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2023 International Conference on Recent Advances in Science and engineering technology, ICRASET 2023

作者： Ojha, Anatha Charan Agarwal, Ankita Swetha, G. Department of Computer Science and Information Technology Bangalore India Maharishi University of Information Technology Maharishi School of Engineering & Technology Uttar Pradesh India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India

ISBN: (纸本)9798350306927

We provide a unique method to enhance cybersecurity threat prediction using generative adversarial networks (GANs). Through competitive training, the generator and discriminator parts of GANs are customized for application in the cybersecurity industry. Three key algorithms underlie this method. First, to create believable threats, GANs are employed in the GAN Architecture for Threat Generation. The discriminator distinguishes between actual and phony threat data, while the generator generates false threat data. These simulated assaults are a significant part of cybersecurity threat predictions. Second, Long Short-Term Memory (LSTM) neural networks are used in the Threat Scenario Prediction Algorithm's Time Series Forecasting Algorithm. To predict possible outcomes, long short-term memory (LSTM) models the temporal dependency of historical threat data. This approach offers perceptions into prospective threats, enabling the adoption of preventative actions. Finally, Markov chains are used by the Threat Evolution Algorithm to depict the dynamic evolution of cybersecurity threats. In order to shed light on how dangers vary over time, it forecasts the chance of a transition from one state of hazard to another. We get a deeper understanding of threat trajectories and how they influence an organization's cybersecurity by adopting a more dynamic approach. This comprehensive approach combines state-of-the-art techniques in threat scenario generation, forecasting, and evolution modeling, providing organizations with a robust toolset to proactively address cybersecurity challenges. © 2023 IEEE.

关键词： Long short-term memory

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Adaptive Protocols for End-to-End Network Management in Underwater High Speed Communications

Adaptive Protocols for End-to-End Network Management in Unde...

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2023 IEEE International Conference on Paradigm Shift in information Technologies with Innovative Applications in Global Scenario, ICPSITIAGS 2023

作者： Sharma, Ritu Das, Neeraj Jebaraj, Solomon Garg, Prateek Murali, M.J. Riddhi, R. Vivekananda Global University Department of Computer Science & Engineering Jaipur India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science and Information Technology Karnataka Bangalore India Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Institute of Engineering and Technology Chitkara University Punjab India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electrical and Electronics Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Information Technology Pune India

ISBN: (纸本)9798350329773

Adaptive protocols for cease-to-give up network management in underwater excessive pace communications are designed to dynamically adjust operating parameters of a communication machine based at the traits of the underwater surroundings. Those adaptive protocols generally appoint a spread of techniques, together with packet size selection, adaptive manipulate of link fee and latency, and mistakes healing mechanisms. The main purpose of those protocols is to maximize the throughput of the underwater verbal exchange machine at the same time as minimizing interference. To achieve these dreams, the adaptive protocols can detect adjustments within the underwater surroundings and alter the communique gadget for this reason. The proposed adaptive protocols can improve the reliability and first-class of the underwater verbal exchange gadget by way of minimizing strength, bandwidth and latency requirements. Additionally, those protocols can reduce the verbal exchange communique machine's postpone version, leading to higher overall performance and smoother statistics switch. © 2023 IEEE.

关键词： Bandwidth

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Enhanced Cognitive Quality Inspection in Manufacturing through Siamese Few-Shot Learning 7

Enhanced Cognitive Quality Inspection in Manufacturing throu...

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7th IEEE Pune Section International Conference, PuneCon 2024

作者： Dharaskar, Yog Parikh, Atharva Aloni, Ketaki Rajput, Vipulkumar Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Purdue University Department of Statistics West Lafayette United States Vishwakarma Institute of Information Technology Department of Electronics and Telecommunication Pune India

ISBN: (纸本)9798331527822

Defect detection in manufacturing remains challenging, with traditional methods relying on inflexible, hardcoded image processing techniques. While deep learning approaches show promise, they often lack scalability across product variations and require extensive training data. This paper presents a novel few-shot learning-based strategy for defect detection, addressing these limitations. Our proposed solution centers on a Siamese Defect classification model utilizing the Swin-L (Shifted Window Transformer) architecture. This approach enables efficient scaling across new products with minimal technical overhead. The models are trained on the industry-standard MVTEC dataset and optimized for real-time processing on embedded GPU devices, ensuring practical applicability in manufacturing environments. The developed prototype incorporates automated removal of defective items while preserving detailed information for subsequent analysis. By combining few-shot learning with advanced model architectures, our solution offers a reliable and highly scalable approach to defect detection. © 2024 IEEE.

