Browsing by Author "Anshul Verma"

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A Comprehensive Analysis of Indian Legal Documents Summarization Techniques
(Springer, 2023) Saloni Sharma; Surabhi Srivastava; Pradeepika Verma; Anshul Verma; Sachchida Nand Chaurasia
In the Legal AI field, the summarization of legal documents is very challenging. Since the Indian case documents are much noisier and poorly organized, the summarization of legal documents can be useful for legal professionals, who often have to read and analyze large amounts of legal text. During the review process of the legal documents, a team of reviewers may be needed to understand and for taking further actions. A branch of text summarization called ‘legal text summarization’ which is concerned with summarizing legal texts, such as court opinions, contracts, and legal briefs may reduce the need of these reviewers. Legal text summarization aims to highlight the key points of a legal document and convey them in a concise form so that decisions can be made in quick manner. In this paper, we experimented on seven machine learning-based summarization models to analyse their performance on judgment report datasets that has been collected from Indian national legal portal. The models that are taken here for the analysis are BART, LexRank, TextRank, Luhn, LSA, Legal Pegasus, and Longformer. We experimented with these models to find which model may perform well on the legal data. As a result, we observed that Legal Pegasus outperforms over all other models in the case legal summarization. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.
A COVID-19 Vaccine Notifier Android App “CoWin Mitra”
(CRC Press, 2023) Vikas Kumar Patel; Anshul Verma; Pradeepika Verma
The government of India has launched a web portal to register and schedule appointments for COVID-19 vaccination. This paper illustrates an android app model through which people can get notified about available slots according to their filters (age-group, dose type, vaccine name, centers). The proposed app checks for available slots for every given time in the background. Users can schedule their appointment and also enable/disable notifications at any time in the app. The project is developed in android studio. The data source is API Setu, and for client side, Java, XML, and SQLite are used. Software Development Life Cycle model and testing methods are used during the development process. ER diagram, message sequence chart, and flow chart have been shown for database, some important functions, and development process, respectively. The project developed is highly efficient, lightweight, and user-friendly. © 2023 selection and editorial matter, Anshul Verma, Pradeepika Verma, Kiran Kumar Pattanaik and Lalit Garg; individual chapters, the contributors.
A fusion of variants of sentence scoring methods and collaborative word rankings for document summarization
(John Wiley and Sons Inc, 2022) Pradeepika Verma; Anshul Verma; Sukomal Pal
Document summarization is an important task in natural language processing that helps deal with the problem of information overload occurring due to the existence of redundant content. Summary generation with highly relevant contents and maximum coverage is particularly challenging which can only be achieved when redundancy is minimized. This article introduces a novel approach for automatic text summarization based on sentence scoring and collaborative ranking to produce summaries with minimal redundancy and improved overall performance of summarization. The proposed model is a fusion of weighted and unweighted features-based sentence scoring methods. To learn optimal weights of text features, it has been modelled as an optimization problem. Moreover, the proposed model exploits the strength of collaborative ranking to generate the summary of a given document. Three similarity factors (proximity, significance and singularity)-based models have been employed to find the similarity between weighted and unweighted sentence scores. The results of the comparison experiment demonstrate that the proposed (PS + Jac) method generates a closer summary to the reference summary with minimal redundant contents. On average, the proposed (PS + Jac) method generates the summaries with 61% accurate contents with greater improved rates up to 40%. The statistical testing also confirms that the performance improvement is significant at a 5% level of significance. © 2022 John Wiley & Sons Ltd.
