Impact of occupancy behavior on building energy efficiency: What’s next in detection and monitoring technologies? by Wenjie Song, John Calautit

“…Particular attention is paid to data-driven methods, including probabilistic models such as Hidden Markov Models (HMMs), classical machine learning algorithms such as Support Vector Machines (SVMs) and K-Nearest Neighbors (KNN), and deep learning architectures such as Convolutional Neural Networks (CNNs), all of which have demonstrated high accuracy in both laboratory and real-world settings. …”
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Developing a Robust Fuzzy Inference Algorithm in a Dog Disease Pre-Diagnosis System for Casual Owners by Kwang Baek Kim, Doo Heon Song, Hyun Jun Park

“…We evaluated three fuzzy inference algorithms—PFCM-R, FHAL, and MNFL. While PFCM-R achieved high accuracy with clean data, it struggled with noisy inputs. …”
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Variational Autoencoder Based Anomaly Detection in Large-Scale Energy Storage Power Stations by Tuo Ji, Pinghu Xu, Dongliang Guo, Lei Sun, Kangji Ma, Yanan Wang, Xuebing Han

“…This study employs an unsupervised deep learning model based on variational autoencoders (VAEs) to perform anomaly detection on real operational data. …”
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An efficient interpretable framework for unsupervised low, very low and extreme birth weight detection. by Ali Nawaz, Amir Ahmad, Shehroz S Khan, Mohammad Mehedy Masud, Nadirah Ghenimi, Luai A Ahmed

“…While traditional approaches to managing class imbalance require labeled data, our study explores the use of unsupervised learning to detect anomalies indicative of low birth weight scenarios. …”
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CLUSTERING ON DISSIMILARITY REPRESENTATIONS FOR DETECTING MISLABELLED SEISMIC SIGNALS AT NEVADO DEL RUIZ VOLCANO by Castellanos-Domínguez César Germán, Alzate Mauricio Orozco

“…In order to reduce the workload for the seismic analysts and to turn classification reliable<br />and objective, the use of supervised learning algorithms has been explored; particularly classifiers built in dissimilarity spaces. …”
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Smart and Secure Healthcare with Digital Twins: A Deep Dive into Blockchain, Federated Learning, and Future Innovations by Ezz El-Din Hemdan, Amged Sayed

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Application of machine learning in predicting adolescent Internet behavioral addiction by Yao Gan, Li Kuang, Xiao-Ming Xu, Ming Ai, Jing-Lan He, Wo Wang, Su Hong, Jian mei Chen, Jun Cao, Qi Zhang

“…ObjectiveTo explore the risk factors affecting adolescents’ Internet addiction behavior and build a prediction model for adolescents’ Internet addiction behavior based on machine learning algorithms.MethodsA total of 4461 high school students in Chongqing were selected using stratified cluster sampling, and questionnaires were administered. …”
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Deep Learning Automated System for Thermal Defectometry of Multilayer Materials by A. S. Momot, R. M. Galagan, V. Yu. Gluhovskii

“…Three neural network modules are used for automated data processing, each of which performs one of the tasks: defects detection and classification, determination of the defect depth and thickness. …”
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Bio-Inspired Object Detection and Tracking in Aerial Images: Harnessing Northern Goshawk Optimization by Agnivesh Pandey, Rohit Raja, Sumit Srivastava, Krishna Kumar, Manoj Gupta, Chanyanan Somthawinpongsai, Aziz Nanthaamornphong

“…Object detection and tracking are crucial tasks in various industries, prompting increased exploration of machine learning, particularly deep learning techniques. …”
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A Review on Machine Learning Models in Injection Molding Machines by Senthil Kumaran Selvaraj, Aditya Raj, R. Rishikesh Mahadevan, Utkarsh Chadha, Velmurugan Paramasivam

“…Some problems include data division, collection, and preprocessing steps, such as considering the inputs, networks, and outputs, algorithms used, models utilized for testing and training, and performance criteria set during validation and verification. …”
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Weirdnodes: centrality based anomaly detection on temporal networks for the anti-financial crime domain by Salvatore Vilella, Arthur Capozzi, Marco Fornasiero, Dario Moncalvo, Valeria Ricci, Silvia Ronchiadin, Giancarlo Ruffo

“…We address this problem with WeirdNodes, a centrality-based methodology for ranked anomaly detection in temporal networks. Unlike many existing approaches that rely on rule-based algorithms or general machine learning models, WeirdNodes harnesses the evolving structure and relationships within financial transaction networks. …”
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Multiobject Tracking in Videos Based on LSTM and Deep Reinforcement Learning by Ming-xin Jiang, Chao Deng, Zhi-geng Pan, Lan-fang Wang, Xing Sun

“…In this paper, we propose a multiobject tracking algorithm in videos based on long short-term memory (LSTM) and deep reinforcement learning. …”
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Unsupervised Learning for Heart Disease Prediction: Clustering-Based Approach by Jetty Janani., Sk Sajida Sultana., Polepalle Ranga Bhavitha., Parusu Vishwitha.

