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Reinforcement learning based route optimization model to enhance energy efficiency in internet of vehicles

SCIENTIFIC REPORTS 2025年第1期15卷 1-24页

作者： Hussain, Quadeer Noor, Ahmad Shukri Mohd Qureshi, Muhammad Mukhtar Li, Jianqiang Bakry, Aghiad Mahmood, Tariq Rehman, Amjad Beijing Univ Technol Fac Informat Technol Beijing 100024 Peoples R China Univ Malaysia Terengganu Fac Ocean Engn & Informat Kuala Nerus 21030 Terengganu Malaysia Abasyn Univ Dept Comp Sci Islamabad 45550 Pakistan Beijing Engn Res Ctr IoT Software & Syst Beijing 100124 Peoples R China Imam Abdulrahman Bin Faisal Univ Coll Comp Sci & Informat Technol Dept Comp Sci POB 1982 Dammam 31441 Saudi Arabia CCIS Prince Sultan Univ Artificial Intelligence & Data Analyt AIDA Lab Riyadh 11586 Saudi Arabia Univ Educ Fac Informat Sci Vehari Campus Vehari 61161 Pakistan

The Internet of Vehicles (IoV) transforms the automobile industry through connected vehicles with communication infrastructure that improves traffic control, safety and information, and entertainment services. However, some issues remain, like data protection, privacy, compatibility with other protocols and systems, and the availability of stable and continuous connections. Specific problems are related to energy consumption for transmitting information, distributing energy loads across the vehicle's sensors and communication units, and designing energy-efficient approaches to processing received data and making decisions in the context of the IoV environment. In the realm of IoV, we propose OptiE2ERL, an advanced Reinforcement Learning (RL) based model designed to optimize energy efficiency and routing. Our model leverages a reward matrix and the Bellman equation to determine the optimal path from source to destination, effectively managing communication overhead. The model considers critical parameters such as Remaining Energy Level (REL), Bandwidth and Interference Level (BIL), Mobility Pattern (MP), Traffic Condition (TC), and Network Topological Arrangement (NTA), ensuring a comprehensive approach to route optimization. Extensive simulations were conducted using NS2 and Python, demonstrating that OptiE2ERL significantly outperforms existing models like LEACH, PEGASIS, and EER-RL across various performance metrics. Specifically, our model extends the network lifetime, delays the occurrence of the first dead node, and maintains a higher residual energy rate. Furthermore, OptiE2ERL enhances network scalability and robustness, making it a superior choice for IoV applications. The simulation results highlight the effectiveness of our model in achieving energy-efficient routing while maintaining network performance under different scenarios. By incorporating a diverse set of parameters and utilizing RL techniques, OptiE2ERL provides a robust solution for the challeng

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Hybrid Retinal Image Enhancement Algorithm for Diabetic Retinopathy Diagnostic Using Deep Learning Model

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IEEE ACCESS 2022年 10卷 73079-73086页

作者： Abbood, Saif Hameed Hamed, Haza Nuzly Abdull Rahim, Mohd Shafry Mohd Rehman, Amjad Saba, Tanzila Bahaj, Saeed Ali Univ Technol Malaysia UTM Fac Engn Sch Comp Skudai 81310 Johor Malaysia Iraqi Minist Hlth Wasit Hlth Directorate Al Kut 52007 Wasit Iraq Prince Sultan Univ Artificial Intelligence & Data Analyt Lab AIDA CCIS Riyadh 11586 Saudi Arabia Prince Sattam bin Abdulaziz Univ MIS Dept Coll Business Adm Alkharj 11942 Saudi Arabia Hadhramout Univ Coll Engn & Petr Dept Comp Engn Al Mukalla 50512 Yemen

