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2nd International Conference on artificial intelligence and Machine Learning Applications, AIMLA 2024

作者： Brinda, B.M. Rajan, C. Geetha, K. Nathiya, N. Raguraman, P.J. Srinivasan, K. Paavai College of Engineering Computer Science and Engineering Namakkal India Information Technology KSRCT College of Technology Namakkal India Excel Engineering College Computer Science and Engineering Namakkal India Paavai College of Engineering Artificial Intelligence and Data Science Namakkal India

ISBN: (纸本)9798350349221

This work offers a comprehensive investigation of sentiment analysis in social media communication through the integration of deep learning techniques with a natural language processing (NLP) methodology. The goal of the project is to create a matching model that can be used in real-world social processes. This model will allow for the precise identification of pertinent content that is dynamically changing and the realtime selection of key phrases based on available data. The paper builds a message sentiment analysis model and an image message multimodal sentiment analysis model, exploring unimodal and multimodal sentiment analysis algorithms in social networks. The study shows how well it works to extract complex emotions from social media writing by fusing advanced deep neural networks - like transformers or recurrent neural networks - with natural language processing techniques. Furthermore, the study presents noteworthy results, such as 96.1% order accuracy for brief texts in deep learning models that have been optimized, sentiment filtering for positive, neutral, and negative comments in social network data that has been successful, and an assessment of sophisticated semantic similarity models that offers a thorough comprehension of their performance in classification tasks. This work provides insightful information that may be used to improve sentiment analysis models and their usefulness in tasks involving semantic similarity and social network data interpretation. © 2024 IEEE.

关键词： Sentiment analysis

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Evaluation and Inference of a Proposed, Improved and Efficient W Model for Software Testing 15

Evaluation and Inference of a Proposed, Improved and Efficie...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Velan, Senthil S. Velan, Sivaranjani Senthil Rubini, P. Sudhakar, K. Information Science and Engineering CMR Institute of Technology Bengaluru India Computer Science and Engineering MVJ College of Engineering Bengaluru India School of Engineering and Technology CMR University Bengaluru India Artificial Intelligence and Data Science NITTE Meenakshi Institute of Technology Bengaluru India

ISBN: (纸本)9798350370249

Software Testing is an important and measured/outcome-oriented field that requires an in-depth analysis for developing new methodologies. This enables the development of high-quality end product resulting in fewer maintenance schedules. Several models have been proposed by research teams of different companies leading to improved development and testing schedules. Even with the availability of these models, a significant allocation of the budget has been dedicated purely on the maintenance of software developed by the IT industry. In order to address this need for improved process models of testing, a new exclusive and efficient W model has been proposed and explained in this paper. This model can improve the quality of end product, namely the high-quality software and further reduce the cost involved for the maintenance. Based on the quantitative measurement the proposed W software testing model, it was found the small scale software developed by adopting this method produces high-quality software with 50% less errors/faults compared to development using classical life cycle model. ©2024 IEEE.

关键词： Computer software selection and evaluation

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Feature Selection based on Variable Universe Rough Set 23

Feature Selection based on Variable Universe Rough Set

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23rd International Conference on Machine Learning and Cybernetics, ICMLC 2024

作者： Wang, Wanting Tsang, Eric C.C. Xu, Weihua Macau University of Science and Technology Faculty of Innovation Engineering Taipa China College of Artificial Intelligence Southwest University Chongqing400715 China

ISBN: (纸本)9798331528041

The variable universe model is an effective tool for handling uncertain knowledge. However, there remains room for improvement in the area of feature selection within this model. Addressing this gap, we propose a lower approximation reduction based on the variable universe model to enhance feature selection tasks. We design and implement corresponding algorithms, demonstrating through experimentation that our approach outperforms traditional method. Specifically, our method not only achieves rapid and effective feature selection but also significantly reduces data dimensionality. Leveraging the unique characteristics of the variable universe model, our approach accurately identifies key features within datasets, thereby enhancing efficiency and accuracy in data analysis. © 2024 IEEE.

