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Negation Handling and Domain Generalization in Sentiment Analysis using Machine Learning Models

Negation Handling and Domain Generalization in Sentiment Ana...

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2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Biradar, Shrnivas Raju, G.T. Divakar, K.M. SJC Institute of Technology Dept. of Computer Science and Engineering Chikkaballapur India SJC Institute of Technology Dept. of Information Science and Engineering Chikkaballapur India

ISBN: (纸本)9798350359688

Conventional lexicon-based approaches to sentiment analysis typically lack the necessary methods to properly identify the negation window, making it impossible to model negation. An enormous increase in sentiment-rich electronic and social media has been observed daily. Negation modifiers cause problems for Sentiment Classification techniques and have the power to entirely change the discourse's meaning. Therefore, it becomes essential to manage them well. Opinion mining or sentiment analysis is the study of.people's attitudes, feelings, and views as they are expressed in written language. It is one of.the busiest text mining and natural language processing research projects. Even though sentiment analysis research has gained popularity in the field of.natural language processing, for this problem, the state-of.the-art machine learning approach is based on Bag of.Words. But the BOW model pays little attention to polarity shift, which could have a distinct overall effect. One of.the main issues with doing sentimental analysis on any given text or sentence is handling polarity shift, which is what this study attempts to address. Sentiment analysis use Natural Language Processing principles to identify negation in the text. Our goal is to identify the negation effect on customer reviews that, although appearing good, are actually negative. The suggested modified negation methodology helps to increase classification accuracy by providing a method for computing negation identification. In terms of.review classification by accuracy, precision, and recall, this approach yielded a noteworthy outcome. When test and training data are from distinct domains, machine learning faces the challenge of.domain generalization. Despite the large body of.research on cross-domain text classification, the majority of.current methods concentrate on one-to-one or many-to-one domain adaptation. Our domain generalization method regularly outperforms state-of.the-art domain adaption methods, a

关键词： Natural language processing systems

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Enhancing Solar Energy Efficiency: A Deep Learning Approach for Dust Detection

Enhancing Solar Energy Efficiency: A Deep Learning Approach ...

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2024 International Conference on Smart Electronics and Communication Systems, ISENSE 2024

作者： Vigna Harish, R. Ramya, A. Siva, S. Santhi, S. Kalaiselvi, S. Anitha, V. National Engineering College Dept. of Information Technology Tuticorin India National Engineering College Dept. of Computer Science and Engineering Tuticorin India

ISBN: (数字)9798331528126

ISBN: (纸本)9798331528126

In recent decades, power generation from PV systems has become increasingly popular. However, several environmental variables, such as dust deposition on PV panels, have significantly reduced PV energy production. Several approaches are under investigation to identify dust and decrease power loss. These include thermal imaging, image processing, sensors, and IoT devices, machine learning, and deep learning. The orientation of.a solar panel and the angular inclination relative to the horizontal plane affect one of.the environmental factors that impact its performance: since they have an impact on how much solar energy that enters into the photovoltaic panel's surface. Dust, for one, is another example of.environmental factors: the quantity of.energy output can be influenced by coming into contact with dust. Dust particles lying on the panel's surface have the potential to reduce light from reaching the solar modules, which would drastically reduces the energy output. Cleaning or reducing modules is critical, but these procedures must be continuously monitored and assessed for their effects. Image processing algorithms can be used to detect dust to optimize solar panels. In this paper, a novel dataset from Kaggle comprising images of.dusty and clean solar panels is presented, aiming to facilitate the research on automated dust detection algorithms. The dataset is utilized to evaluate the performance of.existing state-of.the-art (SOTA) image classification methods. Subsequently, a new Convolutional Neural Network (CNN) architecture, ResNet-50 with an additional attention mechanism, is proposed, specifically designed for solar panel dust detection. The experimental results shows that the proposed method outperforms other SOTA algorithms, achieving an accuracy level of.96.2%. © 2024 IEEE.

关键词： Thermography (imaging)

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Image Enhancement for Iris Analysis Using Deep Learning Algorithms - A Brief Survey 2

Image Enhancement for Iris Analysis Using Deep Learning Algo...

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2nd International Conference on Emerging Trends in information Technology and engineering, ic-ETITE 2024

作者： Poovayar Priya, M. Ezhilarasan, M. Pondicherry Engineering College Dept. of Computer Science and Engineering Puducherry India Pondicherry Engineering College Dept. of Information Technology Puducherry India

ISBN: (纸本)9798350328202

With applications including medical image analysis, picture sharpening and restoration, robot vision, pattern recognition, and video processing, among many others, image enhancement is the main topic in image processing and computer vision. There have been many algorithms for improving images established in the past. More extensive work on image-enhancing techniques has been done utilizing deep learning models in recent years, as these algorithms have achieved their peak in vision applications. In this study, we present a thorough analysis of.machine learning, deep learning, and literature. The literature on image enhancement is then summarised, and future approaches for iris analysis are discussed. © 2024 IEEE.

