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

作者： Sathish, M. Prabha Devi, D. Kanchana, R. Pranesh, R.K. Kumaraguru College of Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore India Department of Computer Science and Engineering Tamil Nadu Sathyamangalam India Sphoorthy Engineering College Department of Artificial Intelligence and Machine Learning Telangana Hyderabad India Kumaraguru College of Technology Department of Information Science and Engineering Tamil Nadu Coimbatore India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379945

The 'Smart Vehicle Monitoring System' presents a comprehensive solution for enhancing road safety and user authentication in the realm of modern transportation. This system integrates advanced technologies to monitor crucial aspects of vehicle usage, ensuring both the rider's safety and the vehicle's safety. The core functionalities encompass monitoring the usage of safety gear such as helmets and detecting alcohol consumption. User authentication is achieved through a combination of fingerprint recognition and Radio-frequency identification (RFID). This multi-layered approach ensures that only authorized individuals can operate the vehicle. The proposed system further extends its capabilities to monitor essential vehicle parameters, including tire pressure and fuel levels. In the event of an accident, a vibration sensor is employed to swiftly detect impact, triggering an alert system through GSM communication. This timely notification ensures prompt assistance and emergency response. Wireless communication plays a vital role in the system, with the helmet connected to an Arduino Mega via the ESP protocol. This acilitates real-time data exchange, allowing for seamless integration of various components. To enhance user interface and information dissemination, the system utilizes an LCD to present real-time status updates. Additionally, the integration of a GPS module enables the sharing of live location data, contributing to improved navigation and overall safety. In conclusion, the Smart Vehicle Monitoring System not only prioritizes the safety of the rider but also addresses authentication concerns and provides a holistic approach to vehicle monitoring. Through the amalgamation of cutting-edge technologies, this system aims to redefine the standards of safety and security in contemporary transportation. © 2024 IEEE.

关键词： Radio frequency identification (RFID)

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Incorporation of Deep learning (DL) Techniques to make Intelligent and Adaptable Wireless Communication Systems

Incorporation of Deep Learning (DL) Techniques to make Intel...

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2023 International Conference on Advances in Computation, Communication and Information technology, ICAICCIT 2023

作者： Tiwari, Anamika Kumar, Sandeep Rengarajan, A. Noida Institute of Engineering and Technology Department of Artificial Intelligence & Machine Learning Uttar Pradesh Greater Noida India Tula's Institute Department of Computer Science and Engineering Dehradun India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350344387

The emergence of Deep learning (DL) was a response to the widespread availability of mobile data and the growing complexity of wireless networks. It is a subfield of machine learning that has been proposed in academic literature. This technology is regarded as a breakthrough in the management of mobile data traffic. Wireless systems generate a significant amount of varied data, which presents computational issues. In order to tackle this problem, a variety of optimization methodologies are being investigated. DL increases wireless communication by enhancing its intelligence and adaptability. This paper offers an overview of the DL platform and several methodologies employed to enable deep learning in wireless communications. Wireless networking involves various disciplines, including networking, routing, and scheduling, all of which incorporate DL. This text encompasses a comprehensive range of topics. The topic encompassed several optimization methodologies and the latest breakthroughs in this domain. © 2023 IEEE.

关键词： Deep learning Fog computing Mobile network Reinforcement learning Wireless network

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An Optimal Scheduling of Energy Storage Units in Renewable Energy Systems Using Strength-Pareto Evolutionary Algorithm

An Optimal Scheduling of Energy Storage Units in Renewable E...

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2024 International Conference on Intelligent Algorithms for Computational intelligence Systems, IACIS 2024

作者： Kumari, Nalli Vinaya Kumar, Komuravelly Sudheer Alsalami, Zaid Harshitha, P. Ramadan, Ghazi Mohamad Jntuh Malla Reddy Engineering College for Women Department of Computer Science and Engineering Hyderabad India Sr University School of Computer Science & Artificial Intelligence Warangal India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India Ahl Al Bayt University College of Mlt Karbala Iraq

ISBN: (纸本)9798350360660

Dual-stage optimization scheduling model by hybrid energy storage for grid-connected renewable energy systems, is proposed in this paper which focuses on both intra-day and day-ahead phases. In day-ahead phase, model improves economic efficiency by considering of price values at its peak value difference. In intra-day phase, its motive is to decrease renewable energy usage and it's related with the day-ahead schedules using accurate, short-term predictions. Strength-pareto evolutionary algorithm 2 (SPEA2) algorithm for optimization is for multiple objective optimizations, achieving optimal and balanced solutions across multiple objectives. This SPEA2 optimization algorithm efficient handles and it enables system to cost-effectively and firmly operate and optimizing scheduling of grid-connected systems by addressing both the aspects of economic and operational. Overall, the model improves economic efficiency, reduces 6.3% the usage of renewable energy, and improves the sustainability power grid through effective integration and utilization of renewable energy sources. © 2024 IEEE.

