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12th International Conference on Soft Computing for Problem Solving, SocProS 2023

作者： Kumar, Gaurav Kumar, Ankit Pandy, Saroj Kumar Singh, Kamred Udham Singh, Teekam Kumar, Lalan Choudhury, Tanupriya Department of Computer Engineering and Applications GLA University UP Mathura India School of Computing Graphic Era Hill University Dehradun India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Department of Master of Computer Application GL Bajaj Institute of Technology and Management Greater Noida India Department of Computer Science and Engineering University of Petroleum and Energy Studies Dehradun India

ISBN: (纸本)9789819732913

This paper aims to provide a comprehensive overview of existing and state-of-the-art fraud and intrusion detection strategies, specifically focusing on incorporating neural networks. We begin by discussing the fundamental concept of data mining and its significance in detecting anomalies. By leveraging advanced machine learning and neural network techniques, we can effectively uncover subtle patterns and irregularities within complex datasets. We explore how the development of anomalies has enhanced our ability to identify and mitigate various threats, such as malware attacks and unlawful practices. Traditional detection strategies have proven effective, but as deep learning progresses, we uncover new opportunities and insights that can significantly enhance the accuracy and efficiency of anomaly detection systems. Throughout this study, we delve into the most cutting-edge approaches, ranging from broad neural networks to shallower architectures, and their applicability to fraud and intrusion detection. We analyze the strengths and limitations of these strategies, shedding light on their performance characteristics, scalability, and interpretability. By addressing these key issues, we aim to provide a comprehensive summary of the latest fraud and intrusion detection advancements driven by deep learning methodologies. This research contributes to the broader understanding of data mining's potential in uncovering anomalies and bolstering security measures across diverse domains. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Intrusion detection

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Physical Layer Security of 6G Vehicular Networks with UAV Systems:First Order Secrecy Metrics,Optimization,and Bounds

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computers, Materials & Continua 2024年第9期80卷 3685-3711页

作者： Sagar Kavaiya Hiren Mewada Sagarkumar Patel Dharmendra Chauhan Faris A.Almalki Hana Mohammed Mujlid Smt.Chandaben Mohanbhai Patel Institute of Computer Applications Charotar University of Science and TechnologyChangaAnand388421India Electrical Engineering Department Prince Mohammad Bin Fahad UniversityP.O.Box 1664Al Khobar31952Kingdom of Saudi Arabia Department of Electronics and Communication Engineering Chandubhai S.Patel Institute of TechnologyCharotar University of Science and TechnologyChangaAnand388421India Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Kingdom of Saudi Arabia

The mobility and connective capabilities of unmanned aerial vehicles(UAVs)are becoming more and more important in defense,commercial,and research ***,their open communication makes UAVs susceptible toundesirablepassive attacks suchas eavesdroppingor ***,the inefficiencyof traditional cryptography-based techniques has led to the addition of Physical Layer Security(PLS).This study focuses on the advanced PLS method for passive eavesdropping in UAV-aided vehicular environments,proposing a solution to complement the conventional cryptography ***,we present a performance analysis of first-order secrecy metrics in 6G-enabled UAV systems,namely hybrid outage probability(HOP)and secrecy outage probability(SOP)over 2×2 Nakagami-m ***,we propose a novel technique for mitigating passive eavesdropping,which considers first-order secrecy metrics as an optimization problem and determines their lower and upper ***,we conduct an analysis of bounded HOP and SOP using the interactive Nakagami-m channel,considering the multiple-input-multiple-output configuration of the UAV *** findings indicate that 2×2 Nakagami-mis a suitable fadingmodel under constant velocity for trustworthy receivers and *** results indicate that UAV mobility has some influence on an eavesdropper’s intrusion during line-of-sight-enabled communication and can play an important role in improving security against passive eavesdroppers.

关键词： Physical layer security 3D positioning 6G communications UAV systems

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A Patient Response Prediction System using Multi-Scale Feature Fusion Semantic Segmentation Network and Med-YOLO 1

A Patient Response Prediction System using Multi-Scale Featu...

