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Artificial Intelligence in Next-Generation Networking: Energy Efficiency Optimization in IoT Networks Using Hybrid LEACH Protocol

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SN Computer Science 2024年第5期5卷 546页

作者： Khan, Surbhi Bhatia Kumar, Ankit Mashat, Arwa Pruthviraja, Dayananda Imam Rahmani, Mohammad Khalid Mathew, Jimson School of Science Engineering and Environment University of Salford Salford United Kingdom Department of Information Technology Guru Ghasidas Vishwavidyalaya Chhattisgarh Bilaspur India Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Rabigh 21911 Saudi Arabia Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Karnataka Manipal 576104 India Department of Computer Science College of Computing and Informatics Saudi Electronic University Riyadh 11673 Saudi Arabia Department of Computer Science and Engineering Indian Institute of Technology Patna Bihar Patna India

The convergence of the Internet of Things (IoT) and Artificial Intelligence (AI) is significantly transforming the landscape of future networking. The Internet of Things (IoT) is a technological paradigm that encompasses embedded systems, wireless sensors, and automation, facilitating the integration of various applications ranging from smart homes to wearable devices. In addition, the advent of artificial intelligence (AI) amplifies this influence by providing data-driven analytics, optimising processes, and presenting novel opportunities for growth. Nevertheless, the widespread adoption of devices within Internet of Things (IoT) networks gives rise to apprehensions regarding increased energy consumption. In order to ensure the longevity of network operations, it is imperative to employ energy-efficient protocols for sensor nodes that possess limited power resources. One example of a protocol that demonstrates this concept is the Low Energy Adaptive Clustering Hierarchy (LEACH) protocol. This protocol effectively divides networks into clusters and dynamically adjusts the cluster heads to optimise the transmission of data to the base stations. Our study enhances the LEACH protocol by incorporating digital twin simulation, thereby enhancing the efficiency of IoT systems. Virtual network models and AI analytics are employed to assess energy consumption and performance. Cache nodes play a crucial role within this framework as they collect data from cluster heads in order to transmit it to the base station. By leveraging artificial intelligence (AI) and simulation techniques, we are able to improve the energy efficiency and reliability of the Internet of Things (IoT) systems. The findings indicate a significant reduction of 83% in non-functioning nodes and a notable increase of 1.66 times in energy levels of nodes compared to conventional approaches. This study highlights a potential direction for energy-efficient, AI-enhanced Internet of Things (IoT) networking through

关键词： Cluster head Clustering Data aggregation Gateway Internet of things LEACH Lifetime

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QoS-Aware Smart Phone-Based User Tracking Application to Prevent Outbreak of COVID-19 in Saudi Arabia

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Computational Intelligence and Neuroscience 2022年第0期2022卷 2273910页

作者： Mashat, Arwa Alabdali, Aliaa M. Faculty of Computing and Information Technology King Abdulaziz University P.O. Box 344 Rabigh21911 Saudi Arabia

Diverse variants of COVID-19 are repeatedly making everyday living unstable. In reality, the conclusive retort of this highly contagious virus still is in incognito mode. The health experts' primary guideline on the possible prevention of this disease outbreak, including a list of restrictions and confinements, is insufficient in case of any public congregation. As a result, the demand for precise and upgraded real-time COVID-19 tracking and prevention-based applications increases. However, most of the existing android-based applications face a lack of data security and reliability that cannot satisfy the additional quality of service (QoS) requirements. This paper proposes an easy-to-operate android-based multifunctional application to track individuals' health situations, allow uploading scanning report by the authorized organization like universities, mosques, school, and hospitals and helps the users to maintain guidelines via manageable steps. This article offers a three-layered QoS aware service-oriented task scheduling model upon multitasking android-based frontend focusing the cognitive-based AI applications in healthcare with a continual learning paradigm. Designed model is competent to optimize heterogeneous service scheduling and can minimize data delivery time, as well as the resource cost. © 2022 Arwa Mashat and Aliaa M. Alabdali.

