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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Om, Hari Kumar Sharma, Avinash Sharda University Sharda School of Engineering and Technology Dept. of Computer Science and Engineering Uttar Pradesh Gr Noida India

ISBN: (纸本)9798350356816

The number of operational information acquired, such as product reviews in several dialects, has expanded as the digital era has progressed. Many businesses, such as online service providers, benefit greatly from information analysis. Sentiment analysis (SA) is the technique of deciphering & categorizing the opinions/viewpoints/sentiments following the content (assessment) of user via text analysis tool (SA). Sentiment Analysis is a Natural Language Processing (NLP) program that is exercised in order to determine opinions of online consumers' comments, evaluations, thoughts, viewpoints and so on. Nowadays, social networking websites like Facebook & Twitter are recurrently utilised to submit consumer assessments regarding a variety of topics like politics, news, movies, fashion, food, etc. Viewpoints, Opinions & Reviews are important in determining the degree of gratification of consumers with a given unit. To determine the polarity, which might be neutral, negative, or positive. This study discusses a Sentiment Analysis technique on different social media platform reviews in Hindi. This paper presents a comparative study of sentiment analysis on Hindi and English texts using machine learning techniques. Datasets are accumulated of Hindi & English texts from various online sources, including social media platforms, news websites, and movie review sites. Various machine learning algorithms, including Naive Bayes, Support Vector Machines, Random Forest, and Logistic Regression, to perform sentiment analysis on the datasets. © 2024 IEEE.

关键词： Decision trees

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WorkoutNet: A Deep Learning Model for the Recognition of Workout Actions from Still Images 3

WorkoutNet: A Deep Learning Model for the Recognition of Wor...

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3rd IEEE International Conference on Intelligent Technologies, CONIT 2023

作者： Dey, Arnab Dutta, Arpita Biswas, Samit Indian Institute of Engineering Science and Technology Dept. of Computer Science and Technology Howrah India

ISBN: (纸本)9798350338607

Workouts or Exercises are some repetitive physical activities essential for maintaining good health. Workouts make us relax and happy, give us more energy, lower the risk of chronic disease, and rejuvenate our bodies and minds. Proper technique and posture are essential to get the most out of any workout;the same workout may lead to injury if performed incorrectly. This paper presents a method to identify a person's workout action from realistic still images. In contrast to video-based action recognition, the challenge is more difficult for still photographs because the Spatio-temporal information is not present in images. This study proposes a deep learning-based model named WorkoutNet to recognize workout actions. The method achieved remarkable results on the Workout Action Image dataset, consisting of 2796 images prepared by us and on the publicly available Exercise image dataset. The suggested approach predicts different workout actions and classifies them into ten categories with high accuracy. The proposed WorkoutNet model attains 92.75% validation accuracy in classifying the workout action images, succeeded by the XceptionNet, VGG-19, DenseNet121, InceptionV3, MobileNet and VGG-16. Further, the classification performance has been evaluated using the F1-Measure and Confusion Matrix. The suggested model can be utilized in real-life to identify workout action images and assist humans doing workouts without a trainer or guide in understanding the right way. © 2023 IEEE.

关键词： Deep learning

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Accelerating Cloud Deployment Through Containerization Technologies

Accelerating Cloud Deployment Through Containerization Techn...

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2024 International Conference on Advances in Computing Research on Science engineering and technology, ACROSET 2024

作者： Lakkadwala, Aayushya Lakkadwala, Prashant Acropolis Institute of Technology and Research Dept. of Computer Science and Engineering Indore India Acropolis Institute of Technology and Research Dept. of Information Technology Indore India

ISBN: (纸本)9798350388800

This research investigates the effectiveness of containerization technologies, with a focus on Docker, in accelerating cloud application deployments. The study examines diverse methodologies aimed at optimizing container image size, refining container orchestration mechanisms for expedited deployments, and evaluating the integration of serverless functions within containers to reduce deployment durations. By synthesizing existing scholarly literature with empirical analyses, this paper aims to elucidate the efficacy of containerization in streamlining cloud deployment processes. It offers insights into optimal practices and identifies prospective avenues for future research and development in the field. © 2024 IEEE.

