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Baseline Isolated Printed Text Image Database for Pashto Script Recognition

Intelligent Automation & Soft Computing 2023年第7期37卷 875-885页

作者： Arfa Siddiqu Abdul Basit Waheed Noor Muhammad Asfandyar Khan M.Saeed H.Kakar Azam Khan Department of Computer Science&Information Technology University of BalochistanQuetta87300Pakistan

The optical character recognition for the right to left and cursive languages such as Arabic is challenging and received little attention from researchers in the past compared to the other Latin ***,the absence of a standard publicly available dataset for several low-resource lan-guages,including the Pashto language remained a hurdle in the advancement of language *** that,a clean dataset is the fundamental and core requirement of character recognition,this research begins with dataset generation and aims at a system capable of complete language *** in view the complete and full autonomous recognition of the cursive Pashto *** first achievement of this research is a clean and standard dataset for the isolated characters of the Pashto *** this paper,a database of isolated Pashto characters for forty four alphabets using various font styles has been *** order to overcome the font style shortage,the graphical software Inkscape has been used to generate sufficient image data samples for each *** dataset has been pre-processed and reduced in dimensions to 32×32 pixels,and further converted into the binary format with a black background and white text so that it resembles the Modified National Institute of Standards and technology(MNIST)*** benchmark database is publicly available for further research on the standard GitHub and Kaggle database servers both in pixel and Comma Separated Values(CSV)formats.

关键词： Text-image database optical character recognition(OCR) pashto isolated characters visual recognition autonomous language understanding deep learning convolutional neural network(CNN)

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Target Detection Algorithm in Crime Recognition Using Artificial Intelligence

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computers, Materials & Continua 2022年第4期71卷 809-824页

作者： Abdulsamad A.AL-Marghilani Computer Science&Information Technology Northern Border UniversityRafhaSaudi Arabia

Presently,suspect prediction of crime scenes can be considered as a classification task,which predicts the suspects based on the time,space,and type of *** digital forensic investigation in a big data environment poses several challenges to the investigational ***,the facial sketches are widely employed by the law enforcement agencies for assisting the suspect identification of suspects involved in crime *** sketches utilized in the forensic investigations are either drawn by forensic artists or generated through the computer program(composite sketches)based on the verbal explanation given by the eyewitness or *** this suspect identification process is slow and difficult,it is required to design a technique for a quick and automated facial sketch *** Learning(ML)and deep learning(DL)models find it useful to automatically support the decision of forensics *** challenge is the incorporation of the domain expert knowledge with DL models for developing efficient techniques to make better *** this view,this study develops a new artificial intelligence(AI)based DL model with face sketch synthesis(FSS)for suspect identification(DLFSS-SI)in a big data *** proposed method performs preprocessing at the primary stage to improvise the image *** addition,the proposed model uses a DL based MobileNet(MN)model for feature extractor,and the hyper parameters of the MobileNet are tuned by quasi oppositional firefly optimization(QOFFO)*** proposed model automatically draws the sketches of the input facial ***,a qualitative similarity assessment takes place with the sketch drawn by a professional artist by the *** there is a higher resemblance between the two sketches,the suspect will be *** validate the effective performance of the DLFSS-SI method,a detailed qualitative and quantitative examination takes *** experimental outcome stated that the DLF

关键词： Artificial intelligence big data deep learning suspect identification face sketch synthesis

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Offshore Software Maintenance Outsourcing Process Model Validation:A Case Study Approach

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computers, Materials & Continua 2023年第3期74卷 5035-5048页

作者： Atif Ikram Masita Abdul Jalil Amir Bin Ngah Adel Sulaiman Muhammad Akram Ahmad Salman Khan Faculty of Ocean Engineering Technology and Informatics University Malaysia TerengganuKuala TerengganuMalaysia Department of Computer Science&Information Technology Faculty of Information TechnologyThe University of LahoreLahore54000Pakistan Department of Computer Science College of Computer Science and Information SystemsNajran UniversityNajranSaudi Arabia Department of Software Engineering Faculty of Information TechnologyThe University of LahoreLahore54000Pakistan

The successful execution and management of Offshore Software Maintenance Outsourcing(OSMO)can be very beneficial for OSMO vendors and the OSMO *** a lot of research on software outsourcing is going on,most of the existing literature on offshore outsourcing deals with the outsourcing of software development *** frameworks have been developed focusing on guiding software systemmanagers concerning offshore software ***,none of these studies delivered comprehensive guidelines for managing the whole process of *** is a considerable lack of research working on managing OSMO from a vendor’s ***,to find the best practices for managing an OSMO process,it is necessary to further investigate such complex and multifaceted phenomena from the vendor’s *** study validated the preliminary OSMO process model via a case study research *** results showed that the OSMO process model is applicable in an industrial setting with few *** industrial data collected during the case study enabled this paper to extend the preliminary OSMO process *** refined version of the OSMO processmodel has four major phases including(i)Project Assessment,(ii)SLA(iii)Execution,and(iv)Risk.

