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.

The complexity of overlapping symptoms and interactions among multiple diseases makes it very difficult to accurately diagnose comorbidities. This article presents an innovative comorbidity diagnosis improvement approach that integrates conflicting group learning and numerous diverse machine learning algorithms such as Random Forest, Gradient Boosting, AdaBoost, Bagging and Extra Trees. The ensemble model is advantageous over individual models because it improves diagnostic accuracy while improving adversarial robustness. Effective adversarial training methods can also be used to strengthen the model against interference which may interfere with the diagnosis. Benchmark comorbidity datasets explore the effectiveness of the proposed method that is not solely more efficient in terms of accuracy compared to other methods, but similarly more efficient in its aptitude to endure adversarial instances. The current study is supposed to be merged into the investigative pipeline to allow for the development of vigorous diagnosis schemes. Of the algorithms tested in the study, the Extra Trees algorithm achieved 91.845% which was the highest performance obtained among the other algorithms.

Abstract Due to its widespread application in the field of public security, anomaly detection in crowd scenes has recently become a hot topic. Some deep learning-based methods led to significant accomplishments in this field. Nevertheless, due to the scarcity of data and the misclassification of queries which most of them suffer to some extent from a sudden and infrequent overfitting. Though, we tried to solve the above problems, understand the long video streams and establish an accurate and reliable security system in order to improve its performance in detecting anomalies. We also referred to the hash technique, which has proven to be the most efficient method used when researching about large-scale image recovery. Thus, this article offers a smart video anomaly detection solution. In this paper, we combine the advantages of both deep hashing and deep auto-encoders to show that tracking changes in deep hash components across time and can be used to detect local anomalies. More precisely, we start with a new technique to minimize the mass of input data and information in order to decrease the time of calculation using a new dynamic frame skipping technique. Then, we propose to measure local anomalies by combining semantic with low-level optical flows to balance the performance and perceptibility. The experimental results illustrate that the proposed methods surpass these baselines for the detection and localization of anomalies.

Abstract Automatic text classification, in which textual data is categorized into specified categories based on its content, is a classic issue in the science of Natural Language Processing. In recent years, there has been a notable surge in research on medical text classification due to the increasing availability of medical data like patient medical records and medical literature. Machine learning and statistical methods, such as those used in medical text classification, have proven to be highly efficient for these tasks. However, a significant amount of manual labor is still required to categorize the extensive dataset utilized for training. Recent research have demonstrated the effectiveness of pretrained language models, including machine learning models, in reducing the time and effort required for feature engineering by medical experts. However, there is no statistically significant enhancement in performance when directly applying the machine learning model to the classification task. In this paper, we present a hybrid machine learning model that combines individual traditional algorithms augmented by a genetic algorithm. However, the improved model is designed to enhance performance by optimizing the weight parameter. In this context, the best single model demonstrated commendable accuracy. In addition, when applying the hybridization approach and optimizing the weight parameters, the results were substantially enhanced. The results underscore the superiority of our augmented hybrid model over individual traditional algorithms. We conduct experiments using two distinct types of datasets: one comprising medical records, such as the Heart Failure Clinical Record and another consisting of medical literature, such as PubMed 20k RCT. So, the objective is to clearly showcase the effectiveness of our approach by highlighting the significant enhancements in accuracy, precision, F1-score and Recall achieved through our improved model.

Human Activity Recognition (HAR) is one of the most important research areas in the fields of health and human-machine interaction. The creation of several artificial intelligence-based models for activity identification has resulted in poor long-term performance in the actual world since these methods are unable to extract spatial and temporal information. Though, Deep learning is starting to replace well-established hand-crafted techniques that rely on expertly built feature extraction and classification techniques in the field of HAR. Nevertheless, it is challenging to acquire an overview of the suitability of these discrete implementations of custom deep architectures for issues ranging from the recognition of manipulative gestures to the identification and segmentation of physical activities. Given these constraints, we develop an innovative Stacked Auto Encoder (SAE) and Extreme Learning Machine (ELM) architecture based on temporal feature to produce a new model for HAR.. This feature is connected to speed of movement and fed to a new neural network architecture as temporal stream. A key step in this approach is the selection of features that characterize the complexity of human actions in time. Results show that the proposed model outperforms many of the existing deep and machine learning techniques.

