\r\n\tLiterature showed the presence of ACE2 receptors on the membrane of erythrocyte or red blood cell (RBC), indicating that erythrocyte (RBC) can be considered as a peripheral biomarker for SARS-C0V2 infection.
\r\n\r\n\tIncreased levels of glycolysis and fragmentation of RBC membrane proteins were observed in the SARS-C0V2 infected patients, demonstrating that not only RBC’s metabolism and proteome but its membrane lipidome could be influenced by SARS-C0V2 infection changing the homeostasis of the infected erythrocyte. This altered RBC may result in the clot and thrombus formation; the major signs of critically ill Covid-19 patients.
\r\n\r\n\tThis book is going to be a succinct source of knowledge not only for the specialists, researchers, academics and the students in this area but for the general public who are concern about the present situation and are interested in knowing about simple non-invasive measures for identifying viral and bacterial infections through their red blood cells.
",isbn:"978-1-83969-121-8",printIsbn:"978-1-83969-120-1",pdfIsbn:"978-1-83969-122-5",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"fa5f4b6ef59e28b6e7c1a739c57c5d2f",bookSignature:"Prof. Kaneez Fatima Shad",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/10494.jpg",keywords:"Spike Protein, Hemoglobin, Proteins for Oxygen Transport, Altered Protein Structures, RBC ACE Receptors, RBC ACE-2 Receptors, Carboxypeptidase, Mas Receptor, Metabolomics, Gas Transport, Glucose-6-Phosphate, Phosphoglycerate",numberOfDownloads:10,numberOfWosCitations:0,numberOfCrossrefCitations:0,numberOfDimensionsCitations:0,numberOfTotalCitations:0,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"October 15th 2020",dateEndSecondStepPublish:"November 30th 2020",dateEndThirdStepPublish:"January 29th 2021",dateEndFourthStepPublish:"April 19th 2021",dateEndFifthStepPublish:"June 18th 2021",remainingDaysToSecondStep:"3 months",secondStepPassed:!0,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"Dr. Shad is a governing body member and mentor of Women in World Neuroscience (WWN), a division of the International Brain Research Organization (IBRO). She is also a member of IBRO-APRC Global Advocacy responsible for brain research funding distribution in this region.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"31988",title:"Prof.",name:"Kaneez",middleName:null,surname:"Fatima Shad",slug:"kaneez-fatima-shad",fullName:"Kaneez Fatima Shad",profilePictureURL:"https://mts.intechopen.com/storage/users/31988/images/system/31988.jpg",biography:"Professor Kaneez Fatima Shad, a neuroscientist with a medical background, received Ph.D. in 1994 from the Faculty of Medicine, UNSW, Australia, followed by a post-doc at the Allegheny University of Health Sciences, Philadelphia, USA. She taught Medical and Biological Sciences in various universities in Australia, the USA, UAE, Bahrain, Pakistan, and Brunei. During this period, she was also engaged in doing research by getting local and international grants (total of over 3.3 million USD) and translating them into products such as a rapid diagnostic test for stroke and other vascular disorders. She published over 60 articles in refereed journals, edited 8 books, and wrote 7 book chapters, presented at 97 international conferences, mentored 34 postgraduate students. Set up a company Shad Diagnostics for the development of cerebrovascular handheld diagnostic tool Stroke meter into a wearable.",institutionString:"University of Technology Sydney",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"4",totalChapterViews:"0",totalEditedBooks:"6",institution:{name:"University of Technology Sydney",institutionURL:null,country:{name:"Australia"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"16",title:"Medicine",slug:"medicine"}],chapters:[{id:"75447",title:"Detection of Benzo[a]Pyrene Diol Epoxide-DNA Adducts in White Blood Cells of Asphalt Plant Workers in Syria",slug:"detection-of-benzo-a-pyrene-diol-epoxide-dna-adducts-in-white-blood-cells-of-asphalt-plant-workers-i",totalDownloads:10,totalCrossrefCites:0,authors:[null]}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"280415",firstName:"Josip",lastName:"Knapic",middleName:null,title:"Mr.",imageUrl:"https://mts.intechopen.com/storage/users/280415/images/8050_n.jpg",email:"josip@intechopen.com",biography:"As an Author Service Manager my responsibilities include monitoring and facilitating all publishing activities for authors and editors. From chapter submission and review, to approval and revision, copy-editing and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. 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Volume refers to the huge amount of data being generated by several sources. Velocity refers to the rate at which this data is being generated and the variety refers to the different type of the data being used [1]. Now a days with so much of data all around the world, the trend in healthcare is shifting from cure to prevention. Hospitals and healthcare systems are good repositories of big data (like patient records, test reports, medical images etc.) that can be utilized to cut the cost in healthcare, to improve reliability and efficiency, and to provide better cure to patients.
\nHealthcare applications require large amounts of computational and communication resources, and involve dynamic access to large amounts of data within and outside the health organization leading to the need for networked healthcare [2]. Data Analysis has always in demand in all the industry as it gives the approximate prediction of how the market is growing [3]. Although the innovations are in the healthcare field, there are some issues that need to be solved, particularly the heterogeneous data fusion and the open platform for data access and analysis [4].
\nToday, the healthcare industry is turning to big data technology to improve and manage medial systems. For this purpose, healthcare companies and organizations are leveraging big data in health informatics [5]. The analysis of big data carried out through different ways. Machine learning algorithm helps in analysis of big data very efficiently [3].
\nFeature selection is an important preprocessing technique used before data mining so that it can reduce the computational complexity of the learning algorithm and remove irrelevant/redundant features to remove noise [16]. Decision Tree is a predictive model of classification, which can be viewed as a Tree like structure [6]. It is simple and gives a fast and accurate result.
\nNeural Network is one of the other machine learning algorithms which showed a lot of modification. Neural Network is an adaptive learning model which adjusts the weight of the connecting links between its neuron [15]. K-Nearest Neighbor model of classification is one of the simplest classification algorithm which work on the classifying the data set based on the nearest neighbor of the existing class label of already trained mode [7]. Naïve Bayesian Classifier has a very good accuracy in classification for large set of data [8].
\nClustering algorithm makes the groups or clusters of homogenous data. It is an unsupervised learning technique. In Partitioned Clustering the number of cluster was defined beforehand. In Hierarchical Clustering we do not need to define the number of clusters in advance [9, 10]. In both of the above approaches the stopping criterion is usually the number of clusters to be achieved; once the required number is achieved the algorithm can be stopped. Different methods are used for the analysis of Big Data in Health Care has been discussed below.
