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1st IEEE International Conference on Electronics, Computing, Communication and Control technology, ICECCC 2024

作者： Singh, Sukhvir Rawat, Daksh Narang, Harshit Das, Purushottam Dhanalakshmi, R. Yadav, Satya Prakash Graphic Era Hill University Computer Science and Engineering Dehradun India Indian Institute of Information Technology Department of Computer Science and Engineering Tamil Nadu Tiruchirappalli India Department of Computer Science and Engineering Greater Noida201306 India

ISBN: (纸本)9798350371802

The COVID-19 pandemic has underscored the importance of preventive measures, with the use of face masks being a crucial aspect of controlling the virus's spread. Thus, in the response to the global imperative to curb the spread of infectious diseases, particularly in the context of the COVID-19 pandemic, this research paper delves into the realm of face mask detection and alert systems. With the ubiquity of technology, the development of automated solutions for ensuring adherence to mask-wearing protocols has become most necessary. This paper provides a thorough examination of existing methodologies for face mask detection, ranging from image& video-based approaches to sophisticated deep learning models. Additionally, explores the integration of alert systems into these detection frameworks, emphasizing real- time processing considerations and user interface design. The review incorporates case studies, ethical considerations, and privacy implications, offering a comprehensive overview of the current state of the art. By elucidating the challenges, successes, and future directions, this research paper contributes to the discourse on leveraging technology for public health measures and sets the stage for continued advancements in face mask detection systems and also provides a comprehensive review of the state-of-the-art in face mask detection technologies and explores the design. © 2024 IEEE.

关键词： COVID-19

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Deep Learning for Wind Speed Forecasting Using Bi-LSTM with Selected Features

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Intelligent Automation & Soft Computing 2023年第3期35卷 3829-3844页

作者： Siva Sankari Subbiah Senthil Kumar Paramasivan Karmel Arockiasamy Saminathan Senthivel Muthamilselvan Thangavel Department of Computer Science and Engineering Sreenivasa Institute of Technology and Management StudiesChittoor517127India School of Information Technology&Engineering Vellore Institute of TechnologyVellore632014India School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India School of Computing College of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathur603203India

Wind speed forecasting is important for wind energy *** the modern era,the increase in energy demand can be managed effectively by fore-casting the wind speed *** main objective of this research is to improve the performance of wind speed forecasting by handling uncertainty,the curse of dimensionality,overﬁtting and non-linearity *** curse of dimensionality and overﬁtting issues are handled by using Boruta feature *** uncertainty and the non-linearity issues are addressed by using the deep learning based Bi-directional Long Short Term Memory(Bi-LSTM).In this paper,Bi-LSTM with Boruta feature selection named BFS-Bi-LSTM is proposed to improve the performance of wind speed *** model identiﬁes relevant features for wind speed forecasting from the meteorological features using Boruta wrapper feature selection(BFS).Followed by Bi-LSTM predicts the wind speed by considering the wind speed from the past and future time *** proposed BFS-Bi-LSTM model is compared against Multilayer perceptron(MLP),MLP with Boruta(BFS-MLP),Long Short Term Memory(LSTM),LSTM with Boruta(BFS-LSTM)and Bi-LSTM in terms of Root Mean Square Error(RMSE),Mean Absolute Error(MAE),Mean Square Error(MSE)and *** BFS-Bi-LSTM surpassed other models by producing RMSE of 0.784,MAE of 0.530,MSE of 0.615 and R2 of *** experimental result shows that the BFS-Bi-LSTM produced better forecasting results compared to others.

关键词： Bi-directional long short term memory boruta feature selection deep learning machine learning wind speed forecasting

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RAPID-AV: Realtime Adaptive and Intelligent Detection For AV's Against DDoS Attacks 18

RAPID-AV: Realtime Adaptive and Intelligent Detection For AV...