The diagnosis of common diseases, in which people suffer from several bad health conditions, is a complex medical challenge. This study investigated the use of machine learning methods combined with the Chi-square feature selection technique to improve the accuracy and efficiency of comorbidity diseases diagnosis. Using various decision trees of machine learning algorithms, random forest, Gradient Boost, AdaBoost, Bagging, and extra trees, this research aimed to improve the identification and prediction of comorbid conditions and ultimately advance early detection and treatment strategies. Using the selection of Chi-Square features helped give priority to the most relevant attributes, reduce noise, and refine the models. The results showed that both the AdaBoost and Gradient Boost algorithms obtained an accuracy rate of 91.33%, confirming their efficacy in comorbidity diseases diagnosis. However, it is essential to ensure their reliability and efficacy in the real clinical environment, including the practical implementation and validation of these methods, to improve patient care and medical decisions.

The last two years witnessed a rapid outbreak of the COVID-19 virus with an exponential increase of critical cases leading to death of many infected people, the social distancing remains the common technique adopted by all countries to reduce the contamination risk and is also recommended by WHO (World Health Organization). According to previous research studies in virology, it is reasonable to maintain a distance between people in public areas. In this paper, we propose a method based on wireless localization and distance between wireless users using both machine learning techniques and correlation-distance model elaboration. We collected indoor RSSI data of WIFI stations using smartphones, perform data pre-processing RSSI and feature selection, we apply classification techniques to determine the location, and finally use the correlation between measurements to estimate distance using a proposed model. Results are assessed in terms of classification and fitting accuracy. Our model is able to determine user's location with an accuracy of 91.1 % using classification and distance discrimination method is proposed to detect any breach of social distancing standard.

Abstract Affective computing is a key research topic in artificial intelligence which is applied to psychology and machines. It consists of the estimation and measurement of human emotions. A person’s body language is one of the most significant sources of information during job interview, and it reflects a deep psychological state that is often missing from other data sources. In our work, we combine two tasks of pose estimation and emotion classification for emotional body gesture recognition to propose a deep multi-stage architecture that is able to deal with both tasks. Our deep pose decoding method detects and tracks the candidate’s skeleton in a video using a combination of depthwise convolutional network and detection-based method for 2D pose reconstruction. Moreover, we propose a representation technique based on the superposition of skeletons to generate for each video sequence a single image synthesizing the different poses of the subject. We call this image: ‘history pose image’, and it is used as input to the convolutional neural network model based on the Visual Geometry Group architecture. We demonstrate the effectiveness of our method in comparison with other methods in the state of the art on the standard Common Object in Context keypoint dataset and Face and Body gesture video database.

The problem addressed in this paper is feature extraction and classification of images. As a solution, we proposed a Deep Wavelet Network architecture based on the Wavelet Network and the Stacked Auto-encoders. In this work, we shifted from the deep learning based on neural networks to deep learning based on wavelet networks. The latter doesn’t change the general form of the Deep Learning based on the Neural Network but it is a novel method that shows the process of feature extraction and explains the system of image classification. Our Deep Wavelet Network is created for the training and the classification phase. After the training phase, a linear classifier is applied. Finally, the experimental test of our method is in the COIL-100 dataset.

ETL process is responsible of the integration of data into the data warehouse. It is about extracting data from different sources, transforming it to deliver quality data of value for analysis, and loading it into the warehouse. In this context, we propose a new approach called Mapping-ELT (M-ELT) is suggested to deal with ELT basic operations and take into account the semantic heterogeneity. In order to accelerate data handling, the Hive is used to improve data warehousing capabilities and Ontology as a solution to treat the problems of semantic heterogeneity. Experimental results confirm that the ELT operation works well, particularly with adapted operations.

