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.

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.

Background of study: Blood cell analysis is vital for diagnosing medical conditions, but traditional manual methods are laborious and error-prone. Deep learning, especially YOLO models, offers automated solutions for medical image analysis. However, the real-world effectiveness of the latest YOLOv11 in blood cell detection is not thoroughly investigated, as general object detection improvements may not translate to biomedical images due to their unique characteristics.Aims and scope of paper: This study systematically compares YOLOv10 and YOLOv11 on a public blood cell detection dataset to assess if YOLOv11's advancements provide tangible benefits for blood cell classification. The goal is to identify the most effective model for accurate and efficient detection in microscopic images, guiding AI-driven diagnostic tool selection.Methods: Both models were trained and tested under identical conditions using the Kaggle Blood Cell Detection Dataset (RBCs, WBCs, Platelets). Images were resized to 640x640 pixels. Performance metrics included mAP (mAP@50 and mAP@50–95), Precision, Recall, F1-score, speed, model complexity, and training time.Result: YOLOv11n consistently showed higher accuracy (mAP50: 0.9279 vs. 0.9120; mAP50-95: 0.6524 vs. 0.6347), particularly for RBCs and WBCs. However, YOLOv11n had longer inference (11.35 ms/image) and postprocessing times (8.64 ms/image) compared to YOLOv10n (7.00 ms/image and 0.90 ms/image). YOLOv11n trained faster (0.311 hours vs. 0.375 hours), with a smaller model size (5.5 MB vs. 5.8 MB), fewer parameters, and reduced computational complexity.Conclusion: YOLOv11n offers superior accuracy and improved training efficiency, making it suitable for medical image object detection where precision is paramount. The increased inference and postprocessing times indicate a performance-speed trade-off. Model selection should balance these factors based on deployment context.

Background of study: Food image classification is a challenging task in computer vision, particularly when dealing with traditional food items that exhibit subtle visual variations. While Convolutional Neural Networks (CNNs) have long been the standard for image recognition, their limitation in capturing long-range dependencies has led to the emergence of Vision Transformers (ViTs). In this context, the classification of Indonesian traditional cakes offers a culturally rich yet complex problem for automated image recognition systems.Aims and scope of paper: This study aims to conduct a comparative analysis between EfficientNet-B0 (CNN-based) and ViT-B/16 (Transformer-based) architectures in classifying eight categories of Indonesian traditional cakes. The research evaluates not only classification accuracy but also the strengths and limitations of each model in handling fine-grained visual distinctions.Methods: Both models were fine-tuned using the “Kue Indonesia” dataset from Kaggle. The methodology includes image preprocessing, model training with consistent parameters, and evaluation using accuracy, precision, recall, and F1-score. A confusion matrix was also used to visualize misclassifications and analyze per-class performance.Result: ViT-B/16 achieved slightly higher accuracy (96.25%) compared to EfficientNet-B0 (95.62%). ViT performed better in classes with subtle variations, such as kue lapis and kue dadar gulung, while EfficientNet-B0 showed superior efficiency and high accuracy on visually distinct cakes.Conclusion: Both CNN and ViT models demonstrate strong performance in traditional food classification. ViT is more robust in fine-grained visual analysis, whereas EfficientNet-B0 is preferable for resource-constrained environments. This study highlights the role of AI in supporting digital preservation of culinary heritage.

Background of study: The widespread adoption of Large Language Models (LLMs) raises concerns about biases that affect fairness and credibility. As LLMs affect areas such as recruitment and customer service, systematic quantitative analysis is essential to identify and mitigate these biases.Aims and scope of paper: This research investigates demographic bias in LLM quantitatively by analyzing sentiment polarity scores across different demographic categories. The goal is to provide a statistically confirmed analysis of sentiment bias and propose mitigation methods, focusing on GPT-4, LLaMA-2, Claude, and BLOOM.Methods: Quantitative analysis was performed on GPT-4, LLaMA-2, Claude, and BLOOM using sentiment and demographic data. Sentiment polarity assessments for gender and racial/ethnic groups were obtained with VADER and TextBlob. Demographic Disparity Score, ANOVA, and Cohen's Kappa assessed the significance and appropriateness of bias. Inter-rater reliability between automated tools and human annotators was also evaluated.Result: Sentiment bias was found in all models, varying by gender and race, particularly in GPT-4 and Claude. Sentiment scores were consistently higher for queries pertaining to females than those pertaining to males across all models, with GPT-4 and Claude showing the largest differences. Claude also showed racial sentiment alignment, favoring queries relating to white people over black people. ANOVA confirmed statistically significant sentiment variation by demographics across all models. High inter-rater reliability validated the sentiment analysis.Conclusion: This study shows demographic bias in GPT-4, LLaMA-2, Claude, and BLOOM, with different sentiment trends across demographic classifications. The models showed more positive sentiment for female questions and a trend towards certain racial groups. These findings indicate an embedded bias in the training data, which raises ethical concerns. Identifying and addressing these biases is critical to ensuring fairness and credibility in real-world LLM applications.

Fingerprint recognition technology is integral to biometric security systems, providing secure and reliable identification through unique human fingerprint patterns. However, challenges such as low contrast, high intra-class variability, and partial fingerprints often compromise the efficiency and accuracy of traditional recognition systems. This research addresses these challenges by employing advanced deep learning techniques, specifically Convolutional Neural Networks (CNNs), to enhance fingerprint recognition performance. We propose a methodological approach that leverages state-of-the-art CNN architectures tailored to capture intricate fingerprint details. The study utilizes the Sokoto Coventry Fingerprint Dataset (SOCOFing), which includes diverse fingerprint types and synthetic alterations to evaluate model performance under realistic conditions. Through a comparative analysis of various CNN configurations, we assessed the models based on efficiency and accuracy, using metrics such as accuracy, precision, recall, and F1-score. Our experimental results demonstrate significant improvements in fingerprint recognition capabilities. The optimized CNN model achieved an accuracy of 98.61%, a precision of 97.12%, a recall of 97.46%, and an F1-score of 97.29%. These results validate the effectiveness of CNNs in handling complex biometric data and underscore their potential to enhance the reliability and security of fingerprint recognition systems. The study concludes that deep learning, through the use of CNNs, offers a powerful solution to the limitations of traditional fingerprint recognition techniques. This will pave the way for more sophisticated and accurate biometric security systems in practical applications. The research findings contribute to ongoing advancements in neural network architectures, enhancing their applicability in increasingly automated and data-driven security environments.