From Misinformation to Insight: Machine Learning Strategies for Fake News Detection by Despoina Mouratidis, Andreas Kanavos, Katia Kermanidis

“…Our approach systematically compares traditional machine learning classifiers (Naïve Bayes, SVMs, Random Forest) with state-of-the-art deep learning models, such as CNNs, LSTMs, and BERT, while incorporating optimized vectorization techniques, including TF-IDF, Word2Vec, and contextual embeddings. …”
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Recent Advances in In Situ 3D Surface Topographical Monitoring for Additive Manufacturing Processes by Vignesh Suresh, Badrinath Balasubramaniam, Li-Hsin Yeh, Beiwen Li

“…Motivated by manufacturing labor shortages, the high skill required to operate and troubleshoot some of these additive manufacturing techniques, and zero-defect manufacturing goals, this paper also explores the metamorphosis towards autonomous AM systems and adaptive process optimization and explores the role and importance of real-time 3D monitoring in that transition.…”
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Gradient pooling distillation network for lightweight single image super-resolution reconstruction by Zhiyong Hong, GuanJie Liang, Liping Xiong

“…Extensive experimental results demonstrate that our proposed method achieves competitive performance while maintaining relatively low resource occupancy of the model. …”
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A fast calculation approach for thermal distribution of aircraft piston pumps based on finite difference method by Jiming MA, Haolun NING

“…Compared with conventional finite element approaches, the proposed method reduces the simulation duration to approximately 10 % of that required by traditional algorithms under identical operational conditions.The developed methodology enables efficient and accurate simulation of critical heat generation mechanisms in piston pumps, including frictional heating and fluid leakage phenomena, while simultaneously providing comprehensive temperature distributions across pump components. …”
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More than a chatbot: a practical framework to harness artificial intelligence across key components to boost digital therapeutics quality by Amit Baumel

“…This framework provides a structured method to integrate AI-driven improvements, while also enabling to focus on a specific component during the optimization process.…”
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Machine learning-based predictive analysis of energy efficiency factors necessary for the HIFU treatment of adenomyosis by Ziyan Liu, Ziyi Liu, Yuan Wang, Xiyao Wan, Xiaohua Huang

“…Predictive features were selected using minimum redundancy maximum relevance (MRMR) and least absolute shrinkage and selection operator (LASSO) methods, and two joint—based on decision tree and random forest algorithms—models were developed for EEF prediction.ResultsThe decision tree model achieved a mean absolute error (MAE) of 8.095 on the test set, while the random forest model exhibited an MAE of 8.231. …”
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Behavioral Economics and Online Donation Behavior on TikTok by Rukiah Rukiah, Ahmad Salman Farid

“…Understanding the behavioral economic factors that influence online donation behavior is crucial for optimizing fundraising strategies. This study employs a qualitative research design, incorporating in-depth interviews and content analysis of donation-related campaigns on TikTok. …”
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A DSP–FPGA Heterogeneous Accelerator for On-Board Pose Estimation of Non-Cooperative Targets by Qiuyu Song, Kai Liu, Shangrong Li, Mengyuan Wang, Junyi Wang

“…Experimental results show that the optimized model achieves a peak throughput of 399.16 GOP/s with less than 1% accuracy loss. …”
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Bridging the Gap: A Review of Machine Learning in Water Quality Control by Herlina Abdul Rahim, Nur Athirah Syafiqah Noramli, Indrabayu

“…ML-driven solutions, including LSTM networks and random forest models, enable real-time anomaly detection (e.g., 85% accurate algal bloom prediction 7 days in advance) and operational optimization (15% cost reduction in wastewater treatment). …”
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Inflow Prediction for Agricultural Reservoirs Using Disaster Prevention Measurement Data: A Comparison of TANK Model and Machine Learning by Bong-Kuk Lee, Joonyoung Choi, Kyoung Jae Lim, Jeongho Han

