Engineering and Technology Journal

Lung and colon cancers are two of the most common and deadly tumors around the world, creating significant public health concerns. Artificial intelligence (AI) and machine learning (ML) have heavily improved cancer research, particularly in early detection, histopathological analysis, and personalized therapy planning. However, despite their remarkable accuracy, ML models sometimes lack transparency, making explainability crucial in medical applications. Although various machine learning (ML)-based classifications for cancer models exist, their interpretation is not understood. The current research overcomes the diagnostic gap by developing a highly accurate system that uses XAI (Explainable Artificial Intelligence) methods to clarify its predictions. We used Kaggle's LC25000 dataset, which included histology images for lung and colon tumors in humans. To determine the best cancer classification strategy, we tested various machine learning algorithms, including Random Forest, Decision Tree, Support Vector Machine (SVM), and Extreme Gradient Boosting. Furthermore, XAI approaches such as LIME (Local Interpretable Model-Agnostic Explanations) and SHAP (Shapley Additive Explanations) were utilized to evaluate model predictions and identify important information affecting classification outcomes. XGBoost confirmed that it was useful in identifying colon and lung cancer by achieving the highest accuracy of 99.80% among the models used. Also, XAI techniques offered useful information on the most significant features. SHAP analysis highlighted LBP and color histogram features as key for distinguishing lung and colon tissues, while LIME confirmed their importance by identifying critical image regions influencing predictions

This study systematically investigates the viscoelastic characteristics and damping performance of polymer composites, examining the interplay between filler morphology, matrix-filler interactions, and structural behavior. The purpose is to understand and predict how different fillers influence key viscoelastic properties to enable tailored composite design. Methods and Key Findings: Dynamic mechanical analysis (DMA) and forced vibration tests were used to characterize temperature- and frequency-dependent viscoelastic properties, including storage modulus (E′), loss modulus (E′′), and damping factor (tanδ). Key results demonstrate distinct effects: Spherical calcium carbonate increased stiffness (E′) by 45% at 15 wt% loading but restricted damping (tan δ) due to agglomeration-induced stress concentrations. In contrast, core-shell rubber particles increased tan δ by 280% through interfacial slip, achieving a damping ratio (ζ) of 0.052 (2.8 times higher than neat epoxy). Nanoclay composites exhibited frequency-dependent damping anisotropy from processing alignment. Hybrid filler systems showed synergistic damping effects within the 10–50 Hz range. Optimal performance occurred at 5 wt% Al₂O₃, balancing moderate stiffness (E′ = 1.5 GPa) with peak damping (tan δ = 0.82). Microstructural analyses (SEM/AFM) correlated maximized interfacial friction and damping with an agglomerate area fraction <10%. A validated multi-scale computational model (<7% error) successfully bridged nanoscale mechanisms to macroscale performance. Significance and Applications: This work provides a predictive framework for designing next-generation composites. It enables tailored material design—prioritizing damping for applications like automotive NVH systems or stiffness for aerospace components—advancing fundamental knowledge of composite viscoelasticity and offering practical strategies for industrial vibration mitigation

Brain tumors are among the most serious neurological diseases, posing significant diagnostic challenges due to their diverse nature and complexity imbalance in medical imaging datasets. To address these challenges, machine learning (ML) has demonstrated high potential in brain tumor classification; nevertheless, its overall performance can suffer when minority classes are underrepresented. This study examines the impact of the Synthetic Minority Over-sampling Technique (SMOTE) on MRI brain tumor classification using handcrafted features, including Gray Level Co-occurence Matrix (GLCM), Histogram of Oriented Gradients (HOG), and their combination (HOG + GLCM), with three classifiers, namely, Logistic Regression (LR), Support Vector Machines (SVM), and k-Nearest Neighbours (KNN). Experiments were carried out using two publicly available datasets. For Dataset 1, SVM with GLCM increased from 55.21 to 57.40% (full SMOTE), but LR with HOG + GLCM increased from 52.4 to 53.49%. For Dataset 2, fold-wise SMOTE increased LR with GLCM from 69.85 to 70.60% and SVM with HOG increased from 92.87 to 93.10% compared to complete SMOTE. The results show that the SMOTE's effect is dependent on feature type, classifier, and augmentation strategy, with fold-wise typically boosting generalization while avoiding information leaking. These findings validate SMOTE as a viable method for improving the type overall performance in imbalanced medical imaging tasks, particularly for weaker texture-based descriptors

Artificial intelligence has introduced both unprecedented capabilities and novel vulnerabilities into database environments, enabling highly adaptive attacks that can evade traditional defenses. This review surveys recent research published between 2022 and 2025 on AI-assisted database security threats and synthesizes the literature to develop a comprehensive taxonomy of emerging attack approaches. We identified five primary classes of AI-based attacks: intelligent
SQL injection attacks, adversarial machine learning strategies targeting database
security systems, data poisoning attacks on AI-based databases, automated
reconnaissance exploits, and sociotechnical manipulations aimed at database
administrators. We systematically reviewed publications on cyber defense stored
in IEEE Xplore, ACM Digital Library, Science Direct, and Scopus databases.
Boolean search terms were used on the databases specific to cyber defense.
Findings indicate that automated SQL injection attacks can escalate the bypass rate
of security systems to over 85% effectiveness. The effectiveness of rule-based
defense systems degrades by 32% when pitted against sophisticated AI-adapted
adversarial attacks. Conversely, machine learning-based defenses maintain a
detection rate of 85 to 95%. To combat advancing techniques, a multilayer
approach that includes adversarial training, anomaly-based intrusion detection,
and automated user behavior analysis and reporting technology should be
employed. This approach utilizes anomaly-based defenses through a monitoring
model. Analysis shows that conventional database defense techniques need to be
upgraded with real-time analytics, dynamic response mechanisms, and zero-day
vulnerability protection to keep pace with the increasingly sophisticated nature of
AI adversarial attacks on database systems.

Recycling rare earth elements, specifically neodymium from magnet scrap, is crucial for advancing sustainable technologies across various industries. In this research, neodymium was recovered by solvent extraction using a stock solution prepared in 200 mL by dissolving 1 g of neodymium (III) nitrate hexahydrate, Nd(NO3)3.6H2O, in 0.5 M nitric acid. Then, it was mixed with 1 M di-(2-ethylhexyl) phosphoric acid (D2EHPA) in Isopar-L, as the organic phase, and 3 M H2SO4, as the aqueous phase, with an organic/aqueous volume ratio of (1:1). Specifically, the study examined the pH variation of the aqueous phase (from 0.5 to 2). The pH of the solution was measured using a pen-type pH meter, where the stripping agent was HNO3 at a concentration of 6 M. The experiments were conducted using a magnetic stirrer at ambient temperature with an agitation speed of 300 rpm for 24 hours. The concentration of metals was measured using an EDX. The extraction efficiency of neodymium increased from 51.7% at a pH of 0.5 to 82.02% at a pH of 1.5, with a slight decrease observed at pH values of 1.7 and 2. Moreover, samples of Mg-Nd alloy were manufactured with various extractant Nd contents of (1, 3, 4, and 5%). The microstructure of the alloys, both before and after corrosion in 3.5% NaCl, was examined using a scanning electron microscope (SEM). The results indicated that the alloys consisted primarily of the α-Mg phase and the Mg12Nd phase, and that the corrosion resistance increased with the increasing amount of neodymium.