Abstract The emotion recognition field has two major issues. On the one hand, it is difficult to find the same emotion state in different persons since they may express the same emotion state in various ways. On the other hand, it is also hard to seek the difference between expressions of the same person because some emotion states are too subtle to discriminate. The focus of this work is to solve these two problems by proposing a new approach of emotion recognition. This novel approach allows our emotion recognition system to classify 18 emotions (primary emotions and their intensities). First, we proposed textual definitions of the intensity emotions. Then, we created our emotion recognition system, which is composed of three stages: pre-treatment, feature extraction and classification. We used the deep learning for the feature extraction and the fuzzy logic for the classification. The experimental test demonstrates the efficiency of our system for primary emotions and their intensities’ classification compared to other methods.

Violent action represents a threat to public security, thus intelligent violence detection became one of the important and challenging topics in video surveillance scenarios for this reason there is a growing appeal of video-surveillance systems. Hence, it’s mandatory for the detection of violent or abnormal activities to avert any casualties which could cause any damages. Distinctly, in this paper, it is possible to create a network to learn spatial-temporal information on all subjects of violence rather than going through each concept separately. In order to construct a new concept for violence detection system, we rely on a strategy of a dynamic frame skipping to reduce the complexity of calculation. However, following the regions of interest in the frame, the overall complexity of the calculation is decreased. Withal, the History of Binary Motion Image for n successive images is used for features extraction to facilitate to model the human behaviors. Then, the biggest regions of interest are extracted in order to find the maximum component represented violence action. Finally, deep neural networks involve three stacked Autoencoders and a Softmax are adopted as an exterior layer for classification.

Abstract Emotion recognition is a key work of research area in brain computer interactions. With the increasing concerns about affective computing, emotion recognition has attracted more and more attention in the past decades. Focusing on geometric positions of key parts of the face and well detecting them is the best way to increase accuracy of emotion recognition systems and reach high classification rates. In this paper, we propose a hybrid system based on wavelet networks using 1D Fast Wavelet Transform. This system combines two approaches: the biometric distances approach where we propose a new technique to locate feature points and the wrinkles approach where we propose a new method to locate the wrinkles regions in the face. The classification rates given by experimental results show the effectiveness of our proposed approach compared to other methods.

Classical signal representation techniques generally use a description of the components on a basis on which the representation of the signal is unique such as wavelets network. Conversely, sparse representations consist in the decomposition of the signal on a dictionary comprising a number of elements much larger than the dimension of the signal. This technique can be widely used for representation, compression, denoising and separation of all types of signals. Consequently, some researches have confirmed that the use of a predefined dictionary is less efficient than a dictionary from training data. So, the idea of this paper is to propose a new technique for the creation of a dictionary using the wavelet decomposition to enhance the sparse representation of images. This technique is based on the combination of sparse coding and the fast wavelet transform algorithms for image representation. Our results obtained using different universal image databases showed greater performances in the representation of images when compared to some methods from the state of the art.

Image classification is an area where deep learning and especially stacked Auto-encoders have really proven their strength. The contributions of this paper lie in the creation of a new classifier to remedy some classification problems. This new method of classification presents a combination of the most used techniques in Deep Learning (DL) and Sparse Coding (SC) in the field of classification. Proposed deep neural networks consist of three stacked Auto-encoders and a Softmax used as an outer layer for classification. The first Auto-encoder is created from a sparse representation of all images of the dataset. The sparse representation of all images represents the decoder part of the first Auto-encoder. Then the transpose of the matrix is applied to get the encoder part. Experiments performed on standard datasets such as ImageNet and the Coil-100 reveal the efficacy of this approach.

The intraspecific nest parasitism is a phenomenon that attracts the attention of biologists. There are bird species like the Slender-billed which contains at most 3 eggs, but their nests can contain four or five eggs. In fact, a genetic study made on a set of nests has shown that one or two of the eggs belong to a second female named by biologists “a parasitic egg”. As the Gull Mockers are protected by the Law, researchers found it difficult to identify parasite eggs without genetic test. Many studies have been done in order to identify the parasitic egg, based on the morphological parameters and the characteristic of the egg’s shell, but these studies haven’t led to good results. Recent Advances in Artificial Intelligence (AI) and particularly Deep Learning (DL) techniques has increased motivation to use this method to quantify parasitic eggs. In this work, we present a new method to quantify a parasitic egg from a dataset of egg’s image. One of the most used techniques is Convolutional Neural Network (CNN). The technique is a supervised learning method used to classify images. We used this technique to extract features from image to characterize any egg. To evaluate our approach, we use 31 lays of eggs form the 92 eggs dataset to test the performance of our proposed method.

