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SC Currents Minimization in Distributed Generation Embedded Distribution Networks with Optimal Application of FCLs

CSEE Journal of Power and Energy Systems 2022年第5期8卷 1388-1397页

作者： irtaza sohail Babar Hussain M.Abubakar Intisar Ali Sajjad M.Faisal Nadeem Muhammad Sarwar Fauji Fertilizer Company Sadiqabad 64350Pakistan EED UETTaxilaPunjab 47050Pakistan

For meeting the increased demand of electrical power,distributed generation(DG)based on renewable energy resources(RERs),has become a potential alternative to large plants based on fossil *** power from environmentally friendly RERs is available at a competitive price due to technological advancements in recent ***,optimal allocation of DG at the distribution network(DN)level may result in power loss reduction,improvement in voltage profile and the network’s overall ***,integration of DG may increase the short circuit(SC)level beyond the capacity of the protection gear,conductors,transformers and other components of a *** high short circuit currents(SCCs)may be reduced by application of a fault current limiter(FCL).However,most of the existing literature proposes optimization of FCLs size by considering only normal configurations of a *** approach is inappropriate as it may fail to produce the desired reduction in SCCs in different N−1 contingency *** this paper,a new strategy is presented that considers both normal as well as various contingency situations for optimal allocation of FCLs in a DN with DG *** strategy is implemented in the IEEE 30-bus system in a MATLAB environment using a genetic algorithm(GA).The simulation results prove that the proposed strategy is effective in determining the optimal FCLs size that restricts the SCCs to a safe level in different operating conditions including N−1 contingencies and thus,improves network safety and *** strategy described in the paper can play an important role in DN planning involving optimal application of DG and FCLs.

关键词： Distributed generation fault current limiters genetic algorithm N−1 contingency optimization

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Modified Anam-Net Based Lightweight Deep Learning Model for Retinal Vessel Segmentation

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Computers, Materials & Continua 2022年第10期73卷 1501-1526页

作者： Syed irtaza Haider Khursheed Aurangzeb Musaed Alhussein College of Computer and Information Sciences King Saud UniversityRiyadh11543Saudi Arabia Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia

The accurate segmentation of retinal vessels is a challenging taskdue to the presence of various pathologies as well as the low-contrast ofthin vessels and non-uniform illumination. In recent years, encoder-decodernetworks have achieved outstanding performance in retinal vessel segmentation at the cost of high computational complexity. To address the aforementioned challenges and to reduce the computational complexity, we proposea lightweight convolutional neural network (CNN)-based encoder-decoderdeep learning model for accurate retinal vessels segmentation. The proposeddeep learning model consists of encoder-decoder architecture along withbottleneck layers that consist of depth-wise squeezing, followed by fullconvolution, and finally depth-wise stretching. The inspiration for the proposed model is taken from the recently developed Anam-Net model, whichwas tested on CT images for COVID-19 identification. For our lightweightmodel, we used a stack of two 3 × 3 convolution layers (without spatialpooling in between) instead of a single 3 × 3 convolution layer as proposedin Anam-Net to increase the receptive field and to reduce the trainableparameters. The proposed method includes fewer filters in all convolutionallayers than the original Anam-Net and does not have an increasing numberof filters for decreasing resolution. These modifications do not compromiseon the segmentation accuracy, but they do make the architecture significantlylighter in terms of the number of trainable parameters and computation *** proposed architecture has comparatively fewer parameters (1.01M) thanAnam-Net (4.47M), U-Net (31.05M), SegNet (29.50M), and most of the otherrecent works. The proposed model does not require any problem-specificpre- or post-processing, nor does it rely on handcrafted features. In addition,the attribute of being efficient in terms of segmentation accuracy as well aslightweight makes the proposed method a suitable candidate to be used in thescreening platforms at

关键词： Anam-Net convolutional neural network cross-database training data augmentation deep learning fundus images retinal vessel segmentation semantic segmentation

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Refined Anam-Net:Lightweight Deep Learning Model for Improved Segmentation Performance of Optic Cup and Disc for Glaucoma Diagnosis

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Computers, Materials & Continua 2024年第7期80卷 1381-1405页

作者： Khursheed Aurangzeb Syed irtaza Haider Musaed Alhussein Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia

