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An Intelligent LLM-Powered Personalized Assistant for Digital Banking Using LangGraph and Chain of Thoughts 22

An Intelligent LLM-Powered Personalized Assistant for Digita...

引用

22nd IEEE International Symposium on Intelligent systems and Informatics, SISY 2024

作者： Easin, Arafat Md Sourav, Saha Tamas, Orosz Data Science and Engineering Dept. Budapest Hungary Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350385601

Text-based applications and chatbots are increasingly popular for delivering banking services and educational tools, offering convenient and efficient solutions for users. Whereas, personalized assistants have transformed user engagement in the digital banking space by utilizing Large Language Models (LLMs) in conjunction with autonomous agents. This study proposes the development of an intelligent personalized assistant for digital banking, utilizing a multi-agent framework based on the LangGraph and Chain of Thoughts (COT) prompting. While COT guarantees context-aware replies, the LangGraph design maps characteristics to nodes to improve user interactions. The objectives of this system are to enhance task efficiency and elevate the capabilities of digital banking assistants. We present a customizable digital banking system powered by LLM-based models, designed to deliver an interactive and personalized banking experience. The system supports a range of services, including adding money, transferring funds, paying bills, accessing telco services like mobile recharge, managing savings interest rates, DPS schemes, fixed deposits, and answering FAQs related to banking information. Therefore, integrating COT for logical reasoning enhances the effectiveness of multi-agent systems, as each single agent benefits from the structured reasoning process. In addition, LangGraph is employed for structured data management, enabling the assistant to support and accelerate various digital banking processes efficiently. The code implementation of this work is available for public access at: https://***/srv-sh/digital-agent. © 2024 IEEE.

关键词： Information management

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A Hybrid Approach Using Cryptography and K-Least Significant Bit Steganography Algorithm in Cloud Computing 1

A Hybrid Approach Using Cryptography and K-Least Significant...

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1st International Conference on Advances in computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Goyal, Anshika Gupta, Bhumika VMSB Uttarakhand Technical University Dept. of Computer Science & Engineering Dehradun India G.B. Pant Institute of Engineering & Technology Dept. of Computer Science & Engineering Pauri India

ISBN: (纸本)9798331518578

The exponential growth of communication technologies has broadened the cloud computing ecosystem horizon to meet main communication needs. However, in-parallel upsurge in online attacks has alarmed industries to ensure optimal data security. The combination of steganography and cryptosystems has been recognized as an enhanced security model for data transfer among the main security systems now in use. In this paper, a hybrid approach using cryptography and K- Least Significant Bit Steganography algorithm in Cloud Computing (HYBC-KLSBS-CC) are proposed. Initially, input text data is retrieved from cloud storage and compressed using Huffman Compression Coding (HCC) to reduce data size. The compressed data is then encrypted with a Hybrid Cryptography Algorithm (Hyb-MRFC-ALC), combining Modified Random Fibonacci Cryptography and Asymmetric Lightweight Cryptography to ensure robust encryption. Finally, encrypted data is embedded within an image using the K-Least Significant Bit (KLSB) steganography technique, providing additional layer of security. The proposed method enhances data confidentiality, integrity, and storage efficiency in cloud environments. The proposed HYBC-KLSBS-CC approach attains 26.89%, 29.49% and 18.64% lower PSNR and 26.89%, 29.49% and 18.64% lower MSE when analyzed through existing techniques like data security dependent on cryptography steganography related with OTP procedure and Huffman coding in the cloud environment (CS-OTP-CE), a novel hybrid cryptographic framework for secure data storage in cloud computing: Integrating AES-OTP and RSA with adaptive key management and Time-Limited access control (AES-OTP-RSA-CC) and Crypto-Stegno dependent model for securing medical information on IOMT platform (CS-SMI-IOMT). © 2025 IEEE.

关键词： Cloud platforms

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Statistical Analysis of the Properties of Geometric Network with Node Mobility 29

Statistical Analysis of the Properties of Geometric Network ...

引用

29th IEEE Asia Pacific Conference on Communications, APCC 2024

作者： Arquam, Md. Tiwari, Utkarsh Kumari, Suchi Iiit Dept. Computer Science and Engineering Haryana Sonepat India Shiv Nadar Institute of Eminence Dept. Computer Science and Engineering Delhi-NCR India

ISBN: (纸本)9798331517816

The movement changes the underlying spatial representation of the participated mobile objects or nodes. In real-world scenarios, such mobile nodes can be part of any biological network, transportation network, social network, human interaction, etc. The change in the geometry leads to the change in various desirable properties of real-world networks, especially in human interaction networks. In real life, human movement is concerned with a better lifestyle where they form their new connections due to the geographical changes. Therefore, in this paper, we design a model for geometric networks with mobile nodes (GNMN) and conduct a comprehensive statistical analysis of their properties. We analyze the effect of node mobility by evaluating key network metrics such as connectivity, node degree distribution, second-hop neighbors, and centrality measures. Through extensive simulations, we observe significant variations in the behavior of geometric networks with mobile nodes. © 2024 IEEE.