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18th IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2024

作者： Kotha, Aditya Venkanna, U. International Institute of Information Technology Department of Computer Science and Engineering Naya Raipur India National Institute of Technology Department of Computer Science and Engineering Warangal India

ISBN: (纸本)9798350391725

Autonomous vehicles (AVs) increasingly rely on vehicle-to-everything (V2X) networks for communication. However, due to the devices' heterogeneity, they are more susceptible to attacks like distributed denial of service (DDoS), resulting in packet loss and transmission delays. Existing solutions to these issues often create bottlenecks and lack post-attack recovery mechanisms. To solve these problems, this paper proposes RAPID-AV, an ML-based approach that uses programmable P4 switches for real-time detection and mitigation of DDoS attacks in AV networks. The proposed system uses a threefold strategy: collect traffic statistics from AV, use P4-based programmable packet processing for anomaly detection, and use majority voting to mitigate attacks. In our experiments, we found that the decision tree algorithm achieves an accuracy of 94.33% in distinguishing traffic from DDoS traffic. In addition, RAPID-AV demonstrated a nearly 10% reduction in CPU load during attacks and maintained consistent communication between AV and RSU. This approach can help improve AV network security while addressing the limitations of existing solutions. © 2024 IEEE.

关键词： Network security

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SA-MSVM:Hybrid Heuristic Algorithm-based Feature Selection for Sentiment Analysis in Twitter

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computer Systems science & engineering 2023年第3期44卷 2439-2456页

作者： C.P.Thamil Selvi R.PushpaLaksmi Department of Computer science and Engineering Sri Ranganathar Institute of Engineering and TechnologyCoimbatoreTamilnadu641009India Department of Information and Technology PSNA College of Engineering and TechnologyDindigulTamilnadu624622India

One of the drastically growing and emerging research areas used in most information technology industries is Bigdata *** is created from social websites like Facebook,WhatsApp,Twitter,*** about products,persons,initiatives,political issues,research achievements,and entertainment are discussed on social *** unique data analytics method cannot be applied to various social websites since the data formats are *** approaches,techniques,and tools have been used for big data analytics,opinion mining,or sentiment analysis,but the accuracy is yet to be *** proposed work is motivated to do sentiment analysis on Twitter data for cloth products using Simulated Annealing incorporated with the Multiclass Support Vector Machine(SA-MSVM)***-MSVM is a hybrid heuristic approach for selecting and classifying text-based sentimental words following the Natural Language Processing(NLP)process applied on tweets extracted from the Twitter dataset.A simulated annealing algorithm searches for relevant features and selects and identifies sentimental terms that customers ***-MSVM is implemented,experimented with MATLAB,and the results are *** results concluded that SA-MSVM has more potential in sentiment analysis and classification than the existing Support Vector Machine(SVM)***-MSVM has obtained 96.34%accuracy in classifying the product review compared with the existing systems.

关键词： Bigdata analytics Twitter dataset for cloth product heuristic approaches sentiment analysis feature selection classification

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Narrative Paragraph Generation for Photo Stream Using Neural Networks 4th

Narrative Paragraph Generation for Photo Stream Using Neura...

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4th International Conference on Communication and Intelligent Systems, ICCIS 2022

作者： Anjali, M.N. More, Tejash Misa, Kumari Nath, Keshab Department of Computer Science and Engineering Indian Institute of Information Technology Kottayam Kerala Kottayam686635 India

ISBN: (纸本)9789819920990

Humans have the innate ability to perceive an image just by looking at it, for us images are not just a collection of objects but a network of interconnected object relationships. The problem arises when a machine tries to inspect an image, hence we try to convert image data to textual data. Despite major achievements in the image captioning field, there is a lack of models that provide concise captions of a given image;moreover, already existing models are so much bigger in size that the number of learning parameters is very high. The objective of this paper is to fill that gap;hence, we propose a model that incorporates an advanced deep convolution neural network to extract image features and an attention GRU with a local attention network to generate captions. We have also identified a class imbalance problem with Flickr dataset. We rectified this problem by adding images of some specific classes. The model has been trained on our improvised Flickr dataset. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Generative adversarial networks

