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4th International Workshop on Information Technologies: Theoretical and Applied Problems, ITTAP 2024

作者： Sorokivskyi, Oleksandr Hotovych, Volodymyr Nazarevych, Oleg Shymchuk, Grygorii Department of Computer Science Faculty of Computer Information Systems and Software Engineering Ternopil Ivan Puluj National Technical University Ternopil Ukraine

In solving the problem of automated analysis of football match video recordings, special video cameras are currently used. This work presents a comparative characterization of known algorithms and methods for video camera calibration, including those utilizing machine learning and neural networks, with the aim of identifying their shortcomings and forming a theoretical foundation for developing modern, more effective methods and algorithms. Specifically, it examines both algorithms that require more input data but operate quickly [1, 2] and more accurate algorithms using machine learning [3, 4, 5, 6, 5]. It is demonstrated that their main drawback is either accuracy or speed. More accurate algorithms using machine learning often do not specify the algorithm's operational speed, which precludes their use in real-time applications. The examined works that emphasize speed frequently lack the accuracy necessary for practical use in real-life scenarios. Copyright © 2024 for this paper by its authors.

关键词： Semantic Segmentation

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Improved Multileader Optimization with Shadow Encryption for Medical Images in IoT Environment

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computers, Materials & Continua 2023年第2期74卷 3133-3149页

作者： Mesfer Al Duhayyim Mohammed Maray Ayman Qahmash Fatma S.Alrayes Nuha Alshuqayran Jaber S.Alzahrani Mohammed Alghamdi Abdullah Mohamed Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam Bin Abdulaziz UniversitySaudi Arabia Department of Information Systems College of Computer ScienceKing Khalid UniversityAbhaSaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh 11671Saudi Arabia Department of Information Systems College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic UniversitySaudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Information and Technology Systems College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt

Nowadays,security plays an important role in Internet of Things(IoT)environment especially in medical services’domains like disease prediction and medical data *** healthcare sector,huge volumes of data are generated on a daily basis,owing to the involvement of advanced health care *** general terms,health care images are highly sensitive to alterations due to which any modifications in its content can result in faulty *** the same time,it is also significant to maintain the delicate contents of health care images during reconstruction ***,an encryption system is required in order to raise the privacy and security of healthcare data by not leaking any sensitive *** current study introduces Improved Multileader Optimization with Shadow Image Encryption for Medical Image Security(IMLOSIE-MIS)technique for IoT *** aim of the proposed IMLOSIE-MIS model is to accomplish security by generating shadows and encrypting them *** do so,the presented IMLOSIE-MIS model initially generates a set of shadows for every input medical ***,shadow image encryption process takes place with the help of Multileader Optimization(MLO)withHomomorphic Encryption(IMLO-HE)technique,where the optimal keys are generated with the help of MLO *** the receiver side,decryption process is initially carried out and shadow image reconstruction process is *** experimentation analysis was carried out on medical images and the results inferred that the proposed IMLOSIE-MIS model is an excellent performer compared to other *** comparison study outcomes demonstrate that IMLOSIE-MIS model is robust and offers high security in IoT-enabled healthcare environment.

关键词： Medical image security image encryption shadow images homomorphic encryption optimal key generation

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Improved-Equalized Cluster Head Election Routing Protocol for Wireless Sensor Networks

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computer systems science & engineering 2023年第1期44卷 845-858页

作者： Muhammad Shahzeb Ali Ali Alqahtani Ansar Munir Shah Adel Rajab Mahmood Ul Hassan Asadullah Shaikh Khairan Rajab Basit Shahzad Department of Computer Science Institute of Southern PunjabMultan5932Pakistan College of Computer Science and Information Systems Najran UniversityNajran61441Saudi Arabia Department of Computer Skills Deanship of Preparatory YearNajran UniversityNajran61441Saudi Arabia Department of Engineering National University of Modern LanguagesIslamabad44000Pakistan

Throughout the use of the small battery-operated sensor nodes encou-rage us to develop an energy-efficient routing protocol for wireless sensor networks(WSNs).The development of an energy-efficient routing protocol is a mainly adopted technique to enhance the lifetime of *** routing protocols are available,but the issue is still *** is one of the most important techniques in the existing routing *** the clustering-based model,the important thing is the selection of the cluster *** this paper,we have proposed a scheme that uses the bubble sort algorithm for cluster head selection by considering the remaining energy and the distance of the nodes in each ***,the bubble sort algorithm chose the two nodes with the maximum remaining energy in the cluster and chose a cluster head with a small *** proposed scheme performs hierarchal routing and direct routing with some energy *** simulation will be performed in MATLAB to justify its performance and results and compared with the ECHERP model to justify its ***,the simulations will be performed in two scenarios,gate-way-based and without gateway to achieve more energy-efficient results.