Diabetic Retinopathy (DR) is a prevalent acute stage of diabetes mellitus that causes vision-effecting abnormalities on the retina. This will cause blindness if not identified early. Because DR not an irreversible procedure, and only vision is preserved via care. Consequently, Early diagnosis and care with DR will significantly minimize the chance of vision loss. In modern ophthalmology, retinal image analysis has become a popular approach to disease diagnosis. The ophthalmologists and computerized systems extensively employ fundus angiography to detect DR-based clinical signs for early detection of DR. fundus photographs are commonly prone to low contrast, noise, and irregular illumination issues due to the complexity of imaging environments such as imaging variety of angles and light conditions. This research presents an Algorithm for improving the quality of images to strengthen the standard of color fundus images by reducing the noise and improving the contrast. The approach includes two main stages: cropping the images to remove insignificant content, then applying the shape crop and gaussian blurring for noise reduction and contrast improvement. The experimental results are evaluated using two standard datasets EyePACS and MESSIDOR. It's clearly shown that the outcomes of feature extraction and classification of enhanced images is outperform the results without applying the enhancement approach. The improved algorithm is also tested in smart hospitals as an IoMT application.

关键词： Retina Image enhancement Feature extraction Image color analysis Histograms Diabetes Image segmentation Image enhancement deep learning diabetic retinopathy retina fundus image healthcare health risks

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Swarm-LSTM: Condition Monitoring of Gearbox Fault Diagnosis Based on Hybrid LSTM Deep Neural Network Optimized by Swarm intelligence Algorithms

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Computers, Materials & Continua 2021年第2期66卷 2041-2059页

作者： Gopi Krishna Durbhaka Barani Selvaraj Mamta Mittal Tanzila Saba Amjad Rehman Lalit Mohan Goyal School of Electrical and Electronics Sathyabama Institute of Science and TechnologyChennai600119India Department of Computer Science and Engineering G.B.Pant Government Engineering CollegeNew Delhi110020India Artificial Intelligence and Data Analytics(AIDA)Laboratory CCISPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Computer Engineering J.C.Bose University of Science and TechnologyFaridabad121006India

Nowadays,renewable energy has been emerging as the major source of energy and is driven by its aggressive expansion and falling *** of the renewable energy sources involve turbines and their operation and maintenance are vital and a difficult *** monitoring and fault diagnosis have seen remarkable and revolutionary up-gradation in approaches,practices and technology during the last *** mostly do use a rotating type of machinery and analysis of those signals has been challenging to localize the *** paper proposes a new hybrid model wherein multiple swarm intelligence models have been evaluated to optimize the conventional Long Short-Term Memory(LSTM)model in classifying the faults from the vibration signals data acquired from the *** helps to analyze the performance and behavioral patterns of the system more effectively and efficiently which helps to suggest for replacement of the unit with higher *** results have demonstrated that the proposed hybrid modeling approach is effective in classifying the faults of the gearbox from the time series data and achieve higher diagnostic accuracy in comparison to the conventional LSTM methods.

关键词： Gearbox long short term memory fault classification swarm intelligence optimization condition monitoring

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Trust Management Frameworks In Multi-Cloud Environment: A Review

Trust Management Frameworks In Multi-Cloud Environment: A Re...

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

作者： Arwa Alajroush Rabia Latif Tanzila Saba College of Computer and Information Sciences Prince Sultan University Riyadh Saudi Arabia Artificial Intelligence and Data Analytics Lab College of Computer and Information Sciences Prince Sultan University Riyadh Saudi Arabia

The term "cloud computing" refers to a method of managing and using resources remotely. Networks, servers, applications, machines, and other resources and storage are all part of the shared and virtualized resource pool that is offered. In addition to providing the flexibility, stability, and scalability many organizations need the services of cloud computing, this reduces the need for setting up the private cloud and deploy separate hardware resources. However, multi-could settings presented several security and trust issues. This paper primary contribution is an examination of contemporary, serious trust challenges that arise in a multi-cloud context. And to put out a survey comparing trust management frameworks for Multi-Cloud environment to address the challenges identified. The suggested solution is given to have a more robust and secure trust framework based on data compliance.

