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Despite its linguistic and cultural significance, the Mehri language has received little attention in natural language processing research. This paper presents a Mehri–Arabic machine translation system based on a multilingual neural model. A custom Mehri–Arabic parallel corpus was constructed and preprocessed using rule-based techniques. The multilingual mT5 model was fine-tuned via transfer learning and evaluated using both automatic metrics and human assessment. Experimental results indicate that satisfactory Arabic translations can be achieved despite limited data availability. This work represents an early computational effort in Mehri– Arabic machine translation and highlights the potential of multilingual pretrained models for supporting under-resourced languages.", "summary_ar": null, "keywords_ar": null, "language_original": "ar", "translation_status": "untranslated", "translation_model": null, "translation_version": null, "content_type": "Research Paper", "publication_year": 2026, "country": "عالمي", "country_raw": "Global", "doi": "https://doi.org/10.59992/ijci.2026.v5n10p1", "doi_verified": null, "citation_count": null, "citation_source": null, "duplicate_group_id": null, "url": "https://doi.org/10.59992/ijci.2026.v5n10p1", "pdf_url": "https://vsrp.co.uk/index.php/ijci/article/download/1922/1938", "journal_name": "International Journal of Computers and Informatics", "publisher": null, "license": null, "language": "ar", "dialect": "العربية الفصحى الحديثة (MSA)", "open_access_status": "hybrid", "relevance_score": 47.0, "quality_score": 95.0, "review_status": "Probably Irrelevant", "ai_enriched_data": {"is_ai_generated": true, "ai_model": "ArabicRadar-Enricher-v1.0", "confidence": 0.92, "enriched_at": "2026-10-08T08:28:01.456924", "inferred_topics": ["معالجة اللغة الطبيعية", "النماذج اللغوية الكبيرة", "المدونات ومجموعات البيانات", "الترجمة الآلية"], "detected_dialect": "العربية الفصحى الحديثة (MSA)", "technologies_extracted": [], "concise_summary": "Machine translation for low-resource and endangered languages remains a challenging task due to the scarcity of parallel corpora and limited computational resources. Despite its linguistic and cultural significance, the Mehri language has received little attention in natural language processing research.", "is_emerging_trend": true, "study_type": "Theoretical / Descriptive", "relevance_explanation": {"matches": [{"keyword": "arabic", "weight": 32.0}], "penalties": [], "calculated_score": 47.0, "category": "Probably Irrelevant"}, "quality_details": {"checks": {"title": 15, "authors": 10, "publication_date": 10, "abstract": 15, "identifiers": 20, "venue_publisher": 10, "taxonomy": 15}, "missing_fields": [], "final_score": 95.0}}, "discovered_at": "2026-10-08T08:28:01.553996", "authors": [{"id": "cf74b33f-cf22-442e-a5e0-4a10257edc9b", "name_ar": null, "name_en": "Sulaiman Maeli", "orcid": null, "orcid_valid": false, "country": "عالمي"}, {"id": "e3fba24c-7b06-477d-be44-c982df9f0f45", "name_ar": null, "name_en": "Khaled Balhaf", "orcid": null, "orcid_valid": false, "country": "عالمي"}, {"id": "06bc5b2b-a3c3-44bb-94c6-053f76c539c3", "name_ar": null, "name_en": "Nabil Mohammed Ali Munassar", "orcid": null, "orcid_valid": false, "country": "عالمي"}, {"id": "f101fce2-c150-4b8b-aff9-4d6a3636c1f7", "name_ar": null, "name_en": "Lamiaa Akoosh", "orcid": null, "orcid_valid": false, "country": "عالمي"}], "topics": [{"id": "a1007f33-9311-4833-b730-5e98b13d0d7f", "label_ar": "Computer science", "label_en": "Computer science", "slug": "computer-science", "category": null}, {"id": "2c4131f0-a68f-40e0-8aa0-53a0f17bc3c7", "label_ar": "Task (project management)", "label_en": "Task (project management)", "slug": "task-project-management", "category": null}, {"id": "298a3121-c302-463b-82c3-9fbb7d688dad", "label_ar": "النماذج اللغوية الكبيرة", "label_en": "النماذج اللغوية الكبيرة", "slug": "النماذج-اللغويه-الكبيره", "category": null}, {"id": "d0666763-b664-4cfd-a314-f9f8d4792283", "label_ar": "معالجة اللغة الطبيعية", "label_en": "معالجة اللغة الطبيعية", "slug": "معالجه-اللغه-الطبيعيه", "category": null}, {"id": "65482177-38d2-497f-a632-0fe9c00f7d13", "label_ar": "المدونات ومجموعات البيانات", "label_en": "المدونات ومجموعات البيانات", "slug": "المدونات-ومجموعات-البيانات", "category": null}, {"id": "1f32e217-c78a-4297-b610-637de27b5183", "label_ar": "الترجمة الآلية للغة العربية", "label_en": "Machine Translation", "slug": "machine-translation", "category": null}, {"id": "551bf8fa-d1ab-4f7b-a546-b32cbb765fd2", "label_ar": "Artificial intelligence", "label_en": "Artificial intelligence", "slug": "artificial-intelligence", "category": null}, {"id": "0513102d-5dee-4688-a967-e0424042c5e3", "label_ar": "Transformer", "label_en": "Transformer", "slug": "transformer", "category": null}, {"id": "86057ebd-6741-4bf9-afa8-216667153a29", "label_ar": "الترجمة الآلية", "label_en": "الترجمة الآلية", "slug": "الترجمه-الاليه", "category": null}, {"id": "e769a0cb-b7d2-41f7-b712-196d466063b8", "label_ar": "Natural language processing", "label_en": "Natural language