The digitalization of the building sector enhances energy efficiency, automation, and maintenance. The digitization of building data is essential for Building Management Systems (BMS), enabling optimized monitoring and control of systems such as heating, ventilation, and air conditioning (HVAC), as well as lighting and security. This paper analyzes ontologies in smart buildings, particularly for HVAC optimization. We propose SEBAO, a new ontology built upon SAREF4BLDG. Furthermore, the proposed framework introduces a novel integration with Elasticsearch for intelligent data indexing and retrieval, and incorporates semantic web services to enhance interoperability and dynamic service discovery. Our framework aims to support adaptive HVAC control for improved energy performance in smart buildings.

The progressive neurodegenerative disease known as Alzheimer’s disease (AD) severely reduces cognitive function. Early and precise diagnosis is critical for successful clinical intervention. In this study, we introduce a novel deep learning architecture that blends a 3D Generative Adversarial Network (GAN) with a Simple Fully Convolutional Network (SFCN) to enhance AD classification from structural MRI (Magnetic Resonance Imaging) data. The original dataset, obtained from the OASIS database, consists of 416 MRI volumes, which are supplemented with 10 synthetic 3D brain scans created by a GAN trained on the same distribution. This augmentation solves class imbalance and data scarcity, which are two frequent problems in medical imaging. Our 3D CNN-GAN model outperformed the baseline model trained without GAN augmentation, with a test accuracy of 84.3%, an AUC of 0.92, sensitivity of 91.7%, and specificity of 84.6%, as well as a 22% increase in early-stage AD diagnosis. These results emphasize the clinical utility of leveraging synthetic data to enhance diagnosis based on deep learning, allowing for more accurate and timely identification of AD in real-world medical environments.

Language impairment is a key biomarker for neurodegenerative diseases such as Alzheimer's dis-ease (AD). With the rapid growth of Large Language Models, natural language processing (NLP) has be-come a preferred modality for the early prediction of AD from speech. In this work, we propose a two-stage process for early detection of AD from transcriptions of speech. The first step involves extracting a discriminative text embedding representation using public models from OpenAI. This embedding serves as input for a machine learning classifier in the second stage. In this paper, we investigate three text embedding models and eight machine learning classifiers, both deep learning (DL) based and non-DL based. The evaluation was conducted using the public ADReSSo dataset of 237 patients. The results show that models “ada-002” and “3-small” produce discriminative embeddings that lead to good performance when combined with a Deep Neural Network in classification, achieving accuracy rates of 83.10% and 84.51 %, respectively.

Alzheimer's disease (AD) is a chronic and irreversible neurological disorder, making early detection essential for managing its progression. This study investigates the coherence of SHAP values with medical scientific truth. It examines three types of features: clinical, demographic, and FreeSurfer extracted from MRI scans. A set of six ML classifiers are investigated for their interpretability levels. This study is validated on the OASIS-3 dataset with binary classification. The results show that clinical data outperforms the others, with a margin of 14% over FreeSurfer features, the second-best features. In the case of clinical features, the explanations provided by the tree-based classifiers consistently align with medical insights. This comparison was calculated using the Kendall Tau distance.

Alzheimer's disease poses a significant challenge for patients and their caregivers, necessitating innovative solutions to enhance patients quality of life and safety. In this article, we propose an assistance system based on a Contextual Bandit (CB) approach to help elderly Alzheimer's patients in performing their activities of daily living autonomously. Our system relies on a human activity recognition system and a prompt system to alert the patient when needed. For decision-making, we employ a Contextual Bandit Reinforcement Learning module capable of identifying and suggesting necessary assistance based on the patient's behavior. We validated the effectiveness of our system by testing it on the DemCare dataset. The results demonstrate the system's ability to provide tailored and responsive assistance to the needs of Alzheimer's patients.

Alzheimer’s disease (AD) is a chronic and irreversible brain disorder, making early detection crucial in managing its progression. This study aims to compare multiple deep learning models for the early detection of Alzheimer’s disease. The research employs advanced techniques, including convolutional neural networks (CNNs) and transfer learning models. Specifically, six deep neural network techniques - 2DCNN, VGG19, DenseNet121, Inception-V3, MobileNet and ResNetl0lv2 are utilized to classify and identify different stages of AD, such as NonDementia, Very Mild Dementia, Mild Dementia and Moderate Dementia. The comparative study is validated on the Kaggle dataset of 6400 MRI images. The results demonstrate that the CNN outperforms the fine-tuned architectures with an accuracy of 99.14% and AUC of 99.84%.

