International Journal of Advances in Artificial Intelligence and Machine Learning
The International Journal of Advances in Artificial Intelligence and Machine Learning (IJAAIML) is a prominent academic journal dedicated to publishing cutting-edge research and developments in the fields of Artificial Intelligence (AI) and Machine Learning (ML). It serves as an essential platform for researchers, practitioners, and professionals worldwide to share innovative ideas, technologies, and empirical studies that contribute to advancing AI and ML. The journal emphasizes both theoretical advancements and practical applications, showcasing how these technologies are shaping various industries, including healthcare, finance, education, robotics, and autonomous systems. IJAAIML covers a wide range of topics within AI and ML, such as deep learning, neural networks, natural language processing (NLP), computer vision, robotics, data mining, reinforcement learning, and AI ethics. The journal is open to diverse types of scholarly contributions, including original research articles, review papers, case studies, technical notes, and surveys. It encourages submissions that introduce novel algorithms, methodologies, and systems, as well as those addressing challenges and proposing new approaches in AI and ML. This broad scope allows the journal to remain at the forefront of technological innovation, providing valuable insights into the latest trends and developments in the field. The journal maintains high academic standards through a rigorous peer-review process, ensuring that each published article is of exceptional quality and originality. Submissions are evaluated by experts in relevant fields based on their significance, innovation, methodology, and clarity. This commitment to quality makes IJAAIML a trusted source of information for a diverse audience, including academic researchers, industry professionals, AI practitioners, and students who seek to stay informed about the latest advances in AI and ML. IJAAIML is committed to global knowledge dissemination, making its publications accessible to researchers and professionals worldwide through its online platform. This approach fosters knowledge exchange and collaboration across borders, enabling the journal to reach a broad international audience. By highlighting state-of-the-art research that addresses real-world problems using AI and ML technologies, IJAAIML plays a significant role in advancing the understanding and application of these technologies. Additionally, the journal explores the ethical, societal, and economic impacts of AI and ML, promoting discussions on responsible AI practices and future directions. By contributing to these conversations, IJAAIML not only advances technological innovation but also encourages the development of AI and ML in a manner that considers broader implications for society. Overall, the International Journal of Advances in Artificial Intelligence and Machine Learning stands as a crucial resource for anyone involved in AI and ML, driving forward the frontiers of these dynamic fields through high-quality, peer-reviewed research.
Articles
27 Documents
<![CDATA[Predicting Thyroid Cancer Recurrence Using Machine Learning: An Artificial Intelligence Approach to Clinical Oncology ]]>
<![CDATA[Aifuobhokhan, Joy]]>;
<![CDATA[Hussain, Ahmad Khalid]]>;
<![CDATA[Ekechi, Chijioke Cyriacus]]>;
<![CDATA[Olanrewaju, Aisha Olasunbo]]>;
<![CDATA[Afuadajo, Emmanuel]]>;
<![CDATA[Bowale, Deborah Adetola]]>;
<![CDATA[Inioluwa, Oluwadare Marvellous]]>
<![CDATA[ International Journal of Advances in Artificial Intelligence and Machine Learning Vol. 2 No. 3 (2025): International Journal of Advances in Artificial Intelligence and Machine Learni ]]>
Publisher : <![CDATA[CV Media Inti Teknologi ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.58723/ijaaiml.v2i3.469
<![CDATA[Background of study: Differentiated thyroid cancer (DTC) accounts for most thyroid malignancies and has favorable survival outcomes, yet up to 30% of patients experience recurrence, placing strain on follow-up systems in resource-limited settings. Conventional staging tools offer limited predictive precision. With increasing interest in machine learning (ML) for precision oncology, there is a need for interpretable, deployable models suitable for low-resource environments.Aims and scope of paper: To develop and validate an interpretable machine learning model for predicting thyroid cancer recurrence and assess its feasibility for deployment in constrained clinical settings, including African oncology contexts.Methods: A retrospective dataset of 383 DTC patients with at least 10-year follow-up was sourced from the UCI Machine Learning Repository. Thirteen demographic, clinical, and treatment-related predictors were included. Data preprocessing involved encoding, scaling, and class balancing using SMOTE. Logistic Regression, Random Forest, K-Nearest Neighbors, and Extreme Gradient Boosting (XGBoost) were trained with hyperparameter tuning via grid search and cross-validation. Performance was evaluated using accuracy, precision, recall, F1 score, and AUC-ROC.Result: XGBoost achieved the best performance with 97% accuracy, 95% recall, 94% precision, and an AUC-ROC of 0.93. The most influential predictors were age, smoking status, T and M staging, ATA risk category, and adenopathy. The final model was deployed as a browser-based decision support tool to enable real-time recurrence risk estimation.Conclusion: This study presents a high-performing and interpretable ML model for predicting DTC recurrence, demonstrating feasibility for use in low-resource oncology settings. External validation with African clinical datasets and integration into electronic health systems is recommended to enhance equity and clinical uptake.]]>
