Narrow Results

Cognitive fault detection and diagnosis systems are systems able to provide timely information about possibly occurring faults without requiring any a priori knowledge about the process generating the data or the possible faults. This ability is crucial in sensor network scenarios where a priori information about the data generating process, the noise level or the dictionary of the possibly occurring faults is generally hard to obtain. We here present a novel cognitive fault detection and isolation system for sensor networks. The proposed solution relies on the modeling of spatial and temporal relationships present in the acquired datastreams and an ensemble of Hidden Markov Model change-detection tests working in the space of estimated parameters for fault detection and isolation purposes. The effectiveness of the proposed solution has been evaluated on both synthetically generated and real datasets.

Emotion recognition plays an indispensable role in human–machine interaction system. The process includes finding interesting facial regions in images and classifying them into one of seven classes: angry, disgust, fear, happy, neutral, sad, and surprise. Although many breakthroughs have been made in image classification, especially in facial expression recognition, this research area is still challenging in terms of wild sampling environment. In this paper, we used multi-level features in a convolutional neural network for facial expression recognition. Based on our observations, we introduced various network connections to improve the classification task. By combining the proposed network connections, our method achieved competitive results compared to state-of-the-art methods on the FER2013 dataset.

In the field of medicine, thermal image processing and analysis play a significant role in the diagnosis, monitoring, and treatment of diseases. For example, during the last decade, several studies have been performed based on thermal image processing for ocular disease diagnosis. This research proposes a unique approach for the classification of subgroups of two retinal vascular diseases, namely diabetic eye disease and age-related macular degeneration (AMD). The class imbalance problem is a well-known issue when working with medical data, where one class is significantly less represented than another class in the dataset. To deal with the class imbalance issue, an ensemble decision tree classifier with a random under-sampling and adaptive boosting (RUSBoost) technique is proposed. The performance of the proposed classifier is compared with various traditional machine learning-based classifiers. Experimental results show that the proposed ensemble tree outperforms other classifiers through high accuracy, $F$-score, and Mathews correlation coefficient (MCC) values in classifying diabetic eye diseases and AMD diseases. The proposed ensemble decision tree distinguishes dry AMD and wet AMD over healthy controls with 95% average accuracy. Also, it classifies diabetic retinopathy (DR) with diabetic macular edema (DME) and DR without DME with 94% average accuracy. The classifier could distinguish dry and wet AMD which did not work around in temperature analysis on the manual temperature measurement. The performance of the automated classification model is on par with the performance of the temperature analysis of OST for DME and DR without DME.

Human Activity Recognition (HAR) is pivotal in ubiquitous computing, offering benefits to human-centric services such as health monitoring, smart homes, and eldercare systems. HAR leverages smartphones, smartwatches, and other wearable devices to collect sensory data annotated with activity labels, which are then used to train machine learning or deep learning models for automatic activity recognition. Effective HAR systems must integrate information from multiple modalities to accurately assist users. This paper introduces the XAttentionHAR model, an innovative cross-modality attention-based ensemble model for HAR. Our approach utilizes a self-attention module to extract features within each modality and an inter-domain cross-attention module to capture and integrate long-term dependencies across domains. The cross-modality attention mechanism enhances the fusion of diverse modalities, enriching the semantic information. We conducted extensive experiments on the WISDM public dataset, which includes accelerometer and gyroscope data from smartwatches and smartphones. Our results demonstrate that XAttentionHAR outperforms other state-of-the-art methods in activity recognition, achieving 98.48% accuracy for smartphone-based HAR and 98.73% accuracy for smartwatch-based HAR, paving the way for improved human-centric services.

Parkinson’s disease (PD) is a progressive neurodegenerative illness that frequently affects phonation, articulation, fluency, and prosody of speech. Speech impairment is a major sign of PD which can be employed for the earlier identification of the disease and provide proper treatment. Besides, the machine learning (ML) models can be commonly employed for PD detection and classification by the use of speech data. Since the speech data has the features of maximum data redundancy, high aliasing, and small sample sizes, dimensionality reduction (DR) techniques become essential for effective PD diagnosis. Therefore, this paper presents a new DR with weighted voting ensemble classification (DR-WVEC) model for PD diagnosis. The presented DR-WVEC model operates on different stages such as pre-processing, DR, classification, and voting process. Primarily, the speech data undergoes min–max normalization process in order to normalize the speech data. Besides, linear discriminant analysis (LDA) technique is applied for reducing the dimensionality of the features. In addition, an ensemble of two ML models, namely extreme learning machine (ELM) and Adaboost models, is employed for classification. Finally, a weighted voting-based classification process is carried out where the integration of two ML models takes place and the highest outcome is chosen as the final results. In order to assess the effective PR diagnostic outcome, an extensive set of simulations were carried out on Parkinson’s telemonitoring dataset. The obtained experimental results reported the betterment of the DR-VWEC technique over the other compared methods in terms of different aspects.

