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computing for Sustainability and Intelligent Future (COMP-SIF), International Conference on

作者： Srigiri Sruthi Deivanai Saravanan Jyotsna C. Dept. of Computer Science Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798331538538

ISBN: (纸本)9798331538545

In the rapidly evolving cryptocurrency markets, accurate price prediction is crucial for investors and traders. This task investigates the efficacy of advanced machine learning and deep learning for forecasting cryptocurrency prices. The proposed idea utilizes Autoregressive Integrated Moving Average (ARIMA) models, Bidirectional Long Short-Term Memory (Bi- LSTM) networks, Gated Recurrent Units (GRU), and Long Short-Term Memory networks improved with Self-Attention (LSTM-Self-Attention). Numerous studies were conducted using the historical data available. Root Mean Squared Error (RMSE) were used to evaluate the models. The study tries to show the impact of outside factors such as economic data and market belief in purchase to improve the precision of the forecast. This research offers investors and policy makers insight that demonstrates the ability of sophisticated machine learning models to navigate the intricacies of cryptocurrency marketplaces. Incorporating these approaches into a single predictive framework enables accurate forecasting and the identification of anomalies in DeFi markets. The findings contribute to the decision making that is strategic DeFi investments, providing tools for better prediction and analysis, and laying the groundwork for future advancements in DeFi analytics.

关键词： Adaptation models Accuracy Biological system modeling Computational modeling Predictive models Data models Cryptocurrency Forecasting Sustainable development Long short term memory

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An IoT Assisted Weather Prediction and Information Monitoring Scheme based on Intensive Learning Strategy

An IoT Assisted Weather Prediction and Information Monitorin...

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

作者： Sonia Jenifer Rayen Minu Susan Jacob S. Nivetha Lekshmi S Raveendran J. Sarojini Premalatha S. Scinthia Clarinda Department of Computer Science and Engineering School of Computing Sathyabama Institute of Science and Technology Chennai

Several of the environmental characteristics we hold dear, including our fundamental ecology, regional climatic variances, and global variety, are threatened caused by widespread industry and increasing urbanization. Problems arise in our everyday lives as a consequence of the challenges we face due of the rapid pace at which things are changing. These problems have their origins in the rapid pace of digitalization and the lack of enough infrastructure for handling and analyzing extremely large data sets. When data generated by the IoT detection layer is inaccurate, partial, or irrelevant, it affects weather forecast reports, which in turn disrupts activities that rely on precise and reliable weather predictions. Some of the main problems in the modern world is getting accurate readings of the surroundings in real time, as doing so presents a number of obstacles. The proposed technology could avoid such events from occurring by constantly monitoring the weather. The suggested solution is built on the cutting edge of embedded system technology and the IoT. Electronic components, sensor nodes, and wireless infrastructure are also part of the system. The primary function of the sensors in this framework is to monitor environmental factors such as temperature, humidity, and gas concentrations. Data collected in this way can be transmitted to external systems, such as databases. Random Forest, Logistic Regression, AdaBoost, and Naive Bayes were all used to compare their respective identification of anomalies scores. These algorithms were applied to the time-based, temperature-based, pressure-based, and humidity-based data produced by the sensors.

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Calligraphy Alphabet Perception Using Artificial Intelligence

Calligraphy Alphabet Perception Using Artificial Intelligenc...

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International Conference on science Technology engineering and Management (ICONSTEM)

作者： Venkatesh S D. Jeevitha J. Anitha Gnanaselvi Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Chennai Department of Computer Science and Engineering Jeppiaar Engineering College Chennai

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365108

The human brain has a simple time analyzing and processing images. The brain is able to rapidly deconstruct and distinguish an image's various components when the eye perceives it. With the Convolutional Neural Network (CNN) as its foundation, this research suggests deep learning conceptual models. When the algorithms are compared, it becomes clear that CNN-based classification of handwritten alphabets performs better than other algorithms in terms of accuracy. The Manual Net, Alex Net, and LeNet Architectures are among the CNN algorithms employed in this research. The convulational layer, max pooling, flattening, feature assortment, rectifier lined unit, and completely linked softmaxx layers are each components of the aforementioned designs. The proposed network is tested using an image dataset comprising 530 training photos and 2756 testing images. The top precision and cost-efficient model will be used in the Django context to build a handler line for supplying the appeal to be recognized and obtaining the productivity outcome of recognized appeal.

