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4th IEEE Global Conference for Advancement in Technology, GCAT 2023

作者： Sasidharan, Anjali K. Rahulnath, R. Department of Computer Science and Application Amrita School of Engineering Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9798350305258

Legal documents contain a wealth of valuable information, making them crucial for knowledge extraction and analysis. Extracting structured relationships between entities in legal texts can significantly enhance our understanding of legal concepts and facilitate the creation of knowledge graphs. In this paper, we propose a structured approach combining relation extraction, graph reasoning, and clustering methods to effectively extract and represent legal relationships. Firstly, we employ state-of-the-art relation extraction techniques to identify relevant entities and their relationships from unstructured legal documents. These techniques leverage natural language processing (NLP) and machine learning algorithms to recognize entity pairs and assign appropriate relationship labels. Next, we leverage graph reasoning to enhance the extracted relationships by incorporating additional contextual information. By representing the legal entities and their relationships as nodes and edges in a knowledge graph, we can apply reasoning algorithms to infer implicit relationships, resolve conflicts, and expand the graph based on the legal domain's rules and regulations. Furthermore, we integrate clustering methods to organize related legal entities and relationships into meaningful clusters within the knowledge graph. By grouping semantically similar entities and relationships, we can uncover patterns, identify common themes, and enable more effective retrieval and analysis of legal information. Our experiments demonstrate that the structured approach, combining relation extraction, graph reasoning, and clustering methods, effectively extracts and represents legal relationships in a knowledge graph. The resulting graph provides a comprehensive and organized representation of legal concepts, facilitating legal research, decision-making, and knowledge discovery in the legal domain. © 2023 IEEE.

关键词： Clustering methods Graph reasoning Knowledge graph creation Legal documents Machine learning Natural language processing Relation extraction

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MemoChat App: An Advancement in Existing WhatsApp 1

MemoChat App: An Advancement in Existing WhatsApp

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Takkar, Hardik Sharma, Krishna Karun Kumar, Nagadamudi Vijay Jha, Manish Kumar Saraswat, Bal Krishna Tyagi, Himani Sharda University Computer Science and Engineering Department Uttar Pradesh Greater Noida India Sharda University Computer Science and Application Department Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350356816

With over 1 billion users in 180 countries, it's no surprise that WhatsApp is one of the most popular instant messaging apps available today. Whether you're looking to make voice or video calls, or simply share messages, photos, and videos with friends and family, this app is an excellent choice. Not only is it free of charge, but it's also incredibly reliable and easy to use. With its user-friendly interface and convenient features, WhatsApp is the perfect way to stay connected with loved ones, no matter where they are in the world. With regular updates, WhatsApp is becoming even more user-friendly and interactive. Though there may be some issues, solutions are always available. By addressing these problems head-on, WhatsApp can provide a unique and unparalleled messaging experience, setting itself apart from the competition. The future of WhatsApp is bright, and no one can't wait to see what's in store! Have you ever encountered the problem of not being able to send messages to specific members of a group? Unfortunately, with the current system, messages sent to a group are received by everyone in the group. However, there are ways to work around this issue. To access the Include and Exclude options while sending a message, simply click on the Arrow symbol located next to the attachment icon. This will open up a new section where you can choose which members to include or exclude from the message. It's a simple and effective way to ensure that your messages are only sent to the intended recipients. © 2024 IEEE.

关键词： Instant messaging applications

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Blockchain and Machine Learning for Data-Driven Insights in Consumer Behavior Analytics 13

Blockchain and Machine Learning for Data-Driven Insights in ...

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13th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2024

作者： Rawat, Bhupender Singh Thapliyal, Nitin Pal, Sunil Kumar Mishra, Nikhil Verma, Rohan College of Smart Computing Coer University Roorkee India Graphic Era Hill University Department of Computer Science & Engineering Dehradun India Axis Colleges Department of Computer Science & Application Kanpur India Department of Computer Science & Engineering Dehradun India

ISBN: (纸本)9798350305463

Comparing with neural network method to more known methods, this article assesses their simplicity and effectiveness. Our approach uses new network pruning, interpretability-based regularization, and gradient-based sensitivity analysis. Numerous dataset trials show that the suggested method is more accurate, faster, and more durable than others. Also easier to understand. It turns out that small neural networks may simplify models without compromising accuracy. This comparative study shows how neural networks can handle complex data and make good predictions. These networks will be increasingly accessible in daily life. © 2024 IEEE.

