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

作者： Rani, M. Jamuna Adnan, Myasar Mundher Saranya, N. Naga Karuna, Gotlur Margarat, G. Simi Sona College of Technology Department of Electronics and Communication Engineering Salem India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Saveetha College of Liberal Arts & Science Simats Department of Computer Applications Chennai India Hyderabad India New Prince Shri Bhavani College of Engineering and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350352931

The prediction of the human's activities and the personality from the social media platforms attains significant growth between the researchers. The statistical data over the human activities represented through the social media platforms is important benefit for the analysis in the predictions of the personalities and other attitudes of the humans. However, the challenges in personality prediction are to introduce effective approach which determines an individual's personality traits from various behavioural data. Hence, to tackle these issues, this research proposes the Mixture Kernel based Least Square Support Vector Machine (MK-LSSVM) approach for the personality prediction. The utilization of mixture kernels in SVM, enables for modelling the challenging relationships in the data, capturing various types of input features and enhancing the prediction accuracy. The standard datasets such as Essay and Myers-Briggs Type Indicator (MBTI) are utilized to implement the MK-LSSVM approach. The Term Frequency and Inverse Gravity Moment (TF-IGM) approach is utilized for the feature extraction, which determines the relationship between the feature sets and features from the datasets. The implemented results demonstrates that the MK-LSSVM approach reaches the superior average accuracy of 89.72% and average precision of 0.89 as compared to the CatBoost algorithm. © 2024 IEEE.

关键词： Prediction models

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Enabling Smart Home Energy Management through Gesture-Based Control and IoT Technology

Enabling Smart Home Energy Management through Gesture-Based ...

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2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Rambe, Khairul Hawani Ou, Chia-Ho International Master Program in Information Technology and Applications Sweden National Pingtung University Department of Computer Science and Information Engineering Taiwan

ISBN: (纸本)9798350324174

The Internet of Things (IoTs) have become ubiquitous in all aspects of public needs today. The application of IoT technology is crucial for promoting energy-saving behavior. This paper presents a control system used for smart home devices based on hand gesture control and Android applications. The hardware design uses the ESP32 microcontroller, which has an in-chip Wi-Fi module, making it very suitable for creating IoT application systems. The software was designed using the Arduino IDE, and the application display design was developed using Kodular Creator, which is used to control the lamps and switches. The experiment results show that the electrical equipment can be activated using the Android application with gesture sensors by waving down, up, left, and right. © 2023 IEEE.

关键词： Internet of things

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Anomaly Detection in Industrial Quality Control with computer Vision and Deep Learning

Anomaly Detection in Industrial Quality Control with Compute...

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2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Mary, A. Jenita Umesh, V. Khairnar, Prerana Nilesh Sahana, R. Sangeethaa, S.N. Shanthi, H.J. Srm Institute of Science and Technology Faculty of Science and Humanities Vadapalani City Campus Department of Computer Science and Applications Chennai India Ksou Mysore Department of It Mysuru India Visvesvaraya Institute of Technology Department of Electronics and Telecommunication Engineering Chincholi Nashik India Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Avadi Department of Artificial Intelligence and Machine Learning Chennai India Bannari Amman Institute of Technology Department of Computer Science & Engineering Sathyamangalam India Hindustan Institute of Technology and Science Deemed to Be University Department of Computer Applications Padur India

ISBN: (纸本)9798350375442

This study examines the impact of the dynamic manufacturing environment on companies' efforts to improve customer satisfaction through product quality enhancement, production cost reduction, and sustainability realisation. The research focuses on the current trends and challenges companies face in adapting in response to the dynamic nature of the industrial industry. This study seeks to offer practical recommendations by analyzing these factors for companies to meet the evolving demands of their customers while ensuring long-term success and competitiveness. This study examines the impact of anomaly detection on product quality, specifically focusing on the commonly employed method of visual quality inspection. This study examines the methods of visual quality inspection for products, comparing manual and automatic approaches. This study examines the limitations of manual inspection as a monotonous task, which can result in the oversight of errors and subjective assessments. In order to keep up with the needs of smart production and the advent of Industry 4.0, manufacturing as a whole is actively seeking computerized quality inspection solutions. Innovative inspection systems can be created through the effective use of big data. This research abstract discusses the widespread use of machine learning in automated quality inspection systems to enhance performance and support data analytics methods. This study uses a convolutional neural network architecture to extract complex features and patterns from images. This enables the model to differentiate between normal and abnormal conditions effectively. This research presents a proposed model trained on a diverse dataset, including a comprehensive collection of potential defects and irregularities. This research abstract presents a proposed framework for validating effectiveness through extensive experiments on datasets from diverse industrial domains. This study presents the findings of a research project that evaluated

