Hasil untuk "Islam"

M. Ayub

Feisal Khan

R. Haniffa, M. Hudaib

A. W. Dusuki, N. Abdullah

Jonathan A. J. Wilson, Jonathan Liu

Riyad Eid, H. El-Gohary

Chris Mallin, H. Farag, Kean Ow-Yong

Jasser Auda

Abbas J Ali, Abdullah Al-Owaihan

L. Yarovaya, Ahmed H. Elsayed, S. Hammoudeh

Abstract We analyse the impact of the COVID-19 pandemic on the spillovers between conventional and Islamic stock and bond markets. We further analyse comparatively whether gold, oil, Bitcoin prices, and VIX and EPU indexes affect the relationships between these markets during the COVID-19 pandemic outbreak. The results show that the Islamic bonds (Sukuk) demonstrate safe haven properties during this pandemic crisis, while the spillovers between conventional and Islamic stock markets become stronger during the pandemic outbreak. COVID-19, Oil and gold are strong predictors of the conventional-Islamic markets spillovers, while Bitcoin is not a significant determinant of these relationships.

Md Takrim Ul Alam, Akif Islam, Mohd Ruhul Ameen et al.

Large language models (LLMs) deployed behind APIs and retrieval-augmented generation (RAG) stacks are vulnerable to prompt injection attacks that may override system policies, subvert intended behavior, and induce unsafe outputs. Existing defenses often treat prompts as flat strings and rely on ad hoc filtering or static jailbreak detection. This paper proposes Prompt Control-Flow Integrity (PCFI), a priority-aware runtime defense that models each request as a structured composition of system, developer, user, and retrieved-document segments. PCFI applies a three-stage middleware pipeline, lexical heuristics, role-switch detection, and hierarchical policy enforcement, before forwarding requests to the backend LLM. We implement PCFI as a FastAPI-based gateway for deployed LLM APIs and evaluate it on a custom benchmark of synthetic and semi-realistic prompt-injection workloads. On the evaluated benchmark suite, PCFI intercepts all attack-labeled requests, maintains a 0% False Positive Rate, and introduces a median processing overhead of only 0.04 ms. These results suggest that provenance- and priority-aware prompt enforcement is a practical and lightweight defense for deployed LLM systems.

Md Jahidul Islam

The adaptation of large-scale Vision-Language Models (VLMs) like CLIP to downstream tasks with extremely limited data -- specifically in the one-shot regime -- is often hindered by a significant "Stability-Plasticity" dilemma. While efficient caching mechanisms have been introduced by training-free methods such as Tip-Adapter, these approaches often function as local Nadaraya-Watson estimators. Such estimators are characterized by inherent boundary bias and a lack of global structural regularization. In this paper, ReHARK (Refined Hybrid Adaptive RBF Kernels) is proposed as a synergistic training-free framework that reinterprets few-shot adaptation through global proximal regularization in a Reproducing Kernel Hilbert Space (RKHS). A multistage refinement pipeline is introduced, consisting of: (1) Hybrid Prior Construction, where zero-shot textual knowledge from CLIP and GPT-3 is fused with visual class prototypes to form a robust semantic-visual anchor; (2) Support Set Augmentation (Bridging), where intermediate samples are generated to smooth the transition between visual and textual modalities; (3) Adaptive Distribution Rectification, where test feature statistics are aligned with the augmented support set to mitigate domain shifts; and (4) Multi-Scale RBF Kernels, where an ensemble of kernels is employed to capture complex feature geometries across diverse scales. Superior stability and accuracy are demonstrated through extensive experiments on 11 diverse benchmarks. A new state-of-the-art for one-shot adaptation is established by ReHARK, which achieves an average accuracy of 65.83%, significantly outperforming existing baselines. Code is available at https://github.com/Jahid12012021/ReHARK.

Nayeb Hasin, Md. Arafath Rahman Nishat, Mainul Islam et al.

An Automatic License Plate Recognition (ALPR) system constitutes a crucial element in an intelligent traffic management system. However, the detection of Bangla license plates remains challenging because of the complicated character scheme and uneven layouts. This paper presents a robust Bangla License Plate Recognition system that integrates a deep learning-based object detection model for license plate localization with Optical Character Recognition for text extraction. Multiple object detection architectures, including U-Net and several YOLO (You Only Look Once) variants, are compared for license plate localization. This study proposes a novel two-stage adaptive training strategy built upon the YOLOv8 architecture to improve localization performance. The proposed approach outperforms the established models, achieving an accuracy of 97.83% and an Intersection over Union (IoU) of 91.3%. The text recognition problem is phrased as a sequence generation problem with a VisionEncoderDecoder architecture, with a combination of encoder-decoders evaluated. It was demonstrated that the ViT + BanglaBERT model gives better results at the character level, with a Character Error Rate of 0.1323 and Word Error Rate of 0.1068. The proposed system also shows a consistent performance when tested on an external dataset that has been curated for this study purpose. The dataset offers completely different environment and lighting conditions compared to the training sample, indicating the robustness of the proposed framework. Overall, our proposed system provides a robust and reliable solution for Bangla license plate recognition and performs effectively across diverse real-world scenarios, including variations in lighting, noise, and plate styles. These strengths make it well suited for deployment in intelligent transportation applications such as automated law enforcement and access control.