Wireless Sensor Networks (WSNs) are scalable research domain with a multitude of application contexts. Sensor nodes deployment is a decisive step that has a major impact on the performance of the network, since it directly influences the cost, the sensing capability and even the WSNs lifetimes. In this paper, we are interested in the placement problem of sensor nodes for WSNs. First, the issue is formulated as constrained multi-objective optimization problem (MOOP). Then, a novel approach based on Multi-Objective Flower Pollination Algorithm (MOFPA) was proposed. This new method aimed to approximate optimal trade-offs among multiple objective functions, which are enhancing the coverage, reducing the network energy dissipation, maximizing the network lifetime and maintaining the connectivity. Finally, we compared the proposed approach with two popular algorithms, namely, the classic Particle Swarm Optimization (PSO) and Non-dominated Sorting Genetic Algorithm II (NSGA-II). The simulation experiments show that our approach outperforms PSO and NSGA-II.

Many techniques are used to solve problems of security in the Internet such as cryptography or watermarking. In this context watermarking is a way for protecting copyright and proving authenticity of a digital data. In this paper, a non blind digital watermark scheme is proposed. It is based on Discrete Cosine transformation (DCT), singular Values Decomposition (SVD) and Beta Chaotic Map (BCM). The experimental results show that this scheme is robust against several attacks compared to other algorithms.

In the multimedia processing, the extraction and the representation of characteristics are considered as an important step. The extraction of the ideal characteristics having the ability to reflect the intrinsic content of the images as complete as possible is still a difficult problem in computer vision. Little research has focused on this problem. This paper presents a new unsupervised method based on the self-organizing map (SOM) for features extraction. Our method consists of two main steps: Extracting the sub-regions of an image according to their points of interest and using the SOM for the different views of an image such as color, texture and shape. Then, combine them to have finally a "Multi-View" vector characteristic. The proposed method is evaluated on three image classification datasets Cloud, Coil100 and CIFAR-10. The classification accuracies of the proposed method for the three datasets are much higher in comparison with the other methods cited in the literature.

As a powerful technique, sparse coding was adopted by several researchers in different approaches. Particularly in image processing, it has attracted a considerable attention. It can be widely used for representation, compression, denoising and separation of all type of signal. Recent works have confirmed that the use of a predefined dictionary is less efficient than a dictionary from training data. According to this idea, this paper proposes a new technique based on wavelet network to create a dictionary to ameliorate the representation and classification of image using sparse coding technique.

The wireless sensor networks (WSN) consist of a set of sensors that are more and more used in surveillance applications on a large scale in different areas: military, Environment, Health ... etc. Despite the minimization and the reduction of the manufacturing costs of the sensors, they can operate in places difficult to access without the possibility of reloading of battery, they generally have limited resources in terms of power of emission, of processing capacity, data storage and energy. These sensors can be used in a hostile environment, such as, for example, on a field of battle, in the presence of fires, floods, earthquakes. In these environments the sensors can fail, even in a normal operation. It is therefore necessary to develop algorithms tolerant and detection of defects of the nodes for the network of sensor without wires, therefore, the faults of the sensor can reduce the quality of the surveillance if they are not detected. The values that are measured by the sensors are used to estimate the state of the monitored area. We used the Non-linear Auto- Regressive with eXogeneous (NARX), the recursive architecture of the neural network, to predict the state of a node of a sensor from the previous values described by the functions of time series. The experimental results have verified that the prediction of the State is enhanced by our proposed model.

To survive the competition, companies always think about having the best employees. The selection is depended on the answers to the questions of the interviewer and the behavior of the candidate during the interview session. The study of this behavior is always based on a psychological analysis of the movements accompanying the answers and discussions. Few techniques are proposed until today to analyze automatically candidate’s non verbal behavior. This paper is a part of a work psychology recognition system; it concentrates in spontaneous hand gesture which is very significant in interviews according to psychologists. We propose motion history representation of hand based on an hybrid approach that merges optical flow and history motion images. The optical flow technique is used firstly to detect hand motions in each frame of a video sequence. Secondly, we use the history motion images (HMI) to accumulate the output of the optical flow in order to have finally a good representation of the hand‘s local movement in a global temporal template.

