EDRAAK
type: channel
description: EDRAAK (E_ISSN: 3078-8412) is a specialized academic journal focused on advancing research in artificial intelligence. Published by Peninsula Publishing Press in Dubai, EDRAAK serves as a platform for researchers, scholars, and industry professionals to share innovative work in AI disciplines, including machine learning, deep learning, robotics, natural language processing, and AI ethics. The journal is dedicated to fostering cutting-edge research that pushes the boundaries of AI technology, providing high-quality, peer-reviewed studies that address both theoretical advancements and practical applications.
publisher: Peninsula Publishing Press
language: en
publication name: EDRAAK
issn: 3078-8412
items: Predictive Intelligence in Public Administration A Machine Learning Framework for Decision Support and Policy Optimization
items: Explainable Artificial Intelligence for Fake News Detection in Digital Media
items: Leaf-by-Leaf Diagnosis: A Custom CNN with Pyramidal Feature Extraction for Plant Disease Classification
items: A Machine Learning-Based Diagnostic Framework for Accurate Tuberculosis Detection
items: Modelling-Based Classification of Sensor Nodes in WBAN Using a Hybrid Backpropagated Mask Convolutional Neural Network
items: AI Hallucinations in Retrieval-Augmented and Generative Systems: A Rigorous Review of Definitions, Failure Mechanisms, Evaluation, and Mitigation Strategies
items: Human Activity Recognition Using Smartphone Sensor Data: A Lightweight Benchmark Study
items: Computational Algorithms for Climate-Smart Agriculture in Sub-Saharan Africa
items: Machine Learning Enhanced Artificial Bee Colony for Solving Inverse Problems: Surrogate-Guided Reconstruction under Measurement Noise
items: Developing an Efficient Deep Learning Model for the Classification of Skin Lesions
items: Detecting Similarity License Plate Vehicle License Via Using Deep CNNs in Complex Surroundings
items: Comparative Evaluation of Ensemble Learning Algorithms for Early Detection of Diabetes
items: Classification Arabic language (Classical Arabic Poetry, Al-Hur Arabic Poetry and Prose) Using Machine Learning
items: Most Cited AI Research (2024–2025): A Cross-Sector Review
items: Applications of Geospatial AI in Human Geography and Spatial Networks: A Literature Review
items: Enhancing Economic Growth Time Series for UAE Forecasting with Deep Learning: A Seq2Seq and Attention-Driven LSTM Approach
items: Using Random Forest Algorithm to Improve Investment Decision Making in Damascus Stock Exchange
items: Deep Learning-Based Neural Network Modeling for Economic Performance Prediction: An Empirical Study on Iraq
items: Machine and Deep Learning Techniques in Cancer Prediction and Risk Stratification Using Bioinformatics in Big Data Era: A Review
items: Application of Deep Learning and Transfer Learning Techniques for Medical Image Classification
items: Economic Performance Classification in Iraq (2000–2023): A Statistical Analysis Using Machine Learning with Support Vector Machines and Random Forest
items: Deep Learning-Based Time Series Forecasting: A Convolutional Neural Network Approach for Predicting Inflation Trends
items: Enhancing Point-of-Interest Recommendation Systems through Multi-Modal Data Integration in Location-Based Social Networks: Challenges and Future Directions
items: Analyzing Temperature Trends in Nouakchott Using ARIMA Modeling
items: Correlation Analysis of COVID-19 Case Trends in Dakar, Senegal
items: Machine Learning for Temperature Analysis in Ouagadougou: A Random Forest Perspective
items: Seasonal Temperature Prediction in Niamey: A Prophet Model Approach
items: Long Short-term Memory Model for Temperature Forecasting in Khartoum, Sudan
items: Multi-view Deep Learning-Based COVID-19 Diagnosis with Chest X-Ray Images: A Comparative Study of SVM and KNN Classifiers
items: Predictive Modeling and Analysis in Genetic Diseases: A Comprehensive Review of Recent Advances

Predictive Intelligence in Public Administration A Machine Learning Framework for Decision Support and Policy Optimization
type: item
description: Data-driven LGT can help identify meaning and reshape the provision of public services, and facilitate expansion in new global and digital business sectors. Hence, the value of machine learning (ML)-based predictive intelligence has increased in the context of providing actionable predictions based on real-life data and policy recommendations. In this paper, a comprehensive machine learning framework is proposed to support the decision-making and policy-making processes in a more advanced manner. The framework includes several prediction algorithms such as Linear Regression, Decision Trees, Random Forest and Artificial Neural Networks to make predictions about the important information provided by the government. The models are demonstrated and analyzed using real-world data from open government portals across different scenarios, like prediction of budget, hospital demand forecasting, as well as emergency source allocation. They are tested for their predictive efficiency with traditional metrics such as the Root Mean Square Error (RMSE), the Mean Absolute Error (MAE) and the Coefficient of Determination (R²). A comparison of the experimental results indicates that Artificial Neural Networks have significant predictive performance, especially for complex and high-dimensional data, whereas Random Forest has a high predictive performance and a better interpretability. Linear Regression and Decision Trees are more useful in terms of transparency and scalability, making them appropriate models for selection of public policies in government systems. They are not a good environment for implementation everywhere, however, including universities, NGOs, or national/international government institutions with many requirements for their implementation. This encompasses application of explainable artificial intelligence (XAI) methods to tackle transparency and trust concerns, in addition to designing more local and adaptive models to enhance generalizability to various administ…
subject: Articles
  topic: section
subject: Predictive Intelligence
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Public Administration
  topic: keywords
subject: Decision Support Systems
  topic: keywords
subject: Policy Optimization
  topic: keywords
creator: Sarmad Muwfak Khazaal
rights: Copyright (c) 2026 Sarmad Muwfak Khazaal https://creativecommons.org/licenses/by/4.0
date: 2026-07-29
publication date: 2026-07-29
starting page: 111
ending page: 121
doi: 10.70470/EDRAAK/2026/009

Explainable Artificial Intelligence for Fake News Detection in Digital Media
type: item
description: The speed with which fake news is being spread through digital channels has presented a great challenge, impacting the public opinion, politics, and social cohesion. Several machine learning and deep learning methods have been suggested for detecting fake news, but most of these models are either highly resource-intensive or do not give insights into the news predictions. In response to these challenges, this paper presents an Explainable Artificial Intelligence (XAI) framework for fake news detection that unites the complementary explainability methods: SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) with the feature extraction technique, Term Frequency-Inverse Document Frequency (TF-IDF) and the Linear Support Vector Machine (Linear SVM) classifier. The framework is based mainly on the GossipCop and PolitiFact subset of the FakeNewsNet repository. The methodology is divided into four phases, namely: data preprocessing, textual feature extraction using TF-IDF, classification using Linear SVM, and explainability analysis of the classification by using both local and global interpretation techniques. The experimental results confirmed the proposed approach with accuracy of 79.40%, precision of 80.51%, recall of 95.34%, F1-score of 87.30%, and ROC-AUC of 81.40%, and by providing transparent explanations of classification using SHAP and LIME. The achieved outcome shows the proposed framework has a good balance between classification accuracy, simplicity of calculation, and the ease of understanding the model. As a result, the proposed approach is an effective solution for detecting fake news in applications, including automated fact-checking, digital journalism and social media content monitoring.
subject: Articles
  topic: section
subject: Explainable Artificial Intelligence (XAI)
  topic: keywords
subject: Fake News Detection
  topic: keywords
subject: Digital Media
  topic: keywords
subject: TF-IDF
  topic: keywords
subject: Linear Support Vector Machine (Linear SVM)
  topic: keywords
subject: SHAP
  topic: keywords
subject: LIME
  topic: keywords
subject: Machine Learning
  topic: keywords
creator: Ilyass Mzili
creator: Otmane Houdaif
creator: Zakaria Benlalia
rights: Copyright (c) 2026 Ilyass Mzili, Otmane Houdaif, Zakaria Benlalia https://creativecommons.org/licenses/by/4.0
date: 2026-07-16
publication date: 2026-07-16
starting page: 97
ending page: 110
doi: 10.70470/EDRAAK/2026/008

