Università italiana, docenti e ChatGPT. La zona grigia tra pratiche lavorative e immaginari by Giovanni Ciofalo, Marco Pedroni, Francesca Setiffi

“…The questionnaire and the interpretation of the results consider two perspectives: a) the culture and everyday life of artificial intelligence and 2) artificial communication, algorithmic thinking, platforms, and work practices. …”
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AI-Enabled Smart Irrigation for Climate-Resilient Agriculture by Khan Roohee, Sharma Pooja

“…The system tries to achieve reduction in waste, optimized water usages and enhancement of crop yield by assimilating advanced machine learning algorithms with real time sensor data. …”
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AI-Driven Telerehabilitation: Benefits and Challenges of a Transformative Healthcare Approach by Rocco Salvatore Calabrò, Sepehr Mojdehdehbaher

“…Artificial intelligence (AI) has revolutionized telerehabilitation by integrating machine learning (ML), big data analytics, and real-time feedback to create adaptive, patient-centered care. …”
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Orchard-Wide Visual Perception and Autonomous Operation of Fruit Picking Robots: A Review by CHEN Mingyou, LUO Lufeng, LIU Wei, WEI Huiling, WANG Jinhai, LU Qinghua, LUO Shaoming

“…It points out that current mapping methods, while effective, still struggle with dynamic changes in the orchard, such as variations of fruits and light conditions. Improved adaptation techniques, possibly through machine learning models that can learn and adjust to different environmental conditions, are suggested as a way forward. …”
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Numerical modeling and experimental estimates of structural member fatigue characteristics by Yu. P. Man’shin, E. Yu. Man’shina

“…Introduction. In the algorithm for predicting the resource of machine parts, models of external actions, fracture resistance, and temporal development of a particular type of damage to these units interact. …”
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A Research Review of Order Allocation in Robotic Mobile Fulfillment Systems by LIU Zhaokai, MOU Jinrui, JIANG Ruyi, WANG Lin

“…Next, to further elucidate related solution methods, this paper introduces research progress in order allocation and multi-robot task scheduling from various perspectives, such as classical optimization methods, heuristic and meta-heuristic algorithms, rule-based strategies, simulation optimization algorithms, as well as artificial intelligence and machine learning techniques. …”
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Planetary Gearbox Fault Diagnosis based on LMD Sample Entropy and ELM by Zhang Ning, Wei Xiuye, Xu Jinhong

“…In order to solve the difficult problem of early fault feature extraction of planetary gearbox and consider that the planetary gearbox vibration signal is coupling and nonlinear，and the signal has multiple transmission paths，a planetary gearbox fault diagnosis method based on Local Mean Decomposition（LMD） and Sample Entropy and Extreme Learning Machine（ELM） is proposed.Firstly，the vibration signal is adaptively decomposed into a plurality of PF components by LMD，and the first four PF components including the main fault information are selected in combination with the correlation coefficient and the variance contribution rate.Secondly，the Sample Entropy of the signal is calculated to form a feature vector.Finally，the feature vector is input into ELM for fault classification.Experiments are carried out on the planetary gearbox test bench，compared with the probabilistic neural network classification algorithm，and compared with the feature vector based on Singular Value Decomposition （SVD）.The results verify the effectiveness of the proposed method.…”
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K-Nearest Neighbors hybrid method for maximum power point tracking under partial shading for photovoltaic power systems by Djamel Guessoum, Maen Takrouri, Mohammad Rabih, Maissa Farhat, Sufian A. Badawi

“…This paper presents a hybrid approach that integrates the K-Nearest Neighbors (KNN) machine learning algorithm with an enhanced local search for optimizing the duty cycle D. …”
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Intelligent hydraulic fracturing under industry 4.0—a survey and future directions by Jing Jia, Qinghu Fan, Jianglu Jing, Kehui Lei, Lichang Wang

“…It identifies four technical challenges: integrating heterogeneous data, developing intelligent decision-making algorithms, adaptive surface equipment adjustments, and multi-machine collaborative control. …”
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Enhancing diabetes risk prediction: A comparative evaluation of bagging, boosting, and ensemble classifiers with SMOTE oversampling by Rabia Asif, Darshana Upadhyay, Marzia Zaman, Srini Sampalli