The video surveillance is one of the key areas in computer vision researches. The scientific challenge in this field involves the implementation of automatic systems to obtain detailed information about individuals and groups behaviors. In particular, the detection of abnormal movements of groups or individuals requires a fine analysis of frames in the video stream. In this article, we propose a new method to detect anomalies in crowded scenes. We try to categorize the video in a supervised mode accompanied by unsupervised learning using the principle of the autoencoder. In order to construct an informative concept for the recognition of these behaviors, we use a technique of representation based on the superposition of human silhouettes. The evaluation of the UMN dataset demonstrates the effectiveness of the proposed approach.

Deep Convolutional Neural Network (DCNN) can be marked as a powerful tool for object and image classification. However, the training stage of such networks is highly consuming in terms of storage space and time. Also, the optimization is still a challenging subject. In this paper, we propose a fast DCNN based on smart dropout and layer skipping. The proposed approach led to improve the speed of the testing stage as well as image classification accuracy. This was possible thanks to three key advantages: First, the rapid way to compute the features using Fast Beta Wavelet Transform. Second, the proposed intelligent dropout method is based on whether or not a unit is efficiently and not randomly selected. Third, it is possible to classify the image using efficient units of earlier layer(s) and skipping all the subsequent hidden layers directly to the output layer. Our experiments were performed on CIFAR-10 and MNIST datasets and the obtained results are very promising.

Automatic identification of television programs in the TV stream is an important task for operating archives and represent a principal source of multimedia information.. The goal of the proposed approach is to enable a better exploitation of this source of video by multimedia services (i.e., TV-On-Demand, catch-up TV), social community, and video-sharing pla forms (Vimeo, Youtube, Facebook...) This paper presents a new spatio-temporal approach to identify the programs in TV stream using deep learning in two main steps. A database for video of visual jingles is constructed for training. In the test we use same jingles program type in order to identify the various program types in the TV stream. The main idea of identification process consists in using the principal of auto-encoder. After presenting the proposed approach, the paper overviews the encouraging experimental results on several streams extracted from different channels and composed of several programs. Comparison experiments to similar works have been carried out on the TRECVID 2017 database. We show significant improvements to TV programs identification exceed 95 %.

In the medical field, research studies have shown that Augmented Reality (AR) based surgical training has a good potential in making the learning process more active. However, lack of displaying the correct occlusion between the real surgical instrument and virtual organ greatly limits the trainee surgeons understanding and reduces the overall system usability. In this paper, we propose a novel mutual occlusion handling method based on surgical instrument tracking and 3D positioning approach in AR environment. Therefore, we introduce a monocular image processing based paradigm that aims at (1) tracking the instrument using both background subtraction and Hough transform method (2) calculating the 3D position of the instrument using the geometry of perspective projection (3) comparing the 3D coordinates of the real instrument with the virtual organ to achieve a realistic AR rendering system. The experimental results show that our approach is highly accurate and can handle the mutual occlusion automatically in real time.

This paper presents a new technique for detection of dental caries that is a bacterial disease that destroys the tooth structure. In our approach, we have achieved a new segmentation method that combines the advantages of fuzzy C mean algorithm and level set method. The results obtained by the FCM algorithm will be used by Level sets algorithm to reduce the influence of the noise effect on the working of each of these algorithms, to facilitate level sets manipulation and to lead to more robust segmentation. The sensitivity and specificity confirm the effectiveness of proposed method for caries detection.

Currently, there are several fall detection systems based on video analysis. However, these systems have not yet reached the desired level of appropriateness and robustness. To reduce the risk of falling in insecure environments, a new method is developed in this paper to detect and predict human fall detection. We adopt, in this approach, a Block Matching motion estimation algorithm based on acceleration and changes of the human body silhouette area, which are obtained from a single surveillance camera. It presents an algorithm to accelerate the fall detection system on based on a local adjustment of the velocity field.