关键词： Training Productivity Accuracy Rectifiers User interfaces Mathematical models Classification algorithms

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Confidence-Guided Feature Alignment for Cloth-Changing Person Re-identification 27th

Confidence-Guided Feature Alignment for Cloth-Changing Pers...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Huang, Sirong Zheng, Huicheng School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangzhou China Guangdong Province Key Laboratory of Information Security Technology Guangzhou China

ISBN: (纸本)9783031781094

Much progress has been made in the field of person re-identification, but changes in clothing have hindered the practical application of long-term person re-identification. Cloth-changing person re-identification (CC-ReID) aims to address this problem, with the main challenge being the extraction of discriminative features unrelated to clothing. Existing methods, which mainly focus on introducing clothing-irrelevant cues such as key points, contours, and 3D shapes, require additional modules for feature extraction, resulting in increased complexity and potential inaccuracies due to dependence on the performance of external models. Few studies directly use the original RGB images to make the model constantly focus on clothing-independent information. In this paper, we propose a Confidence-Guided Feature Alignment Network (CGFA) for CC-ReID. Specifically, we design a confidence module that automatically learns to make confidence adjustments to fine-grained information, prompting the model to mine clothing-independent discriminative features without introducing other modal cues. By transferring knowledge, we encourage the model to learn discriminative identity features that are independent of clothing bias. As a result, the confidence module can be removed during the inference phase. The proposed simple but efficient method uses only RGB modality without additional cues, and can serve as a powerful baseline for CC-ReID to drive future research. Extensive experiments on the CC-ReID datasets demonstrate the effectiveness of the proposed method, which achieves state-of-the-art performance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Feature extraction

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Implementing Real-Time Two-Tier Auctions in the Fashion Industry: A Blockchain-Integrated Full-Stack Approach

Implementing Real-Time Two-Tier Auctions in the Fashion Indu...

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International Conference on Computational Innovations and engineering Sustainability (ICCIES)

作者： Gurupriya M Gayathri R Hareni M D Dhanush Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798331536695

ISBN: (纸本)9798331536701

Implementing an online auction platform with two primary auction types—Standard Auctions and Sealed Bidding Auctions—is the aim of this project. It is a cutting-edge technology that uses Prisma to organize database activities, *** to give a fully responsive user interface, and MongoDB Atlas for scalable database management. Clerk authentication has been enabled for a secure user interaction in login and access by roles. It also incorporates a powerful dashboard to manage products as well as set auction parameters such as type and duration and oversee all bidding activities for admins. Auctions are classified as Men, Women, and Children, and the two auction types would be permissible for that category. A great feature included is the blockchain ledger in which the bid information is stored since that offers transparency, security, and immutability to the whole bidding process. This makes an organization and consumer orientation into a very contemporary option for traditional auction systems, through a better user experience and data integrity.

关键词： Access control Technological innovation Databases Standards organizations Authentication User interfaces User experience Real-time systems Blockchains Sustainable development

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Improving Energy Efficiency using Colliding Bodies Optimization based Routing Protocol for Wireless Sensor Networks 2

Improving Energy Efficiency using Colliding Bodies Optimizat...