关键词： Cloud platforms

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World Coin Currency Detection: A Comparative Study of Machine Learning and Deep Learning

World Coin Currency Detection: A Comparative Study of Machin...

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2024 International Conference on Smart Devices, ICSD 2024

作者： Dumka, Ankur Pandey, Arnav Mishra, Amit Kumar Bhaskarrao Bahadure, Nilesh Tripathi, Neha Pandey, Neeraj Kumar Women Institute of Technology Dept. of Computer Science & Engineering Dehradun India St. Joseph's Academy Dehradun India Graphic Era Hill University Dept. of Computer Science & Engineering Dehradun India Symbiosis Institute of Technology Nagpur Campus Pune India Dept. of Computer Science & Engineering Dehradun India

ISBN: (纸本)9798350394412

In the economy where trade and finance are closely linked it is essential to accurately identify and authenticate various currencies, especially in industries such, as banking, tourism and retail. Conventional methods, for identifying coins typically require inspections or simple automated processes, which might take up a lot of time and are susceptible, to mistakes. The advancements, in machine learning (ML) and deep learning (DL) technologies, over the years have revolutionized currency detection providing efficient solutions. This article discusses the evaluation of machine learning and deep learning approaches to identify types of coins. It explores how algorithms, like MobileNetV2, Support Vector Machines (SVM), Random Forest, Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) perform in distinguishing and classifying coin denominations across currencies. The results of experiments conducted on data sets show how these methods compare in terms of accuracy, effectiveness, and reliability. Furthermore, we examine the advantages and constraints of each approach while suggesting directions for advancing research, in the realm of identifying cryptocurrency. © 2024 IEEE.

关键词： Convolutional neural networks

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An optimal group-wise non-rigid image registration technique based clinical decision support for dementia prediction

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Multimedia Tools and Applications 2025年 1-26页

作者： Mary, F. Sheeja Asanambigai, V. Fred, A. Lenin Assistant Professor Senior Grade School of Computing Dept. of CSE Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Chennai India Computer Science and Engineering Annamalai University Chidambaram India Dept. of CSE Mar Ephraem College of Engineering and Technology Marthandam India

The fundamental challenge in clinical practice is predicting whether patients with cognitive symptoms or impairment will improve or stay stable. Population ageing is a worldwide phenomena with various impacts. Among dementia, Alzheimer disease (AD) is common in the elderly which is the most common form of dementia and may contribute to 60–70% of cases. An early and precise AD diagnosis enhances patient quality of life. In this study, we propose an optimal group-wise non-rigid image registration technique for dementia (AD) prediction. We introduce an improved cuckoo search optimization (ICSO) algorithm to search optimal group-wise dense correspondence in large image sets in non-rigid image registration which reduce dimensionality of the search space. Then, we illustrate a teacher-student learning based optimization algorithm which cooperates with PLS regression to extract deformation which separates the relationship information suitable for each other from the raw relationship information. After that, we compute the distribution of morphologies of age by fitting smooth percentile curves to these scores using hybrid capsule neural network with least Median Square (hybrid CNN-LMS) curve fitting model. Finally, the simulation results of proposed hybrid CNN-LMS technique are compared to the existing methods to analyze the measures. We observed that the accuracy of our proposed hybrid CNN-LMS technique is 96.23% and 95.82% for RSS and ANDI datasets respectively. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Neurodegenerative diseases

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Cardiovascular Disease Prediction Utilizing Machine Learning and Feature Selection with Clonal Selection Algorithm 5

Cardiovascular Disease Prediction Utilizing Machine Learning...

