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Benchmarking Performance of Document Level Classification and Topic Modeling

Computers, Materials & Continua 2022年第4期71卷 125-141页

作者： Muhammad Shahid Bhatti Azmat Ullah Rohaya Latip abid sohail Anum Riaz Rohail Hassan Department of Computer Science COMSATS University IslamabadLahorePakistan Department of Communication Technology and Network Faculty of Computer ScienceUniversiti Putra MalaysiaSelangor43400Malaysia Othman Yeop Abdullah Graduate School of Business(OYAGSB) Universiti Utara Malaysia(UUM)Kuala Lumpur50300Malaysia

Text classification of low resource language is always a trivial and challenging *** paper discusses the process of Urdu news classification and Urdu documents *** is one of the most famous spoken languages in *** implementation of computational methodologies for text classification has increased over ***,Urdu language has not much experimented with research,it does not have readily available datasets,which turn out to be the primary reason behind limited research and applying the latest methodologies to the *** overcome these obstacles,a mediumsized dataset having six categories is collected from authentic Pakistani news *** is a rich but complex *** processing can be challenging for Urdu due to its complex features as compared to other *** frequency-inverse document frequency(TFIDF)based term weighting scheme for extracting features,chi-2 for selecting essential features,and Linear discriminant analysis(LDA)for dimensionality reduction have been *** matrix and cosine similarity measure have been used to identify similar documents in a collection and find the semantic meaning of words in a document FastText model has been *** training-test split evaluation methodology is used for this experimentation,which includes 70%for training data and 30%for testing ***-of-the-art machine learning and deep dense neural network approaches for Urdu news classification have been ***,we trained Multinomial Naïve Bayes,XGBoost,Bagging,and Deep dense neural *** and deep dense neural network outperformed the other *** experimental results show that deep dense achieves 92.0%mean f1 score,and Bagging 95.0%f1 score.

关键词： Deep neural network machine learning natural language processing TFIDF sparse matrix cosine similarity classification linear discriminant analysis gradient boosting

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Multilingual Sentiment Mining System to Prognosticate Governance

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Computers, Materials & Continua 2022年第4期71卷 389-406页

作者： Muhammad Shahid Bhatti Saman Azhar abid sohail Mohammad Hijji Hamna Ayemen Areesha Ramzan COMSATS University Islamabad Lahore CampusLahorePakistan Computer Science Department University of TabukTabukSaudi Arabia

In the age of the internet,social media are connecting us all at the tip of our *** are linkedthrough different social *** social network,Twitter,allows people to tweet their thoughts on any particular event or a specific political body which provides us with a diverse range of political *** paper serves the purpose of text processing of a multilingual dataset including Urdu,English,and Roman *** machine learning solutions for sentiment analysis and train models,collect the data on government from Twitter,apply sentiment analysis,and provide a python library that classifies text *** data contained tweets in three languages:English:200k,Urdu:200k and Roman Urdu:*** different classification models are applied to determine sentiments,and eventually,the use of ensemble technique to move forward with the acquired results is *** Logistic Regression model performed best with an accuracy of 75%,followed by the Linear Support Vector classifier and Stochastic Gradient Descent model,both having 74%***,Multinomial Naïve Bayes and Complement Naïve Bayes models both achieved 73%accuracy.

关键词： Multilingual NLP artificial intelligence government sentiment analysis NLP NLTK ensemble technique multilingual twitter data science

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Defending Adversarial Examples by a Clipped Residual U-Net Model

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Intelligent Automation & Soft Computing 2023年第2期35卷 2237-2256页

作者： Kazim Ali Adnan N.Qureshi Muhammad Shahid Bhatti abid sohail Mohammad Hijji Department of Computer Science Faculty of Information TechnologyUniversity of Central Punjab Lahore54000Pakistan Department of Computer Science COMSAT University IslamabadLahore CampusLahore54000Pakistan Faculty of Computers and Information Technology Computer Science DepartmentUniversity of TabukTabuk47711Saudi Arabia

