Internet of Things plays an important role in agriculture in order to provide an innovative and smart solution to traditional farming. IOT is all about connecting physical devices to the internet and can access from a...
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Social networking sites in the most modernized world are flooded with large data *** the sentiment polarity of important aspects is necessary;as it helps to determine people’s opinions through what they *** Coronavir...
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Social networking sites in the most modernized world are flooded with large data *** the sentiment polarity of important aspects is necessary;as it helps to determine people’s opinions through what they *** Coronavirus pandemic has invaded the world and been given a mention in the social media on a large *** a very short period of time,tweets indicate unpredicted increase of *** reflect people’s opinions and thoughts with regard to coronavirus and its impact on *** research community has been interested in discovering the hidden relationships from short texts such as Twitter and Weiboa;due to their shortness and *** this paper,a hierarchical twitter sentiment model(HTSM)is proposed to show people’s opinions in short *** proposed HTSM has two main features as follows:constructing a hierarchical tree of important aspects from short texts without a predefined hierarchy depth and width,as well as analyzing the extracted opinions to discover the sentiment polarity on those important aspects by applying a valence aware dictionary for sentiment reasoner(VADER)sentiment *** tweets for each extracted important aspect can be categorized as follows:strongly positive,positive,neutral,strongly negative,or *** quality of the proposed model is validated by applying it to a popular product and a widespread *** results show that the proposed model outperforms the state-of-the-art methods used in analyzing people’s opinions in short text effectively.
This paper introduces the development of a solar atlas designed to evaluate both photovoltaic and thermal energy potentials in urban environments. The study focuses on Las Herrerias sector in Cuenca, Ecuador, detailin...
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Deep metric learning has gained significant attention recently due to its promising performance in image retrieval, face recognition, and clustering tasks. Deep metric learning algorithms map the original data from th...
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This paper investigates the active reconfigurable intelligent surfaces (RIS)-assisted integrated sensing and communication (ISAC) system, in which a dual-functional base station (BS) simultaneously transmits communica...
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作者：
Petkar, Taniya
Faculty of Engineering and Technology Department of Computer Science And Medical Engineering Maharashtra Wardha442001 India
This paper presents a novel line-of-control (LoC) monitoring system that leverages the Internet of Things (IoT) to improve border security. The system creates a strong infrastructure for real-time monitoring throughou...
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In the tag recommendation task on academic platforms,existing methods disregard users’customized preferences in favor of extracting tags based just on the content of the ***,it uses co-occurrence techniques and tries...
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In the tag recommendation task on academic platforms,existing methods disregard users’customized preferences in favor of extracting tags based just on the content of the ***,it uses co-occurrence techniques and tries to combine nodes’textual content for *** still do not,however,directly simulate many interactions in network *** order to address these issues,we present a novel system that more thoroughly integrates user preferences and citation networks into article labelling ***,we first employ path similarity to quantify the degree of similarity between user labelling preferences and articles in the citation ***,the Commuting Matrix for massive node pair paths is used to improve computational ***,the two commonalities mentioned above are combined with the interaction paper labels based on the additivity of Poisson *** addition,we also consider solving the model’s parameters by applying variational *** results demonstrate that our suggested framework agrees and significantly outperforms the state-of-the-art baseline on two real datasets by efficiently merging the three relational *** on the Area Under Curve(AUC)and Mean Average Precision(MAP)analysis,the performance of the suggested task is evaluated,and it is demonstrated to have a greater solving efficiency than current techniques.
Adopting the CloudIoT-based healthcare paradigm provides various prospects for medical IT and considerably enhances healthcare services. However, compared to the advanced development of CloudIoT-based healthcare syste...
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The CloudIoT paradigm has profoundly transformed the healthcare industry, providing outstanding innovation and practical applications. However, despite its many advantages, the adoption of this paradigm in healthcare ...
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As global digitization continues to grow, technology becomes more affordable and easier to use, and social media platforms thrive, becoming the new means of spreading information and news. Communities are built around...
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As global digitization continues to grow, technology becomes more affordable and easier to use, and social media platforms thrive, becoming the new means of spreading information and news. Communities are built around sharing and discussing current events. Within these communities, users are enabled to share their opinions about each event. Using Sentiment Analysis to understand the polarity of each message belonging to an event, as well as the entire event, can help to better understand the general and individual feelings of significant trends and the dynamics on online social networks. In this context, we propose a new ensemble architecture, EDSAEnsemble (Event Detection Sentiment Analysis Ensemble), that uses Event Detection and Sentiment Analysis to improve the detection of the polarity for current events from Social Media. For Event Detection, we use techniques based on Information Diffusion taking into account both the time span and the topics. To detect the polarity of each event, we preprocess the text and employ several Machine and Deep Learning models to create an ensemble model. The preprocessing step includes several word representation models: raw frequency, TFIDF, Word2Vec, and Transformers. The proposed EDSA-Ensemble architecture improves the event sentiment classification over the individual Machine and Deep Learning models. Authors
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