Leaf-by-Leaf Diagnosis: A Custom CNN with Pyramidal Feature Extraction for Plant Disease Classification
type: item
description: Plant diseases threaten global agriculture, causing 20–40% yield losses and food insecurity. Current diagnostic methods are costly and lack scalability. While deep learning advances plant disease detection, there remains a need for CNNs with simpler architectures, better generalizability, and lower computational cost. This study presents a novel CNN for multi-class classification of 38 diseases. Trained on a public dataset of over 87,000 RGB images, the architecture comprises five convolutional blocks (filters 32–512) with max pooling and dropout (0.25, 0.4), followed by a 1,500-unit dense layer and SoftMax output. Optimized with Adam (lr=0.0001) and categorical cross-entropy, the model achieved 98% training and 96% validation accuracy with approximately 28.7 million parameters significantly fewer than transfer learning architectures. These results demonstrate an effective balance between predictive performance and computational efficiency, positioning the model as a promising tool for real-world agricultural deployment.
subject: Articles
  topic: section
subject: Plant disease
  topic: keywords
subject: Deep learning
  topic: keywords
subject: Convolutional Neural Networks
  topic: keywords
subject: Image recognition
  topic: keywords
subject: Image processing
  topic: keywords
creator: Ahmad S. Lateef
rights: Copyright (c) 2026 Ahmad S. Lateef https://creativecommons.org/licenses/by/4.0
date: 2026-07-10
publication date: 2026-07-10
starting page: 85
ending page: 96
doi: 10.70470/EDRAAK/2026/007

A Machine Learning-Based Diagnostic Framework for Accurate Tuberculosis Detection
type: item
description: Tuberculosis (TB) is a highly infectious disease with a profound impact on global health, making early and accurate diagnosis crucial for preventing its spread. In recent years, machine learning has emerged as a powerful diagnostic tool capable of enhancing disease detection and optimizing healthcare processes. This study presents a machine learning-based diagnostic model designed to improve the precision and efficiency of TB diagnosis. The proposed framework utilizes a comprehensive dataset comprising clinical and laboratory information, including patient demographics, symptoms, and diagnostic test results. The model operates through several key stages: data collection, preprocessing, feature extraction, feature fusion union, classification, and distribution. Experimental evaluation across multiple datasets demonstrates the model’s robustness and superior performance. On the TB-DS-I dataset, it achieved precision, recall, accuracy, and F-measure scores of 94.84%, 94.56%, 95.85%, and 94.32%, respectively. For the TB-DS-II dataset, it attained precision of 96.74%, recall of 96.5%, F-measure of 96.15%, and accuracy of 97.02%. Similarly, the TB-DS-III dataset yielded outstanding results with 97.83% precision, 97.54% recall, 97.2% F1 score, and 98.03% accuracy, confirming the model’s effectiveness and reliability in accurately classifying individuals as TB-positive or TB-negative.
subject: Articles
  topic: section
subject: Tuberculosis
  topic: keywords
subject: Diagnosing
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: Patients
  topic: keywords
creator: Hina Afzal
creator: Ghulam Mustafa
creator: Asif Nawaz
creator: Muhammad Rizwan Rashid Rana
creator: Tariq Ali
rights: Copyright (c) 2026 Hina Afzal, Ghulam Mustafa, Asif Nawaz, Muhammad Rizwan Rashid Rana, Tariq Ali https://creativecommons.org/licenses/by/4.0
date: 2026-06-03
publication date: 2026-06-03
starting page: 75
ending page: 84
doi: 10.70470/EDRAAK/2026/006

Modelling-Based Classification of Sensor Nodes in WBAN Using a Hybrid Backpropagated Mask Convolutional Neural Network
type: item
description: Wireless Body Area Networks (WBANs) have enhanced cornerstone technology in modern healthcare, enabling continuous monitoring of patients through interconnected wearable sensors. However, their reliability and security remain vulnerable due to untrusted or malicious sensor nodes that can degrade data quality and delay medical responses. This study introduces an integrated intelligent framework that enhances node trust classification indoors healthcare-oriented WBAN environments. The proposed approach combines Principal Component Analysis (PCA) for dimensionality reduction with the Dragonfly optimization algorithm algorithm for optimal feature selection, followed by a Hybrid Backpropagated Mask Convolutional Neural Network (HB-MCNN) for node trust classification. The model was evaluated using a real WBAN dataset under controlled simulation settings. Results demonstrated a classification rate of 99.2%, an accuracy of 98.5%, a packet delivery rate of 98.6%, and a latency of 7.2 ms, while reducing energy consumption to 55.5%. These outcomes confirm the framework’s ability to enhance the reliability and security of medical data transmission by accurately isolating untrusted nodes, reducing communication delays, and improving overall trust management in healthcare monitoring systems.
subject: Articles
  topic: section
subject: WBAN
  topic: keywords
subject: Trust Node Classification
  topic: keywords
subject: Dragonfly optimization algorithm
  topic: keywords
subject: HB-MCNN
  topic: keywords
subject: Feature Selection
  topic: keywords
subject: Convergence Analysis
  topic: keywords
creator: Israa Ibraheem Al Barazanchi
creator: Wahidah Hashim
creator: Reema Thabit
creator: Abdul Samad Bin Shibghatullah
rights: Copyright (c) 2026 Israa Ibraheem Al Barazanchi, Wahidah Hashim, Reema Thabit, Abdul Samad Bin Shibghatullah https://creativecommons.org/licenses/by/4.0
date: 2026-05-10
publication date: 2026-05-10
starting page: 52
ending page: 74
doi: 10.70470/EDRAAK/2026/005

