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1st International Conference on Emerging Technologies for Dependable Internet of Things, ICETI 2024

作者： Al-Hadi, Marwa Abdulrahman Al-Gaphari, Ghaleb Hamoud Al-Baltah, Ibrahim Ahmed Julian, Francisco Bacallao Al-Hadi, Ahmed Abdulrahman Department of Computer Science Faculty of Computer Science and IT Sana’a University Sana’a Yemen Department of Information Technology Faculty of Computer Science and IT Sana’a University Sana’a Yemen Faculty of Science and Engineering Al-Hikma University Sana’a Yemen Cardiac-Center Military Hospital Yemen Department of Science and Technologies Faculty of Engineering and Technology Mohamed Boudiaf University M'sila Algeria Department of Industrial Electronic and Automatic Engineering Faculty of Higher Polytechnic Technology Burgos University Burgos Spain

ISBN: (纸本)9798331533557

In an era when healthcare was becoming increasingly crucial, many developing nations, including Yemen, struggled to provide basic medical services. Nearly half of Yemen's population lacked access to adequate healthcare, with the situation being even more dire in rural and remote areas. While sectors such as industry, agriculture, and environmental science rapidly embraced technological advancements, healthcare systems in these regions lagged, with limited research addressing affordable and accessible solutions. This gap was further highlighted by the global healthcare crisis and the urgent need for innovative, cost-effective approaches. In response, this study outlined the creation and deployment of a cost-effective health monitoring system utilizing Internet of Things (IoT) technology to address these challenges. The system was designed to monitor vital signs, specifically heart rate (HR), using an Arduino Uno and ECG sensor, at a meager total cost of $27.25. It provided real-time monitoring for situations requiring immediate intervention. Power consumption tests demonstrated that the system operated efficiently, consuming between 1.061 and 1.35 watts, making it practical and affordable. Additionally, the potential integration of deep learning techniques promises to enhance the system's accuracy and efficiency. Although this study focused on IoT-based health monitoring, its potential extended beyond this, offering broader implications for future healthcare technology solutions. © 2024 IEEE.

关键词： Electronic health record

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Machine Learning Supported Network Attack Detection with a Novel Method Based on Random Forest-Based Feature Fusion 17

Machine Learning Supported Network Attack Detection with a N...

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17th International Conference on information Security and Cryptology, ISCTurkiye 2024

作者： Cinar, Cihan Dogru, Ibrahim Alper Atacak, Ismail Information Security Engineering Graduate School of Natural and Applied Sciences Gazi University Ankara Turkey Department of Computer Engineering Faculty of Technology Gazi University Ankara Turkey

ISBN: (纸本)9798331533649

Nowadays, Innovative techniques, including artificial intelligence and machine learning (ML), have been employed in attack detection systems across numerous domains to create effective detection mechanisms that identify attacks more quickly and accurately. In this study, we propose a novel machine learning-based method that utilizes Random Forest (RF)-based feature fusion (RFBFF) to detect anomalies in computer networks. This method integrates two basic procedures: feature fusion and classification. In feature fusion, unlike traditional feature selection, all features are utilized in the creation of the new data set. This allows for the incorporation of the impact of each feature on the outcome. The feature fusion procedure generates a reduced dataset by combining similar features based on RF-derived gain scores, while the classification procedure uses this refined dataset to label the attack status in the network using various ML algorithms. Specifically, Decision Tree (DT), K-Nearest Neighbor (k-NN), Additive Logistic Regression (Logitboost), and Naïve Bayes (NB) have been employed as classification algorithms. The effectiveness of the proposed method in network attack detection has been validated by comparing it with ML methods based on RF-based feature selection (RFBFS). For a robust comparison and see the performance of the model in the real world, the NSL-KDD and UNSW_NB15 data sets, which contain data packet examples of normal or attack traffic that can be encountered in the internet environment, categorized with binary classification, were preferred as the benchmark data set. Experimental results demonstrate that the proposed approach excels in detecting network attacks, achieving an accuracy rate of 0.995 on the NSL-KDD dataset and 0.924 on the UNSW-NB15 dataset. Leveraging the gain scores of all features, even if they are relatively low, has been found to positively impact the model's overall performance. © 2024 IEEE.

