المؤلفون: Ali, Adnan, Li, Jinlong, Chen, Huanhuan, Bashir, Ali Kashif

مصطلحات موضوعية: Computer Science - Machine Learning, Computer Science - Artificial Intelligence

الوصف: Graph contrastive learning (GCL) aims to contrast positive-negative counterparts to learn the node embeddings, whereas graph data augmentation methods are employed to generate these positive-negative samples. The variation, quantity, and quality of negative samples compared to positive samples play crucial roles in learning meaningful embeddings for node classification downstream tasks. Less variation, excessive quantity, and low-quality negative samples cause the model to be overfitted for particular nodes, resulting in less robust models. To solve the overfitting problem in the GCL paradigm, this study proposes a novel Cumulative Sample Selection (CSS) algorithm by comprehensively considering negative samples' quality, variations, and quantity. Initially, three negative sample pools are constructed: easy, medium, and hard negative samples, which contain 25%, 50%, and 25% of the total available negative samples, respectively. Then, 10% negative samples are selected from each of these three negative sample pools for training the model. After that, a decision agent module evaluates model training results and decides whether to explore more negative samples from three negative sample pools by increasing the ratio or keep exploiting the current sampling ratio. The proposed algorithm is integrated into a proposed graph contrastive learning framework named NegAmplify. NegAmplify is compared with the SOTA methods on nine graph node classification datasets, with seven achieving better node classification accuracy with up to 2.86% improvement.

الوصول الحر: http://arxiv.org/abs/2406.15044Test

المؤلفون: Rohan, Ali, Rafaq, Muhammad Saad, Hasan, Md. Junayed, Asghar, Furqan, Bashir, Ali Kashif, Dottorini, Tania

مصطلحات موضوعية: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence

الوصف: Livestock health and welfare monitoring has traditionally been a labor-intensive task performed manually. Recent advances have led to the adoption of AI and computer vision techniques, particularly deep learning models, as decision-making tools within the livestock industry. These models have been employed for tasks like animal identification, tracking, body part recognition, and species classification. In the past decade, there has been a growing interest in using these models to explore the connection between livestock behaviour and health issues. While previous review studies have been rather generic, there is currently no review study specifically focusing on DL for livestock behaviour recognition. Hence, this systematic literature review (SLR) was conducted. The SLR involved an initial search across electronic databases, resulting in 1101 publications. After applying defined selection criteria, 126 publications were shortlisted. These publications were further filtered based on quality criteria, resulting in the selection of 44 high-quality primary studies. These studies were analysed to address the research questions. The results showed that DL successfully addressed 13 behaviour recognition problems encompassing 44 different behaviour classes. A variety of DL models and networks were employed, with CNN, Faster R-CNN, YOLOv5, and YOLOv4 being among the most common models, and VGG16, CSPDarknet53, GoogLeNet, ResNet101, and ResNet50 being popular networks. Performance evaluation involved ten different matrices, with precision and accuracy being the most frequently used. Primary studies identified challenges, including occlusion, adhesion, data imbalance, and the complexities of the livestock environment. The SLR study also discussed potential solutions and research directions to facilitate the development of autonomous livestock behaviour recognition systems.

الوصول الحر: http://arxiv.org/abs/2310.13483Test

المؤلفون: Bashir, Ali Kashif, Victor, Nancy, Bhattacharya, Sweta, Huynh-The, Thien, Chengoden, Rajeswari, Yenduri, Gokul, Maddikunta, Praveen Kumar Reddy, Pham, Quoc-Viet, Gadekallu, Thippa Reddy, Liyanage, Madhusanka

مصطلحات موضوعية: Computer Science - Computers and Society, Computer Science - Artificial Intelligence

الوصف: Recent technological advancements have considerately improved healthcare systems to provide various intelligent healthcare services and improve the quality of life. Federated learning (FL), a new branch of artificial intelligence (AI), opens opportunities to deal with privacy issues in healthcare systems and exploit data and computing resources available at distributed devices. Additionally, the Metaverse, through integrating emerging technologies, such as AI, cloud edge computing, Internet of Things (IoT), blockchain, and semantic communications, has transformed many vertical domains in general and the healthcare sector in particular. Obviously, FL shows many benefits and provides new opportunities for conventional and Metaverse healthcare, motivating us to provide a survey on the usage of FL for Metaverse healthcare systems. First, we present preliminaries to IoT-based healthcare systems, FL in conventional healthcare, and Metaverse healthcare. The benefits of FL in Metaverse healthcare are then discussed, from improved privacy and scalability, better interoperability, better data management, and extra security to automation and low-latency healthcare services. Subsequently, we discuss several applications pertaining to FL-enabled Metaverse healthcare, including medical diagnosis, patient monitoring, medical education, infectious disease, and drug discovery. Finally, we highlight significant challenges and potential solutions toward the realization of FL in Metaverse healthcare.
Comment: Submitted to peer review