In this work,we aim to introduce some modifications to the Anam-Net deep neural network(DNN)model for segmenting optic cup(OC)and optic disc(OD)in retinal fundus images to estimate the cup-to-disc ratio(CDR).The CDR is a reliable measure for the early diagnosis of *** this study,we developed a lightweight DNN model for OC and OD segmentation in retinal fundus *** DNN model is based on modifications to Anam-Net,incorporating an anamorphic depth embedding *** reduce computational complexity,we employ a fixed filter size for all convolution layers in the encoder and decoder stages as the network *** modification significantly reduces the number of trainable parameters,making the model lightweight and suitable for resource-constrained *** evaluate the performance of the developed model using two publicly available retinal image databases,namely RIM-ONE and *** results demonstrate promising OC segmentation performance across most standard evaluation metrics while achieving analogous results for OD *** used two retinal fundus image databases named RIM-ONE and Drishti-GS that contained 159 images and 101 retinal images,*** OD segmentation using the RIM-ONE we obtain an f1-score(F1),Jaccard coefficient(JC),and overlapping error(OE)of 0.950,0.9219,and 0.0781,***,for OC segmentation using the same databases,we achieve scores of 0.8481(F1),0.7428(JC),and 0.2572(OE).Based on these experimental results and the significantly lower number of trainable parameters,we conclude that the developed model is highly suitable for the early diagnosis of glaucoma by accurately estimating the CDR.

关键词： Refined Anam-Net parameter tuning deep learning optic cup optic disc cup-to-disc ratio glaucoma diagnosis

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Recognition and Detection of Diabetic Retinopathy Using Densenet-65 Based Faster-RCNN

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Computers, Materials & Continua 2021年第5期67卷 1333-1351页

作者： Saleh Albahli Tahira Nazir Aun irtaza Ali Javed Department of Information Technology College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Computer Science University of Engineering and TechnologyTaxila47050Pakistan Department of Software Engineering University of Engineering and TechnologyTaxila47050Pakistan

Diabetes is a metabolic disorder that results in a retinal complication called diabetic retinopathy(DR)which is one of the four main reasons for sightlessness all over the *** usually has no clear symptoms before the onset,thus making disease identication a challenging *** healthcare industry may face unfavorable consequences if the gap in identifying DR is not lled with effective ***,our objective is to develop an automatic and cost-effective method for classifying DR *** this work,we present a custom Faster-RCNN technique for the recognition and classication of DR lesions from retinal *** pre-processing,we generate the annotations of the dataset which is required for model ***,introduce DenseNet-65 at the feature extraction level of Faster-RCNN to compute the representative set of key ***,the Faster-RCNN localizes and classies the input sample into ve *** experiments performed on a Kaggle dataset comprising of 88,704 images show that the introduced methodology outperforms with an accuracy of 97.2%.We have compared our technique with state-of-the-art approaches to show its robustness in term of DR localization and ***,we performed cross-dataset validation on the Kaggle and APTOS datasets and achieved remarkable results on both training and testing phases.

关键词： Deep learning medical informatics diabetic retinopathy healthcare computer vision

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Pandemic Analysis and Prediction of COVID-19 Using Gaussian Doubling Times

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Computers, Materials & Continua 2022年第7期72卷 833-849页

作者： Saleh Albahli Farman Hassan Ali Javed Aun irtaza Department of Information Technology College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Computer Science University of Engineering and TechnologyTaxilaPakistan Department of Computer Science and Engineering Oakland UniversityMIUSA Department of Computer and Electrical Engineering University of MichiganDearbornUSA

COVID-19 has become a pandemic,with cases all over the world,with widespread disruption in some countries,such as Italy,US,India,South Korea,and *** and reliable detection of COVID-19 is mandatory to control the spread of ***,prediction of COVID-19 spread in near future is also crucial to better plan for the disease *** this purpose,we proposed a robust framework for the analysis,prediction,and detection of *** make reliable estimates on key pandemic parameters and make predictions on the point of inflection and possible washout time for various countries around the *** estimates,analysis and predictions are based on the data gathered fromJohns Hopkins Center during the time span of April 21 to June 27,*** use the normal distribution for simple and quick predictions of the coronavirus pandemic model and estimate the parameters of Gaussian curves using the least square parameter curve fitting for several countries in different *** predictions rely on the possible outcomes of Gaussian time evolution with the central limit theorem of statistics the predictions to be well *** parameters of Gaussian distribution,i.e.,maximumtime and width,are determined through a statisticalχ^(2)-fit for the purpose of doubling times after April 21,*** COVID-19 detection,we proposed a novel method based on the Histogram of Oriented Gradients(HOG)and CNN in multi-class classification scenario i.e.,Normal,COVID-19,viral pneumonia *** results show the effectiveness of our framework for reliable prediction and detection of COVID-19.