<![CDATA[Toward Human-Level Artificial Intelligence: Technological Foundations and Trajectories ]]>
<![CDATA[Aljawad , Ammar]]>
<![CDATA[ International Journal of Advances in Artificial Intelligence and Machine Learning Vol. 2 No. 3 (2025): International Journal of Advances in Artificial Intelligence and Machine Learni ]]>
Publisher : <![CDATA[CV Media Inti Teknologi ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.58723/ijaaiml.v2i3.487
<![CDATA[Background of study: Human-Level Artificial Intelligence (HLAI) represents one of the most formidable dreams in laptop technology, aiming to create structures able to cognition, perception, and reasoning similar to people. Despite splendid development in slender AI programs, the development of Artificial General Intelligence (AGI) remains confined by conceptual, technical, and moral demanding situations.Aims and scope of paper: This paper pursuits to analyze the theoretical foundations, technological enablers, and ethical dimensions of HLAI. It seeks to distinguish HLAI from slender AI and to make clear the clinical, engineering, and societal problems concerned in constructing structures with human-like intelligence.Methods: The have a look at adopts a conceptual and literature-based totally method, synthesizing insights from synthetic intelligence, cognitive science, neuroscience, and ethics. Key frameworks and latest research are reviewed to pick out common concepts, technological developments, and unresolved challenges shaping the evolution of HLAI.Result: The evaluation highlights essential enablers which include device gaining knowledge of, herbal language processing, laptop vision, and robotics as important pathways in the direction of HLAI. Findings screen that at the same time as development in these domain names is good sized, reaching popular intelligence calls for deeper integration of cognitive modeling, neural architectures, and moral alignment mechanisms.Conclusion: The study shows that collaboration across sectors and designs with principles and safety in mind are important steps to create artificial brains that are robust and reliable. This underlines the need for ethical governance and adaptation of human values to cushion the risk associated with uncontrolled AI development.]]>
<![CDATA[Enhancing SQL Code Security and Maintainability: A Deep Learning Based Approach ]]>
<![CDATA[Alghamdi, Faisal]]>;
<![CDATA[Ben Ammar, Boulbaba]]>
<![CDATA[ International Journal of Advances in Artificial Intelligence and Machine Learning Vol. 2 No. 3 (2025): International Journal of Advances in Artificial Intelligence and Machine Learni ]]>
Publisher : <![CDATA[CV Media Inti Teknologi ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.58723/ijaaiml.v2i3.515
<![CDATA[Background of study: SQL injection attacks continue to pose a significant risk to online systems. Traditional rule-based detection regularly fails to identify emerging or disguised attack vectors. Deep learning holds significant promise for robust detection, yet few studies rigorously compare model types or examine how to convey detection results as actionable security advice for developers.Aims and scope of paper: Building on this gap in existing research, this study tests three deep learning models for detecting SQL injection: Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory (BiLSTM), and DistilBERT. The best model is then utilized in a tool that provides developers with risk assessments, warnings about unsafe patterns, and examples of secure queries.Methods: To achieve this, a dataset of 30,919 labeled SQL queries was preprocessed using normalization, syntax validation, and stratified splitting (70/15/15). A dual tokenization approach enabled fair comparisons between architectures. Models were trained using Adam/AdamW optimizers and evaluated for accuracy, precision, recall, F1-score, AUC-ROC, and MCC.Result: Among the tested models, DistilBERT set the performance benchmark, achieving 99.8% accuracy, 99.9% precision, 99.5% recall, and a false positive rate of just 0.1%. CNN and BiLSTM showed strong results, but proved weaker against obfuscated or distributed attacks. The SQL Security Advisor system converts model predictions directly into actionable guidance for developers.Conclusion: In conclusion, our findings indicate that DistilBERT detects SQL injections more effectively than CNN and BiLSTM, particularly when attacks are complex or hidden. By combining detection, explanation, and repair, this approach helps bring research closer to real-world use and supports developers in building more secure systems.]]>
<![CDATA[Diabetic Retinopathy Prediction Using Deep Learning: Insights From CNN ]]>
<![CDATA[Sirisati, Ranga Swamy]]>;
<![CDATA[Navyasri, V.]]>;
<![CDATA[Swathi, T.]]>;