F. M. Donner

How and why did Muslims first come to write their own history? The author argues in this work that the Islamic historical tradition arose not out of idle curiosity, or through imitation of antique models, but as a response to a variety of challenges facing the Islamic community during its first several centuries.In the first part, the author presents an overview of four approaches that have characterized scholarship on the literary sources, including the source-critical and the skeptical approaches, then it discusses historiographical problems raised by the Qur'an and hadith.In the second part, the work analyzes major themes in historical narratives and presents formal and structural characteristics of early Islamic historiography. The monograph concludes with the proposition of a four-stage chronology regarding the evolution of historical writing in Arabic.

Mohd Shukor Harun, K. Hussainey, Khairul Ayuni Mohd Kharuddin et al.

Purpose This study aims to explore the corporate social responsibility disclosure (CSRD) practices of the Islamic banks in the Gulf Cooperation Council (GCC) countries during the period 2010-2014 and examines the determinants of CSRD and its effects on firm value. Design/methodology/approach Based on the Accounting and Auditing Organization for Islamic Financial Institutions Governance Standard No. 7 guidelines and using content analysis, the paper develops a comprehensive CSRD index for GCC Islamic banks. The study applies ordinary least squares regression analysis for hypothesis testing and for finding determinants of respective dependent variables. Findings The results show a very low level of CSRD among the sample Islamic banks in GCC countries. When using corporate governance characteristics to examine the determinants of CSRD, this study provides evidence of a significant positive association between board size and CSRD practice in Islamic banks and a significant negative relationship of chief executive officer (CEO) duality with CSRD, as per expectation. For the economic consequences of CSRD, the study documents an inverse performance effect of CSRD while board size, board composition and CEO duality indicate significant positive effects on firm value. Research limitations/implications The relatively small sample size of GCC Islamic banks may limit the application of the findings to other Islamic financial institutions such as Takaful and the Islamic unit trust company. Practical implications The findings of this study initiate the global debate on the need for corporate governance reform in Islamic banks by providing insights on the role played by corporate governance mechanisms in encouraging and enhancing CSRD practices among Islamic banks. The findings also have important implications for investors, managers, regulatory bodies, policymakers and Islamic banks in the GCC countries. Social implications The results of the study do not support the idea that Islamic banks operating on Islamic principles can meet their social responsibilities through promoting corporate social responsibility (CSR) activities and by differentiating themselves from non-Islamic banks. Originality/value This is the first study to examine the determinants of CSRD in GCC Islamic banks using comprehensive CSRD and corporate governance variables and, therefore, adds value to the existing CSR literature in banking.

M. Hassan, Ashraf Khan, A. Paltrinieri

The aim of this paper is to provide a thorough assessment of Islamic banks’ (IBs) liquidity risk compared to conventional banks (CBs). We firstly investigate the relationship between liquidity and credit risk. Employing a simultaneous structural equation approach, on a comprehensive dataset of 52 IBs and CBs, from selected Organization of Islamic Cooperation Countries for the period of 2007–2015, we find that credit risk and liquidity risk have negative relationship. We then investigate the relationship between liquidity risk and stability, finding a negative relationship just for IBs. We finally show that Islamic banks are better than conventional in managing risks.

Abror Abror, D. Patrisia, Yunita Engriani et al.

Purpose The purpose of this study is to investigate the influential factors of customer loyalty to Islamic banks, namely, service quality, customer satisfaction, customer engagement and religiosity. Design/methodology/approach This study is a survey of 335 Islamic bank customers in West Sumatra, Indonesia. This research deployed purposive sampling and analyzed the data by using covariance-based structural equation modeling. Findings Service quality has a positive and significant impact on customer satisfaction. Religiosity has a significant and negative moderating impact on the service quality–customer satisfaction relationship. Service quality has no significant influence on customer loyalty. Customer satisfaction is a significant antecedent of customer engagement and loyalty. Finally, customer engagement has a significant and positive effect on customer loyalty. Research limitations/implications This study is a combination of cross-sectional and a single-country case. Accordingly, the results may not be representative of other countries. Similar studies in longitudinal data collection are conducted in other countries (e.g. ASEAN countries), which would therefore be worthwhile. Some antecedents of customer loyalty have been neglected in this study (e.g. customer value co-creation and customer commitment); hence, the future study may investigate those factors. Practical implications By considering these Islamic banks’ antecedents, the Islamic banks might enhance their customer loyalty. Also, this study has revealed the moderating role of religiosity in a loyalty relationship. Therefore, it will give insights for the Islamic bank managers in decision-making. Originality/value This study has revealed the moderating role of religiosity on the link between service quality and customer satisfaction in Islamic banks, which is, to the authors’ knowledge, neglected in the previous studies. The customers with high religiosity will have a higher standard of satisfaction and demand a better service quality than the customers with low religiosity. This study has also examined the relationships between service quality, religiosity, customer satisfaction, customer engagement and loyalty as a whole, which have been limited previously.