关键词： data accuracy

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Hybrid RNN Based Text Classification Model for Unstructured data

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SN Computer science 2024年第6期5卷 726页

作者： Sunagar, Pramod Sowmya, B.J. Pruthviraja, Dayananda Supreeth, S. Mathew, Jimpson Rohith, S. Shruthi, G. Cloudthat Technologies Department of Artificial Intelligence and Data Science Ramaiah Institute of Technology Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education School of Computer Science and Engineering REVA University Department of Computer Science and Engineering Indian Institute of Technology Patna Department of ECE Nagarjuna College of Engineering and Technology

The volume of social media posts is on the rise as the number of social media users expands. It is imperative that these data be analyzed using cutting-edge algorithms. This goal is handled by the many techniques used in text categorization. There are a variety of text categorization techniques available, ranging from machine learning to deep learning. Numerical crunching has become easier with less processing time since the emergence of high-end computer facilities. This has led to the development of sophisticated network architectures that can be trained to achieve higher precision and recall. The performance of neural network models which was evaluated by the F1 score is affected by cumulative performance in precision and recall. The current study intends to analyze and compare the performance of the neural network proposed, A Hybrid RNN model that has two layers of BiLSTM and two layers of GRU to that of previous hybrid models. GloVE dataset is used to train the models and their accuracy, precision, recall, and F1 score are used to assess performance. Except for the RNN + GRU model, the RNN + BILSTM + GRU model has a precision of 0.767, a recall of 0.759, and an F1-score of 0.7585. This hybrid model outperforms the others. © The Author(s) 2024.

关键词： Bidirectional long-short-term memory (BiLSTM) Gated recurrent units (GRU) Recurrent neural networks (RNN) Region-based convolutional neural networks (RCNN) Text classification

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Sentiment Analysis of Text and Emoji using Machine Learning Algorithms 2

Sentiment Analysis of Text and Emoji using Machine Learning ...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Rubini, P.E. Sheela, K. Selva Devi, R. Delshi Howsalya Yamuna, R. Penyameen, K. Gowdhamkumar, S. Ibm Karnataka Bengaluru India Kgisl Institute of Technology Department of Artificial Intelligence and Data Science Tamilnadu Coimbatore India Karpaga Vinayaga College of Engineering and Technology Department of Artificial Intelligence and Data Science Tamilnadu Chengalpattu India Vsb College of Engineering Technical Campus Department of Electrical and Electronics Engineering Coimbatore India Velammal College of Engineering and Technology Department of Information Technology Madurai India Training Department Coimbatore India

ISBN: (纸本)9798350384277

A major form of communication after the speech is text. Unlike direct communication through a speech where the tone can be analyzed depending on the pitch and frequency, text analysis is based on the frequency of words in a particular text. A recent survey conducted shows that around 80% of business data is in textual format. All industry and business users including call centers, online reviews, customer surveys, etc., work with raw textual data. This untapped data when mined and analyzed can provide great insights to the organization. Consumers are interested in other's opinions and experiences. Statistics reveal that at least 90% of people are influenced by what they read and hence the negative comments take a huge toll. Over the recent years, sites have started collecting customer reviews for their products. Companies use 'Text Analysis' to manage their content. Textual data when converted into data slices that are easy to automate, processes like product development, decision making, marketing optimization, and business intelligence take place easily. When the text analysis process is automated, it helps analyze large chunks of data within a short period of time and with lesser resources when compared to manual analysis. Automated text analysis is also proven to be faster and scalable along with consistent results. The purpose of this work is to develop a text analysis algorithm that will classify text into either one of the three categories: Positive, Negative, or Neutral. Once the received text is cleaned and pre-processed, it can be classified into one of the categories based on classification techniques like Naive Bayes, Support Vector Machine, etc., although it is observed that the Naive Bayes approach produces better accuracy. Similar to text, emoji can also be converted to its respective textual sequence and then classified. © 2024 IEEE.

