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ResuMatcher: An intelligent Resume Ranking System 3

ResuMatcher: An Intelligent Resume Ranking System

引用

3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Dhobale, Prasad Bhoir, Vinayak Vyavhare, Abhijeet Yelkar, Pratik Dharmadhikari, Pramod A. School of Computer Engineering MIT Academy of Engineering Alandi Pune India

ISBN: (纸本)9798331527549

The growing complexity of recruitment processes necessitates advanced solutions for resume screening and ranking. In this paper, we present ResuMatcher, an AI-powered resume ranking system that leverages the power of Large Language Models (LLMs) for semantic understanding. Unlike traditional keyword-based systems, ResuMatcher evaluates the context and meaning behind terms in resumes and job descriptions, providing a more accurate and efficient matching process. Using models such as BERT and transformer-based architectures, ResuMatcher can assess candidate qualifications by considering the underlying semantics of job requirements and candidate skills. The system was evaluated on a diverse dataset across industries such as IT, healthcare, and finance, and showed a significant improvement in both accuracy and efficiency compared to conventional Applicant Tracking systems (ATS). Results from user testing demonstrated high satisfaction rates among both job seekers and recruiters, highlighting ease of use, relevance of matches, and efficiency gains in the hiring process. © 2025 ieee.

关键词： Semantics

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Dual-Plate Verification System for Vehicle Identifcation using IoT and Embedded systems 3

Dual-Plate Verification System for Vehicle Identifcation usi...

引用

3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Sampath, A.V.S.S. Malagati, Nischal S. Kanapathi, V. Belwal, Meena Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

This research uses a dual-plate verification system for improving vehicle security by using IoT and embedded systems. The system compares front and rear license plates to detect any anomaly, which signals unauthorized modifications. Using real-time image processing and IoT-based data communication, this solution gives us an accurate plate matching system, making sure of reliable vehicle identity verification. Its possible applications include traffic monitoring, parking management, and detecting any unlawful activity. This project is unique because it integrates the dual plate verification system with an Arduino IoT setup. For hassle-free text recognition, this system uses deep learning frameworks combined with a wireless mobile setup that serves as a camera. The system’s accuracy is shown through experiments, displaying its value in improving transportation security with automation. The number plates were detected, and the respective penalties for the vehicles were imposed accordingly. © 2025 ieee.

关键词： Anomaly detection

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intelligent Battery Management System for Electric Vehicle with IoT-based Monitoring, AI-Driven Multiple Fault Diagnosis and Energy Optimization 3

Intelligent Battery Management System for Electric Vehicle w...

引用

3rd international conference on Electronics and Renewable systems, ICEARS 2025

作者： Arun Kumar, P. Shalini, S. Sneha, R. Vasunthra, P. Suganyadevi, S. M Kumarasamy College of Engineering Department of Ece Karur India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509675

Battery related issues have a significant effect on both the performance and safety of electric vehicle (EV). Overheating Battery Lifespan is meant to be reduced by simply the dangers of overheating, as they can even cause thermal runaway, as well as from over and deep revolution. Besides, EV batteries have poor performance even in extreme temperatures, capacity loss, and internal defects. In order to cope with these problems, a proposed IoT based EV battery monitoring and fault detection system provides the real time analysis to promptly notify the battery health. Built in IoT sensors are integrated in this system which monitors critical parameters constantly i.e. temperature, voltage, current and charge levels. For 95-98% battery parameters monitoring accuracy, the collected data is transferred to a centralized cloud platform for real time cloud processing. The system utilizes advanced edge computing techniques and continuous monitoring capable of detecting a fault with 95-98% probability. Electrochemical processes are analyzed with 90-95% accuracy using predictive analytics and machine learning algorithms to ensure optimal battery performance. Moreover, the system achieves energy efficiency of more than 95% through power management and control mechanisms. In 95% of the cases, intelligent charging algorithms will prevent overcharging. This cutting-edge technology dramatically improves EV battery safety, longevity as well as efficiency, assuring reliability and sustainability. © 2025 ieee.

关键词： Predictive analytics

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Investigations of PAPR reduction methods in FBMC System 3

Investigations of PAPR reduction methods in FBMC System

引用

3rd international conference on intelligent and Innovative Technologies in computing, Electrical and Electronics, IITCEE 2025

作者： Samantaray, Rashmi Rani Siddappaji Hkbk College Of Engineering Vtu Department of Electronics and Communication Bmsce Bengaluru India Bms College of Engineering Department of Electronics and Communication Bengaluru India

ISBN: (纸本)9798331515911

The primary concern of the 5G radio network has sparked interest in the development of new waveforms beyond 4G (5th generation networks). Filter Bank Multicarrier (FBMC) waveforms have recently been suggested as the desired one for 5G networks. It has both advantages and disadvantages. One of the biggest drawback of the FBMC broadcast signal is its high Peak to Average Power Ratio (PAPR). It has been widely used in wireless communication to achieve high speed and data rates. A high PAPR system results in substantial distortion of the signal because of the nonlinear power amplifier. Therefore, methods that effectively reduce PAPR are necessary for the realization of the FBMC system. The FBMC system uses the some common techniques as the OFDM system. Comparative analysis of various approaches such as PTS, SLM, Companding, etc are performed by considering their simulation outcomes when it comes to effective spectrum utilization, improved PAPR, along with minimum complexity. © 2025 ieee.

