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: 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
items: Explainable AI for Healthcare: Training Healthcare Workers to Use Artificial Intelligence Techniques to Reduce Medical Negligence in Ghana’s Public Health Act, 2012 (Act 851)
items: The Era of AI: The Impact of Artificial Intelligence (AI) and Machine Learning (ML) on Financial Stability in the Banking Sector
items: A Review of Approaches and Difficulties in the Use of Deep Learning Techniques for COVID-19 Disease Diagnosis on Chest X-Rays
items: A Comprehensive Review of Data Mining Techniques for Diabetes Diagnosis Using the Pima Indian Diabetes Dataset
items: Steps of Design and Implementation of a Diabetes Classification System Using Decision Tree and Bayesian Theory
items: Fortifying AI Against Cyber Threats Advancing Resilient Systems to Combat Adversarial Attacks
items: Revolutionizing Medical Imaging with Artificial intelligence Real-Time Segmentation for Enhanced Diagnostics
items: Deep Learning-Driven Disease Prediction System in Cloud Environments using a Big Data Approach

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

Explainable AI for Healthcare: Training Healthcare Workers to Use Artificial Intelligence Techniques to Reduce Medical Negligence in Ghana’s Public Health Act, 2012 (Act 851)
type: item
description: This analysis examines whether Ghana’s Public Health Act, 2012 (Act 851) imposes adequate legal responsibilities on healthcare facilities concerning personnel training on artificial intelligence (AI) systems and implementation of medical negligence reduction measures. Through an evaluative review of Act 851 provisions on staff qualifications, technology deployment, quality care, safety planning, and risk management benchmarks relative to precedents in Ghana and other countries, critical gaps in binding regulations to incentivize organizational capacity building for mitigating errors, hazards and liabilities from substandard practices were identified. Key recommendations include amending Act 851 to mandate credentialing assurance frameworks, clinical audits, risk assessment models and transparency requirements around reporting quality indicators. Strengthening policy directives will compel internal monitoring, governance, and accountability among healthcare facilities as multilayered negligence prevention strategies. Scientific contributions highlight deficiencies in Ghana’s health legislation regarding contemporary challenges like AI adoption risks and propose legal reforms to modernize regulations to support safer, responsible healthcare delivery nationwide.
subject: Articles
  topic: section
subject: Explainable AI
  topic: keywords
subject: Healthcare Workers
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: Medical Negligence
  topic: keywords
subject: Ghana’s Public Health Act, 2012
  topic: keywords
creator: George Benneh Mensah
creator: Maad M. Mijwil
creator: Mostafa Abotaleb
creator: Guma Ali
creator: Pushan Kumar Dutta
creator: Toufik Mzili
creator: Marwa M. Eid
rights: Copyright (c) 2025 George Benneh Mensah, Maad M. Mijwil, Mostafa Abotaleb, Guma Ali , Pushan Kumar Dutta, Toufik Mzili, Marwa M. Eid https://creativecommons.org/licenses/by/4.0
date: 2025-01-10
publication date: 2025-01-10
starting page: 1
ending page: 6
doi: 10.70470/EDRAAK/2025/001

