Hasil untuk "Islam"

M. Kabir Hassan, Abdel-Hameed M. Bashir

Md. Rashadul Islam

Microgravity induces profound metabolic adaptations in mammalian physiology, yet the molecular mechanisms governing thermogenesis in female white adipose tissue (WAT) remain poorly characterized. This paper presents the first machine learning (ML) analysis of NASA Open Science Data Repository (OSDR) dataset OSD-970, derived from the Rodent Research-1 (RR-1) mission. Using RT-qPCR data from 89 adipogenesis and thermogenesis pathway genes in gonadal WAT of 16 female C57BL/6J mice (8 flight, 8 ground control) following 37 days aboard the International Space Station (ISS), we applied differential expression analysis, multiple ML classifiers with Leave-One-Out Cross-Validation (LOO-CV), and Explainable AI via SHapley Additive exPlanations (SHAP). The most striking finding is a dramatic 12.21-fold upregulation of Ucp1 (Delta-Delta-Ct = -3.61, p = 0.0167) in microgravity-exposed WAT, accompanied by significant activation of the thermogenesis pathway (mean pathway fold-change = 3.24). The best-performing model (Random Forest with top-20 features) achieved AUC = 0.922, Accuracy = 0.812, and F1 = 0.824 via LOO-CV. SHAP analysis consistently ranked Ucp1 among the top predictive features, while Angpt2, Irs2, Jun, and Klf-family transcription factors emerged as dominant consensus classifiers. Principal component analysis (PCA) revealed clear separation between flight and ground samples, with PC1 explaining 69.1% of variance. These results suggest rapid thermogenic reprogramming in female WAT as a compensatory response to microgravity. This study demonstrates the power of explainable AI for re-analysis of newly released NASA space biology datasets, with direct implications for female astronaut health on long-duration missions and for Earth-based obesity and metabolic disease research.

Tousif Islam, Tejaswi Venumadhav, Digvijay Wadekar et al.

We provide a comprehensive analysis of GW190711_030756 and GW200114_020818, two of the most significant binary black hole merger candidates in the IAS catalog, with probabilities of astrophysical origin $p_{\rm astro}=0.99$ and $0.71$, respectively, and signal-to-noise ratios of approximately $10.0$ and $13.4$. We employ numerical relativity surrogate models to infer both the source properties and the remnant properties of these two candidates. We find that both GW190711_030756 and GW200114_020818 are asymmetric-mass binaries, with inferred mass ratios of $0.35^{+0.32}_{-0.15}$ and $\leq 0.20$. In addition, GW200114_020818 is inferred to have a source-frame total mass of approximately $220M_{\odot}$ and highly spinning black holes, with primary (secondary) dimensionless spin magnitudes of $0.96^{+0.03}_{-0.07}$ ($0.84^{+0.13}_{-0.34}$), closely resembling GW231123_135430. We further find that GW200114\_020818 has a confidently negative effective inspiral spin of $χ_{\rm eff}=-0.60^{+0.22}_{-0.13}$ and exhibits strong spin precession, characterized by an effective precession parameter of $χ_{\rm p}=0.60^{+0.21}_{-0.19}$. GW200114_020818 (when considered alongside GW231123_135430) points towards an emerging population of massive, rapidly spinning BBH mergers. While GW231123_135430 is consistent with mergers in globular clusters, producing systems like GW200114_020818 in such environments remains difficult even under hierarchical merger scenarios. The probability that the remnant black hole of GW190711_030756 (GW200114_020818) is retained in its host environment is $0.079$ ($0.0002$), $0.62$ ($0.965$), and $0.997$ ($1$) if the merger occurred in a globular cluster, a nuclear star cluster, or an elliptical galaxy, respectively.

