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IEEE Transactions on AgriFood electronics 2024年第1期2卷 2-11页

作者： Pandey, Harsh Arya, Subhanshu Das, Debanjan Udutalapally, Venkanna International Institute of Information Technology Naya Department of Electronics & Communication Engineering Naya Raipur493661 India National Institute of Technology Department of Computer Science & Engineering Warangal506004 India

Pulses are one of the most important food crops in the world due to their higher protein content, approximately 21%-25%. Therefore, it is crucial to analyze the crop's quality and impurity levels. Stones, pebbles, marble chips, and synthetic dyes, such as lead chromate, metanil yellow, and artificial colors, are some of the impurities added to pulse products, accidentally or on purpose. The existing analysis techniques are mostly laboratory-based, time-consuming, costly, and require human examination. To address this issue, this article presents an intelligent system, FoodExpert, based on image processing that automatically uses an image of a pulse sample to identify the region of interest and essential attributes. Then, machine learning frameworks are used to predict pulse quality and adulteration levels based on the obtained parameters. On the test dataset, the suggested model had a 96% accuracy rate for pulse quality prediction and 94% accuracy for adulteration level prediction. The model was successfully deployed on a Raspberry Pi-based hardware prototype and mobile application. © 2023 IEEE.

关键词： Feature extraction

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Review and Explore the Transformative Impact of Artificial Intelligence (AI) in Smart Healthcare Systems

Review and Explore the Transformative Impact of Artificial I...

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2024 International Conference on Advances in Computing Research on science engineering and Technology, ACROSET 2024

作者： Nagar, Sneha Patidar, Mukesh Sheikh, Rashid Singh, Sudhanshu Kashiv, Ashutosh Jain, Ankit Namdeo, Shravan Kumar Paranjpe, Pranav Mandloi, Devendra Singh Indore Institute of Science and Technology Department of Electronics and Communication Engineering Indore India Parul Institute of Engineering and Technology Parul University Department of Computer Science & Engineering Vadodara India Acropolis Institute of Technology and Research Department of Computer Science & Engineering Indore India Indian Institute of Technology Indore Department of Electronics and Communication Engineering Indore India

ISBN: (纸本)9798350388800

Because of its high level of complexity and data collection capabilities, the healthcare industry employs artificial intelligence (AI) more than other sectors. Artificial intelligence is becoming an increasingly important part of our daily routines. AI's features include extremely low error rates, constant accessibility, quick analysis, and real-time data dissemination. This paper examines the crucial role of artificial intelligence in intelligent healthcare systems, with a particular emphasis on enhancing healthcare delivery. Artificial intelligence in healthcare is rapidly evolving, with the potential to significantly enhance patient care, diagnosis, treatment, and cost distribution. It also presents issues of transparency, honesty, and availability. This study will look into the benefits, drawbacks, and other aspects of artificial intelligence in intelligent healthcare systems. © 2024 IEEE.

关键词： Artificial Intelligence (AI) Ethical Considerations Explainable AI (XAI) Healthcare Resource Allocation Healthcare Systems Smart Treatment Planning

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Towards Explainable AI: Interpretable Models for Complex Decision-making

Towards Explainable AI: Interpretable Models for Complex Dec...

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2024 International Conference on Knowledge engineering and communication Systems, ICKECS 2024

作者： Singh, Jaibir Rani, Suman Srilakshmi, Garaga Lovely Professional University Computer Science & Engineering Punjab Phagwara India Lovely Professional University Electronics & Communication Engineering Punjab Phagwara India Aditya College of Engineering and Technology Electronics & Communication Engineering Andhra Pradesh Surampalem India

ISBN: (纸本)9798350359688

In the rapidly evolving landscape of artificial intelligence (AI), the integration of explainable AI (XAI) models into complex decision-making processes has become paramount. This paper addresses the critical need for transparency and interpretability in AI systems, particularly those involved in high-stakes decisions in sectors such as healthcare, finance, and autonomous systems. Traditional AI models, while powerful, often operate as 'black boxes,' offering little to no insight into their decision-making processes. This opacity can lead to trust issues, ethical concerns, and regulatory challenges. To bridge this gap, we propose a novel framework of interpretable models designed to enhance the explainability of AI without compromising on performance. Our approach leverages state-of-the-art techniques in machine learning, including feature importance analysis and model-agnostic methods, to develop models that are both accurate and interpretable. We demonstrate the efficacy of our proposed models through rigorous testing on complex datasets, showcasing significant improvements in transparency and user trust. Furthermore, our findings reveal that our interpretable models can achieve comparable, if not superior, performance to traditional 'black box' models, thereby challenging the notion that explainability necessarily comes at the cost of accuracy. This work contributes to the burgeoning field of XAI by providing a viable pathway towards the development of AI systems that are not only powerful but also comprehensible and trustworthy. © 2024 IEEE.

