Опубликована: Янв. 27, 2024
Maize, also known as corn (Zea mays), is a staple food for humans and animals alike key component in numerous economic sectors. While maize an essential cereal crop, its robust expansion constantly threatened by unseen foe: leaf diseases that throw shadow across cornfields endanger yields quality. To protect global security, rapid disease diagnosis crucial. In this ground-breaking research, they introduce unique paradigm detection combines the efficacy of Convolutional Neural Networks (CNNs) with comprehension Random Forests. The method centers on three convolutional layers specifically developed to analyze extract complex characteristics from photos leaves. A max-pooling layer sits atop these acts amplification most important elements. Key among innovations incorporation increasing number flatness layers, which allows us tailor degree complexity model specific nuances each dataset therefore guarantee detailed knowledge condition. system promises not just extraordinary accuracy but profound insights into world balancing advantages deep learning conventional machine learning. Extensive experiments validation broad support findings, demonstrating promise hybrid approach equip agricultural stakeholders tools early treatment This major step forward protecting viability farming reinforcing role it plays agriculture.
Язык: Английский
Процитировано
2Remote Patient Monitoring (RPM) with Biosensors: Reforming Healthcare Beyond Walls of Hospital
Опубликована: Янв. 27, 2024
This narrative analysis article investigates the revolutionary potential of biosensors in remote patient monitoring, with goal comprehending their influence on healthcare outside hospital walls. The study dives into narratives around adoption and acceptability using Technology Model (TAM) as a theoretical framework. gives complete overview benefits implications revolutionizing delivery by analyzing wide range literature from different trusted sources. convenience well aspects that for improving practices. promise outcomes, expanding treatment access, is highlighted this study.
Язык: Английский
Процитировано
2Intelligent Computing Methods for Knowledge Discovery and Management: Analysis, Comparison, and Application Domains
Опубликована: Янв. 27, 2024
Knowledge Management (KM) and Artificial Intelligence (AI) at their core are about knowledge. AI provides the mechanism that allows a machine to obtain, acquire, process, use information execute tasks, as well reveal or unlock knowledge may be transmitted people for improved strategic decision-making. The intelligent approaches in process of KDM (Knowledge Discovery Management) can improve efficiency sense time accuracy. Intelligent especially soft computing have ability learn any environment with help logic, reasoning, other abilities. In this paper authors analyses single ICM Combined those provide correct reasonable solutions form main aim study is apply techniques solve day-to-day problems our rural smart digital societies.
Язык: Английский
Процитировано
2Designing a Multi-Class Classification Optimized Model for Requirement based Test Case Prioritization
Опубликована: Янв. 27, 2024
Most of the times we have to test out entire application functionality, for any code modification done cater need larger audiences or bug fixes. This results consumption time and effort retest by executing all suites. In such cases more often regression testing comes rescue, where prioritization techniques are being used overcome limitations testing. Test Case Prioritization (TCP) usually means categorically ranking some higher than others. The main goal TCP is find fault early in process scheduling with help Requirement based Technique (RTCP) order increase effectiveness this research study, developed a supervised machine learning RTCP mechanism significant business requirement relevant feature considered according their priority label as high, medium low multi classes. proposed model validated two datasets collected from internet sources. classifier k-Nearest Neighbor (K-NN), Decision Tree (DT), Random Forest (RF) Bagging Boosting algorithm utilized evaluate features case prioritization. To enhance performance hyper parameter settings altered found drastic change results. achieve cost high detection rate RTCP, an optimized designed which settings. experimental result demonstrates that RF achieved best among other classifiers predicting prioritized reduce required
Язык: Английский
Процитировано
2Task Offloading Technique Selection In Mobile Edge Computing
Опубликована: Янв. 27, 2024
Язык: Английский
Процитировано
2Skin Disease Detection Using Neural Networks
Опубликована: Янв. 27, 2024
Язык: Английский
Процитировано
2An empirical investigation of deep transfer learning approach to automatic detection of coronavirus disease (COVID-19) in CT images
Опубликована: Янв. 27, 2024
Язык: Английский
Процитировано
1Enhanced Citrus Fruit Disease Detection: Combining CNN and Random Forest Models
Опубликована: Янв. 27, 2024
In this study, through an emphasis on the precision, recollection, F1-Score, encouragement, support proportion, etc. accuracy metrics across different criticality stages, indicating varied degrees of disease severity, abstract gives a general summary classification model's performance evaluation. This evaluation is shown in table. The accurate values for each which range between 92.57% to 93.75%, show how accurately model can categorize occurrences within stage. demonstrates its reducing false positives and ensuring that situations are found relevant. properly detecting pertinent cases further demonstrated by recall values, from 92.49% 93.68%, demonstrating capacity reduce negatives. supports classifying diseases ability simultaneously optimize several stages balanced F1-scores, 92.75% 93.51%. balance highlights reliable functioning. With counts vary 865 965, dataset distributed throughout stages. adapt diverse illness severity levels efficiently handle variety function dependably various proportions importance highlighted proportion consistency model. regularly makes correct predictions all phases, with remarkable overall 98%. high level well it classifies diseases. concludes succinct review model, highlighting remember, assistance, metrics. A helpful tool healthcare or management applications, routinely exhibits dependability identifying
Язык: Английский
Процитировано
1Order Abatement of Linear System Using Mixed Method
Опубликована: Янв. 27, 2024
This paper introduces a new mixed approach for Order Abatement (OA) in Linear Systems (LS). The method employs two optimization algorithms, namely the Honey Badger Algorithm (HBA) employed to find out numerator of Abated System (AS) and Bonobo Optimizer (BOA) denominator AS. efficacy this strategy is evaluated on three Large-Scale (LSS) from existing literature, with Integral Square Error (ISE) considered as objective function (OF) OA. primary minimize discrepancy between LSS Proposed AS by identifying unknown coefficients. experimentation conducted using MATLAB 2018a its control system toolbox. Various Performance Indices (PI) well Transient Parameters are estimated compared other contemporary approaches different literatures.
Язык: Английский
Процитировано
1Analyzing Extractive Text Summarization Techniques and Classification Algorithms: A Comparative Study
Опубликована: Янв. 27, 2024
Язык: Английский
Процитировано
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