A COMPARATIVE ANALYSIS OF DEEP TRANSFER LEARNING TECHNIQUES FOR MAMMOGRAPHIC IMAGE CLASSIFICATION by Bhavesh Gupta, Akshay Singh, Anjana Gosain

“…For the same, the Deep Learning algorithms with transfer learning models are utilized, already trained with ImageNet database, and partially training them on the small mammography images database and thus help to diagnose it without the need for large datasets or tissue analysis (biopsy). …”
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A Rule-Based Agent for Unmanned Systems with TDGG and VGD for Online Air Target Intention Recognition by Li Chen, Jing Yang, Yuzhen Zhou, Yanxiang Ling, Jialong Zhang

“…Finally, to have a performance evaluation and application analysis for the algorithm, we carried out a data instance analysis of ATIR for unmanned systems and an air defense warfare simulation experiment based on a Wargame platform; the comparative experiments with the classical k-means, FCNIRM, and the sector-based forward search method verified the effectiveness and feasibility of the proposed agent, which characterizes it as a promising tool or baseline model for the battlefield situational awareness tasks of unmanned systems.…”
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Improving thyroid disorder diagnosis via innovative stacking ensemble learning model by Ayesha Hassan, Shabana Ramzan, Ali Raza, Muhammad Munwar Iqbal, Aseel Smerat, Norma Latif Fitriyani, Muhammad Syafrudin, Seung Won Lee

“…Results A 10-fold cross-validation technique is utilized to ensure robust model evaluation and reduce the risk of overfitting by using one test set for each subset and training on the rest of the subsets. …”
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A new method for early diagnosis and treatment of meniscus injury of knee joint in student physical fitness tests based on deep learning method by Yan Fang, Lu Liu, Qingyu Yang, Shuang Hao, Zhihai Luo

“…The method under consideration has been subjected to evaluation using a well-recognized dataset comprising MRI images knee joint injuries. …”
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ML-Based Control Strategy for PHEV Under Predictive Vehicle Usage Behaviour by Aleksandr Doikin, Aleksandr Korsunovs, Felician Campean, Oscar García-Afonso, Enrico Agostinelli

“…This study, based on extended real-world data (journeys history from 10 vehicles over 12 months), shows that trip patterns can be learnt quite effectively using classic ML classification algorithms. In particular, the RusBoosted ensemble classifier performed consistently well across the heterogeneous dataset (volume of data for training and variable imbalance in the datasets, reflecting the natural variability in the vehicle usage profiles), providing sufficiently accurate predictions for the proposed EMS strategy. …”
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Spatiotemporal inhomogeneity of accuracy degradation in AI weather forecast foundation models: A GNSS perspective by Junsheng Ding, Wu Chen, Junping Chen, Jungang Wang, Yize Zhang, Lei Bai, Yuyan Wang, Xiaolong Mi, Tong Liu, Duojie Weng

“…This temporal and spatial inhomogeneity of accuracy and accuracy degradation are related to AI algorithms and attributes of training data, etc., but these characteristics have not been thoroughly explored and analyzed. …”
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An Effective ABC-SVM Approach for Surface Roughness Prediction in Manufacturing Processes by Juan Lu, Xiaoping Liao, Steven Li, Haibin Ouyang, Kai Chen, Bing Huang

“…Further, to evaluate the optimization performance of ABC in parameters determination of SVM, this study compares the prediction performance of SVM models optimized by well-known evolutionary and swarm-based algorithms (differential evolution (DE), genetic algorithm (GA), particle swarm optimization (PSO), and ABC) and analyzes ability of these optimization algorithms from their optimization mechanism and convergence speed based on experimental datasets of turning and milling. …”
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Open Soil Spectral Library (OSSL): Building reproducible soil calibration models through open development and community engagement. by José L Safanelli, Tomislav Hengl, Leandro L Parente, Robert Minarik, Dellena E Bloom, Katherine Todd-Brown, Asa Gholizadeh, Wanderson de Sousa Mendes, Jonathan Sanderman

“…From independent model evaluation, we found that Cubist comes out as the best-performing ML algorithm for the calibration and delivery of reliable outputs (prediction uncertainty and representation flag). …”
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Delineating flood susceptibility zones using novel ensemble models – An application of evidential belief function, relative frequency ratio, and Shannon entropy by Samuel Yaw Danso, Yi Ma, Isaac Yeboah Addo

“…Furthermore, 13 conditioning parameters were chosen via multicollinearity evaluation. Three bivariate statistical algorithms, namely evidential belief function (EBF), relative frequency ratio (RFR), and Shannon entropy (SE) were combined through basic arithmetic operations to produce nine ensemble scenarios. …”
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Design and assessment of AI-based learning tools in higher education: a systematic review by Jihao Luo, Chenxu Zheng, Jiamin Yin, Hock Hai Teo

