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Women in data Science at Prince Sultan University (WiDS PSU), International Conference of

作者： Tariq Mahmmod Noor Ayesha Muhammad Mujahid Amjad Rehman Artificial Intelligence and Data Analytics (AIDA) Lab CCIS Prince Sultan University Riyadh School of Clinical Medicine Zhengzhou University Zhengzhou China Artificial Intelligence and Data Analytics (AIDA) Lab CIS Prince Sultan University Riyadh City Country

ISBN: (数字)9798350395839

ISBN: (纸本)9798350395846

Lung cancer is a devastating disease that kills around five million people annually, making it a major global threat. Timely recognition is critical to improving patient survival. With a focus on pulmonary nodules in medical imaging, CT scans are essential for the screening and identification of lung cancer. This paper introduces a customized deep-learning framework for lung cancer detection using CT Scans. To achieve an accurate and efficient early-stage lung cancer diagnosis, the model makes use of a large dataset of chest CT images for training and validation. The experimental results show that the model outperforms state-of-the-art methods for lung cancer categorization. The model provides a trustworthy and efficient means of early detection of lung cancer, which has the potential to revolutionize the field of lung cancer diagnostics. The deep framework outperforms existing approaches and highlights the need to apply transfer learning to medical image analysis. Because of the technique’s extraordinary effectiveness, lung cancer research may be significantly impacted, and improvements in early diagnosis and therapy may result.

关键词： Training Deep learning Accuracy Computed tomography Transfer learning Lung cancer Medical treatment

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Identity verification using palm print microscopic images based on median robust extended local binary pattern features and k-nearest neighbor classifier

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MICROSCOPY RESEARCH AND TECHNIQUE 2022年第4期85卷 1224-1237页

作者： Rehman, Amjad Harouni, Majid Karchegani, Negar Haghani Solati Saba, Tanzila Bahaj, Saeed Ali Roy, Sudipta Prince Sultan Univ Artificial Intelligence & Data Analyt Lab CCIS Riyadh 11586 Saudi Arabia Islamic Azad Univ Dept Comp Engn Dolatabad Branch Esfahan Iran Islamic Azad Univ Fac Comp Engn Najafabad Branch Esfahan Iran Prince Sattam bin Abdulaziz Univ MIS Dept Coll Business Adm Al Kharj Saudi Arabia JIO Inst Artificial Intelligence & Data Sci Programme Navi Mumbai Maharashtra India

Automatic identity verification is one of the most critical and research-demanding areas. One of the most effective and reliable identity verification methods is using unique human biological characteristics and biometrics. Among all types of biometrics, palm print is recognized as one of the most accurate and reliable identity verification methods. However, this biometrics domain also has several critical challenges: image rotation, image displacement, change in image scaling, presence of noise in the image due to devices, region of interest (ROI) detection, or user error. For this purpose, a new method of identity verification based on median robust extended local binary pattern (MRELBP) is introduced in this study. In this system, after normalizing the images and extracting the ROI from the microscopic input image, the images enter the feature extraction step with the MRELBP algorithm. Next, these features are reduced by the dimensionality reduction step, and finally, feature vectors are classified using the k-nearest neighbor classifier. The microscopic images used in this study were selected from IITD and CASIA data sets, and the identity verification rate for these two data sets without challenge was 97.2% and 96.6%, respectively. In addition, computed detection rates have been broadly stable against changes such as salt-and-pepper noise up to 0.16, rotation up to 5 degrees, displacement up to 6 pixels, and scale change up to 94%.

关键词： legal identity for all local descriptors median robust extended local binary pattern microscopic palm print images security

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Efficient-gastro: optimized EfficientNet model for the detection of gastrointestinal disorders using transfer learning and wireless capsule endoscopy images

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PEERJ COMPUTER SCIENCE 2024年 10卷 e1902页

作者： Al-Otaibi, Shaha Rehman, Amjad Mujahid, Muhammad Alotaibi, Sarah Saba, Tanzila Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Syst Riyadh Saudi Arabia Prince Sultan Univ Artificial Intelligence & Data Analyt Lab CCIS Riyadh Saudi Arabia King Saud Univ Coll Comp & Informat Sci Dept Comp Sci Riyadh Saudi Arabia

Gastrointestinal diseases cause around two million deaths globally. Wireless capsule endoscopy is a recent advancement in medical imaging, but manual diagnosis is challenging due to the large number of images generated. This has led to research into computer-assisted methodologies for diagnosing these images. Endoscopy produces thousands of frames for each patient, making manual examination difficult, laborious, and error-prone. An automated approach is essential to speed up the diagnosis process, reduce costs, and potentially save lives. This study proposes transfer learning-based efficient deep learning methods for detecting gastrointestinal disorders from multiple modalities, aiming to detect gastrointestinal diseases with superior accuracy and reduce the efforts and costs of medical experts. The Kvasir eight-class dataset was used for the experiment, where endoscopic images were preprocessed and enriched with augmentation techniques. An EfficientNet model was optimized via transfer learning and fine tuning, and the model was compared to the most widely used pre-trained deep learning models. The model's efficacy was tested on another independent endoscopic dataset to prove its robustness and reliability.

