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7th IEEE International Conference on Actual Problems of Unmanned Aerial Vehicles Development, APUAVD 2024

作者： Prystavka, Pylyp Shevchenko, Andrii Rokitianska, Irina National Aviation University Faculty of Computer Science and Technology Department of Applied Mathematics Kyiv03058 Ukraine

ISBN: (纸本)9798331534141

This paper investigates the tracking technology of moving objects from a UAV camera (or streaming video) for systems with limited computational resources, such as modern SBCs. A detector-tracker architecture is proposed, combining the advantages of object detection methods and tracking algorithms. A comparative analysis of the performance and accuracy of various detector and tracker combinations on modern SBCs is conducted. The results demonstrate the high efficiency of the proposed approach for object-tracking tasks under limited computational resources, which has practical significance for UAV-based systems. © 2024 IEEE.

关键词： Object tracking

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A Decision Tree-Based Advisory Recommendation System for Dental Students

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Journal of Mobile Multimedia 2025年第1期21卷 179-196页

作者： Thakaeng, Katayut Temdee, Punnarumol Wicha, Santichai School of Applied Digital Technology Mae Fah Luang University Chiang Rai Thailand Computer and Communication Engineering for Capacity Building (CCC) Research Center School of Applied Digital Technology Mae Fah Luang University Chiang Rai Thailand

This study aims to enhance dental education by developing a model that matches dental students with suitable advisors using Data Mining Classification Techniques. Inadequate guidance from mismatched advisors can hinder students' academic and practical performance, leading to suboptimal educational outcomes. Questionnaires were used to analyze the relationship between students' expectations and advisor behaviors, focusing on three factors: advisor roles, essential qualities, and valuable behaviors. Machine learning models Decision Tree, Neural Networks, and K-Nearest Neighbors (KNN) were employed to categorize data and optimize advisor-student matching. The Decision Tree model demonstrated the highest efficiency, achieving 97.73% accuracy, 100.00% recall, 97.62% precision, and an F1-Score of 98.79%, making it the most effective for predicting advisor characteristics, expectations, and student satisfaction. This research provides a scalable solution for improving advisor-student matching, enhancing decision-making, and ultimately supporting the educational success of dental students. © 2025 River Publishers.

关键词： Advisor behaviour classification data mining decision trees DISC personality graph search machine learning

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A Novel Optimized Deep Convolutional Neural Network for Efficient Seizure Stage Classification

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computers, Materials & Continua 2024年第12期81卷 3903-3926页

作者： Umapathi Krishnamoorthy Shanmugam Jagan Mohammed Zakariah Abdulaziz S.Almazyad K.Gurunathan Department of Biomedical Engineering KIT-Kalaignarkarunanidhi Institute of TechnologyCoimbatore641402India Department of Computer Sciences and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai600062India Department of Computer Sciences and Engineering College of Applied ScienceKing Saud UniversityRiyadh11543Saudi Arabia Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Computer Science and Engineering Mount Zion College of Engineering and TechnologyPudukkottai622507India

Brain signal analysis from electroencephalogram(EEG)recordings is the gold standard for diagnosing various neural disorders especially epileptic *** signals are highly chaotic compared to normal brain signals and thus can be identified from EEG *** the current seizure detection and classification landscape,most models primarily focus on binary classification—distinguishing between seizure and non-seizure *** effective for basic detection,these models fail to address the nuanced stages of seizures and the intervals between *** identification of per-seizure or interictal stages and the timing between seizures is crucial for an effective seizure alert *** granularity is essential for improving patient-specific interventions and developing proactive seizure management *** study addresses this gap by proposing a novel AI-based approach for seizure stage classification using a Deep Convolutional Neural Network(DCNN).The developed model goes beyond traditional binary classification by categorizing EEG recordings into three distinct classes,thus providing a more detailed analysis of seizure *** enhance the model’s performance,we have optimized the DCNN using two advanced techniques:the Stochastic Gradient Algorithm(SGA)and the evolutionary Genetic Algorithm(GA).These optimization strategies are designed to fine-tune the model’s accuracy and ***,k-fold cross-validation ensures the model’s reliability and generalizability across different data *** and validated on the Bonn EEG data sets,the proposed optimized DCNN model achieved a test accuracy of 93.2%,demonstrating its ability to accurately classify EEG *** summary,the key advancement of the present research lies in addressing the limitations of existing models by providing a more detailed seizure classification system,thus potentially enhancing the effectiveness of real-time seizure prediction and management systems in clinic