关键词： Support vector machines

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Advancements in Liver Tumor Detection:A Comprehensive Review of Various Deep Learning Models

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Computer Modeling in engineering & sciences 2025年第1期142卷 91-122页

作者： Shanmugasundaram Hariharan D.Anandan Murugaperumal Krishnamoorthy Vinay Kukreja Nitin Goyal Shih-Yu Chen Deptartment of Artificial Intelligence and Data Science Vardhaman College of EngineeringHyderabad501218India Deptartment of CSE V.S.B College of EngineeringKarur639111India Deptartment of Electrical and Electronics Engineering Vardhaman College of EngineeringHyderabad501218India Centre for Research Impact&Outcome Chitkara UniversityRajpura140401PunjabIndia Department of Computer Science and Engineering School of Engineering and TechnologyCentral University of HaryanaMahendergarh123031HaryanaIndia Department of Computer Science and Information Engineering National Yunlin University of Science and TechnologyYunlin64002Taiwan Intelligence Recognition Industry Service Research Center National Yunlin University of Science and TechnologyYunlin64002Taiwan

Liver cancer remains a leading cause of mortality worldwide,and precise diagnostic tools are essential for effective treatment *** Tumors(LTs)vary significantly in size,shape,and location,and can present with tissues of similar intensities,making automatically segmenting and classifying LTs from abdominal tomography images crucial and *** review examines recent advancements in Liver Segmentation(LS)and Tumor Segmentation(TS)algorithms,highlighting their strengths and limitations regarding precision,automation,and *** metrics are utilized to assess key detection algorithms and analytical methods,emphasizing their effectiveness and relevance in clinical *** review also addresses ongoing challenges in liver tumor segmentation and identification,such as managing high variability in patient data and ensuring robustness across different imaging *** suggests directions for future research,with insights into technological advancements that can enhance surgical planning and diagnostic accuracy by comparing popular *** paper contributes to a comprehensive understanding of current liver tumor detection techniques,provides a roadmap for future innovations,and improves diagnostic and therapeutic outcomes for liver cancer by integrating recent progress with remaining challenges.

关键词： Liver tumor detection liver tumor segmentation image processing liver tumor diagnosis feature extraction tumor classification deep learning machine learning

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Blockchain-Empowered Asynchronous Federated Reinforcement Learning for IoT-Based Traffic Trajectory Prediction

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IEEE Internet of Things Journal 2025年第11期12卷 17095-17109页

作者： Wang, Bin Tian, Zhao Tang, Fengxiao Pan, Heng She, Wei Liu, Wei Zhengzhou University School of Computer and Artificial Intelligence Zhengzhou450002 China Zhengzhou University School of Cyber Science and Engineering Zhengzhou450002 China Central South University School of Computer Science and Engineering Changsha410083 China Zhongyuan University of Technology School of Cyber Space Security Zhengzhou450007 China Zhengzhou University School of Cyber Science and Engineering Zhengzhou Key Laboratory of Blockchain and Data Intelligence Zhengzhou450002 China

Vehicle trajectory prediction plays a crucial role in IoT-based intelligent transportation systems, which can effectively address key issues, such as driving safety and multivehicle collaboration. However, the sensitivity of trajectory data and the reluctance of data holders to share it constrain the prediction model’s ability to capture vehicle behavior patterns in different scenarios. To address the above problems, we propose a blockchain-enabled asynchronous federated proximal policy optimization framework (BE-AFPPO) for the trajectory prediction of self-driving vehicles. First, we propose a curiosity proximal policy optimization (C-PPO) algorithm. The method utilizes a driven exploration strategy to actively motivate the intelligent agent to explore the unknown state space. The avoidance policy model reaches a local optimum when processing trajectory data. In addition, we design historical gated recurrent unit (GRU) and future GRU as input layers. The target’s historical motion features and future trajectory features are extracted, respectively. Then, various data is received through asynchronous federated learning. This model can fully learn the vehicle’s behavior patterns in different scenarios, which improves prediction accuracy. Based on this, we develop a blockchain-based dynamic group practical Byzantine fault tolerance (DG-PBFT) consensus algorithm. This enhances the credibility and integrity of the data while enriching the sources of trajectory data. Finally, we perform the experiments on the publicly available dataset nuScenes. The results demonstrate that the proposed method improves the robustness and accuracy of trajectory prediction. © 2014 IEEE.

关键词： Federated learning

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Investigating the 3D cardiomyocyte arrangement in human interventricular septum sample using X-ray phase-contrast microtomography 17

Investigating the 3D cardiomyocyte arrangement in human inte...

