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EXTREMAL AND CONTINUA SOLUTIONS FOR STOCHASTIC FRACTIONAL DIFFERENTIAL EQUATIONS

Journal of Mathematical sciences (United States) 2025年 1-15页

作者： Benhamida, Ouafaa Dahmani, Zoubir Ibrahim, Rabha W. Laboratory of Pure and Applied Maths Faculty of Sciences UMAB Mostaganem Algeria Near East University Mathematics Research Center Department of Mathematics Near East Boulevard Nicosia /Mersin 10 99138 Türkiye Department of Computer Science and Mathematics Lebanese American University Beirut 13-5053 Lebanon Information and Communication Technology Research Group Scientific Research Center Al-Ayen University Thi-Qar Iraq Laboratory of LAMDA-RO Faculty of Sciences University of Blida 1 Blida 09000 Algeria

The aim of this work is to study two classes of stochastic fractional differential equations via the application of the method of upper and lower solutions combined with the Arzela-Ascoli theorem. We begin by proving an auxiliary result for the integral representation of an Airy-type stochastic problem. The specific symmetry features of an Airy-type stochastic problem depend on the form of the stochastic differential equations (SDE) and the relevant coefficients, it is vital to note. To comprehend each issue’s unique symmetries and their effects, a thorough investigation is necessary. Then, we prove an existence result for extremal solutions for the same problem. Another class of stochastic equations of higher-order type is also studied. We also present some examples to show the validity of the obtained results. At the end, a conclusion follows. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Airy equation Extremal solutions Fractional calculus Fractional differential operator Stochastic fractional differential equation Upper and lower method

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Integration of Deep Learning and Image Segmentation in Computational Modeling for Improved Biospeckle Image Analysis

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SN computer Science 2025年第4期6卷 1-14页

作者： Hamatta, Hatem S. A. Deepika, Ch. L. N. Rjoub, Gaith Kassaymeh, Sofian Manikandan, R. Suman, Sanjay Kumar Bhagyalakshmi, L. Department of Applied Sciences Aqaba University College Al Balqa Applied University Aqaba Jordan Faculty of Information Technology Aqaba University of Technology Aqaba Jordan Department of CSE Koneru Lakshmaiah Education Foundation AP Vaddeswaram India Concordia Institute for Information Systems Engineering Concordia University Montreal Canada Faculty of Information Technology Aqaba University of Technology Aquaba Jordan Department of Electronics and Communication Engineering Panimalar Engineering College TN Chennai India Dept. of AI & DS Sri Shanmugha College of Engineering and Technology and Director R & D Sri Shanmugha Educational Institutions Sankari TN Salem India Department of ECE Rajalakshmi Engineering College TN Chennai India

Recently, deep learning and image segmentation have helped to enhance agricultural quality evaluation, material research, and disease diagnosis. Biospeckle pictures are challenging to process using traditional image processing techniques because of their complexity and blurriness. This is due to the widely recognized challenges associated with comprehending these photographs. Our technique uses convolutional neural networks to identify and distinguish biospeckle patterns. Via models generated on big datasets such as ImageNet, we can speed up the process of building and training models via transfer learning. Stochastic gradient descent, batch normalization, and dropout regularization improve the models, prevent overfitting, and stabilize the training. We employ several strategies to enhance the model. To enhance the model, data augmentation techniques use elastic deformations, flip, scale, and rotate the data. These strategies work together to increase the dataset's variety. The recommended technique yielded dice = 0.95, IoU = 0.94, accuracy = 0.96, precision = 0.95, recall = 0.92, and F1 = 0.93. These performance measures show tremendous improvement. The computational complexity of our method (152 billion floating-point operations, 3.5 GB of CPU memory, 20 ms for inference, and 31 million parameters) ensures constant performance. This approach improves the accuracy and reliability of biospeckle image. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Agriculture Batch normalization Biospeckle image analysis Convolutional neural networks Deep learning Dropout regularization Image segmentation Material science Transfer learning

