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11th International Conference on Soft Computing and Machine Intelligence, ISCMI 2024

作者： Mulerikkal, Jaison Paul Janani, K. Sreelakshmi, P.S. James, Divya Lakshmi, K.S. Sajanraj, T.D. Binu, A. Department of Information Technology India Rajagiri School of Engineering & Technology Kochi India Department of Computer Science and Engineering India Department of Computer Science and Business Systems India Department of Artificial Intelligence and Data Science India Department of Computer Science Loughborough University Leicester United Kingdom

ISBN: (纸本)9798331518127

The goal of this research is to integrate an artificial intelligence framework for predicting Kathakali mudras, a crucial component of the traditional Indian dance style that is renowned for its complex hand and facial motions. Our goal is to protect and improve the accessibility of this traditional art form by utilising deep learning technology. To be more precise, we used the object identification model YOLOv8 together with convolutional neural networks (CNNs) like VGG 19, Inception V3, and ResNet 152 to predict and analyse Kathakali mudras. Furthermore, Few-shot learning (FSL) and support vector machines (SVMs) were used to offer a comparative viewpoint on the efficacy of machine learning method in this situation. The study entailed a thorough analysis of how well-performing different deep learning techniques performed. The predictive accuracy of VGG 19, Inception V3, and ResNet 152 for intricate mudras of Kathakali was subjected to a thorough evaluation process. In addition, an evaluation of YOLOv8's real-time object identification capabilities was conducted in order to investigate its potential in live performance circumstances. The results of the experiment showed that VGG 19 performed better than the other models and had the highest accuracy in mudra prediction. As a result, VGG 19 was chosen as the best algorithm for this particular application. The results of this study demonstrate how artificial intelligence (AI) technologies may support and preserve traditional artistic forms. This AI-integrated framework is a useful tool for scholars, educators, and dancers, promoting a deeper knowledge and enjoyment of this cultural legacy through its accurate analysis and prediction of Kathakali mudras. © 2024 IEEE.

关键词： Convolutional neural networks

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SkinMarkNet: an automated approach for prediction of monkeyPox using image data augmentation with deep ensemble learning models

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Multimedia Tools and Applications 2025年第18期84卷 20177-20193页

作者： Akram, Aqsa Jamjoom, Arwa A. Innab, Nisreen Almujally, Nouf Abdullah Umer, Muhammad Alsubai, Shtwai Fimiani, Gianluca Bahawalpur Pakistan Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia Department of Computer Science and Information Systems College of Applied Sciences AlMaarefa University Riyadh Diriyah13713 Saudi Arabia Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Computer Science The Islamia University of Bahawalpur Bahawalpur Pakistan Department of Computer Science College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University P.O. Box 151 Al-Kharj11942 Saudi Arabia Department of Computer Science University of Salerno Fisciano84084 Italy

Monkeypox, a rare but potentially fatal viral disease, poses a significant public health challenge due to its potential for outbreaks and complications. Detecting monkeypox lesions early and accurately is vital to effectively manage and control the disease. This study introduces a novel method for classifying monkeypox lesions, employing data augmentation methods and a framework based on an ensemble of three transfer learning models called "SkinMarkNet". The dataset used in this research consists of skin lesion images collected from the Kaggle data repository, encompassing diverse demographics and lesion characteristics. This research uses image data augmentation techniques to tackle the scarcity of annotated data. This augmentation enriches the training dataset, thereby improving the model’s ability to perform effectively. Moreover, the novelty of this research work lies in the usage of three popular transfer learning models(Inception, Xception, and ResNet) for feature extraction and ensemble learning. The SkinMarkNet achieves promising results showing an accuracy of 90.615% for monkeypox lesion classification, outperforming traditional machine learning and deep learning methods utilized in recent research works. In addition, thorough comparative analysis is done with machine learning models and contemporary approaches to validate the efficacy of the proposed method. Overall, the findings underscore the potential of leveraging advanced deep learning architectures and data augmentation strategies for improving monkeypox lesion classification, thereby facilitating early diagnosis and intervention in clinical settings and public health surveillance efforts. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Learning systems

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Integrating Multimodal AI Models for Enhanced Oral Cancer Detection and Diagnosis 2

Integrating Multimodal AI Models for Enhanced Oral Cancer De...

