A novel approach for predicting microbe-disease associations by bi-random walk on the heterogeneous network. by Shuai Zou, Jingpu Zhang, Zuping Zhang

Get full text

Corrosion Rate Analysis and Prediction of the Remaining Life of the Research Vessel to Improve Ship Safety Aspects by Andi Cahyo Prasetyo Tri Nugroho, Cahyo Sasmito, Abid Paripurna Fuadi, Dany Hendrik, Cakra Wijaya Kusuma Rahadi, Rizqi Dian Permana, Noor Muhammad Ridha Fuadi

Get full text

PAD-SA: a method for predicting the turnover of scientific researchers based on ADASYN-Stacking algorithm by Tianyi Zhang, Jing Yang, Ru Liu, Qiyuan Feng

Get full text

Prediction of additional hospital days in patients undergoing cervical spine surgery with machine learning methods by Bin Zhang, Shengsheng Huang, Chenxing Zhou, Jichong Zhu, Tianyou Chen, Sitan Feng, Chengqian Huang, Zequn Wang, Shaofeng Wu, Chong Liu, Xinli Zhan

Get full text

Interpretable Prediction of a Decentralized Smart Grid Based on Machine Learning and Explainable Artificial Intelligence by Ahmet Cifci

Get full text

Intelligence Types Predict Different Domains of Emotional Creativity Through Self-Assessed Creativity and Intelligence by Tatiana V. Kornilova, Maria A. Chumakova, Lydia B. Maksarova

Get full text

Nomogram to predict periprosthetic joint infection after total hip arthroplasty using laboratory tests by Junzhe Lang, Zetao Dong, Boyuan Shi, Dongdong Wang, Jiandong Yuan, Lei Chen, Jianqing Gao, Anan Sun, Jiyue Huang, Zhiqiang Xue

Get full text

Out-of-distribution reject option method for dataset shift problem in early disease onset prediction by Taisei Tosaki, Eiichiro Uchino, Ryosuke Kojima, Yohei Mineharu, Yuji Okamoto, Mikio Arita, Nobuyuki Miyai, Yoshinori Tamada, Tatsuya Mikami, Koichi Murashita, Shigeyuki Nakaji, Yasushi Okuno

Get full text

Explainable Deep Learning to Predict Kelp Geographical Origin from Volatile Organic Compound Analysis by Xuming Kang, Zhijun Tan, Yanfang Zhao, Lin Yao, Xiaofeng Sheng, Yingying Guo

Get full text

Maize and soybean yield prediction using machine learning methods: a systematic literature review by Ramandeep Kumar Sharma, Jasleen Kaur, Gary Feng, Yanbo Huang, Chandan Kumar, Yi Wang, Sandhir Sharma, Johnie Jenkins, Jagmandeep Dhillon

Get full text

Predictive Energy Management for Docker Containers in Cloud Computing: A Time Series Analysis Approach by Abdulmohsen Algarni, Iqrar Shah, Ali Imran Jehangiri, Mohammed Alaa Ala'Anzy, Zulfiqar Ahmad

Get full text

Machine Learning‐Based Mobile Application for Predicting Posterior Canal Benign Paroxysmal Positional Vertigo by Emre Soylemez, Sait Demir, Kasım Ozacar

Get full text

Prediction of Influence of Environmental Factors on the Toxicity of Pentachlorophenol on <i>E. coli</i>-Based Bioassays by Sulivan Jouanneau, Gerald Thouand

Get full text

Non-end-to-end adaptive graph learning for multi-scale temporal traffic flow prediction. by Kang Xu, Bin Pan, MingXin Zhang, Xuan Zhang, XiaoYu Hou, JingXian Yu, ZhiZhu Lu, Xiao Zeng, QingQing Jia

Get full text

Remaining useful life prediction method of centrifugal pump rolling bearings based on digital twins by ShengWen Zhou, Li Zhang, Xiaoming Yang, Ruiping Luo, BaiGang Du, Wenhui Zeng

Get full text

Incorporating subjective survival information in mortality and change in health status predictions: A Bayesian approach by Apostolos Papachristos, Dimitrios Fouskakis

Get full text

Classification prediction of load losses in power stations using machine learning multilayer stack ensemble by Bathandekile M. Boshoma, Oluwole S. Akinola, Peter Olukanmi, Peter Olukanmi

Get full text

Enhancing Asset Reliability and Sustainability: A Comparative Study of Neural Networks and ARIMAX in Predictive Maintenance by Salvador Perez-Garcia, Cristina Gonzalez-Gaya, Miguel A. Sebastian

Get full text

Leveraging machine learning and molecular docking techniques to predict novel melanocortin-4 receptor agonists by Stavroula Giakoumopoulou, Paris Christodoulou, Minos-Timotheos Matsoukas, Vasilis Panagiotopoulos, Dionisis Cavouras, Eftichia Kritsi

Get full text

GPCR-A17 MAAP: mapping modulators, agonists, and antagonists to predict the next bioactive target by Ana B. Caniceiro, Ana M. B. Amorim, Nícia Rosário-Ferreira, Irina S. Moreira

“…To address the growing need for effective treatments, the GPCR-A17 Modulator, Agonist, Antagonist Predictor (MAAP) was introduced as an advanced ensemble machine learning model that combines XGBoost, Random Forest, and LightGBM to predict the functional roles of agonists, antagonists, and modulators in GPCR-A17 interactions. …”
Get full text