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1st International Conference on Software, Systems and Information Technology, SSITCON 2024

作者： Shankari, S. Uma Ali, Hassan Mohamed Durga, Boya Vijaya Vadivu, M. Senthil Mangaiyarkarasi, V. Srm Institute of Science and Technology Ramapuram Department of Computer Science and Applications Chennai India College of Technical Engineering the Islamic University Department of Computers Techniques Engineering Najaf Iraq Maisammaguda School of Computer Science and Engineering Hyderabad India Sona College of Technology Department of Electronics and Communication Engineering Salem India New Prince Shri Bhavani College of Engineering and Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350352931

Nowadays, most of the people are getting affected with various health issues such as typhoid, diabetes and cancer. A human immune system plays a crucial role in maintaining and balancing human health and reduces the risk of getting ill. Immunotherapy is a treatment which utilizes human body immunity power to fight cancer. There are many challenges in the existing models such as imbalance data, heterogenic data, uncertainties, and lack of temporal data. These challenges are resolved by imposing deep learning techniques and generating a valid information with the clinical details of patient health conditions. Therefore, a hybrid model name called Multi-Scale Feature Fusion Semantic Segmentation Network (MSSNet) and You Look Only Once (MedYOLO) for feature extraction and detection. Initially, Synthetic Minority Over-sampling Technique (SMOTE) is employed to overcome the issue of imbalanced data;further MSSNet is imposed for extracting the optimal features and detected using MedYOLO. Also, to get detailed information and generate cohort, the data collected from GSC hospital with overall details that are essential to predict the patient response to immunotherapy in cancer treatment. In the end, MSSNet-MedYOLO obtained higher accuracy of 98.16% for an effective prediction of patient response which is better than existing Convolutional Neural Network (CNN). © 2024 IEEE.

关键词： Semantic Segmentation

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Recommendation System for E-Commerce Apparel Stores based on Text-Semantics 2

Recommendation System for E-Commerce Apparel Stores based on...

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2nd International Conference for Innovation in Technology, INOCON 2023

作者： Dash, Bibhuti Bhusan De, Utpal Chandra Behera, Trupti Mayee Samant, Tapaswini Banerjee, Shobhan School of Computer Applications Kalinga Institute of Industrial Technology Bhubaneswar India School of Electronics Engineering Kalinga Institute of Industrial Technology Bhubaneswar India Birla Institute of Technology and Science Department of Engineering Technology Pilani India

ISBN: (纸本)9798350320923

For physical offline stores as association analysis plays an important role in the maximization of sales, similarly, for online E-Commerce stores recommendation systems play a major role to enhance the shopping experience of users by suggesting similar kinds of products based on past search history. Just a single search for a product is sufficient to flood the social media and emails of users with various similar products in the form of advertisements, promotional videos & other posts. In this paper, we have discussed a recommendation system using text-semantics-based methods for ladies' Apparel and finally, a comparative analysis has been done with our previous works on this aspect. © 2023 IEEE.

关键词： Semantics

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Signal Detection of MIMO System with One Bit ADCS Based on Deep Learning 7

Signal Detection of MIMO System with One Bit ADCS Based on D...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Shrivastava, Anurag Maindola, Meenakshi Alabdeli, Haider Gopal, K. Thandra, Nagarjuna Bansal, Saloni Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Tamilnadu Chennai India Graphic Era Deemed to be University Department of Computer Science & Engineering Uttarakhand Dehradun India The Islamic university College of technical engineering Department of computers Techniques engineering Najaf Iraq New Horizon College of Engineering Department of Mechanical Engineering Bangalore India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India Gla University Department of Computer Engineering and Applications Uttar Pradesh Mathura India

ISBN: (纸本)9798350350067

Due to the quantization noise created by analog-to-digital converters (ADCs) with just one bit of resolution, signal identification in Multiple-Input Multiple-Output (MIMO) systems is a very difficult task to perform. This is due to the fact that MIMO systems have many inputs in addition to a large number of outputs. The conventional methods of detection have a difficult time properly handling this noise. In this study, we describe a novel strategy based on deep learning for improving signal identification performance in MIMO systems like these that employ ADCs with just one bit of resolution. The method of deep learning that we recommend takes advantage of the power of neural networks to generate intricate connections from information that has been quantized to one bit. We provide the model with the ability to learn to differentiate between the several separate delivered signals while simultaneously decreasing the impacts of quantization noise by training it on a massive dataset consisting of MIMO channel realisations. This is done by providing it with the opportunity to learn from a variety of MIMO channel realisations. In-depth simulations are carried out in order to evaluate how well our deep learning-based approach to identifying signals works, and the results obtained from these simulations are compared to the findings obtained from more conventional detection methods. The results of our research indicate that deep learning algorithms perform better than traditional ones in terms of bit error rate (BER) and symbol recognition accuracy, particularly in challenging conditions with low signal-to-noise ratios (SNR). © 2024 IEEE.

