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检索条件"机构=Computer Science and Engineering Institute of Aeronautical Engineering"

共 168044 条记录，以下是1-10 订阅

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Data-Driven House Price Prediction 3

Data-Driven House Price Prediction

引用

3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Shalini, N. Kiran, A Sai Sathwik, B. Reddy, G Trishul Aswini, Kilaru Yadav, M Geetha Computer Science and Engineering Institute of Aeronautical Engineering India Computer Science and Engineering Institute of Aeronautical Engineering Hyderabad India

ISBN: (纸本)9798350375190

This research study analyzes the complex and dynamic field of real estate, aiming to reveal the numerous factors influencing home values. The primary objective of this research study is to develop a prediction system using Random Forest (RF). Leveraging a comprehensive dataset encompassing historical real estate data, this study intends to forecast house values by considering a wide array of variables. Key attributes such as the number of bedrooms and bathrooms, property size, location, and proximity to amenities like transportation, hospitals, and schools form the cornerstone of the proposed model. Drawing insights from past price fluctuations, the proposed algorithm enhances forecast accuracy by analyzing previous value trends. Additionally, for comparative analysis, the proposed approach incorporates multiple regression and linear regression models alongside the Random Forests approach, enabling to assess the performance of our primary model against industry-standard techniques. The resultant system offers users detailed insights into the local real estate landscape alongside precise property price projections. Users can compare specific properties with nearby alternatives, empowering them to make informed decisions. Tailored for real estate agents, investors, and homeowners, our system serves as an invaluable resource for navigating the complexities of real estate transactions. The proposed solution aims to facilitate wise and successful real estate decisions by furnishing precise forecasts and revealing market intricacies. © 2024 IEEE.

关键词： Houses

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Classification of Pancreatic Cancer using Kolmogorov-Arnold Networks: An Upgrade on Traditional Machine Learning for Medical Classification 6

Classification of Pancreatic Cancer using Kolmogorov-Arnold ...

引用

6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Vinod, Ratnaparkhi Gaurang Kumar, Manemoni Arun Chowdary, Chinthalapudi Vaahnitha Suvarchala, K. Department of Computer Science and Engineering Institute of Aeronautical Engineering Hyderabad India

ISBN: (纸本)9798331522667

Pancreatic cancer is one of the most fatal cancers, and medical doctors basically face major problems in its early diagnosis. This article throws light on the work involving the application of Kolmogorov-Arnold Networks (KANs), where we deploy a new machine learning paradigm in the diagnosis of pancreatic cancer. The process of classification basically happens in pancreatic cancer using urinary biomarkers. Unlike Logistic Regression and XGBoost, models have struggled with complex and non-linear medical data, KANs can manage large and complex datasets with ease. Using a dataset of urinary biomarkers, trained a KAN model with EBM techniques for feature selection to increase the model's interpretability and feature relevance. The model presented in this paper showed superior performance compared to traditional models in terms of high accuracy and generalization capability. KANs therefore will be very essential in handling efficiently the challenge presented by the vastness of data in medical studies regarding pancreatic cancer detection. Findings from the studies indicate a better potential in reliable use for progression in developing improved detection of pancreatic cancer using the KAN method. The publication contributes to current research that makes innovative uses in machine learning a reality for improvements in diagnosis as well. © 2025 IEEE.

关键词： Diseases

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Enhancing Mental Wellbeing Among College Students Using Autogen

Enhancing Mental Wellbeing Among College Students Using Auto...

引用

2025 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Reddy, Chilukuri Riktha Devi, B. Madhavi Reddy, Kura Pranav Medarametla, Lahari Institute of Aeronautical Engineering Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798331529833

Mental health support is crucial for students, who are vulnerable to challenges due to academic pressures and social expectations. University culture often fosters misconceptions about mental well-being, leading to stigma that discourages students from seeking help. This project introduces an AI Therapist - a conversational agent offering a private, non-judgmental space for students to discuss personal challenges. Using Sentiment Analysis, a Natural Language Processing (NLP) method powered by machine learning, the system evaluates the emotional tone of these interactions. Through this analysis, the AI provides insights into students' mental health over time, visualized through data reports generated with tools like matplotlib. These reports allow students to track their emotional journey and promote self-awareness. The goal is to create an accessible support system that encourages self-reflection, reduces the stigma around mental health, and fosters early intervention. © 2025 IEEE.

关键词： Students

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The Role of Machine Learning in Crime Analysis and Prediction 4

The Role of Machine Learning in Crime Analysis and Predictio...

