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Robust kidney carcinoma prognosis and characterization using Swin-ViT and DeeplabV3+with multi-model transfer learning

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APPLIED SOFT COMPUTING 2025年 170卷

作者： Rehman, Amjad Mahmood, Tariq Saba, Tanzila CCIS Prince Sultan Univ Artificial Intelligence & Data Analyt AIDA Lab Riyadh 11586 Saudi Arabia Univ Educ Dept Informat Sci Vehari Campus Vehari 61100 Pakistan

Chronic kidney disease (CKD) remains a significant health epidemic despite surgical advancements and a global shortage of nephrologists. Medical data analysis and clinical examination can assist healthcare professionals in identifying CKD in its early stages. Computer-based techniques can enhance diagnostic efficiency and accuracy in medical imaging, but current deep-learning-based models have limitations in extracting global information and multi-label classification. The cutting-edge variants of the Vision transformers (ViT) models show promise in capturing information from multiple image regions but require large-scale dataset training. The study proposes a novel approach based on the Swin-ViT model and various modified DL-based pre-trained models (ConvNext, Swint-Unet, MedT, mResNet152V2, EfficientNet-B7, and mVGG19) for prognosis-assisted kidney image and multilabel grading, with changes made to the final layers using the transfer learning paradigm to improve performance and reduce dependence on data scales. Transfer learning can improve classification performance and model generalization in small-scale datasets while reducing training costs. data enhancement and batch size cutting can improve performance, but smaller batches lead to longer training times. The experiment evaluates the Swin-ViT model for multi-label classification of CKD images via transfer learning, demonstrating that it has a stronger multi-label classification performance and that its attention mechanism is beneficial for precision-focused lesions. The study also generates attention mechanism GradCAM based on the Swin-ViT model, improving interpretability. Ablation experiments demonstrate that incorporating CNN and Transformer models outperform single-structure models. Future research can focus on extracting complex diseases and high-level semantic data to boost the model's ability.

关键词： Health issue Chronic renal failure Tumor detection Swin-ViT ConvNext Transfer learning Multilabel grading

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Microscope-Assisted Hypertensive Retinopathy Diagnosis Using Deep Learning Models

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MICROSCOPY RESEARCH AND TECHNIQUE 2025年第0期 2025 Mar 24页

作者： Akbar, Shahzad Shahzore, Usama Saba, Tanzila Alamri, Faten S. Khan, Sadaf S. Khan, Amjad R. Riphah Int Univ Riphah Coll Comp Riphah Artificial Intelligence Res RAIR Lab Faisalabad Pakistan Prince Sultan Univ Artificial Intelligence & Data Analyt Lab CCIS Riyadh Saudi Arabia Princess Nourah Bint Abdulrahman Univ Coll Sci Dept Math Sci Riyadh Saudi Arabia

The retina is the most crucial part of the human eye, and it can be affected due to hypertension. However, retinal abnormalities due to hypertension are termed hypertensive retinopathy (HR). A severe stage of HR can lead to complete blindness if not diagnosed and treated on time. Manually analyzing retinal images for HR diagnosis is time-consuming and prone to errors. This research article provides a novel technique based on U-Net and Dense-Net for automatic HR detection and grading through retinal images. The presented method consists of preprocessing, vessel segmentation, artery or vein (A/V) classification, and vessel width calculation to compute the arteriovenous ratio (AVR). In the preprocessing phase, the Gabor filter is applied to the retinal image to enhance the vascular network of the image. The preprocessed image is fed into the U-Net architecture to segment the vascular network image. The segmented vascular network image is fed into the Dense-Net architecture for A/V classification. The A/V classified vascular network is divided into several artery and vein segments at the bifurcation and crossover points. The A/V segments are labeled for width calculation to compute the AVR. The AVR is a standard parameter for HR detection and grading. The evaluation results show an average accuracy of 99.40% in HR classification and 99.77% in HR grading on the AVRDB dataset. The evaluated results are beneficial for the automatic HR detection and grading for clinical purposes.

