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Unlocking Talent: Exploring the Potential of AI and ML Algorithms in Recruitment Process

Unlocking Talent: Exploring the Potential of AI and ML Algor...

引用

2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Kundu, Sankhadip Palani, Saravanan Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350378092

A Recruitment process is complex and multifaceted. It requires a strategic approach to assess and select suitable candidates for the selected post while mitigating challenges along the way. This study aimed at predicting the likelihood of job candidates being hired, utilizing a diverse range of input features. Leveraging machine learning algorithms applied to a comprehensive dataset spanning over a decade and encompassing various populations, the research emphasizes the interpretability of the model's predictions. By integrating multiple machine learning algorithms, the study achieves remarkable prediction accuracy while providing valuable insights that HR professionals can interpret. Through meticulous analysis, distinct patterns and factors influencing hiring decisions are identified, offering practical implications for recruitment processes. The findings underscore the feasibility of predicting candidate hiring ability prior to employment, thereby facilitating the development of optimized recruitment strategies. This research significantly contributes to enhancing HR decision-making by furnishing a robust framework for evaluating job candidates' potential success. © 2024 IEEE.

关键词： Prediction models

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AgriAid: An Intelligent Farmer Companion Using Deep Learning Approach 2

AgriAid: An Intelligent Farmer Companion Using Deep Learning...

引用

2nd IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024

作者： Ilakiyaselvan, N. Dhandapani, Aarthi Khadar Nawas, K. Bhattacharya, Abhinov Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350306415

The percentage of farmers in India's enormous population is roughly 54.6%. However, despite having such a large population, they only contribute roughly 13.9% of the nation's total yearly GDP. This severe inequality may be reduced by improving farmers' access to information and expert advice. In order to increase crop output and quality, farmers typically depend on agricultural counselors and professionals to provide correct information for crop decision-making. Frequently, advisors or specialists in agriculture are not always available. A voice-based chatbot has several benefits, such as being real-time, constantly available, requiring little to no training to use, and providing instant access to information rather than requiring users to walk convoluted paths. In this study, we explain my design work for a farmer's helper, named Farmer Companion, to meet the needs of both rural and urban Indian farmers utilizing Supervised ML Algorithms and Deep Neural Networks, of which the best one is selected. A comparison of recent models that have been used in the literature is also included in the paper. © 2024 IEEE.

关键词： Artificial Neural Network Natural Language Processing Neural Networks Voice based Query System Voice-based Chatbot

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Vision Beyond the Haze: Advancements in Cataract Detection and Diagnosis 2

Vision Beyond the Haze: Advancements in Cataract Detection a...

引用

2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Kumar, E. Sudheer Reddy, N U. Praneeth Reddy, Depatla Hemika Kanish, Tulasi Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350368413

Eye diseases are among the most critical health issues impacting human life. One of the most frequently encountered eye problems is cataracts, due to which people suffer as they age. The cloudiness that forms on eye lenses is known as cataracts which can significantly damage vision. Diagnosing the disease in its acute stage rather than its onset should not blind the patient. This study presents an improved Cataract detection method using pre-trained convolutional neural networks (CNNs) on an Ocular Disease Intelligent Recognition (ODIR) dataset, Comparing the performance of ResNet50, ResNet101, and VGG19 architectures to find the most effective model for this project. The proposed approach uses deep learning to analyse fundus images, aiming to increase diagnostic accuracy, speed, and reliability. several preprocessing and data Augmentation techniques were utilized to prepare the data set, which provided robust training and improved model generalization. Samples were refined using pre-trained ImageNet weights and adjusted for binary classification of Cataract presence. Among the tested models, ResNet50 exhibited the best performance, achieving an overall accuracy of 99.08% It also showed good evaluation metrics, with a precision rate of 98.15% and 100% recall for cataract images, and 100% precision with 98.23% recall for normal images. The results highlight the potential of ResNet50 to provide accurate and effective cataract diagnosis, especially in areas where access to trained ophthalmologists is inaccessible. This study highlights the importance of using deep learning technologies to improve the early detection and treatment of cataracts, ultimately contributing to better patient outcomes and global eye health progress. © 2024 IEEE.

