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2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Kumar, S. Vinod Sunil, G. Hussein, Ramy Riad Manju Vidhya, S. Sundaram, S. Meenakshi Department of Computer Science and Engineering Secunderabad India Sr University School of Computer Science & Artificial Intelligence Warangal India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Excel Engineering College Department of Artificial Intelligence and Data Science Komarapalayam India Nitte Meenakshi Institute of Technology Department of Cse Bengaluru India

ISBN: (纸本)9798350360660

Facial Emotion Recognition (FER) plays a crucial role in analysing and understanding individual's emotions in the form of non-verbal communication. Existing Deep Learning (DL) based FER methods offered diverse contribution in analysing human emotions but several challenges like extracting accurate spatial and temporal features for accurate classification of emotions is a major concern. To address this, a DL based Facial Recognition and Emotion Detection (FRED) system is proposed by using extended Cohn-Kanade (CK+) dataset as the input. The data is pre-processed first where the Histogram Equalization (HE) is applied and then Convolutional Neural Network (CNN) is used to extract the spatial features while reducing the dimensionality. The extracted features are given to Recurrent Neural Network (RNN) to learn long term dependencies from a temporal sequence of data. The final classification is performed where the Attention Mechanism (AM) is integrated in the RNN to determine the higher weights for better outcome. The results of proposed AMConVnet-RNN showed a superior classification accuracy of 99.54%, precision of 98.63%, and f1-score of 97.39% when compared to existing FER methods like Residual Network-50 (ResNet-50) and Channel Based Attention Mechanism (CBAM) based ResNet50. © 2024 IEEE.

关键词： Convolutional neural networks

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IPL data Analysis and Visualization for Team Selection and Profit Strategy 7

IPL Data Analysis and Visualization for Team Selection and P...

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7th International Conference on Computing Methodologies and Communication, ICCMC 2023

作者： Saranya, G. Swaminathan, Aravind Joel Benjamin, J. Surendran, R. Nelson, Leema Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai campus Chennai India Data Science Middlesex University Dubai United Arab Emirates Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computer Science and Engineering Chitkara University Institute of Engineering & Technology Chitkara University Punjab India

ISBN: (纸本)9781665464086

Day by day, the role of data science and machine learning in cricket is increasing due to the large amount of data generated from a single player on a whole line. The field of data science is the intensive study of data to extract insights and knowledge from the data and apply the acquired knowledge and actionable insights. These available data and statistics are used to predict things like the team's first-innings score and the probability of winning the second team, etc. This study works on Indian Premier League (IPL) data Analysis 2022 and data Visualization for the duration (2008-2020) using Python. This application modules are followed by preprocessing, data analysis, and visualization, and finally create a model that predicts the team's overall score and the probability of winning. When building models, python algorithms such as Numpy is used for S cientific Computing, Pandas used to perform data Analysis, and finally Matplotlib and S eaborn to enable data Visualization. Finally, this paper helps the trainers and owners of the team during the auction to select an emerging player to win the matches in the entire season and to win the trophy and to get profit to the owners by the profit strategy done in this analysis report. © 2023 IEEE.

关键词： data visualization

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Smart Attendance Management using a Self-Supervised Learning Approach 5

Smart Attendance Management using a Self-Supervised Learning...

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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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Computation Analysis for Identifying the Protagonist and Antagonist and their Sentiments in Harry Potter Books 4

Computation Analysis for Identifying the Protagonist and Ant...

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4th International Conference on Artificial Intelligence and Speech Technology, AIST 2022

作者： Tayal, D.K. Vij, Sonakshi Arora, Divya Meena, Bhavna Jain, Prakhar Sharma, Kritik Department of Computer Science and Engineering Indira Gandhi Delhi Technical University for Women Delhi India Department of Artificial Intelligence and Data Science Vivekananda Institute of Professional Studies-Technical Campus Delhi India

ISBN: (纸本)9781665499026

The main goal of sentiment analysis is to find the text polarity (positive or negative). Sentiment analysis can be Natural language processing and computational literary studies have long been interested in sentiment analysis, which can be used to infer relationships between fictional characters. This study uses sentence-based semantic analysis to analyze sentiment in the Harry Potter series. The findings showed that among the primary characters with heroic attributes, Harry Potter scored the most positive polarity;Voldemort scored the most negative polarity, and Hermoine scored the neutral polarity. © 2022 IEEE.

