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A Holistic Approach to Handling Metallic Waste for Environmental Sustainability 3

A Holistic Approach to Handling Metallic Waste for Environme...

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3rd International Conference for Advancement in Technology, ICONAT 2024

作者： Thangam Reddy, Itreddy Shyam Sundar Prakash, Kakarla Bhanu Reddy, Panyam Hemanth Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering India

ISBN: (纸本)9798350354171

Metal waste management includes the gathering, moving, handling, recycling, and getting rid of waste made of metal. Metallic waste comprises a wide range of materials, such as alloys, composites, and ferrous and nonferrous metals. Numerous activities, including industrial processes, manufacturing, construction, and demolition, can result in the production of metallic trash. Handling metallic waste improperly can contaminate the environment and be harmful to human health. Effective metal waste management strategies combine disposal, recycling, reuse, and trash reduction. Reducing the production of metallic waste at the source is the goal of waste reduction. Reuse is the process of finding new uses for metallic waste materials without reprocessing them. Recycling is the process of turning waste metal into new products, like alloys or new metal objects. Disposal is the controlled placement of metallic waste in landfills or other approved locations. © 2024 IEEE.

关键词： Proximity sensors

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Enhanced Art Style Classification with VGG16 and Machine Learning Techniques 5

Enhanced Art Style Classification with VGG16 and Machine Lea...

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5th IEEE Global Conference for Advancement in Technology, GCAT 2024

作者： Karthik, Garikipati Muppala, Tejaswi Vinitha Chowdary, A. Pati, Peeta Basa Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350376685

Art is an expression of human creativity and culture that has greatly impacted history. Classifying art styles is an important process in art and culture to preserve cultural heritage and facilitate educational and research purposes. The proposed work presents a comprehensive study that focuses on classifying 13 different art styles using deep learning techniques. The VGG16 convolutional neural network was employed for feature extraction using its convolutional and pooling layers up to the 'fc1' layer, generating 512 and 4096-dimensional feature vectors. To evaluate the classification performance, seven machine learning classifiers were utilized. The classification accuracy is evaluated before and after applying Principal Component Analysis (PCA), to reduce the dimensionality of the feature vectors. Additionally, the paper explored the direct classification by the VGG16 model. Results prove that deep feature learning with the combination of traditional classifiers of machine learning is efficient. © 2024 IEEE.

关键词： Convolutional neural networks

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Decentralized User Wallet: Transforming Digital Banking with Blockchain 8

Decentralized User Wallet: Transforming Digital Banking with...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Asmitha, M. Kavitha, C.R. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376425

Digital payment applications are now a crucial aspect of our everyday routines, completely changing how to handle money transactions. The use of blockchain technology is becoming more common in online payment systems, providing various advantages such as decentralization and trust, enhanced security, reduced costs, global accessibility, smart contracts that add to the importance of these applications in everyday living. These apps are accessible anytime and anywhere, offering users the flexibility to manage their finances at their convenience, regardless of banking hours. Blockchain data is stored in a series of blocks that are connected in order to apply guaranteed and secure records. This solution also involves the linking of each block represented as a hash, which is a digital signature of the block as well as the block that precedes it. Solving the analytical problem, it is feasible to notice that hashing, guaranteeing the formation of permanent fingerprints for each block, plays a very important role in blockchain. SHA-256 hashing algorithm is widely adopted in many blockchain applications due to the nature of the security in collision measurements. Hashing and blockchain designs help the enterprises to create reliable solutions. Since the web interface is applied to allow full access to the blockchain technology, the healthcare, supply chain management, and finance industries may now build confident systems. © 2024 IEEE.

关键词： Decentralized finance

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Deep Learning for Automated Air Quality Classification Using Image Data: A Comparative Study of GoogleNet and MobileNet 8

Deep Learning for Automated Air Quality Classification Using...

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8th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Thalapilly, Maanav Agasti, Ketone Kisor, G. Anjali, T. Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9798350367904

The following study contributes to the improvement of environmental monitoring by providing an approach for the estimation of air quality in real-time based on deep learning models GoogleNet and MobileNet. With either compromised realtime processing capability or spatial resolution in conventional approaches, these two models can serve as practical and scalable alternatives. Empirical results demonstrate that both GoogleNet and MobileNet can classify AQI levels effectively and transfer learning significantly improves the effectiveness and generalization of comparison for tiny datasets. Class labels include 'AQI-Good' to 'AQI-Hazardous/Severe' Pollution information collected at sites in cities of India and Nepal along with location, time, pollutant concentration. A detailed data collection enhances the capability of models to generate accurate predictions over a range of scenarios and furthers our knowledge of temporal and spatial variability of air quality. Evaluation criteria like recall, accuracy, precision, and F1score portray the stability and reliability of the method. These measures assure their steady performance in a variety of contexts and thus guaranteeing their applicability in real life. Hence, GoogleNet and MobileNet are a few significant developments in the real-time air quality monitoring process, which provides information on would be crucial in dealing with the problems associated with air pollution. © 2024 IEEE.

