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

Enhancing Question-Answering with Knowledge Graph Retrieval ...

引用

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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Exploring the Potential of MANETs: A Study on Raspberry Pi and UAV Integration 3

Exploring the Potential of MANETs: A Study on Raspberry Pi a...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Archana, B. Mahalakshmi, A. Revathi, K. Ashok Kumar, K. Hema, A.B. Dept. of Computer Science and Engineering Sri Ramakrishna Engineering College Coimbatore India Dept. of Information Technology SNS College of Engineering Coimbatore India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527549

This research investigates the feasibility of utilizing Mobile Ad Hoc Networks (MANETs) in conjunction with Raspberry Pi-equipped Unmanned Aerial Vehicles (UAV) swarms. The primary objective is to overcome the limitations of traditional communication infrastructures in dynamic and decentralized environments. Specifically, we address the challenge of establishing reliable and efficient communication links within a swarm of UAVs engaged in various applications, including disaster response, military reconnaissance, and environmental monitoring. By integrating Raspberry Pi devices with UAVs, we aim to create a self-organizing communication network that enables seamless data transmission, coordinated swarm operations, and enhanced mission effectiveness. The research explores different synchronization strategies, including master-slave and decentralized approaches, to optimize swarm coordination. Furthermore, we investigate the potential of incorporating advanced technologies such as machine learning, computer vision, and swarm intelligence to further enhance the autonomy, adaptability, and overall performance of the UAV swarm. © 2025 IEEE.

关键词： Mobile ad hoc networks

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Personal Exercise Trainer Using computer Vision 2

Personal Exercise Trainer Using Computer Vision

引用

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

作者： Hari Ram, S. Ashok, A. Ramalingam, A. Dheenadhayalan, S. National Engineering College Dept. of Computer Science and Engineering Tamil Nadu Kovilpatti India

ISBN: (纸本)9798350328202

The Personal Trainer online application tracks users progress and offers realtime feedback to encourage users in their daily workout routines. To identify and analyze human body movements, the program uses the mediapipe library, which integrates opencv and machine learning methods. The Personal Trainer online app provides an engaging and immersive workout experience by incorporating these technologies. The application supports several exercises, including weightlifting, squats, bicep curls, and pull-ups. The software uses the webcam on the computer to record realtime footage of the user completing the workout after they choose it. The user's body motions are then recognized from the recorded video using opencv and mediapipe, and performance-based feedback is given. Users may quickly move through the various training modes, check their activity history, and measure their overall progress thanks to the app's user-friendly and intuitive interface. © 2024 IEEE.

关键词： computer vision

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Deep Learning Driven Multilingual System for Recognition and Categorization of Medicinal Plants with Extensive Elucidation 9

Deep Learning Driven Multilingual System for Recognition and...

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9th International Conference on science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Azhaguramyaa, V.R. Janet, J. Hariharan, A. Kamaleshwaran, A. Albert Joseph, A. Computer Science And Engineering Sri Krishna College of Engineering And Technology Tamil Nadu Coimbatore India Sri Krishna College of Engineering And Technology Dept. of Computer Science And Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798350365092

Medicinal plant use is widespread throughout many cultures and geographical areas, frequently entwined with customs and knowledge. The use of technology, especially deep learning, to improve the identification and classification of these plants has gained popularity in recent years. This research introduces a new multilingual system with deep learning capabilities that can identify and classify medicinal plants with a great deal of explanation. The term 'deep learning-driven multilingual system for recognition and categorization of medicinal plants with extensive elucidation' describes an advanced technology that carries out several tasks by utilizing deep learning, one of the most advanced machine learning techniques. Based on pictures, the system can recognize and classify a variety of therapeutic plants. Because users may communicate with the system, ask questions, and get information in their native tongue, it is usable by a wide variety of people across the globe. Apart from identification and classification, the method offers comprehensive clarifications or in-depth accounts of the recognized therapeutic herbs. © 2024 IEEE.

关键词： Deep learning

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Deep Learning for Enhanced Consumer Behavior Analysis and Predictive Accuracy

Deep Learning for Enhanced Consumer Behavior Analysis and Pr...

