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International Symposium on Applied Machine Intelligence and Informatics (SAMI)

作者： Khalil Ben Khaled Dönìz Borsos Jozsef Katona Kandó Kalmán Faculty of Electrical Engineering Institute of Microelectronics and Technology Obuda University Budapest Hungary Kándó Kálmán Faculty of Electrical Engineering Institute of Electronics and Communication Systems Obuda University Budapest Hungary Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary GAMF Faculty of Engineering and Computer Science John Von Neumann University Kecskemet Hungary Department of Applied Quantitative Methods Faculty of Finance and Accountancy Budapest Business University Budapest Hungary

ISBN: (数字)9798350379365

ISBN: (纸本)9798350379372

This study presents a comprehensive benchmarking analysis of cryptographic protocols for Internet of Things (IoT) malware defense. The framework was specifically tailored to evaluate cryptographic protocols such as AES-128, AES-256, ChaCha20, RSA (1024-bit, 2048-bit, and 4096-bit), SHA256, SHA512, and HMAC-SHA256 were tested in both standalone and emulated environments. The primary objective was to evaluate the performance and resource consumption of these protocols, focusing on their encryption and hashing efficiency. Key innovations include the comparative analysis of resource consumption and performance efficiency across diverse cryptographic operations, under both real-world and emulated conditions. By identifying protocols like ChaCha20 for high efficiency and minimal resource usage, and RSA 4096-bit for enhanced security at higher computational costs, this study provides actionable insights into the trade-offs between security and performance. These findings offer a foundational reference for selecting optimized cryptographic protocols, advancing IoT malware defense strategies through informed decision-making.

关键词： Performance evaluation Technological innovation Benchmark testing Malware Encryption Internet of Things Reliability Informatics Machine intelligence Cryptographic protocols

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Multimedia Services Problems in Hybrid Network 23

Multimedia Services Problems in Hybrid Network

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23rd Slovak-Czech-Polish Optical Conference on Wave and Quantum Aspects of Contemporary Optics

作者： Štípal, Jiří Trubák, Kamil Látal, Jan Šiška, Petr Vojtěch, Josef Hristov, Hristo Department of Telecommunications Faculty of Electrical Engineering and Computer Science VSB-Technical University of Ostrava 17. listopadu 2172/15 Ostrava-Poruba708 00 Czech Republic CESNET a. l. e. Zikova 1903/4 Prague160 00 6 Czech Republic Department of Software Technologies Plovdiv University "Paisii Hilendarski" Plovdiv4000 Bulgaria

ISBN: (数字)9781510688094

ISBN: (纸本)9781510688087

This work is dealing with actual topic in the field of internet service providers that are solving the question of metallic and optical networks convergence. The aim was to construct an experimental network topology in the form of WAN or LAN topology consisting of GPON network with VDSL2 technology, which led to the creation of a hybrid access network, on which the parameters of multimedia services were then measured. In particular, focusing on the distribution of video streams through SDTV & HDTV (MPEG-2) + HDTV (MPEG-4). As part of the network integrity measurements, tests are performed according to the recommendations of RFC 6349 and ITU-T Y.1564 standard. © 2025 SPIE.

关键词： Multimedia services

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Dual-Sensor Fusion for Real-Time Monitoring of Crystal Methamphetamine Withdrawal Symptoms Using Machine Learning and Gramian Angular Fields

Dual-Sensor Fusion for Real-Time Monitoring of Crystal Metha...