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2nd International Conference on Electronics and Renewable Systems, ICEARS 2023

作者： Prabakaran, P. Shalini, R. Hariharan, N. Marieswari, S. Kumar, Kallakunta Ravi Apoorva, Maturi Gayatri Lakshmi Vels Institute of Science Technology and Advanced Studies School of Computing Sciences Department of Computer Applications Chennai India Vels Institute of Science Technology and Advanced Studies School of Computing Sciences Department of Bca and It Chennai India R.M.K. College of Engineering and Technology Science and Humanities - Eee Thiruvallur601206 India P. S. R. Engineering College Biomedical Engineering India Koneru Lakshmaiah Education Foundation Department of Ece Vaddeswaram India Amrita School of Engineering Ece Department India

ISBN: (纸本)9798350346640

Wireless sensor network (WSN) contains millions of small, low-power gadgets with a lack of resources are memory and battery power. Such gadgets were deployed in a dispersed manner and were often utilized to monitor and sense applications. Owing to constraints and limited resources of WSNs, routing can be a great difficulty. Routing in WSN was the process of choosing the optimal path for data to travel from a source node to a destination node. The goal of routing in WSN offer dependable and effective transmission while minimizing network overhead and energy consumption. This study designs an Energy Efficient Colliding Bodies Optimization based Routing Protocol (EECBO-RP) for WSN. The end goal of the EECBO-RP algorithm lies in the optimal election of routes to a destination. For achieving reasonable results for the minimization of energy exploitation, the EECBO-RP system derives the fitness function using the following variables: residual energy, distance to BS, and node degree. The EECBO-RP technique chooses the relay nodes so that a way that the overall efficiency of the WSN gets maximized. The simulation values of the EECBO-RP system are tested under several dimensions and the outcomes pointed out the betterment of the EECBO-RP technique. © 2023 IEEE.

关键词： Energy efficiency

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Review on Early Prediction of Body Dysmorphic Disorder Using Machine Learning

Review on Early Prediction of Body Dysmorphic Disorder Using...

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International Conference on Smart Technologies For Smart Nation (SmartTechCon)

作者： Harnit Saini Sanjeev Kumar Prasad Department of Computer Science and Engineering Ajay Kumar Garg Engineering College School of Computing Science of Engineering Galgotias University Ghaziabad India School of Computing Science of Engineering Galgotias University Greater Noida India

Body Dysmorphic Disorder (BDD) is a psychiatric condition characterized by excessive preoccupation with perceived defects in one's appearance, often leading to distress and functional impairment. Early prediction of BDD is crucial for timely intervention and improved treatment outcomes. In recent years, machine learning techniques have shown promise in identifying individuals at risk of developing BDD based on various data sources, including facial images, clinical information, and textual data. This review aims to provide an overview of the current state of research on the early prediction of BDD using machine learning, highlighting the methodologies, challenges, and future directions in this emerging field. We conducted a comprehensive literature search and identified relevant studies that employed machine learning algorithms for BDD prediction. The reviewed studies demonstrated the effectiveness of machine learning techniques such as support vector machines, deep learning models, ensemble learning, natural language processing, feature selection and extraction, and clustering in predicting BDD. Despite the promising results, several challenges need to be addressed, including the availability of large and diverse datasets, standardization of evaluation metrics, and interpretability of the models. Future research should focus on refining the existing approaches, integrating multiple data modalities, and developing user-friendly tools for clinical practice. Early prediction of BDD using machine learning holds great potential for improving the identification and intervention of individuals at risk, ultimately leading to better outcomes and quality of life for individuals with BDD.

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A Comprehensive End-to-End Solution for Web Security with Cryptography, Multi-Factor Authentication, and Secure Communication

A Comprehensive End-to-End Solution for Web Security with Cr...

