Cognitive Radio for Smart Grid: Theory, Algorithms, and Security by Raghuram Ranganathan, Robert Qiu, Zhen Hu, Shujie Hou, Marbin Pazos-Revilla, Gang Zheng, Zhe Chen, Nan Guo

“…Cognitive radios are intelligent software defined radios (SDRs) that efficiently utilize the unused regions of the spectrum, to achieve higher data rates. The smart grid is an automated electric power system that monitors and controls grid activities. …”
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A novel brain tumor magnetic resonance imaging dataset (Gazi Brains 2020): initial benchmark results and comprehensive analysis by Seref Sagiroglu, Ramazan Terzi, Emrah Celtikci, Alp Özgün Börcek, Yilmaz Atay, Bilgehan Arslan, Mustafa Caglar Sahin, Kerem Nernekli, Umut Demirezen, Okan Bilge Ozdemir, Kevser Özdem Karaca, Nuh Azgınoğlu

“…To demonstrate the utility of the proposed dataset, different deep learning models were applied to the problem, and these models were tested on various data and models applied for various tasks such as region of interest extraction, whole tumor segmentation, prediction, detection, and classification with accuracy, precision, recall, and F1-score. …”
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Design of Coffee Disease Detection Web Application Using Image Processing: A Case Study of Kyabugimbi-Kajunju Cooperative Farms in Bushenyi District. by Asiimwe, Mark, Kakuru, Ambrose

“…The methodology involves acquiring high-resolution images, employing pre-processing methods to enhance data quality, and implementing state-of-the-art machine-learning algorithms for disease classification. …”
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High-speed threat detection in 5G SDN with particle swarm optimizer integrated GRU-driven generative adversarial network by R. Shameli, Sujatha Rajkumar

“…The attack detection in 5G SDN involves Machine learning (ML) and Deep learning (DL) algorithms to analyze large volumes of network data and identify patterns indicative of attacks. …”
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High‐Impedance Fault Detection in Distribution Networks Based on Support Vector Machine and Wavelet Transform Approach (Case Study: Markazi Province of Iran) by Mohammad Sadegh Attar, Mohammad Reza Miveh

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A Survey on Anti-Money Laundering Techniques in Blockchain Systems by Leyuan Liu, Xiangye Li, Tian Lan, Yakun Cheng, Wei Chen, Zhixin Li, Sheng Cao, Weili Han, Xiaosong Zhang, Hongfeng Chai

“…Looking ahead, the advancement of AML technologies in blockchain systems necessitates progress in several critical areas: the construction of AML datasets capable of addressing data imbalance and annotation uncertainty, development of trusted AML algorithms, design of detection mechanisms for covert financial activities, and formulation of privacy-preserving yet regulation-compliant AML solutions. …”
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StaEn-IDS: An Explainable Stacking Ensemble Deep Neural Network-Based Intrusion Detection System for IoT by Monika Vishwakarma, Nishtha Kesswani

“…The model also addresses the challenge of detecting minority-class attacks in IoT traffic. We evaluate the approach on data derived from the CIC IoT 2022 dataset (pcap files converted to flow records using CICFlowMeter), and demonstrate real-time deployment on a resource-constrained edge device. …”
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Identification of PRKCQ-AS1 as a Keratinocyte-Derived Exosomal lncRNA That Promotes Th17 Differentiation and IL-17 secretion in Psoriasis Through Bioinformatics, Machine L... by Gao P, Gao X, Lin L, Zhang M, Luo D, Chen C, Li Y, He Y, Liu X, Shi C, Yang R

“…Subsequently, exosome-related ncRNAs in psoriasis lesions were identified primarily through weighted gene co-expression network analysis and five machine learning algorithms. Additionally, large-scale integrated single-cell RNA sequencing data and genome-wide association study (GWAS) data were included to investigate the mechanisms of key ncRNA, primarily through immune infiltration analysis, gene set enrichment analysis (GSEA), co-expression analysis, and Mendelian randomization. …”
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A Novel Approach Utilizing Bagging, Histogram Gradient Boosting, and Advanced Feature Selection for Predicting the Onset of Cardiovascular Diseases by Norma Latif Fitriyani, Muhammad Syafrudin, Nur Chamidah, Marisa Rifada, Hendri Susilo, Dursun Aydin, Syifa Latif Qolbiyani, Seung Won Lee

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Improving Forest Above-Ground Biomass Estimation Accuracy Using Multi-Source Remote Sensing and Optimized Least Absolute Shrinkage and Selection Operator Variable Selection Method by Er Wang, Tianbao Huang, Zhi Liu, Lei Bao, Binbing Guo, Zhibo Yu, Zihang Feng, Hongbin Luo, Guanglong Ou

