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

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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Predicting Student Behavior Using a Neutrosophic Deep Learning Model

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Neutrosophic Sets and Systems 2025年 76卷 288-310页

作者： Shitaya, Ahmed Mohamed Wahed, Mohamed El Syed Abd El Khalek, Saied Helemy Ismail, Amr Shams, Mahmoud Y. Salama, A.A. Department of Mathematics and Computer Science Faculty of Science Port Said University Egypt Department of Computer Science Faculty of computer and information sciences Suez Canal University Egypt Department. of Communication and Computer Engineering Air Defense University Egypt Department of Cybersecurity College of Engineering and Information Technology Buraydah Private Colleges Buraydah51418 Saudi Arabia Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh33516 Egypt

We developed an information system using an object-oriented programming language and a distributed database (DDB) consisting of multiple interconnected databases across a computer network, managed by a distributed database management system (DDBMS) for easy access. An intelligent system was designed to assess the difficulty level of preliminary exams and select top-performing advanced students using a Neutrosophic Deep Learning Model. The dataset was randomly split into training (80%) and testing (20%) sets, and the model, trained with the Adam optimizer at a 0.001 learning rate over 50 epochs, incorporated early stopping based on validation loss. This system, implemented at a traditional Egyptian university, achieved a 95% accuracy in predicting student dropout. Student behavior, influenced by personal, environmental, and academic factors, is often evaluated subjectively, leading to inconsistent results. Traditional machine learning approaches struggle with the inherent uncertainty in behavioral data. To address this, we combined neutrosophic theory—a mathematical framework that accounts for truth, falsity, and indeterminacy—with deep learning, which excels at learning complex data relationships, to predict student outcomes such as dropout rates. Evaluating the model on student data, including attendance and grades, showed superior accuracy, achieving a determination coefficient of 0.95, demonstrating the approach's potential for identifying at-risk students and enabling targeted interventions. © 2025, University of New Mexico. All rights reserved.

关键词： Contrastive Learning

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ClipArtGAN: An Application of Pix2Pix Generative Adversarial Network for Clip Art Generation

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Multimedia Tools and Applications 2025年 1-25页

作者： Elnabawy, Reham H. Abdennadher, Slim Hellwich, Olaf Eldawlatly, Seif Technische Universität Berlin Berlin Germany Computer Science and Engineering Department Faculty of Media Engineering and Technology German University in Cairo Cairo Egypt Computer Science Department Faculty of Informatics and Computer Science German International University New Administrative Capital Egypt Computer and Systems Engineering Department Faculty of Engineering Ain Shams University 1 El-sarayat st. Abbassia Cairo Egypt Computer Science and Engineering Department The American University in Cairo Cairo Egypt School of Information Technology New Giza University Giza Egypt

An image can convey a thousand words. This statement emphasizes the importance of illustrating ideas visually rather than writing them down. Although detailed image representation is typically instructive, there are several contexts where simplification of image representations may be more informative. One way to provide an abstract representation of an object is via clip art, which is a simple graphic illustration of an object that can be included in documents or presentations. Clip art representation can be utilized in graphic design for concepts expression and daily life objects illustration. Furthermore, clip art had shown remarkable advantages when compared to real images in terms of editing and manipulation since clip art geometry can be easily edited in addition to the feasibility of storing it in a resolution-independent manner. However, it can be difficult to automatically create clip arts that resemble any object. Here, we propose a novel deep learning model, named ClipArtGAN, that utilizes Generative Adversarial Networks (GANs) to convert a photo of an object to its corresponding clip art. To our knowledge, this is the first GAN model based on Pix2Pix architecture that is capable of automatically generating clip art for any arbitrary object. Three training datasets were collected to train and compare two GAN models based on Pix2Pix and CycleGAN architectures. Our results demonstrate that the mean accuracy obtained using ClipArtGAN utilizing Pix2Pix outperforms that of CycleGAN by 58% and that of Google Images-based search by 54%. These results indicate the efficacy of ClipArtGAN in generating representative clip art images. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Generative adversarial networks

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A learning automata based edge resource allocation approach for IoT-enabled smart cities

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Digital Communications and Networks 2024年第5期10卷 1258-1266页

