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Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), International Conference

作者： Chandradeep Bhatt Anurag Bisht Sanjay Sharma Shefali Gupta Teekam Singh CSE Department Graphic Era Hill University Dehradun India Computer Science and Engineering Graphic Era Hill University Dehradun India Computer Application Department SGRRITS Dehradun India Chitkara University Institute of Engineering and Tech Chitkara University Punjab India Computer Science and Engineering Graphic Era Deemed to be University Dehradun India

The industry for online food delivery has grown quickly, changing how customers access and use food services. Finding foods that are suitable for their diets and leanings among the dozen selections offered to customers and users is frequently difficult. For this study, existing research on customer behavior in online food ordering systems is reviewed in order to identify any gaps where intelligent advocacy systems can be very helpful. The proposed work focuses on utilizing sophisticated machine learning algorithms and approaches to create personalized food advice that takes into consideration user preferences, previous order history, and contextual data. The findings of the empirical study offer priceless insights into the efficiency of intelligent suggestion systems and their influence on user engagement. The results provide guidance for future advancement in the sector and help to understand consumer preferences as well as the difficulties that online meal ordering platforms have had to overcome. My suggested system includes an online food ordering option that makes it simple for clients. My system makes it simple to purchase food online from restaurants and use the mess service. This study looks into how the online food ordering landscape is changing and aims to offer opinions on how intelligent advising systems could improve user experience. The ultimate goal of this is to help companies devise plans to enhance customer service and spur expansion in the online food ordering sector.

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A Multimodal Deep Learning Approach for Advancing Liver Disease Diagnosis and Prognosis Prediction

A Multimodal Deep Learning Approach for Advancing Liver Dise...

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IoT, Communication and Automation Technology (ICICAT), International Conference on

作者： Mukesh Kumar Nitin Arvind Shelke Jagendra Singh Kamal Nain Sharma Rajat Sharma Rakesh Kumar Electronics and Communication Engineering The Technological Institute of Textile & Sciences Bhiwani India School of Computer Science Engineering & Technology Bennett University Greater Noida India School of Computer Application Lovely Professional University Jalandhar India Department of Computer Science and Engineering - SOES G D Goenka University Gurugram India Department of Computer Science and I.T. Magadh University Bodh Gaya India

ISBN: (数字)9798350368109

ISBN: (纸本)9798350368116

Using a multimodal deep learning framework, we have demonstrated the possibility to predict liver disease subtyping and prognosis with high accuracy. The framework includes over 1600 patients, for which medical imaging and clinical data have been collected. Convolutional Neural Networks (CNNs), Recurrent Neural Network (RNNs), and Long short-term memory (LSTMs) have been combined in the work to extract features from the input data and demonstrate correlation between input and output and model complexity. Such an approach allowed analyzing images and predicting liver diseases based on structural abnormality analysis with the use of CNNs. RNNs and LSTMs, in their turn, allowed combining features for multimodal data source analysis and sequential clinical data analysis providing information about patient history. The obtained results were accurate for each feature extraction and modeling, with the best performance demonstrated by CNN with RNN where the accuracy was 97.8% and another CNN with LSTM where the accuracy was around 94.5%. The model proved to be feasible and accurate in the context of its application to hepatology since multiple data sources were combined in the work. This approach allows accounting for all modes of data source and offers a comprehensive assessment including disease diagnosis and prognosis. The obtained results can be also applied to clinical practice where doctors can add data about their patients to receive more information on diagnosis or prognosis for better decision-making. Finally, the idea to use a multilayered feature from multimodal data sources can be transferable to other types of sequential and structural data and applied to other diseases for their diagnosis and prognosis. However, it is essential to test the framework in other settings to refine it or find limitations to its application.

关键词： Deep learning Accuracy Recurrent neural networks Liver diseases Soft sensors Feature extraction Data models Convolutional neural networks Prognostics and health management Long short term memory

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A Bayesian Optimization Framework for Adaptive Node Deployment in Wireless Sensor Networks 14

A Bayesian Optimization Framework for Adaptive Node Deployme...

