Convolutional Variational Autoencoder for Anomaly Detection in On-Load Tap Changers by Fataneh Dabaghi-Zarandi, Hassan Ezzaidi, Michel Gauvin, Patrick Picher, Issouf Fofana, Vahid Behjat

“…Several thresholds based on reconstruction errors are evaluated to detect anomalies, achieving optimal thresholds for each family. …”
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Detection of Hydration on Nominally Anhydrous S-complex Main Belt Asteroids by Maggie McAdam, Cristina Thomas, Lauren McGraw, Andrew Rivkin, Joshua Emery

“…The majority of the observations (27 of 33) have a detectable 3 μ m feature that has at least 1% band depth or greater (within error), indicating the presence of hydration. …”
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Genderly: a data-centric gender bias detection system by Wael Khreich, Jad Doughman

“…Findings reveal that optimized ML models detect surface-level biases effectively, while LLMs excel in identifying biases embedded in semantics, with benevolent sexism being particularly challenging. …”
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Deep Learning-Based Crack Detection on Cultural Heritage Surfaces by Wei-Che Huang, Yi-Shan Luo, Wen-Cheng Liu, Hong-Ming Liu

“…This study employs a deep learning-based object detection model, GoogleNet, to identify cracks in cultural heritage images. …”
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The Development of Digital Twin Baby Incubators for Fault Detection and Performance Analysis by Hatice Kabaoğlu, Emine Uçar, Fecir Duran

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Exploration of Weak Peak Detection Method Based on Gamma Spectra Deconvolution by SONG Yiming, ZHOU Qianqian, CHEN Ye, LIU Dahai, ZHOU Hongzhao, XIAO Wuyun

“…Therefore, it is proved that the method based on spectrum deconvolution to detect weak peaks can effectively improve the weak peaks detection capability of low resolution spectrometers. …”
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Real-time locomotion mode detection in individuals with transfemoral amputation and osseointegration by Bahareh Ahkami, Morten B. Kristoffersen, Max Ortiz-Catalan

“…On average, offline classification resulted in a mean error of 5.7%, while the corresponding mean error during online (real-time) evaluation was 11.6%. …”
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Direct blind multiuser detection without channel estimation and equalizer design by ZHANG Zhi-yong1, LING Yun2

“…The blind equalization and multiuser detection problem for synchronous high-rate DS-CDMA systems in dispersive channels was considered. …”
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Evaluation of paired-end sequencing strategies for detection of genome rearrangements in cancer. by Ali Bashir, Stanislav Volik, Colin Collins, Vineet Bafna, Benjamin J Raphael

“…We further demonstrate how the ability to detect fusion genes depends on the distribution of gene lengths, and we evaluate how different parameters of a sequencing strategy impact breakpoint detection, breakpoint localization, and fusion gene detection, even in the presence of errors that suggest false rearrangements. …”
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Detection of breast cancer using machine learning and explainable artificial intelligence by Tharunya Arravalli, Krishnaraj Chadaga, H Muralikrishna, Niranjana Sampathila, D. Cenitta, Rajagopala Chadaga, K. S. Swathi

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TSHD: Topic Segmentation Based on Headings Detection (Case Study: Resumes) by Majd E. Tannous, Wassim H. Ramadan, Mohanad A. Rajab

“…Evaluation results show a very high F1 score (about 96%) and a very low segmentation error (about 2%). The algorithm can be easily adapted to deal with other textual domains that contain headings in their segments.…”
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Fault detection in beam structure using adaptive immune based approach by Sasmita Sahu, Shakti P. Jena

“…Recently with the application of machine learning approaches and the soft computing, the damage can be detected easily. In this methodology, Clonal Section Algorithm (CSA) has been applied to find out the faults (crack locations and depth) in the structure initially. …”
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Building Damage Visualization Through Three-Dimensional Reconstruction and Window Detection by Ittetsu Kuniyoshi, Itsuki Nagaike, Sachie Sato, Yue Bao

“…Results show that the proposed approach achieved measurement accuracy comparable to or better than traditional methods, with an error reduction of approximately 19% in prefabricated structures and 21.72% in detached houses. …”
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Detection of Banana Diseases Based on Landsat-8 Data and Machine Learning by Renata Retkute, Kathleen S. Crew, John E. Thomas, Christopher A. Gilligan

“…For TR4 in Mozambique, the approach successfully tracked disease progression, revealing clear spatial spread patterns and correlations as high as 0.98 between VI anomalies and disease cases in some pixels. These findings support the potential of our method as a scalable early warning system for banana disease detection.…”
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EXIT Chart Analysis of Expectation Propagation-Based Iterative Detection and Decoding by Fuga Kobayashi, Takumi Takahashi, Shinsuke Ibi, Hideki Ochiai

“…Extensive simulations of multiple-input multiple-output (MIMO) signal detection indicate that EP-based IDD provides superior bit error rate (BER) performance compared to turbo equalization, especially in demanding scenarios involving higher modulation orders and increased spatial multiplexing loads. …”
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Nanoscale Organic Contaminant Detection at the Surface Using Nonlinear Bond Model by Hendradi Hardhienata, Muhammad Ahyad, Fasya Nabilah, Husin Alatas, Faridah Handayasari, Agus Kartono, Tony Sumaryada, Muhammad D. Birowosuto

“…We find for the first time that the SBHM not only identifies the different organic pollutant dyes on the surface, as in conventional SHG detection, but can also determine their relative orientation and different concentrations on the surface. …”
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On-Device System for Device Directed Speech Detection for Improving Human Computer Interaction by Abhishek Singh, Rituraj Kabra, Rahul Kumar, Manjunath Belgod Lokanath, Reetika Gupta, Sumit Kumar Shekhar

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Hydropower Station Status Prediction Using RNN and LSTM Algorithms for Fault Detection by Omar Farhan Al-Hardanee, Hüseyin Demirel

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An Integrated Framework for Cryptocurrency Price Forecasting and Anomaly Detection Using Machine Learning by Hani Alnami, Muhammad Mohzary, Basem Assiri, Hussein Zangoti

“…The deep learning model indicates strong generalization capabilities, suggesting potential advantages on more complex datasets. These findings highlight the importance of combining advanced machine learning techniques and cryptocurrencies to develop a robust framework for cryptocurrency forecasting and anomaly detection.…”
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A Secure and Robust Machine Learning Model for Intrusion Detection in Internet of Vehicles by Pradeep Kumar Tiwari, Shiv Prakash, Animesh Tripathi, Tiansheng Yang, Rajkumar Singh Rathore, Manish Aggarwal, Narendra Kumar Shukla

“…The experimental findings demonstrate the superior performance of the proposed model in detecting and classifying intrusions within IoV.…”
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