关键词： Clean energy

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Protecting Medical Image Transmission: Color Secret Sharing Protocol for Enhanced Security

Protecting Medical Image Transmission: Color Secret Sharing ...

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2024 International Conference on artificial intelligence and Quantum Computation-Based Sensor Applications, ICAIQSA 2024

作者： Maram, Balajee Sai, Manikonda Srinivasa Sesha Vanniappan, Malathy Srinivas, T. Aditya Sai Nagaraju, Pamarthi Prasan, U.D. School of Computer Science and Artificial Intelligence SR University Telangana Warangal506371 India KKR & KSR Institute of Technology and Sciences Department of Information Technology Guntur India SR University Department of Electronics and Communication Engineering Telengana Warangal India Jayaprakash Naryan College of Engineering Artificial Intelligence and Machine Learning Mahabubnagar India Department of CSE Bhimavaram India Aditya Institute of Technology and Management Andhra Pradesh Tekkali India

ISBN: (纸本)9798331517953

The safety in the transmission of medical images cannot be compromised in the digital age, as the information contained is very sensitive. This paper presents a new approach to providing security for medical image transmission using the Color Secret Sharing protocol. This is ensured by the proposed approach of dividing the medical image into many shares, each of which is encoded with independent color information, to ensure that the restoration of the original image can be done only if all the shares are combined. Thus, the risk related to unauthorized access and data breaching is much reduced during the transmission process. Security analysis and experimental validation are provided that demonstrate that the compared methods tend to efficiently resist attacks. This study embeds CSS into medical imaging systems to solve the secure and trustworthy solution on the confidentiality, integrity, and availability of patient data in healthcare communications. © 2024 IEEE.

关键词： Color Secret Sharing Cryptography Healthcare Data Protection Image Encryption Medical Image Security Secure Transmission

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A Comprehensive Review on Probable Measures for Addressing Security Limitations in Internet of Drones

A Comprehensive Review on Probable Measures for Addressing S...

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2023 International Conference on Advances in Computation, Communication and Information technology, ICAICCIT 2023

作者： Gupta, Gaurav Menaka, C. Padmanabhan, P. Tula's Institute Department of Computer Science and Engineering Dehradun India Department of Computer Science and Information Technology Bangalore India Noida Institute of Engineering and Technology Department of Artificial Intelligence & Machine Learning Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350344387

The Internet of Drones (IoD) has garnered significant interest in recent years due to its portable and automated nature and is being utilized across various domains such as the military, search and rescue operations, and entertainment. However, due to the inherently unsecured radio transmission paths in the Internet of Drones (IoD), there are numerous security concerns associated with the data collected, generated, or processed by drones. This paper aims to address the prominent challenges of security and privacy in the IoD. It undertakes a thorough examination of security concerns and corresponding solutions for IoD security. The discussion encompasses the security requirements associated with IoD and highlights the most recent advancements in research pertaining to IoD security. Authentication methods and Blockchain-based schemes are highlighted in this overview of numerous significant security technologies. Through an extensive analysis, we hereby present an examination of the challenges encountered by existing methodologies and propose potential avenues for future research in the field of IoT security. This review highlights the necessity of implementing suitable security measures to tackle IoT security concerns. Furthermore, it emphasizes the importance of developing security solutions that carefully consider the trade-off among the level of security provided and the cost efficiency of implementation. © 2023 IEEE.

关键词： Authentication Blockchain Internet of Drones (IoD) Privacy Security

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Design of Weed Detection Technique Using Yolo Models

Design of Weed Detection Technique Using Yolo Models

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Students Conference on engineering and Systems (SCES)

作者： Sreehari M Ankur Jain Department of computer science engineering specialization Artificial intelligence and Machine Learning Vellore Institiute of Technology Bhopal India