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Real-Time Diagnosis System of COVID-19 Using X-Ray Images and Deep Learning

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IT PROFESSIONAL 2021年第4期23卷 57-62页

作者： Rehman, Amjad Saba, Tanzila Sadad, Tariq Hussain, Ayyaz Tariq, Usman Prince Sultan Univ Riyadh CCIS Artificial Intelligence & Data Analyt Lab Alkharj Saudi Arabia Int Islamic Univ Dept Comp Sci & Software Engn Lahore Pakistan Quaid I Azam Univ Dept Comp Sci Islamabad Pakistan Prince Sattam bin Abdulaziz Univ Coll Comp Engn & Sci Al Kharj Saudi Arabia

The novel coronavirus named COVID-19 has quickly spread among humans worldwide, and the situation remains hazardous to the health system. The existence of this virus in the human body is identified through sputum or blood samples. Furthermore, computed tomography (CT) or X-ray has become a significant tool for quick diagnoses. Thus, it is essential to develop an online and real-time computeraided diagnosis (CAD) approach to support physicians and avoid further spreading of the disease. In this research, a convolutional neural network (CNN)-based Residual neural network (ResNet50) has been employed to detect COVID-19 through chest X-ray images and achieved 98% accuracy. The proposed CAD system will receive the X-ray images from the remote hospitals/healthcare centers and perform diagnostic processes. Furthermore, the proposed CAD system uses advanced load balancer and resilience features to achieve fault tolerance with zero delays and perceives more infected cases during this pandemic.

关键词： Blood CAD Computerised Tomography Convolutional Neural Nets Deep Learning artificial intelligence Diseases Fault Tolerance Health Care Medical Computing Medical Image Processing Medical Information Systems Microorganisms Patient Diagnosis Real Time Diagnosis System COVID 19 Deep Learning Health System Human Body Sputum Computed Tomography Convolutional Neural Network Residual Neural Network Chest X Ray Images CAD System CNN Res Net 50 COVID 19 Pandemics Computed Tomography Medical Services Real Time Systems Convolutional Neural Networks

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A Realistic Image Generation of Face From Text Description Using the Fully Trained Generative Adversarial Networks

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IEEE ACCESS 2021年 9卷 1250-1260页

作者： Khan, Muhammad Zeeshan Jabeen, Saira Khan, Muhammad Usman Ghani Saba, Tanzila Rehmat, Asim Rehman, Amjad Tariq, Usman UET Lahore Alkhawarizmi Inst Comp Sci Lahore 54000 Pakistan UET Lahore Dept Comp Sci & Engn Lahore 54000 Pakistan CCIS Prince Sultan Univ Artificial Intelligence & Data Analyt Lab Riyadh 11586 Saudi Arabia Prince Sattam bin Abdulaziz Univ Coll Comp Engn & Sci Alkharj 16278 Saudi Arabia

Text to face generation is a sub-domain of text to image synthesis. It has a huge impact on new research areas along with the wide range of applications in the public safety domain. Due to the lack of dataset, the research work focused on the text to face generation is very limited. Most of the work for text to face generation until now is based on the partially trained generative adversarial networks, in which the pre-trained text encoder has been used to extract the semantic features of the input sentence. Later, these semantic features have been utilized to train the image decoder. In this research work, we propose a fully trained generative adversarial network to generate realistic and natural images. The proposed work trained the text encoder as well as the image decoder at the same time to generate more accurate and efficient results. In addition to the proposed methodology, another contribution is to generate the dataset by the amalgamation of LFW, CelebA and locally prepared dataset. The dataset has also been labeled according to our defined classes. Through performing different kinds of experiments, it has been proved that our proposed fully trained GAN outperformed by generating good quality images by the input sentence. Moreover, the visual results have also strengthened our experiments by generating the face images according to the given query.

