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\\n\\nLaunching 2021
\\n\\nArtificial Intelligence, ISSN 2633-1403
\\n\\nVeterinary Medicine and Science, ISSN 2632-0517
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\\n\\nNote: Edited in October 2021
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\n\nAfter a robust peer-review process, accepted works are published quickly, thanks to Online First, ensuring research is made available to the scientific community without delay.
\n\nOur innovative Book Series format brings you:
\n\nIntechOpen Book Series will also publish a program of research-driven Thematic Edited Volumes that focus on specific areas and allow for a more in-depth overview of a particular subject.
\n\nIntechOpen Book Series will be launching regularly to offer our authors and editors exciting opportunities to publish their research Open Access. We will begin by relaunching some of our existing Book Series in this innovative book format, and will expand in 2022 into rapidly growing research fields that are driving and advancing society.
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\n\nWe invite you to explore our IntechOpen Book Series, find the right publishing program for you and reach your desired audience in record time.
\n\nNote: Edited in October 2021
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Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"68814",title:"Improving Online Education Using Big Data Technologies",doi:"10.5772/intechopen.88463",slug:"improving-online-education-using-big-data-technologies",body:'Firstly, distance education has been developed mainly to make distance education accessible to all those who cannot follow face-to-face education, whether for reasons of geographical distance, lack of financial resources, or for lack of time. In fact, since the emergence of programming languages, several learning systems have been created that aim to computerize the pedagogical activity in order to facilitate learners’ access to training without having to be physically present. So, e-learning platforms are mainly computer applications dedicated to education [1]. The Francophone community, which is interested in the development of teaching through computer technologies, calls all systems, platforms or software for learning by Computing Environments for Human Learning (CEHL). In this context, a CEHL is a set of tools, systems and learning platforms based on the use of new information and communication technologies to enable to the learner to realize his formation course without constraints of time and distance. They are designed to promote learning and build knowledge for the learner through computer-based learning situations in the form of classes, exercises, or other activities. Many terms have been used to describe this mode of distance learning, including E-learning, online learning, CAT (Computer Assisted Teaching), ITS (Intelligent Tutorial Systems), Distance Education, Distance Training, WBT (Web-Based Training), M-learning (Mobile Learning), MOOC (Massive Open Online Courses), etc. [2]. As e-learning has many advantages such as flexibility, diversity, openness, etc., it becomes an essential way to acquire new knowledge and skills and to take lessons anytime, anywhere and from any device. And, this will not replace face-to-face teaching but greatly enhances the effectiveness of education.
Today, the IT world is experiencing a strong technological development in terms of resource acquisition, data management, and manipulation. This development is marked by the evolution of the cloud computing model and the advent of the new generation of big data technologies. All of these technologies have upset existing practices by introducing new forms of on-demand services, scalable architectures, and distributed approaches for distributed data processing and analysis.
In this context, the cloud aims to meet the strong demand for e-learning from universities and training organizations that need to develop, execute and deploy high-performance applications at an optimized cost. It also aims to enable e-learning professionals to deal with the multiplicity of devices (desktop, smartphones, and tablets), operating systems, programming languages, and development frameworks. Furthermore, big data technologies will allow an e-learning system to automatically evolve and adapt to different situations depending on the learner’s profile and interactions. Indeed, thanks to the integration of big data in CEHL, an online learning system now has the ability to make decisions and make predictions automatically, without the intervention of a human being, through very advanced models and algorithms of machine learning, which is an integral part of big data.
Recent IT innovations have transformed all areas, including the field of distance learning. Indeed, CEHL have always evolved. Since, the computer, at first, the Internet, then, the cloud computing and the big data, at the moment, appeared for educational use. However, these CEHLs have never been perfect. They are constantly asking for improvements and adjustments simply because we are facing many changing realities and technological developments. So systems, structures, and e-learning processes must remain flexible to be able to adjust to these changes. Our work is part of the research work on CEHL and focuses on the integration of new information and communication technologies into distance education. More specifically, this chapter situates in the context of the development and continuous improvement of CEHL through the implementation of an open, adapted and intelligent online learning platform, which takes into account dimensions of resource sharing, availability and quality of the learning service, and at a lower cost.
To do this, it has proved essential to incorporate modern technological innovations into the CEHL. Thus, this work chooses to use the services provided by cloud computing, as a promising IT model, enabling the outsourcing of hardware, software, and platform resources to remote servers of a cloud provider in order to make them accessible on the internet in the form of services while facilitating their acquisition and optimizing their exploitation. Moreover, it uses the latest technological advances through the implementation of advanced technologies of the big data ecosystem in e-learning systems. Big data offers, in addition to massively parallel computational powers and distributed storage capabilities, sophisticated methods, and algorithms dedicated to machine learning to process and extract knowledge from the various types of data produced by e-learning systems, including learner profile information, activities, preferences, results, etc. This work set up a recommendation engine, the world’s most popular big data application, to help and guide the learner to easily identify and select the most relevant educational resources by offering him an intelligent system capable of generate a catalog of courses adapted to their interests and their cognitive level through the analysis of historical data of learning activities.
The integration of big data and the cloud into e-learning systems is one of the main tracks of this research work which aims to take advantage of on-demand services, and exploit powerful technologies of distributed storage, parallel processing, and real-time analysis to solve some problems of e-learning systems, e.g. recommendation, prediction, motivation, and handle huge amounts of heterogeneous data. This section presents the state of the art of big data and cloud in relation to the e-learning domain. It explores the research that has been done by researchers to integrate these three great paradigms by proposing architectures, approaches and use cases.
In this context, the work [3] addresses the incorporation of the big data into the online learning system by proposing a framework to provide a high quality learning service. This framework facilitates the combining of the e-learning field and big data ecosystem in order to benefit from the advanced techniques of data management and analysis. It consists of three layers. Firstly, there is the e-learning layer which includes educational pedagogical methods, teaching contents, and a set of technologies dedicated to education such as Learning Management System (LMS), Virtual Class Room (VCR). This layer also contains learner information, e.g. profile, preferences, interests, and interactions with the learning system. Secondly, there is the big data layer. Indeed, the data collected from the e-learning platform are passed through this extraction and processing layer in order to prepare, to analyze this data which will be transmitted, subsequently to the third layer of results interpretation and data visualization via several presentation techniques provided by dedicated user applications.
This work [4] aims to show how big data can help solve some distance learning problems by exploiting its technologies in learning content analysis to ensure efficiency and reliability e-learning systems. It uses some powerful big data techniques and tools, such as Hadoop, MapReduce, and HDFS. It also aims to propose a methodology to incorporate the tools and Frameworks big data in the field of CEHL. This methodology includes four steps, which are: first, identifying likely sources of educational data. These data can be of various types such as e-learning databases, e-mails, social networks, etc. Then the third step is data extraction which concerns the collection of data from different sources. Next, there is the big data processing step, which consists in choosing the most appropriate programming language and identifies an appropriate algorithm for returning the results requested by the user. The visualization of data is the fourth step that aims to present the results of analyzed data in an interactive visual form for accessible and understandable.
The research work [5] addresses the use of big data in the academic context. It proposes a model for adapting big data technologies with e-learning platforms. This aims to integrate existing LMSs already in universities with the Hadoop Framework deployed as a cloud SaaS service. In order to exploit the data of a traditional LMS system, Hadoop uses two methods for this purpose. The first method is to migrate the relational database from LMS to an HDFS distributed file system. The second mechanism is to transfer the structured data from LMS into a Data Warehouse. After integrating LMS data into the Hadoop Framework, it is possible to apply conversions and filtering to this data, and then perform advanced analysis. These analyses can greatly contribute to the development of an adaptive and personalized learning system.
We note the research work that was done in this field does not provide a real use case or application based on the software libraries of big data ecosystem in online learning systems, such as high performance machine learning techniques of Spark MLlib or scalable algorithms of Mahout Framework. This is why in this work we were interested, firstly, in proposing a new approach for integrating big data, cloud computing and online learning systems. In addition, this chapter attempts to set up a large-scale big data application. It consists in developing a recommendation system capable of providing adapted and personalized courses to each learner according to his preferences, his cognitive level and his learning style.
In the literature, the term big data first appeared in 1997 according to the IEEE digital library archives, in a published scientific article [6] by two NASA researchers: Michael Cox, and David Ellsworth, on the technological challenges of visualizing large data sets and the difficulty of systems in dealing with massive volumes of data.
In 2003, Google has published a paper about the idea behind of its file system [7], and reveals the first secrets of the success of its search engine. One year later, Google developed MapReduce as a parallel and distributed computing model programming for massive data processing. A year later, Doug Cutting and Michael Cafarella, at that time employed at Yahoo and inspired by the principle of MapReduce, develop Nutch Search Engine, which will become today Apache Hadoop [8].
According to International Data Corporation (IDC), “Big Data Technologies describes a new generation of technologies, architectures, tools, and techniques designed for extracting value from very large volumes of a wide variety of data, allowing a high speed of capture, discovery and/or analysis “ [9]. Gartner in his report [10] gave the following definition: “big data brings together data of great variety, arriving in increasing volumes, at high speed. This is called 3V”. In other words, big data is composed of complex and very diverse data. This large data is generated at a high speed as current database systems become unable to handle it. So, big data tools can provide effective solutions to overcome these challenges. NIST suggests that, “Big data is when data volume, acquisition speed, or data representation limits the ability to perform efficient analysis using conventional relational models or requires the use of a significant horizontal scale for effective handling. big data refers to the need to distribute and parallelize the data computing and storage in data-intensive applications” [11].
Specifically, big data can be divided into data science and big data technologies. Data science is “the study of techniques covering the acquisition, conditioning, evaluation and exploitation of data”, while big data technologies are “systems, software libraries, tools, Frameworks with their algorithms associates that allow distributed processing and analysis of big data problems between clusters of machines” [9].
The characteristics of big data are five, or the 5V model. Vs refer to five key elements that are: volume, variety, velocity, veracity, and value. Figure 1 gives a summary of the different characteristics of big data.
Characteristics of big data or 5V.
Volume refers to the huge amount of data to be stored, processed, analyzed and disseminated by big data tools and technologies. Indeed, the volume of data, generated and handled by companies, is constantly increasing. Currently, the data is measured in petabytes, exabytes, even zettabyte.
Variety refers to the variety of formats and types of data. In fact, big data technologies can handle heterogeneous data from various sources. The classic format is that of the relational database, in which the data is stored according to a rigid and organized schema. But currently, more than 80% of the data generated by companies is of the semi-structured and unstructured type, for example, text, image, video, voice, etc. For this, big data offers the ability to gather all these data and analyze them.
Velocity refers to the speed or frequency at which the data are generated and used. This aggregated data must be exploited in real time. This requires high-performance computing and storage powers and robust analysis tools. In this sense, the IaaS services of the cloud prove to be an adequate solution allowing theoretically unlimited computing resources.
Veracity means the validity and quality of the data captured. It is the credibility and reliability of the data on which a data scientist is based to perform analysis in order to make decisions. Therefore, the big data platform and solutions aim to select and search the exact data in giant databases by eliminating useless data, through innovative tools and techniques [12].
Value refers to the ability of big data to derive value from huge masses of heterogeneous data. It’s good to have access to large volumes of data, but we still have to turn them into value. Indeed, it is necessary that the new generation of distributed technologies (Hadoop, Spark, etc.), DBMS NoSQL, and advanced methods, serve something useful and usable for businesses and universities.
