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SSRN 2025年

作者： Gardezi Ahmed, Syed Saim Khan, Mahnoor Bukhari, Syeda Muneeba Khan, Rizwan Ahmed Jawed, Soyiba Department of Biomedical Engineering Faculty of Engineering Salim Habib University Karachi Pakistan Department of Computer Software Engineering College of Electrical and Mechanical Engineering NUST Islamabad Pakistan Department of Physics Faculty of Applied Sciences Riphah International University Islamabad Pakistan Department of Biomedical Engineering Faculty of Engineering NED University Karachi Pakistan Department of Computer Science Faculty of Information Technology Salim Habib University Karachi Pakistan

A multitude of individuals across the globe grapple with motor disabilities. Neural prosthetics utilizing Brain-computer Interface (BCI) technology exhibit promise for improving motor rehabilitation outcomes. The intricate nature of EEG data poses a significant hurdle for current BCI systems. Recently, a qualitative repository of EEG signals tied to both upper and lower limb execution of motor and motor imagery tasks has been unveiled. Despite this, the productivity of the Machine Learning (ML) Models that were trained on this dataset was alarmingly deficient, and the evaluation framework seemed insufficient. To enhance outcomes, robust feature engineering (signal processing) methodologies are implemented. A collection of time domain, frequency domain, and wavelet-derived features was obtained from 16-channel EEG signals, and the Maximum Relevance Minimum Redundancy (MRMR) approach was employed to identify the four most significant features. For classification K Nearest Neighbors, Support Vector Machine, Decision Tree, and Naive Bayes models were implemented with these selected features, evaluating their effectiveness through metrics such as testing accuracy, precision, recall, and F1 Score. By leveraging SVM with a Gaussian Kernel, a remarkable maximum testing accuracy of 92.50% for motor activities and 95.48% for imagery activities is achieved. These results are notably more dependable and gratifying compared to the previous study, where the peak accuracy was recorded at 74.36%. This research work provide an in-depth analysis of the MI Limb EEG dataset and it will help in designing and developing simple, cost-effective and reliable BCI systems for neuro-rehabilitation. © 2025, The Authors. All rights reserved.

关键词： Image enhancement

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Modeling of Sensor Enabled IrrigationManagement for Intelligent Agriculture Using Hybrid Deep Belief Network

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computer Systems science & engineering 2023年第8期46卷 2319-2335页

作者： Saud Yonbawi Sultan Alahmari B.R.S.S.Raju Chukka Hari Govinda Rao Mohamad Khairi Ishak Hend Khalid Alkahtani JoséVarela-Aldás Samih M.Mostafa Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia King Abdul Aziz City for Science and Technology RiyadhKingdom of Saudi Arabia Aditya Engineering College Affiliated to JNTUK(Kakinada) SurampalemIndia Department of MBA Vignan’s Institute of Information and Technology(A)DuvvadaVisakhapatnamAP530049India School of Electrical and Electronic Engineering Engineering CampusUniversiti Sains Malaysia(USM)Nibong TebalPenang14300Malaysia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11564Saudi Arabia SISAu Research Group Universidad Tecnológica IndoaméricaAmbato180103Ecuador Faculty of Computers and Information South Valley UniversityQena83523Egypt

Artificial intelligence(AI)technologies and sensors have recently received significant interest in intellectual *** the application of AI technologies and agriculture sensors in intellectual agriculture is urgently required for the growth of modern agriculture and will help promote smart *** irrigation scheduling systems were highly required in the agricultural field due to their capability to manage and save water deficit irrigation *** learning systems devise an alternative to conventional irrigation management through the automatic elaboration of predictions related to the learning of an *** this motivation,this study develops a modified black widow optimization with a deep belief network-based smart irrigation system(MBWODBN-SIS)for intelligent *** MBWODBN-SIS algorithm primarily enables the Internet of Things(IoT)based sensors to collect data forwarded to the cloud server for examination ***,the MBWODBN-SIS technique applies the deep belief network(DBN)model for different types of irrigation classification:average,high needed,highly not needed,and not *** MBWO algorithm is used for the hyperparameter tuning process.A wideranging experiment was conducted,and the comparison study stated the enhanced outcomes of the MBWODBN-SIS approach to other DL models with maximum accuracy of 95.73%.

