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Enhanced Gorilla Troops Optimizer with Deep Learning Enabled Cybersecurity Threat Detection

computer systems science & engineering 2023年第6期45卷 3037-3052页

作者： Fatma S.Alrayes Najm Alotaibi Jaber S.Alzahrani Sana Alazwari Areej Alhogail Ali M.Al-Sharafi Mahmoud Othman Manar Ahmed Hamza Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Prince Saud Al Faisal Institute for Diplomatic Studies RiyadhSaudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Information Systems College of Computer and Information SciencesKing Saud UniversitySaudi Arabia Department of Computer Science College of Computers and Information TechnologyUniversity of BishaSaudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Recent developments in computer networks and Internet of Things(IoT)have enabled easy access to *** the government and business sectors face several difficulties in resolving cybersecurity network issues,like novel attacks,hackers,internet criminals,and so ***,malware attacks and software piracy pose serious risks in compromising the security of *** can steal confidential data which results infinancial and reputational *** advent of machine learning(ML)and deep learning(DL)models has been employed to accomplish security in the IoT cloud *** article pre-sents an Enhanced Artificial Gorilla Troops Optimizer with Deep Learning Enabled Cybersecurity Threat Detection(EAGTODL-CTD)in IoT Cloud *** presented EAGTODL-CTD model encompasses the identification of the threats in the IoT cloud *** proposed EAGTODL-CTD mod-el mainly focuses on the conversion of input binaryfiles to color images,where the malware can be detected using an image classification *** EAG-TODL-CTD model pre-processes the input data to transform to a compatible *** threat detection and classification,cascaded gated recurrent unit(CGRU)model is exploited to determine class ***,EAGTO approach is employed as a hyperparameter optimizer to tune the CGRU parameters,showing the novelty of our *** performance evaluation of the EAGTODL-CTD model is assessed on a dataset comprising two class labels namely malignant and *** experimental values reported the supremacy of the EAG-TODL-CTD model with increased accuracy of 99.47%.

关键词： Cybersecurity computer networks threat detection internet of things cloud computing deep learning

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A novel deep learning approach for investigating liquid fuel injection in combustion system

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Discover Artificial Intelligence 2025年第1期5卷 1-14页

作者： Inam, Syed Azeem Khan, Abdullah Ayub Ahmed, Noor Mazhar, Tehseen Shahzad, Tariq Khan, Sunawar Saeed, Mamoon M. Hamam, Habib Department of Artificial Intelligence and Mathematical Science Sindh Madressatul Islam University Sindh Karachi Pakistan Department of Computer Science and Information Technology Shaheed Benazir Bhutto University Lyari Karachi Pakistan Department of Computer Science Shaheed Zulfiqar Ali Bhutto Institute of Science and Technology Karachi Pakistan Department of Computer Science School Education Department Government of Punjab Layyah31200 Pakistan School of Computer Science National College of Business Administration and Economics Lahore54000 Pakistan Department of Computer Engineering COMSATS University Islamabad Sahiwal Campus Sahiwal57000 Pakistan Department of Software Engineering Islamia University of Bahawalpur Bahawalnagar62300 Pakistan Sana’a00967 Yemen Faculty of Engineering Uni de Moncton MonctonNBE1A3E9 Canada Department of Electrical and Electronic Engineering Sciences University of Johannesburg Johannesburg South Africa Av. Grandes Ecoles BP 1989 Libreville Gabon Bridges for Academic Excellence Spectrum Center-ville Tunis Tunisia

