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

作者： Sudhakar, S. Kolanchinathan, V.P. Babu, S. Sivasaravana Aakash, M. Arunkumar, B. Arunkumar, V.B. SSM Institute of Engineering and Technology Department of Computer Science and Engineering India Vel Tech High Tech Dr.Rangarajan Dr.Sakunthala Engineering College Department of Electronics and Communication Engineering India

ISBN: (纸本)9798350372816

Mycobacterium tuberculosis, the causative agent of tuberculosis (TB), continues to be a serious worldwide health concern, killing about 1.3 million people in 2022, predominantly in low- and middle-income countries. The urgent need for automation in healthcare has spurred the development of supplementary tools. We have created a method for detecting TB in chest X-rays using advanced techniques such as pre-processing, picture segmentation, augmenting the data, along with Deep Learning model for classification. Our approach involves segmenting the lung region from X-rays using a U-Net model, followed by classification with a convolutional neural network (CNN). This optimized design enhances accuracy and efficiency, making it suitable for mobile deployment and providing a practical solution for TB detection in resource-limited settings. © 2024 IEEE.

关键词： Convolutional neural networks

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A Survey on Applications of Cache-Aided NOMA

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IEEE communications Surveys and Tutorials 2023年第3期25卷 1571-1603页

作者： Bepari, Dipen Mondal, Soumen Chandra, Aniruddha Shukla, Rajeev Liu, Yuanwei Guizani, Mohsen Nallanathan, Arumugam National Institute of Technology Raipur Department of Electronics and Communication Engineering Raipur492010 India National Institute of Technology Durgapur Department of Electronics and Communication Engineering Durgapur713209 India Queen Mary University of London School of Electronic Engineering and Computer Science LondonE1 4NS United Kingdom Mohamed Bin Zayed University of Artificial Intelligence Machine Learning Department Abu Dhabi United Arab Emirates

Contrary to orthogonal multiple-access (OMA), non-orthogonal multiple-access (NOMA) schemes can serve a pool of users without exploiting the scarce frequency or time domain resources. This is useful in meeting the future network requirements (5G and beyond systems), such as, low latency, massive connectivity, users' fairness, and high spectral efficiency. On the other hand, content caching restricts duplicate data transmission by storing popular contents in advance at the network edge which reduces data traffic. In this survey, we focus on cache-aided NOMA-based wireless networks which can reap the benefits of both cache and NOMA;switching to NOMA from OMA enables cache-aided networks to push additional files to content servers in parallel and improve the cache hit probability. Beginning with fundamentals of the cache-aided NOMA technology, we summarize the performance goals of cache-aided NOMA systems, present the associated design challenges, and categorize the recent related literature based on their application verticals. Concomitant standardization activities and open research challenges are highlighted as well. © 1998-2012 IEEE.

关键词： Standardization

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AI-Powered IoT Framework for Enhancing Building Safety through Stability Detection 8

AI-Powered IoT Framework for Enhancing Building Safety throu...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Raja, S.R. Devi, T. Rathi Raj, J. Relin Francis Sankar, V. Krishna Krishnan, R. Santhana Venkatalakshmi, R. Hindustan Institute of Technology and Science Computer Applications Chennai India Christ University Department of Computer Science and Engineering Bangalore India Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Saveetha School of Engineering Chennai India Scad College of Engineering and Technology Department of Civil Engineering Cheranmahadevi 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

ISBN: (纸本)9798350376425

The rapid urbanization and increasing structural complexities of modern buildings have heightened the need for advanced monitoring systems to ensure building safety. The research presents an AI-powered IoT framework that enhances building safety through advanced stability detection mechanisms. The proposed framework employs a novel algorithm, Ensemble Learning with IoT Sensor Data Aggregation (EnIoT-SDA), which integrates ensemble learning techniques with aggregated sensor data to provide accurate and real-time stability assessments of building structures. The effectiveness of EnIoT-SDA was evaluated through a comprehensive simulation analysis, comparing its performance against existing algorithms, including Support Vector Machine (SVM), Gradient Boosting Machines (GBM), and Fuzzy Logic Systems (FLS). Simulation metrics, such as accuracy, false positive rate, computational time, and detection latency, were used to assess and compare the algorithms' performance. The results demonstrated that EnIoT-SDA outperformed the existing methods in several key areas, offering improved accuracy and reduced detection latency, thus establishing its potential as a robust solution for building safety monitoring. The study underscores the significant advancements brought by integrating ensemble learning with IoT sensor data and highlights areas for future research and development in this domain. © 2024 IEEE.

