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The Impact of TV White Space (TVWS) and 5G Networks on Bimodal Learning for Master's Programs in Engineering science at the Higher Institute of technology, University of Bangui

The Impact of TV White Space (TVWS) and 5G Networks on Bimod...

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2025 IEEE international Conference on computational, Communication and Information technology, ICCCIT 2025

作者： Ndassimba, Edgard Zoh-Ponguele, Nathan Kossingou, Ghislain Mervyl Saint-Juste Faculty of Science University of Bangui Department of Computer Engineering Bangui Central African Republic

ISBN: (数字)9798331512965

ISBN: (纸本)9798331512965

In this study, we presente digital solution for training master's students in engineering sciences, using a bimodal learning approach at the Higher Institute of technology of Bangui's University. This proposal addresses the lack of master's-level programs in DevOps and Network Telecommunications, as well as the insufficient expertise of local educators in Information and Communication technology (ICT). It also aims to strengthen the instruction of science, technology, engineering, and mathematics (STEM). The solution is based on remote learning platforms hosted in the public cloud, engaging students and teachers in a bimodal teaching model that delivers simultaneous instruction to both inperson and remote students via a high-speed TV White Space (TVWS) and 5G network. The expected impact is the training of competent engineers capable of successfully carrying out projects in the domain of information and communication technology, thereby contributing to the development of the Central African Republic. © 2025 IEEE.

关键词： Students

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E2E-LANet: Unleashing the Potential of Linear Attention for High-Resolution Medical Image Segmentation via Pretraining Distillation 24

E2E-LANet: Unleashing the Potential of Linear Attention for ...

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5th international symposium on Artificial Intelligence for Medicine science, ISAIMS 2024

作者： Tang, Hui Yu, Gang Cao, Lu School of Computer Science and Engineering Hunan University of Science and Technology Hunan Xiangtan China

ISBN: (纸本)9798400717826

Medical image segmentation plays an important role in computer-aid diagnosis. In the past years, convolutional neural networks, especially the UNet-based architectures with symmetric U-shape encoder-decoder structure and skip connection, have been widely used in a variety of medical imaging segmentation tasks. However, such methods are difficult to capture global context. In contrast, Vision Transformers (ViTs) utilize the self-attention mechanism to obtain long-range dependencies in images. Despite these advantages, the huge computational costs of the self-attention mechanism reduce the inference speed of models, thereby limiting their real-world applications. Meanwhile, hierarchical pixel-level decoding of U-shape architectures increases the complexity of large-size segmentation decoding. To address these limitations, in this paper we propose a linear attention-based end-to-end model for high-resolution medical image segmentation, named E2E-LANet. It consists of two modules: an image encoder based on constructed Efficient Vision Transformer (EVT) that reduces the high quadratic complexity of self-attention to linear complexity and a Query-Supported Mask Decoder (QSMD) that employs independent mask query for each type of medical images in segmentation mask predictions. It focuses on the detailed segmentation decoding of patch-level features to further improve the efficiency of large-size mask predictions. Especially, we leverage the advantages of pretraining distillation to improve the transferability of our model in various segmentation tasks. Experimental results on the nucleus segmentation task and the polyp segmentation task display the important role of pretraining distillation in improving the performance of our framework. E2E-LANet demonstrates superior performance compared to CNNs, ViTs, and latest Mamba-based U-shape architectures. © 2024 Copyright held by the owner/author(s).

关键词： Convolutional neural networks

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A survey on predictive maintenance for manufacturing using data mining techniques 4th

A survey on predictive maintenance for manufacturing using d...

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4th international Conference on computational Methods in science and technology, ICCMST 2024

作者： Mathew, Albin Kaur, Savleen Department of Computer Science Lovely Professional University Punjab India

ISBN: (纸本)9781032911571

This paper delves into the significance of predictive maintenance, a technique leveraging intelligent sensors for efficient data collection and evaluation to prevent problems and accidents. A data-focused predictive maintenance system has been established for manufacturing production lines, facilitating the identification of potential issues before they occur. Machine learning plays a pivotal role in predicting the precise moment when industrial equipment requires repair, ensuring quick problem-solving and adaptive decisionmaking. The paper’s goal is to conduct a comprehensive literature analysis of Data Mining and Machine Learning methods tailored for predictive maintenance, emphasizing those currently under examination in this domain, and showcasing the practical applications of the most advanced Machine Learning techniques. This review provides a unique and in-depth exploration of the evolving landscape of Data Mining and Machine Learning methods within the context of predictive maintenance in manufacturing. Beyond a mere survey, it showcases the potential of cutting-edge Machine Learning methodologies to revolutionize predictive maintenance practices. Ultimately, this review weaves together the threads of technological innovation and practical utility, providing readers with a comprehensive roadmap to navigate the future of predictive maintenance in manufacturing. © 2025 the Author(s).