processing", "slug": "natural-language-processing", "category": null}], "institutions": [], "source_records": [{"id": "ced129e2-9fdb-4f6e-a654-93b949011673", "source_name": "OpenAlex Academic API", "source_url": "https://openalex.org/W7220426688", "extraction_method": "REST_API"}]}, {"id": "ec14a3a5-a5f7-4b94-9523-793c84e97245", "title_ar": null, "title_en": "Domain-adaptive Bengali text summarization: Harnessing transformer-based large language models in closed-set contexts", "title_original": "Domain-adaptive Bengali text summarization: Harnessing transformer-based large language models in closed-set contexts", "abstract_ar": null, "abstract_en": "This work aims to develop a domain-adapted Bengali text summarization model by training and fine-tuning on general and domain-specific datasets with categories such as state, international, and sports. Flan-T5 and mT5 LLMs were trained on the XLSUM Bengali dataset for text summarization, and they were trained on source domains and fine-tuned on the target domains for domain adaptation. In general-domain summarization on the XLSum Bengali dataset, mT5 achieved stronger overall performance than Flan-T5, with ROUGE-1 and BERTScore values of 0.21 and 0.72, respectively. The Flan-T5-XLSUM model outperformed other LLMs, achieving a ROUGE score of 0.79, a BLEU score of 0.13 and a BERTScore of 0.86. A human evaluation involving 28 participants was conducted to assess summary fluency, adequacy, and factual consistency across domains, confirming the qualitative effectiveness of the proposed models. A custom dataset is developed with manually annotated categories for domain adaptation purposes, contributing to domain-specific Bengali data. The ablation study illustrated that pre-training and fine-tuning on domain-specific data significantly enhanced model performance, with Flan-T5 fine-tuned on sports data. Three explainability methods (LIME, SHAP, and BertViz) revealed that geographically and contextually meaningful tokens strongly influenced the domain-adaptive Flan-T5 Bengali summarization model. The adapter-based Flan-T5 architecture enables effective multi-domain fine-tuning with less than 1% of trainable parameters while preserving model performance.", "abstract_original": "This work aims to develop a domain-adapted Bengali text summarization model by training and fine-tuning on general and domain-specific datasets with categories such as state, international, and sports. Flan-T5 and mT5 LLMs were trained on the XLSUM Bengali dataset for text summarization, and they were trained on source domains and fine-tuned on the target domains for domain adaptation. In general-domain summarization on the XLSum Bengali dataset, mT5 achieved stronger overall performance than Flan-T5, with ROUGE-1 and BERTScore values of 0.21 and 0.72, respectively. The Flan-T5-XLSUM model outperformed other LLMs, achieving a ROUGE score of 0.79, a BLEU score of 0.13 and a BERTScore of 0.86. A human evaluation involving 28 participants was conducted to assess summary fluency, adequacy, and factual consistency across domains, confirming the qualitative effectiveness of the proposed models. A custom dataset is developed with manually annotated categories for domain adaptation purposes, contributing to domain-specific Bengali data. The ablation study illustrated that pre-training and fine-tuning on domain-specific data significantly enhanced model performance, with Flan-T5 fine-tuned on sports data. Three explainability methods (LIME, SHAP, and BertViz) revealed that geographically and contextually meaningful tokens strongly influenced the domain-adaptive Flan-T5 Bengali summarization model. The adapter-based Flan-T5 architecture enables effective multi-domain fine-tuning with less than 1% of trainable parameters while preserving model performance.", "summary_ar": null, "keywords_ar": null, "language_original": "ar", "translation_status": "untranslated", "translation_model": null, "translation_version": null, "content_type": "Research Paper", "publication_year": 2026, "country": "عالمي", "country_raw": "Global", "doi": "https://doi.org/10.1371/journal.pone.0357890", "doi_verified": null, "citation_count": null, "citation_source": null, "duplicate_group_id": null, "url": "https://doi.org/10.1371/journal.pone.0357890", "pdf_url": "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0357890&type=printable", "journal_name": "PLoS ONE", "publisher": null, "license": null, "language": "ar", "dialect": "العربية الفصحى الحديثة (MSA)", "open_access_status": "gold", "relevance_score": 15.0, "quality_score": 100.0, "review_status": "Probably Irrelevant", "ai_enriched_data": {"is_ai_generated": true, "ai_model": "ArabicRadar-Enricher-v1.0", "confidence": 0.92, "enriched_at": "2026-10-08T08:28:00.908813", "inferred_topics": ["معالجة اللغة الطبيعية", "النماذج اللغوية الكبيرة", "المدونات ومجموعات البيانات"], "detected_dialect": "العربية الفصحى الحديثة (MSA)", "technologies_extracted": ["BERT"], "concise_summary": "This work aims to develop a domain-adapted Bengali text summarization model by training and fine-tuning on general and domain-specific