AI Hallucinations in Retrieval-Augmented and Generative Systems: A Rigorous Review of Definitions, Failure Mechanisms, Evaluation, and Mitigation Strategies
type: item
description: The research on AI hallucinations undergoes complete evaluation through this review which studies retrieval-augmented generation (RAG) and large language models (LLMs) and multimodal systems and their applications in healthcare and education and law and cybersecurity and business and tourism. The paper uses the bibliography from the source document as its review material to combine different definitions and show the reasons for hallucinations at both the model stage and the pipeline stage and to evaluate detection methods and assessment approaches and to develop a functional system for mitigation methods. Research shows that user trust along with interface behavior and anthropomorphic design and legal accountability and regulatory oversight now determine how hallucinated output systems impact actual operational systems. Three conclusions emerge. The research identifies hallucination as a group of different errors which produce unsupported content and weak grounding and context conflict and false citations and misleading confidence. The research identifies hallucination as a group of different errors which produce unsupported content and weak grounding and context conflict and false citations and misleading confidence. The RAG system implements security features which prevent particular failure modes but it generates new failure modes through its retrieval quality and evidence selection and grounding fidelity systems. The RAG system removes certain failure types but it produces new problems which impact both retrieval operations and evidence selection and grounding process accuracy. A stack which unites corpus control with retrieval validation and constrained generation and uncertainty quantification and human review stands as the most defensible solution for high-risk operational deployment. The most defensible approach for high-risk deployment requires a combination of corpus control and retrieval validation and constrained generation and uncertainty quantification…
subject: Articles
  topic: section
subject: AI hallucination
  topic: keywords
subject: retrieval-augmented generation
  topic: keywords
subject: large language models
  topic: keywords
subject: grounding
  topic: keywords
subject: hallucination detection
  topic: keywords
subject: evaluation metrics
  topic: keywords
subject: trustworthy AI
  topic: keywords
subject: mitigation strategies
  topic: keywords
subject: human-AI trust
  topic: keywords
creator: Toufik Mzili
creator: Ilyass Mzili
creator: Ahmed Abatal
creator: Zahra Oughannou
creator: Adarsh Kumar Arya
creator: Mohamed Kurdi
rights: Copyright (c) 2026 Toufik Mzili, Ilyass Mzili, Ahmed Abatal, Zahra Oughannou, Adarsh Kumar Arya, Mohamed Kurdi https://creativecommons.org/licenses/by/4.0
date: 2026-03-27
publication date: 2026-03-27
starting page: 41
ending page: 51
doi: 10.70470/EDRAAK/2026/004

Human Activity Recognition Using Smartphone Sensor Data: A Lightweight Benchmark Study
type: item
description: Smartphone inertial sensors enable Human Activity Recognition (HAR) which serves as a fundamental task for mobile computing and digital health and context-aware systems because these devices offer both functional sensing abilities and extensive access to real-world environments. The study creates a journal-style lightweight benchmark for smartphone sensor-based HAR through a dataset which contains 4,999 samples and 561 numerical features and 15 subjects and six activity classes after data cleaning and restructuring. The proposed pipeline performs data repair before preprocessing and descriptive statistics and feature grouping and correlation analysis and principal component analysis (PCA) and ANOVA-based feature ranking and model benchmarking. The study tested four lightweight machine learning baselines which included Logistic Regression and Random Forest and k-Nearest Neighbors (KNN) and Gaussian Naive Bayes through an 80/20 train-test split that maintained class distribution. The Logistic Regression model delivered the highest performance with an accuracy of 0.973 and a macro F1-score of 0.9748 yet Random Forest and KNN models also achieved results above 0.95. The analysis of features demonstrated that gravity-based descriptors together with angle-based descriptors produced the most effective recognition results yet the error analysis showed that the most frequent classification errors occurred between sitting and standing positions. The research demonstrates that feature representations which have been designed with purpose maintain their competitive position when used in deployment-focused HAR systems.
subject: Articles
  topic: section
subject: Human Activity Recognition
  topic: keywords
subject: Smartphone Sensors
  topic: keywords
subject: Lightweight Benchmark
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Activity Classification
  topic: keywords
subject: Feature Engineering
  topic: keywords
subject: Principal Component Analysis
  topic: keywords
subject: Mobile Computing
  topic: keywords
creator: Abdulazeez Alsajri
creator: Amani Steiti
rights: Copyright (c) 2026 Abdulazeez Alsajri, Amani Steiti https://creativecommons.org/licenses/by/4.0
date: 2026-03-15
publication date: 2026-03-15
starting page: 22
ending page: 40
doi: 10.70470/EDRAAK/2026/003

Computational Algorithms for Climate-Smart Agriculture in Sub-Saharan Africa
type: item
description: Climate-smart agriculture (CSA) provides a vital framework for enhancing food security, resilience, and mitigation in Sub-Saharan Africa (SSA), where agriculture is highly vulnerable to climate variability and shocks. By integrating practices that sustainably increase productivity and reduce greenhouse gas emissions, CSA addresses the region’s dual challenges of climate change and food insecurity. Computational algorithms offer critical tools for supporting CSA through simulation, predictive analytics, optimization, and decision-making. However, their systematic application to SSA’s agricultural systems remains limited. This study investigates how algorithm development and optimization can strengthen CSA adoption in SSA. The objectives are to examine the current status of CSA practices and adoption drivers, explore algorithmic applications in modelling and resource management, identify integration barriers, and propose scalable pathways for sustainable deployment. A systematic review was conducted across six databases, focusing on literature published between 2010 and 2023. Screening yielded 32 eligible studies, which were synthesized through narrative and thematic analysis. Results highlight the use of algorithms such as particle swarm optimization, neural networks, and evolutionary computation in domains including yield prediction, drought risk assessment, irrigation scheduling, and crop disease detection. Key barriers include budgetary constraints, weak supply chains, policy gaps, and low farmer awareness, while opportunities lie in digital connectivity, climate information services, and institutional support. The findings suggest that integrating computational algorithms into CSA frameworks can enhance adaptive capacity, optimize resource use, and accelerate resilient food system transformations in SSA.
subject: Articles
  topic: section
subject: Climate-smart agriculture
  topic: keywords
subject: Computational algorithms
  topic: keywords
subject: Sub-Saharan Africa
  topic: keywords
subject: Optimization
  topic: keywords
subject: Food security
  topic: keywords
subject: Climate resilience
  topic: keywords
creator: Mulala Jimaima
creator: Fredrick Kayusi
creator: Timothy Mwewa
creator: James Shabiti Mukombwe
creator: Yusuf Umer
creator: Petros Chavula
rights: Copyright (c) 2026 Mulala Jimaima, Fredrick Kayusi, Timothy Mwewa, James Shabiti Mukombwe, Yusuf Umer, Petros Chavula https://creativecommons.org/licenses/by/4.0
date: 2026-02-01
publication date: 2026-02-01
starting page: 11
ending page: 21
doi: 10.70470/EDRAAK/2026/002

