International Journal of Complexity in Applied Science and Technology (7 papers in press)

Regular Issues

• Machine Learning Models based on Financial Data for Stock Trend Predictions  
  by John Phan, Hung-Fu Chang 
  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 companys 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.
  Keywords: Stock Trend Prediction; Fundamental Analysis; Machine Learning; CNN; LSTM; Logistic Regression.
  DOI: 10.1504/IJCAST.2025.10069470
   
  
• Brain tumour identification using improved YOLOv8  
  by Rupesh Dulal, Rabin Dulal 
  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.
  Keywords: brain tumour detection; deep learning; attention; transformer; YOLOv8.
  DOI: 10.1504/IJCAST.2025.10071167
   
  
• FourierNAT: a Fourier-mixing-based non-autoregressive transformer for parallel sequence generation  
  by Andrew Kiruluta 
  Abstract: We present FourierNAT, 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, FourierNAT 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.
  Keywords: non-autoregressive transformer: NAT; Fourier mixing; parallel sequence generation; global spectral operations; NAT architecture.
  DOI: 10.1504/IJCAST.2025.10071491
   
  
• A comprehensive review of machine learning techniques for detecting fraud in banking and payment services  
  by Sushmita Kumari, Kamlesh Kumar, Ashutosh Gaurav 
  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.
  Keywords: fraud detection; mobile payment; machine learning; unsupervised learning; supervised learning.
  DOI: 10.1504/IJCAST.2025.10072040
   
  
• A comparative analysis of optimisation methods for classification on various datasets  
  by Simanta Das, Soumitra Das 
  Abstract: Optimisation studies how a variety of mathematical structures can be analysed through the minimisation or maximisation of a function. In deep learning (DL), optimisation encompasses everything from hyperparameter tuning to weight and bias adjustment until the convergence of a loss or cost function (J), such that the model’s performance metrics and reliability in classification and regression are increased. Over the last few years, stochastic gradient methods and their variants - or adaptive gradient methods - have become very popular, with varying levels of success or otherwise. This study provides a neat comparison of adaptive gradient methods with respect to their accuracies and cross-entropy loss (CEL) in the mentioned tasks; it tested nine optimisation algorithms across three CNN architectures on MNIST, Fashion-MNIST, and CIFAR-10 datasets over 30 epochs. The overall top-performing optimisers were SGD, RMSProp, Adam, and Nadam, whereas Adagrad and Adadelta consistently performed lower.
  Keywords: adaptive gradient methods; optimisation methods; convolutional neural networks; CNNs; MNIST; FashionMNIST; CIFAR10.
  DOI: 10.1504/IJCAST.2025.10072041
   
  
• PreStroke_ML: a machine learning approach to heat stroke prediction  
  by Md. Zahurul Haque, Mimuza Tazvia, Afreen Sultana Kuna 
  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 trainingvalidation 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.
  Keywords: heatstroke prediction; AI in healthcare; public health; early warning system; health risk prevention.
  DOI: 10.1504/IJCAST.2025.10072080
   
  
• A survey of identification and forecasting of healthcare fraud through machine learning  
  by Rasanarayan Chaurasiya, Kirti Jain, Vikas Chaurasia 
  Abstract: Healthcare fraud is a widespread problem that costs billions of dollars annually and has significant societal and financial consequences. Patients may face increased premiums and out-of-pocket expenses as a result of this because it compromises the integrity of healthcare systems. This survey analyses the ongoing philosophies for medical services misrepresentation recognition and expectation, the difficulties confronted, and arising patterns in this basic field. Patients and providers alike are harmed by healthcare fraud, which can lead to decreased quality and increased costs. The vast, complex, and ever-evolving nature of healthcare data has proven to be too much for traditional fraud detection methods. Improved fraud detection and prediction in the healthcare industry may be possible with the help of machine learning. This review looks at how various ML techniques are used to find healthcare fraud, talks about the problems and opportunities, and gives ideas for where research and practice should go in the future.
  Keywords: healthcare fraud; forecasting; machine learning; ML; fraud detection; premiums and expenses.
  DOI: 10.1504/IJCAST.2025.10072845