Niful Islam, Md. Rayhan Ahmed, Nur Mohammad Fahad
et al.
Remote sensing imagery plays a crucial role in many applications and requires accurate computerized classification techniques. Reliable classification is essential for transforming raw imagery into structured and usable information. While Convolutional Neural Networks (CNNs) are mostly used for image classification, they excel at local feature extraction, but struggle to capture global contextual information. Vision Transformers (ViTs) address this limitation through self attention mechanisms that model long-range dependencies. Integrating CNNs and ViTs, therefore, leads to better performance than standalone architectures. However, the use of additional CNN and ViT components does not lead to further performance improvement and instead introduces a bottleneck caused by redundant feature representations. In this research, we propose a fusion model that combines the strengths of CNNs and ViTs for remote sensing image classification. To overcome the performance bottleneck, the proposed approach trains four independent fusion models that integrate CNN and ViT backbones and combine their outputs at the final prediction stage through ensembling. The proposed method achieves accuracy rates of 98.10 percent, 94.46 percent, and 95.45 percent on the UC Merced, RSSCN7, and MSRSI datasets, respectively. These results outperform competing architectures and highlight the effectiveness of the proposed solution, particularly due to its efficient use of computational resources during training.
With the widespread of software systems and applications that serve the Islamic knowledge domain, several concerns arise. Authenticity and accuracy of the databases that back up these systems are questionable. With the excitement that some software developers and amateur researchers may have, false statements and incorrect claims may be made around numerical signs or miracles in the Quran. Reproducibility of these claims may not be addressed by the people making such claims. Moreover, with the increase in the number of users, scalability and availability of these systems become a concern. In addition to all these concerns, extensibility is also another major issue. Properly designed systems can be extensible, reusable and built on top of one another, instead of each system being built from scratch every time a new framework is developed. In this paper, we introduce the QuranResearch.Org system and its vision for scalability, availability, reproducibility and extensibility to serve Islamic database systems.
The advent of Large Language Models (LLMs) has revolutionized Natural Language Processing, yet their application in high-stakes, specialized domains like religious question answering is hindered by challenges like hallucination and unfaithfulness to authoritative sources. This issue is particularly critical for the Persian-speaking Muslim community, where accuracy and trustworthiness are paramount. Existing Retrieval-Augmented Generation (RAG) systems, relying on simplistic single-pass pipelines, fall short on complex, multi-hop queries requiring multi-step reasoning and evidence aggregation. To address this gap, we introduce FARSIQA, a novel, end-to-end system for Faithful Advanced Question Answering in the Persian Islamic domain. FARSIQA is built upon our innovative FAIR-RAG architecture: a Faithful, Adaptive, Iterative Refinement framework for RAG. FAIR-RAG employs a dynamic, self-correcting process: it adaptively decomposes complex queries, assesses evidence sufficiency, and enters an iterative loop to generate sub-queries, progressively filling information gaps. Operating on a curated knowledge base of over one million authoritative Islamic documents, FARSIQA demonstrates superior performance. Rigorous evaluation on the challenging IslamicPCQA benchmark shows state-of-the-art performance: the system achieves a remarkable 97.0% in Negative Rejection - a 40-point improvement over baselines - and a high Answer Correctness score of 74.3%. Our work establishes a new standard for Persian Islamic QA and validates that our iterative, adaptive architecture is crucial for building faithful, reliable AI systems in sensitive domains.
Abdessalam Bouchekif, Samer Rashwani, Heba Sbahi
et al.
This paper evaluates the knowledge and reasoning capabilities of Large Language Models in Islamic inheritance law, known as 'ilm al-mawarith. We assess the performance of seven LLMs using a benchmark of 1,000 multiple-choice questions covering diverse inheritance scenarios, designed to test models' ability to understand the inheritance context and compute the distribution of shares prescribed by Islamic jurisprudence. The results reveal a significant performance gap: o3 and Gemini 2.5 achieved accuracies above 90%, whereas ALLaM, Fanar, LLaMA, and Mistral scored below 50%. These disparities reflect important differences in reasoning ability and domain adaptation. We conduct a detailed error analysis to identify recurring failure patterns across models, including misunderstandings of inheritance scenarios, incorrect application of legal rules, and insufficient domain knowledge. Our findings highlight limitations in handling structured legal reasoning and suggest directions for improving performance in Islamic legal reasoning. Code: https://github.com/bouchekif/inheritance_evaluation
Fakhraddin Alwajih, Abdellah El Mekki, Hamdy Mubarak
et al.
