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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Bahade, Sachin T. Mohan Sawarkar, Mohini Prema, S. Anispremkoilraj, P. Bhongade, Ajay. S Patil, Harshal Nabira Mahavidyalaya Department of Electronics Katol India Bharati Vidyapeeth College of Engineering Instrumentation Engineering Department Navi Mumbai India Sri Krishna College of Technology Department of ECE Coimbatore India KSR Institute for Engineering and Technology CSE Tiruchengode India Bharati Vidyapeeth College of Engineering Department of Mechanical Engineering Maharashtra Navi Mumbai India Computer Science and Engineering Pune India

ISBN: (纸本)9798350327533

Recently, there has been an uptick in both the incidence of house fires and the danger the system pose to people's lives and possessions. Even if a safe distance away from the leak, liquid petroleum gas (LPG) can still ignite. The regulator being left on or a defective rubber hose are the two most common causes of fires. Making a system to alert people of gas leaks is, thus, extremely important. To that end, this research presents a gas leakage alarm system to keep an eye out for emergencies like these. The proposed procedure begins with three stages: data preprocessing, feature selection, and model training. The wavelet de-noising technique is used in preprocessing data. Time domain features are utilized during the feature selection process. Once features have been extracted, the data is used to train XGBoost-A-BiGRU models. The proposed strategy outperforms XGBoost and BiGRU, two well-known rivals. The greatest accuracy of 98.70% was reached using the proposed strategy. © 2024 IEEE.

关键词： Galvanized Steel Pipe (GSP) Gas sensor Leakage Liquid petroleum gas (LPG)

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Estimating the AGV load and a battery lifetime based on the current measurement and random forest application

Estimating the AGV load and a battery lifetime based on the ...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Hanzel, Krzysztof Grzechca, Damian Ziebinski, Adam Chruszczyk, Lukas Janus, Artur Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Department of Telecommunications and Teleinformatics Gliwice Poland Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Department of Electronics Electrical Engineering and Microelectronics Gliwice Poland Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Department of Distributed Systems and Informatics Devices Gliwice Poland Silesian University of Technology Student of Electrical EngineeringFaculty of Electrical Engineering Gliwice Poland

ISBN: (纸本)9798350324457

The paper describes the energy consumption from the battery based on the current measurements for various cases, i.e., speed (PWL adjustment) and loads. The main purpose of the research is to have additional and reliable information about power consumption and battery life estimation for autonomous guided vehicles (AGV). The authors propose a two-step algorithm. In the first step, a linear classifier was proposed. Then, the KNN classifier was tested;however, it did not give satisfactory results, so it was finally decided to use the random forest to estimate the load and PWL. The time domain current measurement is evaluated, and the beforementioned algorithms process the selected statistical measures. It has been proven that a two-step algorithm allows for achieving high accuracy. Based on the current observation, the paper is a good starting point for further investigation of the AGV because it is usually implemented in the AGV - so it does not require additional hardware. Moreover, it can lead to better energy management and increase battery lifetime. © 2023 IEEE.

关键词： AGV load estimation machine learning Prediction of energy consumption random forest

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Enhancing Energy Efficiency and Security in IoTDriven Smart Cities Using Hybrid GAN-ICA Framework 2

Enhancing Energy Efficiency and Security in IoTDriven Smart ...

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2nd IEEE Global Conference on Communications and Information Technologies, GCCIT 2024

作者： Hafeezuddin, Mohammed Jadhav, Archana Renuka, P. Sukumar, P. Ghamande, Manasi Vyankatesh Rajasekhar, Kusuma Geethanjali College of Engineering and Technology Department of Eee Telangana India Pimpri Chinchwad University School of Engineering & Technology Department of Computer Science Engineering Maharashtra India Velammal Engineering College Department of Electronics and Communication Engineering Surapet Tamilnadu Chennai India Department of Computer Science and Engineering Tamilnadu Erode India Vishwakarma Institute of Technology Desh Maharashtra Pune India Department of Ece Dr. B R Ambedkar Konaseema Odalarevu Andhra Pradesh India

