Amazon Reviews allows customers to review the purchased or used products. this type of user-generated content is incredibly helpful for e-commerce businesses; whether it is from existing customers or new ones, every r...
Amazon Reviews allows customers to review the purchased or used products. this type of user-generated content is incredibly helpful for e-commerce businesses; whether it is from existing customers or new ones, every review provides a valuable perspective and feedback on a certain product. Amazon product reviews are an incredibly powerful tool for both buyers and businesses in determining the perceived value of a product, informing decisions, and improving customer trust and loyalty. this study explores the use of machine learning-based methods to assess the sentiment of Amazon product reviews from a range of product categories, including electronics, home appliances, and fashion items. through the use of Natural Language Processing (NLP) and deep learningalgorithms, this study aims to determine whether there is a correlation between customer sentiment and product category. the analysis will further facilitate the development of a model that can accurately predict sentiment scores for any given batch of reviews or comments. According to the study, using Text Blob, Logistic Regression, SVM, and Multinomial Naive Bayes methods can improve classification task accuracy. It produced 91% accuracy with SVM and Logistic Regression and Multinomial Naive Bayes methods are 84% and 87% accuracy with on 4444 positive reviews and 469 negative reviews.
Given that COVID-19 symptoms might be similar to other viral infectious diseases, it becomes difficult to accurately diagnose for COVID-19 without traditional testing strategies like polymerase chain reaction (PCR) te...
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ISBN:
(数字)9781665499583
ISBN:
(纸本)9781665499583
Given that COVID-19 symptoms might be similar to other viral infectious diseases, it becomes difficult to accurately diagnose for COVID-19 without traditional testing strategies like polymerase chain reaction (PCR) testing. As the quarantine and testing requirements have been lifted from most countries, easier and innovative testing strategies are being adopted to maintain high awareness levels in regards to the spread of the disease for both authorities and the public. this paper presents a COVID19 detection strategy that uses Machine learning (ML) models to accurately diagnose for the disease in patients. the Artificial Intelligence (AI)-enabled solution not only serves the purpose of detecting whether patients are diagnosed with COVID, but also to track their daily symptoms and accurately classify the type of viral disease. Different ML models are trained and tested for accuracy and prediction timings. A decentralized approach is taken for the disease prediction, and hence, blockchain is adapted within the solution to ensure the authenticity of the user data. the solution has been implemented to allow users to receive real-time disease diagnosis using a web-based interface.
the speed of the semiconductor technology roadmap was conventionally defined by the scaling of the patterning pitches, withthe main objective of halving the cost per transistor for each subsequent technology node. In...
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As the number of MS Teams, Zoom, and Google Meet users increases with online education, so do the privacy and security vulnerabilities. this study aims to investigate the privacy, security, and usability aspects of fe...
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the rise in mental health problems among people and the need for effective solutions for mental health have led to a lot of research in the machine learning domain for its applications in mental healthcare Many people...
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the rise in mental health problems among people and the need for effective solutions for mental health have led to a lot of research in the machine learning domain for its applications in mental healthcare Many people’s lives have been affected by the exponential rise in mental health and depression, and various factors must be revised. Different situations can have an impact on one’s mental stability. So, taking the factors in mind like gender, age, insomnia, loneliness, depression level, aggressiveness, stress level etc. we are going to analyze the most affecting factors for poor mental health. Nowadays mostly people are facing some kind of mental health problems but very few people are giving attention to their mental health. So, our main purpose is to analyze the factors and find those factors which are responsible for a person’s poor mental health. those factors are also anonymous by the person itself and they don’t even know that they have weak mental health. For that we have taken the initiative to analyze and point out the main causes for poor mental health by circulating a google form Which has questions regarding regular lifestyle and from that we have collected data of different types of persons who are from different professions like students, employees, professors etc. After collecting the data, we had done preprocessing on that data and then we applied machine learningalgorithms for classification and prediction. By applying machine learningalgorithms, we found different types of outputs and by taking those outputs we can say which are more affecting factors on a person’s mental health and we can suggest how people should improve their mental health and what type of things they should avoid for their good mental health.
作者：
Santhosh, C.S.Umesh, K.K.
Department of Computer Applications Karnataka Mysuru570006 India
Department of Information Science and Engineering Karnataka Mysuru570006 India
Coffee has a significant part in the economies of many African, American, and Asian nations since it is a perennial crop and a product used in daily life. For agricultural system modelers, it is still difficult to ant...
