Résumé

Home Health Care (HHC) aims to provide medical care and support services directly to patients in their own homes. The demand for HHC services is steadily increasing due to demographic trends, with a growing preference for receiving care in the home. This trend pushes organizations providing home health care services, to optimize their activities in order to meet this increasing demand efficiently. For this purpose, we propose in this work a new bi-level modeling of the problem, that we termed Bi-level Home Health Care Problem Considering Patients Preferences (Bi-HHCPP) aiming to find an efficient solution corresponding to this design. Existing research studies have focused on optimizing the problem considering only one decision-maker that optimizes both routing and scheduling entities imposed by the problem. This paper is the first to shed light on a new bi-level modeling of the problem involving two hierarchical decision entities: (1) a scheduling entity, and (2) a routing one. The proposed model primarily accounts for nurse qualification, travel costs, and patient preferences on visited nurses. Besides, the proposed mathematical formulation of the problem is tested using the CBC (Coin-or Branch and Cut) optimization solver.

Résumé

Sleep is a vital process for human well-being. Sleep scoring is performed by experts using polysomnograms, that record several body activities, such as electroencephalograms (EEG), electrooculograms (EOG), and electromyograms (EMG). This task is known to be exhausting, biased, time-consuming, and prone to errors. Current automatic sleep scoring approaches are mostly based on single-channel EEG and do not produce explainable results. Therefore, we propose a heterogeneous ensemble learning-based approach where we combine accuracy-based learning classifier systems with different algorithms to produce a robust, explainable, and enhanced classifier. The efficiency of our approach was evaluated using the Sleep-EDF benchmark dataset. The proposed models have reached an accuracy of 89.2% for the stacking model and 87.9% for the voting model, on a multi-class classification task based on the R&K guidelines.

Résumé

The aim of this paper is to classify the driver tiredness and fatigue in automobile via eye tracking and body movements using deep learning based Convolutional Neural Network (CNN) algorithm. Vehicle driver face localization serves as one of the most widely used real-world applications in fields like toll control, traffic accident scene analysis, and suspected vehicle tracking. The research proposed a CNN classifier for simultaneously localizing the region of human face and eye positioning. The classifier, rather than bounding rectangles, gives bounding quadrilaterals, which gives a more precise indication for vehicle driver face localization. The adjusted regions are preprocessed to remove noise and passed to the CNN classifier for real time processing. The preprocessing of the face features extracts connected components, filters them by size, and groups them into face expressions. The employed CNN is the well-known technology for human face recognition. One we aim to extract the facial landmarks from the frames, we will then leverage classification models and deep learning based convolutional neural networks that predict the state of the driver as 'Alert' or 'Drowsy' for each of the frames extracted. The CNN model could predict the output state labels (Alert/Drowsy) for each frame, but we wanted to take care of sequential image frames as that is extremely important while predicting the state of an individual. The process completes, if all regions have a sufficiently high score or a fixed number of retries are exhausted. The output consists of the detected human face type, the list of regions including the extracted mouth and eyes with recognition reliability through CNN with an accuracy of 98.57% with 100 epochs of training and testing.

Résumé

Résumé

Deep Convolutional Neural Network (DCNN) is considered as a popular and powerful deep learning algorithm in image classification. However, there are not many DCNN applications used in medical imaging, because large dataset for medical images is not always available. In this paper, we present two DCNN architectures, a shallow DCNN and a pre-trained DCNN model: AlexNet, to detect breast cancer from 8000 mammographic images extracted from the Digital Database for Screening Mammography. In order to validate the performance of DCNN in breast cancer detection using a big data , we carried out a comparative study with a second deep learning algorithm Stacked AutoEncoders (SAE) in terms accuracy, sensitivity and specificity. The DCNN method achieved the best results with 89.23% of accuracy, 91.11% of sensitivity and 87.75% of specificity.