This study investigates the classification of two prominent movement disorders: Parkinson’s Disease (PD) and Essential Tremor (ET) using a comprehensive machine learning framework. A novel dataset was meticulously created which contains handwriting samples collected at Habib Bourguiba Hospital in Sfax, Tunisia, specifically designed for differentiating between PD and ET. Preprocessing techniques such as image resizing, normalization, and data augmentation were employed to enhance robustness. Feature extraction was performed using the ResNet50 model, effectively capturing essential image characteristics through global average pooling. Recursive Feature Elimination (RFE) was then applied to identify the most significant features, followed by the training and validation of two classification models Random Forest and SVM using these selected features. The performance of these models is rigorously assessed through various metrics, revealing that the Random Forest model attained an accuracy of $92.83 \% \pm 2$, while the SVM model achieved an average accuracy of $94.66 \% \pm 1$. Visualizations such as confusion matrices and ROC curves provide deeper insights into model performance. Overall, the findings demonstrate the potential of machine learning techniques to enhance diagnostic accuracy in distinguishing between PD and ET, ultimately contributing to improved clinical decision-making.

Parkinson’s disease (PD) is a common and progressive neurodegenerative disorder with motor symptoms and a variety of non-motor symptoms. Experts regularly include handwriting as one of the Parkinsonian motor symptoms of PD and as a valuable tool that can aid in diagnosing and tracking the disease’s progression. PD patients have two periods. ‘On’ time is when levodopa is working well and your symptoms are controlled. ‘Off’ time is when levodopa is no longer working well and symptoms such as tremor, rigidity and slow movement re-emerge. To our knowledge, all existing publicly available datasets allow PD to be identified using only one period. No publicly available online handwriting datasets are dedicated to the analysis of PD using these two periods. Therefore, in this paper, we present our new online Arabic handwriting dataset for analysing PD, which we will make publicly available so that it could potentially be used for diagnosis, screening and monitoring the progression of PD. Our dataset was collected from 30 healthy controls and 30 PD patients in both “off” and “on” at the Neurology Department, Habib Bourguiba Hospital, Sfax, Tunisia. All participants performed five different handwriting tasks. The tasks included drawing repetitive ellipses, a spiral, repetitive digits and Arabic word writing. We hope that our new dataset will help researchers in the early detection of Parkinson’s disease, inpatient rehabilitation and quantification of therapeutic effects.

Machine learning has become a key driver of technological development, but the rising demand for AI applications involving human interaction necessitates access to large databases of human image data. However, the use of large real-world image datasets, particularly those that contain faces, has given rise to valid privacy concerns. We examine the critical issue of anonymizing image datasets that contain facial information in this paper. We hope to strike a balance between the requirement for data-driven advancements and preserving people’s right to privacy by addressing these issues. In this paper, we propose a new method named FaceAnonym that de-identifies facial images by projecting them onto the latent space of a GAN model. This allows us to preserve the important characteristics of the face, such as shape, expression, and luminance, while still obscuring the identity of the individual. Finally, our method has been shown to be more effective than other methods at de-identifying facial images. It is also fast and easy to use, making it a practical solution for de-identifying large datasets of facial images.

Machine learning is widely recognized as a key driver of technological progress. Artificial Intelligence (AI) applications that interact with humans require access to vast quantities of human image data. However, the use of large, real-world image datasets containing faces raises serious concerns about privacy. In this paper, we examine the issue of anonymizing image datasets that include faces. Our approach modifies the facial features that contribute to personal identification, resulting in an altered facial appearance that conceals the person's identity. This is achieved without compromising other visual features such as posture, facial expression, and hairstyle while maintaining a natural-looking appearance. Finally, Our method offers adjustable levels of privacy, computationally efficient, and has demonstrated superior performance compared to existing methods.

Our works deals with the problem of Information Retrieval System (IRS) that integrates the human behaviour. This system must be able to recognize the degree of satisfaction of the user of the result found through its facial expression, its physiological state, its gestures and its voice. For this, we propose in this paper an algorithm for recognizing the emotional state of a user during a search session in order to issue the relevant documents that he needs. We present also, the architecture agent of the envisaged system.

We propose in this paper an emotional recognition system based on physiological signals. We adopt the seven basic emotions that are: neutrality, joy, sadness, fear, anger, disgust and surprise. An experiment has been conducted to verify the feasibility of the proposed system. This experience has allowed us to acquire EEG signals and to create an emotional database. For this, we have used the Emotiv EPOC headset. Thereafter, we have chosen the fuzzy logic techniques to classify the EEG signals and to analyze the results.