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2nd IEEE International Conference on Integrated Intelligence and Communication systems, ICIICS 2024

作者： Shantha Kumari, K. Udhya shree, S. Calarany, C. Department of Data Science and Business Systems SRM Institue of Science and Technology Kattankulathur India Department of Computer Science and Engineering Rajiv Gandhi College of Engineering and Technology Pondicherry India Department of Computer Science and Engineering Tagore Government Arts and Science College Pondicherry India

ISBN: (纸本)9798331504960

Oral cancer is a major global health issue marked by high morbidity and mortality rates, primarily due to late-stage diagnosis. This work proposes the Advanced AI models that use multimodal data for early detection and diagnosis of oral cancer. Currently, traditional machine learning algorithms coupled with deep learning algorithms such as Convolutional neural networks (CNNs), focus on image-based analysis for detecting cancer. These works are delimited with minimal robustness and input data efficacy. Through the proposed multimodal AI model, histopathological images and clinical notes of the oral cancer patients are combinedly analyzed by which diagnostic accuracy is enhanced. This framework integrates CNNs for image analysis and Transformer-based models for text analysis, with a sophisticated fusion layer for data integration. Enhanced preprocessing techniques and performance optimization through Vision Transformers (ViTs) and BERT models significantly improve sensitivity, specificity, and F1-score. The multimodal AI model achieved a sensitivity of 0.90, specificity of 0.85, and an F1-score of 0.89, outperforming previous models like SVMs, RF, and CNNs. This indicates a high accuracy in identifying both positive and negative cases, ensuring reliable diagnoses. The results demonstrate a substantial improvement over traditional methods, that lays foundations for early diagnosis. © 2024 IEEE.

关键词： Lung cancer

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Pose-Critical Keypoint Attention model for Dynamic Yoga Pose Classification 2

Pose-Critical Keypoint Attention model for Dynamic Yoga Pose...

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2nd International Conference on computer Vision and Internet of Things, ICCVIoT 2024

作者： Meghana, J.H. Chethan, H.K. Prakash, S.P. Shiva Department of Studies in Computer Science University of Mysore Karnataka Mysore India Department of Computer Science and Engineering Maharaja Institute of Technology Karnataka Mysore India Department of Information Science and Engineering & Computer Science and Business Systems JSS Science and Technology University Karnataka Mysuru India

ISBN: (纸本)9781837242672

Yoga pose detection and classification have garnered considerable attention in recent years due to their significant applications in various domains, including fitness, health monitoring, and rehabilitation programs. Accurate detection and classification of yoga poses not only enhance the effectiveness of fitness regimes but also facilitate the assessment of practitioners' health and recovery processes. However, existing methods often struggle with the challenges posed by high-dimensional data processing, particularly in real-time applications. This paper introduces the Pose-Critical Keypoint Attention (PCKA) model, a novel approach designed to overcome the limitations of traditional methods in yoga pose analysis. The PCKA model employs an advanced attention mechanism to dynamically prioritize the most relevant keypoints from a comprehensive set of 33 extracted keypoints. This dynamic focus allows for efficient classification without the drawbacks associated with static keypoint reduction, ensuring that the model remains adaptable and robust across various poses. Extensive experiments evaluate the performance of the PCKA model against conventional classification frameworks. The results indicate that this model significantly outperforms traditional approaches, achieving an impressive accuracy of 91.08%, precision of 89.70%, recall of 91.93%, and an F1-score of 90.78%. These metrics demonstrate the model's capability to deliver accurate pose classification while providing realtime feedback for corrective measures, which is crucial for users striving to improve their yoga practices. © The Institution of engineering & Technology 2024.

关键词： Data handling

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CART - Web-Based Institutional Placement Management System Using NLP 10

CART - Web-Based Institutional Placement Management System U...

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10th International Conference on Advanced Computing and Communication systems, ICACCS 2024

作者： Priyanka, G. Amritha, P. Gokul Sundar, U. Sanjai, M.B. Vasanth, R. Sri Eshwar College of Engineering and Technology Department of Computer Science and Business Systems Coimbatore India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384369

The 'CART (Career at Right Time)', a pupil/ company information system, is a web- grounded programmed. The design is grounded on the' Placement Cell' that's presently employed in the University for storing and reacquiring information from scholars and companies who are registered in the placement cell. The Placement Cell maintains a large pupil database in which all pupil information, including particular records and academic performance in terms of marks and grades, is stored, as well as company information, similar as the company's capsule, sphere they work in, job descriptions, reclamation process, and so on. This data is recaptured by the software and shown when demanded by the users. © 2024 IEEE.