\nAbdulsalam Yassine et al. [11] have proposed a model that utilizes smart home big data as a means of learning and discovering human activity patterns for health care applications. They proposed the use of frequent pattern mining, cluster analysis and prediction to measure and analyze energy usage changes sparked by occupants’ behavior.
\nMd. Mofijul Islam et al. [12] have proposed a mobility- and resource aware joint virtual-machine migration model for heterogeneous mobile cloud computing systems to improve the performance of mobile Smart health care applications in a Smart City environment.
\nMohammad-Parsa Hosseini et al. [13] focused on an autonomic edge computing framework for processing of big data as part of a decision support system for surgical candidacy, an optimized model for estimation of the epileptogenic network, and an unsupervised feature extraction model.
\nBernhard Schölkopf et al. [14] have designed a class of support vector algorithms for regression and classification.
\nChandra et al. [15] have proposed a approach for using MLP to handle Big data. There was high computational cost and time involved in using MLP for classification of Big data having large number of features. This is a promising technique for handling big data and is the idea extracted for the present research work.
\nHuan Liu et al. [16] have introduced a concepts and algorithms of feature selection, surveys existing feature selection algorithms for classification and clustering, groups and compares different algorithms with a categorizing framework based on search strategies, evaluation criteria, and data mining tasks.
\nMalika Bendechache et al. [17] have proposed a distributed clustering approach to deal efficiently with both phases; generation of local results and generation of global models by aggregation.
\nThe large amounts of data, driven by record keeping, compliance & regulatory requirements, and patient care will historically render for the healthcare industry. While most data is saved in hard copy form, the current trend is towards quick digitization of these large amounts of data. Driven by mandatory requirements and the potential to develop the quality of healthcare delivery meanwhile minimizing the costs, these massive quantities of data known as ‘big data’ hold the promise of supporting a wide range of medical and healthcare functions, admitting between others clinical decision support, disease surveillance, and population health management. Some troubles that exist in big data analysis in health care are, i) to succeed, big data analytics in healthcare requires to be packaged so it is menu driven, user-friendly and transparent. ii) The lag among data collection and processing has to be addressed. iii) The crucial managerial issues of ownership, governance and standards have to be conceived. iv) Continuous data acquisition and data cleansing is another issue.
\nIn the increasingly quick generation of large amounts of data, and across several areas of science, technological and conceptual advances are resulting. The collection and organization of data, the volume, variety, and velocity of current ‘big data’ production inaugurates novel opportunities and challenges in both scale and complexity these are always admitted on research. Also, in health care sector, the dealing of big data has currently get an interesting research topic, as since there are wide amount of medical data’s available in cloud storage.
\nMoreover, the huge number of data records within very large datasets that comprise an extremely high amount of information is conceived to be a very critical issue. Thus processing with sequential algorithm results in greater computational cost in terms of memory space and time complexities. Hence, for discovering the above mentioned issues, a parallel architecture is required to be demonstrated.
\nIn order to minimize the computational complexity and the memory requirement while leading large healthcare data, it is suggested to have a parallel adaptive artificial neural network (AANN) technique applying Map-Reduce programming model for health care analysis from big data in cloud environment. The introduction of abnormality in the medical data records applying the proposed Map-Reduce based Adaptive Artificial Neural Network classification method by the trained data these are determined by suggested approach. The medical data from the cloud has to be first clustered in order to distinguish the similar classes of data associated to any one particular health disorder for better classification of data. Here, the clustering of similar sets of data is done with the help of Fuzzy C means clustering algorithm, so as to develop the classification performance. The dataset separated as sub clusters were afforded to Map-Reducer framework, where AANN is implemented in parallel. Once the clustering is done the normal and abnormal classes of medical data are then learned applying the proposed map decrease programming model based AANN. By training the AANN models, it can be capable to predict for newer data as well.
\nThe map minimized programming model comprises of two phases: (1) Mapper phase and (2) Reducer phase. Data belonging to each cluster are mapped applying separate mappers. Each mapper based AANN receives one training item (i.e. any one data cluster) and then calculates the weights of the network applying the training item in the suggested parallel prediction model. Here, to develop the precision of classification of the data, the proposed AANN method applies the concept of optimization, where the weight factors are maximized by applying Whale Optimization Algorithm. The Reducer separates the test medical record in order to distinguish the health condition established on the mapped data.
\nHere, the schematic diagram of the proposed healthcare application model for the analysis of large datasets is presented in Figure 1.
\nSchematic diagram.
The proposed FCM based Map-Reduce AANN approach comprises of the following phases, 1)Fuzzy C-means (FCM) based Data Grouping 2) Mapper phase involving assigning each data groups to separate Mappers and training Data using Adaptive ANN 3) Reducer Phase consisting of Testing Phase. Each of the steps is detailed in the following sections.
\nEstablished on the membership function, Fuzzy C-means (FCM) is a data clustering technique in which each and every data in that group will comes under one cluster. It will group all the data in to particular number of clusters in high dimensional search space. The degrees of the cluster are determined by the membership function in terms of [0, 1] which affords the flexibility that the data point can belong to more than one cluster.
\nThe proposed method applies FCM for clustering the input large data into smaller groups of similar data. The input data will be grouped into number of clusters randomly and the centroids will be rendered for the clusters during Fuzzy c-means clustering. The clusters are updated established on the membership grade of the data points and the novel centroid is depicted correspondingly at the each iteration.
\nMoreover, how the clustering with fuzzy c means algorithm is made for a set of input samples is afforded below.
\nLet us considering the input sample be,
\nThe input sample is to be separated into ‘\n
The objective function of FCM algorithm is effectively explained as follows.
\nwhere,
\n“\n
“\n
“\n
“\n
“\n
“\n
Now the cluster center calculation is done by Eq. (3),
\nMembership updation is done by Eq. (4),
\nwhere, ‘\n
The input data is clustered into data groups of certain similarity for established on the above procedure of FCM. We found the number of cluster set such as \n
For large scale mobile data process, the mapper is a programming model and a connected implementation. Programmers only required to specify a Map-Reduce job which is composed of Reducer functions and the mapper. A Mapper function receives a key/value pair and generates a set of intermediate key/value pairs. With the same intermediate key, and a Reducer function merges all intermediate values are connected. Here, in parallel, the Mapper receives the clustered data and trains the AANN. Then established on all the Mappers output network model, the Reducers improve an AANN model to predict for unknown/newer data.
\nIn the Mapper phase, the clustered data is now processed. Mapper receives several items of the training sets (i.e. data items from the cluster groups) and accomplishes many mapper tasks. Each Mapper receives one training item (i.e. data items from one cluster group) and then performs AANN learning/training task by maximizing the weight values in the network applying this training item; so as to develop the learning efficiency. Through the AANN algorithm, their outputs are the trained network models resulted. The Mapper process (i.e. the AANN training procedure) is accomplished repeatedly until the expected precision is attained.