关键词： Smart manufacturing

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Harnessing the Potential of the Internet of Things: Navigating Choices Between Fog and Cloud Infrastructure in Agriculture 1

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2nd International Conference on Cyber Intelligence and Information Retrieval, CIIR 2023

作者： Singh, Malikhan Arora, Kavita Pathak, Sonal Quraishi, Suhail Javed Singh, Anupam Ather, Danish Computer Science and Engineering Modern Institute of Technology and Research Centre Rajasthan Alwar India School of Computer Applications Manav Rachna International Institute of Research and Studies Haryana Faridabad India Department of Computer Science and Engineering Graphic era Hill University Dehradun India Amity University in Tashkent Tashkent Uzbekistan

ISBN: (数字)9789819776030

ISBN: (纸本)9789819776023

In recent years, the Internet of Things (IoT) has become a pivotal force in transforming various sectors, with agriculture being a prominent beneficiary. The integration of smart devices, sensors, and data-driven decision-making tools has led to the emergence of a more efficient and sustainable farming landscape. Central to this transformation is the decision of where and how to process and store the vast amounts of data generated by these devices. This paper delves into the practical implications of fog and cloud computing systems in the agricultural domain taking Delhi NCR and region as a sample area by considering factors like efficiency, scalability, and real-world challenges faced by farmers. The aim here is to provide a comprehensive guide to stakeholders in making an informed choice between fog and cloud infrastructure for their IoT-enabled agricultural endeavors. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Smart agriculture

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P2FLF: Privacy-Preserving Federated Learning Framework Based on Mobile Fog Computing

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2023年第17期17卷 72-81页

作者： Ankayarkanni, B. Pani, Niroj Kumar Anand, M. Malathy, V. Bhupati Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Tamil Nadu Chennai India Department of Computer Science Engineering and Applications Indira Gandhi Institute of Technology Odisha Sarang India Department of ECE Dr M.G.R Educational and Research Institute Tamil Nadu Chennai India Department of ECE SR University Telangana Warangal India Department of IoT KL Deemed to be University Andhra Pradesh Vaddeswaram Guntur India

Mobile IoT devices provide a lot of data every day, which provides a strong base for machine learning to succeed. However, the stringent privacy demands associated with mobile IoT data pose significant challenges for its implementation in machine learning tasks. In order to address this challenge, we propose privacy-preserving federated learning framework (P2FLF) in a mobile fog computing environment. By employing federated learning, it is possible to bring together numerous dispersed user sets and collectively train models without the need to upload datasets. Federated learning, an approach to distributed machine learning, has garnered significant attention for its ability to enable collaborative model training without the need to share sensitive data. By utilizing fog nodes deployed at the edge of the network, P2FLF ensures that sensitive mobile IoT data remains local and is not transmitted to the central server. The framework integrates privacy-preserving methods, such as differential privacy and encryption, to safeguard the data throughout the learning process. We evaluate the performance and efficacy of P2FLF through experimental simulations and compare it with existing approaches. The results demonstrate that P2FLF strikes a balance between model accuracy and privacy protection while enabling efficient federated learning in mobile IoT environments. © 2023 by the authors of this article. Published under CC-BY.

关键词： Fog computing

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Pesticide Usage Forecasting for Agriculture using Prophet Model 5

Pesticide Usage Forecasting for Agriculture using Prophet Mo...