ISBN: (数字)9798350374711

ISBN: (纸本)9798350374728

The use of the ADAM (Adaptive Moment Estimation) and SGD (Stochastic Gradient Descent) algorithms to optimize the YOLOv7(You Only Look Once), YOLOv8, and YOLO-NAS models for weed detection in agricultural landscapes is investigated in this study. Offering a thorough comparative study based on mean Average Precision (mAP), recall, and precision measures is the goal. When compared to YOLOv7, YOLOv8 regularly performs better, exhibiting balanced recall and higher precision. With remarkable mAP scores at IOU (Intersection Over Union) 0.50, YOLOv8 ADAM in particular sticks out, demonstrating strong weed identification abilities. The YOLOv7 ADAM exhibits a precision-recall trade-off, with precision being slightly sacrificed for recall. Model superiority is not solely determined by the optimization algorithm used (ADAM vs. SGD); YOLOv8 variations perform admirably. For real-world implementation, practical factors like processing efficiency and deployment viability are even more important than quantitative criteria. This work establishes the foundation for comprehending the complex trade-offs and advantages that are present in every model and algorithm, in addition to helping with the educated selection of the best YOLO models for weed detection. Future research directions in object detection in agriculture, including ensemble techniques and hyperparameter tuning, promise more advancements in this subject as technology develops.

关键词： YOLO Analytical models Accuracy Stochastic processes Ensemble learning Reliability Task analysis

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machine learning model for predicting pancreatic fistula after pancreatoduodenectomy 1

Machine learning model for predicting pancreatic fistula aft...

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1st International Conference of Intelligent Methods, Systems and Applications, IMSA 2023

作者： Saleh, Hager El-Rashidy, Nora Mohamed, Eman Sultan, Ahmad M El Nakeeb, Ayman El-Sappagh, Shaker South Valley University Faculty of Computers and Artificial Intelligence Hurghada Egypt Kafrelsheiksh University Faculty of Artificial Intelligence Machine Learning and Information Retrieval Department Kafrelsheiksh13518 Egypt Damnhour University Faculty of Computers and Information Damnhour Egypt Mansoura University Gastroenterology Surgical Center Faculty of Medicine Mansoura Egypt Galala University Faculty of Computer Science and Engineering Suez435611 Egypt

ISBN: (纸本)9798350335569

Pancreaticoduodenectomy (PD) is a complex surgery used mainly to treat tumors and other pancreas disorders. PD is considered one of the most challenging surgeries because it may have several complications, including bleeding and infections in the surgical area, temporary or permanent diabetes, and pancreatic leakage (PL), which may lead to morbidity and mortality. In this study, we build an accurate and medically oriented machine learning model that predicts PL after PD based on patient markers collected only before the PD operation. The study is made using a real-world dataset for 397 Egyptian patients. The proposed machine learning pipeline starts with a data preprocessing step that handles the missing data values by the median values. In the next step, diverse interpretable classifiers, including logistic regression, random forest, decision tree, support vector machine, XGBoost, and AdaBoost, are utilized to predict the PL. Hyperparameter optimization is done using grid search with k-fold cross-validation. The results indicate that XGBoost achieves the highest marks, outperforming the state-of-the-art techniques in several evaluation metrics (i.e., accuracy= 91%, precision= 90.96%, recall= 91.0%, F1-score= 90.97%, and AUC=89.78%. The resulting model is accurate enough to be medically relevant for PL prediction in real healthcare settings. © 2023 IEEE.

关键词： Surgery

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Harris Hawks Optimization Based Feature Selection on High Dimensional data 2

Harris Hawks Optimization Based Feature Selection on High Di...

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2nd IEEE International Conference on Data Science and Network Security, ICDSNS 2024

作者： Jena, Suvendu Kumar Kumar, Gotte Ranjith Al-Fatlawy, Ramy Riad Ali, S.Rasheed Mansoor Aruna, T.M. Indira Gandhi Institute of Technology Department of Computer Science Engineering and Applications Odisha Sarang India School of Computer Science & Artificial Intelligence Sr University Warangal India College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Jamal Mohamed College Affiliated to Bharathidasan University Department of Computer Science Tiruchirappalli India Artificial Intelligence and Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India

ISBN: (纸本)9798350373110

In recent times, evolutionary algorithms have been demonstrating significant benefits in feature selection due to their simplicity and global search capability. However, most of the existing evolutionary algorithms are computationally intensive for high-dimensional data. In order to decrease computational costs, effective evaluation methods are crucial. In this paper, Harris Hawks Optimization (HHO) technique is proposed for effective feature selection on high dimensional data. Firstly, a Multi-Layer Perceptron (MLP) is used to form an integer optimization problem for efficient feature selection process. Then the proposed HHO method is applied for the process of feature selection. This method has three steps such as Initialization, solutions update and classification. The performance of proposed HHO algorithm is compared with the existing classifiers such as Binary Waterwheel Plant Algorithm (BWWP A) and Community detection based Genetic Algorithm (CDGAFS) and achieved 98.2% of accuracy. © 2024 IEEE.

