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6th International Conference on Communications and Cyber-Physical engineering, ICCCE 2023

作者： Sembeta, Chala Embabo, Amansisa Gangone, Swapna Bharat Kumar, G.J. Department of Computer Science Mattu University Oromia Mattu Ethiopia Department of Computer Science and Engineering Sreenidhi Institute of Science and Technology Hyderabad India

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

关键词： Hotels

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MIRA: An Affordable Design and engineering of an Autonomous Underwater Vehicle 6

MIRA: An Affordable Design and Engineering of an Autonomous ...

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6th International Conference on Control and Robotics, ICCR 2024

作者： Shrote, Sumukh Karunamurthy, K. Gunturu, Shasank Srivatsa, Puvvala Shyam Ganesan, Suganya School of Computer Science and Engineering Vellore Institute of Technology Chennai Chennai India School of Mechanical Engineering Vellore Institute of Technology Chennai Chennai India School of Electronics Engineering Vellore Institute of Technology Chennai Chennai India

ISBN: (纸本)9798331518158

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 an AUV system with Level 4 autonomy, aimed at performing these tasks with high precision and efficiency in challenging underwater environments. To maintain a balance between performance and cost, the AUV relies solely on visual-inertial sensors, eliminating the need for expensive sonar systems. The key features of the proposed system include a low-light multi-sensor vision system for enhanced perception, a robust pressure hull with multiple compartments to protect critical electronics, a static mechanical gripper for subsea manipulation, and an eight-thruster configuration that provides superior maneuverability and stability. These advancements make the AUV a reliable and cost-effective solution for subsea operations, particularly in regions where low-cost alternatives are critical, such as the growing offshore energy sectors of developing nations. © 2024 IEEE.

关键词： Autonomous underwater vehicles

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An Automated Methodology for Detecting Cercospora Infestations in Eggplant using YOLOv10N 4

An Automated Methodology for Detecting Cercospora Infestatio...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Deepan, S. Revathy, Ms. M. Kit - Kalaignarkarunanidhi Institute of Technology Department of Computer Science and Engineering Coimbatore India Cse Kit - Kalaignarkarunanidhi Institute of Technology Coimbatore India

ISBN: (纸本)9798331523923

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 affected percentages using advanced image processing and the YOLOvlON algorithm. A diverse dataset of annotated eggplant leaf images highlights healthy and infected regions. Preprocessing steps, including noise reduction and feature enhancement, ensure accurate detection. Supervised data augmentation enhances the model's adaptability to real-world conditions. The proposed system achieves high precision with competitive metrics, such as mean Average Precision (mAP), precision, and recall. Its lightweight design ensures compatibility with various devices, promising accessible solutions for farmers. Future developments aim to integrate mobile platforms, advancing sustainable and scalable plant disease management in agriculture. © 2025 IEEE.

关键词： Noise abatement

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CLEAN: Cloud-enabled Scalable Blockchain Outsourcing System Using Dynamic Programming 6

CLEAN: Cloud-enabled Scalable Blockchain Outsourcing System ...

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6th ACM International Symposium on Blockchain and Secure Critical Infrastructure, BSCI 2024

作者： Chan, Weilin Wei, Yihang Jiang, Peng Xu, Lei Zhu, Liehuang Yu, Jing School of Computer Science and Technology Beijing Institute of Technology Beijing China Institute of Information Engineering Chinese Academy of Sciences Beijing China

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

关键词： Cloud platforms

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Enhancing Human Action Recognition through advanced Motion Descriptors and Machine Learning 1

Enhancing Human Action Recognition through advanced Motion D...

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1st International Conference on Optimization Techniques for Learning, ICOTL 2023

作者： Ramalingam, Vijay Saravanan, D. Vignesh, S. Rahin Batcha, R. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350328042

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 annotating them accordingly;human action identification involves recognizing actions associated with one or more objects based on analysis of their activities and context. As technology has proliferated rapidly, huge amounts of human-centric video data have accumulated;it has become an obstacle in contemporary video processing systems due to identifying all these actions efficiently. Complex backgrounds compound these challenges, creating error margins that severely hamper many object detection algorithms and creating error margins that compromise performance - particularly with regard to occlusion and distortion issues. This work addresses these obstacles using Apache Spark with in-memory computing and distributed environment for improved recognition of human actions;specifically, a Local Motion Descriptor is employed for feature extraction that calculates intensities between frames that include both motion and appearance elements. Spark MLlib employs random forest to effectively recognize human behavior. Experimental studies are performed to evaluate its activity recognition capabilities and results are benchmarked against popular supervised classification techniques like Decision Trees;results demonstrate that our proposed model outshone other classification techniques, confirming its efficacy in activity recognition. © 2023 IEEE.

关键词： Decision trees

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Investigating the impacts of spatial-temporal variation features of air density on assessing wind power generation and its fluctuation in China

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science China(Technological sciences) 2023年第6期66卷 1797-1814页

作者： REN GuoRui WANG Wei WAN Jie HONG Feng YANG Ke YU DaRen School of Control and Computer Engineering North China Electric Power UniversityBeijing 102206China School of Electrical Engineering and Automation Harbin Institute of TechnologyHarbin 150001China Xi’an Thermal Power Research Institute Co. Ltd.Xi’an 710054China School of Energy Science and Engineering Harbin Institute of TechnologyHarbin 150001China

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.

