The National Police in the system of administrative delinquency prevention subjects by O. M. Muzychuk

“…The article notes that the introduction of martial law in Ukraine has changed the approach to the organisation of police work, since domestic law enforcement agencies have faced challenges previously unknown to science and practice, in particular, in the area of prevention of administrative delinquency among internally displaced persons, in the de-occupied territories, and in the context of hostilities, which required new algorithms and approaches, as well as consideration of the priorities and principles of activity. …”
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Decision vector model in predicting social behavior (on the example of the secondary analysis of the pre-election surveys) by N. S. Babich, S. M. Kharun

“…The article considers a decision vector model that can be used to approximate actual behavior with some formal rules that allow to construct algorithms for predicting future states. The authors tested the reliability of ex post facto predictions on the example of the unsuccessfully predicted 2013 Moscow mayoral elections. …”
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Credit card default prediction using ML and DL techniques by Fazal Wahab, Imran Khan, Sneha Sabada

“…Subsequently, various techniques are employed to preprocess the unprocessed data and visually present the outcomes through the use of exploratory data analysis (EDA). Furthermore, the algorithms are hypertuned to evaluate the enhancement in prediction. …”
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Quantum Key Distribution Applicability to Smart Grid Cybersecurity Systems by Warren Grice, Mohammed Olama, Annabelle Lee, Philip G. Evans

“…However, with the increasing number of sophisticated attacks as well as the increasing computational power, the security of the “classical” cryptographic algorithms is threatened. Quantum information science offers solutions to this problem, specifically quantum key distribution (QKD), which provides a means for the generation and secure distribution of symmetric cryptographic keys. …”
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Ion channel classification through machine learning and protein language model embeddings by Ghazikhani Hamed, Butler Gregory

“…We employ a comprehensive array of machine learning algorithms, including k-Nearest Neighbors, Random Forest, Support Vector Machines, and Feed-Forward Neural Networks, alongside a novel Convolutional Neural Network (CNN) approach. …”
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Load frequency control in renewable based micro grid with Deep Neural Network based controller by Prasantini Samal, Niranjan Nayak, Anshuman Satapathy, Sujit Kumar Bhuyan

“…Microgrids (MGs) offer numerous technical, economic, and environmental benefits, yet they face challenges due to high-frequency deviations caused by the unpredictable nature of the renewable energy source, and variable loads with the integration of Electric Vehicles (EVs). Numerous methods, algorithms, and controllers have been created to address these issues and preserve system stability and efficient load frequency control (LFC). …”
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RASGRF2 as a potential pathogenic gene mediating the progression of alcoholic hepatitis to alcohol-related cirrhosis and hepatocellular carcinoma by Zhengyuan Chen, Danfeng Fan, Tianyi Hang, Xiaoqing Yue

“…After screening with two machine learning algorithms, five shared genes remained. Combining the results of the immune infiltration and bulk transcriptome results from an independent validation cohort, the core shared gene was determined to be RASGRF2. …”
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Binary program taint analysis optimization method based on function summary by Pan YANG, Fei KANG, Hui SHU, Yuyao HUANG, Xiaoshao LYU

“…Taint analysis is a popular software analysis method, which has been widely used in the field of information security.Most of the existing binary program dynamic taint analysis frameworks use instruction-level instrumentation analysis methods, which usually generate huge performance overhead and reduce the program execution efficiency by several times or even dozens of times.This limits taint analysis technology’s wide usage in complex malicious samples and commercial software analysis.An optimization method of taint analysis based on function summary was proposed, to improve the efficiency of taint analysis, reduce the performance loss caused by instruction-level instrumentation analysis, and make taint analysis to be more widely used in software analysis.The taint analysis method based on function summary used function taint propagation rules instead of instruction taint propagation rules to reduce the number of data stream propagation analysis and effectively improve the efficiency of taint analysis.For function summary, the definition of function summary was proposed.And the summary generation algorithms of different function structures were studied.Inside the function, a path-sensitive analysis method was designed for acyclic structures.For cyclic structures, a finite iteration method was designed.Moreover, the two analysis methods were combined to solve the function summary generation of mixed structure functions.Based on this research, a general taint analysis framework called FSTaint was designed and implemented, consisting of a function summary generation module, a data flow recording module, and a taint analysis module.The efficiency of FSTaint was evaluated in the analysis of real APT malicious samples, where the taint analysis efficiency of FSTaint was found to be 7.75 times that of libdft, and the analysis efficiency was higher.In terms of accuracy, FSTaint has more accurate and complete propagation rules than libdft.…”
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Optimization and loss estimation in energy-deficient polygeneration systems: A case study of Pakistan's utilities with integrated renewable energy by Muhammad Shoaib Saleem, Naeem Abas

“…Techniques used for electricity demand forecasting encompass artificial intelligence, artificial neural networks, trend line extrapolations, fuzzy logic, vector support machines, genetic algorithms and expert systems. Demand forecasting becomes even more difficult in polygeneration utilities with renewable energy sources integrated to meet the varying demands. …”
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Optimization of Meat and Poultry Farm Inventory Stock Using Data Analytics for Green Supply Chain Network by Rajnish Kler, Roshan Gangurde, Samariddin Elmirzaev, Md Shamim Hossain, Nhut V. T. Vo, Tien V. T. Nguyen, P. Naveen Kumar

