Instance-Based Lossless Summarization of Knowledge Graph With Optimized Triples and Corrections (IBA-OTC)

Knowledge graph (KG) summarization facilitates efficient information retrieval for exploring complex structural data. For fast information retrieval, it requires processing on redundant data. However, it necessitates the completion of information in a summary graph. It also saves computational time during data retrieval, storage space, in-memory visualization, and preserving structure after summarization. State-of-the-art approaches summarize a given KG by preserving its structure at the cost of information loss. Additionally, the approaches not preserving the underlying structure, compromise the summarization ratio by focusing only on the compression of specific regions. In this way, these approaches either miss preserving the original facts or the wrong prediction of inferred information. To solve these problems, we present a novel framework for generating a lossless summary by preserving the structure through super signatures and their corresponding corrections. The proposed approach summarizes only the naturally overlapped instances while maintaining its information and preserving the underlying Resource Description Framework RDF graph. The resultant summary is composed of triples with positive, negative, and star corrections that are optimized by the smart calling of two novel functions namely merge and disperse . To evaluate the effectiveness of our proposed approach, we perform experiments on nine publicly available real-world knowledge graphs and obtain a better summarization ratio than state-of-the-art approaches by a margin of 10% to 30% with achieving its completeness, correctness, and compactness. In this way, the retrieval of common events and groups by queries is accelerated in the resultant graph

Robust human locomotion and localization activity recognition over multisensory

Human activity recognition (HAR) plays a pivotal role in various domains, including healthcare, sports, robotics, and security. With the growing popularity of wearable devices, particularly Inertial Measurement Units (IMUs) and Ambient sensors, researchers and engineers have sought to take advantage of these advances to accurately and efficiently detect and classify human activities. This research paper presents an advanced methodology for human activity and localization recognition, utilizing smartphone IMU, Ambient, GPS, and Audio sensor data from two public benchmark datasets: the Opportunity dataset and the Extrasensory dataset. The Opportunity dataset was collected from 12 subjects participating in a range of daily activities, and it captures data from various body-worn and object-associated sensors. The Extrasensory dataset features data from 60 participants, including thousands of data samples from smartphone and smartwatch sensors, labeled with a wide array of human activities. Our study incorporates novel feature extraction techniques for signal, GPS, and audio sensor data. Specifically, for localization, GPS, audio, and IMU sensors are utilized, while IMU and Ambient sensors are employed for locomotion activity recognition. To achieve accurate activity classification, state-of-the-art deep learning techniques, such as convolutional neural networks (CNN) and long short-term memory (LSTM), have been explored. For indoor/outdoor activities, CNNs are applied, while LSTMs are utilized for locomotion activity recognition. The proposed system has been evaluated using the k-fold cross-validation method, achieving accuracy rates of 97% and 89% for locomotion activity over the Opportunity and Extrasensory datasets, respectively, and 96% for indoor/outdoor activity over the Extrasensory dataset. These results highlight the efficiency of our methodology in accurately detecting various human activities, showing its potential for real-world applications. Moreover, the research paper introduces a hybrid system that combines machine learning and deep learning features, enhancing activity recognition performance by leveraging the strengths of both approaches

Innovative healthcare solutions: robust hand gesture recognition of daily life routines using 1D CNN

IntroductionHand gestures are an effective communication tool that may convey a wealth of information in a variety of sectors, including medical and education. E-learning has grown significantly in the last several years and is now an essential resource for many businesses. Still, there has not been much research conducted on the use of hand gestures in e-learning. Similar to this, gestures are frequently used by medical professionals to help with diagnosis and treatment.MethodWe aim to improve the way instructors, students, and medical professionals receive information by introducing a dynamic method for hand gesture monitoring and recognition. Six modules make up our approach: video-to-frame conversion, preprocessing for quality enhancement, hand skeleton mapping with single shot multibox detector (SSMD) tracking, hand detection using background modeling and convolutional neural network (CNN) bounding box technique, feature extraction using point-based and full-hand coverage techniques, and optimization using a population-based incremental learning algorithm. Next, a 1D CNN classifier is used to identify hand motions.ResultsAfter a lot of trial and error, we were able to obtain a hand tracking accuracy of 83.71% and 85.71% over the Indian Sign Language and WLASL datasets, respectively. Our findings show how well our method works to recognize hand motions.DiscussionTeachers, students, and medical professionals can all efficiently transmit and comprehend information by utilizing our suggested system. The obtained accuracy rates highlight how our method might improve communication and make information exchange easier in various domains

Enhanced Data Mining and Visualization of Sensory-Graph-Modeled Datasets through Summarization

The acquisition, processing, mining, and visualization of sensory data for knowledge discovery and decision support has recently been a popular area of research and exploration. Its usefulness is paramount because of its relationship to the continuous involvement in the improvement of healthcare and other related disciplines. As a result of this, a huge amount of data have been collected and analyzed. These data are made available for the research community in various shapes and formats; their representation and study in the form of graphs or networks is also an area of research which many scholars are focused on. However, the large size of such graph datasets poses challenges in data mining and visualization. For example, knowledge discovery from the Bio–Mouse–Gene dataset, which has over 43 thousand nodes and 14.5 million edges, is a non-trivial job. In this regard, summarizing the large graphs provided is a useful alternative. Graph summarization aims to provide the efficient analysis of such complex and large-sized data; hence, it is a beneficial approach. During summarization, all the nodes that have similar structural properties are merged together. In doing so, traditional methods often overlook the importance of personalizing the summary, which would be helpful in highlighting certain targeted nodes. Personalized or context-specific scenarios require a more tailored approach for accurately capturing distinct patterns and trends. Hence, the concept of personalized graph summarization aims to acquire a concise depiction of the graph, emphasizing connections that are closer in proximity to a specific set of given target nodes. In this paper, we present a faster algorithm for the personalized graph summarization (PGS) problem, named IPGS; this has been designed to facilitate enhanced and effective data mining and visualization of datasets from various domains, including biosensors. Our objective is to obtain a similar compression ratio as the one provided by the state-of-the-art PGS algorithm, but in a faster manner. To achieve this, we improve the execution time of the current state-of-the-art approach by using weighted, locality-sensitive hashing, through experiments on eight large publicly available datasets. The experiments demonstrate the effectiveness and scalability of IPGS while providing a similar compression ratio to the state-of-the-art approach. In this way, our research contributes to the study and analysis of sensory datasets through the perspective of graph summarization. We have also presented a detailed study on the Bio–Mouse–Gene dataset, which was conducted to investigate the effectiveness of graph summarization in the domain of biosensors