In general, there are three types of data to consider. In addition to structured type managed by a relational database management system, there are two other new types of data, including unstructured and semi-structured ones which are handled by the big data technologies. First, the structured data are those whose set of possible values is determined and known in advance. They respect a predefined model that allows them to be accessed and managed very easily. Structured data is often managed by relational database management systems in the form of tables, and it is handled using a query language such as SQL or PL/SQL. Secondly, the type is the unstructured data which is the opposite of the first type (structured). These are data that do not respect a data schema and are not organized in a predefined way. This type of data can have various formats such as videos, pdf, images, doc, text files, activities on social networks, etc. They are both complex and bulky, and traditional databases cannot manipulate or query them. In addition to structured and unstructured data, there is also a third category, called semi-structured data. Semi-structured data is information that is not stored in a structured dataset, but its structure contains tags that make it easier to manipulate and analyze. Examples of semi-structured data may include XML files, text emails, and JSON documents.
Big data technologies require processing power, speed of execution and huge storage space. This requires big computers with processor, memory, and disk space resources that offer tremendous computing power and performance. In this context, the services of cloud computing can be used. In fact, cloud computing and big data are two inseparable elements. Cloud computing offers theoretically infinite processing power and storage capacity, in addition to the availability of resources. Indeed, we cannot discuss the integration of big data and e-learning platforms without considering the cloud, because it gives the resources needed to deploy big data technologies and tools, as well as learning management systems (LMS). In addition, the cloud offers a preconfigured, ready-to-use environment that incorporates massive data processing technologies.
Big data and the cloud have become key components of any information system, including e-learning systems. As a result, their integration is a major necessity to free themselves from hardware and technical architecture installation issues and to take advantage of the important volumes of data generated by such a system, as well as to gain flexibility in processing and analysis. Identifying useful information from learning data is a big challenge, especially with the significant increase in the amount of data produced every day by online learning platforms. To overcome this problem, big data ecosystem provides advanced technologies, methods, and techniques trough machine learning algorithms in the form of software libraries (APIs, Frameworks, etc.) that are very powerful and easy to use. Such technologies make it possible to prepare and analyze, in a distributed manner, large amounts of data in order to make the best decision and to help e-learning professionals to be able to continuously enrich and enhance their strategies to be adapted to the interests and preferences of each learner [13] (Figure 2).
Integration of big data, online learning systems, and cloud computing.
Infrastructure is the first layer that is the lowest level of the proposed approach. The infrastructure layer is built with compute, storage, and network resources that are virtualized and delivered as services through the cloud. The latter is responsible for providing virtual computing resources and the big data technologies needed to provide e-learning systems with a favorable execution environment. The resources of this layer are scalable. If, for example, when analyzing a massive volume of data, an application requires a huge amount of computing time or disk space, the cloud infrastructure will automatically expand to allocate the resources required by this application. This mechanism allows great flexibility over the traditional approach based on traditional hosting technologies in which server resources are limited. The great advantage of this layer is that it offers a scalable, resilient and fault-tolerant infrastructure.
The second layer of this approach is that of the big data ecosystem. The big data layer includes decentralized storage technologies and distributed large data, massively parallel computing, advanced analysis, optimization and visualization of the processing results. It groups together various big data technologies which can be classified in:
Distributed file systems: stores data in multiple nodes in a cluster in a replicated manner to provide redundancy and high availability. HDFS remains best known as an open source solution for distributed data storage and management.
NoSQL movement: represent the new generation of databases management systems. They allows moving away from the relational model and overcoming the limitations of RDBMS in terms of the amount and types and formats of the data handled. Among the distributed databases are CouchDB (document-oriented), Cassandra and Hbase (column-oriented), which do not impose strict schema rules as in the case of the relational model.
Distributed processing and predictive analysis infrastructure: allows parallel computing of large data sets across machine clusters. The best known open source example of distributed systems is Hadoop developed by the Apache Foundation. They are also tools implementing mathematical methods applied to computing for the analysis of giant databases via predictive models of machine learning. In addition, Apache Spark is a high-performance framework dedicated to the design and creation of large-scale applications for predictive analysis in memory.
The e-learning system represents the third level. This is the application layer represented by the e-learning system containing, in particular, learning management tools (LMS), content management systems (CMS), virtual learning environments (VLE), etc. The information in this layer can be data in the form of educational content, information on the learner or teacher profile, course registrations, etc. These data play a very important role and will be useful in generating personalized learning resources by adapting learning content to the needs of each learner to provide a more appropriate learning platform. To perform this adaptation mechanism, the e-learning system must use the technologies of the lower layer (big data) to exploit advanced predictive models by applying parallel algorithms of machine learning on the learning data.
Generally, an online learning is a platform consisting of hardware, software, and user. The hardware includes memory capacity, network bandwidth, and CPU provided by the IaaS services of cloud. The e-learning system is the software. Users are actors who use the system to communicate, store and process information. The users are mainly the learner, the teacher, and the system administrator. In addition, a fourth player is data scientist can be added who is responsible for configuring, installing, monitoring and controlling the distributed environment of the cluster, as well as developing and deploying analytics models and implementing data mining techniques on platforms. Big data.
Generally, in the different areas, it is a priority to have a clear methodology before starting the implementation phase or the operationalization of a project. This methodology shows the process and the mechanism to better manage big data projects. So, we must address a personalized methodology adapted to the context of e-learning. To do this, we identify the key steps in the big data process that, from the sources of data generated by online learning platforms, extract value and insights to help educational distance to make good decisions. This includes the acquisition, discovery, preparation, modeling, processing and visualization of the results of the data analysis. When data is effectively captured, processed and analyzed, e-learning professionals can have a complete understanding of their learners, educational resources, assessment results, and so on. In this way, they will be able to offer more personalized learning activities, produce relevant teaching strategies, provide adaptive learning to each learner, and improve the quality of educational content. Figure 3 schematizes our methodology for using big data in distance learning environments. It describes the different stages, including big data acquisition and discovery, preparation, modeling, processing, and visualization.
Methodology for dealing with massive data in online learning.
Actually, in order to handle the large volume of data produced by online learning platforms, the data itself must go through a series of five steps:
discovery and acquisition phase describes the process of collecting and discovering the data produced by the distance learning environments, which can be information about the learner (profile, knowledge, skills, etc.), and educational resources including all formats (text, image, video, web page, etc.). They also can captured from learners’ interactions between learners and teachers through social networks, wikis, and forums.
The second phase of our methodology is that of data preparation that comes just after the acquisition and discovery phase. It is a coherent set of operations that retrieve, load, and transform (ELT) multiple data sources. Indeed, this phase includes the integration of the data generated by the e-learning platforms, prepare them and transfer them to be stored in a distributed file system or a NoSQL database such as Cassandra, HBase, etc. Data preparation is a crucial step in the analysis process because it is at this level that we have to filter the collected data to rule out unnecessary or noisy data such as redundancy and keep only those that are relevant and good quality, something that will be used later as input of the analytical model.
Big data modeling is the third phase of our methodology. It aims to specify a suitable method to take advantage of large datasets. To do this, it is necessary to determine the right model to apply on these data. Indeed, during this phase, we must identify the model, determine the appropriate method to use and develop the appropriate algorithm to implement the chosen method. In this sense, big data technologies implement various large-scale machine learning techniques, including: classification, clustering, association rules, regression, collaborative filtering. The best model depends on the type, quality, and size of the data to be analyzed and the available computing resources. It also involves data explorion to learn more about the relationships between variables and then selecting the most appropriate key variables for such a model.
Big data processing represents the fourth phase of our methodology. Actually, big data relies on a parallel computing approach to deal with the intensive processing needs and the increase in data volume. Technologies dedicated to data manipulation are transformed chronologically into batch processing, real-time processing and hybrid computing. Batch processing try to solve to the volume problem, real-time computing respond to speed issues, and hybrid computing is good for both. Distributed computing systems are widely developed, primarily to support the analysis of gigantic data, including Frameworks Hadoop MapReduce [14] and Apache Spark [15].
This phase aims to clearly and effectively visualize and communicate the results of the analysis of the learning data through rich tools and advanced software libraries to synthesize the information of the treatment. This often contains tables, in the form of graphical representations, such as curves, bars, sectors, and histograms. It also describes a set of techniques, software and utilities designed to help e-learning professionals to have a clear view of the enormous data generated by learners.
To validate our model based on the integration of big data technologies and methods in the context of CEHL, a course recommender system has been implemented to show the effectiveness and usefulness of our contribution. It is about a recommendation engine acting as a predictive unit able to adapt to a given learning profile by anticipating its next actions through the suggestion of relevant pedagogical resources that best meet its preferences and interests. The aim is, therefore, to provide the learner with a personalized educational and pedagogical plan according to his profile. For this purpose, our recommender system uses advanced machine learning techniques, especially association rules method for extracting knowledge through the analysis of learning traces. The realization of this system was made using the historical data of learner’s activities collected from the ESTenLigne platform of the Higher School of Technology of Fez. Our recommender system relies on a totally distributed architecture which consists in setting up a large-scale course recommender system. It was developed and tested using the FP-growth parallel algorithm, and the Apache Spark Framework. The deployment of this version is done through the Hadoop distributed cluster infrastructure.
The present work is a part of the ESTenLigne [16] project, which is the result of several years of experience for the development of e-learning in the Sidi Mohamed Ben Abdellah University of Fez. It was started since 2012 by the EST network of Morocco, which aims the development of distance education based on new information and communication technologies through the implementation of open, adapted and free online learning platform, and taking into account the dimensions of exchange, sharing and mutualization of pedagogical resources [17, 18]. Several works have been done as part of this project including the training of experts across e-learning in the context of the Coselearn I project, and teacher training through Franco-Moroccan EST [19] and IUT [20] cooperation [18, 21]. Furthermore, there are some researches that have been done around this project such as the analysis of the use of educational resources where the objective was to analyze the use of pedagogical resources in some courses namely the algorithmic course [22]. Also, a case study for collaboration analysis of online course based on activity theory [23]. In addition, the development an e-learning recommender system based on R environment [24].
In fact, the students have a lot of difficulties and are lost in the diversity of educational resources, particularly the large number of available courses. This requires the adaptation of the teaching to meet the needs of students. To solve these problems, we develop a course recommender system to promote learning to learners through creating a smart solution. It is able to generate the most appropriate courses automatically based on historical data of learner’s activities.
In the area of machine learning, Association rules [25] is an unsupervised learning method, widely used in many areas, including referral engines, online purchase transaction analysis, and flow analysis. Clicks on multiple web pages [26]. Its purpose is to discover relationships between variables in a set of data, which we will call transactions, in the form of interesting association rules. In other words, this method consists in detecting associations between data stored in a giant database. It is a set of powerful exploratory techniques widely used in many sectors but also for scientific research purposes. The most popular application using the association rules is the one concerning the analysis of consumption habits. The power of the association rule method lies in its ability to extract hidden structures in a massive amount of data.
Generally, association rule technique produces a large number of rules, but to select interesting rules in the set of generated relationships, it has two important criteria for determining the quality of a rule by measuring its strength, namely: the minimum thresholds of support and confidence. Support is the percentage (%) of transactions containing the set of items X, while confidence is defined as the percentage (%) of transactions containing X, which also contain Y. Therefore, a force association rule X ⟹ Y should satisfy: supp (X∪Y) ≥ σ and conf (X ⟹ Y) ≥ δ, where σ and δ represent the minimum threshold of support and confidence, respectively.