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5th International Conference on Sustainable Technologies for Industry 5.0, STI 2023

作者： Rahman, Wahidur Aneek, Riasaad Haque Moinuddin, Md. Sakib, Md. Shadman Iqbal, Md. Sadiq Rahman, Mohammad Motiur Uttara University Dept. of Computer Science and Engineering Uttara Dhaka Bangladesh Z.H. Sikder University of Science and Technology Dept. of Computer Science and Engineering Bhedarganj Shariatpur Bangladesh University of Information Technology and Sciences Dept. of Computer Science and Engineering Dhaka Bangladesh Bangladesh University Dept. of Computer Science and Engineering Adabor Bangladesh Mawlana Bhashani Science and Technology University Dept. of Computer Science and Engineering Tangail Bangladesh

ISBN: (纸本)9798350394290

A substantial and rising number of patients suffer from cardiovascular diseases, including heart attacks, heart failure, and other related illnesses. This case surge places increasing pressure on healthcare professionals and administrators while patients grapple with growing medical costs. To address these challenges, an automated system is necessary within the healthcare sector. In this paper, we present a cardiovascular health monitoring system that incorporates Machine Learning techniques. The study employed feature selection techniques on a secondary dataset. The study has used a nature-inspired technique, namely the Clonal Selection Algorithm (CSA) and Maximum Relevance Minimum Redundancy (mRMR) technique, to identify the most prominent feature in the detection of cardio-vascular illness. This approach was combined with a collection of Machine Learning classifiers. A group of experimental data has been listed to assess the suggested model's effectiveness. The research findings indicated a maximum accuracy rate of 100% when employing the proposed algorithms and orientations. The study also discusses the performance analysis for CSA and mRMR using a set of performance evaluation matrices. Based on the obtained results, it can be inferred that the suggested model will likely exhibit a high level of effectiveness in identifying cardiovascular diseases. © 2023 IEEE.

关键词： Feature Selection

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Offline Mathematical Expression Evaluator using Non-Linear SVM

Offline Mathematical Expression Evaluator using Non-Linear S...

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2023 IEEE Global Conference on Information Technologies and Communications, GCITC 2023

作者： Gogula, Susmitha Valli Reddy Pittala, Venkata Dheeraj Chirala, Ashrith Mantena, Krishnam Raju Pillai, Ashwin Raj Gokaraju Rangaraju Institute of Engineering and Technology Dept. of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350308167

The Offline Handwritten Expression Evaluator [OHMEE] is a Machine Learning Model used to recognize and solve the Offline Handwritten Mathematical Expressions [OHME]. The term Offline refers to documents, images, papers and transcripts. We perform Image Pre-processing steps like Image Segmentation, Character Segmentation, Normalization and Binarization to covert the Image data to appropriate training data for the model using OpenCV Library. The character images in the [OHME] are classified and recognized using Non-Linear Support Vector Machines [SVM] and they are converted to appropriate Strings. These Strings are solved using user-defined mathematical methods. These modules are used to evaluate these String expressions and the appropriate results are generated and plotted. © 2023 IEEE.

关键词： Binarization Character Segmentation Image Segmentation Normalization OHME OHMEE OpenCV SVM

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WellBe: An Intelligent Elderly Care and Well-Being Monitoring System Using Deep Learning 4

WellBe: An Intelligent Elderly Care and Well-Being Monitorin...

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4th International Conference on Intelligent Technologies, CONIT 2024

作者： Raotole, Atharv Shirodkar, Sailee Sanjay Shukla, Rahul Sisodia, Jignesh Devadkar, Kailas Patel Institute of Technology Dept. of Information Technology Sardar Mumbai India Patel Institute of Technology Dept. of Computer Science and Engineering Sardar Mumbai India