datasets with categories such as state, international, and sports. Flan-T5 and mT5 LLMs were trained on the XLSUM Bengali dataset for text summarization, and they were trained on source domains and fine-tuned on the target domains for domain adaptation.", "is_emerging_trend": true, "study_type": "Applied Research", "relevance_explanation": {"matches": [], "penalties": [], "calculated_score": 15.0, "category": "Probably Irrelevant"}, "quality_details": {"checks": {"title": 15, "authors": 15, "publication_date": 10, "abstract": 15, "identifiers": 20, "venue_publisher": 10, "taxonomy": 15}, "missing_fields": [], "final_score": 100.0}}, "discovered_at": "2026-10-08T08:28:01.005430", "authors": [{"id": "c0c07f99-6a6b-495c-bbe7-ac2a318734b1", "name_ar": null, "name_en": "Riasat Khan", "orcid": "https://orcid.org/0000-0002-5429-2235", "orcid_valid": false, "country": "عالمي"}, {"id": "c252aee9-2a9c-435c-8af7-728846c081ac", "name_ar": null, "name_en": "Farasha Shamma Yussouf", "orcid": null, "orcid_valid": false, "country": "عالمي"}, {"id": "812e95c4-3772-4a7a-a3b1-ecc4ecf9c891", "name_ar": null, "name_en": "Fahrin Hossain Sunaira", "orcid": null, "orcid_valid": false, "country": "عالمي"}, {"id": "95764f70-a562-44c6-9ab9-7ae988bfa70b", "name_ar": null, "name_en": "Intisar Tahmid Naheen", "orcid": "https://orcid.org/0000-0002-4033-2136", "orcid_valid": false, "country": "عالمي"}, {"id": "5a856fa6-14d0-4c37-9d33-2d067b811256", "name_ar": null, "name_en": "Anusree Roy", "orcid": null, "orcid_valid": false, "country": "عالمي"}, {"id": "693cc3bb-f39c-4683-845c-12d52b78ae16", "name_ar": null, "name_en": "Tateyama Orpa", "orcid": null, "orcid_valid": false, "country": "عالمي"}], "topics": [{"id": "90543570-6f78-4f09-8b4c-a08f2d6a6a24", "label_ar": "Bengali", "label_en": "Bengali", "slug": "bengali", "category": null}, {"id": "a1007f33-9311-4833-b730-5e98b13d0d7f", "label_ar": "Computer science", "label_en": "Computer science", "slug": "computer-science", "category": null}, {"id": "298a3121-c302-463b-82c3-9fbb7d688dad", "label_ar": "النماذج اللغوية الكبيرة", "label_en": "النماذج اللغوية الكبيرة", "slug": "النماذج-اللغويه-الكبيره", "category": null}, {"id": "d0666763-b664-4cfd-a314-f9f8d4792283", "label_ar": "معالجة اللغة الطبيعية", "label_en": "معالجة اللغة الطبيعية", "slug": "معالجه-اللغه-الطبيعيه", "category": null}, {"id": "65482177-38d2-497f-a632-0fe9c00f7d13", "label_ar": "المدونات ومجموعات البيانات", "label_en": "المدونات ومجموعات البيانات", "slug": "المدونات-ومجموعات-البيانات", "category": null}, {"id": "ed59202c-6248-40d1-a3d6-d23d065f8f04", "label_ar": "Automatic summarization", "label_en": "Automatic summarization", "slug": "automatic-summarization", "category": null}, {"id": "551bf8fa-d1ab-4f7b-a546-b32cbb765fd2", "label_ar": "Artificial intelligence", "label_en": "Artificial intelligence", "slug": "artificial-intelligence", "category": null}, {"id": "9231c5f5-e629-4060-af3a-34b464b987e7", "label_ar": "Domain (mathematical analysis)", "label_en": "Domain (mathematical analysis)", "slug": "domain-mathematical-analysis", "category": null}, {"id": "e769a0cb-b7d2-41f7-b712-196d466063b8", "label_ar": "Natural language processing", "label_en": "Natural language processing", "slug": "natural-language-processing", "category": null}], "institutions": [], "source_records": [{"id": "d898fa3f-8ab7-483f-ad0d-4756e17fabee", "source_name": "OpenAlex Academic API", "source_url": "https://openalex.org/W7220367584", "extraction_method": "REST_API"}]}, {"id": "d2c32ccb-84d0-4365-b22e-0685a788ee0b", "title_ar": null, "title_en": "From Accuracy to Responsibility: A Systematic Review of Computational Sustainability, Algorithmic Fairness, and Governance in Transformer-Based Text Classification", "title_original": "From Accuracy to Responsibility: A Systematic Review of Computational Sustainability, Algorithmic Fairness, and Governance in Transformer-Based Text Classification", "abstract_ar": null, "abstract_en": "Background The rapid adoption of Transformer-based architectures such as BERT and RoBERTa has advanced text classification in high-stakes domains, including cyberbullying detection, mental-health screening, and fake-news moderation. However, these advances have increased computational demands and raised concerns regarding environmental sustainability, algorithmic fairness, and responsible governance. This systematic review examines the interrelationships among computational sustainability, Green AI mechanisms, the efficiency–fairness trade-off, and governance in Transformer-based text classification. Methods A systematic literature review was conducted following the PRISMA 2020 guidelines. Studies were identified from five databases: ScienceDirect, Wiley Online Library, IEEE Xplore, Scopus, and ProQuest. A total of 94 studies published between 2020 and 2026 were synthesised to examine strategies for improving computational efficiency, their implications for fairness, and governance mechanisms relevant to responsible Transformer-based text classification. Results Four complementary categories of Green AI mechanisms were identified: model