Deep learning model for patient emotion recognition using EEG-tNIRS data by Mohan Raparthi, Nischay Reddy Mitta, Vinay Kumar Dunka, Sowmya Gudekota, Sandeep Pushyamitra Pattyam, Venkata Siva Prakash Nimmagadda

“…This study presents a novel approach that integrates electroencephalogram (EEG) and functional near-infrared spectroscopy (tNIRS) data to enhance emotion classification accuracy. …”
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Federal Deep Learning Approach of Intrusion Detection System for In-Vehicle Communication Network Security by In-Seop Na, Anandakumar Haldorai, Nithesh Naik

“…It utilizes a federal learning framework which employs Convolutional Neural Networks as well as Long-Term Short Memory. …”
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Identifying Climate Change Impacts On Hydrological Behavior On Large-Scale With Machine Learning Algorithms by Aleksander M. Ivanov, Artem V. Gorbarenko, Maria B. Kireeva, Elena S. Povalishnikova

“…The article presents the results of study of the application of machine learning methods to the problem of classification and identification of different river water regimes in a large region – the European territory of Russia. …”
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A Transformer-Based Reinforcement Learning Framework for Sequential Strategy Optimization in Sparse Data by Zizhe Zhou, Liman Zhang, Xuran Liu, Siyang He, Jingxuan Zhang, Jinzhi Zhu, Yuanping Pang, Chunli Lv

“…A deep reinforcement learning framework is presented for strategy generation and profit forecasting based on large-scale economic behavior data. …”
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Student support and motivation in online learning: evidence from pilot online course implementation by Rasa Tamulienė, Alina Liepinaitienė, Edvinas Ignatavičius

“… Teaching and learning in a virtual environment bring many changes in the implementation of study content. …”
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A Comparative Study of Unsupervised Deep Learning Methods for Anomaly Detection in Flight Data by Sameer Kumar Jasra, Gianluca Valentino, Alan Muscat, Robert Camilleri

“…The paper finds that LSTM, when integrated with a self-attention mechanism, offers notable benefits over other deep learning methods as it effectively handles lengthy time series like those present in flight data, establishes a generalized model applicable across various airports and facilitates the detection of trends across the entire fleet. …”
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Automated Fillet Weld Inspection Based on Deep Learning from 2D Images by Ignacio Diaz-Cano, Arturo Morgado-Estevez, José María Rodríguez Corral, Pablo Medina-Coello, Blas Salvador-Dominguez, Miguel Alvarez-Alcon

“…The object detection method follows a geometric deep learning model based on convolutional neural networks. …”
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Hyperparameter tuned deep learning-driven medical image analysis for intracranial hemorrhage detection. by Naif Almakayeel, E Laxmi Lydia, Oleg Razzhivin, S Rama Sree, Mohammed Altaf Ahmed, Bibhuti Bhusan Dash, S P Siddique Ibrahim

“…This manuscript proposes the design of a Hyperparameter Tuned Deep Learning-Driven Medical Image Analysis for Intracranial Hemorrhage Detection (HPDL-MIAIHD) technique. …”
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Mitigating side channel attacks on FPGA through deep learning and dynamic partial reconfiguration by Sesibhushana Rao Bommana, Sreehari Veeramachaneni, Syed Ershad, MB Srinivas

“…Abstract This paper introduces a framework that combines Deep Learning (DL) models and Dynamic Partial Reconfiguration (DPR) in Field Programmable Gate Arrays (FPGA) to mitigate Side Channel Attacks (SCA). …”
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Spatial datasets for benchmarking machine learning-based landslide susceptibility modelsMendeley Data by Guruh Samodra, Mukhamad Ngainul Malawani, Indranova Suhendro, Djati Mardiatno

“…This article presents a comprehensive dataset developed for benchmarking machine learning-based landslide susceptibility models. …”
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An Intelligent Method for C++ Test Case Synthesis Based on a Q-Learning Agent by Serhii Semenov, Oleksii Kolomiitsev, Mykhailo Hulevych, Patryk Mazurek, Olena Chernyk

“…Most traditional test suite optimization methods treat test cases as atomic units, without analyzing the utility of individual instructions. This paper presents an intelligent method for test case synthesis using a Q-learning agent. …”
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Application of Machine Learning Techniques for Predicting Students’ Acoustic Evaluation in a University Library by Dadi Zhang, Kwok-Wai Mui, Massimiliano Masullo, Ling-Tim Wong

“…Understanding students’ acoustic evaluation in learning environments is crucial for identifying acoustic issues, improving acoustic conditions, and enhancing academic performance. …”
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Enhancing physics learning through feedback: insights from secondary and high school teachers by Gabriela DOMILESCU, Mihaela IORGA

“…By employing a non-experimental transversal research design, the present study aimed to assess the implications of feedback on the achievement of middle and high school students, as perceived by the teachers, in the subject of physics. …”
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Intelligent Resource Allocation via Hybrid Reinforcement Learning in 5G Network Slicing by Zahraa Zakariya Saleh, Maysam F. Abbod, Rajagopal Nilavalan

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A Unified Machine Learning Framework for Li-Ion Battery State Estimation and Prediction by Afroditi Fouka, Alexandros Bousdekis, Katerina Lepenioti, Gregoris Mentzas

“…The accurate estimation and prediction of internal states in lithium-ion (Li-Ion) batteries, such as State of Charge (SoC) and Remaining Useful Life (RUL), are vital for optimizing battery performance, safety, and longevity in electric vehicles and other applications. This paper presents a unified, modular, and extensible machine learning (ML) framework designed to address the heterogeneity and complexity of battery state prediction tasks. …”
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Machine learning for detoxification of aflatoxin M1 by Lactococcus lactis probiotic in kashk production by Maryam Jafari, Roshanak Rafiei Nazari, Mohammad Rezaei, Mojtaba Moazzen, Nabi Shariatifar

“…Therefore, the detoxification of AFM 1 using probiotics combined with machine learning methods presents a practical, feasible, and simple method for predicting detoxification processes based on various parameters related to the probiotic application in managing aflatoxin in dairy products.…”
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Deep Learning Models for Rotated Object Detection in Aerial Images: Survey and Performance Comparisons by Jiaying He, K. L. Eddie Law

“…Rotated object detection in aerial images presents unique challenges due to the variability in object orientation and aspect ratios. …”
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Hybrid deep learning model for density and growth rate estimation on weed image dataset by Anand Muni Mishra, Mukund Pratap Singh, Prabhishek Singh, Manoj Diwakar, Indrajeet Gupta, Anchit Bijalwan

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Deep learning-based interpretable prediction of recurrence of diffuse large B-cell lymphoma by Hussein Naji, Paul Hahn, Juan I. Pisula, Stefano Ugliano, Adrian Simon, Reinhard Büttner, Katarzyna Bozek

“…Abstract Background The heterogeneous and aggressive nature of diffuse large B-cell lymphoma (DLBCL) presents significant treatment challenges as up to 50% of patients experience recurrence of disease after chemotherapy. …”
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Distinguishing Dyslexia, Attention Deficit, and Learning Disorders: Insights from AI and Eye Movements by Alae Eddine El Hmimdi, Zoï Kapoula

“…Key parameters, such as amplitude, latency, duration, and velocity, are extracted and processed to remove outliers and standardize values. Machine learning models, including logistic regression, random forest, support vector machines, and neural networks, are trained using a GroupKFold strategy to ensure patient data are present in either the training or test set. …”
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