关键词： Support vector machines

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SITSL: Strategic Image Tag Recommendation Framework Encompassing Semantics Oriented Learning and Reasoning 2

SITSL: Strategic Image Tag Recommendation Framework Encompas...

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2nd IEEE International Conference for Women in Innovation, Technology and Entrepreneurship, ICWITE 2024

作者： Kumari, Rupa Deepak, Gerard National Institute of Technology Department of Electronics and Communication Engineering Meghalaya India Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Department of Computer Science and Engineering Manipal India

ISBN: (纸本)9798350383287

This paper introduces the concept of a strategic structure for Web Image Tagging that is oriented toward semantics and knowledge centric. It encompasses strategies like LDA (Latent Dirichlet Allocation) based topic modelling and ontology generation through the dataset terms and further help in the metadata generation. The generated metadata is classified using autoencoders classifier to yield the classified instances. In the proposed model, the classifier for logistics is applied in the dataset of web images that integrates features through Lin Similarity from the ontologies generated in the perfect global dataset. The LDA also enhances the topics and contextualizes that for classified instances of the dataset. The normalized compression distance with lance William index helps in semantics similarity computation for semantics measure-based reasoning and learning. Shuffle frog algorithm helps in Optimization helps in refining the initial solutions into a much more optimal solution state to produce in the top-tier images. So, to produce in the top-tier tags for the images and overall precision of dash with a measure of dash and an FDR of dash has been achieved by the proposed framework which is the best-in-class model for web image tagging. © 2024 IEEE.

关键词： Semantics

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Machine Learning-Based Optimization for Identifying Effective Drugs in Breast Cancer on an in Vitro Platform 4

Machine Learning-Based Optimization for Identifying Effectiv...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Saxena, Mahima Rao, P.V.V.S. Eswara Choudhary, Amar Reddy, P. Srinivas Sharma, Vipin Kumar Kumar, Vinish Ajay Kumar Garg Engineering College Department of Computer Science Ghaziabad India Sasi Institute of Technology and Engineering Department of Computer Science Tadepalligudem India Department Electronics and Communication Engineering Bengaluru India Cvr College of Engineering Mechanical Engineering Department Rangareddy India Ims Engineering College Department of Mca India Ghaziabad India

ISBN: (纸本)9798350360165

In this innovative quest for more efficient irrigation, we investigate the combined use of sensor technology and machine intelligence to optimize agricultural water usage. We collect precise data on essential variables, including water flow, temperature, humidity, soil moisture, and water level, every 15 days by promptly deploying sensors in the field to monitor them in real-time. We use the vast amount of data available to us to train machine learning models, including Recurrent Neural Networks (RNN), K-Nearest Neighbours (KNN), Artificial Neural Networks (ANN), and Convolutional Neural Networks (CNN), specifically for streams that are anticipated to have a certain distribution. CNN has exceptional precision with a decoding accuracy of 94.5% in visual signal interpretation, surpassing ANN, KNN, and RNN which achieve lower accuracies of 91.1%, 88.7%, and 83.6% respectively. Our investigation uncovers a fluid interaction between sensor data and model training, leading to each model exhibiting unique characteristics. These findings not only showcase the accuracy with which machine learning can distribute water, but they also signify a noteworthy progress in resource-efficient and sustainable agriculture. In addition to generating mathematical discoveries, our research establishes the foundation for advanced irrigation systems that smoothly integrate mechanical and natural processes in agricultural fields. © 2024 IEEE.

关键词： Soil moisture

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Exploring the Potential of Deep Learning Algorithms in Medical Image Processing: A Comprehensive Analysis 5th

Exploring the Potential of Deep Learning Algorithms in Medic...