The intricacies and instability of introducing cryogenic propellants into the combustion system have piqued the curiosity of scientists studying the procedure. The latest innovation is utilizing data-driven machine learning and deep learning approaches to gain deeper insights into the related difficulties. However, the current work serves as a baseline for future research because relatively few studies have used data-driven methodologies to assess the temperature of liquid fuel injections in combustion systems. The performance of Linear Regression (LR), Random Forest (RF), Extra Trees Regressor (ETR), Polynomial Regression (PR), Support Vector Regressor (SVR), Decision Tree Regressor (DTR), Gradient Boost Regressor (GBR), XGB Regressor (XGBoost), AdaBoost Regressor (ABR), K-Neighbors Regressor (KNR), Long-Short Term Memory (LSTM), Bi-LSTM (Bi-directional Long-Short Term Memory) has all been investigated in this study. The study also suggested a Fully Connected Neural Network (FCNN) to examine its performance and paired it with an Extra Tree Regressor (ETR). The coupled FCNN and Extra Tree Regressor outperform the other algorithms with a Mean Square Error (MSE) of 0.0000005062, Root Mean Square Error (RMSE) of 0.00071148, Mean Absolute Error (MAE) of 0.00020672, and R-squared (R2) value of 0.99998689. Linear Regression, Polynomial Regression, and Support Vector Regressor are found to be the least-performing algorithms. The current work uses machine learning and deep learning methods to make data-driven decisions for liquid fuel injection in the combustion system. © The Author(s) 2025.

关键词： Deep learning

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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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Biometric Verification System UsingHyperparameter Tuned Deep Learning Model

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computer systems science & engineering 2023年第7期46卷 321-336页

作者： Mohammad Yamin Saleh Bajaba Sarah B.Basahel E.Laxmi Lydia Department of Management Information Systems Faculty of Economics and AdministrationKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Business Administration Faculty of Economics and AdministrationKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Management Information Systems Faculty of Economics and AdministrationKing Abdulaziz UniversityJeddahSaudi Arabia Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India

Deep learning(DL)models have been useful in many computer vision,speech recognition,and natural language processing tasks in recent *** models seem a natural fit to handle the rising number of biometric recognition problems,from cellphone authentication to airport security *** approaches have recently been utilized to improve the efficiency of various biometric recognition *** recognition was considered the more reliable and accurate biometric detection method *** recognition has been an active research region in the last few decades due to its extensive applications,from security in airports to homeland security border *** article presents a new Political Optimizer with Deep Transfer Learning Enabled Biometric Iris Recognition(PODTL-BIR)*** presented PODTL-BIR technique recognizes the iris for biometric *** the presented PODTL-BIR model,an initial stage of pre-processing is carried *** addition,the MobileNetv2 feature extractor is utilized to produce a collection of feature *** PODTL-BIR technique utilizes a bidirectional gated recurrent unit(BiGRU)model to recognise iris for biometric ***,the political optimizer(PO)algorithm is used as a hyperparameter tuning strategy to improve the PODTL-BIR technique’s recognition ***-ranging experimental investigation was executed to validate the enhanced performance of the PODTL-BIR *** experimental outcome stated the promising performance of the PODTL-BIR system over other existing algorithms.

关键词： Biometric verification iris recognition political optimizer deep learning feature extraction

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Graph-Based Embedding Improvement Feature Distribution in Videos

Graph-Based Embedding Improvement Feature Distribution in Vi...

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2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Zhang, Junbin Tsai, Pei-Hsuan Tsai, Meng-Hsun National Cheng Kung University Department of Computer Science and Information Engineering Tainan Taiwan National Cheng Kung University Institute of Manufacturing Information and Systems Tainan Taiwan National Yang Ming Chiao Tung University Department of Computer Science Hsinchu Taiwan

ISBN: (纸本)9798350324174

Video understanding is a significant computer vision research subject since online video content is growing exponentially. Feature extraction and representation play a crucial role in video understanding tasks such as classification, segmentation, and recognition. However, the model's learning is ambiguous since adjacent video frames typically have similar RGB features. To address this issue, we present graph-based embedding to enhance video feature distribution. We construct a graph-structured of videos by connecting similar features. Node embedding is generated by utilizing a graph model. Experiments demonstrate that our approach effectively improves feature distribution. The graph attention network (GAT) improves accuracy and editing score by 4% over the visual model. © 2023 IEEE.

关键词： Graphic methods

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Cassava Root Disease Classification Using Deep Learning Techniques 3

Cassava Root Disease Classification Using Deep Learning Tech...

