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Enhancing Text-to-Image Model Evaluation: SVCS and UCICM 6

Enhancing Text-to-Image Model Evaluation: SVCS and UCICM

引用

6th International Conference on Recent Trends in Advance Computing, ICRTAC 2023

作者： Gabriel Joshua, R. Khadar Nawas, K. Varsini, S.R. Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350394702

As the world gradually embraces the integration of AI into various applications, there is a growing need for methods to enhance and evaluate AI-based technologies. One such a remarkable innovation is text to image models, which are currently revolutionizing the content generation, computer vision, design industries etc. Among the existing deep learning techniques utilized for text to image synthesis, stable diffusion model set apart due to its ability to generate high quality images. It transforms images into their latent space representation, mitigating computational memory and time requirements while minimizing instability and complexity. In this paper, we propose two evaluation metrics Semantic Visual Consistency Score (SVCS) and User-Centric Image Coherence Metric (UCICM) to analyze the coherence of the image with respect to the user prompt. SVCS assesses the model's capability to interpret human language, while UCICM is tailored to enhance application-specific image generation through personalization. This paper primarily focuses on the processes entailed in a transfer learning-based stable diffusion model and subsequently evaluates its performance using the newly proposed metrics. © 2023 IEEE.

关键词： Semantics

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Detection of COVID-19 Using CNN and ML Algorithms

Detection of COVID-19 Using CNN and ML Algorithms

引用

International Conference on Data Analytics, Intelligent Computing, and Cyber Security, ICDIC 2020

作者： Raghav Srinivaas, M. Shah, Khanjan Abhishek, B. Jagadeesh Kannan, R. Balasundaram, A. School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9789811941610

As we see coronavirus is the very dangerous diseases and to identify this diseases in one’s body is also not as easy. So during identification of diseases there are many false positive cases we see that person does not have corona and still the prediction comes true and also in some cases, it happens that person has corona but it does not get detected (false negative case). So due to this problem, we here come up with the two approaches and make comparison between these two approaches and decide which one is better to analyze the diseases in the body. We are using CNN to scan chest X-ray dataset and ML algorithms for tabular dataset as it contains many text information too. So in this project, we explain in detail, what is CNN, what is ML, how to implement CNN and ML algorithms on particular dataset, what output we will get as a comparison. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： CNN Corona ML

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Investigation of Assessment Methodologies in Information Security Risk Management 7th

Investigation of Assessment Methodologies in Information Sec...

引用

7th International Conference on Inventive Communication and Computaional Technologies, ICICCT 2023

作者： Rajathi, C. Rukmani, P. School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9789819951659

Information security risk management is a crucial component of every organization's security plan. It comprises the identification, assessment, and prioritization of potential security risks to an organization's information assets as well as the implementation of protective measures or risk management plans. For this method to work, a detailed understanding of an organization's assets, threats, vulnerabilities, and potential impacts of security incidents is required. Effective information security risk management ensures business continuity, safeguards critical information assets, and prevents data breaches. In this study, the key concepts, practices, and tools of information security risk management are discussed. It also looks at the most effective strategies to set up a successful risk management program and identifies emerging trends. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Risk management

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Event analysis of functional near infrared spectroscopy brain signal using Conditional Generative Adversarial Network 1

Event analysis of functional near infrared spectroscopy brai...

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1st International Conference on Innovations in Communications, Electrical and computer engineering, ICICEC 2024

作者： Akila, V. Shirly Edward, A. Sathiya, R. Vasuki, A. Rishi, Priti Anju, M.I. Anita Christaline, J. SRM Institute of Science and Technology Department of Ece Vadapalani Chennai India Vellore Institute of Science and Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350376517