关键词： COVID-19

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iMars: Intelligent Municipality Augmented Reality Service for Efficient information Dissemination based on Deep Learning algorithm in Smart City of Jeddah

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Procedia Computer Science 2019年 163卷 93-108页

作者： Ahmad Hoirul Basori Ahmad Luqman bin Abdul Hamid Andi Besse Firdausiah Mansur Norazah Yusof Faculty of Computing and Information Technology Rabigh King Abdulaziz University Rabigh Makkah 21911 Saudi Arabia Fakulti Sains Komputer dan Matematik Universiti Teknologi Mara Segamat Johor Malaysia

A smart city is one of a huge transformation in these millennia, where it combines latest and advanced technology to overcome the daily basis problem of humans. Artificial intelligence with augmented reality has been highlighted as the potential solution along with IOT and another system to support the smart city system. The smarter Jeddah is part of Saudi Vision 2030 where Saudi government tends aims to transform this second biggest city in the kingdom to be a smart city with a ton of applications that connected with IOT and they can monitor people inside the city in real time. The proposed system focused on providing the resident of the Jeddah city with smart apps that can help them on conducting their daily basis activities (tourism) or even in an emergency situation. The geo-location data and other information will be used as a feeder for deep learning to make the system learn and getting smarter to disseminate information to the user based on their needs, location, emergency factors, etc. Resident can access the municipality public facilities such as Jeddah waterfront location and see what the event happens inside. They also can find the location of other attractive location such as Al-Fakieh aquarium and observe the content of aquarium facilities with virtual reality technology. In term of emergency cases: accident, car stopping and another incident; the proposed apps will be able to inform the police patrol nearby or nearest workshop for tyre or car workshop. This app is capable for wide range utilization to support program of the Ministry of Municipal and Rural Affairs to apprehend the smart city integrated application. The simulation test has confidently proved that deep learning algorithm overcome other classifier Naïve Bayes and Random forest with 67.5% recognition rate. The future work can be integrated with IOT sensor based system to enhance the municipality service in smart city.

关键词： Smart City, Deep Learning Augmented Reality Municipality Service

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Pixel attention meets M-shaped networks: A cutting-edge AI solution for diabetic retinopathy classification and stroke risk prediction

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Biomedical Signal Processing and Control 2025年 110卷

作者： Ahmed A. Alsheikhy Tawfeeq Shawly Yahia Said Aws I. AbuEid Abdulrahman A. Alzahrani Abdulrahman A. Alshdadi Hossam E. Ahmed Electrical Engineering Department College of Engineering Northern Border University Arar Saudi Arabia King Salman Center for Disability Research Riyadh 11614 Saudi Arabia Electrical Engineering Department Faculty of Engineering at Rabigh King Abdulaziz University Jeddah Saudi Arabia Center for Scientific Research and Entrepreneurship Northern Border University Arar Saudi Arabia Department of Information and Communication Technology College of Computing Studies Arab Open University Kuwait Branch Kuwait Department of Information Systems and Technology College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia Electrical Engineering Department Faculty of Engineering Benha University Benha Egypt

Visual impairment is a major global health concern, with Diabetic Retinopathy (DR) recognized as a leading cause of vision loss and blindness among individuals with diabetes. Despite its widespread occurrence, the early detection of DR poses significant challenges due to the subtlety of initial symptoms, the requirement for expert analysis of retinal images, and the drawbacks of traditional diagnostic techniques, which can be time-consuming, subjective, and susceptible to human error. DR impacts the retina and the ocular blood vessels, making timely and accurate diagnosis crucial to prevent irreversible damage. However, recent advancements in Artificial Intelligence (AI) have opened up new avenues in medical diagnostics, offering sophisticated approaches for the detection and classification of DR with impressive accuracy. This article introduces a Pixel Attention-based M−Shaped Architecture (PAM), an innovative AI-powered diagnostic tool designed to enhance the detection and classification of DR. The PAM system consists of two key components: 1) a Pixel Attention (PIAT) network that aids in the precise identification of abnormalities in retinal blood vessels, and 2) an M−shaped neural network architecture that delivers strong segmentation and classification capabilities. Trained and validated on three diverse benchmark datasets with over 35,000 fundus images spanning multiple DR severity levels, PAM exhibits exceptional adaptability across different levels of DR severity (mild, moderate, severe, and proliferative), achieving state-of-the-art results with an accuracy of 98.73%, precision of 98.82%, sensitivity of 98.67%, specificity of 98.72%, F1-score of 99.13%, and a Dice coefficient of 98.74%. Comparative studies demonstrate that PAM outperforms current methodologies, especially in terms of accuracy and F1-score. Its key advantage is its ability to offer healthcare professionals a scalable, efficient, and dependable resource for improving clinical decision-making.