关键词： Agriculture smart farming hyperparameter tuning artificial intelligence irrigation management sensors deep learning

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Numerical Simulation of Full-Scale Square Concrete Filled Steel Tubular (CFST) Columns Under Seismic Loading

Numerical Simulation of Full-Scale Square Concrete Filled St...

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International Conference on Modern Mechanics and Applications, ICOMMA 2020

作者： Phan, Hao D. Lin, Ker-Chun Phan, Hieu T. Faculty of Civil Engineering The University of Danang – University of Science and Technology Danang Viet Nam Department of Civil and Construction Engineering National Taiwan University of Science and Technology Taipei Taiwan National Center for Research on Earthquake Engineering Taipei Taiwan Department of Computer Science and Software Engineering Miami University OxfordOH United States

ISBN: (纸本)9789811632389

This paper presents numerical investigations on the seismic behavior of full-scale square concrete filled steel tubular (CFST) columns. The main objective is to understand the seismic behavior and evaluate the seismic performance of these composite columns under high levels of axial compression. Finite element analysis (FEA) models in ABAQUS software were used to simulate a series of columns subjected to axial compression and cyclic lateral loading. The CFST columns were modeled using eight-node reduced integration brick elements (C3D8R) for the infilled concrete with confinement effect, and four-node reduced integration shell elements (S4R) for the steel tube with consideration of steel-concrete interaction and steel wall’s buckling. The feasibility of the FEA models has been validated by published experimental results. The validated FEA model was further extended to conduct parametric studies with various parameters including width-to-thickness ratio (B/t), concrete strength, and axial compression level. The numerical analysis results reveal that with the same B/t and constituent materials, the higher the axial compression was, the lower the shear strength and the deformation capacity were. Also, the higher axial compression led to earlier local buckling of the steel tube, especially, in the case of the thinner steel wall (B/t of 41.7). The thicker steel wall (B/t of 20.8) resulted in higher strength and larger deformation capacity of the column. Increasing concrete material strength significantly improved the column’s shear strength for both thinner and thicker steel walls, but it led to significant development in deformation for the column having thicker steel walls. This study also reveals that only the square CFST columns with B/t of 20.8 using medium material strengths satisfy the seismic performance demand for the building columns in high seismic zones (ultimate interstory drift ratio (IDRu) not less than 3% radian) under high axial compression (up to 55% of

关键词： Concrete confinement effect Finite element analysis (FEA) model High axial compression Local buckling Seismic performance Square CFST columns Width-to-thickness ratio (B/t)

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Image-Based Criticality-Aware Fire Detection

Image-Based Criticality-Aware Fire Detection

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Frontiers of Information Technology (FIT)

作者： Alina Arshad Jawwad Ahmed Shamsi Muhammad Burhan Khan Narmeen Zakaria Bawany Department of Software Engineering National University of Computer and Emerging Sciences Karachi Pakistan Department of Computer Science National University of Computer and Emerging Sciences Karachi Pakistan Department of Electrical Engineering National University of Computer and Emerging Sciences Karachi Pakistan Faculty of Science Jinnah University for Women Karachi Pakistan

ISBN: (数字)9798331510503

ISBN: (纸本)9798331510510

Context-aware fire detection is a significant task in the era of new urban monitoring. Severe damage might result from fire events. To minimize the occurrence of these events, timely detection of fire accidents is necessary. Various machinelearning and deep learning methods are used for fire detection. Most of them do not focus on the extent of the fire or the damage it causes. All these methods need the detection of the criticality of the fire event. This work proposes an identification system based on Large Vision Models (LVMs) for fire detection. We proposed an intelligent criticality-aware fire detection system that can detect fire and its varying scales from small to large and generate alarming alerts accordingly. The model integrates advanced computer vision techniques with LVMs to explain the hidden context in a textual format. The system is tested on realtime data collected from surveillance cameras and achieves an accuracy of 86.67 % in correctly identifying the context-based criticality of the fire events depicted in the input data. This proposed framework can revolutionize surveillance applications by enhancing security and responding to crucial fire incidents in real-time. The system provides an efficient method for allocating resources and facilitates rapid response to incidents to prioritize activities intelligently. Using this approach, we identified and prioritized fire incidents in the urban environment.