\nArtificial neural network is otherwise named as Neural Network (NN). For calculation, it contains of an interconnected collecting of artificial neurons and procedures data applying a connectionist approach. Here a feed forward neural network (FFNN) inaugurated by this work. The data moves in just a single direction, forward, from the input layers, through the hidden layers, and to the output layers by this system. There are no cycles or circles in the system. The information handling can stretch out over numerous (layers of) units, yet no criticism associations are available, that is, an association reaching out from outputs of units to contributions of units in a similar layer or past layers is not present. There are associations among the processing elements (PEs) in every layer that have a weight (parameter) connected with them. Amid preparing this weight is balanced. The proposed adaptive ANN renders the optimal training network aligned by optimally selecting the interconnection weights among the hidden and output layers applying Whale Optimization Algorithm.
\nInput information is displayed to the system and proliferated through the system until it attains the output layer in FFNN. A predicted output is delivered by this forward procedure. The desired output is subtracted from the actual output and error esteem for the systems is ascertained. The error function can be characterized as:
\nFor altering weights, a couple of traditional researches has applied Backpropagation learning algorithm. In reverse through the system, the calculation begins with the weights among the output layer PE’s and the last hidden layer PE’s and works. Once back propagation has fulfilled, the forward procedure begins once more, and this cycle proceeds until the error among is predicted and actual output are reduced. Rather than back propagation algorithm, the Whale Optimization algorithm is displayed because it can acquire valuable output than back propagation calculation.
\nThe proposed Adaptive Artificial Neural Network model is given in below Figure 2.
Whale optimization approach
Proposed adaptive artificial neural network.
Recently a novel optimization algorithm named whale optimization algorithm (Mirjalili 2016) has been introduced to metaheuristic algorithm by Mirjalili and Lewis. As highly intelligent animals with motion, the whales are conceived. The WOA is inspired by the unique hunting behavior of humpback whales. The humpback whales prefer to hunt krills or small fishes which are close to the surface of sea at normally. Humpback whales use a special unique hunting method named bubble net feeding method. In this method they swim around the prey and produce distinctive bubbles along a circle or 9-shaped path. The mathematical model of WOA is described in the following sections a) Encircling prey b) Bubble net hunting method and c) Search the prey. The steps admitted in the proposed Whale optimization algorithm for rendering the optimal network structure by maximizing the interconnection weights of the neurons are afforded as follows,
\nStep 1: Initialization.
\nThe algorithm is showed by arbitrarily generating the solutions (i.e. the interconnection weight values) that communicates to the result. Here the neural network structure comprising the interconnection weights among the hidden layers and the output layers are referred by the random value in the search space is afforded as:
\nwhere, \n
Step 2: Fitness Calculation.
\nDetermine the fitness of the input solutions on the basis of the Eq. (7). To get the best weight values, the fitness value of the solutions are computed. It’s revealed in below,
\nThe minimum of mean square error (MSE) determines that, how correct the network predicted targets are (i.e. high classification accuracy) in above equation. Hence, for further development, the initial solution with minimum error is chosen as best solution and checked.
\nStep 3: Update position of current solutions towards the best
\nA. Encircling prey
\nThe position of prey (i.e. the current best solution) is distinguished by humpback whale and then it encircles the prey. Towards the best search operator the other search operators will consequently attempt to update their positions when the best search agent is characterized. The updation method is determined by the below equations:
\nwhere ‘\n
The vectors \n
where, \n
B. Bubble-net attacking method (exploitation phase)
\nTo model the bubble-net behavior of humpback whales mathematically two approaches developed are a) Shrinking encircling mechanism and b) Spiral updating position
Shrinking encircling mechanism
The value of \n
b. Spiral updating position
A spiral equation among the position of whale and prey is produced to mimic the helix-shaped movement of humpback whales is as follows:
\nwhere \n
where, \n
c. Search for prey (exploration phase)
To search for prey in exploration phase, the same search approach applied in the exploitation phase established on the variation of the \n
where, \n
The solutions are updated established on the best search agent between the current solutions found from the fitness evaluation step during each iteration. Again, at each time of generating newer weight values, it is aligned to the network and the fitness is determined and established on the back propagation error (i.e. the min MSE), which is the fitness function.
\nStep 4: Termination criteria.
\nOnce the optimal weights are generated for all the networks of the Mappers, the training of the networks is finished. Now the AANN becomes a classifier and it can be generalized to predict for newer data also. The output mapped networks are then forwarded to Reducer phase.
\nTo create the optimal network structure, the WOA algorithm is finished when best weight values are found. Also, the satisfaction of a termination criterion is confirmed when the mean square error is decreased to the needed limit or when the maximum iteration is attained.
\nOnce the optimal weights are rendered for all the networks of the Mappers, the training of the networks is completed. Now the AANN gets a classifier and it can be generalized to predict for newer data also. The output mapped networks are then forwarded to Reducer phase.
\nA Reducer accepts the data element of each Mapper for each Reducer task. With the same intermediate key, and a Reducer function merges all intermediate values connected. Established on the requirement, the Reducers can be customized. The proposed healthcare analysis model needs only one Reducer for improving a classifier model that must separate the patient’s medical records. Here, the Reducer task is to form a robust classifier model from the parallely trained network models. Since the Reducer results in only one classifier network model, it will average all the maximized weight results for each interconnection links found for each training item and find the final optimal weights of the classifier. Here the analyzing data’s (i.e. the unknown/newer data) are separated in the minimized AANN classifier model found from the Reducer phase.
\nThis section comprises result and discussion about the proposed parallel AANN (Adaptive Artificial Neural Network) technique for health care analysis from big data in cloud environment. The proposed algorithm is accomplished through JAVA software and the experimentation is carried out applying a system of having 4 GB RAM and 2.10 GHz Intel i-3 processor.
\nFor estimating the performance of the proposed FCM based accuracy, Map-Reduce model, time, memory, precision, and recall are taken into an account and equated with the existing k-means based Map-Reduce and DBSCAN model. The experimental results for the suggested FCM based Map-Reduce model and other being k-means based Map-Reduce model and DBSCAN are tested in this section. The prediction efficiency is evaluated established on differentiating the number of records and number of mappers.
\nThe performance judgment of the proposed FCM based Map-Reduce model to predict the inauguration of abnormality in the medical data records is established in this section and equated with accomplishing k-means based Map-Reduce and DBSCAN method. The efficiency of our proposed method is evaluated in terms of time, memory, precision, recall and accuracy established on number of records and number of mappers.