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3rd International Conference on Electrical, Electronics, information and Communication Technologies, ICEEICT 2024

作者： Seethalakshmi, K. Bhagyalakshmi, A. Anitha, G. Priyadarshini, S. Pavithra Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science & Engineering Tamil Nadu Chennai India S.A Engineering College Department of Computer Science and Business Systems Tamil Nadu Chennai India St Peter's Institute of Higher Education and Research Department of Information & Technology Tamil Nadu Chennai India

ISBN: (纸本)9798350369083

Crop like cassava plays a crucial role in regions facing food insecurity, serving as a primary food source. However, the susceptibility of cassava to various diseases poses a significant threat to crop yields and livelihoods. In this methodology, the input images undergoes a series of preprocessing steps initially. Scaling and Gaussian smoothing filters are applied to cancel out noise. After preprocessing, the images are subjected to segmentation using the UNet architecture to remove background. Next, feature extraction is conducted utilizing Convolutional Neural Networks (CNNs) to capture relevant features. After future extraction, a dense network is used for classification, facilitating the identification of affected images. With cassava root dataset images, the system achieves an impressive accuracy rate of 99.25%, outperforming existing methods. This advancement not only aids in safeguarding cassava production but also holds promise for the cassava production and export industries by providing an efficient means of detecting diseased crops, thus enabling prompt action to mitigate losses and ensure food security. Additionally, the ease of disease detection offered by the proposed system facilitates quality control in cassava production and enhances the competitiveness of cassava exports in the global market. © 2024 IEEE.

关键词： Convolutional neural networks

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Symbiotic Organisms Search with Deep Learning Driven Biomedical Osteosarcoma Detection and Classification

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computers, Materials & Continua 2023年第4期75卷 133-148页

作者： Abdullah M.Basahel Mohammad Yamin Sulafah M.Basahel Mona M.Abusurrah K.Vijaya Kumar E.Laxmi Lydia Faculty of Economics and Administration King Abdulaziz UniversityJeddahSaudi Arabia E-Commerce Department College of Administrative and Financial SciencesSaudi Electronic UniversityJeddahSaudi Arabia Department of Management Information Systems College of Business AdministrationTaibah UniversityAl-MadinahSaudi Arabia Department of Computer Science and Engineering GITAM School of TechnologyVisakhapatnam CampusGITAM(Deemed to be a University)India Department of Computer Science and Engineering GMR Institute of TechnologyAndhra PradeshRajamIndia

Osteosarcoma is one of the rare bone cancers that affect the individualsaged between 10 and 30 and it incurs high death rate. Early diagnosisof osteosarcoma is essential to improve the survivability rate and treatmentprotocols. Traditional physical examination procedure is not only a timeconsumingprocess, but it also primarily relies upon the expert’s *** this background, the recently developed Deep Learning (DL) models canbe applied to perform decision making. At the same time, hyperparameteroptimization of DL models also plays an important role in influencing overallclassification performance. The current study introduces a novel SymbioticOrganisms Search with Deep Learning-driven Osteosarcoma Detection andClassification (SOSDL-ODC) model. The presented SOSDL-ODC techniqueprimarily focuses on recognition and classification of osteosarcoma usinghistopathological images. In order to achieve this, the presented SOSDL-ODCtechnique initially applies image pre-processing approach to enhance the qualityof image. Also, MobileNetv2 model is applied to generate a suitable groupof feature vectors whereas hyperparameter tuning of MobileNetv2 modelis performed using SOS algorithm. At last, Gated Recurrent Unit (GRU)technique is applied as a classification model to determine proper class *** order to validate the enhanced osteosarcoma classification performance ofthe proposed SOSDL-ODC technique, a comprehensive comparative analysiswas conducted. The obtained outcomes confirmed the betterment of SOSDLODCapproach than the existing approaches as the former achieved a maximumaccuracy of 97.73%.

关键词： Osteosarcoma medical imaging deep learning feature vectors computer aided diagnosis image classification

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DEDGraph: Delay Embedding of Dynamic Graph for Temporal Action Segmentation 6

DEDGraph: Delay Embedding of Dynamic Graph for Temporal Acti...