关键词： Face Image generation Generative adversarial networks Task analysis Decoding Gallium nitride Semantics GAN CNN text to face image generation face synthesis data augmentation legal identity for all

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Energy optimized data fusion approach for scalable wireless sensor network using deep learning-based scheme

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JOURNAL OF NETWORK AND COMPUTER APPLICATIONS 2024年 224卷

作者： Mahmood, Tariq Li, Jianqiang Saba, Tanzila Rehman, Amjad Ali, Saqib CCIS Prince Sultan Univ Artificial Intelligence & Data Analyt AIDA Lab Riyadh 11586 Saudi Arabia Univ Educ Fac Informat Sci Vehari Campus Vehari 61100 Pakistan Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Beijing Engn Res Ctr IoT Software & Syst Beijing 100124 Peoples R China

Energy efficiency and security are critical components of Quality of Service (QoS) and remain a challenge in WSN-assisted IoT owing to its open and resource -limited nature. Despite intensive research on WSN-IoT, only a few have achieved significant levels of energy efficiency and load balancing on clustering nodes. This study proposes a novel approach for dynamic cluster -based WSN-IoT networks to enhance the network's resilience using data fusion techniques and eliminate illogical clustering. The Mean Value and Minimum Distance Method identifies the optimal cluster heads within the network by reducing data redundancy, resulting in improved quality of service, energy optimization, and enhanced lifetime. The proposed fused deep learning -based data mining method (RNN-LSTM) mitigates the data fitting and enhances the dynamic routing and balancing load at the WSN fusion center. The novel approach splits the network into layers, assigning sensor nodes to each layer, drastically reducing latency, data transfers, and the fusion center's overhead. Distinct experiments evaluated the suggested approach's efficacy by varying the hidden layer nodes and signaling intervals. The empirical verdicts exhibit that the presented routing algorithms surpass state-of-the-art conventional routing systems in energy depletion, average latency, signaling overhead, cumulative throughput, and route heterogeneity.

关键词： data fusion Multi-hop clustering Internet of things RNN-LSTM Energy balanced Inclusive innovation

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RDH-based dynamic weighted histogram equalization using for secure transmission and cancer prediction

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MULTIMEDIA SYSTEMS 2021年第2期27卷 177-189页

作者： Abbasi, Rashid Chen, Jianwen Al-Otaibi, Yasser Rehman, Amjad Abbas, Asad Cui, Weiwei Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu Peoples R China King Abdulaziz Univ Dept Informat Syst Jeddah 21589 Saudi Arabia Prince Sultan Univ Artificial Intelligence & Data Analyt Lab CCIS Riyadh Saudi Arabia Univ Cent Punjab Lahore Fac Informat Technol Lahore Pakistan

Image contrast enhancement is a prerequisite and plays a very important role in many image processing field like medical imaging, face recognition, computer-vision, and satellite imaging. In this paper we proposed reversible data hiding based Limited Dynamic Weighted Histogram Equalization techniques for Abnormal Tumor regions which improve the contrast, transmit the hidden secret information, preserve its brightness intensity and original appearance of the image. We have implemented Otsu's method to segment the input image into two sub-histogram regions of interest (ROI) and non-region of interest;furthermore, the sub-histograms ROI region equalized independently without of over-enhancement and any loss of hidden and diagnostic data. Our proposed method is more efficient to precisely preserve the brightness of the image and extract the secret information with contrast image reversibly;besides, different classifiers are used to classify the brain cancer to check the performance of our proposed method.

关键词： Contrast enhancement Secure transmission Reversible data hiding Classification

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Alzheimer's disease unveiled: Cutting-edge multi-modal neuroimaging and computational methods for enhanced diagnosis

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2024年 97卷

作者： Mahmood, Tariq Rehman, Amjad Saba, Tanzila Wang, Yu Alamri, Faten S. CCIS Prince Sultan Univ Artificial Intelligence & Data Analyt AIDA Lab Riyadh 11586 Saudi Arabia Univ Educ Dept Informat Sci Vehari Campus Vehari 61100 Pakistan Shandong Res Inst Ind Technol Jinan Peoples R China Princess Nourah Bint Abdulrahman Univ Coll Sci Dept Math Sci POB 84428 Riyadh 11671 Saudi Arabia

