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Enhancing Question-Answering with Knowledge Graph Retrieval and Generation using LLMs

Enhancing Question-Answering with Knowledge Graph Retrieval ...

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2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Applications, ICAIQSA 2024

作者： Gijre, Supraj Agrawal, Rishi Laddha, Priyash Keswani, Gunjan Shri Ramdeobaba College of Engineering and Management Dept. Computer Science and Engineering Nagpur India School of Computer Science and Engineering Ramdeobaba University Nagpur India

ISBN: (纸本)9798331517953

This paper describes a novel technique to improving Large Language Models (LLMs) for document analysis that employs knowledge graphs and retrieval-augmented generation (RAG). We are working on constructing a chatbot system that can handle and analyze large documents from a variety of fields. Our approach addresses basic LLM issues including context maintenance and hallucination avoidance. The system combines document chunking, vector embedding, and similarity search with graph-based knowledge representation. Users can upload large papers and answer questions accurately. We show that integrating standard information retrieval approaches with graph-based storage and LLM capabilities improves context awareness and response accuracy across a wide range of document genres. This strategy is especially promising for complicated publications such as financial reports. © 2024 IEEE.

关键词： Knowledge graph

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Evaluating the Performance of Machine Learning Models in Handwritten Signature Verification

Evaluating the Performance of Machine Learning Models in Han...

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2024 Asia Pacific Conference on Innovation in Technology, APCIT 2024

作者： Akter, Tahmina Akter, Mst. Sharmin Mahmud, Tanjim Chakma, Rishita Hossain, Mohammad Shahadat Andersson, Karl Port City International University Dept. of Computer Science and Engineering Chittagong Bangladesh Rangamati Science and Technology University Dept. of Computer Science and Engineering Rangamati4500 Bangladesh University of Chittagong Dept. of Computer Science and Engineering Chittagong4331 Bangladesh Luleå University of Technology Cybersecurity Laboratory Luleå97187 Sweden

ISBN: (纸本)9798350361537

Handwritten signatures remain a widely used method for personal authentication in various official documents, including bank checks and legal papers. The verification process is often labor-intensive and time-consuming, necessitating the development of efficient methods. This study evaluates the performance of machine learning models in handwritten signature verification using the ICDAR 2011 Signature and CEDAR datasets. The investigation involves preprocessing, feature extraction using CNN architectures, and optimization techniques. The most effective models undergo a rigorous evaluation process, followed by classification using supervised ML algorithms, such as linear SVM, random forest, logistic regression, and polynomial SVM. The results indicate that the VGG16 architecture, optimized with the Adam optimizer, achieves the satisfactory performance metrics. This study demonstrates the potential of ML methodologies to enhance the efficiency and accuracy of signature verification, offering a robust solution for document authentication. © 2024 IEEE.

关键词： Decision trees

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Sentimental Analysis of Various TV Serials in Society 15

Sentimental Analysis of Various TV Serials in Society

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15th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2023

作者： Sarkar, Ripa Sarkar, Ratna R. BRAC University Dept. of Computer Science and Engineering Dhaka Bangladesh Jahangirnagar University Dept. of Computer Science and Engineering Savar Bangladesh

ISBN: (纸本)9798350324433

Sentiment analysis which involves identifying emotions in the text is a widely studied topic of natural language processing. Many researchers focus on social media content like posts, tweets and reviews for their studies. In this paper, a large number of data from comments about serials on social media and internet forums in Banglish is collected and analyzed. We use NLP techniques to do the sentiment analysis of the comments. This study explores how different kinds of serials have impacted society and concentrates on how they have influenced the creation of Banglish comments. This study aims to conduct a sentiment analysis of Banglish comments generated in response to different genres of serials and to analyze their potential societal implications. Along with Long Short-Term Memory (LSTM), we use several machine-learning algorithms such as Logistic Regression (LR), Multinomial Naive Bayes (MNB), Gaussian Naive Bayes, Decision Tree (DT), AdaBoost, Random Forest (RF) and Support Vector Machine (SVM) to create this model. The SVM gives the highest accuracy among all of the machine learning algorithms. © 2023 IEEE.