关键词： 5G mobile communication systems

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The Effect of Algorithmic Trading systems on Stock Market Volatility 3

The Effect of Algorithmic Trading Systems on Stock Market Vo...

引用

3rd international conference on intelligent and Innovative Technologies in computing, Electrical and Electronics, IITCEE 2025

作者： Charithra, C.M. Vikas, Bhavya Mukund Sharma, N. Bnm Institute of Technology Department of Business Administration Bengaluru India

ISBN: (纸本)9798331515911

For many people, trading in stock market is a popular investment option. Stock price trends has always been an important area of interest for machine learning researchers. Modelling the stock market has always stood as a challenge, despite of many Algorithmic Trading system being available. Algorithmic trading is a mechanism involving use of Artificial Intelligence(AI) for quick trading in stock *** work focuses on understanding the role of algorithmic trading on the capital markets and examines the existence of volatility and test the homogeneousness in the degree of volatility of the chosen Emerging and the developed markets from ***, PP tests and Jarque-Bera tests were conducted to examine the stationarity and normality of the time series data. Returns, SD, ARIMA, ARCH, GARCH, BEKK-GARCH, Granger causality test were applied for the study. The empirical analysis depicted that volatility is present in all stock markets, with its level varying between different markets over time. This will be beneficial to the investors to arrive at right decision. © 2025 ieee.

关键词： Marketplaces

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Design of a Real-Time Crime Monitoring System using Machine Learning 3

Design of a Real-Time Crime Monitoring System using Machine ...

引用

3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Krishnaveni, A. Manikandan, C. Department ofComputer Science Karpagam Academy of Higher Education Coimbatore India

ISBN: (纸本)9798331527549

Crime in public areas poses risks to individuals and society at large. Traditional crime prevention methods face significant hurdles in detecting and tracking crime. Furthermore, the influx of large volumes of data overwhelms security systems and personnel making security enforcement harder. However, technological innovations have positively influenced different fields, including public safety and crime prevention. This study introduces an innovative system for crime prediction and analysis using machine learning, designed to assist law enforcement in identifying criminals in public spaces. The system enables authorities to input detailed information about criminals, including facial data, which is used to train an advanced recognition model. The system’s ability to provide accurate and timely alerts enhances the efficiency of law enforcement operations. This framework leverages state-of-the-art face recognition technology to offer a scalable and robust solution for crime prevention. By integrating machine learning algorithms with intelligent surveillance systems, the proposed approach addresses the growing need for proactive measures to ensure public safety. Further, this system also highlights the potential of technology to transform traditional crime detection methods, paving the way for smarter and more responsive law enforcement practices. Finally, the proposed system attained a 92% face detection accuracy score which was higher than existing approaches. © 2025 ieee.

关键词： Crime prediction exact location facial data machine learning public surveillance recognition model

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Customized IoT based Smart Irrigation for Terrace Plants 6

Customized IoT based Smart Irrigation for Terrace Plants

引用

6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Priya, S. Padma Pavalarajan, S. Abarna, P.S. Manjula devi, C. Abinaya, B. Sukaina, S. Department of Computer Science and Business Systems PSNA College of Engineering and Technology Dindigul India Department of Information Technology Velammal college of Engineering and Technology Madurai India

ISBN: (纸本)9798331522667

An Internet of Things based smart irrigation system for terrace plants is a cutting-edge solution designed to automate and optimize the watering process, ensuring efficient use of water and promoting healthier plant growthSmart irrigation system may vary from field with crops of same variety to terrace plantation with plants of different varieties. As the water requirement may vary based on plant type, environmental condition, growth stage, it is intelligent to provide varying water supply. This Smart irrigation system employs soil moisture to monitor the water content of the terrace garden in real time. Some variety of plants may require minimum quantity of water. The sensors continuously collect data and send it to a central IoT platform or a cloud-based server, where intelligent algorithms analyse the data and determine the precise amount of water needed by the plants. Based on this analysis, the system automatically activates the irrigation mechanism whether it's a drip system, sprinklers, or any other appropriate watering method only when required, thus preventing water wastage and avoiding overwatering or underwatering. © 2025 ieee.