关键词： Offshore outsourcing process model model validation vendor challenges case study

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Deep Learning-Based ECG Classification for Arterial Fibrillation Detection

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computers, Materials & Continua 2024年第6期79卷 4805-4824页

作者： Muhammad Sohail Irshad Tehreem Masood Arfan Jaffar Muhammad Rashid Sheeraz Akram Abeer Aljohani Faculty of Computer Science&Information Technology The Superior UniversityLahore54000Pakistan Intelligent Data Visual Computing Research(IDVCR) Lahore54000Pakistan Department of Computer Science National University of TechnologyIslamabad45000Pakistan Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia Department of Computer Science Applied CollegeTaibah UniversityMedina42353Saudi Arabia

The application of deep learning techniques in the medical field,specifically for Atrial Fibrillation(AFib)detection through Electrocardiogram(ECG)signals,has witnessed significant *** and timely diagnosis increases the patient’s chances of ***,issues like overfitting and inconsistent accuracy across datasets remain *** a quest to address these challenges,a study presents two prominent deep learning architectures,ResNet-50 and DenseNet-121,to evaluate their effectiveness in AFib *** aim was to create a robust detection mechanism that consistently performs *** such as loss,accuracy,precision,sensitivity,and Area Under the Curve(AUC)were utilized for *** findings revealed that ResNet-50 surpassed DenseNet-121 in all evaluated *** demonstrated lower loss rate 0.0315 and 0.0305 superior accuracy of 98.77%and 98.88%,precision of 98.78%and 98.89%and sensitivity of 98.76%and 98.86%for training and validation,hinting at its advanced capability for AFib *** insights offer a substantial contribution to the existing literature on deep learning applications for AFib detection from ECG *** comparative performance data assists future researchers in selecting suitable deep-learning architectures for AFib ***,the outcomes of this study are anticipated to stimulate the development of more advanced and efficient ECG-based AFib detection methodologies,for more accurate and early detection of AFib,thereby fostering improved patient care and outcomes.

关键词： Convolution neural network atrial fibrillation area under curve ECG false positive rate deep learning classification

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Energy Cost Minimization Using String Matching Algorithm in Geo-Distributed Data Centers

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computers, Materials & Continua 2023年第6期75卷 6305-6322页

作者： Muhammad Imran Khan Khalil Syed Adeel Ali Shah Izaz Ahmad Khan Mohammad Hijji Muhammad Shiraz Qaisar Shaheen Department of Computer Science&Information Technology University of Engineering&TechnologyPeshawar25000Pakistan Department of Computer Science Bacha Khan UniversityCharsadda24541Pakistan Computer Science Department University of TabukTabuk47512Saudi Arabia Department of Computer Science Federal Urdu University of ArtsScience and Technology Islamabad44000Pakistan Department of Computer Science and Information Technology The Islamia University of BahawalpurRahim Yar Khan CampusPunjabPakistan

Data centers are being distributed worldwide by cloud service providers(CSPs)to save energy costs through efficient workload alloca-tion *** CSPs are challenged by the significant rise in user demands due to their extensive energy consumption during workload *** research studies have examined distinct operating cost mitigation techniques for geo-distributed data centers(DCs).However,oper-ating cost savings during workload processing,which also considers string-matching techniques in geo-distributed DCs,remains *** this research,we propose a novel string matching-based geographical load balanc-ing(SMGLB)technique to mitigate the operating cost of the geo-distributed *** primary goal of this study is to use a string-matching algorithm(i.e.,Boyer Moore)to compare the contents of incoming workloads to those of documents that have already been processed in a data center.A successful match prevents the global load balancer from sending the user’s request to a data center for processing and displaying the results of the previously processed workload to the user to save *** the contrary,if no match can be discovered,the global load balancer will allocate the incoming workload to a specific DC for processing considering variable energy prices,the number of active servers,on-site green energy,and traces of incoming *** results of numerical evaluations show that the SMGLB can minimize the operating expenses of the geo-distributed data centers more than the existing workload distribution techniques.