Automatic identification of television programs in the TV stream is an important task for operating archives. This article proposes a new spatio-temporal approach to identify the programs in TV stream into two main steps: First, a reference catalogue for video features visual jingles built. We operate the features that characterize the instances of the same program type to identify the different types of programs in the flow of television. The role of video features is to represent the visual invariants for each visual jingle using appropriate automatic descriptors for each television program. On the other hand, programs in television streams are identified by examining the similarity of the video signal for visual grammars in the catalogue. The main idea of the identification process is to compare the visual similarity of the video signal features in the flow of television to the catalogue. After presenting the proposed approach, the paper overviews encouraging experimental results on several streams extracted from different channels and compounds of several programs.

In this paper, we proposed a deep self-organizing map model (Deep-SOMs) for automated features extracting and learning from big data streaming which we benefit from the framework Spark for real time streams and highly parallel data processing. The SOMs deep architecture is based on the notion of abstraction (patterns automatically extract from the raw data, from the less to more abstract). The proposed model consists of three hidden self-organizing layers, an input and an output layer. Each layer is made up of a multitude of SOMs, each map only focusing at local headmistress sub-region from the input image. Then, each layer trains the local information to generate more overall information in the higher layer. The proposed Deep-SOMs model is unique in terms of the layers architecture, the SOMs sampling method and learning. During the learning stage we use a set of unsupervised SOMs for feature extraction. We validate the effectiveness of our approach on large data sets such as Leukemia dataset and SRBCT. Results of comparison have shown that the Deep-SOMs model performs better than many existing algorithms for images classification.

Color is a significant visual characteristic for both human vision and computer processing. Global color descriptors characterize an image by its color distribution or histogram, and discard information about object location as well as content of different colors. In this paper, we proposed a local color descriptor based on indexed matrix wavelet analysis and fuzzy decision support system (FDSS), which we called “Fuzzy Indexed Color (FIC)”. First, the indexed map of each image is analysed using Fast wavelet transform to capture the most relevant color feature content for each color channel R, G and B. Then, a FDSS is proposed for image matching in order to get more flexibility and reliability in making decision. The proposed FIC was evaluated using Google color, ebay data, Soccer and Flower datasets; and the results are very promising.

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.

Face detection has been one of the most studied topics in the computer vision literature due to its relevant role in applications such as video surveillance, human computer interface and face image database management. Here, we will present a face detection approach which contains two steps. The first step is training phase based on Adaboost algorithm. The second step is the detection phase. The proposed approach presents an enhancement of Viola and Jones’ algorithm by replacing Haar descriptors with Beta wavelet. The obtained results have proved an excellent performance of detection not only when a face is in front of the camera but also when it is oriented towards the right or the left. Moreover, thanks to the start period needed for the detection, our approach can be applied during a real time experience.

This work fits into the context of the interpretation of automatic gestures based on computer vision. The aim of our work is to transform a conventional screen in a surface that allows the user to use his hands as pointing devices. These can be summarized in three main steps. Hand detection in a video, monitoring detected hands and conversion paths made by the hands to computer commands. To realize this application, it is necessary to detect the hand to follow. A classification phase is essential, at the control part. For this reason, we resorted to the use of a neuro-fuzzy classifier for classification and a pattern matching method for detection.

The problem addressed in this paper is the classification and recognition of Arabic handwritten characters. As a solution, we present a Neural Network (NN) architecture based on Fast Wavelet Transform (FWT) and Adaboost algorithm. FWT is used to extract character's features, based on Multi-Resolution Analysis (MRA) at different levels of abstraction. These features are used to calculate inputs of hidden layer. After this first step, the features are filtered, using Adaboost algorithm, to select the best corresponding ones to each shape of input characters. The reported results are tested on Arabic handwritten characters dataset with 6000 characters. The classification rate for the different groups of characters are 93.92%. Additionally, the speed of the classification algorithm is tested and reported.

