Small target detection algorithm based on SAHI-Improved-YOLOv8 for UAV imagery: A case study of tree pit detection by Xiuhao Liang, Jun Xiang, Sheng Qin, Yundan Xiao, Lifen Chen, Dongxia Zou, Honglun Ma, Dong Huang, Yongxin Huang, Wei Wei

“…The application of deep learning in tree pit detection of unmanned aerial vehicle (UAV) images has problems such as dense distribution, high density, small size, false detections, missed detections, and high localization error. …”
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Assessing Data Fusion in Sensory Devices for Enhanced Prostate Cancer Detection Accuracy by Jeniffer Katerine Carrillo Gómez, Carlos Alberto Cuastumal Vásquez, Cristhian Manuel Durán Acevedo, Jesús Brezmes Llecha

“…Following this, the datasets for each device were individually analyzed and subsequently merged to enhance the classification results. The data fusion was assessed and it successfully improved the accuracy of detecting prostate-related conditions and distinguishing healthy patients, achieving the highest success rate possible (100%) in classification through machine learning methods, outperforming the results obtained from individual electronic devices.…”
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Electricity Theft Detection in a Smart Grid Using Hybrid Deep Learning-Based Data Analysis Technique by Camille Franklin Mbey, Jacques Bikai, Felix Ghislain Yem Souhe, Vinny Junior Foba Kakeu, Alexandre Teplaira Boum

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Mixed-Embeddings and Deep Learning Ensemble for DGA Classification With Limited Training Data by Christian Morbidoni, Alessandro Cucchiarelli, Luca Spalazzi

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Advanced Phishing Detection: Leveraging t-SNE Feature Extraction and Machine Learning on a Comprehensive URL Dataset by Taha Etem, Mustafa Teke

“…We applied the t-SNE technique for feature extraction, condensing the original 51 features into only 2, while preserving high detection accuracy. We evaluated several machine learning algorithms on both full and reduced datasets, including Logistic Regression, Naive Bayes, k-Nearest Neighbors (kNN), Decision Trees, and Random Forest. …”
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Analysis of Deep Learning Techniques for Vehicle Detection and Reidentification Using Data from Multiple Drones and Public Datasets by FELIPE P.A. EUPHRÁSIO, RAFAEL M. DE ANDRADE, ELCIO H. SHIGUEMORI, LIANGRID L. SILVA, MOISÉS JOSÉ S. FREITAS, NATHAN AUGUSTO Z. XAVIER, ARGEMIRO S.S. SOBRINHO

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Detection of Electrical Fires in Residential Buildings Using a Gradient Boosting Machine Algorithm by Sharaban Taha Ahmed, Fadhil Aula

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Real-Time Postural Disturbance Detection Through Sensor Fusion of EEG and Motion Data Using Machine Learning by Zhuo Wang, Avia Noah, Valentina Graci, Emily A. Keshner, Madeline Griffith, Thomas Seacrist, John Burns, Ohad Gal, Allon Guez

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GAINSeq: glaucoma pre-symptomatic detection using machine learning models driven by next-generation sequencing data by Muhammad Iqbal, Arshad Iqbal, Humaira Ayub, Maqbool Khan, Naveed Ahmad, Yasir Javed, Mohammed Ali Alshara

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Novel Spatio-Temporal Joint Learning-Based Intelligent Hollowing Detection in Dams for Low-Data Infrared Images by Lili Zhang, Zihan Jin, Yibo Wang, Ziyi Wang, Zeyu Duan, Taoran Qi, Rui Shi

“…Finally, we employed adaptive joint learning to obtain the spatio-temporal features. We designed the experiments on the dataset we constructed, and we demonstrated that the method proposed in this paper can handle the low-data (few shots real images) issue. …”
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A Novel YOLOv10-Based Algorithm for Accurate Steel Surface Defect Detection by Liefa Liao, Chao Song, Shouluan Wu, Jianglong Fu

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An Innovative Analysis of Time Series-Based Detection Models for Improved Cancer Detection in Modern Healthcare Environments by Uma Shankari Srinivasan, Venkat Pavithra, Kaliappan Sutha, Sridevi Ramachandiran, Nallathambi Indumathi

“…This enhanced version of time series analysis incorporates multiple layers of data sources and uses advanced machine learning algorithms to identify patterns that could signal the presence of a tumor. …”
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Machine learning models based on routine blood and biochemical test data for diagnosis of neurological diseases by Wanshan Ning, Zhicheng Wang, Ying Gu, Lindan Huang, Shuai Liu, Qun Chen, Yunyun Yang, Guolin Hong

“…So this study aimed to construct machine learning models using the convenient blood routine and biochemical detection data for diagnosis of nervous system diseases. …”
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Machine learning for epithelial ovarian cancer platinum resistance recurrence identification using routine clinical data by Li-Rong Yang, Mei Yang, Liu-Lin Chen, Yong-Lin Shen, Yuan He, Zong-Ting Meng, Wan-Qi Wang, Feng Li, Zhi-Jin Liu, Lin-Hui Li, Yu-Feng Wang, Xin-Lei Luo

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Parallelization of the Bison Algorithm Applied to Data Classification by Simone A. Ludwig, Jamil Al-Sawwa, Aaron Mackenzie Misquith

“…In data science and machine learning, efficient and scalable algorithms are paramount for handling large datasets and complex tasks. …”
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Analysis of different IDS-based machine learning models for secure data transmission in IoT networks by Gladić Dejana, Petrovački Jelena, Sladojević Srdan, Arsenović Marko, Ristić Sonja

“…The aim of this study is to develop a machine learning (ML) model for network traffic classification and attack detection in IoT environments. …”
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Integration of Accelerometers and Machine Learning with BIM for Railway Tight- and Wide-Gauge Detection by Jessada Sresakoolchai, Chayutpong Manakul, Ni-Asri Cheputeh

“…These data are processed and analyzed using supervised machine-learning algorithms to classify and predict potential tight- and wide-gauge events. …”
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High resolution remote sensing image object detection algorithm based on improved YOLOv8 by ZHANG Xia, QIAO Huanyu, CAO Feng

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Evaluating and Enhancing Face Anti-Spoofing Algorithms for Light Makeup: A General Detection Approach by Zhimao Lai, Yang Guo, Yongjian Hu, Wenkang Su, Renhai Feng

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Leveraging self attention driven gated recurrent unit with crocodile optimization algorithm for cyberattack detection using federated learning framework by Manal Abdullah Alohali, Hatim Dafaalla, Mohammed Baihan, Sultan Alahmari, Achraf Ben Miled, Othman Alrusaini, Ali Alqazzaz, Hanadi Alkhudhayr

“…This study proposes a Self-Attention Mechanism-Driven Federated Learning for Secure Cyberattack Detection with Crocodile Optimization Algorithm (SAMFL-SCDCOA) methodology. …”
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