Millets delves into the dynamics of the millets industry, with a particular focus on sales projection and customer segmentation as strategic levers for growth. The research commences with an in-depth analysis of the m...
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Millets delves into the dynamics of the millets industry, with a particular focus on sales projection and customer segmentation as strategic levers for growth. The research commences with an in-depth analysis of the millets market, encompassing production patterns, consumption trends, and emerging market opportunities. It explores the diverse range of millets varieties, their nutritional profiles, and the factors driving consumer preference. By understanding the market landscape, the study identifies key trends and challenges shaping the industry. A core component of this research is the development of a robust sales projection model. Employing advanced statistical and data-driven techniques, the model forecasts future sales based on historical data, market trends, and relevant economic indicators. The model incorporates factors such as consumer demographics, purchasing behavior, and competitive landscape to provide accurate and actionable insights. Customer segmentation is another critical aspect of the study. By applying clustering and profiling methodologies, the research identifies distinct customer segments based on factors such as age, income, dietary preferences, and purchasing habits. This segmentation enables a deeper understanding of customer needs and preferences, facilitating targeted marketing strategies and product development. The integration of sales projection and customer segmentation empowers businesses to make informed decisions, optimize resource allocation, and enhance overall market performance. By aligning product offerings and marketing efforts with customer segments, companies can achieve higher customer satisfaction, increased market share, and improved profitability. This research contributes to the growing body of knowledge on the millets industry by providing valuable insights into market dynamics, sales forecasting, and customer segmentation. The findings offer practical guidance for industry stakeholders, including farmers, processors
Ransomware is one of the most advanced malware which uses high computer resources and services to encrypt system data once it infects a system and causes large financial data losses to the organization and individuals...
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Machine learning(ML)is increasingly applied for medical image processing with appropriate learning *** applications include analyzing images of various organs,such as the brain,lung,eye,etc.,to identify specific flaws...
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Machine learning(ML)is increasingly applied for medical image processing with appropriate learning *** applications include analyzing images of various organs,such as the brain,lung,eye,etc.,to identify specific flaws/diseases for *** primary concern of ML applications is the precise selection of flexible image features for pattern detection and region *** of the extracted image features are irrelevant and lead to an increase in computation ***,this article uses an analytical learning paradigm to design a Congruent Feature Selection Method to select the most relevant image *** process trains the learning paradigm using similarity and correlation-based features over different textural intensities and pixel *** similarity between the pixels over the various distribution patterns with high indexes is recommended for disease ***,the correlation based on intensity and distribution is analyzed to improve the feature selection ***,the more congruent pixels are sorted in the descending order of the selection,which identifies better regions than the ***,the learning paradigm is trained using intensity and region-based similarity to maximize the chances of ***,the probability of feature selection,regardless of the textures and medical image patterns,is *** process enhances the performance of ML applications for different medical image *** proposed method improves the accuracy,precision,and training rate by 13.19%,10.69%,and 11.06%,respectively,compared to other models for the selected *** mean error and selection time is also reduced by 12.56%and 13.56%,respectively,compared to the same models and dataset.
The success of Internet of Things (IoT) services will be determined by how the security of the IoT devices and the networks to which they are connected can be guaranteed. Integrating biometric recognition technology i...
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Dirac-vortex microcavity laser based on InAs/InGaAs quantum dots have been experimentally realized on silicon *** topological laser features a large spectral range and high robustness against variations such as cavity...
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Dirac-vortex microcavity laser based on InAs/InGaAs quantum dots have been experimentally realized on silicon *** topological laser features a large spectral range and high robustness against variations such as cavity size.
Multi-class classification can be solved by decomposing it into a set of binary classification problems according to some encoding rules,e.g.,one-vs-one,one-vs-rest,error-correcting output *** works solve these binary...
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Multi-class classification can be solved by decomposing it into a set of binary classification problems according to some encoding rules,e.g.,one-vs-one,one-vs-rest,error-correcting output *** works solve these binary classification problems in the original feature space,while it might be suboptimal as different binary classification problems correspond to different positive and negative *** this paper,we propose to learn label-specific features for each decomposed binary classification problem to consider the specific characteristics containing in its positive and negative ***,to generate the label-specific features,clustering analysis is respectively conducted on the positive and negative examples in each decomposed binary data set to discover their inherent information and then label-specific features for one example are obtained by measuring the similarity between it and all cluster *** clearly validate the effectiveness of learning label-specific features for decomposition-based multi-class classification.
Today,liver disease,or any deterioration in one’s ability to survive,is extremely common all around the *** research has indicated that liver disease is more frequent in younger people than in older *** the liver’s ...
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Today,liver disease,or any deterioration in one’s ability to survive,is extremely common all around the *** research has indicated that liver disease is more frequent in younger people than in older *** the liver’s capability begins to deteriorate,life can be shortened to one or two days,and early prediction of such diseases is *** several machine learning(ML)approaches,researchers analyzed a variety of models for predicting liver disorders in their early *** a result,this research looks at using the Random Forest(RF)classifier to diagnose the liver disease early *** dataset was picked from the University of California,Irvine ***’s accomplishments are contrasted to those of Multi-Layer Perceptron(MLP),Average One Dependency Estimator(A1DE),Support Vector Machine(SVM),Credal Decision Tree(CDT),Composite Hypercube on Iterated Random Projection(CHIRP),K-nearest neighbor(KNN),Naïve Bayes(NB),J48-Decision Tree(J48),and Forest by Penalizing Attributes(Forest-PA).Some of the assessment measures used to evaluate each classifier include Root Relative Squared Error(RRSE),Root Mean Squared Error(RMSE),accuracy,recall,precision,specificity,Matthew’s Correlation Coefficient(MCC),F-measure,and *** has an RRSE performance of 87.6766 and an RMSE performance of 0.4328,however,its percentage accuracy is *** widely acknowledged result of this work can be used as a starting point for subsequent *** a result,every claim that a new model,framework,or method enhances forecastingmay be benchmarked and demonstrated.
In recent years, cloud computing has witnessed widespread applications across numerous organizations. Predicting workload and computing resource data can facilitate proactive service operation management, leading to s...
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This paper implements a dynamic goalkeeper defense policy based on Deep Reinforcement Learning (DRL) for a soccer robot. This study proposed a multi-directional training approach to enable soccer goalkeepers to learn ...
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Voice is the king of communication in wireless cellular network (WCN). Again, WCNs provide two types of calls, i.e., new call (NC) and handoff call (HC). Generally, HCs have higher priority than NCs because call dropp...
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