关键词： air density spatial-temporal variation power curve wind resource assessment fluctuation ERA-5

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Predicting health insurance claim frauds using supervised machine learning technique 8

Predicting health insurance claim frauds using supervised ma...

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8th IEEE International Conference on science, technology, engineering and Mathematics, ICONSTEM 2023

作者： Veena, K. Supriya, S. Deepa, D. Cruz Antony, J. Karpura Dheepan, G.M. Pavan, Ayila Sri Dharma Siva Pagadalahariprasad Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350347791

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 detection of healthcare scams uses data mining and machine learning approaches. In this work, an effort is made to provide a review of healthcare industry frauds and the methods for spotting them. A proposed solution to address issues in health insurance claims involves using a machine learning model that focuses on utilizing appropriate methods, identifying key data sources, and examining the properties of healthcare data. To gain insight into the patterns of the features, univariate and bivariate analysis are conducted on the data. Afterwards, proper data visualization is performed to determine which feature has the greatest impact. Based on the pre-processed data, a machine learning model is constructed and compared with four algorithms: Logistic Regression, Random Forest, Decision Tree Classifier, and Naive Bayes. The highest accuracy was achieved through the Decision Tree Classifier algorithm, with an accuracy of 97.03%. © 2023 IEEE.

关键词： Health insurance

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Real - Time AI and Machine Learning Applications in Healthcare Management 1

Real - Time AI and Machine Learning Applications in Healthca...

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1st IEEE International Conference on Emerging Research in Computational science, ICERCS 2023

作者： Poornima, D. Premalatha, J. Sarojini Abirami, G. Triveni, K. Bobby, M.P. Sathyabama Institute of Science & Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350359756

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 shortages and affected person document management are among the fundamental problems dealing with the healthcare industry. In an effort to fight those issues, actual-time AI and ML technology offer a demonstration of optimism with the aid of facilitating quick decisions and higher affected person care. Having those technologies work together ensures that time-sensitive healthcare statistics is processed quick, which improves both useful resource allocation and service shipping. However, there are challenges related to the implementation of real-time AI and ML packages in healthcare management. Interoperability, the mixing of disparate records sources, and privateness and security concerns all pose great difficulties. In addition, an intensive and powerful approach is needed to assure the processing of healthcare facts in real-time. To better manipulate patient facts inside electronic health information, an IoT-based totally Artificial Deep Neural Network (IoT-ADNN) is offered in this studies. There are a huge style of revolutionary uses for actual-time (AI-ML) in healthcare administration. These gear provide healthcare carriers with actual-time information, which in flip results in better resource allocation and a more gratifying affected person revel in. In addition, they enhance information security and accessibility, making sure privateness and ease of get entry to to touchy healthcare facts. Data transmission and shipping quotes, postpone estimation accuracy, energy performance, and throughput have all been dramatically improved. The importance and usefulness of actual-time AI and ML systems in healthcare management are emphasised by means of these measurements. © 2023 IEEE.

关键词： Deep neural networks

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Cyber-Ego: The Digital Self 9

Cyber-Ego: The Digital Self

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9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Saravanan, R. Aishwarya, R. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350397376

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 image animation as a combined framework to generates a video of the character speaking the generated content with the character's voice. Text data and audio voice lines have been collected and pre-processed to train the text-to-speech model. While pre-trained models are fine-tuned and used for the speech-driven facial animation model and image animation model. To put everything together in an interactive way, a Flask-based Python web application is created to provide as an interface that handles user inputs and generates the result. © 2023 IEEE.

关键词： Animation

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Ontology-Driven Semantic Interoperability Framework for Multi-Center Healthcare Big Data with Graph-Based Query Analysis

Ontology-Driven Semantic Interoperability Framework for Mult...

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2025 International Conference on Computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Balakrishnan, T. Suresh Karthikeyan, L. Gururama Senthilvel, P. Ebenezar, U. Samson Preetham, B.S. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India Saveetha University Department of Computer Science and Engineering Chennai India Ms Ramaiah Institute of Technology Department of Medical Electronics Bangalore India

ISBN: (纸本)9798331538538

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 terminologies make it difficult to match up and analyze data. In the light of these challenges, there is a proposed solution called the OntologyDriven Semantic Interoperability Framework which applies definitive ontologies of the domain to achieve the interlinking of health data. It thus facilitates data sharing across the different institutions, and also ensures that the data is consistent and can be interpreted in the same way. This component of our approach is called the graph-based query analysis where graph database and query optimization techniques are used to improve the efficiency of the data retrieval process. Our approach also effectively represents healthcare entities and their relationships as a knowledge graph to support human-controllable queries and various semantic reasoning. Moreover, for an automated matching of the different data sets to a common conceptual schema we employ machine learning techniques to automatically map various sources of input to the created ontology. The proposed framework facilitates the access of data, enables researchers and clinicians to make more-informed decisions and helps in big science in the biomedical field. Experimental analysis prove that arsenal is efficient in comparing to traditional relational databases in terms of execution of queries' time and their accuracy. Our work reveals that using the approaches based on ontology can effectively eliminate the issues of semantic mismatch and will contribute to the development of an intelligent environment for healthcare. © 2025 IEEE.

关键词： eHealth

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