The brain's intricate processes have been the subject of an extraordinary amount of detailed study by researchers, who have access to vast quantities of brain signal data. Event-related brain signals are necessary for comprehending cognitive activities such as memory, attention, and decision-making. The brain mechanisms of cognitive processes are revealed by these signals. Deep learning and machine learning are technologies that are promising for the analysis of brain signals related to events. These sophisticated methodologies are capable of detecting intricate data patterns that suggest cognitive activities. This investigation employs deep learning and machine learning to analyze brain signals associated with events. Event-related signal pre-processing, feature extraction, and categorization comprise the primary procedures. Data preparation eliminates noise and anomalies in order to prepare signals for analysis. The objective of feature extraction is to identify the brain signals that most accurately represent cognitive activity. Categorization is the process of classifying these characteristics in order to comprehend their cognitive processes. A comparison is made between deep learning and machine learning in the context of event-related neural signal processing. The comparison is made between support vector machines and decision trees and deep learning methods such as convolutional neural networks and neural networks. Both methodologies are effective in identifying neural correlates of cognitive processes. This method has the potential to enhance our comprehension of the brain's operation and potential treatments, thereby facilitating the development of diagnostic and therapeutic instruments for neurological and psychiatric illnesses. © 2024 IEEE.

关键词： Near infrared spectroscopy

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Video Based Fashion Recommendation System 14

Video Based Fashion Recommendation System

引用

14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Mukherjee, Koushiki Parkavi, K. Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350335095

The fashion industry has grown incredibly over the years giving rise to a number of varied styles. This growth in the variety has also led to a change in the choices of the customers. Customers nowadays are mostly inspired by the style statement of the characters of a movie or a web show. They want to recreate the same look for themselves but fall short of knowing the correct terminology to identify the style. Online shopping platforms deal with a lot of data, so for an efficient search, it is needed that we identify the proper keyword to initiate the search. A style comes into existence through the different combinations of the features of a dress. Thus through this paper, we explore how these features can be used in identifying a style. Further, we propose an approach to recommend clothes similar to a reference character in a video. © 2023 IEEE.

关键词： Nearest neighbor search

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An analysis on Keylogger Attack and Detection based on Machine Learning

An analysis on Keylogger Attack and Detection based on Machi...

引用

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent engineering, ICECONF 2023

作者： Balakrishnan, Yeshaswini Renjith, P.N. School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350334357

A keylogger attack is a type of cyberattack that involves the use of a software program to record keystrokes on a target device. Attacks of this kind can be used to steal sensitive data, including credit card numbers and login credentials. Keylogger attacks are often targeted at specific individuals or organizations, and the attackers may have prior knowledge of the target's systems and configuration. There are a variety of ways to carry out a keylogger attack, and the attacker's choice of method will depend on the type of information they are trying to steal. For example, an attacker may install a hardware keylogger on the target's computer in order to record every keystroke made. As an alternative, the attacker might develop malicious software that captures keystrokes and transmits them to a distant server. Keylogger attacks are difficult to detect, as the keylogger software can be disguised as a legitimate program or run in the background without the user's knowledge. However, there are some signs that a keylogger attack may be taking place, such as unexpected activity on the computer or unusual network traffic. The best way to protect against keylogger attacks is to use a reputable antivirus program and to keep all software up to date. Furthermore, users need to be cautious while opening attachments or clicking on hyperlinks that come from unknown sources. This paper focuses on the evolution of technology over time, as well as the implementation and observation of keylogger attacks. © 2023 IEEE.

关键词： Machine learning

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Hand Gesture-Based AI System for Accessing Windows Applications 4th

Hand Gesture-Based AI System for Accessing Windows Applicati...

引用

4th International Conference on Image Processing and Capsule Networks, ICIPCN 2023

作者： Palivela, Lakshmi Harika Premanand, V. Begum, Afruza School of Computer Science Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9789819970926

Actions speak more than words. In the context of the above statement, the importance of gestures and using them to control a system has become popular. The hand gesture recognition system for opening applications in Windows is a computer vision application that uses a webcam and machine learning models to recognize hand gestures and open specific applications. This system allows users to control their computers using simple hand gestures instead of relying on traditional input devices such as keyboards or mouse. The system uses the Tensor Flow and Media-Pipe libraries for training and detecting hand gestures, respectively. The trained machine learning model recognizes the gestures, and specific applications are opened based on the recognized gesture. The system has potential applications in accessibility, gaming, and virtual reality. In this work, we will discuss potential applications of the hand gesture recognition system for opening applications in Windows, and the second proposed system, which may also act as the control system of PowerPoint presentations, can be made easy and convenient using these gestures-based control. Thus, the proposed model improves the interaction between the human and the computer. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Palmprint recognition

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An Investigation on Machine Learning Algorithms for Crop Yield Prediction

An Investigation on Machine Learning Algorithms for Crop Yie...