关键词： Convolutional neural networks

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Context-Aware Summarization for PDF Documents using Large Language Models 4

Context-Aware Summarization for PDF Documents using Large La...

引用

4th International Conference on Expert Clouds and Applications, ICOECA 2024

作者： Ramprasad, Akshara Sivakumar, P. School of Computer Science and Engineering Vellore Institute of Technology B. Tech Computer Science with specialization in AI & ML Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350385793

In an era characterized by the overflow of textual information, the demand for effective text summarization techniques has become increasingly evident. This research study presents a novel solution to address this demand: the development of a PDF Summarizer Generative AI Platform for context-aware text and document summarization. The developed platform empowers users to generate informative summaries from lengthy textual documents. It features an interface powered by Gradio for enabling user-friendly accessibility. Equipped with essential components such as OpenAI's language models for text comprehension, Chroma vector enables document embedding and conversational retrieval chains for contextual understanding, where the platform offers a comprehensive solution. Users can effortlessly upload documents in various formats, including PDFs, and interactively engage with the system to obtain concise summaries tailored to their specific requirements. © 2024 IEEE.

关键词： Chroma Database (ChromaDB) Gradio LangChain Large Language Models (LLM) OpenAI

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An Integrated Framework for Real-Time Disaster Data Acquisition, Processing, and Resource Optimization in Crisis Management Systems 4

An Integrated Framework for Real-Time Disaster Data Acquisit...

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Saranya, G. Atchaya, M. Harshini, R. Choudhary, Kartik School of Computer Science Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798331529635

The rapid onset and unpredictable nature of disasters necessitate a quick, organized, and efficient response. This paper proposes a comprehensive disaster management platform that integrates real-time disaster information, weather forecasting, and resource management to enhance disaster preparedness and response. The platform collects disaster data from geophysical monitoring stations, processes the data for consistency, and stores it in a MongoDB database. Geospatial visualization tools, such as Folium, are employed to present disaster events on interactive maps, helping decision-makers assess the situation and respond accordingly. Additionally, the platform includes features for weather forecasting and resource management, allowing users to monitor conditions and contribute essential resources in real time. This research demonstrates how leveraging modern technologies can improve disaster management systems, providing a more effective and coordinated response to natural and man-made disasters. © 2024 IEEE.

关键词： Weather forecasting

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Privacy-Preserving Employee Attrition Prediction using Deep Learning and Fully Homomorphic Encryption

Privacy-Preserving Employee Attrition Prediction using Deep ...

引用

2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Rajesh, R. Harshavardhan, S. Kirthana, B. Shreya, V. School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (数字)9798331510022

ISBN: (纸本)9798331510022

Employee attrition presents a significant challenge for organizations, impacting both finances and operations. Mean-while, the automation of human resource processes, including recruitment and performance monitoring, heavily relies on vast amounts of personal data. Traditional predictive models for employee attrition have increasingly turned to deep learning techniques due to their superior accuracy in deciphering complex data patterns. However, these methods raise serious privacy concerns due to their reliance on sensitive employee information. This paper introduces a novel solution by integrating Fully Homomorphic Encryption (FHE) with deep learning, ensuring data privacy without compromising predictive performance. This integration not only enhances attrition prediction accuracy but also maintains employee data confidentiality, potentially improving organizational efficiency and trust in HR processes. © 2024 IEEE.

关键词： Differential privacy

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Advancing Gender, Age and Ethnicity with YOLOv5 and Transfer Learning 6th

Advancing Gender, Age and Ethnicity with YOLOv5 and Transfer...

引用

6th International Conference on Information Systems and Management science, ISMS 2023

作者： Bhattacharya, Varun Tripathy, Balakrushna School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore632014 India

ISBN: (纸本)9783031664090

The detection of gender, age, and ethnicity using machine learning techniques holds significant promise for applications in marketing, healthcare, and law enforcement. Previous methods relied on conventional machine learning and manual feature engineering, which often struggled to capture the complex relationships between facial features and these demographic factors. To overcome these limitations, our novel approach leverages the powerful YOLOv5 framework, integrating transfer learning and deep neural networks. This cutting-edge method excels at accurately classifying gender, age, and ethnicity from facial images, thanks to its deep neural network architecture with advanced object detection capabilities. Our work represents a substantial advancement in gender, age, and ethnicity prediction, showcasing the potential of advanced machine learning techniques. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Adversarial machine learning

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Automated Traffic Signaling System for Ambulance Crossing using IOT 15

Automated Traffic Signaling System for Ambulance Crossing us...

