International Journal of Computer Applications in Technology (7 papers in press)

Regular Issues

• FPGA implementation and Multisim simulation of a new four-dimensional two-scroll hyperchaotic system with coexisting attractors  
  by Sundarapandian Vaidyanathan, Esteban Tlelo-Cuautle, Khaled Benkouider, Aceng Sambas, Ciro Fabian Bermudez-Marquez, Samy Abdelwahab Safaan 
  Abstract: Field-programmable gate array (FPGA) design of a new four-dimensional two-scroll hyperchaotic system is investigated in this work. A detailed system modelling of the new system with a hyperchaotic attractor begins this work with phase plots, which is followed by a bifurcation study of the new system. Special dynamic properties such as multistability and symmetry are also investigated for the new system. Using Multisim software, a circuit model is designed and simulated for the new hyperchaotic system. FPGA design and Multisim simulation of the new system enable practical applications in science and engineering. The implementation of the FPGA design in this work is carried out by applying two numerical schemes, viz. Forward Euler and Trapezoidal methods. Experimental attractors observed in the oscilloscope show good match with the Matlab signal plots.The FPGA hardware resources are detailed for both numerical methods.
  Keywords: hyperchaos; bifurcation; symmetry; phase plots; hyperchaotic system;rnparameters; stability; multistability; circuit model; FPGA implementation.
  
  
• Improving hybrid-layer convolutional neural network system for lung cancer nodule classification using enhanced weight optimisation algorithm  
  by Vikul Pawar, P. Premchand 
  Abstract: In recent times, lung cancer is evolving as a highly life-threatening disease for human beings. According to the WHO, lung cancer disease is the second largest cause of deaths as compared to all other types of cancer. The prevailing available technology is striving to get more exposure in the field of medical science using Computer Assisted Diagnosis (CAD), where image processing is playing a crucial role for detecting the cancerous nodules in computer tomographic images. Augmenting the machine learning techniques with image processing algorithms is becoming a more comprehensive examination of cancer disease in proposed CAD systems. This paper is describes a heuristic approach for lung cancer nodule detection, and the proposed model predominantly consists of the following tasks, which are image enhancement, segmenting ROI (Region of Interest), features extraction, and nodule classification. In pre-processing, primarily the Adaptive Median Filter (AMF) filtering method is applied to eliminate the speckle noise from input CT images of Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): in the LIDC-IDRI dataset, the quality of input image is improved by applying Histogram Equalization (HE) technique with Contrast-Limited Adaptive (CLA) approach. Secondly, in the successive stage the Improved Level-Set (ILS) algorithm is used to segment the ROI. Furthermore, the third step of the projected work is applied to extract the definite learnable texture features and statistical features from the segmented ROI. The extracted features in the subsequent stage of classification are applied to Hybrid-Layer Convolutional Neural Network (HL-CNN) architecture to classify the lung cancer nodule as either benign or malignant. Principally this research is carried out by contributing to each stage of it, where the novel concept of the improved Hybrid-Layer Convolutional Neural Network (HL-CNN) is employed by optimising and selecting the optimal weight using the Enhanced Cat Swarm Optimisation (ECSO) algorithm. The experimental result of the proposed HL-CNN using the weight optimisation algorithm ECSO is achieved an accuracy of 93%, which is comparatively efficient with respect to existing models such as DBN, SVM, CNN, WOA, MFO, and CSO. Moreover, the proposed model conclusively gives a decision on the detected nodule as either benign or malignant.
  Keywords: Computer Assisted Diagnosis (CAD); Computer Vision; Cancer Diagnosis; Image Classification; Image Enhancement; Image Segmentation; Feature Extraction.
  
  
• Prediction model for total amount of coke oven gas generation based on FCM-RBF  
  by Lili Feng, Jun Peng, Zhaojun Huang 
  Abstract: The rational use of Coke Oven Gas (COG) is of great significance to improve the economic efficiency of enterprises. In this paper, a COG generation prediction model based on fuzzy C-mean clustering (FCM) and radial basis function (RBF) neural network is proposed to address the problems such as the difficulty of accurate modelling of COG generation process and the difficulty of real-time flow prediction. Firstly, the coke oven production process is analysed and correlation analysis is used to select the influencing factors. Secondly, the FCM is used to classify the working conditions of the coke oven, and the appropriate number of working conditions is selected through experiments. Finally, the prediction models under different working conditions are established separately by using RBF. The experiments were carried out using actual industrial production data, and the experimental results showed that the model could provide guidance reference for the dispatchers.
  Keywords: coking oven process; fuzzy C-means clustering; prediction model; radial basis function neural network.
  