作者： Sampa Sahoo Kshira Sagar Sahoo Bibhudatta Sahoo Amir H.Gandomi Department of Computer Science and Engineering C.V.Raman Global UniversityBhubaneswarIndia Department of Computer Science and Engineering SRM UniversityAmaravatiIndia Department of Computing Science Umea UniversityUmea90187Sweden Department of Computer Science and Engineering NIT RourkelaIndia Faculty of Engineering and Information Technology University of Technology SydneyAustralia University Research and Innovation Center(EKIK) Obuda University1034 BudapestHungary

The development of the Internet of Things(IoT)technology is leading to a new era of smart applications such as smart transportation,buildings,and smart ***,these applications act as the building blocks of IoT-enabled smart *** high volume and high velocity of data generated by various smart city applications are sent to flexible and efficient cloud computing resources for ***,there is a high computation latency due to the presence of a remote cloud *** computing,which brings the computation close to the data source is introduced to overcome this *** an IoT-enabled smart city environment,one of the main concerns is to consume the least amount of energy while executing tasks that satisfy the delay *** efficient resource allocation at the edge is helpful to address this *** this paper,an energy and delay minimization problem in a smart city environment is formulated as a bi-objective edge resource allocation ***,we presented a three-layer network architecture for IoT-enabled smart ***,we designed a learning automata-based edge resource allocation approach considering the three-layer network architecture to solve the said bi-objective minimization *** Automata(LA)is a reinforcement-based adaptive decision-maker that helps to find the best task and edge resource *** extensive set of simulations is performed to demonstrate the applicability and effectiveness of the LA-based approach in the IoT-enabled smart city environment.

关键词： Edge computing IoT Learning automata Resource allocation Smart city

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Artificial intelligence-inspired comprehensive framework for desertification prediction

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International Journal of Cognitive Computing in engineering 2025年 6卷 462-476页

作者： Alsubai, Shtwai Mosleh, Mogeeb A.A. Al-Hadhrami, Suheer A. Bhatia, Munish College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Alkharj Saudi Arabia Faculty of Engineering & Computing University of Science & Technology Aden Yemen Faculty of Engineering and Information Technology Taiz Univeristy Taiz Yemen Department of Computer Engineering College of Engineering Al Mukalla Hadhramout Yemen Department of Computer Applications National Institute of Technology Kurukshetra India

Desertification greatly affects land deterioration, farming efficiency, economic growth, and health, especially in Gulf nations. Climate change has worsened desertification, making developmental issues in the area even more difficult. This research presents an enhanced framework utilizing the Internet of Things (IoT) for ongoing monitoring, data gathering, and analysis to evaluate desertification patterns. The framework utilizes Bayesian Belief Networks (BBN) to categorize IoT data, while a low-latency processing method on edge computing platforms enables effective detection of desertification trends. The classified data is subsequently analyzed using an Artificial Neural Network (ANN) optimized with a Genetic Algorithm (GA) for forecasting decisions. Using cloud computing infrastructure, the ANN-GA model examines intricate data connections to forecast desertification risk elements. Moreover, the Autoregressive Integrated Moving Average (ARIMA) model is employed to predict desertification over varied time intervals. Experimental simulations illustrate the effectiveness of the suggested framework, attaining enhanced performance in essential metrics: Temporal Delay (103.68 s), Classification Efficacy—Sensitivity (96.44 %), Precision (95.56 %), Specificity (96.97 %), and F-Measure (96.69 %)—Predictive Efficiency—Accuracy (97.76 %) and Root Mean Square Error (RMSE) (1.95 %)—along with Reliability (93.73 %) and Stability (75 %). The results of classification effectiveness and prediction performance emphasize the framework's ability to detect high-risk zones and predict the severity of desertification. This innovative method improves the comprehension of desertification processes and encourages sustainable land management practices, reducing the socio-economic impacts of desertification and bolstering at-risk ecosystems. The results of the study hold considerable importance for enhancing regional efforts in combating desertification, ensuring food security, and formulatin

关键词： Life on land Climate action Desertification Internet of things Artificial neural network

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Data Augmentation and Random Multi-Model Deep Learning for Data Classification