الوصول الحر: http://arxiv.org/abs/2304.00524Test

المؤلفون: Thaliath, Joseph, Niknam, Solmaz, Singh, Sukhdeep, Banerji, Rahul, Saxena, Navrati, Dhillon, Harpreet S., Reed, Jeffrey H., Bashir, Ali Kashif, Bhat, Avinash, Roy, Abhishek

مصطلحات موضوعية: Computer Science - Networking and Internet Architecture, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Signal Processing

الوصف: Network slicing provides introduces customized and agile network deployment for managing different service types for various verticals under the same infrastructure. To cater to the dynamic service requirements of these verticals and meet the required quality-of-service (QoS) mentioned in the service-level agreement (SLA), network slices need to be isolated through dedicated elements and resources. Additionally, allocated resources to these slices need to be continuously monitored and intelligently managed. This enables immediate detection and correction of any SLA violation to support automated service assurance in a closed-loop fashion. By reducing human intervention, intelligent and closed-loop resource management reduces the cost of offering flexible services. Resource management in a network shared among verticals (potentially administered by different providers), would be further facilitated through open and standardized interfaces. Open radio access network (O-RAN) is perhaps the most promising RAN architecture that inherits all the aforementioned features, namely intelligence, open and standard interfaces, and closed control loop. Inspired by this, in this article we provide a closed-loop and intelligent resource provisioning scheme for O-RAN slicing to prevent SLA violations. In order to maintain realism, a real-world dataset of a large operator is used to train a learning solution for optimizing resource utilization in the proposed closed-loop service automation process. Moreover, the deployment architecture and the corresponding flow that are cognizant of the O-RAN requirements are also discussed.
Comment: 7 pages, 3 figures, 1 table

الوصول الحر: http://arxiv.org/abs/2202.01966Test

المؤلفون: Rohan, Ali, Rafaq, Muhammad Saad, Hasan, Md. Junayed, Asghar, Furqan, Dottorini, Tania, Bashir, Ali Kashif

مصطلحات موضوعية: Precision agriculture, Deep learning (DL), Artificial intelligence (AI), Behaviour recognition, Precision livestock farming

الوصف: Livestock health and welfare monitoring is a tedious and labour-intensive task previously performed manually by humans. However, with recent technological advancements, the livestock industry has adopted the latest AI and computer vision-based techniques empowered by deep learning (DL) models that, at the core, act as decision-making tools. These models have previously been used to address several issues, including individual animal identification, tracking animal movement, body part recognition, and species classification. However, over the past decade, there has been a growing interest in using these models to examine the relationship between livestock behaviour and associated health problems. Several DL-based methodologies have been developed for livestock behaviour recognition, necessitating surveying and synthesising state-of-the-art. Previously, review studies were conducted in a very generic manner and did not focus on a specific problem, such as behaviour recognition. To the best of our knowledge, there is currently no review study that focuses on the use of DL specifically for livestock behaviour recognition. As a result, this systematic literature review (SLR) is being carried out. The review was performed by initially searching several popular electronic databases, resulting in 1101 publications. Further assessed through the defined selection criteria, 126 publications were shortlisted. These publications were filtered using quality criteria that resulted in the selection of 44 high-quality primary studies, which were analysed to extract the data to answer the defined research questions. According to the results, DL solved 13 behaviour recognition problems involving 44 different behaviour classes. 23 DL models and 24 networks were employed, with CNN, Faster R-CNN, YOLOv5, and YOLOv4 being the most common models, and VGG16, CSPDarknet53, GoogLeNet, ResNet101, and ResNet50 being the most popular networks. Ten different matrices were utilised for performance evaluation, with precision and accuracy being ...

