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2024 Asia Pacific Conference on Innovation in Technology, APCIT 2024

作者： Thara, Machakanti Navya Chatterjee, Kalyan Raju, Muntha Rout, Sandip Priyadharshini, M. Prasad, Kokkula Shiva Suraj Kumar, Samala Reddy, Mandadi Sriya Nalla Malla Reddy Engineering College Artificial Intelligence & Machine Learning Hyderabad India Nalla Malla Reddy Engineering College Computer Science & Engineering Hyderabad India Government of Odisha Mathematics Government Autonomous College Dept. of Higher Education Odisha Angul India

ISBN: (纸本)9798350361537

Cancer instills fear in people worldwide, with millions succumbing to its grasp each year. Among the various types, lung cancer stands as a prominent contributor to this alarming statistic. Therefore, this novel research Lung Cancer Classification and Detection using Convolutional Neural Network-based Gated Recurrent Unit Neural Network Model (LaCK) has focused on classifying lung cancer, notably Non-Small Cell Lung Cancer, which constitutes the majority of cases. We employed five units of gated recurrent unit neural network (GRU-NN) together to achieve this classification, utilizing logistic regression (LR) and support vector classifier (SVC) models for comparisons. Among all models, the LaCK model demonstrated remarkable accuracy, achieving approximately 98% success rate, outperforming its counterparts. Implementing this model in clinical settings enables medical professionals to establish a precise and automated approach to diagnosing lung cancer. © 2024 IEEE.

关键词： Lung cancer

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Exploratory Data Analysis and Classification of Employee Retention based on Logistic Regression Model 9

Exploratory Data Analysis and Classification of Employee Ret...

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9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Bongale, Anupkumar M Dharrao, Deepak Urolagin, Siddhaling University Symbiosis Institute of Technology Department of Artificial Intelligence and Machine Learning Pune India University Symbiosis Institute of Technology Department of Computer Science and Engineering Pune India Dubai International Academic City Birla Institute of Technology & Science Department of Computer Science and Engineering Dubai United Arab Emirates

ISBN: (纸本)9798350397376

Nowadays, a huge number of companies are using employee data records to understand the employee churn rate in the company. Companies use predictive analytics to predict the chances of an employee leaving the company. Organizations are effectively using Human Resource (HR) predictive analytics to increase revenue and turnover. This paper is about building a predictive model on the employee dataset to predict if an employee is likely to leave or continue service in the employed company. The steps carried out for visualization and Exploratory Data Analysis (EDA) has been highlighted in this paper by bringing out valuable insights from the employee dataset. Observations were made to the features that determine the chances of an employee leaving the company. For the prediction and classification of employee retention, a Logistic Regression (LR) model is applied to the dataset. The article focuses on the analysis of data and understanding the working of the LR model for predicting categorical features. The performance of the applied model was evaluated using a confusion matrix. The accuracy of the model was calculated from values in the confusion matrix. © 2023 IEEE.

关键词： Predictive analytics

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Vehicle Prediction for BUS Identification Output from our Route Testing Real Time Algorithm 5

Vehicle Prediction for BUS Identification Output from our Ro...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Misba, M. Ramya, R. Dickson, Joel Sharmila, L. Kavitha, J. Udayakumar, K. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Artificial Intelligence and Machine Learning Tamil Nadu India St. Joseph's College of Engineering Department of Artificial Intelligence and Data Science Chennai India Bethlehem Institute of Engineering Tamil Nadu Karungal India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu India

ISBN: (纸本)9798331530013

Traffic generation in Indian cities is of a mixed nature, comprising over a dozen types of vehicles that can be broadly categorized into slow-moving and fast-moving vehicles. This study highlights the challenges associated with the inefficient operation of the existing bus system. GPS and mobile networks form integral components of the current system, enabling data storage and management using PostgreSQL. The system provides robust and reliable data to fulfill requests from other software applications. Operating on cloud platforms, it is compatible with all major web browsers. The proposed algorithm continuously tracks the real-time location of buses and their corresponding WMS (Web Map Service) location, alerting users through marker notifications. This paper also explores measures to evaluate the efficiency and effectiveness of bus services. Recognizing that an inadequate public transport system impedes economic growth and development, this study emphasizes the need to enhance the overall quality and accessibility of public transportation. The findings aim to improve the operational efficiency of bus services, contributing to a more effective and sustainable transport system. © 2024 IEEE.

