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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Manoharan, Geetha Sarkar, Prithu Pathak, Punit Ahmad, Khadri Syed Faizz Singh, Arun Vaishnavi, M. Sr University School of Business Telangana Warangal India Amity University Amity School of Communication Kolkata India Auro University School of Liberal Arts and Human Sciences Surat India Srm University Department Computer Science and Engineering Andhra Pradesh India Vikrant University M.P Gwalior India Bannari Amman Institute of Technology Department of Computer Science and Engineering Erode India

ISBN: (纸本)9798350350067

In the recent decade, there has been a rise in social media and various companies are using it as a primary tool for marketing. In order to better understand the emotion and views of the customers, social media sentiment analysis has been used. Machine learning is a rising technology which helps in sentiment analysis in the social media which helps to easily categorize the views and opinions of the customers in certain categories. The major objectives of the study include exploring Machine Learning Approaches for Natural Language Processing and Sentiment Analysis in social media. In this research, the methods of sentiment analysis in online social networks using ML and techniques for grouping have been studied in detail. A secondary research approach has been followed for the research. The major benefits of sentiment analysis on social media have also been presented as a part of the report. In the research it has been found that there are two major types of clarifying sentiments which are supervised and unsupervised learning. The research also reveals that the best learning model for evaluating the Twitter dataset is determined to be CNN using Word to Vector and under instruction. © 2024 IEEE.

关键词： Adversarial machine learning

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Creating Realities: An In-Depth Study of AI-Driven Image Generation with Generative Adversarial Networks 15

Creating Realities: An In-Depth Study of AI-Driven Image Gen...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Dhand, Raman Sidana, Sagar Sinha, Anurag Kumar, Biresh Kumari, Shreya Madhukar Rao, G. Raj, Petheru Al-Khayyat, Ahmed Maheswaran, S. Guru Tegh Bahadur Institute of Technology Department of computer science and engineering India Maharshi Dayanand University Department of computer science and engineering Haryana Rohtak India IGNOU India School of Computing and information Science New Delhi India Amity University Department of computer science and It Jharkhand India Koneru Lakshmaiah Education Foundation Department of computer science and engineering India Reliance Jio Platforms Ltd Bangalore India Islamic University Department of computer science and engineering Iraq Kongu Engineering College Department of Electronics and Communication Engineering Erode Perundurai638060 India

ISBN: (纸本)9798350370249

The field of artificial intelligence has witnessed significant advancements with the advent of Generative Adversarial Networks, revolutionizing image generation techniques. The literature review covers seminal works and recent advancements, highlighting the evolution of image generation techniques. Taxonomy is developed to classify methods based on their underlying principles and applications across various domains including art, fashion, healthcare, and entertainment. Comparative analysis evaluates the effectiveness, efficiency, and applicability of different models, utilizing metrics corresponding Inception Score and Frechet Inception Distance. By conducting a thorough review of existing literature and technologies, we explore the evolution of GAN architectures, from their inception to the latest innovations. We examine various GAN models, including DCGAN, StyleGAN, and CycleGAN, assessing their effectiveness in generating high-fidelity images across diverse applications. In addition to this comprehensive analysis, we present our own implementation of a GAN model tailored for specific image generation tasks. Our model is tested on multiple datasets to evaluate its performance, highlighting improvements in training stability and image quality. We discuss the architectural choices, optimization techniques, and training procedures employed in our implementation. Furthermore, we address the challenges faced in the field, such as mode collapse, training instability, and the need for largescale datasets. Our findings offer valuable insights into optimizing GAN performance and propose potential directions for future research. This comprehensive analysis serves as a foundational reference for researchers and practitioners aiming to leverage GANs for advanced image generation tasks, pushing the boundaries of what is visually conceivable through artificial intelligence. © 2024 IEEE.

关键词： AI computer vision Deep Learning GANs Image generation ML

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Machine Learning Based Early Prediction of Parkinson's Disease for Sustainable Healthcare 1st

Machine Learning Based Early Prediction of Parkinson's Disea...