关键词： arteriovenous ratio artery/vein classification health risks hypertensive retinopathy microscope-assisted imaging visions loss

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Deep Ensemble for Central Serous Microscopic Retinopathy Detection in Retinal Optical Coherence Tomographic Images

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MICROSCOPY RESEARCH AND TECHNIQUE 2025年第0期 2025 Feb 27页

作者： Hassan, Syed Ale Akbar, Shahzad Shoukat, Ijaz Ali Khan, Amjad R. Alamri, Faten S. Saba, Tanzila Riphah Int Univ Riphah Artificial Intelligence Res RAIR Lab Faisalabad Campus Faisalabad Pakistan CCIS Prince Sultan Univ Riyadh Artificial Intelligence & Data Analyt Lab Riyadh Saudi Arabia Princess Nourah Bint Abdulrahman Univ Coll Sci Dept Math Sci Riyadh Saudi Arabia

The retina is an important part of the eye that aids in focusing light and visual recognition to the brain. Hence, its damage causes vision loss in the human eye. Central serous retinopathy is a common retinal disorder in which serous detachment occurs at the posterior pole of the retina. Therefore, detection of CSR at an early stage with good accuracy can decrease the rate of vision loss and recover the vision to normal conditions. In the past, numerous manual techniques have been devised for CSR detection;nevertheless, they have demonstrated imprecision and unreliability. Thus, the deep learning method can play an important role in automatically detecting CSR. This research presents a convolutional neural network-based framework combined with segmentation and post-ocessing for CSR classification. There are several challenges in the segmentation of retinal images, such as noise, size variation, location, and shape of the fluid in the retina. To address these limitations, Otsu's thresholding has been employed as a technique for segmenting optical coherence tomography (OCT) images. Pigments and fluids are present in epithelial detachment, and contrast adjustment and noise removal are required. After segmentation, post-processing is used, combining flood filling, dilation, and area thresholding. The segmented processed OCT scans were classified using the fusion of three networks: (i) ResNet-18, (ii) Google-Net, and (iii) VGG-19. After experimentation, the fusion of ResNet-18, GoogleNet, and VGG-19 achieved 99.6% accuracy, 99.46% sensitivity, 100% specificity, and 99.73% F1 score using the proposed framework for classifying normal and CSR-affected images. A publicly available dataset OCTID comprises 207 normal and 102 CSR-affected images was utilized for testing and training of the proposed method. The experimental findings conclusively demonstrate the inherent suitability and efficacy of the framework put forth. Through rigorous testing and analysis, the results unequ

关键词： central serous microscopic retinopathy (CSR) deep learning health risks optical coherence tomography images retinal surface

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Autonomous and Intelligent Mobile Multimedia Cyber-Physical System with Secured Heterogeneous IoT Network

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MOBILE NETWORKS & APPLICATIONS 2024年第3期29卷 876-885页

作者： Rehman, Amjad Haseeb, Khalid Alruwaili, Fahad F. Ara, Anees Saba, Tanzila CCIS Prince Sultan Univ Artificial Intelligence & Data Analyt AIDA Lab Riyadh 12435 Saudi Arabia Shaqra Univ Coll Comp & Informat Technol Dept Comp & Network Engn Shaqra Saudi Arabia

The Internet of Things (IoT) and smart technologies perform a vital role in gathering data to facilitate extreme environments. These systems offer an autonomous management of real-time multimedia activities and are integrated with future networks. Smart communication and Cyber-Physical Systems (CPS) are tightly integrated with various processes to improve the efficiency, safety, and functionality of mobile networks. In recent decades, many approaches have been proposed to cope with the communication gap between physical objects and heterogeneous devices using adaptive routing techniques. Autonomous Multimedia CPS implementation using sensor technologies is significant for environmental monitoring, surveillance, and smart cities. However, reliable and fault-tolerant CPS communication in multimedia networks remains a research challenge. Most existing schemes deal with device mobility, which consumes energy and high latency communication in a constrained environment. In this research work, we proposed an optimized multimedia communication framework using adaptive intelligence for forwarding routes and decreasing the probability of data loss. It examines environmental parameters and circumstances for a prediction and decision-making system based on Particle Swarm Optimization (PSO). Furthermore, the proposed framework uses edge-enabled security to provide trusted sessions for devices with token-based authentication. Compared to existing work, the proposed framework demonstrates notable performance improvement for throughput, data latency, and alive nodes.

