Artificial Intelligence Systems for Solving Problems of Agro­Industrial Complex Digitalization and Robotization by A. L. Ronzhin, A. I. Savel'ev

“…The digital transformation of agricultural technological processes is substantiated to be relevant in the context of the exacerbating global problems of food security, the agricultural business stagnation in the regions of the northern Non-Black Earth Region, as well as the ESG transformation of the economy.Research purpose To develop control algorithms for unmanned aerial vehicles (UAVs) based on numerical methods of machine learning to ensure the monitoring of the crops state and the improving of the production process planning and operational management.Materials and methods The following methodology was used: the original methods of machine learning, knowledge engineering and computer modeling for organizational and technological processes of technical objects’ life cycle in industry and products’ life cycle in the national economy,  as well as mathematical and algorithmic models, methods and prototypes of proactive automation tools for information, physical and energy interaction of heterogeneous robotic and cyber-physical complexes.Results and discussion Artificial intelligence systems were created for the photogrammetric processing of visible spectrum images and those taken with multispectral video cameras with the construction of orthophotomaps, digital elevation models. …”
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Integrating Sentiment Analysis With Machine Learning for Cyberbullying Detection on Social Media by Maram Fahaad Almufareh, Noor Zaman Jhanjhi, Mamoona Humayun, Ghadah Naif Alwakid, Danish Javed, Saleh Naif Almuayqil

“…State-of-the-art solutions predominantly rely on pre-trained language models and machine learning algorithms; however, these methods are often associated with substantial computational overheads and the development of advanced cyberbullying detection algorithms remains limited. …”
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Current status and outlook of UWB radar personnel localization for mine rescue by ZHENG Xuezhao, MA Jiawen, HUANG Yuan, LI Qiang, REN Jing, LIU Yu

“…Future research directions of UWB radar personnel localization technology for mine rescue operations are proposed: ① optimizing the UWB radar localization system by constructing cross-modal information fusion models and developing highly adaptive signal processing methods to enhance the system's adaptability to post-mining disaster environments; ② improving the applicability of combined static and dynamic target localization by developing hybrid localization algorithms that integrate Bayesian networks or deep belief networks to fuse static and dynamic target features and establishing state-switching-based comprehensive models; ③ improving UWB radar echo processing algorithms, combining adaptive beamforming technology, Multiple Input Multiple Output (MIMO) technology, and optimized K-means++ or entropy-based hierarchical analysis algorithms, effectively distinguishing multi-target position information, and validating their adaptability and reliability in complex environments through extensive simulation experiments.…”
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The influence of mind mapping on computational thinking skills and self-efficacy in students’ learning of graphical programming by Rong Guo, Yan Zheng, Haifei Miao

“…Further analysis of the data showed significant enhancements in their algorithmic thinking and modeling, as well as pattern recognition and evaluation sub-skills, while abstraction and decomposition sub-skills did not show significant improvement. …”
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An Experimental Study of GPR Near-Field Problem in Depth Ranging of Buried Objects by Rex Yui Kei Cham, Wallace Wai Lok Lai

“…Then, the same antenna was used to estimate the cover depth by measuring the time of flight and velocity based on the two velocity algorithms. The results reveal that the ranging accuracy is highly in doubt in reactive near-field and Fresnel regions but is largely improved when the object locates in far-field region where GPR wave propagation becomes plane-type. …”
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Project quality, regulation quality by Elena Mussinelli

“…Instead, deductive design approaches seem to prevail today, due to the growing availability of algorithmic procedures that do not merely support the design process, but develop it in an almost automated manner through conditioning and prevailing indicators and parameters. …”
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A systematic review of deep learning applications in database query execution by Bogdan Milicevic, Zoran Babovic

“…We categorize these approaches into three groups based on how such models are applied: improving performance of index structures and consequently data manipulation algorithms, query optimization tasks, and externally controlling query optimizers through parameter tuning. …”
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A review of machine learning and deep learning for Parkinson’s disease detection by Hajar Rabie, Moulay A. Akhloufi

“…We discuss the preprocessing methods applied, the state-of-the-art models utilized, and their performance. Our evaluation included different algorithms such as support vector machines (SVM), random forests (RF), convolutional neural networks (CNN). …”
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Western disturbances and climate variability: a review of recent developments by K. M. R. Hunt, K. M. R. Hunt, J.-P. Baudouin, A. G. Turner, A. G. Turner, A. P. Dimri, A. P. Dimri, G. Jeelani, Pooja, R. Chattopadhyay, R. Chattopadhyay, F. Cannon, T. Arulalan, T. Arulalan, M. S. Shekhar, T. P. Sabin, E. Palazzi