关键词： Quantization (signal)

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Detection and Mitigation of Attacks in MANETs: A Comprehensive Survey of Security Techniques 4

Detection and Mitigation of Attacks in MANETs: A Comprehensi...

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Mohandas, R. Sivapriya, N. Vaigandla, Karthik Kumar Kirubasankar, K. Chennai Institute of Technology Department of Electronics & Communication Engineering Tamil Nadu Chennai India Cauvery College for Women Department of Computer Applications Tamilnadu Trichy India Balaji Institute of Technology and Science Department of Electronics & Communication Engineering Telangana Warangal India

ISBN: (纸本)9798331529635

MANETs are decentralized and self-organizing networks with no fixed infrastructure. They are very flexible but, at the same time are vulnerable to the huge diversity of security threats. The dynamic topology and the lack of central control along with the reliance on wireless communication expose MANETs to several attacks, which are blackhole, wormhole, Sybil, and DoS attacks. This is a survey paper that covers the most common security threats towards MANETs and probes for different detection and prevention techniques proposed to mitigate those risks. Various methodology are examined, ranging from cryptographic methods and trust-based models to machine learning and intrusion detection systems, comparing the effectiveness, scalability, and adaptability of those approaches in different network environments. The paper further outlines some real strengths and weaknesses of the currently used solutions, indicates potential key challenges, and states the future directions that could form the way for strengthening the security capabilities of MANETs with respect to evolving emerging technologies such as 5G and beyond. The aim is to demonstrate the ongoing state of the attack detection and prevention in MANETs, thereby offering insights into promising research opportunities. © 2024 IEEE.

关键词： Network intrusion

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Smart Home Automation with IoT Sensor Systems for Energy Conservation using Zigbee and Wifi 5

Smart Home Automation with IoT Sensor Systems for Energy Con...

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Jagannathan, Srinivasan Deepa, V. Sakthi Karthi Durai, B. Rajan, M. Sundar Celin, J. Jeya A. Dhamodaran, Sasikala Madanapalle Institute of Technology & Science Department of Computer Applications Madanapalle India Tiruppur Kumaran College for Women Pg and Research Department of Computer Science Tamil Nadu Tiruppur India Kalasalingam Academy of Research and Education Computer Science and Engineering India Arbaminch Institute of Technology Arbaminch University Faculty of Electrical and Computer Engineering Ethiopia Kalasalingam Academy of Research and Education Department of Information Technology Viruthunagar Krishnankoil626126 India Rajasthan Jaipur India

ISBN: (纸本)9798350379945

This study presents a comprehensive study on smart home automation systems utilizing Internet of Things (IoT) sensor technology to achieve efficient energy conservation. The proposed system integrates ZigBee and WiFi communication protocols for effective and reliable connectivity across various smart devices within a home. Through the deployment of interconnected sensors and intelligent controllers, the system allows real-time monitoring and management of household energy usage, optimizing energy consumption and reducing waste. The combination of ZigBee for low-power, long-range sensor networks and WiFi for high-speed data transmission enables seamless interaction between smart appliances, lighting, heating, and cooling systems. Proposesd approach offers a scalable, cost-effective solution for enhancing home automation with an emphasis on energy conservation and sustainability. Also discussed the implementation, challenges, and future potential of IoT-based smart home systems for improved energy efficiency. © 2024 IEEE.