compression, including pruning, knowledge distillation, and quantization; alternative energy-efficient architectures; secure and efficient inference; and lifecycle-oriented governance techniques such as machine unlearning. Across the reviewed studies, these mechanisms reduced parameter counts or energy consumption by up to sixty-fold while generally maintaining comparable predictive performance. However, efficiency gains did not consistently preserve fairness, with some compression strategies increasing subgroup performance disparities, particularly for under-represented languages and demographic groups. Parameter-efficient fine-tuning demonstrated potential for jointly improving efficiency and equity. Persistent governance gaps were also identified, including limited alignment between machine-unlearning techniques and regulatory rights such as the GDPR right to erasure, insufficient transparency in vendor-imposed training decisions, and limited sustainability criteria for evaluating legacy research. Conclusions The findings indicate that computational efficiency, algorithmic fairness, and governance should be considered interconnected dimensions of responsible Transformer-based text classification rather than independent objectives. The review proposes a unified evaluation framework integrating sustainability metrics, fairness-aware evaluation protocols, and governance considerations to support more responsible Transformer deployment and research. The findings also highlight potential contributions to Sustainable Development Goals 9, 10, 12, and 13.", "abstract_original": "Background The rapid adoption of Transformer-based architectures such as BERT and RoBERTa has advanced text classification in high-stakes domains, including cyberbullying detection, mental-health screening, and fake-news moderation. However, these advances have increased computational demands and raised concerns regarding environmental sustainability, algorithmic fairness, and responsible governance. This systematic review examines the interrelationships among computational sustainability, Green AI mechanisms, the efficiency–fairness trade-off, and governance in Transformer-based text classification. Methods A systematic literature review was conducted following the PRISMA 2020 guidelines. Studies were identified from five databases: ScienceDirect, Wiley Online Library, IEEE Xplore, Scopus, and ProQuest. A total of 94 studies published between 2020 and 2026 were synthesised to examine strategies for improving computational efficiency, their implications for fairness, and governance mechanisms relevant to responsible Transformer-based text classification. Results Four complementary categories of Green AI mechanisms were identified: model compression, including pruning, knowledge distillation, and quantization; alternative energy-efficient architectures; secure and efficient inference; and lifecycle-oriented governance techniques such as machine unlearning. Across the reviewed studies, these mechanisms reduced parameter counts or energy consumption by up to sixty-fold while generally maintaining comparable predictive performance. However, efficiency gains did not consistently preserve fairness, with some compression strategies increasing subgroup performance disparities, particularly for under-represented languages and demographic groups. Parameter-efficient fine-tuning demonstrated potential for jointly improving efficiency and equity. Persistent governance gaps were also identified, including limited alignment between machine-unlearning techniques and regulatory rights such as the GDPR right to erasure, insufficient transparency in vendor-imposed training decisions, and limited sustainability criteria for evaluating legacy research. Conclusions The findings indicate that computational efficiency, algorithmic fairness, and governance should be considered interconnected dimensions of responsible Transformer-based text classification rather than independent objectives. The review proposes a unified evaluation framework integrating sustainability metrics, fairness-aware evaluation protocols, and governance considerations to support more responsible Transformer deployment and research. The findings also highlight potential contributions to Sustainable Development Goals 9, 10, 12, and 13.", "summary_ar": null, "keywords_ar": null, "language_original": "ar", "translation_status": "untranslated", "translation_model": null, "translation_version": null, "content_type": "Research Paper", "publication_year": 2026, "country": "عالمي", "country_raw": "Global", "doi": "https://doi.org/10.12688/f1000research.190739.1", "doi_verified": null, "citation_count": null, "citation_source": null, "duplicate_group_id": null, "url": "https://doi.org/10.12688/f1000research.190739.1", "pdf_url": "https://f1000research.com/articles/15-1655/pdf", "journal_name": "F1000Research", "publisher": null, "license": null, "language": "ar", "dialect": "العربية الفصحى الحديثة (MSA)", "open_access_status": "gold", "relevance_score": 15.0, "quality_score": 100.0, "review_status": "Probably