引用

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

作者： Geetha Vadav, M. Rajasekhar, N. Reddy, E. Sripadha Vishal, M. Sai Vishal, G. Institute of Aeronautical Engineering Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350385793

Crime prediction has become essential in modern law enforcement for enabling proactive strategies to enhance public safety. Machine Learning (ML) algorithms, particularly Random Forest, offer a powerful tool for analyzing extensive datasets and uncovering complex patterns. Random Forest's ensemble learning approach is well-suited for managing high-dimensional data and capturing nonlinear relationships. This study provides an in-depth examination of crime prediction with Random Forest, emphasizing its advantages such as feature selection, robustness to noise, and scalability. Theoretical foundations, including bagging learning and decision tree aggregation, are explored, showcasing Random Forest's predictive capabilities. Empirical studies and real-world applications demonstrate its effectiveness across various spatial and temporal scales. Integrating Random Forest-based crime prediction systems into law enforcement practices enables agencies to anticipate trends, allocate resources strategically, and implement targeted interventions for community safety. By leveraging data-driven approaches, law enforcement can effectively address and mitigate criminal activities, contributing to safer communities. © 2024 IEEE.

关键词： Prediction models

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Optimizing Telecom Operations with Segmentation and Churn Prediction 3

Optimizing Telecom Operations with Segmentation and Churn Pr...

引用

3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Neha, Dandu Ameesha, Mohammed Dheeraj, Mukka Sangeeta, K. Dept.Computer Science and Engineering Institute of Aeronautical Engineering Telangana Hyderabad India Institute of Aeronautical Engineering Telangana Hyderabad India

ISBN: (纸本)9798331527549

Client turnover is a significant issue for large organizations, particularly within the telecom sector, where it has a direct impact on profitability. To address this, telecom providers are increasingly focusing on developing predictive models to forecast customer churn and reduce its adverse effects. Identifying the key factors influencing churn is critical for implementing effective retention strategies. This research presents the development of a churn prediction model designed to help telecom companies identify customers at a high risk of leaving. The study involves exploring various data analysis techniques and presenting the results through graphical visualizations. Using machine learning methods, the model leverages large-scale datasets and introduces an innovative approach to feature design and selection. Historical customer data from previous months was utilized for training, evaluation, and validation of the model. Four machine learning algorithms were tested: Logistic Regression, XGBoost, Gradient-Boosted Machines (GBM), Random Forest, and Decision Trees. To further improve model accuracy, parameter tuning was applied during the training phase. The primary objective of this work is to enhance telecom operations by optimizing customer segmentation and churn prediction, ultimately improving both operational efficiency and customer retention. © 2025 IEEE.

关键词： Sales

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Identify Skin Disorder Using Dermoscopic Analysis Through Federated Learning 2

Identify Skin Disorder Using Dermoscopic Analysis Through Fe...

引用

2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Chaitanya, V. Pavan Madhavi Devi, B. Naveen, Batta Akash, Mulkala Institute of Aeronautical Engineering Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798331534950

Skin problems are common health issues around the world, and effective treatment frequently requires an accurate and prompt diagnosis. The objective of this work is to use federated learning to enable dermoscopic analysis for the identification of skin problems. Federated learning reduces the data sharing and privacy violations by decentralizing model training across several collaborating institutions, hence ensuring data security and privacy. To improve data privacy, the weights of the skin condition are kept in both the local client and the global client (federated server). Consequently, the Federated server balances the server for both Local clients. The YOLO8 approach is used to extract the features of the skin condition in order to classify it. The HAM10000 dataset is used to train the VGG19 and Convolutional Neural Networks (CNN) models. The accuracy rates of the local servers is 93% and the global server is 94%. © 2024 IEEE.

关键词： Federated learning

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AI-Driven Solutions with CNN and IoT for Enhancing Visual Impairments 5

AI-Driven Solutions with CNN and IoT for Enhancing Visual Im...

引用

5th International Conference on Emerging Systems and Intelligent Computing, ESIC 2025

作者： Prasad, M. Lakshmi Varshitha, Veeramalli Goud, A. Praneeth Bindu, R. Srinivasulu, Ch. Rao, S. Janardhana Institute of Aeronautical Engineering Dept. of Computer Science and Engineering Dundigal Hyderabad India

ISBN: (纸本)9798331522100

Visual impairment is a problem that affects millions of people worldwide, preventing them from walking independently and performing daily tasks. Some of the challenges that blind people face include crossing the street, reading, driving, and socializing. Despite the scientific community's continuous efforts to treat the eye, these solutions have not yet been found. Traditional aids such as canes are useful but limited. Get in the groove. The main focus of the project is to integrate the YOLOv5 object detection model with a convolutional neural network (CNN). Known for its fast and accurate target detection, YOLOv5, together with CNN, improves the physical ability to detect and classify targets in difficult areas with high accuracy. The eye uses the camera module to continuously monitor the environment and identify various objects in real time;the detected data is converted into speech by text-to-speech (TTS) converters such as Google Text- to-Speech (gTTS) and pyttsx3 This description allows users to get an instant description of their surroundings, unlike helping the blind, it improves their ability to navigate independently rather than just providing references, but there are also good ways to work with better and more reliable assistive technologies. © 2025 IEEE.

