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A Decision Support Model for Improved Routing in Software Defined Vehicular Ad hoc Networks 18

A Decision Support Model for Improved Routing in Software De...

引用

18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Kumar, Manish Raw, Ram Shringar University School of Information Communication and Technology Guru Gobind Singh Indraprastha University Dept. of Computer Science and Engineering New Delhi India New Delhi India

ISBN: (纸本)9789380544519

The inherent dynamism of Vehicular Ad hoc Networks (VANETs), distinguished by rapid movement and frequent changes in network structure, imposes substantial obstacles in effective route management and optimization. The use of Software Defined Networking (SDN) in VANETs enables a centralized control mechanism that provides flexibility and adaptability in the administration of the network. The SDN Controller in SDN is responsible for route determination. There are many factors like the distance between vehicles, movement direction, connection duration, data transfer rate, speed, and vehicle data transmission range that are involved to determine the route. This study introduces an innovative decision support model for Software Defined Vehicular Ad hoc Networks (SD-VANETs) that use the Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to improve the route determination which further enhances the VANETs routing. The model estimates the relative importance of the factors to decide the route by the SDN controller in Sd-VANETs. This work enhances the progress of intelligent routing solutions in VANETs, providing a scalable and efficient way for future ITS advancements. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： AHP MCDM in Routing Multi Criteria Decision Making SD-VANET TOPSIS VANET VANET Routing

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Challenges in Working with Unstructured Data in the LLM Era: NER Processes Using Graph Neural Networks 4

Challenges in Working with Unstructured Data in the LLM Era:...

引用

4th International Conference on Electrical, computer, Communications and Mechatronics engineering, ICECCME 2024

作者： Bouquet, Paolo Molinari, Andrea Sandri, Simone University of Trento Department of Information Engineering and Computer Science Trento Italy University of Trento Dept. of Industrial Engineering Trento Italy Okkam S.r.l. Trento Italy

ISBN: (纸本)9798350391183

The application of neural networks and large language models (LLMs) in the financial technology (fintech) sector has significantly enhanced capabilities of providing better services based on unstructured documents, mainly thanks to the astonishing interactive capabilities of large language models (LLMs). However, in these contexts, precisely recognizing named entities (for example, the beneficiary of an insurance policy) and creating reliable, domain-dependent, private knowledge graphs is more crucial than conversational capabilities. Moreover, optimizing these models for dynamic data environments and ensuring Explainability presents notable challenges. This paper aims to present our approach for the creation of a knowledge graph that uses graph neural nets (GNN) to provide a better named-entity recognition (NER) process, to identify entities in the text precisely and to solve other issues in knowledge graph management, like the co-ference resolution problem or focusing on dynamic data environments and the critical need for Explainability of modern AI-based solutions. The paper presents our preliminary results on one of the various pipeline steps that creates the navigable knowledge graph. © 2024 IEEE.

关键词： Knowledge graph

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An Ameliorated Methodology for Water Supply and Management Using Binary Trees 14

An Ameliorated Methodology for Water Supply and Management U...

引用

14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Arunakumari, B.N. Setty, Vishal R Vivek, Villuri Ramesh, Sujan Sowmyashree, P. Kshama, S.B. Bms Institute of Technology and Management Computer Science and Engineering Karnataka560064 India Bms Institute of Technology and Management Computer Science and Engineering Bangalore India Bms Institute of Technology and Management Dept. of Information Science and Engineering Bengaluru India

ISBN: (纸本)9798350335095

Water supply system is an infrastructure for collection, storage, transmission, and distribution of water to homes, factories, irrigation of agricultural fields, etc. Water holds an important factor in growth of the city. Earlier, lakes and rivers are the major source of water supply to the cities and villages. The rivers followed their own path throughout the city and provided water to some specific areas. Due to this the new areas which were being developed would not have direct connection to the new areas, the people would have to look for other alternatives or use the water available from the underground water beds, by building wells. Water connection to different regions is setup in an organized manner. Even though everything is organized, but there are certain drawbacks such as improper detection in the leakage of pipe lines and failure in the proper use of technology to setup and *** this paper, an attempt has been made to rectify these problems by applying the concept of binary search tree. Also, we have discussed about how pipelines can be setup underground in a smooth manner and using IOT devices to detect leakages or breakages in the pipelines, so that the water is not wasted. © 2023 IEEE.

关键词： Water supply

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Performance Evaluation of Machine Learning Algorithms for Rainfall Prediction Using Dimensionality Reduction Techniques 4

Performance Evaluation of Machine Learning Algorithms for Ra...