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Innovative metallization pattern and technique for industrial rear emitter p-type tunnel oxide passivated contact solar cells

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Journal of Industrial and Engineering Chemistry 2025年 147卷 628-635页

作者： Khokhar, Muhammad Quddamah Yousuf, Hasnain Rahman, Rafi Ur Aida, Maha Nur Madara, Polgampola Chamani Jony, Jaljalalul Abedin Dao, Vinh-Ai Pham, Duy Phong Yi, Junsin Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of Interdisciplinary Program in Photovoltaic System Engineering Sungkyunkwan University Suwon16419 Korea Republic of Department of Future Energy Engineering Sungkyunkwan University Suwon16419 Korea Republic of Department of Physics Faculty of Applied Sciences HCMC University of Technology and Education Ho Chi Minh City700000 Viet Nam College of Information and Communication Engineering Sungkyunkwan University Suwon16419 Korea Republic of

In this study, we aim to enhance the efficiency of p-type tunnel oxide passivated contact (p-TOPCon) solar cells by improving charge carrier extraction while achieving higher fill factor (FF) and open circuit voltage (Voc). We explore various potential methods for the photovoltaic industrial procedure, including optimizing metallization patterns and techniques, such as the utilization of a high efficiency grid pattern (HEGP) in place of the conventional straight finger-line pattern (SFLP). Co-firing conditions, peak firing temperature, and belt speed are investigated for their impact on FF enhancement, along with evaluating the effects of single and double print processes. Furthermore, we examine the efficacy of Nafion treatment as a surface modification technique to enhance FF. Experimental results indicate that optimizing co-firing conditions and employing Nafion treatment significantly increased FF to approximately 83.26% in p-TOPCon solar cells. Adjusting peak firing temperature and belt speed, as well as implementing the double print process, demonstrate superior FF enhancement compared to the single print process. Additionally, Nafion treatment contributes to FF improvement by passivating surface defects and enhancing charge carrier collection. Overall, this study provides valuable insights into optimizing co-firing conditions and surface treatment techniques to enhance p-TOPCon solar cell efficiency (Eff) up to 23.50%, thus advancing high-efficiency photovoltaic technology. © 2024 The Korean Society of Industrial and Engineering Chemistry

关键词： Metallizing

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Hardware-Accelerated Detection of Unauthorized Mining Activities Using YOLOv11 and FPGA

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International Journal of Advanced computer Science and Applications 2025年第4期16卷 967-979页

作者： Ghodhbani, Refka Saidani, Taoufik Kachoukh, Amani Elsayed, Mahmoud Salaheldin Said, Yahia Ahmed, Rabie Center for Scientific Research and Entrepreneurship Northern Border University Arar73213 Saudi Arabia Department of Information Systems-Faculty of Computing and Information Technology Northern Border University Saudi Arabia Department of Computer Sciences-Faculty of Computing and Information Technology Northern Border University Saudi Arabia DCenter for Scientific Research and Entrepreneurship Northern Border University Arar73213 Saudi Arabia Department of Computer Science-Faculty of Computing and Information Technology Northern Border University Rafha Saudi Arabia Mathematics and Computer Science Department-Faculty of Science Beni-Suef University Beni-Suef Egypt

