Template-Type: ReDIF-Article 1.0 Author-Name: John Phan Author-X-Name-First: John Author-X-Name-Last: Phan Author-Name: Hung-Fu Chang Author-X-Name-First: Hung-Fu Author-X-Name-Last: Chang Title: Machine learning models based on financial data for stock trend predictions Abstract: This paper investigates the application of long short-term memory (LSTM), one-dimensional convolutional neural networks (1D CNN), and logistic regression (LR), for predicting stock trends based on fundamental analysis. This research emphasises a company's financial statements and its intrinsic value for stock price trend forecasting. Using a dataset of 269 data points from publicly traded companies across various sectors from 2019 to 2023, we employ key financial ratios and the discounted cash flow (DCF) model for two tasks: annual stock price difference (ASPD) and difference between current stock price and intrinsic value (DCSPIV). Assessing the likelihood of profitability from relationship between financial data and price action, and the current discrepancy between 'true value' and market price, respectively. Our results demonstrate that LR models outperform CNN and LSTM models, achieving an average test accuracy of 74.66% for ASPD and 72.85% for DCSPIV, highlighting the benefits for portfolio managers in their decision-making processes. Journal: Int. J. of Complexity in Applied Science and Technology Pages: 1-14 Issue: 1 Volume: 2 Year: 2026 Keywords: stock trend prediction; fundamental analysis; machine learning; CNN; LSTM; logistic regression. File-URL: http://www.inderscience.com/link.php?id=151883 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcast:v:2:y:2026:i:1:p:1-14 Template-Type: ReDIF-Article 1.0 Author-Name: Rupesh Dulal Author-X-Name-First: Rupesh Author-X-Name-Last: Dulal Author-Name: Rabin Dulal Author-X-Name-First: Rabin Author-X-Name-Last: Dulal Title: Brain tumour identification using improved YOLOv8 Abstract: Accurately identifying the extent of brain tumours remains a major challenge in brain cancer treatment, primarily due to the difficulty in detecting tumour boundaries from MRI scans. Manual detection is time-consuming and requires expert knowledge. In this study, we propose a modified YOLOv8 model for precise brain tumour detection in MRI images. We replaced the traditional non-maximum suppression (NMS) with a real-time detection transformer (RT-DETR) to eliminate hand-designed filtering. Additionally, we integrated ghost convolution to reduce computational costs while maintaining accuracy, and introduced a vision transformer block in the backbone to enhance context-aware feature extraction. The model was trained and tested on a publicly available brain tumour dataset. Experimental results show that our modified YOLOv8 outperforms the original YOLOv8 and other popular object detectors including faster R-CNN, mask R-CNN, YOLOv3-v5, SSD, RetinaNet, EfficientDet, and DETR, achieving a mAP@0.5 of 0.91. Journal: Int. J. of Complexity in Applied Science and Technology Pages: 15-38 Issue: 1 Volume: 2 Year: 2026 Keywords: brain tumour detection; deep learning; attention; transformer; YOLOv8. File-URL: http://www.inderscience.com/link.php?id=151885 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcast:v:2:y:2026:i:1:p:15-38 Template-Type: ReDIF-Article 1.0 Author-Name: Andrew Kiruluta Author-X-Name-First: Andrew Author-X-Name-Last: Kiruluta Title: FourierNAT: a Fourier-mixing-based non-autoregressive transformer for parallel sequence generation Abstract: We present <i>FourierNAT</i>, a novel non-autoregressive transformer (NAT) architecture that leverages Fourier-based mixing in the decoder to generate output sequences in parallel. While traditional NAT approaches often face challenges in capturing global dependencies, our method uses a discrete Fourier transform with learned frequency-domain gating to mix token embeddings across the entire sequence dimension. This design enables efficient propagation of context without explicit autoregressive steps. Empirically, F<SUB align="right"><SMALL>OURIER</SMALL></SUB>NAT achieves competitive results on WMT14 En-De and CNN/DailyMail benchmarks, highlighting that frequency-domain operations can mitigate coherence gaps often associated with NAT generation. Our approach underscores the potential of integrating spectral-domain operations to accelerate and improve parallel text generation. Journal: Int. J. of Complexity in Applied Science and Technology Pages: 63-75 Issue: 1 Volume: 2 Year: 2026 Keywords: non-autoregressive transformer: NAT; Fourier mixing; parallel sequence generation; global spectral operations; NAT architecture. File-URL: http://www.inderscience.com/link.php?id=151886 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcast:v:2:y:2026:i:1:p:63-75 Template-Type: ReDIF-Article 1.0 Author-Name: Sushmita Kumari Author-X-Name-First: Sushmita Author-X-Name-Last: Kumari Author-Name: Kamlesh Kumar Author-X-Name-First: Kamlesh Author-X-Name-Last: Kumar Author-Name: Ashutosh Gaurav Author-X-Name-First: Ashutosh Author-X-Name-Last: Gaurav Title: A comprehensive review of machine learning techniques for detecting fraud in banking and payment services Abstract: In today's digital age, fraud detection in financial services has become essential due to the rapid