“…This paper on the prediction of heart disease addresses the application of unsupervised machine learning algorithms, digs up the latent pattern of risk in the data of patients for early diagnosis, and intervenes. …”
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Bioinformatics and machine learning-driven key genes screening for vortioxetine by Sabire Kılıçarslan, Meliha Merve Hız

“…The original datasets were preprocessed in the second step by detecting and correcting missing and noisy data and then merged. …”
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Trustworthiness of Deep Learning Under Adversarial Attacks in Power Systems by Dowens Nicolas, Kevin Orozco, Steve Mathew, Yi Wang, Wafa Elmannai, George C. Giakos

“…In power grids, DL models such as Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks are commonly utilized for tasks like state estimation, load forecasting, and fault detection, depending on their ability to learn complex, non-linear patterns in high-dimensional data such as voltage, current, and frequency measurements. …”
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Improving EEG based brain computer interface emotion detection with EKO ALSTM model by R. Kishore Kanna, Preety Shoran, Meenakshi Yadav, Mohammad Nadeem Ahmed, Shrikant Burje, Garima Shukla, Anurag Sinha, Mohammad Rashid Hussain, Saifullah Khalid

“…The data was pre-processed using a bandpass filter to remove unwanted frequency noise for the obtained data. …”
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Machine learning‐assisted point‐of‐care diagnostics for cardiovascular healthcare by Kaidong Wang, Bing Tan, Xinfei Wang, Shicheng Qiu, Qiuping Zhang, Shaolei Wang, Ying‐Tzu Yen, Nan Jing, Changming Liu, Xuxu Chen, Shichang Liu, Yan Yu

“…Recent breakthroughs in computing power and algorithmic design, particularly deep learning frameworks that emulate neural processes, have revolutionized POC devices for CVDs, enabling more frequent detection of abnormalities and automated, expert‐level diagnosis. …”
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Anti‐interference lithium‐ion battery intelligent perception for thermal fault detection and localization by Luyu Tian, Chaoyu Dong, Rui Wang, Yunfei Mu, Hongjie Jia

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Capturing the songs of mice with an improved detection and classification method for ultrasonic vocalizations (BootSnap). by Reyhaneh Abbasi, Peter Balazs, Maria Adelaide Marconi, Doris Nicolakis, Sarah M Zala, Dustin J Penn

“…A-MUD and USVSEG outperformed the other methods in terms of true positive rates using default and adjusted settings, respectively, and A-MUD outperformed USVSEG when false detection rates were also considered. For automating the classification of USVs, we developed BootSnap for supervised classification, which combines bootstrapping on Gammatone Spectrograms and Convolutional Neural Networks algorithms with Snapshot ensemble learning. …”
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Cell‐free epigenomes enhanced fragmentomics‐based model for early detection of lung cancer by Yadong Wang, Qiang Guo, Zhicheng Huang, Liyang Song, Fei Zhao, Tiantian Gu, Zhe Feng, Haibo Wang, Bowen Li, Daoyun Wang, Bin Zhou, Chao Guo, Yuan Xu, Yang Song, Zhibo Zheng, Zhongxing Bing, Haochen Li, Xiaoqing Yu, Ka Luk Fung, Heqing Xu, Jianhong Shi, Meng Chen, Shuai Hong, Haoxuan Jin, Shiyuan Tong, Sibo Zhu, Chen Zhu, Jinlei Song, Jing Liu, Shanqing Li, Hefei Li, Xueguang Sun, Naixin Liang

“…Plasma cfDNA was analysed for its epigenetic and fragmentomic profiles using chromatin immunoprecipitation sequencing, reduced representation bisulphite sequencing and low‐pass whole‐genome sequencing. Machine learning algorithms were then employed to integrate the multi‐omics data, aiding in the development of a precise lung cancer detection model. …”
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