A multivariate transformer-based monitor-analyze-plan-execute (MAPE) autoscaling framework for dynamic resource allocation in cloud environment
(Springer, 2025) Bablu Kumar; Anshul Verma; Pradeepika Verma
The rapid advancement of cloud technology has heightened the demand for real-time data processing systems that provide accuracy, flexibility, and scalability. Autoscaling manages cloud resources automatically in real-time, employing either reactive or proactive approaches. Reactive autoscaling adjusts resources based on predefined thresholds but can be inefficient during fluctuating workloads. In contrast, proactive autoscaling predicts future workloads, enabling preemptive resource adjustments to optimize performance. This study proposes an autoscaling approach based on the monitor-analyze-plan-execute (MAPE) framework, which emphasizes proactive strategies by integrating feature selection techniques with a multivariate transformer (MV-Transformer) approach. This MV-Transformer approach excels at capturing long-term dependencies and complex interactions among multiple variables while using less memory. The framework enhances resource provisioning, as evidenced by the lowest under-provisioning value of 0.2892240 and duration of time under-provisioning value of 10.6676060, indicating superior performance. Additionally, the MAPE autoscaling framework achieves an elastic speedup of 2.9818, compared to 1.3200 for Bi-LSTM, 1.0230 for LSTM, and 1.0000 for reactive without autoscaling. The proactive MV-Transformer approach demonstrates significant improvements in resource management by evaluating this elastic speedup and resource provisioning metrics against both reactive without autoscaling and other proactive autoscaling approaches. For real-world implementation, docker desktop and Kubernetes were used to dynamically scale VMs based on workload, orchestrated by the MAPE autoscaling framework. This approach also helps in handling high dynamic workloads and overall efficiency in cloud computing, particularly in scaling and de-scaling. Our implementation codes are available at the following GitHub link: https://github.com/BABLU-KUMAR/MV-Transformer-based-MAPE-Autoscaling-Framework/tree/main. © The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2025.
A Novel Hybrid GA-PSO Algorithm-Based Optimization of Transmission and Expansion Planning
(Springer, 2023) Shweta Mehroliya; Shilpi Tomar; Anoop Arya; Anshul Verma
In the power system environment, transmission and expansion planning (TNEP) is an essential and computationally very challenging problem in power systems. Competent and robust optimization techniques are required to get the optimal solution technically and economically. This paper aims to resolve the transmission and expansion planning problem in less computational time and investment costs using H1GAPSO and H2GAPSO algorithms. Two hybrid progressive algorithms based on the combination of PSO and GA methods are proposed and crossing over the PSO and GA have been implemented in this paper. The focus behind the proposed methods is to merge PSO and GA methods in a combination of parallel and series form, respectively. To validate the proposed hybrid algorithm and to test efficacy in comparison with other methods reported in the literature, it is tested on Garver’s-6 bus, IEEE-14 bus, and IEEE-24 bus test systems using MATLAB. For IEEE-14 and IEEE-24 bus systems, by applying the hybridization, the optimal investment costs are reduced to 520 US$ and 630 US$, respectively and the corresponding computational time in seconds are reduced to 4.3637 s and 4.3788 s. For Garver’s 6 bus system, the computational time are 1.4936 s and 1.1847 s for both hybridization. The results are compared with conventional GA and PSO methods. The simulation and observations of the outcome demonstrate the effectiveness of the proposed hybrid algorithms' time and have the better ability to find the global optimum solution. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.
