Parkinson's Disease PD is a progressive neurodegenerative disorder that affects motor and cognitive functions with early diagnosis being critical for effective clinical intervention Electroencephalography EEG offers a noninvasive and costeffective means of detecting PDrelated neural alterations yet the development of reliable automated diagnostic models remains a challenge In this study we conduct a systematic benchmark of traditional machine learning ML and deep learning DL models for classifying PD using a publicly available oddball task dataset Our aim is to lay the groundwork for developing an effective learning system and to determine which approach produces the best results We implement a unified sevenstep preprocessing pipeline and apply consistent subjectwise crossvalidation and evaluation criteria to ensure comparability across models Our results demonstrate that while baseline deep learning architectures particularly CNNLSTM models achieve the best performance compared to other deep learning architectures underlining the importance of capturing longrange temporal dependencies several traditional classifiers such as XGBoost also offer strong predictive accuracy and calibrated decision boundaries By rigorously comparing these baselines our work provides a solid reference framework for future studies aiming to develop and evaluate more complex or specialized architectures Establishing a reliable set of baseline results is essential to contextualize improvements introduced by novel methods ensuring scientific rigor and reproducibility in the evolving field of EEGbased neurodiagnostics
Deception detection is a significant challenge in fields such as security, psychology, and forensics. This study presents a deep learning approach for classifying deceptive and truthful behavior using ElectroEncephaloGram (EEG) signals from the Bag-of-Lies dataset, a multimodal corpus designed for naturalistic, casual deception scenarios. A Bidirectional Gated Recurrent Unit (Bi-GRU) neural network was trained to perform binary classification of EEG samples. The model achieved a test accuracy of 97%, along with high precision, recall, and F1-scores across both classes. These results demonstrate the effectiveness of using bidirectional temporal modeling for EEG-based deception detection and suggest potential for real-time applications and future exploration of advanced neural architectures.
Wi-Fi sensing is gaining momentum as a non-intrusive and privacy-preserving alternative to vision-based systems for human identification. However, person identification through wireless signals, particularly without user motion, remains largely unexplored. Most prior wireless-based approaches rely on movement patterns, such as walking gait, to extract biometric cues. In contrast, we propose a transformer-based method that identifies individuals from Channel State Information (CSI) recorded while the subject remains stationary. CSI captures fine-grained amplitude and phase distortions induced by the unique interaction between the human body and the radio signal. To support evaluation, we introduce a dataset acquired with ESP32 devices in a controlled indoor environment, featuring six participants observed across multiple orientations. A tailored preprocessing pipeline, including outlier removal, smoothing, and phase calibration, enhances signal quality. Our dual-branch transformer architecture processes amplitude and phase modalities separately and achieves 99.82% classification accuracy, outperforming convolutional and multilayer perceptron baselines. These results demonstrate the discriminative potential of CSI perturbations, highlighting their capacity to encode biometric traits in a consistent manner. They further confirm the viability of passive, device-free person identification using low-cost commodity Wi-Fi hardware in real-world settings.
Commit messages are crucial for documenting software changes, aiding in program comprehension and maintenance. However, creating effective commit messages is often overlooked by developers due to time constraints and varying levels of documentation skills. Our research presents an automated approach to generate commit messages using Machine Learning (ML) and Natural Language Processing (NLP) by developing models that use techniques such as Logistic Regression with TF-IDF and Word2Vec, as well as more sophisticated methods like LSTM. We used the dataset of code changes and corresponding commit messages that was used by Liu et al., which we used to train and evaluate ML/NLP models and was chosen because it is extensively used in previous research, also for comparability in our study. The objective was to explore which ML/NLP techniques generate the most effective, clear, and concise commit messages that accurately reflect the code changes. We split the dataset into training, validation, and testing sets and used these sets to evaluate the performance of each model using qualitative and quantitative evaluation methods. Our results reveal a spectrum of effectiveness among these models, with the highest BLEU score achieved being 16.82, showcasing the models' capability in automating a clear and concise commit message generation. Our paper offers insights into the comparative effectiveness of different machine learning models for automating commit message generation in software development, aiming to enhance the overall practice of code documentation. The source code is available at https://doi.org/10.5281/zenodo.10888106.
The number of applications in Google Play has increased dramatically in recent years. On Google Play, users can write detailed reviews and rate apps, with these ratings significantly influencing app success and download numbers. Reviews often include notable information like feature requests, which are valuable for software maintenance. Users can update their reviews and ratings anytime. Studies indicate that apps with ratings below three stars are typically avoided by potential users. Since 2013, Google Play has allowed developers to respond to user reviews, helping resolve issues and potentially boosting overall ratings and download rates. However, responding to reviews is time-consuming, and only 13% to 18% of developers engage in this practice. To address this challenge, we propose a method to prioritize reviews based on response priority. We collected and preprocessed review data, extracted both textual and semantic features, and assessed their impact on the importance of responses. We labelled reviews as requiring a response or not and trained four different machine learning models to prioritize them. We evaluated the models performance using metrics such as F1-Score, Accuracy, Precision, and Recall. Our findings indicate that the XGBoost model is the most effective for prioritizing reviews needing a response.
