OVERVIEW STUDY OF MOBILE NETWORK TRAFFIC FOR BTS STATIONS by Hoang Van Thuc*, Pham Van Ngoc, Doan Thi Thanh Thao, Vu Chien Thang, Pham Thanh Nam, Mac Thi Phuong

“…In recent years, Machine Learning (ML) has become a crucial and promising tool for forecasting and solving a wide range of complex problems. …”
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INTEGRATING NEURAL NETWORKS INTO SHEET METAL FORMING: A REVIEW OF RECENT ADVANCES AND APPLICATIONS by COSMIN - CONSTANTIN GRIGORAȘ, ȘTEFAN COȘA, VALENTIN ZICHIL

“… In order to predict defects, improve performance, and streamline operations, machine learning techniques are becoming ever more indispensable in manufacturing processes, mainly in sheet metal forming. …”
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Motivation Analysis of Technological Startups Business Models Based on Intelligent Data Mining and Analysis by Xuejiao Ren, Xiaozhou Ding

“…To improve the motivation analysis effect of the technological startups business model, this study combines intelligent data mining technology to analyze related factors and proposes a method of adjusting parameters of machine-learning model based on Bayesian optimization algorithm. …”
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Vers les analyses algorithmiques de l'espace et des territoires by Claire Bailly, Jean Magerand

“…Big data, data mining and machine learning are undergoing unprecedented development. …”
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Association and application in XDR and MR data by Tao LIU, Tao WU, Bin WANG

“…Based on the XDR and MR data records of O domain and combining with the development and practice in big data and machine learning technologies,the analysis of impact factors of correlation rate and accuracy was described in detail via practice with the help of technology of association of XDR and MR data records.By means of the integration of machine learning technology and fingerprint localization algorithm,the position accuracy was promoted constantly using the random forest algorithm,and the rasterization of associated data was realized.Simultaneously,the innovative application scenarios and directions applied to planning,network,customer and market were proposed.Finally,to verify the prospect of the association of XDR and MR data records,two examples in network construction,maintain and market were enumerated.…”
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Qwen-2.5 Outperforms Other Large Language Models in the Chinese National Nursing Licensing Examination: Retrospective Cross-Sectional Comparative Study by Shiben Zhu, Wanqin Hu, Zhi Yang, Jiani Yan, Fang Zhang

“…We also explore whether combining their outputs using machine learning techniques can improve their overall accuracy. …”
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Molecular dynamics simulation based prediction of T-cell epitopes for the production of effector molecules for liver cancer immunotherapy. by Sidra Zafar, Yuhe Bai, Syed Aun Muhammad, Jinlei Guo, Haris Khurram, Saba Zafar, Iraj Muqaddas, Rehan Sadiq Shaikh, Baogang Bai

“…Based on immunoinformatic and integrated machine learning tools, we predicted the potential therapeutic vaccine candidates of liver cancer. …”
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A python approach for prediction of physicochemical properties of anti-arrhythmia drugs using topological descriptors by Huiling Qin, Mudassar Rehman, Muhammad Farhan Hanif, Muhammad Yousaf Bhatti, Muhammad Kamran Siddiqui, Mohamed Abubakar Fiidow

“…Abstract In recent years, machine learning has gained substantial attention for its ability to predict complex chemical and biological properties, including those of pharmaceutical compounds. …”
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Evaluation of the Risk of Recurrence in Patients with Local Advanced Rectal Tumours by Different Radiomic Analysis Approaches by Alaa Khadidos, Adil Khadidos, Olfat M. Mirza, Tawfiq Hasanin, Wegayehu Enbeyle, Abdulsattar Abdullah Hamad

“…Using artificial intelligence, in particular, different machine learning techniques, is a necessary step for better data exploitation. …”
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SURVEY AND PROPOSED METHOD TO DETECT ADVERSARIAL EXAMPLES USING AN ADVERSARIAL RETRAINING MODEL by Thanh Son Phan, Quang Hua Ta, Duy Trung Pham, Phi Ho Truong

“…However, in recent years, machine learning models have been the target of various attack methods. …”
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Evaluation of early student performance prediction given concept drift by Benedikt Sonnleitner, Tom Madou, Matthias Deceuninck, Filotas Theodosiou, Yves R. Sagaert

“…We investigate the performance of different machine learning pipelines on a data set with change in study behavior during the Covid-19 period. …”
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Research on multi-party security collaborative linear regression for computing power networks by PAN Jie, HOU Huifang, CHEN Xi, XUE Zhao, XU Liankun

