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Microscopic segmentation and classification of COVID-19 infection with ensemble convolutional neural network

MICROSCOPY RESEARCH AND TECHNIQUE 2022年第1期85卷 385-397页

作者： Amin, Javeria Anjum, Muhammad Almas Sharif, Muhammad Rehman, Amjad Saba, Tanzila Zahra, Rida Univ Wah Dept Comp Sci Wah Cantt Pakistan Natl Univ Technol NUTECH Islamabad Pakistan COMSATS Univ Islamabad Dept Comp Sci Wah Campus Wah Cantt Pakistan CCIS Prince Sultan Univ Artificial Intelligence & Data Analyt Lab Riyadh Saudi Arabia

The detection of biological RNA from sputum has a comparatively poor positive rate in the initial/early stages of discovering COVID-19, as per the World Health Organization. It has a different morphological structure as compared to healthy images, manifested by computer tomography (CT). COVID-19 diagnosis at an early stage can aid in the timely cure of patients, lowering the mortality rate. In this reported research, three-phase model is proposed for COVID-19 detection. In Phase I, noise is removed from CT images using a denoise convolutional neural network (DnCNN). In the Phase II, the actual lesion region is segmented from the enhanced CT images by using deeplabv3 and ResNet-18. In Phase III, segmented images are passed to the stack sparse autoencoder (SSAE) deep learning model having two stack auto-encoders (SAE) with the selected hidden layers. The designed SSAE model is based on both SAE and softmax layers for COVID19 classification. The proposed method is evaluated on actual patient data of Pakistan Ordinance Factories and other public benchmark data sets with different scanners/mediums. The proposed method achieved global segmentation accuracy of 0.96 and 0.97 for classification.

关键词： Deeplabv3 denoise convolutional neural network (DnCNN) healthcare public health ResNet-18 stack sparse autoencoder deep learning model (SSAE)

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MSegNet: A Multi-View Coupled Cross-Modal Attention Model for Enhanced MRI Brain Tumor Segmentation

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INTERNATIONAL JOURNAL OF COMPUTATIONAL intelligence SYSTEMS 2025年第1期18卷 1-32页

作者： Wang, Yu Xu, Juan Guan, Yucheng Ahmad, Faizan Mahmood, Tariq Rehman, Amjad Shandong Res Inst Ind Technol Jinan Shandong Peoples R China Taian City Cent Hosp Dept Endocrinol Tai An Peoples R China Northeastern Univ Coll Engn San Jose CA USA Qiqihar Med Univ Qiqihar Heilongjiang Peoples R China Prince Sultan Univ Artificial Intelligence & Data Analyt AIDA Lab CCIS Riyadh 11586 Saudi Arabia Univ Educ Fac Informat Sci Vehari Campus Vehari 61100 Pakistan

Brain tumor incidence and mortality rates are increasing due to unique location and treatment challenges. Early detection, robust diagnosis, and prompt treatment are crucial for better clinical evaluations. However, traditional neural network-based diagnostic methods often overlook issues such as variation in multimodality information, loss of spatial information, and under-utilization of boundary information. This study presents the Multi-View Coupled Cross-Modal Attention Network (MSegNet), a novel Transformer-based segmentation framework that integrates cross-modal attention mechanisms and a multi-view architecture. MSegNet is designed to exploit multimodal MRI data's spatial and depth dimensions, effectively capturing nuanced intermodal relationships and modeling long-range dependencies. The proposed framework also employs three data augmentation methods, which help prevent overfitting and improve the performance of segmentation network training, enhancing the model's robustness and generalizability. The proposed model is validated using BraTS2019, BraTS2020 and Figshate brain datasets and is compared against three state-of-the-art 3D segmentation networks. Extensive experiments, including ablation studies and hyperparameter sensitivity analyses, highlight MSegNet's robust performance. The dice scores for the whole tumor (WT), tumor core (TC) and enhancing tumor (ET) regions improved by 13. 96%, 12. 39%, and 11. 83%, respectively, while the Hausdorff distances were reduced by 3.64 mm, 2.98 mm, and 14.72 mm. These results demonstrate the model's efficacy in enhancing segmentation precision, making it a valuable tool for clinical diagnosis and treatment planning.