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5th International Conference on communication and Intelligent Systems, ICCIS 2023

作者： Pal, Ganesh Prasad Pal, Raju Department of Computer Science and Engineering Jaypee Institute of Information Technology Noida India Department of Computer Science and Engineering School of Information and Communication Technology Gautam Buddha University Greater Noida India

ISBN: (纸本)9789819720781

In this study, we comprehensively examine the potential of deep learning algorithms in the domain of medical image processing. Through a systematic analysis of existing literature, we explore the applications, methodologies, and outcomes of these algorithms across diverse medical specialties. Our analysis underscores the transformative impact of convolutional neural networks and transfer learning techniques in enhancing diagnostic accuracy, image segmentation, and disease detection. We discuss challenges such as data variability, ethics, and interpretability while emphasizing the importance of interdisciplinary collaboration. By synthesizing findings and identifying future directions, we highlight the promising role of deep learning in revolutionizing healthcare diagnostics and treatment planning. After conducting a thorough analysis of various literature and empirical studies, we have evaluated the capabilities and drawbacks of deep learning models in managing different medical imaging modalities such as X-rays, MRIs, and CT scans. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： COVID-19

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Machine Learning in Economic Forecasting: A Comprehensive Review 7

Machine Learning in Economic Forecasting: A Comprehensive Re...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Gupta, Arhaan Kumar, Kirana Asha, V. Kumar, S. Naveen Modern School Barakhamba Road New Delhi India Institute of Aeronautical Engineering Department of Electronics and Communication Engineering Telangana Hyderabad India New Horizon College of Engineering Department of Master of Computer Application Karnataka Bangalore India Mlr Institute of Technology Department of Electronics and Communication Engineering Telangana Hyderabad India

ISBN: (纸本)9798350350067

Economic experts investigate links between economic entities by analyzing large volumes of data. Econometrics is the process of using statistical methods on economic data to find these links through independent observations. Conventional econometric techniques evaluate economic data using only mathematical and statistical concepts. Conversely, applied econometrics concentrates on real-world observations, like economic results, market price swings, and forecasts. In recent years, Machine Learning models have become increasingly popular across numerous fields, including economics. When compared to typical econometric models, these models provide more accurate analysis of economic data and have shown to be more efficient in tasks like classification. This study examines some machine learning techniques are especially utilized with economic data. It also explores metrics for econometric model analysis and supervised machine learning techniques. The paper explores the possible future uses of the machine learning models that Economic experts find most appealing. © 2024 IEEE.

关键词： Adversarial machine learning

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Human Stress Detection in and through Sleep using Machine Learning

Human Stress Detection in and through Sleep using Machine Le...

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2024 International Conference on Recent Advances in Electrical, electronics, Ubiquitous communication, and Computational Intelligence, RAEEUCCI 2024

作者： Harshini, V. Kuresan, Harisudha Kancharlawar, Omkar Madhan, V.M. Balamithra, S. Department of Computer Science Engineering Department of Electronics and Communication Engineering Tamilnadu Kattankulathur India SRM Institute of Science and Technology Department of Electronics and Communication Engineering Tamilnadu Kattankulathur India Sathyabama Dental College and Hospital Bachelor of Dental Surgery Chennai India

ISBN: (纸本)9798350354539

The effects of stress on the body and mind may be devastating, and it is a prevalent health problem. The quality of one's sleep is an important health indicator, and stress is a known disruptor of that quality. As a result, gauging a person's stress levels and health risks based on their sleep patterns may be quite informative. One's capacity for making decisions, paying attention, learning, and solving problems are all significantly impacted by stress. Both the disciplines of psychology and computer science have recently dedicated significant resources to the study of stress detection and modelling. Affective states, or the actual experience of having that emotion, are the measuring stick that psychologists use to assess the magnitude of stress. The majority of the progress in stress classification has been made using user-dependent models that cannot generalize to a new user. This results in substantial time investment from new users in training the algorithm to forecast their emotional states. Common mental health issues in children need to be addressed immediately since they may escalate into more serious issues if left untreated. The analysis and diagnosis of medical data are areas where machine learning techniques now excel. Different machine learning algorithms have had their accuracy across the chosen attribute set compared. Data on sleep-related factors, such as heart rate variability and sleep architecture, will be collected by wearable devices and analyzed using machine learning methods such support vector machine (SVM), decision tree (DT), MLP, and Ada Boost classifier. By analyzing a person's sleep patterns with the help of a machine learning model, researchers have shown they can effectively identify signs of stress in people. © 2024 IEEE.