关键词： Smart homes

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Responsive Portfolio Website Using React 2

Responsive Portfolio Website Using React

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2nd IEEE International Conference on Futuristic Technologies, INCOFT 2023

作者： Siva Rama Lingham, N. Aruna, R. Dhandayuthabani, Gowreeswaran Sindhia, M. Maria Jeraldin Krishnasaivignesh, M. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Avadi Chennai India T. John College Gottigere Computer Applications Department Karnataka Bangalore56008 India Jeppiaar Engineering College Chennai Department of Computer Science and Engineering India

ISBN: (纸本)9798350308846

The way people use the Internet has changed significantly with the introduction of third-generation cell phones. Nowadays, websites can be read on a variety of devices of different types and sizes. Since the website should be similarly helpful regardless of the type of device used to view it, this place demands on website design. Responsive web design makes it possible to adjust the visual presentation of web pages depending on the device they are accessed with. Although it is a commonly used tool in web development, it has not been fully explored in technical, aesthetic, and practical terms. To this end, standard definitions for this technique were compiled, and a responsive product was implemented in a paper. A fully responsive portfolio website was created by implementing meta tags and scalable components. The process of creating the product made it possible to understand the possibilities and limitations of flexible web design. © 2023 IEEE.

关键词： CSS Developer Html Pages Portfolio React JS Responsive Technologies Webpage's

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Modelling a Risk-based Network Model for Epileptic Seizure Prediction using Learning Approaches 6

Modelling a Risk-based Network Model for Epileptic Seizure P...

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6th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2022

作者： Anandaraj, A. Alphonse, P.J.A. Sri Eshwar College of Engineering Department of Computer Science and Engineering Coimbatore India National Institute of Technology Department of Computer Applications Tiruchirappalli India

ISBN: (数字)9781665469418

ISBN: (纸本)9781665469418

The process of developing across space and time in the networks of a person with epilepsy occurs through Epileptic seizures. The generalizable technique is developed in this research to predict a particular patient seizure using the evaluation of featurerepresentation to obtain the features from the signals of multichannel EEG. The features are revealed for the signals of EEG using the available parameters. The features are input to the Risk-based Elman learning model (r-ELM) to evaluate feature representation to collectively train the data. The suggested model of r-ELM obtains 0. 096/h as the rate of false prediction, 85% as sensitivity, and 10% as the time in warning to perform the tests from the EEG dataset of CHB-Mn scalp using 10 patients. The suggested method has superiority over the existing results. Various metrics are used in the experiment which shows the epileptic stage as the essential factor affecting seizures' performance. A subject-oriented method for seizure prediction is presented in the proposed system, which is powerful for the unbalanced data and created for any dataset of scalp EEG with no requirement of subject-oriented engineering. © 2022 IEEE.

关键词： Risk assessment

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Machine learning assisted screening of binary alloys for metal-based anode materials

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Journal of Energy Chemistry 2025年第5期104卷 62-68页

作者： Xingyue Shi Linming Zhou Yuhui Huang Chaohui Wu Yongjun Wu Juan Li Zijian Hong State Key Laboratory of Silicon and Advanced Semiconductor Materials School of Materials Science and EngineeringZhejiang UniversityHangzhou 310058ZhejiangChina Nanhu Brain-Computer Interface Institute Hangzhou 311100ZhejiangChina Zhejiang Key Laboratory of Advanced Solid State Energy Storage Technology and Applications Research Institute of Zhejiang University-TaizhouTaizhou 318000ZhejiangChina Inner Mongolia Electric Power Economic and Technological Research Institute Hohhot 010010Inner MongoliaChina College of Materials Science and Engineering Zhejiang University of TechnologyHangzhou 310014ZhejiangChina School of Engineering Hangzhou City UniversityHangzhou 310015ZhejiangChina Institute of Fundamental and Transdisciplinary Research Zhejiang UniversityHangzhou 311121China

Metal alloy anode materials with high specific capacity and low voltage have recently gained significant attention due to their excellent electrochemical performance and the ability to suppress dendrite ***,experimental investigations of metal alloys can be time-consuming and expensive,often requiring extensive experimental design and *** this study,we developed a machine learning model based on the Crystal Graph Convolutional Neural Network(CGCNN)to screen alloy anode materials for seven battery systems,including lithium(Li),sodium(Na),potassium(K),zinc(Zn),magnesium(Mg),calcium(Ca),and aluminum(Al).We utilized data with tens of thousands of alloy materials from the Materials Project(MP)and Automatic FLOW for Materials Discovery(AFLOW)*** any experimental voltage input,we identified over 30 alloy systems that have been experimentally validated with good ***,we predicted over 100 alloy anodes with low potential and high specific *** hope this work to spur further interest in employing advanced machine learning models for the design of battery materials.

关键词： CGCNN Alloy anodes Machine learning Metal-based batteries

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