Machine Learning Enhanced Artificial Bee Colony for Solving Inverse Problems: Surrogate-Guided Reconstruction under Measurement Noise
type: item
description: Inverse problems are fundamental to a variety of scientific and engineering problems, but are commonly ill-posed, nonlinear, and sensitive to measurement errors. In this study, a novel machine learning based ABC framework (ML-ABC) is introduced to enhance the stability of and the efficiency in IP estimation. The proposed strategy includes a surrogate learning, which is used to mimic the objective landscape and guide the injection of candidates during exploration phase, thus achieving faster convergence rate and avoided premature stagnation than those of the conventional ABC algorithm. The method is tested on inverse reconstruction problems in simulations, where unknown model parameters are estimated from noisy observations. Experimental results illustrate that ML-ABC achieves higher reconstruction quality and a more consistent estimate over multiple levels of noise compared to previous methods while having the ability to perform global search. Further, our proposed approach produces interpretable convergence behavior and can be used for error-surface analysis that could help in verifying the reliability of solutions in ill-posed conditions. In summary, the ML-ABC is a feasible hybrid optimization framework to address inverse problems, providing better robustness and computational efficiency.
subject: Articles
  topic: section
subject: Inverse problems
  topic: keywords
subject: Artificial Bee Colony
  topic: keywords
subject: machine learning
  topic: keywords
subject: surrogate modeling
  topic: keywords
subject: parameter estimation
  topic: keywords
subject: optimization
  topic: keywords
subject: noise robustness
  topic: keywords
subject: metaheuristic algorithms
  topic: keywords
creator: Habeeb Al-thabhawee
creator: Ghassan AL-Thabhawee
creator: Hussein Alkattan
rights: Copyright (c) 2026 Habeeb Al-thabhawee, Ghassan AL-Thabhawee, Hussein Alkattan https://creativecommons.org/licenses/by/4.0
date: 2026-01-10
publication date: 2026-01-10
starting page: 1
ending page: 10
doi: 10.70470/EDRAAK/2026/001

Developing an Efficient Deep Learning Model for the Classification of Skin Lesions
type: item
description: This paper proposes a deep learning technique for classifying dermoscopic skin lesion pictures, improving diagnostic precision and clinical decision-making. The model extracts features using ResNet101V2 and infers using Monte Carlo dropout to determine prediction certainty. Grad-CAM additionally shows the model's output-affecting regions. This makes it easy to assess whether the network is targeting therapeutically relevant structures. A modular software system tracked preprocessing, training, and assessment to ensure approach clarity. Repeating trials and making systematic model adjustments is easy with this framework. We processed ISIC using defined metadata and stratified splitting. This was balanced and simple to comprehend how the model operated. Due to a lack of representative samples and an evident class imbalance, the framework performed well for the majority class but could not identify melanoma. Use uncertainty estimate and visual explanation combined to assess model reliability and identify ambiguous predictions. The development of dermatology-focused AI systems that are clinically interpretable and dependable is supported by these components.
subject: Articles
  topic: section
subject: skin lesion classification
  topic: keywords
subject: deep learning
  topic: keywords
subject: ResNet101V2
  topic: keywords
subject: ISIC dataset
  topic: keywords
subject: Monte Carlo dropout
  topic: keywords
subject: explainable AI
  topic: keywords
subject: Grad-CAM
  topic: keywords
subject: medical image analysis
  topic: keywords
creator: Yasmin Makki Mohialden
creator: Saba Abdulbaqi Salman
rights: Copyright (c) 2025 Yasmin Makki Mohialden, Saba Abdulbaqi Salman https://creativecommons.org/licenses/by/4.0
date: 2025-11-20
publication date: 2025-11-20
starting page: 119
ending page: 125
doi: 10.70470/EDRAAK/2025/015

Detecting Similarity License Plate Vehicle License Via Using Deep CNNs in Complex Surroundings
type: item
description: As our society has developed, cars on the road have increased. Manual license plate recognition is challenging since it is significantly slower in real-time than when performed by a machine. The bulk of license plate recognition systems utilized morphological image processing until recently. The detection rate falls drastically under challenging situations (when the license plate is blurry, for example). We suggest a novel approach to license plate recognition as a solution to this problem. It was positioning a license plate. The study presented a deep learning-based vehicle classifier to localize license plates and numbers. Instead of bounding rectangles, the classifier outputs determining quadrilaterals for vehicle number estimate with license plate localization. The suggested DCNN model training began with an initial weight that had been trained without the classification head, resulting in a total training iteration of roughly 9000 for the DCNN model, including transfer learning. The DCNN model could start at an intelligent point and optimize all functional heads. The DCNN obtained 97.5% classification accuracy in "vehicle number estimate and license plate localization.
subject: Articles
  topic: section
subject: ML
  topic: keywords
subject: DCNN
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: Plate image
  topic: keywords
subject: Convolutional Neural Networks (CNN)
  topic: keywords
subject: Segmentation and Image
  topic: keywords
creator: Jinan J. Abdulkareem
creator: Ahmed Sameer Abdulmohsin
creator: Hanan J. Abdulkareem
rights: Copyright (c) 2025 Jinan J. Abdulkareem, Ahmed Sameer Abdulmohsin, Hanan J. Abdulkareem https://creativecommons.org/licenses/by/4.0
date: 2025-10-01
publication date: 2025-10-01
starting page: 111
ending page: 118
doi: 10.70470/EDRAAK/2025/014

Comparative Evaluation of Ensemble Learning Algorithms for Early Detection of Diabetes
type: item
description: Diabetes is a severe metabolic disorder that poses a significant worldwide health challenge. Early detection is crucial to avoid serious complications and reduce healthcare burdens. Despite the rising use of machine learning in medical diagnostics, many studies rely on single algorithms, which limits predictive robustness and effectiveness. This study aims to perform a comparative evaluation of five widely used ensemble learning algorithms, including Random Forest, AdaBoost, XGBoost, LightGBM, and CatBoost on the early-stage diabetes risk prediction dataset. The algorithms have been trained and validated using 10-fold cross-validation, and their performance was assessed with accuracy, precision, recall, F1-score, and AUC. Random Forest outperformed the other models, with an accuracy of 98.46%, an F1-score of 98.75%, and an AUC of 100%, closely followed by LightGBM and CatBoost. The study's findings reveal that ensemble learning approaches, especially Random Forest, demonstrate practical and generalizable predictive performance, providing highly reliable predictions for early diabetes detection that surpass those of other methods. These findings highlight the practical value of ensemble learning in clinical contexts, offering robust, reliable, and interpretable tools to support timely diabetes diagnosis and decision-making.
subject: Articles
  topic: section
subject: Early Diabetes Detection
  topic: keywords
subject: Machine learning
  topic: keywords
subject: Ensemble Learning
  topic: keywords
subject: Classification
  topic: keywords
creator: Abdoul Malik
rights: Copyright (c) 2025 Abdoul Malik https://creativecommons.org/licenses/by/4.0
date: 2025-09-06
publication date: 2025-09-06
starting page: 103
ending page: 110
doi: 10.70470/EDRAAK/2025/013

