International Journal of Computer Applications in Technology (11 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.
  
  
• Intelligent traffic congestion discrimination method based on wireless sensor network front-end data acquisition  
  by Maokai Lai 
  Abstract: Conventional intelligent traffic congestion discrimination methods mainly use GPS terminals to collect traffic congestion data, which is vulnerable to the influence of vehicle time distribution, resulting in poor final discrimination effect. Necessary to design a new intelligent traffic congestion discrimination method based on wireless sensor network front-end data collection. That is to use the front-end data acquisition technology of wireless sensor network to generate a front-end data acquisition platform to obtain intelligent traffic congestion data, and then design an intelligent traffic congestion discrimination algorithm based on traffic congestion rules so as to achieve intelligent traffic congestion discrimination. The experimental results show that the intelligent traffic congestion discrimination method designed based on the front-end data collection of wireless sensor network has good discrimination effect, the obtained discrimination data is more accurate, effective, and has certain application value, which has made certain contributions to reducing the frequency of urban traffic accidents.
  Keywords: wireless sensor network; front-end; Data acquisition; transportation; intelligence; traffic jam; traffic congestion data.
  DOI: 10.1504/IJCAT.2023.10059521
   
  
• Unsupervised VAD method based on short time energy and spectral centroid in Arabic speech case  
  by Hind Ait Mait, Noureddine Aboutabit 
  Abstract: Voice Activity Detection (VAD) distinguishes speech segments from noise or silence areas. An efficient and noise-robust VAD system can be widely used for emerging speech technologies such as wireless communication and speech recognition. In this paper, we propose two versions of an unsupervised Arabic VAD method based on the combination of the Short-Time Energy (STE) and the Spectral Centroid (SC) features for formulating a typical threshold to detect the speech areas. The first version compares only the STE feature to the threshold (STE-VAD). In contrast, the second compares the SC vector and the threshold (SC-VAD). The two versions of our VAD method were tested on 770 sentences of the Arabphone corpus, which were recorded in clean and noisy environments and evaluated under different values of Signal-to-Noise-Ratio. The experiments demonstrated the robustness of the STE-VAD in terms of accuracy and Mean Square Error.
  Keywords: VAD; Arabic speech; voiced segment; unvoiced segment; STE; SC; MSE; Accuracy.
  DOI: 10.1504/IJCAT.2023.10061438
   
  
• Bi-LSTM GRU-based deep learning architecture for export trade forecasting  
  by Vaishali Gupta 
  Abstract: To assess a country's economic outlook and achieve higher economic growth, econometric models and prediction techniques are significant tools. Policymakers are always concerned with the correct future estimates of economic variables to take the right economic decisions, design better policies and effectively implement them. Therefore, there is a need to improve the predictive accuracy of the existing models and to use more sophisticated and superior algorithms for accurate forecasting. Deep learning models like recurrent neural networks are considered superior for forecasting as they provide better predictive results as compared to many of the econometric models. Against this backdrop, this paper presents the feasibility of using different deep-learning neural network architectures for trade forecasting. It predicts export trade using different recurrent neural architectures such as “vanilla recurrent neural network (VRNN)”, “bi-directional long short-term memory network (Bi-LSTM)”, “bi-directional gated recurrent unit (Bi-GRU)” and a hybrid “bi-directional LSTM and GRU neural network”.
  Keywords: Bi-LSTM; GRU; economic forecasting; international trade; recurrent neural network.
  DOI: 10.1504/IJCAT.2024.10061555
   
  
• Electronic management of enterprise accounting files under the condition of informatisation  
  by Xia Liu, Zhengfu Zhao, Yun Zhao 
  Abstract: With the rapid development of computer information technology, the work of accountants has gradually evolved to an electronic trend, and the management of accounting files has also undergone great changes. Combining with the current development trend of informatization, this paper has discussed the electronic management mode of enterprise accounting files under the condition of informatization. Combined with the latest information technology, an enterprise electronic accounting file system is established, and the research and development system is compared with the traditional paper accounting file management. The results have shown that the retrieval and query time of traditional paper accounting files is close to 2 hours. After the implementation of the electronic accounting file system, the retrieval and query time of files can be completed in only 2 minutes, and the query efficiency of files has been increased by nearly 60 times.
  Keywords: accounting files; system development; financial management; electronic management.
  DOI: 10.1504/IJCAT.2024.10062854
   