Md Zahidul Islam

GenAI systems are increasingly used for drafting, summarisation, and decision support, offering substantial gains in productivity and reduced cognitive load. However, the same natural language fluency that makes these systems useful can also blur the boundary between tool and companion. This boundary confusion may encourage some users to experience GenAI as empathic, benevolent, and relationally persistent. Emerging reports suggest that some users may form emotionally significant attachments to conversational agents, in some cases with harmful consequences, including dependency and impaired judgment. This paper develops a philosophical and ethical argument for why the resulting illusion of friendship is both understandable and can be ethically risky. Drawing on classical accounts of friendship, the paper explains why users may understandably interpret sustained supportive interaction as friend like. It then advances a counterargument that despite relational appearances, GenAI lacks moral agency: consciousness, intention, and accountability and therefore does not qualify as a true friend. To demystify the illusion, the paper presents a mechanism level explanation of how transformer based GenAI generates responses often producing emotionally resonant language without inner states or commitments. Finally, the paper proposes a safeguard framework for safe and responsible GenAI use to reduce possible anthropomorphic cues generated by the GenAI systems. The central contribution is to demystify the illusion of friendship and explain the computational background so that we can shift the emotional attachment with GenAI towards necessary human responsibility and thereby understand how institutions, designers, and users can preserve GenAI's benefits while mitigating over reliance and emotional misattribution.

Niluh Merthi Wulandari, Ernawaty Usman, Nina Yusnita Yamin et al.

Good governance and public confidence require high-quality financial reporting. This research is to see the influence of AIS, and Internal control against financial reporting system of quality in Public Health Center (Puskesmas) in Kabupaten Parigi Moutong. The study relies on a quantitative method of research with sample data coming from 72 participants who are the heads of centers, administrative head and treasurer. The data were analyzed by multiple regression analysis with F-test, t-test, and coefficient of determination (R²). The finding indicates that both AIS and Internal Control significantly affect financial reporting quality (F = 53.245; Sig. 0.000). Part of Internal Control has positively and significantly influence (t = 7.378; Sig. 0.000) and AIS has no effect (t = 0.417; Sig. 0.678). The R² value (0.607) is equal to the proportion of both variables that account for 60.7% from financial report quality variance. These observations validate that enhancing internal controls is the main approach to reliable transparent and accountable financial reports among Public Health facilities.

M. Iqbal, P. Molyneux

Khondoker Ittehadul Islam, Gabriele Sarti

Language models have demonstrated remarkable performance on complex multi-step reasoning tasks. However, their evaluation has been predominantly confined to high-resource languages such as English. In this paper, we introduce a manually translated Bangla multi-step reasoning dataset derived from the English Reveal dataset, featuring both binary and non-binary question types. We conduct a controlled evaluation of English-centric and Bangla-centric multilingual small language models on the original dataset and our translated version to compare their ability to exploit relevant reasoning steps to produce correct answers. Our results show that, in comparable settings, reasoning context is beneficial for more challenging non-binary questions, but models struggle to employ relevant Bangla reasoning steps effectively. We conclude by exploring how reasoning steps contribute to models' predictions, highlighting different trends across models and languages.

Yousuf Islam, Sumon Chandra Das, Md. Jalal Uddin Chowdhury

The rapid evolution of machine learning (ML) has brought about groundbreaking developments in numerous industries, not the least of which is in the area of undersea communication. This domain is critical for applications like ocean exploration, environmental monitoring, resource management, and national security. Bangladesh, a maritime nation with abundant resources in the Bay of Bengal, can harness the immense potential of ML to tackle the unprecedented challenges associated with underwater communication. Beyond that, environmental conditions are unique to the region: in addition to signal attenuation, multipath propagation, noise interference, and limited bandwidth. In this study, we address the necessity to bring ML into communication via undersea; it investigates the latest technologies under the domain of ML in that respect, such as deep learning and reinforcement learning, especially concentrating on Bangladesh scenarios in the sense of implementation. This paper offers a contextualized regional perspective by incorporating region-specific needs, case studies, and recent research to propose a roadmap for deploying ML-driven solutions to improve safety at sea, promote sustainable resource use, and enhance disaster response systems. This research ultimately highlights the promise of ML-powered solutions for transforming undersea communication, leading to more efficient and cost-effective technologies that subsequently contribute to both economic growth and environmental sustainability.