关键词： Adversarial machine learning

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Design and Implementation of a LoRa-Based Remote Health Monitoring System with Emergency Alert Capabilities 7

Design and Implementation of a LoRa-Based Remote Health Moni...

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Rajesh, K.P. Vinu Kumar, S.M. Parameshwari, V. Divyaa, N. Nallasivam, Manikanda Prabu Padma Suresh, L. Sri Krishna College of Technology Electronics & Communication Engineering Coimbatore India Sri Krishna College of Technology Mechanical Engineering Coimbatore India Nandha Engineering College Electronics & Communication Engineering Erode India Velalar College of Engineering and Technology Electronics & Communication Engineering Erode India Baselios Mathews II College of Engineering Computer Science and Engineering Kerala Sasthamcotta India

ISBN: (纸本)9798350372816

Saving human lives is essential in the field of medical science. engineering technologies support to achieve this goal. This article explores the integration of Internet of Things and LoRa technology in hospital wristbands to enhance patient care and streamline healthcare operations. The proposed system employs Arduino Pro Mini microcontrollers and LoRa1276 in the LoRa enabled Patient Wrist Band Module (LPWM) to facilitate real-time monitoring of patient vital signs, such as temperature and pulse. The acquired data is transmitted wirelessly to a central nursing station. In the nursing station, a Raspberry Pi equipped with a LoRa receiver device consolidates and displays the data. Grey Wolf Optimization algorithm is used in Raspberry Pi to prioritize patient care based on predefined parameters. The integration of Explainable AI in the receiver module enhances the transparency and trustworthiness of AI-driven recommendations. This supports personalized patient care. This comprehensive approach aims to bridge the healthcare gap in ensuring efficient resource allocation and improved patient outcomes through advanced technological solutions. © 2024 IEEE.

关键词： Remote patient monitoring

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PSO-DBNet for Peak-to-Average Power Ratio Reduction Using Deep Belief Network

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computer Systems science & engineering 2023年第5期45卷 1483-1493页

作者： A.Jameer Basha M.Ramya Devi S.Lokesh P.Sivaranjani D.Mansoor Hussain Venkat Padhy Department of Computer Science and Engineering Hindusthan Institute of TechnologyCoimbatore641032India Department of Computer Science and Engineering Hindusthan College of Engineering and TechnologyCoimbatore641032India Department of Electronics and Communication Engineering Kongu Engineering CollegeErode638060India Department of Computer Science and Engineering Sri Krishna College of Engineering and TechnologyCoimbatore641008India School of Computing Science&Engineering VIT Bhopal UniversityBhopal466114India

Data transmission through a wireless network has faced various signal problems in the past *** orthogonal frequency division multiplexing(OFDM)technique is widely accepted in multiple data transfer patterns at various frequency bands.A recent wireless communication network uses OFDM in longterm evolution(LTE)and 5G,among *** main problem faced by 5G wireless OFDM is distortion of transmission signals in the *** transmission loss is called peak-to-average power ratio(PAPR).This wireless signal distortion can be reduced using various *** study uses machine learning-based algorithm to solve the problem of PAPR in 5G wireless *** transmit sequence(PTS)helps in the fast transfer of data in wireless *** is merged with deep belief neural network(DBNet)for the efficient processing of signals in wireless 5G *** indicates that the proposed system outperforms other existing ***,PAPR reduction in OFDM by DBNet is optimized with the help of an evolutionary algorithm called particle swarm ***,the specified design supports in improving the proposed PAPR reduction architecture.