引用

4th International Conference on Computing, Mathematics and engineering Technologies, iCoMET 2023

作者： Kumari, Sapna Raza, Muhammad Owais Kumari, Arsha Shaheed Zulfikar Ali Bhutto Institute of Science and Technology Dept. of Computer Science Hyderabad Pakistan University of Sufism and Modern Sciences Dept. of Information and Computing Bhitshah Pakistan Mehran University of Engineering & Technology Dept. of Electronics Engineering Jamshoro Pakistan

ISBN: (纸本)9798350335316

In the last few decades, tremendous change is observed in rainfall patterns which are majorly influenced by two major factors 1) climate change and 2) CO2 emission. Erratic rainfall patterns caused catastrophic effects on agriculture and human life in developing countries like Pakistan, where major economic growth is largely dependent on agriculture. The main objective of this study is to evaluate a performance different Machine learning algorithms for forecasting rainfall patterns using dimensionality reduction techniques on climate change indicators. For this purpose rainfall data was collected for Pakistan. Principle component analysis (PCA), Pearson correlation, and Greedy search algorithms were used for feature selection and the evolution of models was performed using Root Mean Square error (RMSE), Root Absolute Error (RAE), and Coefficient of determination metrics. Results show that features obtained using the Pearson correlation produced the least error and Bayesian linear regression performed with the highest accuracy followed by Neural Network regression. © 2023 IEEE.

关键词： Climate change

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Multinomial Naive Bayes Based Machine Learning Analysis of Twitter Sentiment 2

Multinomial Naive Bayes Based Machine Learning Analysis of T...

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2nd International Conference on Edge Computing and Applications, ICECAA 2023

作者： Saravanan, U.K.Balaji Vijay, M. Shreedhar, T. Rajasekar, G. Yashwanth, R. Shakthipriya, P. Sona College of Technology Information Technology Salem India Vmkv Enginerring College Dept. of Computer Science and Engineering Salem India

ISBN: (纸本)9798350347579

Sentiment analysis identifies and categorizes thoughts and feelings expressed in the source text. Social media uses tweets, status updates, and blog columns to generate sensitive data. SA on user-generated data helps understand crowd opinion. Due to slang and spelling errors, Twitter sentiment analysis (TSA) is much more complex than general sentiment analysis. Every day, millions of tweets are generated on various topics. Machine learning algorithms can be used to solve this problem-the proposed machine learning algorithm includes preprocessing, weight calculation, feature extraction, and classification. First, we collected a Kaggle dataset from Twitter sentiment analysis. In the second step, the data preprocessing steps mainly include stop words, emoji, and repeated characters in words and URLs due to their low importance. Then, we used tokenization, normalization, stemming, and lemmatization to achieve better results. In the third step, we computed weights using word2vec to represent the tweets. In the fourth step, we use a feature extraction method consisting of TF-IDF and word embedding to check based on word and noun counts. Finally, TSA can be used to explore sentiment using Multinomial Naïve Bayes (MNB) and Maximum Entropy classification. The proposed machine learning algorithm achieves better results with TSA in terms of recognition accuracy, recall, precision and F-measure. © 2023 IEEE.

关键词： Sentiment analysis

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Fake News Detection via Sentiment Neutralization

Fake News Detection via Sentiment Neutralization

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Huang, Hanjuan Peng, Hsuan-Ting Pao, Hsing-Kuo National Taiwan University of Science and Technology Dept. of Computer Science and Information Engineering Taipei Taiwan Wuyi University College of Mechanical and Electrical Engineering Wuyishan354300 China

ISBN: (纸本)9798350324457

Everybody knows how fake news or disinformation can have big impact on deciding our daily lives. As people rely on social networks rather than reliable news channels to receive information, we no longer have enough protection mechanisms to help us judge between genuine and fake news. In this work, we propose a sentiment-based analysis for fake news detection. The approach leverages a few of the most recent acclaimed techniques, namely, Large Language Models and Artificial Intelligent Generated Content for sentiment analysis and the subsequent fake news detection. We have ChatGPT to help us rewrite a text for its neutralized version. By comparing the two, before and after the neutralization, we figure out their difference, such as different frequencies of sentiment word usage which can help us for fake news detection. Also, we observe an imbalanced improvement between the fake news and genuine ones. Fake news rather than genuine news in general contains more emotional words, or negative sentiment words to be specific. Taking advantage of such observation can indeed help to further improve the detection result. After all, the evaluation confirms the aforementioned statements and shows the superiority of the proposed method to other state-of-the-art methods, in datasets that own different languages. © 2023 IEEE.

关键词： disinformation detection fake news LLM sentiment analysis

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TinyML-Based Gait Recognition for User Detection on an STM32L4 IoT Node 9

TinyML-Based Gait Recognition for User Detection on an STM32...