Addressing Graphviz File Generation Issue in CPN Tools: A Java-Based Solution
(Springer Science and Business Media Deutschland GmbH, 2025) Vikas Kumar Patel; Anshul Verma; Pradeepika Verma
This paper presents a novel approach to resolving a specific bug encountered in Colored Petri Nets (CPN) Tools during the generation of Graphviz files. CPN Tools, a powerful instrument for constructing and analyzing colored petri nets, has been identified to have limitations in graph visualization, particularly when dealing with token value strings or record/product with strings.The CPN Tools generate the Graphviz (dot) file, however it does not take into account color sets of strings, records, and products. If the color set is one of these, after generating the Graphviz file from CPN, Graphviz displays a syntax error. This issue results in a syntax mismatch with Graphviz, hindering the seamless visualization of graphs. To address this problem, the authors have developed a Java-based Graphical User Interface (GUI) program that takes the CPN Tools generated dot file as input, refactors it, and corrects the syntax, thereby enabling error-free graph visualization. The user merely needs to provide the CPN Tools created DOT file to the proposed system. It will make internal corrections and generate the graph. In addition, the user can download the revised version of the DOT file from the system. This solution not only resolves the identified bug but also enhances the overall user experience by simplifying the process of graph generation. The paper provides a detailed description of the bug, the development process of the Java GUI program, enhancing the page hierarchy graph of hierarchical colored petri nets, and its successful application in resolving the bug and generating correct graph. It also discusses the potential impact of this solution on the broader usability and efficiency of CPN Tools. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
An Analytical Study on the Efficacy of Blockchain Frameworks for Student Grievance Management
(Springer, 2024) Harish Kumar; Rajesh Kumar Kaushal; Naveen Kumar; Anshul Verma
Student grievance redressal is an essential indicator of institutional effectiveness and education quality, that ensures a conducive academic environment. Every educational institute provides a 24 × 7 web or mobile platform for students to register their grievances. However, these centralized solutions often lack transparency, exhibit potential biases, and also raise security and privacy concerns that lead to student reluctance to use them. A blockchain-based grievance redressal system can address these issues by providing transparency, immutability, privacy, accountability, and auditability. However, selecting the most suitable blockchain framework is challenging and a tedious task. So, we analyzed the existing studies on performance analysis of blockchain frameworks and existing studies on grievance redressal. The finding from the reviewed studies indicates that 81% of the studies diverged towards Hyperledger fabric and Hyperledger fabric outperforms other frameworks in performance based on key parameters such as transactional throughput and latency. The consensus mechanism selection also significantly impacts the performance of a blockchain framework. RAFT is more efficient than the Kafka and solo consensus mechanism for Hyperledger fabric, in both low and high transaction volumes for read and write operations at various transfer rates. Hyperledger fabric achieves a 94.6% success rate in multiple operations with RAFT consensus as compared to 72.7% with Kafka. The success rate of Hyperledger fabric is reached to 96%, 98.4%, and 96.6% at 25tps, 50tps, and 100tps respectively for write operations whereas during the read operations, it is reached to 99.6. It is also found that the success rate is increased to 99.12% in dual channel network for write operation at varying transfer rates. This study suggests that Hyperledger Fabric is more effective for implementing a blockchain-based student grievance redressal system. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.
An approach for extractive text summarization using fuzzy evolutionary and clustering algorithms
(Elsevier Ltd, 2022) Pradeepika Verma; Anshul Verma; Sukomal Pal
Automatic text summarization schemes are indeed helpful for glancing briefly at the text document. With this motivation, we introduce here a two-stage hybrid model for text summarization task by utilizing the strength of various approaches. In the first step, we cluster the sentences of a document according to their similarity using a partitional clustering algorithm. We then use a linear combination of the normalized Google distance and word mover's distance to differentiate two sentences. The notion of gap statistics is exploited to approximate the number of partitions for the given document needed in the partitional clustering algorithm. We extract the significant sentences from each cluster (partition), which are recognized by their adjusted text feature scores, in the second step. The teaching–learning based optimization approach is used to find the optimal weights for the text features whereas a fuzzy inference system with a full-fledged knowledge base generated by humans is employed to determine the final score of the sentences. Moreover, we have also proposed an exact method to give a solution for the summarization problem by modeling it as an Integer Linear Programming (ILP) problem. We evaluate the proposed methods on three different datasets: DUC 2001, DUC 2002, and CNN. The observed results on these standard datasets manifest the efficacy of the proposed methods. We further show that partitioning a document in an optimal number of clusters plays a major role in content coverage in summaries. The performance of the proposed hybrid method shows that the combination of fuzzy, evolutionary, and clustering algorithms produces good summaries of the documents. © 2022 Elsevier B.V.