“…This study explores advanced machine learning techniques, specifically bagging, boosting, and ensemble methods to improve diabetes risk prediction. …”
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Enhancing Energy Systems and Rural Communities through a System of Systems Approach: A Comprehensive Review by Abdellatif Soussi, Enrico Zero, Alessandro Bozzi, Roberto Sacile

“…The principal areas of interest include the integration of advanced control algorithms and machine learning techniques and the development of robust communication networks to manage interactions between interconnected subsystems. …”
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An Early Detection of Asthma Using BOMLA Detector by Md. Abdul Awal, Md. Shahadat Hossain, Kumar Debjit, Nafiz Ahmed, Rajan Dev Nath, G. M. Monsur Habib, Md. Salauddin Khan, Md. Akhtarul Islam, M. A. Parvez Mahmud

“…It has even been attempted to delineate how the ADASYN algorithm affects the classification performance. The highest accuracy (ACC) and Matthews’s correlation coefficient (MCC) for an Asthma dataset provide 94.35% and 88.97%, respectively, using BOMLA detector when SVC is adapted, and it has been increased to 96.52% and 93.04%, respectively, when ensemble technique is adapted. …”
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Identification of S1 and S2 Heart Sound Patterns Based on Fractal Theory and Shape Context by Ana Gavrovska, Goran Zajić, Vesna Bogdanović, Irini Reljin, Branimir Reljin

“…There has been a sustained effort in the research community over the recent years to develop algorithms that automatically analyze heart sounds. …”
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From word models to executable models of signaling networks using automated assembly by Benjamin M Gyori, John A Bachman, Kartik Subramanian, Jeremy L Muhlich, Lucian Galescu, Peter K Sorger

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Neutrosophic Fuzzy Power Management (NFPM): Tackling Uncertainty in Energy Harvesting for Sensor Networks by Musallam M. AlZubi, Mohamed A. Mohamed, Hanan M. Amer, A. A. Salama

“…Future research directions include exploring the integration of NFPM with machine learning algorithms for predictive energy management, assessing its scalability in larger networks, and examining its applicability in other domains requiring energy management under uncertainty.…”
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ADVANCEMENTS AND EMERGING PERSPECTIVES IN MICROSCOPIC IMAGING

“…Imaging depth and signal fidelity have been improved through the implementation of adaptive optics and wavefront shaping. The integration of microscopic imaging with optogenetics, biosensors, and machine learning approaches has occurred2. …”
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Fault detection in electrical power systems using attention-GRU-based fault classifier (AGFC-Net) by Deepen Khandelwal, Prateek Anand, Mayukh Ray, Sangeetha R. G.

“…Experimental results show that AGFC-Net attains a fault detection accuracy of 99.52%, better than conventional machine learning and deep learning algorithms. The suggested method presents a stronger, adaptive, and scalable solution for autonomous fault diagnosis, opening the door to intelligent and trustworthy fault detection systems in future power grids and industrial applications.…”
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An Analysis of Intelligent Turkish Text Classification Models for Routing Calls in Call Centers: A Case Study on the Republic of Turkiye Ministry of Trade Call Center by Muammer Özdemir, Yasin Ortakcı

“…Using a specific dataset of 20,000 phone call texts collected from the MTCC, the study employs TF-IDF, Word2Vec, and GloVe text vectorization techniques and applies various machine learning algorithms such as K-Nearest Neighbours, Naive Bayes, Support Vector Machines, Adaptive Boosting, Decision Tree and Random Forest for text classification. …”
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Review of Demand Response-based Optimal Scheduling of Electric and Thermal Integrated Energy Systems by Jingshan MO, Guangxian YAN, Na SONG, Mingyang YUAN

“…Artificial intelligence algorithms are primarily divided into methods based on group optimization problems and machine learning algorithms. …”
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Optimising energy distribution and detecting vulnerabilities in networks using artificial intelligence by D. Koshkin, O. Sadovoy, A. Rudenko, V. Sokolik

“…The application of machine learning algorithms, such as convolutional and recurrent neural networks, significantly improved load forecasting accuracy and adaptability to changing network conditions. …”
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