关键词： Deep learning Gastrointestinal Digestive endoscopy Multiclass classification Augmentation Capsule endoscopy Transfer learning

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Enhancing trustworthiness and reliability: advance explainable artificial intelligence framework for real world Sclerosis detection

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PHYSICA SCRIPTA 2024年第10期99卷 105017-105017页

作者： Saba, Tanzila Mujahid, Muhammad Rehman, Amjad Alamri, Faten S. Ayesha, Noor Prince Sultan Univ Artificial Intelligence & Data Analyt Lab CCIS Riyadh 11586 Saudi Arabia Princess Nourah Bint Abdulrahman Univ Coll Sci Dept Math Sci POB 84428 Riyadh 11671 Saudi Arabia Zhengzhou Univ Sch Clin Med Zhengzhou 450001 Henan Peoples R China

In this era, Explainable artificial intelligence (XAI) is being employed in many health-related problems, but it faces challenges because most models produce results that are opaque and interpretable. The goal of explainable AI is to make machine learning, and deep learning models more understandable and accessible to people. Consequently, there is a pressing need for XAI models to enhance trust, given its increasing popularity in the field of medical artificial intelligence. This study explores the XAI nature of machine learning for disease prediction, with a particular focus on transparency and reliability of the results. The study examines the interpretability of artificial intelligence, focusing on issues such as bias, equality, and system reliability. The main theme is to minimize errors, disparities in human understanding, and use artificial intelligence in disease prediction to improve the outcomes for medical patients. The XAI methods were validated on Sclerosis predictions using two important models with fine-tuning their hyperparameters. The experiments demonstrated that the XAI methods outperformed the existing methods, achieving impressive results in terms of accuracy, recall, f1 score, precision, and AUC. The proposed approach achieved 98.53% accuracy using 75%-25% hold-out splitting, and 98.14% accuracy using 10-fold validation. This semantic approach is superior to previous methods by showing the abundance of correct predictions and demonstrating its effectiveness in predicting multiple sclerosis in the real world.

关键词： explainable support vector machine black-box decision tree sclerosis trustworthiness healthcare

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A Machine Learning Method with Threshold Based Parallel Feature Fusion and Feature Selection for Automated Gait Recognition

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JOURNAL OF ORGANIZATIONAL AND END USER COMPUTING 2020年第2期32卷 67-92页

作者： Sharif, Muhammad Attique, Muhammad Tahir, Muhammad Zeeshan Yasmim, Mussarat Saba, Tanzila Tanik, Urcun John COMSATS Univ Islamabad Dept CS Wah Campus Wah Cantt Pakistan HITEC Univ Dept Comp Sci Museum Rd Taxila Pakistan CCIS Prince Sultan Univ Artificial Intelligence & Data Analyt AIDA Lab Riyadh Saudi Arabia Texas A&M Univ Commerce Commerce TX USA

Gait is a vital biometric process for human identification in the domain of machine learning. In this article, a new method is implemented for human gait recognition based on accurate segmentation and multi-level features extraction. Four major steps are performed including: a) enhancement of motion region in frame by the implementation of linear transformation with HSI color space;b) Region of Interest (ROI) detection based on parallel implementation of optical flow and background subtraction;c) shape and geometric features extraction and parallel fusion;d) Multi-class support vector machine (MSVM) utilization for recognition. The presented approach reduces error rate and increases the CCR. Extensive experiments are done on three data sets namely CASIA-A, CASIA-B and CASIA-C which present different variations in clothing and carrying conditions. The proposed method achieved maximum recognition results of 98.6% on CASIA-A, 93.5% on CASIA-B and 97.3% on CASIA-C, respectively.