关键词： Support vector machines

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Intelligent Deep Learning Based Cybersecurity Phishing Email Detection and Classification

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computers, Materials & Continua 2023年第3期74卷 5901-5914页

作者： R.Brindha S.Nandagopal H.Azath V.Sathana Gyanendra Prasad Joshi Sung Won Kim Department of Computing Technologies SRM Institute of Science and TechnologyKattankulathur603203India Department of Computing Science and Engineering Nandha College of TechnologyErode638052India School of Computing Science and Engineering VIT Bhopal UniversityBhopal466114India Department of Computer Science and Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Department of Information and Communication Engineering Yeungnam UniversityGyeongsan-si38541Gyeongbuk-doKorea

Phishing is a type of cybercrime in which cyber-attackers pose themselves as authorized persons or entities and hack the victims’sensitive data.E-mails,instant messages and phone calls are some of the common modes used in *** the security models are continuously upgraded to prevent cyberattacks,hackers find innovative ways to target the *** this background,there is a drastic increase observed in the number of phishing emails sent to potential *** scenario necessitates the importance of designing an effective classification *** numerous conventional models are available in the literature for proficient classification of phishing emails,the Machine Learning(ML)techniques and the Deep Learning(DL)models have been employed in the *** current study presents an Intelligent Cuckoo Search(CS)Optimization Algorithm with a Deep Learning-based Phishing Email Detection and Classification(ICSOA-DLPEC)*** aim of the proposed ICSOA-DLPEC model is to effectually distinguish the emails as either legitimate or phishing *** the initial stage,the pre-processing is performed through three stages such as email cleaning,tokenization and stop-word ***,the N-gram approach is;moreover,the CS algorithm is applied to extract the useful feature ***,the CS algorithm is employed with the Gated Recurrent Unit(GRU)model to detect and classify phishing ***,the CS algorithm is used to fine-tune the parameters involved in the GRU *** performance of the proposed ICSOA-DLPEC model was experimentally validated using a benchmark dataset,and the results were assessed under several *** comparative studies were conducted,and the results confirmed the superior performance of the proposed ICSOA-DLPEC model over other existing *** proposed model achieved a maximum accuracy of 99.72%.

关键词： Phishing email data classification natural language processing deep learning cybersecurity

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Advancing Neurological Health- A Collaborative Ensemble Learning Approach for Enhancing Early Diagnosis of Amyotrophic Lateral Sclerosis (ALS) 10

Advancing Neurological Health- A Collaborative Ensemble Lear...

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10th International Conference on communication and Signal Processing, ICCSP 2024

作者： Srinivasan, Karthik Ravi, R. Durga Pavani, A. Mandadi, Sowmya Srivel, R. Saudi Electronic University Department of Information Technology Riyadh Saudi Arabia J. J College of Engineering and Technology Department of Computer Science and Engineering India Aurora's Technological and Research Institute Department of Computer Science and Engineering India Adhiparasakthi Engineering College Department of Computer Science and Engineering Melmaruvathur India

ISBN: (纸本)9798350353068

In this study, we propose the integration of Support Vector Machines (SVM) into an ensemble learning framework alongside K-Nearest Neighbors (K-NN) and Naive Bayes models to enhance the early diagnosis of Amyotrophic Lateral Sclerosis (ALS). ALS diagnosis is challenging due to its heterogeneous clinical manifestations. Leveraging diverse datasets including genetic data, medical imaging, and clinical records, we train SVM, K-NN, and Naive Bayes models independently. The ensemble approach amalgamates their predictions, harnessing the complementary strengths of each model. Through rigorous performance evaluation encompassing accuracy, precision, recall, and F1-score metrics, we demonstrate the superior diagnostic capabilities of the ensemble. Employing cross-validation ensures the robustness of our model, while fine-tuning hyperparameters optimizes its performance. Integration of SVM into the ensemble framework contributes additional discriminative power, enhancing the accuracy and reliability of ALS diagnosis. Our research underscores the pivotal role of ensemble learning in addressing the complexities of medical diagnosis, offering a notable advancement in ALS diagnostic methodologies. The proposed approach holds promise for improving patient care and setting new standards in clinical practice © 2024 IEEE.

关键词： Support vector machines

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