Large Language Models (LLMs) inherently reflect the vast data distributions they encounter during their pre-training phase. As this data is predominantly sourced from the web, there is a high chance it will be skewed towards high-resourced languages and cultures, such as those of the West. Consequently, LLMs often exhibit a diminished understanding of certain communities, a gap that is particularly evident in their knowledge of Arabic and Islamic cultures. This issue becomes even more pronounced with increasingly under-represented topics. To address this critical challenge, we introduce PalmX 2025, the first shared task designed to benchmark the cultural competence of LLMs in these specific domains. The task is composed of two subtasks featuring multiple-choice questions (MCQs) in Modern Standard Arabic (MSA): General Arabic Culture and General Islamic Culture. These subtasks cover a wide range of topics, including traditions, food, history, religious practices, and language expressions from across 22 Arab countries. The initiative drew considerable interest, with 26 teams registering for Subtask 1 and 19 for Subtask 2, culminating in nine and six valid submissions, respectively. Our findings reveal that task-specific fine-tuning substantially boosts performance over baseline models. The top-performing systems achieved an accuracy of 72.15% on cultural questions and 84.22% on Islamic knowledge. Parameter-efficient fine-tuning emerged as the predominant and most effective approach among participants, while the utility of data augmentation was found to be domain-dependent.
From al-Sufi's tenth-century observation of the Andromeda Galaxy as a "little cloud" to contemporary space missions, Islamic astronomy represents a millennium-spanning tradition of innovation and knowledge. This study traces its trajectory through three phases: the Golden Age (8th to 15th centuries), when scholars such as al-Biruni, al-Battani, and Ibn Sina developed instruments, cataloged the heavens, and refined theories that later influenced Copernicus; a period of decline (late 15th to 17th centuries), shaped by political fragmentation, economic shifts, and the delayed adoption of technologies such as printing and the telescope; and today's revival, marked by observatory collaborations, Olympiad successes, and emerging space programs in Morocco, Iran, Turkey, the UAE, and Saudi Arabia. This comparative analysis with Chinese and European scientific traditions shows how Islamic astronomy provided a vital link in the global history of science, transmitting mathematical rigor, observational methods, and Arabic star names that are still used today. The contemporary resurgence signals the potential for renewed contributions to astrophysics, provided that it is supported by regional observatory networks, space-based research initiatives, and educational frameworks that integrate historical heritage with modern computational science.
This paper presents our approach and results for SubTask 1: Islamic Inheritance Reasoning at QIAS 2025, a shared task focused on evaluating Large Language Models (LLMs) in understanding and reasoning within Islamic inheritance knowledge. We fine-tuned the Fanar-1-9B causal language model using Low-Rank Adaptation (LoRA) and integrated it into a Retrieval-Augmented Generation (RAG) pipeline. Our system addresses the complexities of Islamic inheritance law, including comprehending inheritance scenarios, identifying eligible heirs, applying fixed-share rules, and performing precise calculations. Our system achieved an accuracy of 0.858 in the final test, outperforming other competitive models such as, GPT 4.5, LLaMA, Fanar, Mistral and ALLaM evaluated with zero-shot prompting. Our results demonstrate that QU-NLP achieves near state-of-the-art accuracy (85.8%), excelling especially on advanced reasoning (97.6%) where it outperforms Gemini 2.5 and OpenAI's o3. This highlights that domain-specific fine-tuning combined with retrieval grounding enables mid-scale Arabic LLMs to surpass frontier models in Islamic inheritance reasoning.