关键词： Gait Recognition Geometric Features HOG Features MSVM Classifier Segmentation

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Hot-Spot Zone Detection to Tackle Covid19 Spread by Fusing the Traditional Machine Learning and Deep Learning Approaches of Computer Vision

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IEEE ACCESS 2021年 9卷 100040-100049页

作者： Khan, Muhammad Zeeshan Khan, Muhammad Usman Ghani Saba, Tanzila Razzak, Imran Rehman, Amjad Bahaj, Saeed Ali Univ Engn & Technol Lahore Natl Ctr Artificial Intelligence NCAI Intelligent Criminol Res Lab Al Khawarizmi Inst Comp Sci KICS Lahore 54000 Pakistan Prince Sultan Univ CCIS Artificial Intelligence & Data Analyt Lab AIDA Riyadh 11586 Saudi Arabia Deakin Univ Sch Informat Technol Geelong Vic 3217 Australia Prince Sattam Bin Abdulaziz Univ Coll Business Adm Dept MIS Al Kharj 11942 Saudi Arabia

Corona Virus is a pandemic, and the whole world is affected due to it. Apart from the vaccine, the only cure for this drastic disease is to follow the rules and regulations that avoid further spread. There are different mechanisms like (Social Distancing, Mask Detection, Human occupancy etc.) through which we can able to stop the spread of the coronavirus. In this paper, we proposed hotspot zone detection using the computer vision techniques of deep learning. We have defined the hotspot area as the particular region on which the person touches more than some specified threshold. We further mark that area to some specific color to help the authority take necessary action and disinfect that particular place. To implement this algorithm, we have utilized the human-object interaction concept. We have extracted the dataset of person classes from the publicly available dataset for the person detection and the self-generated dataset to train the algorithm. Different experiments on object detection algorithms (YOLO-v3, Faster RCNN, SSD) for person detection have been performed in this work. We achieved the maximum accuracy in real-time on the YOLO-v3 for person detection. Whereas we have marked the specific area using the template matching algorithm of computer vision techniques. Our proposed algorithm detects the persons and extracts the region of interest points on which the user draws the rectangle. Then we find the intersection over union ratio between the detected person and the region of interest of the marked area to make the decision. We have achieved 88.72% accuracy on person detection in the local environment. Whereas, for the whole system of human-object interaction for detecting the hotspot area zone, we have achieved 86.7% accuracy using the confusion matrix.

关键词： COVID-19 Computer vision Feature extraction Object detection Diseases Task analysis Filtering algorithms Convolution neural network object detection person detection hotspot zone fine-tuning

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Image Fusion Using Wavelet Transformation and XGboost Algorithm

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Computers, Materials & Continua 2024年第4期79卷 801-817页

作者： Shahid Naseem Tariq Mahmood Amjad Rehman Khan Umer Farooq Samra Nawazish Faten S.Alamri Tanzila Saba Department of Information Sciences Division of S&TUniversity of EducationLahore54770Pakistan Artificial Intelligence and Data Analytics(AIDA)Lab CCIS Prince Sultan UniversityRiyadh11586Saudi Arabia Faculty of Information Sciences University of EducationVehari CampusVehari61100Pakistan Department of Computer Science University of Engineering and TechnologyTaxila47050Pakistan Department of Mathematical Sciences College of SciencePrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia

Recently,there have been several uses for digital image *** fusion has become a prominent application in the domain of imaging *** create one final image that provesmore informative and helpful compared to the original input images,image fusion merges two or more initial images of the same *** fusion aims to produce,enhance,and transform significant elements of the source images into combined images for the sake of human visual *** fusion is commonly employed for feature extraction in smart robots,clinical imaging,audiovisual camera integration,manufacturing process monitoring,electronic circuit design,advanced device diagnostics,and intelligent assembly line robots,with image quality varying depending on *** research paper presents various methods for merging images in spatial and frequency domains,including a blend of stable and curvelet transformations,everageMax-Min,weighted principal component analysis(PCA),HIS(Hue,Intensity,Saturation),wavelet transform,discrete cosine transform(DCT),dual-tree Complex Wavelet Transform(CWT),and multiple wavelet *** fusion methods integrate data from several source images of an identical target,thereby enhancing information in an extremely efficient *** precisely,in imaging techniques,the depth of field constraint precludes images from focusing on every object,leading to the exclusion of certain *** tackle thess challanges,a very efficient multi-focus wavelet decomposition and recompositionmethod is *** use of these wavelet decomposition and recomposition techniques enables this method to make use of existing optimized wavelet code and filter *** simulated outcomes provide evidence that the suggested approach initially extracts particular characteristics from images in order to accurately reflect the level of clarity portrayed in the original *** study enhances the performance of the eXtreme Gradient Boosting(XGBoos

关键词： Image fusion max-min average CWT XGBoost DCT inclusive innovations spatial and frequency domain

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Estimating fish weight growth in aquaponic farming through machine learning techniques 3

Estimating fish weight growth in aquaponic farming through m...

