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5th International Conference on Image Processing and Capsule Networks, ICIPCN 2024

作者： Vikram, D. Ajina, H. Gokilapriya, S. Malini, M. Rathinam Technical Campus Department of Computer Science and Engineering Coimbatore India Rathinam Technical Campus Department of Artificial Intelligence and Data Science Coimbatore India Kalingarkarunanidhi Institute of Technology Department of Artificial Intelligence and Machine Learning Coimbatore India Sri G.V.G Visalakshi College for Women Department of Information Technology Udumalpet India

ISBN: (纸本)9798350367171

Organizational efficiency is significantly influenced by automated attendance management systems, yet traditional methods often lack flexibility and reliability. This study proposes a novel approach to transform the Smart Attendance Management System (SAMS) by leveraging recent advances in Self-Supervised learning (SSL). The proposed method enhances accuracy and adaptability without explicit supervision by integrating SSL techniques at multiple stages, including feature extraction, anomaly detection, and data preparation. The proposed system learns meaningful representations from unlabeled attendance data through SSL-based data preprocessing, thereby improving the performance of downstream tasks. Feature extraction by utilizing SSL enables the proposed system to extract robust features enhancing both anomaly detection and attendance tracking. Anomaly detection models trained with SSL frameworks can effectively identify unusual attendance patterns. Additionally, SSL-based continuous learning ensures that the proposed system can adapt to the changing user behaviors and various situations. Supported by SSL, the proposed SAMS offers a more precise, efficient, and flexible solution for managing attendance, positioning it to meet the evolving demands of contemporary businesses across various industries. © 2024 IEEE.

关键词： Self-supervised learning

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Enhanced Radiological Anomaly Detection using Optimized YOLO-NAS Model

Enhanced Radiological Anomaly Detection using Optimized YOLO...

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2024 International Conference on Advances in Modern Age Technologies for Health and Engineering science, AMATHE 2024

作者： Leka Shree, J. Joy, Emmanuel Sri Ram, M.S. Karunya Institute Of Technology And Sciences Division Of Data Science And Cyber Security Coimbatore India Division Of Artificial Intelligence And Machine Learning Karunya Institute Of Technology And Sciences Coimbatore India

ISBN: (纸本)9798350371567

Chest X-rays are commonly used to detect lung diseases. Chest radiographs often have a lot of perceptual errors. The application of image processing in diagnosing lung diseases has improved medical imaging. This has also enabled the creation of databases for classifying chest radiographs. However, current methods are not accurate enough to reliably detect anomalies. Aimed at improving the shortcomings of existing techniques, the proposed approach uses the Automated Neural Architecture Construction (AutoNAC) engine, a proprietary of Neural Architecture Search (NAS). Comparative evaluations against established models, including YOLOv3, YOLOv5, YOLOv7, and YOLOv8, showcase the superiority of YOLO-NAS in mean Average Precision (mAP@0.5), precision, and recall metrics. The originality of this study lies in introducing NAS specifically AutoNAC, to optimize neural network architectures for chest X-ray anomaly detection, marking a significant advancement in diagnostic capabilities in medical imaging. © 2024 IEEE.

关键词： Anomaly detection

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A queuing theory based delay-efficient packet scheduler for machine type communication

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International Journal of Vehicle Information and Communication Systems 2022年第2期7卷 121-132页

作者： Sunita, T.N. Malakreddy, A. Bharathi Department of Computer Science and Engineering Rajiv Gandhi Institute of Technology Karnataka Bengaluru560032 India Department of Artificial Intelligence and Machine Learning Bms Institute of Technology and Management Karnataka Bengaluru560064 India

Lately, there is a drastic change in the telecommunication industry due to emergence of Internet of Things (IoT). machine to machine (M2M) plays a very important role in enabling IoT. As the LTE is mainly suitable for Human-to-Human (H2H) communication, MTC faces some challenges in LTE like radio resource management and uplink-scheduling algorithms are not suitable for MTC. In this paper, we use a queuing theory and propose a packet scheduler, which would model the regular and event-driven traffic and schedule the jobs in queue for request processing. Then using the Markov Chain process, we calculate the queue length and determine the blocking probability. If there is high blocking probability and still there are some requests in queue, then those requests will be processed in virtual server for maximum network usage. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： machine-to-machine communication

