Amyotrophic Lateral Sclerosis (ALS) is a progressive neurodegenerative disorder that presents significant diagnostic challenges due to its heterogeneous clinical manifestations and symptom overlap with other neurological conditions. Early and accurate diagnosis is critical for initiating timely interventions and improving patient outcomes. Traditional diagnostic approaches rely heavily on clinical expertise and manual interpretation of neuroimaging data, such as structural MRI, Diffusion Tensor Imaging (DTI), and functional MRI (fMRI), which are inherently time-consuming and prone to interobserver variability. Recent advances in Artificial Intelligence (AI) and Deep Learning (DL) have demonstrated potential for automating neuroimaging analysis, yet existing models often suffer from limited generalizability across modalities and datasets. To address these limitations, we propose a Transformer-augmented deep learning ensemble framework for automated ALS diagnosis using multi-modal neuroimaging data. The proposed architecture integrates Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Vision Transformers (ViTs) to leverage the complementary strengths of spatial, temporal, and global contextual feature representations. An adaptive weighting-based fusion mechanism dynamically integrates modality-specific outputs, enhancing the robustness and reliability of the final diagnosis. Comprehensive preprocessing steps, including intensity normalization, motion correction, and modality-specific data augmentation, are employed to ensure cross-modality consistency. Evaluation using 5-fold cross-validation on a curated multi-modal ALS neuroimaging dataset demon-strates the superior performance of the proposed model, achieving a mean classification accuracy of 94.5% ± 0.7%, precision of 93.9% ± 0.8%, recall of 92.9% ± 0.9%, F1-score of 93.4% ± 0.7%, spec-ificity of 97.4% ± 0.6%, and AUC-ROC of 0.968 ± 0.004. These results significantly outperform baseline CNN models and highlight the potential of transformer-augmented ensembles in complex neurodiagnostic applications. This framework offers a promising tool for clinicians, supporting early and precise ALS detection and enabling more personalized and effective patient management strategies.

Chronic diseases such as chronic kidney disease (CKD), diabetes, and heart disease remain major causes of mortality worldwide, highlighting the need for accurate and interpretable diagnostic models. However, conventional machine learning methods often face challenges of limited generalization, feature redundancy, and class imbalance in medical datasets. This study proposes an integrated classification framework that unifies three complementary feature paradigms: classical tabular attributes, deep latent features extracted through an unsupervised Long Short-Term Memory (LSTM) encoder, and quantum-inspired features derived from a five-qubit circuit implemented in PennyLane. These heterogeneous features are fused using a feature-wise attention mechanism combined with an AdaBoost classifier to dynamically weight feature contributions and enhance decision boundaries. Experiments were conducted on three benchmark medical datasets—CKD, early-stage diabetes, and heart disease—under both balanced and imbalanced configurations using stratified five-fold cross-validation. All preprocessing and feature extraction steps were carefully isolated within each fold to ensure fair evaluation. The proposed hybrid model consistently outperformed conventional and ensemble baselines, achieving peak accuracies of 99.75% (CKD), 96.73% (diabetes), and 91.40% (heart disease) with corresponding ROC AUCs up to 1.00. Ablation analyses confirmed that attention-based fusion substantially improved both accuracy and recall, particularly under imbalanced conditions, while SMOTE contributed minimally once feature-level optimization was applied. Overall, the attention-guided AdaBoost framework provides a robust and interpretable approach for clinical risk prediction, demonstrating that integrating diverse quantum, deep, and classical representations can significantly enhance feature discriminability and model reliability in structured medical data.

Earthquake damage prediction is essential to improve preparedness and resilience in seismically active regions such as Nepal. This study investigates the application of machine learning models to predict the grade of building damage during seismic events, using building attributes from the 2015 Gorkha earthquake dataset. We employed various algorithms, including Random Forest with both Random Oversampling and SMOTE, Multilayer Perceptron (MLP), Support Vector Machine (SVM), Gradient Boosting, and XGBoost, to tackle class imbalance in damage grades and enhance predictive accuracy. Key building attributes such as construction materials, number of floors, and building age were analyzed to forecast the severity of the damage, which was categorized into three grades. Our findings indicate that the Random Forest model, particularly with SMOTE oversampling, outperformed other techniques, achieving a macro F1-score of 0.67. Both Random Oversampling and SMOTE significantly improved performance over the imbalanced baseline, with Random Oversampling achieving a macro F1-score of 0.65. However, all models faced challenges in accurately predicting Grade 2 damage, underscoring the need for further refinement to address intermediate damage levels. The study highlights the strengths and limitations of various machine learning approaches and emphasizes the significance of feature selection and data balancing methods in improving earthquake damage prediction. It offers valuable insights for policymakers and engineers aiming to enhance earthquake preparedness and develop resilient infrastructure.