The Era of AI: The Impact of Artificial Intelligence (AI) and Machine Learning (ML) on Financial Stability in the Banking Sector
type: item
description: The impact of artificial intelligence and machine learning on financial stability: a socio-legal analysis of the banking sector. The purpose of the present analysis is to identify the impact of artificial intelligence and machine learning on financial stability in the banking sector. The methodology uses the socio-legal model and the technological approach. At the same time, a SWOT analysis is deployed to identify the strengths, weaknesses, opportunities, and threats that artificial intelligence and machine learning carry for the banking industry. The findings suggest a profound potential that these technologies must increase the efficiency, accuracy, and client experience in banking while at the same time providing significant challenges, such as dis-crimination, bias, loss of privacy, and weaker cybersecurity within the sector. Thus, the central message of the analysis is that the future progress of the banking sector with the help of artificial intelligence and machine learning can be achieved if a multi-stakeholder approach that takes full measure of the benefits and risks at the same time is to be implemented. The impact of the analysis is practical – it is a guideline for the policies that will help to implement the development. Meanwhile, the recommendations in terms of the regulatory environment and development of digital literacy and culture can be implemented in the near future. It also affects the academic and theoretical fields, providing an analysis of the question that can be an excellent background for further theoretical developments and policies.
subject: Articles
  topic: section
subject: Artificial intelligence
  topic: keywords
subject: Machine learning
  topic: keywords
subject: Banking
  topic: keywords
subject: Financial stability
  topic: keywords
subject: Sociolegal analysis
  topic: keywords
creator: George Benneh Mensah
creator: Maad M. Mijwil
creator: Mostafa Abotaleb
creator: Sayed M. Tawfeek
creator: Guma Ali
creator: Klodian Dhoska
creator: Ioannis Adamopoulos
rights: Copyright (c) 2024 George Benneh Mensah, Maad M. Mijwil, Mostafa Abotaleb, Sayed M. Tawfeek, Guma Ali, Klodian Dhoska, Ioannis Adamopoulos https://creativecommons.org/licenses/by/4.0
date: 2024-06-16
publication date: 2024-06-16
starting page: 43
ending page: 48
doi: 10.70470/EDRAAK/2024/007

A Review of Approaches and Difficulties in the Use of Deep Learning Techniques for COVID-19 Disease Diagnosis on Chest X-Rays
type: item
description: The global community's healthcare system has faced significant challenges as a result of the COVID-19 outbreak, which has increased demand for prompt and accurate diagnosis. Of these techniques, deep learning-based (DL-based) models have shown a great deal of promise in identifying COVID-19 from medical imaging techniques, especially CT and chest X-ray scans. The latest developments in COVID-19 detection using chest X-ray images are presented in this survey paper. As discussed, various deep learning architectures, including CNN, ResNet, Xception, and hybrid models, have shown promise in accurately classifying cases of COVID-19, pneumonia, and normal lung disease. Furthermore, a comparison is made between the accuracy, sensitivity, specificity, and F1-score level of each individual ADA and ADA with each model. Along with highlighting current drawbacks like data imbalance and overfitting, the paper suggests future development directions to give the model resilience and a range of applications.
subject: Articles
  topic: section
subject: Deep Learning
  topic: keywords
subject: COVID-19 Detection
  topic: keywords
subject: Chest X-ray Imaging
  topic: keywords
subject: Convolutional Neural Networks (CNN)
  topic: keywords
subject: Medical Image Analysis
  topic: keywords
creator: Hind abud-allah
rights: Copyright (c) 2023 Hind abud-allah https://creativecommons.org/licenses/by/4.0
date: 2023-05-04
publication date: 2023-05-04
starting page: 33
ending page: 38
doi: 10.70470/EDRAAK/2023/007

A Comprehensive Review of Data Mining Techniques for Diabetes Diagnosis Using the Pima Indian Diabetes Dataset
type: item
description: Diabetes is a major global health concern, and early diagnosis is crucial for effective management and prevention of complications. This paper presents a comprehensive review of various data mining techniques applied to the diagnosis of diabetes, specifically using the Pima Indian Diabetes dataset. The Pima Indian dataset, a widely used benchmark in diabetes research, contains information on various health-related features such as age, body mass index, insulin levels, and glucose concentration, among others, which are crucial for predicting the onset of diabetes. The review explores a range of classification algorithms, including decision trees, support vector machines (SVM), logistic regression, k-nearest neighbors (KNN), and artificial neural networks (ANNs), discussing their performance, strengths, and limitations in predicting diabetes. In order to increase the models' accuracy and efficiency, the paper also emphasizes the importance of preprocessing procedures like feature selection, data cleaning, and normalization. It also contrasts the evaluation metrics accuracy, precision, recall, F1-score, and area under the ROC curve (AUC) that are used to evaluate the performance of these models. We hope that this review will shed light on the best data mining methods for diagnosing diabetes, with a focus on scalability, interpretability, and model optimization. The findings of this review suggest that while traditional techniques like decision trees and logistic regression are effective, more complex models such as support vector machines and neural networks tend to yield higher prediction accuracy. However, the trade-off between model complexity and interpretability remains a key challenge in the deployment of these techniques for clinical decision-making. The paper concludes by suggesting future directions for improving diabetes diagnosis through the integration of advanced machine learning methods and big data analytics.
subject: Articles
  topic: section
subject: Diabetes diagnosis
  topic: keywords
subject: data mining
  topic: keywords
subject: Pima Indian Diabetes dataset
  topic: keywords
subject: classification algorithms
  topic: keywords
subject: machine learning
  topic: keywords
creator: Hadeel M Saleh
rights: Copyright (c) 2024 Hadeel M Saleh https://creativecommons.org/licenses/by/4.0
date: 2024-04-15
publication date: 2024-04-15
starting page: 39
ending page: 42
doi: 10.70470/EDRAAK/2024/006