Artificial Intelligence technology has made a huge leap forward. However, the current systems still fall short when faced with the catastrophic challenge of forgetting. Deep neural networks face huge limitations, including the tendency to forget learned tasks as they perform new tasks. While neuroscience research suggests that the human brain synapses can overcome this challenge through the ability to adapt based on historical information, applying similar mechanisms to deep neural networks to prevent forgetting previous data proves to be a monumental task.In this study, our focus lies on using these improved deep networks to provide assistance to people afflicted with Alzheimer’s disease, a prevalent condition among the elderly. Due to cognitive impairments, individuals with Alzheimer’s often require ongoing support to carry out daily activities necessary for their well-being. To tackle the issue of forgetfulness in deep neural networks, specifically binary neural networks, and grant them the ability of multitasking and continuous learning, we propose an effective training method. This can be achieved by treating the hidden weights as semi-modifiable variables and adapting the training methodology accordingly. The proposed remote monitoring system consists of two main components. The first component is the Human Activity and Patient Behavior Monitoring (HAR) Identification Unit, which focuses on monitoring and identifying patterns in the activities and behaviors of Alzheimer’s patients. The second component is a support unit that detects anomalies and behavioral problems and provides appropriate warnings. This system is based on augmentative learning technology. As a result of our research, we have developed auxiliary systems specifically designed for Alzheimer’s patients. Activity details are as follows. We achieved excellent results for Activity 1 (Task 1) with an accuracy of 99.51% and Activity 2 (Task 2) was also well-addressed by our system, reaching an accuracy of 96.63%. Finally, we conducted a comparative study, comparing our system’s performance complexity with previous systems using the Dem@care Dataset.

Alzheimer's disease is a chronic brain disease with multi-factorial causes that begins in the middle of life. It affects the patient in many ways, like the ability to perform daily life activities. In this paper, we proposed an assistance system for Alzheimer's patients to assist them in performing their activities of daily living autonomously. The developed system is based on a human activity recognition system and a prompt system to provide alerts to the patient in case of need. To detect the anomalies in the patient's behavior and provide assistance, we used a reinforcement learning (RL) module as a decision-making system. This module may be responsible for identifying and prompting the patient's wanted assistance based on his or her behavior. The efficiency of the proposed system was proven after testing using the Dem Care dataset.

Multiple sclerosis (MS) is a chronic autoimmune inflammatory disease that damages the central nervous system by causing small lesions in the brain. In this study, we present the fusion of four features extraction methods such as the 3D Local Binary Pattern (3D-LBP), 3D Decimal Descriptor Patterns (3D-DDP), Local Binary Pattern from Three Orthogonal Planes (LBP-TOP) and Decimal Descriptor Patterns from Three Orthogonal Planes (DDP-TOP) with Convolutional Neural Network (CNN) for MS classification using three 3D MRI sequences datasets T1, T2 and PD from 3D BrainWeb dataset. We implement twelve CNN models and apply each method with each of the CNN models on T1, T2 then PD MRI sequences. The experimental results demonstrate that 3D-DDP and DDP-TOP methods are the most robust and, for the contrast change effect of MRI sequences on the classification results, T2 yields the best performance.

This paper introduces a novel approach for human activities recognition (HAR) based on body articulations (joints) that represent the connection between bones in the human body which join the skeletal system such as the knee, shoulder and hand, and which are made to allow different degrees and types of movement. To implement our system, we used PoseNet to extract articulation points, which will be classified employing transfer learning approach to recognize the activity. The created system will be named in the rest of the paper (PTLHAR). The experimental results show that the proposed approach provides a significant improvement over state-of-the-art methods.

Extreme learning Machine is a well known learning algorithm in the field of machine learning. It's about a feed forward neural network with a single-hidden layer. It is an extremely fast learning algorithm with good generalization performance. In this paper, we aim to compare the Extreme learning Machine with wavelet neural networks, which is a very used algorithm. We have used six benchmark data sets to evaluate each technique. These datasets Including Wisconsin Breast Cancer, Glass Identification, Ionosphere, Pima Indians Diabetes, Wine Recognition and Iris Plant. Experimental results have shown that both extreme learning machine and wavelet neural networks have reached good results.