This paper examines current trends in Artificial Intelligence (AI), with a particular emphasis on the application of the Python programming language in educational settings. The existing literature reveals a gap in reviews regarding Python's role in AI applications within the education sector. This paper highlights and describes the most effective and popular Python libraries, particularly those relevant to AI in educational settings, that have emerged in recent years. Additionally, it presents basic and widely used AI applications in education that utilize Python. The research method employed is a state-of-the-art review, which involves searching the literature using specific keywords and applying inclusion and exclusion criteria for quality and reliability. In the findings, synthesis, and discussion section, results are presented in tables to facilitate use by researchers and practitioners, allowing for easier application. The tables include information on the most well-known and frequently used Python libraries and AI applications, the grouping categories of Python libraries, their tasks and functions, as well as how Python libraries can be applied in education. Moreover, the paper explores AI methods that enhance teaching and learning using Python tools and connects learning theories with these AI methods. During the discussion, the paper provides an analysis to assist new researchers and practitioners, especially those working with AI in education, on how to effectively leverage AI through Python. It also emphasizes the overall benefits that Python brings to the field of AI in education. The paper also offers various suggestions for addressing gaps in the existing literature.

Tomato crop yields face significant threats from plant diseases, with existing deep learning solutions often computationally prohibitive for resource-constrained agricultural settings; to address this gap, we propose Efficient Disease Attention Network (EDANet), a novel lightweight architecture combining depthwise separable convolutions with hybrid attention mechanisms for efficient Tomato disease recognition. Our approach integrates channel and spatial attention within hierarchical blocks to prioritize symptomatic regions while utilizing depthwise decomposition to reduce parameters to only 104,043 (multiple times smaller than MobileNet and EfficientNet). Evaluated on ten tomato disease classes from PlantVillage, EDANet achieves 97.32% accuracy and exceptional (~1.00) micro-AUC, with perfect recognition of Mosaic virus (100% F1-score) and robust performance on challenging cases like Early blight (93.2% F1) and Target Spot (93.6% F1). The architecture processes 128×128 RGB images in ~23ms on standard CPUs, enabling real-time field diagnostics without GPU dependencies. This work bridges laboratory AI and practical farm deployment by optimizing the accuracy-efficiency tradeoff, providing farmers with an accessible tool for early disease intervention in resource-limited environments.

The daily exchange of informatics over the Internet has both eased the widespread proliferation of resources to ease accessibility, availability and interoperability of accompanying devices. In addition, the recent widespread proliferation of smartphones alongside other computing devices has continued to advance features such as miniaturization, portability, data access ease, mobility, and other merits. It has also birthed adversarial attacks targeted at network infrastructures and aimed at exploiting interconnected cum shared resources. These exploits seek to compromise an unsuspecting user device cum unit. Increased susceptibility and success rate of these attacks have been traced to user's personality traits and behaviours, which renders them repeatedly vulnerable to such exploits especially those rippled across spoofed websites as malicious contents. Our study posits a stacked, transfer learning approach that seeks to classify malicious contents as explored by adversaries over a spoofed, phishing websites. Our stacked approach explores 3-base classifiers namely Cultural Genetic Algorithm, Random Forest, and Korhonen Modular Neural Network – whose output is utilized as input for XGBoost meta-learner. A major challenge with learning scheme(s) is the flexibility with the selection of appropriate features for estimation, and the imbalanced nature of the explored dataset for which the target class often lags behind. Our study resolved dataset imbalance challenge using the SMOTE-Tomek mode; while, the selected predictors was resolved using the relief rank feature selection. Results shows that our hybrid yields F1 0.995, Accuracy 0.997, Recall 0.998, Precision 1.000, AUC-ROC 0.997, and Specificity 1.000 – to accurately classify all 2,764 cases of its held-out test dataset. Results affirm that it outperformed bench-mark ensembles. Result shows the proposed model explored UCI Phishing Website dataset, and effectively classified phishing (cues and lures) contents on websites.

This study examines the application of Educational Data Mining (EDM) to predict the academic per-formance of first-year students at the Catholic University of Bukavu and the Higher Institute of Edu-cation (ISP) in the Democratic Republic of Congo. The primary objective is to develop a model that can identify at-risk students early, providing the university with a tool to enhance student support and academic guidance. To address the challenges posed by data imbalance (where successful cases outnumber failures), the study adopts a hybrid methodological approach. First, the SMOTE algorithm was applied to balance the dataset. Then, a stacking classification model was developed to combine the predictive power of multiple algorithms. The variables used for prediction include the National Exam score (PEx), the secondary school track (Humanities), and the type of prior institution (public, private, or religious-affiliated schools), as well as age and sex. The results demonstrate that this approach is highly effective. The model is not only capable of predicting success or failure but also of forecasting students' performance levels (e.g., honors or distinctions). Moreover, the use of the Apriori association rule mining algorithm allowed the identification of faculty-specific success profiles, transforming prediction into an interpretable decision-support tool. This research makes several significant contributions. Practically, it provides the University of Bukavu with a tool for student orientation and early risk detection. Methodologically, it illustrates the effectiveness of a combined approach to EDM in an African context. However, the study acknowledges certain limitations, including the non-public nature of the data and the geographical specificity of the sample. It therefore proposes avenues for future research, such as the integration of Explainable AI (XAI) techniques for more refined and transparent analysis of the results.