关键词： Image enhancement

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Leveraging Deep Learning for Identification of.Medicinal Plant Species 2

Leveraging Deep Learning for Identification of Medicinal Pla...

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2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Vidya, H.A. Narasimha Murthy, M.S. Thara, D.K. KIT Tiptur Vtu Dept. of Computer Science and Engineering Belagavi India BMSIT&M Bengaluru Vtu Dept. of Information Science and Engineering Belagavi India CIT Gubbi Vtu Dept. of Information Science and Engineering Belagavi India

ISBN: (数字)9798350373110

ISBN: (纸本)9798350373110

Identifying medicinal plant species is crucial in plant taxonomy because it allows us to harness the therapeutic properties of.medicinal plants for human health. This information can be beneficial for the advancement of.new drugs and treatments and for promoting the sustainable use of.medicinal plants. Allopathic medicine has side effects in many cases. Traditional medicine, like Ayurveda, provides alternative treatments with minor side effects and helps protect human health. The decline of.many medicinal plant species highlights the need for botanical research and biodiversity conservation. Artificial intelligence builds a gap between applications of.computer technology in plant taxonomy. The goal of.the study is to develop a reliable deep learning system for the recognition of.medicinal plant species, which can subsequently aid in their preservation and support ongoing research efforts in this field. This paper focuses on a deep learning approach based on Convolutional Neural Network (CNN) to identify three distinct categories of.medicinal plants. The model proposed uses CNN architecture and two pre-trained models, InceptionV3 and ResNet50, to classify three classes of.medicinal plants, namely neem, peepal, and tulsi. The CNN model has an accuracy of.97.14% and a loss of.0.1274, the ResNet50 model has an accuracy of.97.06% and a loss of.0.1474, and the InceptionV3 model has an accuracy of.100% and a loss of.0.0199 in identifying the medicinal plant species. © 2024 IEEE.

关键词： Medicinal chemistry

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Cao Robot for Taiwanese/English Knowledge Graph Application 27

Cao Robot for Taiwanese/English Knowledge Graph Application

引用

27th Conference on the Oriental COCOSDA International Committee for the Co-Ordination and Standardisation of.Speech Databases and Assessment Techniques, O-COCOSDA 2024

作者： Lee, Chang-Shing Wang, Mei-Hui Tseng, Guan-Ying Yue, Chao-Cyuan Hsieh, Hao-Chun Reformat, Marek National University of Tainan Dept. of Computer Science and Information Engineering Tainan Taiwan University of Alberta Dept. of Electrical and Computer Engineering EdmontonAB Canada

ISBN: (纸本)9798331506032

This paper proposes a Content Attention Ontology (CAO) robot for constructing Taiwanese/English Knowledge Graphs (KGs) by prompting audio or texts to Large Language Models (LLMs), including TAIDE, Zephyr, and Llama 3.1. The collected data includes lecture videos from the IEEE WCCI 2024 in Japan and the 2024 National Language Development Forum in Taiwan, along with students' learning data from the 2024 Summer School on Taiwanese/English Human and Robot Co-Learning at Rende Elementary School (RDES). In addition, the fundamental concepts of.Computational Intelligence (CI) and Quantum CI (QCI) learning were incorporated into the study. The generative KGs highlight important concepts, relations, and communities within the collected teaching and learning data. Additionally, we utilized data from subjects wearing braincomputer interface (BCI) devices while speaking Taiwanese/English to generate KGs. We also compared the differences in these KGs and analyzed the similarities between the transcribed texts of.lectures and learners. In the future, we plan to expand the CAO robot to more validation fields across Taiwan, aiming to engage young students in speaking Taiwanese while concurrently enhancing their English language skills through interaction with the robot. © 2024 IEEE.

关键词： Knowledge graph

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Adaptive Forward Error Correction for Voice over IP with Random Linear Network Coding 2

Adaptive Forward Error Correction for Voice over IP with Ran...

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2nd IEEE Virtual Conference on Communications, VCC 2024

作者： Su, Yi-Chiuan Wu, Quincy National Chi Nan University Dept. of Computer Science and Information Engineering Nantou Taiwan

ISBN: (纸本)9798331530099

This paper proposes the use of.Random Linear Network Coding (RLNC) as an erasure code in the forward error correction (FEC) mechanism for Internet telephony. It also designs and implements a forward error correction algorithm applied to the Real-time Transport Protocol (RTP), which we call Adaptive Systematic RLNC (AS-RLNC). The algorithm learns the current packet loss rate of.the network through the Real-time Transport Control Protocol (RTCP) and dynamically adjusts the number of.redundant packets sent. Compared with traditional forward error correction methods based on block codes, this approach can enhance the reliability of.data transmission and improve audio quality by 31%. © 2024 IEEE.

关键词： Internet telephony

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Using Blockchain for Incentive-Driven FHIR-based Health Record Exchange 24

Using Blockchain for Incentive-Driven FHIR-based Health Reco...