关键词： Bonn EEG dataset cross-validation genetic algorithm batch normalization seizure classification stochastic gradient

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Energy-Aware Congestion Control Technique Based on Hybrid Weight-Based Optimization Algorithm in Wireless Mobile Communication Networks

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Journal of The Institution of Engineers (India): Series B 2025年第1期106卷 189-206页

作者： Jeen Shene, S. Sam Emmanuel, W.R. Vimal Kumar Stephen, K. Department of Computer Science & Research Centre Nesamony Memorial Christian College Marthandam Affiliated to Manonmaniam Sundaranar University Tamil Nadu Tirunelveli India Department of Information Technology University of Technology and Applied Sciences Ibra Ibra400 Oman

Energy-aware congestion-free data routing is one of the critical challenges in successful data transmission for mobile sensor communication environments. Conventional techniques suffer from limitations such as high packet drop, ineffective Cluster Head (CH) selection, and unnecessary data retransmission, leading to high energy usage in mobile networks. To overcome these challenges, we propose Weight-based Prominent Wolf Preference Grey wolf optimization (2WP-GWO) with congestion control route selection through Separation Enhanced Elephant Herd Optimization algorithm (SEEHO). In this model, we enhance the efficiency of the standard GWO algorithm by prioritizing the location of the fittest wolves at each generation. Furthermore, we fix the priority level based on adopting weight measures to highly rank the fittest wolf (efficient CH), thereby lowering the influence of other mobile nodes over specific time intervals (generations). Additionally, congestion control is integrated into the SEEHO algorithm to find congestion-free paths for data transmission. This approach significantly improves network lifetime, reduces data traffic, and optimizes congestion-free optimal path selection. The proposed method is validated with other techniques using different performance measures, indicating promising results compared to other state-of-the-art approaches. © The Institution of Engineers (India) 2024.

关键词： Energy harvesting

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Water Level Controller with Weather Forecasting Using 8051 Microcontroller 7

Water Level Controller with Weather Forecasting Using 8051 M...

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7th International Conference on Electronics, Communication and Aerospace technology, ICECA 2023

作者： Kaniskaa, M.S. Madanagopal, C. Deepa, M. Hariprasath, K. PSG Institute of Technology and Applied Research Department of Electronics and Communications India Vellore Institute of Technology and Applied Research Department of Computer Science India

ISBN: (纸本)9798350340600

Water management is a salient role of engineers due to its importance in everyday activities. The proposed work aims to eliminate over- and under-irrigation, as well as water waste, by monitoring soil moisture and enabling automatic switching of the pumping system to adjust the moisture level. Along with the soil moisture levels, the weather forecast is also considered to regulate the moisture level. The proposed design implements a water level controller by tracking the soil moisture levels using an 8051 microcontroller and interfacing it with sensors. A threshold value is set, for both lower and higher values of soil moisture and the live value is constantly compared. When the value is below or above the respective lower and higher threshold values, the motor pumping system is automatically turned on or off respectively. The output values are observed using a web application via an Arduino board. The design also uses Node MCU which is integrated with ThingSpeak to aggregate and store data. The web application has an additional feature of weather forecasts, which is live data obtained using an Application Programming Interface (API) key with Weatherstack API. This weather forecast is used to control the motor and automatically change its state based on the soil's present state of moisture level and the weather that day. This feature makes the design more reliable and practical for integrating it with mainstream agriculture and gardening activities. © 2023 IEEE.

