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4th International Conference on Intelligent engineering and Management, ICIEM 2023

作者： Paul, Tushar Raj, Rohit Garg, Priyansh Jain, Shruti Jaypee University of Information Technology Department of Electronics and Communication Engineering Himachal Pradesh Solan India Jaypee University of Information Technology Department of Computer Science and Engineering Himachal Pradesh Solan India

ISBN: (纸本)9798350341126

A water quality monitoring system can aid in preserving the environment, ensuring the security of nearby water sources, and fostering economic growth in rural areas. This results in the development of a system, employing the Internet of Things (IoT) and Machine Learning to monitor the quality of water. This paper discusses the characteristics of water that explains whether it is fit for human consumption or not. The pH and Turbidity sensors are dipped in water samples acquired from wells, lakes, rivers, ponds, and other places are used to inform the development of an effective model. The data will be delivered from the sensors to the IDE, where it will then be sent to the cloud server. The model effectively accounts for test tables, where 1 indicates the water is fit for drinking and 0 indicates the water is unfit. The model is validated by employing Support Vector Machine, Random Forest and XG Boost methods. The maximum accuracy of 95.12% was observed using XGBoost. We have successfully implemented the real-time data for the water quality monitoring system for rural areas using Machine Learning and IoT which lets us know whether the water is fit for drinking or not. © 2023 IEEE.

关键词： Internet of things

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Optimizing Antenna Design for 6G Applications Using Machine Learning Techniques

Optimizing Antenna Design for 6G Applications Using Machine ...

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2024 International Conference on Artificial Intelligence and Emerging Technology, Global AI Summit 2024

作者： Sharma, Praveen Kumar Yadav, Vivek Prakash Mukesh, Aashiwal Nirmal Jawale, Sourabh Kumar, Ankit Research Center for Electrical and Information Technology Seoul National University of Science and Technology Seoul01811 Korea Republic of FEAL: Flexible Electronics and Antenna Lab Department of Electronics and Communication Engineering Pilani India FEAL: Flexible Electronics and Antenna Lab Department of Electronics and Communication Engineering Mumbai India Medicaps University Department of Computer Science and Engineering Indore India Seoul National University of Science and Technology Department of Computer Science and Engineering Seoul01811 Korea Republic of

ISBN: (纸本)9798350379716

The emergence of 6G networks requires innovative antenna designs that operate at extremely high frequencies. The proposed paper explores the application of advanced machine learning techniques to optimize the design of antennas for 6G applications at 15 GHz. Traditional design methods are often time-consuming and depend on expert knowledge, whereas machine learning can automate and expedite this process. We developed a dataset incorporating critical antenna design parameters such as geometric dimensions and applied algorithms to predict key performance metrics like return loss, gain, and bandwidth. Our results demonstrate that neural networks, in particular, offer superior predictive accuracy, significantly reducing design iteration times. This integration of machine learning into antenna design not only improves performance but also facilitates the development of adaptive antenna systems, meeting the stringent demands of 6G networks. This research highlights the potential of machine learning to revolutionize antenna design, enabling rapid and efficient development of next-generation communication systems. © 2024 IEEE.

关键词： Smart antennas

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Network Traffic Prediction Using Radial Kernelized-Tversky Indexes-Based Multilayer Classifier

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computer Systems Science & engineering 2022年第3期40卷 851-863页

作者： M.Govindarajan V.Chandrasekaran S.Anitha Department of Computer Applications Velalar College of Engineering and TechnologyErode638012India Department of Electronics and Communication Engineering Velalar College of Engineering and TechnologyErode638012India Department of Information Technology Kongu Engineering CollegePerunduraiIndia

Accurate cellular network traffic prediction is a crucial task to access Internet services for various devices at any *** the use of mobile devices,communication services generate numerous data for every *** the increasing dense population of data,traffic learning and prediction are the main components to substantially enhance the effectiveness of demand-aware resource allocation.A novel deep learning technique called radial kernelized LSTM-based connectionist Tversky multilayer deep structure learning(RKLSTM-CTMDSL)model is introduced for traffic prediction with superior accuracy and minimal time *** RKLSTM-CTMDSL model performs attribute selection and classification processes for cellular traffic *** this model,the connectionist Tversky multilayer deep structure learning includes multiple layers for traffic prediction.A large volume of spatial-temporal data are considered as an input-to-input ***,input data are transmitted to hidden layer 1,where a radial kernelized long short-term memory architecture is designed for the relevant attribute selection using activation function *** obtaining the relevant attributes,the selected attributes are given to the next *** index function is used in this layer to compute similarities among the training and testing traffic *** similarity index outcomes are given to the output *** value is used as basis to classify data as heavy network or normal ***,cellular network traffic prediction is presented with minimal error rate using the RKLSTM-CTMDSL *** evaluation proved that the RKLSTM-CTMDSL model outperforms conventional methods.