<![CDATA[Akhila, M.]]>;
<![CDATA[Astried, Astried]]>
<![CDATA[ International Journal of Advances in Artificial Intelligence and Machine Learning Vol. 2 No. 3 (2025): International Journal of Advances in Artificial Intelligence and Machine Learni ]]>
Publisher : <![CDATA[CV Media Inti Teknologi ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.58723/ijaaiml.v2i3.420
<![CDATA[Background of study: Diabetic Retinopathy (DR) is a severe microvascular complication of diabetes mellitus that can lead to vision loss if not detected early. With over 93 million individuals affected globally, the need for accurate and efficient diagnostic systems has become urgent. Traditional screening methods depend on manual interpretation of fundus images by ophthalmologists, which is time-consuming and prone to subjectivity.Aims: This research seeks to create and assess a deep learning diagnostic model designed to reliably identify the severity levels of diabetic retinopathy using retinal fundus images. The research also explores model interpretability using Shapley Additive Explanations (SHAP) to increase transparency in AI-assisted medical diagnosis.Methods: Convolutional Neural Networks (CNNs) were implemented using transfer learning with pretrained architectures such as ResNet50 and InceptionV3. The EyePACS dataset, containing images categorized into five DR severity levels, was used for model training. Preprocessing techniques, including contrast enhancement, histogram equalization, and data augmentation, improved image quality and model generalization. The models were optimized with the Adam and assessed through accuracy, precision, recall, F1-score, and AUC. Additionally, SHAP analysis was employed to interpret and illustrate the model’s predictions.Results: The proposed CNN-based model achieved 98.5% accuracy, with a sensitivity and specificity of 0.99, demonstrating strong performance across multiple DR stages. Comparison with existing studies revealed a notable improvement in diagnostic accuracy. SHAP visualizations confirmed that critical retinal features such as microaneurysms, hemorrhages, and cotton-wool spots were key predictors influencing model decisions.Conclusion: The findings validate the efficacy of deep learning, particularly CNNs, in enhancing early detection and classification of diabetic retinopathy. The integration of SHAP interpretability bridges the gap between AI predictions and clinical trust, making this approach a promising tool for large-scale automated DR screening and supporting ophthalmologists in timely diagnosis and treatment.]]>
<![CDATA[User-Friendly Interface and Comprehensive Features for Hostel Management ]]>
<![CDATA[Kolan, Helini]]>;
<![CDATA[Mungi, Keerthana]]>;
<![CDATA[Somayajula, Lekhana]]>;
<![CDATA[Achanta, Harshitha]]>;
<![CDATA[Edulakanti, Vaishnavi]]>;
<![CDATA[Haryanto, Haryanto]]>
<![CDATA[ International Journal of Advances in Artificial Intelligence and Machine Learning Vol. 2 No. 3 (2025): International Journal of Advances in Artificial Intelligence and Machine Learni ]]>
Publisher : <![CDATA[CV Media Inti Teknologi ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.58723/ijaaiml.v2i3.456
<![CDATA[Background of study: Hostel administration in academic institutions has traditionally relied on manual processes such as paper-based record keeping, in-person registration, and ad-hoc maintenance communication. These fragmented practices often lead to inefficiencies, delays, miscommunication, and data inaccuracy. As student populations grow and operational demands increase, institutions require modernized systems that integrate automation, usability, and real-time information management to improve service delivery and resource allocation. However, existing solutions frequently lack comprehensive features, scalability, or user-centric design, indicating a clear gap in the availability of accessible and robust digital hostel management platforms.Aims: This study aims to design and implement a user-friendly, web-based Hostel Management System (HMS) that consolidates key administrative operations including student registration, room allocation, maintenance reporting, and occupancy tracking within a unified interface. The scope encompasses database design, workflow automation, interface usability, security provisions, and system evaluation through functional demonstrations.Methods: The system was developed following an Agile methodology, enabling iterative refinement based on user feedback. Dataset acquisition involved collecting student, room, facility, and maintenance information, followed by preprocessing steps such as data cleaning, normalization, and categorization to ensure accuracy. The architecture employed modular design principles, a web-based interface for multi-device accessibility, and security measures such as encrypted storage and role-based access control. Functional testing, integration testing, and user acceptance trials validated system performance and reliability.Result: The implemented HMS successfully automated core hostel processes improved real-time data access, and significantly reduced manual workload for administrative staff. Features such as automated room allocation, maintenance request tracking, virtual hostel viewing, and dashboard-based monitoring demonstrated high usability and operational effectiveness. User feedback indicated enhanced transparency, faster response times, and improved overall efficiency in hostel management.Conclusion: The proposed system provides a scalable, secure, and intuitive solution that modernizes hostel operations. By integrating comprehensive features within a user-friendly platform, the HMS enhances administrative productivity and student satisfaction. Its modular architecture and cloud-ready design position it for future enhancements, including AI-driven analytics, mobile integration, and predictive resource planning.]]>