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computers, Materials & Continua 2023年第3期74卷 5191-5207页

作者： Fatma Harby Adel Thaljaoui Durre Nayab Suliman Aladhadh Salim EL Khediri Rehan Ullah Khan Computer Science Department Future Academy-Higher Future Institute for Specialized Technological StudiesEgypt Department of Computer Systems Engineering Faculty of Electrical and Computer EngineeringUniversity of Engineering and TechnologyPeshawar25120Pakistan Department of Information Technology College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Computer Sciences Faculty of Sciences of GafsaUniversity of GafsaGafsaTunisia

In the machine learning(ML)paradigm,data augmentation serves as a regularization approach for creating ML *** increase in the diversification of training samples increases the generalization capabilities,which enhances the prediction performance of classifiers when tested on unseen *** learning(DL)models have a lot of parameters,and they frequently ***,to avoid overfitting,data plays a major role to augment the latest improvements in ***,reliable data collection is a major limiting ***,this problem is undertaken by combining augmentation of data,transfer learning,dropout,and methods of normalization in *** this paper,we introduce the application of data augmentation in the field of image classification using Random Multi-model Deep Learning(RMDL)which uses the association approaches of multi-DL to yield random models for *** present a methodology for using Generative Adversarial Networks(GANs)to generate images for data *** experiments,we discover that samples generated by GANs when fed into RMDL improve both accuracy and model *** across both MNIST and CIAFAR-10 datasets show that,error rate with proposed approach has been decreased with different random models.

关键词： Data augmentation generative adversarial networks classification machine learning random multi-model deep learning

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Optimizing Image Retrieval in Cloud Servers with TN-AGW: A Secure and Efficient Approach

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Journal of The Institution of Engineers (India): Series B 2025年第2期106卷 459-473页

作者： Ponnuviji, N.P. Nirmala, G. Kokila, M. L. Sworna Priyadharshini, S. Indra Department of Computer Science and Engineering RMK College of Engineering and Technology Puduvoyal Thiruvallur India Department of Computer Science and Engineering RMD Engineering College Chennai India Department of Computing Technologies School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Tamil Nādu Kattankulathur Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai India

The increasing use of cloud-based image storage and retrieval systems has made ensuring security and efficiency crucial. The security enhancement of image retrieval and image archival in cloud computing has received considerable attention in transmitting data and ensuring data confidentiality among cloud servers and users. Various traditional image retrieval techniques regarding security have developed in recent years but they do not apply to large-scale environments. This paper introduces a new approach called Triple network-based adaptive grey wolf (TN-AGW) to address these challenges. The TN-AGW framework combines the adaptability of the Grey Wolf Optimization (GWO) algorithm with the resilience of Triple Network (TN) to enhance image retrieval in cloud servers while maintaining robust security measures. By using adaptive mechanisms, TN-AGW dynamically adjusts its parameters to improve the efficiency of image retrieval processes, reducing latency and utilization of resources. However, the image retrieval process is efficiently performed by a triple network and the parameters employed in the network are optimized by Adaptive Grey Wolf (AGW) optimization. Imputation of missing values, Min–Max normalization, and Z-score standardization processes are used to preprocess the images. The image extraction process is undertaken by a modified convolutional neural network (MCNN) approach. Moreover, input images are taken from datasets such as the Landsat 8 dataset and the Moderate Resolution Imaging Spectroradiometer (MODIS) dataset is employed for image retrieval. Further, the performance such as accuracy, precision, recall, specificity, F1-score, and false alarm rate (FAR) is evaluated, the value of accuracy reaches 98.1%, the precision of 97.2%, recall of 96.1%, and specificity of 917.2% respectively. Also, the convergence speed is enhanced in this TN-AGW approach. Therefore, the proposed TN-AGW approach achieves greater efficiency in image retrieving than other existing

关键词： Extraction

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Performance analysis of naive vehicular named data networks

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International Journal of Wireless and Mobile Computing 2025年第3期28卷 234-244页

作者： Nithin Rao, R. Sharma, Rinki Department of Computer Science and Engineering Ramaiah University of Applied Sciences Karnataka Bangalore India