引用

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

作者： Pattabiraman, V. Parvathi, R. Jai Ganesh, C. Kaushal, C. Mohnish Kumaar, D. Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331300579

The research aims to improve traffic management and response time of emergency vehicles, specifically ambulances, at four-lane traffic junctions. The primary objectives are to detect the presence of emergency vehicles, reduce traffic congestion, and design an efficient traffic management system. The proposed solution combines radio frequency (RF) technology and sound sensing for emergency vehicle detection, along with density-based traffic regulation using ultrasonic sensors. The methods employed include installing RF transmitters in ambulances and RF receivers with sound sensors at traffic junctions, interfaced with an Arduino microcontroller. The project's outcomes are expected to include more efficient traffic flow, faster passage for emergency vehicles, and overall reduced traffic congestion. As a result, the response time of emergency services will be improved, contributing to saving lives and promoting sustainable urban transportation infrastructure. The scope of this project will include traffic management in urban environments, specifically at four-lane traffic junctions. The proposed system has the potential to be scaled up and adapted for larger or more complex traffic networks, as well as integrated with other smart city technologies, enhancing traffic management and emergency response efficiency in various urban settings. © Grenze Scientific Society, 2024.

关键词： Traffic congestion

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Enhancing Cyberbullying Detection Through Keyword Filtering: A Comparative Study of ML and DL Approaches

Enhancing Cyberbullying Detection Through Keyword Filtering:...

引用

2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Kaarthika, R. Hemamalini, R. Sujithra Kanmani, R. School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (数字)9798331540685

ISBN: (纸本)9798331540685

Cyberbullying represents the use of online platforms like social media, gaming platforms, and similar platforms to harass a person, hurt a person, or threaten a person. In this study, keyword filtering is introduced to enhance cyberbullying detection. Various machine learning models, including Logistic Regression, Random Forest, and Support Vector Machine, as well as a deep learning model Convolutional Neural Network, are implemented on original and keyword-labelled datasets. The performance of these models is compared using various evaluation metrics. Our findings demonstrate that the performance of models on the keyword-labeled dataset is significantly higher, with Logistic Regression achieving 98.24%, Random Forest at 99.77%, SVM at 99.67%, and CNN at 99.95% accuracy. This study highlights the effectiveness of keyword filtering features in enhancing the detection of cyberbullying, providing a robust approach to creating safer online environments. © 2024 IEEE.

关键词： Support vector regression

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Performance Analysis of Machine Learning Algorithms for Covid-19 Detection using Cough Analysis

Performance Analysis of Machine Learning Algorithms for Covi...

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2024 International Conference on Recent Innovation in Smart and Sustainable technology, ICRISST 2024

作者： Subitha, D. Kavitha, J.C. Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (数字)9798350309140

ISBN: (纸本)9798350309140

With an alarming rate of Covid-19 cases worldwide, the challenge for early detection of this disease is growing. There are many methods available for the detection of Covid-19 but they can be tiresome and difficult. The more common method of testing is reverse transcriptase-polymerase chain reaction (RTPCR) testing which requires direct contact with the patient during the testing that further causes more chance for disease spread. But, the method of testing that uses coughing sounds was found to be more safe method of testing as this does not require the presence of patient in the lab rather the pre-recorded audio wave is enough to perform the testing. Machine Learning algorithms are a great promise for detection of Covid-19 disease. There are many different approaches for detection involving wide range of inputs. Likewise, this paper shows about the possibility of detecting Covid-19 using cough sound of the patient, by extracting required input features from the audio file using short time Fourier transform method. Simulation results shows the performance of different classification algorithms in terms of Accuracy, Precision, Recall and F1-Score for the detection of Covid-19. This paper has developed a web application which can detect the presence of corona virus. The audio recording of cough is loaded as the input in to the app which is then sent to deep learning model at the back end that returns the Covid infection result at the front end of the application. This also shows the accuracy of various algorithms in the web application. © 2024 IEEE.

关键词： COVID-19

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