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Detecting AI-generated images with CNN and Interpretation using Explainable AI

Detecting AI-generated images with CNN and Interpretation us...

引用

Contemporary computing and Communications (InC4), IEEE International Conference on

作者： Bharathi Mohan G Prasanna Kumar Rangarajan Akilesh rao S Mandava Sukesh Abinandhini D M Jaikanth Y Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (数字)9798350383652

ISBN: (纸本)9798350383669

The rapid proliferation of digitally manipulated images in various media outlets has necessitated the development of robust and reliable methods for distinguishing between authentic and synthetic visual content. In response to this imperative, this research leverages state-of-the-art deep learning techniques and an extensive dataset of ‘real’ and ‘fake’ images to create an AI model for image identification. The study encompasses a meticulous data collection process, preprocessing steps, the selection of the DenseNet121 model architecture, model training, and evaluation of the model's performance through a range of metrics, including accuracy (93.42), precision, recall, and the Area Under the ROC Curve (AUC). To enhance the transparency and interpretability of the model, employed the Gradient-weighted Class Activation Mapping (Grad-CAM) technique, enabling to visualize the regions within images that influenced the model's decision-making. The research provides valuable insights into the development and evaluation of AI systems for image identification, particularly in the context of distinguishing between real and fake content. Furthermore, the interpretability techniques shed light on the model's decision-making process. This research contributes to the field of image authentication and paves the way for enhanced image classification in the era of synthetic media.

关键词： Heating systems Training Visualization Accuracy Decision making Data models Stability analysis

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Forecasting India’s Air Quality: A Machine Learning Approach for Comprehensive Analysis and Prediction

Forecasting India’s Air Quality: A Machine Learning Approac...

引用

Intelligent Technologies (CONIT), International Conference on

作者： Osho Kothari Nabin Kumar Sah Konda V S Harshith Kumar Priyanka C Nair Nalini Sampath Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798350349900

ISBN: (纸本)9798350349917

With rising industrialization, India confronts increasing difficulties in maintaining air quality regulations. This research proposes a comprehensive analysis and prediction framework based on machine learning approaches for assessing and forecasting the Air Quality Index (AQI) in different place of India. This model takes into account a wide variety of pollutant concentrations that are monitored at regular intervals. This approach, which uses historical data from numerous Indian cities, employs a variety of machine learning methods, including k-Neighbour regression, decision tree regressor, linear regression, and random forest regressor, in addition to support vector regressor. Every model had performed well but the Decision Tree Regression and Random forest has performed best achieving RMSE value as 0.0016 and 0.00095 respectivelly whereas R-Squared value as 0.99 for both the model, indicating strong performance and a substantial progress in air quality prediction. The environmental health risks associated with air pollution have been mitigated by the addition of important knowledge.

关键词： Atmospheric modeling Urban areas Linear regression Predictive models Vectors Regulation Forecasting

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Enhancing Potato Leaf Disease Detection: A Comparative Study of CNN Architectures with K-means Segmentation and Data Augmentation

Enhancing Potato Leaf Disease Detection: A Comparative Study...

引用

BIT Conference (BITCON), International

作者： Rekha R Nair Ebin P. M Tina Babu Department of Computer Science and Engineering Alliance School of Advanced Computing Alliance University Bengaluru India

ISBN: (数字)9798331518394

ISBN: (纸本)9798331518400

Potato, the third most consumed crop globally, faces significant challenges from leaf diseases. This research proposes an automated deep learning system for detecting and classifying potato leaf diseases. Utilizing computer vision techniques, the study evaluated three CNN architectures (VGG19, VGG16, ResNet50) on a 2,580-image dataset. The VGG16 model, enhanced with K-means segmentation and data augmentation, achieved 97.1% accuracy in identifying healthy and blight-infected leaves. Proposed GAN-based data enhancement further improved performance to 98.2%. By integrating advanced image processing and machine learning, the approach offers a promising solution for early disease detection, enabling farmers to implement timely crop management interventions.