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International Conference on New Trends in Computing Sciences (ICTCS)

作者： Omar Amr Ali Ahmed Ayman Atia Software Engineering Department October University for Modern Sciences and Arts (MSA) Cairo Egypt HCI-LAB Faculty of Computers and Artificial Intelligence Helwan University Faculty of Computer Science October University for Modern Sciences and Arts (MSA) Giza Egypt

ISBN: (数字)9798331523411

ISBN: (纸本)9798331523428

Managing withdrawal from crystal metham-phetamine is challenging due to limitations in existing wearable technology, such as single sensor setups and accuracy issues. This research proposes a dual-sensor system that combines ankle and wrist sensors, enhanced by a Kalman filter and sensor fusion techniques to improve data precision and responsiveness. Data quality was enhanced using techniques like Synthetic Minority Over-sampling Technique (SMOTE) and adding Gaussian noise, and developed advanced pattern recognition abilities using Gramian Angular Field (GAF) transformations. The use of these transformations through Convolutional Neural Networks (CNN) and ConvLSTM significantly enhances the ability to detect withdrawal symptoms. The sophisticated identification methods integrated into a smartphone app built with Flutter and hosted on a FastAPI backend enable greater support for individuals recovering from drug use disorders.

关键词： Deep learning Wrist Ankle Crystals Sensor fusion Real-time systems Convolutional neural networks Kalman filters Biomedical monitoring Monitoring

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Gender Classification from Pashto Handwritten Text Images

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IEEE Access 2025年 13卷 81736-81754页

作者： Sultan, Khan Ahmad, Riaz Muhammad, Siraj Almutairi, Sulaiman Abohashrh, Mohammed Ullah, Khalil Namoun, Abdallah Hussain, Ibrar Shaheed Benazir Bhutto University Department of Computer Science Dir Upper Khyber Pakhtunkhwa Sheringal75660 Pakistan Qassim University College of Applied Medical Sciences Department of Health Informatics Qassim52571 Saudi Arabia King Khalid University College of Applied Medical Sciences Department of Basic Medical Sciences Abha61421 Saudi Arabia University of Malakand Department of Software Engineering Dir Lower Khyber Pakhtunkhwa Malakand18800 Pakistan Islamic University of Madinah AI Center Faculty of Computer and Information Systems Madinah42351 Saudi Arabia Shaheed Benazir Bhutto University Sheringal Quality Enhancement Cell Khyber Pakhtunkhwa Dir Upper75660 Pakistan

computer vision (CV) is a subfield of computer science that enables machines to perceive, interpret, and understand visual data. It combines image processing, analysis, and machine learning to extract meaningful insights from images and videos. One key application of CV is the classification of gender from handwritten text images-a complex task, especially in languages such as Pashto due to the intricacy of the script and the limited resources. This research presents a novel approach to gender classification using Pashto Handwritten Text Images (PHTI). The study evaluates and compares the performance of traditional machine learning and modern deep learning models: Support Vector Machine (SVM) and You Only Look Once version 8 (YOLO8), a CNN-based object detection framework. The PHTI dataset, introduced in this study, consists of 36,086 text line images written by 200 male and 200 female native Pashto writers. The goal is to assess how effectively SVM and YOLO8 can classify gender based on visual handwriting patterns in the Pashto language. The experimental results reveal that YOLO8 outperforms SVM in classification accuracy. Although SVM, a widely used machine learning model, achieved an accuracy of 61.29%, YOLO8 reached a significantly higher accuracy of 77% in the same data set. This performance gap highlights the advantages of using advanced deep learning models for complex pattern recognition tasks in CV. This research provides a pioneering contribution by focusing on Pashto, a low-resource language, in the context of gender classification using handwritten text images. It opens new pathways for applying CV in regional languages and supports the development of intelligent systems capable of handling various scripts. © 2013 IEEE.