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6th International Symposium on computer, Consumer and Control, IS3C 2023

ISBN: (纸本)9798350301953

In real-world interactive applications, where videos are generated in real-time and require immediate feedback, online segmentation has practical advantages over offline inference. Many excellent previous models have been developed for offline scenarios, while real-time prediction for temporal action segmentation (TAS) is a difficult task. Some interactive applications can tolerate a certain amount of delay. In this paper, we propose a node delay embedding of a dynamic graph for real-time TAS. We transform the video stream into a dynamic graph stream that evolves over time. We define past, current, and future nodes to construct sub-graphs at each step. Specifically, future nodes are sampled using our proposed node delay method. A graph model is utilized to aggregate past, current, and future node information to update the representation of current nodes and predict their labels. To the best of our knowledge, it is the first real-time TAS graph model with delay embedding. Experiments show that delay embedding enhances node representation and improves performance. Overall, our proposed approach provides a promising solution for real-time TAS. © 2023 IEEE.

关键词： Embeddings

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Adaptive-extremum seeking control algorithm to seek the odor source using a multi-agent system 41

Adaptive-extremum seeking control algorithm to seek the odor...

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第41届中国控制会议

作者： Meh Jabeen Qing-Hao Meng Li-Cheng Jin Sheng Jin Zhen-Yu Deng Institute of Robotics and Autonomous Systems School of Electrical and Information EngineeringTianjin University Department of Software Engineering The University of Lahore Tianjin Fire Science and Technology Research Institute of MEM

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

This study aims to seek the odor source using a multi-agent *** main challenge of the odor source seeking is to search the optimal extremum in the presence of multiple local extrema in a gradient complex *** this,an adaptive-extremum seeking control(adaptive-ESC) algorithm has been *** are navigated to the vicinity of the source based on an estimated gradient using *** the proposed algorithm,both the integral gain and the amplitude of the perturbation signal are adjusted *** local convergence of the algorithm has been proved using averaging *** performance of the proposed algorithm is validated through simulations by employing a multi-agent system.

关键词： Odor source seeking Extremum seeking control Adaption approaches

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A hybrid approach for medical images classification and segmentation to reduce complexity

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Innovations in systems and software engineering 2023年第1期19卷 33-46页

作者： Kumar, Ankit Bhatia, Surbhi Bhardwaj, Rajat Singh, Kamred Udham varshney, Neeraj Raja, Linesh Department of Computer Engineering and Applications GLA University Chaumuhan Uttar Pradesh Mathura281406 India Department of Information Systems College of Computer Science and Information Technology King Faisal University Dammam31451 Saudi Arabia Chitkara University Institute of Engineering and Technology Chitkara University Chandigarh-Patiala National Highway Punjab Rajpura140401 India Department of Computer Science and Information Engineering National Cheng Kung University Dasyue Street Tainan Tainan701 Taiwan School of Computing Graphic Era Hill University Bell Road Uttarakhand Dehradun248002 India Department of Computer Apllications Manipal University Jaipur Rajasthan Jaipur303007 India

The computational domain facilitates the performance of novel and innovative medical research and development tasks by providing support and computational power. This analysis method forecasts the future by analyzing the data we now have. The method may be divided into three primary phases: preprocessing, feature extraction, and classification. The research presented here aimed to improve the precision with which heart disease could be predicted across three distinct phases. The first step is thoroughly examining the databases kept at the UCI computer repository. In this study, we use the dataset’s five different algorithms, decision tree, Naive Bayes, random forest, KNN, and support vector machine, to compare their respective performances. In addition to age, the suggested revolutionary technique considers other characteristics such as pulse rate, cholesterol, and so on, which was not the case in earlier studies. In the past, age was the primary consideration in analysis and illness prediction. Compared to more traditional methods, improved prediction accuracy is achieved by modifying the study’s primitive properties. Third, this research introduced a novel hybrid classification model by fusing support vector machines and k-nearest neighbor classification techniques. A k-nearest neighbor classifier will do the heavy lifting to classify the data, while support vector machines will extract the dataset’s features. The accuracy rates for the various prediction methods decision tree, KNN, Naive Bayes, random forest, support vector machine, and proposed method range from 72.53% to 87.32% to 87.39% to 81.34%, respectively. The new technique decreases execution value by 5 % and increases accuracy by up to 8 %. The suggested model outperforms state-of-the-art approaches in terms of accuracy and implementation speed. © 2022, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.

关键词： Decision trees

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