\nIn the medical data records, an effective method should minimize the time needed to predict the inauguration of abnormality. The proposed FCM based Map-Reduce model decreases the time while equating with other accomplishing k-means based Map-Reduce and DBSCAN method.
\nFigure 3 establishes the time needed for prediction of abnormality applying our proposed FCM based Map-Reduce model and k-means based Map-Reduce and DBSCAN model while the number of records rises. This clearly establishes that our proposed FCM based Map-Reduce model decreases the time needed for prediction of abnormality while equating with other accomplishing k-means based Map-Reduce and DBSCAN method.
\nTime taken by FCM based Map-Reduce; k-means based Map-Reduce model for prediction.
An effective method should decrease the requirement of memory. The proposed FCM based Map-Reduce model decreases the requirement of memory while equating with other accomplishing k-means based Map-Reduce and DBSCAN method.
\nFigure 4 demonstrates the memory needed for our proposed FCM based Map-Reduce model and k-means based Map-Reduce and DBSCAN model while the number of records increases. This clearly establishes that our proposed FCM based Map-Reduce model decreases the memory requirement while equating with other accomplishing k-means based Map-Reduce and DBSCAN method.
\nMemory requirement for FCM based Map-Reduce and k-means based Map-Reduce model.
The method with high precision will be more efficient. The proposed FCM based Map-Reduce model maximizes the precision while equating with other being k-means based Map-Reduce and DBSCAN method.
\nFigure 5 establishes the precision level for our proposed FCM based Map-Reduce model and k-means based Map-Reduce and DBSCAN model while the number of records rises. This clearly demonstrates that our proposed FCM based Map-Reduce model rises the precision level while equating with other being k-means based Map-Reduce and DBSCAN method.
\nPrecision for FCM based Map-Reduce; k-means based Map-Reduce and DBSCAN model.
The method with high recall is said to be more effective. The proposed FCM based Map-Reduce model rises the recall while equating with other being k-means based Map-Reduce and DBSCAN method.
\nWhile the number of records rises, the Figure 6 establishes the recall for the proposed FCM based Map-Reduce model and k-means based Map-Reduce and DBSCAN model. This clearly establishes that the proposed FCM based Map-Reduce model rises the recall while equating with other being k-means based Map-Reduce and DBSCAN method.
\nRecall for FCM based Map-Reduce; k-means based Map-Reduce and DBSCAN model.
The method with high accuracy is said to be more effective. With other being k-means based Map-Reduce and DBSCAN method, for the proposed FCM based Map-Reduce model raises the accuracy while comparing.
\nFigure 7 establishes the accuracy for the proposed FCM based Map-Reduce model and k-means based Map-Reduce and DBSCAN model while the number of records rises. This clearly demonstrates that the proposed FCM based Map-Reduce model raises the accuracy while equating with other accomplishing k-means based Map-Reduce and DBSCAN method.
\nAccuracy for FCM based Map-Reduce; k-means based Map-Reduce and DBSCAN model.
In the medical data records, an effective method should decrease the time needed to predict the presence of abnormality. The proposed FCM based Map-Reduce model decreases the time while equating with other accomplishing k-means based Map-Reduce and DBSCAN method.
\nFigure 8 establishes the time needed for prediction of abnormality applying the proposed FCM based Map-Reduce model and k-means based Map-Reduce model while the number of mapper rises. This clearly demonstrates that the proposed FCM based Map-Reduce model decreases the time needed for prediction of abnormality while equating with other accomplishing k-means based Map-Reduce method.
\nTime taken by FCM based Map-Reduce; k-means based Map-Reduce model for prediction.
An effective method should decrease the requirement of memory. While equating with other accomplishing k-means based Map-Reduce and DBSCAN method, for the proposed FCM based Map-Reduce model decreases the requirement of memory.
\nWhile the number of mapper rises, Figure 9 establishes the memory needed for the proposed FCM based Map-Reduce model and k-means based Map-Reduce model and k-means base model. This clearly demonstrates that the proposed FCM based Map-Reduce model decreases the memory requirement while equating with other accomplishing k-means based Map-Reduce and DBSCAN method.
\nMemory requirement for FCM based Map-Reduce; k-means based Map-Reduce and DBSCAN model.
The method with high precision will be more effective. The proposed FCM based Map-Reduce model raises the precision while equating with other accomplishing k-means based Map-Reduce method.
\nWhile the number of mapper rises, Figure 10 establishes the precision level for the proposed FCM based Map-Reduce model and k-means based Map-Reduce model. While equating with other accomplishing k-means based, Map-Reduce method this clearly demonstrates that the proposed FCM based Map-Reduce model rises the precision level.
\nPrecision for FCM based Map-Reduce; k-means based Map-Reduce and DBSCAN model.
The method with high recall is said to be more effective. Our proposed FCM based Map-Reduce model raises the recall while comparing with other accomplishing k-means based Map-Reduce method.
\nFigure 11 establishes the recall for the proposed FCM based Map-Reduce model and k-means based Map-Reduce and DBSCAN model while the number of mapper rises. This clearly demonstrates that the proposed FCM based Map-Reduce model raises the recall while equating with other accomplishing k-means based Map-Reduce and DBSCAN method.
\nRecall for FCM based Map-Reduce; k-means based Map-Reduce and DBSCAN model.
The method with high accuracy is said to be more effective. With other being k-means based Map-Reduce and DBSCAN method, for the proposed FCM based Map-Reduce model raises the accuracy while comparing.
\nWhile the number of mapper increases, the Figure 12 establishes the accuracy for the proposed FCM based Map-Reduce model and k-means based Map-Reduce and DBSCAN model. This clearly demonstrates that the proposed FCM based Map-Reduce model raises the accuracy while equating with other accomplishing k-means based Map-Reduce and DBSCAN method.
\nAccuracy for FCM based Map-Reduce; k-means based Map-Reduce and DBSCAN model.
The presented research method have improved a FCM based Mapreduce programming model for the implementation parallel calculating applying Adaptive Artificial Neural Network approach for the prediction of abnormality of medical records. The proposed FCM based Mapreduce model is equated with the accomplishing k-means based Mapreduce and DBSCAN model and tested in terms of different evaluates like time, memory, precision, recall and accuracy by differentiating the data size as well as the number of mappers. It can be seen from the results that, all the values found for the proposed method is better when equated to the being method. Moreover, the time and memory requirements are very much minimized when the number of mappers is raised. This establishes the efficiency of proposed model and so the proposed application can be applicable for handling large healthcare databases in cloud environment.