As part of our research, we applied the rules of association technique in the context of a computer environment for human learning dedicated to e-learning. Therefore, a transaction in our case is represented by the learner profile. Likewise, the items are replaced by all available resources in the database. A transaction is represented by a learner’s enrollment in a number of courses during his or her learning path. We can therefore define the support (1) and the confidence (2) as follows:
FP-growth [27] (Frequent Pattern Growth) is a very powerful algorithm for extracting the most frequent elements from large data sets by allowing a very fast discovery of the association rules without generation of candidates, which requires more memory and time processor. In fact, the generation and testing of candidates requires several analyzes of the database. By using FP-growth, the number of database scans is reduced to two. The first scan aims to count the support of each item, the non-frequent items are deleted, while the frequent items are sorted in descending order of support, in the form of a list of frequent items (L). Then, in the second scan the algorithm builds the FP-tree structure with the creation and insertion of the different nodes. These operations constitute the first step of the algorithm. On the other hand, the second step is to extract sets of frequent elements from the constructed FP tree. The FP-growth algorithm is based on the “divide and conquer” strategy of breaking down a problem into subproblems. First, it compresses frequent itemsets represented in the database using a compact data structure called FP-Tree (frequent-pattern tree) whose branches contain the possible item associations. The FP-growth method transforms the problem of finding the longest frequent itemset by searching for the smaller one and its concatenation with the corresponding suffix. This reduces the cost of research.
This section introduces all the big data technologies used as well as their different components. Each technology has a definite role and participates in the process of extracting prediction knowledge by providing a list of highly recommended courses according to the needs and interests of each learner. These technologies are deployed on a cluster infrastructure of nodes that are interconnected via network protocols to be able to communicate and exchange data during the analysis of learning traces. These technologies can be organized in three layers. Figure 4 describes the different big data frameworks used to develop the large-scale course recommendation system.
Big data technologies.
All implemented technologies can be grouped in 3 layers:
First, there is the layer of distributed data storage. We chose to use Hadoop’s Distributed File System (HDFS). In fact, HDFS is a fault-tolerant file system capable of managing distributed data across large clusters. It has a master/worker architecture. HDFS provides high performance access to large amounts of data. It creates an abstraction of hard drive resources to allow the management of distributed physical storage of multiple nodes as if there is only one storage space. In the HDFS architecture, data is managed across the cluster, in different Datanodes, by the workers in the form of block-structured files. The locations of these blocks and the namespace of the files and directories are kept in the Namenode component in the master node [28].
In the second level, Yarn [29] is found as the node cluster resource manager. This is a Hadoop module dedicated to scheduling and executing tasks, at the same time, on a number of computers in a cluster. It is also responsible for managing disk resources, memory, CPU and cluster network. Yarn’s main idea is to separate resource management from the computational model. Indeed, Yarn will take care to rent the necessary resources and to distribute the basic tasks on different units of calculation of the machines of a cluster.
Finally, the upper layer represented by the Spark Framework [15] is responsible for the manipulation and analysis of the data. It is used to apply association rules techniques to learner learning data, collated from the ESTenLigne project. To implement our course recommendation system, Scala was chosen as the development language. The advantage of Spark is its ability to support multiple programming languages such as Java, Python and R. This framework provides many libraries. In our use case, we focused on just three components: Spark SQL, Spark DataFrames, and Spark MLlib.
Spark SQL allows you to connect to the moodle LMS database and execute SQL queries.
Spark DataFrames is a Spark module for structured data processing.
Spark MLlib implements several machine learning algorithms, including the FP-growth parallel algorithm.
In general, our approach is to generate recommendations by analyzing the traces of learning activities that are the source of knowledge in the process of personalization of learning resources provided to learners. The entry of the system thus consists of the history of course registrations, imported from the database of the e-learning platform. Figure 5 describes this architecture in detail.
Distributed architecture of course recommendation system.
In the beginning, we need to load the data produced by the learners’ interactions with the ESTenLigne platform. Then, this data, loaded by the Spark SQL library, is processed in a distributed manner using the Spark Framework that runs on a Hadoop cluster and uses the Yarn Resource Manager. Indeed, Apache Spark provides a special library dedicated to machine learning techniques, called MLlib. This library proposes an implementation of the parallel FP-growth algorithm in the Scala language. Subsequently, the prepared data is analyzed using the FP-growth algorithm of the Spark MLlib library. Then, Spark connects to Hadoop HDFS to store the data on machine clusters. Then, the recommendation system generates the catalog of the most relevant courses. Finally, the results of the recommendation engine can be presented to the user in order to guide them and suggest the educational resources most suited to their interests. Thus, the learner can go through the courses recommended by our system and begin to learn those that suit his cognitive level and preferences.
Besides, our system uses the open source tool Ganglia. It is a highly scalable, distributed and scalable solution for monitoring large environments such as clusters and grids, as well as measuring performance and resource consumption such as CPU utilization, memory, and data storage of each node of the cluster. It also controls and visualizes network traffic, such as bandwidth usage or the amount of data transported over the network.
Considering the high cost of installing a physical infrastructure of big data clusters, the use of virtualization tools for a distributed architecture represents an alternative solution to configure, develop, test and validate our course recommendation system. The Hadoop multi-node configuration is done in a fully distributed environment consisting of three nodes. The machine with the IP address: 192.168.56.101 works as the Hadoop master, which contains the Hadoop components, namely ResourceManager and NameNode. The master node runs Hadoop processes to manage and coordinate cluster tasks and services. In fact, the virtual machine identified by the IP address 192.168.56.101 works as a master and worker at the same time. The other machines in the cluster are workers. The worker nodes are responsible for running the processes or the basic tasks of the parallel application. They also provide resources to the cluster to perform the processing of data assigned by the master. As shown in Figure 6, they respectively have machines with the IP addresses 192.168.56.102 and 192.168.56.103.
Hadoop cluster configuration.
In order to build our big data infrastructure, we have prepared a cluster of three virtual machines. As a virtualization solution we used the free and popular VirtualBox solution. With the available computing resources, we created three virtual machines by installing the Ubuntu 18.04.1 LTS operating system on each node of the Hadoop cluster. These nodes are connected to each other using a private LAN. The capacity and configuration of all virtual machines are described in Table 1.
Machine | Network | Cores | Memory | Disk |
---|---|---|---|---|
Master | 192.168.56.101 (master.domain.com) | 8 Core i5 (7th Gen) | 8 GB | 32 GB |
Worker1 | 192.168.56.102 (worker1.domain.com) | 8 Core i5 (7th Gen) | 8 GB | 32 GB |
Worker2 | 192.168.56.103 (worker2.domain.com) | 8 Core i5 (7th Gen) | 8 GB | 32 GB |
Configuration of Hadoop cluster nodes.
After configuring the virtual cluster network, we unzipped and installed Hadoop 3.1.1 and Spark 2.3.1 at the master node of the cluster. Then we moved the installation folder of both Frameworks to the worker nodes using the SSH protocol. Similarly, we used the power of Secure Copy (SCP) to get the same copy of Apache Hadoop and Spark. The Java version 1.8 has been installed on each node and we have configured a password-free ssh between the nodes so that the master Hadoop node can connect, start, stop, and execute tasks in different workers.
To run parallel FP-growth, we had to specify the minimum support and confidence thresholds in order to find the strongest correlations between course enrollments in learning activity traces. The number of interesting association rules changes according to the value of the support, the confidence and the size of the database. Thus, we used the minimum support threshold of 5% and we set 60% as the minimum confidence threshold. In fact, the course recommendation system generates two types of results that meet the specified support and confidence criteria. First, it finds the list of frequent courses in the database of the e-learning system based on the calculation of the support and the confidence of each itemset, it keeps only the list of itemsets that satisfy the condition of the minimum threshold of confidence. The 10 main rules of interest, ordered according to the confidence measure, are shown in Table 2.
Rule | Antecedent | Consequent | Confidence |
---|---|---|---|
[1] | [46, 11] | [45] | 1.000 |
[2] | [45, 11] | [46] | 0.975 |
[3] | [46] | [45] | 0.952 |
[4] | [45] | [46] | 0.919 |
[5] | [7, 6] | [18] | 0.868 |
[6] | [7] | [18] | 0.818 |
[7] | [6, 18] | [7] | 0.785 |
[8] | [7, 18] | [6] | 0.733 |
[9] | [6] | [18] | 0.711 |
[10] | [7] | [6] | 0.690 |
Parallel FP-growth results.
According to the results obtained in Table 2, the rule of association between courts {11 and 46} and {45} has the greatest confidence. Similarly, the rule of association between the courts {7} and {6} has the lowest confidence. Based on calculated values and support and confidence, we can clearly identify the courses most likely to be followed by learners, and most relevant to recommend them. For example, rule 1 {11, 46} = > {45} has the greatest confidence, that is, the strongest. Our system therefore suggests course 45 to students who have already enrolled in courses {11 and 46}. According to the results of Table 2, the confidence of rule 1 is 100%, because, at the level of the database, we find that 56 students have enrolled in courses {11 and 46}, and that these 56 them also enrolled in course {45}. For association rule number 2, the analysis of historical student registration data from the ESTenligne platform shows that 57 students took courses {11 and 45}, and 56 of them followed also the course {46}. So, the confidence of association rule 2 is 98%. Therefore, our recommendation system recommends the course {46} to students enrolled in courses {11 and 45}. With regard to association rule number 3, there are 123 learners enrolled in the course {46}, of which 118 are also enrolled in course {45}. Thus, the confidence of rule 3 is 95%. Our system therefore offers the {45} course to students enrolled in {46}. For the 10 strongest association rules, we note that the confidence values are between 0.69 (69%) and 1.00 (100%), which demonstrates that we have achieved good results. We can therefore conclude that the course recommendation system that we have proposed can provide relevant teaching resources by recommending courses suitable to each learner in order to guide them through their learning path.
Learner ID | Items (course) | Recommendations |
---|---|---|
541 | [46, 6, 11, 45] | [18, 7] |
720 | [45, 46, 15] | [14] |
19 | [6] | [18, 7] |
277 | [18, 14, 6, 9, 3, … | [43] |
287 | [17, 43, 42, 9, 2, … | [6] |
155 | [15, 17, 42, 9] | [14, 43] |
1157 | [6] | [18, 7] |
184 | [40, 6, 43, 42] | [18, 7] |
274 | [7, 6, 15, 4, 3, … | [43] |
766 | [15, 45, 46] | [14] |
Prediction results of the course recommendation system.
After establishing the recommendation model using the training dataset (70%), it can also be used to predict the result of recommending courses to learners through the database. Test (test dataset) (30%). The recommendation system generates a catalog of courses for each learner profile. Table 3 shows the top 10 predictions out of a total of 48 predictions.
The results of the prediction of the recommendation system that we have developed gives for each learner (id) the list of courses in which he participates and a catalog of the predictions of the courses. For example, our course recommendation system suggests courses 18 and 7 to the learner (id = 541) who is already enrolled in courses 46, 6, 11 and 45. Also, he recommends course 14 to the learner (id = 720) which follows courses 45, 46 and 15 courses, etc.
This chapter aims to integrate the new generation of information and communication technologies, especially the big data ecosystem, in Computing Environments For Human Learning dedicated to online learning. In fact, big data provides a wide range of tools and systems for distributed storage, massively parallel processing, and predictive analytics. This set of technologies can be used in the processing and analysis of massive data produced by learners interactions. It offers high-level frameworks allowing a lot of advantages to greatly improve the quality and disponibility of distance learning platforms.
So, our model can really improve the online learning field which every learner can have the maximum benefits from that. Furthermore, pedagogical teams and administrators of e-learning platforms have valuable tools and advanced APIs for analyzing data in order to improve learning strategies, make better decisions and offer a big variety of new learning methods.