Enhancing Vehicle Detection and Tracking in UAV Imagery: A Pixel Labeling and Particle Filter Approach

Systems must be capable of detecting and tracking autonomous vehicles for intelligent management and control of transportation. Even though several methods are used to create intelligent systems for traffic monitoring, this article explains how to detect and track vehicles using pixel labeling and particle filter algorithms. We suggested a novel technique that segments the image using image segmentation to retrieve the foreground objects. We have divided our proposed model into the following steps: at first, geo-referencing is used to find the exact location; secondly, the images are denoised by using pre-processing; image segmentation is used to separate the background from the foreground; multiple objection detection is performed using the random forest to classify different objects; vehicles are detected through a method called template matching; after this, the vehicles are counted using histogram of oriented gradients (HOG); and after counting, the tracking of vehicles is obtained using particle filter; lastly, the trajectories are predicted by comparing the rectangular centroid of each car against the frame number and using it as a time stamp reference, the last match that the tracking algorithm obtained for each vehicle was recorded and used to estimate the trajectories. Our model outperforms current traffic monitoring approaches in terms of detection and tracking accuracy by 0.87 and 0.92, respectively using the Aerial Car dataset and 0.84 and 0.88, respectively, using the AU-AIR datasets. Vehicle recognition, traffic density detection, traffic flow analysis, and pedestrian route generation are all possible uses for the proposed system. It can transform traffic management and improve overall road safety due to its powerful algorithms and cutting-edge technologies. The system’s adaptability makes it a significant asset in modern transportation, from optimizing signal timing to improving pedestrian navigation

Anticancer properties and mechanism insights of α-hederin

α-Hederin is a natural bioactive molecule very abundant in aromatic and medicinal plants (AMP). It was identified, characterized, and isolated using different extraction and characterization technologies, such as HPLC, LC-MS and NMR. Biological tests have revealed that this natural molecule possesses different biological properties, particularly anticancer activity. Indeed, this activity has been investigated against several cancers (e.g., esophageal, hepatic, breast, colon, colorectal, lung, ovarian, and gastric). The underlying mechanisms are varied and include induction of apoptosis and cell cycle arrest, reduction of ATP generation, as well as inhibition of autophagy, cell proliferation, invasion, and metastasis. In fact, these anticancer mechanisms are considered the most targeted for new chemotherapeutic agents’ development. In the light of all these data, α-hederin could be a very interesting candidate as an anticancer drug for chemotherapy, as well as it could be used in combination with other molecules already validated or possibly investigated as an agent sensitizing tumor cells to chemotherapeutic treatments

Genkwanin: An emerging natural compound with multifaceted pharmacological effects

Plant bioactive molecules could play key preventive and therapeutic roles in chronological aging and the pathogenesis of many chronic diseases, often accompanied by increased oxidative stress and low-grade inflammation. Dietary antioxidants, including genkwanin, could decrease oxidative stress and the expression of pro-inflammatory cytokines or pathways. The present study is the first comprehensive review of genkwanin, a methoxyflavone found in several plant species. Indeed, natural sources, and pharmacokinetics of genkwanin, the biological properties were discussed and highlighted in detail. This review analyzed and considered all original studies related to identification, isolation, quantification, investigation of the biological and pharmacological properties of genkwanin. We consulted all published papers in peer‐reviewed journals in the English language from the inception of each database to 12 May 2023. Different phytochemical demonstrated that genkwanin is a non-glycosylated flavone found and isolated from several medicinal plants such as Genkwa Flos, Rosmarinus officinalis, Salvia officinalis, and Leonurus sibiricus. In vitro and in vivo biological and pharmacological investigations showed that Genkwanin exhibits remarkable antioxidant and anti-inflammatory activities, genkwanin, via activation of glucokinase, has shown antihyperglycemic activity with a potential role against metabolic syndrome and diabetes. Additionally, it revealed cardioprotective and neuroprotective properties, thus reducing the risk of cardiovascular diseases and assisting against neurodegenerative diseases. Furthermore, genkwanin showed other biological properties like antitumor capability, antibacterial, antiviral, and dermato-protective effects. The involved mechanisms include sub-cellular, cellular and molecular actions at different levels such as inducing apoptosis and inhibiting the growth and proliferation of cancer cells. Despite the findings from preclinical studies that have demonstrated the effects of genkwanin and its diverse mechanisms of action, additional research is required to comprehensively explore its therapeutic potential. Primarily, extensive studies should be carried out to enhance our understanding of the molecule's pharmacodynamic actions and pharmacokinetic pathways. Moreover, toxicological and clinical investigations should be undertaken to assess the safety and clinical efficacy of genkwanin. These forthcoming studies are of utmost importance in fully unlocking the potential of this molecule in the realm of therapeutic applications