Development of a 1.0 KVA Fuelless Generator by Blessing Ngozi Goodluck Aliemeke, Munirat Abdullawal Momoh, Mercy Onoshioze Asekhome, Christian Azuka Anani, Afoke Chief Vincent

“…This power producing mechanism is aimed to develop a targeted 1KVA power capacity through innovative design strategies.  Detailed graphical modeling of the orthographic projection and isometric views brought about enhanced machine components development for stability and reliability. …”
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Justification of Technological Schemes and Parameters of Robotic Milking Parlors by S. S. Ruzin, F. E. Vladimirov, S. S. Yurochka, G. A. Dovgerd

“…(Conclusions) The authors revealed the optimal ratio of the number of milking posts and medical stations – 13:1. …”
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Modification of the WaldBoost algorithm to improve the efficiency of solving pattern recognition problems in real-time by A. N. Chesalin, S. Ya. Grodzenskiy, M. Yu. Nilov, A. N. Agafonov

“…The results are confirmed by statistical modeling on several data sets. It is noted that the results can be applied in the refinement of other cascade classification algorithms.…”
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Integration of artificial intelligence in reverse logistics process: enhancing decision-making across planning, execution, and control stages by Pascual Cortes Pellicer, Faustino Alarcon, David Pérez-Perales

“…Future research should focus on overcoming adoption barriers, advancing predictive models, and integrating AI with other Industry 4.0 technologies to enhance RLP outcomes. …”
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Virtual Reality in Building Evacuation: A Review by Ming-Chuan Hung, Ching-Yuan Lin, Gary Li-Kai Hsiao

“…Future research should integrate VR with artificial intelligence and machine learning for predictive and adaptive evacuation models. …”
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AI-Driven Chatbot for Real-Time News Automation by Fahim Sufi, Musleh Alsulami

“…This study presents an AI-driven chatbot designed for real-time news automation, integrating advanced natural language processing techniques, knowledge graphs, and generative AI models to improve news summarization and correlation analysis. …”
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Multi‐Target Drug Design in Alzheimer's Disease Treatment: Emerging Technologies, Advantages, Challenges, and Limitations by Md Saad Hossain, Md Hamed Hussain

“…Future research should focus on optimizing delivery systems, improving in silico modeling, and translating multi‐target strategies into clinically viable therapies for AD. …”
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Identification of digital twins to guide interpretable AI for diagnosis and prognosis in heart failure by Feng Gu, Andrew J. Meyer, Filip Ježek, Shuangdi Zhang, Tonimarie Catalan, Alexandria Miller, Noah A. Schenk, Victoria E. Sturgess, Domingo Uceda, Rui Li, Emily Wittrup, Xinwei Hua, Brian E. Carlson, Yi-Da Tang, Farhan Raza, Kayvan Najarian, Scott L. Hummel, Daniel A. Beard

“…Incorporating these features into prognostic AI models improved performance, transferability, and interpretability compared to models using only clinical variables. …”
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Tool wear prediction based on XGBoost feature selection combined with PSO-BP network by Zhangwen Lin, Yankun Fan, Jinling Tan, Zhen Li, Peng Yang, Hua Wang, Weiwei Duan

“…Subsequently, time-domain, frequency-domain, and time–frequency domain features are extracted from the preprocessed data using FFT and wavelet packet decomposition, followed by feature screening for tool wear mapping via Pearson correlation and XGBoost feature importance analysis as model input. Finally, PSO is employed to optimize BPNN parameters. …”
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Research on deep reinforcement learning based intelligent shop scheduling method by Zihui LUO, Chengling JIANG, Liang LIU, Xiaolong ZHENG, Huadong MA

“…The unprecedented prosperity of the industrial internet of things (IIoT) has opened up a new path for the traditional industrial manufacturing model.Intelligent shop scheduling is one of the key technologies to achieve the overall control and flexible production of the whole production process.It requires an effective plan with a minimum makespan to allocate multiple processes and multiple machines for production scheduling.Firstly, the shop scheduling problem was defined as a Markov decision process (MDP), and a shop scheduling model based on the pointer network was established.Secondly, the job scheduling process was regarded as a mapping from one sequence to another, and a new shop scheduling algorithm based on deep reinforcement learning (DRL) was proposed.By analyzing the convergence of the model under different parameter settings, the optimal parameters were determined.Experimental results on different scales of public data sets and actual production data sets show that the proposed DRL algorithm can obtain better performances.…”
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An Intelligent Technique for Coordination and Control of PV Energy and Voltage-Regulating Devices in Distribution Networks Under Uncertainties by Tolulope David Makanju, Ali N. Hasan, Oluwole John Famoriji, Thokozani Shongwe

