Search

Manual investigation of chest radiography (CXR) images by physicians is crucial for effective decision-making in COVID-19 diagnosis. However, the high demand during the pandemic necessitates auxiliary help through image analysis and machine learning techniques. This study presents a multi-threshold-based segmentation technique to probe high pixel intensity regions in CXR images of various pathologies, including normal cases. Texture information is extracted using gray co-occurrence matrix (GLCM)-based features, while vessel-like features are obtained using Frangi, Sato, and Meijering filters. Machine learning models employing Decision Tree (DT) and Random Forest (RF) approaches are designed to categorize CXR images into common lung infections, lung opacity (LO), COVID-19, and viral pneumonia (VP). The results demonstrate that the fusion of texture and vessel-based features provides an effective ML model for aiding diagnosis. The ML model validation using performance measures, including an accuracy of approximately 91.8% with an RF-based classifier, supports the usefulness of the feature set and classifier model in categorizing the four different pathologies. Furthermore, the study investigates the importance of the devised features in identifying the underlying pathology and incorporates histogram-based analysis. This analysis reveals varying natural pixel distributions in CXR images belonging to the normal, COVID-19, LO, and VP groups, motivating the incorporation of additional features such as mean, standard deviation, skewness, and percentile based on the filtered images. Notably, the study achieves a considerable improvement in categorizing COVID-19 from LO, with a true positive rate of 97%, further substantiating the effectiveness of the methodology implemented. Graphic Abstract [ABSTRACT FROM AUTHOR]

Cardiovascular diseases remain one of the main threats to human health, significantly affecting the quality and life expectancy. Effective and prompt recognition of these diseases is crucial. This research aims to develop an effective novel hybrid method for automatically detecting dangerous arrhythmias based on cardiac patients’ short electrocardiogram (ECG) fragments. This study suggests using a continuous wavelet transform (CWT) to convert ECG signals into images (scalograms) and examining the task of categorizing short 2-s segments of ECG signals into four groups of dangerous arrhythmias that are shockable, including ventricular flutter (C1), ventricular fibrillation (C2), ventricular tachycardia torsade de pointes (C3), and high-rate ventricular tachycardia (C4). We propose developing a novel hybrid neural network with a deep learning architecture to classify dangerous arrhythmias. This work utilizes actual electrocardiogram (ECG) data obtained from the PhysioNet database, alongside artificially generated ECG data produced by the Synthetic Minority Over-sampling Technique (SMOTE) approach, to address the issue of imbalanced class distribution for obtaining an accuracy-trained model. Experimental results demonstrate that the proposed approach achieves high accuracy, sensitivity, specificity, precision, and an F1-score of 97.75%, 97.75%, 99.25%, 97.75%, and 97.75%, respectively, in classifying all the four shockable classes of arrhythmias and are superior to traditional methods. Our work possesses significant clinical value in real-life scenarios since it has the potential to significantly enhance the diagnosis and treatment of lifethreatening arrhythmias in individuals with cardiac disease. Furthermore, our model also has demonstrated adaptability and generality for two other datasets. [ABSTRACT FROM AUTHOR]

The field of scene recognition, which lies at the crossroads of computer vision and artificial intelligence, has experienced notable progress because of scholarly pursuits. This article introduces a novel methodology for scene recognition by combining convolutional neural networks (CNNs) with feature selection techniques based on mutual information (MI). The main goal of our study is to address the limitations inherent in conventional unimodal methods, with the aim of improving the precision and dependability of scene classification. The focus of our research is around the formulation of a comprehensive approach for scene detection, utilizing multimodal deep learning methodologies implemented on a solitary input image. Our work distinguishes itself by the innovative amalgamation of CNN- and MI-based feature selection. This integration provides distinct advantages and enhanced capabilities when compared to prevailing methodologies. In order to assess the effectiveness of our methodology, we performed tests on two openly accessible datasets, namely, the scene categorization dataset and the AID dataset. The results of these studies exhibited notable levels of precision, with accuracies of 100% and 98.83% achieved for the corresponding datasets. These findings surpass the performance of other established techniques. The primary objective of our end-to-end approach is to reduce complexity and resource requirements, hence creating a robust framework for the task of scene categorization. This work significantly advances the practical application of computer vision in various real-world scenarios, leading to a large improvement in the accuracy of scene recognition and interpretation. [ABSTRACT FROM AUTHOR]