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A Machine Learning-Based Multi-Model Approach for Predicting Post-Deep Brain Stimulation Outcomes in Alzheimer’s disease Patients

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Recent Patents on Engineering 2025年

作者： Thakur, Gopal Kumar Kulkarni, Shridhar Raguru, Jaya Krishna Tayubi, Iftikhar Aslam Rao, P Srinivasa Mudarakola, Lakshmi Prasad Reddy, P Chandra Shaker Department of Data Sciences Harrisburg University of Science & Technology Harrisburg PA United States Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Computer Science Faculty of Computing and Information Technology Rabigh (FCITR) King Abdulaziz University Jeddah Saudi Arabia School of CSE Department of ISE Jain University Karnataka Bengaluru India Computer Science and Engineering Institute of Aeronautical Engineering Telangana Hyderabad India School of Computer Science and Artificial Intelligence SR University Warangal India

Objectives and Background: Deep-brain stimulation (DBS) is a demonstrated rehabilitation for Alzheimer's Disease (AD), regularly resulting in an upgrade of engine work. In any case, some unfortunate incidental upshots can occur past DBS, which can deteriorate the patient's satisfaction. In this way, the medical group must painstakingly choose patients over whom to perform DBS. In the past, there have been a few endeavors to associate pre-usable information and DBS experimental results, generally centered on engine symptomatology. Objectives & Methodology: A machine learning (ML)-based strategy called a multimodel pipeline alluded to as FlowModel is designed and implemented to anticipate an enormous number of DBS medical results for AD. It is composed of a patented Artificial Neural Network (ANN) for processing medical data, using a conventional image handling technique to extract morphological biomarkers from T1 imaging, and employing a Support Vector Machine (SVM) to perform regressions. We authenticated it in 256 AD patients who underwent DBS. Results: The FlowModel demonstrated high predictive precision, achieving a correlation coefficient of as high as 0.82 and successfully predicting 63 out of 84 clinical outcome scores. Importantly, FlowModel outperformed traditional linear models, demonstrating the capability to learn effectively from preoperative data alone. Conclusion: The uniqueness of this study lies in its ability to predict multiple clinical outcomes across different modalities, independent of stimulation parameters. The introduction of this ML pipeline contributes to the precision medicine landscape by providing a robust, data-driven tool to select patients for DBS, potentially improving clinical decision-making and patient outcomes. © 2025 The Author(s). Published by Bentham Science Publishers.

关键词： AD clinical prediction DBS machine learning neural networks support vector machine

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Forgetting User Preference in Recommendation Systems with Label-Flipping

Forgetting User Preference in Recommendation Systems with La...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Alshehri, Manal A. Zhang, Xiangliang Computer Science Cemse Thuwal23955-6900 Saudi Arabia King Abdulaziz University Faculty of Computing and Information Technology Rabigh Saudi Arabia University of Notre Dame Department of Computer Science and Engineering Notre Dame United States

ISBN: (纸本)9798350324457

Recommendation systems play a crucial role in identifying users' preferences based on their historical interaction records and those of other users. However, the ability to 'forget' certain users' preferences is indispensable for ensuring user privacy and maintaining recommendation accuracy. It is essential to accommodate a user's request to exclude their behavioral data from the recommendation system when necessary. Likewise, if certain data corrupts the system, its impact should be removed to restore system performance. In this paper, we propose FlipRec, a general and efficient framework for recommendation models to 'forget' the preferences of specific users while retaining the model's performance for all other users. Our concept of forgetting user preferences is inspired by the label-flipping attack, a technique where the labels of some training samples are inverted to adversarially manipulate the weights of the trained model. FlipRec adjusts the recommendation model weights to forget the targeted users by flipping their interaction records $y \in \{ 0,1\}$. To preserve the model's performance for the remaining users, we augment the fine-tuning data with samples from users who have interacted with the same items as the targeted users. This ensures minimal impact on these users during the 'forgetting' process. FlipRec has been validated on both contentbased recommendation models and collaborative filtering models. The experimental results show that FlipRec outperforms stateof-the-art unlearning methods in terms of efficiency, the ability to forget targeted users, and the preservation of performance for the remaining users. © 2023 IEEE.