Developing an automatic arabic sign language recognition system is of great importance, it can be used as a communication means between hearing-impaired and other people.

The success of the deep learning and specifically learning layer by layer led to many impressive results in several contexts that include neural network. This gave us the idea to apply this principle of learning on wavelet network because it is an active research topic at the moment. This paper present our approach for image classification by the combination of two techniques of learning: the wavelet network and the deep learning. We try to classify images in a supervised way following by an unsupervised learning using the principle of autoencoder. Experiments on two databases COIL-100 and MNIST show that our approach gives good results for the two classifiers that we used.

This paper presents a novel hand posture recognizer based on separator wavelet networks (SWNs). Aiming at creating a robust and rapid hand posture recognizer, we have contributed by proposing a new training algorithm for the wavelet network classifier based on fast wavelet transform (FWN). So, the contribution resides in reducing the number of WNs modeling training data. To make that, inspiring from the adaboost feature selection method, we thought to create SWNs (n-1 WNs for n classes) instead of modeling each training sample by its wavelet network (WN). By proposing the new training algorithm, the recognition phase will be positively influenced. It will be more rapid thanks to the reduction of the number of comparisons between test images WNs and training WNs. Comparisons with other works, employing universal hand posture datasets are presented and discussed. Obtained results have shown that the new hand posture recognizer is comparable to previously established ones.

In last years, the emergence of 3D shape in face recognition is due to its robustness to pose and illumination changes. These attractive benefits are not all the challenges to achieve satisfactory recognition rate. Other challenges such as facial expressions and computing time of matching algorithms remain to be explored. In this context, we propose our 3D face recognition approach using 3D wavelet networks. Our approach contains two stages: learning stage and recognition stage. For the training we propose a novel algorithm based on 3D fast wavelet transform. From 3D coordinates of the face (x,y,z), we proceed to voxelization to get a 3D volume which will be decomposed by 3D fast wavelet transform and modeled after that with a wavelet network, then their associated weights are considered as vector features to represent each training face . For the recognition stage, an unknown identity face is projected on all the training WN to obtain a new vector features after every projection. A similarity score is computed between the old and the obtained vector features. To show the efficiency of our approach, experimental results were performed on all the FRGC v.2 benchmark.

Driving security is an important task for human society. The major challenge in the field of accident avoidance systems is the driver vigilance monitoring. The lack of vigilance can be noticed by various ways, such as, fatigue, drowsiness and distraction. Hence, the need of a reliable driver’s vigilance decrease detection system which can alert drivers before a mishap happens. In this paper, we present a novel approach for vigilance estimation based on multilevel system by combining head movement analysis and eyes blinking. We have used Viola and Jones algorithm to analyse head movement and a classification system using wavelet networks for eyelid closure measuring. The contribution of our application is classifiying the vigilance state at multi level. This is different from the binary-class (awakening or hypovigilant state) existing in most popular systems.

Image classification is an important task within the field of computer vision. In this paper we propose a new wavelet network classifier (WNC) based on the cascaded architecture. This classifier is characterized by its new learning approach and its novel architecture which brings a novel robust test way. So, our contributions in this paper reside in two major points. The first one is the proposition of a new training algorithm which overcomes lacuna detected in the latest version of WN learning approach. Hence, our new approach creates separator WNs discriminating classes (n -- 1 WNs to classify n classes) instead of creating a WN for each training image. This contribution makes very rapid the classification process by reducing the number of comparisons between test images WNs and training WNs. The second contribution is the proposition of a novel architecture which brings a new test approach radically different to those employed in ancient WN versions. By the new architecture which is based on the cascade notion, we aim at reducing the number of kernels employed in the approximation of test images. Experiments, using well known benchmarks, show that our new classifier is very robust and rapid compared to already existing ones.