关键词： Sensitivity analysis

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Multiprocessor Task Scheduling Optimization for Cyber-Physical System Using an Improved Salp Swarm Optimization Algorithm

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SN computer Science 2024年第1期5卷 184页

作者： Acharya, Biswaranjan Panda, Sucheta Ray, Niranjan K. Department of Computer Application Veer Surendra Sai University of Technology Burla Odisha Sambalpur 768018 India School of Computer Engineering KIIT Deemed to be University Odisha Bhubaneswar 751024 India

Salp Swarm Algorithm (SSA) is a bio-inspired optimization algorithm used in this paper to optimize the multiprocessor scheduling process in the current cyber-physical system. Although SSA is mainly utilized in terms of local search, in our case, an improved version has been introduced with the use of a Local Search Algorithm (LSA) and binary SSA, namely Improved SSA (ISSA). More to the point, eight optimization algorithms are compared with this proposed ISSA namely SSA, Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Grey Wolf Optimizer (GWO), Jaya Algorithm (JAYA), Chaotic Squirrel Search Algorithm (CSSA), Quantum-inspired Binary Chaotic Salp Swarm Algorithm (QBCSSA) and Space Transformation Search (STS) with SSA is termed as STS-SSA. The performance of ISSA along with the other 6 meta-heuristic and 2 improved versions of SSA algorithms are compared with 12 traditional benchmark functions and evaluated for 100 and 300 dimensions. Convergent curves have also been demonstrated and the proposed ISSA has been shown to find a global optimum within the very initial phase of iterations. For calculating the efficiency of the proposed algorithm, the gear train design problem has been employed. The proposed algorithm has demonstrated higher accuracy rates and better convergent values than the other applied algorithms. © 2024, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Benchmark function Cyber-physical system Gear train strategy Global optima Grid computing Local search algorithm (LSA) Multiprocessor task scheduling Salp swarm algorithm

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SSHTDNS: A Blockchain-Based Secure, Scalable, and High-Throughput Domain Name System 3

SSHTDNS: A Blockchain-Based Secure, Scalable, and High-Throu...

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3rd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Singh, Baldev Kavita Vivekananda Global University Department of Computer Science Engineering Jaipur India Chandigarh School of Business Department of Master of Computer Application India

ISBN: (纸本)9798350399264

The Internet's crucial domain name system is in charge of translating domain names into IP addresses. However, because of its dispersed structure with a hub, it is subject to significant security concerns. Since the creation of Bitcoin, blockchain has drawn a lot of attention owing to its decentralized, trustworthy, and secure features. In this article, we suggest SSHTDNS, a brand-brand-new domain name system built on the blockchain. SSHTDNS selects the consortium chain form to ensure the management of Top-Level Domain names, and these consortium nodes use linkable ring signature technology for identity anonymity and vote fairness. Shading protocol is used in multi-chain structures to increase the system's scalability and throughput. A domain name system that combines these methods can be safe, scalable, and high-throughput. © 2023 IEEE.

关键词： Blockchain

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Resource Management allocation in the CC Environments with Multi-Objective Optimization system 3

Resource Management allocation in the CC Environments with M...

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3rd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Bhambu, Pawan Kaur, Gursimran Vivekananda Global University Department of Computer Science Engineering Jaipur India Chandigarh School of Business Department of Master of Computer Application India

ISBN: (纸本)9798350399264

These days, the topic of cloud computing has become a very popular paradigm for providing services online. For the cloud data center to deliver cloud infrastructure services in response to user demand, a set of virtualized resources are required. In order to lower the degree as a result of resource loss and SLA violations, the necessity for optimum resource management becomes a crucial problem in cloud data centers. These issues are classified as NP-hard, and meta heuristics approaches are well suited to solving them. The article develops a multi-objective hybrid fruit fly optimization (MOHFO)-based method for SLA-conscious dynamic resource management in cloud data centers. For the fruit fly optimization technique, the Bald Eagle Search (BES) optimization behavior search capabilities. To balance resource waste and SLA breaches, the proposed solution employs dynamic consolidation and deployment of virtual machines (VMs) mechanism. The suggested technique utilizing Clouds to simulate different data center designs, using QoS measures to assess the experimental outcomes. The proposed MOHFO technique is assessed and contrasted to existing optimization strategies in terms of resource wastage, energy use, communication costs, and migration frequency in order to provide cloud computing with QoS-aware optimal resource provisioning. © 2023 IEEE.

