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Optimizing User Recommendations with Variational Autoencoders: Insights from MovieLens-1M and BookCrossing Datasets

Optimizing User Recommendations with Variational Autoencoder...

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computer Vision and Machine Intelligence (CVMI), International Conference on

作者： Kumari Samridhi Bam Bahadur Sinha Trinanjan Das Electronics and Communication Engineering Indian Institute of Information Technology Ranchi India Computer Science and Engineering National Institute of Technology Sikkim India Computer Science and Engineering Indian Institute of Information Technology Ranchi India

ISBN: (数字)9798350376876

ISBN: (纸本)9798350376883

Recommender systems utilize algorithms and data to predict user preferences based on their past choices. While these systems can be highly accurate, this increased accuracy can sometimes lead to predictability and monotony. In the realm of big data, recommender systems are both prevalent and valuable. Research on these systems has intensified in recent years, accompanied by a growing number of datasets. This paper examines the application of Variational Autoencoder (VAE)-based recommender systems using the MovieLens 1M and BookCrossing datasets. The study primarily evaluates the performance of the VAE model using Root Mean Square Error (RMSE) as the key metric. The VAE model was trained on these datasets to predict user preferences and generate personalized recommendations. Experimental results revealed an RMSE of less than 1 for both datasets, indicating the model’s proficiency in accurately estimating user ratings for items. These findings suggest that VAE-based recommender systems are promising for enhancing recommendation accuracy and personalization. This research contributes to the understanding of VAE models within the context of recommender systems and provides insights for future research and potential improvements in this field.

关键词： Measurement computer vision Accuracy Mean square error methods Predictive models Prediction algorithms Root mean square Recommender systems Machine intelligence Context modeling

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Swin Transformer with Convolution Neural Network for City Change Detection from Satellite Images 3

Swin Transformer with Convolution Neural Network for City Ch...

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3rd International Conference on Integrated Circuits and communication Systems, ICICACS 2025

作者： Patil, Shilpa Kumar, Sharan Alasady, Heba Priyadarshini, R. Kumar, B. Rajesh Department of Electronics and Communication Engineering H.K.E. Society's Sir M Visvesvaraya College of Engineering Raichur India Vtu Belagavi India College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Information Science and Engineering Bengaluru India Dhanalakshmi Srinivasan College of Engineering Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798331508456

Satellite images of high resolution are more accessible for semantic understanding of urban multi-temporal. Current approaches failed to fine-grained features and possess poor ability to acquire long-range space time information, and therefore the micro changes are missed and the edges of change types are smoothed. Therefore, in this research Swin Transformer with Convolution Neural Network (ST-CNN) is proposed for city change detection from satellite images. The ST -CNN utilizes hierarchical representations with self-attention mechanisms that improve feature extraction and spatial aspects of the understanding by changes in urban form. Besides capturing whole local-global context images, ST architectures also considered long-distance dependencies in a way that does not limit them to traditional CNN architectures. The preprocessing uses three methods such as augmentation, Bilateral Filtering (BF) and normalization. The data quality is improved through image augmentation which included horizontal flipping, rotation and brightness adjustment procedures. BF uses smooth images while keeping edges intact by carrying out non-linear pixel value combinations from nearby spots. Normalization process transforms RGB values through a scale adjustment to establish a standard range which reduces illumination effects while improving feature extraction quality. The ST-CNN achieves better results of 99.58% accuracy, 98.27% recall, 98.62% precision, 98.56% f1-score and 95.13% Intersection over Union (IoU) for Hi-UCD dataset which is better than baseline models. © 2025 IEEE.

关键词： Convolutional neural networks

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Fiber Optic communication Systems for Next-Generation Smart Cities 3

Fiber Optic Communication Systems for Next-Generation Smart ...

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3rd IEEE International Conference on Automation and Computation, AUTOCOM 2025

作者： Patil, Shrinivas Pandian, R. Naval, Preeti Jain, Romil Biswas, Sourav Karnataka Bangalore India Sathyabama Institute of Science and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Centre of Research Impact and Outcome Chitkara University Punjab Rajpura140417 India Department of Computer Science and Engineering Odisha Bhubaneswar India

ISBN: (纸本)9798331542375

The evolution of urbanization and the variety of information communication technologies have given birth to the idea of Smart Cities those territories that aspire to improve the lifestyle standards for their inhabitants through development technologies. communication network infrastructures are the foundation of a smart city where information and services seamlessly flow between them. This era of communication faces challenges not only in reaching millions of users through relatively limited current process technologies but also in the increased demand for high throughput and low latency services, etc. Fiber optic communication systems are the most promising and effective solutions, with properties like high bandwidth, back-to-back transmission at low delay periods, and immunity to electromagnetic noise source devices among various existing commercial technologies. They utilize fiber optic cables to transmit data in the form of light pulses, which enables a faster and less susceptible means of communication. Fiber optic communications systems will grow as the need for smart city applications increases. Designs of next-generation fiber optic systems will meet smart city requirements, including high-speed data transmission, low power consumption and costeffectiveness. These configurations employ advanced technologies, including wavelength division multiplexing, coherent detection and modulation techniques for improved speed and distance capabilities. © 2025 IEEE.

