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Recognition of Handwritten Words from Digital Writing Pad Using MMU-SNet

Intelligent Automation & Soft Computing 2023年第6期36卷 3551-3564页

作者： V.Jayanthi S.Thenmalar Department of Computer Science and Engineering SRM Institute of Science and TechnologyKattankulathurTamilnaduIndia

In this paper,Modified Multi-scale Segmentation Network(MMU-SNet)method is proposed for Tamil text *** texts from digi-tal writing pad notes are used for text *** words recognition for texts written from digital writing pad through text file conversion are challen-ging due to stylus pressure,writing on glass frictionless surfaces,and being less skilled in short writing,alphabet size,style,carved symbols,and orientation angle *** pressure on the pad changes the words in the Tamil language alphabet because the Tamil alphabets have a smaller number of lines,angles,curves,and *** small change in dots,curves,and bends in the Tamil alphabet leads to error in recognition and changes the meaning of the words because of wrong alphabet ***,handwritten English word recognition and conversion of text files from a digital writing pad are performed through various algorithms such as Support Vector Machine(SVM),Kohonen Neural Network(KNN),and Convolutional Neural Network(CNN)for offline and online alphabet *** proposed algorithms are compared with above algorithms for Tamil word *** proposed MMU-SNet method has achieved good accuracy in predicting text,about 96.8%compared to other traditional CNN algorithms.

关键词： Digital handwritten writing pad tamil text recognition syllable dialect

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Application of Digital Interactive Experience System for Cultural and Tourism based on Augmented Reality with YOLOv3

Application of Digital Interactive Experience System for Cul...

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2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Liang, Dan Changchun Institute of Technology Department of Computer Technology and Engineering Changchun China

ISBN: (纸本)9798350360660

Now a day's tourism industry in Asia is more than ever being modernized by innovative technologies, an undisputable reality that was converting the sector in different ways. To create a memorable experience there were no embracing innovations and feedback mechanism are there for better engagement. In this paper, the proposed Augmented Realities with You Only Look Once (AR-YOLOv3) detects any cultural and tourism objects with marker less AR through an interactive application. Initially, the user and admin login to the web-based system and the details are saved to database for further accessibility. The scale invariant feature transform method is used to extract Scale Invariant Feature Transform (SIFT) features from the image. To achieve better performance the SIFT features are divided into threads by using multithreaded approach. These thread frames are given as input to YOLOv3 algorithm for a real-time object detection. The simulation setup for proposed AR-YOLOv3 achieved a better results including accuracy 85.83%, precision 87.29% and recall 88.49%. © 2024 IEEE.

关键词： Leisure

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A Unified Multi-Domain Face Normalization Framework for Cross-domain Prototype Learning and Heterogeneous Face Recognition

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IEEE Transactions on Information Forensics and Security 2025年 20卷 5282-5295页

作者： Pang, Meng Zhang, Wenjun Lu, Yang Cheung, Yiu-Ming Zhou, Nanrun Nanchang University School of Mathematics and Computer Sciences China Xiamen University Department of Computer Science and Technology School of Informatics China Hong Kong Baptist University Department of Computer Science Hong Kong Hong Kong Shanghai University of Engineering Science School of Electronic and Electrical Engineering China

Face normalization is a critical technique for improving the robustness and generalizability of face recognition systems by reducing intra-personal variations arising from expressions, poses, occlusions, illuminations, and domain shifts. Existing normalization methods, however, often lack the flexibility to handle multi-factorial variations and exhibit limited cross-domain adaptability. To address these challenges, we propose a Unified Multi-Domain Face Normalization Network (UMFN), which is designed to process facial images with diverse variations from various domains and reconstruct frontal, neutralized facial prototypes in the target domain. As an unsupervised domain adaptation model, the UMFN facilitates concurrent training across multiple cross-domain datasets and demonstrates robust prototype reconstruction capabilities. Notably, the UMFN functions as a joint prototype and feature learning framework, extracting domain-agnostic identity features through a decoupling mapping network and adversarial training with a feature domain classifier. Furthermore, we design an efficient Heterogeneous Face Recognition (HFR) network that integrates these domain-agnostic features and the identity-discriminative features extracted from normalized prototypes, enhanced by contrastive learning to improve identity recognition accuracy. Empirical evaluation on multiple cross-domain benchmark datasets validate the effectiveness of the UMFN for face normalization and the superiority of the HFR network for heterogeneous face recognition. © 2005-2012 IEEE.