“…With the rapid development of science and technology, machine learning has become a key factor driving the progress of enterprises. …”
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Biometric monitoring system based on K-means ＆MTLS-SVM algorithm by Jingming XIA, Lingling TANG, Ling TAN, Han ZHENG

“…In a nonmedical biometric monitoring system,the monitoring parameters are preceded with machine learning for precision promotion of diagnosis and prediction.Considering the problems of insufficient information mining and low prediction accuracy in multi task time series,both supervised and unsupervised machine learning techniques were applied to predict the physical condition of the remote health care.These techniques were K-means for clustering the similar group of data and MTLS-SVM model for training and testing historical data to perform a trend prediction.In order to evaluate the effectiveness of the method,the proposed method was compared with MTLS-SVM method.The experimental results show that the proposed method has higher prediction accuracy.…”
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Multi-label feature selection algorithm based on joint mutual information of max-relevance and min-redundancy by Li ZHANG, Cong WANG

“…Feature selection has played an important role in machine learning and artificial intelligence in the past decades.Many existing feature selection algorithm have chosen some redundant and irrelevant features,which is leading to overestimation of some features.Moreover,more features will significantly slow down the speed of machine learning and lead to classification over-fitting.Therefore,a new nonlinear feature selection algorithm based on forward search was proposed.The algorithm used the theory of mutual information and mutual information to find the optimal subset associated with multi-task labels and reduced the computational complexity.Compared with the experimental results of nine datasets and four different classifiers in UCI,the proposed algorithm is superior to the feature set selected by the original feature set and other feature selection algorithms.…”
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Android malware detection method based on deep neural network by Fan CHAO, Zhi YANG, Xuehui DU, Yan SUN

“…Android is increasingly facing the threat of malware attacks.It is difficult to effectively detect large-sample and multi-class malware for traditional machine learning methods such as support vector machine,method for Android malware detection and family classification based on deep neural network was proposed.Based on the comprehensive extraction of application components,Intent Filter,permissions,and data flow,the method performed an effective feature selection to reduce dimensions,and conducted a large-sample detection and multi-class classification for malware based on deep neural network.The experimental results show that the method can conduct an effective detection and classification.The accuracy of binary classification between benign and malicious Apps is 97.73%,and the accuracy of family multi-class classification can reach 93.54%,which is higher than other machine learning algorithms.…”
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Survey on model inversion attack and defense in federated learning by Dong WANG, Qianqian QIN, Kaitian GUO, Rongke LIU, Weipeng YAN, Yizhi REN, Qingcai LUO, Yanzhao SHEN

“…As a distributed machine learning technology, federated learning can solve the problem of data islands.However, because machine learning models will unconsciously remember training data, model parameters and global models uploaded by participants will suffer various privacy attacks.A systematic summary of existing attack methods was conducted for model inversion attacks in privacy attacks.Firstly, the theoretical framework of model inversion attack was summarized and analyzed in detail.Then, existing attack methods from the perspective of threat models were summarized, analyzed and compared.Then, the defense strategies of different technology types were summarized and compared.Finally, the commonly used evaluation criteria and datasets were summarized for inversion attack of existing models, and the main challenges and future research directions were summarized for inversion attack of models.…”
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Evaluation of Provincial Economic Resilience in China Based on the TOPSIS-XGBoost-SHAP Model by Zhan Wu

“…The aim of this research is to propose a framework for measuring and analysing China’s economic resilience based on the XGBoost machine learning algorithm, using Bayesian optimization (BO) algorithm, extreme gradient-boosting (XGBoost) algorithm, and TOPSIS method to measure China’s economic resilience from 2007 to 2021. …”
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Integrated Bioinformatics Identifies FREM1 as a Diagnostic Gene Signature for Heart Failure by Chenyang Jiang, Weidong Jiang

“…This study is aimed at integrating bioinformatics and machine learning to determine novel diagnostic gene signals in the progression of heart failure disease. …”
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Semantic segmentation using synthetic images of underwater marine-growth by Christian Mai, Jesper Liniger, Simon Pedersen

“…The dataset includes environmental classes like seawater and seafloor, offshore structures components, ship hulls, and several marine growth classes. The machine-learning models were trained using transfer learning and data augmentation techniques.ResultsTesting showed high accuracy in segmenting synthetic images. …”
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PD_EBM: An Integrated Boosting Approach Based on Selective Features for Unveiling Parkinson's Disease Diagnosis With Global and Local Explanations by Fahmida Khanom, Mohammad Shorif Uddin, Rafid Mostafiz

“…PD_EBM leverages machine learning (ML) algorithms and a hybrid feature selection approach to enhance diagnostic accuracy. …”
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