关键词： Genetic algorithms

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Efficient Prompting for LLM-Based Generative Internet of Things

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IEEE Internet of Things Journal 2025年第1期12卷 778-791页

作者： Xiao, Bin Kantarci, Burak Kang, Jiawen Niyato, Dusit Guizani, Mohsen University of Ottawa School of Electrical Engineering and Computer Science OttawaONK1N 6N5 Canada Guangdong University of Technology School of Automation Guangzhou510006 China Nanyang Technological University School of Computer Science and Engineering Jurong West Singapore Singapore Mohamed bin Zayed University of Artificial Intelligence Machine Learning Department Abu Dhabi United Arab Emirates

Large language models (LLMs) have demonstrated remarkable capacities on various tasks, and integrating the capacities of LLMs into the Internet of Things (IoT) applications has drawn much research attention recently. Due to security concerns, many institutions avoid accessing state-of-the-art commercial LLM services, requiring the deployment and utilization of open-source LLMs in a local network setting. However, open-source LLMs usually have more limitations regarding their performance, such as their arithmetic calculation and reasoning capacities, and practical systems of applying LLMs to IoT have yet to be well-explored. Therefore, we propose an LLM-based Generative IoT (GIoT) system deployed in the local network setting in this study. To alleviate the limitations of LLMs and provide service with competitive performance, we apply prompt engineering methods to enhance the capacities of the open-source LLMs, design a Prompt Management Module and a Postprocessing Module to manage the tailored prompts for different tasks and process the results generated by the LLMs. To demonstrate the effectiveness of the proposed system, we discuss a challenging table question answering (Table-QA) task as a case study of the proposed system, as tabular data is usually more challenging than plaintext because of their complex structures, heterogeneous data types and sometimes huge sizes. We conduct comprehensive experiments on the two popular Table-QA data sets, and the results show that our proposal can achieve competitive performance compared with state-of-the-art LLMs, demonstrating that the proposed LLM-based GIoT system can provide competitive performance with tailored prompting methods and is easily extensible to new tasks without training. © 2014 IEEE.

关键词： Generative adversarial networks

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Efficient feature fusion model withmodified bidirectional LSTM for automatic Parkinson's disease classification

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International Journal of Information technology (Singapore) 2024年第6期16卷 3963-3971页

作者： Reshma, S. Chennakesavulu, M. Patil, Shweta Sidagounda Lamani, Manjunath Ramanna Dept of Artificial Intelligence and Machine Learning Dayananda Sagar College of Engineering Bangalore India Department of Electronics and Communication Engineering Rajeev Gandhi Memorial College of Engineering and Technology (Autonomous) Andhra Pradesh Nandyal 518501 India Rajarambapu Institute of Technology Rajaramnagar Uran Islampur India CHRIST(Deemed to Be University) Kanmanike Kumbalgudu Mysore Road Karnataka Bangalore 560074 India

The majority of people affected by Parkinson’s disease (PD) are middle-aged and older. The condition causes a variety of severe symptoms, including tremors, limited flexibility, and slow movements. As Parkinson’s disease develops with changing symptoms and growing severity, the importance of computer-aided diagnosis based on algorithms cannot be highlighted. Gait recognition technology appears to be a potential path for Parkinson's disease identification since it captures unique properties of a person’s walking pattern without requiring active participation, providing stability and non-intrusiveness. To begin, the median filter is used to remove noise from the input images received during data collection. This paper describes a new method for finding local and global features in gait images to assess the severity of Parkinson’s *** features are extracted using a stacked autoencoder, and global features are obtained using an Improved Convolutional Neural Network (ICNN). The Enhanced Sunflower Optimisation (ESO) technique is used to improve the CNN model's performance by optimizing hyperparameters such as batch size, learning rate, and number of convolutional layers. To classify PD severity, a modified bidirectional LSTM (MBi-LSTM) classifier receives input in the form of a combination of local and global features. The proposed model's performance is completely evaluated with the GAIT-IT and GAIT-IST datasets, which include key measures such as accuracy, precision, recall, and the F-measure. This study improves the diagnosis of Parkinson’s disease by introducing a non-intrusive real-time monitoring system capable of early detection and prevention. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2024.

关键词： Enhanced sunflower optimisation Gait recognition GAIT-IT GAIT-ITS Improved convolutional neural network Median filter Modified bidirectional LSTM Parkinson’s disease

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