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An Integrated Agricultural Financial Inclusion System Leveraging Supervised Machine Learning & Data Analytics 6

An Integrated Agricultural Financial Inclusion System Levera...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Rathi, Snehal Khedkar, Vijayshri Jadhav, Rutuja Kamate, Mugdha Potdar, Apurva Kamble, Prachi Computer Department Vishwakarma Institute of Information Technology Pune India Department of Computer Science & Engineering Symbiosis Institute of Technology Pune India

ISBN: (纸本)9798331522667

Agriculture is backbone of many economies but still farmers often face many challenges in accessing financial services or available resources for them, making informed crop choices and participating in government initiatives. Because of many farmers are not aware about all these things, to address these concerns, the proposed "Agricultural Financial Inclusion System" provides a comprehensive platform which integrates essential services with aim of improving farmers’ production and their financial awareness. Our system has a crop recommendation feature that helps farmers to choose the best suitable crop for their soil in the farm by taking climate and soil type into account. In order to make sure that farmers are aware about various available services/resources or any government schemes and able to take advantage of them, the platform also offers essential information on a variety of government initiatives, saving plans etc. By combining all these features, the Agricultural Financial Inclusion System encourage farmers to make smart choices which help them to enhance agricultural productivity and financial stability. In order to create more successful and sustainable agriculture sector, this integrated approach hopes to fill the gap between farmers and technology & resources they need. © 2025 IEEE.

关键词： Smart agriculture

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A Case Study on Context-Aware Neural Machine Translation with Multi-Task Learning 25

A Case Study on Context-Aware Neural Machine Translation wit...

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25th Annual Conference of the European Association for Machine Translation, EAMT 2024

作者： Appicharla, Ramakrishna Gain, Baban Pal, Santanu Ekbal, Asif Bhattacharyya, Pushpak Department of Computer Science and Engineering Indian Institute of Technology Patna India Wipro AI Lab45 London United Kingdom School of AI and Data Science Indian Institute of Technology Jodhpur India Department of Computer Science and Engineering Indian Institute of Technology Bombay India

ISBN: (纸本)9781068690709

In document-level neural machine translation (DocNMT), multi-encoder approaches are common in encoding context and source sentences. Recent studies (Li et al., 2020) have shown that the context encoder generates noise and makes the model robust to the choice of context. This paper further investigates this observation by explicitly modelling context encoding through multi-task learning (MTL) to make the model sensitive to the choice of context. We conduct experiments on cascade MTL architecture, which consists of one encoder and two decoders. Generation of the source from the context is considered an auxiliary task, and generation of the target from the source is the main task. We experimented with German-English language pairs on News, TED, and Europarl corpora. Evaluation results show that the proposed MTL approach performs better than concatenation-based and multi-encoder DocNMT models in low-resource settings and is sensitive to the choice of context. However, we observe that the MTL models are failing to generate the source from the context. These observations align with the previous studies, and this might suggest that the available document-level parallel corpora are not context-aware, and a robust sentence-level model can outperform the context-aware models. © 2024 The authors, © 2024 European Association for Machine Translation.

关键词： Neural machine translation

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Enhancing Accuracy of Handwritten Character Segmentation Using Region-Based Convolutional Neural Networks (R-CNN) 1

Enhancing Accuracy of Handwritten Character Segmentation Usi...

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1st International Conference on AIML-Applications for engineering and technology, ICAET 2025

作者： Varpe, Kanchan Sakhare, Sachin Joshi, Rahul Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Department of Computer Science and Engineering Pune India