Steps of Design and Implementation of a Diabetes Classification System Using Decision Tree and Bayesian Theory
type: item
description: An effective tool for the early identification and diagnosis of diabetes mellitus is intended to be developed through the design and implementation of a diabetes categorization system utilizing decision trees and Bayesian theory. To increase classification accuracy and dependability, this system integrates two potent machine learning techniques: Bayesian classifiers and decision trees. While Bayesian theory offers a probabilistic framework that improves decision-making under uncertainty, decision trees provide an understandable way to represent decision-making based on input attributes. The system starts with data preprocessing, which includes feature selection, normalization, and handling missing values. Next, decision tree methods like ID3 or C4.5 are applied to create classification models based on patient attributes. The categorization is then further refined by incorporating prior knowledge and uncertainty using Bayesian techniques, especially Naive Bayes.. Performance parameters like accuracy, precision, recall, and F1-score are assessed while testing the system using publically accessible diabetic datasets. The results show that the hybrid model, which combines Bayesian theory with decision trees, provides a reliable solution for diabetes categorization and offers a promising way for doctors to more accurately diagnose diabetes. Furthermore, real-time data can be added to the system to enable ongoing observation and forecasting.
subject: Articles
  topic: section
subject: Diabetes mellitus
  topic: keywords
subject: Classification System
  topic: keywords
subject: Decision Trees
  topic: keywords
subject: Bayesian Theory
  topic: keywords
creator: Hadeel M Saleh
rights: Copyright (c) 2023 Hadeel M Saleh https://creativecommons.org/licenses/by/4.0
date: 2023-04-17
publication date: 2023-04-17
starting page: 27
ending page: 32
doi: 10.70470/EDRAAK/2023/006

Fortifying AI Against Cyber Threats Advancing Resilient Systems to Combat Adversarial Attacks
type: item
description: The emerging complexity of the threats which the safer world faces as of now, let alone the adversary attacks, presents great troubles to the orthodox systems of cybersecurity. This research focuses on the use of AI to design and develop robust systems that prevent such shifting threats. As a result, the proposed adaptive defense mechanisms utilize RL in this investigation to contend with real-time emergent attack patterns. At the same time, Ensemble Methods are applied to improve the anomaly detection non-sensitive approaches by using several machine learning models in order to minimize the false positives and maximize the true positives. The utility of the proposed framework is assessed by using benchmarking data sets such as NSL-KDD and CICIDS 2017 to represent various adversarial settings. The results shown in the study show that RL is able to manage dynamic threats successfully with high detection ratio and low response time. Ensemble Methods supplement this by strengthening the reliability of detection and shortening the error margin. The study reveals that AI has implications for optimizing the possibilities of cybersecurity systems, as well as for the defense systems themselves, as the implementation of flexible and fundamentally suitable approaches is already possible at the present stage.
subject: Articles
  topic: section
subject: AI
  topic: keywords
subject: Cybersecurity
  topic: keywords
subject: Adversarial Attacks
  topic: keywords
subject: Reinforcement Learning
  topic: keywords
subject: Adaptive Defense
  topic: keywords
creator: Lal Hussain
rights: Copyright (c) 2024 Lal Hussain https://creativecommons.org/licenses/by/4.0
date: 2024-03-05
publication date: 2024-03-05
starting page: 26
ending page: 31
doi: 10.70470/EDRAAK/2024/004