关键词： Data quality

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Sapota Quality: Bruise Detection and Shelf Life Prediction 5

Sapota Quality: Bruise Detection and Shelf Life Prediction

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Raviteja, Mellacheruvu Reddy, Vanguru Venkata Varun Kumar Aqibuddin, Shaik Soppa, Mithil Teja Sarath, S. Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9798350379945

The shelf life and consumer appeal of sapota, a tropical fruit, are influenced by fluctuations in its freshness and flavor over time. Apart from classifying bruises, this study looks into the application Using thermal imaging to estimate the shelf life and identify maturity of sapota. This research study intends to predict the shelf life of the sapota, maturity of the sapota and bruises detection for this we collected dataset using fluke thermal camera of 60 sapota fruits, each day for 5 days in order to capture the life cycle of sapota from unripe to ripen state. The weights are monitored for further analysis. Further, a seperate dataset is collected for bruise detection by intentionally damaging the fruit for bruise detection part. The dataset consists of ripen and unripen images together with the number of days that have passed since the photo was taken, along with weights on particular day. For these tasks, various convolutional neural networks CNN ResNet, VGG16 and MobileNet are used. In terms of ripeness classification, shelf life prediction and sapota bruise detection, the efficiencies of the models are assessed and accuracy we got is 93 percent for the best model. These findings suggest that the thermal imaging in combination with deep learning techniques for non-destructive quality Evaluation of sapota fruit may assist in reducing food waste. © 2024 IEEE.

关键词： Shelf life

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Auto-Scaling Anomaly Detection in Cloud computing Models using MAPE-K Loop 15

Auto-Scaling Anomaly Detection in Cloud Computing Models usi...

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

作者： Mahesh, Nikhil Supriya, M. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

computing paradigms are changing at a faster pace leading to highly complex and dynamic environments, thereby demanding resource management acumen. Pivoting in these paradigms, auto scaling ensures optimal resource allocation, sufficient to accommodate variable demands. However, the dynamic nature of such settings poses challenges in maintaining reliability and efficiency in auto-scaling methodologies. This paper proposal solves this challenge through a dependable anomaly detection system intended for auto-scaling in cloud computing frameworks. This work tries to design, develop, and validate the framework of anomaly detection for resource consumption and workload patterns using a MAPE-K loop. The envisioned framework is meant to use machine learning techniques and methodologies of data analysis to track in real time system parameters for the early identification of potential scalability issues. In terms of scores for all metrics, it has performed better with the MAPE-K loop, and the R2 score for all metrics came out to be 1. © 2024 IEEE.

关键词： Resource allocation

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Augmented Data Protection: Fortifying Confidentiality through CBBPH approach 15

Augmented Data Protection: Fortifying Confidentiality throug...

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

作者： Tripathy, Aayushi Jeeban Tiwari, Utkarsh Anirudh, S. Ashwani, Siddhant Kavitha, C.R. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350370249

The foundation of contemporary data security, guaranteeing integrity and confidentiality in digital communications, is referred to as cryptography. On the other side, image steganography strengthens data security by providing a stealthy way to hide information within seemingly same images. A new level of data camouflage is added by the tactile writing system Braille, whose small representation makes effective use of pixel components for storage. Columnar transposition is also applied to improve security by further obscuring the contained data. To improve data security, this paper presents the Columnar Binary Braille Pixel Hiding (CBBPH) approach, a revolutionary technology that combines Braille encoding, image steganography, encryption, and columnar transposition synergistically. The proposed method involves encoding plaintext messages into Braille, transposing them using columnar transposition, and then embedding them into the pixels in the least important portions of the image. The CBBPH strategy represents a significant breakthrough in data security techniques as experimental results show that it generates higher values in Peak Signal-to-Noise Ratio (PSNR) metric computations, indicating superior visual fidelity. ©2024 IEEE.