Sayed Sadiqul Islam, Tony J. Puthenpurakal

Let $A$ be a regular ring containing a field of characteristic $p>0$ and let $R=A[x_1,\ldots,x_m,y_1,\ldots,y_n]$ be standard bigraded over $A$, i.e., $\operatorname{bideg}(A)=(0,0)$, $\operatorname{bideg}(x_i)=(1,0)$ and $\operatorname{bideg}(y_j)=(0,1)$ for all $i$ and $j$. Assume that $M=\bigoplus_{i,j} M_{(i,j)}$ is a bigraded $F_R$-finite, $F_R$-module. We use Lyubeznik's theory of $F$-finite, $F$-modules from \cite{Lyu-Fmod} to study the bigraded components of $M$. The properties we study include vanishing, rigidity, Bass numbers, associated primes, and injective dimension of the components of $M$. As an application we show that if $(A,\mathfrak{m})$ is regular local ring containing a field of characteristic $p>0$, $R/I$ is equidimensional, $\operatorname{Bproj}(R/I)$ is Cohen-Macaulay and non-empty, then $H^j_I(R)_{(m,n)}=0$ for all $(m,n)\geq (0,0)$ and all $j>\operatorname{height} I$.

Sayed Sadiqul Islam, Tony J. Puthenpurakal

Let $R=K[x_1,\ldots,x_m]$ where $K$ is an uncountable algebraically closed field of characteristic $0$. For a prime ideal $P$ of $R$, let $μ_j(P,M)$ be the $j$-th Bass number of an $R$-module $M$ with respect to the prime $P$. For $1\leq g\leq m-1$, we construct a set $\mathcal{S}_g(t)$ such that $\mathcal{S}_g(t)\subseteq \mathcal{S}_g(t+1)$ for all $t\geq 1$ and $\bigcup_{t\geq 1} \mathcal{S}_g(t)=\operatorname{Spec}_g(R)=\{P\in \operatorname{Spec}(R)\mid \operatorname{height}P=g\}$. Let $\mathcal{T}$ be a Lyubeznik functor on $\operatorname{Mod}(R)$. We prove that there exists some function $φ^g_i: \mathbb{N}^2\rightarrow \mathbb{N}$ which is monotonic in both the variables such that $μ_i(P,\mathcal{T}(R))\leq φ^g_i(e(\mathcal{T}(R)),t)$ for all $P\in \mathcal{S}_{g}(t)$. In particular, the result holds for composition of local cohomology functors of the form $ H^{i_1}_{I_1}(H^{i_2}_{I_2}(\dots H^{i_r}_{I_r}(-)\dots)$.

Mohammad Tariqul Islam, Du Liu, Deblina Sarkar

Centered kernel alignment (CKA) is a popular metric for comparing representations, determining equivalence of networks, and neuroscience research. However, CKA does not account for the underlying manifold and relies on numerous heuristics that cause it to behave differently at different scales of data. In this work, we propose Manifold approximated Kernel Alignment (MKA), which incorporates manifold geometry into the alignment task. We derive a theoretical framework for MKA. We perform empirical evaluations on synthetic datasets and real-world examples to characterize and compare MKA to its contemporaries. Our findings suggest that manifold-aware kernel alignment provides a more robust foundation for measuring representations, with potential applications in representation learning.

Suhrid Gupta, Muhammed Tawfiqul Islam, Rajkumar Buyya

Edge computing allows for the decentralization of computing resources. This decentralization is achieved through implementing microservice architectures, which require low latencies to meet stringent service level agreements (SLA) such as performance, reliability, and availability metrics. While cloud computing offers the large data storage and computation resources necessary to handle peak demands, a hybrid cloud and edge environment is required to ensure SLA compliance. Several auto-scaling algorithms have been proposed to try to achieve these compliance challenges, but they suffer from performance issues and configuration complexity. This chapter provides a brief overview of edge computing architecture, its uses, benefits, and challenges for resource scaling. We then introduce Service Level Agreements, and existing research on devising algorithms used in edge computing environments to meet these agreements, along with their benefits and drawbacks.