关键词： Bubble sort algorithm gateway energy thresholds wireless sensor networks

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SS-DBSCAN: Epsilon Estimation with Stratified Sampling for Density-Based Spatial Clustering of Applications with Noise

SS-DBSCAN: Epsilon Estimation with Stratified Sampling for D...

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2023 International Conference on Automation, control and Electronics engineering, CACEE 2023

作者： Monko, Gloriana Joseph Kimura, Masaomi Shibaura Institute of Technology Graduate School Department of Functional Control Systems Tokyo Japan Shibaura Institute of Technology Department of Computer Science and Engineering Tokyo Japan

ISBN: (纸本)9798350309201

Clustering algorithms are crucial in uncovering hidden patterns and structures within datasets. Among the density-based clustering algorithms, DBSCAN (Density-Based Spatial Clustering of Applications with Noise) has gained considerable attention for its effectiveness in various applications. However, determining appropriate parameter values for this algorithm remains a challenging task. This paper presents a novel methodology for eps parameter estimation for an improved DBSCAN, namely SS-DBSCAN. The experimental results across nine datasets demonstrate the efficacy of our proposed method in accurately determining clusters with eps value from SS-DBSCAN algorithm. The clusters identified using estimated eps values by SS-DBSCAN align well with the inherent structure of the datasets, yielding better cluster results than the manually set parameters and other methods used for automatic estimations of the eps for DBSCAN. Our approach adapted well to the peculiarities of each dataset, whether dealing with different scales, dimensions, or densities;it proved the versatility and robustness across various datasets, thereby emphasizing its generalizability and potential for broader applications. © 2023 IEEE.

关键词： Parameter estimation

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A Customizable Virtual Assistant Enhanced with Vision and Speech Capabilities 12th

A Customizable Virtual Assistant Enhanced with Vision and Sp...

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12th International Conference on Recent Trends in Computing, ICRTC 2024

作者： Christy Jeba Malar, A. Deva Priya, M. Naveen Kumaran, R. Sanjay Abhishek, J. Bhuvana, S. Karthick, S. Department of Information Technology Sri Krishna College of Technology Tamilnadu Coimbatore India Department of Computer Science and Engineering Karpagam College of Engineering Tamilnadu Coimbatore India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India Department of Computer Science and Engineering SRM Institute of Science and Technology Uttar Pradesh Delhi–NCR Campus Ghaziabad India

ISBN: (纸本)9789819788354

Recent advancements in Smart Assistants (SAs) as well as home automation have captured the attention of both researchers and consumers. Virtual Assistants (VAs) that are speech-enabled are commonly referred to as smart speakers. They offer a wide range of network-based services. In some cases, they seamlessly link to smart environments, introducing innovative and efficient user interfaces. Nevertheless, these devices present their own set of challenges and limitations. The distinctive features of this technology, particularly its hand-free operation handled by voice and implementation of voice-user interface create a unique landscape. Existing technology adoption models, however, fall short in offering a comprehensive explanation for the adoption of this innovative technology. To gain deeper insights into the motivation behind approving and using in-home voice assistants, this work shifts its focus towards examining voice interactions. Utilization of speech recognition for converting speech into text enables constructing an attractive personalized assistant. This development facilitates seamless tasks such as sending emails without manual typing, conducting Google searches without opening the browser, and accomplishing various daily activities including playing music or launching a preferred IDE, all with a single voice command. To assess its performance, the system is seamlessly integrated with home automation (open-source) environment and operated continuously for some days. During this period, users are encouraged to interact with it, and the results demonstrate increased accuracy, reliability and appeal. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Speech recognition

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Periodic Physical Activity Information Segmentation, Counting and Recognition From Video

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IEEE Access 2023年 11卷 23019-23031页

