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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Deshmukh, Sanskruti Bajaj, Aaryaman Vishwakarma Institute of Technology Department of Artificial Intelligence and Data Science Pune India Srm Institute of Science and Technology Department of Computing Technologies Chennai Kattankulathur India

ISBN: (纸本)9798350376425

The proposed system presents a novel approach to job recommendation and resume enhancement, leveraging advanced Natural Language Processing (NLP) techniques and Retrieval-Augmented Generation (RAG). We introduce a comprehensive system that combines semantic search, FAISS-based similarity matching, and a fine-tuned GPT-2 model to provide personalized job recommendations and actionable resume improvement suggestions. The evaluation, conducted through offline metrics and online A/B testing with 5,000 users, demonstrates significant improvements over traditional keyword-matching approaches. The RAG-based system achieved a 25.8% increase in recommendation relevance (Precision@10), a 45.7% increase in user engagement, and a 41.2% increase in job application rates. Furthermore, users who applied the system's resume enhancement suggestions saw a 9.0% improvement in subsequent job matches. These results highlight the potential of AI-driven systems to revolutionize online recruitment and career development, while also emphasizing the need for ongoing consideration of ethical implications and fairness in AI-assisted hiring processes. © 2024 IEEE.

关键词： Semantics

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Career Prediction Website using Machine Learning 3

Career Prediction Website using Machine Learning

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3rd IEEE International Conference on Intelligent Technologies, CONIT 2023

作者： Rane, Milind Kalal, Sahil Chandegara, Jenil Kakkad, Toshish Jain, Vishwas Jagtap, Simran Vishwakarma Institute of Technology Department of Electronics and Telecommunication Pune India Vishwakarma Institute of Technology Department of Artificial Intelligence and Data Science Pune India

ISBN: (纸本)9798350338607

Most of the students across the Country are constantly in delirium about their career path after their senior secondary schooling. Mostly at the age of 18 students do not have the maturity that they should require in order to choose a right career path. Almost all the students have a series of questions and a complex and confounding thought process of which field to carry on after 12th. Also most people have doubts about whether they have adequate skills or not. So in this paper basically we discuss career prediction which uses basic web development consisting of an exhaustive questionnaire and machine learning approaches like Decision trees, KNN algorithm, Classification to predict a career or field which the student can pursue as per his interest. Python programming language is used in implementing Machine learning classifiers and algorithms. © 2023 IEEE.

关键词： Python

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LesionAid: vision transformers-based skin lesion generation and classification – A practical review

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Multimedia Tools and Applications 2025年 1-22页

作者： K, Mallikharjuna Rao Krishna, Ghanta Sai Supriya, Kundrapu Sorgile, Meetiksha Data Science and Artificial Intelligence International Institute of Information Technology Naya Raipur India Computer Science and Engineering International Institute of Information Technology Naya Raipur India

Skin cancer is one of the most prevalent forms of human cancer. It is recognized mainly visually, beginning with clinical screening and continuing with the dermoscopic examination, histological assessment, and specimen collection. Deep convolutional neural networks (CNNs) perform highly segregated and potentially universal tasks against a classified fine-grained object. This study suggests a novel multi-class prediction framework that uses ViT and ViTGAN to categories skin lesions. To address the class disparity, GANs (Generative Adversarial Networks) based on vision transformers are used. The framework comprises four main phases: ViTGANs, Image processing, and explainable AI. Phase 1 consists of generating synthetic images using ViTGAN to balance all the classes in the dataset. To enhance the amount of the data, the second phase involves using various morphological processes and data augmentation techniques. In phases three and four, a ViT model for edge computing systems that can recognize patterns and classify skin lesions from the user's skin that is visible in the picture is developed. In phase 3, after classifying the lesions into the desired class with ViT, we will use explainable AI (XAI) that leads to more explainable results (using activation maps, etc.) while ensuring high predictive accuracy. The results demonstrate that the model used for generating synthetic images has achieved an FID score of 13.32 and the ViT model has achieved 99.2% as its training accuracy and 97.4% as its validation accuracy. The whole framework is compared with the existing frameworks for skin lesion detection. And explainable AI (XAI) is used in the proposed framework in order to increase model transparency and boost trust among users by illustrating the main factors affecting predictions. This interpretability helps physicians in making more informed decisions by providing clear insights into the model's reasoning. © The Author(s), under exclusive licence to Springer science+Bu

关键词： Generative adversarial networks

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Blockchain-Based Academic Bank of Credit Management System for Higher Education: An Innovative Approach to Student Credentialing and Progression 2

Blockchain-Based Academic Bank of Credit Management System f...