العلاقة: https://rgu-repository.worktribe.com/output/2383131Test; https://rgu-repository.worktribe.com/file/2383131/1/ROHAN%202024%20Application%20of%20deep%20learning%20%28VOR%29Test

الإتاحة: https://doi.org/10.1016/j.compag.2024.109115Test
https://rgu-repository.worktribe.com/file/2383131/1/ROHAN%202024%20Application%20of%20deep%20learning%20%28VOR%29Test
https://rgu-repository.worktribe.com/output/2383131Test

المؤلفون: Tariq, Usman, Ahmed, Irfan, Bashir, Ali Kashif, Khan, Muhammad Attique

الوصف: This paper explores how deep learning enhances Internet of Things (IoT) cybersecurity, examining advanced methods like convolutional and recurrent neural networks for detailed IoT data analysis. It highlights the importance of real-time threat detection and classification, focusing on innovative Graph Neural Networks and Transformer Models for better network security. The study also considers Federated Learning and Edge Computing for decentralized, privacy-friendly data handling, and Explainable AI for clarity in decision-making. It addresses the growing challenges of creating scalable, adaptable deep learning models for ever-changing IoT environments and cyber threats, emphasizing the need for ongoing research in developing resilient IoT cybersecurity solutions. The analysis further reveals that deep learning techniques are increasingly effective in anomaly detection and predictive maintenance, reducing false positives, and adapting to new types of cyber threats dynamically. Specifically, it emphasizes how Transformer Models and Graph Neural Networks offer promising results in contextualizing and mitigating complex multi-stage cyber-attacks, enhancing the robustness of IoT systems against evolving threats.

وصف الملف: application/pdf

العلاقة: https://e-space.mmu.ac.uk/634351Test/; https://kurdishstudies.net/menu-script/index.php/KS/article/view/1623Test; https://e-space.mmu.ac.uk/634351/1/7-Paper%2BEmail%2B14.pdfTest; Tariq, Usman , Ahmed, Irfan , Bashir, Ali Kashif ORCID logoorcid:0000-0001-7595-2522 and Khan, Muhammad Attique (2024) Securing the evolving IoT with deep learning: a comprehensive review. Kurdish Studies, 12 (1). pp. 3426-3454. ISSN 2051-4883

الإتاحة: https://e-space.mmu.ac.uk/634351/1/7-Paper%2BEmail%2B14.pdfTest

المؤلفون: Tariq, Usman, Ahmed, Irfan, Khan, Muhammad Attique, Bashir, Ali Kashif

المساهمون: Prince Sattam Bin Abdulaziz University

المصدر: Indonesian Journal of Electrical Engineering and Computer Science; Vol 35, No 1: July 2024; 520-541 ; 2502-4760 ; 2502-4752 ; 10.11591/ijeecs.v35.i1

مصطلحات موضوعية: Computer and Informatics, Algorithmic economy, Deep learning, Economic analysis, Innovation growth, Predictive modelling, Sectoral transformation

الوصف: Deep learning (DL) is increasingly recognized for its effectiveness in analyzing and forecasting complex economic systems, particularly in the context of Pakistan's evolving economy. This paper investigates DL's transformative role in managing and interpreting increasing volumes of intricate economic data, leading to more nuanced insights. DL models show a marked improvement in predictive accuracy and depth over traditional methods across various economic domains and policymaking scenarios. Applications include demand forecasting, risk evaluation, market trend analysis, and resource allocation optimization. These processes utilize extensive datasets and advanced algorithms to identify patterns that traditional methods cannot detect. Nonetheless, DL's broader application in economic research faces challenges like limited data availability, complexity of economic interactions, interpretability of model outputs, and significant computational power requirements. The paper outlines strategies to overcome these barriers, such as enhancing model interpretability, employing federated learning for better data privacy, and integrating behavioral and social economic theories. It concludes by stressing the importance of targeted research and ethical considerations in maximizing DL's impact on economic insights and innovation, particularly in Pakistan and globally.

