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International Conference on Inventive Computation Technologies (ICICT)

作者： Shashank Patel Mudita Pandey Rajeswari D Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (数字)9798350359299

ISBN: (纸本)9798350359305

Skin cancer is a predominant and possibly lethal condition that distresses people across the world. Primary detection and precise finding are crucial for leveraging efficacious treatment and enhanced patient consequences. This research study explores the application off Artificial Intelligence (AI) resolutions for the primary finding and analysis of skin cancer. The study leverages a dataset of dermatological images and employs various ML methods, including Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and decision trees, to analyse the existence of skin cancer with a higher degree of accuracy. By utilizing a combination of image processing and feature extraction, the AI model demonstrates higher performance in classifying skin lesions into malignant or benign categories. The proposed model has achieved an accuracy rate of 98.52%, making the AI-based system a promising tool for dermatologists and healthcare professionals.

关键词： Support vector machines Technological innovation Dermatology Transfer learning Collaboration Medical services Skin

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Experimental Analysis on IVPM Implementation for Image Processing Workflows

Experimental Analysis on IVPM Implementation for Image Proce...

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Advances in computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： Sidhant Sharma Md Anas Hasan Abhivansh Bhatnagar Valadri Pardhavan Reddy K. Sornalakshmi Department of Data Science and Business Systems School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

IPVM (Interplanetary Virtual Machine) is a versatile protocol, an extension of IPFS capable of handling a diverse range of computations, spanning from small-scale to large-scale tasks. It plays a pivotal role in driving the transition towards open decentralized computing infrastructure, shifting from conventional centralized cloud services. By making use of IPFS content addressing, IPVM streamlines computation processes, facilitating distributed scheduling, caching, memoization, and adaptive optimization. This exploration highlights the emergence of a decentralized ecosystem promising enhanced resilience, scalability, and inclusivity in computing frameworks.

关键词： Processor scheduling Scalability Image processing Ecosystems Streaming media InterPlanetary File System Task analysis

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Disease Detection in Arecanut using Convolutional Neural Network

Disease Detection in Arecanut using Convolutional Neural Net...

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Advances in computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： V Arun Karthik Jatin Shivaprakash D Rajeswari Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

Arecanut, also known as betel nut, is a tropical crop predominantly grown in India. The country holds the second position globally in terms of the production and consumption of arecanut. Throughout its life cycle, the areca nut plant is susceptible to a variety of diseases affecting its roots, trunk, leaves, and fruits. Some of these diseases are visible to the naked eye, while others are not. Farmers traditionally analyze every crop to detect any signs of disease, a process that is extremely *** this study, we propose an automatic system that aids in detecting the diseases of arecanut leaves using Convolutional Neural Networks (CNN) and deep learning algorithms. A Convolutional Neural Network is a Deep Learning algorithm that takes an image as input, assigns learnable weights and biases to various features in the image, and then learns from the results to distinguish one feature from another. This approach significantly reduces the manual effort required in disease detection and increases the efficiency of the process. A datset is developed with many photos of healthy and sick arecanut leaves in order to train and evaluate the CNN model. An 80:20 split of the dataset was made into training and testing data. The goal of training the model over multiple epochs was to maximize validation and test accuracy while minimizing loss. It was discovered that the suggested method for diagnosing arecanut illnesses was precise and successful. This study contributes to the ongoing efforts to leverage technology in agriculture, with the aim of improving crop health and productivity. This study indicate that the proposed system can accurately identify diseases in arecanut leaves with a good and best accuracy. The system was able to distinguish between healthy and diseased leaves, and suggest appropriate remedies for the detected diseases. These findings demonstrate the potential of using Convolutional Neural Networks and deep learning algorithms in the field of agriculture, par

关键词： Deep learning Training Productivity Plant diseases Accuracy Crops Agriculture

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Music Recommendation based on Facial Expressions using data Augmentation

Music Recommendation based on Facial Expressions using Data ...

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International Conference on Innovative Mechanisms for Industry Applications (ICIMIA)

作者： Tanisha Kapoor Arnaja Ganguly D Rajeswari Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India

Emotion recognition from facial expressions plays a pivotal role in human-computer interaction, psychology, marketing, and various other domains. This research paper presents a comprehensive study and implementation of an automated emotion recognition system based on deep learning techniques. The project addresses the challenge of accurately classifying a wide range of emotions from facial images in real-time. Leveraging Convolutional Neural Networks (CNNs) and modern deep learning approaches, the system demonstrates high accuracy in identifying emotions, including happiness, sadness, anger, and more. This project used the image dataset FER-2013 for the mood prediction portion of the project, and Spotify music data to predict emotions for music recommendation. Both the datasets utilized are freely available on Kaggle. The proposed system can achieve an accuracy of 91.23% in predicting the users’ emotion based on their facial expressions.