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3rd IEEE International Conference on Intelligent Technologies, CONIT 2023

作者： Kumar, Purushottam Tiwari, Pranav Srinivasulu Reddy, U. Machine Learning & Data Analytics Lab CoE in Artificial Intelligence National Institute of Technology Trichy India Indian Institute of Information Technology Tiruchirappalli Computer Science and Engineering Trichy India National Institute of Technology Machine Learning & Data Analytics Lab Department of Computer Applications Trichy India

ISBN: (纸本)9798350338607

Due to the ever-growing population, rapid urbanization, unusual environmental change, and dwindling water supply, the food production from conventional farming techniques won't be able to keep up with increasing food demand for long, leading to severe food scarcity. Aquaponics is emerging as a viable alternative to food cultivation techniques but lacks efficiency, hindering its adoption on a large-scale production basis. The adoption of artificial intelligence (AI) techniques in smart aquaponic farming can further contribute to increasing the yield in aquaponics. In the study, both linear and non-linear regression algorithms such as linear regression, lasso regression, ridge regression, KNN regression, SVR, and decision tree regression were trained on aquaponic fish pond dataset, evaluated on the basis of multiple metrics like MAE, MSE, RMSE, and RMSLE, and a competitive analysis was done to determine the best algorithm for predicting the harvest weight of the fish in aquaponic ponds. The results showed that KNN regression and decision tree regression algorithms have lowest error and highest accuracy of 99.15% and 99.1% respectively, for predicting the harvest weight of the fish. The results obtained could be scaled up to commercial levels and used in regulating nutrient concentration and aquatic environmental conditions © 2023 IEEE.

关键词： Fish

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Query-by-visual-search: multimodal framework for content-based image retrieval

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JOURNAL OF AMBIENT intelligence AND HUMANIZED COMPUTING 2020年第11期11卷 5629-5648页

作者： Bibi, Ruqia Mehmood, Zahid Yousef, Rehan Mehmood Saba, Tanzila Sardaraz, Muhammad Rehman, Amjad Univ Engn & Technol Dept Software Engn Taxila 47050 Pakistan Univ Engn & Technol Dept Comp Engn Taxila 47050 Pakistan Prince Sultan Univ Artificial Intelligence & Data Analyt AIDA Lab CCIS Riyadh 11586 Saudi Arabia COMSATS Univ Islamabad Dept Comp Sci Attock Campus Islamabad 43600 Pakistan

Content-based image retrieval (CBIR) states the procedure of recovering images having similar visual content against a query image from image datasets. In CBIR, the selection of redundant and irrelevant features from images results in the semantic gap issue, which occurs during feature representation and machine learning process. The robust image representation for effective and efficient image retrieval mainly depends upon robust feature selection and classification, which also reduces the semantic gap problem of CBIR. This paper proposed an innovative method for effective and efficient CBIR. The method uses sparse complementary features for vigorous image representation, optimal feature selection based on locality-preserving projection, fuzzy c-means clustering, and soft label support vector machine for robust image classification. In CBIR, smaller and larger sizes of codebook improve the recall and precision (accuracy) of the system, respectively. Due to this reason, the proposed method introduces complementary features based on a larger size codebook, which is assembled using two small sizes of codebooks to increase CBIR performance. The three well-known image datasets (i.e. Corel-1000, Corel-1500, and Holidays) are used to assess the performance of the proposed method. The experimental evaluation highlights promising results as compared to recent methods of CBIR.

关键词： Content-based image retrieval Multifractal features Soft label SVM Salient objects Bag-of-words model

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Facial Emotion Recognition Using Conventional Machine Learning and Deep Learning Methods: Current Achievements, Analysis and Remaining Challenges

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INFORMATION 2022年第6期13卷 268页

作者： Khan, Amjad Rehman CCIS Prince Sultan Univ Artificial Intelligence & Data Analyt AIDA Lab Riyadh 11586 Saudi Arabia

Facial emotion recognition (FER) is an emerging and significant research area in the pattern recognition domain. In daily life, the role of non-verbal communication is significant, and in overall communication, its involvement is around 55% to 93%. Facial emotion analysis is efficiently used in surveillance videos, expression analysis, gesture recognition, smart homes, computer games, depression treatment, patient monitoring, anxiety, detecting lies, psychoanalysis, paralinguistic communication, detecting operator fatigue and robotics. In this paper, we present a detailed review on FER. The literature is collected from different reputable research published during the current decade. This review is based on conventional machine learning (ML) and various deep learning (DL) approaches. Further, different FER datasets for evaluation metrics that are publicly available are discussed and compared with benchmark results. This paper provides a holistic review of FER using traditional ML and DL methods to highlight the future gap in this domain for new researchers. Finally, this review work is a guidebook and very helpful for young researchers in the FER area, providing a general understating and basic knowledge of the current state-of-the-art methods, and to experienced researchers looking for productive directions for future work.

关键词： facial expressions facial emotion recognition (FER) technological development healthcare security deep learning and traditional classification methods

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