关键词： Graphics processing unit

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IoT'S Impact on Development in the Energy Sector

IoT'S Impact on Development in the Energy Sector

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Menaka, C. Singh, Swati Sindgi, Anupama Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India

ISBN: (纸本)9798350357769

The major facilitators of green energy changes and worldwide warming mitigation are the incorporation of alternative energy sources and electricity usage optimisation. There are several uses for modern technology, such as the Internet of Issues, in the field of energy, including supply, delivery, and demand. IoT may be used to increase consumption of clean energy, improve energy efficiency, and lessen the harmful impacts associated with electricity use. This essay summarises the material that has been published on the use of IoT in the energy sector generally and smart electrical grids in specific. Additionally, we go through IoT technologies that enable it involving the internet of things and various platforms for data processing. We also discuss the privacy and security issues that arise when using IoT in the energy industry, as well as a few potential fixes such blockchain technology. A description of the importance of IoT in the system's optimization is given to supervisors, energy the economists, including politicians in the energy sector by this survey. © 2023 IEEE.

关键词： Alternative Energy Sources Blockchain technology and System Optimization Clean Energy Consumption Data Processing Platforms Delivery Demand Development Electricity Usage Optimization Energy Sector Green Energy Impact IoT Privacy Security Issues Supply

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Federated Learning and Decentralized IoT Cooperation

Federated Learning and Decentralized IoT Cooperation

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2023 International Conference on Advances in Computation, Communication and information technology, ICAICCIT 2023

作者： Kumar, Dinesh Krishna, Vijaya Manju Bargavi, S.K. Maharishi University of Information Technology Maharishi School of Engineering & Technology Uttar Pradesh India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350344387

In an effort to lessen the difficulties caused by large-scale transfer of data on mobile phone networks, edge buffering tech has emerged as a result of the introduction of Internet of Things (IoT) apps by developers and carriers. Current optimization-based methods often struggle to adjust to changing circumstances. In order to solve these problems, most modern learning-based technologies are presented in a centralized way, which wastes network resources on refresher as well as data sharing. Study conducted present the Coordinated Deep Regeneration Edge Caching System (FADE) in the above setting. FADE enables decentralized cooperative development of a common prediction model among base stations. Base stations start local education with their initial sets of parameters and then upload almost perfect local circumstances to take part in international training sessions. Study conducted prove that convergence of FADE, displaying the intended convergence features. FADE demonstrates a considerable improvement over centralized deep training and reinforced approaches via trace-driven trials, resulting in a 92% decrease in performance deterioration and a mean 60% lower system costs. Especially, it outperforms the state-of-the-art methods in this field. © 2023 IEEE.

关键词： Content Replacement Cooperative Edge Caching Decentralized IoT Cooperation Deep Reinforcement Learning Edge Caching FADE (Federated Deep renewal learning-based cooperating Edge caching) Federated Learning Internet of Things (IoT) QoS (Quality of Service) and System Payment

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computer Science Education Facing Unconventional Odds: Case Studies from the Arab World

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ACM Inroads 2024年第1期15卷 6-17页

作者： Aly, Sherif G. Echihabi, Karima Eldawlatly, Seif Shuaib, Khaled Tekli, Joe Department of Computer Science and Engineering The American University in Cairo Cairo Egypt Department of Computer Science Mohamed VI Polytechnic University Ben Guerir Morocco Department of Computer Science and Engineering The American University in Cairo AUC Avenue New Cairo Cairo11835 Egypt Ain Shams University Cairo Egypt College of Information Technology United Arab Emirates University Abu Dhabi United Arab Emirates Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon

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Optimising Business Processes in Project Tracking using CRM 2

Optimising Business Processes in Project Tracking using CRM

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2nd IEEE International Conference on Emerging Research in Computational Science, ICERCS 2024

作者： Ramprasath, J. Saranya, N. Arun Kumar, M. Chandru Vignesh, C. Ramprakash, P. Krishnaraj, N. Dr. Mahalingam College of Engineering and Technology Department of Information Technology Pollachi India Sri Krishna College of Technology Department of Computer Science and Engineering Coimbatore India Sri Krishna College of Technology Department of Electronics and Communication Engineering Coimbatore India Vellore Institute of Technology School of Computer Science and Engineering Vellore India Sri Krishna College of Technology Department of AI & DS Coimbatore India

ISBN: (纸本)9798331534950

Customer relationship management, called CRM, is essential for businesses looking to sustain profitable growth in today's fiercely competitive business environment. This essay examines cutting-edge methods to improve clients in the digital era and thoroughly explains CRM practices. The research examines best-serving customers through various touchpoints by integrating data analytics, AI, and personalized marketing strategies. In addition, it explores the role of organizational alignment and a customer-centric culture in developing enduring customer relationships. The study uses theoretical frameworks and practical data to highlight the main obstacles and changes associated with implementing efficient CRM procedures. As a result, synthesizing pertained research and case studies provides insights into new trends and industry best practices for building long-lasting relationships with customers. The results highlight the significance of proactive involvement, ongoing innovation, and ethicizing deliberations in fostering trust and allegiance across various consumer categories. This paper adds to the current conversation on CRM by providing practice advice that companies can use to adjust to and prosper in a constantly changing market. © 2024 IEEE.