Classification Arabic language (Classical Arabic Poetry, Al-Hur Arabic Poetry and Prose) Using Machine Learning
type: item
description: Many languages globally have made significant advances in electronically studying and classifying texts. Making electronic text a great alternative to manual classification by saving time, cost, and effort. However, Arabic has not seen similar progress due to several limitations faced by researchers, such as the complexity of the language, the scarcity of related research, and the use of classical Arabic. Additionally, the poetry presents further challenges, like the reliance on a single activation function. This paper introduces a new method for classifying the Arabic text (Prose, classical Arabic poetry and Al-hur Arabic poetry) based on distinctive features that identify the type of Arabic text. Prepressing data is crucial in this approach as it enhances classification accuracy.
subject: Articles
  topic: section
subject: Arabic Text Classification
  topic: keywords
subject: Classical Arabic Poetry
  topic: keywords
subject: Al-Hur Arabic Poetry
  topic: keywords
subject: Machine Learning Algorithms (SVM, LSVC, Naïve Bayes)
  topic: keywords
subject: Natural Language Processing (NLP)
  topic: keywords
creator: Munef Abdullah Ahmed
creator: Raed Abdulkareem Hasan
creator: Mostafa Abdulghafoor Mohammed
creator: Peter Mwangi
creator: Tirus Muya
rights: Copyright (c) 2025 Munef Abdullah Ahmed, Raed Abdulkareem Hasan, Mostafa Abdulghafoor Mohammed, Peter Mwangi, Tirus Muya https://creativecommons.org/licenses/by/4.0
date: 2025-07-04
publication date: 2025-07-04
starting page: 94
ending page: 104
doi: 10.70470/EDRAAK/2025/012

Most Cited AI Research (2024–2025): A Cross-Sector Review
type: item
description: The blistering pace of generative and foundational AI models being deployed in 2024 and 2025 is transforming experiences in education, healthcare, science, sustainability and business. This narrative review consolidates findings from the 50 most cited peer reviewed publications in this time frame, providing a cross-cutting overview on the state of development, the application and the challenges concerning technology. We start by discussing the architectural origins behind both large language models, multimodal generators, as well as domain-specific foundation models including SpectralGPT and scGPT. Then, we evaluate their application on vertical-industrial applications of academic teaching, clinical diagnosis, supply chain operation, and environmental monitoring. The review discusses also important ethical and societal issues, including fairness, explainability, AI angst, and academic responsibility. We also discuss lingering technical challenges including hallucination, data privacy, and availability barriers despite recent progress made. Finally, we discuss some of the emerging frontiers that this work opens and their exciting implications, including controllable generation, symbolic-neural integration, and divergence between open and proprietary model ecosystems. This citation-motivated review provides a timely snapshot of how the most influential research is leading the development of generative AI across domains.
subject: Articles
  topic: section
subject: Generative AI
  topic: keywords
subject: Foundational Models
  topic: keywords
subject: Cross-Sector Applications
  topic: keywords
subject: Ethical Challenges
  topic: keywords
subject: Symbolic-Neural Integration
  topic: keywords
creator: Musaria Karim Mahmood
creator: Ali Rachini
rights: Copyright (c) 2025 Musaria Karim Mahmood, Ali Rachini https://creativecommons.org/licenses/by/4.0
date: 2025-03-20
publication date: 2025-03-20
starting page: 85
ending page: 93
doi: 10.70470/EDRAAK/2025/011

Applications of Geospatial AI in Human Geography and Spatial Networks: A Literature Review
type: item
description: GeoAI is the internationally recognized term that describes the powerful nexus of artificial intelligence, spatial science, and big data analytics presents new and innovative avenues for understanding and addressing the challenges and opportunities faced by human societies at a geographic scale. This literature review aims to consolidate the key applications of GeoAI specifically for human geography and spatial networks, demonstrating its revolutionary potential in fields such as urban planning, population mobility, social network analysis, health geography, or environmental justice. The review describes the methodologies used from machine and deep learning to graph neural networks and the enabling geospatial technologies, which include GIS, remote sensing and spatial databases. Here we summarize our comprehensive review of existing studies, their limitations, challenges in the field such as spatial data bias, absence of ground truth, computation efficiency, ethical issues, and interpretability of AI models. Other emerging approaches such as real-time GeoAI, smart cities and digital twins’ integration, explainable AI and human-centered approaches are also covered. Findings This review highlights the importance of cross-domain collaboration and ethical guidelines to ensure GeoAI technologies not only bring the best technical expertise, but also are responsibly and inclusively embedded into geographic decision-making.
subject: Articles
  topic: section
subject: GeoAI
  topic: keywords
subject: Human Geography
  topic: keywords
subject: Spatial Networks
  topic: keywords
subject: machine learning
  topic: keywords
subject: Smart Cities
  topic: keywords
creator: Huda Jassim Haseeb Saleh
creator: Judith Ratu Tandi Arrang
creator: Luis Miguel Oliveira de Barros Cardoso
rights: Copyright (c) 2025 Huda Jassim Haseeb Saleh, Judith Ratu Tandi Arrang, Luis Miguel Oliveira de Barros Cardoso https://creativecommons.org/licenses/by/4.0
date: 2025-03-15
publication date: 2025-03-15
starting page: 76
ending page: 84
doi: 10.70470/EDRAAK/2025/010

Enhancing Economic Growth Time Series for UAE Forecasting with Deep Learning: A Seq2Seq and Attention-Driven LSTM Approach
type: item
description: This study aims to enhance economic growth forecasting in the United Arab Emirates (UAE) by implementing a Seq2Seq deep learning model with an attention-driven Long Short-Term Memory (LSTM) network. Traditional statistical models often fail to capture the complex temporal dependencies and nonlinear trends inherent in economic time series data. To address these limitations, this research employs a structured methodology, beginning with data collection from the World Bank, including macroeconomic indicators such as GDP growth, inflation, trade balance, investment flows, and employment rates. Preprocessing steps involve handling missing values, normalization, and feature engineering. The proposed Seq2Seq LSTM model utilizes an encoder-decoder structure with an attention mechanism to assign dynamic weights to critical time points, improving forecasting accuracy. The model is trained using the Adam optimizer and evaluated using RMSE, MAE, and MAPE metrics. Results demonstrate superior predictive performance compared to traditional approaches, with improved generalization on unseen data. Findings suggest that attention-enhanced deep learning models provide more reliable economic forecasts, aiding policymakers in decision-making. Future work should explore hybrid models, incorporate external economic shocks, and optimize hyperparameter tuning for further accuracy improvements.
subject: Articles
  topic: section
subject: Economic Forecasting
  topic: keywords
subject: Time Series
  topic: keywords
subject: Seq2Seq Model
  topic: keywords
subject: Attention Mechanism
  topic: keywords
subject: LSTM
  topic: keywords
creator: Ioannis Adamopoulos
creator: Antonios Valamontes
creator: John T. Karantonis
creator: Panagiotis Pantelis Tropaitis
creator: Niki Syrou
rights: Copyright (c) 2025 Ioannis Adamopoulos, Antonios Valamontes, John T. Karantonis, Panagiotis Pantelis Tropaitis, Niki Syrou https://creativecommons.org/licenses/by/4.0
date: 2025-03-02
publication date: 2025-03-02
starting page: 62
ending page: 75
doi: 10.70470/EDRAAK/2025/009

Using Random Forest Algorithm to Improve Investment Decision Making in Damascus Stock Exchange
type: item
description: This research aims to explore the potential of using the Random Forest algorithm to improve investment decision-making in the Damascus Stock Exchange. Google Trends data related to key economic indicators were analyzed to develop a predictive model capable of identifying potential investment opportunities and reducing losses. The results demonstrated the algorithm's effectiveness in classifying investment decisions, achieving 62% accuracy in identifying investment opportunities. The analysis also revealed that search patterns for gold prices played a significant role in predicting investment decisions, emphasizing their importance to the Damascus market.
subject: Articles
  topic: section
subject: Random Forest
  topic: keywords
subject: Google Trends
  topic: keywords
subject: Damascus Stock Exchange
  topic: keywords
subject: Investment Decision-Making
  topic: keywords
subject: Syrian Economy
  topic: keywords
creator: Khder Alakkari
creator: Bushra Ali
rights: Copyright (c) 2025 Khder Alakkari, Bushra Ali https://creativecommons.org/licenses/by/4.0
date: 2025-02-25
publication date: 2025-02-25
starting page: 57
ending page: 61
doi: 10.70470/EDRAAK/2025/008