关键词： Deep learning Image segmentation Crops Computer architecture Data augmentation Generative adversarial networks Data models Faces Diseases Residual neural networks

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Pan-Sharpening Using Fusion of 2D-DFRFT, 2D-DWT, and CNN

Pan-Sharpening Using Fusion of 2D-DFRFT, 2D-DWT, and CNN

引用

Artificial Intelligence and Quantum Computation-Based Sensor Application (ICAIQSA), International Conference on

作者： Gadiraju Hemanth Sai Varma Gudivada Lohit Sai Phaniveer Tippa Srivallabh Tripuramallu Pramodh Nidhin Prabhakar T V Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798331517953

ISBN: (纸本)9798331517960

In recent years, the demand for high-resolution remote sensing imaging has increased in a variety of fields, including environmental monitoring, urban planning, agriculture, and disaster management. Multispectral imaging, which gathers information at several wavelengths, can provide useful insights for such applications. However, the spatial resolution of multi- spectral images frequently falls short of the necessary criteria for thorough analysis. Pansharpening approaches overcome this issue by combining high-resolution panchromatic (PAN) images with lowerresolution multispectral data, thereby improving spa- tial details while keeping spectral features. In this article, we present a unique pan-sharpening technique that makes use of the synergies between the two-dimensional discrete fractional Fourier transform (2D-DFRFT), the twodimensional discrete wavelet transform (2D-DWT), and convolutional neural network (CNN) models. By combining these complex mathematical transforms with deep learning approaches, we provide a holistic solution for improving both spectral and spatial resolution. Our method's main innovation is its multi-stage procedure, which starts with converting the PAN and multispectral images using 2DDFRFT and 2D-DWT, then extracts features using a CNN model. Subse- quent high-pass filtering of the converted images refines spatial information, allowing for a more precise fusion process. The inclusion of angle parameters associated with both 2D-DFRFT and 2D-DWT provides an additional degree of freedom, allowing for finer control over the fusion process and producing better results. We test the performance of our proposed pan-sharpening system using real-world satellite pictures from IKONOS and GeoEye-1 via extensive experiments in MATLAB.

关键词： Technological innovation Satellites Quantum computing Urban planning Process control Feature extraction Mathematical models Convolutional neural networks Spatial resolution Remote sensing

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Encryption scheme using Mealy Machine

Encryption scheme using Mealy Machine

引用

Global Conference for Advancement in Technology (GCAT)

作者： Hemanth Ramasubbu Sricharan Silaparasetty Mehul Singh Bhakuni Niharika Panda Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798350376685

ISBN: (纸本)9798350376692

This novel encryption scheme leverages a Mealy machine for dynamic encryption. To significantly enhance data security, it enforces strong key requirements (with mixed uppercase letters, digits, and special characters). The encryption process involves pre-processing data, creating a character mapping matrix, integrating a Mealy machine based on the matrix sum for additional complexity, generating and validating a strong key, and manipulating the data with the key matrix and Mealy machine outputs. Decryption follows a similar approach to recover the original data, making the system efficient for both encryption and decryption. This work paves the way for a secure and versatile encryption solution.

关键词： Data protection Encryption Delays Complexity theory Data mining Data communication Optimization

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Real-Time Object Detection using Mobile Robot Captured Images: A Deep Learning Approach

Real-Time Object Detection using Mobile Robot Captured Image...

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Innovation in Technology (INOCON), IEEE International Conference for

作者： Hema Srivarshini Ch N Preeti Sreekar Praneeth Marri Suja Palaniswamy Peeta Basa Pati Department of Computer Science & Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

Robots are becoming more and more prevalent in a variety of industries, such as transportation, agriculture, healthcare, and so forth. Deep learning is an area of study that is rapidly expanding and is applied in many fields including robots. For a robot to understand its environment and carry out tasks, object recognition is essential. This paper proposes a deep learning method that leverages both pre-existing data and real-time data for robotic object recognition which will enable robots to operate in challenging conditions. Using real-time data, CNN with decision tree is tested after being trained on both pre-existing and real-time data which yields 96% accuracy. A model has been created for reliable and accurate object recognition for robots.

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Generalized Multilingual AI-Powered System for Detecting Fake News in India: A Comparative Analysis of Machine Learning Algorithms

Generalized Multilingual AI-Powered System for Detecting Fak...

引用

Advances in Data engineering and Intelligent computing Systems (ADICS), International Conference on

作者： M Sai Mahesh Thanusri V Deepak K Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (数字)9798350364828

ISBN: (纸本)9798350364835

The impact of fake news in the present-day world is very high. It has the potential to alter anything irrespective of the profile. Not all fake news is dangerous but some may even ruin the lives of many. There are a lot of existing Fake News Detecting models but most of them mainly struggle to achieve and maintain the accuracy of the model as well as consistent updating of the datasets. Some models face difficulty in multi-language detection while other models struggle in automatic language detection. The proposed model serves as a solution for all the above-mentioned problems and it also includes web scrapping. The cross-dataset experimentation is performed using machine learning techniques for impactful results which primarily focus on Indian news. This model outperforms existing standards with an astonishing 99% accuracy using the logistic regression algorithm.

