Infection in apple leaves is typically brought on by unanticipated weather conditions such as rain, hailstorms, draughts, and fog. As a direct consequence of this, the farmers suffer a significant loss of productivity. It is essential to be able to identify apple leaf diseases in advance in order to prevent the occurrence of this disease and minimise losses to productivity caused by it. The research offers a bibliometric analysis of the effectiveness of artificial intelligence in diagnosing diseases affecting apple leaves. The study provides a bibliometric evaluation of apple leaf disease detection using artificial intelligence. Through an analysis of broad current developments, publication and citation structures, ownership and cooperation patterns, bibliographic coupling, productivity patterns, and other characteristics, this scientometric study seeks to discover apple diseases. Nevertheless, numerous exploratory, conceptual, and empirical studies have concentrated on the identification of apple illnesses. However, given that disease detection is not confined to a single field of study, there have been very few attempts to create an extensive science map of transdisciplinary studies. In bibliometric assessments, it is important to take into account the growing amount of research on this subject. The study synthesises knowledge structures to determine the trend in the research topic. A scientometric analysis was performed on a sample of 214 documents in the subject of identifying apple leaf disease using a scientific search technique on the Scopus database for the years 2011-2022. In order to conduct the study, the Bibliometrix suite's VOSviewer and the web-based Biblioshiny software were also utilised. Important journals, authors, nations, articles, and subjects were chosen using the automated workflow of the software. Furthermore, citation and co-citation checks were performed along with social network analysis. In addition to the intellectual and social organisation of the meadow, this investigation reveals the conceptual structure of the area. It contributes to the body of literature by giving academics and practitioners a strong conceptual framework on which to base their search for solutions and by making perceptive recommendations for potential future research areas.
- MeSH
- Bibliometrics MeSH
- Databases, Factual MeSH
- Fabaceae * MeSH
- Humans MeSH
- Malus * MeSH
- Software MeSH
- Artificial Intelligence MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
COVID-19 has depleted healthcare systems around the world. Extreme conditions must be defined as soon as possible so that services and treatment can be deployed and intensified. Many biomarkers are being investigated in order to track the patient's condition. Unfortunately, this may interfere with the symptoms of other diseases, making it more difficult for a specialist to diagnose or predict the severity level of the case. This research develops a Smart Healthcare System for Severity Prediction and Critical Tasks Management (SHSSP-CTM) for COVID-19 patients. On the one hand, a machine learning (ML) model is projected to predict the severity of COVID-19 disease. On the other hand, a multi-agent system is proposed to prioritize patients according to the seriousness of the COVID-19 condition and then provide complete network management from the edge to the cloud. Clinical data, including Internet of Medical Things (IoMT) sensors and Electronic Health Record (EHR) data of 78 patients from one hospital in the Wasit Governorate, Iraq, were used in this study. Different data sources are fused to generate new feature pattern. Also, data mining techniques such as normalization and feature selection are applied. Two models, specifically logistic regression (LR) and random forest (RF), are used as baseline severity predictive models. A multi-agent algorithm (MAA), consisting of a personal agent (PA) and fog node agent (FNA), is used to control the prioritization process of COVID-19 patients. The highest prediction result is achieved based on data fusion and selected features, where all examined classifiers observe a significant increase in accuracy. Furthermore, compared with state-of-the-art methods, the RF model showed a high and balanced prediction performance with 86% accuracy, 85.7% F-score, 87.2% precision, and 86% recall. In addition, as compared to the cloud, the MAA showed very significant performance where the resource usage was 66% in the proposed model and 34% in the traditional cloud, the delay was 19% in the proposed model and 81% in the cloud, and the consumed energy was 31% in proposed model and 69% in the cloud. The findings of this study will allow for the early detection of three severity cases, lowering mortality rates.
- MeSH
- Algorithms MeSH
- COVID-19 * MeSH
- Internet of Things * MeSH
- Humans MeSH
- Delivery of Health Care MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- MeSH
- Safety MeSH
- Cloud Computing MeSH
- Diabetes Mellitus * prevention & control MeSH
- Diabetic Retinopathy diagnostic imaging MeSH
- Digital Technology methods instrumentation MeSH
- Medical Informatics * methods instrumentation MeSH
- Humans MeSH
- Prediabetic State prevention & control MeSH
- Blood Glucose Self-Monitoring methods statistics & numerical data MeSH
- Software classification MeSH
- Telemedicine methods instrumentation MeSH
- Internal Medicine methods instrumentation MeSH
- Treatment Outcome MeSH
- Check Tag
- Humans MeSH
Cyber-attack detection via on-gadget embedded models and cloud systems are widely used for the Internet of Medical Things (IoMT). The former has a limited computation ability, whereas the latter has a long detection time. Fog-based attack detection is alternatively used to overcome these problems. However, the current fog-based systems cannot handle the ever-increasing IoMT's big data. Moreover, they are not lightweight and are designed for network attack detection only. In this work, a hybrid (for host and network) lightweight system is proposed for early attack detection in the IoMT fog. In an adaptive online setting, six different incremental classifiers were implemented, namely a novel Weighted Hoeffding Tree Ensemble (WHTE), Incremental K-Nearest Neighbors (IKNN), Incremental Naïve Bayes (INB), Hoeffding Tree Majority Class (HTMC), Hoeffding Tree Naïve Bayes (HTNB), and Hoeffding Tree Naïve Bayes Adaptive (HTNBA). The system was benchmarked with seven heterogeneous sensors and a NetFlow data infected with nine types of recent attack. The results showed that the proposed system worked well on the lightweight fog devices with ~100% accuracy, a low detection time, and a low memory usage of less than 6 MiB. The single-criteria comparative analysis showed that the WHTE ensemble was more accurate and was less sensitive to the concept drift.
- MeSH
- Bayes Theorem MeSH
- Big Data MeSH
- Early Diagnosis MeSH
- Internet of Things * MeSH
- Publication type
- Journal Article MeSH
Ve zdravotnictví, stejně jako v jiných oborech, se výpočetní technika a internet staly jejich integrální součástí. Počet aplikací, které používají VT, dramaticky narůstá. IS v nemocnicích jsou dnes velice komplexní systémy, které mají své koncové body na každém pracovišti. Umožňují dokonalou evidenci událostí, výměnu a sdílení dat i jejich archivaci pro budoucí potřeby. V nemocnicích výrazně roste i počet instalovaných diagnostických přístrojů, které poskytují informace v obrazové formě. Data, která představují jeden snímek, jsou dnes několikanásobně větší než v minulosti. Roste i počet vyšetření. Co se dnes jeví jako akutní problém, je nárůst objemu a uchovávání dat. Bude výpočetní technika instalovaná v nemocnicích postačovat na pokrytí stále narůstajících potřeb? Jednou z aktuálních otázek budoucnosti jistě bude, jak zajistit udržitelnost a rozvoj těchto systémů. Nebude jedním z řešení použití externích služeb, např. komerčních cloudů, které nabízejí nejen dostatečné kapacity pro zpracování, ale i uložení dat?.