关键词： String matching optimization geo-distributed data centers geographical load balancing green energy

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Fusion Strategy for Improving Medical Image Segmentation

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computers, Materials & Continua 2023年第2期74卷 3627-3646页

作者： Fahad Alraddady E.A.Zanaty Aida HAbu bakr Walaa M.Abd-Elhafiez Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Information System Department Faculty of Computers and InformationSohag UniversitySohagEgypt Mathematical and Computer Science Department Faculty of ScienceAswan UniversityAswanEgypt College of Computer Science&Information Technology Jazan UniversityJazanKingdom of Saudi Arabia Computer Science Department Faculty of Computers and Artificial IntelligenceSohag UniversitySohagEgypt

In this paper,we combine decision fusion methods with four metaheuristic algorithms(Particle Swarm Optimization(PSO)algorithm,Cuckoo search algorithm,modification of Cuckoo Search(CS McCulloch)algorithm and Genetic algorithm)in order to improve the image *** proposed technique based on fusing the data from Particle Swarm Optimization(PSO),Cuckoo search,modification of Cuckoo Search(CS McCulloch)and Genetic algorithms are obtained for improving magnetic resonance images(MRIs)*** algorithms are used to compute the accuracy of each method while the outputs are passed to fusion *** order to obtain parts of the points that determine similar membership values,we apply the different rules of incorporation for these *** proposed approach is applied to challenging applications:MRI images,gray matter/white matter of brain segmentations and original black/white images Behavior of the proposed algorithm is provided by applying to different medical *** is shown that the proposed method gives accurate results;due to the decision fusion produces the greatest improvement in classification accuracy.

关键词： Decision fusion particle swarmoptimization(PSO) cuckoo search algorithm CS McCulloch algorithm genetic algorithm CT and MRI

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Automated Brain Hemorrhage Classification and Volume Analysis

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computers, Materials & Continua 2023年第4期75卷 2283-2299页

作者： Maryam Wardah Muhammad Mateen Tauqeer Safdar Malik Mohammad Eid Alzahrani Adil Fahad Abdulmohsen Almalawi Rizwan Ali Naqvi Department of Computer Science Air University Multan CampusMultan60000Pakistan Department of Information Technology Bahauddin Zakariya UniversityMultan60800Pakistan Department of Engineering and Computer Science College of Computer Science and Information TechnologyAl Baha UniversityAl BahaSaudi Arabia School of Computer Science&Information Technology King Abdulaziz UniversityJeddahSaudi Arabia Department of Intelligent Mechatronics Engineering Sejong UniversitySeoul05006Korea

Brain hemorrhage is a serious and life-threatening condition. It cancause permanent and lifelong disability even when it is not fatal. The wordhemorrhage denotes leakage of blood within the brain and this leakage ofblood from capillaries causes stroke and adequate supply of oxygen to thebrain is hindered. Modern imaging methods such as computed tomography(CT) and magnetic resonance imaging (MRI) are employed to get an idearegarding the extent of the damage. An early diagnosis and treatment can savelives and limit the adverse effects of a brain hemorrhage. In this case, a deepneural network (DNN) is an effective choice for the early identification andclassification of brain hemorrhage for the timely recovery and treatment of anaffected person. In this paper, the proposed research work is divided into twonovel approaches, where, one for the classification and the other for volumecalculation of brain hemorrhage. Two different datasets are used for twodifferent techniques classification and volume. A novel algorithm is proposedto calculate the volume of hemorrhage using CT scan images. In the firstapproach, the ‘RSNA’ dataset is used to classify the brain hemorrhage typesusing transfer learning and achieved an accuracy of 93.77%. Furthermore,in the second approach, a novel algorithm has been proposed to calculate thevolume of brain hemorrhage and achieved tremendous results as 1035.91mm3and 9.25 cm3, using the PhysioNet CT scan tomography dataset.