关键词： Sentiment analysis

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Performance Evaluation of Ensemble Learning Techniques for Prediction

Performance Evaluation of Ensemble Learning Techniques for P...

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2024 IEEE International Conference on computer Vision and Machine Intelligence, CVMI 2024

作者： Ramya, S. Srinath, S. Tuppad, Pushpa Jssstu Dept. Computer Science & Engineering Mysore India Jssstu Dept. Environmental Engineering Mysore India

ISBN: (纸本)9798350376876

The paper employed a range of ensemble learning techniques to forecast Chemical Oxygen Demand (COD) levels in Wastewater Treatment Plants (WWTP). By harnessing ensemble learning, the main objective is to enhance the accuracy and robustness of COD prediction models by capitalizing on the diverse strengths of multiple algorithms. Our experiment encompassed two distinct WWTP datasets, allowing us to comprehensively assess the efficiency and effectiveness of three ensemble methods. Firstly, Bagging operates by creating multiple versions of a predictor and combining their outputs to form an aggregated prediction. Secondly, Boosting algorithms such as AdaBoost, GBM, and XGBoost sequentially constructed powerful learners, focusing on previously misclassified instances to bolster predictive performance. Finally, Stacking methodologies combined predictions from multiple models using a meta-learner. The analysis of the results for various models across the two wastewater treatment plant datasets (WWTPDS1 and WWTPDS2) reveals distinct performance levels. The Bagging model exhibits a reasonable performance with WWTPDS1 (MSE: 0.0016, R2: 0.9351) and a slightly lower performance with WWTPDS2 (MSE: 0.0033, R2: 0.6787). The Boosting model outperforms with lower MSE and higher R2 values for both datasets (WWTPDS1 MSE: 0.0010, R2: 0.9751;WWTPDS2 MSE: 0.0028, R2: 0.7384), indicating its better predictive accuracy and reliability. The Stacking model evaluated in the training phase, it showcases exceptionally low MSE and high R2 values (WWTPDS1 MSE: 0.0004, R2: 0. 9 7 1 2;WWTPDS2 MSE: 0.0009, R2: 0.9213). In the testing phase, the Stacking model maintains strong performance (WWTPDS1 MSE: 0.0013, R2: 0. 9 4 9 4;WWTPDS2 MSE: 0.0028, R2: 0. 7 1 9 1). © 2024 IEEE.

关键词： Wastewater treatment

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Investigating the Efficacy of Diverse Machine Learning Classifiers for Parkinson's Disease Detection 15

Investigating the Efficacy of Diverse Machine Learning Class...

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

作者： Roy, Sourabarna Debbarma, Tina Pal, Tannistha Duraisamy, Prakash National Institute of Technology Agartala Dept. of Computer Science and Engineering Agartala India University of Wisconsin- Green Bay Dept. of Computer Science and Engineering WI54311 United States

ISBN: (纸本)9798350370249

To detect Parkinson's disease, we compare the effectiveness of K-Nearest Neighbors (KNN), Logistic Regression (LR), Support Vector Machines (SVM), and Random Forest (RF) algorithms. Utilizing a dataset with clinical and biomedical features, we preprocess the data to handle missing values and standardize the features. Subsequently, we train each algorithm with the preprocessed data and evaluate their performance using metrics like accuracy, precision, recall, and F1-score. Our results indicate that all four algorithms achieve excellent accuracy in diagnosing Parkinson's disease, with KNN slightly outperforming the others. However, the selection of the algorithm may depend on specific needs such as interpretability and computational efficiency. Additionally, we conduct a feature importance analysis to identify the most relevant features for Parkinson's disease identification, offering insights that can aid in early diagnosis and disease management. © 2024 IEEE.

关键词： Logistic regression

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Investigating Requirements and Expectations of Wearable Telexistence Robotic Systems 29

Investigating Requirements and Expectations of Wearable Tele...