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1st International Conference on Sustainable Development through Machine Learning, Artificial Intelligence, and IoT, ICSD 2023

作者： Chauhan, Ritu Mehta, Khushi Alankar, Bhavya Kaur, Harleen Artificial Intelligence and IoT Lab Center for Computational Biology and Bioinformatics Amity University UP Noida India Amity Institute of Information Technology Amity University UP Mumbai India Department of Computer Science and Engineering School of Engineering Sciences and Technology Jamia Hamdard New Delhi India

ISBN: (纸本)9783031470547

The healthcare sector and technology are working hand-in-hand, which has led researchers to focus on Parkinson's disease. In a world where medical science and technology intertwined, there was a group of researchers determined to unravel the mysteries of Parkinson's disease. Medical science with machine learning algorithms, aimed to develop a robust prediction model for diagnosing this complex neurological disorder. The researchers explored various algorithms including Xgboost, Naive Bayes, Tree Classifier, KNN model, SVC model, Logistic Model, Random Forest, AdaBoost, and Gradient Boosting, using a diverse dataset encompassing clinical measurements, genetic information, and demographic factors. The researchers utilized various machine learning algorithms to uncover patterns and correlations, shed light on relationships, make accurate predictions, foster insights, differentiate Parkinson's disease, understand odds and probabilities, tackle uncertainty, and improve accuracy. By combining these algorithms, they constructed a prediction model for the accurate diagnosis of Parkinson's disease, bringing us closer to unraveling its mysteries. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Adaptive boosting

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Comparative Analysis of Machine Learning Models for Diabetes Prediction

Comparative Analysis of Machine Learning Models for Diabetes...

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International Conference on System Modeling & Advancement in Research Trends (SMART)

作者： Anshita Shukla Rahul Kumar Vikrant Chole Jhankar Moolchandani Santosh Sahu Ashok Kumar Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Gwalior Madhya Pradesh India

ISBN: (数字)9798350380583

ISBN: (纸本)9798350380590

This paper aims at comparing performance of various machine learning models for the task of predicting diabetes using one of the public datasets. The research assesses the big data analysis in the determination of the possibilities of risk of diabetes and the preferred models of Logistic Regression and KNearest Neighbors (KNN). Performance measures including accuracy, precision and recall were used in assessing the performance of the two models. The study do reveals the high level of potential of both the models yet reveals that Logistic Regression just edges a bit ahead of KNN in terms of accuracy and precision. These results show the potential of using machine learning in hospital diagnosis and specifically in diagnosis of Chronic diseases such as diabetics.

关键词： Logistic regression Analytical models Accuracy Hospitals Machine learning Predictive models Nearest neighbor methods Market research Data models Diabetes

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Demosaicing of Multispectral Imaging Camera data using Neural Networks

Demosaicing of Multispectral Imaging Camera data using Neura...

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BIT Conference (BITCON), International

作者： Sumita Gupta Sapna Gambhir Saru Dhir Simran Sapui Rana Majumdar Deepsikha Naskar Department of Computer Science & Engineering Amity School of Engineering & Technology Amity University UP Department of Computer Science College of Engineering and Computing George Mason University USA Dept. of Information Technology Amity School of Engineering & Technology Amity University UP India Department of Computer Science Sister Nivedita University

ISBN: (数字)9798331518394

ISBN: (纸本)9798331518400

In most digital cameras, the sensor uses a Bayer filter array to capture the image. This array records only one colour, either blue or green or red for every image pixel, resulting in a mosaic image. To retrieve the missing colour information in the captured image making use of cross-channel interpolation is referred to as demosaicing. Demosaicing, especially in the context of colour demosaicing (CDM), plays a vital role as the first step in obtaining high-quality colour images with single-chip cameras. Traditional demosaicing methods, used to reconstruct colour images from raw sensor data in digital cameras, have several drawbacks. They often result in a loss of spatial resolution, introduce artifacts, lack robustness in challenging conditions, and may require manual tuning for optimal performance. In contrast, neural networks offer advantages like end-to-end learning, improved image quality, robustness, flexibility, and state-of-the-art performance. They learn complex mappings directly from raw data, adapt to various conditions, and produce visually pleasing, high-resolution images. However, they require substantial training data and computational resources, making them less suitable for resource-constrained applications. In this paper, conventional interpolation methods are compared to deep-learning based approaches for image demosaicing. To validate the approach to image demosaicing, aerial images captured from the Mars Colour Camera have been used.