Vehicular Named Data Networks (VNDN) is a content centric approach for vehicle networks. The fundamental principle of addressing the content rather than the host, suits vehicular environment. There are numerous challenges such as interest/data flooding, routing, naming schemes, channel constraints and security, among others. The proposed work aims to analyse the behaviour of the naive VNDN under diverse scenarios, which is crucial for devising effective forwarding, caching and security strategies to enhance VNDN’s performance. In this paper, the naive VNDN architecture is implemented and its performance is evaluated by considering the metrics such as Throughput, Packet Delivery Ratio (PDR), Interest Satisfaction Delay (ISD), Interest Satisfaction Ratio (ISR), Copies of Data Packets Processed (CDPP) and Hop Count Difference (HCD). These metrics are tested against varying vehicle speed, network size and interests generated by each vehicle in the network. The simulation results provide insights that should be considered while developing the forwarding strategies and caching mechanisms that will improve the performance of VNDN. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： caching content forwarding data packets delay naming timestamp

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Advancing Activity Recognition with Multimodal Fusion and Transformer Techniques

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IEEE Sensors Journal 2025年第11期25卷 19632-19649页

作者： Aidarova, Saltanat Nurmakhan, Temirlan Myrzakhan, Rakhat Fazli, Siamac Yazici, Adnan Nazarbayev University School of Engineering and Digital Sciences Department of Computer Science Astana Kazakhstan

In the field of Human Activity Recognition (HAR), the precise identification of human activities from time-series sensor data is a complex yet vital task, given its extensive applications across various industries. This study introduces an advanced HAR technique that markedly improves activity recognition by combining multimodal sensor fusion with a transformer-based attention mechanism. Our methodology begins with rigorous preprocessing of the raw data from multiple sensors, focusing on cleaning and normalizing the data to create ideal conditions for subsequent analysis. We then apply an innovative sensor fusion strategy alongside a transformer-based attention mechanism to accurately and comprehensively detect human activities. The effectiveness of our method was rigorously evaluated on two widely recognized benchmark datasets in HAR research, Extrasensory and UCI-HAR, both known for their complexity and broad usage. The evaluation concentrates on the model’s ability to precisely classify primary human activities, particularly in near-real-time situations. The findings demonstrate that our model excels in accuracy and adeptly identifies primary human activities, marking a notable progression in HAR technology. This enhancement highlights the strong potential of our proposed approach, combining sensor fusion and attention mechanisms to advance activity recognition in real-life settings. © 2001-2012 IEEE.

关键词： Sensor data fusion

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Hybrid two-level protection system for preserving pre-trained DNN models ownership

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Neural Computing and Applications 2024年第34期36卷 21415-21449页

作者： Fkirin, Alaa Moursi, Ahmed Samy Attiya, Gamal El-Sayed, Ayman Shouman, Marwa A. Department of Electrical Engineering Faculty of Engineering Fayoum University Fayoum Egypt Computer Science and Engineering Department Faculty of Electronic Engineering Menoufia Governorate Menouf32952 Egypt

Recent advancements in deep neural networks (DNNs) have made them indispensable for numerous commercial applications. These include healthcare systems and self-driving cars. Training DNN models typically demands substantial time, vast datasets and high computational costs. However, these valuable models face significant risks. Attackers can steal and sell pre-trained DNN models for profit. Unauthorised sharing of these models poses a serious threat. Once sold, they can be easily copied and redistributed. Therefore, a well-built pre-trained DNN model is a valuable asset that requires protection. This paper introduces a robust hybrid two-level protection system for safeguarding the ownership of pre-trained DNN models. The first-level employs zero-bit watermarking. The second-level incorporates an adversarial attack as a watermark by using a perturbation technique to embed the watermark. The robustness of the proposed system is evaluated against seven types of attacks. These are Fast Gradient Method Attack, Auto Projected Gradient Descent Attack, Auto Conjugate Gradient Attack, Basic Iterative Method Attack, Momentum Iterative Method Attack, Square Attack and Auto Attack. The proposed two-level protection system withstands all seven attack types. It maintains accuracy and surpasses current state-of-the-art methods. © The Author(s) 2024.

关键词： Deep neural networks

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