<![CDATA[Pixelcraft: AI-Powered Artistic Innovation ]]>
<![CDATA[Fouziya, MD]]>;
<![CDATA[Sruthi, Avula]]>;
<![CDATA[Prathina , Paila]]>;
<![CDATA[Sushma, Parsha]]>;
<![CDATA[Rithika, Madas]]>;
<![CDATA[Wibawa, Helmie Arif]]>
<![CDATA[ International Journal of Advances in Artificial Intelligence and Machine Learning Vol. 2 No. 3 (2025): International Journal of Advances in Artificial Intelligence and Machine Learni ]]>
Publisher : <![CDATA[CV Media Inti Teknologi ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.58723/ijaaiml.v2i3.460
<![CDATA[Background of study: Recent breakthroughs in Artificial Intelligence (AI) have significantly advanced text-to-image generation, enabling machines to convert natural language descriptions into realistic visual outputs. Stable Diffusion has emerged as a promising solution, offering high-fidelity results with improved controllability and accessibility. To leverage these strengths, this study introduces PixelCraft, an AI-powered text-to-image generation system designed to support creative, educational, and industrial applications.Aims: The purpose of this paper is to design, develop, and evaluate PixelCraft, an intuitive AI system that generates coherent images from textual prompts using Stable Diffusion.Methods: PixelCraft integrates a Stable Diffusion pipeline implemented using Hugging Face libraries and wrapped in a Tkinter-based graphical interface for seamless user interaction. The system processes user prompts, executes diffusion-based denoising stages, and outputs generated images that can be viewed and saved. A structured evaluation was conducted using widely accepted performance metrics, including CLIP similarity scores, Fréchet Inception Distance (FID), and Structural Similarity Index Measure (SSIM). Comparative analyses were performed against models such as BigGAN, VQ-VAE-2, and DALL·E-2.Result: Experimental findings show that PixelCraft achieves strong semantic alignment and visual coherence, yielding an average CLIP score of 0.95, an FID score of ~15, and an SSIM of 0.91. These results outperform several benchmark models, demonstrating superior consistency across both simple and moderately complex prompts.Conclusion: PixelCraft effectively demonstrates Stable Diffusion's ability to generate high-quality images from natural-language descriptions. The system provides a practical, accessible platform for artists, educators, and digital content creators, significantly reducing barriers associated with traditional design tools.]]>
<![CDATA[A Web-based Decision Support Platform for Student Performance Prediction using Machine Learning ]]>
<![CDATA[AlOtaibi, Nadiah Fahad]]>
<![CDATA[ International Journal of Advances in Artificial Intelligence and Machine Learning Vol. 2 No. 3 (2025): International Journal of Advances in Artificial Intelligence and Machine Learni ]]>
Publisher : <![CDATA[CV Media Inti Teknologi ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.58723/ijaaiml.v2i3.542
<![CDATA[Background of study: Students confront more complicated academic decisions, ranging from course selection to study planning, but lack individualized, data-driven support. While machine learning has shown potential in forecasting performance, most educational technologies are institution-specific, technically obscure, or isolated from real-time student demands.Aims: This project develops, assesses, and implements a web-based Decision Support Platform that uses machine learning to provide students with individualized, real-time academic success forecasts and practical advice.Methods: The study tested seven regression models using RMSE, MAE, and R² on a 20% holdout set. Nineteen behavioral, socioeconomic, and academic characteristics were preprocessed, and the most important predictors were statistically rated. The dataset included 10,000 high school pupils. The best-performing model was incorporated into a dynamic React-Flask web interface to enable real-time prediction.Result: Among the tested models, LightGBM outperformed all other options with the best prediction accuracy (R2 = 0.730, RMSE = 1.954). Prior scores, study hours, and attendance were important predictors. With a sub-second latency, the deployed platform was able to produce real-time predictions based on user input.Conclusion: In conclusion, our findings indicate that academic planning may become insight-driven rather than intuition-based with the use of LightGBM-powered decision assistance. This initiative bridges the gap between educational machine learning research and equitable, real-world effect by putting predictive analytics in the hands of students, enabling them to make proactive, well-informed decisions about their academic futures.]]>