“…Sentinel 2, Sentinel 1, Landsat 8 OLI, ALOS-2 PALSAR-2, Light Detection and Ranging, and Digital Elevation Model (DEM) data were used in this study. …”
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Wearable Sensors-Based Intelligent Sensing and Application of Animal Behaviors: A Comprehensive Review by Luyu Ding, Chongxian Zhang, Yuxiao Yue, Chunxia Yao, Zhuo Li, Yating Hu, Baozhu Yang, Weihong Ma, Ligen Yu, Ronghua Gao, Qifeng Li

“…Current behavior classification relies predominantly on traditional machine learning or deep learning approaches with high-frequency data acquisition. …”
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A human-on-the-loop approach for labelling seismic recordings from landslide site via a multi-class deep-learning based classification model by Jiaxin Jiang, David Murray, Vladimir Stankovic, Lina Stankovic, Clement Hibert, Stella Pytharouli, Jean-Philippe Malet

“…Recent advances in machine learning have introduced algorithms for classifying seismic events associated with landslides, such as earthquakes, rockfalls, and smaller quakes. …”
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RANFIS-Based Sensor System with Low-Cost Multi-Sensors for Reliable Measurement of VOCs by Keunyoung Kim, Woosung Yang

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Machine-Learning Parsimonious Prediction Model for Diagnostic Screening of Severe Hematological Adverse Events in Cancer Patients Treated with PD-1/PD-L1 Inhibitors: Retrospective... by Seok Jun Park, Seungwon Yang, Suhyun Lee, Sung Hwan Joo, Taemin Park, Dong Hyun Kim, Hyeonji Kim, Soyun Park, Jung-Tae Kim, Won Gun Kwack, Sung Wook Kang, Yun-Kyoung Song, Jae Myung Cha, Sang Youl Rhee, Eun Kyoung Chung

“…The study aimed to develop a simple machine learning (ML) model for predicting irHAEs associated with PD-1/PD-L1 inhibitors. …”
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D-SPDH: Improving 3D Robot Pose Estimation in Sim2Real Scenario via Depth Data by Alessandro Simoni, Guido Borghi, Lorenzo Garattoni, Gianpiero Francesca, Roberto Vezzani

“…The working scenario is the Sim2Real, i.e., the system is trained only with synthetic data and then tested on real sequences, thus eliminating the time-consuming acquisition and annotation procedures of real data, common phases in deep learning algorithms. …”
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Solar FaultNet: Advanced Fault Detection and Classification in Solar PV Systems Using SwinProba‐GeNet and BaBa Optimizer Models by Praveen Kumar Balachandran, Muhammad Ammirrul Atiqi Mohd Zainuri, Faisal Alsaif

“…The work is therefore targeted at trying to address these challenges in the development of an efficient and reliable model that could be applicable in the fault detection for solar PV systems. It also proposes the Solar FaultNet‐a novel deep learning‐based approach that significantly improves fault detection performance in solar PV systems and integrates the model with state‐of‐the‐art ML techniques like CNN and LSTM to capture inherent complex patterns and interdependencies of fault data. …”
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Decision Making Support System for the Diagnostics of the Cardiovascular System Pathologies by the X-ray Images of the Chest by A. G. Radzhabov

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A Comprehensive Review of Smartphone and Other Device-Based Techniques for Road Surface Monitoring by Saif Alqaydi, Waleed Zeiada, Ahmed El Wakil, Ali Juma Alnaqbi, Abdelhalim Azam

“…Machine learning algorithms, particularly CNNs, are utilized to classify road anomalies, enhancing detection accuracy and system efficiency. …”
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Providing contexts for classification of transients in a wide-area sky survey: An application of noise-induced cluster ensemble by Tossapon Boongoen, Natthakan Iam-On, James Mullaney

“…In particular, samples are clustered to form data contexts to which different learning strategies may be applied. …”
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D-optimal candexch algorithm-enhanced machine learning UV-spectrophotometry for five-analyte determination in novel anti-glaucoma formulations and ocular fluids: four-color sustain... by Omkulthom Al kamaly, Lateefa A. Al-Khateeb, Michael K. Halim, Noha S. katamesh, Galal Magdy, Ahmed Emad F. Abbas

“…The key novelty was using the D-optimal design generated by MATLAB's candexch algorithm to construct a robust validation set, overcoming random data splitting limitations in machine learning chemometric methods and ensuring unbiased evaluation across concentrations. …”
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