关键词： Convolutional neural networks

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A Study of Various Optimization Algorithms and Entropy Measures for Image Segmentation 18

A Study of Various Optimization Algorithms and Entropy Measu...

引用

18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Gonthina, Nagamani Prasad, L. V. Narasimha Institute of Aeronautical Engineering Dept. of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9789380544519

One of the most challenging jobs in image processing techniques is image segmentation. To identify the objects of interest in an image, we segment the image into different parts and extract the interesting objects. Previous studies have addressed various techniques for image segmentation, like clustering, thresholding, watershed, neural networks, etc. However, a segmentation technique alone cannot achieve better-segmented results. Much research is being carried out by combining the segmentation technique with either an optimization algorithm or entropy measures to enhance the efficiency of segmented results in the segmentation process. So, there is a need to study various segmentation techniques combined with optimization algorithms or entropy measures. Recent studies focused only on segmentation techniques, entropy measures, or optimization algorithms rather than on these combinations. This paper presents various works based on optimization techniques and entropy measures combined with clustering and thresholding techniques of image segmentation. From the study it is observed that most of the work is carried out in combining thresholding-based image segmentation techniques with optimization algorithms and entropy measures. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Entropy measures Image segmentation Optimization algorithms Segmentation techniques

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Generative AI Empowered Skin Cancer Diagnosis: Advancing Classification Through Deep Learning 5

Generative AI Empowered Skin Cancer Diagnosis: Advancing Cla...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Thoviti, Sindhu Harshitha Varma, Bhupathiraju Karthik Sai, Seelam Nikhil Prasanna, B. Lakshmi Institute of Aeronautical Engineering Department of Computer Science and Information Tecnology Hyderabad India Institute of Aeronautical Engineering Hyderabad India

ISBN: (纸本)9798331518097

Effective diagnostic techniques are still needed for the early diagnosis and treatment of skin cancer, which is still a serious health concern. This paper presents a Generative AI-powered approach for the diagnosis of skin cancer that uses deep learning techniques to enhance classification accuracy and reliability. Current diagnostic methods face challenges like class imbalance, limited dataset diversity, and dependency on manual labeling. Recent advancements in AI, including CNNs and GANs, offer promising solutions but are hindered by issues such as overfitting and lack of generalizability. By using a Convolutional Neural Network (CNN) for classification and training over 200 epochs, the model achieved an accuracy of 92%. Precision, recall, and F1-score are measures of evaluation metrics to ensure the performance of the model is coherent, for which data augmentation was used to handle class imbalance. A Generative Adversarial Network was used with this model to create synthetic images, thus augmenting 1000 samples over 600 epochs. This type of data augmentation made a difference in the generalization capability of the model and greatly contributed to more robust classification outcomes and advancements in the automated detection of skin cancer. © 2024 IEEE.

关键词： Generative adversarial networks

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Enhancing Skin Cancer Diagnosis Through Optimized Convolutional Neural Network in a Smart Health Care System 5

Enhancing Skin Cancer Diagnosis Through Optimized Convolutio...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Hussain, Mohd Mudassir Akhil, Lakkam Sai Prakash, Talasila Om Lakshmi Prasanna, B. Institute of Aeronautical Engineering Department of Computer Science and Information Technology Hyderabad India Institute of Aeronautical Engineering Hyderabad India

ISBN: (纸本)9798331518097

Skin cancer presents one of the most complex public health problems in many countries worldwide. Accurate and time-sensitive diagnosis may help in suitable treatment and enhanced patient benefits. The study is titled as 'Improving Skin Cancer Classification using Optimized Convolutional Neural Networks.' Here, it advances early diagnosis and categorization of this kind of skin cancer applying more sophisticated methodologies of artificial intelligence. Preprocessing and augmentation of the ISIC 2019 dataset improve the quality and diversity of the data set, solving problems of scarcity and imbalance. By using CNN architectures such as EfficientNetV2 and InceptionResNetV2, results have had high accuracy rates, 97.3% and 96.1%, respectively. The automated diagnostic reports with images can be downloaded as PDF files. It uses an AI-driven methodology in an enhanced way to improve diagnostic accuracy and early intervention towards better patient outcomes. © 2024 IEEE.

关键词： Electronic health record

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