关键词： 5G wireless network orthogonal frequency division multiplexing signal distortion peak to average power ratio partial transmit sequence deep belief network

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AI-Driven Resource and communication-Aware Virtual Machine Placement Using Multi-Objective Swarm Optimization for Enhanced Efficiency in Cloud-Based Smart Manufacturing

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computers, Materials & Continua 2024年第12期81卷 4743-4756页

作者： Praveena Nuthakki Pavan Kumar T. Musaed Alhussein Muhammad Shahid Anwar Khursheed Aurangzeb Leenendra Chowdary Gunnam Department of Computer Science&Engineering Koneru Lakshmaiah Education FoundationGuntur522302India Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of AI and Software Gachon UniversitySeongnam-Si13120Republic of Korea Department of Electronics and Communication Engineering SRM UniversityAmaravati522502India

Cloud computing has emerged as a vital platform for processing resource-intensive workloads in smart manu-facturing environments,enabling scalable and flexible access to remote data centers over the *** these environments,Virtual Machines(VMs)are employed to manage workloads,with their optimal placement on Physical Machines(PMs)being crucial for maximizing resource ***,achieving high resource utilization in cloud data centers remains a challenge due to multiple conflicting objectives,particularly in scenarios involving inter-VM communication dependencies,which are common in smart manufacturing *** manuscript presents an AI-driven approach utilizing a modified Multi-Objective Particle Swarm Optimization(MOPSO)algorithm,enhanced with improved mutation and crossover operators,to efficiently place *** approach aims to minimize the impact on networking devices during inter-VM communication while enhancing resource *** proposed algorithm is benchmarked against other multi-objective algorithms,such as Multi-Objective Evolutionary Algorithm with Decomposition(MOEA/D),demonstrating its superiority in optimizing resource allocation in cloud-based environments for smart manufacturing.

关键词： Resource utilization smart manufacturing efficiency inter VM communication virtual machine placement cloud computing multi-objective optimization

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Machine Learning Model Comparison for Flood Detection Application from Satellite Images 2

Machine Learning Model Comparison for Flood Detection Applic...

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

作者： Sonavale, Amruta Chakkaravarthy, Midhun Rao, Surampudi Srinivasa Salleh, Hishamuddin Bin M. Jadhav, Jagannath Lincoln University College Department of Computer Science and Multimedia Malaysia Malla Reddy College of Engineering and Technology Department of Electronics and Communication Engineering Telangana India Kle College of Engineering and Technology Department of Electronics and Communication Engineering Chikodi India

ISBN: (纸本)9798350373110

Backgrounds: Flooding, a rapidly increasing and increasingly frequent natural disaster, requires effective management, even if unavoidable, and the development of an effective classifier for image detection is crucial for effective response. Methods: The study uses machine learning methods like closest K-nearest neighbors, logistic regression, support vector classification, and decision tree as classifiers. The accuracy of these algorithms varies. The dataset, consisting of ground and space-based sources, is used to determine their accuracy rates. The most accurate classifier is used for forecasting flooding in advance, allowing for faster evacuation and care for those in need. Result: In this case, we could anticipate the likelihood of flooding by analyzing historical rainfall data. The support vector machine produced an efficient accuracy of 93.92%. Conclusion: The accuracy of each classifier is shown, and detection is performed mostly with great precision. The outcomes of any image tests conducted are shown as well. © 2024 IEEE.

关键词： Adversarial machine learning

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Dynamic Data Placement Strategy with Network Security Issues in Distributed Cloud Environment for Medical Issues: An Overview

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Recent Advances in computer science and communications 2024年第6期17卷 25-38页

作者： Pradhan, Devasis Behera, Majusha Gheisari, Mehdi Department of Electronics and Communication Engineering Acharya Institute of Technology Karnataka Bangalore560107 India School of Electronics Engineering Campus 12 KIIT deemed to be University Odisha Bhubaneswar India Department of Cognitive Computing Institute of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

The rapid integration of distributed cloud systems in the healthcare industry has profoundly impacted the management of valuable medical data. While this advancement has significantly improved data handling, protecting sensitive healthcare information in such a complex environment remains daunting. This comprehensive study explores the crucial intersection between dynamic data placement strategies and network security concerns in distributed cloud environments, particularly healthcare. After establishing the significance and context of this research, the survey delves into the growing need to safeguard medical data within the everevolving landscape of cloud-based healthcare systems. It lays out fundamental concepts, such as dynamic data placement and network security, highlighting their unique implications in the medical domain. Ultimately, this survey sheds light on the most effective approaches for balancing dynamic data placement and network security in the healthcare sector. This research delves into examining many tactics, evaluating their effectiveness in handling delicate medical information, and presenting tangible use cases. A key focus of this investigation is the fusion of data organization and network safety within the healthcare industry. It investigates the adaptability of dynamic data positioning techniques in fortifying network security and safeguarding against potential threats unique to the healthcare sector. Case studies of the successful implementation of these strategies in healthcare establishments are also included. © 2024, Bentham science Publishers. All rights reserved.

