GPU-Accelerated Fock Matrix Computation with Efficient Reduction by Satoki Tsuji, Yasuaki Ito, Haruto Fujii, Nobuya Yokogawa, Kanta Suzuki, Koji Nakano, Victor Parque, Akihiko Kasagi

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QSAR Model for Prediction of some Non-Nucleoside Inhibitors of Dengue Virus Serotype 4 NS5 using GFA-MLR Approach by Samuel Adawara, Gideon Shallangwa, Paul Mamza, Abdulkadir Ibrahim

“…Thus, the model can be used to predict the activity of new chemicals within its applicability domain. …”
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CFD investigation and ANN prediction of heat transfer coefficient for fully developed turbulent air flow around double V-baffle turbulators by Abdulaziz Alasiri, H.E. Fawaz

“…The ANN model demonstrates excellent predictive performance, yielding values close to 1 for R2 and r, along with extremely low values for MSE, MAPE, MSLE, and log-cosh loss (0.01, 0.6 %, 0.001, and 0.01, respectively), demonstrating the ANN model's high predictive accuracy.…”
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Predicting Pathological Complete Response Following Neoadjuvant Therapy in Patients With Breast Cancer: Development of Machine Learning–Based Prediction Models in a Retrospective S... by Chun-Chi Lai, Cheng-Yu Chen, Tzu-Hao Chang

“…ObjectiveThe objective of this study was to develop robust, machine learning–based prediction models for pCR following neoadjuvant therapy, leveraging clinical, laboratory, and imaging data. …”
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Band Selection Algorithm Based on Multi-Feature and Affinity Propagation Clustering by Junbin Zhuang, Wenying Chen, Xunan Huang, Yunyi Yan

“…A similarity matrix is then constructed by integrating multi-feature information. The AP algorithm clusters the bands of the hyperspectral images to achieve effective band dimensionality reduction. …”
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DS-YOLOv7: Dense Small Object Detection Algorithm for UAV by Tao Sun, Haonan Chen, Haiying Liu, Lixia Deng, Lida Liu, Shuang Li

“…It pays more attention to edge information of small objects and reduces the rate of missing detection. Dimensionality reduction techniques focus on reducing model parameters to facilitate the deployment of algorithms on lightweight devices. …”
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Development of machine learning models for predicting non-remission in early RA highlights the robust predictive importance of the RAID score-evidence from the ARCTIC study by Gaoyang Li, Shrikant S. Kolan, Franco Grimolizzi, Joseph Sexton, Giulia Malachin, Guro Goll, Tore K. Kvien, Tore K. Kvien, Nina Paulshus Sundlisæter, Manuela Zucknick, Siri Lillegraven, Espen A. Haavardsholm, Espen A. Haavardsholm, Bjørn Steen Skålhegg

“…The model performance was evaluated through five independent unseen tests with nested 5-fold cross-validation. The predictive power of each feature was assessed using a composite measure derived from individual algorithm estimates.ResultsThe model demonstrated a mean AUC-ROC of 0.75-0.76, with mean sensitivity of 0.77-0.81, precision (also referred to as Positive Predictive Value) of 0.77-0.79 and specificity of 0.63-0.66 across the criteria. …”
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An integrated cloud system based serverless android app for generalised tractor drawbar pull prediction model using machine learning by Harsh Nagar, Rajendra Machavaram, Ambuj, Peeyush Soni, Subhajit Saha, T. Subhash Chandra Bose

Subjects: “…Tractor drawbar pull prediction…”
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Spatiotemporal Multivariate Weather Prediction Network Based on CNN-Transformer by Ruowu Wu, Yandan Liang, Lianlei Lin, Zongwei Zhang

“…Finally, we demonstrated the excellent effect of STWPM in multivariate spatiotemporal field weather prediction by comprehensively evaluating the proposed algorithm with classical algorithms on the ERA5 dataset in a global region.…”
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A Method for Predicting Coal-Mine Methane Outburst Volumes and Detecting Anomalies Based on a Fusion Model of Second-Order Decomposition and ETO-TSMixer by Qiangyu Zheng, Cunmiao Li, Bo Yang, Zhenguo Yan, Zhixin Qin