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Uncertainty-induced Incomplete Multi-Omics Integration Network for Cancer Diagnosis

Uncertainty-induced Incomplete Multi-Omics Integration Netwo...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Gao, Peipei Du, Ling Qiao, Sibo Yin, Nan Tiangong University School of Computer Science and Technology Tianjin China Tiangong University School of Software Tianjin China Mohamed Bin Zayed University of Artificial Intelligence Department of Machine Learning Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350386226

Cancer diagnosis with incomplete multi-omics data is inevitable since there are widely missing arbitrary omics in real-world applications. Currently, although much progress has been made in handling missing data, existing methods for cancer diagnosis still struggle to obtain credible predictions due to the relatively high uncertainty of missing omics data. In this paper, we propose a trusted and attention-based multi-omics integrated framework for cancer diagnosis using incomplete multi-omics data (TAIMONET). For the raw omics data, the framework first builds an attention-based shared encoder to pick out important features and align different omics data. Next, it utilizes generative adversarial learning to impute the missing omics features based on the extracted available omics features. Meanwhile, different reconstructed complete omics data are weighted and fused according to the attention mechanism. Moreover, it uses true-class-probability to calibrate the classification results and improve the reliability of the results. Extensive experiments on four real-world multimodal medical datasets are conducted. Compared to state-of-the-art methods, the superior performance and trustworthiness of our proposed model are clearly validated. © 2024 IEEE.

关键词： Lung cancer

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machine learning Approach: Consumer Buying Behavior Analysis

Machine Learning Approach: Consumer Buying Behavior Analysis

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2022 IEEE Pune Section International Conference, PuneCon 2022

作者： Sharma, Anjali Pratap, Aradhana Vyas, Kishan Mishra, Sashikala Symbiosis Institute Technology Of Artificial Intelligence and Machine Learning Pune India University Department of Computer Science and Engineering Pune India

ISBN: (纸本)9781665498975

The rise of multiple company competitors during the COVID-19 outbreak resulted in fierce competition among competing firms for new clients and the retention of current ones. As a result of the foregoing, exceptional customer service is required, regardless of the size of the organization. Furthermore, any company's ability to know each of its customers' desires will provide it an advantage when it comes to providing specialized customer care and establishing customized marketing plans for them. The term 'Consumer Buying Behavior Analysis' refers to a comprehensive assessment of the company's ideal clients/customers. In this project, we're utilizing the K-Means Algorithm to divide clients into two groups: 'Highly Active Customers' and 'Least Active Customers.' Then, utilizing the Apriori Algorithm, we use Association Rule Mining to recommend the best goods to clients based on their purchasing history and associations. We take one step further and use Logistic Regression to validate our Clustering operation by doing Binary Classification with our clusters as the label, resulting in accuracy and an F1 score of 91%. © 2022 IEEE.

关键词： K-means clustering

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Unveiling the Vision: A Comprehensive Review of computer Vision in AI and ML

Unveiling the Vision: A Comprehensive Review of Computer Vis...

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2024 International Conference on Advances in Data Engineering and Intelligent Computing Systems, ADICS 2024

作者： Laad, Meena Maurya, Ratan Saiyed, Najeeb Symbiosis Institute Of Technology Dept. Of Applied Science Maharashtra Pune India Symbiosis Institute Of Technology Dept. Of Artificial Intelligence And Machine Learning Maharashtra Pune India