\nThe supply chain plays a crucial role in modern businesses by allowing them to achieve efficiency, responsiveness, and success. Over the past several decades, the scale of businesses has expanded, the number of geographic locales involved in the production process has grown, and product portfolios have diversified. As a result, the supply chain has grown from a traditional network of manufacturers and suppliers, to a vast ecosystem made of various products that move through multiple parties and require cooperation among stakeholders [1]. Additionally, due to the rapid evolution of e-commerce, the demand for improved product visibility and source-to-store traceability has never been higher. However, the inefficiency of data sharing in current supply chain networks has dramatically impacted the operations of retailers and manufacturers. For example, information gaps between data collected by factories and by retailers make it challenging to trace product history and offer customized products.
To overcome these challenges and improve supply chain performance, industries have explored innovative technologies that support efficient collaboration and coordination within and among different organizations [2, 3]. Among these technologies, blockchain provides a promising future and allows the supply chain to provide better visibility, transparency, and acuity of transactions throughout the entire process [4]. The blockchain technology that powers cryptocurrency has caught the attention of businesses, especially those in supply chain management. A 2017 study indicated that nearly 62% of supply chain executives claimed to have engaged with blockchain technology [5]. Although blockchain-based applications in the supply chain are still in their early stages, we believe this technology will significantly remodel the supply chain system [6, 7, 8]. Analysts forecast that blockchain technology can help supply chain management gain one-third improvement in most of its common processes [9]. A blockchain network is as a distributed ledger—transactions are contained in blocks that are linked together in chronological order to form a tamper-proof chain, which is usually stored in all network nodes [10, 11]. As such, blockchain technology provides a means to create tamper-proof logs of business activities and transactions [12]. Transaction data are immutable because they cannot be tampered with once they are distributed, accepted, and validated by network consensus and stored in the blocks [13]. By eliminating intermediaries to achieve trust among all stakeholders, efficiency improves and cost is reduced for the entire supply chain.
Despite the general acceptance that blockchain technology facilitates faster, more easily auditable interactions and allows for the exchange of immutable data among supply chain partners [14], it will take time for this technology to be adopted and to revolutionize the supply chain. Currently, most applications of blockchain are conceptual expositions, and empirical evidence on the implementation of it is limited [15]. Furthermore, few studies have been conducted on the challenges of deploying blockchain in the supply chain, such as organizational readiness, technical expertise, scalability, and compatibility with existing systems. Therefore, this study will provide a systematic analysis of how blockchain technology fits in the supply chain network and discuss potential challenges with its implementation.
Supply chain encompasses the end-to-end flow, including the physical and correlated data flow of raw material, products, information, and money. It plays a unique and critical role in businesses and determines the performance of organizations. Supply chain manages or is involved in sourcing, procurement, manufacturing, distribution, and logistics, and, thus, affects speed-to-market, the cost of a product, service perception, and capital requirements in businesses [16]. Supply chain integrates a set of fragmented and often geographically discrete processes into a cohesive system to deliver value to the customer. The core functions and operations of a typical supply chain network are illustrated in Figure 1.
Supply chain and operations.
Evolving customer requirements, challenges from competition, geographically separated operations, and the adoption of new business models (such as e-commerce) make the current supply chain a highly complex system. Over the past decade, e-commerce and hand-held digital devices have substantially changed the daily lives of people, especially in the ways they shop. There is an ever-increasing demand for customized products, a simplified and efficient shopping experience, and transparency about the value and provenance of goods. These needs bring new opportunities to businesses but impose significant challenges to current supply chains. These outdated supply chains struggle to improve demand management, to provide data visibility for the entire flow, or to track goods from raw material to end consumer—all of which are tremendously complex. Furthermore, the old technology of today’s supply chain fails to provide adequate risk management, to reduce costs, or to meet rapidly changing market requirements. We summarize the main challenges in current supply chains here:
Lack of traceability: In the last few years, traceability has become crucial for supply chains to address, especially in regard to customer service and planning and forecasting in business operations. However, it is difficult to deploy a centralized system in an interconnected network, especially where trust among participants is limited. Instead, there are several discrete systems among involved parties that consist of various databases that impede product tracking throughout the entire supply chain network [17].
Stakeholder distrust: Trust is an essential factor in supply chain management, and an effective supply chain network must be built on a solid foundation of it [18]. However, distrust among participants is the single greatest obstacle to improving supply chain networks [19]. Consequently, most stakeholders in the network primarily rely on third-party intermediaries to serve as agents of trust and to verify transactions, which dramatically increase operational cost and reduce process efficiency.
Limited transparency: The term “transparency” in the supply chain refers to the extent to which all stakeholders own a shared understanding of and access to accurate and adequate information about products [20, 21]. A transparent supply chain network improves trust among stakeholders and guarantees the integrity of products and associated data. However, the discrete databases in current supply chain networks offer minimal transparency, and most of the useful information in them is lost when products and data are transferred from one stakeholder to another. Furthermore, there are issues with inconsistent data sharing, relying on paper documentation, and inadequate interoperability. These critical challenges remain despite years of significant research investment. The crisis of Chipotle Mexican Grill outlets [7] is an important and sad example of how the current supply chain system is inefficient at, and possibly incapable of, offering transparency throughout the entire lifecycle of products.
Outdated means of data sharing: In current supply chain networks, data are shared between many organizations using paper-based documentation. Oftentimes, important documents, such as bills of lading, letters of credit, invoices, insurance policies, and various certificates, must travel with their associated goods around the world [22]. For example, about 200 communications were needed for Maersk, a global transport and logistics company, to complete a single shipment of frozen goods from Mombasa to Europe in 2014 [23]. These communications created a stack of documents about 25 centimeters in height [24]. Constrained by this outdated and inefficient data sharing method, ships and airplanes are often delayed in ports when the paperwork does not match the carried goods [22].
Compliance challenges: Currently, businesses have to meet increasingly strict regulatory standards to provide safe products and services to customers. Recently, the U.S. Food and Drug Administration and Federal Trade Commission adopted several standards to increase food safety and offer full visibility of food flows in the supply chain. However, under current supply chain processes, it is difficult to obtain this information from a variety of stakeholders and to develop a database that complies with new standards.
Blockchain is an innovational technology that enhances customer service, drives end-to-end value, and increases the efficiency of operations [25]. Additionally, it allows distrusting or unfamiliar stakeholders to create shared and secure data records [26]. In sum, when an exchange of valuable data and goods is necessary, blockchain technology expedites transactions, streamlines the process, enhances transparency, reduces waste, and, ultimately, reduces cost [27]. Consequently, new types of internet and associated business models have been built off of this robust technology [22]. Blockchain promises to be the primary driver of secure and efficient economic and social systems in the future.
