HouseGanDi: A Hybrid Approach to Strike a Balance of Sampling Time and Diversity in Floorplan Generation by Azmeraw Bekele Yenew, Beakal Gizachew Assefa, Elefelious Getachew Belay

“…Floorplan synthesis is the process of generating new, realistic floor plans for buildings and homes using machine learning and generative models. In recent years, various generative methods, including GANs and diffusion models, have been utilized for the task of floorplan generation, demonstrating promising advancements in architectural design and planning. …”
Get full text

Time series extrinsic regression for reconstructing missing electron temperature in tokamak by Minglong Wang, Chenguang Wan, Jingjing Lu, Zhi Yu, Bingjia Xiao, Yanlong Li, Xiaoxue He, Zhengping Luo, Qiping Yuan, Yemin Hu, Jiangang Li

“…Sensor failures, data acquisition errors, or limitations in diagnostic systems (e.g. absent electron temperature diagnostic) pose significant challenges for experimental data analysis, physical integrated simulation, and the design and optimization of tokamak experiment. To address this, we propose a data-driven machine learning approach based on time series extrinsic regression (TSER) to reconstruct missing electron temperature data in tokamak experiments. …”
Get full text

Deep reinforcement learning Lane-Change Decision-Making for autonomous vehicles based on motion primitives library in hierarchical action space by Guizhe Jin, Zhuoren Li, Bo Leng, Minhua Shao

Get full text

A hybrid framework for estimating photovoltaic dust content based on UAV hyperspectral images by Peng Zhu, Hao Li, Pan Zheng

Get full text

Comparison of CNN Architectures for Pre-Cancerous Cervical Lesion Classification Based on Colposopy Images Using IARC and AnnoCerv Datasets by Sigit Prasetyo Noprianto, Siti Nurmaini, Dian Palupi Rini

Get full text

A Pipeline for Multivariate Time Series Forecasting of Gas Consumption in Pelletization Process by Thadeu Pezzin Melo, Jefferson Andrade, Karin Satie Komati

“…Although AutoML did not outperform the statistical model in terms of RMSE values, regarding training time, AutoML models were significantly more efficient than the statistical approach, optimizing computational resource usage and enabling faster model adjustments. …”
Get full text

A pre-trained deep potential model for sulfide solid electrolytes with broad coverage and high accuracy by Ruoyu Wang, Mingyu Guo, Yuxiang Gao, Xiaoxu Wang, Yuzhi Zhang, Bin Deng, Mengchao Shi, Linfeng Zhang, Zhicheng Zhong

“…Yet most existing machine-learning models are trained on narrow chemistry, requiring retraining for each new system, which wastes transferable knowledge and incurs significant cost. …”
Get full text

Assessing habitat suitability for aoudad (Ammotragus lervia) reintroduction in southeastern morocco to promote ecotourism by Lahbib Naimi, El Mahi Bouziane, Lamya Benaddi, Abdeslam Jakimi, Mohamed Manaouch

“…To begin with, an extensive inventory of 88 remaining sites where these Barbary sheep still living was conducted, and precise measurements of three topographical parameters were collected at each site. Subsequently, a machine learning algorithm called Bagging was employed to develop a predictive model. …”
Get full text

A deep operator network for Bayesian parameter identification of self-oscillators by Tobias Sugandi, Bayu Dharmaputra, Nicolas Noiray

Get full text

A Hybrid Approach for Forecasting Occupancy of Building’s Multiple Space Types by Iqra Rafiq, Anzar Mahmood, Ubaid Ahmed, Ahsan Raza Khan, Kamran Arshad, Khaled Assaleh, Naeem Iqbal Ratyal, Ahmed Zoha

“…After the feature selection, Machine Learning (ML) models such as Extreme Gradient Boosting (XgBoost), Adaptive Boosting (AdaBoost), Long Short-Term Memory (LSTM) and Categorical Boosting (CatBoost) are employed to predict occupants’ count in each room. …”
Get full text