作者： Cheng, Sheng-Hsien Sarwar, Muhammad Atif Daraghmi, Yousef-Awwad Ik, Tsi-Ui Li, Yih-Lang National Yang Ming Chiao Tung University College of Computer Science Department of Computer Science Hsinchu30010 Taiwan Palestine Technical University-Kadoorie Department of Computer Systems Engineering TulkaremP310 Palestine

The research on complex human body motion including sports and workout activity recognition is a major challenge and long-lasting problem for the computer vision community. Recent development in deep learning algorithms to track people's workout activity characteristics based on video sensors can be used to infer the human body pose for further analysis. Specifically, tracking complex body movements while performing multi-pose physical exercise helps individuals provide fine granularity feedback including activity repetition counting and activity recognition. Therefore, this research proposes a system that provides a repetition counter and activity recognition of physical exercise from video frames (extracted 3D human skeleton using VIBE) based on the deep semantic features and repetitive segmentation algorithm. The proposed system locates both ends of the activity's action and segments the activity into multiple unit actions which improves activity recognition, time intervals, # of sets, and other quantitative values of activity. The proposed system is evaluated on the physical activities dataset named "NOL-18 Exercise"through extensive experiments. The proposed system results show that the accuracy of the repetitive action segmentation is 96.27% with 0.23% time error, and action recognition reaches 99.06%. The system can be employed to fitness or rehabilitation centers and used for treating patients. © 2013 IEEE.

关键词： Sports

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Advancing Emotion Recognition in Visual Data with Artificial Intelligence

Advancing Emotion Recognition in Visual Data with Artificial...

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2024 International Conference on Knowledge engineering and Communication systems, ICKECS 2024

作者： Anusree, K. Bhuvaneshvari, B. Balakrishnan, D. Nandhagopal, S. Chellakannu, Aarthy Logeshwari, S. K S R Institute for Engineering and Technology Department of Information Technology Namakkal India KGiSL Institute of Technology Department of Computer Science and Business Systems Coimbatore India K P R Institute of Engineering and Technology Department of Computer Science Engineering Coimbatore India Dhanalakshmi Srinivasan University Department of Computer Science & Engineering Tiruchirappalli India

ISBN: (纸本)9798350359688

The burgeoning discipline of affective computing, which sits at the nexus of AI and psychology, aims to improve our capacity to comprehend and analyze human emotions as they manifest themselves in visual data. This abstract provides a succinct overview of the problem, highlighting the key elements that characterize the discipline's significance and ramifications. Emotion recognition in visual data requires sophisticated artificial intelligence techniques, including computer vision algorithms and deep learning models. These technologies have made it feasible to accurately deduce emotional states from facial expressions, body language, and even speech sentiment. Facial expression analysis has gained popularity in particular because it may be used to recognize a variety of emotions © 2024 IEEE.

关键词： Convolutional neural networks

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IoT-Supported Indoor Navigation System Using Machine Learning Technique 12th

IoT-Supported Indoor Navigation System Using Machine Learnin...

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12th International Conference on Recent Trends in Computing, ICRTC 2024

作者： Christy Jeba Malar, A. Deva Priya, M. Bhuvana, S. Vidhya, K. Sudharsan, S. Suhita, S. Department of Information Technology Sri Krishna College of Technology Tamilnadu Coimbatore India Department of Computer Science and Engineering Karpagam College of Engineering Tamilnadu Coimbatore India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9789819788354

Indoor Navigation System (INS) supports seamless movement of objects within confined spaces in smart environments. In this paper, a novel INS that relies on ESP32-based Received Signal Strength Indication (RSSI) measurements for precise estimation of distance using K-Nearest Neighbour (K-NN) algorithm is proposed. The system architecture includes three ESP32 nodes functioning as clients, and ESP8266 combined with ESP32 acting as a server. To overcome the limitations of Global Positioning System (GPS) within an indoor environment, Machine Learning (ML)-based prediction models are employed to estimate distance between nodes and predict user locations along with the distance between source and destination. This approach not only addresses the challenges posed by GPS in indoor settings, but also shows the potential of ESP32 devices and predictive algorithms for enhancing navigation accuracy and reliability. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Global positioning system

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A novel U-net model for brain tumor segmentation from MRI images

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Alexandria engineering Journal 2025年 126卷 220-230页