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29th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications, RTCSA 2023

作者： Iskandar, Abdullah Al-Sada, Mohammed Halabi, Osama Nakajima, Tatsuo Waseda University Dept. of Computer Science and Engineering Tokyo Japan Qatar University Dept. of Computer Science and Engineering Doha Qatar

ISBN: (纸本)9798350337860

Telexistence refers to various technologies that enable a high sense of embodiment and interaction capabilities with remote environments. Although numerous telexistence systems have been explored in previous works of literature, no prior studies have investigated the usability of telexistence systems for daily use. Therefore, this study focuses on Piton, which is a novel wearable telexistence robot which has a high degree of freedom in a snake-shaped design. Piton offers a unique opportunity for remote interaction and manipulation in diverse environments, owing to its flexible movement, which enables users to adapt to various scenarios with ease. The robot's wearability is also an essential feature that allows the user to wear it as a backpack. However, despite its promising features, deeper investigation is necessary to explore potential use cases for Piton in users' daily lives and the challenge of Piton. © 2023 IEEE.

关键词： Robots

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EMRChain: Electronic Medical Records Management System using Blockchain 3

EMRChain: Electronic Medical Records Management System using...

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3rd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Rajasekharan, Arjun Koshy, Rani College of Engineering Trivandrum Dept. of Computer Science and Engineering Kerala Thiruvananthapuram India College of Engineering Trivandrum Department of Computer Science and Engineering Kerala Thiruvananthapuram India

ISBN: (纸本)9798350354348

Electronic Medical Records (EMRs) are traditionally managed by central authorities, posing significant security risks such as data breaches, limited interoperability, and restricted patient control. This system leverages blockchain technology to address these issues, ensuring data remains immutable, transparent, and highly secure. Utilizing the Ethereum network and IPFS technology, our system provides a decentralized framework for storing and sharing medical records. Healthcare professionals, patients, and authorized parties can seamlessly exchange and access records through Ethereum's decentralized structure and smart contracts, enabling automated and auditable interactions. To enhance confidentiality, all data uploaded to IPFS undergoes AES 256-bit encryption, with the encrypted Content Identifier (CID) stored on the blockchain. This ensures that intercepted CIDs cannot be accessed without the decryption key. IPFS technology facilitates decentralized storage and retrieval, eliminating the need for a centralized server and improving scalability and resilience. Patients retain full control over their health information, granting or revoking access as necessary. This system ensures secure patient identification, precise access management, and data confidentiality while allowing authorized parties to track and trace record access and modifications. Our blockchain-based EMR management system addresses the inherent risks of traditional centralized systems by providing a secure, transparent, and decentralized solution. By leveraging Ethereum and IPFS, it enhances data security, patient control, and interoperability, thus paving the way for more robust and patient-centric healthcare information management. This approach ensures that healthcare data remains secure, patients maintain control over their information, and authorized parties can effectively manage and access the records. © 2024 IEEE.

关键词： Ethereum

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Microwave Marvels: Design and Computational Analysis of an Ultra-thin Metamaterial Absorber for C, X and K Band 6

Microwave Marvels: Design and Computational Analysis of an U...

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6th International Conference on Electrical engineering and Information and Communication Technology, ICEEICT 2024

作者： Tasnim, Nowshin Rattre, Naziya Hasan Shawon, Sarowar Morshed Alam, Mohammed Shamsul Alam, Touhidul Chittagong University of Engineering & Technology Dept. of Computer Science & Engineering Chittagong4349 Bangladesh International Islamic University Chittagong Dept. of Computer Science & Engineering Chittagong4318 Bangladesh University of Science & Technology Chittagong Dept. of Computer Science & Engineering Chattogram4202 Bangladesh Universiti Kebangsaan Malaysia Bangi43600 UKM Malaysia

