Routing Protocols in Underwater Acoustic Sensor Networks: A Quantitative Comparison by Guangjie Han, Na Bao, Li Liu, Daqiang Zhang, Lei Shu

“…The impact of water currents on the routing algorithms is also analyzed in our simulation. Experimental results demonstrate that E-PULRP provides high data delivery ratio at the cost of end-to-end delay. …”
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A comprehensive review of deepfakes in medical imaging: Ethical concerns, detection techniques and future directions by Pradepan P, Gladston Raj S, Juby George K

“…In addition, we present an experimental study that evaluates how well different deep learning models detect deepfakes in a lung CT scan dataset, demonstrating both the potential and limitations of current detection methods. …”
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Improving SLICES crystal representation through CHGNet integration and parameter tuning by Bizhu Zhang, Kedeng Wu, Chang Zhang, Hang Xiao, Liangliang Zhu

“…To bridge this gap, we present an enhanced approach to the simplified line-input crystal-encoding system (SLICES) representation by incorporating the Crystal Hamiltonian Graph Neural Network (CHGNet) machine learning force field model. Comprehensive evaluations across multiple datasets (MP-20, MP21-40, and MOF) using different neighbor recognition algorithms (EconNN and CrystalNN) show that our approach outperforms the original in reconstructing structures with fewer than 20 atoms per unit cell, achieving up to a 1.34% improvement in reconstruction rate (from 92.55% to 93.89%). …”
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Adversarial attacks dataset for low light image enhancementMendeley Data by Axel Martinez, Matthieu Olague, Gustavo Olague, Emilio Hernandez, Julio Cesar Lopez-Arredondo

“…As a result, the necessity to create algorithms that can withstand harmful and subtle disturbances has garnered interest, as both humans and machines can significantly influence how programs perform. …”
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Machine Learning-Driven Transcriptome Analysis of Keratoconus for Predictive Biomarker Identification by Shao-Hsuan Chang, Lung-Kun Yeh, Kuo-Hsuan Hung, Yen-Jung Chiu, Chia-Hsun Hsieh, Chung-Pei Ma

“…<b>Objective:</b> This study employed multiple machine learning algorithms to analyze the transcriptomes of keratoconus patients, identifying feature gene combinations and their functional associations, with the aim of enhancing the understanding of keratoconus pathogenesis. …”
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Optimized intelligent learning for groundwater quality prediction in diverse aquifers of arid and semi-arid regions of India by Imran Khan, Sarwar Nizam, Apoorva Bamal, Abdul Majed Sajib, Mir Talas Mahammad Diganta, Mohd Azfar Shaida, S.M. Ashekuzzaman, Stephen Nash, Agnieszka I. Olbert, Md Galal Uddin

“…This study employs the eXtreme Gradient Boosting (XGB) algorithm, demonstrating strong predictive capabilities within the RMS-WQI model across diverse aquifers of Rajasthan. …”
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η-ensemble path integral Monte Carlo approach to the free energy of the warm dense electron gas and the uniform electron liquid by Tobias Dornheim, Panagiotis Tolias, Zhandos A. Moldabekov, Jan Vorberger

“…Specifically, we present an in-depth study of the relevant algorithmic details such as the choice of the free weighting parameter and the choice of the optimum number of intermediate η-steps to connect the real, nonideal system (η=1) with the ideal limit (η=0). …”
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Unpacking Performance Variability in Deep Reinforcement Learning: The Role of Observation Space Divergence by Sooyoung Jang, Ahyun Lee

“…Deep Reinforcement Learning (DRL) algorithms often exhibit significant performance variability across different training runs, even with identical settings. …”
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Development of Ai-Based Crop Quality Grading Systems using Image Recognition by Dusi Prerna, Sharma Pooja

“…The data given is 10,000 labeled images across five quality grades from various crop types under different lighting conditions for robustness. The system is then evaluated and doesn't perform well, showing that Transfer Learning outperforms other baselines with 95.8% of accuracy, whereas CNN, Random Forest, and SVM get 92.1%, 87.4% and 85.9% respectively. …”
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A Comparative Study for Localization of Forgery Regions in Images by Mustafa Ozden, Canberk Sahin

