Research ArticleOpen accessIn the abstractNear Infrared Spectroscopy Identification Method for Five Chemical Types of Cinnamomum camphoraBailian Tu, Yueting Zhang, Yongjie Zheng et al.To more quickly and accurately identify the five chemical types of Cinnamomum camphora, a desktop and portable near-infrared spectrometer was used to collect the near-infrared spectra of the leaves of the five chemical types of C. camphora. Combined with chemometric methods, the near-infrared spectra of different chemical types of C. camphora were discriminated and analyzed. Using a desktop near-infrared spectrometer … …American Journal of Biochemistry and Biotechnology, Vol. 21, Issue 1 · 20 Feb 2025 · 5,288 accessesView PDFCite
Research ArticleOpen accessIn the abstractClassification of Finger Vein Image Using Convolutional Neural NetworkAhmed S. Hameed, Shawkat K. Guirguis, Hend A. ElsayedCurrently available technologies can perform rather well, but their effectiveness is mostly contingent on how well the venous images being analyzed are quality images. Finger vein features have garnered significant attention in the past few years as a potential means of automatic user identification. A significant amount of daily usage goes into the very vital personal identification procedure. The identification … …Journal of Computer Science, Vol. 21, Issue 2 · 6 Jan 2025 · 4,712 accessesView PDFCite
Research ArticleOpen accessIn the abstractA Misuse and Anomaly Intrusion Detection System Using Hybrid Supervised and Unsupervised Data Mining ApproachesHoma Molavi, Mohammad KhanbabaeiIntrusion Detection Systems (IDSs) play a crucial role in monitoring computer network security. Data mining and machine learning techniques facilitate the identification of intrusion patterns within large volumes of data. Hybrid data mining models have become increasingly popular in IDSs due to their enhanced effectiveness. This study presents a novel hybrid IDS that combines misuse and anomaly detection by integrating … …Journal of Computer Science, Vol. 22, Issue 6 · 6 Jul 2026 · 999 accessesView PDFCite
Research ArticleOpen accessIn the abstractSEAchain: A Privacy-Preserving Dual-Credential Model on BlockchainS. Balachander, A. MuruganAll facets of daily life are now impacted by transaction data. The complex and heterogeneous network created by millions of users, devices, online services, and applications makes digital identity management more difficult. Users can maintain control over their personal information via distributed identity, a potentially useful model for resolving identity issues. Nevertheless, current state-of-the-art techniques are inappropriate because of persistent … …Journal of Computer Science, Vol. 22, Issue 5 · 25 May 2026 · 1,310 accessesView PDFCite
Research ArticleOpen accessIn the abstractDetecting Anomalies in IoT Using Intuitionistic Fuzzy Clustering AlgorithmsVijo Arul Selvi M., Fehmin Nadira Laskar, Fokrul Alom Mazarbhuiya et al.Intuitionistic Fuzzy Sets (IFS) are convenient ways to represent the vagueness and uncertainty inherent in any dataset. There are many uses of these. One such use is the Fuzzy C-Means (FCM) algorithm, which clusters the data objects into a pre-assigned (c) number of fuzzy clusters, which has been adopted in many fields, namely, pattern classification, anomaly detection, fraud identification, etc. … …Journal of Computer Science, Vol. 22, Issue 2 · 2 Mar 2026 · 1,600 accessesView PDFCite
Research ArticleOpen accessIn the abstractIncorporation of Modified Region Growing into FCM Clustering for Extraction of Tumors in MR ImagesLovepreet Singh Brar, Jaget Singh, Bhawana Agrawal et al.Due to the tremendous growth of medical Magnetic Resonance (MR) images, it becomes an essential requirement for an automated extraction of diagnostically salient parts of the images to refine the transmission and storage process. Research method: This paper proposes an automated unsupervised machine learning segmentation technique named fuzzy c-means clustering based on modified region growing algorithm (RG-FCM) for the extraction … …Journal of Computer Science, Vol. 21, Issue 12 · 12 Feb 2026 · 1,477 accessesView PDFCite
Research ArticleOpen accessIn the abstractCrime Rate Analysis and Mapping from Socio-Economic Data Using Deep Neural NetworksSanthosh Sankara, Nadeson SugithaCrime prediction is the attempt to identify and mitigate future criminal activity. Crime is typically "unpredictable"; it cannot be predicted in advance. Addressing the roots of crime has long been a priority for researchers. Extracting causality from data is difficult if the relevant data aspects are not selected. In this research activity, an innovative classification framework is defined which is … …Journal of Computer Science, Vol. 20, Issue 10 · 17 Aug 2024 · 5,089 accessesView PDFCite
Research ArticleOpen accessIn the abstractResearch on Cotton Top Bud Target Detection Algorithm Based on Improved RetinaNetJikui Zhu, Shijie Lin, Fengkui Zhang et al.To solve the problems of low accuracy and high miss rate in the recognition of cotton apical buds during mechanical topping, an enhanced method based on the RetinaNet network is proposed for the accurate identification of cotton apical buds under natural light. The traditional RetinaNet algorithm is validated to improve the recall rate and average accuracy of cotton apical bud … …American Journal of Biochemistry and Biotechnology, Vol. 20, Issue 1 · 17 Apr 2024 · 5,128 accessesView PDFCite
Research ArticleOpen accessIn the abstractBayesian Analysis of Longitudinal Ordinal Data Using Non-Identifiable Multivariate Probit ModelsXiao ZhangMultivariate probit models have been explored foranalyzing longitudinal ordinal data. However, the inherent identification issuein multivariate probit models requires the covariance matrix of the underlyinglatent multivariate normal variables to be a correlation matrix and thushinders the development of efficient Bayesian sampling methods. It is knownthat non-identifiable models may produce Markov Chain Monte Carlo (MCMC)samplers with better convergence and mixing than … …Journal of Mathematics and Statistics, Vol. 18, Issue 1 · 14 Nov 2022 · 6,044 accessesView PDFCite
Research ArticleOpen accessIn the abstractDetection of Rotten Fresh-Cut Cauliflowers based on Machine Vision Technology and Watershed Segmentation MethodJianxin Xue, Liang Huang, Bingyu Mu et al.In this study, machine vision technology was used to separate the samples and detect the rotting degrees of fresh-cut cauliflowers. First, the improved watershed algorithm was used for the segmentation of fresh-cut cauliflower samples and the extraction of single-sample. Then, three color models, a gray co-occurrence matrix and two feature extraction algorithms were used to extract the color, texture and … …American Journal of Biochemistry and Biotechnology, Vol. 18, Issue 2 · 13 Apr 2022 · 6,083 accessesView PDFCite