Review of pedestrian trajectory prediction methods by Linhui LI, Bin ZHOU, Weiwei REN, Jing LIAN

“…With the breakthrough of deep learning technology and the proposal of large data sets, the accuracy of pedestrian trajectory prediction has become one of the research hotspots in the field of artificial intelligence.The technical classification and research status of pedestrian trajectory prediction were mainly reviewed.According to the different modeling methods, the existing methods were divided into shallow learning and deep learning based trajectory prediction algorithms, the advantages and disadvantages of representative algorithms in each type of method were analyzed and introduced.Then, the current mainstream public data sets were summarized, and the performance of mainstream trajectory prediction methods based on the data sets was compared.Finally, the challenges faced by the trajectory prediction technology and the development direction of future work were prospected.…”
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Efficient Pathfinding on Grid Maps: Comparative Analysis of Classical Algorithms and Incremental Line Search by Amr Elshahed, Majid Khan Bin Majahar Ali, Ahmad Sufril Azlan Mohamed, Farah Aini Binti Abdullah, Ts. Lee Jian Aun

“…On average, ILS achieved a 87.31% reduction in execution time and a 71.44% reduction in node expansions compared to their standard counterparts. …”
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Acoustic impedance inversion via voting stacked regression (VStaR) algorithms by Lutfi Mulyadi Surachman, Sanlin I. Kaka, Abdullatif Al-Shuhail

“…To refine the AI estimation, we used stacking and voting regression algorithms, with depth, two-way travel time (TWTT), and nine seismic attributes as inputs. …”
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Dimensionality cutback and deep learning algorithms efficacy as to the breast cancer diagnostic dataset by Gennady Chuiko, Denys Honcharov

“…The results indicate that the dimensionality of the Wisconsin Breast Cancer dataset, which is increasingly becoming the "gold standard" for diagnosing Malignant-Benign tumors, can be significantly reduced without losing predictive power. The Deep Learning algorithms in WEKA deliver excellent performance for both supervised and unsupervised learning, regardless of whether dealing with full or reduced datasets.…”
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Comparison of Energy Consumption Optimization in Sugar Factory Using Meta-Heuristic Algorithms by M. Boroun, M. Ghahderijani, A. A. Naseri, B. Beheshti

“…The total energy input reduction with the genetic algorithm was 17.05%, while the imperialist competitive algorithm achieved a higher reduction of 26.40%. …”
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Interior Radiation Noise Reduction Method Based on Multiframe Processing in Infrared Focal Plane Arrays Imaging System by Yiyang Li, Weiqi Jin, Zhihao Liu

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Performance comparison of machine learning algorithms for condition monitoring of tapered roller bearings by Harshal Aher, Nilesh Ghuge

“…This paper investigated the implementation of machine learning algorithms for health monitoring and fault detection of tapered roller bearings (TRBs) (30205 J2/Q, 30206 J2/Q and 30207 J2/Q). …”
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Low Speed Longitudinal Control Algorithms for Automated Vehicles in Simulation and Real Platforms by Mauricio Marcano, José A. Matute, Ray Lattarulo, Enrique Martí, Joshué Pérez

“…In that sense, this paper presents a use case where three longitudinal low speed control techniques are designed, tuned, and validated using an in-house simulation framework and later applied in a real vehicle. Control algorithms include a classical PID, an adaptive network fuzzy inference system (ANFIS), and a Model Predictive Control (MPC). …”
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Diabetes Mellitus Disease Prediction and Type Classification Involving Predictive Modeling Using Machine Learning Techniques and Classifiers by B. Shamreen Ahamed, Meenakshi S. Arya, S. K. B. Sangeetha, Nancy V. Auxilia Osvin

“…Various Machine-Learning (ML) algorithms are being used in order to predict and detect the disease to avoid further complications of health. …”
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Generating the Flood Susceptibility Map for Istanbul with GIS-Based Machine Learning Algorithms by Zehra Koyuncu, Ömer Ekmekcioğlu

