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Intelligent Machine Learning Based EEG Signal Classification Model

Computers, Materials & Continua 2022年第4期71卷 1821-1835页

作者： Mesfer Al Duhayyim Haya Mesfer Alshahrani Fahd N.Al-Wesabi Mohammed Abdullah Al-Hagery Anwer Mustafa Hilal Abu sarwar Zaman Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversitySaudi Arabia Department of Computer Science King Khalid UniversityMuhayel AseerSaudi Arabia&Faculty of Computer and ITSana’a UniversitySana’aYemen Department of Computer Science College of ComputerQassim UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlkharjSaudi Arabia

In recent years,Brain-Computer Interface(BCI)system gained much popularity since it aims at establishing the communication between human brain and *** systems are applied in several research areas such as neuro-rehabilitation,robots,exoeskeletons,***(EEG)is a technique commonly applied in capturing brain *** is incorporated in BCI systems since it has attractive features such as noninvasive nature,high time-resolution output,mobility and *** classification process is highly essential in decision making process and it incorporates different processes namely,feature extraction,feature selection,and *** this motivation,the current research paper presents an Intelligent Optimal Fuzzy Support Vector Machine-based EEC recognition(IOFSVM-EEG)model for BCI *** Component Analysis(ICA)technique is applied onto the proposed IOFSVM-EEG model to remove the artefacts that exist in EEG signal and to retain the meaningful EEG ***,Common Spatial Pattern(CSP)-based feature extraction technique is utilized to derive a helpful set of feature vectors from the preprocessed EEG ***,OFSVM method is applied in the classification of EEG signals,in which the parameters involved in FSVM are optimally tuned using Grasshopper Optimization Algorithm(GOA).In order to validate the enhanced EEG recognition outcomes of the proposed IOFSVM-EEG model,an extensive set of experiments was *** outcomes were examined under distinct *** experimental results highlighted the enhanced performance of the presented IOFSVM-EEG model over other state-of-the-art methods.

关键词： Brain computer interface EEG recognition human computer interface machine learning parameter tuning FSVM

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Artificial Intelligence Enabled Apple Leaf Disease Classification for Precision Agriculture

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Computers, Materials & Continua 2022年第3期70卷 6223-6238页

作者： Fahd N.Al-Wesabi Amani Abdulrahman Albraikan Anwer Mustafa Hilal Majdy M.Eltahir Manar Ahmed Hamza Abu sarwar Zamani Department of Computer Science King Khalid UniversityMuhayel AseerSaudi Arabia Faculty of Computer and IT Sana’a UniversityYemen Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlkharjSaudi Arabia

Precision agriculture enables the recent technological advancements in farming sector to observe,measure,and analyze the requirements of individual fields and *** recent developments of computer vision and artificial intelligence(AI)techniques find a way for effective detection of plants,diseases,weeds,pests,*** the other hand,the detection of plant diseases,particularly apple leaf diseases using AI techniques can improve productivity and reduce crop ***,earlier and precise apple leaf disease detection can minimize the spread of the *** works make use of traditional image processing techniques which cannot assure high detection rate on apple leaf *** this motivation,this paper introduces a novel AI enabled apple leaf disease classification(AIE-ALDC)technique for precision *** proposed AIE-ALDC technique involves orientation based data augmentation and Gaussian filtering based noise removal *** addition,the AIE-ALDC technique includes a Capsule Network(CapsNet)based feature extractor to generate a helpful set of feature ***,water wave optimization(WWO)technique is employed as a hyperparameter optimizer of the CapsNet ***,bidirectional long short term memory(BiLSTM)model is used as a classifier to determine the appropriate class labels of the apple leaf *** design of AIE-ALDC technique incorporating theWWO based CapsNetmodel with BiLSTM classifier shows the novelty of the *** range of experiments was performed to showcase the supremacy of the AIE-ALDC *** experimental results demonstrate the promising performance of the AIEALDC technique over the recent state of art methods.