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5th IEEE International Conference for Emerging Technology, INCET 2024

作者： Wani, Afshan Hassan Moharana, Bhimasen Samant, Tapaswini De, Utpal Chandra Dey, Snehasis Banerjee, Shobhan Department of Computer Science and Engineering Lovely Professional University Punjab India School of Electronics Engineering Kalinga Institute of Industrial Technology Bhubaneswar India School of Computer Applications Kalinga Institute of Industrial Technology Bhubaneswar India College of Engineering Bhubaneswar India Department of Engineering Technology Birla Institute of Technology and Science Pilani India

ISBN: (纸本)9798350361155

Agriculture plays a vital role in our day-to-day life for the life sustenance of not just human beings but also various domestic and livestock animals. In addition to this, agriculture also contributes significantly to the economy of a country, impacting its growth across the globe. India had always been an agriculture-dominated country before the companies from the West started investing in India. But alongside agriculture come various challenges such as infestation by insects or weeds which eventually lead to the damage of crops and even diseases. But pesticides can even contribute to the contamination of soil, water, and other vegetation in areas where they are been used. In this paper, we have used the Prophet model to study the use of pesticides and analyze the trends of usage of organic pesticides over inorganic pesticides. © 2024 IEEE.

关键词： Pesticides

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Shortest Route ∗ Bidirectional Search Algorithm for Predicting Shortest Routes in Traffic 4

Shortest Route ∗ Bidirectional Search Algorithm for Predict...

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4th International Conference on Smart Electronics and Communication, ICOSEC 2023

作者： Lakshna, A. Gokila, S. Ramesh, K. Hindustan Institute of Technology and Science Department of Computer Applications Chennai India Sri Krishna College of Engineering and Technology Department of Cse Coimbatore India

ISBN: (纸本)9798350300888

Road traffic congestion is a pressing issue that plagues urban areas, causing delays, inefficiency, and environmental problems. Smart traffic management systems aim to tackle this problem by utilizing advanced Shortest route ∗ Bidirectional Search algorithms. The Shortest route ∗ bidirectional search algorithm(SRSBSA) in smart traffic systems is used to efficiently avoid road traffic congestion and find the shortest route to reach the destination. Simultaneously exploring the search space from both the starting point and the destination, the algorithm significantly reduces the computational effort required to find the optimal path. distance, driver behavior, estimated travel considers road, type of holiday, peak hours, tr flow, and road capacities, vehicle characteristics. It considers the characteristic road type, holiday typing one-way streets, turn road capacities, and d limits, to generate accurate and feasible route suggestions to determine the best shortest route. The algorithm leverages heuristic estimates and evaluates various route options to guide drivers toward the shortest route. Implementing the Shortest route ∗ bidirectional Search algorithm in smart traffic produces the result in high accuracy level of 95%. This leads to improved travel time, increased transportation efficiency, and a more sustainable urban environment. © 2023 IEEE.

关键词： Traffic congestion

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Intelligent Energy Management Energy Storage Systems Using Machine Learning

Intelligent Energy Management Energy Storage Systems Using M...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Govindasamy, Chinnaraj Dayana, T.R Nisha Adkar, Siddhika Dilip Prakash, D. Shyam Gupta, A. Shilpa Trivedi, Sanjeev Kumar Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamilnadu Chennai602105 India Vels Institute of Science Technology and Advance Studies Department of Computer Science Chennai India School of Computer Science Engineering and Applications D y Patil International University Akurdi India Department of Computer Science and Business Systems Sri Eshwar College of Engineering Coimbatore India Department of Electrical and Electronics Engineering Aurora's Technological Research Institute Uppal Hyderabad India Khwaja Moinuddin Chishti Language University Faculty of Engineering and Technology Department of Electronics Lucknow India

ISBN: (纸本)9798350375237

A nevertheless-emerging generation called cloud computing permits customers to pay for services on a usage-based foundation. Internet-primarily based IT offerings are supplied through cloud computing, at the same time as virtualization enables the availability of PC sources. The muse of cloud computing is the information center, which is made up of networked computers, cables, electricity components, and different components that host and shop corporate records. With cloud facts centers, high overall performance has continually been the most critical concern, yet it compromises strength utilization. The key hassle is to lessen power consumption at the same time as preserving provider nice and performance a good way to stability device performance and strength intake. A detailed grasp of strength use styles within the cloud environment is needed for our suggested technique. We look at power consumption tendencies and reveal how, through the use of the right optimization standards based on our strength intake models, we can keep more strength in cloud records facilities. All through the prediction section, tablet optimization, which has a 97 percent accuracy rate, permits this era to provide extra correct future price forecasts. © 2024 IEEE.