关键词： Adversarial machine learning

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Attention-based neural machine translation with quality analysis on low-resource Digaru-English Pairs

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International Journal of Information technology (Singapore) 2025年第1期17卷 575-589页

作者： Rushanti, Kri Kakum, Nabam Sambyo, Koj Department of Computer science and Engineering National Institute of Technology Jote Arunachal Pradesh India

Attention-based neural machine translation (NMT) has significantly improved translation quality for well-resourced language pairs. However, enhancing translation accuracy for low-resource languages remains a challenge due to limited datasets and technical constraints. Despite these challenges, the latest NMT method has notably improved translation precision for language combinations with limited data by employing techniques aimed at boosting the training models. This study focuses on improving translations for the low-resource North-East Indian language, Digaru. We utilize attention-based NMT and the Transformer model with hyperparameter tuning, experimenting with datasets of 5k, 10k, and 50k sentences. Our optimized model shows a notable improvement in BLEU scores over the base models, resulting in significantly better translation accuracy. Additionally, human evaluations and multidimensional quality metrics (MQM) confirm the enhanced translation quality. © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： BLEU Global attention Hyper-parameter optimization MQM Transformer

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A Machine Learning Approach and Methodology for Solar Radiation Assessment Using Multispectral Satellite Images

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Annals of Data Science 2023年第4期10卷 907-932页

作者： Verma, Preeti Patil, Sunil Department of Computer Science Engineering Vedica Institute of Technology RKDF University Bhopal India

In this paper, machine learning based method for the estimation of solar radiation in earth surface is presented. To design the machine learning model, multispectral (visible and infrared) satellite images of the very high-resolution from multiple locations are considered as primary data. The satellite images in visible and infrared bands, altitude, latitude, longitude, month, day, time, solar zenith angle, solar azimuth angle, viewing zenith angle, and viewing azimuth angle are used as input to the machine learning, while the solar radiation is taken as output variable. The paper specifics the entire procedure, including data collection, pre-processing, and feature selection, as well as the selection of the best machine learning algorithm, measurements, and validation. The impact of each input feature in estimating the solar radiation is also analyzed using correlation methods. SOLCAST datasets are used for Carcassonne city in the France. The analysis of correlations provides how variables are connected or linked. The Pearson correlation, Kendall rank correlation, Spearman correlation, and Phi K correlations are used in the present study and useful correlations exist because they allow us to anticipate future behaviour by relating the relevant parameters (such as azimuth angle, cloud capacity, dew point temp, air temp, DHI, DNI, horizontal component of beam radiation (Ebh), GHI, precipitable water, relative humidity, surface pressure, wind direction, wind speed, zenith, albedo daily). From the correlation results, neural network algorithm has been adopted using most relevant parameters to validate the results. Researchers and scientists may use the method to build high-efficiency solar devices like solar power plants and photovoltaic cells. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Solar radiation Solar power plant Machine learning algorithm Multispectral satellite images

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Machine Learning for Cloud Cover Detection Using Multispectral Satellite Images

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Annals of Data Science 2023年第6期10卷 1543-1557页

作者： Verma, Preeti Patil, Sunil Department of Computer Science Engineering Vedica Institute of Technology RKDF University Bhopal India

Reliable cloud detection in satellite based remote sensing applications is a vital pre-processing step. It can be viewed as a classification technique by partitioning objective pixels into cloud or non-cloud shadow classes. However, certain cloud pixels, particularly narrow pixels in the cloud, can be regarded as a blend of cloud reflection and land-based objects. Hence, it is difficult to classify them using a single classifier. In the present study, a technique based on an ensemble classifier is proposed to identify these pixels. The proposed ensemble classifier uses the Extreme Learning Machine, Naïve Byes, and K-Nearest Neighbour classifier. The model is trained and evaluated using the Spatial Procedures for Automated Removal of Cloud and Shadow datasets. The findings of the analysis indicate that the proposed classifier provides better classification accuracy. The performance of the model other than RGB band is also analyze and results shows that adding images from different bands significantly improves the results. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Machine learning Cloud cover detection Remote sensing Satellite images

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Transformers@DravidianLangTech-EACL2024: Sentiment Analysis of Code-Mixed Tamil Using RoBERTa 4

Transformers@DravidianLangTech-EACL2024: Sentiment Analysis ...

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4th Workshop on Speech, Vision, and Language Technologies for Dravidian Languages, DravidianLangTech 2024

作者： Singhal, Kriti Bedi, Jatin Computer Science and Engineering Department Thapar Institute of Engineering and Technology India

ISBN: (纸本)9798891760783

Sentiment analysis has been an active field of research for over 20 years and has gained immense popularity due to its applications in both academia and industry. Sentiment Analysis of code-mixed posts and comments on social media, especially in Dravidian languages, is gaining more and more traction. This paper describes the team Transformers’ submission to the Sentiment Analysis in Tamil shared task organized by DravidianLangTech 2024 workshop at EACL 2024. A BERT-based architecture, RoBERTa was used for the shared task. The best macro average F1-score achieved was 0.212. We secured the 5th rank in the Sentiment Analysis shared task in Tamil. © 2024 Association for Computational Linguistics.