Deep Learning-Based Neural Network Modeling for Economic Performance Prediction: An Empirical Study on Iraq
type: item
description: This study investigates the application of deep learning-based neural networks for predicting Iraq’s economic performance. Traditional econometric models impose restrictive assumptions that limit their predictive accuracy, especially in volatile economic environments. To overcome these limitations, we propose an artificial neural network (ANN) model trained on six key macroeconomic indicators: Gross Domestic Product (GDP), inflation rate, unemployment rate, exchange rate, trade volume, and government spending. The dataset spans from 2000 to 2023, sourced from authoritative economic institutions. The methodology incorporates feature scaling, hyperparameter tuning, and backpropagation optimization to minimize mean squared error (MSE) and enhance generalization performance. The model is validated through cross-validation and out-of-sample testing. Descriptive statistical analysis highlights the variability of macroeconomic indicators, while the ANN model effectively captures nonlinear dependencies. The results indicate that GDP and government spending are the most influential factors in economic performance prediction, while unemployment rate and exchange rate exhibit lower predictive significance. The model demonstrates superior accuracy compared to traditional regression-based approaches, with minimal error in both training and testing phases. This research contributes to the empirical literature on machine learning in economic forecasting by presenting a robust alternative to conventional predictive models. The findings provide policymakers with valuable insights for designing data-driven economic policies. Future work should explore hybrid models integrating deep learning with traditional econometrics to improve interpretability while maintaining predictive power.
subject: Articles
  topic: section
subject: Deep Learning
  topic: keywords
subject: Economic Forecasting
  topic: keywords
subject: Neural Networks
  topic: keywords
subject: Macroeconomic
  topic: keywords
subject: Prediction
  topic: keywords
creator: Atheel Sabih Shaker
creator: Guma Ali
creator: Wamusi Robert
creator: Habib Hassan
rights: Copyright (c) 2025 Atheel Sabih Shaker, Guma Ali, Wamusi Robert, Habib Hassan https://creativecommons.org/licenses/by/4.0
date: 2025-02-20
publication date: 2025-02-20
starting page: 47
ending page: 56
doi: 10.70470/EDRAAK/2025/007

Machine and Deep Learning Techniques in Cancer Prediction and Risk Stratification Using Bioinformatics in Big Data Era: A Review
type: item
description: Cancer remains a global health challenge, and early edge detection is crucial for improving patient outcomes and survival rates. Traditional diagnostic methods often face limitations in sensitivity and specificity, emphasizing the need to innovate approaches to enhance cancer diagnosis. This paper summarizes the recent applications and methodologies of Machine learning(ML) and/or Deep learning(DL) in bioinformatic for cancer prediction and diagnosis and how can be successfully employed to tackle problems such as patient classification, gene clustering, and biomarkers identification. This review constraints on three types of cancer: prostate, gastric, and colorectal.
subject: Articles
  topic: section
subject: Bioinformatics
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: Cancer
  topic: keywords
subject: Big data
  topic: keywords
creator: Rana Abdulrahman Lateef
rights: Copyright (c) 2024 Rana Abdulrahman Lateef https://creativecommons.org/licenses/by/4.0
date: 2024-10-03
publication date: 2024-10-03
starting page: 118
ending page: 127
doi: 10.70470/EDRAAK/2024/015

Application of Deep Learning and Transfer Learning Techniques for Medical Image Classification
type: item
description: The advancements in deep learning (DL) and transfer learning (TL) have transformed artificial intelligence, particularly in image classification. This research examines the theoretical foundations of DL and TL, focusing on their applications in medical image classification, specifically distinguishing between COVID-19, viral pneumonia, and normal lung conditions. By leveraging GPU-enabled high-performance computing and large labeled datasets, DL models particularly Convolutional Neural Networks (CNNs) such as ResNet50 and VGG16 have achieved superior accuracy compared to traditional machine learning methods. This study explores feature extraction using pre-trained models, the implementation of classifiers like Support Vector Machines (SVM) and K-Nearest Neighbors (KNN), and the integration of multi-view learning techniques, such as early fusion. The results demonstrate the effectiveness of DL and TL in improving classification performance, highlighting their significant potential to advance global healthcare diagnostics.
subject: Articles
  topic: section
subject: Deep Learning (DL)
  topic: keywords
subject: Transfer Learning (TL)
  topic: keywords
subject: Medical Image Classification
  topic: keywords
subject: Convolutional Neural Networks (CNNs)
  topic: keywords
subject: Feature Extraction
  topic: keywords
creator: Tam Sakirin
creator: Rachid Ben Said
rights: Copyright (c) 2025 Tam Sakirin, Rachid Ben Said https://creativecommons.org/licenses/by/4.0
date: 2025-02-12
publication date: 2025-02-12
starting page: 38
ending page: 46
doi: 10.70470/EDRAAK/2025/006

Economic Performance Classification in Iraq (2000–2023): A Statistical Analysis Using Machine Learning with Support Vector Machines and Random Forest
type: item
description: This research investigates the classification of Iraq's economic performance from 2000 to 2023 using machine learning methodologies, specifically Support Vector Machines (SVM) and Random Forest (RF). The study aims to leverage macroeconomic indicators to develop predictive models for economic state classification. The dataset comprises six key variables gross domestic product, inflation rate, unemployment rate, exchange rate, trade volume, and government spending selected for their economic relevance. The methodology employs a composite Economic Performance Index for binary categorization ("Good" or "Poor"), followed by the application of SVM and RF algorithms. Model performance is evaluated using metrics such as AUC, F1-score, precision, recall, and MCC. Results indicate that SVM demonstrates superior classification accuracy compared to Random Forest, highlighting its effectiveness in capturing complex economic relationships. Gross domestic product, inflation, and government spending are identified as the most influential factors. This research contributes to the growing intersection of machine learning and macroeconomic forecasting, providing policymakers with data-driven insights for economic policy evaluation.
subject: Articles
  topic: section
subject: Macroeconomic
  topic: keywords
subject: Policy evaluation
  topic: keywords
subject: Predictive modeling
  topic: keywords
subject: Algorithm comparison
  topic: keywords
subject: Feature importance
  topic: keywords
creator: Hussein Alkattan
creator: Benson Turyasingura
creator: Byamukama Willbroad
creator: Abd Al Karim Jaafar
rights: Copyright (c) 2025 Hussein Alkattan, Benson Turyasingura , Byamukama Willbroad, Abd Al Karim Jaafar https://creativecommons.org/licenses/by/4.0
date: 2025-02-01
publication date: 2025-02-01
starting page: 29
ending page: 37
doi: 10.70470/EDRAAK/2025/005

