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作者： Ricky Mohanty Subhendu Kumar Pani Amjad Rehman Noor Ayesha Artificial Intelligence & Data Analytics Lab CCIS Prince Sultan University School of Clinical Medicine Zhengzhou University

The signals produced by an electrocardiogram(ECG) are made up of intricate pattern sequences that have a periodic *** pattern sequences contain an initial P-wave,which denotes the beginning of an ECG wave,a QRS sequence,which denotes the intensity of the pulse,and a T segment,which denotes the conclusion of the *** such as PR interval,QRS interval,QT interval,ST interval,R to R interval,*** employed to recognize chronic,ischemic,and other cardiac *** wave patterns need the simultaneous execution of many high-complexity signal processing operations to be classified into cardiac *** pre-processing,feature extraction,feature selection,classification into epileptic and non-epileptic seizures,and post-processing are some of these *** each of these operations,researchers create a wide range of *** algorithms' performance differs significantly in terms of the quantity of leads utilized for ECG collection,filtering effectiveness,feature extraction & selection effectiveness,and classifier ***,researchers and system designers have become unclear when choosing the optimum algorithm set for an *** text offers a thorough analysis & design of fuzzy CNN model of a broad range of epileptic & non-epileptic seizure classification techniques to lessen *** neural network(CNN) based models outperform other models in terms of general-purpose performance,whereas application-specific deployments need the employment of customized fuzzy CNN models.A significant area of research & development is presented by the observation that fuzzy logic techniques are not observed while constructing ECG classification *** on these findings,a new fuzzy logic-based classification method is provided in this text that employs quantization techniques to transform input ECG signals into fuzzy *** these parameters and a specially created CNN model,it was possible to a

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Next-Generation Human Activity Recognition Using Locality Constrained Linear Coding Combined With Machine Learning (NG-HAR-LCML)

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Healthcare Technology Letters 2025年第1期12卷

作者： Maryam Shabbir Fahad Ahmad Saad Awadh Alanazi Muhammad Hassan Khan Jianqiang Li Tariq Mahmood Shahid Naseem Muhammad Anwar Department of Computer Sciences Bahria University Lahore Punjab Pakistan Department of Computer Science Faculty of Computing & Information Technology University of the Punjab Lahore Pakistan School of Computing Faculty of Technology University of Portsmouth Portsmouth UK Portsmouth Artificial Intelligence and Data Science Centre (PAIDS) University of Portsmouth Portsmouth UK Department of Computer Science College of Computer and Information Sciences Jouf University Sakaka Aljouf Saudi Arabia The School of Software Engineering Beijing University of Technology Beijing China Artificial Intelligence and Data Analytics (AIDA) Lab CCIS Prince Sultan University Riyadh Saudi Arabia Department of Information Sciences University of Education Vehari Pakistan Faculty of Information Sciences Division Science &Technology University of Education Lahore Pakistan

Accurate Human Activity Recognition (HAR) is a critical challenge with wide-ranging applications in healthcare, assistive technologies, and human-computer interaction. Traditional feature extraction methods often struggle to capture the complex spatial and temporal dynamics of human movements, leading to suboptimal classification performance. To address this limitation, this study introduces a novel encoding approach using Locality-Constrained Linear Coding (LLC) to enhance the discriminative power of hand-crafted features extracted from low-cost wearable sensors—an accelerometer and a gyroscope. The proposed LLC-based encoding scheme enables robust feature representation, improving the accuracy of HAR models. The encoded features are classified using a diverse set of Machine Learning (ML) and Deep Learning (DL) algorithms, including Support Vector Machine (SVM), Logistic Regression (LR), Naive Bayes (NB), K-Nearest Neighbours (KNN), AdaBoost, Gradient Boosting Machine (GBM), and Deep Belief Network (DBN). Extensive quantitative evaluations demonstrate that LLC significantly outperforms conventional feature encoding techniques, leading to improved classification accuracy. Among the tested models, DBN achieves a state-of-the-art accuracy of 99%, highlighting its superiority for HAR tasks. The contributions of this research are threefold: (1) it establishes the necessity of an advanced encoding scheme (LLC) for feature enhancement in HAR, (2) it provides a rigorous comparative analysis of multiple ML and DL classifiers, and (3) it introduces a scalable and cost-effective HAR framework suitable for real-world applications. Performance is comprehensively assessed using robust evaluation metrics such as Area Under the Curve (AUC), Classification Accuracy (CA), F1 Score, Precision, Recall, and Matthews Correlation Coefficient (MCC). The findings of this study offer new insights into feature encoding for HAR, setting a foundation for future advancements in sensor-based act

关键词： feature encoding human activity recognition locality constrained linear coding support vector machine wearable sensors

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Customized Deep Learning Framework with Advanced Sampling Techniques for Lung Cancer Detection using CT Scans

Customized Deep Learning Framework with Advanced Sampling Te...

