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Oversampling-Enhanced Feature Fusion-Based Hybrid ViT-1DCNN Model for Ransomware Cyber Attack Detection

Computer Modeling in Engineering & Sciences 2025年第2期142卷 1667-1695页

作者： muhammad armghan latif Zohaib Mushtaq Saifur Rahman Saad Arif Salim Nasar Faraj Mursal muhammad Irfan Haris Aziz Department of Computer and Information System Cleveland State UniversityOhio44115USA Department of Electrical Electronics and Computer SystemsCollege of Engineering and TechnologyUniversity of SargodhaSargodha40100Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Department of Mechanical Engineering College of EngineeringKing Faisal UniversityAl Ahsa31982Saudi Arabia Department of Mechanical Industrial and Energy System EngineeringUniversity of SargodhaSargodha40100Pakistan

Ransomware attacks pose a significant threat to critical infrastructures,demanding robust detection *** study introduces a hybrid model that combines vision transformer(ViT)and one-dimensional convolutional neural network(1DCNN)architectures to enhance ransomware detection *** common challenges in ransomware detection,particularly dataset class imbalance,the synthetic minority oversampling technique(SMOTE)is employed to generate synthetic samples for minority class,thereby improving detection *** integration of ViT and 1DCNN through feature fusion enables the model to capture both global contextual and local sequential features,resulting in comprehensive ransomware *** on the UNSW-NB15 dataset,the proposed ViT-1DCNN model achieved 98%detection accuracy with precision,recall,and F1-score metrics surpassing conventional *** approach not only reduces false positives and negatives but also offers scalability and robustness for real-world cybersecurity *** results demonstrate the model’s potential as an effective tool for proactive ransomware detection,especially in environments where evolving threats require adaptable and high-accuracy solutions.

关键词： Ransomware attacks cybersecurity vision transformer convolutional neural network feature fusion encryption threat detection

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Improving Thyroid Disorder Diagnosis via Ensemble Stacking and Bidirectional Feature Selection

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Computers, Materials & Continua 2024年第3期78卷 4225-4241页

作者： muhammad armghan latif Zohaib Mushtaq Saad Arif Sara Rehman muhammad Farrukh Qureshi Nagwan Abdel Samee Maali Alabdulhafith Yeong Hyeon Gu Mohammed A.Al-masni Department of Computer and Information System Cleveland State UniversityOhio44115USA Department of Electrical Electronics and Computer SystemsCollege of Engineering and TechnologyUniversity of SargodhaSargodha40100Pakistan Department of Mechanical Engineering College of EngineeringKing Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Biomedical Engineering Riphah International UniversityIslamabad44000Pakistan Department of Electrical Engineering Riphah International UniversityIslamabad44000Pakistan Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Artificial Intelligence College of Software and Convergence TechnologySejong UniversitySeoul05006Korea

Thyroid disorders represent a significant global health challenge with hypothyroidism and hyperthyroidism as two common conditions arising from dysfunction in the thyroid *** and timely diagnosis of these disorders is crucial for effective treatment and patient *** research introduces a comprehensive approach to improve the accuracy of thyroid disorder diagnosis through the integration of ensemble stacking and advanced feature selection *** forward feature selection,sequential backward feature elimination,and bidirectional feature elimination are investigated in this *** ensemble learning,random forest,adaptive boosting,and bagging classifiers are *** effectiveness of these techniques is evaluated using two different datasets obtained from the University of California Irvine-Machine Learning Repository,both of which undergo preprocessing steps,including outlier removal,addressing missing data,data cleansing,and feature *** experimentation demonstrates the remarkable success of proposed ensemble stacking and bidirectional feature elimination achieving 100%and 99.86%accuracy in identifying hyperthyroidism and hypothyroidism,*** enhancing detection accuracy,the ensemble stacking model also demonstrated a streamlined computational complexity which is pivotal for practical medical *** significantly outperformed existing studies with similar objectives underscoring the viability and effectiveness of the proposed *** research offers an innovative perspective and sets the platform for improved thyroid disorder diagnosis with broader implications for healthcare and patient well-being.