Alzheimer's disease (AD), characterized by stages like Early and Late Mild Cognitive Impairment (EMCI and LMCI), is a growing global concern. Accurate diagnosis is crucial for delaying its onset. Biomarkers like medical imaging and deep learning technology have been developed to address low early AD diagnosis accuracy. Deep learning algorithms can revolutionize the medical system by creating automatic diagnosis models and promoting artificial intelligence. However, existing studies have limitations, such as not considering complementarity between multiple features and ignoring spatial topological properties in the brain structure. It introduces two novel AD classification methods: Depth Double Deep Learning Method of Linear Attention Network (D3LM-LAN) based on Resnet-50, which incorporates a bilinear residual network structure, dual- channel data input, and hybrid attention module for enhanced PET and MRI multimodal data classification;and a machine learning approach using a Multi-Core Support Vector Machine (MLM-MCSVM) that improves softmax classifiers, optimizing data features and kernel functions via Particle Swarm Optimization. These models aid in effective intervention and personalized treatment planning, while the MLM-MCSVM model optimizes early detection with advanced feature selection. Together, these models enhance understanding of AD's heterogeneity, supporting evidence-based clinical practices and patient care. The study uses MRI and PET data to conduct two- and four-class experiments, testing AD vs. Normal Control (NC), MCI vs. NC, AD vs. MCI, EMCI, and LMCI. D3LM-LAN achieved accuracies of 97.74%, 95.01%, 93.82% and 95.69 % in two- class experiments. MLM-MCSVM further improved classification performance, demonstrating enhanced early diagnosis capabilities with accuracies of 98.59%, 96.53%, 95.77%, and 96.32%.

关键词： Alzheimer's disease Multimodal data Hybrid attention mechanism Multi-label classification Health issues

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Efficient deep learning-based approach for malaria detection using red blood cell smears

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SCIENTIFIC REPORTS 2024年第1期14卷 1-16页

作者： Mujahid, Muhammad Rustam, Furqan Shafique, Rahman Montero, Elizabeth Caro Alvarado, Eduardo Silva de la Torre Diez, Isabel Ashraf, Imran Prince Sultan Univ Artificial Intelligence & Data Analyt AIDA Lab CCIS Riyadh 11586 Saudi Arabia Univ Coll Dublin Sch Comp Sci Dublin D04 V1W8 Ireland Yeungnam Univ Dept Informat & Commun Engn Gyongsan Gyeongbuk South Korea Univ Europea Atlantico Santander 39011 Spain Univ Int Iberoamericana Arecibo PR 00613 USA Univ Int Cuanza EN250 Cuito Angola Univ Int Iberoamericana Campeche 24560 Mexico Univ La Romana La Romana Dominican Rep Univ Valladolid Dept Signal Theory Commun & Telemat Engn Valladolid 47011 Spain

Malaria is an extremely malignant disease and is caused by the bites of infected female mosquitoes. This disease is not only infectious among humans, but among animals as well. Malaria causes mild symptoms like fever, headache, sweating and vomiting, and muscle discomfort;severe symptoms include coma, seizures, and kidney failure. The timely identification of malaria parasites is a challenging and chaotic endeavor for health staff. An expert technician examines the schematic blood smears of infected red blood cells through a microscope. The conventional methods for identifying malaria are not efficient. Machine learning approaches are effective for simple classification challenges but not for complex tasks. Furthermore, machine learning involves rigorous feature engineering to train the model and detect patterns in the features. On the other hand, deep learning works well with complex tasks and automatically extracts low and high-level features from the images to detect disease. In this paper, EfficientNet, a deep learning-based approach for detecting Malaria, is proposed that uses red blood cell images. Experiments are carried out and performance comparison is made with pre-trained deep learning models. In addition, k-fold cross-validation is also used to substantiate the results of the proposed approach. Experiments show that the proposed approach is 97.57% accurate in detecting Malaria from red blood cell images and can be beneficial practically for medical healthcare staff.

关键词： Malaria detection EfficientNet Transfer learning Disease detection

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