The Internet of Medical Things (IoMT) is mainly concerned with the efficient utilisation of wearable devices in the healthcare domain to manage various processes automatically, whereas machine learning approaches enable these smart systems to make informed decisions. Generally, broadcasting is used for the transmission of frames, whereas congestion, energy efficiency, and excessive load are among the common issues associated with existing approaches. In this paper, a machine learning-enabled shortest path identification scheme is presented to ensure reliable transmission of frames, especially with the minimum possible communication overheads in the IoMT network. For this purpose, the proposed scheme utilises a well-known technique, i.e., Kruskal’s algorithm, to find an optimal path from source to destination wearable devices. Additionally, other evaluation metrics are used to find a reliable and shortest possible communication path between the two interested parties. Apart from that, every device is bound to hold a supplementary path, preferably a second optimised path, for situations where the current communication path is no longer available, either due to device failure or heavy traffic. Furthermore, the machine learning approach helps enable these devices to update their routing tables simultaneously, and an optimal path could be replaced if a better one is available. The proposed mechanism has been tested using a smart environment developed for the healthcare domain using IoMT networks. Simulation results show that the proposed machine learning-oriented approach performs better than existing approaches where the proposed scheme has achieved the minimum possible ratios, i.e., 17% and 23%, in terms of end to end delay and packet losses, respectively. Moreover, the proposed scheme has achieved an approximately 21% improvement in the average throughput compared to the existing schemes. [ABSTRACT FROM AUTHOR]

This paper presents an analysis of the effectiveness of the element interaction model in a vehicular ad hoc network (VANET). An analysis of the mathematical model and its numerical solution for the system of boundary device interactions in the traditional configuration of roadside unit (RSU) placement using single- and dual-channel connection between on-board units (OBU) and RSU is given. In addition, the model efficiency is improved using a clustering approach. The efficiency evaluation is based on calculating the percentage of unprocessed requests generated by OBUs during their mobility, the average power consumption and the magnitude of the delay in transmitting and processing the generated requests in the OBU–RSU system. The traditional and cluster models are compared. The results obtained in this paper show that each of the proposed models can be effectively implemented in mobile nodes and will significantly reduce the overall expected query processing time to improve the organization and algorithmic support of VANET. Along with this, it is shown that the developed approach allows for efficient power consumption when combining RSUs into clusters with a shared cache. The novelty of solving the problems is due to the lack of a comprehensive model that allows the distribution and prediction of the parameters and resources of the system for different computational tasks, which is essential when implementing and using V2X technology to solve the problems of complex management of VANET elements. [ABSTRACT FROM AUTHOR]

Pedestrian detection and tracking are vital elements of today’s surveillance systems, which make daily life safe for humans. Thus, human detection and visualization have become essential inventions in the field of computer vision. Hence, developing a surveillance system with multiple object recognition and tracking, especially in low light and night-time, is still challenging. Therefore, we propose a novel system based on machine learning and image processing to provide an efficient surveillance system for pedestrian detection and tracking at night. In particular, we propose a system that tackles a two-fold problem by detecting multiple pedestrians in infrared (IR) images using machine learning and tracking them using particle filters. Moreover, a random forest classifier is adopted for image segmentation to identify pedestrians in an image. The result of detection is investigated by particle filter to solve pedestrian tracking. Through the extensive experiment, our system shows 93% segmentation accuracy using a random forest algorithm that demonstrates high accuracy for background and roof classes. Moreover, the system achieved a detection accuracy of 90% usingmultiple templatematching techniques and 81% accuracy for pedestrian tracking. Furthermore, our system can identify that the detected object is a human. Hence, our system provided the best results compared to the state-ofart systems, which proves the effectiveness of the techniques used for image segmentation, classification, and tracking. The presented method is applicable for human detection/tracking, crowd analysis, and monitoring pedestrians in IR video surveillance. [ABSTRACT FROM AUTHOR]