关键词： Predictive maintenance

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Energy-Efficient Cloud Computing Through Reinforcement Learning-Based Workload Scheduling

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international Journal of Advanced computer science and Applications 2025年第4期16卷 645-656页

作者： Malipatil, Ashwini R Paramasivam, M.E. Gulyamova, Dilfuza Saravanan, Aanandha Ramesh, Janjhyam Venkata Naga Muniyandy, Elangovan Ghodhbani, Refka Department of Computer Science and Engineering BNM Institute of Technology Bangalore India Department of ECE Sona College of Technology Salem India Computer Engineering Department University of Information Technologies in Tashkent Tashkent Uzbekistan Department of ECE Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of CSE Graphic Era Hill University Dehradun248002 India Department of CSE Graphic Era Deemed to be University Uttarakhand Dehradun248002 India Department of Biosciences-Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Applied Science Research Center Applied Science Private University Amman Jordan Center for Scientific Research and Entrepreneurship Northern Border University Arar73213 Saudi Arabia

The basis for current digital infrastructure is cloud computing, which allows for scalable, on-demand computational resource access. Data center power consumption, however, has skyrocketed because of demand increases, raising operating costs and their footprint. Traditional workload scheduling algorithms often assign performance and cost priority over energy efficiency. This paper proposes a workload scheduling method utilizing deep reinforcement learning (DRL) that adjusts dynamically according to present cloud situations to ensure optimal energy efficiency without compromising performance. The proposed method utilizes Deep Q-Networks (DQN) to perform feature engineering to identify key workload parameters such as execution time, CPU and memory consumption, and subsequently schedules tasks smartly based on these results. Based on evaluation output, the model brings down the latency to 15 ms and throughput up to 500 tasks/sec with 92% efficiency in load balancing, 95% resource usage, and 97% QoS. The proposed approach yields improved performance in terms of key parameters compared to conventional approaches such as Round Robin, FCFS, and heuristic methods. These findings show how reinforcement learning can significantly enhance the scalability, reliability, and sustainability of cloud environments. Future work will focus on enhancing fault tolerance, incorporating federated learning for decentralized optimization, and testing the model on real-world multi-cloud infrastructures. © (2025), (science and Information Organization). All Rights Reserved.

关键词： Deep reinforcement learning

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Comparison of different pre-trained deep learning models for classification of soil moisture content 4th

Comparison of different pre-trained deep learning models for...

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4th international Conference on computational Methods in science and technology, ICCMST 2024

作者： Ansari, Mohammad Shaharyar Gautam, Prateek Raj Centre for Advanced Studies AKTU Lucknow India School of Computer Science UPES Dehradun India

ISBN: (纸本)9781032911571

India is a country that relies heavily on agriculture, and the growth of plants and crops depends heavily on soil moisture. Water resources can be sustained by measuring soil moisture and keeping the soil moisture content balanced. The weight of moisture soil and dry soil differences are calculated as is the usual method for determining the soil moisture content. As time goes on, new methods based on various sensors are introduced. Machine learning and deep learning have become increasingly powerful techniques in recent times, and they can be applied to the classification of various soil moisture levels. In the field of deep learning, convolution neural networks are employed as a tool for image classification. In this study, a dataset has been created by downloading similar images based on a class. Based on CNN, several pre-trained models exist to make the classification possible. This algorithm is used to analyze the images on various factors like texture, color, hue, and shapes. Some pre-trained model is applied to the created dataset and after tuning the hyperparameter of all models, the best accuracy achieved so far is 94.62%. © 2025 the Author(s).

关键词： Soil moisture

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Human activity recognition using residual deep network with extreme learning 4th

Human activity recognition using residual deep network with ...

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4th international Conference on computational Methods in science and technology, ICCMST 2024

作者： Bohra, Garima Jha, C.K. Sharma, Neelam Department of Computer Science Banasthali Vidyapith Rajasthan India

ISBN: (纸本)9781032911571

In recent years, human activity recognition (HAR) has emerged as an important topic of research due to the multiple applications it has found in various fields, including fitness and strength surveillance, residential care, biometric verification, aged care, and many others. Sensors are a great alternative for activity recognition because of their widespread use. The bulk of wearable sensors, including accelerometer, gyroscope, camera, GPS, microphone, compass, and others, are built into the newest smart devices. Such sensors measure different properties of an object, are simple to use, and are inexpensive. Researchers using machine learning (ML) now have new potential to accurately recognize human behaviours thanks to the usage of sensors in the HAR sector. Deep learning (DL) is gaining popularity among HAR researchers because it outperforms standard machine learning (ML) approaches. In this paper, hybrid features of mechanisms and learning are used by employing deep learning and machine learning;deep learning uses RESNET-50 and machine learning uses XG boosting. In experiments, the proposed approach was validated by a 10-cross comparison with the state-of-the-art approach. In experiment also classified human activities and intruders. © 2025 the Author(s).

关键词： Active learning

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A heart diseases prediction model using machine learning and deep learning techniques 4th

A heart diseases prediction model using machine learning and...

