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User Impersonation Detection and Authentication Using Image Based OTP

User Impersonation Detection and Authentication Using Image ...

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Innovations in Power and Advanced computing Technologies (i-PACT)

作者： A Raja Rajendran Gokul Raj J Sharath Kumar K Mukeshvar A Radhika G Department of Computer Science and Engineering Amrita School of Computing Coimbatore India

In this age of the internet and technology, it is very important to be vigilant with one's devices and have safe and secure passwords to avoid several types of crimes. In this paper, we concentrate on two types of attacks: user impersonation and shoulder surfing. User impersonation is the act where one person pretends to be you or someone else to create confusion or obtain information that is private to you. To prevent this, we discuss a method to classify a person as authorised or unauthorised, utilising data from the user's mobile device's accelerometer and gyroscope sensors [1]. One of the simplest methods scammers use to acquire your personal information is by shoulder surfing, which may be as easy as simply looking over your shoulder. It is extremely common and hard to prevent in crowded areas. We proposed a method of authentication that uses OTPs and graphical password techniques and would prove effective against shoulder surfing attacks.

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OCEAN - Ovarian Cancer subtypE clAssification and outlier detectioN using DenseNet121

OCEAN - Ovarian Cancer subtypE clAssification and outlier de...

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International Conference on Image Information Processing

作者： P Paayas R Annamalai Computer Science and Engineering Amrita Vishwa Vidyapeetham School of Computing Chennai

ISBN: (数字)9798350371406

ISBN: (纸本)9798350371413

In our exploration, we tackle the significant hurdle of diagnosing ovarian cancer, a formidable challenge within the field of oncology. This cancer type often remains undetected until its advanced stages due to inconspicuous symptoms and the absence of reliable early detection methods. To combat this issue, we propose a method utilizing DenseNet121, a deep learning approach, for the multi-classification of ovarian cancer sub-types based on histopathology images. We leverage the UBC Ovarian Cancer data-set obtained from Kaggle, encompassing whole slide images and tissue microarrays with various dimensions. Among the sub-types covered in this dataset are clear cell (CC), endometrioid (EC), high-grade serous (HGSC), low-grade serous (LGSC) and mucinous (MC). To address the challenge posed by large image sizes up to 100,000 x 50,000 pixels and the diverse morphological characteristics present in the images, we have devised comprehensive data augmentation techniques. Our DenseNet121 model demonstrates robust generalization capabilities by effectively learning features from this varied data-set. A thorough experiment with challenging cases of ovarian cancer sub-types resulted in 99.6% accuracy and 100% F1-score in the multi class classification. Ovarian cancer detection systems based on this approach may be developed to detect the disease early and accurately.

关键词： Deep learning Representation learning Histopathology Computational modeling Sensitivity and specificity Data augmentation Oncology

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LyEmoBERT: Classification of lyrics' emotion and recommendation using a pre-trained model

LyEmoBERT: Classification of lyrics' emotion and recommendat...

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2022 International Conference on Machine Learning and Data engineering, ICMLDE 2022

作者： Revathy, V.R. Pillai, Anitha S. Daneshfar, Fatemah School of Computing Sciences Hindustan Institute of Technology and Science Chennai India Department of Computer Engineering University of Kurdistan Sanandaj Iran

Music plays a significant role in evoking human emotions. Thanks to the quick proliferation of smartphones and mobile internet, music streaming applications and websites have made the music emotion recognition task even more active and exciting. However, music emotion recognition faces significant challenges too. These include inaccessibility of data, unavailability of large data volume, and the lack of other emotionally relevant features. While emotionally relevant features can be identified by analyzing lyrics and audio signals, the availability of datasets annotated with a lyrical emotion remains a constant challenge. This study uses the Music4All dataset to evaluate the lyrical features relevant for the identification of four important human emotions-happy, angry, relaxed, and sad. This was done with the help of several machine learning algorithms based on a semantic psychometric model. A transfer learning approach was also used to understand the feelings of the lyrics from an in-domain dataset and then predict the emotion of the target dataset. Further, it was observed that the BERT model improves the overall accuracy of the model (92%). A simple lyrics recommender system is also built using the Sentence Transformer model. © 2023 The Authors. Published by Elsevier B.V.