关键词： Soil moisture

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A variable structure model of neurons’ application in computer vision

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Multimedia Tools and Applications 2025年 1-11页

作者： Sridi, Alaeddine Attia, Mouna Bouallegue, Kais Electronics and Micro-Electronics Laboratory Faculty of Sciences of Monastir University of Monastir Monastir Tunisia The Higher Institute of Computer Science and Communication Technologies of Hammam Sousse University of Sousse Sousse Tunisia Laboratory of Computer Science Parallelism and Intelligent Communicating Systems Faculty of Sciences of Tunisia University of Tunis ELManar Tunis Tunisia Department of Electrical Engineering Higher Institute of Applied Sciences and Technology of Sousse University of Sousse Sousse Tunisia

The neuron doctrine defines the neuron as the basic unit of the nervous system, which drives the dynamic behavior of our organs. This has led to neurons becoming the focus of modern neuroscience research and to the rise of neurocomputing, in which we try to digitally simulate human behavior. The goal being to better understand the human nervous system and gain more control over neuron-related diseases, helping doctors establish faster and more reliable medical diagnoses. In this context, we aim to simulate eye diseases and vision problems by convolving input images using a new class of neural networks based on a variable structure model of neurons presented in [1]. In this work, we focus on recreating the common convolution filters as a first step. This is the first time in neurocomputing literature that dendritic neurons have been used for dynamic convolution kernels generation and eye diseases simulation. We start with a presentation of our upgrades to the model’s flexibility and accuracy. Then, we propose a novel kernel generator algorithm to convolve the input images. Finally, we show its promising results in creating the outline, pixelization, and night convolution kernels, as well as control some metrics, including brightness and data retention. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Neurons

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Behaviour recognition based on the integration of multigranular motion features in the Internet of Things

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Digital Communications and Networks 2024年第3期10卷 666-675页

作者： Lizong Zhang Yiming Wang Ke Yan Yi Su Nawaf Alharbe Shuxin Feng School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu611731China Beijing Institute of Remote Sensing Equipment Beijing100039China Applied College Taibah UniversityMedina42353Saudi Arabia

With the adoption of cutting-edge communication technologies such as 5G/6G systems and the extensive development of devices,crowdsensing systems in the Internet of Things(IoT)are now conducting complicated video analysis tasks such as behaviour *** applications have dramatically increased the diversity of IoT ***,behaviour recognition in videos usually requires a combinatorial analysis of the spatial information about objects and information about their dynamic actions in the temporal *** recognition may even rely more on the modeling of temporal information containing short-range and long-range motions,in contrast to computer vision tasks involving images that focus on understanding spatial ***,current solutions fail to jointly and comprehensively analyse short-range motions between adjacent frames and long-range temporal aggregations at large scales in *** this paper,we propose a novel behaviour recognition method based on the integration of multigranular(IMG)motion features,which can provide support for deploying video analysis in multimedia IoT crowdsensing *** particular,we achieve reliable motion information modeling by integrating a channel attention-based short-term motion feature enhancement module(CSEM)and a cascaded long-term motion feature integration module(CLIM).We evaluate our model on several action recognition benchmarks,such as HMDB51,Something-Something and *** experimental results demonstrate that our approach outperforms the previous state-of-the-art methods,which confirms its effective-ness and efficiency.

关键词： Behaviour recognition Motion features Attention mechanism Internet of things Crowdsensing

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Deep Learning-Based ECG Classification for Arterial Fibrillation Detection

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computers, Materials & Continua 2024年第6期79卷 4805-4824页

作者： Muhammad Sohail Irshad Tehreem Masood Arfan Jaffar Muhammad Rashid Sheeraz Akram Abeer Aljohani Faculty of Computer Science&Information Technology The Superior UniversityLahore54000Pakistan Intelligent Data Visual Computing Research(IDVCR) Lahore54000Pakistan Department of Computer Science National University of TechnologyIslamabad45000Pakistan Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia Department of Computer Science Applied CollegeTaibah UniversityMedina42353Saudi Arabia