关键词： Virtual machine

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Enhanced face recognition using rank-level fusion with multi-feature vectors and interval type-3 fuzzy set 3

Enhanced face recognition using rank-level fusion with multi...

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3rd IEEE Applied Signal Processing Conference, ASPCON 2023

作者： Ghosh, Manas Sing, Jamuna Kanta Rcc Institute of Information Technology Department of Computer Application Kolkata India Jadavpur University Department of Computer Science & Engineering Kolkata India

ISBN: (纸本)9798350325881

In the near past, interval type-3 fuzzy set theory has been introduced as a more effective means of addressing uncertainty in decision-making than its fuzzy set counterparts. This paper suggests an approach to face recognition which integrates multiple feature vectors and interval type-3 fuzzy sets for fuzzy rank-level fusion. Use of multiple feature vectors resolves inter-class resemblances while incorporating interval type-3 fuzzy membership function in generating fuzzy rank reduces the uncertainty due to intra-class divergences. The output of the classifier is regarded as confidence factor. For each feature vector, the fuzzy ranks are generated by formulating an interval type-3 fuzzy membership function for each of top-ordered H classes based on confidence factors utilizing all training feature vectors and those of the training samples of the respective class. Multiple fuzzy ranks, built upon interval type-3 fuzzy set, are generated by employing of multiple feature vectors. To determine the final score of a class, summation of the fuzzy rank complements is combined with the complement of arithmetic mean of respective classifier's output. Final scores are evaluated to conclude test image's class. Two publicly accessible face databases are used to evaluate the method and it has shown that the solution, advocated here, exhibits an improved effectiveness over its existing related counterparts. © 2023 IEEE.

关键词： Face recognition

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Enhancing Fault Tolerance and Load Balancing in Cloud computing for improved e-healthcare Systems Performance 2

Enhancing Fault Tolerance and Load Balancing in Cloud comput...

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2nd International Conference on Ambient Intelligence in Health Care, ICAIHC 2023

作者： Priyadarshini, Aliva Pradhan, Sateesh Kumar Laha, Suprava Ranjan Kumar Nayak, Debasish Swapnesh Utkal University Department of Computer Science & Application Odisha Bhubaneswar India Utkal University Department of Computer Science and Application Odisha Bhubaneswar India Siksha 'O' Anusandhan University Department of Computer Science & Engineering ITER Odisha Bhubaneswar India

ISBN: (纸本)9798350344882

Cloud Computing is increasingly being recognized as a valuable virtual resource-sharing solution. Due to its ability to provide solutions across different industries, the number of Cloud computing users has grown substantially. Load balancing distributes the workload across multiple servers to optimize resource utilization and prevent overload, while fault tolerance ensures that the system can continue to function even in the event of failures. In an e-healthcare system, where downtime or errors can have severe consequences for patients, fault tolerance and load balancing become even more critical. Load-balancing techniques are of utmost importance in enhancing the effectiveness of cloud servers. We have comprehensively examined the primary elements that impact fault tolerance in load-balancing task migration within cloud computing. These factors have been assessed by employing various methods to evaluate their influence on fault tolerance in load balancing. By implementing these techniques, e-healthcare systems can improve their ability to handle traffic surges and ensure patient safety and satisfaction. © 2023 IEEE.

