Data Augmentation Framework for Improved Classification in Object Detectors by Ioan-Alexandru Herdea, Divya Tiwari, John Oyekan, Ashutosh Tiwari

“…Deep Learning techniques have been classified as a significant advancement for data-driven industries such as electrical machine manufacturing. …”
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Principal component analysis and fine-tuned vision transformation integrating model explainability for breast cancer prediction by Huong Hoang Luong, Phuc Phan Hong, Dat Vo Minh, Thinh Nguyen Le Quang, Anh Dinh The, Nguyen Thai-Nghe, Hai Thanh Nguyen

“…In the final phase, SHapley Additive exPlanations, Local Interpretable Model-agnostic Explanations, and Gradient-weighted Class Activation Mapping were used for the interpretability and explainability of machine-learning models, aiding in understanding the feature importance and local explanations, and visualizing the model attention. …”
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Improving pluvial flood simulations with a multi-source digital elevation model super-resolution method by Y. Zhu, Y. Zhu, P. Burlando, P. Y. Tan, C. Geiß, C. Geiß, S. Fatichi

“…Accordingly, this study underscores the practical value of machine learning techniques that leverage publicly available global datasets to generate DEMs that allow for the enhancement of flood simulations.…”
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GNSTAM: Integrating Graph Networks With Spatial and Temporal Signature Analysis for Enhanced Android Malware Detection by Yogesh Kumar Sharma, Deepak Singh Tomar, R. K. Pateriya, Surendra Solanki

“…Federated learning preserves user privacy by training the model across a distributed network. …”
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ASSESSMENT OF TRANSMISSION EFFECTS BETWEEN “CORRUPTION-DIGITIZATION-ECONOMIC GROWTH” by Adriana Surovičová, Victoria Bozhenko, Anton Boyko, K.Yu. Petrenko

“… The last decade has seen the rapid development of digital information technology, the intellectualization of control systems, the increase in the number and capacity of mobile and computer devices, and the accumulation of large amounts of data and its processing through machine learning algorithms, which inevitably leads to new opportunities for economic development. …”
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HouseGanDi: A Hybrid Approach to Strike a Balance of Sampling Time and Diversity in Floorplan Generation by Azmeraw Bekele Yenew, Beakal Gizachew Assefa, Elefelious Getachew Belay

“…Floorplan synthesis is the process of generating new, realistic floor plans for buildings and homes using machine learning and generative models. In recent years, various generative methods, including GANs and diffusion models, have been utilized for the task of floorplan generation, demonstrating promising advancements in architectural design and planning. …”
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WiPy-RT: A Fast Ray Tracing Modeling Platform for RIS-Assisted Wireless Channels by Mohammadreza Farashahi, Boon-Chong Seet, Xue Jun Li

“…The interdisciplinary nature of WiPy-RT, combining techniques from machine learning and computational electromagnetics, represents a significant step forward in addressing the challenges of modeling next-generation wireless systems.…”
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AutoML based workflow for design of experiments (DOE) selection and benchmarking data acquisition strategies with simulation models by Xukuan Xu, Donghui Li, Jinghou Bi, Michael Moeckel

“…This paper introduces a workflow for conducting DOE comparative studies using automated machine learning. Based on a practical definition of model complexity in the context of machine learning, the interplay of systematic data generation and model performance is examined considering various sources of uncertainty: this includes uncertainties caused by stochastic sampling strategies, imprecise data, suboptimal modeling, and model evaluation. …”
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Optimization of engine parameters and emission profiles through bio-additives: Insights from ANFIS Modeling of Diesel Combustion by Abbas Rohani, Javad Zareei, Kourosh Ghadamkheir, Seyed Alireza Farkhondeh

“…Various machine learning configurations and training algorithms were employed to optimize the model's performance. …”
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Predictive Model for Diagnosis of Gestational Diabetes in the Kurdistan Region by a Combination of Clustering and Classification Algorithms: An Ensemble Approach by Rasool Jader, Sadegh Aminifar

“…Intelligent systems designed by machine learning algorithms are remodelling all fields of our lives, including the healthcare system. …”
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Research on Interval Probability Prediction and Optimization of Vegetation Productivity in Hetao Irrigation District Based on Improved TCLA Model by Jie Ren, Delong Tian, Hexiang Zheng, Guoshuai Wang, Zekun Li

