Enhancing accuracy through ensemble based machine learning for intrusion detection and privacy preservation over the network of smart cities by Mudita Uppal, Yonis Gulzar, Deepali Gupta, Jayant Uppal, Mukesh Kumar, Shilpa Saini

“…The dataset utilized for anomaly-based detection techniques is KDDCup99 dataset, on which the different algorithms have been applied. The goal is to gain knowledge about data integrity and improve the predictive power of data. …”
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Sensor-Based Rock Hardness Characterization in a Gold Mine Using Hyperspectral Imaging and Portable X-Ray Fluorescence Technologies by Saleh Ghadernejad, Kamran Esmaeili, Mariano P. Consens

“…Three ML algorithms, including Random Forest Regressor (RFR), Adaptive Boosting (AdaBoost), and Multivariate Linear Regression (MLR), were applied to develop predictive hardness models considering three scenarios: using chemical features, using refined spectral features, and their combination. …”
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Enhancing Kidney Disease Diagnosis Using ACO-Based Feature Selection and Explainable AI Techniques by Abbas Jafar, Myungho Lee

“…However, the performance of previous automated approaches has often been hindered by suboptimal feature selection and algorithms’ “black-box” nature, which adversely affect their interpretability and clinical applicability. …”
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Investigation of the impact of service parameters on the degree of passenger satisfaction based on the application of the apparatus of neural networks by S. S. Pastukhov, K. V. Stel’Mashenko

“…The approbation results of the apparatus of neural networks on sets of marketing data concerning various aspects of passenger service at stations and in trains are presented, as well as an estimation of the efficiency of using neural networks in comparison with the algorithms of ordinal logistic and linear regression. …”
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Teaching a neural network modeling socio-economic development of the region by S. V. Romanchukov, O. G. Berestneva, L. A. Petrova

“…Under these conditions, the expansion of the mathematical apparatus and the more active introduction of information technologies (including in the area of Big Data analysis and the construction of predictive models based on artificial neural networks) can be viable. …”
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Distinguish the Value of the Benign Nevus and Melanomas Using Machine Learning: A Meta-Analysis and Systematic Review by Suli Li, Yihang Chu, Ying Wang, Yantong Wang, Shipeng Hu, Xiangye Wu, Xinwei Qi

“…This suggests that state-of-the-art ML-based algorithms for distinguishing melanoma from benign nevi may be ready for clinical use. …”
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Nuevos modelos para la Caracterización, Detección y Diagnóstico de Fallas en Máquinas Eléctricas Rotativas by Jair Elías Araujo Vargas, Dilan Yesid Franklin Coronel, Victor Manuel Arias Ruiz

“…Therefore, modern techniques based on machine learning offer significant improvements in fault detection and prediction, allowing the identification of complex patterns and more precise diagnoses, expanding the capabilities of conventional approaches to facilitate predictive maintenance. …”
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Parallel Gaussian Elimination Method by Muhammad Muhammad Ali

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Chemoresistance factors in non-small cell lung cancer by А. I. Shevchenko, А. P. Коlesnik, А. V. Каdzhoian, V. А. Кuzmenko

“…Basing on current data we tried to analyze prognostic and predictive value of main chemoresistance factors in NSCLC. …”
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Adjusted imbalance ratio leads to effective AI-based drug discovery against infectious disease by Ons Masmoudi, Afef Abdelkrim, Emna Harigua-Souiai

“…We trained five machine learning and six deep learning algorithms to predict the anti-pathogen activity of chemical compounds, derived from PubChem Bioassays targeting four infectious diseases. …”
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Demographic forecast modelling using SSA-XGBoost for smart population management based on multi-sources data. by Jin Wang, Shihan Ma, Qing Lv, Qiang Li

“…Population prediction could provide effective data support for social and economic planning and decision-making, especially for the sub-national population forecasting accurately. …”
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Visceral adiposity index as a predictor of metabolic dysfunction-associated steatotic liver disease: a cross-sectional study by Tuo Zhou, Xiang Ding, Linjie Chen, Qianxiong Huang, Linfang He

“…Weighted multivariable regression models, subgroup analyses, and machine learning algorithms were used to evaluate associations and predictive performance. …”
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A machine learning approach for the diagnosis of obstructive sleep apnoea using oximetry, demographic and anthropometric data by Zhou Hao Leong, Shaun Ray Han Loh, Leong Chai Leow, Thun How Ong, Song Tar Toh

“…Seven commonly used supervised learning algorithms were trained with the data. Sensitivity (recall), specificity, positive predictive value (PPV) (precision), negative predictive value, area under the receiver operating characteristic curve (AUC) and F1 measure were reported for each model. …”
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Integrating Machine Learning, SHAP Interpretability, and Deep Learning Approaches in the Study of Environmental and Economic Factors: A Case Study of Residential Segregation in Las... by Jingyi Liu, Yuxuan Cai, Xiwei Shen

“…By integrating traditional econometric techniques with machine learning and deep learning models, the study investigates (1) the correlation between housing prices, environmental quality, and segregation; (2) the differentiated impacts on various ethnic groups; and (3) the comparative effectiveness of predictive models. Among the tested algorithms, LGBM (Light Gradient Boosting) delivered the highest predictive accuracy and robustness. …”
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A Decomposition-Integration Framework of Carbon Price Forecasting Based on Econometrics and Machine Learning Methods by Zhehao Huang, Benhuan Nie, Yuqiao Lan, Changhong Zhang

“…The framework subsequently employs GARCH models for predicting high-frequency components and a gated recurrent unit (GRU) neural network optimized by the grey wolf algorithm for low-frequency components. …”
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Speech Signal Enhancement Techniques by Chouki Zegar, Abdelhakim Dahimene

“… Speech enhancement and noise reduction have wide applications in speech processing. …”
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Adaptive Spatial Modulation Based Visible Light Communications: SER Analysis and Optimization by Jin-Yuan Wang, Jian-Xia Zhu, Sheng-Hong Lin, Jun-Bo Wang

“…The ASM-based and the candidate-reduction-ASM-based optimization algorithms are proposed to solve the problem, respectively. …”
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Recent Facial Image Preprocessing Techniques: A Review by Rendra Soekarta, Ku Ruhana Ku-Mahamud

“…This review analyzes recent advancements in facial recognition and classification algorithms, emphasizing the critical role of preprocessing techniques in enhancing the algorithms’ accuracy, reliability, and efficiency. …”
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Complex, Temporally Variant SVD via Real ZN Method and 11-Point ZeaD Formula from Theoretics to Experiments by Jianrong Chen, Xiangui Kang, Yunong Zhang

“…In addition, with the use of the 11-point and other ZeaD formulas, five discrete-time SVD (DTSVD) algorithms are further acquired. Meanwhile, theoretical analyses and numerical experimental results substantiate the correctness and convergence of the proposed CTSVD model and DTSVD algorithms.…”
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The Analysis of Communication Strategy of Disabled Sports Information Based on Deep Learning and the Internet of Things by Wanglong Wang, Qingwen Liu, Chuan Shu

“…The ever-growing landscape of Internet of Things (IoT) technology and the evolution of deep learning algorithms have ushered in transformative changes in the communication strategy for disseminating information on disabled sports. …”
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