“…Effective inventory optimization algorithms have been shown to be able to evaluate a significant portion of previous sales data and anticipate inventory future demand by taking seasonality and lead times into account. …”
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Design, Multiperspective Investigations, and Performance Analysis of Multirotor Unmanned Aerial Vehicle for Precision Farming by Darshan Kumar Jayaram, Vijayanandh Raja, Beena Stanislaus Arputharaj, Hussein A. Z. AL-bonsrulah

“…In this experiment, AI algorithms were used to lemon leaves. Three AI systems were tested on different datasets to forecast plant stress by analyzing leaves due to technical constraints. …”
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Predictive modelling of hexagonal boron nitride nanosheets yield through machine and deep learning: An ultrasonic exfoliation parametric evaluation by Jerrin Joy Varughese, Sreekanth M․S․

“…A suite of machine learning regression models including Adaptive Boosting (AdaBoost) Regressor, Random Forest (RF) Regressor, Linear Regressor (LR), and Classification and Regression Tree (CART) Regressor, was employed alongside a deep neural network (DNN) architecture optimized using various algorithms such as Adaptive Moment Estimation (Adam), Root Mean Square Propagation (RMS Prop), Stochastic Gradient Descent (SGD), and Limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS). …”
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IMPLEMENTATION OF LEARNING MANAGEMENT SYSTEMS WITH GENERATIVE ARTIFICIAL INTELLIGENCE FUNCTIONS IN THE POST-PANDEMIC ENVIRONMENT by Denis-Cătălin Arghir

“…To demonstrate the system's effectiveness, a curriculum was crafted for a specialized field of study - Artificial Intelligence (AI), with a specific focus on the practical application of Machine Learning algorithms. This curriculum incorporates theoretical and practical application components, complemented by a suite of assessment tools and assignments tailored to the proposed subject. …”
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An improved soft voting-based machine learning technique to detect breast cancer utilizing effective feature selection and SMOTE-ENN class balancing by Indu Chhillar, Ajmer Singh

“…However, if the dataset contains duplicate or irrelevant features, machine learning-based algorithms are unable to give the intended results. …”
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Continuously tracking of moving object by a combination of ultra-high frequency radio-frequency identification and laser range finder by Yulu Fu, Ran Liu, Hua Zhang, Gaoli Liang, Shafiq ur Rehman, Lixiang Liu

“…Two-dimensional laser range finders can provide the distance to the objects but require complicated recognition algorithms to acquire the identity of object. This article proposes an innovative method to track the locations of dynamic objects by combining radio-frequency identification and laser ranging information. …”
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Machine learning-based prediction of antipsychotic efficacy from brain gray matter structure in drug-naive first-episode schizophrenia by Xiaodong Guo, Enpeng Zhou, Xianghe Wang, Bingjie Huang, Tianqi Gao, Chengcheng Pu, Xin Yu

“…At 1-year follow-up, patients were categorized into the rehabilitation and non-rehabilitation groups. Machine learning algorithms were applied to predict treatment outcomes based on GM volume, cortical thickness, and gyrification index, and the model performance was evaluated. …”
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Pharmacogenomics in psychiatry: Practice recommendations from an Asian perspective (2024) by Shih Ee Goh, Saumya Shekhar Jamuar, Siew Eng Chua, Derrick Chen Kuan Yeo, Jerome Hern Yee Goh, Chee Hon Chin, Mohamed Zakir Karuvetil, Ee Lian Lee, Daniel Shuen Sheng Fung, Giles Ming Yee Tan

“…Direct-to-consumer pharmacogenomic panels that assay multiple genes and analyse them via proprietary algorithms, are not presently recommended in Singapore’s psychiatric setting due to inconclusive evidence on clinical outcomes. …”
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Low-cost and scalable machine learning model for identifying children and adolescents with poor oral health using survey data: An empirical study in Portugal. by Susana Lavado, Eduardo Costa, Niclas F Sturm, Johannes S Tafferner, Octávio Rodrigues, Pedro Pita Barros, Leid Zejnilovic

“…Logistic regression models with variables selected through low-variance filtering and recursive feature elimination outperformed various others trained with complex machine learning algorithms based on precision@k metric, outperforming also random selection and expert rule-based models in identifying students with poor oral health. …”
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Aspiring to clinical significance: Insights from developing and evaluating a machine learning model to predict emergency department return visit admissions. by Yiye Zhang, Yufang Huang, Anthony Rosen, Lynn G Jiang, Matthew McCarty, Arindam RoyChoudhury, Jin Ho Han, Adam Wright, Jessica S Ancker, Peter Ad Steel

“…Multiple machine learning algorithms were evaluated, including deep significance clustering (DICE), regularized logistic regression (LR), Gradient Boosting Decision Tree, and XGBoost. …”
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Study on the temperature prediction model of residual coal in goaf based on ACO-KELM by ZHAI Xiaowei, WANG Chen, HAO Le, LI Xintian, HOU Qinyuan, MA Teng

“…Compared to the prediction models based on extreme learning machine (ELM) and random forest (RF) algorithms, the ACO-KELM model achieved an average absolute error of 0.0701 ℃ and a root mean square error (RMSE) of 0.0748 ℃ on the test set, reducing these errors by 65% and 195%, respectively, compared to the ELM-based model, and by 53% and 156%, respectively, compared to the RF-based model. …”
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