To implement big data technologies in e-learning systems, this chapter has designed and developed a course recommender system to provide an adaptive learning solution, which consists of adapting teaching resources to individual preferences and needs of each learner. The implementation of the proposed course recommendation engine uses machine learning techniques, in particular, the association rules technique to find all the interesting relationships from historical student enrollment data. The results obtained show the effectiveness of our system in terms of quality and relevance of course recommendation and execution time performance thanks to the decentralization approach of the processing and analysis of data. The deployment of our system is done in the distributed infrastructure of Hadoop and the powerful in-memory processing and advanced analysis of the Spark Framework. The implemented Spark application is based on a completely different approach which consists, in fact, in distributing the processing and the data on several nodes each of which carries out specific tasks in order to execute the FP-growth algorithm in parallel on different machines of the cluster. The distribution mechanism of computing and storage solves the problem of limiting available resources while speeding up the execution speed and the cost of processing.
Nepal is a small landlocked country located in Southeast Asia surrounded by India (South, East, and West) and China (North), which is geographically highly diverse and fairly rich in its variety of endangered flagship species, that is, wild animal and plants. The conservation of endangered wild animal species is a major challenge to government conservation of endangered wild animal species is a major threat as well as a challenge to government in developing countries particularly Nepal to protect the loss and degradation of natural habitats, poaching, and illegal trade activities of their crucial body parts for making pharmaceutical products, cosmetic, and other purpose [1]. Therefore, monitoring tools can be helpful to minimize the risk of possible human-animal conflict and also above described conditions especially to extinction of endangered wild animals in Nepal [2, 3].
\nHowever, the Government of Nepal has already successfully used the modern technologies such as radio collars and camera trap to monitor and conserve some endangered species like tigers and vulture at protected areas [4, 5, 6, 7]. In the other hand, monitoring technologies have also several limitations, for example, camera trapping and radio collar require a long period of time as well as high costs [8]. Noninvasive genetic analysis from scat, urine, and hair samples have become widely used in wildlife research study, where molecular markers helps to identify the species, sex, and individuals [9, 10]. noninvasive genetic analysis from scat, urine, and hair samples have become widely used in wildlife research study, and additionally molecular markers can also help to identify the species, sex, and individuals [9, 10]. Noninvasive genetic analysis technique is more safe and avoids the potential harmful effects from direct contacts with endangered wild animal species while performing research at protected areas [11]. In Nepal, more than dozens of previous studies on endangered wild animal species have been successfully recognized by applying the noninvasive genetic analysis insight into population structure, mitochondrial (mt) DNA of cytochrome b and D-loop control region sequencing, effective population size, phylogenetic inference, and other modeling [9, 10, 12, 13] approach as described in Table 1 [14, 15, 16, 17, 18, 19, 20, 21].
\nEndangered species | \nGenetic techniques | \nConservation location | \nMonitoring method | \nReference | \n||||
---|---|---|---|---|---|---|---|---|
Common name | \nScientific name | \nSample | \nSample size | \nGenetically profile remarks | \nGenetic analysis | \n|||
Snow leopard | \nScats | \n71 putative scats | \n(i) 19 (27%) scats: genetically identified; 10 (53%) successfully genotype at 6 microsatellite loci. (ii) Total 9 individual snow leopards; 4 individual identified at SPNP (1 male and 3 females) and 5 (2 male and 3 females) at KCA. | \nPCR; mitochondrial cytochrome b gene/carnivore specific/species /sex/individual identification | \n(SPNP) (KCA) | \nNoninvasive genetic analysis | \n[14] | \n|
Bengal tiger | \nFeceal sample/tigerparts/skin vespieces/blood smear | \n( | \n(i) All forensic samples ( (ii) Geo-source location for 9 of the 14 samples with 6 ± 8 nuclear DNA microsatellite loci (iii) 6 samples were assigned to BNP and 1 was an exact match to a female tiger previously profiled according to their fecal DNA reference database. (iii) 2 tiger samples were assigned to SWR (iv) 1 from CNP | \nPCR; mt DNA cytochrome-b (CYT-B)/species/sex/individual identification | \nCNP BNP SWR PWR | \nNoninvasive genetic analysis/genetic structure analysis/Geo-source assignment of unknown tigers/Molecular forensic techniques | \n[15] | \n|
Himalayan black bear | \nFresh bear feces/hair sample | \nn = 126 fecal samples n = 21 hair samples | \n8 microsatellite loci by using 147 samples: 60 individual identified. | \nPCR: mt control region (CR) D-loop/cytochrome-b/whole mitogenome sequencing | \nACA | \nNon-invasive genetic diversity/microsatellite genotyping/mitochondrial DNA sequencing/phylogenetic inference | \n[16] | \n|
Hanuman langur | \nFecal sample | \n67 non-invasive fecal sample | \n(i) DNA sequences; successfully; 67 non-invasively collected fecal samples belonging to 18 wild Hanuman langur troops covering the entire distribution range of the species in Nepal. (ii) identified 37 haplotypes from the concatenated CR + CYTB (2230 bp) sequences | \nPCR: mt DNA control region (R = 1090 bp), cytochrome B (CYTB=1140 bp) sequences | \nKRS GRS KMRS | \nNon-invasive genetic diversity/population genetic structure/molecular analysis/paleodistribution modeling | \n[17] | \n|
Assam macaques | \nFecal sample | \n227 fecal samples collected from 39 wild troops | \nThe mt DNA fragment complete control region (1121 bp): recovered from 208 fecal samples defining into 54 haplotye. | \nPCR: mt DNAcomplete control region (1121 bp) | \n\n | Non-invasive genetic analysis/population genetic structure/paleodistribution constrcution | \n[18] | \n|
Tiger | \nFecal sample (Scat) | \n770 scat samples | \n(i).770 scat samples; 412 (57%) (ii) Out of 10 microsatellite loci; 8 markers identify 78 individual tigers (iii) Sex was genetically identified 353 scat samples; 255 samples (male) and 98 female | \nPCR:mt DNA cytochrome B fragment (162 bp)/species/sex/individual identification | \nSuNP BNP BaNP CNP PNP | \nNon-invasive genetic analysis/genetic structure/population structure/genetic variation/contemporay gene flow/potential population | \n[19] | \n|
Snow leopard Himalayan Wolf | \nScat samples | \nN = 573 leopard scat samples N = 236 wolf scat samples | \nN = 182 Wolf = 57 | \nPCR:mt DNA cytochrome B/species/sex/individual identification | \nACA MCA | \nNon-invasive genetic analysis | \n[20] | \n|
Himalayan Wolf | \nFecal samples | \nN = 6 fecal samples | \nOut 6 fecal samples; 5 samples were successfully ampfified | \nPCR: mt DNA control region CR locus (220 bp) | \nACA | \nNon-invaasive genetic analysis/molecular analysis/phyolgenetic analysis | \n[21] | \n
Non-invasive genetic analysis studies were identified among the endangered wild animals species at protected areas in Nepal from 2011-2018.
SPNP, Shey Phoksundo National Park; KCA, Kangchanjunga Conservation Area; BNP, Bardia National Park; CNP, Chitwan National Park; PWR, Parsa WildlifeReserve; SWR, Suklaphanta Wildlife Reserve; ACA, Annapurna Conservation Area; KRS, Koshi River System; GRS, Gandaki River System; KMRS, Karnali-Mahakali River System; SuNP, Suklaphanta National Park; PNP, Paras National Park; MCA, Manasula Conservation Area.
Nepal has a total area of 147,181 square kilometers of which protected area mainly includes forest land area of different altitudes in terai, hills, and mountains. This possesses a multitude of landscape and maintains vast biodiversity in the Palearctic and Indo-Malayan ecozones. In Nepal, altitude ranges from 67 m in the southeastern Terai to 8848 m at the highest peak in the world “the Mount Everest.” It has 11 bioclimatic zones which are ranging from lower tropical below 500 m to nival above 5000 m in the high Himalayas containing 9 terrestrial eco regions with 36 vegetation types. Nepal attains 10th rank in terms of richest flowering plant diversity in Asia having 1120 species of nonflowering plants and 5160 species of flowering plants. According to recording made by zoologists, Nepal has 181 mammal species, 844 bird species, 100 reptile species, 43 amphibian species, 185 fresh water fish species, and 635 butterfly species. For protection of biodiversity, the Government of Nepal has established 20 protected areas since 1973 AD consisting 10 national parks, 3 wildlife reserves, 6 conservation areas, and 1 hunting reserve. Suklaphata and Parsa wildlife reserves were upgraded to national parks in 2017 AD. Nine different Ramsar sites have been declared between 1988 and 2008.
\nWild animal population is monitored for different reasons like to know the status of species whether it is in endangered or threatened state, biological interest or research purposes, game management, population being monitored to assess recovery or progress of any threatened species, biological diversity study, to know the effects of human management actions, land use patterns, etc. [7, 22, 23].
\nVery much careful consideration has to be done in collecting information about wild animals for monitoring wildlife population. Direct observation of animal and behavior observation is important for wildlife population monitoring. Tracks and dung of animal, thermal cameras or attaching monitoring devices like collars, leg bands, and data loggers are also used for population monitoring [7].
\nThe purpose of this review study is to highlight the different population monitoring techniques being applied in Nepal for different endangered wild animals and their application in the field condition. Based on these applied techniques, counting of animals and their status and further research and conservation strategy are made.
\nAlthough conservation practice has come since long time in Nepal, only few biodiversity monitoring systems exist. Among existed system, the address is only for some mammal species of Terai region and one species of mountain (snow leopard). In monitoring process, repeated measurements are taken, and comparison is made to understand the cause of change.
\nTherefore, biodiversity monitoring includes carrying out repeated survey works to find out the size and extent of population of certain species. It also includes quality of habitat of certain species analysis results to find out trend and rate of change of species being monitored. By using standard methods and different field protocols, data is collected, and it is analyzed to determine the rate of progress. In the field data collection procedure ecologists and managers will be assisted by data analysis and GIS techniques.
\nGIS allows team to identify areas where they live. By overlying within a GIS environment, areas which match all criteria can be identified within minutes. This saves much fieldwork time, especially on hills and mountains where most wildlife habitat areas need to be accessed only on foot. Another important contribution made by GIS is in locating sample plots, which is crucial for validating datasets and results obtained from them. By the use of GIS, sample plot location can be identified either randomly or systematically. Thus identified sample plots can be overlaid within the three-dimensional GIS model, which will be helpful in providing exact impression of real field location. Location of these plots can be extracted from the GIS and will be identified in the field using GPS.
\nGIS uses uniform addressing format in the form of latitude, longitude, and altitude which is another advantage. Geostatistical techniques offer interpolation tools that take into consideration continuously changing spatial variables, which cannot be performed with general statistics. Such interpolation tools are useful for generating stoking density, especially of floral biodiversity [23, 24, 25].
\nAbundance (N) and density (D) are important population parameters estimated for tiger monitoring programs. Photographic capture-recapture through camera trap is a most reliable technique for estimating tiger population as it being elusive species and the use of the unique identification patterns on each individual. Capture-recapture models provide a statistically robust framework to estimate species abundance, particularly when a population is said to be closed to births, deaths, immigration, or emigration during the survey period [26]. For camera trapping protected area or forest is divided into grids of 2 km × 2 km area, and a pair of cameras is placed in each grid cells. Field sampling design is determined by the size of the survey area and availability of logistics like camera number and personnel. Camera trapping was conducted in shifting blocks as described by Royle et al. [25] in each protected area and surrounding forests, which are divided into several blocks. Stations for camera trap are determined through prior sign survey of area like pugmarks, scrapes, and scat. In each station, two cameras are placed facing each other at a height of 45 cm above ground and were mounted on trees or posts on either side of a forest trail or road, with a distance of 6–8 m between the two cameras. In every grid cameras are placed for the standard sampling period of 15 nights. Each camera and memory card is given a unique identification number for data recording and maintenance purposes. Camera traps were checked every second day to ensure they were operating effectively.