An Efficient Deep Learning Technique for Driver Drowsiness Detection
(Springer, 2024) Abhineet Ranjan; Sanjeev Sharma; Prajwal Mate; Anshul Verma
Deep learning techniques allow us to learn about a person’s behavior based on pictures and videos. Using digital cameras, the system can identify and classify a person’s behavior based on images and videos. This paper aims to present a method for detecting drivers’ drowsiness based on deep learning. To determine which transfer learning technique best suits this work, we used DenseNet169, MobileNetV2, ResNet50V2, VGG19, InceptionV3, and Xception on the dataset. The dataset used in this paper is the Driver Drowsiness Dataset (DDD), which is publicly available on Kaggle. This dataset consists of 41,790 RGB images, and each image has a size of 227 × 227, which has 2 classes: drowsy and not drowsy. The Drivers Drowsiness Dataset is based on the images extracted from the real-life Drowsiness dataset (RLDD). After comparing the results coming from all 6 models, the highest accuracy achieved was 100% by ResNet50V2, and various parameters are calculated like accuracy, F1 score, etc. Additionally, this work compared the results with existing methods to demonstrate its effectiveness. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.
An Extensive Investigation on Lyapunov Optimization-based Task Offloading Techniques in Multi-access Edge Computing
(Springer, 2025) Vandna Rani Verma; Pushkar; Bablu Kumar; Anshul Verma; Vishnu Sharma; Pankaj Kumar Tripathi
Technological advancements have heightened the demand for real-time applications with minimal energy consumption on resource-constrained devices, often facing storage, computational power, and battery life limitations. Multi-access edge computing mitigates these challenges by offloading data and computational tasks to nearby edge servers, improving task execution efficiency. Despite the progress in task-offloading techniques, real-time processing and energy consumption issues remain. Lyapunov optimization offers a promising approach to address these challenges by optimizing task allocation and resource management in dynamic environments. This paper provides a comprehensive review of task offloading techniques using Lyapunov optimization, focusing on energy consumption and latency. It examines these techniques through classification, theoretical frameworks, and mathematical analyses, while also detailing Lyapunov optimization algorithms, workflows, advantages, and metrics. The paper includes an in-depth comparative analysis of Lyapunov-based algorithms in the context of task offloading, highlighting their benefits and challenges. Finally, it identifies emerging research opportunities and suggests future directions based on recent advancements in the field. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.
An in-depth and insightful exploration of failure detection in distributed systems
(Elsevier B.V., 2024) Bhavana Chaurasia; Anshul Verma; Pradeepika Verma
In today's world, everyone wants a good profit with a tiny investment and distributed computing is a boon for this purpose. Cloud computing, fog computing, and the Internet of Things (IoT) are well-known examples of distributed computing which provide good computing services and performance. However, providing reliable services in a real environment, which is failure-prone, remains a challenge. To address this issue, failure detectors are used in distributed systems, which are abstract modules responsible for detecting and monitoring the activity of nodes in order to determine whether they are faulty or not. In this paper, an approach is presented for the systematic literature review of failure detectors in distributed systems. Further, many existing review and survey papers on failure detectors are critically analyzed along with their key contributions and limitations. The classification of distributed systems is presented on the basis of the nodes’ properties and the components of system models are described in detail. Various issues and challenges related to agreement and failure problems are also explored. The strengths and limitations of various existing failure detectors are discussed along with their comparative evaluation. Finally, fault-tolerance and recovery techniques are discussed and analyzed. © 2024 Elsevier B.V.