关键词： Ensemble learning random forests boosting dimensionality reduction machine learning smart healthcare computer aided diagnosis

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Video Analytics Framework for Human Action Recognition

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Computers, Materials & Continua 2021年第9期68卷 3841-3859页

作者： muhammad Attique Khan Majed Alhaisoni Ammar armghan Fayadh Alenezi Usman Tariq Yunyoung Nam Tallha Akram Department of Computer Science HITEC University TaxilaTaxila47080Pakistan College of Computer Science and Engineering University of Ha’ilHa’ilSaudi Arabia Department of Electrical Engineering College of EngineeringJouf UniversitySakakaSaudi Arabia College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-KhrajSaudi Arabia Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea Department of Computer Science COMSATS University IslamabadWah Campus47040Pakistan

Human action recognition(HAR)is an essential but challenging task for observing human *** problem encompasses the observations of variations in human movement and activity identification by machine learning *** article addresses the challenges in activity recognition by implementing and experimenting an intelligent segmentation,features reduction and selection framework.A novel approach has been introduced for the fusion of segmented frames and multi-level features of interests are *** entropy-skewness based features reduction technique has been implemented and the reduced features are converted into a codebook by serial based fusion.A custom made genetic algorithm is implemented on the constructed features codebook in order to select the strong and wellknown *** features are exploited by a multi-class SVM for action *** experimental results are undertaken on four action datasets,namely,Weizmann,KTH,Muhavi,and WVU *** achieved the recognition rate of 96.80%,100%,100%,and 100%*** reveals that the proposed action recognition approach is efficient and well accurate as compare to existing approaches.

关键词： Video analytics action recognition features classification entropy data analytic

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Fruit Leaf Diseases Classification: A Hierarchical Deep Learning Framework

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Computers, Materials & Continua 2023年第4期75卷 1179-1194页

作者： Samra Rehman muhammad Attique Khan Majed Alhaisoni Ammar armghan Fayadh Alenezi Abdullah Alqahtani Khean Vesal Yunyoung Nam Department of Computer Science HITEC UniversityTaxila47080Pakistan Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Electrical Engineering College of EngineeringJouf UniversitySakakah72311Saudi Arabia College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversityAl-Kharj16242Saudi Arabia Department of ICT Convergence Soonchunhyang UniversityAsan31538Korea

Manual inspection of fruit diseases is a time-consuming and costly because it is based on naked-eye *** authors present computer vision techniques for detecting and classifying fruit leaf *** of computer vision techniques are preprocessing original images for visualization of infected regions,feature extraction from raw or segmented images,feature fusion,feature selection,and *** following are the major challenges identified by researchers in the literature:(i)lowcontrast infected regions extract irrelevant and redundant information,which misleads classification accuracy;(ii)irrelevant and redundant information may increase computational time and reduce the designed model’s *** paper proposed a framework for fruit leaf disease classification based on deep hierarchical learning and best feature *** the proposed framework,contrast is first improved using a hybrid approach,and then data augmentation is used to solve the problem of an imbalanced *** next step is to use a pre-trained deep model named Darknet53 and fine-tune ***,deep transfer learning-based training is carried out,and features are extracted using an activation function on the average pooling ***,an improved butterfly optimization algorithm is proposed,which selects the best features for classification using machine learning *** experiment was carried out on augmented and original fruit datasets,yielding a maximum accuracy of 99.6%for apple diseases,99.6%for grapes,99.9%for peach diseases,and 100%for cherry *** overall average achieved accuracy is 99.7%,higher than previous techniques.