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14th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2025

作者： Priyadarshi, Rahul Rout, Jayant Kumar Vishwakarma, Anish Kumar Dhengre, Nikhil Ranjan, Rakesh Verma, Amit Faculty of Engineering and Technology ITER SOA Bhubaneswar India School of Computer Application KIIT Bhubaneswar India School of Computer Science UPES Uttarakhand Dehradun India Department of ECE IIIT Maharashtra Nagpur India

ISBN: (纸本)9798331531935

Wireless Sensor Networks (WSNs) require efficient node deployment strategies to optimize network longevity, energy consumption, and coverage. This paper introduces a novel approach for strategic node deployment in WSNs using Bayesian Optimization (BO). A BO-based framework that iteratively modifies node positions to maximize network performance metrics, such as energy efficiency and coverage ratio, while minimizing execution time is proposed. Through extensive simulations across diverse deployment scenarios, the proposed method is evaluated and demonstrates superior performance compared to both traditional and machine learning (ML)-based techniques. The results indicate significant improvements in coverage and energy efficiency, alongside competitive execution times. These findings highlight the potential of BO to enhance WSN deployment strategies, offering a scalable and adaptive solution for real-world applications. Future research will focus on integrating BO with other optimization techniques and extending the approach to dynamic environments and varied network configurations. © 2025 IEEE.

关键词： Bayesian Optimization Coverage Ratio Energy Efficiency Node Deployment Wireless Sensor Networks

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An Analysis of Encapsulating Security Payload in Wireless Network Security 1

An Analysis of Encapsulating Security Payload in Wireless Ne...

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1st IEEE International Conference on Emerging Research in Computational science, ICERCS 2023

作者： Verma, Devika A. Singh, Vaishali Beemkumar, N. Shree, Ritu Minhas, Deepak Yuvaraj, S. Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Maharishi University of Information Technology Maharishi School of Engineering and Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Mechanical Engineering Karnataka Bangalore India Vivekananda Global University Department of Computer Science & Application Jaipur India Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India

ISBN: (纸本)9798350359756

The Encapsulating Safety Payload (ESP) is a necessary part of the IPsec suite of protocols, imparting a critical layer of security within a Wi-Fi community. This paper aims to evaluate ESP and its software in wireless community protection comprehensively. ESP capabilities as a secured payload encapsulating data via imparting integrity, authentication, and confidentiality. It commonly accomplishes this by using a mixture of cryptography and tunneling to shield the contents of the packets from alteration, eavesdropping, and unauthorized get entry. Moreover, this paper outlines the distinctive ESP modes and cipher suites, presenting how they may be used to defend data being despatched over a Wi-Fi community. ESP additionally affords numerous safety enhancements, such as protection from replay assaults, packet length control, and critical negotiation. Furthermore, this paper explores the challenges associated with implementing and deploying ESP in wireless networks. Subsequently, the paper will pay attention to the numerous overall performance issues for ESP utilization by analyzing the effect of latency, throughput, and power intake on ESP-enabled Wi-Fi networks. © 2023 IEEE.

关键词： Authentication

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Fog Computing in IoT: Architecture and applications

Fog Computing in IoT: Architecture and Applications

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International Conference on science Technology engineering and Management (ICONSTEM)

作者： Anurag Vijay Agrawal Shashi Gowridurga A Suresh Balakrishnan T Anwar Ahamed Shaikh Kalaipriya V Department of Electronics and Communication Engineering J. P. Institute of Engineering and Technology Meerut Uttar Pradesh India Department of Computer Application CCS University Meerut Department of Computer Science and Engineering Velammal Institute Of Technology Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Thandalam Tamil Nadu India Department of Computer Science and Engineering KoneruLakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Department of Computer Science and Engineering RMK College of Engineering and Technology Puduvoyal Tamil Nadu India

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365108

This exploration paper explores the paradigm of Fog Computing as a transformative armature in the Internet of effects(IoT) geography. Fog Computing represents a decentralized approach, positioning computing coffers closer to the edge of the network, thereby addressing quiescence, bandwidth, and data sequestration challenges current in traditional polycentric IoT infrastructures. The paper provides an in-depth analysis of Fog Computing's armature, expounding its crucial factors similar to fog bumps, middleware, and edge bias. likewise, it investigates the different operations of Fog Computing in IoT, ranging from realtime data analytics, effective resource operation, to enhanced security and sequestration measures. By bridging the gap between edge bias and centralised pall waiters, Fog Computing emerges as a vital result, fostering the elaboration of scalable, effective, and responsive IoT ecosystems. This exploration contributes precious perceptivity to the architectural nuances and practical operations, paving the way for the integration of Fog Computing as a foundation in the ever-expanding realm of IoT technologies.