\nTiger habitat occupancy surveys are conducted across all potential tiger habitats. A grid cell each (15 km × 15 km) was laid across the survey area. Each grid cell is further divided into sub-cells. To include an element of randomness in the spatial distribution of survey routes, one sub-cell per grid cell coded as tiger habitat was randomly selected prior to the survey [27]. The number of spatial replicates per grid cell (i.e. km walked) was proportional to the percentage of tiger habitat [27]. For grid cells with 100% tiger habitat, we sampled 40 km in the cell touching random grid in every sampling route. Each contiguous 1 km segment was considered as a “spatial replicate” [28, 29, 30]. The field team walked along trails, roads, ridgelines, and river and stream beds in selected sub-cells, searching for tiger signs (scats, scrapes, pugmarks, kills, and urination sites), prey signs (dung, footprints, calls, and sightings), and human disturbance (wood cutting, lopping, grazing, poaching, etc.) following high probability tiger sign areas [28]. Observations were recorded for every 100 m section of the transect walk (Figure 1).
\nCamera trapping grid (Bardiya National Park).
The greater one-horned rhino was once believed widespread throughout the northern floodplains and nearby foothills of the Indian subcontinent between Indo-Myanmar border in the east and Sindh River basin, Pakistan, in the west; now greater one-horned rhinoceros are currently restricted in few protected areas in northeastern India and lowland terai of Nepal. Different techniques used for rhino monitoring in Nepal are mentioned below.
\nAccording to different physical demarcation like rivers and other field knowledge, potential rhino habitats (both inside and outside the protected areas) are divided into blocks. Reconnaissance surveys are also conducted in the more difficult areas to refine the blocks and plan for the sweeping operation. Where necessary, tall grasses are cut and burnt to improve visibility to observe animals. Elephant is an essential component for this census. Elephants are lined up over 5 km long with visible gap between the elephants and moved parallel along transects marked on the map to sweep individual blocks. Experienced staffs with camera, radio communication, GPS receivers, and maps are assigned to manage sections of the sweep to coordinate the operation. Radio communication is essential to maintain distance of approximately 50 m in dense forest and 100–200 m in open grassland so that rhino will not be missed. Maps of area to be covered on each census day are provided to each observer for navigation purposes. The maps in conjunction with the GPS receivers are extremely useful to ensure groups of elephants did not break from the census line particularly in thick forests (Figure 2).
\nTrend of Rhino Population in Nepal and Chitwan National Park.
A rhino data recording booklet is designed to standardize the accurate recording of rhino identification features and demographic data. The booklet convenient to fit in a shirt pocket is put together to form a convenient way of recording all of the details about each greater one-horned rhino. The observers are trained to look for and draw/note any features such as horn shape, ear tears, skin folds, deformities, epidermal knobs, body scars, or tail shape that would make a rhino distinguishable. They are trained to correctly fill in the following information:
Date and time
GPS location
Standardized age class (adult, subadult, or calf)
Sex (male, female, or unidentified sex)
Habitat type (tall grassland, short grassland, Sal forest, riverine forest, wetland, other)
Distinct rhino identification features (ears and horn, body and tail features)
Body condition (good, average, poor)
Group composition
Activity
ID-based rhino monitoring is carried out in low population density area where population can differentiate easily and manually track and monitor individual animals and their movements. Intensive ID-based rhino monitoring is a simple monitoring system where each individual rhino is assigned a particular ID or name based on its distinct physical appearance and body features. All potential rhino habitats are divided into several blocks for ID-based rhino monitoring. The basic equipment required for rhino monitoring comprises cameras, binoculars, GPS, maps, and data forms. Based on the field information of an individual rhino, a master ID file is prepared which is an invaluable tool for monitoring the rhino population. Rhino identification master files are used to control quality of rhino sighting data recorded in field sighting forms and trace the record of each rhino. The files also help to capture and transfer the knowledge/skills of the highly experienced key observers for future generation. The photographic sequences kept in the files enable to assess the changes in the animals over time and allow development of guidelines on horn and body size appearance with rhino age [31]. This also helps to monitor the animals for anti-poaching control. It is therefore essential that the information in the files is accurate and kept up-to-date by the data controllers with change in information like calving number, inter-calving intervals, and number of calves.
\nOnce rhinos are individually identifiable, their details are maintained in population databases which assist greatly in ensuring that information can be derived to meet the needs both at park and national (metapopulation management) levels [31].
\nVHF, GPS, and satellite radio transmitter neck collars are modern radio-tracking technology which facilitates the monitoring of rhino. However, these are considerably more expensive. The transmitters can also include mortality sensors that change the frequency of the signal after a predetermined period of immobility.
\nThe radio-tracking technology can be cost effective in certain situations such as:
To monitor severe poaching activity.
To monitor post-release adaptation of translocated rhinos to assess the adaptation in new habitat.
Where there is insufficient monitoring capacity to ensure regular sightings through recognition of identity features
Mark-recapture methods provide population estimates. Mark-recapture population estimation software can be used for this purpose provided:
Rhino sighting throughout subpopulation over a period of time.
Equal attention been paid to monitoring both identifiable and “clean” rhinos.
There are enough sightings of adults and independent subadult rhinos.
This technique of estimation is useful in populations where not all animals are individually identifiable and information are collected by nonspecialized teams of anti-poaching patrols and other staff on an ongoing ad hoc basis. Additional knowledge about the population will be derived (mortality, introductions, and removals in a population and where known calves have become independent of their mothers), are obtained through estimation process, and violates classical mark-recapture estimation. In these situations, periodic discrete surveys of a rhino subpopulation can be used to generate population estimates, using basic methods of mark-recapture population monitoring. Such estimates may have a lower degree of accuracy and precision than those that would be derived through the more continuous monitoring but will nonetheless be useful.
\nFirst national red panda population count was made in 2016 by Red Panda Network in collaboration with the Government of Nepal. During this count program, habitat survey was also conducted. This study was conducted in 35 different districts of Nepal, and presence of red panda population was obtained in 23 districts of the country including 7 protected areas. This study provided information on deforestation level, habitat quality of red panda, and effect of climate change in their range.
\nA mobile application is being used to monitor movement of red panda, in eastern hill districts, including Panchthar, Ilam, and Taplejung. Wildlife activists who have received the phones have been entrusted with recording situation, speed, appearance, direction, and signs of the red panda and uploading them into the network through the Internet. GPS, camera, and mobile apps are used to monitor the red panda in Nepal [32, 33, 34, 35].
\nPhotographic capture-recapture (CR) methods are sometimes adopted to estimate elephant population densities. However, this approach faces many functional challenges posed by difficulties in temporal sampling and unreliable identification of individuals on variable traits. Data of elephants were collected by photograph from roads and along water hole.
\nIf an elephant is seen, it is recorded as fresh encounter, and its detailed description is made. Every elephant’s morphological characteristics are classified as variable traits (holes and tears), fixed traits (tusk characteristics and ear fold shape), and tail traits (tail length, tail brush shape). Height of animal and age are recorded. Each animal is marked with different identity code [36].
\nCamera trapping is also applied for the monitoring of snow leopard. Beside this satellite radio collaring in Kanchenjunga Conservation Area of eastern Nepal has been started since 2013 in collaboration with WWF Nepal. Both Conservation Committee and citizen scientists are trained in GPS handling and camera trapping and monitoring of snow leopards and their prey. The use of advanced technology in wildlife research such as noninvasive genetic analysis, camera trap surveys, and GPS-satellite telemetry presents better opportunity to conduct ecology and behavioral studies of snow leopards and their prey and habitats [24].
\nGharial population monitoring is carried by sweep operation in the river system with potential habitat of gharial. River system is divided into several blocks. Each block is observed by a team of observers on boat. They will move along the river system and record all presence of gharial along with the activity of them. Monitoring is carried out continuously for 3 days. Average number is calculated with a record of 3 days.
\nA study on population status and distribution of Gharial in Nepal was commissioned by the Department of National Parks and Wildlife Conservation and WWF Nepal in 2008 aiming to update the existing status and distribution pattern of gharial and also to assess the threats in gharial conservation in Nepal. Data were collected from direct count by the use of opportunistic search method and questionnaire survey from Karnali and Babai Rivers of western Nepal, Narayani and Rapti Rivers of central Nepal, and Koshi River of eastern Nepal [37].
\nSwamp deer count is carried out at the reserve by Suklaphanta Wildlife Reserve in partnership with Terai Arc Landscape Program and Suklaphanta Conservation Program, NTNC. The reserve is home to the world’s largest herd of swamp deer. The count was based on direct head count of animals. Swamp deer is a protected species of Nepal and listed in Appendix I of CITES.
\nFor the census, protected area and surrounding forest are divided into several blocks. Sex, age, location, group size, habitat, and time are recorded. An elephant is deployed along with the observers; they will ride in the back of the elephant and count the individual head. Global positioning system (GPS), digital camera, binocular, maps, and communication sets were used for the census purpose. In Nepal, the Gaur is listed as protected animal under National Parks and Wildlife Conservation Act 2029, as vulnerable in IUCN Red Data Book, and is listed in Appendix I of CITES.
\nMusk deer is one of the genera called primitive deer that produced musk and high value of raw materials for making cosmetic as well as pharmaceutical products which is why musk can sell up to US $45,000 per kilogram (2.2 pounds) on the international market. However, musk is produced in the gland of males; later it can be extracted from live animals; for that purpose humans kill the musk deer to remove the entire sac having yields only about 25 g (1/40 of a kilogram) composed of the brown waxy substance [38, 39]. Due to increased price of musk deer on the international market, activities of poaching and trade of musk deer have been reported to rapidly increase in the Himalayan region of Nepal [40]. The study from neighboring country also revealed that poaching and trade of the species of musk is also a threat to conservation in Uttarakhand state of India [41]. The population of musk deer rapidly decrease due to many reasons such as poaching for musk pod, habitat overlapping, excess harvesting of forest resources like grass, shrubs, and timber for the human benefits, causing a serious problem for the survival of musk deer in the Himalayan region of Nepal [42, 43]. Grazing of livestock within protected areas might also be a possible risk and grazing of livestock within protected areas might also be a possible risk and threat to musk deer for no availability of enough diet to survive and high chance of disease transmission from livestock to musk deer and vice versa [40].
\nHowever, there was only limited information on musk deer and lack of detailed study on the distribution as well as threat in few protected areas in Nepal. Noninvasive genetic analysis was carried out to know information on the population status, distribution, habitat, seasonal diet, altitude range, survival life span, genetic diversity, and phylogenetic analysis of musk deer in Nepal.
\nAlthough Nepal is a small country, it has a vast biodiversity found from very low land to the highest mountain in the world. Conservation has come a long way in Nepal; few biodiversity monitoring systems exist and do address for some large species like tiger, greater one-horned rhino, elephant, crocodile, swamp deer, musk deer, snow leopard, red panda, gaur, etc. This review provides information on different population monitoring techniques that have been used in Nepal for endangered wildlife species. In Nepal, wildlife population is basically monitored to assess progress or recovery of endangered or threatened species and research purposes and to know the effect of human management actions. Different methods such as directly observing animals and their behavior in situ, looking for signs like tracks and dung of animals, radar, thermal cameras, capture/mark/release of animals, or attaching or implanting monitoring devices (e.g., collars, leg bands, back packs, or data loggers) and noninvasive methods using fecal, hair, etc. are used for population monitoring. In Nepal population monitoring is being done for few wildlife species only although it has many species. So, population monitoring of other species is also important for making proper conservation plan. In developed countries more advance monitoring technologies are being developed and adapted too. So it needs to collaborate with different biodiversity conservation organizations to work together and share technology and make monitoring more comprehensive. This present review study will provide strong recommendations to community persons, leaders, conservation NGO/INGO, and government bodies to prepare the future action plan strategies about the conservation and monitoring of flagship endangered wild animal species at protected areas in Nepal. It is important to know further detailed information on population status, distribution, habitat, seasonal diet, altitude range, survival life span, genetic diversity, and phylogenetic inferences of each particularly endangered wild animal found at protected areas in Nepal.