Deep learning-based systems have succeeded in many computer vision ***,it is found that the latest study indicates that these systems are in danger in the presence of adversarial *** attacks can quickly spoil deep learning models,e.g.,different convolutional neural networks(CNNs),used in various computer vision tasks from image classiﬁcation to object *** adversarial examples are carefully designed by injecting a slight perturbation into the clean *** proposed CRU-Net defense model is inspired by state-of-the-art defense mechanisms such as MagNet defense,Generative Adversarial Net-work Defense,Deep Regret Analytic Generative Adversarial Networks Defense,Deep Denoising Sparse Autoencoder Defense,and Condtional Generattive Adversarial Network *** have experimentally proved that our approach is better than previous defensive *** proposed CRU-Net model maps the adversarial image examples into clean images by eliminating the adversarial *** proposed defensive approach is based on residual and U-Net *** experiments are done on the datasets MNIST and CIFAR10 to prove that our proposed CRU-Net defense model prevents adversarial example attacks in WhiteBox and BlackBox settings and improves the robustness of the deep learning algorithms especially in the computer visionﬁ*** have also reported similarity(SSIM and PSNR)between the original and restored clean image examples by the proposed CRU-Net defense model.

关键词： Adversarial examples adversarial attacks defense method residual learning u-net cgan cru-et model

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Detection of Toxic Content on Social Networking Platforms Using Fine Tuned ULMFiT Model

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Intelligent Automation & Soft Computing 2023年第1期35卷 15-30页

作者： Hafsa Naveed abid sohail Jasni Mohamad Zain Noman Saleem Rao Faizan Ali Shahid Anwar Department of Software Engineering Faculty of ScienceUniversity of LahorePakistan Department of Computer Science COMSATS University IslamabadLahore CampusPakistan Institute for Big Data Analyics and Artifcial Intelligence(IBDAAI) Kompleks Al-KhawarizmiUniversii Tekoogi MARA40450Shah AlamSelangorMalaysia TechnoGenics SMC PVT LTD LahorePakistan Department of Computer and Infomation Science Universiti Teknologi PETRONASBandar Ser IskandarTronohPerakMalaysia Depatment of Information Engineering Technology National Skils University IslamabadSector H-8/1Faiz AhmedFaiz RoadIslamabadPakistan

Question and answer websites such as Quora,Stack Overflow,Yahoo Answers and Answer Bag are used by *** users post questions on these websites to get the answers from domain specific *** websites are multilingual meaning they are available in many different *** problem for these types of websites is to handle meaningless and irrelevant *** this paper we have worked on the Quora insincere questions(questions which are based on false assumptions or questions which are trying to make a statement rather than seeking for helpful answers)dataset in order to identify user insincere questions,so that Quora can eliminate those questions from their platform and ultimately improve the communication among users over the ***,a research was carried out with recurrent neural network and pretrained glove word embeddings,that achieved the F1 score of *** proposed study has used a pre-trained ULMFiT *** model has outperformed the previous model with an F1 score of 0.91,which is much higher than the previous studies.

关键词： Machine learning text mining quora mining artificial intelligence natural language processing

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Deep Image Restoration Model: A Defense Method Against Adversarial Attacks

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Computers, Materials & Continua 2022年第5期71卷 2209-2224页

作者： Kazim Ali Adnan N.Quershi Ahmad Alauddin Bin Arifin Muhammad Shahid Bhatti abid sohail Rohail Hassan Department of Information Technology University of Central PunjabLahore54000Pakistan Department of Communication Technology and Network Faculty of Computer Science and Information TechnologyUniversity Putra MalaysiaSalengor43400Malaysia Department of Computer Science Comsats University IslamabadLahore Campus54000Pakistan Othman Yeop Abdullah Graduate School of Business University Utara MalaysiaKuala Lumpur50300Malaysia

These days,deep learning and computer vision are much-growing fields in this modern world of information *** learning algorithms and computer vision have achieved great success in different applications like image classification,speech recognition,self-driving vehicles,disease diagnostics,and many *** success in various applications,it is found that these learning algorithms face severe threats due to adversarial *** examples are inputs like images in the computer vision field,which are intentionally slightly changed or *** changes are humanly *** are misclassified by a model with high probability and severely affects the performance or *** this scenario,we present a deep image restoration model that restores adversarial examples so that the target model is classified correctly *** proved that our defense method against adversarial attacks based on a deep image restoration model is simple and state-of-the-art by providing strong experimental results *** have used MNIST and CIFAR10 datasets for experiments and analysis of our defense *** the end,we have compared our method to other state-ofthe-art defense methods and proved that our results are better than other rival methods.

