Comparative Study of Time Series Analysis Algorithms Suitable for Short-Term Forecasting in Implementing Demand Response Based on AMI by Myung-Joo Park, Hyo-Sik Yang

“…This paper compares four time series forecasting algorithms—ARIMA, SARIMA, LSTM, and SVM—suitable for short-term load forecasting using Advanced Metering Infrastructure (AMI) data. …”
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EDGE DETECTION TECHNIQUE BASED ON BILATERAL FILTERING AND ITERATIVE THRESHOLD SELECTION ALGORITHM AND TRANSFER LEARNING FOR TRAFFIC SIGN RECOGNITION by Milind PARSE, Dhanya PRAMOD

“…Furthermore, the precision rate, recall rate, and F1 scores are evaluated for the CNN models. With the German Traffic Sign Benchmark database (GTSRB), the proposed algorithm and Inception V3 CNN model gives promising results when it receives the edge-detected images for training and testing.…”
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Development of an artificial intelligence-based algorithm to classify images acquired with an intraoral scanner of individual molar teeth into three categories. by Nozomi Eto, Junichi Yamazoe, Akiko Tsuji, Naohisa Wada, Noriaki Ikeda

“…Then, the algorithm was generated through transfer learning and training with images acquired from cadavers by intraoral scanning. …”
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Optimal intrusion detection for imbalanced data using Bagging method with deep neural network optimized by flower pollination algorithm by Hussein Ridha Sayegh, Wang Dong, Bahaa Hussein Taher, Muhanad Mohammed Kadum, Ali Mansour Al-madani

“…The RB Bagging strategy derives multiple RB training subsets from the original dataset and proper class weights are incorporated into the fitness function to attain unbiased DNN models. …”
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Machine learning algorithms for diabetic kidney disease risk predictive model of Chinese patients with type 2 diabetes mellitus by Lu-Xi Zou, Xue Wang, Zhi-Li Hou, Ling Sun, Jiang-Tao Lu

“…This study aimed to set up DKD risk prediction models based on machine learning algorithms (MLAs) in patients with type 2 DM (T2DM).Methods The electronic health records of 12,190 T2DM patients with 3-year follow-ups were extracted, and the dataset was divided into a training and testing dataset in a 4:1 ratio. …”
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Predicting Weaning Weight of Romanov Lambs From Biometric Measurements Before Weaning Age Using Machine Learning Algorithms by Mehmet Eroğlu, Ali Osman Turgut, Mürsel Küçük, Muhammed Furkan Önen

“…The study employed random forest (RF), classification and regression trees (CART), gradient boosting (GB), eXtreme gradient boosting (XGBoost) and CatBoost algorithms. The data was standardized to eliminate scale differences and divided into training (80%) and test (20%) sets. …”
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Research on Collaborative Optimization Strategy of Railway Signal Nonlinear Control System Based on BBO Algorithm and Multi-objective Optimization by Xue Li, Yixuan Yang, Zheng Li, Hui He

“…This study focuses on exploring collaborative optimization strategies for a nonlinear control system of railway signals based on the BBO algorithm. Currently, the railway signal control system faces performance bottlenecks such as response lag and local optima due to parameter coupling when dealing with multi-objective optimization problems like train operating speed and signal delays. …”
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Identification of matrix stiffness-related molecular subtypes in HCC via integrating multi-omics analysis and machine learning algorithms by Hanqi Li, Jiayi Zhang, Yu Shi, Huanhuan Wang, Ruida Yang, Shaobo Wu, Yue Li, Xue Yang, Qingguang Liu, Liankang Sun

“…A matrix stiffness-related signature comprising 57 genes was constructed by evaluating 101 machine learning algorithm combinations. …”
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An artificial intelligence algorithm for the detection of pulmonary ground-glass nodules on spectral detector CT: performance on virtual monochromatic images by Zhong-Yan Ma, Hai-lin Zhang, Fa-jin Lv, Wei Zhao, Dan Han, Li-chang Lei, Qin Song, Wei-wei Jing, Hui Duan, Shao-Lei Kang

“…Abstract Background This study aims to assess the performance of an established an AI algorithm trained on conventional polychromatic computed tomography (CT) images (CPIs) to detect pulmonary ground-glass nodules (GGNs) on virtual monochromatic images (VMIs), and to screen the optimal virtual monochromatic energy for the clinical evaluation of GGNs. …”
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Enhanced ANN-Based MPPT for Photovoltaic Systems: Integrating Metaheuristic and Analytical Algorithms for Optimal Performance Under Partial Shading by Alpaslan Demirci, Idriss Dagal, Said Mirza Tercan, Hasan Gundogdu, Musa Terkes, Umit Cali

“…Furthermore, the ANN model was developed and trained using an extensive dataset that includes diverse shading scenarios, irradiation levels, and temperature conditions, with metaheuristic algorithms playing a key role in enhancing its training process. …”
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Coffee Leaf Rust Disease Detection and Implementation of an Edge Device for Pruning Infected Leaves via Deep Learning Algorithms by Raka Thoriq Araaf, Arkar Minn, Tofael Ahamed