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2nd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2023

作者： Mali, Manisha Aherkar, Sakshi Waykos, Prajwal Shukla, Aishwarya Vishwakarma Institute of Information and Technology Computer Eng. Pune India Artificial Intelligence and Data Science Vishwakarma Institute of Information and Technology Pune India

ISBN: (纸本)9798350333763

The traditional higher education system has long relied on manual processes for managing student credentials, resulting in inefficiencies and delays in certification and credit recognition. In response to the new guidelines set by the University Grants Commission (UGC) regarding a credit-based system, this research paper presents a novel blockchain-based credit management platform for higher education institutions. Leveraging the decentralized and immutable nature of blockchain technology, this system aims to revolutionize student credentialing, credit accumulation, and academic progression. The proposed platform employs a public blockchain network, with different colleges and the central university acting as nodes, ensuring transparency, security, and tamper-resistant storage of student information and academic records. The implementation starts with students registering their information, including personal details and academic history, on the blockchain. This step guarantees the authenticity and accessibility of their records throughout their academic journey. The blockchain-based credit management system enhances the student's ownership of their academic records, fostering a decentralized, secure, and trustless environment. The use of smart contracts streamlines administrative processes, reducing bureaucratic overheads, and improving the overall efficiency of the education ecosystem. Furthermore, the transparency offered by the blockchain ensures that students' achievements are accurately recognized and acknowledged by potential employers and other institutions. © 2023 IEEE.

关键词： Blockchain

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Solarsense: Enhancing Energy Efficiency Through Iot-Enabled Solar Tracking 2

Solarsense: Enhancing Energy Efficiency Through Iot-Enabled ...

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2nd IEEE International Conference on Interdisciplinary Approaches in technology and Management for Social Innovation, IATMSI 2024

作者： Thopate, Kaushalya Gawade, Mayuri Kakade, Surabhi Bahir, Suvarna Kulkarni, Mukund Jaiswal, Krishna Nilesh Computer Engineering Vishwakarma Institute of Technology Pune India Artificial Intelligence and Data Science Vishwakarma Institute of Technology Pune India Computer Engineering Trinity Academy of Engineering Pune India Electronics and Telecommunication Vishwakarma Institute of Technology Pune India

ISBN: (纸本)9798350360523

The surging global demand for energy is primarily fueled by a burgeoning world population and elevated living standards. However, with traditional fossil fuels like oil dwindling in availability, there is an urgent need to shift towards renewable energy sources. Given the rapid progress in renewable energy technologies, a key objective of the project is to develop a solar energy tracking panel that can rotate for enhanced power generation. This innovative panel, capable of adjusting its position to optimize energy output by tracking the sun, proves invaluable in monitoring power consumption. The design of this rotatable solar energy tracking panel, incorporating four essential components (solar energy tracking panels, light-dependent resistors (LDRs), an Arduino, and a motor), is intricately explained in this study. This configuration not only serves as an advanced educational tool but also provides engineering technology students with a comprehensive understanding of alternative energy sources like solar power. © 2024 IEEE.

关键词： Fossil fuels

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A Semantic Approach for Automated Hiring using artificial intelligence & Computer Vision 8

A Semantic Approach for Automated Hiring using Artificial In...