  
• Application of artificial intelligence in enterprise human resource management and employee performance evaluation  
  by Qingguo Nie 
  Abstract: With the rapid development of artificial intelligence technology, significant breakthroughs have been made in its application in many fields. Especially in field of enterprise human resource management and employee performance evaluation, AI has demonstrated its powerful ability to optimise and improve performance. This study explores the application AI in enterprise human resource management and how to use AI to evaluate employee performance. The research includes analysing and comparing existing AI-driven human resource management models, evaluating how AI can help improve employee performance and leadership styles, and designing and developing human resource management computer systems for enterprise employees. Through empirical research and case analysis, this study proposes a new AI-optimised employee performance evaluation model and explores its application and effect in practice. At present, artificial intelligence technology has been widely used in various fields of daily life, especially in corporate human resource management, providing better support for the development of enterprises.
  Keywords: artificial intelligence; enterprise human resource management; employee performance evaluation; AI optimised human resource model.
  DOI: 10.1504/IJCAT.2024.10062884
   
  
• Numerical simulation of financial fluctuation period based on non-linear equation of motion  
  by Guixian Tian 
  Abstract: The traditional numerical simulation method of financial fluctuation cycle does not focus on the study of nonlinear financial fluctuation, but has problems such as high numerical simulation error and long time. In order to solve this problem, this paper introduces the nonlinear equation of motion to optimize the numerical simulation method of financial fluctuation cycle. A comprehensive analysis of the components of the financial market, the establishment of a financial market network model, and the acquisition of relevant financial data under the support of the model. Based on the collection of financial data, set up financial volatility index, measuring cycle, the financial wobbles, to establish the nonlinear equations of motion, financial wobbles, The simulation results show that, compared with the traditional method, the numerical simulation of the proposed method has high precision, low error and short time, which provides relatively accurate reference data for the stable development of regional economy.
  Keywords: non-linear equation of motion; financial fluctuation; fluctuation period; numerical simulation.
  DOI: 10.1504/IJCAT.2023.10063134
   
  
• Research on machine reading comprehension for BERT and its variant neural network models  
  by Yanfeng Wang, Ning Ma, Wenrong Lv 
  Abstract: Currently, machine reading comprehension model is mainly models primarily rely on the basis of LSTM network networks with the gate mechanism. In the present this study, we employ BERT and its variant pre training-trained language model are used models to conduct research and experiment experimentation on the DuReader dataset. It is found We find that improved mask methods enhanced masking techniques, such as full -word mask masking and dynamic mask masking, can notably significantly enhance the model's performance of the model in machine reading comprehension tasks. Therefore, the ROUGE-L and BLEU-4 values of Consequently, the best RoBERTRoBERTa-wwm-ext model on the test set are achieves ROUGE-L and BLEU-4 scores of 51.02% and 48.14% separately, on the test set, respectively, which are 19.12% and 8.94% higher than the benchmark model. In addition, in view of the problem that the, Moreover, addressing the issue of suboptimal model performance is not optimal when the data dealing with large-scale is large data and the relatively dispersed effective information is relatively dispersed, this paper adopts employs a three-step preprocessing of approach for the dataset.
  Keywords: machine reading comprehension; BERT pre-training language model; masking mode.
  DOI: 10.1504/IJCAT.2024.10063844
   
  
• Leveraging the internet of behaviours and digital nudges for enhancing customers financial decision-making  
  by Imane Moustati, Noreddine Gherabi, Mostafa Saadi 
  Abstract: Human behaviour, which is led by the human, emotional, and occasionally fallible brain, is highly influenced by the environment in which choices are presented. This research paper explores the synergistic potential of the Internet of Behaviours (IoB) and digital nudges in the financial sector as new avenues for intervention, while shedding light on the IoB benefits and the digital nudges' added value in these financial settings. Afterward, it proposes an IoB-nudges conceptual model to explain how these two concepts would be incorporated, and investigates their complementary relationship and benefits for this sector. Finally, the paper also discusses key challenges to be addressed by the IoB framework.
  Keywords: internet of behaviour; IoB; nudge theory; digital nudges; financial decision-making.
  DOI: 10.1504/IJCAT.2024.10065772