وصف الملف: application/pdf

العلاقة: https://ijeecs.iaescore.com/index.php/IJEECS/article/view/36919/18433Test; https://ijeecs.iaescore.com/index.php/IJEECS/article/view/36919Test

الإتاحة: https://doi.org/10.11591/ijeecs.v35.i1.pp520-541Test
https://doi.org/10.11591/ijeecs.v35.i1Test
https://ijeecs.iaescore.com/index.php/IJEECS/article/view/36919Test

المؤلفون: Guo, Zhiwei, Yu, Keping, Guo, Tan, Bashir, Ali Kashif, Imran, Muhammad, Guizani, Mohsen

مصطلحات موضوعية: Computer Science - Information Retrieval, Computer Science - Artificial Intelligence

الوصف: With the prevalence of Internet of Things (IoT)-based social media applications, the distance among people has been greatly shortened. As a result, recommender systems in IoT-based social media need to be developed oriented to groups of users rather than individual users. However, existing methods were highly dependent on explicit preference feedbacks, ignoring scenarios of implicit feedback. To remedy such gap, this paper proposes an implicit feedback-based group recommender system using probabilistic inference and non-cooperative game(GREPING) for IoT-based social media. Particularly, unknown process variables can be estimated from observable implicit feedbacks via Bayesian posterior probability inference. In addition, the globally optimal recommendation results can be calculated with the aid of non-cooperative game. Two groups of experiments are conducted to assess the GREPING from two aspects: efficiency and robustness. Experimental results show obvious promotion and considerable stability of the GREPING compared to baseline methods.
Comment: I don't want to share this paper

الوصول الحر: http://arxiv.org/abs/2102.00835Test

المؤلفون: Uddin, Zahoor, Altaf, Muhammad, Bilal, Muhammad, Nkenyereye, Lewis, Bashir, Ali Kashif

مصطلحات موضوعية: Electrical Engineering and Systems Science - Audio and Speech Processing, Computer Science - Machine Learning, Computer Science - Sound, Electrical Engineering and Systems Science - Signal Processing, Statistics - Machine Learning, 68T45, 68T10, 62H30, C.2, C.2.4, G.3

الوصف: Owing to small size, sensing capabilities and autonomous nature, the Unmanned Air Vehicles (UAVs) have enormous applications in various areas, e.g., remote sensing, navigation, archaeology, journalism, environmental science, and agriculture. However, the unmonitored deployment of UAVs called the amateur drones (AmDr) can lead to serious security threats and risk to human life and infrastructure. Therefore, timely detection of the AmDr is essential for the protection and security of sensitive organizations, human life and other vital infrastructure. AmDrs can be detected using different techniques based on sound, video, thermal, and radio frequencies. However, the performance of these techniques is limited in sever atmospheric conditions. In this paper, we propose an efficient unsupervise machine learning approach of independent component analysis (ICA) to detect various acoustic signals i.e., sounds of bird, airplanes, thunderstorm, rain, wind and the UAVs in practical scenario. After unmixing the signals, the features like Mel Frequency Cepstral Coefficients (MFCC), the power spectral density (PSD) and the Root Mean Square Value (RMS) of the PSD are extracted by using ICA. The PSD and the RMS of PSD signals are extracted by first passing the signals from octave band filter banks. Based on the above features the signals are classified using Support Vector Machines (SVM) and K Nearest Neighbor (KNN) to detect the presence or absence of AmDr. Unique feature of the proposed technique is the detection of a single or multiple AmDrs at a time in the presence of multiple acoustic interfering signals. The proposed technique is verified through extensive simulations and it is observed that the RMS values of PSD with KNN performs better than the MFCC with KNN and SVM.
Comment: 25 pages, 10 figures, accepted for the publication in future issue of "Computer Communications (2020)"

الوصول الحر: http://arxiv.org/abs/2003.01519Test

المؤلفون: Arul, Rajakumar, Raja, Gunasekaran, Almagrabi, Alaa Omran, Alkatheiri, Mohammed Saeed, Hussain, Chauhdary Sajjad, Bashir, Ali Kashif

مصطلحات موضوعية: Computer Science - Cryptography and Security

الوصف: Millions of devices are going to participate in 5G producing a huge space for security threats. The 5G specification goals require rigid and robust security protocol against such threats. Quantum cryptography is a recently emerged term in which we test the robustness of security protocols against Quantum computers. Therefore, in this paper, we propose a security protocol called Quantum Key GRID for Authentication and Key Agreement (QKG-AKA) scheme for the dynamic security association. This scheme is efficiently deployed in Long Term Evolution (LTE) architecture without any significant modifications in the underlying base system. The proposed QKG-AKA mechanism is analyzed for robustness and proven safe against quantum computers. The simulation results and performance analysis show drastic improvement regarding security and key management over existing schemes.
Comment: 9 PAGES, 9 FIGURES

الوصول الحر: http://arxiv.org/abs/1911.00692Test