关键词： Geography Data analysis Ecosystems Mathematics Distance measurement Internet Security

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Improved DHOA-Fuzzy Based Load Scheduling in IoT Cloud Environment

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computers, Materials & Continua 2022年第5期71卷 4101-4114页

作者： R.Joshua Samuel Raj V.Ilango Prince Thomas V.R.Uma Fahd N.Al-Wesabi Radwa Marzouk Anwer Mustafa Hilal Department of Information Science&Engineering CMR Institute of TechnologyBengaluru560037India Department of Computer Application CMR Institute of TechnologyBangalore560037India School of Computing Woldia Institute of TechnologyWoldia UniversityEthiopia Department of Commerce School of CommerceFinance and AccountancyChrist UniversityBangalore560029India Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Faculty of Computer and IT Sana’a UniversityYemen Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlkharjSaudi Arabia

Internet of things (IoT) has been significantly raised owing to thedevelopment of broadband access network, machine learning (ML), big dataanalytics (BDA), cloud computing (CC), and so on. The development of IoTtechnologies has resulted in a massive quantity of data due to the existenceof several people linking through distinct physical components, indicatingthe status of the CC environment. In the IoT, load scheduling is realistictechnique in distinct data center to guarantee the network suitability by fallingthe computer hardware and software catastrophe and with right utilize ofresource. The ideal load balancer improves many factors of Quality of Service(QoS) like resource performance, scalability, response time, error tolerance,and efficiency. The scholar is assumed as load scheduling a vital problem inIoT environment. There are many techniques accessible to load scheduling inIoT environments. With this motivation, this paper presents an improved deerhunting optimization algorithm with Type II fuzzy logic (IDHOA-T2F) modelfor load scheduling in IoT environment. The goal of the IDHOA-T2F is todiminish the energy utilization of integrated circuit of IoT node and enhancethe load scheduling in IoT environments. The IDHOA technique is derivedby integrating the concepts of Nelder Mead (NM) with the DHOA. Theproposed model also synthesized the T2L based on fuzzy logic (FL) systemsto counterbalance the load distribution. The proposed model finds usefulto improve the efficiency of IoT system. For validating the enhanced loadscheduling performance of the IDHOA-T2F technique, a series of simulationstake place to highlight the improved performance. The experimental outcomesdemonstrate the capable outcome of the IDHOA-T2F technique over therecent techniques.

关键词： Load scheduling internet of things cloud computing metaheuristics fuzzy logic

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Actual Rate Prediction: Extension in Real Estate Prediction using Machine Learning

Actual Rate Prediction: Extension in Real Estate Prediction ...

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Advances in Computing, Communication and Networking (ICAC2N), International Conference on

作者： Archana Archana Prachi Bhatnagar Narendra Singh Manoj Kumar Sharma Gaurav Kumar Mahima Shankar Pandey Nidhi Bansal Department of Computer Science and Engineering IIMT University Meerut Uttar Pradesh India Department of Computer Science and Application IIMT University Meerut Uttar Pradesh India Department of Computer Science and Engineering Panchwati Institute of Engineering and Technology Meerut Uttar Pradesh India Department of Computer Science and Engineering Vishveshwarya Group of Institute Dadri GB Nagar Uttar Pradesh India Department of Computer Science and Engineering Galgotia College of Engineering and Technology Greater Noida Uttar Pradesh India Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune India

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

Machine learning algorithms always perform well to solve real-life problems encountered in daily life. Due to large data sets, analysis is also becoming too complex to predict anything. A lot of calculations are being done to find the exact patterns and values to get accurate results. Here machine learning algorithms are giving tremendous performance while applying various classification techniques as it gives algorithms based on specifications for different aspects. In different cities, forecasting is necessary as people must move from one place to another due to the pandemic situation. A case study has also been conducted with real estate real data by applying machine learning algorithms. A new model has also been implemented with Jupyter Notebook in the proposed paper and has received some positive feedback.

关键词： Machine learning algorithms Accuracy Pandemics Urban areas Machine learning Big Data Prediction algorithms Planning Classification algorithms Forecasting

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A Conflicting Evidence Fusion Method Based on Game Theory Combination Weighting

A Conflicting Evidence Fusion Method Based on Game Theory Co...