关键词： Machine learning

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A Paradigm Shift in Real-Time Detection: The YOLOv7 Approach to Trainable Freebie Optimization 3

A Paradigm Shift in Real-Time Detection: The YOLOv7 Approach...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Vinitha, A. Jeevitha, S. Kumar, N.B. Mahesh Sathish Kumar, L. Saravanan, D. Karpagam Academy of Higher Education Department of Computer Applications Tamilnadu Coimbatore India Kalasalingam Academy of Research and Education Department of Computer Science and Information Technology Krishnankoil Tamilnadu Virudhunagar India Hindusthan Institute of Technology Computer Science and Engineering Tamil Nadu Coimbatore India Madhya Pradesh Sehore India

ISBN: (纸本)9798331509675

Significant progress has been made in real-time object detection, with YOLOv7 emerging as a state-of-the-art model that achieves the highest levels of accuracy and speed. This study combines Bag-of-Freebies (BoF) strategies with YOLOv7 to improve performance without incurring extra computational expenses. Our tests show that adding BoF preserves real-time inference capabilities while increasing mAP@0.5 by 3.5%. The accuracy and FPS of YOLOv7 with BoF are superior to those of the current YOLO-based models (YOLOv4, YOLOv5, YOLOv6). According to the results, YOLOv7 with BoF sets a new standard for real-time object detection by achieving a mAP@0.5 of 55.3% and 83 FPS on the COCO dataset. © 2025 IEEE.

关键词： Contrastive Learning

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A Hybrid Watermarking Scheme Using DWT and Haar Transform for Image Authentication 3rd

A Hybrid Watermarking Scheme Using DWT and Haar Transform fo...

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3rd International Conference on Advanced Computing and applications, ICACA 2024

作者： Jana, Sachchidananda Pal, Pabitra Mahadani, Asim Kumar Giri, Debasis Department of Computer Science & Applications Prabhat Kumar College Contai West Bengal Purba Medinipur721404 India Department of Computer Applications Maulana Abul Kalam Azad University of Technology West Bengal Nadia741249 India Department of Computer Science and Engineering Bankura Unnayani Institute of Engineering West Bengal Bankura India Department of Information Technology Maulana Abul Kalam Azad University of Technology West Bengal Nadia741249 India

ISBN: (纸本)9789819747986

The safe transfer and preservation of medical images is crucial in the age of digital healthcare. Watermarking medical images is an essential method of adding patient data, hospital logos, and other pertinent information to these images without compromising their confidentiality, integrity, or validity. To overcome the aforementioned difficulties, this abstract offers a unique and reliable medical image watermarking system that makes use of Particle Swarm Optimization (PSO), Discrete Wavelet Transform (DWT), and Singular Value Decomposition (SVD) approaches. There are three main phases to the suggested plan. The medical image is first broken down into its frequency components using DWT, which makes watermark embedding more effective. Subsequently, the PSO method is utilized to maximize the choice of image segments for incorporation, guaranteeing that the watermark remains undetectable and robust against diverse attacks. In the last step, the watermark’s strength is increased by using the SVD approach to embed it in the singular values of certain image blocks. Comprehensive tests are conducted to assess the efficacy of the suggested approach, taking into account performance measures like the Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR), as well as resistance against typical assaults such as noise addition, compression, and cropping. The testing findings show that the method effectively embeds the watermark while maintaining image quality and making sure it is robust to attack. The suggested method is appropriate for clinical and telemedicine applications as it also protects the confidentiality and integrity of patient data. By advancing the development of effective and safe medical image watermarking methods, this research helps to protect the privacy and veracity of critical healthcare data. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Discrete wavelet transforms

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