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

作者： Bharti, Jigyasa Dongre, Snehlata G H Raisoni College of Engineering Dept. of Computer Science and Engineering Nagpur India

ISBN: (纸本)9798350319019

The accelerating digital transformation in retail necessitates advanced analytical approaches to effectively comprehend and predict consumer behavior. Existing methodologies in retail data mining often fall short in handling the complexity and heterogeneity of data, resulting in suboptimal predictive accuracy and operational efficiency. This work addresses these limitations by introducing a novel deep learning framework that synergizes the capabilities of Graph Neural Networks (GNNs) with Q Learning and VARMAx models. The proposed model adeptly fuses data from diverse sources such as Twitter, Amazon, Facebook, and Flipkart, creating a comprehensive analysis platform. By leveraging the relational data structure processing power of GNNs and the dynamic decision-making proficiency of Q Learning, along with the time series predictive strength of VARMAx models, this approach offers a more holistic understanding of consumer trends. The efficacy of this model is demonstrated through extensive testing on varied data samples encompassing social media, e-commerce platforms, and tweets. The results are compelling: an 8.3% increase in accuracy, 8.5% enhancement in precision, 6.5% improvement in recall, and a significant 10.4% reduction in delay compared to existing classification methods. The impact of this work is far-reaching, offering retailers a robust tool for strategic decision-making, enhanced customer insight, and a competitive edge in the rapidly evolving digital marketplace. This approach not only propels the retail industry forward but also sets a new benchmark in the application of deep learning for consumer behavior analysis. © 2024 IEEE.

关键词： Data mining

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FBG Sensor Design and Analysis for Early Detection of Cancer 18

FBG Sensor Design and Analysis for Early Detection of Cancer

引用

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

作者： Sharan, S.C. Dhruva, H.K. Mary Anitha, T. Roy, Neil Sharan, Preeta Madhwesha Moudgalya, R. The Oxford College of Engineering Dept. of Computer Science and Engineering Bangalore India The Oxford College of Engineering Dept. of Mca Bangalore India Delhi Technological University Dept. of Computer Science and Engineering New Delhi India The Oxford College of Engineering Dept. of Electronics and Communication Engineering Bangalore India The Oxford College of Engineering Dept. of Science and Humanities Bangalore India

ISBN: (纸本)9789380544519

Several of the current procedures for detecting cancer, such as mammography, ultrasound, MRI, and biopsy, are either expensive, painful, intrusive, or have limitations in accuracy and sensitivity. As a result, there is a need for a simple, noninvasive, and cost-effective tool for detecting cancer at an early stage. Fiber Bragg grating (FBG) sensors offer a wide range of uses in primary care and biomedical applications for intelligent sensing. Furthermore, fiber-optic FBGs have several benefits that set them apart. The most noteworthy of these applications, of course, are incredibly valuable human health indicators such as blood pressure, heart rate, and body temperature. Temperature and blood pressure vary depending on a person's physical, involuntary, nervous, and mental state. Therefore, measuring vital parameters, especially temperature can help in the early identification of symptoms of a disease. Research suggests that temperature variation is observed in cancerous cells (breast, cervix, and blood vessels). FBGs can be utilized as thermal sensors to measure temperature changes with great sensitivity and precision. FBG sensors also face some challenges, such as low signal-to-noise ratio, cross-sensitivity, and environmental interference. Therefore, optimizing the Q factor of FBG sensors is crucial for improving their performance and reliability for cancer identification. In the current study, an optimized FBG sensor was designed and simulated. The average Q-factor obtained in 244.26. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Blood cancer FBG Fiberoptic Hela cells MCF-7 cells modulation OptiGrating SMF

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Automated Leukemia Diagnosis from Microscopic Blood Smear Images Using Adapted Inception V3

Automated Leukemia Diagnosis from Microscopic Blood Smear Im...

引用

2024 International Conference on Big Data Analytics in Bioinformatics, DABCon 2024

作者： Pujitha, M Vani Srikantam, Srinija Sahithi, Ch Timmasarti, Akhila Velagapudi Ramakrishna Siddhartha Engineering College Dept. of Computer Science and Engineering Vijayawada India

ISBN: (纸本)9798350378047

Leukemia, a cancer characterized by the rapid overproduction of abnormal blood cells, presents notable diagnostic and treatment challenges. Identifying leukemia subtypes promptly and accurately is vital for devising effective treatments and improving patient outcomes. This study proposes an advanced Inception V3 model optimized for the automatic identification of leukemia from microscopic images of blood smears. By integrating cutting-edge deep learning methods and a customized model architecture, this system differentiates between Acute Lymphoblastic Leukemia (ALL), Acute Myeloid Leukemia (AML), Chronic Lymphocytic Leukemia (CLL), Chronic Myeloid Leukemia (CML), and healthy samples. The model benefits from preprocessing techniques like image resizing, normalization, and augmentation to enhance its performance. Through transfer learning, the Inception V3 model is fine-tuned on a rich dataset covering various leukemia subtypes. Experimental results demonstrate the model's high effectiveness, achieving 100% accuracy during training and 99.72% accuracy in testing, proving the system's reliability for automated leukemia detection. © 2024 IEEE.