Illegal mining activities present significant environmental, economic, and safety challenges, particularly in remote and under-monitored regions. Traditional surveillance methods are often inefficient, labor-intensive, and unable to provide real-time insights. To address this issue, this study proposes a computer vision-based solution leveraging the state-of-the-art YOLOv11 Nano and Small models, fine-tuned for the detection of illegal mining activities. A specific dataset comprising aerial and ground-level images of mining sites was curated and annotated to train the models for identifying unauthorized excavation, equipment usage, and human presence in restricted zones. The proposed system integrates the hardware-software design of YOLOv11 on the PynqZ1 FPGA, offering a highperformance, low-latency, and energy-efficient solution suitable for real-time monitoring in resource-constrained environments. This hardware-accelerated approach combines FPGA's parallel processing capabilities with the lightweight deep learning models, enabling efficient deployment for automated illegal mining detection. By providing a scalable, real-time monitoring tool, this work contributes to the development of automated enforcement tools for the mining industry, ensuring better control and surveillance of mining activities. To validate the efficiency of deep learning deployment on edge devices, YOLOv11n was implemented on an FPGA, utilizing 70% of available LUTs, 50% of FFs, and 80% of DSPs, with 8.3 Mbits of on-chip memory. The design achieved 100.33 GOP/s throughput, 18 FPS at 55 ms latency, consuming 4.8 W, and delivering an energy efficiency of 20.90 GOP/s/W. © (2025), (Science and information Organization). All rights reserved.

关键词： Mining industry

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EnVoR: Multi-Granular Gold Price Prediction Using Ensemble Voting Regression

EnVoR: Multi-Granular Gold Price Prediction Using Ensemble V...

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Artificial Intelligence, Applications, Innovation and Ethics (AI2E), International Conference for

作者： Rajkumar Kannan Samuel Giftson Durai Abdul Raouf Khan Kalaiarasi Sonai Muthu Department of Computer Science Bishop Heber College (Autonomous) Tiruchirappalli India Department of Information Technology University of Technology and Applied Sciences Muscat Oman Department of Computer Sciences King Faisal University Alhassa Saudi Arabia Faculty of Information Science and Technology Multimedia University Malaysia

ISBN: (数字)9798331513733

ISBN: (纸本)9798331513740

Price prediction and forecasting have been important topics in the financial world. Among all commodities, such as gold, silver, platinum and others, gold has been the prime and forefront commodity which decides the price movements of other commodities. This paper proposes a unique multi-granular gold price prediction methodology, called EnVoR, by leveraging voting based regression tree models and utilizing simple, cumulative and exponential moving average values as features. Our proposed EnVoR predicts gold prices of next day, next week, next month and next quarter seasons. Extensive experiments are conducted on the gold price daily data from December 2011 to June 2024 to predict the future prices at various granularity levels. Experimental results on the gold price data have confirmed that the proposed EnVoR methodology has given superior performance in terms of highest regression accuracy and the lowest error values for MSE, RMSE, MAE and MAPE for predicting next day, next week, next month and next quarter price values. The proposed EnVoR model can be used by financial institutions and stock market analysts for providing buy, sell or hold recommendations to their clients.

关键词： Gold Silver Technological innovation Tracking Biological system modeling Market research Vectors Random forests Regression tree analysis Long short term memory

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Load Balancing in Cloud Computing based on Ant Colony Optimization and Crow Search Algorithm 27

Load Balancing in Cloud Computing based on Ant Colony Optimi...

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27th International Conference on Advanced Communications Technology, ICACT 2025

作者： Alsheavi, Amar N. Wang, Xingfu Zhao, Wei Hawbani, Ammar Alhusaini, Naji Ahmed, Ibrahim Abdulrab Ismail, A.S. School of Computer Science and Technology University of Science and Technology of China Anhui Hefei230027 China School of Computer Science and Technology Anhui University of Technology Anhui China School of Computer Science Shenyang Aerospace University Shenyang110136 China School of Computer and Information Engineering Chuzhou University Anhui Chuzhou239000 China Computer Department Applied College Najran University Najran66462 Saudi Arabia College of Life Sciences and Oceanography Shenzhen University Shenzhen518060 China Faculty of Science Zagazig University Zagazig44519 Egypt