The basic concepts of blockchain were introduced by Satoshi Nakamoto in Bitcoin [28], a digital cryptocurrency that can work without the need of a trusted intermediary. It offers a distributed ledger that tracks and sustains a tamper-proof record of transactions in a decentralized network. In essence, it is a unique database system that is created, replicated, synchronized, and maintained by all participants in the decentralized network. Blockchain operates in a decentralized peer-to-peer network [29] to validate and store all transactions in a consensus that is agreed upon by all nodes in the network, without any central authority to validate the transaction (as with an intermediary). All completed and validated transactions are logged in the distributed ledger in a verifiable, secure, transparent, and permanent manner along with a timestamp and other details [30]. In this way, the exchange of tangible and intangible data and assets among participants can be recorded digitally. Each stakeholder maintains a copy of the synchronized ledger, which prevents a single point of system failure or data loss [22]. When changes are made, such as adding a new block, all copies in the network are simultaneously updated, and records are permanently registered in all ledgers [31]. These changes are stored into blocks that create a chain [32], where a block is linked to the preceding one by storing its hash (a unique data that is mapped from the given block) [33]. Figure 2 shows the fundamental chained architecture of a blockchain network.
The architecture of a data chain in a blockchain network.
In Figure 2, notice that except for the first block (called the genesis block), each block has its hash as a unique ID that includes the hash of the previous block. In this way, a chronological chain is formed. Additionally, the hash mechanism provides enhanced data security. Usually, a block stores a set of time-stamped transactions that are validated by stakeholders in the network. Once it gains consensus, the block is accepted and stored by all parties in the blockchain and can no longer be modified. Therefore, trust in and transparency of transactions between organizations are significantly improved.
Since the introduction and success of Bitcoin, many blockchain-based platforms can be categorized as either a permissionless or permissioned blockchain. Virtually, anyone can join and participate anonymously in a permissionless blockchain network. Accordingly, it is also called a public blockchain, and these two notions will be used interchangeably in the remaining sections. Within this type of network, trust among users is limited or nonexistent. To overcome this lack, miners (detailed later) are introduced to validate transactions.
In contrast, permissioned blockchain is a network for a group of identified users operating under a governance model, called a consensus, to improve transactional trust. To join this type of network, new users need permission from the majority of the group or a delegated user; hence, it is also called a private blockchain, and we use both notions interchangeably in this paper. These networks facilitate trust among users and do not require costly miners. More efficient consensus protocols (such as the Byzantine fault tolerant protocol) validate data, improve network throughput, and reduce the latency of transactions.
Blockchain technology has many unique features that allow for the creation of a verifiable, secure, transparent, and immutable distributed ledger, the core characteristics of which are summarized as follows:
Versatile value exchange: Blockchain provides a secure and efficient platform for recording the transactions of intellectual property rights, the provenance of services and goods, asset ownership, cryptocurrency exchange, and more.
Distributed governance: A blockchain network is not controlled by any designated authority, organization, or person, and the need for trusted intermediaries to verify transactions is eliminated. It is a distributed database that provides secure and validated data for all participants in the network simultaneously. Thus, there is full transparency along the entire stream of transactions, and assets and data can be transferred between several organizations in a quick and efficient way.
Decentralized architecture: The ledger is decentralized and stored in all nodes (i.e., individual stakeholder databases) of the network, and failure of it at a central infrastructural point is not possible. Therefore, it fosters a robust network that improves the quality, reliability, and availability of services and information.
Logically centralized: With only one transaction record shared with and agreed upon by all participants, a blockchain network behaves like a logically centralized system.
Data transparency: Blockchain technology allows for a highly transparent network that is visible to each stakeholder at all times. This dramatically reduces the chances of illegal transactions.
Immutable data: Once a block with a set of transactions is verified by the consensus and stored in the chain, the encapsulated data can no longer be modified.
Enhanced data security: Blockchain technology utilizes asymmetric cryptography and digital signature algorithms to ensure data security and individual identity.
To cater to the vastly different needs of unique businesses and users, many blockchain networks are created, and each contains a slightly different set of features; however, a basic foundation remains the same for all. As an example, we use Bitcoin, the first and the most successful permissionless blockchain system, to illustrate the key components of typical data flow in a blockchain network:
Block: A data structure that is used to collect a set of transactions and is protected by adding a hash value to ensure the integrity of stored data. It is an essential component and is deployed in all blockchain networks.
Digital wallet: A secure repository for a user to store the private and public key pair. It interacts with the Bitcoin network so a user can receive and send digital currency (Bitcoins) and monitor their balance.
Node: A client who participates in transactional activities on the blockchain network. First and most importantly, a node owns a complete and permanent copy of the ledger that consists of all historical transactions. It works as a cornerstone to store a full copy of the tamper-proof ledger in each node in a blockchain network. Second, a node contributes to the network by broadcasting transactions and enabling miners to validate and create blocks.
Miner: A miner, a special user in the Bitcoin network, collects and validates all broadcasted transactions and creates new blocks. It competes with other miners in the network to solve a mathematical puzzle, widely known as a proof-of-work problem. The first to win the puzzle adds a new block to the chain and gains a specific amount of reward, such as a small number of Bitcoins. When a block is added, all nodes synchronize their local copy, ensuring their ledger is up-to-date. A miner or mining procedure is used for validation in many permissionless blockchains, whereas validation is executed by nodes under the control of a consensus in most permissioned blockchains.
Consensus: An agreement between nodes in a blockchain network that submits transactional information, and is one of the most critical components of blockchain technology. A blockchain network is updated via the deployed consensus protocol to ensure that transactions and blocks are ordered correctly, to guarantee the integrity and consistency of the distributed ledger, and, ultimately, to enhance trust between stakeholders (nodes). Additionally, a consensus algorithm can help a distributed or decentralized network unanimously make a decision [11, 29]. Prevalent consensus algorithms include proof-of-work, proof-of-stake, Byzantine fault tolerance, delegated proof-of-stake, proof-of-elapsed time, and proof-of-authority matched [34, 35].