ISBN: (纸本)9798350388916

The rapid emergence of IoT-powered smart cities has generated substantial apprehensions regarding energy efficiency and security. Current technologies frequently fail to adequately address these concerns in a comprehensive manner. This study presents a hybrid framework that combines GAN with ICA to improve energy efficiency and security in smart city infrastructures. The design of the model utilizes GANs to generate synthetic data and ICA to extract independent characteristics, resulting in improved accuracy and security of IoT systems. The accuracy of our model surpassed that of previous technologies, reaching 97.5%. Additionally, our model achieved a precision of 95.0% and an F1 score of 95.6%. As a result, our model has established a new benchmark in smart city research. The results showcase the model's capacity to enhance smart city operations, rendering it a worthwhile addition to the field. © 2024 IEEE.

关键词： Generative adversarial networks

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Advancing Cloud Security Frameworks Implementing Distributed Ledger Technology for Robust Data Protection and Decentralized Security Management in Cloud Computing Environments 2

Advancing Cloud Security Frameworks Implementing Distributed...

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2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Rajasekar, P. Kalaiselvi, K. Shanmugam, Raju Tamilselvan, S. Pandian, A. Pasumpon Department of ECE Sri Krishna College of Technology Kovaipudur Tamil Nadu Coimbatore India Department of Electronics and Communication Engineering Hindusthan College of Engineering and Technology Tamil Nadu Coimbatore India Unitedworld School of Computational Intelligence Karnavati University Gujarat India Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Saveetha University Tamil Nadu Chennai India Department of Computer Science and Engineering CARE College of Engineering Tamilnadu Trichy India

ISBN: (纸本)9798350383867

Cloud computing caused the paradigm shift in business performance, from scalability to flexibility as well as affordability in the management of computing resources. Nevertheless, ensuring security of the data remains a key issue in the cloud due to the centralized interactions between service provider and a single vulnerability in the network can put rest of the data at risk. Consequently, the application of DLT, in addition to other solutions such as DLT, demonstrates a big step towards the improvement of cloud security frameworks. This article focuses on the implementation of DLT for data protection applications that are based on a solid dataset and decentralized security procedures in cloud environments. DLT utilization, like blockchain, provides an organization with integrity data, transparency, and fitness to counter cyber threats. Architecture stacks up sponsors such as cloud providers, users, and applications and DLT is responsible for the security of the transaction and the security policy enforcement. Customers use those services via desktop based application with transparent security policy arisen from DLT. Moreover, DLT also does identity management in the way that is decentralized, data sharing in the manner that it is secure, and the compliance to regulatory frameworks, which in addition reduces the trust and privacy risks in the cloud ecosystems. The proposed architecture is genuinely a big jump toward protecting cloud security from centralized control interruptions and thus, enhancing resilience and stability in cloud computing. © 2024 IEEE.

关键词： Cloud security

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Enhancing Quality-of-Service in LoRa Low-Power Wide-Area Networks via Multi-Hop Optimized Radio Resource Management 2

Enhancing Quality-of-Service in LoRa Low-Power Wide-Area Net...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Jasim Alzubaidi, Laith H. Sunil, G. Praveena, Hirald Dwaraka Sathishkumar, N. Manjula, B.M. The Islamic University Najaf Iraq Sr University School of Computer Science and Artificial Intelligence Warangal India School of Engineering Mohan Babu University Erstwhile Sree Vidya Nikethan Engineering College Department of Ece Andhra Pradesh Tirupati India Kpr Institute of Engineering and Technology Department of Electronics and Communication Engineering Coimbatore India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350317558