  
• Slope stability assessment based on a combined weight-cloud model  
  by Decheng Zhang, Jielu He, Yunjun Yang 
  Abstract: To evaluate the stability of slopes accurately and reduce the risk of landslide accidents during slope treatment, this study focuses on four aspects: slope morphology, rock mass characteristics, geological structure, and triggering factors. Based on these factors and the intrinsic relationships among indicators, the study refines them into 12 secondary indicators to establish a risk assessment system for slope instability. This research aims to address limitations in previous studies, where the combined influence of subjective judgment and objective data was not fully integrated, and uncertainties in slope evaluation indicators were not effectively managed. To improve the accuracy of weighting, a combination of subjective weighting and objective weighting methods is adopted. The cloud model is used to solve the uncertainty and imprecision of the impact evaluation index, and the slope stability grade is determined according to the principle of maximum certainty. Finally, the method is applied to an engineering example, and the slope stability grade obtained is consistent with the actual slope stability grade, demonstrating that this method can accurately evaluate slope stability.
  Keywords: slope instability; stability evaluation; combined weighting; cloud model.
  DOI: 10.1504/IJCAT.2025.10078410
   
  
• Improving autonomous networks using predictive offloading in mobile-edge computing  
  by Himanshi Babbar, Shalli RANI 
  Abstract: The integration of Mobile Edge Computing (MEC) with Vehicular Edge Computing (VEC) to enhance the efficiency of autonomous networks is presented in this research. Traditional offloading strategies rely on reactive approaches, which fail to anticipate workload variations, leading to high latency, suboptimal resource allocation, and increased energy consumption. To address these challenges, we propose an intelligent predictive offloading method that dynamically distributes computing workloads among edge servers and vehicles. The method leverages Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V) communications to optimise task transfer and mobility prediction. Our approach reduces computational latency by up to 40% and decreases communication costs by 25%, as demonstrated through simulations in a high-speed vehicular environment. Additionally, we show that predictive offloading improves resource utilisation efficiency by 90%, compared to 65% in reactive offloading methods. The proposed framework significantly enhances real-time performance for applications such as traffic management and autonomous driving by minimising delays and ensuring seamless task execution. These results establish predictive offloading as a viable solution for next-generation intelligent transportation systems.
  Keywords: autonomous networks; vehicles communication; mobile edge computing; performance evaluation.
  DOI: 10.1504/IJCAT.2026.10079442
   
  
• An effective and efficient local features reduction scheme for time series classification  
  by Manel Khadiche, Bachir Boucheham, Salah Bougueroua 
  Abstract: Dynamic Time Warping (DTW), the most famous time series alignment method, is known for its high complexity and, often, its weakness in dealing with local details. We propose a variant of the DTW with the aim of enhancing its performance on both above-mentioned aspects. The proposed method is based on two operators: One-Dimensional Local Binary Pattern (1D-LBP), for retaining the most relevant and discriminative features, and the Ramer Douglas Peucker (RDP) line simplification algorithm, for data reduction. These operators are followed by application of the DTW on the so-obtained reduced features. Accordingly, the proposed method is called LBP-RDPDTW. The LBP-RDPDTW is fed to the K-Nearest Neighbors (KNN) classifier, for classification purposes. When applied to the 29 image outlines datasets from the UCR (University of California at Riverside) time series classification and clustering benchmark, the so-obtained classifier outperformed competitive existing methods, including the DTW based KNN.
  Keywords: DTW; one-dimensional local binary pattern; Ramer–Douglas–Pecker algorithm; K-nearest neighbours classifier; UCR time series classification; clustering benchmark.
  DOI: 10.1504/IJCAT.2026.10080346
   
  
• Intelligent decision learning for multi-constraint resource allocation in vehicular networks  
  by Yile Chen 
  Abstract: Vehicular networks increasingly require real-time resource allocation that jointly optimizes spectrum scheduling, transmit power, and computation offloading under stringent latency, energy, and reliability constraints. However, time-varying topology, incomplete interference information, and bursty service demands make conventional heuristic scheduling and unconstrained reinforcement learning prone to constraint violations and queue instability. We propose LPD-AC, a Lyapunov-guided primal--dual actor--critic framework that couples drift-plus-penalty stabilization with Lagrangian dual updates to enforce multi-dimensional budgets while learning long-horizon efficient policies. The algorithm employs feasible action construction for conflict-free RB assignment and bounded power control, and admits a multi-timescale stochastic approximation convergence guarantee to a projected KKT set. Experiments on four public V2X datasets demonstrate that LPD-AC consistently improves throughput and latency while substantially reducing steady-state queue backlog and achieving robust performance under tight latency--power regimes. These results indicate that integrating stability-aware learning with principled constraint handling yields a reliable decision engine for multi-constraint vehicular networking.
  Keywords: vehicular networks; V2X communications; multi-constraint optimisation; Lyapunov optimisation; primal–dual learning.
  DOI: 10.1504/IJCAT.2026.10081101