Efficient Task Scheduling and Load Balancing in Fog Computing for Crucial Healthcare Through Deep Reinforcement Learning by Prashanth Choppara, Bommareddy Lokesh

“…Comparative performance analysis indicated a 30% reduction in task completion times, a 40% reduction in operational latency, and a 25% improvement in fault tolerance relative to traditional scheduling approaches. …”
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Dynamic Water Scheduling in the Northwest River Delta Basin Based on Minimum Discharge Flow Control in Cross-section by WU Di, FANG Chunyan, SHANG Yunfei

“…In response to the problem of insufficient future demand forecasting and poor scheduling balance caused by traditional watershed water scheduling methods relying on historical data and fixed rules, a dynamic water scheduling method for the Northwest River Delta Basin based on minimum discharge flow control of cross-sections was proposed. By using the grey prediction model, the production and domestic water consumption in the downstream areas of the Northwest River Delta Basin was accurately predicted. …”
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Twin Support Vector Regression Model Based on Heteroscedastic Gaussian Noise and Its Application by Shiguang Zhang, Ge Feng, Feng Yuan, Shuangle Guo

“…The main purpose of twin support vector regression (TSVR) is to find linear or nonlinear relationships in sample data, and then predict future data. TSVR is the decomposition of a large convex quadratic programming problem into two small convex quadratic programming problems. …”
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Applications, Challenges, and Future Perspectives of Artificial Intelligence in Psychopharmacology, Psychological Disorders and Physiological Psychology: A Comprehensive Review by Mohammad Hossein Salemi, Elham Foroozandeh, Molouk Khademi Ashkzari

“…Personalized medicine, powered by AI, predicts individual medication responses, minimizing side effects and optimizing outcomes. …”
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Analytical approach for modelling of thread crimp in jacquard woven two-dimensional fabrics by Brigita Kolcavová Sirková, Iva Mertová

“…The theoretical predicted values of thread crimp in jacquard fabrics were compared with experimentally obtained values. …”
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The System Research and Implementation for Autorecognition of the Ship Draft via the UAV by Wei Zhan, Shengbing Hong, Yong Sun, Chenguang Zhu

“…In order to solve the above problems, this paper introduces the computer image processing technology based on deep learning, and the specific process is divided into three steps: first, the video sampling is carried out by the UAV to obtain a large number of pictures of the ship draft reading face, and the images are preprocessed; then, the deep learning target detection algorithm of improved YOLOv3 is used to process the images to predict the position of the waterline and identify the draft characters; finally, the prediction results are analyzed and processed to obtain the final reading results. …”
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Towards a New Taxonomy of Pattern-Making in the Visuo-Spatial Domain in Early Childhood Based on Zygonic Theory and the Sounds of Intent Framework of Musical Development by Adam Ockelford, Sarah McCarthy, Sue Gifford, Helen Thouless, Sharon Kirk, Michael Thorpe

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Real-time classification of EEG signals using Machine Learning deployment by Swati CHOWDHURI, Satadip SAHA, Samadrita KARMAKAR, Ankur CHANDA

“…This study proposes a machine learning-based approach for predicting the level of students' comprehension with regard to a certain topic. …”
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Forecasting Ultrafine Dust Concentrations in Seoul: A Machine Learning Approach by Sophia Park, Myeong Jun Kim

“…Using daily data from 1 January 2018 to 30 June 2023, this study employed the Boruta algorithm, a variable selection technique based on the random forest model, to identify the most influential predictors for predicting PM2.5 concentrations. …”
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Monitoring the Concentrations of Na, Mg, Ca, Cu, Fe, and K in <i>Sargassum fusiforme</i> at Different Growth Stages by NIR Spectroscopy Coupled with Chemometrics by Sisi Wei, Jing Huang, Ying Niu, Haibin Tong, Laijin Su, Xu Zhang, Mingjiang Wu, Yue Yang