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Enhancing face recognition performance: a comprehensive evaluation of deep learning models and a novel ensemble approach with hyperparameter tuning

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Soft computing 2024年第20期28卷 12399-12424页

作者： Selvaganesan, Jana Sudharani, B. Shekhar, S. N. Chandra Vaishnavi, K. Priyadarsini, K. Raju, K. Srujan Rao, T. Srinivasa Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R & ampD Institute of Science and Technology Avadi Tamil Nadu Chennai600062 India Guntur Dist. Andhra Pradesh Vadlamudi India Department of Electronics and Communication Engineering Sreenidhi Institute of Science and Technology Telangana Hyderabad501301 India Department of Computer Science and Engineering Sona College of Technology Tamil Nadu Salem636005 India Department of Data Science and Business Systems School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India Department of Computer Science and Engineering CMR Technical Campus Medchal Telangana Hyderabad501401 India Department of Electronics and Communication Engineering Aditya Engineering College Surampalem533437 India

In response to growing security concerns and the increasing demand for face recognition (FR) technology in various sectors, this research explores the application of deep learning techniques, specifically pre-trained Convolutional Neural Network (CNN) models, in the field of FR. The study harnesses the power of five pre-trained CNN models—DenseNet201, ResNet152V2, MobileNetV2, SeResNeXt, and Xception—for robust feature extraction, followed by SoftMax classification. A novel weighted average ensemble model, meticulously optimized through a grid search technique, is introduced to augment feature extraction and classification efficacy. Emphasizing the significance of robust data pre-processing, encompassing resizing, data augmentation, splitting, and normalization, the research endeavors to fortify the reliability of FR systems. Methodologically, the study systematically investigates hyperparameters across deep learning models, fine-tuning network depth, learning rate, activation functions, and optimization methods. Comprehensive evaluations unfold across diverse datasets to discern the effectiveness of the proposed models. Key contributions of this work encompass the utilization of pre-trained CNN models for feature extraction, extensive evaluation across multiple datasets, the introduction of a weighted average ensemble model, emphasis on robust data pre-processing, systematic hyperparameter tuning, and the utilization of comprehensive evaluation metrics. The results, meticulously analyzed, unveil the superior performance of the proposed method, consistently outshining alternative models across pivotal metrics, including Recall, Precision, F1 Score, Matthews Correlation Coefficient (MCC), and Accuracy. Notably, the proposed method attains an exceptional accuracy of 99.48% on the labeled faces in the wild (LFW) dataset, surpassing erstwhile state-of-the-art benchmarks. This research represents a significant stride in FR technology, furnishing a dependable and accurate

关键词： Convolutional neural networks

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Deepfake Detection System

Deepfake Detection System

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Networking and Communications (ICNWC), International Conference on

作者： Shaswat Shrivas Aditya Rai M. Lakshmi Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (数字)9798350365269

ISBN: (纸本)9798350365276

Detecting deepfake content presents a formidable challenge, necessitating advanced methodologies. This paper proposes a holistic strategy employing Facenet_pytorch, MTCNN, and InceptionResnetV1 for robust deepfake detection. Facenet_pytorch serves as the cornerstone for facial feature recognition, while MTCNN efficiently identifies faces in images during preprocessing. InceptionResnetV1 scrutinizes visual details to detect subtle anomalies indicative of manipulation, such as unnatural facial expressions and incongruent lip synchronization. Our approach underscores the importance of maintaining a delicate balance between accurate detection and individual privacy rights. By leveraging these advanced models, we achieve significant strides in differentiating manipulated content from authentic media, contributing to the ethical deployment of deepfake detection technologies.

关键词： Deepfakes Visualization Social networking (online) Law enforcement Forensics Entertainment industry Biodiversity

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Speech Emotion Recognition Using data Augmentation

Speech Emotion Recognition Using Data Augmentation

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Advances in computing, Communication and Applied Informatics (ACCAI), International Conference on

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

Speech emotion recognition (SER) is a vital aspect of human-computer interaction systems, enabling applications such as virtual assistants, affective computing, and mental health monitoring. In this work, deep learning methods to audio data to suggest a fresh approach to SER are applied. While still in its infancy, CNN is capable of accurately identifying emotions in human speech, but it requires careful study in order to produce reliable *** studywas done using a heterogeneous dataset that included recordings of different emotional states from many sources, such as TESS, RAVDESS, SAVEE, CREMAD, and others. A deep neural network model was built with many fully connected layers and ReLU activation functions after preprocessing and feature extraction. The suggested method’ s effectiveness was assessed using common performance indicators like F1-score, recall, accuracy, and precision. Our tests produced encouraging outcomes, with an accuracy of 84% on the test dataset. This demonstrates how well the suggested technique works to discern emotions from speech audio information.

关键词： Emotion recognition Accuracy Virtual assistants Speech recognition Mental health Feature extraction Recording

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Detection and Classification of Skin Cancer by Using a Parallel Deconvolutional Network Model 1

Detection and Classification of Skin Cancer by Using a Paral...