关键词： Computer vision deep learning convolutional neural networks adversarial examples adversarial attacks adversarial defenses

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MELex: The Construction of Malay-English Sentiment Lexicon

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Computers, Materials & Continua 2022年第4期71卷 1789-1805页

作者： Nurul Husna Mahadzir Mohd Faizal Omar Mohd Nasrun Mohd Nawi Anas ASalameh Kasmaruddin Che Hussin abid sohail Faculty of Computer and Mathematical Sciences Universiti Teknologi MARA(UiTM)Kedah08400MerbokKedahMalaysia Department of Decision Sciences School of Quantitative SciencesUniversiti Utara Malaysia06010KedahMalaysia Disaster Management of Institute School of Technology Management and LogisticUniversiti Utara Malaysia06010KedahMalaysia Department of Management Information System College of Business AdministrationPrince Sattam Bin Abdulaziz University165Al-KharjSaudi Arabia Faculty of Entrepreneurship and Business Universiti Malaysia KelantanMalaysia Department of Computer Science COMSATS University IslamabadLahorePakistan

Currently,the sentiment analysis research in the Malaysian context lacks in terms of the availability of the sentiment ***,this issue is addressed in this paper in order to enhance the accuracy of sentiment *** this study,a new lexicon for sentiment analysis is constructed.A detailed review of existing approaches has been conducted,and a new bilingual sentiment lexicon known as MELex(Malay-English Lexicon)has been *** MELex involves three activities:seed words selection,polarity assignment,and synonym *** approach differs from previous works in that MELex can analyze text for the two most widely used languages in Malaysia,Malay,and English,with the accuracy achieved,is 90%.It is evaluated based on the experimentation and case study approaches where the affordable housing projects in Malaysia are selected as case *** finding has given an implication on the ability of MELex to analyze public sentiments in the Malaysian *** novel aspects of this paper are ***,it introduces the new technique in assigning the polarity score,and second,it improves the performance over the classification of mixed language content.

关键词： Machine learning data sciences artificial intelligence opinion mining sentiment analysis sentiment lexicon lexicon-based bilingual lexicon

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De-Noising Brain MRI Images by Mixing Concatenation and Residual Learning(MCR)

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Computer Systems Science & Engineering 2023年第5期45卷 1167-1186页

作者： Kazim Ali Adnan N.Qureshi Muhammad Shahid Bhatti abid sohail Muhammad Hijji Atif Saeed Department of Computer Science Faculty of Information TechnologyUniversity of Central Punjab LahoreLahore54000Pakistan Department of Computer Science COMSAT University IslamabadLahore CampusLahore54000Pakistan Faculty of Computers and Information Technology Computer Science DepartmentUniversity of TabukTabuk47711Saudi Arabia

Brain magnetic resonance images(MRI)are used to diagnose the different diseases of the brain,such as swelling and tumor *** quality of the brain MR images is degraded by different noises,usually salt&pepper and Gaussian noises,which are added to the MR images during the acquisition *** the presence of these noises,medical experts are facing problems in diagnosing diseases from noisy brain MR ***,we have proposed a de-noising method by mixing concatenation,and residual deep learning techniques called the MCR de-noising *** proposed MCR method is to eliminate salt&pepper and gaussian noises as much as possible from the brain MRI *** MCR method has been trained and tested on the noise quantity levels 2%to 20%for both salt&pepper and gaussian *** experiments have been done on publically available brain MRI image datasets,which can easily be accessible in the experiments and result *** Structure Similarity Index Measure(SSIM)and Peak Signal-to-Noise Ratio(PSNR)calculate the similarity score between the denoised images by the proposed MCR method and the original clean ***,the Mean Squared Error(MSE)measures the error or difference between generated denoised and the original *** proposed MCR denoising method has a 0.9763 SSIM score,84.3182 PSNR,and 0.0004 MSE for salt&pepper noise;similarly,0.7402 SSIM score,72.7601 PSNR,and 0.0041 MSE for Gaussian noise at the highest level of 20%*** the end,we have compared the MCR method with the state-of-the-art de-noising filters such as median and wiener de-noising filters.

关键词： MR brain images median filter wiener filter concatenation learning residual learning MCR de-noising method

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Competency Driven Resource Evaluation Method for Business Process Intelligence

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Computers, Materials & Continua 2021年第10期69卷 1141-1157页

作者： abid sohail Dhanapal Durai Dominic Mohammad Hijji Muhammad Arif Butt Department of Computer Science COMSATS University IslamabadLahorePakistan Universiti Teknologi PETRONAS Bandar Seri IskandarTronoh PerakMalaysia Department of Computer Science University of TabukTabukSaudi Arabia Punjab University College of Information Technology University of the PunjabLahorePakistan