关键词： Network intrusion

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Privacy Protection in Learning Management Systems’ Mobile technology-Based Learning Analytics

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2025年第6期19卷 197-208页

作者： Rawat, Manoj Kumar Jha, Suresh Kumar Sree Lakshmi, A. Venkata Ramana, J. Gonge, Sudhanshu S. De, Arunava Department of Computer Science and Engineering Medi-Caps University Madhya Pradesh Indore India Manipal University Rajasthan Jaipur India Geetanjali College of Engineering and Technology Telangana Hyderabad India Department of MBA KL Business School Koneru Lakshmaiah Education Foundation Vaddeswaram Campus Guntur District Andhra Pradesh India Department of Computer Science & Information Technology Symbiosis Institute of Technology Lavale A Constituent of Symbiosis International University Maharashtra Pune India Faculty of Information Technology Gopal Narayan Singh University Bihar Jamuhar India

Mobile technology use in education is changing, much like in the business and health sectors. Research is now conducted on designing user-centric platforms that allow people to engage in teaching and learning activities. Although the learning management systems (LMS) field supports students and academic endeavors, the application’s high requirements prevent it from having the desired effect. More significantly, although mobile applications are conve-nient, there is a chance that user data could be maliciously exploited and eavesdropped. Due to the communication overhead of sending raw data to the central learning processor, wire-less application structures cannot adapt to the original structure of mobile learning, which calls for data and processing heads to have a centralized entity. The goal is to offer fresh viewpoints and approaches that bring educational assessment up to date. This project aims to give educators scientifically based assessment tools and tactics by thoroughly examining the use of mobile technology in academic assessment. This will allow for more accurate teaching management and individualized instruction. © 2025 by the authors of this article. Published under CC-BY.

关键词： Mobile computing

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Lightweight Deep Residual Network for Motorcyclist Behavior Recognition Using Inertial Sensors 8

Lightweight Deep Residual Network for Motorcyclist Behavior ...

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8th International Conference on information technology, InCIT 2024

作者： Hnoohom, Narit Mekruksavanich, Sakorn Jitpattanakul, Anuchit Mahidol University Faculty of Engineering Image Information and Intelligence Laboratory Department of Computer Engineering Nakhon Pathom Thailand University of Phayao Department of Computer Engineering School of Information and Communication Technology Phayao Thailand King Mongkut's University of Technology Faculty of Applied Science Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics North Bangkok Thailand

ISBN: (纸本)9798350366303

Motorcyclist behavior recognition (MBR) is a significant factor in improving road safety and preventing accidents. Inertial sensors, such as accelerometers, gyroscopes, and magnetometers, have shown promising potential for capturing the dynamic behavior of motorcyclists. However, existing approaches often require complex models and significant computational resources, limiting their deployment in real-world scenarios. This study presents a MotoNeXt architecture, which is a light deep residual network for correctly identifying the behavior of motorcyclists using data from inertial sensors. Our approach leverages the power of deep learning while maintaining a compact model size, making it suitable for resource-constrained environments. The proposed network incorporates multi-kernel residual blocks to facilitate the learning of hierarchical features and improve gradient flow during training. We evaluate our method on a publicly available benchmark dataset, MB-IMU, collected from multiple riders under various riding conditions. Experimentation has shown that our lightweight model achieves cutting-edge performance, outperforming a variety of existing techniques. In Scenario I, using accelerometer, gyroscope, and magnetometer data, MotoNeXt achieved an accuracy result of 78.90088% along with an F1-score of 81.56738%. According to Scenario II, which included additional sensor data such as VelInc, OriInc, and Euler angles, MotoNeXt further improved its performance, achieving an accuracy result of 83.29735% along with an F1-score of 84.95352%. The proposed approach offers a practical solution for real-time MBR, enabling the development of intelligent transportation systems and rider assistance technologies. Our experiments highlight the effectiveness of lightweight deep learning models in extracting meaningful patterns from inertial sensor data. This opens up opportunities for further investigation in this field. © 2024 IEEE.

关键词： Accelerometers

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The Critical Role of Industrial Eco-Parks in Preserving the Environment of the Western Balkans

The Critical Role of Industrial Eco-Parks in Preserving the ...

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International Conference of Experimental and Numerical Investigations and New Technologies, CNNTech 2024

作者： Mitrovic, Aleksandra Vuruna, Mladen Mutavči, Vjačeslav Faculty of Information Technology and Engineering University "Union – Nikola Tesla" Belgrade Serbia Computer-Mechanical Engineering Section The Academy of Applied Technical Studies Belgrade Belgrade Serbia

ISBN: (纸本)9783031786341

The Western Balkans, a region characterized by rich biodiversity and natural resources, face substantial environmental challenges due to industrial activities. Industrial eco-parks offer a sustainable solution to these issues, playing a crucial role in preserving the environment while fostering economic development. These eco-parks employ advanced technologies and sustainable practices to significantly reduce air, water and soil pollution. By optimizing resource use through recycling, reuse, and waste reduction, they conserve natural resources and lower operational costs for businesses. Moreover, the integration of green spaces and sustainable landscaping within eco-parks supports local biodiversity and maintains ecological balance. Beyond environmental benefits, industrial eco-parks contribute to economic and social development. They create job opportunities, attract investments, and drive regional economic growth. Additionally, eco-parks enhance the quality of life for surrounding communities by reducing pollution-related health risks and providing green recreational areas. They also serve as hubs for innovation and education, promoting collaboration between businesses, academic institutions, and governments. Strategically, industrial eco-parks support the Western Balkans’ sustainable development goals and align with regional strategies to achieve environmental and economic objectives. For countries aspiring to join the European Union, adopting eco-parks demonstrates compliance with EU environmental standards, facilitating the integration process. Furthermore, the development of eco-parks promotes cross-border cooperation, strengthening regional stability and shared environmental responsibility. It is particularly important to emphasize that industrial eco-parks are essential for the environmental and economic future of the Western Balkans. Their adoption can lead to significant pollution reduction, resource conservation, and biodiversity protection while offering