Pervasive Computing (PC) opens up the room for the adoption of devices very close to end users that gives the opportunity to interact with them and execute various applications to facilitate their every day activities. Pervasive applications are supported by the evolution of the Internet of Things (IoT) as well as the Edge Computing (EC) that offer vast infrastructures where data can be collected and processed. EC acts as the mediator between the IoT and the Cloud becoming the middle point where data are transferred before they become the subject of processing by Cloud services. IoT devices can assist in the collection of data and EC nodes could play the role of intermediate processing points executing the desired tasks requested by applications. Any processing can be affected by the presence of missing values that may jeopardize the quality of the outcomes. In this paper, we propose a setting where EC nodes play the aforementioned processing role for data reported by IoT devices and adopt an ensemble scheme for data imputation in the case where missing values are present. Our model relies on the local view of the IoT devices reporting a data vector with a missing value, the view of the group that consists of the IoT nodes with a high similarity in the reported data and a probabilistic approach that reveals the statistics of data as realized in the group of similar reports. The proposed scheme continuously detects the correlation between the incoming data streams and efficiently combines the available data vectors before it is in a position to suggest replacements for missing values. The envisioned aggregation mechanism is capable of resulting the appropriate replacements aligned with the aforementioned views on the collected data. Our ensemble model relies on a number of similarity metrics and statistics to derive the final outcome. The paper reports on the description of the proposed model and elaborates on its validation based on various evaluation scenarios.

Video content analysis (VCA) is the process of analyzing the contents in the video for various applications. Video classification and content analysis are two of the most difficult challenges that computer vision researchers must solve. Object detection plays an important role in the VCA and is used for identification, detection and classification of objects in the images. The Chaser Prairie Wolf optimization-based deep Convolutional Neural Network classifier (CPW opt-deep CNN classifier) is used in this research to identify and classify the objects in the videos. The deep CNN classifier correctly detected the objects in the video, and the CPW optimization boosted the deep CNN classifier’s performance, where the decision-making behavior of the chasers is enhanced by the sharing nature of the prairie wolves. The classifier’s parameters were successfully tuned by the enabled optimization, which also aids in producing better results. The Ensemble model developed for the object detection adds value to the research and is initiated by the standard hybridization of the YOLOv4 and Resnet 101 model, which evaluated the research’s accuracy, sensitivity, and specificity, improving its efficacy. The proposed CPW opt-deep CNN classifier attained the values of 89.74%, 89.50%, and 89.19% while classifying objects in dataset 1, 91.66%, 86.01%, and 91.52% while classifying objects in dataset 2, compared to the preceding method that is efficient.

The aim of this paper is to develop a model to classify the stance expressed in social media texts. More specifically, the work presented focuses on tweets. In stance detection (SD) tasks, the objective is to identify the stance of a person towards a target of interest. In this paper, a model for SD is established and its variations are evaluated using different classifiers. The single models differ based on the pre-processing and the combination of features. To reduce the dimensionality of the feature space, analysis of variance (ANOVA) test is used. Then, two classifiers are employed as base learners including Random Forests (RF) and Support Vector Machines (SVM). Experimental analyses are conducted on SemEval dataset that is used as a benchmark for SD. Finally, the base learners that resulted from different design alternatives, are combined into three ensemble models. Experimental results show the significance of the used features and the effectiveness of a manually built dictionary that is used in the pre-processing stage. Moreover, the proposed ensembles outperform the state-of-the-art models in the overall test score, which suggests that ensemble learning is the best tool for effective SD in tweets.

The enormous amount of data contained in the video image has grown rapidly along with surveillance, greatly outpacing the capacity of human resources to handle it effectively. Smart surveillance retrieval is an essential component of any modern video surveillance system, considerably boosting the effectiveness, precision, and interoperability of the system. The use of face recognition and other cutting-edge technology in the security surveillance system is rapidly rising. Therefore, in this article, the distributed deep convolutional neural network (DCNN) and distributed deep BiLSTM is proposed to efficiently detect the face from the video. One of the major contributions involved in this research relies on the key frame selection, where four unique distance measurement techniques are fused, and is named hybrid distance- key frame selection. The Tri birds optimization (TBO) technique selects the best solution from a large number of solutions for the ensemble model classifier engaged in face recognition. The ensemble model classifier incorporates various hyper-parameters that are optimally trained. Multiple test videos with 401 and 802 test videos are used as the input for the TBO-ensemble model that attains 97% accuracy, 98.33% precision, recall, and f-measure for epoch 50 and the 500 number of retrievals, respectively.

Breast cancer is one of the leading causes of untimely deaths among women in various countries across the world. This can be attributed to many factors including late detection which often increase its severity. Thus, detecting the disease early would help mitigate its mortality rate and other risks associated with it. This study developed a hybrid machine learning model for timely prediction of breast cancer to help combat the disease. The dataset from Kaggle was adopted to predict the breast tumor growth and sizes using random tree classification, logistic regression, XBoost tree and multilayer perceptron on the dataset. The implementation of these machine learning algorithms and visualization of the results was done using Python. The results achieved a high accuracy (99.65%) on training and testing datasets which is far better than traditional means. The predictive model has good potential to enhance early detection and diagnosis of breast cancer and improvement of treatment outcome. It could also assist patients to timely deal with their condition or life patterns to support their recovery or survival.