关键词： Artificial intelligence apple leaf plant disease precision agriculture deep learning data augmentation

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Liver-Tumor Detection Using CNN ResUNet

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Computers, Materials & Continua 2021年第5期67卷 1899-1914页

作者： Muhammad Sohaib Aslam Muhammad Younas Muhammad Umar sarwar Muhammad Arif Shah Atif Khan MIrfan Uddin Shafiq Ahmad Muhammad Firdausi Mazen Zaindin Department of Computer Science Government College University FaisalabadFaisalabad38000Pakistan Department of IT and Computer Science Pak-Austria Fachhochschule Institute of Applied Sciences and TechnologyHaripur22620Pakistan Department of Computer Science Islamia College PeshawarPeshawarKPPakistan Institute of Computing Kohat University of Science and TechnologyKohatPakistan Department of Industrial Engineering College of EngineeringKing Saud UniversityRiyadh11421Saudi Arabia Department of Statistics and Operations Research College of ScienceKing Saud UniversityRiyadh11421Saudi Arabia

Liver tumor is the fifth most occurring type of tumor in men and the ninth most occurring type of tumor in women according to recent reports of Global cancer statistics *** are several imaging tests like Computed Tomography(CT),Magnetic Resonance Imaging(MRI),and ultrasound that can diagnose the liver tumor after taking the sample from the tissue of the *** tests are costly and *** paper proposed that image processing through deep learning Convolutional Neural Network(CNNs)ResUNet model that can be helpful for the early diagnose of tumor instead of conventional *** existing studies have mainly used the two Cascaded CNNs for liver segmentation and evaluation of Region Of Interest(ROI).This study uses ResUNet,an updated version of U-Net and ResNet Models that utilize the service of Residential *** apply over method on the 3D-IRCADb01 dataset that is based on CT slices of liver tumor affected *** results showed the True Value Accuracy around 99%and F1 score performance around 95%.This method will be helpful for early and accurate diagnose of the Liver tumor to save the lives of many patients in the field of Biotechnology.

关键词： Liver tumor diagnose ResUNet CNNs segmentation

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Enhanced Crow Search with Deep Learning-Based Cyberattack Detection in SDN-IoT Environment

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Intelligent Automation & Soft Computing 2023年第6期36卷 3157-3173页

作者： Abdelwahed Motwakel Fadwa Alrowais Khaled Tarmissi Radwa Marzouk Abdullah Mohamed Abu sarwar Zamani Ishfaq Yaseen Mohamed I.Eldesouki Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversityMeccaSaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt Department of Information System College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAlKharj16436Saudi Arabia

The paradigm shift towards the Internet of Things(IoT)phe-nomenon and the rise of edge-computing models provide massive poten-tial for several upcoming IoT applications like smart grid,smart energy,smart home,smart health and smart transportation ***,it also provides a sequence of novel cyber-security *** IoT networks provide several advantages,the heterogeneous nature of the network and the wide connectivity of the devices make the network easy for *** result in financial loss and data breaches for organizations and ***,it becomes crucial to secure the IoT environment from such *** this motivation,the current study introduces an effectual Enhanced Crow Search Algorithm with Deep Learning-Driven Cyberattack Detection(ECSADL-CAD)model for the Software-Defined Networking(SDN)-enabled IoT *** presented ECSADL-CAD approach aims to identify and classify the cyberattacks in the SDN-enabled IoT *** attain this,the ECSADL-CAD model initially pre-processes the *** the presented ECSADL-CAD model,the Reinforced Deep Belief Network(RDBN)model is employed for attack *** last,the ECSA-based hyperparameter tuning process gets executed to boost the overall classification outcomes.A series of simulations were conducted to validate the improved outcomes of the proposed ECSADL-CAD *** experimental outcomes confirmed the superiority of the proposed ECSADL-CAD model over other existing methodologies.

