Integrating Machine Learning for Enhanced Agricultural Productivity: A Focus on Bananas and Arecanut in the Context of India’s Economic Growth by B. S. Saruk, G. Mokesh Rayalu

“…Assist yield projections may provide governments and policymakers with valuable information to make well-informed choices about food security, import–export policies, and resource allocation. It facilitates national- and regional-level food supply planning. …”
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Machine learning in predicting firm performance: a systematic review by Yaseen Hezam, Hoa Luong, Lilian Anthonysamy

“…It reveals a predominance of classification methods in ML, with neural networks, logistic regression, and decision trees being the most frequently employed algorithms. These findings underscore the potential of ML techniques to provide a more nuanced and accurate prediction of firm performance by integrating diverse data sources and attributes. …”
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Crop yield prediction using machine learning: An extensive and systematic literature review by Sarowar Morshed Shawon, Falguny Barua Ema, Asura Khanom Mahi, Fahima Lokman Niha, H.T. Zubair

“…In order to ensure food security and optimize resource allocation, precise crop yield prediction has become essential due to the growing global population and the effects of climate change on agricultural production. …”
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Optimizing integration techniques for UAS and satellite image data in precision agriculture — a review by Aliasghar Bazrafkan, C. Igathinathane, Nonoy Bandillo, Paulo Flores

“…Integrated UAS and satellite data impact precision agriculture, contributing to improved resolution, monitoring capabilities, resource allocation, and crop performance evaluation. …”
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Collaborative governance model for spoil disposal and gully infill land creation near open-pit coal mines by Shaogang LEI, Jianying ZHANG, Chang LIU, Liang WANG, Zhenwang JIA

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Leveraging AI for early cholera detection and response: transforming public health surveillance in Nigeria by Adamu Muhammad Ibrahim, Mohamed Mustaf Ahmed, Shuaibu Saidu Musa, Usman Abubakar Haruna, Mohammed Raihanatu Hamid, Olalekan John Okesanya, Aishat Muhammad Saleh, Don Eliso Lucero-Prisno III

“…AI technologies, including predictive modeling and ML algorithms such as random forests and convolutional neural networks (CNNs), can analyze diverse data sources—such as meteorological, environmental, and health records—to detect patterns and predict outbreaks. …”
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A maChine and deep Learning Approach to predict pulmoNary hyperteNsIon in newbornS with congenital diaphragmatic Hernia (CLANNISH): Protocol for a retrospective study. by Ilaria Amodeo, Giorgio De Nunzio, Genny Raffaeli, Irene Borzani, Alice Griggio, Luana Conte, Francesco Macchini, Valentina Condò, Nicola Persico, Isabella Fabietti, Stefano Ghirardello, Maria Pierro, Benedetta Tafuri, Giuseppe Como, Donato Cascio, Mariarosa Colnaghi, Fabio Mosca, Giacomo Cavallaro

“…The native sequences from fetal magnetic resonance imaging (MRI) will be collected. Data from different sources will be integrated and analyzed using ML and DL, and forecasting algorithms will be developed for each outcome. …”
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Incorporating Contextual Factors into a Comprehensive Analysis of Operational Efficiency and Service Quality in Healthcare Sector by Utsav Pandey, Sanjeet Singh

“…In the first stage, an output-oriented Data Envelopment Analysis is employed to model the interdependency between operational efficiency and service quality by assessing the allocation of the input resources for achieving these two objectives. …”
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Perspective of Chinese GF-1 high-resolution satellite data in agricultural remote sensing monitoring by Qing-bo ZHOU, Qiang-yi YU, Jia LIU, Wen-bin WU, Hua-jun TANG

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Processing streams in a monitoring cloud cluster by Alexey N. Nazarov

“…This requires to study the methodological possibilities of organization to study of parallel processing high-speed streaming services with the processing of huge amounts of bit data, and, simultaneously, to estimate the necessary computational resource. In the conditions of high dynamics of changes in the bit rate of information generation from the source, a model of the bit rate of Discretized Stream (DStream) formation is proposed, which has a common application. …”
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Two-sided Energy Storage Cooperative Scheduling Method for Transmission and Distribution Network Based on Multi-agent Attention-deep Reinforcement Learning by CHEN Shi, ZHU Yujie, LIU Yihong, XU Liuchao, TANG Guodeng

“…An improved Shapley value is used to allocate additional income, providing a cooperative incentive. …”
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Development and validation of an interpretable multi-task model to predict outcomes in patients with rhabdomyolysis: a multicenter retrospective cohort studyResearch in context by Chunli Liu, Jie Shi, Fengjuan Wang, Duo Li, Yu Luo, Bofan Yang, Yunlong Zhao, Li Zhang, Dingwei Yang, Heng Jin, Jie Song, Xiaoqin Guo, Haojun Fan, Qi Lv

“…Early and accurate prediction of acute kidney injury (AKI), disease severity, renal replacement therapy (RRT) requirements, and mortality risk is essential for timely identification of high-risk individuals, personalized treatment planning, and optimal allocation of healthcare resources. We aimed to develop and externally validate an interpretable multi-task machine learning (ML) model to predict four clinical outcomes in patients with rhabdomyolysis: AKI, disease severity, the need for RRT, and in-hospital mortality. …”
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Proximity-based solutions for optimizing autism spectrum disorder treatment: integrating clinical and process data for personalized care by Annarita Vignapiano, Annarita Vignapiano, Francesco Monaco, Francesco Monaco, Stefania Landi, Luca Steardo, Carlo Mancuso, Claudio Pagano, Gianvito Petrillo, Alessandra Marenna, Martina Piacente, Stefano Leo, Carminia Marina Ingenito, Rossella Bonifacio, Benedetta Di Gruttola, Marco Solmi, Marco Solmi, Marco Solmi, Marco Solmi, Maria Pontillo, Giorgio Di Lorenzo, Giorgio Di Lorenzo, Alessio Fasano, Alessio Fasano, Alessio Fasano, Giulio Corrivetti, Giulio Corrivetti

“…A central data hub, the Master Data Plan (MDP), will aggregate and analyze information from diverse sources, feeding AI algorithms that can identify risk factors for ASD, personalize treatment plans based on individual needs, and even predict potential relapses. …”
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IntelliGrid AI: A Blockchain and Deep-Learning Framework for Optimized Home Energy Management with V2H and H2V Integration by Sami Binyamin, Sami Ben Slama

“…The core of IntelliGrid AI is an advanced Q-learning algorithm that intelligently allocates energy resources. …”
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The methodology for calculating the number of teaching staff using economically feasible norms by L. A. Krokhmal, Yu. A. Kovshun

“…It has been established that the planned number of teaching staff is lower than the planned staffing level, calculated based on the volume of contact work hours, which makes it possible to encourage universities to restructure their curricula without going beyond funding limits.Conclusons and Relevance:the proposed approach allowed to conduct the calculation of teaching staff in accordance with the curriculum with a standardized volume of financial resources allocated to finance the remuneration of teaching staff of a specific educational program. …”
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