Enterprises are continuously aiming at improving the execution of processes to achieve a competitive *** of the established ways of improving process performance is to assign the most appropriate resources to each task of the ***,evaluations of business process improvement approaches have established that a method that can guide decision-makers to identify the most appropriate resources for a task of process improvement in a structured way,is *** is because the relationship between resources and tasks is less understood and advancement in business process intelligence is also *** address this problem an integrated resource classification framework is presenting that identifies competence,suitability,and preference as the relationship of task with ***,only the competence relationship of human resources with a task is presented in this research as a resource competence ***,the competency calculation method is presented as a user guider layer for business process intelligencebased resource competence *** computed capabilities serve as a basic input for choosing the most appropriate resources for each task of the *** of method is illustrated through a heathcare case study.

关键词： Data sciences artificial intelligence business process management business process improvement process warehouse data warehouse resource competency resource competency modeling health care

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An Intelligent Graph Edit Distance-Based Approach for Finding Business Process Similarities

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Computers, Materials & Continua 2021年第12期69卷 3603-3618页

作者： abid sohail Ammar Haseeb Mobashar Rehman Dhanapal Durai Dominic Muhammad Arif Butt Department of Computer Science COMSATS University IslamabadLahorePakistan Faculty of Information and Communication Technology Universiti Tunku Abdul RahmanKamparPerakMalaysia Universiti Teknologi PETRONAS Bandar Seri IskandarTronoh PerakMalaysia Punjab University College of Information Technology University of the PunjabLahorePakistan

There are numerous application areas of computing similarity between process *** includes finding similar models from a repository,controlling redundancy of process models,and finding corresponding activities between a pair of process *** similarity between two process models is computed based on their similarity between labels,structures,and execution *** attempts have been made to develop similarity techniques between activity labels,as well as their execution ***,a notable problem with the process model similarity is that two process models can also be similar if there is a structural variation between ***,neither a benchmark dataset exists for the structural similarity between process models nor there exist an effective technique to compute structural *** that end,we have developed a large collection of process models in which structural changes are handcrafted while preserving the semantics of the ***,we have used a machine learning-based approach to compute the similarity between a pair of process models having structural and label ***,we have evaluated the proposed approach using our generated collection of process models.

关键词： Machine learning intelligent data management similarities of process models structural metrics dataset graph edit distance process matching artificial intelligence

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Change in mean postoperative pain in patients undergoing tonsillectomy with cold water versus usual practice:A pragmatic trial

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World Journal of Otorhinolaryngology-Head and Neck Surgery 2024年第1期10卷 24-28页

作者： Haissan Iftikhar Nida Zahid Adan Zubair Mohammad Wasif Nabeel N.Baig Khadijah abid Muhammad Ozair Awan Muhammad sohail Awan Department of Otolaryngology Queen Elizabeth HospitalBirminghamUK Department of Surgery Aga Khan University HospitalKarachiPakistan College of Physicians and Surgeons Higher Education CommissionKarachiPakistan Department of UGME Aga Khan UniversityKarachiPakistan

Introduction:Tonsillectomy is one of the most common procedures performed in *** are various methods to perform tonsillectomies(cold and hot).Thermal damage and inflammation of the surrounding tissue using monopolar cautery is said to point to increased pain whereas immediate cooling of the burnt area is thought to reduce pain owing to heat *** objective was to determine the difference in mean post‐tonsillectomy pain scores among patients irrigated with cold water(4℃)and in patients not irrigated with cold *** and Methods:A quasi‐experimental trial was conducted from January 2016 to December 2017 at a tertiary care *** tonsillectomies were carried out with monopolar cautery at a power of 20 *** either received cold water irrigation post tonsillectomy of the tonsillar bed(intervention arm)or no irrigation(control arm).The pain score was measured on Days 0,1,3,5,and *** scores were charted on a visual analog scale on the respective ***:Seventy‐eight patients were included in the *** mean age of our patients was 10.26(4.24)years old in the intervention arm,and 11.95(4.19)years old in the control *** was observed that the pain was significantly lower in patients with cold water irrigation of the tonsillar fossa on Days 1,3,5,and 7(p=0.001).There were no readmissions postprocedure and none of our patients developed any complications ***:In this trial,we report a reduced pain score at all days of observation in the intervention *** of the tonsillar fossa is a safe,cost-effective,and less technically demanding technique that could be employed to reduce postoperative *** studies with randomization,blinding and a larger sample size could further improve on our results.

关键词： cold water tonsillectomy monopoalr tonsillectomy postoperative pain tonsillectomy

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