关键词： Transformer model Brain tumor segmentation Multi-modal fusion Cross-modal attention Health Issue

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Mobile Application Usability Evaluation: A Study Based on Demography

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IEEE ACCESS 2022年 10卷 41512-41524页

作者： Ali, Wahaj Riaz, Omer Mumtaz, Shahzad Khan, Amjad Rehman Saba, Tanzila Bahaj, Saeed Ali Islamia Univ Bahawalpur Fac Comp Bahawalpur 63100 Pakistan Prince Sultan Univ Artificial Intelligence & Data Analyt Lab CCIS Riyadh 11586 Saudi Arabia Prince Sattam Bin Abdulaziz Univ Coll Business Adm MIS Dept Al Kharj 11942 Saudi Arabia

An application or product is considered "usable" if it is pleasing, easy to use, and works as expected user interface. Most companies majorly focus on the application's functional requirements but put minimal effort into user experience (usability). Consumers' adaptation of these applications depends on the number of features and user interface. In this work, an android based application, "Houzcalls" is used for usability study using PACMAD usability model. This work is focused on the variations in PACMAD attributes based on the participants' education and age. Participants are segregated into two major groups FG1 and FG2 based on their education. All the participants with more than 10 years of education are in FG1 while others in FG2. Each focal group is divided into four subgroups Under 25, 25-35, 36-45, and Over 45 based on their ages. The results have shown participants in FG1 have shown more Effectiveness, Efficiency, Satisfaction, Learnability, and Memorability. In contrast, they have committed fewer Errors and shown less Cognitive Load during usability testing as compared to FG2. These variations can also be seen age-wise as generally "Under 25 and 25-35" subgroups have shown better results than other subgroups. It is inferred from the study that application usability and acceptability can be increased by considering the general population during development which includes all groups of people based on education and age.

关键词： Usability Testing Education Load modeling Software ISO Standards Mobile applications HCI usability testing attribute-based evaluation PACMAD usability model UI test design test execution focus groups age education mobile application requirement engineering

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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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Transforming educational insights: strategic integration of federated learning for enhanced prediction of student learning outcomes

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JOURNAL OF SUPERCOMPUTING 2024年第11期80卷 16334-16367页

作者： Farooq, Umer Naseem, Shahid Mahmood, Tariq Li, Jianqiang Rehman, Amjad Saba, Tanzila Mustafa, Luqman Beijing Univ Technol Fac Informat Technol Beijing 100024 Peoples R China Univ Education Fac Informat Sci Div Sci & Technol Lahore 54000 Pakistan CCIS Prince Sultan Univ Artificial Intelligence & Data Analyt AIDA Lab Riyadh 11586 Saudi Arabia Univ Educatio Fac Informat Sci Vehari Campus Vehari 61161 Pakistan Beijing Engn Res Ctr IoT Software & Syst Beijing 100124 Peoples R China

Numerous educational institutions utilize data mining techniques to manage student records, particularly those related to academic achievements, which are essential in improving learning experiences and overall outcomes. Educational data mining (EDM) is a thriving research field that employs data mining and machine learning methods to extract valuable insights from educational databases, primarily focused on predicting students' academic performance. This study proposes a novel federated learning (FL) standard that ensures the confidentiality of the dataset and allows for the prediction of student grades, categorized into four levels: low, good, average, and drop. Optimized features are incorporated into the training process to enhance model precision. This study evaluates the optimized dataset using five machine learning (ML) algorithms, namely support vector machine (SVM), decision tree, Naive Bayes, K-nearest neighbors, and the proposed federated learning model. The models' performance is assessed regarding accuracy, precision, recall, and F1-score, followed by a comprehensive comparative analysis. The results reveal that FL and SVM outperform the alternative models, demonstrating superior predictive performance for student grade classification. This study showcases the potential of federated learning in effectively utilizing educational data from various institutes while maintaining data privacy, contributing to educational data mining and machine learning advancements for student performance prediction.

关键词： Prediction Federal learning Machine learning Learning outcome EDM SVM Inclusive innovation

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An adaptive and secure routes migration model for the sustainable cloud of things

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2023年第2期26卷 1631-1642页

作者： Islam, Naveed Haseeb, Khalid Rehman, Amjad Alam, Teg Jeon, Gwanggil Islamia Coll Peshawar Dept Comp Sci Peshawar Pakistan CCIS Prince Sultan Univ Artificial Intelligence & Data Analyt AIDA Lab Riyadh 11586 Saudi Arabia Prince Sattam Bin Abdulaziz Univ Dept Ind Engn Coll Engn Al Kharj 16273 Saudi Arabia Incheon Natl Univ Dept Embedded Syst Engn Incheon 22012 South Korea