Md Shariful Islam, M. Sohel Rahman

Log management is crucial for ensuring the security, integrity, and compliance of modern information systems. Traditional log management solutions face challenges in achieving tamper-proofing, scalability, and real-time processing in distributed environments. This paper presents a blockchain-based log management framework that addresses these limitations by leveraging blockchain's decentralized, immutable, and transparent features. The framework integrates a hybrid on-chain and off-chain storage model, combining blockchain's integrity guarantees with the scalability of distributed storage solutions like IPFS. Smart contracts automate log validation and access control, while cryptographic techniques ensure privacy and confidentiality. With a focus on real-time log processing, the framework is designed to handle the high-volume log generation typical in large-scale systems, such as data centers and network infrastructure. Performance evaluations demonstrate the framework's scalability, low latency, and ability to manage millions of log entries while maintaining strong security guarantees. Additionally, the paper discusses challenges like blockchain storage overhead and energy consumption, offering insights for enhancing future systems.

Md Tauhidul Islam, Lei Xing

Advancements in deep learning are revolutionizing science and engineering. The immense success of deep learning is largely due to its ability to extract essential high-dimensional (HD) features from input data and make inference decisions based on this information. However, current deep neural network (DNN) models face several challenges, such as the requirements of extensive amounts of data and computational resources. Here, we introduce a new learning scheme, referred to as deep-and-wide learning (DWL), to systematically capture features not only within individual input data (intra-data features) but also across the data (inter-data features). Furthermore, we propose a dual-interactive-channel network (D-Net) to realize the DWL, which leverages our Bayesian formulation of low-dimensional (LD) inter-data feature extraction and its synergistic interaction with the conventional HD representation of the dataset, for substantially enhanced computational efficiency and inference. The proposed technique has been applied to data across various disciplines for both classification and regression tasks. Our results demonstrate that DWL surpasses state-of-the-art DNNs in accuracy by a substantial margin with limited training data and improves the computational efficiency by order(s) of magnitude. The proposed DWL strategy dramatically alters the data-driven learning techniques, including emerging large foundation models, and sheds significant insights into the evolving field of AI.

Sara Saad Soliman, Ahmed Younes, Islam Elkabani et al.

Due to an information explosion on the internet, there is a need for the development of aggregated search systems that can boost the retrieval and management of content in various formats. To further improve the clustering of Arabic text data in aggregated search environments, this research investigates the application of advanced natural language processing techniques, namely stacked autoencoders and AraBERT embeddings. By transcending the limitations of traditional search engines, which are imprecise, not contextually relevant, and not personalized, we offer more enriched, context-aware characterizations of search results, so we used a K-means clustering algorithm to discover distinctive features and relationships in these results, we then used our approach on different Arabic queries to evaluate its effectiveness. Our model illustrates that using stacked autoencoders in representation learning suits clustering tasks and can significantly improve clustering search results. It also demonstrates improved accuracy and relevance of search results.

Shahran Rahman Alve, Muhammad Zawad Mahmud, Samiha Islam et al.

The Internet of Things (IoT) is expanding at an accelerated pace, making it critical to have secure networks to mitigate a variety of cyber threats. This study addresses the limitation of multi-class attack detection of IoT devices and presents new machine learning-based lightweight ensemble methods that exploit its strong machine learning framework. We used a dataset entitled CICIoT 2023, which has a total of 34 different attack types categorized into 10 categories, and methodically assessed the performance of a substantial array of current machine learning techniques in our goal to identify the best-performing algorithmic choice for IoT application protection. In this work, we focus on ML classifier-based methods to address the biocharges presented by the difficult and heterogeneous properties of the attack vectors in IoT ecosystems. The best-performing method was the Decision Tree, achieving 99.56% accuracy and 99.62% F1, indicating this model is capable of detecting threats accurately and reliably. The Random Forest model also performed nearly as well, with an accuracy of 98.22% and an F1 score of 98.24%, indicating that ML methods excel in a scenario of high-dimensional data. These findings emphasize the promise of integrating ML classifiers into the protective defenses of IoT devices and provide motivations for pursuing subsequent studies towards scalable, keystroke-based attack detection frameworks. We think that our approach offers a new avenue for constructing complex machine learning algorithms for low-resource IoT devices that strike a balance between accuracy requirements and time efficiency. In summary, these contributions expand and enhance the knowledge of the current IoT security literature, establishing a solid baseline and framework for smart, adaptive security to be used in IoT environments.

Zinnat Ara Moni, Zahid Hasan, Md. Shaheen Alam et al.