\nNational Trust for Nature Conservation, Chitwan, Nepal.
\nNone.
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His studies in robotics lead him not only to a PhD degree but also inspired him to co-found and build the International Journal of Advanced Robotic Systems - world's first Open Access journal in the field of robotics.",institutionString:null,institution:{name:"TU Wien",country:{name:"Austria"}}},{id:"441",title:"Ph.D.",name:"Jaekyu",middleName:null,surname:"Park",slug:"jaekyu-park",fullName:"Jaekyu Park",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/441/images/1881_n.jpg",biography:null,institutionString:null,institution:{name:"LG Corporation (South Korea)",country:{name:"Korea, South"}}},{id:"465",title:"Dr.",name:"Christian",middleName:null,surname:"Martens",slug:"christian-martens",fullName:"Christian Martens",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Rheinmetall (Germany)",country:{name:"Germany"}}},{id:"479",title:"Dr.",name:"Valentina",middleName:null,surname:"Colla",slug:"valentina-colla",fullName:"Valentina Colla",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/479/images/358_n.jpg",biography:null,institutionString:null,institution:{name:"Sant'Anna School of Advanced Studies",country:{name:"Italy"}}},{id:"494",title:"PhD",name:"Loris",middleName:null,surname:"Nanni",slug:"loris-nanni",fullName:"Loris Nanni",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/494/images/system/494.jpg",biography:"Loris Nanni received his Master Degree cum laude on June-2002 from the University of Bologna, and the April 26th 2006 he received his Ph.D. in Computer Engineering at DEIS, University of Bologna. On September, 29th 2006 he has won a post PhD fellowship from the university of Bologna (from October 2006 to October 2008), at the competitive examination he was ranked first in the industrial engineering area. He extensively served as referee for several international journals. He is author/coauthor of more than 100 research papers. He has been involved in some projects supported by MURST and European Community. His research interests include pattern recognition, bioinformatics, and biometric systems (fingerprint classification and recognition, signature verification, face recognition).",institutionString:null,institution:null},{id:"496",title:"Dr.",name:"Carlos",middleName:null,surname:"Leon",slug:"carlos-leon",fullName:"Carlos Leon",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Seville",country:{name:"Spain"}}},{id:"512",title:"Dr.",name:"Dayang",middleName:null,surname:"Jawawi",slug:"dayang-jawawi",fullName:"Dayang Jawawi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Technology Malaysia",country:{name:"Malaysia"}}},{id:"528",title:"Dr.",name:"Kresimir",middleName:null,surname:"Delac",slug:"kresimir-delac",fullName:"Kresimir Delac",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/528/images/system/528.jpg",biography:"K. Delac received his B.Sc.E.E. degree in 2003 and is currentlypursuing a Ph.D. degree at the University of Zagreb, Faculty of Electrical Engineering andComputing. His current research interests are digital image analysis, pattern recognition andbiometrics.",institutionString:null,institution:{name:"University of Zagreb",country:{name:"Croatia"}}},{id:"557",title:"Dr.",name:"Andon",middleName:"Venelinov",surname:"Topalov",slug:"andon-topalov",fullName:"Andon Topalov",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/557/images/1927_n.jpg",biography:"Dr. Andon V. Topalov received the MSc degree in Control Engineering from the Faculty of Information Systems, Technologies, and Automation at Moscow State University of Civil Engineering (MGGU) in 1979. He then received his PhD degree in Control Engineering from the Department of Automation and Remote Control at Moscow State Mining University (MGSU), Moscow, in 1984. From 1985 to 1986, he was a Research Fellow in the Research Institute for Electronic Equipment, ZZU AD, Plovdiv, Bulgaria. In 1986, he joined the Department of Control Systems, Technical University of Sofia at the Plovdiv campus, where he is presently a Full Professor. He has held long-term visiting Professor/Scholar positions at various institutions in South Korea, Turkey, Mexico, Greece, Belgium, UK, and Germany. And he has coauthored one book and authored or coauthored more than 80 research papers in conference proceedings and journals. His current research interests are in the fields of intelligent control and robotics.",institutionString:null,institution:{name:"Technical University of Sofia",country:{name:"Bulgaria"}}},{id:"585",title:"Prof.",name:"Munir",middleName:null,surname:"Merdan",slug:"munir-merdan",fullName:"Munir Merdan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/585/images/system/585.jpg",biography:"Munir Merdan received the M.Sc. degree in mechanical engineering from the Technical University of Sarajevo, Bosnia and Herzegovina, in 2001, and the Ph.D. degree in electrical engineering from the Vienna University of Technology, Vienna, Austria, in 2009.Since 2005, he has been at the Automation and Control Institute, Vienna University of Technology, where he is currently a Senior Researcher. His research interests include the application of agent technology for achieving agile control in the manufacturing environment.",institutionString:null,institution:null},{id:"605",title:"Prof",name:"Dil",middleName:null,surname:"Hussain",slug:"dil-hussain",fullName:"Dil Hussain",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/605/images/system/605.jpg",biography:"Dr. Dil Muhammad Akbar Hussain is a professor of Electronics Engineering & Computer Science at the Department of Energy Technology, Aalborg University Denmark. Professor Akbar has a Master degree in Digital Electronics from Govt. College University, Lahore Pakistan and a P-hD degree in Control Engineering from the School of Engineering and Applied Sciences, University of Sussex United Kingdom. Aalborg University has Two Satellite Campuses, one in Copenhagen (Aalborg University Copenhagen) and the other in Esbjerg (Aalborg University Esbjerg).\n· He is a member of prestigious IEEE (Institute of Electrical and Electronics Engineers), and IAENG (International Association of Engineers) organizations. \n· He is the chief Editor of the Journal of Software Engineering.\n· He is the member of the Editorial Board of International Journal of Computer Science and Software Technology (IJCSST) and International Journal of Computer Engineering and Information Technology. \n· He is also the Editor of Communication in Computer and Information Science CCIS-20 by Springer.\n· Reviewer For Many Conferences\nHe is the lead person in making collaboration agreements between Aalborg University and many universities of Pakistan, for which the MOU’s (Memorandum of Understanding) have been signed.\nProfessor Akbar is working in Academia since 1990, he started his career as a Lab demonstrator/TA at the University of Sussex. After finishing his P. hD degree in 1992, he served in the Industry as a Scientific Officer and continued his academic career as a visiting scholar for a number of educational institutions. In 1996 he joined National University of Science & Technology Pakistan (NUST) as an Associate Professor; NUST is one of the top few universities in Pakistan. In 1999 he joined an International Company Lineo Inc, Canada as Manager Compiler Group, where he headed the group for developing Compiler Tool Chain and Porting of Operating Systems for the BLACKfin processor. The processor development was a joint venture by Intel and Analog Devices. In 2002 Lineo Inc., was taken over by another company, so he joined Aalborg University Denmark as an Assistant Professor.\nProfessor Akbar has truly a multi-disciplined career and he continued his legacy and making progress in many areas of his interests both in teaching and research. 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Because of this aspect, the framework of modern forensic medicine includes a new field, that of forensic genetics, that mostly involves working with investigations that have human genotype identification as a goal.",book:{id:"5259",slug:"forensic-analysis-from-death-to-justice",title:"Forensic Analysis",fullTitle:"Forensic Analysis - From Death to Justice"},signatures:"Raluca Dumache, Veronica Ciocan, Camelia Muresan and Alexandra Enache",authors:[{id:"179199",title:"Dr.",name:"Raluca",middleName:null,surname:"Dumache",slug:"raluca-dumache",fullName:"Raluca Dumache"},{id:"181860",title:"Prof.",name:"Alexandra",middleName:null,surname:"Enache",slug:"alexandra-enache",fullName:"Alexandra Enache"},{id:"190151",title:"Dr.",name:"Camelia",middleName:null,surname:"Muresan",slug:"camelia-muresan",fullName:"Camelia Muresan"},{id:"190153",title:"Dr.",name:"Veronica",middleName:null,surname:"Ciocan",slug:"veronica-ciocan",fullName:"Veronica Ciocan"}]},{id:"19164",doi:"10.5772/19434",title:"Epidemiology and Diagnostic Problems of Electrical Injury in Forensic Medicine",slug:"epidemiology-and-diagnostic-problems-of-electrical-injury-in-forensic-medicine",totalDownloads:6433,totalCrossrefCites:5,totalDimensionsCites:6,abstract:null,book:{id:"243",slug:"forensic-medicine-from-old-problems-to-new-challenges",title:"Forensic Medicine",fullTitle:"Forensic Medicine - From Old Problems to New Challenges"},signatures:"William Dokov and Klara Dokova",authors:[{id:"34961",title:"Dr.",name:"Klara",middleName:null,surname:"Dokova",slug:"klara-dokova",fullName:"Klara Dokova"},{id:"34976",title:"Dr.",name:"Klara",middleName:null,surname:"Dokova",slug:"klara-dokova",fullName:"Klara Dokova"}]}],mostDownloadedChaptersLast30Days:[{id:"50789",title:"Molecular Genetics and its Applications in Forensic Sciences",slug:"molecular-genetics-and-its-applications-in-forensic-sciences",totalDownloads:4655,totalCrossrefCites:4,totalDimensionsCites:7,abstract:"The way to medico legal identification was open at the end of the twenty‐first century by the “digital fingerprinting” represented by the multifactorial phenotypical trait, determined by both polygenic and environmental factors, followed by group‐specific antigens, or with specificity for blood and tissue, and ending with the DNA molecule in use today. Because of this aspect, the framework of modern forensic medicine includes a new field, that of forensic genetics, that mostly involves working with investigations that have human genotype identification as a goal.",book:{id:"5259",slug:"forensic-analysis-from-death-to-justice",title:"Forensic Analysis",fullTitle:"Forensic Analysis - From Death to Justice"},signatures:"Raluca Dumache, Veronica Ciocan, Camelia Muresan and Alexandra Enache",authors:[{id:"179199",title:"Dr.",name:"Raluca",middleName:null,surname:"Dumache",slug:"raluca-dumache",fullName:"Raluca Dumache"},{id:"181860",title:"Prof.",name:"Alexandra",middleName:null,surname:"Enache",slug:"alexandra-enache",fullName:"Alexandra Enache"},{id:"190151",title:"Dr.",name:"Camelia",middleName:null,surname:"Muresan",slug:"camelia-muresan",fullName:"Camelia Muresan"},{id:"190153",title:"Dr.",name:"Veronica",middleName:null,surname:"Ciocan",slug:"veronica-ciocan",fullName:"Veronica Ciocan"}]},{id:"19160",title:"Death Scene Investigation from the Viewpoint of Forensic Medicine Expert",slug:"death-scene-investigation-from-the-viewpoint-of-forensic-medicine-expert",totalDownloads:27471,totalCrossrefCites:2,totalDimensionsCites:7,abstract:null,book:{id:"243",slug:"forensic-medicine-from-old-problems-to-new-challenges",title:"Forensic Medicine",fullTitle:"Forensic Medicine - From Old Problems to New Challenges"},signatures:"Serafettin Demirci and Kamil Hakan Dogan",authors:[{id:"30612",title:"Prof.",name:"Kamil Hakan",middleName:null,surname:"Dogan",slug:"kamil-hakan-dogan",fullName:"Kamil Hakan Dogan"},{id:"32211",title:"Dr.",name:"Serafettin",middleName:null,surname:"Demirci",slug:"serafettin-demirci",fullName:"Serafettin Demirci"}]},{id:"57199",title:"Negative Autopsy in Infant and Juvenile Population: Role of Cardiac Arrhythmias",slug:"negative-autopsy-in-infant-and-juvenile-population-role-of-cardiac-arrhythmias",totalDownloads:1417,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"Negative autopsy is a post-mortem examination in which a comprehensive analysis does not provide a cause of death. These include situation of death, anatomical and histological analysis, toxicology and microbiological study. A low part of autopsies remain without a conclusive cause of death, but all these cases are usually seen in young population, apparently healthy who died suddenly and unexpectedly. In these situations a cardiac arrhythmia is suspected as cause of death