“…Furthermore, Machine Learning (ML) models were trained as controllers to determine the reactive-power setpoints for the PVSIs and DSTATCOMs as well as the optimal OLTC tap position required for voltage stability in the network. …”
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Detecting microcephaly and macrocephaly from ultrasound images using artificial intelligence by Abraham Keffale Mengistu, Bayou Tilahun Assaye, Addisu Baye Flatie, Zewdie Mossie

“…Future work needs to be done on optimizing model performance, trying complex models, and expanding datasets to improve generalizability. …”
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The effectiveness, equity and explainability of health service resource allocation—with applications in kidney transplantation & family planning by Joris van de Klundert, Harwin de Vries, Francisco Pérez-Galarce, Nieves Valdes, Felipe Simon

“…Additional resources are required and need to be allocated effectively and equitably. Resource allocation models, however, have struggled to accurately predict effects and to present optimal allocations, thus hampering effectiveness and equity improvement. …”
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A Framework for Data‒Driven Fault Diagnosis of Numerical Spacecraft Propulsion Systems by Kazushi Adachi, Samir Khan, Shinichi Nakasuka, Kohji Tominaga, Seiji Tsutsumi, Yu Daimon, Taiichi Nagata

“…Future work will focus on enhancing the framework's capability to address unknown anomalies through advanced machine learning techniques. The study addresses the limitations imposed by computational resources and sensor installation constraints through Sequential Forward Selection (SFS) for optimized sensor placement and feature selection. …”
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High-resolution RGB image dataset for wheat seed varietal identification and purity assessmentZenodoMendeley Data by Mehreen Nawaz, Sadaf Safder, Shazia Riaz, Saqib Ali

“…Therefore, many researchers have turned to computer vision, machine learning, and deep learning models. However, this requires a sizable dataset of a specific location, which is limited and unavailable. …”
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On Variable-Universe Fuzzy Control for Drive Chain of Front-End Speed Regulated Wind Generator by Hongwei Li, Kaide Ren, Haiying Dong, Shuaibing Li

“…To ensure that the FSR wind turbine performs as a general synchronous generator, this paper firstly modeled the drive chain and then proposed to use the variable-universe fuzzy approach for the drive chain control. …”
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Development of Fast Analytical Method for the Detection and Quantification of Honey Adulteration Using Vibrational Spectroscopy and Chemometrics Tools by Omar Elhamdaoui, Aimen El Orche, Amine Cheikh, Brahim Mojemmi, Rachid Nejjari, Mustapha Bouatia

“…For quantitative analysis, we used partial least-squares regression (PLS-R) and the support vector machine (SVM) to develop optimal calibration models. …”
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SUPPLEMENTS TO THE PROBLEM OF ENERGY CONSUMPTION IN REDUCING LIGNOCELLULOSES BIOMASS SIZE TO PRODUCE ENERGY by Raluca SFÎRU, Petru CARDEI, Cornelia MURARU-IONEL, Vergil MURARU

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NEW ORGANIZATION PROCESS OF FEATURE SELECTION BY FILTER WITH CORRELATION-BASED FEATURES SELECTION METHOD by Olga Solovei

“… The subject of the article is feature selection techniques that are used on data preprocessing step before building machine learning models. In this paper the focus is put on a Filter technique when it uses Correlation-based Feature Selection (further CFS) with symmetrical uncertainty method (further CFS-SU) or CFS with Pearson Correlation (further CFS-PearCorr). …”
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Artificial Intelligence and Smart Technologies in Safety Management: A Comprehensive Analysis Across Multiple Industries by Jiyoung Park, Dongheon Kang

“…AI-driven solutions, such as predictive analytics, machine learning algorithms, IoT sensor integration, and digital twin models, are shown to proactively identify and mitigate potential hazards, optimize energy consumption, and enhance operational efficiency. …”
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