关键词： Software defined networks artificial intelligence cybersecurity deep learning internet of things

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3D Head Pose Estimation through Facial Features and Deep Convolutional Neural Networks

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Computers, Materials & Continua 2021年第2期66卷 1757-1770页

作者： Khalil Khan Jehad Ali Kashif Ahmad Asma Gul Ghulam sarwar Sahib Khan Qui Thanh Hoai Ta Tae-Sun Chung Muhammad Attique Department of Computer Science and Software Engineering Pak-Austria Fachhochschule:Institute of Applied Sciences and TechnologyHaripur-KPKPakistan Department of Computer Engineering and Department of AI Convergence Network Ajou UniversitySuwon16499South Korea Hamad Bin Khalifa University DohaQatar Department of Statistics Shaheed Benazir Bhutto Women UniversityPeshawarPakistan Department of Software Engineering University of Azad Jammu and KashmirPakistan Department of Electronics and Telecommunications Politecnico di TorinoTorino10129Italy Institute of Research and Development Duy Tan UniversityDanang550000Vietnam Department of Computer Engineering Ajou UniversitySuwon16499Korea Department of Software Sejong UniversitySeoul05006South Korea

Face image analysis is one among several important cues in computer *** the last five decades,methods for face analysis have received immense attention due to large scale applications in various face analysis *** parsing strongly benefits various human face image analysis tasks inducing face pose *** this paper we propose a 3D head pose estimation framework developed through a prior end to end deep face parsing *** have developed an end to end face parts segmentation framework through deep convolutional neural networks(DCNNs).For training a deep face parts parsing model,we label face images for seven different classes,including eyes,brows,nose,hair,mouth,skin,and *** extract features from gray scale images by using *** train a classifier using the extracted *** use the probabilistic classification method to produce gray scale images in the form of probability maps for each dense semantic *** use a next stage of DCNNs and extract features from grayscale images created as probability maps during the segmentation *** assess the performance of our newly proposed model on four standard head pose datasets,including Pointing’04,Annotated Facial Landmarks in the Wild(AFLW),Boston University(BU),and ICT-3DHP,obtaining superior results as compared to previous results.

关键词： Face image analysis face parsing face pose estimation

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Deep Reinforcement Learning Enabled Smart City Recycling Waste Object Classification

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Computers, Materials & Continua 2022年第6期71卷 5699-5715页

作者： Mesfer Al Duhayyim Taiseer Abdalla Elfadil Eisa Fahd NAl-Wesabi Abdelzahir Abdelmaboud Manar Ahmed Hamza Abu sarwar Zamani Mohammed Rizwanullah Radwa Marzouk Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Information Systems-Girls Section King Khalid UniversityMahayil62529Saudi Arabia Department of Computer Science King Khalid UniversityMuhayel AseerSaudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen Department of Information Systems King Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversitySaudi Arabia Department of Mathematics Faculty of ScienceCairo UniversityGiza12613Egypt Corresponding Author:Manar Ahmed Hamza.Email:ma.hamza@psau.edu.sa

The Smart City concept revolves around gathering real time data from citizen,personal vehicle,public transports,building,and other urban infrastructures like power grid and waste disposal *** understandings obtained from the data can assist municipal authorities handle assets and services *** the same time,the massive increase in environmental pollution and degradation leads to ecological imbalance is a hot research ***,the progressive development of smart cities over the globe requires the design of intelligent waste management systems to properly categorize the waste depending upon the nature of *** of the commonly available wastes are paper,paper boxes,food,glass,*** order to classify the waste objects,computer vision based solutions are cost effective to separate out the waste from the huge dump of garbage and *** to the recent developments of deep learning(DL)and deep reinforcement learning(DRL),waste object classification becomes possible by the identification and detection of *** this aspect,this paper designs an intelligence DRL based recycling waste object detection and classification(IDRL-RWODC)model for smart *** goal of the IDRLRWODC technique is to detect and classify waste objects using the DL and DRL *** IDRL-RWODC technique encompasses a twostage process namely Mask Regional Convolutional Neural Network(Mask RCNN)based object detection and DRL based object *** addition,DenseNet model is applied as a baseline model for the Mask RCNN model,and a deep Q-learning network(DQLN)is employed as a ***,a dragonfly algorithm(DFA)based hyperparameter optimizer is derived for improving the efficiency of the DenseNet *** order to ensure the enhanced waste classification performance of the IDRL-RWODC technique,a series of simulations take place on benchmark dataset and the experimental results pointed out the better performance over the recent techniques