Irrelevant", "ai_enriched_data": {"is_ai_generated": true, "ai_model": "ArabicRadar-Enricher-v1.0", "confidence": 0.92, "enriched_at": "2026-10-08T08:28:00.402728", "inferred_topics": ["النماذج اللغوية الكبيرة", "المدونات ومجموعات البيانات", "استرجاع المعلومات والبحث الدلالي"], "detected_dialect": "العربية الفصحى الحديثة (MSA)", "technologies_extracted": ["BERT"], "concise_summary": "Background The rapid adoption of Transformer-based architectures such as BERT and RoBERTa has advanced text classification in high-stakes domains, including cyberbullying detection, mental-health screening, and fake-news moderation. However, these advances have increased computational demands and raised concerns regarding environmental sustainability, algorithmic fairness, and responsible governance.", "is_emerging_trend": true, "study_type": "Theoretical / Descriptive", "relevance_explanation": {"matches": [], "penalties": [], "calculated_score": 15.0, "category": "Probably Irrelevant"}, "quality_details": {"checks": {"title": 15, "authors": 15, "publication_date": 10, "abstract": 15, "identifiers": 20, "venue_publisher": 10, "taxonomy": 15}, "missing_fields": [], "final_score": 100.0}}, "discovered_at": "2026-10-08T08:28:00.526803", "authors": [{"id": "aed04500-a703-4732-8df3-ac4d8bfdc966", "name_ar": null, "name_en": "Nur Wachid Hidayatulloh", "orcid": "https://orcid.org/0000-0002-8230-8425", "orcid_valid": false, "country": "عالمي"}, {"id": "7a519bbb-dc55-410e-846a-9f2b0d90b8a9", "name_ar": null, "name_en": "Rehan Nur Setiawan", "orcid": null, "orcid_valid": false, "country": "عالمي"}, {"id": "713d4ac8-bc53-4acc-8f61-3c7cd88a2a91", "name_ar": null, "name_en": "Bobby Riana Dwi Atmojo", "orcid": "https://orcid.org/0009-0003-8833-9817", "orcid_valid": false, "country": "عالمي"}, {"id": "38202d7b-9bb0-4674-ac8c-f94adf7f12c9", "name_ar": null, "name_en": "Anastasia Amanda Harumdiana Tobong", "orcid": "https://orcid.org/0009-0000-4376-0593", "orcid_valid": false, "country": "عالمي"}, {"id": "32b0a077-056c-4419-bf20-fd53e9b8098e", "name_ar": null, "name_en": "Fransiskus Pati Soge", "orcid": null, "orcid_valid": false, "country": "عالمي"}, {"id": "2ad43fbc-1927-4ada-9997-42e08b80faf4", "name_ar": null, "name_en": "Vicky Enjellino Osvaldo Effendi Doko", "orcid": "https://orcid.org/0009-0004-7755-8034", "orcid_valid": false, "country": "عالمي"}, {"id": "22af1282-872e-4dc1-993b-3125d7113eb5", "name_ar": null, "name_en": "Putri Juliarti", "orcid": null, "orcid_valid": false, "country": "عالمي"}, {"id": "051a9ce9-494e-4645-9d3b-e1725c431368", "name_ar": null, "name_en": "M Rizki Madya", 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Existing machine learning approaches tend to focus on incorporating dependencies between words and their significance among texts under email analysis. This study offers an innovative solution based on deep learning using Long Short-Term Memory (LSTM) model that includes an attention mechanism. The proposed framework incorporates an LSTM unit to retain the long term dependencies in the email text and an attention mechanism that focuses on the most relevant words for efficient sentiment classification. Hyperparameter tuning is performed for the proposed model via the Bayesian optimization method for better convergence and generalization. For the experimental evaluations, various baseline machine learning and deep learning methods are applied and their performances are compared with the attention-based deep learning models. From the experimental findings, the proposed Attention-based LSTM model outperformed the other models with an accuracy of 98.63%, F1-Score of 90.39%, and Area Under the Curve (AUC) of 0.99. It is also confirmed from the comparison study that the use of deep learning models is better than other techniques in terms of understanding sequential dependencies in the input data. Further improvement in the accuracy of the classification results is achieved through the application of an attention-based technique that emphasizes informative aspects. The incorporation of explainable AI methods into the framework provides valid explanations for model predictions, interpretability and reliability. This study reveals the effectiveness of attention-enhanced sequence models for robust and scalable email sentiment classification.", "abstract_original": "The analysis of email sentiment is an important aspect of natural language processing because it allows the automated detection of user sentiments. Existing machine learning approaches tend to focus on incorporating dependencies between words and their significance among texts under email analysis. This study offers an innovative solution based on deep learning using Long Short-Term Memory (LSTM) model that includes an attention mechanism. The proposed framework incorporates an LSTM unit to retain the long term dependencies in the email text and an attention mechanism that focuses on the most relevant words for efficient sentiment classification. Hyperparameter tuning is performed for the proposed model via the Bayesian optimization method for better convergence and generalization. For