关键词： Cloud computing

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application of hybrid capsule network model for malaria parasite detection on microscopic blood smear images

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Multimedia Tools and applications 2025年第7期84卷 3459-3485页

作者： Kumar, S. Aanjan Muchahari, Monoj Kumar Poonkuntran, S. Kumar, L. Sathish Dhanaraj, Rajesh Kumar Karthikeyan, P. Department of Computer Application and Science Institute of Engineering and Management West Bengal Kolkata India School of Computing Science and Engineering VIT Bhopal University Bhopal-Indore Highway Kothrikalan Madhya Pradesh Sehore466114 India Pune India Department of Electronics & Communication Engineering Velammal College of Engineering & Technology Tamil Nadu Madurai625009 India

Today, malaria is a dangerous disease caused by Plasmodium parasites and transmitted by the bite of Anopheles mosquitoes. It is estimated that more than 200,000 cases of malaria will occur every day worldwide, and more than 400,000 people will die from malaria. Early diagnosis of malaria is important to mitigate the morbidity and mortality of the disease. In this paper, a fully trained capsule neural network combined with a convolutional neural network model is proposed for malaria detection from blood smear microscopic images. In malaria diagnosis, the hybrid CapsNet model can detect malaria in blood samples. The hybrid model is optimized based on malaria data in microscopic images of infected and uninfected blood. Images are processed and enhanced through rotation before being fed into the hybrid CapsNet model. The CapsNet hybrid model is examined for malaria data with a learning rate of 0.07 and a batch size of 20. Hybrid CapsNet model shows improved results in detecting malaria from microscopic images measured against the currently available standard malaria test. Constraints like malaria parasite detection rate, accuracy, F1 index and blood smear classification is used to gauge the attainment of the hybrid CapsNet model. Detection rate of 99%, accuracy of 99.08% and FAR of 0.97% is attained by the hybrid CapsNet model as contrary to the traditional state-of-the-art model, the DSCN-Net, where the detection rate was 98%, the accuracy was 97.2%, and the FAR was 0.99%. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Diseases

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Intrusion Detection System Through Deep Learning in Routing MANET Networks

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Intelligent Automation & Soft Computing 2023年第7期37卷 269-281页

作者： Zainab Ali Abbood DoguÇagdaşAtilla Çagatay Aydin Department Electrical and Computer Engineering Altinbas UniversityIstanbulTurkey Department Computer Technology Engineering Al-Esraa UniversityBaghdadIraq Electric Autonomous and Unmanned Vehicles Application and Research CentreAltinbas UniversityIstanbulTurkey Faculty of Engineering and Architecture Altinbas UniversityIstanbulTurkey Department Electrical and Electronics Engineering Ege UniversityIzmirTurkey

Deep learning(DL)is a subdivision of machine learning(ML)that employs numerous algorithms,each of which provides various explanations of the data it consumes;mobile ad-hoc networks(MANET)are growing in *** reasons including node mobility,due to MANET’s potential to provide small-cost solutions for real-world contact challenges,decentralized management,and restricted bandwidth,MANETs are more vulnerable to security *** protecting MANETs from attack,encryption and authentication schemes have their ***,deep learning(DL)approaches in intrusion detection sys-tems(IDS)can adapt to the changing environment of MANETs and allow a sys-tem to make intrusion decisions while learning about its mobility in the *** are a secondary deﬁance system for mobile ad-hoc networks *** since they monitor network trafﬁc and report anything ***,many scientists have employed deep neural networks(DNNs)to address intrusion detection *** paper used MANET to recognize com-plex patterns by focusing on security standards through efﬁciency determination and identifying malicious nodes,and mitigating network attacks using the three algorithms presented Cascading Back Propagation Neural Network(CBPNN),Feedforward-Neural-Network(FNN),and Cascading-Back-Propagation-Neural-Network(CBPNN)(FFNN).In addition to Convolutional-Neural-Network(CNN),these primary forms of deep neural network(DNN)building designs are widely used to improve the performance of intrusion detection systems(IDS)and the use of IDS in conjunction with machine learning(ML).Further-more,machine learning(ML)techniques than their statistical and logical methods provide MANET network learning capabilities and encourage adaptation to differ-ent *** with another current model,The proposed model has better average receiving packet(ARP)and end-to-end(E2E)*** results have been obtained from CBP,FFNN and CNN 74%,82%and 85%,respectively,by the ti

关键词： ARP CBPNN CNN DNN DL E2E FFNN IDS ML MANET security

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