关键词： Electronic health record

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Enhancing the efficiency of lung cancer screening: predictive models utilizing deep learning from CT scans

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Neural Computing and Applications 2025年第17期37卷 11459-11477页

作者： Tawfeek, Medhat A. Alrashdi, Ibrahim Alruwaili, Madallah Shaban, Warda M. Talaat, Fatma M. Department of Computer Science Faculty of Computers and Information Menoufia University Shebin Elkom32511 Egypt Department of Computer Science College of Computer and Information Sciences Jouf University Sakakah Saudi Arabia Department of Computer Engineering and Networks College of Computer and Information Sciences Jouf University Sakakah Saudi Arabia Department of Communication and Electronics Engineering Nile Higher Institute for Engineering and Technology Mansoura Egypt Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh Egypt Faculty of Computer Science and Engineering New Mansoura University Gamasa35712 Egypt

Lung cancer is the most lethal form of cancer. This paper introduces a novel framework to discern and classify pulmonary disorders such as pneumonia, tuberculosis, and lung cancer by analyzing conventional X-ray and CT scan images called lung cancer risk prediction (LCRP) model. LCRP has four modules, namely data collection and preprocessing, data augmentation module, image segmentation module, and prediction module. Actually, LCRP employs three deep learning models;sequential model, functional model, and transfer model on publicly available training datasets. Convolutional neural networks (CNNs) have emerged as a highly effective field in machine learning, particularly for image datasets in the field of biomedical applications. The primary goal is to validate these models by comparing their performance with other models in order to determine their effectiveness in addressing challenging datasets. Our research has revealed a noteworthy enhancement in the efficiency of binary and multi-class classification using mask R-CNN image segmentation. During the model training process, a combination of Adam and stochastic gradient descent dual optimizers has been used to improve performance. LCRP have outperformed current pre-trained models by minimizing training parameters, computational costs, and overhead. It introduces 98.5% accuracy, 88.7% specificity, 89% sensitivity, 89.2% precision, and 89.09% F-measure. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Lung cancer

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Assessing Social Media's Impact on Mental Health: A Gaussian Naive Bayes Approach 4

Assessing Social Media's Impact on Mental Health: A Gaussian...

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4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Nanditha, S. Joy, Helen K. Kounte, Manjunnth R. Sridevi, R. Jayarani, A. Electa Alice Vanusha, D. Christ University Department of Computer Science Bangalore560029 India School of Electronics and Communication Engineering Reva University Bangalore India Ks Institute of Technology Department of Electronics and Communication Engineering Bangalore India Srm Institute of Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350387490

In recent times, social media has gained immense popularity. However, there is a growing concern among individuals regarding its impact on mental well-being. Extensive research has been conducted to unravel the intricate connection between the use of social media and mental health. This study thoroughly examines the existing literature by incorporating various studies that cover topics such as machine learning for analysing depression-related sentiments and the effects of social media on specific demographic groups. The aim is to develop a comprehensive understanding of the relationships between these factors. In order to gain a deeper understanding of mental health indicators, a dataset consisting of 481 records was analysed using various techniques including pre-processing, correlation analysis, data visualisation, and Gaussian Naive Bayes predictive modelling. Based on our findings, it appears that individuals who engage with social media may experience negative impacts on their mental well-being. During our investigation, we conducted tests on different machine learning models and discovered that Gaussian Naive Bayes yielded the highest level of accuracy. The presence of multicollinearity among independent variables emphasises the importance of being cautious when interpreting research findings. This study makes a valuable contribution to our understanding of the complexities involved in using social media platforms while managing mental health issues. By focusing on the intersection of technology and psychiatric illnesses, it lays the groundwork for future research and interventions in this important area. © 2024 IEEE.

关键词： Adversarial machine learning

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