关键词： Cellular network traffic prediction connectionist Tversky multilayer deep structure learning attribute selection classification radial kernelized long short-term memory

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Designing a Resilient SDN Framework with IoT Integration for Enhanced Network Reliability 7

Designing a Resilient SDN Framework with IoT Integration for...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Jegadeesan, S. Aishwarya, S.S. Harshini, R.G. Diraviam, A. Justin Sujatha, S. Krishnan, R. Santhana Velammal College of Engineering And Technology Department of Information Technology Madurai India Scad College of Engineering And Technology Department of Computer Science And Engineering Cheranmahadevi India Adhiyamaan College of Engineering Department of Information Technology Hosur India Scad College of Engineering And Technology Department of Electronics And Communication Engineering Cheranmahadevi India

ISBN: (纸本)9798350359299

The ever-expanding Internet of Things (IoT) landscape poses significant challenges for network reliability and resilience. This research investigates the design and implementation of a resilient Software-Defined Networking (SDN) framework with Internet of Things (IoT) integration, aiming to enhance network reliability. The proposed model, referred to as HybridSDN-IoT Resilience (HyRes), introduces a hybrid SDN approach by amalgamating traditional and distributed SDN strategies. This hybridization seeks to balance the advantages of centralized control and distributed resilience within the network architecture. To estimate the efficacy of the HyRes model implementation, comprehensive simulation analyses are conducted, comparing its performance against established algorithms in the SDN domain. Metrics such as network latency, packet loss, and throughput are employed to assess the reliability of the network under varying conditions. The simulation results offer significant performance results for the resilience and adaptability of HyRes in comparison to existing algorithms, showcasing its potential to mitigate network failures and enhance overall reliability. The proposed HyRes model not only establishes a foundation for resilient SDN architectures but also discourses the precise challenges in the integration of diverse IoT devices. By emphasizing adaptability and responsiveness, the research advances the discourse on the practical realization of reliable SDN frameworks in the context of emerging IoT technologies. © 2024 IEEE.

关键词： Software defined networking

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Adaptive Intrusion Detection in Cyber-Physical Systems using Reinforcement Learning-Based Autoencoders 2

Adaptive Intrusion Detection in Cyber-Physical Systems using...

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

作者： Rajathi, N. Divya, S. Anitha Elavarasi, S. Saritha, G. Jeya ramya, V. Department of Information Technology Kumaraguru College of Technology Coimbatore India Department of Computer Science and Business System Bannari Amman Institute of Technology Erode India Department of Computer Science and Engineering Sona College of Technology Salem India Department of Electronics and Communication Engineering Sri Sai Ram Institute of Technology Chennai India Department of Electronics and Communication Engineering Panimalar Engineering College Chennai India

ISBN: (纸本)9798331504960

The increasing integration of Cyber-Physical Systems (CPS) in critical infrastructure presents unique challenges for ensuring robust cybersecurity. Traditional Intrusion Detection Systems (IDS) often struggle with the dynamic, complex nature of CPS environments, making adaptive solutions essential. This paper presents an Adaptive Intrusion Detection System (AIDS) that leverages Reinforcement Learning-Based Autoencoders (RL-AE) to detect and respond to cyber intrusions in CPS environments. The proposed model combines the power of autoencoders for unsupervised anomaly detection with reinforcement learning to continuously adapt and improve detection accuracy. Experimental results on benchmark CPS datasets demonstrate that the RL-AE-based IDS achieves a detection accuracy of 98.3%, a precision rate of 96.8%, and a recall rate of 97.5%, effectively minimizing false positives to 2.4%. The system also demonstrated rapid adaptability, with a convergence time of less than 50 iterations in response to new attack patterns. These results highlight the model's ability to enhance system resilience and promote secure and efficient CPS operations in industrial and critical infrastructure applications. This approach provides a scalable and flexible IDS solution, promoting secure and resilient CPS operations in real-time, high-stakes environments. © 2024 IEEE.

关键词： Network intrusion

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Quantum Prototype Clustering: Advancing AI Enhanced Machine Learning 9

Quantum Prototype Clustering: Advancing AI Enhanced Machine ...

