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Implementing HR Management System with an AI Powered ChatBot

Implementing HR Management System with an AI Powered ChatBot

引用

Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： D Susitra Pratheesh G Karthiga JD K Dhanalakshmi B Shamreen Ahamed Sylvia Grace J Dept. of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai Tamil Nadu

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Human Resource (HR) management is one important section that increases the success of an organization to handle some important functions like employee data manipulation, attendance tracking, payroll processing, and performance evaluation. With the management of these processes becoming key to the success of small-scale startups, it poses a very big challenge for many of them to be able to manage these processes efficiently and cost efficient. By contrast, the old era of traditional HR systems was based on database driven architectures that are expensive in terms of operational cost, require storage space and complex infrastructural needs. These limitations often make traditional HR systems impossible for a startup that would prefer lightweight, affordable, and scalable solutions. Startups need robust platforms to streamline HR work streams, but often lack with financial resources for building complex database driven systems. In this paper, an AI powered HR management system is developed to simplify employee management processes for small scale startups. In this system. cache memory is used instead of traditional databases, thereby the operational cost of the system will be reduced for storage and retrieval of employee details. Automated Attendance Management enhanced with Face Recognition technology, Payroll Management, Leave Management, Feedback Submission modules are included. It is integrated with an AI powered chatbot, which facilitates a smooth interaction by the employees with query for information, submit leave requests and pull up all payroll information etc. A user friendly dashboard for a consolidated view of employee profiles, attendance records, leave approvals, and payroll data can be served over an optimized and time efficient HR workflow. This system addresses the needs of the startups such as flexible usage, cost effective operations and improved resource management.

关键词： Adaptation models Costs Databases Face recognition Cache memory Organizations Chatbots Data models Resource management Artificial intelligence

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A Study of Deep Learning Models in Diagnosis and Prediction of Chronic Diseases 3

A Study of Deep Learning Models in Diagnosis and Prediction ...

引用

3rd International Conference on Issues and Challenges in Intelligent Computing Techniques, ICICT 2022

作者： Begum, Sayyada Hajera Vidyullatha, P. Koneru Lakshmaiah Education Foundation Dept of Computer Science and Engineering A.P Vaddeswaram India

ISBN: (纸本)9781665482684

With the introduction of big data and its incredible advancement in image procurement devices, transformation of medical data into valuable knowledge has become an important challenge in the area of bioinformatics. The medical images procured require huge analysis and diagnosis of images using AI techniques like Machine and Deep Learning (ML, DL) that yields automated diagnosis solutions. Deep learning methods can provide optimized and precise solutions for medical image diagnosis and can be an important methodology for imminent health care applications. Deep learning in biomedical image classification, automated image classification can aid in effective and early treatment of various diseases thus reducing mortality and morbidity rate. This paper gives an introduction to Deep Learning models and reviews some contemporary deep learning models and their application in detecting various diseases. © 2022 IEEE.

关键词： Image classification

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A Comparative Analysis of Deep Learning Based Approaches for DeepFake Identification 6

A Comparative Analysis of Deep Learning Based Approaches for...

引用

6th International Conference on Futuristic Trends in Networks and Computing Technologies, FTNCT 2024

作者： Duhan, Krity Kajal, Abhishek Dept. of Computer Science and Engineering GJUS and T Hisar125001 India

One of the major concerns in the present global environment is the creation and utilization of deepfakes. In this work, we have examined the issues and challenges that are created by the deepfake in the security and surveillance mechanisms. Deepfakes are used for by passing the facial identification or biometrics system that are normally used as surveillance mechanisms. Previously the only issue was to find the authentic person through the photographs that they have put on their identity proofs but nowadays it is extended to find the forged pics that are manipulated with the usage of AI based methods like deepfakes. Deepfakes are used in the current environments for bypassing the security by impersonation and providing false information and thus become a source of danger especially in the politics arena and entertainment landscape. Deep learning has the tendency to mitigate the impact of deepfakes to considerable extent by incorporating the same in the techniques develop for the identification of deepfakes. This paper provides the comprehensive review of the deepfake related work that has been done by researchers with the usage of deep learning approaches which could identify the fake images, videos to significant extent in various contexts along with the mechanism used for deepfake creation and identification in general. Moreover, the comparative analysis of the existing techniques has been done considering a range of factors like dataset used, technique used, accuracy, AUC, data type used for deepfake identification etc. It has been inferred that majority of the researchers have used the FaceForensics++, Celeb-DF, DFDC dataset and have utilized CNN technique for the deepfake identification primarily on images. © 2025 The Author(s). Published by Elsevier B.V.

关键词： Deep learning

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Classification of Benign and Malignant Skin Lesions using Convolutional Neural Networks (CNN)

Classification of Benign and Malignant Skin Lesions using Co...