ISBN: (纸本)9798350364828

In the era of rapid technological advancement, computer Vision has emerged as a transformative force, reshaping the landscape of artificial intelligence (AI) and machine learning (ML). This comprehensive review paper aims to delve into the intricate evolution, methodologies, applications, challenges, and future trajectories of computer Vision. Moving beyond a mere exploration of technical intricacies, our objective is to present a holistic narrative that encapsulates the profound impact of computer Vision on AI and ML and its repercussions across society. The journey begins by traversing the philosophical and historical roots of computer Vision, unraveling the threads that connect human visual perception to the development of artificial vision. By exploring the historical evolution from early image processing to the current era of deep learning, we seek to elucidate the intellectual milestones that have shaped the field. Methodologically, this paper navigates through both traditional approaches and contemporary deep learning paradigms. It dissects traditional methods, emphasizing their enduring relevance and influence on modern computer Vision applications. In parallel, exploring deep learning delves into established architectures all the nuanced impact of design choices on interpretability and explain ability. Applications form a cornerstone of our review, with an enriched focus on case studies that spotlight the transformative influence of computer Vision. Beyond the traditional domains of image recognition, we delve into the healthcare renaissance, where computer Vision contributes to diagnostics, drug discovery, and personalized medicine. Furthermore, we explore its role in smart cities, extending beyond surveillance to urban planning, traffic management, and environmental monitoring. © 2024 IEEE.

关键词： computer vision

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Sine Cosine Reptile Search Algorithm with Grid Search Support Vector machine based Ransomware Detection and Classification 2

Sine Cosine Reptile Search Algorithm with Grid Search Suppor...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Adnan, Myasar Mundher Devi, S. Vaag Deepthi, V.S. Raghunathareddy, M.V. Yamsani, Nagendar The Islamic University Najaf Iraq City Engineering College Department of Artificial Intelligence and Machine Learning Karnataka Bangalore India Cyber Security Dayananda Sagar College of Engineering Department of Computer Science and Engineering Karnataka Bangalore India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India SR University School of Computer Science and Artificial Intelligence Warangal India

ISBN: (纸本)9798350317558

The ransomware exploits the system data and takes off the significant information of the user without any intimation. Moreover, the ransomware furtively directs that information to the servers which are organized by the attackers. In recent years, many researchers and scientists discovered anti-malware products to identify known malware. But these methods are not robust to detect complicated and packed malware. To overcome these problems, the Sine Cosine Reptile Search Algorithm with Grid Search Support Vector machine (SCRSA-GSSVM) is proposed for ransomware detection and classification. The Drebin dataset is employed in this paper and min-max standardization is utilized aimed at preprocessing. The SCRSA is utilized for feature selection and GSSVM is utilized for classification. The SVM is tuned by GS which reduces the noise and false positives to enhance the model performance. Performance of SCRSA-GSSVM is assessed with presentation measure of accuracy, precision, recall and f1-score. The SCRSA-GSSVM attains 99.85% accuracy, 99.83% precision, 99.83% recall and 99.78% f1-score which is better when compared to Random Forest (RF) and artificial Neural Network (ANN). © 2024 IEEE.

关键词： +Support Vector machine Grid Search Min-max Normalization Ransomware Reptile Search Algorithm

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Exploratory Analysis of Heart Attack and Breast Cancer Early Stage Prediction 3

Exploratory Analysis of Heart Attack and Breast Cancer Early...

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3rd International Conference on Trends in Electrical, Electronics and computer Engineering, TEECCON 2024

作者： Vandana, C.P. Kashyap, M Shivani Yashas, D. Singh, Neha New Horizon College of Engineering Dept of Information Science & Engg. Bengaluru India New Horizon College of Engineering Dept of Artificial Intelligence and Machine Learning Bengaluru India

ISBN: (纸本)9798331505714

This paper involves the development of a web based platform with an integrated machine learning algorithm to address problems pertaining to heart disease and breast cancer. It enables users to select either the heart attack or breast cancer datasets and use machine learning models such as Logistic Regression, K-Nearest Neighbor (KNN), Support Vector machine (SVM), Random Forest and XG Boost. The website provides basic information about the datasets such as the total number of records in a dataset, number of classes in a dataset, and also allows users to generate graphs for better visualization of the data. The evaluation of the performance of each of the models is based on various metrics such as accuracy, F1 score, precision, recall and mean squared error. This way it is possible to assess the performance of the models in relation to each of the available data sets which makes it easy for users to pick the most effective model. There are also inputs for the users which would be their health indicators like age, sex, level of cholesterol, heart rate among others. Using these values as well as the picked model, the website estimates the possibility of the user developing heart disease or breast cancer. Apart from predicting health risks, there is an emphasis on the evaluation of health risks by different machine learning models and thus assists users in finding the most accurate prediction model regarding their healthcare problems. Overall, the website provides both predictive healthcare insights and a deeper understanding of how various algorithms perform in real-world scenarios. © 2024 IEEE.