An optimal three-tier prioritization-based multiflow scheduling in cloud-assisted smart healthcare
(Academic Press, 2025) Sarthak; Anshul Verma; Pradeepika Verma
Internet of Things is significantly advancing the development of modern interconnected networks. Coordinated with cloud computing, this technology becomes even more powerful, cost-effective, and reliable. These advancements are rapidly being integrated into modern healthcare through innovations such as smart ambulances, remote monitoring systems, and smart hospitals, enhancing tracking, analysis, and alerting capabilities. However, these innovations also bring challenges, particularly in task allocation and resource management for safety-critical systems that must meet stringent quality of service while efficiently utilizing resources. This paper introduces a new heuristic Three-Tier Prioritization based Multiflow Scheduling (TTPMS) approach for smart healthcare in cloud, utilizing the adaptive multi-criteria decision-making. The proposed TTPMS algorithm prioritizes tasks across three levels, considering factors such as urgency, deadlines, budget, and impact value within the workflow, and then dynamically selects the most suitable virtual machine for allocation. Performance comparisons were made against traditional approaches like the Prioritized Sorted Task-Based Algorithm (PSTBA) and the Max–Min algorithm. Experiments conducted using the Eclipse IDE with Java, demonstrated that the proposed approach significantly outperforms traditional algorithms across multiple metrics, including success rates for deadlines and budgets, as well as the resource utilization. It achieved a 98% deadline adherence rate, outperforming Max–Min (93%) and PSTBA (60%). Additionally, TTPMS surpassed budget adherence metrics, achieving a 76% success rate compared to PSTBA (72%) and Max–Min (70%). For combined adherence to both deadlines and budgets, TTPMS achieved a 74% success rate, outperforming PSTBA (33%) and Max–Min (63%). These results highlight the effectiveness of TTPMS in scheduling the healthcare applications. © 2025 Elsevier Ltd
Analysis and Implementation of Microservices Using Docker
(Springer Science and Business Media Deutschland GmbH, 2023) Keshav Sharma; Anshul Verma; Pradeepika Verma
Implementation of micro-services is among the most challenging and important tasks in the field of Computer Science. For implementing the microservice architecture, Docker has been used in the current scenario. Creating a machine learning model that works on one computer is not really difficult. But creating a model that can scale and run on all types of servers around the world, it’s more challenging. This work proposed a way to deploy a certain type of trained model in microservice architecture using Docker to use anywhere. For practical implementation, two models for machine learning has been used. One that addresses the handwritten digit recognition problem which has been an open problem in pattern classification since a long time, and another model resolves the issue of house pricing based on several attributes. The purpose of implementation of machine learning model in microservice architecture using Docker is to enable a method from which anyone can use a machine learning model without worrying for their machine configuration and dependencies of the machine learning model. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Analysis of oncogene protein structure using small world network concept
(Bentham Science Publishers, 2020) Neetu Kumari; Anshul Verma
Background: The basic building block of a body is protein which is a complex system whose structure plays a key role in activation, catalysis, messaging and disease states. Therefore, careful investigation of protein structure is necessary for the diagnosis of diseases and for the drug designing. Protein structures are described at their different levels of complexity: primary (chain), secondary (helical), tertiary (3D), and quaternary structure. Analyzing complex 3D structure of protein is a difficult task but it can be analyzed as a network of interconnection between its component, where amino acids are considered as nodes and interconnection between them are edges. Objective: Many literature works have proven that the small world network concept provides many new opportunities to investigate network of biological systems. The objective of this paper is analyzing the protein structure using small world concept. Methods: Protein is analyzed using small world network concept, specifically where extreme condition is having a degree distribution which follows power law. For the correct verification of the proposed approach, dataset of the Oncogene protein structure is analyzed using Python programming. Results: Protein structure is plotted as network of amino acids (Residue Interaction Graph (RIG)) using distance matrix of nodes with given threshold, then various centrality measures (i.e., degree distribution, Degree-Betweenness correlation, and Betweenness-Closeness correlation) are calculated for 1323 nodes and graphs are plotted. Conclusion: Ultimately, it is concluded that there exist hubs with higher centrality degree but less in number, and they are expected to be robust toward harmful effects of mutations with new functions. © 2020 Bentham Science Publishers.
Automatic Cauliflower Disease Detection Using Fine-Tuning Transfer Learning Approach
(Springer, 2024) Noamaan Abdul Azeem; Sanjeev Sharma; Anshul Verma
Plants are a major food source worldwide, and to provide a healthy crop yield, they must be protected from diseases. However, checking each plant to detect and classify every type of disease can be time-consuming and would require enormous expert manual labor. These difficulties can be solved using deep learning techniques and algorithms. It can check diseased crops and even categorize the type of disease at a very early stage to prevent its further spread to other crops. This paper proposed a deep-learning approach to detect and classify cauliflower diseases. Several deep learning architectures were experimented on our selected dataset VegNet, a novel dataset containing 656 cauliflower images categorized into four classes: downy mildew, black rot, bacterial spot rot, and healthy. We analyzed the results conducted, and the best test accuracy reached was 99.25% with an F1-Score of 0.993 by NASNetMobile architecture, outperforming many other neural networks and displaying the model’s efficiency for plant disease detection. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.