引用

International Conference on ent Computing and Control Systems (ICICCS)

作者： K. Dhanalakshmi Suraj Kumar Sudhir Mishra D. Susitra R. Lalitha V. Dharani Dept. of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai Tamilnadu India

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

Skin cancer is one of the common forms of cancers, and if it is diagnosed early, then the possibility of survival increases greatly. Currently, the diagnosis is carried out based on expertise and visual inspection, which are cumbersome and prone to human errors. In the present paper use CNN for skin cancer detection using a deep learning approach. A large dataset of dermoscopic images of several categories of skin lesions is used as training data, which may contain both malignant and benign samples, for example, melanoma, basal cell carcinoma, squamous cell carcinoma, and seborrheic keratosis. In fact, the proposed CNN architecture extracts the relevant information of the images, such as texture, shapes, and patterns, to classify such skin lesions into any one of these pre-specified categories. The model will highly determine both benign and malignant lesions with the highest accuracy, thus providing an accurate machine for automated screening of skin cancer. Experimental results showed that it outperformed the techniques used for diagnostics in speed, accuracy, and scalable early detection of skin cancers. This would help in more informed decisions by the dermatologists and thus lead to better patient outcomes while burdening the least members of the healthcare providers. This paper aims at proving the possibility of using deep learning to improve and automate skin-cancer diagnosis. The objective of this work is to leverage deep learning to classify skin lesions into malignant (melanoma, basal cell carcinoma, squamous cell carcinoma) and benign (seborrheic keratosis) categories with high accuracy.

关键词： Deep learning Accuracy Squamous cell carcinoma Basal cell carcinoma Pipelines Medical services Melanoma Skin Lesions Convolutional neural networks

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Epileptic Seizure State Classification from Scalp EEG using Self-Organizing Maps 3

Epileptic Seizure State Classification from Scalp EEG using ...

引用

3rd International Conference on Electronics and Sustainable Communication Systems, ICESC 2022

作者： Visalini, K. Alagarsamy, Saravanan Kalasalingam Academy of Research & Education Dept. of Computer Science and Engineering Tamilnadu Virudhunagar India

ISBN: (数字)9781665479714

ISBN: (纸本)9781665479714

Epileptic seizures afflict around one percent of the worldwide human population, are typified by excessive neuronal firing in the human brain, and have a significant influence on the quality of life of the patients. This study demonstrates a Self Organizing Map-based architecture for ternary classification of seizure states into ictal, pre-ictal, and inter-ictal states. The standardized annotated dataset comprising EEG recordings of 24 pediatric individuals collected at Children's Hospital Boston (CHB-MIT) was used to train the model and evaluate its performance. The suggested model achieved a mean accuracy of 95.63 percent. The model was shown to be similar to advanced classification models on the benchmark dataset, with mean sensitivity rate of 93.51 percent. © 2022 IEEE.

关键词： Self organizing maps

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Advanced Cryptocurrency and Stock Prices Forecasting via Bidirectional Long Short-Term Memory Model

Advanced Cryptocurrency and Stock Prices Forecasting via Bid...

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Data science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： Ramsriprasaath D R. Bhavani S. Pavithra Dept. of Computer Science and Engineering Chennai Institute of Technology Chennai India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

Truthful prediction of the values of the cryptocurrencies and stocks is critical in the financial marketplace. We developed a Bi-Lstm model to forecast the next day prices of Bitcoin, Ethereum, and Microsoft stock. The model expands the temporal resolution to the 60-day sequence window, making predictions of financial time series more reliable due to higher temporal dependencies. The effectiveness of the model is evaluated using three error metrics: Hence, use of Mean Squared Error, Root Mean Squared Error, and Mean Absolute Percentage Error was employed to guarantee accurate evaluation. Anticipating prices for the year 2025, the proposed system provides information for overall long-term tendencies and possible gains from investments. The findings reveal that the application of Bi-Lstm model can avert the categories of fluctuation that characterises the financial market and is a powerful instrument for making future predictions. The future work might improve the accuracy of the predictions by incorporating other features like market sentiment and macroeconomic indicators.

关键词： Sentiment analysis Recurrent neural networks Accuracy Operating systems Time series analysis Bitcoin Predictive models Macroeconomics Blockchains Long short term memory

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Balancing Safety and Security in Autonomous Driving Systems: A Machine Learning Approach with Safety-First Prioritization

Balancing Safety and Security in Autonomous Driving Systems:...

引用

computer Security Applications Conference Workshops (ACSAC Workshops), Annual

作者： Afshin Hasani Mehran Alidoost Nia Reza Ebrahimi Atani Dept. of Computer Engineering University of Guilan Rasht Iran Faculty of Computer Science and Engineering Shahid Beheshti University Tehran Iran

ISBN: (数字)9798331532819

ISBN: (纸本)9798331532826

In the era of autonomous vehicles (AVs), ensuring the safety and security of the system is an ongoing challenge, particularly when faced with increasingly cyber-attacks such as GPS spoofing and man-in-the-middle. This paper presents a novel reinforcement learning (RL)-based framework that prioritizes safety over security concerns in AVs, ensuring that life-critical tasks such as collision avoidance and speed control are always maintained, even in the presence of security threats. Our system leverages the MAPE-K loop to dynamically adapt to changing conditions, using RL in the Analyzing phase to make real-time decisions that maximize cumulative safety rewards while addressing security risks that directly affect operational integrity. We demonstrate how our RL-based self-adaptive system mitigates security threats while maintaining safe driving behaviors through various simulations and experiments. The proposed system shows significant improvements in collision avoidance and threat detection, ultimately ensuring that the AV’s safety is preserved in even the most challenging scenarios.

