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Benchmarking Performance of Document Level Classification and Topic Modeling

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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

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

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

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

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

National guidelines for the diagnosis and treatment of hilar cholangiocarcinoma

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World Journal of Gastroenterology 2024年第9期30卷 1018-1042页

作者： Faisal Saud Dar Zaigham Abbas Irfan Ahmed Muhammad Atique Usman Iqbal Aujla Muhammad Azeemuddin Zeba Aziz Abu Bakar Hafeez bhatti Tariq Ali Bangash Amna Subhan Butt Osama Tariq Butt Abdul Wahab Dogar Javed Iqbal Farooqi Faisal Hanif Jahanzaib Haider Siraj Haider Syed Mujahid Hassan Adnan Abdul Jabbar Aman Nawaz Khan Muhammad Shoaib Khan Muhammad Yasir Khan Amer Latif Nasir Hassan Luck Ahmad Karim Malik Kamran Rashid sohail Rashid Mohammad Salih Abdullah Saeed Amjad Salamat Ghias-un-Nabi Tayyab Aasim Yusuf Haseeb Haider Zia Ammara Naveed Department of Hepatopancreatic Biliary Surgery&Liver Transplant Pakistan Kidney and Liver Institute&Research CentreLahore 54000Pakistan Department of Hepatogastroenterology and Liver Transplantation Dr.Ziauddin University HospitalKarachi 75600SindhPakistan University of Aberdeen Aberdeen B243FXUnited Kingdom Department of Pathology Pakistan Kidney and Liver Institute&Research CentreLahore 54000Pakistan Department of Gastroenterology&Hepatology Pakistan Kidney and Liver Institute&Research CentreLahore 54000Pakistan Department of Radiology Aga Khan UniversityKarachi 74800Pakistan Department of Oncology Hameed Latif HospitalLahore 54000Pakistan Division of Hepatopancreatic Biliary Surgery&Liver Transplantation Shifa International HospitalIslamabad 44000Pakistan Department of Hepatopancreatic Biliary Surgery&Liver Transplant Shaikh Zayed Hospital and Post Graduate Medical InstituteLahore 54000Pakistan Department of Medicine Aga Khan University HospitalKarachi 74800Pakistan Department of Liver Transplant Pir Abdul Qadir Shah Jeelani Institute of Medical SciencesGambat 66020Pakistan Department of Medicine&Gastroenterology Lifecare Hospital and Research CentrePeshawar 25000Khyber PakhtunkhwaPakistan Department of Hepatopancreatobiliary&Liver Transplant Bahria International HospitalLahore 54000Pakistan Department of Surgery Hepatopancreatobiliary&Liver TransplantDow University of Health SciencesKarachi 74800Pakistan Department of Gastroenterology Hepatology&NutritionPir Abdul Qadir Shah Jeelani Institute of Medical SciencesGambat 66020Pakistan Department of Oncology Dr.Ziauddin HospitalKarachi 74800Pakistan Department of Radiology Rehman Medical InstitutePeshawar 25000Pakistan Army Liver Transplant Unit Pak Emirates Military HospitalRawalpindi 46000Pakistan Department of Gastroenterology Sindh Institute of Urology and TransplantationKarachi 75500Pakistan Rashid Nursing Home and Cancer Clinic Rashid Nursing Home and Cancer ClinicRawalpindi 46000Pakistan Department of Gastroentero

A consensus meeting of national experts from all major national hepatobiliary centres in the country was held on May 26,2023,at the Pakistan Kidney and Liver Institute&Research Centre(PKLI&RC)after initial consultations with the *** Pakistan Society for the Study of Liver Diseases(PSSLD)and PKLI&RC jointly organised this *** effort was based on a comprehensive literature review to establish national practice guidelines for hilar cholangiocarcinoma(hCCA).The consensus was that hCCA is a complex disease and requires a multidisciplinary team approach to best manage these *** coordinated effort can minimise delays and give patients a chance for curative treatment and effective *** diagnostic and staging workup includes high-quality computed tomography,magnetic resonance imaging,and magnetic resonance *** cytology or biopsy utilizing endoscopic retrograde cholangiopancreatography is a mainstay for ***,histopathologic confirmation is not always required before *** ultrasound with fine needle aspiration of regional lymph nodes and positron emission tomography scan are valuable adjuncts for *** only curative treatment is the surgical resection of the biliary tree based on the Bismuth-Corlette *** patients with unresectable hCCA can be considered for liver *** chemotherapy should be offered to patients with a high risk of *** use of preoperative biliary drainage and the need for portal vein embolisation should be based on local multidisciplinary *** with acute cholangitis can be drained with endoscopic or percutaneous biliary *** chemotherapy with cisplatin and gemcitabine has shown improved survival in patients with irresectable and recurrent hCCA.

关键词： Hilar cholangiocarcinoma Bismuth-Corlette classification Memorial Sloan Kettering Cancer Centre Staging Preoperative biliary drainage Portal vein embolisation Surgical resection Hepatectomy