关键词： K-Nearest Neighbor (KNN) Logistic Regression Predictive Analytics Random Forest Support Vector machine (SVM) XGBoost

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Comparing machine learning Algorithms for Real-Time Forest Fire Detection using Color, Motion, and Shape Features 5

Comparing Machine Learning Algorithms for Real-Time Forest F...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Dixit, Rinku Sharma Choudhary, Shailee Lohmor Mahaveerakannan, R. Kaveya, E. New Delhi Institute of Management Department of Artificial Intelligence and Machine Learning Delhi110062 India Saveetha School of Engineering Department of Computer Science and Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India

ISBN: (纸本)9798331518097

Forest fires are extremely dangerous to ecosystems, animals, and humans;thus, it is crucial to detect them early on to limit the harm they cause. Presenting a novel framework for the detection of forest fires is the objective of this study. To increase detection speed and accuracy, this framework mixes colour, motion, and form information with machine learning algorithms. To capture the dynamic nature of fire - its ever-changing hues, distorted forms, and unending motion - an iterative method is employed. Colour probabilities are determined during the segmentation stage before going to the verification step. The categorisation stage and subsequent picture processing both make use of colour histograms. These characteristics allow the system to distinguish between flames and other things more effectively. Evaluating the framework's efficacy involved using intersection over union (IoU) ratios and frame-based evaluation. While IoU is used to test the system's ability to localise the fire inside an image, frame-based assessment is used to test the accuracy of fire detection. Tests on the VisiFire dataset proved the framework's dependability with an 89.97% true-positive rate and a 10.03% false-negative rate. And with an average frame rate of 21.70 FPS, the system is fast enough for real-time processing workloads. Considering these results, it seems like the suggested approach could greatly enhance forest fire early warning capabilities. It can also maintain a high degree of accuracy while providing quicker response times. Protecting forest ecosystems, reducing human life danger, and improving disaster management are all possible outcomes of integrating this technology into early warning systems. This framework is a huge step forward in the world of modern fire detection systems. © 2024 IEEE.

关键词： Fire detectors

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Estimating fish weight growth in aquaponic farming through machine learning techniques 3

Estimating fish weight growth in aquaponic farming through m...

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3rd IEEE International Conference on Intelligent Technologies, CONIT 2023

作者： Kumar, Purushottam Tiwari, Pranav Srinivasulu Reddy, U. Machine Learning & Data Analytics Lab CoE in Artificial Intelligence National Institute of Technology Trichy India Indian Institute of Information Technology Tiruchirappalli Computer Science and Engineering Trichy India National Institute of Technology Machine Learning & Data Analytics Lab Department of Computer Applications Trichy India

ISBN: (纸本)9798350338607

Due to the ever-growing population, rapid urbanization, unusual environmental change, and dwindling water supply, the food production from conventional farming techniques won't be able to keep up with increasing food demand for long, leading to severe food scarcity. Aquaponics is emerging as a viable alternative to food cultivation techniques but lacks efficiency, hindering its adoption on a large-scale production basis. The adoption of artificial intelligence (AI) techniques in smart aquaponic farming can further contribute to increasing the yield in aquaponics. In the study, both linear and non-linear regression algorithms such as linear regression, lasso regression, ridge regression, KNN regression, SVR, and decision tree regression were trained on aquaponic fish pond dataset, evaluated on the basis of multiple metrics like MAE, MSE, RMSE, and RMSLE, and a competitive analysis was done to determine the best algorithm for predicting the harvest weight of the fish in aquaponic ponds. The results showed that KNN regression and decision tree regression algorithms have lowest error and highest accuracy of 99.15% and 99.1% respectively, for predicting the harvest weight of the fish. The results obtained could be scaled up to commercial levels and used in regulating nutrient concentration and aquatic environmental conditions © 2023 IEEE.

关键词： Fish

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