关键词： Sentiment analysis

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Graph Ranked Clustering Based Biomedical Text Summarization Using Top k Similarity

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computer Systems Science & engineering 2023年第6期45卷 2333-2349页

作者： Supriya Gupta Aakanksha Sharaff Naresh Kumar Nagwani Department of Computer Science and Engineering National Institute of TechnologyRaipur492001ChhattisgarhIndia

Text Summarization models facilitate biomedical clinicians and researchers in acquiring informative data from enormous domain-specific literature within less time and *** and selecting the most informative sentences from biomedical articles is always *** study aims to develop a dual-mode biomedical text summarization model to achieve enhanced coverage and *** research also includes checking the fitment of appropriate graph ranking techniques for improved performance of the summarization *** input biomedical text is mapped as a graph where meaningful sentences are evaluated as the central node and the critical associations between *** proposed framework utilizes the top k similarity technique in a combination of UMLS and a sampled probability-based clustering method which aids in unearthing relevant meanings of the biomedical domain-specific word vectors and finding the best possible associations between crucial *** quality of the framework is assessed via different parameters like information retention,coverage,readability,cohesion,and ROUGE scores in clustering and non-clustering *** significant benefits of the suggested technique are capturing crucial biomedical information with increased coverage and reasonable memory *** configurable settings of combined parameters reduce execution time,enhance memory utilization,and extract relevant information outperforming other biomedical baseline *** improvement of 17%is achieved when the proposed model is checked against similar biomedical text summarizers.

关键词： Biomedical text summarization UMLS BioBERT SDPMM clustering top K similarity PPF HITS page rank graph ranking

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Multiattribute Deep CNN-Based Approach for Detecting Medicinal Plants and Their Use for Skin Diseases

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第3期6卷 710-724页

作者： Dalvi, Prachi Kalbande, Dhananjay R. Rathod, Surendra Singh Dalvi, Harshal Agarwal, Amey Bharatiya Vidya Bhavans Sardar Patel Institute of Technology Department of Computer Engineering Mumbai400058 India Fr. Conceicao Rodrigues College of Engineering Department of Electronics and Computer Science Mumbai400050 India Dwarkadas J Sanghvi College of Engineering Department of Information Technology Mumbai400056 India

Skin health is a critical concern for humans, especially in geographical areas where environmental conditions and lifestyle factors adversely affect their condition, leading to a prevalence of skin diseases. This issue is exacerbated in rural regions, like parts of India, where a notable dermatologist shortage exists, leading to overlooked skin diseases. In response, the use of medicinal plants for dermatological purposes has been a longstanding tradition. However, traditional plant identification often relies on a single attribute, such as leaves or flowers, which can be unreliable due to seasonal variations. This article introduces a novel approach for accurately identifying medicinal plants using a multiattribute deep convolutional neural network. This approach aims to bridge the gap in healthcare access by empowering individuals to recognize and utilize these plants effectively. Our objective is to develop a robust deep CNN model trained on a diverse dataset of images encompassing leaves, trunks, and seeds of medicinal plants associated with skin health. Our findings demonstrate that the model achieves high accuracy in plant identification, effectively addressing the limitations of single-attribute methods. This research not only contributes to the field of medicinal plant classification but also empowers individuals to make informed decisions about their skin health while preserving valuable traditional knowledge. © 2024 IEEE.

关键词： Medicinal chemistry

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Towards smart technologies with integration of the internet of things, cloud computing, and fog computing

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International Journal of Networking and Virtual Organisations 2023年第1期29卷 73-124页

作者： Ahlawat, Chanchal Krishnamurthi, Rajalakshmi Department of Computer Science and Engineering Jaypee Institute of Information Technology Noida201309 India

In the recent era of technology, the internet of things (IoT) plays a tremendous role in enhancing the quality of human life through smart devices and sensing the real-world environment. IoT aims to interconnect anything from anywhere via the internet. The existing literature lacks in providing deeper research insight toward a broader perspective of integrating IoT, cloud computing (CC), and fog computing (FC). Hence, this paper aims to provide a comprehensive review of integrating these main emerging technologies and four major objectives are addressed. The first objective identifies the primary characteristics of IoT to solve real-life problems. The second objective addresses the issues that necessitate the integration of IoT with other emerging technologies. The third objective involves how the integration of IoT with CC elevates the scope of providing a solution to real-life problems. The final objective addresses the limitations of the integration of IoT with CC and how FC overcomes these limitations. © 2023 Inderscience Enterprises Ltd.

关键词： Internet of things

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