关键词： Buses

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Hybrid Optimization Based BERT Model for Drug Detection in NLP 3

Hybrid Optimization Based BERT Model for Drug Detection in N...

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3rd IEEE Mysore Sub Section International Conference, MysuruCon 2023

作者： Kruthika, C.G. Namitha Devi, N.N. Madlur, Vijayananda V Asha, K. Aruna, T.M. Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India Vidya Vikas Institute of Engineering and Technology Department of Computer Science and Engineering Mysuru India Mangalore Institute of Technology and Engineering Department of Computer Science and Engineering Moodabidri India Reva University Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350340358

When it comes to natural language processing (NLP), sentence embedding is a hot topic because of the knowledge it transfers to subsequent tasks. Recent studies have shown that sequential neural networks, which can be trained on large quantities of data, can be used to encode info through a one-way analysis of text sequences, making them a powerful tool for drug detection. The purpose of this study is to smear cutting-edge deep learning techniques to publicly available data in order to accurately detect and identify adverse medication reactions that have not been recorded by medical professionals (open data). This study presents a deep learning-based strategy employing Enhanced Encoder on a dataset of 10,000 reviews culled from WebMD and *** and labelled by hand (IBERT). The model is created by integrating the BERT with many parallel blocks of a single-layer deep Convolutional Neural Network (CNN) that use varying kernel sizes and filters. To deal with ambiguity, the biggest obstacle to NLP, such a combination is helpful. Cuckoo Search (CS) and Antlion Optimization (ALO) are coupled to create a hybrid optimization strategy for determining the best values for the proposed model's hyper-parameters. Results from a number of different classification methods demonstration that the suggested model meaningfully outdoes the state-of-The-Art models. © 2023 IEEE.

关键词： Embeddings

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Mesoscopic Insights: Orchestrating Multi-Scale & Hybrid Architecture for Image Manipulation Localization 39

Mesoscopic Insights: Orchestrating Multi-Scale & Hybrid Arch...

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39th Annual AAAI Conference on artificial intelligence, AAAI 2025

作者： Zhu, Xuekang Ma, Xiaochen Su, Lei Jiang, Zhuohang Du, Bo Wang, Xiwen Lei, Zeyu Feng, Wentao Pun, Chi-Man Zhou, Ji-Zhe College of Computer Science Sichuan University China Engineering Research Center of Machine Learning and Industry Intelligence Ministry of Education of China China The Hong Kong Polytechnic University Hong Kong Faculty of Science and Technology University of Macau China

ISBN: (纸本)157735897X

The mesoscopic level serves as a bridge between the macroscopic and microscopic worlds, addressing gaps overlooked by both. Image manipulation localization (IML), a crucial technique to pursue truth from fake images, has long relied on low-level (microscopic-level) traces. However, in practice, most tampering aims to deceive the audience by altering image semantics. As a result, manipulation commonly occurs at the object level (macroscopic level), which is equally important as microscopic traces. Therefore, integrating these two levels into the mesoscopic level presents a new perspective for IML research. Inspired by this, our paper explores how to simultaneously construct mesoscopic representations of micro and macro information for IML and introduces the Mesorch architecture to orchestrate both. Specifically, this architecture i) combines Transformers and CNNs in parallel, with Transformers extracting macro information and CNNs capturing micro details, and ii) explores across different scales, assessing micro and macro information seamlessly. Additionally, based on the Mesorch architecture, the paper introduces two baseline models aimed at solving IML tasks through mesoscopic representation. Extensive experiments across four datasets have demonstrated that our models surpass the current state-of-the-art in terms of performance, computational complexity, and robustness. Copyright © 2025, Association for the Advancement of artificial intelligence (***). All rights reserved.