“…This superior performance of the RidgeCV model can be attributed to its effective learning of the relationship between inflow data and optimal moving average rainfall, as well as the prevention of overfitting through regularization. …”
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Deep Temporal Clustering of Pathological Gait Patterns in Post-Stroke Patients Using Joint Angle Trajectories: A Cross-Sectional Study by Gyeongmin Kim, Hyungtai Kim, Yun-Hee Kim, Seung-Jong Kim, Mun-Taek Choi

“…Through this method, six optimal clusters were selected with a silhouette score of 0.2831, which is a relatively higher value compared to other clustering algorithms. …”
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Optimisation of the adaptive neuro-fuzzy inference system for adjusting low-cost sensors PM concentrations by Martina Casari, Piotr A. Kowalski, Laura Po

“…Notably, incorporating only PM2.5, relative humidity and temperature as features yields optimal performance while mitigating overfitting issues. …”
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Enhancing phase change thermal energy storage material properties prediction with digital technologies by Minghao Yu, Jing Liu, Cheng Chen, Mingyue Li

“…IntroductionIn the field of materials science, the prediction of material properties plays a critical role in designing new materials and optimizing existing ones. Traditional experimental approaches, while effective, are resource-intensive and time-consuming, often requiring extensive trial-and-error methods. …”
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Efficient base metal exploration in northern New Brunswick, Canada through a hybrid ANN integrated with ABC and PSO methods by Claire Dusabemariya, Wei Qian, Romuald Bagaragaza, Ajibola Richard Faruwa, Ali Mossad

“…By integrating Artificial Neural Networks (ANN) with metaheuristic algorithms, specifically Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO), the research optimizes resistivity inversion techniques to enhance geophysical exploration. …”
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Empirical Evaluation and Analysis of YOLO Models in Smart Transportation by Lan Anh Nguyen, Manh Dat Tran, Yongseok Son

“…By examining the performance of various YOLO models across datasets, IoU thresholds, and object classes, we make six observations on these three aspects while aiming to identify optimal models for road transportation scenarios. …”
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Deepmol: an automated machine and deep learning framework for computational chemistry by João Correia, João Capela, Miguel Rocha

“…Despite its potential to revolutionize the field, researchers are often encumbered by obstacles, such as the complexity of selecting optimal algorithms, the automation of data pre-processing steps, the necessity for adaptive feature engineering, and the assurance of model performance consistency across different datasets. …”
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Design and Analysis of a Serial Manipulator for Pick and Drop Objects for Material Handling at Uiri Metal Forming Workshop. by Behangana, Abert

“…A-36 steel emerged as the optimal choice due to its superior weight-to-structural stiffness ratio, while 6061 aluminum proved advantageous for weight-sensitive applications. …”
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Data-driven fault detection and positioning of eccentric rolls in roll-to-roll systems using wrap angle and sensor proximity by Yoonjae Lee, Minjae Kim, Jaehyun Noh, Gyoujin Cho, Changwoo Lee

“…Classification algorithms were used to compare the performance of models employing these optimized FCM-based features against traditional models utilizing a full suite of 40 statistical features. …”
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HSI Reconstruction: A Spectral Transformer With Tensor Decomposition and Dynamic Convolution by Le Sun, Xihan Ma, Xinyu Wang, Qiao Chen, Zebin Wu

“…Conversely, traditional iterative optimization algorithms are built upon sound mathematical derivations. …”
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Fortifying IoT Infrastructure Using Machine Learning for DDoS Attack within Distributed Computing-based Routing in Networks by Sharaf Aldeen Abdulkadhum Abbas, Abdullahi Abdu Ibrahim

“…This research aims to compare the key machine learning approaches, Namely Support Vector Machines (SVM), Random Forest (RF) and Decision Trees (DT), in their ability to classify Intrusion Detection Systems (IDS) via routing networks over distributed computing systems. In addition, Algorithms perform quality control to determine the optimal hyperplane for the given data, Find neighboring data points and preserve the structure of the tree. …”
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