“…This study addresses this gap through a systematic literature review with two main objectives: (1) to summarize the design features of AI-based learning tools currently employed in higher education, focusing on aspects such as algorithm types, training datasets, modes of information presentation, and their roles in the learning process; and (2) to assess their impacts on college students’ cognitive, skill-based, and affective learning outcomes. …”
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Fuzzy Random Prediction Model of Frost Heave Characteristics of Horizontal Frozen Metro Contact Channel in Coastal Area by Yao Yafeng, Zhang Zhemei, Wang Wei, Li Yongheng, Li Siqi, Wei Chenguang

“…With the aim of improving the deficiency of traditional BP neural network algorithms in solving fuzzy random engineering problems, random factor and mean square error between layers are used to modify the evaluation function of the network model. …”
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Artificial Intelligence and Li Ion Batteries: Basics and Breakthroughs in Electrolyte Materials Discovery by Haneen Alzamer, Russlan Jaafreh, Jung-Gu Kim, Kotiba Hamad

“…In this study, we outlined the fundamental processes involved in applying AI to this domain, including data processing, feature engineering, model training, testing, and validation. We also discussed the quantitative evaluation of structure–property relationships in electrolytic systems, which is guided by AI methods. …”
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A Study of Deep Learning Models for Audio Classification of Infant Crying in a Baby Monitoring System by Denisa Maria Herlea, Bogdan Iancu, Eugen-Richard Ardelean

“…This paper presents a comprehensive evaluation of deep learning models for infant cry detection, analyzing the performance of various architectures on spectrogram and MFCC feature representations. …”
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Enhanced water saturation estimation in hydrocarbon reservoirs using machine learning by Ali Akbari, Ali Ranjbar, Yousef Kazemzadeh, Dmitriy A. Martyushev

“…Nine well log parameters—Depth (DEPT), High-Temperature Neutron Porosity, True Resistivity, Computed Gamma Ray, Spectral Gamma Ray, Hole Caliper, Compressional Sonic Travel Time, Bulk Density, and Temperature—were used as input features to train and test five ML algorithms: Linear Regression, Support Vector Machine (SVM), Random Forest, Least Squares Boosting, and Bayesian methods. …”
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Prediction of river dissolved oxygen (DO) based on multi-source data and various machine learning coupling models. by Yubo Zhao, Mo Chen

“…Firstly, DWT-db4 was used to denoise the noisy water quality feature data; secondly, the meteorological data were simplified into four principal components by KPCA; finally, the water quality features and meteorological principal components were inputted into the GWO-optimized XGBoost model as features for training and prediction. The prediction performance of the model was comprehensively assessed by comparison with other machine learning models using MAE, MSE, MAPE, NSE, KGE and WI evaluation metrics. …”
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Surrogate modeling of passive microwave circuits using recurrent neural networks and domain confinement by Kaustab C. Sahu, Slawomir Koziel, Anna Pietrenko-Dabrowska

“…The proposed procedure ensures building models of outstanding predictive power while using small training datasets, which is beyond the capabilities of benchmark algorithms.…”
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Inferring Travel Modes from Cellular Signaling Data Based on the Gated Recurrent Unit Neural Network by Yanchen Wang, Fei Yang, Li He, Haode Liu, Li Tan, Cheng Wang

“…Taking F score as an example, the outcome of the GRU-based method is about 6% to 7% higher than methods based on other machine learning algorithms. Considering the identification accuracy and model training time comprehensively, the method suggested in this paper outperforms the other three deep learning-based methods, namely, recurrent neural network (RNN), long short-term memory network (LSTM), and bidirectional long short-term memory network (Bi-LSTM). …”
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Data-driven axial compressive strength investigation of FRP-confined coral aggregate concrete by Chang Zhou, Kai-Di Peng, Yu-Lei Bai

“…A dataset comprising 115 samples is created, and eight input features are selected for developing and evaluating ML models. Besides, six empirical formulae are used to compare their performance against the ML models. …”
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Environmental Risk Mitigation via Deep Learning Modeling of Compressive Strength in Green Concrete Incorporating Incinerator Ash by Amin Amraee, Seyed Azim Hosseini, Farshid Farokhizadeh, Mohammad Hassan Haeri

“…A database for deep learning modeling was created using Convolutional Neural Networks (CNNs) and the Multi-Verse Optimizer (MVO) algorithm. After evaluating the efficiency and structure of the deep learning model through MATLAB coding, the focus shifted to analyzing the sensitivity of the input parameters on the output parameter using MATLAB for coding, training, and evaluation. …”
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Development of an Optimal Machine Learning Model to Predict CO₂ Emissions at the Building Demolition Stage by Gi-Wook Cha, Choon-Wook Park

“…It exhibited very high accuracy with R² values of 0.997, 0.983, and 0.984 for the training, test, and validation sets, respectively. …”
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