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17th IEEE International Conference on Signal Processing, ICSP 2024

作者： Li, Wenfeng Li, Zhaorui Du, Yongbin Wang, Shunli Zhengzhou University of Science and Technology Institute of Big Data and Artificial Intelligence Zhengzhou China Harbin Institute of Technology School of Instrumentation Science and Engineering Harbin China Heilongjiang University of Science and Technology School of Electrical and Control Engineering Harbin China

ISBN: (纸本)9798350387384

A good knowledge of the three-dimensional cardiomyocyte arrangement in interventricular septum is crucial for understanding both ventricle's functions. In this preliminary study, we investigate the local myocardial architecture in a human interventricular septum sample imaged by X-ray phase-contrast micro-tomography. This imaging modality allows us to visualize the complex structure of myocardial tissue at high resolution. We first cut the reconstruction data into sub-volume of 32×32×32 voxels (i.e. 112×112×112 μm3) in size, and compute the elevation angle of the local cardiomyocyte using Fast Fourier Transform based method. Then, we describe the distribution of cardiomyocyte elevation angle in the sample globally and locally. We note that, in part of regions, the running of cardiomyocyte aggregates is winding. Though the function of this specific cardiomyocyte arrangement manner is unclear, its existence is inspiring and merits further investigation. © 2024 IEEE.

关键词： Fast Fourier transforms

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The Future of Chatbots: Survey on Generative Pretrained Models, Ernie, and Gemini

The Future of Chatbots: Survey on Generative Pretrained Mode...

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2024 International Conference on Innovations and Challenges in Emerging Technologies, ICICET 2024

作者： Raut, Abhishek Katti, Jayashree Pimpri Chinchwad College of Engineering Nigidi Artificial Intelligence and Data Science Department of Information Technology Pune India Pimpri Chinchwad College of Engineering Department of Information Technology Nigidi Pune India

ISBN: (纸本)9798350319019

The evolution of chatbots from rule-based, retrieval-based, contextual systems, to hybrid and generative AIpowered models. It aimsto solve complex natural language processing and task-specific challenges. The finding explores innovative self-attention mechanisms in the Generative Pretrained Transformer (GPT). The GPT model training and fine-tuning methods include discriminative fine-tuning, zeroshot and few-shot learning, parallel processing, and reinforcement learning with human feedback. Revolutionary multimodal AI models like GPT-4 (Open-AI), Ernie (Baidu), and Gemini (Google). The study discovers the advantages and limitations of chatbots. © 2024 IEEE.

关键词： Reinforcement learning

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Ecowatch: A Comprehensive NodeMCU-Based System for Solar Power Oversight 2

Ecowatch: A Comprehensive NodeMCU-Based System for Solar Pow...

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2nd IEEE International Conference on Interdisciplinary Approaches in technology and Management for Social Innovation, IATMSI 2024

作者： Thopate, Kaushalya Kambare, Shweta Manohar Kulkarni, Mukund Gawade, Mayuri Jadhav, Balasaheb Jaiswal, Krishna Nilesh Vishwakarma Institute of Technology Computer Engineering Pune India Vishwakarma Institute of Technology Artificial Intelligence and Data Science Pune India Vishwakarma Institute of Technology Electronics and Telecommunicarion Pune India

ISBN: (纸本)9798350360523

Globally, solar power systems are becoming more and more popular as a renewable energy source. However, to maximize their effectiveness and guarantee their dependable functioning, these systems must be effectively managed and monitored. In this study, we suggest utilizing the open-source IoT platform NodeMCU to create a solar power monitoring system. Our solution gives owners of solar power systems complete monitoring capabilities by gathering, processing, and visualizing data in real time from solar panels, batteries, and other system components. Because of its scalable and affordable design, the system can be used for both home and business purposes. We show through our research that our NodeMCU-based solar power monitoring system is both practical and efficient. This research introduces an innovative approach to monitoring and optimizing solar power systems through the implementation of NodeMCU in an Internet of Things (IoT) framework. The study's findings significantly contribute to the field of renewable energy management by advocating for a more efficient and scalable solution, thereby fostering broader acceptance and utilization of renewable energy sources to achieve a sustainable and environmentally friendly future. © 2024 IEEE.

关键词： Solar energy

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