关键词： Fiber to the x

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Reconfigurable Intelligent Surface as an Access Point: Probability of Error Analysis for Discrete Phase Shifters 2nd

Reconfigurable Intelligent Surface as an Access Point: Pro...

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2nd International Conference on Computational electronics for Wireless communications, ICCWC 2022

作者： Anjana, B.S. Kumaravelu, Vinoth Babu Jadhav, Hindavi Murugadass, Arthi Sayeed, Md. Shohel Alhadidi, Basim Department of Communication Engineering School of Electronics Engineering Vellore Institute of Technology Tamil Nadu Vellore India Sreenivasa Institute of Technology and Management Studies Andhra Pradesh Chittoor India Multimedia University Melaka Malaysia Department of Computer Information Systems Prince Abullah Bin Ghazi Faculty of Information and Communication Technology Al-Balqa Applied University Salt Jordan

ISBN: (纸本)9789811966606

A fresh line of research for future wireless networks is called reconfigurable intelligent surface (RIS)-aided communication. We developed one-bit and two-bit discrete phase shifter facilitated RIS for access point (AP) scenario in this study. In the AP scenario, RIS is considered as a source and it can transmit the information. When RIS is utilized as an AP, it can generate the phase angles needed for constellation mapping as well as compensate for channel distortion. The proximity of the radio frequency (RF) signal source to RIS minimizes the impact of fading between RIS and AP. The exact phase compensation of each RIS component is very difficult practically. Consequently, we can enhance the system’s overall performance using a discrete phase shifter. We are evaluating the effectiveness of one- and two-bit discrete phase shifters over intelligent RIS-AP and blind RIS-AP methods. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Access point (AP) Blind communication Discrete phase shifters Intelligent communication Reconfigurable intelligent surfaces (RIS)

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Quantum Inspired Marine Predator Algorithm with Long Short-Term Memory for Detection and Classification of Chronic Kidney Disease 2

Quantum Inspired Marine Predator Algorithm with Long Short-T...

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2nd IEEE International Conference on Integrated Intelligence and communication Systems, ICIICS 2024

作者： Srikanth, T. Mohamed, Hassan Jayaprakash, D. Vasanthakumar, G.U. Vijayakumari, G. Maisammaguda Hyderabad India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Information Technology Sona College of Technology Salem India Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Electronics and Communication Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai India

ISBN: (纸本)9798331504960

Chronic Kidney Disease (CKD) is a continual decline of the kidney working time being and it is a progressive condition. The main problem with CKD is, it is asymptomatic in its early stages, this makes the early detection challenging. In order to detect the symptoms of the disease and to figure out the exact stage of the disease advance technologies have been introduced. The traditional methods failed to diagnosing the disease accurately in early stage. The paper proposed a Quantum Inspired Marine Predator Algorithm (QIMPA) in order to predict and classify the chronic kidney disease. This algorithm is employed with the marine predators using Brownian and Levy flights that uses hunting strategies. This strategy works in three phases, firstly, prey’s movements and strategies are noted how it escapes from the predator, then a balanced approach is created between predator and prey. Finally, in this phase, optimization process produces a Levy flight pattern to capture the predator and prey moves at low velocity. Long Short-Term Memory (LSTM) is utilized for the classification, it works by capturing the long-range values of features that enhances the diagnosing kidney disease. The proposed QIMPA model obtained 99.04% for CKD dataset compared to the existing methods. © 2024 IEEE.

关键词： Diseases

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An Intelligent Agent Based Optimal Scheduling & Congestion Free Routing Scheme to Enhance QoS of NFV Enabled Heterogeneous Wireless Sensor Network Framework

An Intelligent Agent Based Optimal Scheduling & Congestion F...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Prathap C S Sivaranjani Sathya M Prabhakaran G Rajalakshmi J Kavin Kumar K GokulChandrasekaran Department of Computer Science and Engineering NadarSaraswathi College of Engineering and Technology Theni Department of Computer Science and Engineering SethuInstituteofTechnology Virudhnagar Department of Information Technology Nadar Saraswathi College of Engineering and Technology Theni Department of Electronics and Communication Engineering Nandha Engineering College Erode India Department of Electronics and Communication Engineering Sethu Institute of Technology Virudhunagar Department of Electronics and Communication Engineering Kongu Engineering College Perundurai Erode Department of Electronics and Communication Engineering Karpagam Institute of Technology Coimbatore India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