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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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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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Early Stage Kidney Chronic Diseases Diagnosis using Feature Fusion and Deep Learning Algorithms 17

Early Stage Kidney Chronic Diseases Diagnosis using Feature ...

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17th International Conference on Developments in eSystems Engineering, DeSE 2024

作者： Saba, Tanzila Mujahid, Muhamamd Ayesha, Noor Kolivand, Mahyar Artificial Intelligence & Data Analytics Lab CCIS Prince Sultan University Riyadh11586 Saudi Arabia School of Clinical Medicine Zhengzhou University Henan ZhengzhouRP 450001 China Department of Medicine University of Liverpool Liverpool United Kingdom School of Computing and Digital Technologies Staffordshire University Staffordshire United Kingdom

ISBN: (纸本)9798350368697

Chronic Kidney Disease (CKD) is a global health issue due to pre-existing conditions like anemia, diabetes, and hypertension. Early warning signals are commonly neglected, complicating diagnosis and treatment. This study uses high-performance data mining techniques to provide self-analysis and prognostic tools for medical professionals. Clinical decision support using domain experts and specialists aids CKD prognosis and rehabilitation. We use advanced data mining to evaluate demographic characteristics, test findings, and patient medical information to uncover insights and patterns. We used a modified artificial neural network to extract characteristics from a dataset and long-term and short-term memory to classify chronic kidney disease stages. LSTM algorithms improve the accuracy of CKD diagnosis and stage prediction. Initial validation tests demonstrate a 92.23% accuracy gain over traditional methods. The binary classification AUC was 99.95% and the multi-class classification was 94.13% with the proposed technique. LSTM algorithms use temporal dynamics in patient data to enhance diagnostic accuracy, early diagnosis, therapy customization, and healthcare costs. We proposed a novel CKD diagnostic and prognosis paradigm and test multiple classification algorithms for disease diagnosis and staging. Multiple methods improve reliability and account for CKD diagnostic peculiarities. © 2024 IEEE.

关键词： Chronic kidney disease (CKD) Classifier fusion Health rehabilitation Healthcare

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Prediction of Environmental Earth Surface Temperature using hybrid machine learning model

Prediction of Environmental Earth Surface Temperature using ...

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

作者： Aisha Dawood Arwa Alsehibani Safia Dawood Amjad Rehman Artificial Intelligence & Data Analytics Lab (AIDA) CCIS Prince Sultan University Riyadh Saudi Arabia SE Department Alfaisal University Riyadh Saudi Arabia

Global warming increasingly became a research topic of high importance that have attracted attention of many scientists worldwide. According to the annual 2020 global climate report, global yearly temperature has increased 0.08C per decade since 1880. In this study, the global earth temperature has been analyzed including temperature records since 1850. And it has been observed that globally the average temperature has increased by almost 10C degrees in the last century. Also, this study developed a model for temperature prediction using linear regression and random forest regression. Also, the study reflected its finding on Saudi Arabia and found over 3C degrees over the last 100 years. The proposed models achieved accuracies 99.52 % and 99.58 %, respectively, with mean square error 0.28 and 0.24.

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A Novel Multi-Scale Deep Learning Approach for the Early Detection of Alzheimer's Disease Using fMRI 5

A Novel Multi-Scale Deep Learning Approach for the Early Det...

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5th International Conference on Computer, Big data and artificial intelligence, ICCBD+AI 2024

作者： Rehman Butt, Ateeq Ur Hamid, Isma Nawaz, Qamar Mahmood, Tariq Zhang, Xiang Yaqub, Muhammad National Textile University Department of Computer Science Faisalabad Pakistan University of Agriculture Department of Computer Science Faisalabad Pakistan Prince Sultan University Artificial Intelligence and Data Analytics Lab Ccis Riyadh11586 Saudi Arabia Chinese Academy of Sciences Institute of Biophysics Beijing100101 China Hunan University School of Biomedical Sciences Changsha China

ISBN: (纸本)9798331533991

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that remains challenging to diagnose in its early stages. This study presents a novel 3D multi-scale Convolutional Neural Network (CNN) model that utilizes resting-state functional MRI (rs-fMRI) data to classify different stages of Alzheimer's disease. The proposed model integrates multi-scale feature extraction from the hippocampal region, optimizing the detection of fine-grained and global patterns indicative of AD progression. The model was evaluated on a balanced dataset of 300 subjects, applying cross-validation to ensure robustness across multiple binary classification tasks. Our experimental results demonstrate a classification accuracy of up to 93% for distinguishing Alzheimer's Disease from Cognitively Normal subjects. High sensitivity, specificity, and precision were observed across all classification tasks, indicating the model's effectiveness in early-stage Alzheimer's detection. These findings suggest the proposed approach could enhance clinical diagnosis and facilitate timely interventions for AD patients. © 2024 IEEE.