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ISBN:
(纸本)9781665456357
Coffee has a significant part in the economies of many African, American, and Asian nations since it is a perennial crop and a product used in daily life. For agricultural system modelers, it is still difficult to anticipate coffee output with any degree of accuracy given the environmental, meteorological, and soil fertility factors that influence it. Out of the 103 different varieties of class coffee bean variation that are commercially traded worldwide, the two most significant types of coffee assortment filled in India are Arabica and Robust. In order to investigate and construct a predictive model for the development of coffee planters to make accurate judgments in time during bad conditions in advance, we are taking the most important plantation crop in India, namely, coffee. In order to quantify the effect of agronomic parameters to obtain a decent coffee yield, we thus present a framework for coffee yield prediction utilizing machine learning probabilistic techniques. Here, we take into account the historical dataset from the Central Coffee Research Institute (CCRI), Karnataka, for the year (2008-2019). We are taking into account agronomic elements like Age, Soil Nutrients: Organic Carbon (OC), Phosphorus (P), Potassium (K), Alkaline (pH), and Respective Yield Obtained in Chikkamagaluru Region, Karnataka State, India, for the forecast of coffee yield. Multiple Linear Regression, Lasso Regression, and Elastic Net Regression are three different predictive regression algorithmsthat are used for prediction;the results of each are compared, examined, and tabulated. the best accurate coffee yield estimate during the autonomous testing phase was produced using an elastic net (ENET) regression model (R2 = 0.26 kg ha 1, RMSE = 136.95 kg ha 1, and MAE = 111.41 kg ha 1). this contrasted withthe less accurate Lasso regression model (R2 = 0.25 kg ha-1, RMSE = 137.64 kg ha-1 and MAE = 111.96 kg ha-1) and MLR (R2 = 0.25 kg ha-1, RMSE = 137.71 kg ha-1 and MAE = 112.02 kg h
With traffic demands growing exponentially, a great number of new network applications emerging, traffic load balancing and resource utilization have become the key issues that severely affect network performance of d...
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In the ever-increasing diversity of cloud environment, the recommendation of web service-related applications based on service quality is a basic tasks that both service providers and web developers are very intereste...
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the routing problem poses a significant combinatorial optimization (COP) challenge that cannot be effectively solved by using traditional methods due to its NP-hardness. Recent studies have shown that Graph Attention ...
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ISBN:
(纸本)9789819947607;9789819947614
the routing problem poses a significant combinatorial optimization (COP) challenge that cannot be effectively solved by using traditional methods due to its NP-hardness. Recent studies have shown that Graph Attention Networks (GAT) [1] hold promise in addressing this problem. However, existing models have neglected the importance of distance matrix information between nodes, rendering the trained model less effective. Furthermore, the ability of existing models to solve asymmetric routing problems on real road networks is limited, as these models solely rely on the coordinates of the nodes. In this paper, we propose a novel model that incorporates a hybrid structure of edge-graph attention network (E-GAT) and edge-embedded multi-head attention (E-MHA) as the encoder. Besides, the edges are embedded to obtain graph structure information directly, and it allows us to capture the correlation between different nodes and avoid potential noise. Experimental results demonstrate that our proposed method outperforms existing methods in routing problems of varying scales while maintaining good compatibility and generalization ability. In addition, our method effectively addresses the Travelling Salesman Problem (TSP) and the Capacitated Vehicle Routing Problem (CVRP) under both symmetric and asymmetric distance matrix.
this paper introduces a real-time face detection technology based on TMS320C6201. through the confidential communication between each subsystem, the synchronization of each subsystem is completed, and the real-time fa...
this paper introduces a real-time face detection technology based on TMS320C6201. through the confidential communication between each subsystem, the synchronization of each subsystem is completed, and the real-time face recognition, feature code extraction, and the closest face matching are carried out. Firstly, Grabcut foreground extraction method is used for pre-background segmentation of recognized images to reduce external interference, and then face detection and identification are carried out according to the segmentation effect. A parallel MPI program is developed by transforming the traditional serialization-based face information updating method into a parallel method. this paper applies existing MPI-based methods and existing web-based facial information acquisition methods to improve the efficiency of existing face recognition technologies. It realizes the distributed processing of the update algorithm in the original face recognition system and enhances the ability of the system to process a large amount of data to achieve the purpose of improving the system performance. the experimental results show that the system combining grab cut and Adaboost algorithm can improve the recognition rate and detection rate, and the recognition speed is faster.
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