The paper aims to demonstrate how the concept of omnisubjectivity can be drawn upon in an attempt to solve philosophical problems pertinent to the divine attribute of omniscience in the Islamic context. Notably, we will address the charge that the concept of omniscience is logically impossible and incoherent. We will argue that omnisubjectivity could be an attribute of God in the Islamic paradigm. Furthermore, we will show that this attribute can be inferred from the primary Islamic source; the Qur’an, and that it sufficiently responds to the historical problems in terms of understanding omniscience faced by Islamic philosophers and theologians. We will argue that omnisubjectivity fulfills the conditions of both groups without facing common problems. Lastly, we will mention the benefits of adopting this model and show some philosophical and theological implications within an Islamic framework.
We examine the contribution of Islamic legal institutions to the comparative economic decline of the Middle East behind Latin Europe, which can be observed since the late Middle Ages. To this end, we explore whether the sacralization of Islamic law and its focus on the Sharia as supreme, sacred and unchangeable legal text, which reached its culmination in the 13th century had an impact on economic development. We use the population size of 145 cities in Islamic countries and 648 European cities for the period 800-1800 as proxies for the level of economic development, and construct novel estimates of the number of law schools (i.e. madaris) and estimate their contribution to the pre-industrial economic development. Our triple-differences estimates show that a higher density of madrasas before the sacralization of Islamic law predicts a more vibrant urban economy characterized by higher urban growth. After the consolidation of the sharia sacralization of law in the 13th century, greater density of law schools is associated with stagnating population size. We show that the economic decline of the Middle East can be partly explained by the absence of legal innovations or substitutes of them, which paved the way for the economic rise of Latin Europe, where ground-breaking legal reforms introduced a series of legal innovations conducive for economic growth. We find that the number of learned lawyers trained in universities with law schools is highly and positively correlated with the western European city population. Our counterfactual estimates show that almost all Islamic cities under consideration would have had much larger size by the year 1700 if legal innovations comparable to those in Western Europe were introduced. By making use of a series of synthetic control and difference-in-differences estimators our findings are robust against a large number of model specification checks.
Md Mijanur Rahman, Ashik Uzzaman, Sadia Islam Sami
et al.
Abstract This study introduces a novel encoder–decoder framework based on deep neural networks and provides a thorough investigation into the field of automatic picture captioning systems. The suggested model uses a “long short‐term memory” decoder for word prediction and sentence construction, and a “convolutional neural network” as an encoder that is skilled at object recognition and spatial information retention. The long short‐term memory network functions as a sequence processor, generating a fixed‐length output vector for final predictions, while the VGG‐19 model is utilized as an image feature extractor. For both training and testing, the study uses a variety of photos from open‐access datasets, such as Flickr8k, Flickr30k, and MS COCO. The Python platform is used for implementation, with Keras and TensorFlow as backends. The experimental findings, which were assessed using the “bilingual evaluation understudy” metric, demonstrate the effectiveness of the suggested methodology in automatically captioning images. By addressing spatial relationships in images and producing logical, contextually relevant captions, the paper advances image captioning technology. Insightful ideas for future study directions are generated by the discussion of the difficulties faced during the experimentation phase. By establishing a strong neural network architecture for automatic picture captioning, this study creates opportunities for future advancement and improvement in the area.
Islamophobic language on online platforms fosters intolerance, making detection and elimination crucial for promoting harmony. Traditional hate speech detection models rely on NLP techniques like tokenization, part-of-speech tagging, and encoder-decoder models. However, Graph Neural Networks (GNNs), with their ability to utilize relationships between data points, offer more effective detection and greater explainability. In this work, we represent speeches as nodes and connect them with edges based on their context and similarity to develop the graph. This study introduces a novel paradigm using GNNs to identify and explain hate speech towards Islam. Our model leverages GNNs to understand the context and patterns of hate speech by connecting texts via pretrained NLP-generated word embeddings, achieving state-of-the-art performance and enhancing detection accuracy while providing valuable explanations. This highlights the potential of GNNs in combating online hate speech and fostering a safer, more inclusive online environment.
