Application of Machine Learning in Fault Detection And Classification in Power Transmission Lines by Michel Evariste Tshodi, Nathanael Kasoro, Freddy Keredjim, ALbert Ntumba Nkongolo, Jean-Jacques Katshitshi Matondo, Paul Mbuyi Balowe, Laurent Kitoko

“…Subsequently, machine learning techniques are employed to detect faults in the transmission between lines, and machine learning models are utilized to classify the faults. …”
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Bio inspired optimization techniques for disease detection in deep learning systems by A. Ashwini, Vanajaroselin Chirchi, S. Balasubramaniam, Mohd Asif Shah

“…Bio-inspired methodologies have exhibited significant potential in addressing critical challenges in illness detection across many data types. It seeks to tackle the problem by creating bio-inspired optimization methods to enhance efficient and equitable deep learning for illness diagnosis. …”
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Road Detection, Monitoring, and Maintenance Using Remotely Sensed Data by Nicholas Fiorentini, Massimo Losa

“…The ten papers published in this issue explore diverse methodologies, including novel deep learning algorithms for road inventory, novel methods for pavement crack detection, AI-enhanced ground-penetrating radar (GPR) imaging for subsurface assessment, high-resolution optical satellite imagery for unpaved road assessment, and aerial orthophotography for road mapping. …”
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Energy efficient hierarchical clustering based dynamic data fusion algorithm for wireless sensor networks in smart agriculture by Dhamodharan Srinivasan, Ajmeera Kiran, S. Parameswari, Jeevanantham Vellaichamy

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A Comprehensive Survey on the Usage of Machine Learning to Detect False Data Injection Attacks in Smart Grids by Kiara Nand, Zhibo Zhang, Jiankun Hu

“…This article provides a comprehensive survey on the application of machine learning techniques for detecting False Data Injection Attacks (FDIA) in smart grids. …”
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Machine learning-assisted early detection of keratoconus: a comparative analysis of corneal topography and biomechanical data by Arkadiusz Syta, Arkadiusz Podkowiński, Tomasz Chorągiewicz, Robert Karpiński, Jakub Gęca, Dominika Wróbel-Dudzińska, Katarzyna E Jonak, Dariusz Głuchowski, Marcin Maciejewski, Robert Rejdak, Kamil Jonak

“…This study investigates the application of machine learning models to identify keratoconus based on corneal topography and biomechanical data. …”
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FOA-BDNet: A behavior detection algorithm for elevator maintenance personnel based on first-order deep network architecture by Zengming Feng, Tingwen Cao

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Hybrid Deep Learning Techniques for Improved Anomaly Detection in IoT Environments by Hanan Abbas Mohammad

“…Hence, a number of researchers are attempting to build an intrusion detection system utilizing machine learning and deep learning algorithms. …”
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A Systematic Literature Review of Machine Learning-Based Personality Trait Detection Using Electroencephalographic Data by Celina Rieck, Pascal Penava, Ricardo Buettner

“…That is why machine learning-based detection of personality traits using electroencephalographic data has gained increasing attention. …”
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Machine Learning-Based Harvest Date Detection and Prediction Using SAR Data for the Vojvodina Region (Serbia) by Gordan Mimić, Amit Kumar Mishra, Miljana Marković, Branislav Živaljević, Dejan Pavlović, Oskar Marko

“…In this study, the determination and prediction of harvest dates for different crops were performed by applying machine learning techniques on C-band synthetic aperture radar (SAR) data. …”
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Enhanced schizophrenia detection using multichannel EEG and CAOA-RST-based feature selection by Mohammad Abrar, Abdu Salam, Ahmed Albugmi, Fahad Al-otaibi, Farhan Amin, Isabel de la Torre, Thania Candelaria Chio Montero, Perla Araceli Arroyo Gala

“…Thus, our research introduces a novel approach by integrating the multichannel EGG, Crossover-Boosted Archimedes Optimization Algorithm (CAOA), and Rough Set Theory (RST) for schizophrenia detection. …”
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Fault Detection in MV Switchgears Through Unsupervised Learning of Temperature Conditions by Grazia Iadarola, Alessandro Mingotti, Virginia Negri, Susanna Spinsante

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Machine Learning-Enhanced Dual-Band Plasmonic Sensing for Simultaneous Qualitative and Quantitative Detection of Biomolecules in the Mid-Infrared Region by Yunwei Chang, Ang Bian

“…However, when it comes to molecules with different kinds and concentrations, detection technology and data processing remain a challenging task. …”
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Analysis of Possibilities to Automate Detection of Unscrupulous Microfinance Organizations based on Machine learning Methods by Yu. M. Beketnova

“…The aim of the paper is to study the possibilities to automate detection of unscrupulous microfinance organizations based on machine learning methods in order to promptly identify and suppress illegal activities by regulatory authorities. …”
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Deep learning-based feature selection for detection of autism spectrum disorder by Ibrahim Nafisah, Nermine Mahmoud, Ahmed A. Ewees, Mohamed G. Khattap, Abdelghani Dahou, Safar M. Alghamdi, Ibrahim A. Fares, Mohammed Azmi Al-Betar, Mohammed Azmi Al-Betar, Mohamed Abd Elaziz, Mohamed Abd Elaziz

“…Feature selection is enhanced through an optimized Hiking Optimization Algorithm (HOA) that integrates DynamicOpposites Learning (DOL) and Double Attractors to improve convergence toward the optimal subset of features.ResultsThe proposed model is evaluated using multiple ASD datasets. …”
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Applicability of Artificial Intelligence in Smart Healthcare Systems for Automatic Detection of Parkinson’s Disease by Harikumar Pallathadka, S.J.R.K. Padminivalli V., M. Vasavi, P. Nancy, Mohd Naved, Harish Kumar, Samrat Ray

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Decision Tree for Determining Hospital Treatment for Covid-19 Patients Based on Hematology Parameters Using the C5.0 Algorithm by Joko Riyono, Christina Eni Pujiastuti, Supriyadi Supriyadi, Dody Prayitno, Aina Latifa Riyana Putri

“…Performance evaluated using the Confusion Matrix method produces an accuracy value of 78% which is considered quite good, where testing with the C5.0 Algorithm uses a training and testing data ratio of 40:60. …”
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Deep learning-based improved transformer model on android malware detection and classification in internet of vehicles by Naif Almakayeel

“…The deep learning (DL) model is an efficient tool for detecting various malware variants. …”
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A Statistical Framework to Detect and Quantify Operator-Learning Curves in Medical Device Safety Evaluation by Ssemaganda HC, Davis SE, Govindarajulu US, Koola JD, Mao J, Westerman DM, Perkins AM, Speroff T, Ramsay CR, Sedrakyan A, Ohno-Machado L, Matheny ME, Resnic FS

“…LE were detected in 2065 of the 2291 datasets for which learning was specified (sensitivity: 90%; specificity: 88%; LR: 7). …”
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Fusion of transfer learning with nature-inspired dandelion algorithm for autism spectrum disorder detection and classification using facial features by G. Elangovan, N. Jagadish Kumar, J. Shobana, M. Ramprasath, Gyanendra Prasad Joshi, Woong Cho

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