关键词： Smart cities deep reinforcement learning computer vision image classification object detection waste management

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Ureteroscopic Management of Ureteral Calculi: Pneumatic versus Holmium: YAG Laser Lithotripsy

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Open Journal of Urology 2016年第3期6卷 36-42页

作者： sarwar Noori Mahmood Diar Hameed Bajalan Department of Surgery School of Medicine University of Sulaimania Iraq

Background: Among various intracorporeal lithotripters, pneumatic lithotripter has become the widely used tool for the treatment of urinary stones. Recently the holmium: YAG laser has been used with a wide range of potential urological applications, including intracorporeal lithotripsy of urinary calculi. Purpose: Compare the effectiveness and complications of treatment for ureteric stones between holmium laser lithotripsy and pneumatic lithotripsy. Material and Methods: Comparison of 100 patients presented with ureteric stones, group one (50) of whom were treated with pneumatic lithotripsy and group two (50) with holmium laser was done and the effectiveness and complications of both were analyzed. Results: There was no difference in patient age, sex, stone size and location of stones between the two groups. The immediate stone free rates were 88% in the holmium: YAG group and 66% in the pneumatic lithotripsy group (p < 0.05). The four weeks stone free rates were 98% and 94% respectively (p = 0.07). The mean ± SD operative time in the holmium: YAG group (40 ± 26 min) was shorter than those with pneumatic lithotripsy group (60 ± 40 min). Postoperative stay in hospital was less than 24 hours in holmium: YAG group (70.4%) and shorter than those for pneumatic group (29.6%) (p < 0.002). Post treatment complications such as ureteral perforation were encountered in only two patients who underwent pneumatic lithotripsy. Other complications, such as mucosal injury, ureteral perforation and postoperative fever;there was no statistically significant difference between the two groups. While light hematuria was found more frequently in the pneumatic group (14%) in comparison to laser group (12%) (p = 0.02). On the other hand, the overall complication rates between the two groups was statistically significant (8%) laser group vs. (32%) pneumatic group (p = 0.003). Conclusions: Holmium: YAG laser lithotripsy was associated with shorter operation time and postoperative hospitalizat

关键词： Ureteral Calculus Ureteroscopy Lithotripsy Laser

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Gaussian Optimized Deep Learning-based Belief Classification Model for Breast Cancer Detection

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Computers, Materials & Continua 2022年第11期73卷 4123-4138页

作者： Areej A.Malibari Marwa Obayya Mohamed K.Nour Amal S.Mehanna Manar Ahmed Hamza Abu sarwar Zamani Ishfaq Yaseen Abdelwahed Motwakel Department of Industrial and Systems Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh 11671Saudi Arabia Department of Biomedical Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia

With the rapid increase of new cases with an increased mortality rate,cancer is considered the second and most deadly disease *** cancer is the most widely affected cancer worldwide,with an increased death rate *** to radiologists’processing of mammogram images,many computer-aided diagnoses have been developed to detect breast *** detection of breast cancer will reduce the death rate *** early diagnosis of breast cancer using the developed computer-aided diagnosis(CAD)systems still needed to be enhanced by incorporating innovative deep learning technologies to improve the accuracy and sensitivity of the detection system with a reduced false positive *** paper proposed an efficient and optimized deep learning-based feature selection approach with this *** model selects the relevant features from the mammogram images that can improve the accuracy of malignant detection and reduce the false alarm *** learning is used in the extraction of features *** ext,a convolution neural network,is used to extract the *** two feature vectors are fused and optimized with enhanced Butterfly Optimization with Gaussian function(TL-CNN-EBOG)to select the final most relevant *** optimized features are applied to the classifier called Deep belief network(DBN)to classify the benign and malignant *** feature extraction and classification process used two datasets,breast,and *** to the existing methods,the optimized deep learning-based model secured 98.6%of improved accuracy on the breast dataset and 98.85%of improved accuracy on the MIAS dataset.