关键词： Deep learning computer vision Surveillance Computational modeling Urban areas Cameras Real-time systems Security Information technology Context modeling

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Code Smell Detection Using Whale Optimization Algorithm

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computers, Materials & Continua 2021年第8期68卷 1919-1935页

作者： Moatasem M.Draz Marwa S.Farhan Sarah N.Abdulkader M.G.Gafar Department of Software Engineering Faculty of Computers and InformationKafrelsheikh UniversityKafr ElsheikhEgypt Faculty of Informatics and Computer Science British University in EgyptCairoEgypt Department of Information Systems Faculty of Computers and Artificial IntelligenceHelwan UniversityCairoEgypt Department of Computer Science Faculty of Computers and Artificial IntelligenceHelwan UniversityCairoEgypt Faculty of Computer Studies Arab Open UniversityCairoEgypt Department of Computer Science College of Science and Humanities in Al-SulailPrince Sattam bin Abdulaziz UniversityKharjSaudi Arabia Department of Machine Learning and Information Retrieval Faculty of Artificial IntelligenceKafrelsheikh UniversityKafr ElsheikhEgypt

software systems have been employed in many fields as a means to reduce human efforts;consequently,stakeholders are interested in more updates of their *** smells arise as one of the obstacles in the software *** are characteristics of software source code that indicate a deeper problem in *** smells appear not only in the design but also in software *** smells introduce bugs,affect software maintainability,and lead to higher maintenance *** code smells can be formulated as an optimization problem of finding the best detection *** researchers have recommended different techniques to improve the accuracy of code smell detection,these methods are still unstable and need to be *** research has sought only to discover a few at a time(three or five types)and did not set rules for detecting their *** research improves code smell detection by applying a search-based technique;we use the Whale Optimization Algorithm as a classifier to find ideal detection *** this algorithm,the Fisher criterion is utilized as a fitness function to maximize the between-class distance over the withinclass *** proposed framework adopts if-then detection rules during the software development life *** rules identify the types for both medium and large *** are conducted on five open-source software projects to discover nine smell types that mostly appear in *** proposed detection framework has an average of 94.24%precision and 93.4%*** accurate values are better than other search-based algorithms of the same *** proposed framework improves code smell detection,which increases software quality while minimizing maintenance effort,time,and ***,the resulting classification rules are analyzed to find the software metrics that differentiate the nine code smells.

关键词： software engineering intelligence search-based software engineering code smell detection software metrics whale optimization algorithm fisher criterion

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Training Artificial Neural Networks by Coordinate Search Algorithm

Training Artificial Neural Networks by Coordinate Search Alg...

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2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023

作者： Rokhsatyazdi, Ehsan Rahnamayan, Shahryar Miyandoab, Sevil Zanjani Bidgoli, Azam Asilian Tizhoosh, H.R. Lab Ontario Tech University Department of Electrical Computer and Software Engineering OshawaON Canada Brock University Department of Engineering St. CatharinesON Canada Wilfrid Laurier University Faculty of Science WaterlooON Canada Rhazes Lab Mayo Clinic Department of Artificial Intelligence and Informatics RochesterMN United States