关键词： Cyber physical system Intelligent authentication Multimedia system Particle Swarm Optimization (PSO) Infrastructural development

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AI-driven IoT-fog analytics interactive smart system with data protection

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EXPERT SYSTEMS 2025年第1期42卷

作者： Haseeb, Khalid Saba, Tanzila Rehman, Amjad Abbas, Naveed Kim, Pyoung Won CCIS Prince Sultan Univ Artificial Intelligence & Data Analyt AIDA Lab Riyadh Saudi Arabia Islamia Coll Peshawar Dept Comp Sci Peshawar Pakistan Incheon Natl Univ Coll Educ Incheon South Korea

In recent decades, fog computing has contributed significantly to the expansion of smart cities. It generated numerous real-time data and coped with time-constraint applications. They use sensors, physical objects, and network standards to monitor health imaging, traffic surveillance, industrial management, and so forth. Interactive applications have been proposed for the Internet of Things (IoT) to control wireless channels and improve communication. However, most of the existing lack of handing network interference and a reliable monitoring process. Moreover, many solutions are vulnerable to external threats, resulting in inconsistent and untrustworthy information for end users. Thus, this article proposes a framework that considers possible shortest paths to provide the most reliable and low-latency healthcare decision system using Q-learning. In addition, fog devices offer a trusted transmission interference system and are kept secure. The proposed framework is specially designed for rapid real-time medical data processing while enforcing robust security throughout the IoT-based transmission process. To identify the health sensors in pairwise objects with the initial computing cost, the proposed framework applies graph theory. It also extracts the most effective and least loaded communication edges by examining the behaviour of devices. Moreover, the identities of devices are verified using lightweight timestamps and secret information, accordingly, it decreases the privacy threats.

关键词： healthcare image mobile intelligence reliable processing smart systems

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ConTrust: A Novel Context-Dependent Trust Management Model in Social Internet of Things

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IEEE ACCESS 2022年 10卷 46526-46537页

作者： Latif, Rabia Prince Sultan Univ Artificial Intelligence & Data Analyt Lab Coll Comp & Informat Sci CCIS Riyadh 11586 Saudi Arabia

The global population is around 7.4 billion people. This population density requires connectivity to improve the standard of living by transmitting and receiving variety of services. As a result, numerous forms of communication among objects are required for our everyday living demands, independent of their nature. Furthermore, to create a good relationship, every object that is regarded as associate of another object should have distinct criteria such as scalability, interoperability, and trustworthiness. Many security threats, however, have an impact on the social interaction between objects in a social internet of things (SIoT) context, including illegal admittance and suspicious behavior owing to a lack of verification architecture. Others include attempting to provide a proper viewpoint of a malicious object to earn the trust of other objects. As a result, there is a requirement for an acceptable method to check the behavior of objects such as capability, commitment, reliability, and previous job satisfaction before proceeding with any type of job assignment. This will aid in distinguishing between malicious and trustworthy objects by anticipating their upcoming behavior, allowing better judgments regarding service assignment to be made. This study proposes a context-dependent trust management technique (ConTrust) for choosing and allocating jobs in a SIoT environment. The feature-property match approach, as well as the combination of capability, commitment, and satisfaction, were utilized to increase the efficiency of trust assessment and the resolution of context-dependent difficulties. The proposed trust model considers job characteristics, object capabilities and honesty, and the impact of malicious conduct. The experimental results show that the proposed ConTrust model is viable and capable of ensuring the reliability and efficacy of SIoT service sharing between objects as compared to the benchmark models considered in this work.