Mohamad Usman, Tarun Kanti Ghosh, SK Firoz Islam

We present a theoretical study of spin Hall phenomenon in a nanoribbon of a two-dimensional electronic system with Rashba and Dresselhaus spin-orbit coupling. We model the electronic system by a square lattice in real space. We show that such nanoribbon can give rise to a number of additional spin degeneracy points as well as anticrossing points, apart from the $Γ$ point, between two opposite spin subbands. We compute the SHC and demonstrate that it diverges and gives rise to a resonance when the chemical potential passes through those spin degenerate or anticrossing points. Contrary to the previous studies, here such resonance emerges even without any external perturbation like magnetic field or light. We also examine the spin Nernst effect and find that it shows clear peaks at the anticrossing and spin degeneracy points, consistent with the Mott relation at low temperature. Finally, we also investigate the signature of such additional spin degeneracy and anticrossing points in the longitudinal conductance by using the retarded Green function approach in lattice model. The finite width induced subbands are reflected in the longitudinal conductance, which takes quantized values of $2n e^{2}/{h}$ where $n$ denotes the number of bands occupied by the chemical potential with each band having spin split subbands. We also note that anticrossing that occurs at low energy between two opposite spin subbands could be also detected via longitudinal conductance.

Nazmus Sakib, Simeon Wuthier, Amanul Islam et al.

Distributed peer-to-peer (P2P) networking delivers the new blocks and transactions and is critical for the cryptocurrency blockchain system operations. Having poor P2P connectivity reduces the financial rewards from the mining consensus protocol. Previous research defines beneficalness of each Bitcoin peer connection and estimates the beneficialness based on the observations of the blocks and transactions delivery, which are after they are delivered. However, due to the infrequent block arrivals and the sporadic and unstable peer connections, the peers do not stay connected long enough to have the beneficialness score to converge to its expected beneficialness. We design and build Dynamic Peer Beneficialness Prediction (DyPBP) which predicts a peer's beneficialness by using networking behavior observations beyond just the block and transaction arrivals. DyPBP advances the previous research by estimating the beneficialness of a peer connection before it delivers new blocks and transactions. To achieve such goal, DyPBP introduces a new feature for remembrance to address the dynamic connectivity issue, as Bitcoin's peers using distributed networking often disconnect and re-connect. We implement DyPBP on an active Bitcoin node connected to the Mainnet and use machine learning for the beneficialness prediction. Our experimental results validate and evaluate the effectiveness of DyPBP; for example, the error performance improves by 2 to 13 orders of magnitude depending on the machine-learning model selection. DyPBP's use of the remembrance feature also informs our model selection. DyPBP enables the P2P connection's beneficialness estimation from the connection start before a new block arrives.

Soyabul Islam Lincoln, Mirza Mohd Shahriar Maswood

A common neurodegenerative disease, Alzheimer's disease requires a precise diagnosis and efficient treatment, particularly in light of escalating healthcare expenses and the expanding use of artificial intelligence in medical diagnostics. Many recent studies shows that the combination of brain Magnetic Resonance Imaging (MRI) and deep neural networks have achieved promising results for diagnosing AD. Using deep convolutional neural networks, this paper introduces a novel deep learning architecture that incorporates multiresidual blocks, specialized spatial attention blocks, grouped query attention, and multi-head attention. The study assessed the model's performance on four publicly accessible datasets and concentrated on identifying binary and multiclass issues across various categories. This paper also takes into account of the explainability of AD's progression and compared with state-of-the-art methods namely Gradient Class Activation Mapping (GradCAM), Score-CAM, Faster Score-CAM, and XGRADCAM. Our methodology consistently outperforms current approaches, achieving 99.66\% accuracy in 4-class classification, 99.63\% in 3-class classification, and 100\% in binary classification using Kaggle datasets. For Open Access Series of Imaging Studies (OASIS) datasets the accuracies are 99.92\%, 99.90\%, and 99.95\% respectively. The Alzheimer's Disease Neuroimaging Initiative-1 (ADNI-1) dataset was used for experiments in three planes (axial, sagittal, and coronal) and a combination of all planes. The study achieved accuracies of 99.08\% for axis, 99.85\% for sagittal, 99.5\% for coronal, and 99.17\% for all axis, and 97.79\% and 8.60\% respectively for ADNI-2. The network's ability to retrieve important information from MRI images is demonstrated by its excellent accuracy in categorizing AD stages.