关键词： Fruit diseases data augmentation contrast enhancement deep learning improved butterfly optimization

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Efficient and Cost-Effective Vehicle Detection in Foggy Weather for Edge/Fog-Enabled Traffic Surveillance and Collision Avoidance Systems

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Computers, Materials & Continua 2024年第10期81卷 911-931页

作者： Naeem Raza muhammad Asif Habib Mudassar Ahmad Qaisar Abbas Mutlaq BAldajani muhammad Ahsan latif Department of Computer Science National Textile UniversityFaisalabad37610Pakistan College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia Department of Computer Science University of AgricultureFaisalabad38000Pakistan

Vision-based vehicle detection in adverse weather conditions such as fog,haze,and mist is a challenging research area in the fields of autonomous vehicles,collision avoidance,and Internet of Things(IoT)-enabled edge/fog computing traffic surveillance and monitoring *** and cost-effective vehicle detection at high accuracy and speed in foggy weather is essential to avoiding road traffic collisions in *** evaluate vision-based vehicle detection performance in foggy weather conditions,state-of-the-art Vehicle Detection in Adverse Weather Nature(DAWN)and Foggy Driving(FD)datasets are self-annotated using the YOLO LABEL tool and customized to four vehicle detection classes:cars,buses,motorcycles,and *** state-of-the-art single-stage deep learning algorithms YOLO-V5,and YOLO-V8 are considered for the task of vehicle ***,YOLO-V5s is enhanced by introducing attention modules Convolutional Block Attention Module(CBAM),Normalized-based Attention Module(NAM),and Simple Attention Module(SimAM)after the SPPF module as well as YOLO-V5l with *** vehicle detection accuracy parameters and running speed is validated on cloud(Google Colab)and edge(local)*** mAP50 score of YOLO-V5n is 72.60%,YOLOV5s is 75.20%,YOLO-V5m is 73.40%,and YOLO-V5l is 77.30%;and YOLO-V8n is 60.20%,YOLO-V8s is 73.50%,YOLO-V8m is 73.80%,and YOLO-V8l is 72.60%on DAWN *** mAP50 score of YOLO-V5n is 43.90%,YOLO-V5s is 40.10%,YOLO-V5m is 49.70%,and YOLO-V5l is 57.30%;and YOLO-V8n is 41.60%,YOLO-V8s is 46.90%,YOLO-V8m is 42.90%,and YOLO-V8l is 44.80%on FD *** vehicle detection speed of YOLOV5n is 59 Frame Per Seconds(FPS),YOLO-V5s is 47 FPS,YOLO-V5m is 38 FPS,and YOLO-V5l is 30 FPS;and YOLO-V8n is 185 FPS,YOLO-V8s is 109 FPS,YOLO-V8m is 72 FPS,and YOLO-V8l is 63 FPS on DAWN *** vehicle detection speed of YOLO-V5n is 26 FPS,YOLO-V5s is 24 FPS,YOLO-V5m is 22 FPS,and YOLO-V5l is 17 FPS;and YOLO-V8n is 313 FPS,YOLO-V8s is 182 FPS,YOLO

关键词： Vehicle detection YOLO-V5 YOLO-V5s variants YOLO-V8 DAWN dataset foggy driving dataset IoT cloud/edge/fog computing

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12-Tungstophosphoric acid niched in Zr-based metal-organic framework:a stable and efficient catalyst for Friedel-Crafts acylation

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Science China Chemistry 2018年第4期61卷 402-411页

作者： latif Ullah Guoying Zhao Zichen Xu Hongyan He muhammad Usman Suojiang Zhang Beijing Key Laboratory of Ionic Liquids Clean Process Key Laboratory of Green Process EngineeringState Key Laboratory of Multiphase Complex SystemsInstitute of Process EngineeringChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China Center of Excellence in Nanotechnology King Fahd University of Petroleum and MineralsDhahran 3126Saudi Arabia

Heteropoly acids(HPA) are well known for their versatile solid acid catalysis in diverse chemical reactions, however they suffer from low surface area(<10 m^2/g) and leaching into the reactions media, which reduce their prospects as industrial ***, a novel hybrid material HPW@Zr-BTC,composed of 12-tungstophoric acid(HPW) and Zr^(Ⅳ)-benzene tri-carboxylate(Zr-BTC) metal-organic framework(MOF), was prepared via one-pot solvothermal method. Excellent HPW loading up to 32.3 wt% was achieved, and HPW@Zr-BTC composite proved to be highly stable, besides the crystalline morphology of Zr-BTC was intact. The catalytic activity of the hybrid composite was explored via Friedel-Crafts acylation of anisole with benzoyl *** 28.2 wt% HPW@Zr-BTC showed excellent catalytic performance, with 99.4% anisole conversion and 97.6% yield(pmethoxybenzophenone) under solvent free conditions. Excellent retention of catalytic activity was achieved after at least five consecutive runs due to non-observable HPW leaching. The promising activity and stability of the catalyst forecasted its potential industrial applications.