关键词： Sentiment analysis

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250m Daytime Real-Time LED-OWC System

250m Daytime Real-Time LED-OWC System

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2023 Asia Communications and Photonics Conference/2023 International Photonics and Optoelectronics Meetings, ACP/POEM 2023

作者： Wang, Shulei Qu, Yuanzhe Chen, Jian Yu, Zongbi Chen, Yanhao Yuan, Yongchun Yuan, Shidong Zhang, Qianwu Zhang, Junjie Shanghai Institute for Advanced Communication and Data Science Shanghai University Key Laboratory of Specialty Optics and Optical Access Networks Shanghai China Engineering No.1 Shanghai Institute of Technical Physics Chinese Academy of Sciences Shanghai University Shanghai China Engineering No.1 Shanghai Institute of Technical Physics Chinese Academy of Sciences Shanghai China Key Laboratory of Specialty Optics and Optical Access Networks Shanghai China

We have demonstrated a simple LED-OWC system based on FPGA. From the indoor and field tests, the realtime extreme communication sensitivity and transmission distance at 1Mbps OOK are of -41.93dBm and 250m respectively... 详细信息

ISBN: (纸本)9798350312614

关键词： LED-OWC Real-time optical communication system

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Secure and Low-Complexity Medical Image Exchange Based on Compressive Sensing and LSB Audio Steganography

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Informatica (Slovenia) 2023年第6期47卷 65-74页

作者： Al-Chaab, Wafaa Abduljabbar, Zaid Ameen Abood, Enas Wahab Nyangaresi, Vincent Omollo Mohammed, Hussein M. Ma, Junchao Department of Mathematics College of Science University of Basrah Basrah61004 Iraq Department of Computer Science College of Education for Pure Sciences University of Basrah Basrah61004 Iraq Technical Computer Engineering Department Al-Kunooze University College Basrah61001 Iraq Huazhong University of Science and Technology Shenzhen Institute Shenzhen430074 China Jaramogi Oginga Odinga University of Science & Technology Bondo40601 Kenya Department of Computer Science Shatt Al-Arab University College Basra61001 Iraq College of Big Data and Internet Shenzhen Technology University Shenzhen518118 China

Patients’ data constitutes the vast majority of information exchanged over the Internet. The magnitude of maintaining its security and privacy directly correlates to the importance and privacy of the patients themselves. The manipulation of this data negatively impacts the patient's life and treatment, thus the researchers were inclined to find solutions for this confidentiality challenge by concealing the data to prevent unauthorized access. The process of data concealment is more complex when dealing with large amounts of data. Often, the proportions of the medical image are sizable, which creates challenges when compressing data while maintaining high accuracy. In this paper, a system for securing and compressing medical images based on the Compressive Sensing (CS) principle is presented. The medical image is divided into 8×4 sub-matrices that are multiplied by a 3×8 sensor matrix consisting of Gaussian random numbers. The proposed solution reduces the image's original size by about 30% and conceals it as a random distribution inside an audio (wav) file for more security, using the LSB technique for low complexity. For reconstruction, the sensed image is multiplied by the pseudo-inverse of Moore-Penrose. The statistical analysis proved the efficacy of the system in compression and recovery with reduced cost and time consumption, combined with reduced distortion of the cover file;it was also judged to be increasingly efficient compared to previous research. © 2023 Slovene Society Informatika. All rights reserved.

关键词： Compressed sensing

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AI-Driven Zero Trust Architecture: Enhancing Cyber-Security Resilience 8

AI-Driven Zero Trust Architecture: Enhancing Cyber-Security ...

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8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Chokkanathan, K. Karpagavalli, S.M. Priyanka, G. Vanitha, K. Anitha, K. Shenbagavalli, P. Madanapalle Institute of Technology & Science Department of Artificial Intelligence India Erode Sengunthar Engineering College Department of M.Tech Computer Science and Engineering Erode India Karpagam Academy of Higher Education Department of Computer Science and Engineering Coimbatore India Csi College of Engineering Department of Information Technology Coimbatore India Department of Artificial Intelligence and Data Science Rathinam Technical Campus Coimbatore India

ISBN: (纸本)9798331505462

In the ever-changing field of cybersecurity, conventional defenses that rely on boundaries are no longer adequate to safeguard against sophisticated and persistent assaults. The Zero Trust Architecture (ZTA) paradigm, powered by AI, provides a strong answer by removing the concept of inherent trust within a network and demanding ongoing verification for each access request. This article examines the combination of artificial intelligence (AI) with Zero Trust principles to improve the resilience of cyber-security. This study demonstrates the enhanced accuracy, sensitivity, and specificity of AI-driven techniques in comparison to conventional security models. The findings highlight the significance of utilizing AI-driven Zero Trust Architecture (ZTA) to protect contemporary digital infrastructures. It provides robust protection against emerging cyber threats as well as addressing challenges related to data privacy and system integration. The paper also explores potential areas for future study and the practical consequences of implementing AI-driven Zero Trust Architecture (ZTA) in different organizational settings. © 2024 IEEE.

关键词： Adversarial machine learning

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Optimizing Healthcare Delivery through CloudBased Clinical Decision Support Systems

Optimizing Healthcare Delivery through CloudBased Clinical D...