关键词： Prediction coronavirus time-series prediction gaussian mathematical model pandemic spreading

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A Genetic Based Leader Election Algorithm for IoT Cloud Data Processing

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Computers, Materials & Continua 2021年第8期68卷 2469-2486页

作者： Samira Kanwal Zeshan Iqbal Aun irtaza Rashid Ali Kamran Siddique Department of Computer Science University of Engineering and TechnologyTaxila47050Pakistan School of Intelligent Mechatronics Engineering Sejong UniversitySeoulKorea School of Electrical and Computer Engineering Department of Information and Communication TechnologyXiamen University MalaysiaSepang43900Malaysia

In IoT networks,nodes communicate with each other for computational services,data processing,and resource *** of the time huge data is generated at the network edge due to extensive communication between IoT ***,this tidal data is transferred to the cloud data center(CDC)for efficient processing and effective data *** CDC,leader nodes are responsible for higher performance,reliability,deadlock handling,reduced latency,and to provide cost-effective computational services to the ***,the optimal leader selection is a computationally hard problem as several factors like memory,CPU MIPS,and bandwidth,etc.,are needed to be considered while selecting a leader amongst the set of available *** existing approaches for leader selection are monolithic,as they identify the leader nodes without taking the optimal approach for leader ***,for optimal leader node selection,a genetic algorithm(GA)based leader election(GLEA)approach is presented in this *** proposed GLEA uses the available resources to evaluate the candidate nodes during the leader election *** the first phase of the algorithm,the cost of individual nodes,and overall cluster cost is computed on the bases of available *** the second phase,the best computational nodes are selected as the leader nodes by applying the genetic operations against a cost function by considering the available *** GLEA procedure is then compared against the Bees Life Algorithm(BLA).The experimental results show that the proposed scheme outperforms BLA in terms of execution time,SLA Violation,and their utilization with state-of-the-art schemes.

关键词： IoT cloud computing datacenter leader election algorithm machine learning genetic algorithm

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Defocus blur detection using novel local directional mean patterns(LDMP)and segmentation via KNN matting

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Frontiers of Computer Science 2022年第2期16卷 110-122页

作者： Awais KHAN Aun irtaza Ali JAVED Tahira NAZIR Hafiz MALIK Khalid Mahmood MALIK Muhammad Ammar KHAN Department of Computer Science University of Engineering and TechnologyTaxila 47050Pakistan Department of Electrical and Computer Engineering University of MichiganDearbornMI 48128USA Department of Software Engineering University of Engineering and TechnologyTaxila 47050Pakistan Department of Computer Science and Engineering Oakland UniversityMI 48309USA

Detection and segmentation of defocus blur is a challenging task in digital imaging applications as the blurry images comprise of blur and sharp regions that wrap significant information and require effective methods for information *** defocus blur detection and segmentation methods have several limitations i.e.,discriminating sharp smooth and blurred smooth regions,low recognition rate in noisy images,and high computational cost without having any prior knowledge of images i.e.,blur degree and camera ***,there exists a dire need to develop an effective method for defocus blur detection,and segmentation robust to the above-mentioned *** paper presents a novel features descriptor local directional mean patterns(LDMP)for defocus blur detection and employ KNN matting over the detected LDMP-Trimap for the robust segmentation of sharp and blur *** argue/hypothesize that most of the image fields located in blurry regions have significantly less specific local patterns than those in the sharp regions,therefore,proposed LDMP features descriptor should reliably detect the defocus blurred *** fusion of LDMP features with KNN matting provides superior performance in terms of obtaining high-quality segmented regions in the ***,the proposed LDMP features descriptor is robust to noise and successfully detects defocus blur in high-dense noisy *** results on Shi and Zhao datasets demonstrate the effectiveness of the proposed method in terms of defocus blur *** and comparative analysis signify that our method achieves superior segmentation performance and low computational cost of 15 seconds.