growth and complexity of online transactions. Machine learning techniques are widely used to detect unusual activities in real time. This paper focuses on fraud in banking and payment services and proposes a three-stage review framework: formulating research questions, defining the research methodology, and analysing existing literature. The review reveals that supervised and unsupervised learning algorithms, such as Naïve Bayes, K-nearest neighbours, deep learning, support vector machine, decision tree, artificial neural network, XGBoost, and AdaBoost, are commonly applied for fraud detection. These models are evaluated using metrics like precision, recall, and F-score. Ensemble methods that combine multiple algorithms are also shown to improve detection accuracy. Finally, the review highlights future research directions, especially the need to strengthen wallet payment systems by developing more robust and efficient fraud detection algorithms to ensure secure digital transactions. Journal: Int. J. of Complexity in Applied Science and Technology Pages: 76-96 Issue: 1 Volume: 2 Year: 2026 Keywords: fraud detection; mobile payment; machine learning; unsupervised learning; supervised learning. File-URL: http://www.inderscience.com/link.php?id=151887 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcast:v:2:y:2026:i:1:p:76-96 Template-Type: ReDIF-Article 1.0 Author-Name: Md. Zahurul Haque Author-X-Name-First: Md. Zahurul Author-X-Name-Last: Haque Author-Name: Mimuza Tazvia Author-X-Name-First: Mimuza Author-X-Name-Last: Tazvia Author-Name: Afreen Sultana Kuna Author-X-Name-First: Afreen Sultana Author-X-Name-Last: Kuna Title: PreStroke_ML: a machine learning approach to heat stroke prediction Abstract: Heatstroke is an increasingly critical public health concern, intensified by rising global temperatures and the growing frequency of extreme heat events. This study addresses the urgent need for timely and accurate heatstroke risk prediction by leveraging machine learning techniques. The primary objective is to develop a predictive model capable of identifying individuals at risk based on environmental and physiological data. An extensive dataset of 81,215 instances and 69 features underwent thorough preprocessing and analysis. Four machine learning algorithms - decision tree, random forest, logistic regression, and light gradient boosting machine (LightGBM) - were implemented and evaluated. Among these, LightGBM achieved the highest accuracy of 99.93%, demonstrating superior predictive performance and generalisation capability, as validated through confusion matrices and training-validation accuracy curves. Feature selection played a crucial role in optimising model effectiveness. The findings underscore the potential of machine learning as a valuable tool in predictive healthcare. Future work will focus on integrating real-time sensor data, enabling personalised risk assessments, and deploying a mobile-based alert system to enhance heatstroke prevention. This research contributes to proactive public health strategies through an AI-driven framework for early detection and intervention. Journal: Int. J. of Complexity in Applied Science and Technology Pages: 97-107 Issue: 1 Volume: 2 Year: 2026 Keywords: heatstroke prediction; AI in healthcare; public health; early warning system; health risk prevention. File-URL: http://www.inderscience.com/link.php?id=151888 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcast:v:2:y:2026:i:1:p:97-107 Template-Type: ReDIF-Article 1.0 Author-Name: Takang Achuo Albert Enow Author-X-Name-First: Takang Achuo Albert Author-X-Name-Last: Enow Author-Name: Hermine Bille Ngalle Author-X-Name-First: Hermine Bille Author-X-Name-Last: Ngalle Author-Name: Mangaptche Eddy Leonard Ngonkeu Author-X-Name-First: Mangaptche Eddy Leonard Author-X-Name-Last: Ngonkeu Title: Graph cuts segmentation enhances machine-assisted plant disease diagnosis under tight data constraints Abstract: Effective plant disease diagnosis is key to sustainable farming, but data scarcity remains a significant hurdle. This research examined machine learning models trained on very small datasets enhanced by varied image processing techniques to identify the most reliable approach. The Random Walk Segmented dataset initially seemed more promising, excelling in both precision and accuracy. However, its performance faltered with a random-like ROC-AUC score, suggesting unreliability. In contrast, the Graph Cuts Segmented dataset, despite trailing in precision and accuracy, demonstrated greater consistency with a higher ROC-AUC score. These results highlight the critical need to use diverse metrics for evaluating machine learning models, emphasising that reliability cannot rely solely on accuracy. The study sheds light on advancing plant disease diagnosis in environments constrained by limited data, paving the way for more robust solutions tailored to resource-scarce contexts. Journal: Int. J. of Complexity in Applied Science and Technology Pages: 39-62 Issue: 1 Volume: 2 Year: 2026 Keywords: data scarcity; plant disease diagnosis; machine learning; graph cuts segmentation; random walk. File-URL: http://www.inderscience.com/link.php?id=151909 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcast:v:2:y:2026:i:1:p:39-62