Prediction of Fractal Dimension in Shale CT and its Robustness to Interference Based on Convolutional Neural Networks by SUN Dingwei, WANG Lei, YANG Dong, HUANG Xudong, JIA Yichao

Get full text

Fitness exercise evaluation system based on improved DTW algorithm by Tinghang Guo, Qianhan Yin, Xinlin Liu, Yue Sun, Zhuanping Qin, Yu Han, Guangda Lu

Get full text

Deep Learning-Based Keras Network Formulation for Predicting the Shear Capacity of Squat RC Walls and Sensitivity Analysis by Badie H. Sulaiman, Amer M. Ibrahim, Hadeel J. Imran

“…The most comprehensive dataset of 1424 RC squat wall test specimens collected from the published literature to date has been used to develop the proposed deep learning model as well as three well-known machine learning models: RF, ANN, and LR. …”
Get full text

Unmanned Aerial Vehicle Remote Sensing for Monitoring Fractional Vegetation Cover in Creeping Plants: A Case Study of <i>Thymus mongolicus</i> Ronniger by Hao Zheng, Wentao Mi, Kaiyan Cao, Weibo Ren, Yuan Chi, Feng Yuan, Yaling Liu

Get full text

Integrating testing and modeling methods to examine the feasibility of blended waste materials for the compressive strength of rubberized mortar by Amin Muhammad Nasir, Nassar Roz-Ud-Din, Khan Kaffayatullah, Ul Arifeen Siyab, Khan Mubasher, Qadir Muhammad Tahir

“…Similarly, partial dependence plot analysis suggests that SF, MP, and GP have a comparable effect on the fc′{f}_{\text{c}}^{^{\prime} } of rubberized mortar. The machine learning models demonstrated a significant resemblance to test results. …”
Get full text

Unraveling shared diagnostic genes and cellular microenvironmental changes in endometriosis and recurrent implantation failure through multi-omics analysis by Dongxu Qin, Yongquan Zheng, Libo Wang, Zhenyi Lin, Yao Yao, Weidong Fei, Caihong Zheng

“…A total of 16 key genes were identified, which were predominantly expressed in fibroblasts. Through machine learning, the optimal model combining RF and XGBoost was selected to identify the shared diagnostic genes PDIA4 and PGBD5. …”
Get full text

Rotor Location During Atrial Fibrillation: A Framework Based on Data Fusion and Information Quality by Miguel A. Becerra, Diego H. Peluffo-Ordoñez, Johana Vela, Cristian Mejía, Juan P. Ugarte, Catalina Tobón

“…Fuzzy inference was applied for situation and risk assessment, followed by IQ mapping using a support vector machine by level. Finally, the IQ criteria were optimized through a particle swarm optimization algorithm. …”
Get full text

Interpretability Study on the Fault Diagnosis Model of the Heat pipe/ Vapor Compression Composite Air Conditioning System by ZHANG Yiqi, HUANG Shuoquan, LI Xiuming, DI Yanqiang, SONG Mengjie, HAN Zongwei

“…This study develops a composite fault diagnosis model based on typical machine learning algorithms, compares the diagnostic performance of different models, and finally conducts interpretability research on the diagnostic models using the SHAP method. …”
Get full text

EF yolov8s: A Human–Computer Collaborative Sugarcane Disease Detection Model in Complex Environment by Jihong Sun, Zhaowen Li, Fusheng Li, Yingming Shen, Ye Qian, Tong Li

“…Subsequently, five basic instance segmentation models of YOLOv8 were used for comparative analysis, validated using nutrient deficiency condition videos, and a human–machine integrated detection model for nutrient deficiency symptoms at the top of sugarcane was constructed. …”
Get full text

Review on Deep Learning and Neural Network Implementation for Emotions Recognition by Renas Rajab Asaad

“…Three Promising neural network architectures are customized, trained. and subjected to various classification tasks, after which the best performing network is further optimized. The applicability of the final model is portrayed in a live video application that can instantaneously return the emotion of the user. …”
Get full text