“…All labeled images were used to train the YOLOv5 and YOLOv8 algorithms through the convolutional neural network (CNN). …”
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Artificial intelligence-based prescription of personalized scalp cosmetics improved the scalp condition: efficacy results from 100 participants by Bo Ri Kim, Min Jae Kim, Jieun Koo, Hwa-Jung Choi, Kyung Ho Paik, Soon Hyo Kwon, Hye-Ryung Choi, Chang Hun Huh, Jung Won Shin, Dong-sun Park, Jung-Im Na

“…However, it requires trained evaluators.Aim: To develop artificial intelligence (AI) algorithms for assessment of scalp conditions and to assess the feasibility of AI-based recommendations on personalized scalp cosmetics.Methods: Using EfficientNet, convolutional neural network (CNN) models (SPI-AI) ofeach scalp feature were established. 101,027 magnified scalp images graded according to the SPI scoring were used for training, validation, and testing the model Adults with scalp discomfort were prescribed shampoos and scalp serums personalized according to their SPI-AI-defined scalp types. …”
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Causal Discovery Evaluation Framework in the Absence of Ground-Truth Causal Graph by Tingpeng Li, Lei Wang, Danhua Peng, Jun Liao, Li Liu, Zhendong Liu

“…To address this, we propose a novel method for evaluating causal discovery algorithms without needing real causal graphs. …”
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Advanced neural network modeling with Levenberg–Marquardt algorithm for optimizing tri-hybrid nanofluid dynamics in solar HVAC systems by A. Aziz, S.A.H. Shah, H.M.S. Bahaidarah, T. Zamir, T. Aziz

“…To address this, we propose a nanofluid-based thermal cooling model and develop an advanced computational solver using an Artificial Neural Network (ANN) trained with the Levenberg–Marquardt algorithm (LMA-TNN). …”
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Supervised machine learning algorithms for the classification of obesity levels using anthropometric indices derived from bioelectrical impedance analysis by Rodrigo Yáñez-Sepúlveda, Aldo Vásquez-Bonilla, Rodrigo Olivares, Pablo Olivares, Juan Pablo Zavala-Crichton, Claudio Hinojosa-Torres, Catalina Muñoz-Strale, Frano Giakoni-Ramírez, Josivaldo de Souza-Lima, Jacqueline Páez-Herrera, Jorge Olivares-Arancibia, Tomás Reyes-Amigo, Guillermo Cortés-Roco, Juan Hurtado-Almonacid, Eduardo Guzmán-Muñoz, Nicole Aguilera-Martínez, José Francisco López-Gil, Boryi A. Becerra-Patiño, Juan David Paucar-Uribe, Exal Garcia-Carrillo, Vicente Javier Clemente-Suárez

“…This study aimed to evaluate and compare the performance of various supervised machine learning algorithms in classifying obesity levels using anthropometric indices derived from bioelectrical impedance analysis (BIA). …”
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A comparison of supervised machine learning algorithms and feature vectors for MS lesion segmentation using multimodal structural MRI. by Elizabeth M Sweeney, Joshua T Vogelstein, Jennifer L Cuzzocreo, Peter A Calabresi, Daniel S Reich, Ciprian M Crainiceanu, Russell T Shinohara

“…Each MRI study has a manual lesion segmentation that we use to train and validate the supervised classification algorithms. …”
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Predicting COVID-19 severity in pediatric patients using machine learning: a comparative analysis of algorithms and ensemble methods by Babak Pourakbari, Setareh Mamishi, Sepideh Keshavarz Valian, Shima Mahmoudi, Reihaneh Hosseinpour Sadeghi, Mohammad Reza Abdolsalehi, Mahmoud Khodabandeh, Mohammad Farahmand

“…Machine learning offers a powerful approach to analyzing complex datasets and predicting outcomes, yet its application in pediatric COVID-19 remains limited. This study evaluates the performance of machine learning algorithms in predicting disease severity among pediatrics. …”
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Decoding basic emotional states through integration of an fNIRS-based brain-computer interface with supervised learning algorithms. by Ayşenur Eser, Sinem Burcu Erdoğan

“…Classification performances of three machine learning algorithms, namely the k-Nearest Neighbors (kNN), Ensemble (Subspace kNN) and Support Vector Machines (SVM), in two class and three class classification of positive, neutral and negative states were evaluated with ten runs of a tenfold cross-validation procedure through splitting the data into test, train and validation groups at each run. …”
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DS-YOLO: A dense small object detection algorithm based on inverted bottleneck and multi-scale fusion network by Hongyu Zhang, Guoliang Li, Dapeng Wan, Ziyue Wang, Jinshun Dong, Shoujun Lin, Lixia Deng, Haiying Liu

“…To address this significant challenge, Dense and Small YOLO (DS-YOLO), a dense small object detection algorithm based on YOLOv8s, is proposed in this paper. …”
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Combining machine learning algorithms for bridging gaps in GRACE and GRACE Follow-On missions using ERA5-Land reanalysis by Jaydeo K. Dharpure, Ian M. Howat, Saurabh Kaushik, Bryan G. Mark

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