ISBN: (纸本)9798350355611

Handwritten character segmentation plays a pivotal role in the performance of Optical Character Recognition (OCR) systems. This paper introduces an innovative approach to enhancing segmentation accuracy using Region-Based Convolutional Neural Networks (R-CNN). Traditional methods often struggle with issues such as varying character sizes, overlapping, and inconsistent writing styles, leading to suboptimal recognition results. The proposed R-CNN model addresses these challenges by effectively localizing and segmenting individual characters from handwritten text images. The methodology involves training the R-CNN on a comprehensive dataset of handwritten characters, annotated to capture diverse styles and complexities. The model leverages deep learning's feature extraction capabilities and region-based localization to accurately segment characters, even in challenging scenarios like connected or touching characters. Comparative experiments show that the proposed approach outperforms conventional techniques in terms of segmentation accuracy and robustness. Results demonstrate significant improvements, with the R-CNN model achieving higher precision and recall rates across multiple handwritten datasets, including different languages and scripts. This advancement holds promise for enhancing OCR systems used in applications such as automated document processing and digital archiving. Future work will focus on integrating this segmentation method with advanced recognition models to further boost overall handwritten text recognition performance. © 2025 IEEE.

关键词： Convolutional neural networks

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Web Scraping and Job Recommender System 2

Web Scraping and Job Recommender System

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2nd International Conference on Informatics, ICI 2023

作者： Sinha, Koustubh Sharma, Priyansh Sharma, Harshit Asawa, Krishna Jaypee Institute of Information Technology Computer Science Engineering Delhi India Jaypee Institute of Information Technology Computer Science Engineering Noida India

ISBN: (纸本)9798350343830

Web scraping is the process of autonomously obtaining data from webpages using computer software or tools. It has grown in popularity in recent years because of the volume of data available on the internet and the necessity for organizations and individuals to analyze and utilize this data to get the necessary and important information out of it for further analysis and application. Web scraping, together with automation tools, can create wonders. In this paper, we have focused on the latest web scraping techniques and their applications. One of the ideas is to assist those looking for employment on different firm job portals, which is discussed in this paper. Job tracking and scraping collect data from multiple platforms and are web-based tools that help job searchers locate the ideal job for them based on their categories and organize their job search more effectively. People are always on the lookout for a place where they can get their desired information so that they can easily apply for a job. After the collection of data based on user profile attributes, we recommend their success rate based on previous records and an appropriate machine learning model, and we also suggest what skills they can improve to land that job. Web scraping tools allow us to extract the necessary data from different websites in real time and present it to the user. We have discussed various ways to implement scraping and propose a job algorithm to process the extracted data and make it into meaningful information to help users land the best job. © 2023 IEEE.

关键词： Recommender systems

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Exploring Data as Quantum Bits: Analysis of Quantum Image Encoding Patterns for NISQ era

Exploring Data as Quantum Bits: Analysis of Quantum Image En...

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2023 IEEE International Conference on Quantum Technologies, Communications, Computing, Hardware and Embedded Systems Security, iQ-CCHESS 2023

作者： Gopakumar, Parvathy Rubell Marion Lincy, G. Indian Institute of Information Technology Kottayam Department of Computer Science and Engineering Kerala Pala Kottayam India

ISBN: (纸本)9798350314946

The generall efficiency of a computational system, be it simple or complex, presentation of the data plays a crucial role in getting the most out of it. Classical information theory, quantum mechanics, and computer science collectively influence Quantum information in comprehending how information can be encoded, transmitted, and modified using quantum systems. Quantum mechanics allows quantum information to incorporate a variety of properties, such as quantum encoding of classical information, storing coherent details in quantum superpositions, quantum computations and entanglement, which occurs when two or more qubits become highly correlated and share an unbreakable quantum state. There are numerous barriers and constraints in the broad field of quantum computation, ranging from dealing with Decoherence to ensuring error correction. Moreover, the qubit, a fundamental computational unit, is challenging to create, manipulate, and store. To ensure the quality of the Quantum Algorithms, the area of quantum data encoding has been thoroughly researched and explored as relevant results can be obtained by adequate encoding. In this paper, we reviewed the terminology and physical modeling of qubits, as well as the encoding approaches used to transform data from classical to quantum models. Furthermore, different encoding schemes for Quantum Image Processing were explored to determine the efficiency of quantum pixel representation(QPIXL) framework for effective application in NISQ era. © 2023 IEEE.

关键词： Qubits

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