关键词： Music

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Generative AI: A Review on Models and Applications 2

Generative AI: A Review on Models and Applications

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2nd IEEE International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Kaswan, Kuldeep Singh Dhatterwal, Jagjit Singh Malik, Kiran Baliyan, Anupam School of Computing Science and Engineering Galgotias University UP Greater Noida India Koneru Lakshmaiah Education Foundation Artificial Intelligence and Data Science AP Guntur India Matu Ram Institute of Engineering and Management Computer Science and Engineering Haryana Rohtak India Chandigarh University Department of Computer Science and Engineering Chandigarh India

ISBN: (纸本)9798350369960

Generative Artificial Intelligence (AI) stands as a transformative paradigm in machine learning, enabling the creation of complex and realistic data from latent representations. This review paper comprehensively surveys the landscape of Generative AI, encompassing its foundational concepts, diverse models, training methodologies, applications, challenges, recent advancements, evaluation metrics, and ethical dimensions. The paper begins by introducing Generative AI's significance across various domains, presenting its pivotal role in producing synthetic data with applications spanning image synthesis, text generation, music composition, drug discovery, and more. The objectives lie in elucidating the foundational concepts, delving into model intricacies, unveiling the training procedures, exploring its application landscape, addressing challenges, envisioning future directions, and discussing ethical ramifications. The foundational section elucidates the diverse array of generative models, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), flow-based models, Generative Reinforcement Learning (GRL), and advanced hybrid architectures. Subsequently, evaluation metrics ranging from Inception Score to perceptual similarity metrics and human evaluations are surveyed to assess generative model performance. Finally, ethical considerations underscore the necessity for addressing biases, misuse, intellectual property concerns, and the call for responsible AI development and regulation in the Generative AI landscape. © 2023 IEEE.

关键词： Advanced Hybrid Architectures Flow-Based Models Generative Adversarial Networks (GANs) Generative Reinforcement Learning (GRL) Variational Autoencoders (VAEs)

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Recursive Descent Parser for Abstract Syntax Tree Visualization of Mathematical Expressions

Recursive Descent Parser for Abstract Syntax Tree Visualizat...

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International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS)

作者： Shiva Teja Pecheti Basavadeepthi H M Nithin Kodurupaka Meena Belwal Department of Computer Science and Engineering Amrita School of Computing Bengaluru India

This study presents a novel method for finding and evaluating mathematical expressions using Abstract Syntax Trees (AST). The approach is particularly adaptable for both academic and professional applications since it accurately parses complex equations and accommodates a large number of mathematical operations. The AST model ensures a hierarchical expression structure, enabling accurate and efficient evaluation. Parsed expressions are improved by incorporating interactive graph vi-sualisation, which simplifies mathematical computations and engineering tasks for understanding and application. The project's complete approach to detecting mathematical expressions stands out since it takes into account a wide range of functions and variables and ultimately enhances the system's accuracy and adaptability. The visual representation of AST also helps with debugging and checking the accuracy of parsed expressions, which adds to its significance in the field of mathematical expression recognition.

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Machine Learning Approaches for Myers-Briggs Personality Prediction

Machine Learning Approaches for Myers-Briggs Personality Pre...

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International conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： Sethulakshmi Santhosh Meenakshi M Avani F S Abhishek Anjali T Department of Computer Science and Engineering Amrita School of Computing Amritapuri India

In order to predict Myers-Briggs personality types from text input, this research article compares the abilities of Stochastic Gradient Descent (SGD), Naive Bayes, k-Nearest Neighbours (KNN), and Logistic Regression models. The Myers-Briggs Type Indicator(MBTI) captures distinctive patterns of behaviour, cognition, and preferences of human personality. It divides people into one of sixteen personality types. This study assesses the accuracy, support, recall, precision, and F1-score for the chosen models using a dataset made up of text inputs and matching Myers-Briggs types. The project attempts to determine the most successful model among the evaluated ways for precisely predicting Myers-Briggs personality types through extensive experimentation and review. The findings of this study have applications in building accurate and reliable models for personality prediction utilizing natural language processing methods.

