Data protection in databases is critical for any organization,as unauthorized access or manipulation can have severe negative *** detection systems are essential for keeping databases *** in technology will lead to si...
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Data protection in databases is critical for any organization,as unauthorized access or manipulation can have severe negative *** detection systems are essential for keeping databases *** in technology will lead to significant changes in the medical field,improving healthcare services through real-time information ***,reliability and consistency still need to be *** against cyber-attacks are necessary due to the risk of unauthorized access to sensitive information and potential data ***-ruptions to data items can propagate throughout the database,making it crucial to reverse fraudulent transactions without delay,especially in the healthcare industry,where real-time data access is *** research presents a role-based access control architecture for an anomaly detection ***,the Structured Query Language(SQL)queries are stored in a new data structure called *** pentaplets allow us to maintain the correlation between SQL statements within the same transaction by employing the transaction-log entry information,thereby increasing detection accuracy,particularly for individuals within the company exhibiting unusual *** identify anomalous queries,this system employs a supervised machine learning technique called Support Vector Machine(SVM).According to experimental findings,the proposed model performed well in terms of detection accuracy,achieving 99.92%through SVM with One Hot Encoding and Principal Component Analysis(PCA).
An earthquake happens when the energy from the fracture of rock strata within the crust causes the earth to vibrate. Earth's surface experiences seismic waves because of this energy, which is produced by tectonic ...
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Biomedical image processing acts as an essential part of severalmedical applications in supporting computer aided disease diagnosis. MagneticResonance Image (MRI) is a commonly utilized imaging tool used tosave glioma...
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Biomedical image processing acts as an essential part of severalmedical applications in supporting computer aided disease diagnosis. MagneticResonance Image (MRI) is a commonly utilized imaging tool used tosave glioma for clinical examination. Biomedical image segmentation plays avital role in healthcare decision making process which also helps to identifythe affected regions in the MRI. Though numerous segmentation models areavailable in the literature, it is still needed to develop effective segmentationmodels for BT. This study develops a salp swarm algorithm with multi-levelthresholding based brain tumor segmentation (SSAMLT-BTS) model. Thepresented SSAMLT-BTS model initially employs bilateral filtering based onnoise removal and skull stripping as a pre-processing phase. In addition,Otsu thresholding approach is applied to segment the biomedical imagesand the optimum threshold values are chosen by the use of SSA. Finally,active contour (AC) technique is used to identify the suspicious regions in themedical image. A comprehensive experimental analysis of the SSAMLT-BTSmodel is performed using benchmark dataset and the outcomes are inspectedin many aspects. The simulation outcomes reported the improved outcomesof the SSAMLT-BTS model over recent approaches with maximum accuracyof 95.95%.
Dementia is a neurological disorder that affects the brain and its functioning,and women experience its effects more than men *** care often requires non-invasive and rapid tests,yet conventional diagnostic techniques...
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Dementia is a neurological disorder that affects the brain and its functioning,and women experience its effects more than men *** care often requires non-invasive and rapid tests,yet conventional diagnostic techniques are time-consuming and *** of the most effective ways to diagnose dementia is by analyzing a patient’s speech,which is cheap and does not require *** research aims to determine the effectiveness of deep learning(DL)and machine learning(ML)structures in diagnosing dementia based on women’s speech *** study analyzes data drawn from the Pitt Corpus,which contains 298 dementia files and 238 control files from the Dementia Bank *** learning models and SVM classifiers were used to analyze the available audio samples in the *** methodology used two methods:a DL-ML model and a single DL model for the classification of diabetics and a single DL *** deep learning model achieved an astronomic level of accuracy of 99.99%with an F1 score of 0.9998,Precision of 0.9997,and recall of *** proposed DL-ML fusion model was equally impressive,with an accuracy of 99.99%,F1 score of 0.9995,Precision of 0.9998,and recall of ***,the study reveals how to apply deep learning and machine learning models for dementia detection from speech with high accuracy and low computational *** research work,therefore,concludes by showing the possibility of using speech-based dementia detection as a possibly helpful early diagnosis *** even further enhanced model performance and better generalization,future studies may explore real-time applications and the inclusion of other components of speech.
Social media(SM)based surveillance systems,combined with machine learning(ML)and deep learning(DL)techniques,have shown potential for early detection of epidemic *** review discusses the current state of SM-based surv...
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Social media(SM)based surveillance systems,combined with machine learning(ML)and deep learning(DL)techniques,have shown potential for early detection of epidemic *** review discusses the current state of SM-based surveillance methods for early epidemic outbreaks and the role of ML and DL in enhancing their ***,every year,a large amount of data related to epidemic outbreaks,particularly Twitter data is generated by *** paper outlines the theme of SM analysis for tracking health-related issues and detecting epidemic outbreaks in SM,along with the ML and DL techniques that have been configured for the detection of epidemic *** has emerged as a promising ML technique that adaptsmultiple layers of representations or features of the data and yields state-of-the-art extrapolation *** recent years,along with the success of ML and DL in many other application domains,both ML and DL are also popularly used in SM *** paper aims to provide an overview of epidemic outbreaks in SM and then outlines a comprehensive analysis of ML and DL approaches and their existing applications in SM ***,this review serves the purpose of offering suggestions,ideas,and proposals,along with highlighting the ongoing challenges in the field of early outbreak detection that still need to be addressed.
Today, recommendation systems are everywhere, making a variety of activities considerably more manageable. These systems help users by personalizing their suggestions to their interests and needs. They can propose var...
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Today, recommendation systems are everywhere, making a variety of activities considerably more manageable. These systems help users by personalizing their suggestions to their interests and needs. They can propose various goods, including music, courses, articles, agricultural products, fertilizers, books, movies, and foods. In the case of research articles, recommendation algorithms play an essential role in minimizing the time required for researchers to find relevant articles. Despite multiple challenges, these systems must solve serious issues such as the cold-start problem, article privacy, and changing user interests. This research addresses these issues through the use of two techniques: hybrid recommendation systems and COOT optimization. To generate article recommendations, a hybrid recommendation system integrates features from content-based and graph-based recommendation systems. COOT optimization is used to optimize the results, inspired by the movement of water birds. The proposed method combines a graph-based recommendation system with COOT optimization to increase accuracy and reduce result inaccuracies. When compared to the baseline approaches described, the model provided in this study improves precision by 2.3%, recall by 1.6%, and mean reciprocal rank (MRR) by 5.7%.
Image segmentation is critical in medical image processing for lesion detection, localisation, and subsequent diagnosis. Currently, computer-aided diagnosis (CAD) has played a significant role in improving diagnostic ...
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Healthcare systems face challenges in certifying the security, accuracy, and efficiency of data gathered from IoT sensors. Confidentiality and integrity of patient information are paramount while optimizing resource u...
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Feature selection is an essential step in text classification tasks to enhance model performance, reduce computational complexity, and mitigate the risk of overfitting. Filter-based methods have gained popularity for ...
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Techniques that exploit spectral-spatial information have proven to be very effective in hyperspectral image classification. Joint sparse representation classification (JSRC) is one such technique which has been exten...
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