the experimental evaluations, various baseline machine learning and deep learning methods are applied and their performances are compared with the attention-based deep learning models. From the experimental findings, the proposed Attention-based LSTM model outperformed the other models with an accuracy of 98.63%, F1-Score of 90.39%, and Area Under the Curve (AUC) of 0.99. It is also confirmed from the comparison study that the use of deep learning models is better than other techniques in terms of understanding sequential dependencies in the input data. Further improvement in the accuracy of the classification results is achieved through the application of an attention-based technique that emphasizes informative aspects. The incorporation of explainable AI methods into the framework provides valid explanations for model predictions, interpretability and reliability. 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محاسن الشعر وآدابه لابن رشيق القيرواني&nbsp;للأدب العربي (دراسات عليا)، تم نشرة من قِبل مركز التميز والتعليم الإلكتروني (المحاضرات المصورة). الجامعة الإسلامية (غزة) - كلية الآداب، قسم..."}, "discovered_at": "2026-09-15T23:52:11.619298", "authors": [{"id": "32420f8b-a31b-4ab8-ac67-0abeaebca50c", "name_ar": "محمود الأسطل", "name_en": null, "orcid": null, "orcid_valid": false, "country": "العالم العربي"}], "topics": [], "institutions": [], "source_records": [{"id": "c36cf388-c78c-430a-b24f-f8bda3ab5c32", "source_name": "قاعدة بيانات الإنتاج المعرفي للغة العربية (Google Sheet / Excel Registry)", "source_url": "https://www.youtube.com/watch?v=s2C0gWzXvWY&amp;list=PL9fwy3NUQKwYO3pfz9aWIUfJKyOcsrmKG&amp;index=7", "extraction_method": "GOOGLE_SHEET_PIPELINE"}]}, {"id": "4803acab-dac4-49f7-992c-c2f0ad3f54f5", "title_ar": "محاضرة 7: الأنوار في محاسن الأشعار لأبي الحسن الشمشاطي", "title_en": null, "title_original": "محاضرة 7: الأنوار في محاسن الأشعار لأبي الحسن الشمشاطي", "abstract_ar": "<p>شرح العمدة في محاسن الشعر وآدابه لابن رشيق القيرواني&nbsp;للأدب العربي (دراسات عليا)، تم نشرة من قِبل مركز التميز والتعليم الإلكتروني (المحاضرات المصورة). الجامعة الإسلامية (غزة) - كلية الآداب، قسم اللغة العربية.</p>", "abstract_en": null, "abstract_original": "<p>شرح العمدة في محاسن الشعر وآدابه لابن رشيق القيرواني&nbsp;للأدب العربي (دراسات عليا)، تم نشرة من قِبل مركز التميز والتعليم الإلكتروني (المحاضرات المصورة). الجامعة الإسلامية (غزة) - كلية الآداب، قسم اللغة العربية.</p>", "summary_ar": null, "keywords_ar": null, "language_original": "ar", "translation_status": "untranslated", "translation_model": null, "translation_version": null, "content_type": "تسجيلات الفيديو", "publication_year": 2023, "country": "العالم العربي", "country_raw": "العالم العربي", "doi": null, "doi_verified": null, "citation_count": null, "citation_source": null, "duplicate_group_id": null, "url": "https://www.youtube.com/watch?v=2CVaEtl1LRI&amp;list=PL9fwy3NUQKwYO3pfz9aWIUfJKyOcsrmKG&amp;index=8", "pdf_url": null, "journal_name": null, "publisher": null, "license": null, "language": "ar", "dialect": "العربية الفصحى", "open_access_status": "unknown", "relevance_score": 75.0, "quality_score": 60.0, "review_status": "Needs Review", "ai_enriched_data": {"is_ai_generated": false, "relevance_explanation": {"matches": [{"keyword": "العربية", "weight": 25.0}, {"keyword": "اللغة العربية", "weight": 35.0}], "penalties": [], "calculated_score": 75.0, "category": "Relevant"}, "quality_details": {"checks": {"title": 15, "authors": 10, "publication_date": 10, "abstract": 15, "identifiers": 10, "venue_publisher": 0, "taxonomy": 0}, "missing_fields": ["venue_publisher", "topics"], "final_score": 60.0}, "source_nid": "409147", "db_type_ar": "تسجيلات الفيديو", "inferred_topics": [], "detected_dialect": "العربية الفصحى", "concise_summary": "<p>شرح العمدة في محاسن الشعر وآدابه لابن رشيق القيرواني&nbsp;للأدب العربي (دراسات عليا)، تم نشرة من قِبل مركز التميز والتعليم الإلكتروني (المحاضرات المصورة). الجامعة الإسلامية (غزة) - كلية الآداب، قسم..."}, "discovered_at": "2026-09-15T23:52:11.619298", "authors": [{"id": "9c5d8ac9-5120-4842-b366-5619f6553839", "name_ar": "نبيل الحلبي", "name_en": null, "orcid": null, "orcid_valid": false, "country": "العالم العربي"}], "topics": [], "institutions": [], "source_records": [{"id": "ba97c3ff-7278-4bfd-9be6-a54d3aa95090", "source_name": "قاعدة بيانات الإنتاج المعرفي للغة العربية (Google Sheet / Excel Registry)", "source_url": "https://www.youtube.com/watch?v=2CVaEtl1LRI&amp;list=PL9fwy3NUQKwYO3pfz9aWIUfJKyOcsrmKG&amp;index=8", "extraction_method": "GOOGLE_SHEET_PIPELINE"}]}, {"id": "b528fd0a-35d6-4b48-b515-0a2dff81ab94", "title_ar": "محاضرة 12: نشوار المحاضرة وأخبار المذاكرة للقاضي التنوخي", "title_en": null, "title_original": "محاضرة 12: نشوار المحاضرة وأخبار المذاكرة للقاضي التنوخي", "abstract_ar": "<p>شرح نشوار المحاضرة وأخبار المذاكرة للقاضي التنوخي&nbsp;للأدب العربي (دراسات عليا)، تم نشرة من قِبل مركز التميز والتعليم الإلكتروني (المحاضرات المصورة). الجامعة الإسلامية (غزة) - كلية الآداب، قسم اللغة العربية.</p>", "abstract_en": null, "abstract_original": "<p>شرح نشوار المحاضرة وأخبار المذاكرة للقاضي التنوخي&nbsp;للأدب العربي (دراسات عليا)، تم نشرة من قِبل مركز التميز والتعليم الإلكتروني (المحاضرات المصورة). الجامعة الإسلامية (غزة) - كلية الآداب، قسم اللغة العربية.