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Advancing Optical Tomography Image Analysis: Exploring Convolutional Neural Network Model Variants for Retinal Damage Detection 10

Advancing Optical Tomography Image Analysis: Exploring Convo...

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10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and computer engineering, UPCON 2023

作者： Sathyan, S. Chanchal, M. Raghul, S. Jeyakumar, G. Amrita Vishwa Vidyapeetham Amrita School of Physical Sciences Department of Mathematics Coimbatore India Cmr Institute of Technology Department of Information Science and Engineering Bengaluru India Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350382471

Retina is a vital component of the eye;it plays a crucial role in visual perception. Any damage to the retina can lead to vision impairment and severe ocular diseases. Early detection and accurate diagnosis of retinal conditions is crucial. In the domain of medical image analysis, especially within the scope of retinal imaging, convolutional neural network (CNN)-based deep learning models have exhibited promising results. This paper involves the detection of retinal conditions such as Diabetic Macular Edema (DME), Choroidal Neovascularization (CNV) and Drusen. The paper presents a comparative study of CNN models for the detection of these retinal damages using Optical Coherence Tomography (OCT) images. This study focuses on evaluating the performance of popular CNN architectures, like Residual Networks (ResNet), Dense Convolutional Networks (DenseNet), Inception Networks, Visual Geometry Group (VGG). Also, the paper provides a comparative analysis of different depth variants of deep learning models such as ResNet (18, 50 and 101), DenseNet (121, 169, and 201), Inception-V3, Inception-ResNet-V2, and VGG (16 and 19). These models are assessed on a dataset containing OCT images of Normal, DME, CNV and Drusen retinal conditions. The effectiveness of the models are evaluated using evaluation metrics such as accuracy, precision, recall and F1 score. Additionally, visualizations such as confusion matrix, training accuracy versus validation accuracy plots, training loss versus validation loss plots are utilized to provide a better understanding of the model performance. The idea is to explore the impact of depth on the model performance and understand how deeper models demonstrate an enhanced ability to learn complex features and capture intricate details within the retinal data. Exploring these depth variants aims to provide insights into the advantages and potential trade-offs associated with increasing depth of deep learning models for retinal damage detection tasks. © 2

关键词： choroidal neovascularization deep learning DenseNet diabetic macular edema drusen Inception networks medical image analysis ocular diseases optical coherence tomography (OCT) ResNet retina retinal imaging VGG

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S2AC: ENERGY-BASED REINFORCEMENT LEARNING WITH STEIN SOFT ACTOR CRITIC 12

S2AC: ENERGY-BASED REINFORCEMENT LEARNING WITH STEIN SOFT AC...

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12th International Conference on Learning Representations, ICLR 2024

作者： Messaoud, Safa Mokeddem, Billel Xue, Zhenghai Pang, Linsey An, Bo Chen, Haipeng Chawla, Sanjay Qatar Computing Research Institute Hamad Bin Khalifa University Qatar School of Computer Science and Engineering Nanyang Technological University Singapore SalesForce United States Skywork AI Data Science William & Mary United States

Learning expressive stochastic policies instead of deterministic ones has been proposed to achieve better stability, sample complexity, and robustness. Notably, in Maximum Entropy Reinforcement Learning (MaxEnt RL), the policy is modeled as an expressive Energy-Based Model (EBM) over the Q-values. However, this formulation requires the estimation of the entropy of such EBMs, which is an open problem. To address this, previous MaxEnt RL methods either implicitly estimate the entropy, resulting in high computational complexity and variance (SQL), or follow a variational inference procedure that fits simplified actor distributions (e.g., Gaussian) for tractability (SAC). We propose Stein Soft Actor-Critic (S2AC), a MaxEnt RL algorithm that learns expressive policies without compromising efficiency. Specifically, S2AC uses parameterized Stein Variational Gradient Descent (SVGD) as the underlying policy. We derive a closed-form expression of the entropy of such policies. Our formula is computationally efficient and only depends on first-order derivatives and vector products. Empirical results show that S2AC yields more optimal solutions to the MaxEnt objective than SQL and SAC in the multi-goal environment, and outperforms SAC and SQL on the MuJoCo benchmark. Our code is available at: https://***/SafaMessaoud/S2AC-Energy-Based-RL-with-Stein-Soft-Actor-Critic. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Reinforcement learning

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Predictive Modeling for Cirrhosis Diagnosis: A Machine Learning Exploration 1

Predictive Modeling for Cirrhosis Diagnosis: A Machine Learn...