“…Superior CARS-PLS models were established for Na, Mg, Ca, Cu, Fe, and K with root mean square error of prediction (<i>RMSEP</i>) values of 0.8196 × 10³ mg kg⁻¹, 0.4370 × 10³ mg kg⁻¹, 1.544 × 10³ mg kg⁻¹, 0.9745 mg kg⁻¹, 49.88 mg kg⁻¹, and 7.762 × 10³ mg kg⁻¹, respectively, and coefficient of determination of prediction (<i>R_P</i>²) values of 0.9787, 0.9371, 0.9913, 0.9909, 0.9874, and 0.9265, respectively. …”
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Genetic Subtype‐Based International Prognostic Index Prognostic Model in Diffuse Large B‐Cell Lymphoma by Lan Mi, Jili Deng, Jiayue Qin, Chen Zhang, Lixia Liu, Shunli Yang, Libin Chen, Hua‐Jun Wu, Haojie Wang, Jun Zhu, Hong Chen, Feng Lou, Shanbo Cao, Yuqin Song, Weiping Liu

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COOPERATIVE MODEL FOR OPTIMIZATION OF EXECUTION OF THREADS ON MULTI-CORE SYSTEM by A. A. Prihozhy, O. N. Karasik

“…It optimizes the execution order of the  computational operations and the operations of data exchange, decreases the overall time of the multithread application  execution by means of the reduction of the critical path in the concurrent algorithm graph, increases the application throughput at the growth of the number of threads, and excludes the competition among threads that is specific for preemptive multitasking...............................…”
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Automated CATS system for distance learning by Yu. B. Popova

“…These mathematical methods made it possible to develop adaptability algorithms, their software implementation and testing in the educational process. …”
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AgriChainSync: A Scalable and Secure Blockchain-Enabled Framework for IoT-Driven Precision Agriculture by Hassam Ahmed Tahir, Walaa Alayed, Waqar Ul Hassan, Fahad Nabi, Truong X. Tran

“…The rapid advancement of precision farming, automated irrigation systems, and predictive analytics has revolutionized agriculture, but these innovations also introduce new challenges, particularly in data integrity and security. …”
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SHapley Additive exPlanations (SHAP) for Landslide Susceptibility Models: Shedding Light on Explainable AI by H. Al-Najjar, B. Kalantar, B. Pradhan, G. Beydoun, N. Ueda

“…Various evaluation metrics, including overall accuracy and precision-recall, are employed to assess the predictive capabilities of each model. The findings reveal the strengths and limitations of both models, providing valuable insights for stakeholders and decision-makers involved in land use planning and disaster preparedness. …”
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A smarter approach to liquefaction risk: harnessing dynamic cone penetration test data and machine learning for safer infrastructure by Shubhendu Vikram Singh, Sufyan Ghani

“…ML models, including Support Vector Machine (SVM) optimized with Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), Genetic Algorithm (GA), and Firefly Algorithm (FA), were employed to predict the e/qd ratio using key geotechnical parameters, such as fine content, peak ground acceleration, reduction factor, and penetration rate. …”
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Variable Speed Limit Strategies Based on the Macro Hierarchical Control Traffic Flow Model by Shubin Li, Tao Wang, Hualing Ren, Baiying Shi, Xiangke Kong, Jianyong Chai, Xuejuan Wang

“…The dynamic OD estimation model is used to produce the real traffic information, which is loaded to the traffic network. Then, the prediction information of traffic variables and the VSL strategy are introduced to macro hierarchical control traffic flow model. …”
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Tailoring orthotropic mechanical and thermal properties in nonlinear contact, vibration and thermal problems via kriging-based optimization by Yicong Zhou, Jinyan Duan, Guoxin Mao, Jun Hong

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PointNet++ (MSG-DSLS): A Classification Framework With Dynamic Step-Size Loop-Sampling for High-Density Tree Point Clouds by Zhen Liu, Ying Quan, Bin Wang, Jianyang Liu, Mingze Li

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Dispatching strategy of shared bicycles for morning peak tidal travel based on spatial distribution by Xiangyu Lei, Jiaqi Yang

“…The study identifies the spatial distribution characteristics of the tidal phenomenon by analyzing the riding order and electronic fence data and combining them with the KD-Tree algorithm; subsequently, a demand prediction model based on the XGBoost algorithm and a hierarchical scheduling model based on the greedy algorithm are constructed to dynamically optimize the spatial distribution of the bicycle resources with the demand fluctuation as the guide. …”
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