关键词： Image quality Interpolation Visualization Image color analysis Neural networks Training data Robustness Digital cameras Spatial resolution Tuning

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Enhanced Brain Tumor Diagnosis Using Transfer Learning with Vision Transformers and Topological Data Analysis

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Procedia computer science 2025年 258卷 4048-4059页

作者： Dhananjay Joshi Bhupesh Kumar Singh Kapil Kumar Nagwanshi Nitin S. Choubey Research Scholar at Amity School of Engineering and Technology Amity University Rajasthan Jaipur 303007 India Amity School of Engineering and Technology Amity University Rajasthan Jaipur 303007 India Computer Science & Engineering Guru Ghasidas Vishwavidyalaya (A Central University) Bilaspur India Department of Computer Science Mukesh Patel School of Technology Management & Engineering Shirpur SVKM’s Narsee Monjee Institute of Management Studies (NMIMS) Deemed-To-University Mumbai-400056 India

The development of deep learning in brain tumor diagnosis has evolved due to the demand for effective medical image assessment methods. Early attempts utilizing basic machine learning algorithms and convolutional neural networks (CNNs) faced challenges in accuracy, precision, and stability across various datasets. This paper presents the TransTopoDx system, a novel deep learning architecture designed to enhance brain tumor diagnosis by integrating large datasets with topological data analysis (TDA) and vision transformers (ViTs). ViTs, pre-trained with transfer learning on extensive image databases and fine-tuned on specific brain tumor datasets, serve as feature extractors, significantly improving the model’s capacity to discern tumor characteristics. Additionally, TDA is employed using the Mapper algorithm to identify patterns in imaging data and facilitate classification. The feature extraction process involves two parallel paths for ViTs and TDA, with results merged in a fusion layer to enhance tumor prediction accuracy. To optimize weight tuning and mitigate overfitting, dropout techniques and data augmentation is used. The TransTopoDx system, implemented in Python, achieves an impressive overall accuracy of 99.92%, with a Precision of 99.90% and Recall of 99.87%. The incorporation of transfer learning in ViTs training and TDA is crucial for improving diagnostic efficacy.

关键词： Advanced Deep Learning Regularization Figshare Dataset

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Comparative Analysis of LSTM and Random Forest Algorithms for Sentiment Classification in Movie Reviews 3

Comparative Analysis of LSTM and Random Forest Algorithms fo...

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

作者： Purohit, Shardha Rajput, Aarfa Vats, Satvik Mudgal, Rusha Shah, Owais Ahmad Verma, Aditya Noida International University School of Journalism & Mass Communication Gautam Budh Nagar India Graphic Era Hill University Department of Computer Science & Engineering Dehradun India Amity University Amity School of Communication Manesar Gurugram India K. R. Mangalam University School of Engineering & Technology Gurugram India

ISBN: (纸本)9798350375190

This study conducts sentiment analysis on 50,000 movie reviews, comparing the traditional Random Forest classifier with a Long Short-Term Memory (LSTM) network. The Random Forest achieved robust metrics across accuracy, precision, recall, and F1-score (0.85), while the LSTM excelled in capturing the complex language patterns inherent in the reviews through its sequence-based processing. This study has refined the data by removing irrelevant characters and employing tokenization and lemmatization to enhance model training. Despite its higher computational requirements, the LSTM showed potential in detailed sentiment analysis. Future directions include exploring other neural architectures, enlarging the dataset, and practical applications to improve model efficacy. The study highlights key insights into sentiment analysis methodologies and their implications for both academic and practical realms. © 2024 IEEE.

关键词： Long short-term memory

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Deep Learning Model for Weather Prediction

Deep Learning Model for Weather Prediction

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Computing, sciences and Communications (ICCSC), International Conference on

作者： Shubhanshi Singh Neha Tyagi dept. Computer Science and Engineering Amity School of Engineering and Technology Noida India

ISBN: (数字)9798350353648

ISBN: (纸本)9798350353655

Weather forecasting predicts atmospheric conditions at a particular time and place. It is an important task with a wide range of applications, including agriculture, transportation, and disaster prevention. Traditional weather forecasting methods rely on physical models of the atmosphere, which can be complex and computationally expensive. In recent years, deep learning techniques are effective for weather forecasting. Deep learning models can learn complex patterns from data, which can be used to predict future weather conditions accurately. This project aims to create a deep-learning model for forecasting weather. A huge dataset of historical meteorological data, including radar, satellite, and surface measurements, will be used to train the model. Numerous meteorological factors, such as temperature, precipitation, and wind speed, will be predicted by the model. We will compare the deep learning model's performance with conventional weather forecasting techniques. Creating a deep learning model that is more accurate and effective than current approaches is the project's main objective. The initiative may result in improved weather forecasts that are prompt and accurate, better agricultural, transportation, and disaster prevention decision-making, and less money lost because of weather-related incidents. Possible obstacles for the project are gathering and purifying a sizable dataset of meteorological data, using this data to train a deep learning model, and then comparing the model's output with more conventional techniques.