This paper gives a review of the deep learning history and proposes a new approach to supervised image classification by the combination of two techniques of learning: the wavelet network and the deep learning. This new approach consists of performing the classification of one class versus all the other classes of the dataset by the reconstruction of a convolutional deep neural wavelet network. This network is obtained using a series of stacked auto-encoders and a linear classifier. The experimental test of our approach performed on "COIL-100" dataset demonstrates that our model is remarkably efficient for image classification compared to a known classifier.

Image data is of immense practical importance in medical informatics. In teeth-related radiograph research, the information of teeth shape is the most critical factor for achieving highly automated diagnosis. Automated image segmentation, which aims at automated extraction of region boundary features, plays a fundamental role in understanding image content for searching and mining in medical image. Therefore, accurate segmentation is an essential but difficult task due to low contrast between regions of interest and uneven exposure of the dental X-ray image. To address this problem, several segmentation approaches have been proposed in the literature, with many of them providing rather promising results. In this paper, we will look at a model by Chan-Vese that detects objects not defined by gradient. We will then implement this algorithm on the GPU and see what kind of speedup we can get compared to serial CPU implementations. Finally we will quantity our results as well as make a qualitative evaluation of the method with respect to how it performs for segmenting medical images.

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.

In this paper we invoke a new approach for the multi-objective routing problems in Wireless Sensor Networks (WSNs). Our approach improves more than one single Quality of Services (QoS) exigency such as energy consumption and delay. However, the classical routing protocols in conventional network optimize a single objective or QoS parameters. The proposed approach adapted a multi-objective evolutionary algorithm (MOEA), specifically, the improved Strength Pareto Evolutionary Algorithm (SPEA2) in order to improve the QoS in WSNs. Our simulation results show that the SPEA2 algorithms are efficient in solving routing problems and are capable of finding the Pareto optimal Set. Additionally, we demonstrate that this approach provides better trade-off solutions in comparison to the classical routing protocols.

In this paper, a new approach to supervised image classification is suggested. It's conducted by the combination of two techniques of learning: the wavelet network and the deep learning. This new approach consists of performing the classification of one class versus all the other classes of the dataset by the reconstruction of a convolutional deep neural wavelet network. This network is obtained using a series of stacked auto-encoders and a linear classifier. Finally, a local contrast normalization and an intelligent pooling are applied to our network. The experimental test of our approach performed on Arabic Printed Text Image (APTI) dataset demonstrates that our model is remarkably efficient for image classification compared to a known classifier.

This paper presents an emotion recognition system based on Beta wavelet network using the Fast Wavelet Transform in order to improve the performance of this network. The proposed system can be summarized in two main steps: training stage and classification stage. Comparing with many algorithms which suffer from the low classification rates and the long executing time the rates given by our experimental results show the effectiveness of the FWT.

This paper focuses on the issue of emotion recognition. It describes an emotion recognition system based on facial expression which contains four steps: detection of face's elements, localization of feature points, their tracking during a movie and facial expression classification. The first step is realized by the famous Viola and Jones algorithm. To localize feature points we have developed an automatic and easy method. To track them we used the optical flow. Finally the classification step is based on wavelet network using Fast Wavelet Transform FWT. The experimental results demonstrated the efficiency of our system.

This work is in the field of human-computer communication, namely in the field of gestural communication. The objective was to develop a system for gesture recognition. This system will be used to control a computer without a keyboard. The idea consists in using a visual panel printed on an ordinary paper to communicate with a computer.

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 virtual object control method of augmented reality scene. We have based on control approach on speech recognition. The idea came from human-machine interaction. The speech recognition system is based on wavelet network. In this paper, we have briefly described the used toolkit to do with the augmented reality. Then, we present the speech recognition approach the training and recognition approach. Finally, we present the results.

Elastic Bunch Graph Matching EBGM is a face recognition algorithm that is distributed with CSU's Evaluation of Face Recognition Algorithms System. The algorithm recognizes novel faces by first localizing a set of landmark features and then measuring similarity between these features. Both localization and comparison uses Gabor jets extracted at landmark positions. In order to improve the performance of the face recognition system[7][8], we have associated Beta filters to the EBGM technique. This choice of Beta filters is advanced by the performance of these functions in many applications of classification and pattern recognition.