“…In total, four different methods were tested on eight datasets, and their performance was compared using metrics such as accuracy, precision, recall, dice similarity coefficient, and F1 score. …”
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Ternary Historical Memory-Based Robust Clustered Particle Swarm Optimization for Dynamic Berth Allocation and Crane Assignment Problem by Ruiqi Wu, Shiming Mao, Yi Sun

“…Experimental evaluations on 25 dynamic BACAP benchmark instances show that THM-RCPSO achieves the lowest average vessel dwell time in 22 out of 25 cases, with the lowest overall average rank among five tested algorithms. …”
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System Layout and Grasp Efficiency Optimization for a Multirobot Waste Sorting System by Bart Engelen, Sander Teck, Jef R. Peeters, Karel Kellens

“…The findings demonstrate substantial improvements in picking efficiency of up to 3.6% and pick rates up to 37.5%, underscoring the potential of advanced heuristic algorithms in robotic waste sorting systems. Future work will focus on refining gripper designs and exploring predictive algorithms to further enhance grasp success rates.…”
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A multi-scale multi-channel CNN introducing a channel-spatial attention mechanism hyperspectral remote sensing image classification method by Ru Zhao, Chaozhu Zhang, Dan Xue

“…First, the data set is divided into two kinds of pixel module, and then different channels are used for feature extraction. …”
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Speech emotion recognition using long-term average spectrum by Huerta-Hernández Luis David, Meléndez-Acosta Nayeli Joaquinita, Troncoso-Romero David Ernesto, Ramírez-Pacheco Julio César, León-Borges Jose Antonio

“…In this work, a new framework for emotion recognition is proposed based on the long-term average spectrum (LTAS). Our framework is evaluated through a comparative study, where classifiers such as artificial neural network, K-nearest neighbours, logistic regression, Bayesian algorithms, tree-based logistics, and support vector machine were used. …”
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Profiling triple-negative breast cancer-specific super-enhancers identifies high-risk mesenchymal development subtype and BETi-Targetable vulnerabilities by Qing-shan Chen, Rui-zhao Cai, Yan Wang, Ge-hao Liang, Kai-ming Zhang, Xiao-Yu Yang, Dong Yang, De-Chang Zhao, Xiao-Feng Zhu, Rong Deng, Jun Tang

“…The biological significance of CRCs in these different TNBC subtypes and their influence on BETi sensitivity were evaluated using in vitro and in vivo models. …”
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Development of IIOT-Based Pd-Maas Using RNN-LSTM Model with Jelly Fish Optimization in the Indian Ship Building Industry by PNV Srinivasa Rao, PVY Jayasree

“…The validation of the proposed predictive maintenance model optimization with different types of deep learning algorithms shows that our proposed methodology gives an improved accuracy of 98.9336% which is higher than any other models.   …”
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Development of machine learning models to predict clinical outcome and recovery time in dogs with parvovirus enteritis by Negin Sanaei, Mohamad Zamani-Ahmadmahmudi, Seyed Mahdi Nassiri

“…With the advent of machine learning methods/algorithms, various models can be developed using a combination of clinical and non-clinical variables to predict clinical outcome in different diseases with higher efficiency compared to traditional biomarkers. …”
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Prediction of persistent type II endoleak after endovascular aortic repair using machine learning based on preoperative clinical data and radiomic by Jinqing Mo, Qi Liu, Kangjie Wang, Lin Huang, Chen Yao

“…Six common machine learning algorithms were used to predict persistent T2ELs based on preoperative features. …”
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Efficient Real-Time Object Detection and Classification Using mmWave Radar and Jetson Xavier NX by Mohamed Lamane, Abdessamad Klilou, Mohamed Tabaa

“…Recently, mmWave radars have been used in several automotive and industrial applications to provide the position and speed of detected objects using FMCW algorithms. However, this radar is not able to recognize or classify the detected objects. …”
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Transforming Wind Data into Insights: A Comparative Study of Stochastic and Machine Learning Models in Wind Speed Forecasting by Türker Tuğrul, Sertaç Oruç, Mehmet Ali Hınıs

“…In these analyses, five different models were created with the assistance of cross-correlation. …”
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