关键词： Complex networks

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AI-Driven Dynamic Network Slicing for Resource Optimization in 5G Networks: Implementation and Performance Evaluation

AI-Driven Dynamic Network Slicing for Resource Optimization ...

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2024 International Conference on Cybernation and Computation, CYBERCOM 2024

作者： Jain, Ayushi Jain, Prateek Verma, Aditya Gopal, Lisa Chaudhary, Preeti 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 Graphic Era Deemed to Be University Scholar Dept. of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798331542344

We introduce in this paper an AI-based dynamic network slicing model for resource allocation targeting 5G networks. It does this by using machine learning algorithms that help the model learn and adapt to live traffic conditions so as to intelligently manage different service types, including eMBB (enhanced Mobile Broadband), URLLC (Ultra-Reliable Low Latency Communication) and mMTC (massive Machine Type Communication). This leads to substantial improvements in three of the four core performance metrics, latency, throughput and energy efficiency (at a low sacrifice of when it comes to QoS adherence). Their work also demonstrates that AI technologies hold the promise for revolutionary network management, offering a scalable solution that can address ever-increasing requirements of next-generation telecommunications, by surpassing the capabilities of well-established static slicing techniques. © 2024 IEEE.

关键词： 5G mobile communication systems

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The Stability and Computational Complexity Analysis of Tree-seed Algorithm for Frequency-Constrained 10-bar Truss Problem 5

The Stability and Computational Complexity Analysis of Tree-...

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5th IEEE International Conference on Communications, Information, Electronic and Energy systems, CIEES 2024

作者： Cinar, Ahmet Cevahir Nur Erman, Kadriye Selçuk University Faculty of Technology Dept. of Computer Engineering Konya Turkey Kirikkale University Faculty of Engineering and Natural Sciences Dept. of Computer Engineering Kirikkale Turkey

ISBN: (纸本)9798350352863

Optimization studies in truss problems produce different results according to different optimization conditions, which leads to significant changes in the results obtained. This paper presents a comprehensive analysis of the Tree-Seed Algorithm (TSA) in solving the frequency-constrained 10-bar truss optimization problem, a well-known benchmark in structural engineering characterized by its complex, non-linear, and multi-modal design space. The TSA, inspired by natural processes of tree growth and seed dispersion, is evaluated in the truss problem for its stability, computational efficiency, and ability to find optimal solutions. Through rigorous testing involving 51 independent runs, the TSA consistently achieves optimal solutions with a mean objective function value of 554.4549 and a standard deviation of 1.4049. The algorithm demonstrates rapid convergence, typically within 100 iterations. The design parameters of the frequency-constrained 10-bar truss optimization problem were found by TSA within stability. These findings highlight the TSA's effectiveness and reliability, establishing it as a robust tool for tackling complex engineering optimization problems and suggesting its potential for broader applications in structural optimization. © 2024 IEEE.

关键词： Trusses

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Elevating Disease Prediction: A Stacking Ensemble Learning Approach 3

Elevating Disease Prediction: A Stacking Ensemble Learning A...

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3rd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Kavitha, T. Trivikram, Kammara Manobhi Ram Reddy, B. Shashank, S. Sushmitha, H. Rahul, B. New Horizon College Of Engineering Dept. Of Computer Engineering Bangalore India

ISBN: (纸本)9798350318609

Timely and accurate disease diagnosis is critical for effective treatment and cost reduction. This project develops a web app utilizing machine learning to predict diseases from user-entered symptoms and recommend nearby specialists. An ensemble model stacking Logistic Regression, Random Forest, KNN, MLP, and SVM classifiers with XGBoost as the meta-learner was created using a diverse dataset of symptoms, diseases, and medical specialists. The Flask-based app features user authentication, symptom input, and Google Maps API integration for doctor recommendations. The ensemble model accurately predicts potential diseases and relevant specialists nearby, providing an interactive platform for instant diagnosis and treatment access to reduce complications and costs. Rigorous testing ensured seamless deployment, empowering early disease identification and access to appropriate healthcare. The ensemble achieved 97% accuracy, outperforming individual base learners, while KNN emerged as the top individual model with 92.3% accuracy. © 2024 IEEE.

关键词： Forecasting

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Gaze Detection and Text Extraction from Images using CNN with DBSCAN-based Filter Design 11

Gaze Detection and Text Extraction from Images using CNN wit...