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computer, Vision and Intelligent Technology (ICCVIT), International Conference on

作者： Ziye Wang Jianyu Xiao School of Computer Science and Technology Huaibei Normal University Huaibei China Anhui Engineering Research Center for Intelligent Computing and Application on Cognitive Behavior Huaibei China

ISBN: (数字)9798331540043

ISBN: (纸本)9798331540050

This essay introduces a new approach to address the challenges of applying Dempster-Shafer evidence theory to tackling high-conflict evidence. We first employed Jousselme’s distance and compatibility coefficients to measure confidence and support, then converted these values into vectors. Next, we applied game theory to convert the vectors into a system of linear equations,from which we obtained a corresponding combination of weight coefficients that we used to modify the initial evidence. Finally, we applied the Dempster-Shafer combination rule to achieve evidence fusion. A case analysis shows that our proposed method provides more sensible fusion results for high-conflict evidence.

关键词： computer vision Accuracy Evidence theory Data integration Reliability theory Vectors Game theory Convergence

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Hybrid Bioinspired Algorithms for UWSNs: Combining Genetic Algorithm and PSO

Hybrid Bioinspired Algorithms for UWSNs: Combining Genetic A...

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2024 IEEE International Conference on Communication, Computing and Signal Processing, IICCCS 2024

作者： Vijayaraghavan, P. Vasanthi, P. Almusawi, Muntather Thakur, Shivani Varma, Pankaj Anakath Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Sri Ramakrishna Engineering College Department of Mathematics Tamil Nadu Coimbatore India The Islamic University College Of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq The Islamic University Of Al Diwaniyah College Of Technical Engineering Department Of Computers Techniques Engineering Al Diwaniyah Iraq Chandigarh Group of Colleges Chandigarh Engineering College Department of Computer Application Punjab Jhanjeri Mohali140307 India Ies College of Technology Department of Computer Science & Engineering M.P. Bhopal India Saveetha Institute of Medical and Technical Sciences Department of Cse Tamil Nadu Chennai India

ISBN: (纸本)9798350390759

This work proposes a GA-PSO Metaheuristic Algorithm to improve the throughputs and network efficiency of the UWSNs based on using a new technology of WSNs which is Underwater Wireless Sensor Networks. In response to the difficulties of underwater communication, a hybrid GA-PSO algorithm is designed to fit the needs, where GA excels in search capabilities, and PSO accomplishes optimization adjustments. The realistic network model was developed using realistic network topology with MATLAB NS-3 and the integrated GA-PSO hybrid approach was found to significantly enhance the throughput energy efficiency and overall network lifetime as compared to the other routing protocols GA & PSO individually. Based on these findings, it could therefore be concluded that the proposed hybrid bioinspired algorithms can potentially help enhance the efficiency and dependability of UWSNs significantly. © 2024 IEEE.

关键词： Genetic algorithms

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Dynamic Textual Prompt For Rehearsal-free Lifelong Person Re-identification

arXiv

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arXiv 2024年

作者： Chen, Hongyu Jiao, Bingliang Wang, Wenxuan Wang, Peng Zhang, Yanning The National Engineering Laboratory for Integrated AeroSpace-Ground-Ocean Big Data Application Technology School of Computer Science Northwestern Polytechnical University Xi’an710060 China

Lifelong person re-identification attempts to recognize people across cameras and integrate new knowledge from continuous data streams. Key challenges involve addressing catastrophic forgetting caused by parameter updating and domain shift, and maintaining performance in seen and unseen domains. Many previous works rely on data memories to retain prior samples. However, the amount of retained data increases linearly with the number of training domains, leading to continually increasing memory consumption. Additionally, these methods may suffer significant performance degradation when data preservation is prohibited due to privacy concerns. To address these limitations, we propose using textual descriptions as guidance to encourage the ReID model to learn cross-domain invariant features without retaining samples. The key insight is that natural language can describe pedestrian instances with an invariant style, suggesting a shared textual space for any pedestrian images. By leveraging this shared textual space as an anchor, we can prompt the ReID model to embed images from various domains into a unified semantic space, thereby alleviating catastrophic forgetting caused by domain shifts. To achieve this, we introduce a task-driven dynamic textual prompt framework in this paper. This model features a dynamic prompt fusion module, which adaptively constructs and fuses two different textual prompts as anchors. This effectively guides the ReID model to embed images into a unified semantic space. Additionally, we design a text-visual feature alignment module to learn a more precise mapping between fine-grained visual and textual features. We also developed a learnable knowledge distillation module that allows our model to dynamically balance retaining existing knowledge with acquiring new knowledge. Extensive experiments demonstrate that our method remarkably outperforms SOTAs under various settings. Our model even outperforms rehearsal-based state-of-the-art approaches by

关键词： Semantics

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