关键词： Trust management Reliability Internet of Things Security Scalability Adaptation models Context modeling Context based trust management SIoT trust management

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Classification of cancer types based on microRNA expression using a hybrid radial basis function and particle swarm optimization algorithm

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MICROSCOPY RESEARCH AND TECHNIQUE 2024年第5期87卷 1052-1062页

作者： Soleimani, Masoumeh Harooni, Aryan Erfani, Nasim Khan, Amjad Rehman Saba, Tanzila Bahaj, Saeed Ali Clemson Univ Dept Math & Stat Sci Clemson SC USA Isfahan Univ Technol Dept Elect & Comp Engn Esfahan Iran Islamic Azad Univ Dept Comp Engn Dolatabad Branch Esfahan Iran Prince Sultan Univ Artificial Intelligence & Data Analyt Lab CCIS Riyadh Saudi Arabia Prince Sattam Bin Abdulaziz Univ Coll Business Adm MIS Dept Al Kharj Saudi Arabia Prince Sultan Univ Artificial Intelligence & Data Analyt Lab CCIS Riyadh 11586 Saudi Arabia

The diagnosis and treatment of cancer is one of the most challenging aspects of the medical profession, despite advances in disease diagnosis. MicroRNAs are small noncoding RNA molecules involved in regulating gene expression and are associated with several cancer types. Therefore, the analysis of microRNA data has become one of the most important areas of cancer research in recent years. This paper presents an improved method for cancer-type classification based on microRNA expression data using a hybrid radial basis function (RBF) and particle swarm optimization (PSO) algorithm. Two datasets containing microRNA information were used, and preprocessing and normalization operations were performed on the raw data. Feature selection was carried out by using the PSO algorithm, which can identify the most relevant and informative features in the data along with helping to prioritize them. Using a PSO algorithm for feature selection is an effective approach to microRNA analysis. This enhances the accuracy and reliability of cancer-type classifications based on microRNA expression data. In the proposed method, we, respectively, achieved an accuracy of 0.95% and 0.91% on both datasets, with an average of 0.93%, using an improved RBF neural network classifier. These results demonstrate that the proposed method outperforms previous works. Research Highlights center dot To enhance the accuracy of cancer-type classifications based on microRNA expression data. center dot We present a minimal feature selection method using particle swarm optimization to reduce computational load & radial basis function to improve accuracy.

关键词： cancer health risks microRNA particle swarm optimization radial basis function neural network

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LSTMDD: an optimized LSTM-based drift detector for concept drift in dynamic cloud computing

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PEERJ COMPUTER SCIENCE 2024年 10卷 e1827页

作者： Mehmood, Tajwar Latif, Seemab Jamail, Nor Shahida Mohd Malik, Asad Latif, Rabia Natl Univ Sci & Technol NUST Sch Elect Engn & Comp Sci SEECS Islamabad Pakistan Prince Sultan Univ Coll Comp & Informat Sci CCIS Artificial Intelligence & Data Analyt Lab Riyadh Saudi Arabia

This study aims to investigate the problem of concept drift in cloud computing and emphasizes the importance of early detection for enabling optimum resource utilization and offering an effective solution. The analysis includes synthetic and realworld cloud datasets, stressing the need for appropriate drift detectors tailored to the cloud domain. A modified version of Long Short-Term Memory (LSTM) called the LSTM Drift Detector (LSTMDD) is proposed and compared with other top drift detection techniques using prediction error as the primary evaluation metric. LSTMDD is optimized to improve performance in detecting anomalies in nonGaussian distributed cloud environments. The experiments show that LSTMDD outperforms other methods for gradual and sudden drift in the cloud domain. The findings suggest that machine learning techniques such as LSTMDD could be a promising approach to addressing the problem of concept drift in cloud computing, leading to more efficient resource allocation and improved performance.

关键词： Concept drift Drift detection Cloud usage trace CPU usage Memory usage Machine learning

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LSTM based Soft-Sensor for Estimating Nitrate Concentration in Aquaponics pond 3

LSTM based Soft-Sensor for Estimating Nitrate Concentration ...