关键词： Steganography

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Detection and Classification of Respiratory Syndromes in Original and modified DCGAN Augmented Neonatal Infrared Datasets 5

Detection and Classification of Respiratory Syndromes in Ori...

引用

5th International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2024

作者： Sarath, S. Nair, Jyothisha J. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

In the current pandemic scenarios, a non-invasive method for determining a neonate's respiratory rate and categorizing them using a deep learning technique is highly pertinent. Acquiring an infrared neonatal dataset for detecting and classifying respiratory syndromes is challenging. The limited number of infrared videos and images representing different types of syndromes is a tremendous challenge to the accuracy of the deep learning model. This paper uses the Deep Convolutional Generative Adversarial Networks(DCGAN) with gradient penalty for the data augmentation. The Discriminator in a standard DCGAN architecture is a convolutional neural network (CNN) that receives an image as input and outputs a single scalar value that indicates the likelihood that the input image is real or fake. Adding a gradient penalty adds a regularisation term to the loss function. This modification helps to stabilize training by preventing mode collapse and generating higher-quality images. The augmented dataset helped to make the original imbalanced dataset more balanced and increased the size of the original dataset. When the accuracies of the deep learning models trained on the original and balanced augmented neonatal datasets were compared in this work, the model based on the balanced augmented dataset performed better. © 2024 The Authors. Published by Elsevier B.V.

关键词： Generative adversarial networks

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Cutting-Edge Steel Damage Classification Powered by Cloud Technology 15

Cutting-Edge Steel Damage Classification Powered by Cloud Te...

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

作者： Reddy, Pennabadi Devendra Reddy, K Satya Sampath Gnaneswarachary, P. Reddy, P. Hemanth Prashanth, Mrudula Rajagopal, Shinu M. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

In this era of rapid technological advancement, the detection of metal damage has become increasingly crucial across various industries. The proposed work 'Cutting-Edge Steel Damage Classification Powered by Cloud Technology' aims to build an advanced system for real-time steel damage classification utilizing AWS services includes Rekognition, Lambda, and API Gateway that enables users to upload multiple images simultaneously for damage detection and classification, which will be an innovative approach to industrial defects detection which leads to their prevention. Such benefits of fast, accurate and scalable defect detection make it a critical asset for businesses expecting for exact and real-time solutions in every aspect. This study uses F1 score, precision, recall, and assumed threshold are as evaluation parameters. The use of AWS services is not just helping with model training and deployment, but it also provides failover mechanism and scaling capabilities to accommodate the varying workload demands. The model has the advantage of having rapid training time: 1.005 hours this shows the cloud infrastructure scalability and computational efficiency in dealing with the tasks requiring machine learning of complex nature. © 2024 IEEE.

关键词： Amazon API Gateway Amazon S3 AWS Lambda AWS Rekognition Cloud computing Damage Classification Grazing Pitted Steel detection

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A Personalized Visual Communication App for Children with Autism 3

A Personalized Visual Communication App for Children with Au...

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3rd IEEE North Karnataka Subsection Flagship International Conference, NKCon 2024

作者： Karki, Ankit Namitha, Kalakunnath Singh, Kunal Agrawal, Ronak Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amritapuri India

ISBN: (纸本)9798350364569

Autism Spectrum Disorder (ASD) is a condition that impacts interaction, communication and behavior usually showing signs, around the age of two. This paper presents a novel mobile application developed using Flutter to help children with ASD improve cognitive and communication skills through personalized, AI-powered learning modules, interactive quizzes, and visual aids. The proposed app aims to empower individuals with autism, facilitate learning and social interactions and equip parents with tools for monitoring progress. = The application includes communication features, tailored educational modules and progress monitoring capabilities. It leverages Flutter for platform functionality ensuring accessibility on both Android and iOS devices. The user friendly interface, symbols, for communication and secure parent dashboard enhance usability. Initial assessments indicate increased user engagement improved communication skills and greater independence. With a technology that offers interactive forms of communication, personalized learning modules, and dynamic assessments to match a student's progress with their performance. This app is available for Android and iOS devices through Flutter, a technology that enables it to work in both platforms. The app is easily accessible by users due to its user-friendly layout, visual communication symbols, and secure parent dashboard. Preliminary evaluations indicate increased user participation, enhanced communication, and higher self-reliance. Compared to others apps, this one adopts a more broad approach, emphasizing communication, education, and life skills. © 2024 IEEE.

关键词： Vision aids

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