Average Time Based PRoPHET Routing Protocol for Opportunistic Networks
(Springer Science and Business Media Deutschland GmbH, 2023) Mehul Kumar Gond; Mohini Singh; Anshul Verma; Pradeepika Verma
An Opportunistic Network is an intermittently connected Mobile Ad-hoc Networks that exploits the communication opportunity between the nodes for data transmission whenever they are within the communication range of each other even for a short time. In contrast to the Mobile Ad-hoc Networks, Opportunistic Networks follow store-carry-forward approach for the data transmission. Routing in this type of network depends on many factors, like the direction of the node's movement, the supported interface bandwidth (Bluetooth, high-speed Internet, etc.), the node's speed, and the node's buffer size. In this research work, a context-aware routing protocol is proposed that uses the frequency of contacts among nodes as context information. The frequency of meetings between any two nodes is found to be a good heuristic to identify the message's best forwarder. The proposed routing protocol is simulated on opportunistic network environment (ONE) simulator and the results are compared with the most prominent routing protocols of context-oblivious and context-aware classes, and it was found that the proposed routing protocol performs better than other protocols in terms of delivery probability and buffer average time. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Bibliometric Analysis of Blockchain in the Healthcare Domain
(Tsinghua University Press, 2023) Shilpi Garg; Rajesh Kumar Kaushal; Naveen Kumar; Anshul Verma
As an innovation, Blockchain has transformed numerous industries and sparked the interest of the research community due to its abundance of benefits, opening up diverse research routes in the healthcare sector in the last decade. With Health 4.0 becoming ubiquitous in the healthcare industry, end-user transactions are being carried out on a decentralized network, making Blockchain profitable to meet the demands of the modern healthcare sector. Therefore, a detailed analysis of Blockchain is very crucial. This study emphasizes the evolution of science and the preliminary research of Blockchain in healthcare through bibliometric analysis. All the data are extracted from the Scopus database, and the VOSviewer tool is used for analysis. A total of 1152 Scopus articles published between 2018 and 2022 are examined. Results reveal that in 2022, the field of Blockchain experienced a notable increment in the number of publications and a significant growth rate. IEEE Access became well known in this field and had a large number of citations. It is observed that China and India are the leading countries in terms of publications on Blockchain. This study offers a number of recommendations that amateur and professional researchers can use as a benchmark before commencing a Blockchain investigation in the future. © All articles included in the journal are copyrighted to the ITU and TUP.
Comparative Analysis of YOLO Models for Plant Disease Instance Segmentation
(Institute of Electrical and Electronics Engineers Inc., 2024) Agamjot Singh; Aryan Yadav; Anshul Verma; Prashant Singh Rana
Accurate detection and segmentation of plant diseases are essential for sustaining agricultural productivity and global food security. This report conducts a comparative analysis of three state-of-the-art YOLO models-YOLOv5, YOLOv7, and YOLOv8-emphasizing their efficiency in instance segmentation of plant diseases. The research employs a comprehensive dataset featuring various crops like rice, sugarcane, wheat, bell pepper, potato, and tomato, each suffering from different diseases. The methodology includes fine-tuning YOLO models with pretrained weights and optimizing them with the curated dataset.The assessment of performance is based on crucial metrics such as recall, mean average precision (MAP) and precision. YOLOv8 exhibits superior performance, with over 9 0 % average precision across all disease categories, significantly outperforming YOLOv5 and YOLOv7. This report offers detailed insights into the architectural features, training procedures, and evaluation metrics of each model. It also addresses the implications of the findings for practical agricultural applications, highlighting the role of advanced deep learning techniques in improving crop protection and management strategies. Despite the positive results, the report acknowledges limitations like dataset dependency and challenges in real-world deployment. © 2024 IEEE.