In transport systems in particular, LoRa wireless communication has become an essential technology for intelligent critical infrastructures. Optimizing transmission policy parameters, including carrier frequency, bandwidth, coding rate, and spreading factor, is crucial for achieving excellent Quality of Service (QoS) for LoRa systems;however, current methods have not fully optimized all LoRa parameters. In order to efficiently share information, the proposed Multiobjective Q Learning based LoRa (MQ-LoRa) uses a hybrid topology consisting of a cluster root rotating tree, wherein IoT nodes adhere to a cluster topology inspired by cellular tissues, while gateways follow a tree topology. For cluster construction, a membrane-inspired clustering algorithm is created, and influence scores are used to determine which header node is best. The study presents Data QoS Ranking for IoT nodes, which uses the new LoRa frame structure field QoS Ranking (QRank) to classify data into priority and non-priority categories. The Soft Actor Critic (SAC) fast deep reinforcement learning model is used for optimal transmission policy enforcement, taking into account signal-to-interference-plus-noise ratio, QRank, and other environmental characteristics. After that, the transmission policy is optimized taking carrier frequency, bandwidth, coding rate, and spreading factor into account. Furthermore, a multi-hop routing method with concurrent optimization that uses shuffling shepherd and mayfly optimization rates routes according to fitness standards. A multi-weighted sum approach is used to establish a weighted duty cycle in LoRa IoT networks to minimize resource inefficiency and loss of information. Using the NS3.26 LoRaWAN module, performance evaluation measures energy consumption, latency, throughput, packet receipt ratio, and packet rejection ratio, among other parameters. Comparison with popular LoRa techniques shows that the suggested MQ-LoRa performs better energy consumption of 56 mJ t

关键词： LoRA Mayfly Quality of Service Rank Shuffled Shepherd Optimization Soft Actor Critic

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Exploring Convolutional Neural Networks for Fingerprint Damage Detection and Classification

Exploring Convolutional Neural Networks for Fingerprint Dama...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Swathi, R. Kansal, Vipashi Shyamala Valsalan, Prajoona Vekariya, Vipul Taqui, Syed Noeman Prasad v Potluri Siddhartha Institute of Technology Department of Electrical and Electronics Engineering Andhra Pradesh Vijayawada521108 India Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttarakhand Dehradun248002 India Government Polytechnic for Women Department of Electronics and Communication Engineering Karnataka Ramannagara562159 India Dhofar University Department of Electrical and Computer Engineering Dhofar Salalah Oman Parul Institute of Engineering and Technology Parul University Department of Computer Science and Engineering Post Limda Gujarat Waghodia391760 India Saveetha School of Engineering Simts Saveetha University Department of Vlsi Microelectronics Tamil Nadu Chennai602105 India

ISBN: (纸本)9798350375237

This study proposes a novel approach to fingerprint classification using Deep Convolutional Neural Networks (CNNs), leveraging datasets from prominent sources like the National Institute of Standards and Technology (NIST), Fingerprint Verification Competition (FVC), and the SHUFFLE database. The research involves a systematic pipeline, beginning with data collection and preprocessing to create a comprehensive dataset that represents real-world variations in fingerprint patterns. A tailored CNN architecture is designed to capture hierarchical features essential for accurate classification, trained on the amalgamated dataset using state-of-the-art optimization algorithms and loss functions. The model's performance is rigorously evaluated across diverse fingerprint patterns using separate datasets from NIST, FVC, and SHUFFLE, showcasing its superiority and adaptability compared to existing benchmark methods. Additionally, the study addresses privacy and ethical considerations associated with biometric data usage, ensuring the model's reliability and applicability in various fingerprint recognition applications. © 2024 IEEE.

关键词： Database systems

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IoT-Integrated Leaf Analysis for Early Plant Disease Detection in Agriculture Using Hybrid Ensemble Models 4

IoT-Integrated Leaf Analysis for Early Plant Disease Detecti...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Pulugu, Dileep Praveen, R.V.S. Moharana, Rajeeb Lochan Raju, Meena Naga Revathy, P. Saravanan, K. Malla Reddy College of Engineering and Technology Department of Cse Hyderabad India Department of Digital Engineering and Assurance Hyderabad India College of Agriculture Department of Seed Science and Technology Kalahandi India Srm University Department of Ece Vijayawada India Narsimha Reddy Engineering College Department of Computer Science and Engineering Hyderabad India Sakthi Polytechnic College Department of Electronics and Communication Engineering Erode India