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2024 OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Ebenezar, U. Samson Jenila Vennila, G. Balakrishnan, T. Suresh Krishnan, Prabhakar Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Care College of Engineering Department of Computer Science and Engineering Tamil Nadu Trichy India Mohan Babu University School of Computing Department of Ai & Ml Andhra Pradesh Tirupati India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Amrita Vishwa Vidyapeetham Center for Cyber Security Systems and Networks Amritapuri-campus India

ISBN: (纸本)9798350373783

The experiment aims at evaluating how four machine learning algorithms work: Decision Tree, Random Forest, SVM, and Neural Network, in a task, of binary classification. Models were evaluated using comprehensive metrics such as accuracy, precision, recall, F-score and AUC that revealed a good diagnostic performance. Models selected for the experiment and parameters tested on real datasets show steady changes in performance. The Neural Network model remained on top, achieving accuracy, precision, recall, F-score and AUC-ROC of 91.8%, 92.1%, 91.3%, 91.7% and 0.93 respectively. Random Forest displayed a competitive accordance along with the accuracy of 88.6%, precision of 89.2%, recall of 88.7%, and the F-score of 89.0%, and the AUC of 0.91. From the results, Support Vector Machine and Decision Tree models posted lower accuracy, recall, and fscores compared to the Neural Network and Random Forest models in most cases. These reveal the need for the improvement of the suitable machine learning model that comes from specific task needs as well as the consideration of each one of factors including accuracy, interpretability, and computational efficiency. Extensive and development of the model may result in more comprehensive understanding of how the model leads classifying of real-world applications such as medical diagnoses, fraud detection and target marketing. © 2024 IEEE.

关键词： Random forests

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A consistent estimator for confounding strength

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Mathematical Statistics and Learning 2024年第3-4期7卷 189-220页

作者： Rendsburg, Luca Vankadara, Leena Chennuru Ghoshdastidar, Debarghya von Luxburg, Ulrike Department of Computer Science and Tübingen AI Center University of Tübingen Maria-von-Linden-Straße 6 Baden-Württemberg Tübingen 72076 Germany School of Computation Information and Technology and Munich Data Science Institute Technical University of Munich Boltzmannstraße 3 Bavaria Garching 85748 Germany

Regression on observational data can fail to capture a causal relationship in the presence of unobserved confounding. Confounding strength measures this mismatch, but estimating it requires itself additional assumptions. A common assumption is the independence of causal mechanisms, which relies on concentration phenomena in high dimensions. While high dimensions enable the estimation of confounding strength, they also necessitate adapted estimators. In this paper, we derive the asymptotic behavior of the confounding strength estimator by Janzing and Schölkopf (2018) and show that it is generally not consistent. We then use tools from random matrix theory to derive an adapted, consistent estimator. © 2024 European Mathematical Society.

关键词： confounding high-dimensional linear regression observational data random matrix theory

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Agricultural Water Conservation through AI-Enhanced Solar IoT networks 3

Agricultural Water Conservation through AI-Enhanced Solar Io...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Maniraj, S.P. Monisha, J. Amutha, R. Kavitha, R. Shriidhar, P.J. Srinivasan, C. Srm Institute of Science and Technology School of Computing Department of Data Science and Business Systems Kattankulathur Tamilnadu Chennai India Tamil Nadu Chennai India Ultra College of Engineering and Technology Department of Electrical and Electronics Engineering Tamil Nadu Madurai India Chennai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Vinayaka Missions Kirupananda Variyar Engineering College Department of Computer Science and Engineering Tamil Nadu Salem India Saveetha Universityi Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331509675

With the increasing water shortage and climatic uncertainty, creative strategies for effective water management in agriculture are necessary. This research investigates the integration of Artificial Intelligence (AI), particularly Artificial Neural networks (ANN), with solar-powered Internet of Things (IoT) networks to enhance water utilization in agricultural activities. The proposed system facilitates accurate irrigation scheduling using real-time data from soil moisture sensors, meteorological stations, and crop health monitoring devices. The ANN model analyses this data to predict ideal irrigation schedules and volumes, minimizing water waste while improving crop yields. The solar-powered IoT framework provides sustainability and reduces dependence on traditional energy sources. Field trials indicate that the AI-enhanced system may promote water saving by as much as 30% relative to traditional irrigation techniques. Moreover, using AI enables adaptive learning, enabling the system to enhance its irrigation tactics using historical and real-time data. This novel method conserves water resources and fosters sustainable agriculture practices, coinciding with global initiatives to address water shortages and assure food security. The findings underscore the potential of technology-based solutions to revolutionize agricultural water management for a sustainable future. © 2025 IEEE.

关键词： Smart agriculture

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