and genetic testing is recommended despite not regularly performed. Sudden death is a natural and unexpected decease that occurs in apparently healthy people, or whose disease was not severe enough to expect a fatal outcome. It can be due to several pathologies, usually of cardiac cause and called sudden cardiac death. In infants and young people, both long QT syndrome and catecholaminergic polymorphic ventricular tachycardia are main causes in negative autopsies. These genetic diseases lead to ventricular fibrillation, syncope and sudden cardiac death in a normal heart. Unfortunately, sudden cardiac death could be the first manifestation of the diseases, being early identification and prevention a crucial point in current medical practice. This chapter focuses on sudden death and negative autopsy in young population, mainly due to cardiac arrhythmias.",book:{id:"6262",slug:"post-mortem-examination-and-autopsy-current-issues-from-death-to-laboratory-analysis",title:"Post Mortem Examination and Autopsy",fullTitle:"Post Mortem Examination and Autopsy - Current Issues From Death to Laboratory Analysis"},signatures:"Georgia Sarquella-Brugada, Sergi Cesar, Anna Fernandez-Falgueras,\nMaria Dolores Zambrano, Anna Iglesias, Josep Brugada, Ramon\nBrugada and Oscar Campuzano",authors:[{id:"54165",title:"Prof.",name:"Ramon",middleName:null,surname:"Brugada",slug:"ramon-brugada",fullName:"Ramon Brugada"},{id:"54168",title:"Dr.",name:"Oscar",middleName:null,surname:"Campuzano",slug:"oscar-campuzano",fullName:"Oscar Campuzano"},{id:"218478",title:"Dr.",name:"Georgia",middleName:null,surname:"Sarquella-Brugada",slug:"georgia-sarquella-brugada",fullName:"Georgia Sarquella-Brugada"},{id:"218479",title:"Dr.",name:"Sergi",middleName:null,surname:"Cesar",slug:"sergi-cesar",fullName:"Sergi Cesar"},{id:"218480",title:"MSc.",name:"Anna",middleName:null,surname:"Fernandez-Falgueras",slug:"anna-fernandez-falgueras",fullName:"Anna Fernandez-Falgueras"},{id:"218482",title:"Dr.",name:"Maria Dolores",middleName:null,surname:"Zambrano",slug:"maria-dolores-zambrano",fullName:"Maria Dolores Zambrano"},{id:"218483",title:"MSc.",name:"Anna",middleName:null,surname:"Iglesias",slug:"anna-iglesias",fullName:"Anna Iglesias"},{id:"218484",title:"Prof.",name:"Josep",middleName:null,surname:"Brugada",slug:"josep-brugada",fullName:"Josep Brugada"}]},{id:"57778",title:"Defining Dental Age for Chronological Age Determination",slug:"defining-dental-age-for-chronological-age-determination",totalDownloads:2606,totalCrossrefCites:1,totalDimensionsCites:3,abstract:"Dental age assessment is one of the most reliable methods of chronological age estimation used for criminal, forensic and anthropologic purposes. Visual, radiographic, chemical and histological techniques can be used for dental age estimation. Visual method is based on the sequence of eruption of the teeth and morphological changes that are caused due to function such as attrition, changes in color that are indicators of aging. Radiographs of the dentition can be used to determine the stage of dental development of the teeth from initial mineralization of a tooth, crown formation to root apex maturation. Histological methods require the preparation of the tissues for detailed microscopic examination. The chemical analysis of dental hard tissues determines alterations in ion levels with age, whereas the histological and chemical methods are invasive methods requiring extraction/sectioning of the tooth. In this chapter, the different techniques and considered studies were overviewed in conjunction with their advantages and disadvantages. It needs to be taken into consideration that rather than restricting on one age estimation technique, using the other available techniques additionally and performing repetitive measurements may be beneficial for accurate age estimation.",book:{id:"6262",slug:"post-mortem-examination-and-autopsy-current-issues-from-death-to-laboratory-analysis",title:"Post Mortem Examination and Autopsy",fullTitle:"Post Mortem Examination and Autopsy - Current Issues From Death to Laboratory Analysis"},signatures:"Fatma Deniz Uzuner, Emine Kaygısız and Nilüfer Darendeliler",authors:[{id:"172009",title:"Dr.",name:"Fatma Deniz",middleName:null,surname:"Uzuner",slug:"fatma-deniz-uzuner",fullName:"Fatma Deniz Uzuner"},{id:"200985",title:"Dr.",name:"Emine",middleName:null,surname:"Kaygisiz",slug:"emine-kaygisiz",fullName:"Emine Kaygisiz"},{id:"222232",title:"Prof.",name:"Nilufer",middleName:null,surname:"Darendeliler",slug:"nilufer-darendeliler",fullName:"Nilufer Darendeliler"}]},{id:"50757",title:"Forensic Analysis of the Wakayama Arsenic Murder Case",slug:"forensic-analysis-of-the-wakayama-arsenic-murder-case",totalDownloads:2582,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"This is a review paper of forensic analysis of a murder case of Wakayama arsenic poisoning incident. The influence of this case on scientific research was not small in such a way that papers related to PTSD, disaster medical, copycats, chemical analysis, unwanted chemicals in food, terrorism, and so on were published. The forensic analyses on Wakayama arsenic poisoning incidence have characteristic that SPring-8, a largest synchrotron radiation facility, was used, as well as many other analytical techniques, but now most of the forensic analyses submitted from the prosecutor have been revealed to be fabrication, hiding the truth by logarithmic calculations, and therefore not scientific. Most of the testimonies at the court by the analysts were also lies. Examples of such false analyses are explained.",book:{id:"5259",slug:"forensic-analysis-from-death-to-justice",title:"Forensic Analysis",fullTitle:"Forensic Analysis - From Death to Justice"},signatures:"Jun Kawai",authors:[{id:"180878",title:"Prof.",name:"Jun",middleName:null,surname:"Kawai",slug:"jun-kawai",fullName:"Jun Kawai"}]}],onlineFirstChaptersFilter:{topicId:"180",limit:6,offset:0},onlineFirstChaptersCollection:[],onlineFirstChaptersTotal:0},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:8,limit:8,total:0},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:9,numberOfPublishedChapters:90,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:108,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:33,numberOfPublishedChapters:330,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:14,numberOfPublishedChapters:145,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:9,numberOfPublishedChapters:140,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!0},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:123,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:11,numberOfPublishedChapters:112,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2632-0517",doi:"10.5772/intechopen.73681",isOpenForSubmission:!0}],sshSeriesList:[{id:"22",title:"Business, Management and Economics",numberOfPublishedBooks:1,numberOfPublishedChapters:22,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:11,numberOfOpenTopics:1,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!0},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:5,numberOfUpcomingTopics:0,issn:"2753-6580",doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. 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Saxena is a vice dean and professor at King George's Medical University, Lucknow, India. His research interests involve understanding the molecular mechanisms of host defense during human viral infections and developing new predictive, preventive, and therapeutic strategies for them using Japanese encephalitis virus (JEV), HIV, and emerging viruses as a model via stem cell and cell culture technologies. His research work has been published in various high-impact factor journals (Science, PNAS, Nature Medicine) with a high number of citations. He has received many awards and honors in India and abroad including various Young Scientist Awards, BBSRC India Partnering Award, and Dr. JC Bose National Award of Department of Biotechnology, Min. of Science and Technology, Govt. of India. 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She is currently an Adjunct Professor at Feevale University in Medicine and Biomedicine courses and a permanent professor of the Academic Master\\'s Degree in Virology. She has experience in the field of Microbiology, with an emphasis on Bacteriology, working mainly on the following topics: bacteriophages, bacterial resistance, clinical microbiology and food microbiology.",institutionString:null,institution:{name:"Universidade Feevale",country:{name:"Brazil"}}},{id:"229220",title:"Dr.",name:"Amjad",middleName:"Islam",surname:"Aqib",slug:"amjad-aqib",fullName:"Amjad Aqib",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/229220/images/system/229220.png",biography:"Dr. Amjad Islam Aqib obtained a DVM and MSc (Hons) from University of Agriculture Faisalabad (UAF), Pakistan, and a PhD from the University of Veterinary and Animal Sciences Lahore, Pakistan. Dr. Aqib joined the Department of Clinical Medicine and Surgery at UAF for one year as an assistant professor where he developed a research laboratory designated for pathogenic bacteria. Since 2018, he has been Assistant Professor/Officer in-charge, Department of Medicine, Manager Research Operations and Development-ORIC, and President One Health Club at Cholistan University of Veterinary and Animal Sciences, Bahawalpur, Pakistan. He has nearly 100 publications to his credit. His research interests include epidemiological patterns and molecular analysis of antimicrobial resistance and modulation and vaccine development against animal pathogens of public health concern.",institutionString:"Cholistan University of Veterinary and Animal Sciences",institution:{name:"University of Agriculture Faisalabad",country:{name:"Pakistan"}}},{id:"333753",title:"Dr.",name:"Rais",middleName:null,surname:"Ahmed",slug:"rais-ahmed",fullName:"Rais Ahmed",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/333753/images/20168_n.jpg",biography:null,institutionString:null,institution:{name:"University of Agriculture Faisalabad",country:{name:"Pakistan"}}},{id:"62900",title:"Prof.",name:"Fethi",middleName:null,surname:"Derbel",slug:"fethi-derbel",fullName:"Fethi Derbel",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/62900/images/system/62900.jpeg",biography:"Professor Fethi Derbel was born in 1960 in Tunisia. He received his medical degree from the Sousse Faculty of Medicine at Sousse, University of Sousse, Tunisia. He completed his surgical residency in General Surgery at the University Hospital Farhat Hached of Sousse and was a member of the Unit of Liver Transplantation in the University of Rennes, France. He then worked in the Department of Surgery at the Sahloul University Hospital in Sousse. Professor Derbel is presently working at the Clinique les Oliviers, Sousse, Tunisia. His hospital activities are mostly concerned with laparoscopic, colorectal, pancreatic, hepatobiliary, and gastric surgery. He is also very interested in hernia surgery and performs ventral hernia repairs and inguinal hernia repairs. He has been a member of the GREPA and Tunisian Hernia Society (THS). During his residency, he managed patients suffering from diabetic foot, and he was very interested in this pathology. For this reason, he decided to coordinate a book project dealing with the diabetic foot. Professor Derbel has published many articles in journals and collaborates intensively with IntechOpen Access Publisher as an editor.",institutionString:"Clinique les Oliviers",institution:null},{id:"300144",title:"Dr.",name:"Meriem",middleName:null,surname:"Braiki",slug:"meriem-braiki",fullName:"Meriem Braiki",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/300144/images/system/300144.jpg",biography:"Dr. Meriem Braiki is a specialist in pediatric surgeon from Tunisia. She was born in 1985. She received her medical degree from the University of Medicine at Sousse, Tunisia. She achieved her surgical residency training periods in Pediatric Surgery departments at University Hospitals in Monastir, Tunis and France.\r\nShe is currently working at the Pediatric surgery department, Sidi Bouzid Hospital, Tunisia. Her hospital activities are mostly concerned with laparoscopic, parietal, urological and digestive surgery. She has published several articles in diffrent journals.",institutionString:"Sidi Bouzid Regional Hospital",institution:null},{id:"229481",title:"Dr.",name:"Erika M.",middleName:"Martins",surname:"de Carvalho",slug:"erika-m.