Inertial measurement unit technology for gait detection: a comprehensive evaluation of gait traits in two Italian horse breeds by Vittoria Asti, Michela Ablondi, Arnaud Molle, Andrea Zanotti, Matteo Vasini, Alberto Sabbioni

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An enhanced machine learning approach with stacking ensemble learner for accurate liver cancer diagnosis using feature selection and gene expression data by Amena Mahmoud, Eiko Takaoka

“…This study presents an advanced machine learning approach for liver cancer diagnosis using gene expression data, combining feature selection techniques with a stacking ensemble learning model. …”
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Estimated Disease Progression Trajectory of White Matter Disruption in Unilateral Temporal Lobe Epilepsy: A Data-Driven Machine Learning Approach by Daichi Sone, Noriko Sato, Yoko Shigemoto, Iman Beheshti, Yukio Kimura, Hiroshi Matsuda

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Deep-Learning-Based Computer-Aided Grading of Cervical Spinal Stenosis from MR Images: Accuracy and Clinical Alignment by Zhiling Wang, Xinquan Chen, Bin Liu, Jinjin Hai, Kai Qiao, Zhen Yuan, Lianjun Yang, Bin Yan, Zhihai Su, Hai Lu

“…<b>Objective:</b> This study aims to apply different deep learning convolutional neural network algorithms to assess the grading of cervical spinal stenosis and to evaluate their consistency with clinician grading results as well as clinical manifestations of patients. …”
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Mitigating Catastrophic Forgetting in Pest Detection Through Adaptive Response Distillation by Hongjun Zhang, Zhendong Yin, Dasen Li, Yanlong Zhao

“…Pest detection in agriculture faces the challenge of adapting to new pest species while preserving the ability to recognize previously learned ones. …”
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Research on unsupervised domain adaptive bearing fault diagnosis method by WU ShengKai, SHAO Xing, WANG CuiXiang, GAO Jun

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A combined feature selection approach for malicious email detection based on a comprehensive email dataset by Han Zhang, Yong Shi, Ming Liu, Libo Chen, Songyang Wu, Zhi Xue

“…We summarize two major challenges in the current field of malicious email detection using machine learning algorithms. (1) Current works on malicious email detection use different datasets and lack a unified and comprehensive open source dataset standard for evaluating detection performance. …”
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Hierarchical Clustering Approach With Distribution Similarity Strategy for Multi-Class Botnet Labeling in Real-World Traffic by Ta-Chun Lo, Che-Hsien Lin, Jyh-Biau Chang, Ce-Kuen Shieh

“…The proposed system effectively overcomes the limitations of existing botnet detection approaches, with its improved performance stemming mainly from its ability to increase the volume of labeled data significantly.…”
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Enhancing highway security and wildlife safety: Mitigating wildlife–vehicle collisions with deep learning and drone technology by Nandutu Irene, Atemkeng Marcellin, Okouma Patrice, Mgqatsa Nokubonga, Fendji Jean Louis Ebongue Kedieng, Tchakounte Franklin

“…Here, we intend to create awareness about wildlife fencing, using drone technology and computer vision algorithms to recognize and detect wildlife fences and associated features. …”
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Multi-point estimation weldment recognition and estimation of pose with data-driven robotics design by Meng XiangYi

“…With the estimated points in the Weldmart, entropy points are tracked and estimated for fault estimation and fault detection. Through the data-driven approach, machine learning model is employed for the recognition and estimation of weldment with the robotics. …”
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Feature Selection for Hypertension Risk Prediction Using XGBoost on Single Nucleotide Polymorphism Data by Lailil Muflikhah, Tirana Noor Fatyanosa, Nashi Widodo, Rizal Setya Perdana, Solimun, Hana Ratnawati

“…This study aims to develop a feature selection model using the XGBoost algorithm to identify specific single nucleotide polymorphisms (SNPs) as biomarkers for detecting hypertension risk. …”
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End-of-Line Quality Control Based on Mel-Frequency Spectrogram Analysis and Deep Learning by Jernej Mlinarič, Boštjan Pregelj, Gregor Dolanc

“…The proposed approach addresses these issues by learning discriminative patterns directly from raw sensor data and automating the classification process. …”
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Overheating Defect Detection of Composite Insulator Based on Mask R-CNN by Yi GAO, Lianfang TIAN, Qiliang DU

“…The results show that the algorithm proposed in this paper has a high detection accuracy of 100% for the infrared images of composite insulators with serious and urgent defects, but has false detection occurrence for the infrared images without overheating defects or with general defects. …”
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A novel smart baby cradle system utilizing IoT sensors and machine learning for optimized parental care by Kunal Chandnani, Suryakant Tripathy, Ashutosh Krishna Parbhakar, Kshitij Takiar, Urvi Singhal, P. Sasikumar, S. Maheswari

“…Microcontrollers like Raspberry Pi and NodeMCU use intelligent machine-learning algorithms to process the collected data and trigger adaptive responses. …”
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Application and Analysis of the MFF-YOLOv7 Model in Underwater Sonar Image Target Detection by Kun Zheng, Haoshan Liang, Hongwei Zhao, Zhe Chen, Guohao Xie, Liguo Li, Jinghua Lu, Zhangda Long

“…Nevertheless, conventional image recognition algorithms encounter several obstacles, including intricate underwater settings, poor-quality sonar image data, and limited sample quantities, which hinder accurate identification. …”
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AI-Powered Eye Tracking for Bias Detection in Online Course Reviews: A Udemy Case Study by Hedda Martina Šola, Fayyaz Hussain Qureshi, Sarwar Khawaja

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Towards Safer Cities: AI-Powered Infrastructure Fault Detection Based on YOLOv11 by Raiyen Z. Rakin, Mahmudur Rahman, Kanij F. Borsa, Fahmid Al Farid, Shakila Rahman, Jia Uddin, Hezerul Abdul Karim

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MicrocrackAttentionNext: Advancing Microcrack Detection in Wave Field Analysis Using Deep Neural Networks Through Feature Visualization by Fatahlla Moreh, Yusuf Hasan, Bilal Zahid Hussain, Mohammad Ammar, Frank Wuttke, Sven Tomforde

“…This extreme class imbalance poses a challenge for deep learning models with different microscale cracks, as the network can be biased toward predicting the majority class, generally leading to poor detection accuracy for the under-represented class. …”
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An end-to-end real-time pollutants spilling recognition in wastewater based on the IoT-ready SENSIPLUS platform by Luca Gerevini, Gianni Cerro, Alessandro Bria, Claudio Marrocco, Luigi Ferrigno, Michele Vitelli, Andrea Ria, Mario Molinara

“…The data processing is based on a commercial Micro Control Unit (MCU), including an anomaly detection module, a classification module, and a false positive reduction module, all based on machine learning algorithms that have a computational complexity suitable for low-cost hardware implementation.An extensive experimental campaign on different contaminants has been carried out to train machine-learning algorithms suitable for low-cost and low-power MCU. …”
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IDEEA: information diffusion model for integrating gene expression and EEG data in identifying Alzheimer’s disease markers by Enes Ozelbas, Tuba Sevimoglu, Tamer Kahveci

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