Revolutionizing Medical Imaging with Artificial intelligence Real-Time Segmentation for Enhanced Diagnostics
type: item
description: Machine Intelligent or AI has become a proved tool with high accuracy and efficiency in medical imaging diagnoses. Thus, this paper aims at developing and analyzing the feasibility of using AI-based real-time image segmentation based on models such as Vision Transformers (ViT) and Convolutional Neural Networks (CNN). Previous attempts on segmentation problems have focused on CNNs, but the self-attention approach in ViT poses a distinct possibility since this archetypal model captures global contexts in images and may be particularly beneficial when dealing with challenging medical data. To assess the effectiveness of these approaches, publicly available datasets including ISIC for skin lesion segmentation and BraTS for brain tumor analysis are used. These datasets are highly challenging because of their shapes, as well as the different image resolutions of objects and their overlapping areas, so they are perfect for evaluating segmentation models. The presented models are trained with TensorFlow and PyTorch, and the accuracy is evaluated in terms of intersection over union (IoU) and Dice coefficient. However, the time required to process one image to analyze the results is taken with a view of establishing real-time applicability. Experiments show that with ViT, the segmentation accuracy is higher than that of CNN and the Dice Score is higher by 0.15 while the computation time is lower by 30%. Bath and space-party enhancements to TOF and MI allow quicker, more accurate diagnoses to be returned to the radiologist and reduce the likelihood of errors. In addition, the paper demonstrates that ViT-based models are resilient to variability in medical imaging tasks while maintaining high accuracy and effectiveness. This study proves the capabilities of AI in healthcare advancement when ViT becomes a part of clinical practice. We will leave future work for the exploration of the mix of models, such as CNN-ViT in order to fine-tune the results for specific diagnostic .
subject: Articles
  topic: section
subject: AI
  topic: keywords
subject: Vision Transformers
  topic: keywords
subject: CNN
  topic: keywords
subject: Medical Imaging
  topic: keywords
subject: Diagnostic Accuracy
  topic: keywords
creator: Jungpil Shin
rights: Copyright (c) 2024 Jungpil Shin https://creativecommons.org/licenses/by/4.0
date: 2024-02-18
publication date: 2024-02-18
starting page: 18
ending page: 25
doi: 10.70470/EDRAAK/2024/003

Deep Learning-Driven Disease Prediction System in Cloud Environments using a Big Data Approach
type: item
description: Disease detection has gained great significance in the big data cloud systems as health organizations try to accrue manifold benefits from massive amounts of data to achieve better outcomes in patients. The ability to detect diseases with better efficiency can achieve timely interventions, better resource allocations, and enhanced treatment strategies. However, there are several challenges with the implementation of such systems regarding the integration of different kinds of data, handling imbalanced datasets, and ensuring that real-time processing is capable of supporting clinical decisions. This paper addresses these challenges by proposing a novel disease prediction model on a cloud environment, incorporating the strengths of both Recurrent Neural Networks and Convolutional Neural Networks. The proposed RNN+CNN-based model can effectively capture temporal dependencies and spatial features from different data sources, like medical images and time-series patient data. This model has given improved results through extensive experimentation with different key evaluation metrics like accuracy, precision, recall, F1-score, and AUC-ROC, which are quite higher than other disease prediction systems. Besides, the proposed model is optimized for light speed in inference times, reaching an incredible processing speed of 0.4 seconds per prediction. Thus, the Big Data approach provided within the cloud infrastructure will ensure the predictability and accessibility without bounded local computational resources, opening advanced predictive analytics capabilities to healthcare practitioners.
subject: Articles
  topic: section
subject: Big Data Systems
  topic: keywords
subject: Disease Prediction
  topic: keywords
subject: Cloud
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: Machine Learning
  topic: keywords
creator: E. Srividhya
creator: Jose P
creator: V. Anusuya
creator: K. Jaya Deepthi
creator: P. Gopalsamy
creator: S. Gopalakrishnan
rights: Copyright (c) 2024 E. Srividhya, Jose P, V. Anusuya, K. Jaya Deepthi, P. Gopalsamy, S. Gopalakrishnan https://creativecommons.org/licenses/by/4.0
date: 2024-01-23
publication date: 2024-01-23
starting page: 8
ending page: 17
doi: 10.70470/EDRAAK/2024/002