ISBN: (纸本)9791188428137

With the rapid increase in cloud services and the increasing shift toward them, balancing the cloud load has become a critical research issue. The increasing demand from customers for technology services worldwide is largely due to its direct impact on performance quality. Therefore, to provide better service quality, it is essential to consider reducing response time and cost in load balancing. This paper addresses the load-balancing problem by optimizing response time and computing cost in cloud computing systems. Specifically, we first formulated the load balancing problem in cloud computing mathematically by designing an objective function that optimizes response time and cost, including constraints to ensure task assignment to a single virtual machine (VM) and resource limits are not exceeded. To solve this optimization problem, we proposed a hybrid algorithm, ACOCSA, that combines Ant Colony Optimization (ACO) and Crow Search Algorithm (CSA). We implemented the ACOCSA algorithm, evaluated the response time, cost, and load balancing metrics, and found that our algorithm performed better in terms of response time and computational cost, and showed good fairness among VMs. © 2025 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Ant colony optimization

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Interpretable Arabic Hate Speech Detection using Large Language Model

Interpretable Arabic Hate Speech Detection using Large Langu...

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Advanced Innovations in Smart Cities (ICAISC), International Conference on

作者： Majed Alwateer Ibrahim Gad Mahmoud Elmarhomy Ghada Elmarhomy Hasan Hashim Malik Almaliki El-Sayed Atlam Department of Computer Science College of Computer Science and Engineering Taibah University Saudi Arabia Computer Science Department Faculty of Science Tanta University Tanta Egypt Computer Science and Information College of Applied Taibah University Saudi Arabia Department of Information Systems College of Computer Science and Engineering Taibah University Saudi Arabia

ISBN: (数字)9798331506995

ISBN: (纸本)9798331507008

Social media platforms serve as significant spaces for users to have conversations, discussions and express their opinions. However, anonymity provided to users on these platforms allows the spread of hate speech and other offensive material. Due to the wide-ranging nature of these platforms, there is a critical need to automatically detect and report occurrences of hate speech. There are various detection methods, but many of them operate as black boxes, lacking interpretability and explainability by design. To address the lack of interpretability, this study explores the development of an interpretable framework to detect hate speech in Arabic using large language models (LLMs). The proposed approach combines advanced natural language processing techniques with interpretable machine learning methods to enhance understanding of model decisions. The experimental results demonstrate that the model achieves high accuracy while maintaining interpretability, enabling users to understand the reasoning behind the detections. The proposed method achieves an accuracy of 0.846%, with a precision of 0.843% and a recall of 0.846%, outperforming existing Arabic hate speech detection models. These results show the effectiveness of combining LLM with interpretability for this critical task, providing a reliable and transparent solution for automated moderation of harmful content.

关键词： Technological innovation Accuracy Social networking (online) Smart cities Large language models Hate speech Text categorization Writing Reliability Stakeholders

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Interpretable Arabic Hate Speech Detection using Large Language Model 2

Interpretable Arabic Hate Speech Detection using Large Langu...

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2nd International Conference on Advanced Innovations in Smart Cities, ICAISC 2025

作者： Alwateer, Majed Gad, Ibrahim Elmarhomy, Mahmoud Elmarhomy, Ghada Hashim, Hasan Almaliki, Malik Atlam, El-Sayed College of Computer Science and Engineering Taibah University Department of Computer Science Saudi Arabia Tanta University Faculty of Science Computer Science Department Tanta31527 Egypt College of Applied Taibah University Computer Science and Information Saudi Arabia College of Computer Science and Engineering Taibah University Department of Information Systems Saudi Arabia

ISBN: (纸本)9798331506995

关键词： Arabic Hate Speech Detection Large Language Mode XAI

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TAGE: Trustworthy Attribute Group Editing for Stable Few-shot Image Generation 16

TAGE: Trustworthy Attribute Group Editing for Stable Few-sho...