Speech recognition is a specialized pattern recognition task with several applications such as vocal command system, dictating machines, and understanding systems. In recent years, research on pattern recognition has increased by developing various methods and algorithms for different applications. In this paper, we proposed a novel training algorithm based on the fast Beta wavelet transform for speech recognition. This approach has many advantages compared to other algorithms. The majority of the old approaches need to inverse matrix, which can be computationally intensive. However, the new algorithm is computed by the iterative application of fast wavelet transform to compute connection weights. To highlight our approach, we compared its experimental results to those of the old ones.

Face emotion recognition is one of the most important and rapidly advanced active research areas of computer science. A new method for facial expression recognition based on wavelet network classifier is proposed in this paper. It allows us the detection of six basic emotions other than the neutral one: (Joy, surprise, sadness, anger, fear and disgust) The process is composed of three principle steps: face detection, features extraction and classification. The effectiveness of our proposed algorithm is experimentally demonstrated by using well-known test database: the extended cohen-kanade database.

Road safety is an important challenge for human society. Fatigue at the wheel is a serious problem causing thousands of road accidents each year. The major issue in the field of driving security systems is driver vigilance control. In this paper, we present a new method for driver's vigilance level measurement using a multi parameter system based on head movement estimation, eyes blinking analysis and face emotion recognition based on wavelet networks classification system.

This paper aims at developing a novel approach for speech recognition based on wavelet network learnt by fast wavelet transform (FWN) including a fuzzy decision support system (FDSS). Our contributions reside in, first, proposing a novel learning algorithm for speech recognition based on the fast wavelet transform (FWT) which has many advantages compared to other algorithms and in which major problems of the previous works to compute connection weights were solved. They were determined by a direct solution which requires computing matrix inversion, which may be intensive. However, the new algorithm was realized by the iterative application of FWT to compute connection weights. Second, proposing a new classification way for this speech recognition system. It operated a human reasoning mode employing a FDSS to compute similarity degrees between test and training signals. Extensive empirical experiments were conducted to compare the proposed approach with other approaches. Obtained results show that the new speech recognition system has a better performance than previously established ones.

This paper presents a new cascaded hybrid Wavelet Network Classifier (CHWNC) designed for hand gesture recognition in real time applications. This paper contains two key contributions. The first is the amelioration of our previous works in the classification domain employing wavelet networks (WN). Precisely, by ameliorating the training way of the latest wavelet network classifier (WNC) version by representing each training class by one WN instead of creating a WN for each training image. This contribution makes very rapid the test phase by reducing the number of comparisons between test images WNs and training WNs. The second contribution is the proposition of a new wavelet network architecture including the cascade notion which decomposes the WN on a set of stages. The new architecture has as aim not only to make recognitions robust and rapid but also to reject as fast as possible gestures which must not be considered by the system (spontaneous gestures). Experiments, based on a well known hand posture dataset, show that our method is very robust and rapid compared to already existing ones.

In this paper, a novel learning algorithm of wavelet networks based on the Fast Wavelet Transform (FWT) is proposed. It has many advantages compared to other algorithms, in which we solve the problem in previous works, when the weights of the hidden layer to the output layer are determined by applying the back propagation algorithm or by direct solution which requires to compute the matrix inversion, this may cause intensive computation when the learning data is too large. However, the new algorithm is realized by iterative application of FWT to compute the connection weights. Furthermore, we have extended the novel learning algorithm by using Levenberg–Marquardt method to optimize the learning functions. The experimental results have demonstrated that our model is remarkably more refreshing than some of the previously established models in terms of both speed and efficiency.

Image classification is an important task in computer vision. In this paper, we propose a supervised method for image classification based on a fast beta wavelet networks (FBWN) model. First, the structure of the wavelet network is detailed. Then, to enhance the performance of wavelet networks, a novel learning algorithm based on the Fast Wavelet Transform (FWTLA) is proposed. It has many advantages compared to other algorithms, in which we solve the problem of the previous works, when the weights of the hidden layer to the output layer are determinate by applying the back propagation algorithm or by direct solution which requires to compute matrix inversion, this may be intensive computation when the learning data is too large. However, the new algorithm is realized by the iterative application of FWT to compute connection weights. In the simulation part, the proposed method is employed to classify images. Comparisons with classical wavelet network classifier are presented and discussed. Results of comparison have shown that the FBWN model performs better than the previously established model in the context of training run time and classification rate.