A Topical Review of Quantum and Classical Machine Learning Approaches to Disaster Escape Routing Problems by A. Vinil, Parameswaran Iyer, Jetain Chetan, Aniket Bembale, Nagendra Singh

“…The pathfinding problem in a graph has been solved using several classical algorithms, notably Dijkstra’s and A* algorithms. …”
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Innovative approach for gauge-based QPE in arid climates: comparing neural networks and traditional methods by Bayan Banimfreg, Ernesto Damiani, Vesta Afzali Gorooh, Duncan Axisa, Luca Delle Monache, Youssef Wehbe

“…Results The neural network model outperformed traditional interpolation techniques, achieving a 47% reduction in Root Mean Square Error (RMSE), and a 0.56 increase in R2 compared to the best-performing IDW variant (RS-IDW). …”
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Machine-Learning-Driven Approaches for Assessment, Delegation, and Optimization of Multi-Floor Building by Abtin Baghdadi, Harald Kloft

“…The significance of this research lies in its ability to automate and accelerate complex structural analysis using Adaptive Neuro-Fuzzy Inference Systems (ANFISs), achieving an average error of less than 2% in multi-variable prediction scenarios. The results were compared against reference calculations and ETABS simulations to validate its effectiveness, demonstrating deviations of less than 3%. …”
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Integrative analysis of bulk and single-cell transcriptomic data reveals novel insights into lipid metabolism and prognostic factors in hepatocellular carcinoma by Feiyu Qi, Guiming Zha, Yanfang Zhang, Sihua Liu, Yuhang Yang, Wanliang Sun, Dongdong Wang, Zhong Liu, Zheng Lu, Dengyong Zhang

“…Immune-related analysis was conducted based on seven algorithms for immune cell infiltration prediction. …”
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Innovative framework for fault detection and system resilience in hydropower operations using digital twins and deep learning by Jun Tan, Raoof Mohammed Radhi, Kimia Shirini, Sina Samadi Gharehveran, Zamen Parisooz, Mohsen Khosravi, Hossein Azarinfar

“…We developed a hybrid approach integrating a Digital Twin model of the hydropower system with advanced Deep Learning algorithms for real-time monitoring and predictive analysis. …”
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Study on debris flow vulnerability of ensemble learning model based on spy technology A case study of upper Minjiang river basin by Yutao Chen, Ning Li, Fucheng Xing, Han Xiang, Zilong Chen

“…Since it is challenging to predict debris flows with precision using traditional methods, machine learning algorithms have been used more and more in this field in recent years. …”
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Genomic selection optimization in blueberry: Data‐driven methods for marker and training population design by Paul Adunola, Luis Felipe V. Ferrão, Juliana Benevenuto, Camila F. Azevedo, Patricio R. Munoz

“…Our contribution in this study is threefold: (i) for the genotyping resource allocation, the use of genetic data‐driven methods to select an optimal set of markers slightly improved prediction results for all the traits; (ii) for the long‐term implication, we carried out a simulation study and emphasized that data‐driven method results in a slight improvement in genetic gain over 30 cycles than random marker sampling; and (iii) for the phenotyping resource allocation, we compared different optimization algorithms to select training population, showing that it can be leveraged to increase predictive performances. …”
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Gaussian barebone mechanism and wormhole strategy enhanced moth flame optimization for global optimization and medical diagnostics. by Jingjing Ma, Zhifang Zhao, Lin Zhang

“…Hence, this paper introduces a novel enhanced MFO algorithm (BWEMFO) designed to improve algorithmic performance. …”
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IHML: Incremental Heuristic Meta-Learner by Onur Karadeli, Kıymet Kaya, Şule Gündüz Öğüdücü

“…The landscape of machine learning constantly demands innovative approaches to enhance algorithms’ performance across diverse tasks. Meta-learning, known as “learning to learn” is a promising way to overcome these diversity challenges by blending multiple algorithms. …”
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Unmasking social bots: how confident are we? by James Giroux, Gangani Ariyarathne, Alexander C. Nwala, Cristiano Fanelli