关键词： Logistic regression Machine learning algorithms Machine learning Information age Data engineering Complexity theory Fake news

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Comparative Study on Synthetic and Natural Error Analysis with BART & MarianMT

Comparative Study on Synthetic and Natural Error Analysis wi...

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International Conference for Convergence of Technology (I2CT)

作者： Rohit R SA Gandheesh Gayatri Sanjana Sannala Peeta Basa Pati Department of Computer Science & Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798350394474

ISBN: (纸本)9798350394481

Text is essential for communication, information sharing, knowledge acquisition, and analysis. It shapes public opinion, supports education, and drives online content, making it crucial in various domains. While there are various language models utilized for text analysis and text correction, there is little to no survey conducted on these model’s behavior and limitations. This work deals with studying BART and MarianMT language models behavior to an input dataset consisting of two types of errors, Synthetic and Natural. Synthetic errors are efficient to create and test, whereas Natural errors are more common and close to real world occurring errors. The models were trained and tested with the generated data, the results highlighted that BART exhibited consistent outputs towards both Synthetic and Natural errors and hence revealing a break-even point at the vicinity of 26% Synthetic error introduction. Conversely, the performance of MarianMT was comparatively diminished for Synthetic errors in contrast to Natural errors. These findings provide valuable insights into the behavior and capabilities of the models.

关键词： Training Surveys Analytical models Text analysis Error analysis Shape Knowledge acquisition

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Predictive Analytics for Anticipating Adverse Drug Reactions to ameliorate Drug Development and Prescription Practices

Predictive Analytics for Anticipating Adverse Drug Reactions...

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International Conference on Inventive computing and Informatics (ICICI)

作者： Ch. Aneesh Gembali Saumik K.V.V. Varun Rajesh M Sreevidya B Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798350373295

ISBN: (纸本)9798350373301

Integrating Artificial Intelligence into Health Care involves employing AI technologies and Algorithms to reinforce various facets of Medical Domain including discovery of the drugs and its subsequent development. However, with the advent of various drugs for several medical conditions, comes the side effects. This work aims to present a comprehensive approach to drug side effect prediction using a machine learning algorithm for Drug organization and analysis of extensive drug-related datasets. Utilizing advanced data-driven methodologies techniques, the system aims to predict potential side effects early in the drug development lifecycle. The repository used serves as a crucial infrastructure for managing diverse data, including information on drugs, chemical properties, and historical side effect records. The main objective of this research work is to enhance patient safety by minimizing adverse drug reactions and reducing treatment discontinuation rates, thereby mitigating the economic impact on pharmaceutical companies. The system enables informed decision-making throughout the drug development process by empowering researchers, healthcare practitioners, and regulatory bodies with an integrated decision-support the DBMS, acting as the backbone for the development of this predictive model. The proposed model integrated into drug development, aims to revolutionize drug safety practices, offering a proactive and data-driven approach. The predicted outcomes have broader implications for advancing healthcare technologies, optimizing drug development practices, and contributing to more efficient and informed pharmaceutical sciences.

关键词： Drugs Machine learning algorithms Medical conditions Precision medicine Machine learning Predictive models Prediction algorithms

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Aural Signatures: Audio Fingerprinting Techniques for Real-Time Audio Recognition

Aural Signatures: Audio Fingerprinting Techniques for Real-T...

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Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： B. Sharan Nripesh Reddy B. Sai Venkat I. Manohar S Abhishek Anjali T Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (数字)9798350396157

ISBN: (纸本)9798350396164

Audio fingerprinting is an essential technique for copyright protection and music recognition. The capacity to accurately categorize audio content and spot possible copyright violations is vital for musicians, content creators, and streaming services. With its valuable solutions for copyright protection, content monitoring, and music recommendation, this study has significant implications for the music industry. In the research, the research focused on developing robust audio fingerprinting methods for precise audio content recognition. These tactics have shown to be remarkably accurate and quite effective. The approach is based on the use of strong hashing techniques and the extraction of various audio aspects. Long Short-Term Memory (LSTM) along with SVM, KNN, and CNN were employed in the research. Notably, LSTM outperformed other models, showcasing an outstanding accuracy rate of 93 %. The results hold particular promise for massive music platforms and instantaneous content matching. The proposed methods have demonstrated the effectiveness of these strategies in identifying audio material, even under challenging conditions.

关键词： Deep learning Accuracy Music Fingerprint recognition Copyright protection Real-time systems Data models

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