This paper presents a Machine Learning (ML) application that involves Long Short-Term Memory (LSTM) Artificial Neural Network (ANN). The proposed LSTM-ANN. architecture addresses the prediction challenge of sleep/wake phases in order to minimize night wake stages in elderly patients. Accurate predictions guarantee a good sleep quality for elderly patients and support medication adherence. For this, Vivago® Care watch technology (IST Vivago® Oy) is used in this work. Each watch was advantaged by actimetric functionality. Watches were worn on the wrist of the considered elderly patients. Automatically, one recording was sent every minute to the base station located in the nursery room, where the recorded data are stored and backed up. Experimental results are encouraging and also they are a rationale and evidence for the allocation of investments for developing online monitoring systems for sleep/wake phase’s prediction using only previous historical data. Our proposed LSTM-ANN proposal despite the novelty of this subject and the lack of literature, highlights prototypes results. is accurate for Personal Health Management (PHM) applications.

In 1988, Fere discovered the Electrodermal activity (EDA) and it was defined originally as the property of human skins. Nowadays, it is well known as the characteristics of the human body that causes an incessant variation of the electrical skin potential. In this work, the EDA signal is used to detect the Polyneuropathy (PNP) disease. The main two steps of the proposed strategy is to extract several features via EDA signals and to classify them in two classes (Healthy case and PNP case) by using Support Vector Machine (SVM) algorithm. For this purpose, four different domains of feature extraction are investigated (morphology, time, frequency and time-frequency). The Emrirical Mode Decomposition (EMD) algorithm is used to decompose original EDA to some sub-signals ranged from high to low frequency order. Consequently, the time-frequency domain is investigated, and the EDA analyse is performed considering diffirent frequency ranges. Then, the extracted features were classified using SVM and 83.79% of accuracy was achieved. Compared to previous works, experimental results show that the proposed method is truthful for PNP detection.

Melanoma is the most lethal form of skin cancer, but early detection can lead to effective treatment. Subsequently, the main concern of the health management community is to create efficient systems to detect melanoma earlier by utilizing computer vision systems since the traditional screening methods are manual, time-consuming, and inaccurate in some cases. These systems use measurable visual components describing the shape, color, and texture. These features are extracted based on rules invented by dermatologists to determine the malignancy of skin lesions. In this paper, we propose a novel approach to melanoma detection based on the “ugly duckling” concept, which suggests that nevi in the same individual usually resemble each other, and malignant melanomas often do not follow this pattern. Our method uses a convolutional neural network architecture to extract feature vectors from dermatoscopic images of skin lesions. Then, out-liers are detected by applying principal component analysis. The outliers are indicative of potential melanoma lesions. We evaluate the performance of our method using a dataset of dermatoscopic images. Our proposed method has shown the potential to improve melanoma detection rates.

This paper introduces a method for classifying and recognizing flying objects using trajectory features and artificial neural networks (ANN). Initially, the video sequence undergoes processing through a Gaussian mixture model (GMM) to detect and track the flying objects. Then, we will extract trajectory features from our dataset and use them to feed ANN for flying object classification. These features include turning angle, speed, acceleration and centroid distance function. The classical ANN is applied for feature vector classification to discriminate between birds and drones. Experimental results are conducted to showcase the effectiveness of the proposed method in classifying drones and birds. Moreover, the automated approach can be valuable in aiding military services to differentiate drones from other objects.

Wildfire is an unplanned natural disaster that can threaten human lives. The assessment of wildfires and smoke flows is difficult and needs systems with good precision and rapidity. This study proposes an effective workflow for detecting fire and smoke in RGB video surveillance images with two basic stages: we suggest first to enhance the image quality and denoise the noisy images with a lightweight convolutional encoder decoder architecture then we detect fire and smoke with a preprocessing steps combined with an adaptive level set algorithm. The experimental results of the proposed method prove its ability and efficiency in detecting fires and smokes with a minimum of false negatives regions and over than 90% value of Jaccard index.

Early fire forest detection is crucial for fast and effective intervention. Many research have been done on this subject starting by sensor based systems and arriving to image processing which leverage the computer vision advancements. Our work refers to one of the latest algorithms in forest fire detection: YOLO. We present in this paper a detailed description of the architecture of the YOLO algorithm with an emphasis to the YOLOv6 which is the latest version of the YOLO algorithms. The performance of the studied algorithm is evaluated on a personal database containing 28334 images, with 10534 forest fire images and 17800 non-fire images. The experimental results of applying the YOLOv6 proved the efficiency of the method in fast and accurate fires detection even in large size images and low resolutions. This result makes the studied algorithm so suitable for both satellite and ground based images analysis.

To develop an efficient device that helps dermatologists to early evaluate and inspect a specific kind of skin disease, computer vision systems have been intensively studied. These systems replace the traditional screening ways which are manual and time-consuming. These systems use some measurable visual component describing the shape, color, and texture of skin diseases to recognize them and to specify their malignancy. This article will be concentrated on the importance of using some statistical features and extracting the most relevant features of texture-colored images by calculating their degree of characterization. Using these highly-rated static textural features, non-fatal skin disease and melanoma classification results are presented and discussed.

Fires have become a more serious hazard to people's lives, property, and environment. Compared with the traditional techniques of fire detection, image technologies play a very promising role to overcome the problem of high false alarm rate. However, a major issue with these methods is their fastidious and long-time generation. In fact, the implemented algorithms are often produced using multi-feature technique, including chromatic characteristics, dynamic features, texture features and contour features. Therefore, we provide, in this paper, a study of some supervised machine learning algorithm for fire and smoke images recognition, and we compare it to a proposed model based on convolution neural network (CNN) algorithm. To do this, we consider a proper database composed by a total of 28334 images classified into three categories: 7329 fire images, 9205 smoke images and 11800 other images.

Abstract In recent decades, global warming has contributed to an increase in the number and intensity of wildfires destroying millions hectares of forest areas and causing many casualties each year. Firemen must therefore have the most effective means to prevent any wildfire from breaking out and to fight the blaze before being unable to contain and extinguish it. This article will present a new network architecture based on Convolutional Neural Network to detect and locate smoke and fire. This network generates fire and smoke masks in an RGB image by segmentation. The purpose of this work is to help firemen in assessing the extent of fire or monitor an incipient fire in real time with a camera embedded in a vehicle. To train this network, a database with the corresponding images and masks has been created. Such a database will allow to compare the performances of different networks. A comparison of this network with the best segmentation networks such as U‐Net and Yuan networks has highlighted its efficiency in terms of location accuracy, reduction of false positive classifications such as clouds or haze. This architecture is also efficient in real time.

Research on video analysis for fire detection has become a hot topic in computer vision. However, the conventional algorithms use exclusively rule-based models and features vector to classify whether a frame is fire or not. These features are difficult to define and depend largely on the kind of fire observed. The outcome leads to low detection rate and high false-alarm rate. A different approach for this problem is to use a learning algorithm to extract the useful features instead of using an expert to build them. In this paper, we propose a convolutional neural network (CNN) for identifying fire in videos. Convolutional neural network are shown to perform very well in the area of object classification. This network has the ability to perform feature extraction and classification within the same architecture. Tested on real video sequences, the proposed approach achieves better classification performance as some of relevant conventional video fire detection methods and indicates that using CNN to detect fire in videos is very promising.