关键词： Conferences Velocity control Reinforcement learning Real-time systems Threat assessment Safety Security Collision avoidance Autonomous vehicles Global Positioning System

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A Study on a Framework for Initial Counseling for Vulnerable Populations in Welfare Blind Spots Based on LLM

A Study on a Framework for Initial Counseling for Vulnerable...

引用

International Conference on Knowledge and Smart Technology (KST)

作者： Siyoon Sung Jemin Kim Junhyuk Kim Sangeun Park Junho Jeong Dept. Computer Science and Engineering Dongguk University Seoul Republic of Korea

ISBN: (数字)9798331520403

ISBN: (纸本)9798331520410

This study proposes a method to enhance the quality of counseling and automate initial counseling in welfare counseling systems by utilizing Large Language Models (LLMs) to correct errors occurring in the Speech-to-Text (STT) process. The Silver framework consists of an STT correction and evaluation model, a conversation model, and a summary model, aiming to simultaneously improve the flexibility and accuracy of counseling. In this study, the STT correction and evaluation model improved the quality of text correction, while the conversation model enabled natural conversation. Additionally, the summary model effectively organized and verified counseling content. Experimental results demonstrated that the STT correction model achieved 82 % accuracy in evaluation and 74 % accuracy in correction, while the conversation progress and summary models recorded 68 % and 92 % accuracy, respectively. These findings highlight the effective applicability of LLM-based technologies in welfare counseling.

关键词： Employee welfare Silver Accuracy Large language models Evaluation models Oral communication Chatbots Speech to text

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Solving Task-Select Problems Using a Quantum Annealer

Solving Task-Select Problems Using a Quantum Annealer

引用

2025 IEEE International Conference on Consumer Electronics, ICCE 2025

作者： Mita, Koki Yachi, Yuta Fukada, Keisuke Togawa, Nozomu Dept. of Computer Science and Communications Engineering Waseda University Tokyo Japan

ISBN: (纸本)9798331521165

To tackle environmental and social challenges, we face numerous tasks that need to be solved. However, due to limited time and resources, it is unrealistic to solve all tasks. Therefore, prioritizing tasks to ensure efficient handling and meeting criteria is essential. When the number of tasks is large, prioritizing the optimal combination of tasks on classical computers becomes highly time-consuming. We define this prioritization as a combinatorial optimization problem, termed the task-select problem, and address it using a quantum annealer. A quantum annealer is specialized hardware designed to efficiently solve combinatorial optimization problems by transforming them into Quadratic Unconstrained Binary Optimization (QUBO) problems. In this paper, we propose a QUBO formulation for the task-select problem. Experimental evaluations demonstrate that our approach enables solving the task-select problem with up to 100 items using a real quantum annealer. © 2025 IEEE.

关键词： Ising model

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Utilization of Decentralized Finance (DeFi) and Distributed Ledger Technology (DLT) in Banking operations

Utilization of Decentralized Finance (DeFi) and Distributed ...

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Intelligent Computing and Sustainable Innovations in Technology (IC-SIT), International Conference on

作者： Ch Sree Kumar Akhilendra Pratap Singh K Hemant K Reddy Dept. of Computer Science National Institute of Technology Meghalaya Meghalaya India Dept. of Computer Science and Engineering VIT-AP Vijayawada India

ISBN: (数字)9798350369175

ISBN: (纸本)9798350369182

Distributed Ledger Technology (DLT) is a decentralized database system where transactions are recorded and verified across multiple nodes. Its key features include immutability, time-stamping, and consensus-based validation. Numerous DLT applications are in supply chain management, intellectual property, cross border payments, energy trading, real estate, and online donations. DeFi, a combination of cryptocurrency and blockchain technology, offers financial services without intermediaries. Hence transactions with various digital assets based on cryptocurrency price feeds needs an automated framework. Smart contracts, self-executing contracts with terms directly written into code, automate processes and reduce manual intervention. This paper proposes a decentralized financial trading model using the AAVE protocol. AAVE is a decentralized platform that allows for transactions with various digital assets based on cryptocurrency price feeds. Proposed model uses Chainlink, a decentralized oracle network, to provide accurate and reliable price feeds. IPFS is used for data storage, while Graph is employed for indexing and querying blockchain data. The paper presents an example of a DeFi protocol to simulate banking operations, showcasing the potential of DeFi and DLT in revolutionizing traditional banking processes.

关键词： Accuracy Distributed ledger Finance Banking Decentralized applications Blockchains Cryptocurrency Feeds Reliability Indexing

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