关键词： Lung cancer

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Deadline Constrained Least Laxity Time Based Cloudlet Scheduling

Deadline Constrained Least Laxity Time Based Cloudlet Schedu...

引用

2024 IEEE International Conference on Computing, Semiconductor, Mechatronics, Intelligent Systems and Communications, COSMIC 2024

作者： Datta, Lipika College of Engineering and Management Dept. of Computer Science and Engineering West Bengal Kolaghat India

ISBN: (纸本)9798331517892

Users send their requests to cloud service providers, who then deliver the services over networked systems. With a high volume of requests, it is crucial to schedule cloudlets effectively to ensure that most meet their specified deadlines for optimal performance. This paper focuses on calculating Both slack time and laxity time for cloudlets across all virtual machines and scheduling them accordingly. It also considers the rearrangement of cloudlets already assigned to virtual machines to accommodate new cloudlets. The CloudSim simulation environment is utilized to schedule cloudlets by evaluating their slack time and laxity time across various virtual machines. This approach helps in selecting the most suitable virtual machine for executing the cloudlets. By incorporating laxity time into the scheduling decisions, the system becomes more balanced, reduces makespan, and thus enhances overall performance. The proposed method has been shown to reduce task rejection by at least 10% and the makespan by an average of 17% when compared to the EFD algorithm. © 2024 IEEE.

关键词： Virtual machine

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A Review on Phishing Threats and Data Securityin Online Trading Systems using Artificial Intelligence Techniques 2

A Review on Phishing Threats and Data Securityin Online Trad...

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2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Murugun, S. Haniah, Sheikh Koti, Shambhavi M. Sahani, Saurav Shekhar, S.N. Dept. of Computer Science and Engineering Sri Venkateshwara College of Engineering Bengaluru562157 India

ISBN: (纸本)9798350383867

Phishing threats pose significant risks to online trading systems, threatening the security and integrity of financial transactions. In this paper, we present a comprehensive review of phishing threats and data securityin online trading systems, focusing on the application of artificial intelligence (AI) techniques for detection andprevention. We discuss the evolving landscape of online trading, highlighting the risks associated with technological disruptions, malware, and phishing attacks. The paper explores various AI-based approaches, including machine learning and natural language processing, for detecting and mitigating phishing threats in real time. Additionally, we review recent studies on phishing classification techniques, AI-enabled detection mechanisms, cyber security awareness training, and deep learning-based approaches for phishing detection. Through a systematic review of existing literature, we identify challenges such as data availability, model interpretability, and scalability, and propose future research directions to enhance cyber security defenses against evolving phishing threats in online trading systems. Furthermore, we are Enhancing user education and awareness which plays a crucial role in effectively combating phishing attacks. Also, highlighting the need for collaboration between industry stakeholders, policymakers, and researchers to develop effective strategies for protecting online traders and investorsfrom financial fraud and identity theft. © 2024 IEEE.

关键词： Phishing

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Neural Harvest : Advancements in Crop Yield Forecasting 7

Neural Harvest : Advancements in Crop Yield Forecasting

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Seelam, Srinivasa Reddy Thivari, Charitha Sai Parabattina, Bhagath Narendra Kumar, G. Sumanth, K. Lakireddy Bali Reddy College of Engineering Dept. of Computer Science And Engineering Mylavaram India

ISBN: (纸本)9798350359299

Crop yield forecasting involves predicting the amount of crops that a farmer can expect from their field. This prediction is based on several factors, including soil type and environmental conditions. It is a crucial issue for the farming industry, which is the backbone of any nation's economy. Thanks to advancements in Artificial Intelligence, farmers can benefit from accurate crop yield estimations. This research study presents a Feed Forward Neural Network architecture that enables farmers to get accurate crop yield estimates and finally discusses about the implementation of the procedure and its outcome. © 2024 IEEE.

关键词： Feedforward neural networks

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