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machine learning-based prediction models for electoral outcomes in India: a comparative analysis of exit polls from 2014–2021

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Quality and Quantity 2024年第1期59卷 313-338页

作者： Barthwal, Anurag Bhatt, Mamta Avikal, Shwetank Prakash, Chandra Department of Artificial Intelligence and Machine Learning School of Smart Computing COER University Vardhmanpuram Uttarakhand Roorkee India Guru Jambheshwar University of Science and Technology Haryana Hisar India School of Management Sciences Graphic Era University Uttarakhand Dehradun India

Political researchers and scientists have long sought to forecast the results of upcoming elections in a democratic setup. Prediction approaches have been combined to improve prediction accuracy. In this work, we focus on India, utilizing exit polls spanning the last 8 years to develop machine learning-based prediction models. Exit polls provided by major media agencies for both provincial as well as the union legislative assemblies have been used as inputs to improve prediction accuracy. The training set comprises of the seats projected for the United Progressive Alliance (UPA) and the National Democratic Alliance (NDA) in state and central government elections from 2014 through 2020, providing ample data for training across a wide range of short and long-term trends. The testing set includes seats projected for the UPA and the NDA in 2021, across states including Assam, Kerala, Pondicherry, Tamil Nadu and West Bengal, ensuring validation of the prediction models using undisclosed ‘out of sample’ data. Trained models have been used to provide projected seats for the 2021 legislative assembly elections in the states of Assam, Kerala, Pondicherry, Tamil Nadu and West Bengal. Efficient machine learning approaches viz. support vector regression (SVR), extreme gradient boosting (XGBoost) and deep CNN-LSTM networks have been explored as predictors. The performance of the predictive models is evaluated by utilizing the metrics such as relative error (RE%), normalised mean square error (NRMSE), fractional bias (FB), coefficient of determination (R²) and normalised absolute error (NAE). XGBoost exhibits the lowest R² value of 0.97, indicating superior fit to the observed data compared to other methods. Additionally, it demonstrates the lowest FB value of −0.03, suggesting minimal prediction bias. Notably, XGBoost also yields the lowest NRMSE, NAE, RE% values (4.11, 2.73 and 0.97% respectively), highlighting its superior accuracy in predicting election

关键词： CNN-LSTM Election outcome prediction Exit polls State legislature SVR XGBoost

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Enhancing Education Infrastructure through machine learning: A Framework for Classifying and Prioritizing Schools

Enhancing Education Infrastructure through Machine Learning:...

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Frontier Technologies and Solutions (ICFTS), International Conference on

作者： S Ujjain SV. Akash Ishwar D Dhanush V. Thilak Kumar Department of Computer Science and Business Systems Rajalakshmi Engineering College Chennai Department of Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Chennai

ISBN: (数字)9798331502768

ISBN: (纸本)9798331502775

Education is not just a standard but sets the foundation, empowering students and opportunities in the future. This project proposes the classification of the schools using the “Samagra Shiksha” framework which the Ministry of Education sets. We developed an AI-driven system to classify the schools as structured or odd using UDISE PLUS data. The study describes the methodology of machine learning algorithms such as Isolation Forest and Light Gradient Boosting machine implementation on key factors like grade structure, teacher quality, teacher-student ratio, and infrastructure score. It brings the significance of zone-wise classification and aiding policymakers in prioritizing interventions. This can help the policy makers aware of the condition of the schools and gives them more clarity for allocating resources. By classifying the schools based on their quality metrics, we found that out of 10,48,575 government schools, 85.03% of schools were structured and 14.97% were odd. This highlights the robustness of both algorithms and the critical role of model selection in classifying schools.

关键词： machine learning algorithms Scalability Education Forestry Boosting Classification algorithms Stakeholders Resource management Standards Testing

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Big Data in Social Media: Analyzing Trends, Patterns and Challenges 2

Big Data in Social Media: Analyzing Trends, Patterns and Cha...