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16th International Conference on Signal Processing systems, ICSPS 2024

作者： Zhang, Ruicheng Huang, Guoheng Huo, Yejing Yuan, Xiaochen Zhou, Zhizhen Wu, Shiting Zhong, Guo School of Computer Science and Technology Guangdong University of Technology Guangzhou510006 China Faculty of Applied Sciences Macao Polytechnic University China Huizhou Boluo Power Supply Bureau Guangdong Power Grid Co. Ltd. 516001 China School of Information Science and Technology Guangdong University of Foreign Studies Guangzhou510006 China

ISBN: (纸本)9781510689251

Generative Adversarial Networks (GANs) have emerged as a prominent research focus for image editing tasks, leveraging the powerful image generation capabilities of the GAN framework to produce remarkable results. However, prevailing approaches are contingent upon extensive training datasets and explicit supervision, presenting a significant challenge in manipulating the diverse attributes of new image classes with limited sample availability. To surmount this hurdle, we introduce TAGE, an innovative image generation network comprising three integral modules: the Codebook Learning Module (CLM), the Code Prediction Module (CPM) and the Prompt-driven Semantic Module (PSM). The CPM module delves into the semantic dimensions of category-agnostic attributes, encapsulating them within a discrete codebook. This module is predicated on the concept that images are assemblages of attributes, and thus, by editing these category-independent attributes, it is theoretically possible to generate images from unseen categories. Subsequently, the CPM module facilitates naturalistic image editing by predicting indices of category-independent attribute vectors within the codebook. Additionally, the PSM module generates semantic cues that are seamlessly integrated into the Transformer architecture of the CPM, enhancing the model’s comprehension of the targeted attributes for editing. With these semantic cues, the model can generate images that accentuate desired attributes more prominently while maintaining the integrity of the original category, even with a limited number of samples. We have conducted extensive experiments utilizing the Animal Faces, Flowers, and VGGFaces datasets. The results of these experiments demonstrate that our proposed method not only achieves superior performance but also exhibits a high degree of stability when compared to other few-shot image generation techniques. © 2025 SPIE.

关键词： Image coding

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QSVG: Quaternion Vision Graph Neural Network with Structure Inference for Texture Classification 16

QSVG: Quaternion Vision Graph Neural Network with Structure ...

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16th International Conference on Signal Processing systems, ICSPS 2024

作者： Wen, Haiyuan Huang, Guoheng Cheng, Lianglun Liao, Xianglian Wu, Shiting Yuan, Xiaochen Zhong, Guo School of Computer Science and Technology Guangdong University of Technology Guangzhou510006 China Huizhou Boluo Power Supply Bureau Guangdong Power Grid Co. Ltd. Huizhou China Faculty of Applied Sciences Macao Polytechnic University China School of Information Science and Technology Guangdong University of Foreign Studies Guangzhou510006 China

ISBN: (纸本)9781510689251

In the realm of texture classification, while traditional approaches like Bag of Words (BoW) and Convolutional Neural Networks (CNNs) are commonly used, recent research highlights the remarkable flexibility offered by Graph Neural Networks (GNNs). Specifically, GNNs can effectively represent image blocks as one-dimensional multi-channel node features from three-dimensional data, interconnecting node feature channels. However, existing methods tend to process channels separately during graph convolution, overlooking critical inter-channel connections that are integral to capture intricate relationships between texture features, potentially limiting the effectiveness of GNNs in texture classification. Moreover, textures contain vital global information essential for classification performance, but GNNs tend to focus on information from similar neighboring nodes, which can restrict their capability to capture the global feature of the entire image during data processing. To address the aforementioned challenges, we propose a novel GNN architecture called Quaternion Structure inference Vision Graph neural network (QSVG) for texture classification, which combines quaternions and structure inference. Unlike previous approaches, QSVG comprises two key components: first, Quaternion Max-related convolution (Qmax), which uses the Hamilton transform to enhance node characterization by exploiting inter-channel connections;second, Structure Inference Block (SIF Block), which enriches node information through Bidirectional Gated Recurrent Unit (Bi-GRU)-based structure inference, fusing graph neural network node features with global features. Experimental results on three standard texture datasets demonstrate the competitive classification performance of our method. © 2025 SPIE.

关键词： Graph neural networks

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