作者： Obayya, Marwa Alshuhail, Asma Mahmood, Khalid Alanazi, Meshari H. Alqahtani, Mohammed Aljehane, Nojood O. Almansour, Hamad Al-Hagery, Mohammed Abdullah Department of Biomedical Engineering College of Engineering Princess Nourah bint Abdulrahman University PO Box 84428 Riyadh11671 Saudi Arabia Department of Information Systems College of Computer Sciences & Information Technology King Faisal university Saudi Arabia Department of Information Systems Applied College at Mahayil King Khalid University Saudi Arabia Department of Computer Science College of Sciences Northern Border University Arar Saudi Arabia Department of Information System and Cyber Security College of Computing and Information Technology University of Bisha Bisha61922 Saudi Arabia Department of Computer Science Faculty of Computers and Information Technology University of Tabuk Tabuk Saudi Arabia Applied Collage Najran University Najran Saudi Arabia Department of Computer Science College of Computer Qassim University Saudi Arabia

Segmentation of brain tumors aids in diagnosing the disease early, planning treatment, and monitoring its progression in medical image analysis. Automation is necessary to eliminate the time and variability associated with traditional segmentation methods. Convolutional neural networks (CNNs) and U-Net architectures have demonstrated their efficiency and effectiveness in segmenting brain tumors from MRI images using deep learning techniques. The paper presents an improved U-Net-based segmentation algorithm that integrates nested skip paths to improve encoder-decoder feature fusion. The performance of segmentation was optimized by utilizing a variety of activation functions and loss functions, including Dice Loss and Intersection over Union (IoU). A high level of accuracy was demonstrated in the proposed model when it was evaluated using the LGG Segmentation Dataset. The proposed approach for segmenting medical images has been shown to be both robust and efficient in a comparative analysis. © 2025 The Authors

关键词： Convolutional neural networks

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Optimizing Lettuce Crop Growth Modeling with XGBoost-SVM and Gaussian Process Regression Fusion 8th

Optimizing Lettuce Crop Growth Modeling with XGBoost-SVM and...

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8th International Conference on Inventive Communication and Computational Technologies, ICICCT 2024

作者： Rukumani Khandhan, C. Gothai, E. Kanagaraju, P. Rajkumar, S. Seenivasan, D. Anusurya, R. Department of Computer Science and Engineering Kongu Engineering College Erode Perundurai638060 India Department of Computer Science and Engineering Sri Shanmugha College of Engineering and Technology Salem India Department of Computer Science and Engineering K.S. Rangasamy College of Technology Namakkal Tiruchengode637 215 India Department of Computer Science and Business Systems M. Kumarasamy College of Engineering Karur639113 India Department of Computer Technology-PG Kongu Engineering College Tamil Nadu Perundurai638060 India

ISBN: (纸本)9789819777099

Agriculture plays a pivotal role in ensuring food security and sustainable development. Accurate prediction of crop growth and yield is essential for optimizing agricultural practices and resource allocation. This study presents an integrated approach employing various machine learning techniques to predict crop growth, exemplified by a dataset of 70 plant samples. The dataset includes environmental factors such as temperature, humidity, soil composition, and pH levels, as well as temporal information like growth days. The dataset goes through a thorough preprocessing step that includes resolving missing values, eliminating duplicates, and, encoding categorical variables. To make sure that every variable contributes equally to the models, feature scaling approaches like Z-score standardization and Min–Max scaling are used. The probabilistic outputs of the SVM and XGBoost models are combined in an ensemble technique to improve prediction accuracy. This fusion strategy utilizes the strengths of each model and reduces individual model biases. In addition to classification models, Gaussian Process Regression (GPR) is utilized to provide probabilistic crop growth predictions. GPR offers a continuous output that can be used for yield estimation and uncertainty quantification. Model performance is evaluated using evaluation measures including R-squared (R2), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). These measures offer information on the precision, accuracy, and capacity of the models to explain crop growth variance. This work suggests using Gaussian Process Regression, a machine learning technique, to improve the outcomes of crop growth monitoring for lettuce in aeroponic vertical farming systems. Compared to previous models, this one generates higher prediction results of 0.99% and has lower error rates of 0.32 (MSE), 0.56 (RMSE), and 0.20 (MAE). It is also very resilient to outliers. © The Author(s), under exclusive license to Springer Nature Singa

关键词： Crop rotation

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