Deep Learning-Based Time Series Forecasting: A Convolutional Neural Network Approach for Predicting Inflation Trends
type: item
description: This study investigates the application of Convolutional Neural Networks (CNNs) for forecasting inflation trends in Egypt, aiming to enhance the accuracy of economic predictions by capturing complex, non-linear temporal dependencies in time series data. Traditional econometric models, such as ARIMA and VAR, often struggle to model volatile and dynamic economic conditions, prompting the exploration of deep learning techniques. The proposed CNN-based model leverages historical inflation data from 1960 to 2023, sourced from the World Bank, to predict future inflation trends. The methodology involves data preprocessing, feature extraction using convolutional layers, and prediction through fully connected layers, optimized using the Adam optimizer. Performance metrics, including Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and the Coefficient of Determination (R²), demonstrate the model's robustness, with an RMSE of 9.2113 and an R² of 0.8911 on the testing dataset. The results indicate a steady upward trend in inflation from 2024 to 2030, with rates rising from 12.45% to 16.35%, accompanied by widening confidence intervals reflecting increased uncertainty over longer horizons. The study concludes that CNNs offer a reliable framework for inflation forecasting, outperforming traditional methods in capturing non-linear patterns. Recommendations include integrating additional economic indicators and exploring hybrid models to further enhance predictive accuracy. This research contributes to the growing application of deep learning in economic forecasting, providing valuable insights for policymakers and researchers.
subject: Articles
  topic: section
subject: Neural Networks
  topic: keywords
subject: Inflation Forecasting
  topic: keywords
subject: Time Series Analysis
  topic: keywords
subject: Economic Prediction
  topic: keywords
subject: Deep Learning
  topic: keywords
creator: Mostafa Abotaleb
creator: El-Sayed M. El-kenawy
creator: Klodian Dhoska
rights: Copyright (c) 2025 Mostafa Abotaleb, El-Sayed M. El-kenawy, Klodian Dhoska https://creativecommons.org/licenses/by/4.0
date: 2025-01-24
publication date: 2025-01-24
starting page: 19
ending page: 28
doi: 10.70470/EDRAAK/2025/004

Enhancing Point-of-Interest Recommendation Systems through Multi-Modal Data Integration in Location-Based Social Networks: Challenges and Future Directions
type: item
description: The rapid development of location-based networks (LBSNs) has transformed many aspects of tourism and management by changing the way travelers interact with destinations. However, changes in data analysis pose a major challenge to POI computational models, limiting their ability to generate accurate, reliable language and information. This study reviews recent advances in integrating multiple datasets, machine learning algorithms, and artificial intelligence to improve POI recommendations. In particular, it explores integration techniques, opinion analysis, and context-awareness techniques to mitigate data constraints. Furthermore, the study highlights the importance of real-time monitoring and creative strategies in sustainable management, especially in improving user experience and trust. The transparency and reliability of AI-based recommendations are essential for user adoption, optimization, and informed decision-making. This study also demonstrates the integration of spatial, temporal, and spatial information to improve the accuracy of recommendations and address issues such as bias, variability, and change over time. Using artificial intelligence (AI) and machine learning (ML), this paper presents future directions for generating accurate, user-friendly, and adaptive POI recommendations. Addressing data inconsistencies and enabling informed decision-making can lead to robust and sustainable LBSN-based systems, ultimately transforming the tourism experience and services of the region.
subject: Articles
  topic: section
subject: Artificial Intelligence (AI) in Tourism
  topic: keywords
subject: Data Sparsity Solutions
  topic: keywords
subject: System-Based Identification Systems (LBSNs)
  topic: keywords
subject: Consumer Engagement
  topic: keywords
subject: Consumer Trust
  topic: keywords
creator: P.K. Dutta
creator: Anuj Kumar
creator: Şehnaz Sakici
creator: Benneh Mensah
rights: Copyright (c) 2025 P.K. Dutta, Anuj Kumar, Şehnaz Sakici, Benneh Mensah https://creativecommons.org/licenses/by/4.0
date: 2025-01-20
publication date: 2025-01-20
starting page: 12
ending page: 18
doi: 10.70470/EDRAAK/2025/003

Analyzing Temperature Trends in Nouakchott Using ARIMA Modeling
type: item
description: This research examines the temperature inconstancy in Nouakchott through the application of the Autoregressive Coordinates Moving Average ARIMA model for time series estimating Historical climate records traversing from 2000 to 2020 were utilized with 80 of the data apportioned for model preparing and 20 for approval The created ARIMA show was utilized to extend temperature patterns up to 2026 uncovering a steady upward direction in temperature trends The observed historical data displayed considerable seasonal changes and interannual variability while the forecasted trends demonstrate a consistent increase These discoveries offer vital bits of knowledge for climate adjustment techniques and policy development in parched environments The ponder underscores the viability of ARIMA in capturing complex worldly patterns in temperature data illustrating its potential for environmental and climate risk assessment applications.
subject: Articles
  topic: section
subject: Time series analysis
  topic: keywords
subject: ARIMA model
  topic: keywords
subject: temperature forecasting
  topic: keywords
subject: climate variability
  topic: keywords
subject: seasonal trends
  topic: keywords
creator: Mustafa Husham Abbas
creator: Amr Hosny Helal
creator: Amir Salah
rights: Copyright (c) 2024 Mustafa Husham Abbas, Amr Hosny Helal, Amir Salah https://creativecommons.org/licenses/by/4.0
date: 2024-09-10
publication date: 2024-09-10
starting page: 113
ending page: 117
doi: 10.70470/EDRAAK/2024/014

Correlation Analysis of COVID-19 Case Trends in Dakar, Senegal
type: item
description: The COVID-19 pandemic has postured significant challenges around the world, necessitating compelling predictive models for educated decision-making. This study utilizes a regression model to analyze and forecast COVID-19 case trends in Dakar Senegal by utilizing historical case data the demonstrates distinguishes patterns and predicts future case tallies supporting in proactive public health mediations. The results illustrate the model's capacity to capture the increasing trend of diseases with sensible accuracy. This study contributes to scourge modeling endeavors advertising experiences for policymakers to implement convenient preventive measures. Future work may coordinate extra variables such as vaccination rates and portability data to improve predictive accuracy.
subject: Articles
  topic: section
subject: Predictive Analytics
  topic: keywords
subject: COVID-19
  topic: keywords
subject: Dakar
  topic: keywords
subject: CRU data
  topic: keywords
subject: Regression Model
  topic: keywords
creator: Joseph Pascal Sène
creator: Fredrick Kayusi
creator: Fatima Sule Mohammed
creator: Byamukama Willbroad
creator: Chinenye Agnes Ariwaodo
creator: Benard Mugisha
rights: Copyright (c) 2024 Joseph Pascal Sène, Fredrick Kayusi, Fatima Sule Mohammed, Byamukama Willbroad, Chinenye Agnes Ariwaodo, Benard Mugisha https://creativecommons.org/licenses/by/4.0
date: 2024-08-16
publication date: 2024-08-16
starting page: 106
ending page: 112
doi: 10.70470/EDRAAK/2024/013