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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Dharshan, A. Kumar, Purushottam Ravimaran, S. Srinivasulu Reddy, U. Saranathan College of Engineering Department of Artificial Intelligence and Data Science Trichy India National Institute of Technology Artificial Intelligence Machine Learning & Data Analytics Lab Trichy India National Institute of Technology Department of Computer Applications Machine Learning & Data Analytics Lab Trichy India

ISBN: (纸本)9798350381931

In the field of aquaponics, where fish and plants coexist in a symbiotic environment, closely monitoring nitrate levels in the water is crucial due to their profound impact on aquatic and plant well-being. Traditional nitrate measurement methods are often time-consuming and costly. Various approaches, including first principles, IoT-based sensors, and machine learning-based soft sensors, have been attempted to address this challenge. However, these efforts face challenges such as expensive sensors, infrequent data collection, multistage data processing using limited sensor types, and the need for regular maintenance like cleaning and calibration. Additionally, varied environmental conditions affect sensor suitability for different water environments, and even some machine learning-based soft sensors have proven inaccurate. In response, soft sensors, especially deep learning-based ones, have gained prominence in industrial applications for their adaptability and accuracy. These sensors provide real-time insights into complex processes without requiring expensive hardware. In this study, an innovative solution was introduced using Long Short-Term Memory (LSTM) technology, a neural network architecture in deep learning known for capturing complex temporal patterns. LSTM is well-suited for modeling and predicting nitrate concentration changes in aquaponics, trained with extensive data collected from various aquaponic ponds. Through rigorous evaluation, a remarkable MSE value of 0.00074 and an impressive R-squared score of 0.98 were achieved, holding potential for scaling up to commercial applications, benefiting aquaponics operations, supporting researchers, and enhancing sustainability and productivity in aquaponic systems. © 2024 IEEE.

关键词： Nitrates

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Blockchain-Enabled Intelligent IoT Protocol for High-Performance and Secured Big Financial data Transaction

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2024年第2期11卷 1667-1674页

作者： Saba, Tanzila Haseeb, Khalid Rehman, Amjad Jeon, Gwanggil Prince Sultan Univ Coll Comp & Informat Sci CCIS Artificial Intelligence & Data Analyt AIDA Lab Riyadh 12435 Saudi Arabia Incheon Natl Univ Coll Informat Technol Dept Embedded Syst Engn Incheon 22012 South Korea

In the recent era, the communication network with the support of many wireless technologies is giving benefits for remote access. Such a communication model increases the flexibility for data storage with the management of network resources efficiently with the integration of the Internet of Things (IoT). Although, the industrial Internet of Things (IIoT) has enabled the development of numerous machine learning-based solutions to provide real-time applications. However, most of the solutions are not prepared to cope with heterogeneous services and the huge amount of device data in the digital world. Furthermore, conducting financial transactions over the Internet raises several security issues. Such restrictions compromise the sensitive data of financial institutions and also degrade the trust of network users in the system. Thus, this article presents a secured blockchain model for high-performance computing in a big data environment, which aims to protect the business activities for financial interaction with intelligent services of software-defined network (SDN) architecture. First, to keep the security credentials, the SDN controller creates an association between the IoT devices and maintains local and global records. Second, the machine learning approach is explored using reliable and fault-tolerant methods to support the network scalability and extract the updated routing information for transmitting financial data. The proposed protocol also provides data integrity with a high level of network availability and copes with the financial security of big data by investigating cryptographic approaches. Our proposed protocol is tested using simulation, and various experiments are performed to show its efficacy in terms of network throughput, computing overhead, data delay, response time, and dropped packets as compared to tunicate swarm algorithm-based optimized routing mechanism (TORM) and RouteChain.

关键词： Protocols Blockchains Routing Security Big data Logic gates Reliability Big data blockchain distributed tasks financial security software-defined network (SDN)

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