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4th international Conference on computational Methods in science and technology, ICCMST 2024

作者： Rajput, Indrajeet Rathore, Deepak Kumar Shrivastava, Deshdeepak Shah, Pooja Desai, Toral M. Computer Engineering Indus University India Department of CSE LNCT Bhopal India Information Technology ITM Gwalior India IT Department VSITR Kadi India Department of CSE Indus University India

ISBN: (纸本)9781032911571

The modern era’s busy schedule often leads to an unhealthy lifestyle, causing anxiety and depression. This work aims to detect heart disease symptoms at an early stage to reduce the risk of heart attacks and improve performance parameters using different machine learning techniques. In this study, conducted experimental work using the Kaggle datasets within the Python framework. Here applied several machine learning techniques, including LR, DT, SVM, NB, and k-NN. All these classification techniques were used as binary classifiers, where the target variable had two values, 0 and 1. The simulation results indicate that the LR (Logistic Regression) demonstrated superior accuracy and performance parameter values, like accuracy and other parameters in machine learning techniques and our proposed deep learning model gives better result than the LR model. © 2025 the Author(s).

关键词： Adversarial machine learning

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A survey of data-driven insights into air quality trends and prediction in India 4th

A survey of data-driven insights into air quality trends and...

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4th international Conference on computational Methods in science and technology, ICCMST 2024

作者： Kabilan, K. Jebathangam, J. Department of Computer Science VISTAS Chennai India

ISBN: (纸本)9781032911571

Air pollution is a major health and environmental issue in increasingly urbanizing countries like India. This study examines ML and DL implementations on Indian Air Quality Index (AQI) datasets from 2015 to 2020. The research seeks to identify patterns, anticipate trends, and guide air pollution mitigation decisions. The study carefully analyses AQI parameters, explores data, and uses powerful ML and DL algorithms to forecast AQI and identify pollution hotspots. Technical features include missing value imputation, feature engineering, model selection criteria, hyper parameter tuning procedures, and model performance assessment measures. Key results show that ML and DL models can accurately forecast air quality, identify pollution sources, and capture temporal and geographical fluctuations. The article suggests integrating more data sources, exploring advanced deep learning methods, and developing dynamic air quality control systems. This study uses data-driven insights and multidisciplinary techniques to improve our knowledge of air quality dynamics in India and create the framework for proactive air pollution mitigation and public health policies. © 2025 the Author(s).

关键词： Data quality

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Smart city using blockchain with artificial intelligence 4th

Smart city using blockchain with artificial intelligence

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4th international Conference on computational Methods in science and technology, ICCMST 2024

作者： Gupta, Ayush Gupta, Ritikesh Shukla, Dhirendra Kumar Dagur, Arvind Department of Computer Science Engineering Galgotias University Uttar Pradesh Greater Noida India

ISBN: (纸本)9781032911571

Modern cities all over the world are undergoing significant transformations in order to install stunning architecture, offer smart services to citizens, create a safe, sustainable, and clean environment, and prepare areas for vulnerable populations. The primary driver behind many corporations’ forays into the realms of community development, health, water extraction, sanitation, products and consumption, preserving dynamism, housing, screening, and accessibility is the introduction of new technology. We are investigating these consequences in light of vision and information technology advancements over the years. To resurrect these sectors and build a new, safe, and sustainable megacity, new technology and skills must be made available. This combination offers a thorough comprehension of the societal issues that big cities face. It focuses on two recent technological investigates the potential of blockchain technology and artificial intelligence (AI) to better society. It also looks at the various applications of blockchain and edge AI. We examine the value of walking sweats in the domains of smart mobility and smart dynamism rather than relying solely on an ultra-modern smart megacity. In order to circumvent the law for commercial traffic, it outlines a clever algorithm that uses detection, counting, and speed recognition. It also discusses the usage of blockchain in secure, private information transport and exchange between buses and cars. business knowledge. This study should be read in conjunction with other artificial intelligence research papers that investigate the applications of edge AI and blockchain. © 2025 the Author(s).

关键词： Sustainable city

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Advancements in breast cancer detection: Harnessing the power of machine learning for diagnosis 4th

Advancements in breast cancer detection: Harnessing the powe...

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4th international Conference on computational Methods in science and technology, ICCMST 2024

作者： Singla, Ishdeep Kang, Sandeep Kashyap, Divyanshu Singh, Paramjot Gupta, Ishaan Chaudhary, Gagan Computer Science Engineering Chandigarh University Mohali Gharuan India

ISBN: (纸本)9781032911571

Breast cancer remains a significant health challenge globally, emphasizing the critical need for improved detection methodologies. The paper’s research explores the integration of machine learning techniques for enhancing breast cancer diagnosis accuracy. Leveraging algorithms including random forest, XGBoost, K-nearest neighbour and logistic regression various predictive models are established and evaluated. The primary focus lies on recall as the pivotal evaluation metric, with accuracy, F1-score and precision also considered. To mitigate dimensional inconsistencies, data standardization is employed, followed by feature selection via statistical tests, resulting in an optimal subset of features for model input. The K-nearest neighbour model employs cross-validation to determine the optimal parameters, addressing the challenge of sample imbalance through stratified sampling for training and testing set extraction. The findings not only validate the efficacy of machine learning in breast cancer detection but also underscore the significance of methodological considerations in optimizing predictive models for diagnosis. This research contributes to the ongoing efforts in advancing initial discovery strategies, ultimately improving patient consequences. © 2025 the Author(s).

关键词： Adversarial machine learning

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