关键词： label-flipping attack machine unlearning Recommendation models

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Enhancing Traffic Engineering Policy in SDN Addressing TCAM limitations using Bloom Filters 4

Enhancing Traffic Engineering Policy in SDN Addressing TCAM ...

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4th IEEE Global Conference for Advancement in technology, GCAT 2023

作者： Ghosh, Akash Barik, Lalbihari Rout, Suchismita Rawat, Bhupender Singh Jena, Prakash Chandra Patra, Sudhansu Shekhar School of Computer Applications KIIT Deemed To be University Bhubaneswar India Department of Information Systems Faculty of Computing and Information Technology in Rabigh King Abdul Aziz University Saudi Arabia College of Smart Computing COER University Roorkee India Department of Computer Sc and Engg. Hi-Tech College of Engineering Bhubaneswar India

ISBN: (纸本)9798350305258

With the increasing demand for new technologies, the speed of delivering a packet is becoming the major focus of various industries. The generation of huge traffic is becoming the bottleneck of the current scenario. By segregating the control plane from the data plane, Software Defined Networking (SDN) is able to many such issues to certain extent. But the traffic engineering problem still remains as an important topic to focus on. Ternary Content Addressable Memory (TCAM) is capable of solving many such issues with the cost of its cost and power issues. For the effective use of TCAM, various algorithms and mechanisms have been proposed by many researchers. Bloom filter is one of the popular ways which is able to provide effective solutions after collaborating with TCAM. An algorithm has been proposed which is able to find the exact match of the entry in TCAM. The use of a CRC generator and multiple hashing tables makes it effective at finding the presence of a flow rule in TCAM, in very little time. The major focus is on the fast search procedure for finding the exact match of the flow rule. The number of false positives (FPs) is reduced to a lower percentage value as compared to the existing approaches. © 2023 IEEE.

关键词： Bloom Filter False positive IP addressing multi-hashing Prefix match SDN TCAM Traffic Engineering

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In silico analysis of detrimental mutation in EPHB2 gene causing Alzheimer’s disease

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BMC Genomics 2014年第2期15卷 1-2页

作者： Iftikhar Aslam Tayubi Omar M Barukab Sayane Shome Faculty of Computing and Information Technology King Abdulaziz University Rabigh 21911 Saudi Arabia School of Biosciences and Technology Vellore Institute of Technology Vellore 632014 India

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Drug formulation studies on regulation of BCL-2 family for treatment of autism

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BMC Genomics 2014年第2期15卷 1-2页

作者： Iftikhar A Tayubi Sayane Shome Sarosh Hashmi Faculty of Computing and Information Technology King Abdulaziz University Rabigh 21911 Saudi Arabia School of Biosciences and Technology Vellore Institute of Technology Vellore 632014 India

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A-LIOn - Alignment Learning through Inconsistency negatives of the aligned Ontologies 17

A-LIOn - Alignment Learning through Inconsistency negatives ...

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17th International Workshop on Ontology Matching, OM 2022

作者： Alghamdi, Sarah M. Zhapa-Camach, Fernando Hoehndorf, Robert Computational Bioscience Research Center Computer Electrical & Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology 4700 KAUST Thuwal23955 Saudi Arabia King Abdul-Aziz University Faculty of Computing and Information Technology Rabigh25732 Saudi Arabia

Ontologies play an important role in sharing and reusing knowledge. Several ontologies have been developed to describe a particular domain but from different perspectives from communities of developers and users. This has led to the existence of multiple ontologies covering the same or a different domain with varying degrees of variability. Ontology Alignment is typically used to identify correspondences between semantically related elements of two or more ontologies in order to address this problem. We propose A-LIOn a system that learns alignments by combining lexical and semantic approaches as well as machine learning. The system utilizes OWL EL reasoning for negative sampling which is iteratively used to inform the correction of the learning of the alignments. We demonstrate that A-LIOn produces alignments that are coherent with respect to OWL EL. © 2022 Copyright for this paper by its authors.

关键词： Ontology

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