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： Kanderi Johith Kumar Kandlapalli Aravind Sai Adapala Dharshan Reddy Chinthamreddy Pranay Daiwik Reddy Shinu M Rajagopal Dept. of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

In today's digital landscape, securing sensitive information has become an essential priority in the modern digital era for both individuals and organizations. This project introduces a comprehensive web application that integrates advanced encryption and decryption functionalities alongside multifactor authentication (MFA) and secure payment processing. The application supports various cryptographic algorithms, including AES, DES, Triple DES, Blowfish, etc., allowing users to securely encrypt and decrypt data through dedicated routes accessed over secure HTTPS connections. To further enhance security, robust multi-factor authentication mechanisms, such as OTP-based challenge-response and QR-code-enabled 2FA, have been implemented to prevent unauthorized access to sensitive operations. The platform also incorporates PGP encryption powered by Keybase, enabling users to securely exchange encrypted emails. Additionally, a secure payment gateway is integrated using Stripe, ensuring sensitive payment data is encrypted and handled safely. With a strong focus on data integrity, user privacy, and modern web security standards, this project delivers an all-encompassing solution for safeguarding sensitive information and facilitating secure communication. By addressing common vulnerabilities and security risks, the platform stands as a reliable and scalable tool for data protection.

关键词： Multi-factor authentication Standards organizations Passwords Organizations Logic gates HTTP Tokenization Encryption Reliability Internet of Things

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An efficient and balanced feature pyramid network: shared-FPN

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Optoelectronics Letters 2025年

作者： Tian Sulong Qin Longfei Pang Wenchao Gao Ying Zhao Dexin School of Computer Science and Engineering， Tianjin University of Technology Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology， Tianjin University of Technology

In the field of object detection， it is challenging to achieve a balance between neck complexity and accuracy. To address this issue， we propose an efficient and decoupled neck module called Shared-FPN(Shared Feature Pyramid Network). Our design aims to effectively address the trade-off between these two factors. Neck modules are crucial for effectively fusing multi-scale information in neural network models. However， traditional neck designs， with their single structure， have struggled to significantly improve detection performance. In recent years， the incorporation of neural architecture search techniques in neck design has led to an increase in model complexity. This trade-off between model complexity and detection performance highlights the need for more efficient neck design approaches. Not only does Shared-FPN not increase the number of model parameters and FLOPs， but it also can be easily ported to any of the detection models. It improves on PAFPN by using transposed convolution with a large convolution kernel as the upsampling module， designing spatial pyramidal pooling-fast downsampling(SPPFD) based on shared pooling， and designing shared convolution as the right part module. To evaluate the performance of Shared-FPN in object detection tasks， we conducted experiments on object detection datasets. The results show that Shared-FPN achieved excellent performance across all sizes. In particular， on the VOC 2012 dataset， which contains mostly large objects， Shared-FPN's mAP was improved by 11.2% compared to FPN with YOLOX-s as the detector. On the COCO dataset， which contains mostly small objects， Shared-FPN's mAP was improved by 7.8% compared to FPN and 7.1% compared to PAFPN with Faster RCNN as the detector. Shared-FPN can be easily inserted into any of the detectors for better performance in various scenarios， such as small， medium or large objects.

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Meta Learning for Complexity-Aware Optimal Sampling in Few-Shot Intent Classification 8

Meta Learning for Complexity-Aware Optimal Sampling in Few-S...

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8th International Conference on Trends in Electronics and Informatics, ICOEI 2025

作者： Krishnendu, S. Venugopalan, Manju Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering India

ISBN: (纸本)9798331544607

Intent classification plays a crucial role in applications such as virtual assistants and chatbots, enabling an accurate determination of the user's intention and providing relevant responses. Minimal supervision models that train from minimal labeled data have become important since they can train models using less amount of data as manually labeling data is time-consuming. To address this problem, the proposed model utilizes a few-shot learning mechanism approach using a metalearning architecture for intent classification. Model-Agnostic Meta Learning model is implemented to enhance generalization given the scenario of constrained labeled samples. Model Agnostic Meta Learning is tested using uniform and complexity aware optimal sampling based methods to assess the impact of data imbalance and robustness of the model. The experimental results on the Banking77 dataset show that the Meta Agnostic Meta Learning architectures in a complexity aware non-uniform sampling setting are able to achieve 100% of the results achieved by full train models when integrated with ML models. DL models on the other hand did not report a considerable enhancement when migrated from a uniform to a non-uniform sampling setting. © 2025 IEEE.

关键词： Zero-shot learning

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