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9th International Conference on Signal Processing and communication, ICSC 2023

作者： Bangroo, Ishan Shivansh Kumar, Ravi Department of Computer and Information Science and Engineering University of Florida FL United States Department of Electronics and Communication Engineering Jaypee University of Engineering & Technology M.P. India

ISBN: (纸本)9798350383201

The quantization reconstruction of classical machine learning algorithms is an important research direction in quantum machine learning. Clustering, a widely applied algorithm in machine learning, also holds high research value when quantized. Currently, most quantum machine learning algorithms, including those employing AI techniques, face challenges such as difficulty in replication and the lack of intuitive comparisons with classical algorithms. A quantum prototype clustering algorithm (QPC) is proposed to address these issues, which can be easily deployed on existing general-purpose quantum computing devices. This approach first utilizes single-qubit rotation properties to find the feature mapping with minimal information loss, creating single-qubit rotations from two-dimensional feature data. Then, based on the characteristics of multi-qubit entanglement and entanglement system collapse, a quantum circuit for generating specific quantum entanglement systems and measuring the collapse results of entanglement systems is designed. By establishing the relationship between controlled qubit rotation angles in the entanglement system and the collapse results of the entanglement system and combining the definition of Minkowski distance, a quantum distance is derived for evaluating the similarity of input samples. The quantum distance measurement module, with the same input-output format as the distance calculation module in classical computers, can directly replace the Minkowski distance calculation in prototype clustering without modification, thus reconstructing classical prototype clustering algorithms, with AI included, into QPC. Multiple repeated experiments on publicly available datasets from Kaggle and Scikitlearn demonstrate that with AI integration, QPC exhibits no significant differences in evaluation metrics, such as average sample centre distance, compared to classical prototype clustering algorithms. © 2023 IEEE.

关键词： Clustering algorithms

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Enhancing real time object detection for autonomous driving using YOLO-NAS algorithm with CLEO optimizer

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International Journal of information Technology (Singapore) 2025年第3期17卷 1321-1328页

作者： Nandal, Priyanka Pahal, Sudesh Malik, Sunesh Sehrawat, Neetu Mamta Department of Computer Science and Engineering Maharaja Surajmal Institute of Technology New Delhi India Department of Electronics and Communication Engineering Maharaja Surajmal Institute of Technology New Delhi India Department of Information Technology Maharaja Surajmal Institute of Technology New Delhi India Department of Computer Science and Engineering Panipat Institute of Engineering & Technology New Delhi India

For autonomous driving to operate in a safe and effective manner, efficient and precise object detection is essential. The efficacy of the network model is heavily challenged because of the high-speed movement of vehicles and the dynamic nature of the surrounding environment that need the detection of objects of various scales. With notable improvements over earlier iterations, the You Only Look Once-Neural Architecture Search (YOLO-NAS) algorithm is a strong contender for improving real-time object recognition in autonomous vehicles. YOLO-NAS integrated with chimpanzee leader election optimization (CLEO) has the potential to greatly enhance the safety and dependability of autonomous driving systems by utilizing its sophisticated capabilities and skill fully combining it with the vehicle’s perception system. The BDD100K dataset serves as the training and evaluation set for our suggested technique. The YOLO-NAS model with CLEO optimizer achieves mean average precision (mAP) of 83%, which is significantly higher than the mAP of 65.7% attained by the YOLO-NAS model without the optimizer. This improvement highlights the effectiveness of the CLEO optimizer in enhancing detection precision. The proposed approach was compared with existing algorithms and demonstrated remarkable performance in terms of recall, precision, and mAP. It is seen that our proposed model is better by striking equilibrium between detection speed and accuracy. Based on our findings, object detection performance for autonomous driving applications is much enhanced when utilizing our suggested approach. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Autonomous vehicles Deep learning Object detection YOLO-NAS

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Effective Return Rate Prediction of Blockchain Financial Products Using Machine Learning

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computers, Materials & Continua 2023年第1期74卷 2303-2316页

作者： K.Kalyani Velmurugan Subbiah Parvathy Hikmat A.M.Abdeljaber T.Satyanarayana Murthy Srijana Acharya Gyanendra Prasad Joshi Sung Won Kim PG and Research Department of Computer Science Marudupandiyar College(Affiliated to Bharathidasan UniversityTiruchirappalli)Thanjavur613403India Department of Electronics and Communication Engineering Kalasalingam Academy of Research and EducationKrishnankoil626126TamilnaduIndia Department of Computer Science Faculty of Information TechnologyApplied Science Private UniversityAmmanJordan Department of Information Technology Chaitanya Bharathi Institute of TechnologyHyderabadTelangana500075India Department of Convergence Science Kongju National UniversityGongju32588Korea Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Department of Information and Communication Engineering Yeungnam UniversityGyeongsan-siGyeongbuk-do38541Korea