Md. Saidul Islam, Syed Farid Uddin Farhad, Md. Saidul Islam
et al.
A light source of selective functionalities of wavelengths, illumination periods, and intensities is desirable for investigating performance parameters as well as the quality of different layers and interfaces of solar cells. Conventional light sources used for these types of research are expensive, space-consuming, cumbersome to work with, and have limited functionalities. We have developed a light source with variable wavelength, intensity, and illumination period to address these issues using an illumination period control unit, voltage regulator, neutral density filter, alterable light emitting diodes, etc. As a proof-of-concept, we employed our constructed light source to investigate the intensity, wavelength, illumination period modulated photovoltaic, and impedance properties of inorganic thin film solar cells such as cadmium telluride (CdTe) and copper zinc tin sulfide (CZTS) using lights of wavelength 410, 520, and 635 nm. We hope to use this light source for photophysical and photochemical studies of metal oxide materials used for renewable energy research.
Penelitian ini bertujuan untuk mengetahui peningkatan kemampuan motorik kasar anak umur 5- 6 tahun melalui permainan tapak kaki kupu-kupu. Penelitian ini menggunakan metode penelitian tindakan kelas yakni sebanyak 12 orang anak di RA Bi Al Nazhar. Setiap siklus dalam penelitian ini terdapat 4 fase yang dilalui yaitu: perencanaan, penerapan, observasi serta refleksi. Fokus penelitian yang dilakukan adalah bagaimana permainan tapak kaki kupu-kupu dalam meningkatkan kemampuan motorik kasar seorang anak. Terdapat 2 siklus dalam penelitian ini masing-masing dilakukan dalam 5 pertemuan. Subjek dalam penelitian ini terdiri dari kelompok B di RA Bi Al- Nazhar Dalam proses pengumpulan data, penelitian ini menggunakan teknik observasi. Berdasarkan temuan tersebut, permainan tapak kaki kupu-kupu ternyata dapat meningkatkan kemampuan motorik kasar anak. Hasil penelitian menunjukkan bahwa angka hasil pemantauan sebelum tindakan umumnya berasal dari tahap pra siklus, dengan rata-rata (26,56%) sedangkan yang berasal dari siklus I pertemuan 5 (64,06 %) dan siklus II pertemuan 5 (82,03 %). Hasil penelitian yang didapat adalah bahwa melalui permainan tapak kaki kupu-kupu dapat meningkatan kemampuan motorik kasar anak kelompok B di RA Bi Al- Nazhar, hasil ini teruji dengan terdapatnya peningkatan kemampuan motorik kasar anak. Setiap siklus penelitian terdapat peningkatan pada kemampuan motorik kasar anak melalui permainan tapak kaki kupu- kupu
The Indonesian terms of disease names are unique. Despite being different from their medical terms, Indonesian terms of disease names contain elements of figurative language. This study aims to analyze stylistic naming. The data were Corpora from articles, social media forums, and online news on health for 2013-2023, involving 1.206.281.985 tokens from the Indonesian-Leipzig Corpora Collection (ILC) and 39.294 tokens collected for the 2023 Health Forum Corpus (HF). Data analysis concerned the wordlist and collocation feature to see the frequency, trend, and pattern, and the concordance feature examined the language style of the names. The study does not find any evidence of changes in health terms over the past decade, such as “penyakit jantung” (heart disease), "headache," and "hospital." However, it does uncover some interesting findings regarding the formation of disease names. Affixation and compounding are the primary word formation processes. The stylistic elements of disease names were hyperbolic figures, such as "gagal ginjal" (chronic kidney disease), and symbolic figures, such as "kaki gajah" (filariasis) and "mata ikan" (clavus). In conclusion, the names of diseases followed a particular pattern, but the specific terminology used might vary based on linguistic factors and cultural understanding.