关键词： Convolutional neural networks

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Review on 3D printers

Review on 3D printers

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作者： Hoshang Kolivand Luke Bailey Shiva Asadianfam Amjad Rehman Department of Computer Science Liverpool John Moores University Department of Computer Engineering Islamic Azad University Qom Branch Artificial Intelligence & Data Analytics Lab (AIDA) CCIS Prince Sultan University

In this paper of the report,it is all the different research in the that has been done to understand more about the subject of 3D printers and the health *** this paper all the different research that was done was information that is very closely related to subjects previously stated,although these papers are very professional and informative the focus of this part of the paper is to better show all the different gaps in the information and what it could be missing that could possibly be showed in this paper that they have missed out on.

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AI-Enhanced Mammographic Screening: Optimizing Breast Lesion Detection and Classification with the Robust ECA-Net152 Model

AI-Enhanced Mammographic Screening: Optimizing Breast Lesion...

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Computer, Big data and artificial intelligence (ICCBD+AI), International Conference on

作者： Tariq Mahmood Tanzila Saba Amjad Rehman Xiang Zhang Muhammad Yaqub Artificial Intelligence and Data Analytics Lab (AIDA) CCIS Prince Sultan University Riyadh Kingdom of Saudi Arabia Chinese Academy of Sciences Institute of Biophysics Beijing China School of Biomedical Sciences Hunan University Changsha China

ISBN: (数字)9798331533991

ISBN: (纸本)9798331534004

Breast cancer poses a significant threat to women’s health, and early screening and diagnosis are crucial for improving treatment outcomes and reducing mortality. Computer-aided diagnosis (CAD) systems have been developed to help physicians classify benign and malignant lesions in mammograms. The paper introduces a new pre-processing technique for mammographic datasets, addressing issues like tissue overlap and image noise. The technique includes denoising, segmentation, contrast adjustment, and data augmentation algorithms, enhancing CNN input and generalization. The ECA-Net152 model, which combines channel attention and focal loss function, is introduced to classify breast lesions. The method achieves an AUC of 0.960 and performs better through experimental comparisons and GradCAM visualization. This study contributes to AI-assisted diagnosis and early screening of breast cancer.

关键词： Solid modeling Transfer learning Noise reduction Noise data visualization Mortality Medical services Breast cancer Mammography Lesions

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Covid-19 Detection by using Deep learning-based Custom Convolution Neural Network (CNN)

Covid-19 Detection by using Deep learning-based Custom Convo...

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International Conference on Innovative Computing (ICIC)

作者： Mazhar Javed Awan Muhammad Wasif Imtiaz Muhammad Usama Amjad Rehman Noor Ayesha Hafiz Muhammad Faisal Shehzad University of Management and Technology Lahore Pakistan Artificial Intelligence & Data Analytics Lab (AIDA) CCIS Prince Sultan University Riyadh Saudi Arabia School of Clinical Medicine Zhengzhou University Zhengzhou Henan RP China University of Sargodha Pakistan

ISBN: (纸本)9781665400923

The early diagnosis and treatment of COVID-19 has been a challenge all over the world. It is challenging to manufacture many testing kits and even then, their accuracy rate is very low. Studies carried out recently show that chest x-ray images are of great help in the diagnosis of COVID-19. In this study, we have developed a COVID-19 detection model that by observing the chest x-ray images of the patient, detects that either the patient is affected by COVID-19 or not. The model is developed using a custom Convolutional Neural Network (CNN) that differentiates between COVID-19 and healthy x-ray images so that the patient can be diagnosed and quarantined on time to prevent the spread of the pandemic. We used two different datasets which are publicly available for the training and validation of this model. Upon completion, the proposed model yields an accuracy of almost 98%. Upon further training, our model will be able to be used as a COVID-19 detection tool in hospitals worldwide and will play a vital role in early detection and timely containment of the pandemic.

关键词： COVID-19 Training Pandemics Hospitals Neural networks Convolutional neural networks X-ray imaging

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