</p>", "summary_ar": null, "keywords_ar": null, "language_original": "ar", "translation_status": "untranslated", "translation_model": null, "translation_version": null, "content_type": "تسجيلات الفيديو", "publication_year": 2023, "country": "العالم العربي", "country_raw": "العالم العربي", "doi": null, "doi_verified": null, "citation_count": null, "citation_source": null, "duplicate_group_id": null, "url": "https://www.youtube.com/watch?v=dtWGDe0M2Yc&amp;list=PL9fwy3NUQKwYO3pfz9aWIUfJKyOcsrmKG&amp;index=13", "pdf_url": null, "journal_name": null, "publisher": null, "license": null, "language": "ar", "dialect": "العربية الفصحى", "open_access_status": "unknown", "relevance_score": 75.0, "quality_score": 60.0, "review_status": "Needs Review", "ai_enriched_data": {"is_ai_generated": false, "relevance_explanation": {"matches": [{"keyword": "العربية", "weight": 25.0}, {"keyword": "اللغة العربية", "weight": 35.0}], "penalties": [], "calculated_score": 75.0, "category": "Relevant"}, "quality_details": {"checks": {"title": 15, "authors": 10, "publication_date": 10, "abstract": 15, "identifiers": 10, "venue_publisher": 0, "taxonomy": 0}, "missing_fields": ["venue_publisher", "topics"], "final_score": 60.0}, "source_nid": "409152", "db_type_ar": "تسجيلات الفيديو", "inferred_topics": [], "detected_dialect": "العربية الفصحى", "concise_summary": "<p>شرح نشوار المحاضرة وأخبار المذاكرة للقاضي التنوخي&nbsp;للأدب العربي (دراسات عليا)، تم نشرة من قِبل مركز التميز والتعليم الإلكتروني (المحاضرات المصورة). الجامعة الإسلامية (غزة) - كلية الآداب، قسم ال..."}, "discovered_at": "2026-09-15T23:52:11.619298", "authors": [{"id": "12bd421c-2703-4d25-b950-6edff04fa59b", "name_ar": "علي أبو عودة", "name_en": null, "orcid": null, "orcid_valid": false, "country": "العالم العربي"}], "topics": [], "institutions": [], "source_records": [{"id": "579f8daa-ed2a-4fb7-aaa3-3ebd862df7aa", "source_name": "قاعدة بيانات الإنتاج المعرفي للغة العربية (Google Sheet / Excel Registry)", "source_url": "https://www.youtube.com/watch?v=dtWGDe0M2Yc&amp;list=PL9fwy3NUQKwYO3pfz9aWIUfJKyOcsrmKG&amp;index=13", "extraction_method": "GOOGLE_SHEET_PIPELINE"}]}, {"id": "f002fdbf-359f-4e90-805d-8a68df52bd28", "title_ar": "محاضرة 11: نفح الطيب من غصن الأندلسي الرطيب للمقري", "title_en": null, "title_original": "محاضرة 11: نفح الطيب من غصن الأندلسي الرطيب للمقري", "abstract_ar": "<p>شرح نفح الطيب من غصن الأندلي الرطيب للمقري&nbsp;للأدب العربي (دراسات عليا)، تم نشرة من قِبل مركز التميز والتعليم الإلكتروني (المحاضرات المصورة). الجامعة الإسلامية (غزة) - كلية الآداب، قسم اللغة العربية.</p>", "abstract_en": null, "abstract_original": "<p>شرح نفح الطيب من غصن الأندلي الرطيب للمقري&nbsp;للأدب العربي (دراسات عليا)، تم نشرة من قِبل مركز التميز والتعليم الإلكتروني (المحاضرات المصورة). الجامعة الإسلامية (غزة) - كلية الآداب، قسم اللغة العربية.</p>", "summary_ar": null, "keywords_ar": null, "language_original": "ar", "translation_status": "untranslated", "translation_model": null, "translation_version": null, "content_type": "تسجيلات الفيديو", "publication_year": 2023, "country": "العالم العربي", "country_raw": "العالم العربي", "doi": null, "doi_verified": null, "citation_count": null, "citation_source": null, "duplicate_group_id": null, "url": "https://www.youtube.com/watch?v=ASs1VUNMbS8&amp;list=PL9fwy3NUQKwYO3pfz9aWIUfJKyOcsrmKG&amp;index=13", "pdf_url": null, "journal_name": null, "publisher": null, "license": null, "language": "ar", "dialect": "العربية الفصحى", "open_access_status": "unknown", "relevance_score": 75.0, "quality_score": 60.0, "review_status": "Needs Review", "ai_enriched_data": {"is_ai_generated": false, "relevance_explanation": {"matches": [{"keyword": "العربية", "weight": 25.0}, {"keyword": "اللغة العربية", "weight": 35.0}], "penalties": [], "calculated_score": 75.0, "category": "Relevant"}, "quality_details": {"checks": {"title": 15, "authors": 10, "publication_date": 10, "abstract": 15, "identifiers": 10, "venue_publisher": 0, "taxonomy": 0}, "missing_fields": ["venue_publisher", "topics"], "final_score": 60.0}, "source_nid": "409151", "db_type_ar": "تسجيلات الفيديو", "inferred_topics": [], "detected_dialect": "العربية الفصحى", "concise_summary": "<p>شرح نفح الطيب من غصن الأندلي الرطيب للمقري&nbsp;للأدب العربي (دراسات عليا)، تم نشرة من قِبل مركز التميز والتعليم الإلكتروني (المحاضرات المصورة). الجامعة الإسلامية (غزة) - كلية الآداب، قسم اللغة الع..."}, "discovered_at": "2026-09-15T23:52:11.619298", "authors": [{"id": "9143ec0f-3ca2-447f-974c-63ba76961864", "name_ar": "عمر المصري", "name_en": null, "orcid": null, "orcid_valid": false, "country": "العالم العربي"}], "topics": [], "institutions": [], "source_records": [{"id": "35bf20c2-bf12-46c9-a940-2cbf7a76833f", "source_name": "قاعدة بيانات الإنتاج المعرفي للغة العربية (Google Sheet / Excel Registry)", "source_url": "https://www.youtube.com/watch?v=ASs1VUNMbS8&amp;list=PL9fwy3NUQKwYO3pfz9aWIUfJKyOcsrmKG&amp;index=13", "extraction_method": "GOOGLE_SHEET_PIPELINE"}]}, {"id": "760cbae6-b375-451d-84bb-ebe53bf5a003", "title_ar": "محاضرة 3: لباب الآداب لأسامة ابن المنقذ", "title_en": null, "title_original": "محاضرة 3: لباب الآداب لأسامة ابن المنقذ", "abstract_ar": "<p>شرح لباب الآداب لأسامة ابن المنقذ، للأدب العربي (دراسات عليا)،&nbsp;تم نشرة من قِبل مركز التميز والتعليم الإلكتروني (المحاضرات المصورة). الجامعة الإسلامية (غزة) - كلية الآداب، قسم اللغة العربية.