The National Stock Exchange of India (NSE) has presented Nifty 50 Shariah and Nifty 500 Shariah indices to provide unconventional indices for Sharia-compliant companies. These indices follow Sharia laws and can be used in portfolios that are culturally dependable commodities for investors who do not wish to put their money into the undesired business. NSE witnessed big movements in the indices during the Covid-19 period. This study seeks to understand the association between Nifty 500 Sharia and Nifty 50 Sharia and their respective selected indexes, Nifty 500 and Nifty 50, during the Covid-19 pandemic. The period from 27/01/2020 to 31/05/2022 has been taken for this study. The techniques applied, like correlation, co-integration, GMM, etc. based on the objectives of this paper. We conclude that the return of Sharia indices is better compared to the other indices. Also, stocks compliant with Sharia Indices are less risky and a better alternative for the portfolio during pandemic times.
<p>This study aims to evaluate whether the idea of ultimate reality in world religions contributes to the characteristics of the world religion paradigm, which is hierarchical cosmology or “subject-object cosmology.” Several research on this topic claims that one of the characteristics of the world religion paradigm is its hierarchical perspective. Discussing this issue is important to distinguish the world religions as the paradigm and the world religions as the most widely embraced religion. This study argues that the hierarchical perspective of the world religion paradigm can be rooted in the idea of ultimate reality, that there is a supreme, foremost, and most principal reality in the continuity of this universe, namely the supernatural or God. The hierarchical cosmology consists of three main domains: supernatural/God, culture/human, and nature. This study uses a literature study methodology, relying on books, journals, and texts related to research questions. This study finds that the world religion paradigm or hierarchical cosmology or “subject-object cosmology” is prominent, especially in Abrahamic religions such as Islam, Christianity, and Judaism, even though the concept of ultimate reality in these three religions is different.</p><p align="left"> </p><p><em>Penelitian ini bertujuan untuk mengevaluasi apakah gagasan tentang realitas tertinggi dalam agama-agama dunia turut berkontribusi membentuk karakteristik paradigma agama dunia, yaitu kosmologi hierarkis atau “kosmologi subjek-objek”. Beberapa penelitian tentang topik ini mengklaim bahwa salah satu karakteristik paradigma agama dunia adalah perspektifnya yang hierarkis. Membahas masalah ini penting untuk membedakan agama-agama dunia sebagai paradigma dan agama-agama dunia sebagai agama yang paling banyak dianut. Kajian ini berpendapat bahwa perspektif hierarkis paradigma agama dunia dapat berakar pada gagasan tentang realitas tertinggi, bahwa ada realitas tertinggi, utama, dan paling utama dalam kelangsungan alam semesta ini, yaitu supernatural atau Tuhan. Kosmologi hierarkis terdiri dari tiga domain utama: supernatural/Tuhan, budaya/manusia, dan alam. Penelitian ini menggunakan metodologi studi kepustakaan, dengan mengandalkan buku, jurnal, dan teks-teks yang berkaitan dengan pertanyaan-pertanyaan penelitian. Kajian ini menemukan bahwa paradigma agama dunia atau kosmologi hierarkis atau “kosmologi subjek-objek” menonjol, terutama dalam agama-agama Abrahamik seperti Islam, Kristen, dan Yudaisme, meskipun konsep realitas tertinggi dalam ketiga agama tersebut berbeda. </em></p>
Islamic education is a future choice, and a reference in developing human potential and the embryo of world civilization. However, Islamic education does not always run smoothly, and in fact, it seems static because the education that is carried out cannot be separated from the system and the laws of life that take place. Therefore, Islamic education must be managed in a professional and quality manner. This paper aims to offer a progressive Islamic education model. Through literature studies from various literatures, which are then described and analysed with a qualitative approach, it shows that the Islamic education model must be designed and oriented towards empowering and developing human potential, so as to produce competitive and productive Human Resources. The basic models and styles of progressive Islamic education are able to create positive forces that can influence and determine human attitudes in life. The stronger the quality and potential of the human person, the more they will be able to have a visionary perspective and be able to realize and deepen the meaning of Islamic education in life as a determinant of identity. Thus, the Islamic education system will immediately be reformulated in accordance with the dynamics of the times, market needs, and based on local wisdom.