关键词： Sustainable development goals

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Agent Selection Framework for Federated Learning in Resource-Constrained Wireless Networks

IEEE Transactions on Machine Learning in Communications and ...

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IEEE Transactions on Machine Learning in Communications and Networking 2024年 2卷 1265-1282页

作者： Raftopoulou, Maria Da Silva, Jose Mairton B. Litjens, Remco Vincent Poor, H. Van Mieghem, Piet Uppsala University Department of Information Technology Uppsala751 05 Sweden Delft University of Technology Faculty of Electrical Engineering Mathematics and Computer Science Delft2628 CD Netherlands Department of Networks The Hague2595 DA Netherlands Princeton University Department of Electrical and Computer Engineering PrincetonNJ08544 United States

Federated learning is an effective method to train a machine learning model without requiring to aggregate the potentially sensitive data of agents in a central server. However, the limited communication bandwidth, the hardware of the agents and a potential application-specific latency requirement impact how many and which agents can participate in the learning process at each communication round. In this paper, we propose a selection metric characterizing each agent's importance with respect to both the learning process and the resource efficiency of its wireless communication channel. Leveraging this importance metric, we formulate a general agent selection optimization problem, which can be adapted to different environments with latency or resource-oriented constraints. Considering an example wireless environment with latency constraints, the agent selection problem reduces to the 0/1 Knapsack problem, which we solve with a fully polynomial approximation. We then evaluate the agent selection policy in different scenarios, using extensive simulations for an example task of object classification of European traffic signs. The results indicate that agent selection policies which consider both learning and channel aspects provide benefits in terms of the attainable global model accuracy and/or the time needed to achieve a targeted accuracy level. However, in scenarios where agents have a limited number of data samples or where the latency requirement is very stringent, a pure learning-based agent selection policy is shown to be more beneficial during the early or late stages of the learning process. © 2023 CCBY.

关键词： Federated learning

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A novel myocarditis detection combining deep reinforcement learning and an improved differential evolution algorithm

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CAAI Transactions on Intelligence technology 2024年第6期9卷 1347-1360页

作者： Jing Yang Touseef Sadiq Jiale Xiong Muhammad Awais Uzair Aslam Bhatti Roohallah Alizadehsani Juan Manuel Gorriz Department of Computer System and Technology Faculty of Computer Science and Information TechnologyUniversiti MalayaKuala LumpurMalaysia Centre for Artificial Intelligence Research(CAIR) Department of Information and Communication TechnologyUniversity of AgderGrimstadNorway Department of Creative Technologies Air UniversityIslamabadPakistan School of Information and Communication Engineering Hainan UniversityHaikouHainanChina Institute for Intelligent Systems Research and Innovation(IISRI) Deakin UniversityWaurn PondsVictoriaAustralia Data Science and Computational Intelligence Institute University of GranadaGranadaSpain

Myocarditis is a serious cardiovascular ailment that can lead to severe consequences if not promptly *** is triggered by viral infections and presents symptoms such as chest pain and heart *** detection is crucial for successful treatment,and cardiac magnetic resonance imaging(CMR)is a valuable tool for identifying this ***,the detection of myocarditis using CMR images can be challenging due to low contrast,variable noise,and the presence of multiple high CMR slices per *** overcome these challenges,the approach proposed incorporates advanced techniques such as convolutional neural networks(CNNs),an improved differential evolution(DE)algorithm for pre-training,and a reinforcement learning(RL)-based model for *** this method presented a significant challenge due to the imbalanced classification of the Z-Alizadeh Sani myocarditis dataset from Omid Hospital in *** address this,the training process is framed as a sequential decision-making process,where the agent receives higher rewards/penalties for correctly/incorrectly classifying the minority/majority ***,the authors suggest an enhanced DE algorithm to initiate the backpropagation(BP)process,overcoming the initialisation sensitivity issue of gradient-based methods like back-propagation during the training *** effectiveness of the proposed model in diagnosing myocarditis is demonstrated through experimental results based on standard performance ***,this method shows promise in expediting the triage of CMR images for automatic screening,facilitating early detection and successful treatment of myocarditis.