In recent times,financial globalization has drastically increased in different ways to improve the quality of services with advanced *** successful applications of bitcoin Blockchain(BC)techniques enable the stockholders to worry about the return and risk of financial *** stockholders focused on the prediction of return rate and risk rate of financial ***,an automatic return rate bitcoin prediction model becomes essential for BC financial *** newly designed machine learning(ML)and deep learning(DL)approaches pave the way for return rate predictive *** study introduces a novel Jellyfish search optimization based extreme learning machine with autoencoder(JSO-ELMAE)for return rate prediction of BC financial *** presented JSO-ELMAE model designs a new ELMAE model for predicting the return rate of financial ***,the JSO algorithm is exploited to tune the parameters related to the ELMAE model which in turn boosts the classification *** application of JSO technique assists in optimal parameter adjustment of the ELMAE model to predict the bitcoin return *** experimental validation of the JSO-ELMAE model was executed and the outcomes are inspected in many *** experimental values demonstrated the enhanced performance of the JSO-ELMAE model over recent state of art approaches with minimal RMSE of 0.1562.

关键词： Financial products blockchain return rate prediction model machine learning parameter optimization

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Optimizing the Shopping Experience Through Smart Trolleys with Integrated Deep Learning Techniques 4

Optimizing the Shopping Experience Through Smart Trolleys wi...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Akhila, T.S. Presskila, X. Arogya Kalyanakumar, P. Krishnan, R. Santhana Sahanapreethi, M. Sundaravadivel, P. Dhanalakshmi College of Engineering Department of Electronics and Communication Engineering Tambaram Chennai India Scad College of Engineering and Technology Department of Computer Science and Engineering Cheranmahadevi India Velammal College of Engineering and Technology Department of Information Technology Madurai India Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Psna College of Engineering and Technology Department of Information Technology Dindigul India Saveetha Engineering College Department of Artificial Intelligence and Machine Learning Chennai India

ISBN: (纸本)9798331540364

This research study presents an innovative smart shopping trolley system designed to enhance the retail shopping experience by integrating advanced deep learning techniques and sensor technology. The system begins with a secure membership card login process, enabling personalized customer interaction. Upon entry into the trolley system, products can be easily added and identified through a camera utilizing EfficientN et, a deep learning model trained on the store's inventory. The system incorporates an infrared motion sensor and a weight sensor to ensure accuracy in product registration, enhancing reliability and preventing errors. As the shopping process unfolds, a Transformer-based recommendation system analyzes real-time choices and shopping history to provide tailored product suggestions displayed on the trolley's LCD screen. To enhance security, an Autoencoder neural network monitors for anomalies in product identification and removal, alerting store personnel in case of discrepancies. Upon completing the shopping process, a QR code is generated for payment, streamlining the checkout experience. This smart shopping trolley system optimizes the shopping process, leveraging real-time data to improve efficiency, security, and overall customer satisfaction in the retail environment. The system aims to revolutionize traditional shopping methods by applying deep learning and IoT technologies and create a seamless, engaging consumer experience. © 2024 IEEE.

关键词： Customer satisfaction

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Zérosdetect: Phishing URL Detection with Quantum-Driven Zero-shot Learning 9

Zérosdetect: Phishing URL Detection with Quantum-Driven Zer...

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9th International Conference on Signal Processing and communication, ICSC 2023

ISBN: (纸本)9798350383201

Phishing, a prevalent cyber danger in contemporary times, involves the fraudulent impersonation of legitimate websites with the intention of deceiving users into divulging confidential information. The insufficiency of classic phishing detection tools has become apparent as fraudsters continue to develop new strategies;these approaches mostly depend on variables such as URL character sequences, site content, and visual resemblance. The present study highlights the "Zérosdetect" approach, a quantum-powered technique for detecting phishing URLs using zero-shot learning, a robust model that makes use of both the zero-shot learning's adaptability and the computational advantageous nature of quantum computing, it mitigates the need for extensive previous knowledge of phishing URL attributes. In order to detect previously undiscovered phishing attacks, quantum neural network have been deployed to convert URL data into quantum spaces, therefore using the computational benefits of quantum systems. Quantum layers embedded inside Qnodes are a key part of the present system, calculating gradients at a faster pace, optimizing the network performance by rapidly calculating gradients, making the solution both efficient and forward-thinking. The present study lays the groundwork for future cybersecurity initiatives in the era of quantum computing, enhancing the potential to predict new cases of phishing. © 2023 IEEE.

关键词： Learning systems

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