“…Despite the progress made in developing multiple sophisticated social bot detection algorithms and tools, bot detection remains a challenging, unsolved problem that is fraught with uncertainty due to the heterogeneity of bot behaviors, training data, and detection algorithms. …”
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Artificial intelligence-based framework for early detection of heart disease using enhanced multilayer perceptron by Monir Abdullah

“…The proposed framework demonstrates significant potential for the early detection and prediction of cardiac-related diseases. Experimental results indicate that the Enhanced Multilayer Perceptron (EMLP) model outperformed the other algorithms in terms of accuracy, precision, F1-score, and recall, underscoring its efficacy in cardiac disease detection.…”
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A METHOD FOR INVESTIGATING MACHINE LEARNING ATTACKS ON ARBITER-TYPE PHYSICALLY UNCLONABLE FUNCTIONS by Yuri A. Korotaev

“…The research infers that ANNs outperform traditional machine learning algorithms in carrying out attacks on Arbiter PUFs. …”
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Performance Evaluation of Data Compression Systems Applied to Satellite Imagery by Lilian N. Faria, Leila M. G. Fonseca, Max H. M. Costa

“…This paper presents an overview and evaluation of some compression algorithms suitable for remote sensing applications. …”
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Machine Learning to Assess Relatedness: The Advantage of Using Firm-Level Data by Giambattista Albora, Andrea Zaccaria

“…We also discuss the effect of using parameter optimization and community detection algorithms to identify clusters of related companies and products, finding that a partition into a higher number of blocks decreases the computational time while maintaining a prediction performance well above the network-based benchmarks.…”
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Reversible data hiding in encrypted domain based on fine-grained access control by ZHANG Minqing, PENG Shen, JIANG Chao, DI Fuqiang, DONG Yufeng

“…To improve the embedding rate and security of reversible data hiding algorithms in encrypted images for cloud environments, ciphertext-policy attribute-based encryption and reversible data hiding in encrypted images were organically integrated, and a reversible data hiding algorithm in encrypted images based on fine-grained access control was proposed. …”
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Experimental and numerical investigations on the bidirectional thermal contact performance by Chen Wang, Mingjun Qiu, Huijing Liu, Jun Hong, Feiyu Gu, Lifei Chen, Tao Wang, Hao Guan, Qiyin Lin

“…Subsequently, bidirectional thermal contact performance consisting of TCR ratio and thermal rectification coefficient was analyzed under varying temperatures and pressures. Additionally, a prediction model for TCR was developed using the Levenberg-Marquardt (L-M) algorithm. …”
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Investigating the contributory factors influencing speeding behavior among long-haul truck drivers traveling across India: Insights from binary logit and machine learning technique... by Balamurugan Shandhana Rashmi, Sankaran Marisamynathan

“…The analysis results showed that RF demonstrated superior performance in predicting speeding behavior over other competing algorithms with accuracy (0.80), F1 score (0.77), and AUROC (0.81). …”
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A comprehensive machine learning for high throughput Tuberculosis sequence analysis, functional annotation, and visualization by Md. Saddam Hossain, Md. Parvez Khandocar, Farzana Akter Riti, Md. Yeakub Ali, Prithbey Raj Dey, S M Jahurul Haque, Amira Metouekel, Atrsaw Asrat Mengistie, Mohammed Bourhia, Farid Khallouki, Khalid S. Almaary

“…Such algorithms provided prediction accuracies of 0.963, 0.739, 0.773, 0.866, and 0.866 sequentially. …”
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Enhanced estimation of reference evapotranspiration using hybrid deep learning models and remote sensing variables by Tze Ying Fong, Yuk Feng Huang, Ren Jie Chin, Chai Hoon Koo

“…They managed to improve the accuracy of the prediction in most of the cases, with the highest R2 = 0.805 and the lowest prediction errors, MAE = 0.265 mm/day, RMSE = 0.343 mm/day and NRMSE = 0.096. …”
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Detection of child depression using machine learning methods. by Umme Marzia Haque, Enamul Kabir, Rasheda Khanam

“…Although model performance varied somewhat, RF outperformed all other algorithms in predicting depressed classes by 99% with 95% accuracy rate and 99% precision rate in 315 milliseconds (ms).…”
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