ISBN: (纸本)9798350352931

Crop diseases threaten food security and agricultural production. For prompt interventions, disease management, and loss reduction, crop disease outbreaks must be detected early. Precision Agriculture (PA) promotes sustainable agriculture by developing predictive plant disease prevention technology using ICTs. This paper presents a multi-step illness detection system using contemporary sensors and images. System architecture includes preprocessing, feature extraction, model training, and disease detection. Crop conditions are monitored using temperature, colour, and humidity sensors. High-resolution (HR) picture preprocessing improves raw images for predictive analysis. Leaf pictures provide geometric characteristics including area, perimeter, and rectangularity to improve model correctness. Model Driven ERL trained a plant disease prediction model. Combining sensor data with machine learning algorithms in PA yielded 89.47% plant disease prediction accuracy. A scalable technique to early disease identification in agriculture makes this technology potential for proactive plant disease control. This model's precision makes it suitable for real-world PA systems, reducing disease-related crop losses and ensuring agricultural output sustainability. © 2024 IEEE.

关键词： Deep reinforcement learning

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Enhancing Healthcare Delivery Through IoT Applications in Remote Patient Monitoring and Telemedicine 3

Enhancing Healthcare Delivery Through IoT Applications in Re...

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3rd IEEE North Karnataka Subsection Flagship International Conference, NKCon 2024

作者： Yogi, Kottala Sri Gowda V, Dankan Manoj Kumar, S.B. Nithya, R. Begum, A.Yasmine Suganthi.n Symbiosis Institute of Business Management Hyderabad Symbiosis International University Department of Operations Pune India Bms Institute of Technology and Management Department of Electronics and Communication Engineering Karnataka Bangalore India Bgsit Adichunchanagiri University Faculty of Engineering Management & Technology Dept. of Ece Karnataka 571448 India Vivekanandha College of Engineering for Women Department of Computer Science and Engineering Tamilnadu Namakkal India Mohan Babu University School of Engineering Department of Electronics and Communication Engineering Tirupati India Dayananda Sagar College of Engineering Department of Electrical and Electronics Engineering Karnataka Bangalore India

ISBN: (纸本)9798350364569

This paper presents how IoT plays a vital role in improving delivery of healthcare services especially through remote patient monitoring and telemedicine. The proposed system also employs IoT devices aimed at passing various health indicators like;pulse rate, blood glucose levels, blood pressure, and physical activity to continually and objectively provide real-time data in the body. This particular study, the participants with chronic diseases, normal population and elderly people also participated in the test of the system. Some of the findings include;it is possible to measure the daily steps corresponding to the a calorie expenditure, the sleep score ranging between 60-80 and oxygen saturation percentages ranging from 96%-99% in various postures. The system also proved the correlation between the percentage of variation in ambient temperature and the percentage of variation in the pulse rate which was quite sensitive to the changes in temperatures. Based on these observations, it is possible to conclude that IoT-based monitoring can improve patient's conditions as long as underlying issues will be detected at an early stage and necessary actions will be taken in time. The study also also demonstrates the application of telemedicine in making healthcare more accessible especially for the people from rural settings or in developing countries. The results show a good direction regarding the future of the system;however, security issues, privacy relating to data as well as integration are key hurdles in this connection. © 2024 IEEE.

关键词： Blood pressure

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Expeditious Diagnosis of Cerebral Aneurysm on MR Brain Angiogram Images Utilizing Soft Computing Techniques 7

Expeditious Diagnosis of Cerebral Aneurysm on MR Brain Angio...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Anjaneya, L.H. Maheswari, K. Revathi, N. Siva, K.P. Soudagar, Manzoore Elahi M. Dhanraj, Joshuva Arockia Bapuji Institute of Engineering And Technology Department of Electrical And Electronics Engineering Karnataka Davangere577004 India Cmr Technical Campus Department of Computer Science And Engineering Hyderabad Telangana Kandlakoya501401 India Panimalar Engineering College Department of Electronics And Communication Engineering Tamil Nadu Chennai600123 India Sri Eshwar College of Engineering Department of Computer Science And Business Systems Tamil Nadu Coimbatore641202 India Saveetha Institute of Medical And Techinical Sciences Saveetha School of Engineering Department of Vlsi Microelectronics Tamil Nadu Chennai602105 India Department of Mechatronics Engineering Chennai Tamil Nadu Padur603103 India