In a typical open and permissionless blockchain network such as Bitcoin, when a user starts a transaction, the digital wallet verifies and signatures the transaction before broadcasting it to all nodes in the network. The verified transaction is added to a block that collects a set of new transactions. Miners validate the block, and once validated, the block is added to the existing blockchain by all nodes. This completes the transaction. The following is an illustration of typical data flow within the Bitcoin network:
A typical permissioned blockchain follows a similar data flow to that illustrated in Figure 3, where a signature is added to the transaction, which is then submitted or broadcasted to the network and added to a block. After the block is validated, the transaction is permanently stored in the chain. Permissioned blockchain differs from permissionless blockchain by how blocks and transactions are validated. To gain better performance and lower latency, most permissioned blockchain networks deploy efficient consensus protocols (e.g., the Byzantine fault tolerance consensus used by Hyperledger Fabric) that nodes use for validation.
Data flow of an open blockchain network [27].
The term “smart contract” was first proposed by Nick Szabo, and defined as “a set of promises, specified in the digital form, including protocols within which the parties perform on these promises” [36]. The smart contract concept was integrated into Ethereum’s blockchain network to facilitate, verify, and enforce contract negotiations and to improve the contract performance. Before transactions are conducted in a blockchain network, a smart contract that defines the conditions, obligations, rights, and concepts between stakeholders is created. This information is recorded as executable computer code to reduce ambiguity. Smart contracts are stored and shared in a distributed ledger that all participants have access to. These contracts automatically self-execute when all of the pre-set conditions are satisfied within a blockchain network. Thus, stakeholders who agreed upon a smart contract have more trust for each other and have a reduced risk of error and fraud [37]. The following details additional advantages of smart contracts:
Cost-saving: by eliminating intermediaries and reducing process time;
Accurate: all agreements, conditions, etc. are recorded in terms of computer codes that provide a more accurate and efficient means of information storage;
Speedy: Whenever the pre-defined conditions are met, the smart contract is executed autonomously and in real-time;
Transparent: Smart contracts are available and fully visible to all participants involved in the network; and
Secure: Smart contracts are stored using encryption and are distributed on all nodes of the blockchain network simultaneously.
There are many blockchain platforms with different consensus algorithms, development tools, and programming languages [38]. We introduce a few important blockchain platforms and applications herein.
Bitcoin: The initial and most famous blockchain network to offer crypto-currency transactions. It was launched in 2009 and has rapidly grown to be a significant currency system both online and offline. Since the mid-2010s, increasingly more businesses have begun accepting Bitcoin as payment. At the time of this writing (March 2019), the market capitalization of Bitcoin was about $68 billion [39]—it takes around 10 minutes to create a new block [40].
Ethereum: An open-source blockchain platform that was introduced by Buterin [41] and first launched in 2015. It is the first, and possibly the most advanced, blockchain network to introduce smart contracts for decentralized applications (Dapps). The primary Ethereum network serves as a public blockchain network; however, it is also possible to create a private blockchain network based on Ethereum. Quorum [42] is one such example and deploys the Ethereum network to create an enterprise-ready distributed ledger and smart contract platform, both of which contribute to faster processing. In Ethereum’s main network where a majority of transactions take place, it takes about 10–15 seconds to create a new block [43]. However, the number of transactions processed per minute is still as limited as Bitcoin.
Hyperledger fabric: An open-source, private blockchain network that is designed for enterprise applications. Hyperledger Fabric was established under the Linux Foundation and is maintained by a variety of organizations [44]. It employs a configurable architecture that provides various features, such as distributed ledger frameworks, smart contract engines, pluggable consensus protocols, user interfaces, and more. These versatile characteristics allow for a broad range of business applications, including finance, insurance, supply chain, healthcare, and human resources.
Skuchain: A blockchain network that is designed for enterprise supply chains in global trade [45]. It creates a zero-knowledge collaborative platform for global supply chains and provides precise control in inventory procurement across all partners, reducing friction and the costs of supply chain processes.
Sweetbridge: A blockchain-based application that enables real-time financial systems to assure transactional data are trustworthy between different parties. It integrates trusted identity, smart legal contracts, smart accounting, and payment rails into a transaction for all parties to see in real-time [46].
Zervnetwork: A decentralized trading platform based on blockchain technology. It aims to provide frictionless transactions among all participants within the defense industry [47].
IOTA: An open-source distributed ledger that is being built to power the future of the Internet of Things (IoT) with feeless microtransactions and data integrity for machines [48].
In recent years, Blockchain technology has been recognized as a critical technology with inherent capabilities to dramatically improve supply chain efficiency [49, 50, 51]. A study from Eye for transport stated that more than 16% of the 300 companies surveyed agree that data interchange, tracking, and visibility are the foremost reasons to deploy blockchain technology in the supply chain [52]. However, we discuss the benefits, challenges, and risks of integrating blockchain technology in the supply chain and introduce several pilot initiatives below.
The adaptation of blockchain technology can significantly alleviate or even eliminate the aforementioned problems in today’s supply chain. Blockchain technology empowers the supply chain with improved efficacy, efficiency, and transparency and reduced transactional time and cost. There are many ways blockchain technology benefits the supply chain:
Advanced traceability: With the adoption of blockchain technology, traceability within the supply chain is greatly improved; it produces a fully auditable trail of all items flowing through the network. Combined with IoT-based devices, such as RFID technology, a blockchain-enabled supply chain can automatically collect the item-level data of massive quantities of products in real-time. Additionally, this information is associated with timestamps and collection locations to form an audit trail that is complete, accurate, and easy-to-access, from the product’s origin to the customer. Furthermore, thanks to the immutability of blockchain data and the digital signatures required to confirm information ownership, data stored in this chain offers a secure and full history of any item in the entire supply chain. In the event of a compromised product, improved traceability enables the source of the issue to be identified more quickly, which reduces the cost of recalling products and improves disruption resolution between stakeholders. Advanced traceability gives stakeholders and customers more confidence in a product’s authenticity and quality.
Improved transparency: Blockchain technology provides reliable identity management in the supply chain [53] by enabling all parties to know who is performing what actions, at what time, and where. This information is stored and shared in distributed ledgers that can be conveniently accessed by involved and authenticated stakeholders. Through the integration of physical and digital flows across the supply chain, the connectivity of multiple trading partners will improve [54, 55]. Therefore, a blockchain-enabled supply chain with its transparent and complete inventory of product flow helps businesses make better forecasts and decisions. Additionally, improved transparency serves as a powerful tool for fighting fraud and counterfeiting.
Boosted efficiency: One of the primary motivations for implementing blockchain technology is to replace the outdated, paper-heavy processes in place today. As one of the benefits of digitalization, the logically centralized data ledger provides up-to-date local copies to all stakeholders within the network. All transactions are committed and immediately validated by all involved parties, and data are automatically synchronized to each party’s local copy. Blockchain technology makes it safer and faster to maintain the quality of transactions and associated data [56] by reducing human error and eliminating the need for third-party intermediaries and for local ledger reconciliation. Finally, the autonomous and self-executing blockchain-based smart contract replaces tedious processes and improves flexibility in supply chain management.
