Aspect-based sentiment analysis (ABSA) also known as phrase level, is a specific level of sentiment analysis task which tries to find the polarity of various aspects of an entity. It identifies aspects with in the giv...
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ISBN:
(纸本)9789819971367
Aspect-based sentiment analysis (ABSA) also known as phrase level, is a specific level of sentiment analysis task which tries to find the polarity of various aspects of an entity. It identifies aspects with in the given opinion and predicts the polarity corresponding to each aspect with in the text. In this research work, we proposed an aspect-based sentiment analysis for hotel services in Afaan Oromo texts using deep learning approach. Implementing this technique for hotel industries will have many significances. Tracking users’ opinions on the services they provide will help the organization understand their value in a competitive environment and to understand their customer opinion about the services. As there is no publicly available dataset used for this task, we have prepared an aspect-based sentiment analysis dataset from scratch by manually collecting the opinions from hotel customers by distributing a questionnaire. We have collected 1155 datasets for this research work through manually distributed questionnaires. The proposed solution has four steps: manual data collection, preprocessing, aspect category classification, and sentiment polarity classification. The classification process compares five deep learning models, CNN, LSTM, GRU, BiLSTM, and CNN-BiLSTM. In aspect category classification, we have six categories of aspects: Nyaata (Food), Dhugaatii (Drink), Bakka (Location), Gatii (Price), Keessummeessu (Reception), and Ciisicha (Room). In sentiment polarity classification, the polarity of each aspects is classified into either Negative or Positive classes. The result shows that the BiLSTM performed better than all the other models in both aspect category classification and sentiment polarity classification tasks. The BiLSTM model achieved an accuracy of 93.4% and an F1 score of 93% in the aspect category classification tasks. In the polarity classification task, BiLSTM achieved an accuracy of 87% and an F1 score of 88.9%. BiLSTM has been selected as a
Autonomous underwater vehicles (AUVs) are be-coming crucial for complex subsea tasks such as pipeline in-spection, subsea valve manipulation, and autonomous docking. This paper presents the design and development of a...
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Cercospora leaf spot, a damaging fungal disease in eggplants, threatens agricultural productivity and farmer livelihoods. This study introduces an automated system for early detection of Cercospora infestations and af...
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Blockchain technology has garnered significant attention in academic and industrial domains due to its ability to establish a secure and trustworthy environment. As blockchain techniques continue to advance, there is ...
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ISBN:
(纸本)9798400706387
Blockchain technology has garnered significant attention in academic and industrial domains due to its ability to establish a secure and trustworthy environment. As blockchain techniques continue to advance, there is a growing demand for computing resources in dimensions like storage, data processing, and network bandwidth. To meet this demand, leveraging cloud computing as an off-chain resource for scalable on-chain services has emerged as a viable solution. However, allocating cloud resources in heterogeneous cloud computing environments presents challenges due to their inherent complexity. Native cloud environments encompass diverse cloud service providers with varying capabilities, pricing models, and performance characteristics. Given the cloud's capacity to scale resources based on demand, this paper introduces a novel approach called the Cloud-enabled Scalable Blockchain (CLEAN) outsourcing model. The CLEAN model aims to develop a scalable blockchain system that minimizes costs and enhances performance. We propose a dynamic programming algorithm considering influential factors such as cloud service costs, availability, and execution time. The algorithm aims to minimize expenses while ensuring efficient resource allocation. Experimental evaluations involving rigorous analysis have been conducted to assess the effectiveness of the proposed approach. The results indicate that CLEAN outperforms the Greedy Algorithm and Genetic Algorithm (GA) by maintaining relatively low latency across all the CLEAN settings. Additionally, CLEAN demonstrates lower energy consumption compared to the Greedy Algorithm and GA, with up to a 50% and 30% reduction, respectively, as the number of transactions increases. Furthermore, the experiments determine the optimal number of orderers for the three settings to balance the trade-off between time cost and performance. Moreover, the findings also reveal that simply increasing the number of orderers in the cloud does not guarantee improv
Understanding human activities from videos presents a formidable challenge in computer Vision. Intelligent video systems primarily specialize in automatically recognizing human actions within video sequences and annot...
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Air density plays an important role in assessing wind *** density significantly fluctuates both spatially and *** literature typically used standard air density or local annual average air density to assess wind *** p...
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Air density plays an important role in assessing wind *** density significantly fluctuates both spatially and *** literature typically used standard air density or local annual average air density to assess wind *** present study investigates the estimation errors of the potential and fluctuation of wind resource caused by neglecting the spatial-temporal variation features of air density in *** air density at 100 m height is accurately calculated by using air temperature,pressure,and *** spatial-temporal variation features of air density are firstly *** the wind power generation is modeled based on a 1.5 MW wind turbine model by using the actual air density,standard air densityρst,and local annual average air densityρsite,***ρstoverestimates the annual wind energy production(AEP)in 93.6%of the study *** significantly affects AEP in central and southern China *** more than 75%of the study area,the winter to summer differences in AEP are underestimated,but the intra-day peak-valley differences and fluctuation rate of wind power are ***ρsitesignificantly reduces the estimation error in *** AEP is still overestimated(0-8.6%)in summer and underestimated(0-11.2%)in *** for southwest China,it is hard to reduce the estimation errors of winter to summer differences in AEP by usingρ***ρsitedistinctly reduces the estimation errors of intra-day peak-valley differences and fluctuation rate of wind power,but these estimation errors cannot be ignored as *** impacts of air density on assessing wind resource are almost independent of the wind turbine types.
The healthcare sector is growing quickly, making it a complex system. At the same time, fraud in this sector is becoming a serious issue. Misuse of the medical insurance systems is one of the problems. The automated d...
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Real-time Artificial Intelligence (AI) and Machine Learning (ML) programs have emerged as a number one motive force for considerably altering patient care and management inside the healthcare control area. Staff short...
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Cyber Ego expands to Cyber and Alter-ego. In this proposed paper, I have integrated four deep learning models for performing the tasks - natural language generation, text-to-speech, speech-driven facial animation and ...
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This has led to several challenges in attaining semantic compatibility of the large multi-center health care data systems obtained across different systems. IVERSITY of representation of data, formats, and terminologi...
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