关键词： Sales

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An Adaptive Genetic Algorithm-Based Load Balancing-Aware Task Scheduling Technique for Cloud Computing

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computers, Materials & Continua 2022年第12期73卷 6103-6119页

作者： Mohit Agarwal Shikha Gupta Deptartment of Computer Science&Engineering School of Engineering&TechnologySharda UniversityGreater NoidaUttar Pradesh201319India Department of Information Technology Maharaja Agrasen Institute of TechnologyDelhi110086India

Task scheduling in highly elastic and dynamic processing environments such as cloud computing have become the most discussed problem among *** scheduling algorithms are responsible for the allocation of the tasks among the computing resources for their execution,and an inefficient task scheduling algorithm results in under-or over-utilization of the resources,which in turn leads to degradation of the ***,in the proposed work,load balancing is considered as an important criterion for task scheduling in a cloud computing environment as it can help in reducing the overhead in the critical decision-oriented *** this paper,we propose an adaptive genetic algorithm-based load balancing(GALB)-aware task scheduling technique that not only results in better utilization of resources but also helps in optimizing the values of key performance indicators such as makespan,performance improvement ratio,and degree of *** concept of adaptive crossover and mutation is used in this work which results in better adaptation for the fittest individual of the current generation and prevents them from the *** simulator has been used to carry out the simulations and obtained results establish that the proposed GALB algorithm performs better for all the key indicators and outperforms its peers which are taken into the consideration.

关键词： Cloud computing genetic algorithm(GA) load balancing makespan resource utilization task scheduling

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Control the Interval Estimation to Enhance the Performance of Optimized Link State Routing 4

Control the Interval Estimation to Enhance the Performance o...

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4th IEEE International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and computer engineering, MI-STA 2024

作者： Ishqeem, Omar Ammar Sarreb, Rebah Daw Hatif Libya Company Libyan Post Telecommunications & Information Technology Company Libya Sabratha university Faculty of Engineering Rigdalin Dept. of Computer Engineering Libya

ISBN: (纸本)9798350372632

This paper investigates the creation of self-organizing Mobile Ad hoc Networks (MANETs) employing Optimized Link State Routing Protocol (OLSR) with Multipoint Relay (MPR). The routing protocol in MANET controls the management of node mobility by using metrics to evaluate the quality of link states. The primary concept of OLSR is to choose the MPR that identifies the route from the source to the destination node. The MPR relays Hello and Topology Control (TC) messages between nodes used to discover and distribute link state information. The Hello and TC messages interval plays a critical role in the link state in the network. Accordingly, link failure for a few seconds may lead to a significant performance deterioration. This work enhances the performance of OLSR by estimating the efficient Hello and TC message interval for faster route recovery. Many experiments are simulated and analyzed by a set of parameters such as MPR count, TC traffic sent, throughput, delay, and data dropped. All our results revealed that the proposed Control of the Interval Estimation-Optimized Link State Routing (CIE-OLSR) has better performance than the traditional OLSR. © 2024 IEEE.

关键词： Mobile ad hoc networks

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A Voting Ensemble based Machine Learning Approach to Predict Cardiovascular Disease 26

A Voting Ensemble based Machine Learning Approach to Predict...

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26th International Conference on computer and information technology, ICCIT 2023

作者： Abidin, Ahmad Jainul Borhan, Rownak Akhter, Arifa Jahan, Sanjeda Based, Md Abdul Kader Zilany, Abdul Bangladesh University of Professionals Information and Communication Technology Dhaka Bangladesh Nara Institute of Science and Technology Information Science Nara Japan Jagannath Univeristy Computer Science and Engineering Dhaka Bangladesh Dhaka International University Computer Science and Engineering Dhaka Bangladesh Gono Bishwabidyalay Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350359015

Heart disease has a huge impact on the worldwide mortality picture and accounts for a large number of annual deaths. This rate is growing due to factors like unhealthy diets, lack of physical activity, and tobacco consumption. As a result, adopting preventative measures and early detection has the potential to lessen the severity of the condition. This research aims to create a machine learning model that uses patients' medical variables as input to predict cardiac disease. To predict cardiovascular disease, this study analyzes various machine learning classifiers. The best outcome is finally obtained when employing the voting classifier ensemble technique. Random Forest and Gradient Boost combined technique and Random Forest and KNN combined approach-based voting classifiers both achieved 90% accuracy. © 2023 IEEE.

关键词： boosting ensemble cardiovascular disease (CVD) heart disease prediction Machine learning voting classifier

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