The application of deep learning techniques in the medical field,specifically for Atrial Fibrillation(AFib)detection through Electrocardiogram(ECG)signals,has witnessed significant *** and timely diagnosis increases the patient’s chances of ***,issues like overfitting and inconsistent accuracy across datasets remain *** a quest to address these challenges,a study presents two prominent deep learning architectures,ResNet-50 and DenseNet-121,to evaluate their effectiveness in AFib *** aim was to create a robust detection mechanism that consistently performs *** such as loss,accuracy,precision,sensitivity,and Area Under the Curve(AUC)were utilized for *** findings revealed that ResNet-50 surpassed DenseNet-121 in all evaluated *** demonstrated lower loss rate 0.0315 and 0.0305 superior accuracy of 98.77%and 98.88%,precision of 98.78%and 98.89%and sensitivity of 98.76%and 98.86%for training and validation,hinting at its advanced capability for AFib *** insights offer a substantial contribution to the existing literature on deep learning applications for AFib detection from ECG *** comparative performance data assists future researchers in selecting suitable deep-learning architectures for AFib ***,the outcomes of this study are anticipated to stimulate the development of more advanced and efficient ECG-based AFib detection methodologies,for more accurate and early detection of AFib,thereby fostering improved patient care and outcomes.

关键词： Convolution neural network atrial fibrillation area under curve ECG false positive rate deep learning classification

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computer Python Network Security Monitoring System Based on Intelligent Algorithms

Computer Python Network Security Monitoring System Based on ...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE International Conference on

作者： Ren Na Rui Xiang Department of Mathematics and Computer Engineering Ordos Institute of Applied Technology Ordos China

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

In response to the increasingly complex and diverse network security threats, a network security monitoring system is introduced that combines intelligent algorithms and Python software to achieve efficient and accurate real-time security monitoring and protection. Firstly, the system collects network traffic and log data through libraries such as Scapy and Pyshark, and performs preprocessing. Then, using Python's machine learning library Scikit learn, features are extracted, including traffic patterns, protocol types, source IP (Internet Protocol) addresses, etc., to construct a classification model. Next, based on the characteristics of network attacks, the random forest algorithm in supervised learning is selected to train the annotated attack samples. The false alarm rate of the system is 3.1%, indicating that when detecting the spread of malicious software, the system occasionally misjudges normal file transfers as malicious behavior. The system has significantly improved the efficiency and accuracy of network security monitoring through the application of intelligent algorithms, and has broad application prospects.

关键词： Accuracy Software algorithms Telecommunication traffic Network security Feature extraction Real-time systems Classification algorithms Monitoring Random forests Python

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The Intersection of computer Engineering and Intellectual Property Law 15

The Intersection of Computer Engineering and Intellectual Pr...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Bansal, Shweta Sharma, Sachin Sagar, Sipra Ahmad, Shamim Mishra, Rahul Pandey, Arvind Kumar K. R. Mangalam University School of Engineering & Technology Department of Computer Science & Engineering Haryana Gurugram122103 India Dev Bhoomi Uttarakhand University School of Engineering and Computer Uttarakhand 248007 India Invertis University Department of Applied Sciences Uttar Pradesh Bareilly243123 India Chitkara University Institute of Engineering and Technology Chitkara University Centre for Interdisciplinary Research in Business and Technology Punjab India Arka Jain University Department of Computer Science and Information Technology Jharkahnd Jamshedpur India

ISBN: (纸本)9798350370249

laptop engineering and intellectual belongings regulation are beautiful fields, however their intersection has grow to be increasingly more extensive in current years. With technology advancing speedy, computer engineering has end up important in all industries, even as highbrow assets law has turn out to be critical in protecting the rights of creators and innovators. computer engineering involves the layout, improvement, and implementation of pc hardware and software program application systems. The era maintains to conform, computer engineers are constantly growing new merchandise and generation that push the bounds of innovation. however, with the ones improvements come demanding situations related to highbrow assets rights. As such, laptop engineers must have complete understanding of intellectual belongings regulation to shield their creations and navigate criminal problems, patent infringement, and alternate mystery robbery. however, highbrow assets regulation encompasses a set of criminal policies and rules that guard ideas, creations, and improvements from unauthorized use or duplication. © 2024 IEEE.

关键词： Creations Duplication Industries Protecting Unauthorized

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