关键词： dodeca-tungstophosphoric acid Zr-BTC metal-organic frameworks one-pot solvothermal synthesis heterogeneouscatalysis anisole acylation high temperature XRD

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Crops Leaf Diseases Recognition:A Framework of Optimum Deep Learning Features

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Computers, Materials & Continua 2023年第1期74卷 1139-1159页

作者： Shafaq Abbas muhammad Attique Khan Majed Alhaisoni Usman Tariq Ammar armghan Fayadh Alenezi Arnab Majumdar Orawit Thinnukool Department of Computer Science HITEC UniversityTaxilaPakistan Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh 11671Saudi Arabia College of Computer Engineering and Science Prince Sattam Bin Abdulaziz UniversityAl-Kharaj11942Saudi Arabia Department of Electrical Engineering College of EngineeringJouf UniversitySakakahSaudi Arabia Faculty of Engineering Imperial College LondonLondonSW72AZUK College of Arts Mediaand TechnologyChiang Mai UniversityChiang Mai50200Thailand

Manual diagnosis of crops diseases is not an easy process;thus,a computerized method is widely *** couple of years,advancements in the domain ofmachine learning,such as deep learning,have shown substantial ***,they still faced some challenges such as similarity in disease symptoms and irrelevant features *** this article,we proposed a new deep learning architecture with optimization algorithm for cucumber and potato leaf diseases *** proposed architecture consists of five *** the first step,data augmentation is performed to increase the numbers of training *** the second step,pre-trained DarkNet19 deep model is opted and fine-tuned that later utilized for the training of fine-tuned model through transfer *** features are extracted from the global pooling layer in the next step that is refined using Improved Cuckoo search *** best selected features are finally classified using machine learning classifiers such as SVM,and named a few more for final classification *** proposed architecture is tested using publicly available datasets–Cucumber National Dataset and Plant *** proposed architecture achieved an accuracy of 100.0%,92.9%,and 99.2%,*** with recent techniques is also performed,revealing that the proposed method achieved improved accuracy while consuming less computational time.

关键词： Crops diseases preprocessing convolutional neural network features optimization machine learning

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A Systematic Literature Review of Machine Learning and Deep Learning Approaches for Spectral Image Classification in Agricultural Applications Using Aerial Photography

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Computers, Materials & Continua 2024年第3期78卷 2967-3000页

作者： Usman Khan muhammad Khalid Khan muhammad Ayub latif muhammad Naveed muhammad Mansoor Alam Salman A.Khan Mazliham Mohd Su’ud College of Computing and Information Sciences Karachi Institute of Economics and TechnologyKarachi75190Pakistan Faculty of Computing and Informatics(FCI) Multimedia UniversityCyberjaya63100Malaysia Faculty of Computing Riphah International UniversityIslamabad46000Pakistan Faculty of Engineering and Information Technology School of Computer ScienceUniversity of Technology SydneySydneyAustralia

Recently,there has been a notable surge of interest in scientific research regarding spectral *** potential of these images to revolutionize the digital photography industry,like aerial photography through Unmanned Aerial Vehicles(UAVs),has captured considerable *** encouraging aspect is their combination with machine learning and deep learning algorithms,which have demonstrated remarkable outcomes in image *** a result of this powerful amalgamation,the adoption of spectral images has experienced exponential growth across various domains,with agriculture being one of the prominent *** paper presents an extensive survey encompassing multispectral and hyperspectral images,focusing on their applications for classification challenges in diverse agricultural areas,including plants,grains,fruits,and *** meticulously examining primary studies,we delve into the specific agricultural domains where multispectral and hyperspectral images have found practical ***,our attention is directed towards utilizing machine learning techniques for effectively classifying hyperspectral images within the agricultural *** findings of our investigation reveal that deep learning and support vector machines have emerged as widely employed methods for hyperspectral image classification in ***,we also shed light on the various issues and limitations of working with spectral *** comprehensive analysis aims to provide valuable insights into the current state of spectral imaging in agriculture and its potential for future advancements.