ISBN: (纸本)9798350385779

Metamaterial absorbers are an advancement in material science as they provide more advantages in comparison to conventional materials. Achieving miniaturization with a multilayered structure is the primary challenge here. In this study, a novel metamaterial absorber design is proposed for microwave application. The proposed design is shaped as a squad of four segments where each segment contains an octagonal split ring and a central ring. The multi-layered design of the absorber helps to secure a high absorption rate. An extensive range of research is done to determine particular measurement units, and geometric shapes with the help of CST Studio Suit 2019. Here both ground and patch are designed using copper and the separator is designed with Roger. Moreover, the thickness of the absorber is only 3.4 mm which ensures its ultra-thin and lightweight nature. With an elaborated numerical analysis, it has been shown that the proposed absorber provides 90% to 99% absorption at the C, X, and K bands. Moreover, a concise computational analysis is accomplished with FR4 lossy, Rogers RT6010LM lossy, and Rogers RT5870LM lossy as dielectric constant. The key contribution of this research is the high absorption rate, coupled with minimal thickness and simple structure. © 2024 IEEE.

关键词： Metamaterials

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Classifying wireless IOT ICU traffic with machine learning models 19

Classifying wireless IOT ICU traffic with machine learning m...

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19th International Conference on Future Networks and Communications, FNC 2024 / 21st International Conference on Mobile Systems and Pervasive Computing, MobiSPC 2024 / 14th International Conference on Sustainable Energy Information Technology, SEIT 2024

作者： Sibai, Fadi N. Sibai, Ahmad Electrical & Computer Engineering Dept. Gulf University for Science and Technology Mishref Kuwait Computer Science & Engineering Dept. University of South Florida TampaFL33620 United States

In this work, we classified the wireless internet of things (IoT) traffic of the IoT Health intensive care unit (IHI) dataset which belongs to three general classes: patient monitoring, environment monitoring, and network attack. We trained and tested 7 machine learning (ML) models with Orange 3 including kNN, Decision Tree (tree), SVM, Random Forest (RF), Neural Network (NN), Gradient Boosting (GB), and AdaBoost (AB). With the original dataset, 5 ML models performed perfect classification. After pruning the dataset columns by keeping the features with the highest correlations with the label in the dataset, good classifications were obtained with only 4 TCP/IP features by the Gradient Boosting, kNN, and RF models with MSEs in the range 0.008-0.011, and R2s in the range 0.978-0.984. With only 6 MQTT features, Gradient Boosting, RF, Tree, and NN were the top classifiers with MSEs in the range 0.073-0.074, and R2s in the range 0.856-0.859. This work demonstrates the effectiveness of guiding the feature pruning process by the values of the correlation coefficients in order to minimize the long training times of ML models while achieving good accuracies. © 2024 The Authors. Published by Elsevier B.V.

关键词： correlation coefficient feature pruning Internet of things IOT health intensive care unit dataset machine learning network security

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Multi-Layered Security Techniques for Protecting Cooperative Relay Networks from Eavesdropping

Multi-Layered Security Techniques for Protecting Cooperative...

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2024 International Conference on Cybernation and Computation, CYBERCOM 2024

作者： Upadhyay, Deepak Sharma, Kunj Bihari Upadhyay, Abhay Gupta, Mridul Dhondiyal, Shiv Ashish Asudani, Deepak Suresh Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era Deemed to Be University Dept. of Computer Science and Engineering Dehradun India Pune India

ISBN: (纸本)9798331542344

In this paper, we study the multi-layered security approaches for cooperative relay networks against passive eavesdropping adversaries. Specifically, we investigated physical layer approaches (i.e., beamformed and artificial noise injections) to improve the signal defence ability at a much lower eavesdropping capacity. Trade-offs in these cryptographic methods were studied in terms of encryption strength vs. decryption time under different loads on the network. The security of the network layer was assessed by measuring packet delivery ratios and data integrity violations under malicious nodes. Results show that multi-layer security is performed at the cost of performance, but much better network defence mechanisms are proposed overall. While this end-To-end approach strikes a reasonable balance between security and real-world performance considerations, it presents itself as a sensible solution for securing network communications against eavesdropping threats. © 2024 IEEE.

关键词： Network layers

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