Depression remains one of the most serious mental health challenges worldwide and is often underdiagnosed due to social stigma and limited access to medical services. With the proliferation of social media as a medium for self-expression, these platforms provide new opportunities for early detection of depressive symptoms through digital footprints. However, most prior research has primarily focused on linguistic features derived from text, overlooking behavioral dynamics that also reflect psychological states. To address this gap, we propose Depress-HybridNet, a hybrid deep learning framework that integrates linguistic embeddings with behavioral activity patterns. The architecture combines a BERT-BiLSTM encoder for linguistic feature extraction, a multi-layer perceptron for behavioral feature encoding, and an adaptive attention-based fusion mechanism to integrate multimodal signals optimally. Experiments conducted on the publicly available Kaggle Depression Dataset demonstrate that Depress-HybridNet consistently outperforms strong baselines, including fine-tuned BERT, achieving state-of-the-art results (F1 = 0.92, AUC = 0.93). A further ablation study highlights the critical role of behavioral features and the attention fusion layer in improving performance. In addition, clinical validation by licensed psychologists confirmed a high degree of consistency between the model’s predictions and expert judgment, underscoring its real-world applicability. These findings underscore the importance of modelling depression as a multifaceted phenomenon, rather than a purely linguistic task, and establish a reproducible benchmark for future research.

The integration of Internet of Things (IoT) with cloud computing has revolutionized healthcare systems, offering scalable and real-time patient monitoring. However, optimizing response times and energy consumption remains crucial for efficient healthcare delivery. This research evaluates various algorithmic approaches for workload migration and resource management within IoT cloud-based healthcare systems. The performance of the implemented algorithm in this research, Hybrid Dynamic Programming and Long Short-Term Memory (Hybrid DP+LSTM), was analyzed against other six key algorithms, namely Gradient Optimization with Back Propagation to Input (GOBI), Deep Reinforcement Learning (DRL), improved GOBI (GOBI2), Predictive Offloading for Network Devices (POND), Mixed Integer Linear Programming (MILP), and Genetic Algorithm (GA) based on their average response time and energy consumption. Hybrid DP+LSTM achieves the lowest response time (82.91ms) with an energy consumption of 2,835,048 joules per container. The outcome of the analysis showed that Hybrid DP+LSTM have significant response times improvement, with percentage increases of 89.3%, 79.0%, 83.8%, 97.0%, 99.8%, and 99.94% against GOBI, GOBI2, DRL, POND, MILP, and GA, respectively. In terms of energy consumption, Hybrid DP+LSTM outperforms other approaches, with GOBI2 (3,664,337 joules) consuming 29.3% more energy, DRL (2,973,238 joules) consuming 4.9% more, GOBI (4,463,010 joules) consuming 57.4% more, POND (3,310,966 joules) consuming 16.8% more, MILP (3,005,498 joules) consuming 6.0% more, and the GA (3,959,935 joules) consuming 39.7% more. The result of ablation of the Hybrid DP+LSTM model achieves a 47.05% improvement over DP-only (156.57ms) and a 70.64% improvement over LSTM-only (282.41ms) in response time. On the energy efficiency side, Hybrid DP+LSTM shows 22.80% improvement over LSTM-only (3,671,51 joules), but 7.34% underperformance compared to DP-only (2,640,93). These research findings indicate that the Hybrid DP+LSTM technique provides the best trade-off between response time and energy efficiency. Future research should further explore hybrid approaches to optimize these metrics in IoT cloud-based healthcare systems.

Accurate classification of dry bean varieties is essential for enhancing agricultural productivity, ensuring seed quality, and supporting market standardization. Traditional classification methods are time-consuming, labor-intensive, and prone to error, particularly when dealing with morphologically similar varieties. While recent data-driven approaches have shown promising performances, they often suffer from limited varietal representation, inadequate handling of high-resolution image data, and suboptimal dimensionality reduction techniques. To address these challenges, this study proposes a novel hybrid classification pipeline that integrates a fine-tuned InceptionV3 for low- and high-level feature extraction, Principal Component Analysis (PCA) for efficient dimensionality reduction, and a Support Vector Machine (SVM) for final classification. The model was evaluated on a comprehensive dataset combining 14 dry bean varieties from Dogan et al. with five locally sourced varieties, totaling 19 distinct classes. Extensive experimental results showed that the proposed model achieved an accuracy of 94.00% and 80.01% on benchmark and combined datasets, respectively, outperforming state-of-the-art approaches.