关键词： Deep learning computed tomography magnetic resonance imaging brain hemorrhage classification volume analysis

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Fake News Classification: Past, Current, and Future

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computers, Materials & Continua 2023年第11期77卷 2225-2249页

作者： Muhammad Usman Ghani Khan Abid Mehmood Mourad Elhadef Shehzad Ashraf Chaudhry Department of Computer Science University of Engineering and TechnologyLahore54890Pakistan Department of Computer Science&Information Technology Abu Dhabi UniversityAbu Dhabi59911United Arab Emirates Department of Software Engineering Faculty of Engineering and ArchitectureNisantasi UniversityIstanbulTurkey

The proliferation of deluding data such as fake news and phony audits on news web journals,online publications,and internet business apps has been aided by the availability of the web,cell phones,and social *** can quickly fabricate comments and news on social *** most difficult challenge is determining which news is real or ***,tracking down programmed techniques to recognize fake news online is *** an emphasis on false news,this study presents the evolution of artificial intelligence techniques for detecting spurious social media *** study shows past,current,and possible methods that can be used in the future for fake news *** different publicly available datasets containing political news are utilized for performing *** supervised learning algorithms are used,and their results show that conventional Machine Learning(ML)algorithms that were used in the past perform better on shorter text *** contrast,the currently used Recurrent Neural Network(RNN)and transformer-based algorithms perform better on longer ***,a brief comparison of all these techniques is provided,and it concluded that transformers have the potential to revolutionize Natural Language Processing(NLP)methods in the near future.

关键词： Supervised learning algorithms fake news classification online disinformation transformers recurrent neural network(RNN)disinformation transformers recurrent neural network(RNN)

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Probability Based Regression Analysis for the Prediction of Cardiovascular Diseases

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computers, Materials & Continua 2023年第6期75卷 6269-6286页

作者： Wasif Akbar Adbul Mannan Qaisar Shaheen Mohammad Hijji Muhammad Anwar Muhammad Ayaz Department of Computer Science NFC Institute of Engineering and TechnologyMultanPakistan Faculty of Computer Science&Information Technology University of MalayaKuala LumpurMalaysia Department of Computer Science and Information Technology The Islamia University of BahawalpurRahim Yar Khan CampusPunjabPakistan Faculty of Computers and Information Technology University of TabukTabuk47512Saudi Arabia Department of Information Sciences Division of Science and TechnologyUniversity of EducationLahorePakistan Sensor Networks and Cellular Systems(SNCS)research center University of TabukTabuk47512Saudi Arabia

Machine Learning(ML)has changed clinical diagnostic procedures *** in Cardiovascular Diseases(CVD),the use of ML is indispensable to reducing human *** studies focused on disease prediction but depending on multiple parameters,further investigations are required to upgrade the clinical ***-layered implementation of ML also called Deep Learning(DL)has unfolded new horizons in the field of clinical *** formulates reliable accuracy with big datasets but the reverse is the case with small *** paper proposed a novel method that deals with the issue of less data *** by the regression analysis,the proposed method classifies the data by going through three different *** the first stage,feature representation is converted into probabilities using multiple regression techniques,the second stage grasps the probability conclusions from the previous stage and the third stage fabricates the final *** experiments were carried out on the Cleveland heart disease *** results show significant improvement in classification *** is evident from the comparative results of the paper that the prevailing statistical ML methods are no more stagnant disease prediction techniques in demand in the future.

关键词： Machine learning heart disease cardiac disease deep regression regression learning

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Nonlinear Components of a Block Cipher over Eisenstein Integers

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computers, Materials & Continua 2023年第12期77卷 3659-3675页

作者： Mohammad Mazyad Hazzazi Muhammad Sajjad Zaid Bassfar Tariq Shah Ashwag Albakri Department of Mathematics College of ScienceKing Khalid UniversityAbha61413Saudi Arabia Department of Mathematics Quaid-I-Azam UniversityIslamabad45320Pakistan Department of Information Technology University of TabukTabuk71491Saudi Arabia Department of Computer Science College of Computer Science&Information TechnologyJazan UniversityJazan45142Saudi Arabia

In block ciphers,the nonlinear components,also known as substitution boxes(S-boxes),are used with the purpose to induce confusion in *** the last decade,most of the work on designing S-boxes over the points of elliptic curves,chaotic maps,and Gaussian integers has been *** main purpose of these studies is to hide data and improve the security levels of crypto *** this work,we design pair of nonlinear components of a block cipher over the residue class of Eisenstein integers(EI).The fascinating features of this structure provide S-boxes pair at a time by fixing three ***,in the same way,by taking three fixed parameters only one S-box is obtained through a prime field-dependent Elliptic curve(EC),chaotic maps,and Gaussian *** newly designed pair of S-boxes are assessed by various tests like nonlinearity,bit independence criterion,strict avalanche criterion,linear approximation probability,and differential approximation probability.

关键词： Eisenstein integers residue class of Eisenstein integers block cipher S-boxes analysis of S-boxes

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