关键词： Breast cancer detection computer-aided diagnosis(CAD) deep learning CNN entropy butterfly optimization

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Safety of Overnight Hospitalization after Transurethral Resection of Prostate

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Open Journal of Urology 2016年第1期6卷 1-6页

作者： sarwar N. Mahmood Ismaeel Aghaways Department of Surgery Faculty of Medical Sciences School of Medicine University of Sulaymaniyah Sulaymaniyah Iraq

Background: Monopolar transurethral resection of prostate has long been a standard method of managements of benign prostatic hyperplasia. The safe and superior efficacy of transurethral resection of prostate (TURP) always argues strongly for maintaining it as the primary mode of therapy for patients with benign prostatic hyperplasia (BPH). There is a trend toward early catheter removal after transurethral resection of prostate (TURP) even to the extent of performing it as a day case. We explored the safety and feasibility of early catheter removal and discharging the patient without catheter after TURP. Materials and methods: Forty patients who underwent monopolar TURP were included in a prospective study. The decision to remove catheters on the first morning after surgery was based on the color of the catheter effluent, absence of clots, normal vital signs and adequate urine output. Patients who voided successfully were discharged on the same day as catheter removal. Results: Among the forty patients whose catheters were removed on first postoperative day, 38 patients (95%) voided successfully, and were discharged on the same day. However, two out of forty patients (5%) were recatheterized due to urethral discomfort during micturition. The catheter was removed on the next day. Mean overall duration of catheterization was 18.36 hours, and overall length of patient hospitalization was 21.68 hours. Conclusions: Overnight hospitalization and early catheter removal after transurethral prostatectomy are an appropriate, safe and effective way of patient care with minimal morbidity.

关键词： Benign Prostatic Hyperplasia Catheter Removal Length of Hospital Stay Transurethral Resection of Prostate

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Weather Forecasting Prediction Using Ensemble Machine Learning for Big Data Applications

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Computers, Materials & Continua 2022年第11期73卷 3367-3382页

作者： Hadil Shaiba Radwa Marzouk Mohamed K Nour Noha Negm Anwer Mustafa Hilal Abdullah Mohamed Abdelwahed Motwakel Ishfaq Yaseen Abu sarwar Zamani Mohammed Rizwanullah Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Faculty of Science Mathematics and Computer Science DepartmentMenoufia UniversityEgypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt

The agricultural sector’s day-to-day operations,such as irrigation and sowing,are impacted by the ***,weather constitutes a key role in all regular human *** forecasting must be accurate and precise to plan our activities and safeguard ourselves as well as our property from ***,wind speed,humidity,wind direction,cloud,temperature,and other weather forecasting variables are used in this work for weather *** research works have been conducted on weather *** drawbacks of existing approaches are that they are less effective,inaccurate,and *** overcome these issues,this paper proposes an enhanced and reliable weather forecasting *** well as developing weather forecasting in remote *** data analysis and machine learning techniques,such as Gradient Boosting Decision Tree,Random Forest,Naive Bayes Bernoulli,and KNN Algorithm are deployed to anticipate weather conditions.A comparative analysis of result outcome said in determining the number of ensemble methods that may be utilized to improve the accuracy of prediction in weather *** aim of this study is to demonstrate its ability to predict weather forecasts as soon as *** evaluation shows our ensemble technique achieves 95%prediction ***,for 1000 nodes it is less than 10 s for prediction,and for 5000 nodes it takes less than 40 s for prediction.

关键词： Weather forecasting KNN random forest gradient boosting decision tree naive bayes bernoulli

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