In wireless sensor network (WSN), congestion is regarded as an unavoidable issue as of today’s scenario at which the data traffic is increased to their aggregated channel capacity. This results in the consequence of buffer overflowing which leads to packets drop thereby reducing packet delivery ratio and degrades network throughput. Also, routing is the most favored approaches for the minimization of nodes energy consumption and the enhancement of Heterogeneous WSNs throughput. Since, the problem of routing has been proved to be an NP-hard, it has been realized that optimization-based technique offers better performance. For solving these issues, this paper proposes a congestion free optimal routing scheme based on Genetic algorithm-based distance aware-Low Energy Adaptive Clustering Hierarchy (GADA-LEACH) protocol to predict intelligent agent path. The major intention of this work is to enhance the efficiency of energy and QoS (throughput and delay) in edge-cloud based IoT environment of data transmission. Initially, the system model is initialized and the graph construction is made for the process of clustering using Spectral graph theory (SGT) at which the cluster head (CH) selection is made. Then, the scheduling process is carried by means of Packet weighted based ensemble optimization scheduling (PW-EOS) scheme. The NFV enabled optimal edge node is selected using Reconfigured Cuckoo Search optimizer (RCSO) after which the intelligent agent moving path in the Heterogeneous Network (HetNet) are predicted by GADA-LEACH technique. From this, an effective routing path is determined among CH and an optimized Edge node. Eventually, the performance assessment is carried in terms of throughput, energy consumption, survival rate, PDR, End-to-End delay and the outcomes conquered are compared with existing methods to attest the proficiency of proposed scheme.

关键词： Wireless sensor networks Energy consumption Protocols Quality of service Optimal scheduling Routing Throughput Scheduling Delays Intelligent agents

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Range-angle Imaging Based on the Prior of Target Scattering Characteristics in Distributed Arrays 2

Range-angle Imaging Based on the Prior of Target Scattering ...

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2nd IEEE International Conference on Signal, information and Data Processing, ICSIDP 2024

作者： Mei, Yongjie Zhang, Guangbin Huang, Tianyao Wang, Yan Beijng Institute of Technology School of Information and Electronics Beijing China Yangtze Delta Region Academy Beijing Institute of Technology Radar System and Intelligent Signal Processing Jiaxing China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing China

ISBN: (纸本)9798331515669

We consider the problem of target range-angle imaging using distributed arrays. High resolution imaging requires a large array aperture. However, due to the limited number of array elements, the subarrays are usually sparsely located, which results in severe under-sampling and ambiguity problem, degrading the imaging performance. To this end, we propose a range-angle imaging method based on the prior of target scattering characteristic to resolve the ambiguity. The main idea is to use the information increment of priors to reduce the dependence on the measurements. Particularly, we first statistically extract the prior distribution of target scattering intensity by data analysis on the target images. Then we use these priors to design weighting parameters and achieve the ambiguity-free images by solving a weighted sparse recovery problem. Finally, numerical simulation results verify the feasibility of the proposed method. © 2024 IEEE.

关键词： ambiguity distributed array Range-angle imaging the prior of target scattering characteristics weight

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Cyber Defence Based on Artificial Intelligence and Neural Network Model in Cybersecurity 8

Cyber Defence Based on Artificial Intelligence and Neural Ne...

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8th IEEE International Conference on Science, Technology, engineering and Mathematics, ICONSTEM 2023

作者： Sugumaran, D. Mahaboob John, Y.M. Mary C, Jansi Sophia Joshi, Kireet Manikandan, G. Jakka, Geethamanikanta Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Information Technology Tamil Nadu Chennai India Mahendra College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Salem India Idhaya Engineering College for Women Department of Computer Science and Engineering Tamilnadu Kallakurichi India Graphic Era Deemed to Be University Department of Computer Science Engineering Uttrakhand Dehradun India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Electronics and Communication Engineering Thandalam India University of the Cumberlands Department of Information Technology FremontCA United States

