Bridging neuroscience and AI: a survey on large language models for neurological signal interpretation by Sreejith Chandrasekharan, Jisu Elsa Jacob

“…In general, this survey aims to stimulate research in the area of EEG disease diagnostics by effectively using LLMs and associated techniques in machine learning pipelines.…”
Get full text

Machine Learning Impact on Modern Business Intelligence by Raziyeh Moghaddas, Farinaz Tanhaei, Maryam Al Moqbali, Solmaz Safari

“…The findings of this research work provide a comparative analysis of model efficiency that highlights both the capabilities and limitations of each model. …”
Get full text

Interpretable Machine Learning for High-Accuracy Reservoir Temperature Prediction in Geothermal Energy Systems by Mohammadali Ahmadi

“…By integrating advanced machine learning with enhanced feature engineering, this research provides a robust framework for improving reservoir temperature prediction, contributing to the sustainable development of geothermal energy in alignment with sustainable energy development.…”
Get full text

Tooth segmentation in panoramic dental radiographs using deep convolution neural network -Insights from subjective analysis by Suvarna Bhat, Gajanan K. Birajdar, Mukesh D. Patil

“…UNet++ with the combined data set and after fine-tunning hyperparameter gives the best results (IOU 0.8619 and Dice coefficient 0.9258). …”
Get full text

Machine Learning-Based Approach for HIV/AIDS Prediction: Feature Selection and Data Balancing Strategy by Abdul Mizwar A Rahim, Ahmad Ridwan, Bambang Pilu Hartato, Firman Asharudin

“…The study concludes that combining feature selection, data balancing, and machine learning techniques significantly improves predictive performance, making it a valuable approach for early detection and clinical decision support in HIV/AIDS diagnosis. Future research may explore hyperparameter tuning and real-world clinical data integration to enhance practical applicability.…”
Get full text

Deep learning with ensemble-based hybrid AI model for bipolar and unipolar depression detection using demographic and behavioral based on time-series data by Naga Raju Kanchapogu, Sachi Nandan Mohanty

“…While not clinically validated, the model serves as a methodological foundation for future research with real-world datasets.…”
Get full text

Model reduction of structural mechanical response in the time domain by Xin Yan, Xinyu Guo, Ningya He, Jinglong Shi, Daquan Zhao

“…A hyperparameter tuning and optimization procedure for neural network models is proposed, exploring batch size, hidden layer type, activation function, cells number, epochs number, and number of hidden layers. …”
Get full text

Adaptive Sliding Mode Control for Quadrotor UAVs Under Disturbances Using Multi-Layer Perceptron by Mir Mikael Fatemi, Adel Akbarimajd

“…Another key contribution of this research lies in the substantial reduction of the chattering phenomenon, a common limitation of traditional SMC systems. …”
Get full text

Improved salp swarm algorithm-driven deep CNN for brain tumor analysis by Umang Kumar Agrawal, Nibedan Panda, Ghanshyam G. Tejani, Seyed Jalaleddin Mousavirad

Get full text

The machine learning algorithm based on decision tree optimization for pattern recognition in track and field sports. by Guomei Cui, Chuanjun Wang

“…However, the training data of this model comes from the simulation environment, which may deviate from the real game data. Future research can verify the generalization ability of the model through more actual data.…”
Get full text

Analysis of IoT-Collected Radish Growth Data Using Deep Neural Networks by Ivan P. Malashin, Vadim S. Tynchenko, Andrei P. Gantimurov, Vladimir A. Nelyub, Aleksei S. Borodulin

“…By using physiological and morphological data from various radish species and treatment conditions, this research explores the effectiveness of GA in fine-tuning DNN hyperparameters to achieve high classification accuracy. …”
Get full text

Coal Price Forecasting Using CEEMDAN Decomposition and IFOA-Optimized LSTM Model by Zhuang Liu, Xiaotuan Li

“…Future research will aim to refine the model further to adapt to diverse market conditions and enhance forecasting accuracy.…”
Get full text

Automated System Using HMM for Lung Disease Recognition Based on Cough Sounds by Shing-Tai Pan, Bo-Kai Lee, You-Qian Wu

“…Cough sound is a biological marker that can be used to detect diseases by observing the differences in waveforms between healthy and sick individuals. Previous research has used cough sounds to identify single lung diseases and mental illnesses. …”
Get full text

Penerapan Deep Convolutional Generative Adversarial Network Untuk Menciptakan Data Sintesis Perilaku Pengemudi Dalam Berkendara by Michael Stephen Lui, Fitra Abdurrachman Bachtiar, Novanto Yudistira

“…Dataset used in this research is primary dataset of common driver activity collected in simulation scenario with the size of two thousand images. …”
Get full text

Predictive models of sepsis-associated acute kidney injury based on machine learning: a scoping review by Jie Li, Manli Zhu, Li Yan

“…In addition, a single data source, insufficient assessment of clinical utility, lack of model bias assessment, and hyperparameter adjustment may be the main reasons for the low quality of the current research.Conclusions However, studies on the nondeath prognostic outcomes, the long-term clinical outcomes, and the subtype identification models are insufficient. …”
Get full text

An integrated AI-driven framework for maximizing the efficiency of heterostructured nanomaterials in photocatalytic hydrogen production by Pramod N. Belkhode, Shrikant M. Awatade, Chander Prakash, Sagar D. Shelare, Deepali Marghade, Sameer Sheshrao Gajghate, Muhamad M. Noor, Milon Selvam Dennison

“…Ultimately, it accelerates the discovery of novel heterostructured nanomaterials, enhancing efficiency, scalability, and yield, thus moving closer to sustainable hydrogen production with improvements in photolytic efficiency, setting a benchmark for AI-assisted research.…”
Get full text

Modeling women cyclists' perceived security: A comparison of machine learning techniques by Peyman Noorbakhsh, Navid Khademi, Phromphat Thansirichaisree

“…While prior studies have explored certain aspects of perceived security —mainly for pedestrians—the application of Machine Learning (ML) models to predict cyclists’ perceived security remains a relatively developing research area. This study estimates women cyclists’ perceived security by leveraging environmental features derived from data collected through a virtual reality (VR)-based bicycle experimental system.An inferential approach was employed, combining Automated ML (AutoML) tools, consecutive Grid Search (GS) and Random Search (RS) hyperparameter optimization, and various ensemble techniques to provide insights into the comparative performance of available ML models. …”
Get full text

Enhanced interpretable thyroid disease diagnosis by leveraging synthetic oversampling and machine learning models by Ali Raza, Fatma Eid, Elisabeth Caro Montero, Irene Delgado Noya, Imran Ashraf

“…Thyroid illness affects millions worldwide and can significantly impact their quality of life if left untreated. This research aims to propose an effective artificial intelligence-based approach for the early diagnosis of thyroid illness. …”
Get full text

Real‐Time Hot‐Rolled Coil Placement Recommendation System with Data‐Driven Model by Chihun Lee, Da Seul Shin, Youn Hee Kang, Kanghyouk Choi, Dong Yong Park, Junsuk Rho

Get full text

Detecting small seamounts in multibeam data using convolutional neural networks by Tobias Ziolkowski, Colin W. Devey, Agnes Koschmider

“…Beyond geological mapping, automated seamount detection has broad applications in marine ecology, environmental monitoring, and plate tectonics research. Future work should focus on integrating physical principles and geological constraints, such as typical seamount morphology, size distributions, and tectonic setting, to improve classification accuracy.…”
Get full text