“…The ability to predict the volume of methane outbursts in coal mines is critical for the prevention of methane outburst accidents and the assurance of coal-mine safety. …”
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Big data technology for teaching quality monitoring and improvement in higher education - joint K-means clustering algorithm and Apriori algorithm by Yang Li, Haiyu Zhang

“…In order to cope with these challenges, the study proposes a research method of big data technology based on joint K-mean clustering algorithm and association rule mining algorithm. The study first analyzes the teaching quality monitoring and evaluation indexes using the K-mean algorithm. …”
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Smart agriculture using IoT for automated irrigation, water and energy efficiency by Subir Gupta, Subrata Chowdhury, Ramya Govindaraj, Kassian T.T. Amesho, Sumarlin Shangdiar, Timoteus Kadhila, Sioni Iikela

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Research on Oil and Gas-Bearing Zone Prediction and Identification Based on the SVD–K-Means Algorithm—A Case Study of the WZ6-1 Oil-Bearing Structure in the Beibu Gulf Basin, South China Sea by Zhilong Chen, Renyi Wang, Biao Xu, Jianghang Zhu

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Optimized Reduction in Draft Tube Pressure Pulsation for a Francis Turbine by J. Lu, R. Tao

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Predicting postpartum female sexual interest/arousal disorder via adiponectin and biopsychosocial factors: a cohort-based decision tree study by Saiedeh Sadat Hajimirzaie, Najmeh Tehranian, Amin Golabpour, Ahmad Khosravi, Seyed Abbas Mousavi, Afsaneh Keramat, Mehdi Mirzaii

“…To better diagnose individuals at risk of postpartum complications, predictive models utilizing data mining and machine learning techniques can be instrumental. …”
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Enhanced prediction of heating value of municipal solid waste using hybrid neuro-fuzzy model and decision tree-based feature importance assessment by Oluwatobi Adeleke, Obafemi O. Olatunji, Tien-Chien Jen, Iretioluwa Olawuyi

“…This study proposes a hybrid network of adaptive neuro-fuzzy inference system (ANFIS) with genetic algorithm (GA) to predict the higher heating value (HHV) of municipal solid waste (MSW). …”
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Optimizing Apache Spark MLlib: Predictive Performance of Large-Scale Models for Big Data Analytics by Leonidas Theodorakopoulos, Aristeidis Karras, George A. Krimpas

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Quantitative method for network security situation based on attack prediction by Hao HU, Run-guo YE, Hong-qi ZHANG, Ying-jie YANG, Yu-ling LIU

“…To predict the attack behaviors accurately and comprehensively as well as to quantify the threat of attack,a quantitative method for network security situation based on attack prediction was proposed.By fusing the situation factors of attacker,defender and network environment,the capability of attacker and the exploitability rate of vulnerability were evaluated utilizing the real-time detected attack events,and the expected time-cost for attack-defense were further calculated.Then an attack prediction algorithm based on the dynamic Bayesian attack graph was designed to infer the follow-up attack actions.At last,the attack threat was quantified as the security risk situation from two levels of the hosts and the overall network.Experimental analysis indicates that the proposed method is suitable for the real adversarial network environment,and is able to predict the occurrence time of attack accurately and quantify the attack threat reasonably.…”
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Quantitative method for network security situation based on attack prediction by Hao HU, Run-guo YE, Hong-qi ZHANG, Ying-jie YANG, Yu-ling LIU

“…To predict the attack behaviors accurately and comprehensively as well as to quantify the threat of attack,a quantitative method for network security situation based on attack prediction was proposed.By fusing the situation factors of attacker,defender and network environment,the capability of attacker and the exploitability rate of vulnerability were evaluated utilizing the real-time detected attack events,and the expected time-cost for attack-defense were further calculated.Then an attack prediction algorithm based on the dynamic Bayesian attack graph was designed to infer the follow-up attack actions.At last,the attack threat was quantified as the security risk situation from two levels of the hosts and the overall network.Experimental analysis indicates that the proposed method is suitable for the real adversarial network environment,and is able to predict the occurrence time of attack accurately and quantify the attack threat reasonably.…”
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An easy iris center detection method for eye gaze tracking system by Mingxin Yu, Yingzi Lin, Xiaoying Tang, Jing Xu, David Schmidt, Xiangzhou Wang, Yikang Guo

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