ISBN: (纸本)9781665430654

Training Artificial Neural Networks (ANNs) poses a challenging and critical problem in machine learning. Despite the effectiveness of gradient-based learning methods, such as Stochastic Gradient Descent (SGD), in training neural networks, they do have several limitations. For instance, they require differentiable activation functions, and cannot optimize a model based on several independent non-differentiable loss functions simultaneously;for example, the F1-score, which is used during testing, can be used during training when a gradient-free optimization algorithm is utilized. Furthermore, the training (i.e., optimization of weights) in any DNN can be possible with a small size of the training dataset. To address these concerns, we propose an efficient version of the gradient-free Coordinate Search (CS) algorithm, an instance of General Pattern Search (GPS) methods, for training (i.e., optimizing) neural networks. The proposed algorithm can be used with non-differentiable activation functions and tailored to multi-objective/multi-loss problems. Finding the optimal values for weights of ANNs is a large-scale optimization problem. Therefore instead of finding the optimal value for each variable, which is the common technique in classical CS, we accelerate optimization and convergence by bundling the variables (i.e., weights). In fact, this strategy is a form of dimension reduction for optimization problems. Based on the experimental results, the proposed method is comparable with the SGD algorithm, and in some cases, it outperforms the gradient-based approach. Particularly, in situations with insufficient labeled training data, the proposed CS method performs better. The performance plots demonstrate a high convergence rate, highlighting the capability of our suggested method to find a reasonable solution with fewer function calls. As of now, the only practical and efficient way of training ANNs with hundreds of thousands of weights is gradient-based algorithms such

关键词： Artificial Neural Network (ANN) Coordinate Search Expensive Optimization Gradient-free Large-Scale Optimization Stochastic Gradient Descent (SGD)

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Editorial:Special Topic on Federated Learning for IoT and Edge Computing

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ZTE Communications 2022年第3期20卷 1-2页

作者： PAN Yi CUI Laizhong CAI Zhipeng LI Wei Faculty of Computer Science and Control Engineering at Shenzhen Institute of Advanced Technology Chinese Academy of SciencesChina the Academy of the United Nations Sciences and Technology Organization Ukrainian Academy of Engineering Sciences American Institute for Medical and Biological Engineering Institute of Engineering Technology the College of Computer Science and Software Engineering at Shenzhen University China Department of Computer Science and the College of Business at Georgia State University USA the Center for Distributed and High Performance Computing School of Computer ScienceThe University of Sydney the Australia-China Joint Research Center on Energy Informatics and Demand Response Technologies

Recent years have witnessed the proliferation of Internet of Things(IoT),in which billions of devices are connected to the Internet,generating an overwhelming amount of *** is challenging and infeasible to transfer an... 详细信息

关键词： cloud generating data.

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LDE-UNet: A Novel Model for Rapid COVID-19 Diagnosis via CT Image Segmentation

LDE-UNet: A Novel Model for Rapid COVID-19 Diagnosis via CT ...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： QiSong Wang JiaRou Wu YingZhuo Wang HuiLi Qu Shanling Nie Hai Yang Department of Computer Science and Engineering East China University of Science and Technology Shanghai China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China Faculty of Engineering The University of Sydney Australia

The COVID-19 pandemic has had a profound impact on human society. It has highlighted the need for faster diagnostic methods. Research has shown that combining semantic segmentation with traditional medical approaches can significantly accelerate the process. To address this, leveraging COVID-19 CT images, our team designs a revolutionary semantic segmentation model called Level of Detail Enhancement U-Net (LDE-UNet), which shows the lesion area on CT images. By introducing the LDE block, the model has the unique advantage of overcoming the loss of data details during the downsampling process by emphasizing and transmitting details at the same level. Our SOTA model outperforms the second-best model by at least 0.7% in the most critical indicator precision. Compared with other models, LDE-UNet’s strong reliability determines its ability to be used in the medical field to accelerate the localization and division of lesion areas on CT images by professional doctors, thus completing patient diagnosis faster. In addition, we also propose a standardized method for processing medical images.