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8th IEEE International Conference for Convergence in technology, I2CT 2023

作者： Ghadekar, Premanand Kabra, Aadesh Gangwal, Ketan Kinage, Atharv Agarwal, Kirti Chaudhari, Kshitij Information Technology Vishwakarma Institute of Technology Maharashtra Pune411037 India Vishwakarma Institute of Technology Artificial Intelligence and Data Science Maharashtra Pune411037 India

ISBN: (纸本)9798350334012

This research paper presents an automated interview system that utilizes natural language processing and deep learning techniques to conduct job interviews. The system comprises 4 modules, the first one being resume classification system, which is built on state-of-the-art NLP architecture and transformer-based BERT model. The model uses Named Entity Recognition and Text Classification to extract relevant information from the resume. The resume classification algorithm is based on the principle of context relevance, and skill mentions in the resumes are validated through an analysis of the candidate's experience and completed projects before being classified. The subsequent module utilizes the output from the resume model to generate a set of relevant interview questions for the candidate through utilizing natural language processing techniques and semantic analysis. The difficulty of the following questions is adjusted owing to the interviewee's answer, very much like what human HRs do. Such questions are extracted with the help of web scraping by selenium driver. One of the most innovative parts of this system is its ability to reduce bias in the hiring process by using machine learning models to evaluate candidate responses. The following module assesses the candidate's personality during the interview using the CNN and LSTM models. In addition to generating relevant interview questions, the system also detects potential instances of misconduct or malpractice during the interview process using Open CV'S DNN module and a quantized model. Overall, this system has the potential to revolutionize the way companies conduct job interviews and improve the hiring process. © 2023 IEEE.

关键词： Natural language processing systems

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Adaptive Tutoring Systems: Crafting Personalized Learning Experiences 2

Adaptive Tutoring Systems: Crafting Personalized Learning Ex...

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2nd International Conference on Advances in Computation, Communication and Information technology, ICAICCIT 2024

作者： Patil, Amruta Awati Joshi, Hrushikesh Shinde, Prajakta Jadhav, Jyoti Pise, Ganesh Department of Information Technology Pune Institute of Computer Technology Maharashtra Pune India Department of Artificial Intelligence and Data Science Vishwakarma Institute of Technology Maharashtra Pune India

ISBN: (纸本)9798331541217

The rapid advancement of educational technology has spurred the development of Adaptive Tutoring Systems (ATS) to meet the growing demand for personalized learning in digital environments. This paper introduces the Personalized Adaptive Learning Recommendation System (PALRS), a novel framework designed to enhance learner engagement and performance by delivering customized learning experiences. The system integrates deep reinforcement learning (DRL) algorithms to recommend not only individual courses but also complete learning paths tailored to students' preferences, learning styles, and real-time progress. Through continuous learner interaction, PALRS refines its recommendations, optimizing the educational journey to suit each user's needs. The primary objective is to address the limitations of traditional one-size-fits-all learning systems by offering a dynamic, adaptive solution that evolves with the learner. In real-world experiments involving 500 students, PALRS demonstrated a 30% improvement in learning outcomes and a 25% increase in user satisfaction compared to non-adaptive systems. Additionally, learners using PALRS completed courses 15% faster, with a higher retention of knowledge. These findings underscore the potential of PALRS to revolutionize digital education by providing context-aware, data-driven learning experiences. Contributions include (1) the design and implementation of a robust adaptive recommendation system using DRL, (2) a detailed evaluation of its effectiveness in improving learner outcomes, and (3) insights into the scalability of such systems across diverse educational platforms. These results confirm the efficacy of PALRS in delivering highly personalized, efficient, and engaging learning experiences. © 2024 IEEE.

关键词： Contrastive Learning

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Drowsy Driver Detection System using Computer Vision and Transfer Learning for Preventing Road Accidents 3

Drowsy Driver Detection System using Computer Vision and Tra...

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3rd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Bagal, Naisargi Dingreja, Kashish Atterkar, Atharva Bachhav, Prasad Sable, Nilesh P. Yenkikar, Anuradha Alkunte, Ritesh Vishwakarma Institute of Information Technology Department of Computer Science and Engineering - Artificial Intelligence Pune India