关键词： defocus blur detection local directional mean patterns image matting sharpness metrics blur segmentation

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An automated framework for advertisement detection and removal from sports videos using audio-visual cues

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Frontiers of Computer Science 2021年第2期15卷 185-188页

作者： Abeer TOHEED Ali JAVED Aun irtaza Hassan DAWOOD Hussain DAWOOD Ahmed S.ALFAKEEH Department of Software Engineering University of Engineering and Technology-TaxilaTaxila47050Pakistan Department of Computer&Network Engineering University of JeddahJeddah21577Saudi Arabia Information System Department King Abdulaziz UniversityJeddah21577Saudi Arabia

1 Introduction Advertisements detection and replacement with different ads based on the user preferences is employed during sports rebroadcasts that offers more value to both the distributor and *** advertisements detection is a laborious activity and demands an urgent need to develop automated advertisement detection techniques to save the time,storage space,and transmission bandwidth.

关键词： automated visual framework

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Deep Learning Approach for Hand Gesture Recognition:Applications in Deaf Communication and Healthcare

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Computers, Materials & Continua 2024年第1期78卷 127-144页

作者： Khursheed Aurangzeb Khalid Javeed Musaed Alhussein Imad Rida Syed irtaza Haider Anubha Parashar Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Kingdom of Saudi Arabia Department of Computer Engineering College of Computing and InformaticsUniversity of SharjahSharjah27272United Arab Emirates Laboratory Biomechanics and Bioengineering University of Technology of CompiegneCompiegne60200France Department of Computer Science and Engineering Manipal University JaipurJaipur303007India

Hand gestures have been used as a significant mode of communication since the advent of human *** facilitating human-computer interaction(HCI),hand gesture recognition(HGRoc)technology is crucial for seamless and error-free *** technology is pivotal in healthcare and communication for the deaf *** significant advancements in computer vision-based gesture recognition for language understanding,two considerable challenges persist in this field:(a)limited and common gestures are considered,(b)processing multiple channels of information across a network takes huge computational time during discriminative feature ***,a novel hand vision-based convolutional neural network(CNN)model named(HVCNNM)offers several benefits,notably enhanced accuracy,robustness to variations,real-time performance,reduced channels,and ***,these models can be optimized for real-time performance,learn from large amounts of data,and are scalable to handle complex recognition tasks for efficient human-computer *** proposed model was evaluated on two challenging datasets,namely the Massey University Dataset(MUD)and the American Sign Language(ASL)Alphabet Dataset(ASLAD).On the MUD and ASLAD datasets,HVCNNM achieved a score of 99.23% and 99.00%,*** results demonstrate the effectiveness of CNN as a promising HGRoc *** findings suggest that the proposed model have potential roles in applications such as sign language recognition,human-computer interaction,and robotics.

关键词： Computer vision deep learning gait recognition sign language recognition machine learning

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Gastric Tract Disease Recognition Using Optimized Deep Learning Features

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Computers, Materials & Continua 2021年第8期68卷 2041-2056页

作者： Zainab Nayyar Muhammad Attique Khan Musaed Alhussein Muhammad Nazir Khursheed Aurangzeb Yunyoung Nam Seifedine Kadry Syed irtaza Haider Department of Computer Science HITEC UniversityTaxila47040Pakistan Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea Department of Mathematics and Computer Science Faculty of ScienceBeirut Arab UniversityBeirutLebanon

Artificial intelligence aids for healthcare have received a great deal of *** one million patients with gastrointestinal diseases have been diagnosed via wireless capsule endoscopy(WCE).Early diagnosis facilitates appropriate treatment and saves *** learning-based techniques have been used to identify gastrointestinal ulcers,bleeding sites,and ***,small lesions may be *** developed a deep learning-based best-feature method to classify various stomach diseases evident in WCE ***,we use hybrid contrast enhancement to distinguish diseased from normal ***,a pretrained model is fine-tuned,and further training is done via transfer *** features are extracted from the last two layers and fused using a vector length-based *** improve the genetic algorithm using a fitness function and kurtosis to select optimal features that are graded by a *** evaluate a database containing 24,000 WCE images of ulcers,bleeding sites,polyps,and healthy *** cubic support vector machine classifier was optimal;the average accuracy was 99%.

关键词： Stomach cancer contrast enhancement deep learning optimization features fusion

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