Using Wireless Sensor Networks to Achieve Intelligent Monitoring for High-Temperature Gas-Cooled Reactor by Jianghai Li, Jia Meng, Xiaojing Kang, Zhenhai Long, Xiaojin Huang

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Beyond raw data: AI-driven biosensor fusion for enhancing athletic performance by Peng Luo, Jun Song

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Detection of terrain feature points from digital elevation models using contour context by Jiapei Hu, Xuejun Liu, Bo Wu

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Optimization of IoT-Based Motion Intelligence Monitoring System by Jian Qiao, Zhendong Zhang, Enqing Chen

“…The system provides a multiuser-oriented communication method and realizes accurate and reliable human movement monitoring, which has a wide application prospect in the fields of physical education and rehabilitation training.…”
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A novel data representation framework based on nonnegative manifold regularisation by Yan Jiang, Wei Liang, Jintian Tang, Hongbo Zhou, Kuan-Ching Li, Jean-Luc Gaudiot

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Artificial intelligence, generative artificial intelligence and research integrity: a hybrid systemic review by Khalid H. Arar, Hamit Özen, Gülşah Polat, Selahattin Turan

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A novel motion key frame extraction and video stream classification based on reinforcement learning and feature fusion by Hongbo Cui, Tao Feng, Jinhui Zheng

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Research on the evolution of college online public opinion risk based on improved Grey Wolf Optimizer combined with LSTM model. by Chao Cao, Ziyu Li, Lingzhi Li, Fanglu Luo

“…This research proposes a public opinion crisis prediction model that applies the Grey Wolf Optimizer (GWO) algorithm combined with long short-term memory (LSTM) and implements it to analyze a trending topic on Sina Weibo to validate its prediction accuracy. …”
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AI-driven pharmacovigilance: Enhancing adverse drug reaction detection with deep learning and NLP by Dr. Bharti Khemani, Dr. Sachin Malave, Samyukta Shinde, Mandvi Shukla, Razzaq Shikalgar, Harshita Talwar

“…This research underscores the potential of predictive modeling to enhance pharmacovigilance efforts and ensure safer clinical trial outcomes. • The research methodology includes a comparison of supervised learning algorithms, such as Logistic Regression, Random Forest, Gradient Boost, CNN, and genetic algorithms, to identify patterns and anomalies in clinical trial data. …”
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DCLTV: An Improved Dual-Condition Diffusion Model for Laser-Visible Image Translation by Xiaoyu Zhang, Laixian Zhang, Huichao Guo, Haijing Zheng, Houpeng Sun, Yingchun Li, Rong Li, Chenglong Luan, Xiaoyun Tong

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Consistency Regularization for Semi-Supervised Semantic Segmentation of Flood Regions From SAR Images by G. Savitha, S. Girisha, Pundarika Sughosh, Dasharathraj K. Shetty, Jayaraj Mymbilly Balakrishnan, Rahul Paul, Nithesh Naik

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EDWNet: A Novel Encoder–Decoder Architecture Network for Water Body Extraction from Optical Images by Tianyi Zhang, Wenbo Ji, Weibin Li, Chenhao Qin, Tianhao Wang, Yi Ren, Yuan Fang, Zhixiong Han, Licheng Jiao

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Semantic influence of programming on the development of thinking of students: background, research and prospects by M. U. Mukasheva, Y. V. Payevskaya

“…Altynsarin with the support of the grant from the Ministry of Education and Science of the Republic of Kazakhstan. …”
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On the Potential of Bayesian Neural Networks for Estimating Chlorophyll-a Concentration from Satellite Data by Mohamad Abed El Rahman Hammoud, Nikolaos Papagiannopoulos, George Krokos, Robert J. W. Brewin, Dionysios E. Raitsos, Omar Knio, Ibrahim Hoteit

“…Our results suggest that BNNs perform at least as well as established methods, and they could achieve 20–40% lower mean squared errors when additional input variables are included, such as the sea surface temperature and its climatological mean alongside the coordinates of the prediction. The BNNs offer means for uncertainty quantification by estimating the probability distribution of [CHL-a], building confidence in the [CHL-a] predictions through the variance of the predictions. …”
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Harnessing AI forward and backward chaining with telemetry data for enhanced diagnostics and prognostics of smart devices by Muhammad Shoaib Farooq, Rizwan Pervez Mir, Atif Alvi, Kilian Tutusaus, Eduardo Garcia Villena, Fadwa Alrowais, Hanen Karamti, Imran Ashraf

“…The capacity to precisely diagnose and preemptively predict potential failures holds the potential to considerably amplify maintenance efficiency, diminish downtime, and optimize resource allocation. …”
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Wearable Artificial Intelligence for Sleep Disorders: Scoping Review by Sarah Aziz, Amal A M Ali, Hania Aslam, Alaa A Abd-alrazaq, Rawan AlSaad, Mohannad Alajlani, Reham Ahmad, Laila Khalil, Arfan Ahmed, Javaid Sheikh

“…The primary selection criterion was the inclusion of studies that utilized AI algorithms to detect or predict various sleep disorders using data from wearable devices. …”
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Micro hole drilling and multi criteria optimization of soda lime glass via ultrasonic assisted rotary electrochemical discharge drilling by Sahil Grover, Viveksheel Rajput, Sanjay Kumar Mangal, Sarbjit Singh, Sandeep Singh, Shubham Sharma, Ehab El Sayed Massoud, Dražan Kozak, Jasmina Lozanovic

“…Machine learning-based algorithms are also used to predict the responses using Random Forest and Gradient Boost approaches. …”
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Enhancing PV feed-in power forecasting through federated learning with differential privacy using LSTM and GRU by Pascal Riedel, Kaouther Belkilani, Manfred Reichert, Gerd Heilscher, Reinhold von Schwerin

“…By leveraging advanced FL algorithms such as FedYogi and FedAdam, we propose a method that not only predicts sequential energy data with high accuracy, achieving an R2 of 97.68%, but also adheres to stringent privacy standards, offering a scalable solution for the challenges of smart grids analytics, thus clearly showing that the proposed approach is promising and worth being pursued further.…”
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MHC2-SCALE enhances identification of immunogenic neoantigens by Joshua G. Gober, Aude-Hélène Capietto, Reyhane Hoshyar, Martine Darwish, Richard Vandlen, Jonathan L. Linehan, Lélia Delamarre, Adel M. ElSohly

“…We validated MHC-II peptide candidates predicted by the immune epitope database (IEDB) algorithm, as well as uncovered many true and immunogenic MHC-II binders that were not predicted by IEDB. …”
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Differentiating Pulmonary Nodule Malignancy Using Exhaled Volatile Organic Compounds: A Prospective Observational Study by Guangyu Lu, Zhixia Su, Xiaoping Yu, Yuhang He, Taining Sha, Kai Yan, Hong Guo, Yujian Tao, Liting Liao, Yanyan Zhang, Guotao Lu, Weijuan Gong

“…We applied five machine learning (ML) algorithms to develop predictive models which were evaluated using area under the curve (AUC), sensitivity, specificity, and other relevant metrics. …”
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