ISBN: (纸本)9798350354348

Major traffic accidents are attributed to driver fatigue, according to study on the topic. Driver drowsiness is a state in which the driver of a car is on the verge of falling asleep or losing consciousness. It can be brought on by a number of variables, including biological, physical, psychological, and other factors, all of which can impede and inhibit safe driving. The several methods that can be applied to the implementation of a system for the detection of driver sleepiness will be reviewed in this paper. Improving the ability to identify drowsiness in real time through computer vision is the main objective of all technologies. Thus, our work also pertains to the identification of driver drowsiness, whereby a motorist's tiredness can be ascertained by first recognizing their face, followed by eye tracking. The system compares the extracted eye image with the dataset. The system used the dataset and predicts using transfer learning with our base model as InceptonV3 to identify that it could alert the driver with an alarm if the driver's eyes were closed for a predetermined amount of time, and it could resume monitoring if the driver's eyes were open following the alarm alert. We established a score that grew if the eyes were closed and dropped if they were open. This study aims to reduce traffic accidents by solving the sleepiness detection problem with InceptionV3 with an accuracy of 95.46% compare to 92.11% of ***, driving drowsiness detection - which assesses a driver's degree of fatigue using facial recognition and eye tracking becomes the main focus of our research. © 2024 IEEE.

关键词： Face recognition

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FedLRes: enhancing lung cancer detection using federated learning with convolution neural network (ResNet50)

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Neural Computing and Applications 2025年第14期37卷 8273-8284页

作者： Usharani, C. Selvapandian, A. Department of Artificial Intelligence and Data Science Ramco Institute of Technology Rajapalayam India Department of Electronics and Communication Engineering Gnanamani College of Technology Namakkal India

Lung cancer remains a leading global cause of mortality, necessitating efficient early detection. Lung cancer image analysis plays a pivotal role, yet current manual segmentation by oncologists is laborious. Our innovative (FedLRes) approach proposes a comprehensive system for automated diagnoses of lung cancer using federated learning with ResNet50. The lung cancer dataset from the Iraq-Oncology Teaching Hospital/National Centre for Cancer Diseases was gathered by the IQ-OTH/NCCD. The dataset consists of 1097 images, split into training (822 images) and validation (275 images) sets. The training set includes 312 normal, 420 malignant, and 90 benign cases. The training set's data are further enhanced by using data augmentation techniques, which are then applied to normalise images differences before being fed into the federated learning with ResNet50 architecture. This approach combines deep learning models trained on different datasets allowing for improvising accuracy and generalisation. The federated learning approach enables the use of distributed data while ensuring data privacy and security. The proposed approach compared with different state of art algorithms. Through rigorous experimentation, our system showcases remarkable advancements a classification accuracy of 99.40%. This innovative approach, utilising 3D input CT scan data, offers a potent and precise tool for early detection and effective treatment strategies against the scourge of lung cancer. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Lung cancer

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Network Diffusion Framework to Simulate Spreading Processes in Complex Networks

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Big data Mining and Analytics 2024年第3期7卷 637-654页

作者： MichałCzuba Mateusz Nurek Damian Serwata Yu-Xuan Qiu Mingshan Jia Katarzyna Musial Radosław Michalski Piotr Bródka Department of Artificial Intelligence Wrocław University of Science and TechnologyWrocław 50-370Poland Data Science Institute University of Technology SydneyUltimoNSW 2007Australia

With the advancement of computational network science,its research scope has significantly expanded beyond static graphs to encompass more complex *** introduction of streaming,temporal,multilayer,and hypernetwork approaches has brought new possibilities and imposed additional *** instance,by utilising these advancements,one can model structures such as social networks in a much more refined manner,which is particularly relevant in simulations of the spreading ***,the pace of advancement is often too rapid for existing computational packages to keep up with the functionality *** results in a significant proliferation of tools used by researchers and,consequently,a lack of a universally accepted technological stack that would standardise experimental methods(as seen,e.g.,in machine learning).This article addresses that issue by presenting an extended version of the Network Diffusion ***,a survey of the existing approaches and toolkits for simulating spreading phenomena is shown,and then,an overview of the framework ***,we report four case studies conducted with the package to demonstrate its usefulness:the impact of sanitary measures on the spread of COVID-19,the comparison of information diffusion on two temporal network models,and the effectiveness of seed selection methods in the task of influence maximisation in multilayer *** conclude the paper with a critical assessment of the library and the outline of still awaiting challenges to standardise research environments in computational network science.

关键词： computational framework seed selection influence maximisation spreading models temporal networks multilayer networks network science network control

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