ISBN: (纸本)9798350359299

It is always a challenging task for radiologists to detect Cerebral Aneurysms (CA). Unenhanced magnetic resonance angiography (MRA) has become a popular screening tool because of its non-invasiveness, lack of contrast, and ionizing radiation. Because of its screening role, MRA requires a high recognition rate. This paper presents Machine Learning (ML) methods for detecting CA in MRA data. MRA scans of normal and aneurysms are collected and evaluated. This research study is divided into two sections: segmentation and classification. First, K-Means Clustering (KMC) is used for aneurysm segmentation from MR images. Second, image classification is done. For that, the appropriate features are retrieved using feature extraction and selection. Then Gray Level Run Length Matrix (GLRLM) is used for feature extraction and the Genetic Algorithm (GA) for feature selection. The GLRLM and GA features are provided independently to the ML models for training and testing. The ML models used in the research work are Artificial Neural Networks (ANN) and Modified Fuzzy C-Means (MFCM). The metrics are employed to evaluate the GLRM and GA features with ANN and MFCM. The combination of GA features with ANN produces the best results when compared to other combinations. The GA with ANN obtains an outstanding 98.33% accuracy, 100% specificity and precision, 96.67% sensitivity, and 98.31% F1 score. © 2024 IEEE.

关键词： Genetic algorithms

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Smart Agriculture-Based Food Quality Analysis with Healthcare Security System Using Cloud Machine Learning Model

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Remote Sensing in Earth Systems Sciences 2024年第4期7卷 389-398页

作者： Vaithianathan, V. Rebecca, B. Begum, M. Fathima Sundarraj, Antony Allwyn Kumar, M. Prema Kiran, Ajmeera Department of Electronics and Communication Engineering Sri Sivasubramaniya Nadar College of Engineering Kalavakkam Chennai 603110 India Department of Computer Science and Engineering Marri Laxman Reddy Institute of Technology and Management Hyderabad Telangana Dundigal 500043 India Assistant Professor Department of Computer Science and Engineering (Cyber Security) Madanapalle Institute of Technology and Science Andhra Pradesh-517325 Madanapalle India Department of Food Technology Sri Shakthi Institute of Engineering and Technology Coimbatore Tamilnadu Coimbatore India Dept. of ECE Shri Vishnu Engineering College for Women (A) Andhra Pradesh Bhimavaram 534202 India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

The primary sector of our Indian economy is agriculture. Today’s world’s most important and vital industries are agriculture and irrigation. Information and communication technology must be used in our agriculture sectors in order to support farmers and agriculturalists in improving their practices across the entire crop-growing and post-harvest processes. It is still challenging to define processed food, even in light of mounting research that suggests consuming more ultra-processed food has negative health effects. This research proposes novel technique in smart agriculture model based on cloud machine learning in cloud machine learning and healthcare system. By leveraging IoT and cloud-based data to create data-driven product decisions, cloud computing in agriculture can assist farmers with smart agriculture practices. Here, the input has been collected as food quality based on smart agriculture using a cloud model as well as processed for noise removal and normalisation. Then, based on this data, the healthcare security system has been analysed using reinforcement adversarial stacked encoder with Markov fuzzy algorithm. The experimental analysis has been carried out for various food quality datasets in terms of training accuracy, random precision, normalised correlation ratio, F-1 score, and AUC. The proposed technique achieved training accuracy of 96%, F-1 score of 94%, random precision of 97%, normalised correlation ratio of 93%, and AUC of 92%. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Adversarial stacked encoder Cloud machine learning Cloud machine learning Healthcare system Smart agriculture

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