Software-defined networks (SDN) have gained a lot of attention in recent years as a technique to develop smart systems with a help of the Internet of Things (IoT). Its powerful and centralized architecture makes a balanced contribution to the management of sustainable applications through efficient processes. These networks also systematically keep track of mobile devices and decrease the extra overheads in the communication cost. Many solutions are proposed to cope with data transferring for the critical system, however, mobile devices, on the other hand, require long-distance communication links with minimal retransmissions. Furthermore, the mobile network is highly infected by security attacks and compromised the IoT architecture for both the intermediate layers and end-users. Therefore, this paper presents an adaptive routes migration model for sustainable applications with the collaboration of SDN architecture and limits the disconnectivity time in data transporting along with efficient management of network services. Moreover, its centralized controller fetches the updated information from low-level smart devices and supervised their monitoring efficiently. The proposed model also secures the cloud of things (CoTs) from network threats and protects private data. It provides three levels of security algorithms and supports adaptive computing systems. The proposed model was tested using simulations, and the findings showed that it outperformed other existing studies in terms of packet delivery ratio by 13%, packet loss rate by 15%, transmission error by 22%, computing cost by 17%, and latency by 18%.

关键词： Mobile network Technological development Risk assessment Intelligent computing Cloud of things

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Cloud-edge load balancing distributed protocol for IoE services using swarm intelligence

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2023年第5期26卷 2921-2931页

作者： Saba, Tanzila Rehman, Amjad Haseeb, Khalid Alam, Teg Jeon, Gwanggil Prince Sultan Univ CCIS Artificial Intelligence & Data Analyt AIDA Lab Riyadh 11586 Saudi Arabia Islamia Coll Peshawar Dept Comp Sci Peshawar 25120 Pakistan Prince Sattam Bin Abdulaziz Univ Coll Engn Dept Ind Engn Al Kharj 11942 Saudi Arabia Incheon Natl Univ Dept Embedded Syst Engn Incheon 22012 South Korea

Rapid development of the Internet of Everything (IoE) and cloud services offer a vital role in the growth of smart applications. It provides scalability with the collaboration of cloud servers and copes with a big amount of collected data for network systems. Although, edge computing supports efficient utilization of communication bandwidth, and latency requirements to facilitate smart embedded systems. However, it faces significant research issues regarding data aggregation among heterogeneous network services and objects. Moreover, distributed systems are more precise for data access and storage, thus machine-to-machine is needed to be secured from unpredictable events. As a result, this research proposed secured data management with distributed load balancing protocol using particle swarm optimization, which aims to decrease the response time for cloud users and effectively maintain the integrity of network communication. It combines distributed computing and shift high cost computations closer to the requesting node to reduce latency and transmission overhead. Moreover, the proposed work also protects the communicating machines from malicious devices by evaluating the trust in a controlled manner. Simulation results revealed a significant performance of the proposed protocol in comparison to other solutions in terms of energy consumption by 20%, success rate by 17%, end-to-end delay by 14%, and network cost by 19% as average in the light of various performance metrics.

关键词： Particle swarm optimization Cloud-edge Internet of everything Security analysis Technological development

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Deep Learning and Shape-Driven Combined Approach for Breast Cancer Tumor Segmentation

Deep Learning and Shape-Driven Combined Approach for Breast ...

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International Conference on Advancements in Computational Sciences (ICACS)

作者： Mudassar Ali Haoji Hu Tufail Muhammad Muhammad Ahsan Qureshi Tariq Mahmood College of Information Science and Electronic Engineering Zhejiang University Hangzhou China Department of Computer Science Air University Islamabad Pakistan Artificial Intelligence and Data Analytics Lab CCIS Prince Sultan University Riyadh Kingdom of Saudi Arabia.

ISBN: (数字)9798331533038

ISBN: (纸本)9798331533045

Our aim is to enhance the performance of segmenting breast cancer from medical images by overcoming major challenges, including data insufficiency and complexity when training a model. Using the INbreast dataset, it proposes a hybrid method for improving the segmentation accuracy and computational efficiency by combining M3D-Neural Cellular Automata with Shape-Guided Segmentation. This work aims at optimal performance on complex tasks of tumor segmentation by leveraging shape priors and reducing the need for extensive training data. Results confirm that significant improvements in both accuracy and robustness are achieved, which reduces computation time and improves tumor boundary delineation. The hybrid approach provides a time- and cost-effective method for precise detection in breast cancer diagnosis and treatment.