&nbsp;</p>", "abstract_en": null, "abstract_original": "<p>شرح لباب الآداب لأسامة ابن المنقذ، للأدب العربي (دراسات عليا)،&nbsp;تم نشرة من قِبل مركز التميز والتعليم الإلكتروني (المحاضرات المصورة). الجامعة الإسلامية (غزة) - كلية الآداب، قسم اللغة العربية.&nbsp;</p>", "summary_ar": null, "keywords_ar": null, "language_original": "ar", "translation_status": "untranslated", "translation_model": null, "translation_version": null, "content_type": "تسجيلات الفيديو", "publication_year": 2023, "country": "العالم العربي", "country_raw": "العالم العربي", "doi": null, "doi_verified": null, "citation_count": null, "citation_source": null, "duplicate_group_id": null, "url": "https://www.youtube.com/watch?v=4DS0zTcc9zs&amp;list=PL9fwy3NUQKwYO3pfz9aWIUfJKyOcsrmKG&amp;index=4", "pdf_url": null, "journal_name": null, "publisher": null, "license": null, "language": "ar", "dialect": "العربية الفصحى", "open_access_status": "unknown", "relevance_score": 75.0, "quality_score": 60.0, "review_status": "Needs Review", "ai_enriched_data": {"is_ai_generated": false, "relevance_explanation": {"matches": [{"keyword": "العربية", "weight": 25.0}, {"keyword": "اللغة العربية", "weight": 35.0}], "penalties": [], "calculated_score": 75.0, "category": "Relevant"}, "quality_details": {"checks": {"title": 15, "authors": 10, "publication_date": 10, "abstract": 15, "identifiers": 10, "venue_publisher": 0, "taxonomy": 0}, "missing_fields": ["venue_publisher", "topics"], "final_score": 60.0}, "source_nid": "409143", "db_type_ar": "تسجيلات الفيديو", "inferred_topics": [], "detected_dialect": "العربية الفصحى", "concise_summary": "<p>شرح لباب الآداب لأسامة ابن المنقذ، للأدب العربي (دراسات عليا)،&nbsp;تم نشرة من قِبل مركز التميز والتعليم الإلكتروني (المحاضرات المصورة). الجامعة الإسلامية (غزة) - كلية الآداب، قسم اللغة العربية.&nb..."}, "discovered_at": "2026-09-15T23:52:11.619298", "authors": [{"id": "450fc068-3dcd-49be-adcd-80097a71f736", "name_ar": "عطية زيدان حسين", "name_en": null, "orcid": null, "orcid_valid": false, "country": "العالم العربي"}], "topics": [], "institutions": [], "source_records": [{"id": "308ffc11-354b-42ff-bcd7-40f2fe23220b", "source_name": "قاعدة بيانات الإنتاج المعرفي للغة العربية (Google Sheet / Excel Registry)", "source_url": "https://www.youtube.com/watch?v=4DS0zTcc9zs&amp;list=PL9fwy3NUQKwYO3pfz9aWIUfJKyOcsrmKG&amp;index=4", "extraction_method": "GOOGLE_SHEET_PIPELINE"}]}, {"id": "b57c6c2f-6ac3-4ba4-b59b-cfc0896d76e5", "title_ar": "علامات التأنيث - المحاضرة 24 - اللغة العربية - المستوى الأول 2", "title_en": null, "title_original": "علامات التأنيث - المحاضرة 24 - اللغة العربية - المستوى الأول 2", "abstract_ar": "<p>تناولت المحاضرة علامات التأنيث للمستوى الأول.&nbsp;برنامج أكاديمية زاد (الافتراضية).</p>", "abstract_en": null, "abstract_original": "<p>تناولت المحاضرة علامات التأنيث للمستوى الأول.&nbsp;برنامج أكاديمية زاد (الافتراضية).</p>", "summary_ar": null, "keywords_ar": null, "language_original": "ar", "translation_status": "untranslated", "translation_model": null, "translation_version": null, "content_type": "تسجيلات الفيديو", "publication_year": 2023, "country": "العالم العربي", "country_raw": "العالم العربي", "doi": null, "doi_verified": null, "citation_count": null, "citation_source": null, "duplicate_group_id": null, "url": "https://www.youtube.com/watch?v=6vDsqAJezq8", "pdf_url": null, "journal_name": null, "publisher": null, "license": null, "language": "ar", "dialect": "العربية الفصحى", "open_access_status": "unknown", "relevance_score": 75.0, "quality_score": 53.0, "review_status": "Needs Review", "ai_enriched_data": {"is_ai_generated": false, "relevance_explanation": {"matches": [{"keyword": "العربية", "weight": 25.0}, {"keyword": "اللغة العربية", "weight": 35.0}], "penalties": [], "calculated_score": 75.0, "category": "Relevant"}, "quality_details": {"checks": {"title": 15, "authors": 10, "publication_date": 10, "abstract": 8, "identifiers": 10, "venue_publisher": 0, "taxonomy": 0}, "missing_fields": ["venue_publisher", "topics"], "final_score": 53.0}, "source_nid": "409253", "db_type_ar": "تسجيلات الفيديو", "inferred_topics": [], "detected_dialect": "العربية الفصحى", "concise_summary": "<p>تناولت المحاضرة علامات التأنيث للمستوى الأول.&nbsp;برنامج أكاديمية زاد (الافتراضية).</p>..."}, "discovered_at": "2026-09-15T23:52:11.619298", "authors": [{"id": "3aa1e268-c0c5-476c-ae2e-d0f7fa4b5135", "name_ar": "د. عبدالله المسلمي", "name_en": null, "orcid": null, "orcid_valid": false, "country": "العالم العربي"}], "topics": [], "institutions": [], "source_records": [{"id": "004cf0df-b1d8-49b9-8cc5-4ff180e19db8", "source_name": "قاعدة بيانات الإنتاج المعرفي للغة العربية (Google Sheet / Excel Registry)", "source_url": "https://www.youtube.com/watch?v=6vDsqAJezq8", "extraction_method": "GOOGLE_SHEET_PIPELINE"}]}]