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Hybrid Evolutionary Algorithm Based Relevance Feedback Approach for Image Retrieval

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computers, Materials & Continua 2022年第1期70卷 963-979页

作者： Awais Mahmood Muhammad Imran Aun Irtaza Qammar Abbas Habib Dhahri Esam Mohammed Asem Othman Arif Jamal Malik Aaqif Afzaal Abbasi College of Applied Computer Science King Saud University(Almuzahmiyah Campus)Riyadh11543Saudi Arabia Shaheed Zulfikar Ali Bhutto Institute of Science and Technology(Islamabad Campus) 44000Pakistan University of Engineering and Technology Taxila Pakistan International Islamic University Islamabad44000Pakistan Faculty of Sciences and Technology University of KairouanSidi Bouzid4352Tunisia Department of Software Engineering Foundation University IslamabadIslamabad44000Pakistan

Searching images fromthe large image databases is one of the potential research areas of multimedia *** most challenging task for nay CBIR system is to capture the high level semantic of *** researchers of multimedia domain are trying to fix this issue with the help of Relevance Feedback(RF).However existing RF based approaches needs a number of iteration to fulfill user’s *** paper proposed a novel methodology to achieve better results in early iteration to reduce the user interaction with the *** previous research work it is reported that SVM based RF approach generating better results for ***,this paper focused on SVM based RF *** enhance the performance of SVM based RF approach this research work applied Particle Swarm Optimization(PSO)and Genetic Algorithm(GA)before applying SVM on user *** main objective of using thesemeta-heuristic was to increase the positive image sample size from *** steps PSO is applied by incorporating the user feedback and secondly GA is applied on the result generated through PSO,finally SVM is applied using the positive sample generated through *** proposed technique is named as Particle Swarm Optimization Genetic Algorithm-Support Vector Machine Relevance Feedback(PSO-G A-SVMRF).Precisions,recall and F-score are used as performance metrics for the assessment and validation of PSO-GA-SVM-RF approach and experiments are conducted on coral image dataset having 10908 *** experimental results it is proved that PSO-GA-SVM-RF approach outperformed then various well known CBIR approaches.

关键词： Feature selection image retrieval particle swarm optimization

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Generating synthetic data on agricultural crops with DCGAN

Generating synthetic data on agricultural crops with DCGAN

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International Conference of Advanced Informatics: Concept, Theory and Application (ICAICTA)

作者： Fieter Brain Pasaribu Luh Joni Erawati Dewi Kadek Yota Ernanda Aryanto Ketut Agus Seputra Pariwate Varnakovida Ni Ketut Kertiasih Computer Science Universitas Pendidikan Ganesha Singaraja Indonesia Software Engineering Universitas Pendidikan Ganesha Singaraja Indonesia Faculty of Science King Mongkut's University of Technology Thonburi Thailand

ISBN: (数字)9798331520311

ISBN: (纸本)9798331520328

Convolutional Neural Network (CNN) is one of the deep learning architectures that is very effective for handling images. CNN is able to automatically extract important features from images, making it very suitable for various image processing tasks such as classification, object detection, and segmentation. However, even though CNN has great capabilities, one important thing to note is the amount of data. A considerable amount of data is needed for the CNN model to work optimally and avoid overfitting. To handle this problem, a synthetic data augmentation process is used using the Deep Convolutional Generative Adversarial Network (DCGAN) method. The generator network contained in the DCGAN model has a latent space dimension input whose value can vary. The size of the latent space dimension is very important in enabling data or image reconstruction during the training process. This study tested latent space dimension values on a corn plant dataset totaling 9159. The latent space values used in this experiment were 64, 100, and 128. In addition, this study also tested different batch sizes, namely 64 and 128. The model was evaluated using Fre'chet Inception Distance (FID) and Inception Score (IS). From the evaluation results, the best score on FID was 0.018001 and IS 1.239421. The greater the latent space value, the more realistic and clear the image results will be. Likewise, the smaller the batch size value, the more realistic and clear the image results.

关键词： Training Plants (biology) Generative adversarial networks Feature extraction Data augmentation Size measurement Data models Stability analysis Convolutional neural networks Synthetic data

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