Contents of Volume 34 (2024)

1/2024

1/2024

  • [1] Sudharson D., Gomathi R., Selvam L., (India)
    Software Reliability Analysis by Using the Bidirectional Attention Based Zeiler-Fergus Convolutional Neural Network, pp. 1-25

      Full text         https://doi.org/10.14311/NNW.2024.34.001

    Abstract: Software quality assurance relies heavily on software reliability as one of its primary metrics. Numerous studies have been conducted to identify the software reliability. Improved software dependability may be studied using a triangular approach that includes software modeling, measurement, and improvement. Each of these steps is critical to the development of a solid software system. Improved accuracy in calculating dependability is critical to managing the quality of software. It has been discovered that deep learning algorithms are excellent methods of assessing many aspects of software dependability. Software systems contain distinct characteristics that can be addressed using deep learning techniques. In this study, a deep-learning-based bidirectional attention-based Zeiler-Fergus convolutional neural network (BA-ZFCNN) technique has been suggested to assess software dependability. In the beginning, the data were standardized by using the scalable error splash method. This approach was then used to extract the software fault-related characteristics using hypertuned evolutionary salp swarm optimization (HESSO). Finally, the Zeiler-Fergus convolutional neural network based on bidirectional attention (BA-ZFCNN) may be used to assess software dependability. The suggested method is used to forecast how many defects or failures there are in a software product. AR1 software defect data is widely used to test the effectiveness of deep learning and traditional machine learning methods. The experimental results reveal that the proposed method’s accuracy (96.7%) is higher than the current techniques’ accuracy.

  • [2] Beneš V., Svítek M., Michalíková A., Melicherčík M. , (CZ, SK) ,
    Situation Model of the Transport, Transport Emissions and Meteorological Conditions, pp. 27-36

      Full text         https://doi.org/10.14311/NNW.2024.34.002

    Abstract: Air pollution in cities and the possibilities of reducing this pollution represent one of the most important factors that today’s society has to deal with. This paper focuses on a systemic approach to traffic emissions with their relation to meteorological conditions, analyzing the effect of weather on the quantity and dispersion of traffic emissions in a city. Using fuzzy inference systems (FIS) the model for predicting changes in emissions depending on various conditions is developed. The proposed model is based on traffic, meteorology and emission data measured in Prague, Czech Republic. The main objective of the work is to provide insight into how urban planners and policymakers can plan and manage urban transportation more efficiently with environmental protection in mind.

  • [3] Halim A.A., Mustafa W.A., Nasir A.S.A., Ismail S., Alquran H., (Malaysia, Jordan) ,

    Nucleus Cell Segmentation on Pap Smear Image Using Bradley Modification Algorithm, pp. 37-51

      Full text         https://doi.org/10.14311/NNW.2024.34.003

    Abstract: Early detection of cervical cancer can help patients obtain the best treatment through various means. In general, computer-aided diagnosis has a high impact on the accuracy, reliability, and convenience of cervical cancer. However, several limitations have been faced through the design process in detecting or classifying the cells, such as variation of image features and low-image resolution. Moreover, shape indifference is one of the limitations in terms of image processing scope. The metrics used to measure the size and shape of the cells have not been developed to distinguish the differences between the shape of the objects. This paper focused on the detection and segmentation of the nucleus cell region in Pap smear images based on Bradley local thresholding. The proposed method evolved several steps, such as color adjustment, k-means, and a Bradley modification algorithm. Based on image quality assessment (IQA), the numerical evaluation results indicate that the proposed approach has segmented a full area of the nucleus cell region significantly and efficiently compared to the original Bradley algorithm. We obtained F-measure (98.62%), sensitivity (99.13%), and accuracy (97.96%). It has also been proven that the proposed method can effectively address the issue of low contrast and black noise. Hence, the proposed method differs from the previous research in terms of color disproportion adjustment and the modification of Bradley’s algorithm for Pap smear image convenience.

  • [4] Saranya K., Pandiyan P.M., Hema C.R. , (India)
    Certain Investigations on Feature Selection Technique Using Artificial Immune Systems for EEG Color Visualization Classification, pp. 53-71

      Full text         https://doi.org/10.14311/NNW.2024.34.004

    Abstract: This paper aims to extract and select the significant features of electroencephalogram (EEG) signals and classify the visual stimulation of distinct colors. In this work, a novel method for selecting distinct colors using EEG signals called affinity artificial immune and Daubechies wavelet time-based learning (AAIDWTL) is proposed. Initially, the EEG signals were collected in a controlled environment and an in-built band-pass filter was applied to remove the artifacts. The filtered signals were converted into frequency domain signals using least squarebased short-term Fourier transform. After that, by utilizing Daubechies wavelet statistical time-based feature extraction model the time domain features were extracted. Followed by, computationally efficient features were selected using an affinity artificial immune-based feature selection model. The selected features were classified using a polynomial kernel multiclass classification-based machine learning algorithm and achieved an accuracy of 97.5% when compared with other methods like linear discriminant analysis (LDA) which obtained only 92%. Furthermore, while utilizing the proposed method classification time was considerably less when compared to LDA. The experimental result shows that the proposed color stimulation of the EEG signals method achieved greater improvement in terms of both classification time and classification accuracy with a minimum false positive rate.