Greater security: It is nearly impossible to impact blockchain technology through hacking attacks like those that threaten centralized databases of intermediaries (e.g., banks). It is structured so that when there is an attempted hack into a specific block, all preceding blocks in the entire history must also be tampered with. Thus, blockchain provides a more secure way to maintain a log of business activities and transactions [12].
Enhanced trust: The transactions of a blockchain-based supply chain are created and recorded based on peer-to-peer interaction that can be trusted by the associated digital signatures. Additionally, a reliable identity management mechanism [53] allows for the collection of time, location, and other data at every action on a product in the supply chain. All data are synchronized to all stakeholders in real-time, which enhances trust among stakeholders within the supply chain network.
Easy compliance: A blockchain-enabled supply chain network records all transactions with precise details, such as timestamps, environmental conditions, and location. These accurate, tamper-proof records can serve as the source of a business’s data integrity and be easily accessed for regulations and compliance.
Although blockchain technology is widely recognized as a promising solution for issues with today’s supply chain, the application of it requires significant changes in both technological and cultural contexts. Additionally, more comprehensive evaluations of it are needed to unveil and address its challenges before the full potential of this new technology can be realized [22, 57].
Throughput and performance: Due to its decentralized architecture, each transaction is approved by all or a majority of nodes in a blockchain network. This approval process limits the throughput of a blockchain network; for example, Bitcoin, a public blockchain, can only process from 3 to 30 transactions per second. However, a private blockchain-based supply chain network must process far more transactions, possibly thousands per second, for the entire system. Thus, it is imperative to improve the transaction capacity of blockchain technology for full scalability. Fortunately, a private blockchain network’s ability to improve the throughput of transactions may mitigate this processing challenge.
Standardization: Standardization is a critical concern for the adoption of blockchain technology in the supply chain. In essence, this technology offers a ubiquitous and general-purpose platform for digital data sharing and permanent storage. Interestingly, a major question still remains: what content and format should be adopted for transactional data that facilitates interpretation by all participants? A data standard must be established and agreed upon by the entire supply chain community. However, there is no existing standard that can be adapted for this purpose. In recent years, much effort, such as EPCIS [58] that proposed GS1, has been made to overcome this gap, however, it is still not widely accepted and implemented in supply chains.
Data privacy: The immutability and transparency of blockchain technology raise a concern with data privacy when deployed for supply chains. Once data are stored in blockchains it cannot be changed, and, thus, it is imperative that a reliable mechanism that protects users’ privacy is designed. The task of balancing an individual’s right to privacy in an open blockchain network is very challenging. Currently, most blockchain networks, such as Bitcoin, provide limited control to users over the data and where they can transfer it to [22]. Most networks offer only pseudonymity to its users for privacy, so, although transactions are public for all nodes, the real identity of their owners is never revealed. This is unacceptable for supply chains, as nobody is willing to leak information to competitors about Confidential detail or the amount of merchandise moving in a network. Furthermore, with the limited number of stakeholders in the supply chain, it would be easy to figure out the owner of the transactional data and anonymity would disappear. To address this, private blockchain technology (such as Hyperledger Fabric) can support the creation of a channel for limited and trusted parties who are involved in specific transactions [44]. In this way, an unauthenticated user is forbidden to join the channel or access its data. It should be noted that a blockchain network can be designed to only serve as metadata of the workflow and the contents and details of all transactions within it are stored in external data repositories. Therefore, this technology provides a log of transactions on which no private data are stored [13].
Since late 2016, retail giants Walmart and IBM worked together for a pilot project to develop a blockchain-based system for tracking produce in the U.S. and pork in China. The project traced each product and collected its associated data, including origin farm/factory, storage temperature, and serial number. With this technology, tracking reports for each product were produced within minutes and the speed and accuracy of identifying and recalling contaminated food products were significantly improved [59]. On May 31, 2017, Walmart released the results of this pilot project and reported that blockchain technology helped them trace the origin of Chinese pork and U.S. mangoes in 2.2 seconds, which would normally take as long as several weeks in a traditional supply chain platform [60].
Intel conducted a public demo to explore the implementation of blockchain technology for tracking seafood in the supply chain. They aimed to create a network that assists multiple parties with food storage condition (i.e., temperature) control and with tracking food from sea to table. Several public records of this project are available on the Traceability Blockchain website [61]. These records detail how to use blockchain technology to collect seafood product data (i.e., locations, timestamps, owners, temperatures, etc.) from fishermen, transports, and restaurants within the entire supply chain network. This seafood blockchain can foster more trust between customers and sellers, improve and expedite the food safety network, and enhance consumer experiences.
In 2018, el Maouchi introduced TRADE, a fully transparent and decentralized traceability system for the supply chain that leverages blockchain technology [17]. It is a single system in which multiple participants can transfer and track products flowing through the supply chain. Additionally, it enables customers and other parties in the system to view and verify product data. Experiments show that each actor on the TRADE system can create about 351 and validate 437 transactions per second.
Since August 2018, IBM and Maersk (the world’s largest shipping company) have teamed up to create TradeLens, a blockchain-based system for the global supply chain. TradeLens aims to create a platform for multiple trading parties to securely share databases containing massive amounts of transactional information, and to build a more collaborative environment for global trading. This system is a powerful tool for establishing a single and consistent shared status of each transaction in near real-time while maintaining stakeholder confidentiality. Reports show that TradeLens significantly reduced delays caused by documentation errors and reduced the transit time associated with shipping packaging materials to manufactures in the U.S. up to 40% [62].
Introduced in 2009 as the foundation for Bitcoin, blockchain technology shows the significant capacity to benefit today’s supply chain. It provides a decentralized platform that shares any type of transaction and that records information with an immutable and permanent historical trail. We believe it has a significant future in the supply chain, as it promises to deliver an efficient, transparent, and collaborative network for organizations to quickly and securely share data across the variety of supply chain sectors and processes. This technology allows businesses to build a more flexible and responsible supply chain, and to robustly address new external and internal challenges.
"I work with IntechOpen for a number of reasons: their professionalism, their mission in support of Open Access publishing, and the quality of their peer-reviewed publications, but also because they believe in equality. Throughout the world, we are seeing progress in attracting, retaining, and promoting women in STEMM. IntechOpen are certainly supporting this work globally by empowering all scientists and ensuring that women are encouraged and enabled to publish and take leading roles within the scientific community." Dr. Catrin Rutland, University of Nottingham, UK
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