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39th Annual ACM Symposium on Applied Computing, SAC 2024

作者： Rosar, Moritz Chuang, Kun-Ta Dept. of Computer Science and Information Engineering National Cheng Kung University Tainan Taiwan

ISBN: (纸本)9798400702433

Medical data is highly sensitive data, and patients are particularly concerned about the exposure of.their identities [1]. A study found that 64% of.participants were agreeable to sharing their health data when anonymized, highlighting the importance of.privacy in medical data sharing [5]. © 2024 Copyright held by the owner/author(s).

关键词： Sensitive data

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Cybersecurity Threat Detection Using Deep Learning and Anomaly Detection Techniques

Cybersecurity Threat Detection Using Deep Learning and Anoma...

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2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Rajendran, T. Mohamed Imtiaz, N. Jagadeesh, K. Sampathkumar, Balaji Rajalakshmi Institute of Technology Department of Computer Science and Engineering Chennai India Nagarjuna College of Engineering and Technology Dept. of Information Science and Engineering Bangalore India Sri Venkateswaraa College of Technology Dept. of Computer Science and Engineering Chennai India Akshaya College of Engineering and Technology Dept. of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350359688

Through the use of.combined deep learning and anomaly detection approaches, this research investigates the area of.cybersecurity threat detection. The study proves the framework's extraordinary success in recognizing as well as mitigating diverse cyber dangers, including insider threats and zero-day attacks, through practical testing and case studies. The framework establishes itself as a proactive cybersecurity solution adaptive to the changing threat landscape due to its high accuracy of.95%, low amount of.false positives 5%, and quick reaction times. The recommendations cover user training, scalability, regulatory compliance, including cross-disciplinary collaboration. Behavioral analysis, AI explainability, IoT security, and countering quantum computing concerns should be the main areas of.future research. These revelations help the cybersecurity industry become more resilient and well-prepared. In this research investigation, experiments on threat detection were conducted using deep learning algorithms, with a specific focus on Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). The findings of.the study reveal that a detection accuracy of.93% was achieved by CNN, while a higher accuracy of.96% in threat detection was exhibited by RNN. © 2024 IEEE.

关键词： Zero-day attack

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Face Recognition via Thermal Imaging: A Comparative Study of.Traditional and CNN-Based Approaches 24

Face Recognition via Thermal Imaging: A Comparative Study of...

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8th International Conference on Machine Learning and Sof. Computing, ICMLSC 2024

作者： Lin, Shinfeng Chen, Luming Chen, Wensheng Chang, Boyu Dept. of Computer Science and Information Engineering National Dong Hwa University Hualien Taiwan

ISBN: (纸本)9798400716546

In this article, a face recognition via thermal imaging: a comparative study of.traditional and CNN-based approaches is proposed. The methodology comprises two distinct components: traditional face recognition and CNN-based face recognition. In the traditional face recognition, we employ Random Forest (RF) and Support Vector Machine Classifier (SVM) techniques. Conversely, CNN-based face recognition leverages Convolutional Neural Networks (CNN) to identify individuals. Our research involves a comprehensive evaluation conducted across different databases, including the PUCV Drunk Thermal Face (PUCV-DTF) and the UCH Thermal Temporal Face (UCH-TTF) datasets. To emulate real-life scenarios, we introduce elements such as glasses, face mask, and noise into the original thermal images during experimentation. The recognition rates of.traditional and CNN-based methods for small size and middle size databases achieve 90% and 100%, respectively, under a challenging condition, wearing glasses and mask. Experimental results demonstrate the feasibility and effectiveness of.our proposed method, showcasing its robustness in tackling various challenges. © 2024 Owner/Author.

关键词： Infrared imaging

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Strategies for Helping Anchor-Based Trackers Learn re-ID Features for Smart City Surveillance

Strategies for Helping Anchor-Based Trackers Learn re-ID Fea...

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2024 IEEE International Conference on Consumer Electronics, ICCE 2024

作者： Chen, Xiu-Zhi Li, Mu-Chuan Chen, Yen-Lin National Taipei University of Technology Dept. of Computer Science & Information Engineering Taipei Taiwan

ISBN: (纸本)9798350324136

Re-identification has become a crucial issue in computer vision today as it allows for tracking objects in both continuous and discontinuous scenarios. Despite achieving perfect detection results, anchor-based trackers encountered difficulties in effectively learning re-identification features, due to various issues. This research proposes strategies aimed at improving the capability of.anchor-based trackers to learn high-quality re-identification (re-ID) features. The model developed through our strategies can extract more distinct features and achieve almost 0.57 Multiple Object Tracking Accuracy (MOTA) on MOT20, even under a limited training dataset. This result indicates that our proposed strategies hold potential for improving the performance of.anchor-based trackers. © 2024 IEEE.

关键词： Anchor-Based Tracker Re-Identification Smart City Surveillance

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