关键词： Electric grounding

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A Comparative Study of Convolutional Neural Networks for Oregano Diseases Classification in A Novel Dataset 31

A Comparative Study of Convolutional Neural Networks for Ore...

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31st IEEE International Conference on Electronics, Electrical engineering and Computing, INTERCON 2024

作者： Chaparro-Cruz, Israel N. Santana-Carbajal, Oliver I. Osco-Mamani, Erbert F. Universidad Nacional Jorge Basadre Grohmann Department of Computer Science and Systems Engineering Tacna Peru

ISBN: (纸本)9798350378344

Peru is South America's largest producer and ex-porter of oregano (Origanum vulgare L.), with the Tacna region being the most significant contributor, cultivating 2,499 hectares and producing 11,946 tons, accounting for 65% of the national production. To mitigate the impact of oregano diseases, which lead to substantial crop losses and negatively affect the economy, it is crucial to develop and upgrade technologies for disease management. Accurate classification of oregano diseases can prevent quality deterioration and yield reductions. However, the availability of data and the use of advanced technologies in oregano cultivation are still in their early stages. This study addresses these shortcomings by developing and publishing a novel public dataset for the classification of the most common oregano diseases-Alternaria, Powdery Mildew (Oidium), and Mosaic-using crops from Susapaya - Tarata in Tacna, Peru. To classify oregano diseases, we conducted a comprehensive comparative study, applying Transfer Learning and Fine-Tuning techniques to evaluate the performance of VGG16, MobileNet, MobileNetV2, and NasNetLarge architectures with modifications in the classifier. The experiments revealed that: (i) Fine-Tuning improved validation set accuracy by 4.47% compared to Transfer Learning, (ii) the VGG16 architecture achieved the highest accuracy at 0.993921, (iii) MobileNet and MobileNetV2 models performed similarly, (iv) the NASNetLarge model showed poor performance when only Transfer Learning was applied, and (v) there was a strong significant relationship (R2= 0.84) between the number of parameters and size (in MB) with the performance of the architectures on our dataset. We ensure the reproducibility of the results by making the novel dataset and the best model publicly available on GitHub. © 2024 IEEE.

关键词： Convolutional neural networks

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Empirical techniques for effort estimation in designing effective ML models

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International Journal of computers and Applications 2025年第2期47卷 226-234页

作者： Padmanabha Reddy, Y.C.A. Kosuru, Sai Sathwik Sirisalla, Nageswara Rao Vivekananda, G.N. Perala, Venkata Akanksha Department of Artificial Intelligence and Machine Learning Chaitanya Bharati Institute of Technology Telangana Hyderabad India Department of CSE B V Raju Institute of Technology Telangana Narsapur India Department of Computer Science and Engineering K.S.R.M. College of Engineering AP Kadapa India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

Machine Learning Research often involves the use of diverse libraries, modules, and pseudocodes for data processing, cleaning, filtering, pattern recognition, and computer intelligence. Quantization of Effort Required for the above cumulative processes is rarely discussed in the existing works and The time to reach the desired level of the model's functionality is essential to gauge the environment training for the model training and pre-deployment testing. In this study, we empirically defined the manual-cum-computational effort required for the model development in terms of 2-time factors: time-to-live (time until 1st code modification) and time-for-modification (time between 2 codebase changes) taken together in a mathematical model to compute Effort Factor(quantitative measure of determining effort needed to design ML algorithms). The study is novel in terms of how the effort required to create ML models is calculated with a particular focus on the manual effort direction. The results and the findings obtained can be used for determining the total time needed to synthesize ML models and frameworks in terms of code change cycles and implementation strategies marking a 25% performance increase in the current method above the Standard Pipeline. © 2025 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Pipeline codes

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Comparative Study of Facial Expression Recognition Using Facial Landmark in e-Learning System 11

Comparative Study of Facial Expression Recognition Using Fac...