关键词： Machine learning deep learning unmanned aerial vehicles multi-spectral images image recognition object detection hyperspectral images aerial photography

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Impairments Approximations in Assembled mmWave and Radio Over Fiber Network

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Computers, Materials & Continua 2022年第12期73卷 4885-4895页

作者： muhammad Irfan Farman Ali Fazal muhammad Saifur Rahman Ammar armghan Yousaf Khan Faisal Althobiani Rehan Shafiq Mohammed Alshareef Mohammad E.Gommosani Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Department of Electrical Engineering Qurtuba University of Science and ITDera Ismail Khan29050Pakistan Department of Electrical Engineering University of Engineering TechnologyMardan23200Pakistan Department of Electrical Engineering College of EngineeringJouf UniversitySakakaSaudi Arabia Faulty of Electrical Engineering University of Engineering and TechnologyPeshawarPakistan Faculty of Maritime Studies King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Electrical Engineering Sarhad University of Science and Information TechnologyPeshawar25220Khyber PukhtunkhwaPakistan Ports&Maritime Transportation Faculty of Maritime Studies King Abdulaziz University JeddahSaudi Arabia Nautical Science Department Faculty of Maritime StudiesKing Abdulaziz UniversityJeddahSaudi Arabia

The fiber nonlinearity and phase noise(PN)are the focused impairments in the optical communication system,induced by high-capacity transmission and high laser input *** channels include high-capacity transmissions that cannot be achieved at the end side without aliasing because of fiber nonlinearity and PN ***,addressing of these distortions is the basic objective for the 5G mobile *** this paper,the fiber nonlinearity and PN are investigated using the assembled methodology of millimeter-wave and radio over fiber(mmWave-RoF).The analytical model is designed in terms of outage probability for the proposed mmWave-RoF *** performance of mmWave-RoF against fiber nonlinearity and PN is studied for input power,output power and length using peak to average power ratio(PAPR)and bit error rate(BER)measuring *** simulation outcomes present that the impacts of fiber nonlinearity and PNcan be balanced for a huge capacity mmWave-RoF model applying input power carefully.

关键词： Fiber nonlinearity phase noise radio over fiber network advanced modulation system

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Velocity and Structural Modeling of Mesozoic Chiltan Limestone and Goru Formation for Hydrocarbon Evaluation in the Bitrisim Area, Lower Indus Basin, Pakistan

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Acta Geologica Sinica(English Edition) 2016年第1期90卷 258-275页

作者： Sarfraz KHAN Zahid latif muhammad HANIF Irfan U. JAN Shahid IQBAL National Centre of Excellence in Geology(NCEG) University of PeshawarPakistan Earth Sciences Department Quaid-e-Azam University(QAU)IslamabadPakistan

The present study focuses on building a workflow for structural interpretation and velocity modeling and implementing to Jurassic-Cretaceous succession （Chiltan Limestone and Massive sand of the Lower Goru Formation）. 2D-Migrated seismic sections of the area are used as data set and in order to confirm the presence of hydrocarbons in the study area, P and S-wave seismic velocities are estimated from single-component seismic data. Some specific issues in the use of seismic data for modeling and hydrocarbon evaluation need to deal with including distinguishing the reservoir and cap rocks, and the effects of faults, folds and presence of hydrocarbons on these rocks. This study has carried out the structural interpretation and modeling of the seismic data for the identification of traps. The results demonstrate existence of appropriate structural traps in the form of horst and grabens in the area. 2D and 3D velocity modeling of the horizons indicates the presence of high velocity zones in the eastern half of the study while relatively low velocity zones are encountered in the western half of the area. Two wells were drilled in the study area （i.e. Fateh-01 and Ichhri-01） and both are dry. Immature hydrocarbons migration is considered as a failure reason for Fateh-01 and Ichhri-01 well.

关键词： 2D and 3D Time-Depth models velocity modeling structural modeling Chiltan limestone Goru Formation

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