关键词： Deep learning Accuracy Computational modeling Atmospheric modeling Weather forecasting Transportation Predictive models Data models Agriculture Meteorology

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Analyzing the Impact of AI Adoption on Job Market Dynamics and Employment Trends Using a Synthetic Dataset

Analyzing the Impact of AI Adoption on Job Market Dynamics a...

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International Conference on System Modeling & Advancement in Research Trends (SMART)

作者： Vikrant Chole Santosh Sahu Jhankar Moolchandani Ashok Kumar Rahul Kumar Anshita Shukla Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Madhya Pradesh Gwalior India

ISBN: (数字)9798350380583

ISBN: (纸本)9798350380590

This paper aims at establishing the effects of adopting artificial intelligence in the job market based on an artificially generated set of 500 jobs. In the following analysis, the impacts of AI Adoption levels on salary distributions, job growth, as well as the risks of job automation are examined with the help of such statistical methods as multiple linear regression, logistic regression, classification algorithms. The results show a positive relationship between AI utilization and job creation in industries where AI augments human work but reveal higher automation threats for industries with repetitive activities. Also, AI adoption corresponds to higher wages particularly in the advanced skill jobs but the jobs at high automation risk enjoy wage premium due to job insecurity. This paper acknowledges the fact that AI adoption is both a benefit and a threat that calls for careful executive actions on best strategies for managing changes in the workforce. Such future work should replicate these effects using actual data as well as religious and ethical effects on a wider scale.

关键词： Industries Logistic regression Ethics Automation Statistical analysis Linear regression Market research Remuneration Artificial intelligence Synthetic data

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GPNST: An Improved Graphical Password Authentication Scheme Leveraging Neural Style Transfer Technique on Smartphones

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International Journal of Information Security 2025年第3期24卷 1-34页

作者： Ray, Palash Giri, Debasis Obaidat, Mohammad S. Jana, Manasi Sasmal, Mahuya Alenazi, Mohammed J. F. Haldia Institute of Technology West Bengal Haldia India Maulana Abul Kalam Azad University of Technology West Bengal Nadia India King Abdullah II School of Information Technology The University of Jordan Amman Jordan School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Department of Computational Intelligence School of Computing SRM University SRM Nagar TN Kattankulathur603203 India School of Engineering The Amity University UP Noida201301 India King Saud University Riyadh Saudi Arabia

The growing prevalence of smartphones and the increasing dependence on digital services have made it crucial to have authentication techniques that are both secure and user-friendly. This study examines the effectiveness and usability of graphical passwords named GPNST as an alternative authentication method on mobile devices. Graphical passwords use users’ capacity to remember visuals or patterns, offering a possible equilibrium between security and user pleasure. GPNST is a two-factor authentication technique that requires users to remember content and style images, or stylized images, as portfolio images. Additionally, a unique key token is assigned to each user device and securely sent to the server using the reversible data hiding approach, therefore mitigating several potential attacks. The research uses a combination of quantitative analysis to evaluate the strength of passwords and qualitative evaluations to measure user satisfaction and memorability. We investigate the influence of several design elements in graphical passwords, such as the intricacy of images and the layout of grids, on both the level of security and ease of use. In addition, the study evaluates the robustness of graphical passwords against popular forms of attacks, such as shoulder surfing and brute-force efforts. The findings provide valuable insights regarding the practicality of incorporating graphical passwords into smartphone authentication systems. This has implications for designers, developers, and users who are looking for secure and user-friendly solutions in the constantly changing field of mobile technology. The GPNST scheme demonstrates outstanding performance in our assessment test, with an accuracy rate of up to 94.80% and a login time of 9.61 seconds. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Static random access storage

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