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11th IEEE International Conference on Advances in Computing and Communications, ICACC 2024

作者： Jose, Jithin Ramanujan, Ajeesh College Of Engineering Trivandrum Dept. Of Computer Science And Engineering Trivandrum India

ISBN: (纸本)9798350379136

Gaze detection and text extraction are pivotal technologies in the domain of human-computer interaction and computer vision, enabling applications such as assistive technologies, user interface optimization, and automated content analysis. This paper presents a novel method that identifies the user's gaze direction and extracts the necessary portion of text, which constitutes the user's region of interest, using a Convolutional Neural Network (CNN) integrated with a spatial attention mechanism. A Density-Based Spatial Clustering of Applications with Noise (DBSCAN)-based filter is incorporated into the system to remove textual noise from the extracted image, thereby improving the model's accuracy and reducing computation time. The proposed method demonstrates reduced dependency on the CNN compared to previous approaches, resulting in enhanced real-time operation capability and lower latency. Computational optimization and real-time performance were thoroughly evaluated, showing that our system not only achieves high accuracy in gaze detection and text extraction but also maintains efficient processing speeds suitable for dynamic applications. This advancement offers significant improvements for applications in assistive technology, educational tools, and interactive media, where precise and efficient gaze detection and text extraction are critical. © 2024 IEEE.

关键词： Convolutional neural networks

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ARNAV: computer vision and Machine Learning-Based Augmented Reality-Based Indoor Navigation System 5

ARNAV: Computer vision and Machine Learning-Based Augmented ...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Sai Suryaa, K. Saravana Kumar, C. Sudharsan, S. Anandkumar, K.M. Easwari Engineering College Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350383522

Introducing an advanced Augmented Reality (AR) indoor navigation system that uses computer vision for precise positioning and obstacle detection which eliminates the need for GPS. The system is implemented on Android devices using Unity. The system works based on a 3D model of the navigational space that provides the user with unique interior Point of Interests with AR guided overlays for navigation. It uses an advanced mapping and machine learning techniques for real time positioning and obstacle detection. The ARNAV system is independent of GPS making it reliable in complex indoor spaces where GPS fails. Additionally, in emergency situations where there is low visibility the system provides an advanced AR walls feature to guide users to reach the nearest exit by using AR generated walls and overlays. The retrace path feature which is designed especially for patients with cognitive impairments like Alzheimer's which allows the user to retrace the path that they have already traversed using the AR overlay. Additionally, it provides AR based reminders that helps them complete day to day tasks. In conclusion the ARNAV revolutionizes the AR technology to create a safe, dynamic and user centric system that handles emergency evacuation and needs of patients with special cognitive needs. © 2024 IEEE.

关键词： Augmented Reality computer Vision Indoor Navigation System Mobile Application

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Towards Automated Lip Reading: Developing Marathi Lip Reading Datasets and Neural Network Frameworks 4

Towards Automated Lip Reading: Developing Marathi Lip Readin...

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4th International Conference on Intelligent Technologies, CONIT 2024

作者： Kulkarni, Apurva Kirange, Dnyaneshwar Khemachandra Ssbt College of Engineering and Technology Dept. of Computer Engineering Jalgaon India

ISBN: (纸本)9798350349900

This paper introduces an innovative method for automating lip-reading, with a specific focus on the Marathi language. Lip-reading plays a crucial role in aiding those with hearing impairments, but automating it presents significant challenges, especially for languages like Marathi lacking sufficient datasets. To tackle this, we propose a novel approach to automatic lip-reading, accompanied by the development of a specialized Marathi dataset. Leveraging advancements in computer vision and deep learning, our model deciphers linguistic content from lip movements, trained on this dataset. We employ various neural network architectures, including feed-forward, recurrent, and convolutional networks, to extract vital visual features crucial for accurate language interpretation Our primary goal is to provide a robust solution for automated lip-reading tailored specifically to regional languages like Marathi, aiming to enhance accessibility for individuals with hearing impairments, particularly in linguistically diverse contexts. This paper primarily discusses detailed exploration of dataset creation, neural network architectures for lip-reading system, we demonstrate the feasibility and potential impact of our approach. Our study underscores the significance of giving priority to regional languages in technological advancements to promote inclusivity for all individuals. © 2024 IEEE.

关键词： Convolutional neural networks

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Review of Smart Document Verification System for Traffic Rule Violation 15

Review of Smart Document Verification System for Traffic Rul...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Deshmane, Shruti Raghatwan, Jyoti Patil, Sagar Kirad, Yash Gupta, Shweta Dept. of Computer Engineering RMD Sinhgad School of Engineering Pune India

ISBN: (纸本)9798331300579

A prevalent issue with missing or expired vehicle paperwork has prompted the development of a digital service utilizing QR codes within the documentation. This innovation allows Rapid Transport Office (RTO) officers to swiftly access vehicle details and necessary documents via smartphone scans. Traffic cameras equipped with QR code capabilities also verify documentation, issuing fines or notifications when needed. Users benefit from renewal reminders, streamlining processes, and lowering fine risks. This service not only saves time and resources but also encourages compliance with documentation standards, effectively addressing the persistent challenges of missing or outdated paperwork in the realm of vehicle management. © Grenze Scientific Society, 2024.

关键词： QR Code RTO Officer Smart Document Verification Traffic Rule Violation

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