引用

9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Hassan, Hateem Abdullah, M. Hussain, Muhammad Zunnurain Hasan, Muhammad Zulkifl Mustafa, Muzzamil Khalid, Aqsa Awan, Rimsha Hussain, Usman Khan, Zohaib Ahmed Javaid, Arslan University Lahore Department of Computer Engineering Information Technology Punjab Pakistan Bahria University Lahore Campus Dept. of Computer Science Pakistan University of Central Punjab Lahore Pakistan Faculty of Information Technology Department of Computer Science Pakistan National College of Business Administration And Economics Department of Computer Science Lahore Pakistan Information Technology University Lahore Pakistan University of Home Economics Pakistan

ISBN: (纸本)9798350394474

Gait recognition stands out as a robust biometric tool for discerning users. Yet, adapting it to devices with limited resources, such as the STM32L4 microcontroller, demands cutting-edge strategies. This research unveils an innovative TinyML methodology, leveraging a specially-designed Convolutional Neural Network (CNN). Notably, our bespoke CNN model showcased a remarkable 97.3% accuracy in pinpointing users on the STM32L4 system. © 2024 IEEE.

关键词： Convolutional neural networks

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Recognition Technology with Machine Learning into Swallow Ecological Education 5

Recognition Technology with Machine Learning into Swallow Ec...

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5th IEEE Eurasian Conference on Educational Innovation, ECEI 2022

作者： Wu, Wen-Chuan Wang, Tsai-Chu Wang, Chao-Yuan Yu, Yu-Han National Ilan University Dept. of Computer Science and Information Engineering Yilan Taiwan

ISBN: (纸本)9781665433181

In recent years, artificial intelligence (AI) technology has become the hottest industry trend due to the improvement of computing capability for computer hardware. AI refers to making machines have the same logical thinking and behavior patterns as humans, including learning, perception, reasoning, self-correction, and so on. At present, many industries, such as marketing, finance, transportation, agriculture, and medical care, have integrated AI applications. In this paper, we intend to apply recognition technology to ecological education of swallows. The computer is able to recognize the swallow's behavior and record it, instead of only using the nest-cam. When students observe the swallows, they are informed of relevant knowledge immediately for learning, without the teacher's explanation. This way of learning through technology is easy to attract the attention of students and is more effective. © 2022 IEEE.

关键词： Students

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FEZ: Flexible and Efficient Zoom-In for Ultra-Large Image Classification

FEZ: Flexible and Efficient Zoom-In for Ultra-Large Image Cl...

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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Lu, Cheng-Han Hung, Shih-Hao National Taiwan University Dept. of Computer Science and Information Engineering Taipei Taiwan

ISBN: (纸本)9781665480451

Mainstream deep learning algorithms are designed for typical images sizing from 224 × 224 to 600 × 600 pixels, which are relatively small compared to the whole-slide images (WSI) with billions of pixels, causing existing methods to be inefficient due to computational and memory constraints. Furthermore, most WSIs are annotated with slide-level labels, as patch-level annotations are time-consuming and have to be conducted by experienced pathologists. At the same time, the regions of interest (ROIs), e.g., where cancer, tumor, and bacteria reside, would occupy relatively small areas. Thus, we propose Flexible and Efficient Zoom-In (FEZ) as a method to precisely locate ROIs to accelerate ultra-large image classification. FEZ initially examines a low-resolution WSI with a policy network (PN) to select the potentially critical ROIs and zooms in the selected regions using medium-resolution images to select finer-grain ROIs with another PN. Eventually, FEZ loads the high-resolution patches and classifies them with a value network (VN) to make a prediction. Since FEZ only loads a few selected patches instead of the high-resolution WSI, it dramatically reduces the image loading time and the computation time needed for analyzing the patches. Experimental results show that FEZ is capable of delivering similar accuracy with higher speed than a recently proposed whole-slide training method (WST) and existing sampling methods such as Multiple Instance Learning (MIL) and CLAM. FEZ is 42× and 22× faster than MIL on the Camelyon16 and TCGA Lung Cancer datasets, respectively. With FEZ, we can reduce the classification time for ultra-large images from minutes to seconds to support interactive applications. © 2022 IEEE.

关键词： Image classification

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A multi-point capacitive sensing smart clothing applied to posture detection

A multi-point capacitive sensing smart clothing applied to p...

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2024 IEEE International Conference on Consumer Electronics, ICCE 2024

作者： Yang, Chang-Ming Wu, Shu-Cing Liao, Kuo-Cheng Wu, Ze-We Chen, Shih-Hung Chen, Chi-Chun Ming Young Biomedical Corp. Miaoli Taiwan National Chin-Yi University of Technology Prospective Technology of Electrical Engineering and Computer Science Taichung Taiwan National Chin-Yi University of Technology Dept. of Computer Science and Information Engineering Taichung Taiwan National Chin-Yi University of Technology Dept. of Electronic Engineering Taichung Taiwan

ISBN: (纸本)9798350324136

This study introduced a system of multi-point capacitive sensing smart garment designed for posture detection. This smart garment utilized multiple capacitive sensors placed at different positions to monitor changes in body posture movements. These sensors were capable of detecting subtle variations between the garment and the wearer's body and translating this data into understandable information. By analyzing the collected data, this smart garment can provide real-Time monitoring of the user's posture, such as standing, sitting, or lying down. © 2024 IEEE.

关键词： garment multi-point capacitive sensing posture respiration

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