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2nd International Conference on machine learning and Autonomous Systems, ICMLAS 2025

作者： Jikar, Nayan Tale, Yash Tale, Abhay Barhate, Aditya Verma, Prateek Jikar, Aman Datta Meghe Institute of Higher Education and Research Sawangi Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Sawangi Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Engineering Maharashtra Wardha442001 India

ISBN: (纸本)9798331505745

The huge volumes of data produced in social media provide both new possibilities and challenges to analytics. The present paper emphasizes Big Data analytics and machine learning (ML) methods to uncover trends, patterns, and user behavior from social media-related data. Such vital concepts, like the 'Four Vs' of Big Data: Volume, Velocity, Variety, and Veracity, and innovative tools for datacollection, storage, and preprocessing are covered here as well. Sentiment analysis, trend forecasting, and user behaviormodeling are some of the examples of what is possible using ML and artificial intelligence (AI) for deriving actionable insights. Marketing campaigns, crisis communications, and political campaigns are some of the scenarios that are covered to bring out real world applications of such technologies, along with their transformative effects. Lastly, data privacy, quality, and scalability issues are noted, along with ethical concerns and biases. Directions for the future point to developments in real-time data analysis, anonymization, and the changing role of AI in social media platforms. This research emphasizes the significance of Big Data and machine learning in driving innovation and decision-making in the digital era. © 2025 IEEE.

关键词： Tweets

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Particle Swarm Optimization for Efficient Brain Tumor Classification Using InceptionV3 Deep learning Model 9

Particle Swarm Optimization for Efficient Brain Tumor Classi...

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Akhila, P. Prabaharan, G. Pandiiyan, K. Swarna, S.L. Hussain, A. Sakthivelu, U. B V Raju Institute of Technology Department of Artificial Intelligence and Machine Learning Telangana Narsapur India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Avadi Chennai India Srm Arts and Science College Department of Computer Science with Artificial Intelligence Kattankulathur Chengalpattu India Sona College of Technology Department of Computer Science Science and Engineering Junction Main Road Tamil Nadu Salem India College of Engineering and Technology Srm Institute of Science and Technology Department of Networking and Communications Tamil Nadu Kattankulathur India

ISBN: (纸本)9798350377972

This research proposes a novel framework for brain tumor classification utilizing a combination of deep learning and optimization techniques. The framework employs an autoencoder network for image preprocessing, followed by SegNet for accurate tumor segmentation. The core classification is performed using the InceptionV3 model, further optimized through Particle Swarm Optimization (PSO) to enhance predictive performance. The proposed system achieved a classification accuracy of 98.75% on a dataset of 7,023 MRI images, demonstrating its effectiveness in accurately classifying brain tumors. This research contributes to the advancement of medical image analysis and provides a valuable tool for supporting clinicians in the early diagnosis and treatment of brain tumors. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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AI-Driven Thermal Imaging: A Comprehensive Web-Based Tool for Non-Invasive Medical Diagnostics

AI-Driven Thermal Imaging: A Comprehensive Web-Based Tool fo...

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International Symposium on Advanced Computing and Communication (ISACC)

作者： Aniruddha Ghosh Sayan Snigdha Pal Sumedha Das Spandan Bhattacharya Arijit Bhattacharya Nilanjana De Sudipta Sahana Computer Science and Engineering(Artificial Intelligence and Machine Learning) Institute of Engineering & Management Kolkata University of Engineering and Management Kolkata West Bengal India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Thermography is an innovative technique that creates a heatmap of different portions of the human body by using infrared (IR) radiation where color scales vary for various temperature regions. This work's scope aims to extend thermal imaging applications with machine learning algorithms in conjunction with Azure API services to analyze thermal images and return a preliminary diagnosis. The project consists of generating heatmaps for better visualization results, analyzing them through image processing techniques, and feeding the information to Azure’s Custom Vision API to detect anomalies and classify health conditions and finally producing a proper report. This research allows individuals and healthcare professionals to have a quick, non-invasive diagnostic tool that empowers health monitoring and preventive care. It combines the capabilities of contemporary technologies with uses directed toward health practice, leading to inimical patient outcomes and true practices.

关键词： Visualization machine learning algorithms Image color analysis Infrared heating Infrared imaging Thermal analysis Medical diagnosis Predictive analytics Intelligent systems Monitoring

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