“…Random forest (RF), stochastic gradient boosting (SGB), and XGBoost algorithms were used. The best predictive performance was obtained with the XGBoost algorithm, followed by SGB and RF, respectively. …”
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MODELLING FLUCTUATIONS OF GROUNDWATER LEVEL USING MACHINE LEARNING ALGORITHMS IN THE SOKOTO BASIN by Samson Alfa, Haruna Garba, Augustine Odeh

“…Among the models, the XGBoost algorithm demonstrated the highest performance, providing precise predictions that closely aligned with the actual groundwater levels. …”
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How low can you go: evaluating electrode reduction methods for EEG-based speech imagery BCIs by Maurice Rekrut, Maurice Rekrut, Johannes Ihl, Tobias Jungbluth, Antonio Krüger, Antonio Krüger

“…In this study, we evaluated several electrode reduction algorithms in combination with various feature extraction and classification methods across three distinct EEG-based speech imagery datasets to identify the optimal number and position of electrodes for SI-BCIs. …”
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Anomaly detection using unsupervised machine learning algorithms: A simulation study by Edmund Fosu Agyemang

“…Through systematic analysis on a synthetically simulated dataset, the study assessed each algorithm’s predictive performance using accuracy, precision, recall, and F1 score specifically for outlier detection. …”
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Research on intelligent control of coal slime flotation based on the WOA-GRU model by DOU Zhiheng, WANG Ranfeng, QIN Xinkai, CHAI Yuqing, LI Pinyu, LIU Shutong

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Impact of Right-Hand Polarized Signals in GNSS-R Water Detection Algorithms by Jilun Peng, Estel Cardellach, Weiqiang Li, Serni Ribo, Antonio Rius

“…This analysis can offer a deeper understanding of RHCP data and yield predictive insights prior to the HydroGNSS launch. In this study, we initially analyzed coherence indicators in incoherently averaged dual-polarized signals, and subsequently, applied these indicators to a random forest classifier, similar to the HydroGNSS surface inundation algorithm. …”
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Review of Fault Detection and Diagnosis Methods in Power Plants: Algorithms, Architectures, and Trends by Camelia Adela Maican, Cristina Floriana Pană, Daniela Maria Pătrașcu-Pană, Virginia Maria Rădulescu

“…A novel taxonomy of diagnostic configurations, mapping system types, sensor use, algorithmic strategy, and functional depth is proposed. …”
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Deep Reinforcement Learning for Automated Insulin Delivery Systems: Algorithms, Applications, and Prospects by Xia Yu, Zi Yang, Xiaoyu Sun, Hao Liu, Hongru Li, Jingyi Lu, Jian Zhou, Ali Cinar

“…Advances in continuous glucose monitoring (CGM) technologies and wearable devices are enabling the enhancement of automated insulin delivery systems (AIDs) towards fully automated closed-loop systems, aiming to achieve secure, personalized, and optimal blood glucose concentration (BGC) management for individuals with diabetes. While model predictive control provides a flexible framework for developing AIDs control algorithms, models that capture inter- and intra-patient variability and perturbation uncertainty are needed for accurate and effective regulation of BGC. …”
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The Construction of an Universal Linearized Control Flow Graph for Static Code Analysis of Algorithms by V. A. Bitner, N. V. Zaborovsky

“…That fact was demonstrated by the example code of the Peterson mutual execution algorithm for 2 threads.…”
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IMPLEMENTATION OF DYNAMIC AND FAST MINING ALGORITHMS ON INCREMENTAL DATASETS TO DISCOVER QUALITATIVE RULES by Pannangi Naresh, R. Suguna

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Quantum Computing-Accelerated Kalman Filtering for Satellite Clusters: Algorithms and Comparative Analysis by Shreyan Prakash, Raj Bhattacherjee, Sainath Bitragunta, Ashutosh Bhatia, Kamlesh Tiwari

“…Our quantum computing-based approach achieves a significant improvement in prediction accuracy and a reduction in mean absolute error compared to classical Kalman filtering techniques. …”
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