ISBN: (纸本)9798350349900

Deep Learning refers to a subset of Machine Learning that utilizes deep neural networks to simulate the complex decision-making process of the human brain. In recent years, DL has made remarkable progress in addressing multiple unique challenges faced by most elderly individuals. While there have been significant contributions to independent systems catered for the elderly, there is the existence of an overall integrated AI platform that unifies the use of individual modules for an optimized solution. This paper introduces 'WellBe,' an innovative AI system that encompasses an alarm-based fall detection component, a facial emotion recognition (FER) system and a built-in personalized chatbot all under a user-friendly website. The novelty of the work extends beyond the aforementioned components, owing to the implementation of an optimized FER system able to handle wrinkles and changes in skin texture, thereby tailored exclusively for the elderly demographic. The system leverages a custom feature extraction module, fusing facial and wrinkle features, in addition to extending a convolutional neural network (CNN) architecture for accurate emotion classification, achieving promising performance with precision, recall, and F1-scores of 0.87, 0.91, and 0.89 for the 'Happy' (one of the predicted emotions) class, respectively. The fall detection component, on the other hand, utilizes a pre-trained YOLOv7 Pose model for the real-time identification of falls, sitting at a performance accuracy of 95.3%. Finally, the personalized chatbot is implemented using a rule-based approach enhanced with the Jaccard similarity algorithm, to facilitate prompt assistance and a sense of companionship to the end user. Overall, the integration of these components into a cohesive website offers a comprehensive solution for enhancing the quality of life for elderly individuals living alone, addressing loneliness, safety, and overall well-being. © 2024 IEEE.

关键词： Convolutional neural networks

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Mosquito Species Classification through Wingbeat Analysis: A Hybrid Machine Learning Approach

Mosquito Species Classification through Wingbeat Analysis: A...

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2023 International Conference on Next-Generation Computing, IoT and Machine Learning, NCIM 2023

作者： Rony, Jahid Hasan Karim, Nazmul Rouf, Md Abdur Noor, Sabah Binte Siddiqui, Fazlul Hasan Dhaka University of Engineering & Technology Gazipur Dept. of Computer Science and Engineering Gazipur Bangladesh

ISBN: (纸本)9798350316001

The potential of utilizing the features of wingbeat sound to detect different species of flying mosquitoes is explored in this paper. By analyzing the environmental sound using machine learning, it becomes possible to identify as well as classify different species of flying mosquito before it spreads mosquito-borne diseases. To accomplish the identification and classification, this paper presents a hybrid model that combines Convolutional Neural Networks (CNN) and Support Vector Machines (SVM) to analyze the audio in order to classify different species. The proposed model shows significant accuracy for detecting and classifying different species of flying mosquitoes as well as mitigate the weakness of individual models. If the hybrid model is widely used, could help to reduce the spread of mosquito-borne diseases and related fatalities. © 2023 IEEE.

关键词： Convolutional neural networks

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Ultra-Wideband Pattern Reconfigurable Antenna for Base station Applications 5

Ultra-Wideband Pattern Reconfigurable Antenna for Base stati...

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5th IEEE India Council International Subsections Conference, INDISCON 2024

作者： Bhickta, Shalini Jaglan, Naveen Kumar, Amit Jaypee University of Information Technology Dept. of Electronics and Communication Engineering Waknaghat Solan India Jaypee University of Information Technology Dept. of Computer Science & Engineering and Information Technology Waknaghat Solan India

ISBN: (纸本)9798350376753

The proposed work aims at 5G base station antenna communication applications. The Reconfigurable Antenna is built with an idea of parasitic loaded elements in antenna. The proposed design has a zig-zag S-shape structure with a driven Monopole antenna located along the central axis and a parasitic structure enfolding around the monopole. Reconfiguration is accomplished by adjusting the PIN diodes bias voltages. The 2.44-9.46 GHz frequency range i.e., the Ultra-Wide-Band frequency spectrum is used at which this reconfigurable antenna is designed to operate. This paper has three radiation pattern which are obtained by two PIN diodes. Beam-steering capability in the elevation plane is achieved i.e., θ (0°-180°). The outcome shows the design's potential and its wide range concurrent applications for base station antennas. © 2024 IEEE.

关键词： Monopole antennas

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