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1st IEEE International Conference on Electronics, computing, Communication and Control Technology, ICECCC 2024

作者： Sudeesh Kumar, V. Natarajan, B. Murali, P. Selvam, Prabu Venkatraman, K. Nagarajan, N.R. Amrita Vishwa Vidyapeetham Dept. of Artificial Intelligence Chennai India School of Computer science and Engg. VIT University Chennai India Saveetha school of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer science and Engg. Chennai India Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of CSE Chennai India K.Ramakrishnan College of Engineering Department of ECE Trichy India

ISBN: (纸本)9798350371802

Cirrhosis manifests as a condition wherein healthy liver tissue is gradually replaced by scar tissue, resulting in a decline in liver function. This ailment frequently correlates with autoimmune disorders, non-alcoholic fatty liver disease, excessive alcohol intake, and persistent liver conditions such as hepatitis B or C. As cirrhosis progresses, it can cause liver failure and various complications such as ascites, portal hypertension, hepatic encephalopathy, and an increased risk of liver cancer. Since the liver plays a crucial role in filtering blood and performing essential functions, individuals with acute liver failure are more prone to infections, particularly those affecting the circulatory, respiratory, and urinary systems. Kidney failure is a common consequence of liver failure, especially in cases of acetaminophen overdose, as this medication can damage both the liver and kidneys. In the realm of healthcare, the integration of machine learning has enabled the early prediction of liver cirrhosis using historical datasets. The aim of this research is to create a machine learning model that utilizes artificial intelligence to forecast the occurrence of liver cirrhosis using supplied data. By employing hyperparameter tuning techniques to enhance accuracy, this research employs machine learning techniques like logistic regression and Gradient Boosted Classifier using XGBoost. Utilizing GridSearch within XGBoost, this research work achieves an impressive accuracy rate of 98%, accompanied by precision, recall, and F1-scores of 96%, 97%, and 96%, respectively. These findings hold promise for the advancement of more sophisticated and accurate liver cirrhosis prediction models, thereby benefiting researchers in the field. © 2024 IEEE.

关键词： Machine learning

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Detection of Alzheimer's Disease progression from Structural MRIs using ConvMixer based Deep Learning Model 3

Detection of Alzheimer's Disease progression from Structural...

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3rd International Conference on Machine Learning and Data engineering, ICMLDE 2024

作者： Lashkary, Mahak Bansal, Kanika Jain, Samyak Shukla, Praveen Kumar Tekchandani, Hitesh Dhaka, Vijaypal Singh School of Computing and Intelligent Systems Manipal University Jaipur Rajasthan Jaipur303007 India School of Computer Science and Engineering Manipal University Jaipur Rajasthan Jaipur303007 India Department of Artificial Intelligence and Data Science Koneru Lakshmaiah Education Foundation Telangana Hyderabad500075 India

Alzheimer's Disease (AD) is a progressive neurodegenerative disorder that significantly impacts cognitive functions and quality of life. Affecting over 50 million people worldwide, early and accurate detection of AD progression is crucial for timely intervention and management. This paper presents a reliable method for classifying Alzheimer's stages using Deep Learning based ConvMixer model on structural MRI data. The proposed deep learning based convmixer model significantly utilizes the patch based learning with depthwise separable convolution to achieve improved performance. The dataset used in this work has been extracted from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and comprises of three classes: Alzheimer's Disease (AD), Cognitive Impairment (CI), and Cognitively Normal (CN). The proposed approach demonstrated exceptional performance, achieving an accuracy of 99.88%, specificity of 99.87%, sensitivity of 99.90 and precision of 99.95%. The achieved results demonstrate the proposed model's accuracy in distinguishing Alzheimer's stages, making it a promising tool for early diagnosis and disease progression monitoring. © 2025 The Author(s).

关键词： Deep learning

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