Machine Learning for Temperature Analysis in Ouagadougou: A Random Forest Perspective
type: item
description: Temperature variety analysis is pivotal for understanding climate patterns and foreseeing future changes especially in parched and semiarid regions This study investigates the application of the RF algorithm for temperature prediction in Ouagadougou Burkina Faso leveraging historical climate data The demonstrate is prepared on Climate Investigate Unit CRU data to evaluate its prescient performance in capturing seasonal and long-term temperature patterns Comparative analysis is conducted to evaluate the viability of RF against conventional forecasting strategies The results show that the RF model illustrates tall predictive accuracy making it a dependable tool for temperature estimating in bone-dry climates These discoveries contribute to climate versatility techniques and decision-making for sustainable natural arranging within the region.
subject: Articles
  topic: section
subject: Random Forest
  topic: keywords
subject: prophet model
  topic: keywords
subject: Arid Climate
  topic: keywords
subject: Predictive Modeling
  topic: keywords
creator: Guma Ali
creator: Wamusi Robert
rights: Copyright (c) 2024 Guma Ali, Wamusi Robert https://creativecommons.org/licenses/by/4.0
date: 2024-08-01
publication date: 2024-08-01
starting page: 101
ending page: 105
doi: 10.70470/EDRAAK/2024/012

Seasonal Temperature Prediction in Niamey: A Prophet Model Approach
type: item
description: Precise seasonal temperature forecasting is basic for climate versatility agrarian planning and vitality administration in parched regions This study presents a data driven approach for foreseeing seasonal temperature trends in Niamey utilizing the Prophet model a vigorous timeseries forecasting method created by Facebook Historical climate data from the Climate Inquire about Unit CRU dataset was analyzed and Prophet was utilized to model long-term temperature patterns The results demonstrate a steady warming trend with temperature variances captured viably by the model The proposed approach illustrates the capability of Prophet in dealing with complex seasonal varieties whereas keeping up high predictive exactness These discoveries give important bits of knowledge for policymakers and partners in climate sensitive divisions Future work may explore hybrid models joining extra climatic factors to improve prescient execution.
subject: Articles
  topic: section
subject: Seasonal temperature prophet model
  topic: keywords
subject: forecasting
  topic: keywords
subject: climate change
  topic: keywords
subject: CRU data
  topic: keywords
subject: machine learning
  topic: keywords
creator: Ahmed T. Al-Hasani
creator: Mohamed Al-Qaraguli
rights: Copyright (c) 2025 Ahmed T. Al-Hasani, Mohamed Al-Qaraguli https://creativecommons.org/licenses/by/4.0
date: 2025-01-14
publication date: 2025-01-14
starting page: 7
ending page: 11
doi: 10.70470/EDRAAK/2025/002

Long Short-term Memory Model for Temperature Forecasting in Khartoum, Sudan
type: item
description: This ponder presents LSTM model to forecast temperature trends for Khartoum Sudan utilizing (CRU) data The dataset spans multiple a long time and has been split into 80 for training and 20 for testing to guarantee strong model evaluation The LSTM model successfully captures complex transient conditions and seasonality patterns within the historical temperature data The results demonstrate a sensible predictive performance with the model appearing an increasing temperature trend up to the year 2026 The discoveries suggest that deep learning models particularly LSTM are appropriate for long-term climate forecasting in bone-dry regions such as Khartoum where temperature changes are significant.
subject: Articles
  topic: section
subject: LSTM model
  topic: keywords
subject: forecasting
  topic: keywords
subject: Khartoum
  topic: keywords
subject: CRU data
  topic: keywords
subject: time series analysis
  topic: keywords
creator: Benson Turyasingura
creator: Fatima Sule Mohammed
creator: Nyagong Santino David Ladu
creator: Gilbert Ituka
creator: Byamukama Willbroad
creator: Mohammed Ahmed Mohammed
rights: Copyright (c) 2024 Benson Turyasingura, Fatima Sule Mohammed, Nyagong Santino David Ladu, Gilbert Ituka, Byamukama Willbroad, Mohammed Ahmed Mohammed https://creativecommons.org/licenses/by/4.0
date: 2024-07-20
publication date: 2024-07-20
starting page: 78
ending page: 83
doi: 10.70470/EDRAAK/2024/010

Multi-view Deep Learning-Based COVID-19 Diagnosis with Chest X-Ray Images: A Comparative Study of SVM and KNN Classifiers
type: item
description: This study focuses on the detection of COVID-19 using chest X-ray images. With the rapid global spread of the highly contagious disease, early and accurate detection is crucial to prevent further transmission. We propose a multi-view approach that leverages different deep-learning methods to detect COVID-19 based on chest X-ray images. The framework presented in this study aims to capture both complementary and correlative information from multiple views. By using pre-trained deep convolutional neural network (CNN) models such as ResNet50 and VGG16 to extract deep features from the X-ray images. Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) algorithms are then employed to classify the X-ray images based on the learned feature representations. The single-view models using VGG16 and ResNet50 with SVM achieved accuracy scores of 97% and 98% respectively. Similarly, using KNN, the single-view models achieved accuracy scores of 89% and 95%. Recognizing the potential of multi-view learning to improve generalization performance. We utilized early fusion to integrate the outputs of the pre-trained models and fed them into SVM and KNN classifiers. The multi-view model achieved accuracy scores of 98% and 92% respectively. The experimental results demonstrate that the multi-view deep learning methods outperformed the single-view deep models in detecting COVID-19 from chest X-ray images. The findings of this study have practical implications as they can assist experts in early diagnosis of COVID-19 cases, highlighting the effectiveness of the proposed methods.
subject: Articles
  topic: section
subject: Deep Learning
  topic: keywords
subject: Transfer Learning
  topic: keywords
subject: Feature Extraction
  topic: keywords
subject: Multi-View Classification
  topic: keywords
subject: SVM
  topic: keywords
creator: Hind abud-allah
rights: Copyright (c) 2024 Hind abud-allah https://creativecommons.org/licenses/by/4.0
date: 2024-07-05
publication date: 2024-07-05
starting page: 59
ending page: 77
doi: 10.70470/EDRAAK/2024/009

Predictive Modeling and Analysis in Genetic Diseases: A Comprehensive Review of Recent Advances
type: item
description: This paper provides a comprehensive analysis of current advancements in predictive modeling and genetic disease classification. We delve into various machine learning techniques and text mining technologies that have significantly contributed to understanding genetic disorders and extracting valuable information from vast unstructured data. Our literature review examines key studies from recent years that have utilized machine learning models, including Naive Bayes, support vector machines, and deep learning frameworks, to improve the predictive accuracy of genetic disease outcomes. This work is aimed at enhancing the framework for predicting complex diseases using advanced computational methods.
subject: Articles
  topic: section
subject: Predictive Modeling
  topic: keywords
subject: Genetic Diseases
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Text Mining
  topic: keywords
subject: Deep Learning
  topic: keywords
creator: Rana Khalid Hamad
rights: Copyright (c) 2024 Rana Khalid Hamad https://creativecommons.org/licenses/by/4.0
date: 2024-06-22
publication date: 2024-06-22
starting page: 49
ending page: 58
doi: 10.70470/EDRAAK/2024/008