In the past two decades, there has been a lot of work on computer vision technology that incorporates many tasks which implement basic filtering to image classification. Themajor research areas of this field include object detection and object recognition. Moreover, wireless communication technologies are presently adopted and they have impacted the way of education that has been changed. There are different phases of changes in the traditional system. Perception of three-dimensional (3D) from two-dimensional (2D) image is one of the demanding tasks. Because human can easily perceive but making 3D using software will take time manually. Firstly, the blackboard has been replaced by projectors and other digital screens so such that people can understand the concept better through visualization. Secondly, the computer labs in schools are now more common than ever. Thirdly, online classes have become a reality. However, transferring to online education or e-learning is not without challenges. Therefore, we propose a method for improving the efficiency of e-learning. Our proposed system consists of twoand-a-half dimensional (2.5D) features extraction using machine learning and image processing. Then, these features are utilized to generate 3D mesh using ellipsoidal deformation method. After that, 3D bounding box estimation is applied. Our results show that there is a need to move to 3D virtual reality (VR) with haptic sensors in the field of e-learning for a better understanding of real-world objects. Thus, people will have more information as compared to the traditional or simple online education tools. We compare our result with the ShapeNet dataset to check the accuracy of our proposed method. Our proposed system achieved an accuracy of 90.77% on plane class, 85.72% on chair class, and car class have 72.14%. Mean accuracy of our method is 70.89%. [ABSTRACT FROM AUTHOR]

Skin cancer is one of the widespread diseases among existing cancer types. More importantly, the detection of lesions in early diagnosis has tremendously attracted researchers' attention. Thus, artificial intelligence (AI)-based techniques have supported the early diagnosis of skin cancer by investigating deep-learning-based convolutional neural networks (CNN). However, the current methods remain challenging in detecting melanoma in dermoscopic images. Therefore, in this paper, we propose an ensemble model that uses the vision of both EfficientNetV2S and Swin-Transformer models to detect the early focal zone of skin cancer. Hence, we considerthat the former architecture leads to greater accuracy, while the latter model has the advantage of recognizing dark parts in an image. We have modified the fifth block of the EfficientNetV2S model and have included the Swin-Transformer model. Our experiments demonstrate that the constructed ensemble model has attained a higher level of accuracy over the individual models and has also decreased the losses as compared to traditional strategies. The proposed model achieved an accuracy score of 99.10%, a sensitivity of 99.27%, and a specificity score of 99.80%. [ABSTRACT FROM AUTHOR]

Identifying human actions and interactions finds its use in many areas, such as security, surveillance, assisted living, patient monitoring, rehabilitation, sports, and e-learning. This wide range of applications has attracted many researchers to this field. Inspired by the existing recognition systems, this paper proposes a new and efficient human-object interaction recognition (HOIR) model which is based on modeling human pose and scene feature information. There are different aspects involved in an interaction, including the humans, the objects, the various body parts of the human, and the background scene. The main objectives of this research include critically examining the importance of all these elements in determining the interaction, estimating human pose through image foresting transform (IFT), and detecting the performed interactions based on an optimizedmulti-feature vector. The proposed methodology has six main phases. The first phase involves preprocessing the images. During preprocessing stages, the videos are converted into image frames. Then their contrast is adjusted, and noise is removed. In the second phase, the human-object pair is detected and extracted from each image frame. The third phase involves the identification of key body parts of the detected humans using IFT. The fourth phase relates to three different kinds of feature extraction techniques. Then these features are combined and optimized during the fifth phase. The optimized vector is used to classify the interactions in the last phase. The MSR Daily Activity 3D dataset has been used to test this model and to prove its efficiency. The proposed system obtains an average accuracy of 91.7% on this dataset. [ABSTRACT FROM AUTHOR]

Latest advancements in vision technology offer an evident impact on multi-object recognition and scene understanding. Such sceneunderstanding task is a demanding part of several technologies, like augmented reality-based scene integration, robotic navigation, autonomous driving, and tourist guide. Incorporating visual information in contextually unified segments, convolution neural networks-based approaches will significantly mitigate the clutter, which is usual in classical frameworks during scene understanding. In this paper, we propose a convolutional neural network (CNN) based segmentation method for the recognition of multiple objects in an image. Initially, after acquisition and preprocessing, the image is segmented by using CNN. Then, CNN features are extracted from these segmented objects, and discrete cosine transform (DCT) and discrete wavelet transform (DWT) features are computed. After the extraction of CNN features and computation of classical machine learning features, fusion is performed using a fusion technique. Then, to select theminimal set of features, genetic algorithm-based feature selection is used. In order to recognize and understand the multi-objects in the scene, a neuro-fuzzy approach is applied. Once objects in the scene are recognized, the relationship between these objects is examined by employing the object-to-object relation approach. Finally, a decision tree is incorporated to assign the relevant labels to the scenes based on recognized objects in the image. The experimental results over complex scene datasets including SUN Red Green Blue-Depth (RGB-D) and Cityscapes' demonstrated a remarkable performance. [ABSTRACT FROM AUTHOR]