Convergence of blockchain and IoT for managing decentralized medical records
(Nature Research, 2025) Rajesh Kumar Kaushal; Kumar Naveen; Ekkarat Boonchieng; Shrikant A. Mapari; Vinay Kukreja; Anshul Verma
This research introduces a strategy to integrate blockchain technology with Internet of Things. Amalgamation of blockchain technology and Internet of Things is vital as one of them offers to connect patients remotely and other provides a higher level of privacy, secure decentralized system and immutable data storage. The studies in the past were merely storing patient vitals overlooking the significance of medical reports. A medical history remains incomplete without storing medical reports. This research is offering a technique to store both medical reports and patient vitals on the blockchain ledger. To prevent overwhelming the blockchain network, a node.js application is developed which store the medical reports on the IPFS server and retrieve their corresponding hash values. Thereafter, these hash values along with the patient details and vitals are then transmitted to blockchain ledger. This study makes use of MAX30100 and DS18B20 sensors to monitor heart rate, blood oxygen and body temperature. ESP32 microcontroller is used to integrate these sensors and fetch their data periodically. Hyperledger fabric blockchain framework is used for maintaining the ledger and Hyperledger caliper tool is used to evaluate the overall performance of the proposed system. The performance is computed on three key parameters: reliability, throughput and latency. Reliability is evaluated in two phases, one with caliper tool and another with real RPM (Remote Patient Monitoring) unit. In the first phase, caliper tool transmitted 1500 transactions which are then verified by reading the ledger. In the second phase, RPM unit transmitted 480 transactions to blockchain ledger within 8 h. This study confirms that all transmitted transactions are successfully recorded on the blockchain ledger without any loss or failure. Medical reports submitted on IPFS server are also cross verified and found to be intact. The second experiment is carried out using two, four and eight workers attempting to execute 1000 transactions cumulatively at 40, 80 and 160 TPS (Transactions Per Second) respectively. It is noteworthy that when caliper tool is configured to execute transactions at 40 and 80 TPS, the achieved TPS remain unchanged. In contrast to this, when caliper tool is configured to send transactions at 160 TPS it could only achieve the transaction rate of 94 TPS. The peak of average latency is recorded as 0.45 s when transactions are executed at 94 TPS. The lowest latency is observed as 0.24 s at 40 TPS. As far as the throughput is concerned, the highest throughput is observed as 91.4 TPS when the caliper tool is attempting to execute transactions at 94 TPS. The system could achieve throughput of 39.8 and 79.4 TPS when caliper attempts to send transactions at 40 and 80 TPS respectively. The unique contribution of this study is to converge Hyperledger fabric blockchain framework, InterPlanetary File System (IPFS) and Internet of Things health sensors to develop a comprehensive solution for storing and retrieving the medical histories of remote patients, effectively managing both patient vitals and complex medical reports without compromising reliability and overall throughput. © The Author(s) 2025.
Critical Analysis of Train Operation Simulators
(Springer, 2024) Vikas Kumar Patel; Anshul Verma; Pradeepika Verma
Railway transportation is a cost-effective and reliable mode of transportation. The construction of an entire railway network is a challenging task that requires careful planning and execution. Infrastructure and schedule are just two of the many elements that must be accurate and cheap for greater performance. Train simulators are computer-based simulations of rail transport operations that can help in the planning, creation, and administration of effective train operations. These simulators can model various aspects of rail transport, such as train movements, signaling systems, and track layouts, allowing railway operators to test and optimize their operations before implementing them in the real world. In this study, 20 simulators with various parameters like simulator types, category, working, etc. were examined together with a total of top 10 companies that offer railway-related services for the year 2022. The results of this study shall offer insightful knowledge about the simulation, creation, and administration of effective train operations. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.