ISBN: (纸本)9798350347791

Cybersecurity is an increasingly critical aspect of modern society, with cyber attacks becoming more sophisticated and frequent. Artificial intelligence (AI) and neural network models have emerged as promising tools for improving cyber defense. This paper explores the potential of AI and neural network models in cybersecurity, focusing on their applications in intrusion detection, malware detection, and vulnerability analysis. Intruder detection, or "intrusion detection,"is the process of identifying Invasion of Privacy to a computer system. AI-based security systems that can spot intrusions (IDS) use AI-powered packet-level network traffic analysis and intrusion detection patterns to signify an assault. Neural network models can also be used to improve IDS accuracy by modeling the behavior of legitimate users and detecting anomalies. Malware detection involves identifying malicious software on a computer system. AI-based malware machine-learning algorithms are used by detecting systems to assess the behavior of software and recognize patterns that indicate malicious activity. Neural network models can also serve to hone the precision of malware identification by modeling the behavior of known malware and identifying new *** analysis involves identifying weaknesses in a computer system that could be exploited by attackers. AI-based vulnerability analysis systems use machine learning algorithms to analyze system configurations and identify potential vulnerabilities. Neural network models can also be used to improve the accuracy of vulnerability analysis by modeling the behavior of known vulnerabilities and identifying new ***, AI and neural network models have significant potential in cybersecurity. By improving intrusion detection, malware detection, and vulnerability analysis, they can help organizations better defend against cyber attacks. However, these technologies also present challenges, including a lack of understanding of the impo

关键词： Neural network models

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Integrity of Code and IoT Validation of Resource Utilization in Micro Control Unit

Integrity of Code and IoT Validation of Resource Utilization...

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2023 International Conference on New Frontiers in communication, Automation, Management and Security, ICCAMS 2023

作者： Kushwaha, Ritesh Kumar Ravikumar, Parimi Lvd Mohammed, Muzammil H Sasthi Kumar, C. Chandigarh University Department of Electronics & Communication Engineering Punjab Mohali India Shri Vishnu Engineering College for Women Deparmnent of Information Technology Bhimavaram India Taif University Department of Information Technology Taif Saudi Arabia Karpagam Academy of Higher Education Department of Computer Science Coimbatore India

ISBN: (纸本)9798350317060

Cybersecurity for the Internet of Things is gaining more and more attention as the number of IoT devices has increased and the number of attack stargeting these devices continues to grow exponentially. However, the significant focus areas for security measures have been guaranteeing services' constant availability and protecting users' private data. But code integrity is not receiving the attention it deserves because these devices lack the horsepower to provide more robust safeguards. This issue necessitates more study into adapting standard procedures for code integrity to IoT gadgets. This paper describes a virtual machine-based technique for ensuring the correct execution of code on the Internet of Things. The feasibility of a virtual machine tailored to safeguard software during firmware upgrades is demonstrated here. The suggested method's low resource requirements on popular microcontrollers have been verified experimentally. The acquired findings prove that this secured technique for coding may work in practice. © 2023 IEEE.

关键词： Internet of things

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Impact of Cloud Computing on Agricultural Advancement using Data Mining Algorithms 2

Impact of Cloud Computing on Agricultural Advancement using ...

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2nd International Conference on Automation, Computing and Renewable Systems, ICACRS 2023

作者： Saravanan, S.K. Nisha, F. Robin Rohit, V. Lenin, J. Selvam, P.D. Rajmohan, M. Department of BigData and Network Security Saveetha School of Engineering SIMATS Tamil Nadu Chennai India School of Engineering and Technology Computer Science & Engineering CMR University Karnataka Bengaluru India Computer Science & Engineering School of Computer Science & Engineering and Information Science Presidency University Karnataka Bengaluru India Department: Computer Science & Engineering Alliance College of Engineering and Design Alliance University Karnataka Bengaluru India Department of Electronics and Communication Engineering Vel Tech High Tech Dr.Rangarajan Dr.Sakunthala Engineering College Tamil Nadu Chennai India Department of Electronics and Communication Engineering Hindustan Institute of Technology and Science Tamil Nadu Chennai India

ISBN: (纸本)9798350340235

Incorporating Internet of Things (IoT) algorithms, Ensemble Learning, and Explainable AI (XAI) into the Reinforcement Learning framework provides a comprehensive solution. The framework's dynamic paradigm for precision agriculture increases output while decreasing resource consumption. The system adapts to its environment in real-time based on what it has learned via its previous experiences, thanks to Reinforcement Learning. By incorporating IoT algorithms, agricultural machinery may more easily gather and exchange data with cloud servers, improving precision and flexibility. By merging many models to provide reliable results, Ensemble Learning allows us to significantly enhance our predictive abilities. The usage of XAI, which provides context and justification for computations, has the potential to increase public confidence in AI-powered agricultural systems. It demystifies decision-making and arms participants with relevant information. In order to take use of the scalability and processing capacity of cloud resources, agricultural algorithms may now be readily installed and controlled with the help of this framework. Data mining algorithms have the potential to change agriculture by fostering sustainable practices and addressing challenges of global food security, as this research elucidates. © 2023 IEEE.

关键词： Agricultural Innovation Cloud Computing Data Mining Algorithms Ensemble Learning Explainable AI (XAI) Internet of Things (IoT) Precision Agriculture Reinforcement Learning Resource Optimization Sustainable Practices

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