-de-carvalho",fullName:"Erika M. de Carvalho",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/229481/images/6397_n.jpg",biography:null,institutionString:null,institution:{name:"Oswaldo Cruz Foundation",country:{name:"Brazil"}}},{id:"186537",title:"Prof.",name:"Tonay",middleName:null,surname:"Inceboz",slug:"tonay-inceboz",fullName:"Tonay Inceboz",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/186537/images/system/186537.jfif",biography:"I was graduated from Ege University of Medical Faculty (Turkey) in 1988 and completed his Med. PhD degree in Medical Parasitology at the same university. I became an Associate Professor in 2008 and Professor in 2014. I am currently working as a Professor at the Department of Medical Parasitology at Dokuz Eylul University, Izmir, Turkey.\n\nI have given many lectures, presentations in different academic meetings. I have more than 60 articles in peer-reviewed journals, 18 book chapters, 1 book editorship.\n\nMy research interests are Echinococcus granulosus, Echinococcus multilocularis (diagnosis, life cycle, in vitro and in vivo cultivation), and Trichomonas vaginalis (diagnosis, PCR, and in vitro cultivation).",institutionString:"Dokuz Eylül University",institution:{name:"Dokuz Eylül University",country:{name:"Turkey"}}},{id:"71812",title:"Prof.",name:"Hanem Fathy",middleName:"Fathy",surname:"Khater",slug:"hanem-fathy-khater",fullName:"Hanem Fathy Khater",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/71812/images/1167_n.jpg",biography:"Prof. Khater is a Professor of Parasitology at Benha University, Egypt. She studied for her doctoral degree, at the Department of Entomology, College of Agriculture, Food and Natural Resources, University of Missouri, Columbia, USA. She has completed her Ph.D. degrees in Parasitology in Egypt, from where she got the award for “the best scientific Ph.D. dissertation”. She worked at the School of Biological Sciences, Bristol, England, the UK in controlling insects of medical and veterinary importance as a grant from Newton Mosharafa, the British Council. Her research is focused on searching of pesticides against mosquitoes, house flies, lice, green bottle fly, camel nasal botfly, soft and hard ticks, mites, and the diamondback moth as well as control of several parasites using safe and natural materials to avoid drug resistances and environmental contamination.",institutionString:null,institution:{name:"Banha University",country:{name:"Egypt"}}},{id:"99780",title:"Prof.",name:"Omolade",middleName:"Olayinka",surname:"Okwa",slug:"omolade-okwa",fullName:"Omolade Okwa",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/99780/images/system/99780.jpg",biography:"Omolade Olayinka Okwa is presently a Professor of Parasitology at Lagos State University, Nigeria. She has a PhD in Parasitology (1997), an MSc in Cellular Parasitology (1992), and a BSc (Hons) Zoology (1990) all from the University of Ibadan, Nigeria. She teaches parasitology at the undergraduate and postgraduate levels. She was a recipient of a Commonwealth fellowship supported by British Council tenable at the Centre for Entomology and Parasitology (CAEP), Keele University, United Kingdom between 2004 and 2005. She was awarded an Honorary Visiting Research Fellow at the same university from 2005 to 2007. \nShe has been an external examiner to the Department of Veterinary Microbiology and Parasitology, University of Ibadan, MSc programme between 2010 and 2012. She is a member of the Nigerian Society of Experimental Biology (NISEB), Parasitology and Public Health Society of Nigeria (PPSN), Science Association of Nigeria (SAN), Zoological Society of Nigeria (ZSN), and is Vice Chairperson of the Organisation of Women in Science (OWSG), LASU chapter. She served as Head of Department of Zoology and Environmental Biology, Lagos State University from 2007 to 2010 and 2014 to 2016. She is a reviewer for several local and international journals such as Unilag Journal of Science, Libyan Journal of Medicine, Journal of Medicine and Medical Sciences, and Annual Research and Review in Science. \nShe has authored 45 scientific research publications in local and international journals, 8 scientific reviews, 4 books, and 3 book chapters, which includes the books “Malaria Parasites” and “Malaria” which are IntechOpen access publications.",institutionString:"Lagos State University",institution:{name:"Lagos State University",country:{name:"Nigeria"}}},{id:"273100",title:"Dr.",name:"Vijay",middleName:null,surname:"Gayam",slug:"vijay-gayam",fullName:"Vijay Gayam",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/273100/images/system/273100.jpeg",biography:"Dr. Vijay Bhaskar Reddy Gayam is currently practicing as an internist at Interfaith Medical Center in Brooklyn, New York, USA. He is also a Clinical Assistant Professor at the SUNY Downstate University Hospital and Adjunct Professor of Medicine at the American University of Antigua. He is a holder of an M.B.B.S. degree bestowed to him by Osmania Medical College and received his M.D. at Interfaith Medical Center. His career goals thus far have heavily focused on direct patient care, medical education, and clinical research. He currently serves in two leadership capacities; Assistant Program Director of Medicine at Interfaith Medical Center and as a Councilor for the American\r\nFederation for Medical Research. As a true academician and researcher, he has more than 50 papers indexed in international peer-reviewed journals. He has also presented numerous papers in multiple national and international scientific conferences. His areas of research interest include general internal medicine, gastroenterology and hepatology. He serves as an editor, editorial board member and reviewer for multiple international journals. His research on Hepatitis C has been very successful and has led to multiple research awards, including the 'Equity in Prevention and Treatment Award” from the New York Department of Health Viral Hepatitis Symposium (2018) and the 'Presidential Poster Award” awarded to him by the American College of Gastroenterology (2018). He was also awarded 'Outstanding Clinician in General Medicine” by Venus International Foundation for his extensive research expertise and services, perform over and above the standard expected in the advancement of healthcare, patient safety and quality of care.",institutionString:"Interfaith Medical Center",institution:{name:"Interfaith Medical Center",country:{name:"United States of America"}}},{id:"93517",title:"Dr.",name:"Clement",middleName:"Adebajo",surname:"Meseko",slug:"clement-meseko",fullName:"Clement Meseko",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/93517/images/system/93517.jpg",biography:"Dr. Clement Meseko obtained DVM and PhD degree in Veterinary Medicine and Virology respectively. He has worked for over 20 years in both private and public sectors including the academia, contributing to knowledge and control of infectious disease. Through the application of epidemiological skill, classical and molecular virological skills, he investigates viruses of economic and public health importance for the mitigation of the negative impact on people, animal and the environment in the context of Onehealth. \r\nDr. Meseko’s field experience on animal and zoonotic diseases and pathogen dynamics at the human-animal interface over the years shaped his carrier in research and scientific inquiries. He has been part of the investigation of Highly Pathogenic Avian Influenza incursions in sub Saharan Africa and monitors swine Influenza (Pandemic influenza Virus) agro-ecology and potential for interspecies transmission. He has authored and reviewed a number of journal articles and book chapters.",institutionString:"National Veterinary Research Institute",institution:{name:"National Veterinary Research Institute",country:{name:"Nigeria"}}},{id:"158026",title:"Prof.",name:"Shailendra K.",middleName:null,surname:"Saxena",slug:"shailendra-k.-saxena",fullName:"Shailendra K. Saxena",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRET3QAO/Profile_Picture_2022-05-10T10:10:26.jpeg",biography:"Professor Dr. Shailendra K. Saxena is a vice dean and professor at King George's Medical University, Lucknow, India. His research interests involve understanding the molecular mechanisms of host defense during human viral infections and developing new predictive, preventive, and therapeutic strategies for them using Japanese encephalitis virus (JEV), HIV, and emerging viruses as a model via stem cell and cell culture technologies. His research work has been published in various high-impact factor journals (Science, PNAS, Nature Medicine) with a high number of citations. He has received many awards and honors in India and abroad including various Young Scientist Awards, BBSRC India Partnering Award, and Dr. JC Bose National Award of Department of Biotechnology, Min. of Science and Technology, Govt. of India. Dr. Saxena is a fellow of various international societies/academies including the Royal College of Pathologists, United Kingdom; Royal Society of Medicine, London; Royal Society of Biology, United Kingdom; Royal Society of Chemistry, London; and Academy of Translational Medicine Professionals, Austria. He was named a Global Leader in Science by The Scientist. He is also an international opinion leader/expert in vaccination for Japanese encephalitis by IPIC (UK).",institutionString:"King George's Medical University",institution:{name:"King George's Medical University",country:{name:"India"}}},{id:"94928",title:"Dr.",name:"Takuo",middleName:null,surname:"Mizukami",slug:"takuo-mizukami",fullName:"Takuo Mizukami",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/94928/images/6402_n.jpg",biography:null,institutionString:null,institution:{name:"National Institute of Infectious Diseases",country:{name:"Japan"}}},{id:"233433",title:"Dr.",name:"Yulia",middleName:null,surname:"Desheva",slug:"yulia-desheva",fullName:"Yulia Desheva",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/233433/images/system/233433.png",biography:"Dr. Yulia Desheva is a leading researcher at the Institute of Experimental Medicine, St. Petersburg, Russia. She is a professor in the Stomatology Faculty, St. Petersburg State University. She has expertise in the development and evaluation of a wide range of live mucosal vaccines against influenza and bacterial complications. Her research interests include immunity against influenza and COVID-19 and the development of immunization schemes for high-risk individuals.",institutionString:'Federal State Budgetary Scientific Institution "Institute of Experimental Medicine"',institution:null},{id:"238958",title:"Mr.",name:"Atamjit",middleName:null,surname:"Singh",slug:"atamjit-singh",fullName:"Atamjit Singh",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/238958/images/6575_n.jpg",biography:null,institutionString:null,institution:null},{id:"252058",title:"M.Sc.",name:"Juan",middleName:null,surname:"Sulca",slug:"juan-sulca",fullName:"Juan Sulca",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/252058/images/12834_n.jpg",biography:null,institutionString:null,institution:null},{id:"191392",title:"Dr.",name:"Marimuthu",middleName:null,surname:"Govindarajan",slug:"marimuthu-govindarajan",fullName:"Marimuthu Govindarajan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/191392/images/5828_n.jpg",biography:"Dr. M. 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He serves as an editorial board member in various national and international scientific journals.",institutionString:null,institution:null},{id:"274660",title:"Dr.",name:"Damodar",middleName:null,surname:"Paudel",slug:"damodar-paudel",fullName:"Damodar Paudel",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/274660/images/8176_n.jpg",biography:"I am DrDamodar Paudel,currently working as consultant Physician in Nepal police Hospital.",institutionString:null,institution:null},{id:"241562",title:"Dr.",name:"Melvin",middleName:null,surname:"Sanicas",slug:"melvin-sanicas",fullName:"Melvin Sanicas",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/241562/images/6699_n.jpg",biography:null,institutionString:null,institution:null},{id:"117248",title:"Dr.",name:"Andrew",middleName:null,surname:"Macnab",slug:"andrew-macnab",fullName:"Andrew Macnab",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of British Columbia",country:{name:"Canada"}}},{id:"322007",title:"Dr.",name:"Maria Elizbeth",middleName:null,surname:"Alvarez-Sánchez",slug:"maria-elizbeth-alvarez-sanchez",fullName:"Maria Elizbeth Alvarez-Sánchez",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Universidad Autónoma de la Ciudad de México",country:{name:"Mexico"}}},{id:"337443",title:"Dr.",name:"Juan",middleName:null,surname:"A. 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