关键词： Machine vision

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Optimal meso-granularity selection for classification based on Bayesian optimization

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Knowledge-Based Systems 2025年 318卷

作者： Chen, Qiangqiang Yan, Mengyu Li, Jinhai Wang, Xizhao Faculty of Science Kunming University of Science and Technology Yunnan650500 China Data Science Research Center Kunming University of Science and Technology Yunnan650500 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China

Multi-granularity formal concept analysis is a research field that integrates granular computing and formal concept analysis. It provides valuable opportunities to analyze problems at multiple levels of granularity, which has been becoming a key tool for data mining and knowledge discovery. In multi-granularity formal concept analysis, it is an important challenge to select the optimal granularity. Existing methods for optimal granularity selection typically focus on a single granularity of attributes, without considering meso-granularity layers or the combination of different attribute granularities. Furthermore, these methods fail to reduce redundant attributes effectively. This paper aims to solve this issue and further improve the performance of classification. Specifically, we first introduce the meso-granularity class-attribute blocks to address these limitations. Then, we introduce information gain and design an optimal meso-granularity selection algorithm by combining meso-granularity class-attribute blocks with decision information. Furthermore, we propose the concept of core attributes and define their internal and external significance, which is beneficial for attribute reduction. Finally, the optimal meso-granularity combination is selected by using Bayesian optimization, which improves the efficiency of the meso-granularity combination for classification. The effectiveness of the proposed model is validated through experiments on real-world datasets. The experimental results demonstrate that the proposed model outperforms existing methods, achieving the best classification performance among the compared methods. All codes have been released at https://***/yqlinux/OMSFC. © 2025 Elsevier B.V.

关键词： Attribute reduction Bayesian optimization Classification Multi-granularity of data Optimal meso-granularity selection

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Leveraging Edge Intelligence for Solar Energy Management in Smart Grids

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IEEE Access 2025年 13卷 88093-88104页

作者： Hoang, Trong-Minh Pham, Tuan-Anh Nguyen, Van-Nhan Doan, Duc-Thang Dao, Nhu-Ngoc Posts and Telecommunications Institute of Technology Telecommunication Faculty Hanoi 100000 Viet Nam FPT Software AI Center Department of EdgeAI Hanoi 10000 Viet Nam Dai Nam University Information Technology Faculty Hanoi 100000 Viet Nam Sejong University Department of Computer Science and Engineering Seoul 05006 South Korea

The rapid advancement of renewable energy, particularly solar power, has been significantly supported by the Internet of Things (IoT) infrastructure, enabling optimized energy management in smart grid systems. However, accurate long-term solar energy forecasting remains a critical challenge, especially exploiting specific characteristics of data locality at the edge. This paper introduces an edge intelligence-driven hybrid deep learning model that integrates Temporal Convolutional Networks (TCN) and Gated Recurrent Units (GRU) for precise solar energy prediction. The proposed model leveraged TCN’s capability to capture long-range temporal dependencies and GRU’s efficiency in sequential data processing. To optimize performance for edge computing environments, we employed Neural Architecture Search, model pruning, and quantization techniques, reducing both model size and complexity. The model has been validated using two benchmark datasets (Alibaba Competition and GEFCom), achieving an impressive mean squared error of 0.0051 on long-term forecasts. The results demonstrate that our approach ensures high forecasting accuracy while maintaining computational efficiency, making it well-suited for real-time, IoT-enabled solar energy applications in smart grids. © 2013 IEEE.

关键词： edge intelligence hybrid deep learning Internet of Things solar energy forecasting

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Modelling distillate output of a solar distiller with eco-friendly wood-based steam generation basin using machine learning model and starling murmuration optimizer

International Journal of Thermofluids

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International Journal of Thermofluids 2025年 27卷

作者： Al-Betar, Mohammed Azmi Muskens, Otto L. Hammoodi, Karrar A. Abd Elaziz, Mohamed Shambour, Qusai Yousef Fraihat, Salam Elsheikh, Ammar H. College of Engineering and Information Technology Ajman University Ajman United Arab Emirates Faculty of Engineering and Physical Sciences University of Southampton Southampton SO17 1BJ United Kingdom College of Engineering University of Al Maarif Al Anbar31001 Iraq Department of Mathematics Faculty of Science Zagazig University Zagazig44519 Egypt Faculty of Computer Science and Engineering Galala University Suez435611 Egypt Software Engineering Department Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan Department of Production Engineering and Mechanical Design Faculty of Engineering Tanta University Tanta31527 Egypt Faculty of Engineering Pharos University in Alexandria Egypt