关键词： classification differential evolution myocarditis reinforcement learning

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Effective Diagnosis of Lung Cancer via Various Data-Mining Techniques

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Intelligent Automation & Soft Computing 2023年第4期36卷 415-428页

作者： Subramanian Kanageswari D.Gladis Irshad Hussain Sultan S.Alshamrani Abdullah Alshehri Bharathiar University Coimbatore641046India Bharathi Women’s College Chennai600108India Faculty of Electrical and Computer Engineering University of Engineering and TechnologyPeshawar25000PeshawarPakistan Department of Information Technology College of Computer and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Information Technology Al Baha UniversityP.O.Box 1988Al Baha65431Saudi Arabia

One of the leading cancers for both genders worldwide is lung *** occurrence of lung cancer has fully augmented since the early 19th *** this manuscript,we have discussed various data mining techniques that have been employed for cancer *** to air pollution has been related to various adverse health *** work is subject to analysis of various air pollutants and associated health hazards and intends to evaluate the impact of air pollution caused by lung *** have introduced data mining in lung cancer to air pollution,and our approach includes preprocessing,data mining,testing and evaluation,and knowledge ***,we will eradicate the noise and irrelevant data,and following that,we will join the multiple informed sources into a common *** that source,we will designate the information relevant to our investigation to be regained from that *** that,we will convert the designated data into a suitable mining *** patterns are abstracted by utilizing a relational suggestion rule mining *** patterns have revealed information,and this information is categorized with the help of an Auto Associative Neural Network classiﬁcation method(AANN).The proposed method is compared with the existing method in various *** conclusion,the projected Auto associative neural network and relational suggestion rule mining methods accomplish a high accuracy status.

关键词： Relational association rule mining auto associative neural network preprocessing data mining biological neural network

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Enhancing the Evaluation Performance of Convolutional Neural Networks-Based Vehicle Classification Systems

Enhancing the Evaluation Performance of Convolutional Neural...

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2023 International Conference on Cognitive Computing and Complex Data, ICCD 2023

作者： Kiyindou, Michel Precieux Sunday, Samuel Enobong Hong, Zhou Huaiyin Institute of Technology Faculty of Electronic Information Engineering Huai'an City China Huaiyin Institute of Technology Foreign Expeert Workshop Huai'an Faculty of Computer Engineering Huai'an City China

ISBN: (纸本)9798350306293

The rapid advancement of artificial intelligence, particularly deep learning, has spurred its integration into diverse domains. Among these, the application of deep learning in vehicle classification stands out as a vital focus within intelligent transportation systems, road traffic planning, safety warning, and driverless driving. In this research, a comprehensive approach of CNN's evolution such as LeNet, AlexNet, and other CNN-based vehicle classification techniques are enhanced for a better performance, while also investigating R-CNN, YOLO, and related methods for vehicle detection. Evaluative datasets for these methods are highlighted. Moreover, the enhancement of the computing power of the Graphics Processing Unit (GPU) and the sharp increase in the amount of data, deep learning based on convolutional neural networks has become a research hotspot. To evaluate the accuracy and efficiency of our vehicle detection methods, RCNN and YOLO series datasets were used to evaluate these methods and an the development of CNN as the main line was introduced and use to analyze the vehicle classification methods based on LeNet, AlexNet and other CNNs. This research is pivotal for vehicle tracking, traffic management, and autonomous driving progress. In this landscape, convolutional neural networks (CNNs), self-encoder neural networks, and deep belief networks take prominence, with CNNs evolving as the most dynamic. Notably, CNN's development has led to refined vehicle classification through models like LeNet, AlexNet, VGG, and GoogLeNet. The paper concludes by outlining prospective research directions in this domain. © 2023 IEEE.

关键词： Convolutional neural networks

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Modeling Directional Overcurrent Relays using Modelica 3

Modeling Directional Overcurrent Relays using Modelica

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3rd IEEE International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and computer engineering, MI-STA 2023

作者： Qasem, Montaser. A. Dhaw Aljadi, Ali Enbiah, Ezzedin Muftah Faculty of Technical Sciences Department of Electrical Engineering Bani Waled Libya Faculty of Technical Sciences Department of Computer Engineering and Information Technology Bani Waled Libya

ISBN: (纸本)9798350319897

Phase direction and over current elements are relied upon to provide fast and Reliable protection throughout the power system. Although directional relays have been successfully applied to many years, but the System disruptions present the most challenges. The directional relay model is implemented in the Modelica language in this study. The modules will be merged into the final relay model after being individually tested, and that model will then be put through several fault simulations. Thereafter, the results obtained from this model will be compared with other obtained from a previously prepared model using MATLAB. Finally, a sample power network was implemented in order to evaluate the relays performance in a system. © 2023 IEEE.

关键词： MATLAB

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