ABSTRACT Background Cancer is the second leading cause of human mortality worldwide. Extracellular vesicles (EVs) from liquid biopsy samples are used in early cancer detection, characterization, and surveillance. Exosomes are a subset of EVs produced by all cells and present in all body fluids. They play an important role in the development of cancer because they are active transporters capable of carrying the contents of any type of cell. The objective of this review was to provide a brief overview of the clinical implication of exosomes or exosomal components in cancer diagnosis and prognosis. Methods An extensive review of the current literature of exosomes and their components in cancer diagnosis and prognosis were carried out in the current study. Results Tumor cells release exosomes that contribute to the formation of the pre‐metastatic microenvironment, angiogenesis, invasion, and treatment resistance. On the contrary, tumor cells release more exosomes than normal cells, and these tumor‐specific exosomes can carry the genomic and proteomic signature contents of the tumor cells, which can act as tools for the diagnosis and prognosis of patients with cancers. Conclusion This information may help clinicians to improve the management of cancer patients in clinical settings in the future.

Nilam Sari, Azimah Dianah, Hafidhah et al.

Introduction: Business actor satisfaction is a feeling based on expectations regarding a product or service. Serve, and the level of satisfaction felt will vary according to the conformity with which service exists. If that reality exceeds hope, then the service can be said to be of good quality. If it is below expectations, the service can be bad; if it is above expectations, it can be said to be good. That service is satisfying. Objective: This study aims to research halal certification services to satisfy business actors in Banda Aceh City. Are these certification services quality indicators influencing the satisfaction of business actors as customers? Method: This study uses a quantitative approach with the Structural Equation Model - Partial Least Squares (SEM-PLS) method. The test was conducted using the Smart PLS 3.0 application, with the respondent category being business actors. One hundred business actors in Banda Aceh City have issued halal certification for their products, and there are 100 people. Results: The research country found that all variables, namely reliability, responsiveness, certainty, empathy, tangible evidence, and price, significantly influence business actors’ satisfaction. Implications: This shows that improving the quality of halal certification services in Banda Aceh City can also increase business actors' satisfaction

Syamsul Aripin, Nana Meily Nurdiansyah, Armai Arief et al.

Purpose – This study aims to gain an in-depth understanding of the effectiveness and reformulation of Islamic Religious Education (IRE) in the modern era. Design/methods/approach – The research method used is a literature study with a descriptive qualitative approach. The data sources and data collection techniques used were obtained from books, journals, or scientific articles, and the analysis technique used was content analysis. Findings – The results of the study show that, first, changes to Islamic Religious Education in the modern era are very complex, because the elements of modernity continue to undergo changes in terms of teaching, material, methods, and approaches. The effectiveness of Islamic Religious Education (IRE) in the modern era requires a reformulation of learning that is contextual and adaptive. Therefore, the differences that arise due to the development of science and technology need to be seen as a gift that enriches comprehensive and holistic understanding; second, learning is directed at solving the problems of modernity faced by Muslims through scientific, social, and religious approaches; third, the delivery of Islamic knowledge is not dogmatic, but emphasizes historical analysis so that students are able to understand the dynamics of the development of Islamic teachings; fourth, text-based learning methods must be balanced with critical analysis of social realities to make them more applicable; fifth, strengthening the spiritual dimension through Sufism is a fundamental requirement in shaping religious character; sixth, the effectiveness of Islamic Education is not only measured by individual piety, but also by its contribution to building social piety.    Research implications/limitations – Data sources are limited to online and offline scientific literature based on literature reviews as the primary source. Future researchers can use more in-depth techniques and methods such as research and development or case studies. Originality/value – The results of this study provide knowledge and implications for IRE teaching models and their reformulation to contribute to the challenges and progress of the times, so that synergy between various transformations can be achieved.

Khondoker Ittehadul Islam

In this paper, we conduct experiment to analyze whether models can classify offensive texts better with the help of sentiment. We conduct this experiment on the SemEval 2019 task 6, OLID, dataset. First, we utilize pre-trained language models to predict the sentiment of each instance. Later we pick the model that achieved the best performance on the OLID test set, and train it on the augmented OLID set to analyze the performance. Results show that utilizing sentiment increases the overall performance of the model.