Abstract Chronic kidney disease (CKD) is a major public health concern with rising prevalence and huge costs associated dialysis transplantation. Early prediction of CKD can reduce the patient's risk progression to end‐stage failure. Artificial intelligence offers more intelligent expert healthcare services in diagnosis. In this work, deep learning model built using neural networks (DNN) an adaptive moment estimation optimization function predict early‐stage CKD. The care applications...

Social media and in particular, microblogs are becoming an important data source for disease surveillance, behavioral medicine, public healthcare. Topic Models widely used microblog analytics analyzing integrating the textual within a corpus. This paper uses health tweets as attempts clustering by topic models. The traditional models, such Latent Semantic Indexing (LSI), Probabilistic Schematic (PLSI), Dirichlet Allocation (LDA), Non-negative Matrix Factorization (NMF), integer Joint...

Phishing attacks are one of the most serious hazards to individuals and businesses in today's digital world. Many performed each month with goal convincing consumers that they visiting a reputable website or online application order obtain account information. A strategy is provided for detecting these types assaults by modifying existing Document Object Model (DOM) comparison tools such as Proportional Distance, false positive negative, favicon image recognition algorithm. There may be...

Breast cancer poses a significant global health challenge, demanding enhanced diagnostic accuracy and streamlined medical history documentation. This study presents holistic approach that harnesses the power of artificial intelligence (AI) machine learning (ML) to address these pressing needs. comprehensive methodology for breast diagnosis generation, integrating data collection, feature extraction, learning, AI-driven history-taking. The research employs systematic ensure accurate efficient...

Detecting diseases in fruits and leaves is crucial all sectors of agriculture, including small farms, large-scale commercial operations, urban agriculture. Diseases can reduce crop yield, affect product quality, spread to other plants, leading significant economic losses. Traditional methods disease detection involve visual inspection by trained personnel, which be time-consuming, expensive, prone errors. The hybrid deep learning approach, combines CNNs RNNs, has shown promise accurately...

Topic models are widely used in building clusters of documents for more than a decade, yet problems occurring choosing the optimal number topics. The main problem is lack stable metric quality topics obtained during construction topic models. authors analyzed from previous works, most determining non-parametric and determined by using perplexity coherence measures concluded that they not applicable solving this problem. In paper, we parametric method, which an extension traditional model...

Brain disease is considered a major cause of increased mortality worldwide. Clinical decision support system (CDSS) utilized for predicting individuals with brain in its earlier state. This work proposes novel prediction approach by handling the dataset issues, where an improved SMOTE sampling used balancing target data distribution. Then, Extreme Convolutional Network Model ([Formula: see text] better accuracy. For validation purpose, two publicly available ADNI-1 and ADNI-2 online datasets...

Driver drowsiness detection methods have received significant research interest in recent years and been used a variety of contexts, including driver activity tracking visual attention monitoring. Every year, drowsiness-related accidents result serious injuries fatalities. To address this global issue, researchers are developing new technologies to detect drowsiness. The objective article is present detailed review using various techniques. Some the challenges faced while systems lack...

The proliferation of the Internet has led to emergence malware as a significant cyber threat. Malware refers any application software that executes harmful behavior, such taking data or conducting espionage. According Kaspersky Labs, is executable program specifically destroy genuine user's computer by spreading virus in different ways. To detect and identify malicious threats malware, there are several machine learning techniques. So,this study evaluates performance algorithms based on...

There are now an absurdly large number of available products as a result the expansion e-commerce, making it challenging for customers to find and choose goods would enjoy. To assist in finding items that suit their interests, online shops have turned recommender systems. Traditional systems do, however, constraints AI Big Data Analytics can get beyond. This study uses forecast customer preferences buying.

Text data clustering is performed for organizing the set of text documents into desired number coherent and meaningful sub-clusters. Modeling in terms topics derivations a vital task clustering. Each tweet considered as document, various topic models perform modeling tweets. In existing models, tendency tweets assessed initially based on Euclidean dissimilarity features. Cosine metric more suitable informative assessment, especially Thus, this paper develops novel cosine external interval...

Social networks are an excellent source for users to share or exchange information on topics. Twitter is the most prioritized social network concerning health-related topics aspects of users. Latent Dirichlet Allocation (LDA) widely used technique cluster tweets documents using a derivation hidden conceptual Ailment Topic Aspect Model (ATAM) extended LDA model that implemented, especially clustering health based ailments types. It restricted determines three ailments, which include disease,...

Artificial Intelligence has been showing monumental growth in filling the gap between capabilities of humans and machines. Researchers scientists work on many aspects to make new things happen. Computer Vision is one them. To system visualize, neural networks are used. Some well-known Neural Networks include CNN, Feedforward (FNN), Recurrent (RNN) so on. Among them, CNN correct choice for computer vision because they learn relevant features from an image or video similar human brain. In this...

This research work describes a novel approach to social network-based data clustering using Sampling-based Multiview-points and Cosine-based similarity metrics of Hybrid Topic models reduce execution time increase the accuracy. method uses combination topic modelling datasets analysis. The model generates set topics network analysis identifies relationships between points. with min-max random sampling are used calculate different views proposed can be applied various fields, including...

The majority of organizations use an agile software development methodology. Standard analysis and design processes are abandoned due to the enormous demand generating product within time budget. This may result in a lack high-quality while components not constructively reused. identified at later stage component approaches. To address such challenges, methodology for extracting demand-based domain-specific from repository was developed. process reusing current is described depth with...

We are working on detecting the symptoms of Corona virus also known as Covid-19 in this project. From diagnosis to treatment, COVID-19 has posed a significant threat journalists, researchers, physicians, and organizations all over world. Analysts nonstop gain proper knowledge order monitor pandemic their respective areas. As result, it is critical incorporate an intelligent system alternative option for preventing COVID-19-related effects or death society. The determination level risk, with...

Brain disease prognosis is considered a hot research topic where the researchers intend to predict clinical measures of individuals using MRI data evaluate pathological stage and identifies progression disease. With lack incomplete scores, various existing learning-based approaches simply eradicate score without ground truth computation. It helps restrict training samples with robust reliable models during learning process. The major disadvantage prior adoption hand-crafted features, as...

Abstract In document clustering labeled and unlabeled documents are organized into a desired number of coherent meaningful sub-clusters. Topic models useful in extracting cluster tendency from Twitter-based data clusters. Evaluating performance with reliable metric is one the unsolved problems topic clustering. previous study validity metrics have been proposed under Euclidean distance measure, these underperform when dealing numerical databases for large corpus datasets. this paper, to...

Purpose The purpose of this study for eHealth text mining domains, cosine-based visual methods (VM) assess the clusters more accurately than Euclidean; which are recommended tweet data models assessment. Such VM determines concerning a single viewpoint or none, less informative. Multi-viewpoints (MVP) were used addressing informative assessment health-care documents and to demonstrate analysis cluster tendency. Design/methodology/approach In paper, authors proposed MVP-based by using...