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11th International Conference on Electrical engineering, computer science and Informatics, EECSI 2024

作者： Bachtiar, Fitra Abdurrachman Nainggolan, Cesilia Natasya Soetedjo, Riza Setiawan Rokhmawati, Retno Indah Muflikhah, Lailil Brawijaya University Department of Informatics Engineering Faculty of Computer Science Malang Indonesia Brawijaya University Faculty of Computer Science Department of Information Systems Malang Indonesia

ISBN: (纸本)9798350355314

The e-Iearning system or distance learning system has become important, especially during the COVID-19 pan-demic. Several tertiary institutions have made the e-Iearning system an alternative teaching and learning activity. Research on the use of e-Iearning needs to be explored to obtain innovative, creative, and fun learning. One aspect that can be examined in e-learning is students' emotional response to the learning activities. The emotional state of students directly or indirectly affects the learning process. The student's emotional responses can be measured by their facial expressions. In this study, an exploration was carried out on the facial expressions shown by students in HSS Learning, an e-Iearning for learning web programming. The student's facial expressions were extracted using 68 pairs of facial landmarks and additional 6 facial feature vectors. The total number of features used is 142. Machine learning methods for classification are applied to determine the performance of several classification models, including the k-Nearest Neighbor (k- NN) algorithm, Logistic Regression, and Support Vector Machine (SVM). Machine learning performance evaluation is done using the Fl-score and confusion matrix. The results show that the accuracy of the k-NN, Logistic Regression, and SVM are 91.4%, 92.2%, and 92.1 %, respectively. However, the Fl-score macro values are relatively low due to data imbalance with the k- NN, Logistic Regression, and SVM models are 0.250, 0.272, and 0.243, respectively. While the study provides insight into the student learning experience in e-Iearning settings, this study needs to be explored more. © 2024 IEEE.

关键词： Contrastive Learning

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Exploring Deep Learning Methods for computer Vision Applications across Multiple Sectors:Challenges and Future Trends

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computer Modeling in engineering & sciences 2024年第4期139卷 103-141页

作者： Narayanan Ganesh Rajendran Shankar Miroslav Mahdal Janakiraman SenthilMurugan Jasgurpreet Singh Chohan Kanak Kalita School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India Department of Computer Science and Engineering Koneru Lakshmaiah Education FoundationVaddeswaram522502India Department of Control Systems and Instrumentation Faculty of Mechanical EngineeringVSB-Technical University of OstravaOstrava70800Czech Republic Department of Computer Science and Engineering Vel Tech High Tech Dr.Rangarajan Dr.Sakunthala Engineering CollegeChennai600062India Department of Mechanical Engineering and University Centre for Research&Development Chandigarh UniversityMohali140413India Department of Mechanical Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyAvadi600062India

computer vision(CV)was developed for computers and other systems to act or make recommendations based on visual inputs,such as digital photos,movies,and other *** learning(DL)methods are more successful than other traditional machine learning(ML)methods *** techniques can produce state-of-the-art results for difficult CV problems like picture categorization,object detection,and face *** this review,a structured discussion on the history,methods,and applications of DL methods to CV problems is *** sector-wise presentation of applications in this papermay be particularly useful for researchers in niche fields who have limited or introductory knowledge of DL methods and *** review will provide readers with context and examples of how these techniques can be applied to specific areas.A curated list of popular datasets and a brief description of them are also included for the benefit of readers.

关键词： Neural network machine vision classification object detection deep learning

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Skill Mastery Solutions: Your Path to Excellence 10

Skill Mastery Solutions: Your Path to Excellence

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10th International Conference on Advanced Computing and Communication systems, ICACCS 2024

作者： Saranya, E. Ajay, K. Hardika, S. Jayasudha, C. Kishore, R.E. Sri Eshwar College of Engineering and Technology Department of Computer Science and Business Systems Coimbatore India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384369

'Skillstorm' is an innovative and competitive website that harnesses the power of cutting-edge technologies to create a safe and engaging online environment. Developed primarily using Python and Flask, Skillstorm integrates machine learning algorithms to combat cyberbullying, ensuring a positive user experience. This project leverages HashMaps for efficient data management and employs a dynamic front-end interface built with HTML, CSS, and JavaScript. The backend of Skillstorm is powered by Python and Flask, providing a robust and scalable foundation for handling user data, interactions, and website functionality. Skillstorm utilizes machine learning techniques to automatically detect and mitigate instances of cyberbullying and harassment. This proactive approach ensures a safe and welcoming environment for all users. HashMaps are employed for efficient data storage and retrieval, optimizing the performance of the website and enhancing user experience. 'Skillstorm' represents a forward-thinking and comprehensive project that combines Python, Flask, machine learning, HashMaps, HTML, CSS, and JavaScript to create a competitive website that prioritizes user safety and positive interactions in an increasingly digital world. Powered by cutting-edge Python frameworks, we have created a dynamic and immersive experience that will ignite your passion, inspire excellence, and propel you to new heights of achievement. At SkillStorm, we believe that true talent knows no borders. © 2024 IEEE.

关键词： Python

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