“…Precise vegetation productivity monitoring and forecasting are crucial for the global carbon cycle. Traditional machine learning algorithms frequently experience overfitting when processing high-dimensional time-series data or substantial numbers of outliers, impeding the accurate prediction of various vegetation metrics. …”
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Evaluation of net-zero materials in mortar bricks with predictive modelling using random forest and gradient boosting techniques by Golla Uday kiran, Nakkeeran Ganesan, Dipankar Roy, Sumant Nivarutti Shinde, Mamillapalli Indumathi, George Uwadiegwu Alaneme, Val Hyginus Udoka Eze, Kuzmin Anton

“…Additionally, a significant reduction in water absorption was recorded. Machine learning models, including Random Forest and Gradient Boosting, were employed to predict mechanical behavior. …”
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Towards Automated Quality Control in Industrial Systems: Developing Markov Decision Process Model for Optimized Decision-Making by Katerina Mitkovska-Trendova, Robert Minovski, Verica Bakeva, Simeon Trendov, Dimitar Bogatinov

“…The study focuses on improving the sub-key element of quality-accuracy within a Performance Measurement System (PMS) framework, specifically targeting scrap minimization and cost reduction. The research employs a mathematical model that integrates vector random processes, each representing critical factors such as machine condition, operator behaviour, tools, and materials. …”
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Deep Learning-Based Spatial Prediction of Landslide Risk in Coastal Areas Using GIS and Multicriteria Decision Making: A DeepLabV3+ Approach by Huyong Yan, Asad Khan, Ahsan Jamil, Belkendil Abdeldjalil, Taoufik Saidani, Nazih Y. Rebouh

“…On Google Colab with GPU acceleration, the model is trained and verified and then further improved for computational efficiency on a Mac M1 machine. …”
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Psychological distress in adolescence and later economic and health outcomes in the United States population: A retrospective and modeling study. by Nathaniel Z Counts, Noemi Kreif, Timothy B Creedon, David E Bloom

“…<h4>Methods and findings</h4>This analysis estimated the relationship between psychological distress in those aged 15 to 17 years in 2000 and economic and health outcomes approximately 10 years later, accounting for an array of explanatory variables using machine learning-enabled methods. The cohort was from the National Longitudinal Study of Youth 1997 and nationally representative of those aged 12 to 18 years in 1997. …”
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A novel multi-source data-driven energy consumption prediction model for Venlo-type greenhouses in China by Yangda Chen, Aiqun Bao, Yapeng Li, Yingfeng Xiang, Wanlong Cai, Zhaoqiang Xia, Jialei Li, Mingyang Ning, Jing Sun, Haixi Zhang, Xianpeng Sun, Xiaoming Wei

“…Optimizing energy strategies and implementing predictive models for energy consumption are essential for more efficient management and reduction of greenhouse operational energy costs. …”
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End-to-end neural automatic speech recognition system for low resource languages by Sami Dhahbi, Nasir Saleem, Sami Bourouis, Mouhebeddine Berrima, Elena Verdú

“…The rising popularity of end-to-end (E2E) automatic speech recognition (ASR) systems can be attributed to their ability to learn complex speech patterns directly from raw data, eliminating the need for intricate feature extraction pipelines and handcrafted language models. …”
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MoSeq based 3D behavioral profiling uncovers neuropathic behavior changes in diabetic mouse model by Akm Ashiquzzaman, Eunbin Lee, Brahnu Fentaw Znaub, An Nazmus Sakib, Geehoon Chung, Sang Seong Kim, Young Ro Kim, Hyuk-Sang Kwon, Euiheon Chung

“…This study employed MoSeq-based 3D behavioral profiling combined with unsupervised machine learning to identify subtle yet significant alterations in nicotinamide (NA)- and streptozotocin (STZ)-induced DN mouse models. …”
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BiCrossNet: resource-efficient cross-view geolocalization with binary neural networks by Federico Fontana, Thomas Jantos, Jan Steinbrener, Luigi Cinque, Gian Luca Foresti, Bernhard Rinner

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Classification of Lung Nodule Using Hybridized Deep Feature Technique by Malin Bruntha, Immanuel Alex Pandian, Siril Sam Abraham

“…The hybridization has been carried out between handcrafted features and deep features. The machine learning algorithms such as SVM and Logistic Regression have been used to classify the nodules based on the features. …”
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