引用

2023 International Conference on System, Computation, Automation and Networking, ICSCAN 2023

作者： Singh, Raj Bhavadharini, R.M. School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350315127

The agriculture sector of India is the backbone of Indian economy and has contributed 18.06% in the country total GDP in the year 2021-22. With the rise in population of India, the demand for agriculture production from the same land has also increased. As the crop demand increases, it will be supportive to the formers if they can predict the crop yield in future. But the task of crop yield prediction is challenging due to factors such as unpredictable rainfall trends, multiple climatic conditions and seasonal production trends. Recently Machine learning techniques are widely used for weather forecasting, crop recommendation, weed management, fertilizer recommendation and so on in Agriculture sector. All the data taken as input are real time data, however the performance of the machine learning models vary with the amount of ground truth data. Machine learning algorithms namely Multiple Linear Regressor, Random Forest algorithm, Decision Tree Gradient Boosting, and Extreme Gradient Boost, are evaluated in the proposed work to predict specific crop yield. The performance metrics used to evaluate the these Machine Learning algorithms are Accuracy, R2, and Adjusted R2. The proposed work achieves an accuracy of 93.95% accuracy, 92.06% of R2 score,89.95% of adjusted R2 score for gradient boosting, an accuracy of 93.95%, R2 score of 93.45% and adjusted R2 score of 91.25 for extreme gradient boosting algorithms. © 2023 IEEE.

关键词： Crops

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Machine Learning for Myocardial Infarction complication: A Metrics-Based Investigation of Different Models 6

Machine Learning for Myocardial Infarction complication: A M...

引用

6th International Conference on Recent Trends in Advance Computing, ICRTAC 2023

作者： Thomas Abraham, J.V. Sriram, R. Shashaank, V. Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350394702

Myocardial infarction, known to be heart attack, is still a major global health concern. Recognizing and categorizing MI complications in a timely manner is crucial for effective patient care and better outcomes. In this study, we present an innovative approach that harness power of machine learning to classify complications associated with myocardial infarction using a wide range of clinical data. Our study draws from an extensive dataset that encompasses patient records, imaging data, and lab results. An integral component of our approach involves employing feature selection techniques to pinpoint the most relevant predictors from the vast pool of clinical information. This not only makes the model more understandable but also enhances its predictive accuracy by curbing overfitting and computational complexities. We make use of advanced ML algorithms such as random forests, support vector machines, and deep neural networks, to delve into the intricate relationships hidden within the data. © 2023 IEEE.

关键词： Support vector machines

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Data-Driven Player Recruitment in Football 2

Data-Driven Player Recruitment in Football

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2nd International Conference on Automation, Computing and Renewable Systems, ICACRS 2023

作者： Singh, Asit Pradeep Suguna, M. School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350340235

In today's corporate-driven football landscape, teams aim to maximize profits and competitiveness by investing heavily in player recruitment, scouting, and facilities. This pursuit of excellence often involves significant financial outlays, including lucrative contracts to attract promising talents. However, this approach doesn't always yield the expected results, as teams sign numerous players each year. To mitigate risks and enhance performance, football has increasingly turned to data-driven approaches, employing various metrics to assess player abilities, team strategies, and managerial impacts. Machine learning has emerged as a valuable tool for making more informed decisions, saving resources, and adding a technical dimension to the sport, enhancing the experience for fans worldwide. © 2023 IEEE.

关键词： Data Football Machine Learning Model

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