“…</p> <p>A number of step changes in understanding are synthesised. Use of new modelling frameworks and tracking algorithms has significantly improved knowledge of WD structure and variability, and a more frequentist approach can now be taken. …”
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Evaluating Binary Classifiers for Cardiovascular Disease Prediction: Enhancing Early Diagnostic Capabilities by Paul Iacobescu, Virginia Marina, Catalin Anghel, Aurelian-Dumitrache Anghele

“…This research highlights the value of advanced machine learning techniques in healthcare, addressing key challenges and laying a foundation for future studies aimed at improving predictive models for CVD prevention.…”
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Credit Risk Prediction Using Machine Learning and Deep Learning: A Study on Credit Card Customers by Victor Chang, Sharuga Sivakulasingam, Hai Wang, Siu Tung Wong, Meghana Ashok Ganatra, Jiabin Luo

“…The results indicate that XGBoost outperforms other models, achieving an accuracy of 99.4%. The outcomes from this study suggest that effective credit risk analysis would aid in informed lending decisions, and the application of machine-learning and deep-learning algorithms has significantly improved predictive accuracy in this domain.…”
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DPD-v2: Generalised deep particle diffusometry for varied particle shapes and experimental conditions by Pranshul Sardana, Steven T. Wereley

“…In microfluidic set-ups, the observed particles are often defocused and their motion is affected by factors such as fluid flow, which leads to high errors for conventional and deep learning-based PD (DPD) algorithms. This work improves the performance of DPD models by updating their architecture, avoiding temporal averaging in the input, and exploring the impact of various choices during training. …”
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A Comprehensive Approach to Rustc Optimization Vulnerability Detection in Industrial Control Systems by Kaifeng Xie, Jinjing Wan, Lifeng Chen, Yi Wang

“…Compiler optimization is a critical component for improving program performance. However, the Rustc optimization process may introduce vulnerabilities due to algorithmic flaws or issues arising from component interactions. …”
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Task-Driven Real-World Super-Resolution of Document Scans by Maciej Zyrek, Tomasz Tarasiewicz, Jakub Sadel, Aleksandra Krzywon, Michal Kawulok

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Optimized customer churn prediction using tabular generative adversarial network (GAN)-based hybrid sampling method and cost-sensitive learning by I Nyoman Mahayasa Adiputra, Paweena Wanchai, Pei-Chun Lin

“…Additionally, this study provided a robustness measurement for algorithms, demonstrating that CostLearnGAN outperforms other sampling methods in improving the performance of classical machine learning models with a 5.68 robustness value on average.…”
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Applications of Machine Learning for Wine Recognition Based on ¹H-NMR Spectroscopy by Ariana Raluca Hategan, Adrian Pirnau, Dana Alina Magdas

“…The present study aims to explore the possibility of applying machine learning (ML) algorithms for the development of reliable wine authentication instruments able to encompass the exhaustive performances offered by learning-based methods, and, at the same time, to propose reference practices for improving the recognition ability of the developed models. …”
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Chondrogenic Cancer Grading by Combining Machine and Deep Learning with Raman Spectra of Histopathological Tissues by Gianmarco Lazzini, Mario D’Acunto

“…In particular, in the last years several studies have demonstrated how the diagnostic performances of RS can be significantly improved by employing machine learning (ML) algorithms for the interpretation of Raman-based data. …”
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ARCHITECTURE AND FUNCTIONALITY OF INTEGRATED INFORMATION SYSTEM FOR ANALYSIS OF POTENTIAL OF RENEWABLE ENERGY SOURCES by B. A. Tonkonogov, S. P. Kundas, A. E. Moroz

“…The required functionality of system has led to the solution of a number of problems in the development of appropriate software modules that implement methods, models and algorithms for assessing the energy potential and economic efficiency of the use of renewable energy sources (RES). …”
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Leveraging advanced technologies for early detection and diagnosis of oral cancer: Warning alarm by Saantosh Saravanan, N. Aravindha Babu, Lakshmi T, Mukesh Kumar Dharmalingam Jothinathan

“…Specialized algorithms such as the Recombination-Based Improved Population Optimization Parallel Covariance Matrix Adaptation Evolution Strategy (RB-IPOP CMA-ES) allow for better accuracy of deep learning models, as this enhances the performance of developing models for early diagnosis of oral cancer. …”
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Quantum-enhanced intelligent system for personalized adaptive radiotherapy dose estimation by Radhey Lal, Rajiv Kumar Singh, Dinesh Kumar Nishad, Saifullah Khalid

“…The system efficiently models radiation transport and predicts patient-specific dose distributions by integrating quantum algorithms, deep learning, and Monte Carlo simulations. …”
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