关键词： Sprinkler systems (irrigation)

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IoT based Thermal Management and State of Charge Estimation of LFP battery 3

IoT based Thermal Management and State of Charge Estimation ...

引用

3rd international conference on intelligent and Innovative Technologies in computing, Electrical and Electronics, IITCEE 2025

作者： Anand, Deepthi Paraag, V. Naik, Sahana Kavana, T.M. Vasudha, Karanam Shubha Rao, K. Bnm Institute of Technology Dept. of Eee Bangalore India

ISBN: (纸本)9798331515911

Efficient storage of electricity is pivotal in modern applications, necessitating the use of batteries. However, enclosing batteries in compact spaces, such as power plants or electric vehicles, often leads to excessive heat generation, diminishing efficiency and longevity. Heat generation primarily stems from the charging and discharging processes. A liquid cooling system, powered by a motor connected to the battery system, circulates fluid externally around the battery layers to manage temperature, optimizing the battery's performance within the ideal range of 25°C to 35°C. Thermal Modelling of the battery is implemented in systems to understand how temperature changes affect their performance, efficiency, and reliability. IoT-based Battery Thermal Management System (IoT-BTMS) is introduced resulting in a sophisticated solution utilizing sensors, communication technologies, and data analytics. This system monitors and regulates battery temperature in real-time, based on sensed data a relay will enable cooling system if temperature values exceed the limit values or when the system becomes unstable, the cooling system continues until the system stabilizes and temperatures fall within limits. © 2025 ieee.

关键词： Cooling systems

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Bayesian-Informed Hyperdimensional Learning for intelligent and Efficient data Processing 24

Bayesian-Informed Hyperdimensional Learning for Intelligent ...

引用

43rd international conference on Computer-Aided Design, ICCAD 2024

作者： Barkam, Hamza Errahmouni Das, Tamoghno Poduval, Prathyush Jeong, SungHeon Yeung, Calvin Solitan, Mostafa Imani, Mohsen University of California Irvine IrvineCA United States

ISBN: (纸本)9798400710773

In machine learning (ML), near-sensor AI is transforming edge computing by reducing response times and data transmission, ultimately saving energy and bandwidth. Despite challenges like limited computational resources and the need for transparent decision-making, this approach aims to enhance the intelligence and autonomy of edge devices. Our research presents a novel framework that adds a layer of abstract intelligence to sensors, boosting system efficiency and accuracy through transparent, interpretable sub-symbolic AI. We combine Bayesian algorithms with hyperdimensional computing (HDC), inspired by the human brain’s operational efficiency, to deliver an energy-efficient solution matching the accuracy of traditional cloud systems without constant server dependence. This framework uses a binary classifier with Bayesian insights to choose the best data processing location—locally or in the cloud—adapting to data environments. Our method ensures cloud-level performance while significantly reducing energy consumption, improving the sustainability of sensor-based systems. It also enables continual adaptation and learning directly at the sensor level, enriching cloud models with fresh edge insights. Our results have shown to bridge the gap from around 38% quality loss between the standalone near-sensor HDC model and the SOTA cloud-based model to improve the quality loss to only 9% while simultaneously saving 45.34% of energy by not using the cloud. This framework paves the way for more sustainable, efficient, and accurate edge computing in the ML landscape by bridging the gap between simple near-sensor models and their advanced cloud-based counterparts. © 2024 Copyright is held by the owner/author(s).

关键词： data reduction

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Research on Precision Optimization of Deep Learning Method based on VAT-LSTM and Transformer Encoder in Financial data Prediction 1

Research on Precision Optimization of Deep Learning Method b...

引用

1st international conference on intelligent systems and Computational Networks, ICISCN 2025

作者： You, Yue Shanghai University School of Management Shanghai China

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529246

This article addresses the challenge of improving prediction accuracy for financial data using two advanced deep learning methods: VAT-LSTM and Transformer Encoder. Financial time series data often present significant challenges for traditional econometric models due to their complex characteristics, such as high noise levels, low signal-to-noise ratios, nonlinearity, and strong volatility. In order to overcome these problems, we propose two innovative models: incorporating VAT standardization into LSTM and constructing a VAT-LSTM model to enhance the stability of the model and reduce the generalization error. Through empirical analysis of the data of the Shanghai Composite Index (2015-2022), VAT-LSTM has short term, medium term, in the long term forecast, LSTM, RNN, GRU, BP, SVR, it is better than the traditional model (such as arima), the second model based on transformer encoder and attention. In particular, the prediction accuracy of various time ranges is improved. Comparison with traditional models shows that the Transformer Encoder provides the best performance across different time dimensions. Overall, the proposed models excel in forecasting financial data, with promising results in short-, medium-, and long-term prediction. However, there is potential for further optimization in long-term forecasting. This research not only contributes new insights for financial data prediction but also has wide application potential in the field. © 2025 ieee.

关键词： Prediction models

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