Yield augmentation of solar distiller (SD) using low-cost modifications with minimum environmental impacts is highly desirable. In this study, we developed an eco-friendly steam generation device made of carbonized Ficus sycomorus wood to boost the yield of the conventional single-basin solar distiller. Moreover, a hybrid model composed of a long short-term memory (LSTM) network and a Starling murmuration optimizer (SMO) was developed to predict the yield of the established SD. To verify the accuracy of the model, three other models were employed and the distillate yield was predicted. The predicted data of all models was evaluated using different error measures. These models were standalone LSTM and LSTM optimized by an artificial hummingbird algorithm (AHA) or manta ray foraging optimizer (MRFO). Real field data were employed during the training and testing of all models. The thermal performance of the distiller with the steam generation device was compared with that of a traditional distiller based on exergy output, exergy efficiency, and energy efficiency. The distillate yield, exergy efficiency, and energy efficiency of the modified SD were enhanced by 34 %, 49 %, and 40 %, respectively, compared with that of the traditional distiller. Moreover, the total amount of produced drinkable water using the modified SD could reach a high value of 6.1 L/day with a low cost of 0.014 $/L. © 2025

关键词： Exergy

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Two-stage object detection in low-light environments using deep learning image enhancement

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PeerJ computer Science 2025年 11卷 e2799-e2799页

作者： Al-refai, Ghaith Elmoaqet, Hisham Al-Refai, Abdullah Alzu’bi, Ahmad Al-Hadhrami, Tawfik Alkhateeb, Abedalrhman Department of Mechatronics Engineering German Jordanian University Amman Jordan Software Engineering Department King Hussein School of Computing Science Princess Sumaya University College for Technology Amman Jordan Department of Computer Science Faculty of Computer and Information Technology Jordan University of Science and Technology Irbid Jordan Department of Computer Science School of Science and Technology Nottingham Trent University Nottingham United Kingdom Department of Computer Science Lakehead University Thunder Bay Canada

This study presents a two-stage object detection system specifically tailored for low-light conditions. In the initial stage, supervised deep learning image enhancement techniques are utilized to improve image quality and enhance features. The second stage employs a computer vision algorithm for object detection. Three image enhancement algorithms—ZeroDCE++, Gladnet, and two-branch exposurefusion network for low-light image enhancement (TBEFN)—were assessed in the first stage to enhance image quality. YOLOv7 was utilized in the object detection phase. The ExDark dataset, recognized for its extensive collection of low-light images, served as the basis for training and evaluation. No-reference image quality evaluators were applied to measure improvements in image quality, while object detection performance was assessed using metrics such as recall and mean average precision (mAP). The results indicated that the two-stage system incorporating TBEFN significantly improved detection performance, achieving a mAP of 0.574, compared to 0.49 for YOLOv7 without the enhancement stage. Furthermore, this study investigated the relationship between object detection performance and image quality evaluation metrics, revealing that the image quality evaluator NIQE exhibited a strong correlation with mAP for object detection. This correlation aids in identifying the features that influence computer vision performance, thereby facilitating its enhancement. © 2025 Al-refai et al.

关键词： Image quality

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An intelligent framework for sarcasm detection in Arabic tweets using deep learning with Al-Biruni earth radius optimization algorithm

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Alexandria engineering Journal 2025年 127卷 562-572页

作者： AL-anazi, Reema G. A. Alzaidi, Muhammad Swaileh Eltahir, Majdy M. Almukadi, Wafa Sulaiman Alajmani, Samah Hazzaa Darem, Abdulbasit A. Alshahrani, Mohammed Alhebaishi, Nawaf Department of Arabic Language and Literature College of Humanities and Social Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of English Language College of Language Sciences King Saud University P. O. Box 145111 Riyadh Saudi Arabia Department of Information Systems Applied College at Mahayil King Khalid University Saudi Arabia Department of Software Engineering College of Engineering and Computer Science University of Jeddah Saudi Arabia Department of Information Technology College of Computers and Information Technology Taif University P.O. Box 11099 Taif21944 Saudi Arabia Department of Computer Science College of Science Northern Border University Arar Saudi Arabia Computer Department Applied College Najran University Najran66462 Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia

Social media networks play a major role in expressing people's feelings, reviews, and thoughts. One of the popular linguistic patterns to express or criticize one's ideas with ridicule is sarcasm, where they have unintended and intended meanings in written text. The sarcastic text is used to reverse the polarity of the sentiment. As a result, sarcasm detection in the text positively affects the sentimental analysis (SA) tasks and improves accuracy. The sarcasm recognition of Arabic content is constrained even though Arabic is the most popular language for web content sharing, and yet still naive owing to various challenges, such as the insufficient data sources, the multiple dialects, and the morphological structure of the Arabic language. This study presents an Artificial Intelligence Driven Sarcasm Detection using Optimal Deep Learning (AIDSD-ODL) technique on Arabic tweets. In the AIDSD-ODL technique, a hyperparameter-tuned DL model is utilized to identify sarcastic or non-sarcastic Arabic tweets. At the initial stage, the AIDSD-ODL method undergoes data pre-processing to convert the input tweets into a compatible format. In the next phase, the Glove word embedding process is used in the AIDSD-ODL technique. The self-attention bidirectional long short-term memory (SA-BiLSTM) model is utilized for sarcasm detection. The Al-Biruni Earth Radius (BER) model is implemented for the hyperparameter selection process to enhance the performance of the SA-BiLSTM network. The experimental results of the AIDSD-ODL method are examined under the Arabic tweets dataset. The comparison study of the AIDSD-ODL method portrayed a superior accuracy value of 94.20 % over existing models. © 2025 The Authors

关键词： Deep learning

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Multi-Modal Multi-Kernel Graph Learning for Autism Prediction and Biomarker Discovery

IEEE Transactions on Computational Biology and Bioinformatic...

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IEEE Transactions on Computational Biology and Bioinformatics 2025年第2期22卷 842-854页

作者： Jin Liu Junbin Mao Hanhe Lin Hulin Kuang Shirui Pan Xusheng Wu Shan Xie Fei Liu Yi Pan Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha China Xinjiang Engineering Research Center of Big Data and Intelligent Software School of software Xinjiang University Wulumuqi China Hunan Province Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha China School of Science and Engineering University of Dundee Dundee U.K. School of Information and Communication Technology Griffith University Gold Coast QLD Australia Shenzhen Health Development Research and Data Management Center Shenzhen China Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

Graph learning-based multi-modal integration and classification is one of the most challenging tasks for disease prediction. To effectively offset the negative impact among modalities in the process of multi-modal integration and heterogeneous information extractions from graphs, we propose a novel method called Multi-modal Multi-Kernel Graph Learning (MMKGL). To solve the problem of negative impact among modalities, we propose a multi-modal graph embedding module to construct a multi-modal graph. Different from conventional methods that manually construct static graphs for all modalities, each modality generates a separate graph by adaptive learning, where a function graph and a supervision graph are introduced for optimization during the multi-graph fusion embedding process. We then propose a multi-kernel graph learning module to extract heterogeneous information from the multi-modal graph. The information in the multi-modal graph at different levels is aggregated by convolutional kernels with different receptive field sizes, followed by generating a cross-kernel discovery tensor for disease prediction. Our method is evaluated on the benchmark Autism Brain Imaging Data Exchange (ABIDE) dataset and outperforms the state-of-the-art methods. In addition, discriminative brain regions associated with autism are identified by our model, providing guidance for the study of autism pathology.

关键词： Diseases Autism Random access memory Data mining Kernel Electronic mail Training Neuroimaging Graph convolutional networks Bioinformatics

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