关键词： Deep learning Training Image segmentation Accuracy Shape Training data Breast cancer data models Robustness Real-time systems

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Optimised stacked machine learning algorithms for genomics and genetics disorder detection in the healthcare industry

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FUNCTIONAL & INTEGRATIVE GENOMICS 2024年第1期24卷 23-23页

作者： Rehman, Amjad Mujahid, Muhammad Saba, Tanzila Jeon, Gwanggil Prince Sultan Univ Artificial Intelligence & Data Analyt Lab CCIS Riyadh 11586 Saudi Arabia Incheon Natl Univ Dept Embedded Syst Engn Incheon 610101 South Korea

With recent advances in precision medicine and healthcare computing, there is an enormous demand for developing machine learning algorithms in genomics to enhance the rapid analysis of disease disorders. Technological advancement in genomics and imaging provides clinicians with enormous amounts of data, but prediction is still mostly subjective, resulting in problematic medical treatment. Machine learning is being employed in several domains of the healthcare sector, encompassing clinical research, early disease identification, and medicinal innovation with a historical perspective. The main objective of this study is to detect patients who, based on several medical standards, are more susceptible to having a genetic disorder. A genetic disease prediction algorithm was employed, leveraging the patient's health history to evaluate the probability of diagnosing a genetic disorder. We developed a computationally efficient machine learning approach to predict the overall lifespan of patients with a genomics disorder and to classify and predict patients with a genetic disease. The SVM, RF, and ETC are stacked using two-layer meta-estimators to develop the proposed model. The first layer comprises all the baseline models employed to predict the outcomes based on the dataset. The second layer comprises a component known as a meta-classifier. Results from the experiment indicate that the model achieved an accuracy of 90.45% and a recall score of 90.19%. The area under the curve (AUC) for mitochondrial diseases is 98.1%;for multifactorial diseases, it is 97.5%;and for single-gene inheritance, it is 98.8%. The proposed approach presents a novel method for predicting patient prognosis in a manner that is unbiased, accurate, and comprehensive. The proposed approach outperforms human professionals using the current clinical standard for genetic disease classification in terms of identification accuracy. The implementation of stacked will significantly improve the field of biomed

关键词： Genomics Machine learning Detection Healthcare Medical history

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Efficient and trusted autonomous vehicle routing protocol for 6G networks with computational intelligence

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ISA TRANSACTIONS 2023年 132卷 61-68页

作者： Haseeb, Khalid Rehman, Amjad Saba, Tanzila Bahaj, Saeed Ali Wang, Huihui Song, Houbing Islamia Coll Peshawar Dept Comp Sci Peshawar 25120 Pakistan CCIS Prince Sultan Univ Artificial Intelligence & Data Analyt AIDA Lab Riyadh 11586 Saudi Arabia Prince Sattam Bin Abdulaziz Univ MIS Dept Coll Business Adm Alkharj 11942 Saudi Arabia St Bonaventure Univ Cybersecur Program St Bonaventure NY 14778 USA Univ Maryland Baltimore Cty Secur & Optimizat Networked Globe Lab SONG Lab Baltimore MD 21250 USA

The Internet of Things (IoT) and wireless sensors have collaborated with many real-time environments for the collection and processing of physical data. Mobile networks with sixth-generation (6G) technologies provide support for emerging applications using Connected and Autonomous Vehicles (CAV) and observe critical conditions. Although, autonomous vehicle-based routing solutions have presented significant development toward reliable and inter-vehicle communications. However, there are numerous research obstacles in terms of data delivery and transmission latency due to the unpredictable environment and changing states of IoT sensors. Therefore, this work presents an efficient and trusted autonomous vehicle routing protocol using 6G networks, which aims to guarantee high quality of service and data coverage. Firstly, the proposed protocol establishes a routing process using a simulated annealing optimization technique and improves energy optimization between IoT-based vehicles, and under difficult circumstances, it statistically guarantees the optimal solution. Secondly, it provides a risk-aware security system due to reliable session-oriented communication with network edges among connected vehicles and avoids uncertainties in the autonomous system. The proposed protocol is verified using simulations for varying vehicles and varying iterations that indicates a green communication system for the autonomous system with authenticity and system intelligence. (c) 2022 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： Autonomous system 6G network Green communication Computational intelligence Disaster risk reduction Technological development

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