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1st IEEE International Interdisciplinary Humanitarian Conference for Sustainability, IIHC 2022

作者： Sangeetha, M. Karthikeyini, C. Vasundhara, S. Saravanan, D. Arunkumar, G. School of Computing SRMIST Department of Data Science and Business Systems Kattankulathur C India Excel Engineering College Department of ECE Tamil Nadu Komarapalayam India G.Narayanamma Institute of Technology &science Department of Humanities & Mathematics JNTUH Hyderabad India IFET College of Engineering Department of CSE Tamil Nadu Villupuram India Madanapalle Institute of Technology & Science Department of CSE Andhra Pradesh Madanapalle India

ISBN: (纸本)9781665456876

Skin Cancer is one of the most common cancer forms in many countries, it is considered to be one of the dangerous types in the sense that it is lethal and its occurrence over time has been dramatically high. It is one of the deadliest cancers among all diseases and has a large rate of mortality. The efficiency of the earlier approaches to assess one of the most hazardous melanoma diagnosis in dermoscopic criteria are not up to the mark. Therefore, in this research, the work has been carried out in three stages in order to detect melanoma in an efficient manner. In the first stage, prior to the implementation of the image segmentation technique, noise elimination and pre-processing steps are carried out to remove the noise and to achieve better execution results. This segmentation model focuses on the separation of the interesting portions from the background and collects the necessary information from the neighboring pixels of the same category. Gaussian analytical patterns are used to handle the heterogeneous regions/sections of dermoscopy images whose mean and variance can be dynamic. In addition, the third stage of work is based on the texture classification, which is proposed as the CSTC-Mel Identification Model. © 2022 IEEE.

关键词： Machine learning

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Fraud Detection in Financial Transactions: A Machine Learning Approach

Fraud Detection in Financial Transactions: A Machine Learnin...

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International Conference on science Technology Engineering and Management (ICONSTEM)

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365108

Financial fraud presents substantial risks to individuals and financial institutions globally, necessitating efficient detection mechanisms to mitigate probable fatalities. In this study, the development and evaluation of machine learning (ML) models for detecting fraudulent activities in mobile money transactions are investigated. Using a synthetic dataset, realworld transaction scenarios involving a variety of transaction kinds and features are simulated. The effectiveness of many machine learning (ML) methods, such as LGBM, random forests, XGBoost, and logistic regression, in spotting fraudulent transactions is investigated through data preparation, feature engineering, and model-building procedures. Employing techniques such as SMOTE-Tomek resampling and hyperparameter tuning enhances the performance and robustness of our models. Our results reveal that the XGBoost classifier emerges as the top-performing model, exhibiting a remarkable accuracy of 99.95%. The findings underscore the potential of MLbased approaches to bolster security and trust in mobile financial services, contributing to the ongoing advancement of fraud detection methodologies in the financial sector.

关键词： Analytical models Accuracy Computational modeling Finance data models Fraud Security

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Neuroimaging Precision: Crafting a Stacked VGG19-Logistic Fusion for Breakthrough Early Detection of Alzheimer's

Neuroimaging Precision: Crafting a Stacked VGG19-Logistic Fu...

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International Conference on science Technology Engineering and Management (ICONSTEM)

作者： G. Anbu Selvi T. Karthick Department of Computer Science and Engineering SRMIST Kattankulathur Chennai India Department of Data Science and Business Systems SRMIST Kattankulathur School of Computing Chennai India

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365108

Several medical investigations have demonstrated that Alzheimer's disease (AD) manifests itself long before the formal diagnosis of dementia. These studies have led to the identification of numerous ideal biomarkers associated with Alzheimer's symptoms. Consequently, the imperative for early diagnosis has become evident, necessitating a high-performance computational tool capable of efficiently processing vast amounts of data. Early detection of AD not only offers valuable insights into the condition's progression but also presents a significant opportunity to benefit from timely and effective treatment. This ground-breaking study pioneers an innovative approach to the early detection and diagnosis of AD by crafting a sophisticated model SVGGL, which blends the power of stacked VGG19 neural networks with the interpretability of logistic regression. Leveraging neuro-imaging data, specifically the insights provided MRI, and our model aims to discern subtle structural and functional alterations in the brains of individuals at risk for or in the early stages of AD. The combination of feature extraction from stacked VGG19 models and subsequent logistic regression classification yields a precise and efficient classifier with an outstanding accuracy of 98.24%. With such a high degree of accuracy, the model can reliably differentiate among healthy people and those with Alzheimer's, opening the door to potential early intervention and individualized treatment plans. The study not only sheds light on the intricate neuro pathological landscape of AD but also propels us toward a future where cutting-edge technology and data-driven insights, exemplified by our high-accuracy model, converge to tackle the challenges posed by this prevalent neurodegenerative disorder.

关键词： Neuroimaging Logistic regression Accuracy Computational modeling Brain modeling Feature extraction data models

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