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2nd IEEE International Conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2023

作者： Rangari, Anushka Anil Das, Swagat Rajeswari, D. College of Engineering and Technology Srm Institute of Science and Technology School of Computing Department of Data Science and Business Systems Tamilnadu Kattankulathur603203 India

ISBN: (纸本)9798350315905

Recognition of cursive handwritten text from photographs is a method for finding handwritten text. Identification is difficult because every author has a distinctive writing style. The proposed work uses cutting-edge technologies including RNN, KNN, MLP, and CRNN twith VGG16 to push the boundaries of character and handwriting recognition. Additionally, it compares neural networks to CNNwith VGG16 and finds that neural networks are more accurate. In the first stage, image acquisition, the scanned image is acquired along with its normalization, feature extraction, and segmentation. Additionally, it combines with the most effective feature extraction technique to improve the accuracy of our model. It also compares the accuracy with six uniquepretreat. approach, which are image segmentation image data generator data augmentation closed loop pre-processing, sigmoid Stretching and geometric *** provides the highest accuracy (98.36%) when using data augmentation. CNN has the highest accuracy (95.25%) when using picture segmentation. The third experiment's results for the image data generator show that CNN produced the best results (92.47%), while the highest model, RNN, gave closed loop processing accuracy of 92.20%. a change in geometry KNN has the highest accuracy at 95.39%. © 2023 IEEE.

关键词： Image segmentation

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Sustainable Teaching and Learning E-learning Model towards Redesigning Transformative Learning Model for Knowledge Sharing & Infrastructure Enhancements Post COVID-19

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Recent Patents on engineering 2025年第2期19卷 1-12页

作者： Veeramanickam, M.R.M. Kaur, Gaganpreet Acakpovi, Amevi Vadrevu, Pavan Kumar Ciro, Rodriguez R. Kalpana, A.V. Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Software Engineering Universidad Nacional Mayor de San Marcos Lima Peru Electrical and Electronic Engineering Department Accra Technical University Accra Ghana Andhra Pradesh India Department of Software Engineering Universidad Nacional Mayor de San Marcos UNMSM Lima Peru Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur Tamilnadu Chennai India

Background: Recently, e-learning has become a very basic, integral part of technology-based learning. Wide trends are increasing day by day because of the demands and its usage based on working remotely due to highly penetrated mobile handheld devices and digital media. The smart campus infrastructure has played a vital role to its full extent towards Z millennium students in the 20th century. The teaching and learning accessibility depends on terms of various cost-based afforda-ble platforms, either with synchronous learning or asynchronous mode of learning. Methods: The current patent research explores the changeling leading to infrastructural reforms as per the need for digital media for e-learning during and after COVID-19 spreads. The perspectives in 2 forms of research study are: 1st working on infrastructural needs and demands for the smart campuses and online learning challenges and 2nd is working on platforms technology utilization for better accessible resources for all learners. This work studied different aspects during and after COVID-19, leading to the importance of uninterrupted internet access, phone, hardware and relia-bility, etc. In this work, the importance of gamification study and flipped classrooms for enhancing learner performance to highly engage them in learning environments focused research model on learner engagement on Gamified perceiving study with Smart PLS-SEM was investigated. Promoting sustainability in its entirety through knowledge transfer and contributions to address various challenges in the redesign of learners' syllabi to meet educational needs, emphasizing online learning to integrate various modes of learner platforms, personalized teaching and learn-ing, peer-to-peer communication for learner enhancement, and student engagement through gami-fication are studied. Results: Learners who are enrolled at the school, college, and university levels of education increased exponentially post-COVID-19. More than 90% responded to sch

关键词： Curricula

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Explainable AI in Blockchain System for Decentralized Governance

Explainable AI in Blockchain System for Decentralized Govern...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Chahar, Suman Kaur, Kawaljit Kaswan, Kuldeep Singh Dhatterwal, Jagjit Singh University Institute of Computing Chandigarh University Mohali India Galgotias University Department of Computer Science and Engineering Greater Noida India Koneru Lakshmaiah Education Foundation Department of Artificial Intelligence and Data Science Andhra Pradesh India

ISBN: (纸本)9798331508685

Decentralized governance is going through radical changes due to the innovative use of blockchain which allows decisions to be made in a transparent, secure and more im- portantly, immutable way, without the authority of a single individual or party. Thus, the introduction of artificial intelligence (AI) in blockchain governing systems complicates such thing as transparency and trust, especially in the case of deep AI systems. But more on this later - this is where Explainable AI or XAI comes into the picture. XAI methods enable understanding of the reasons behind the choices made by AI models and solves the "black box"problem that is predominant in machine learn- ing. When applied to decentralised governance, XAI provides clarifications of AI's decision making in such areas as voting, resource distribution and the enactment of smart contracts. This way, the stakeholders are confident that the decision-making is fair and impartial and this is made possible by the use of XAI. This paper aims to find out more about the applicability of XAI concept in blockchain -based governance systems in terms of bias and fairness in automated decisions and explanation of the decisions made. The reasons why interpretability plays the crucial role in decentralized systems are explained with the help of the comparison between the classical artificial intelligence and the Xin Intelligent. The study also looks at various use cases such as, Decentralized Autonomous Organizations (DAOs), and voting systems implemented on blockchain platforms and how XAI can enhance the performance of the governance systems. As this study shows, XAI is vital in building trust, improving responsibility, and guaranteeing balanced majorities in decentralized blockchain governance structures. © 2025 IEEE.

关键词： Smart contract

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Crop Yield Prediction Using Multimodal Meta-Transformer and Temporal Graph Neural Networks

IEEE Transactions on AgriFood Electronics

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IEEE Transactions on AgriFood Electronics 2024年第2期2卷 545-553页

作者： Sarkar, Somrita Dey, Anamika Pradhan, Ritam Sarkar, Upendra Mohan Chatterjee, Chandranath Mondal, Arijit Mitra, Pabitra Indian Institute of Technology Kharagpur Centre for Computational and Data Sciences Kharagpur721302 India Indian Institute of Technology Kharagpur Department of Industrial and Systems Engineering Kharagpur721302 India Indian Institute of Technology Kharagpur Department of Agricultural and Food Engineering Kharagpur721302 India Indian Institute of Technology Patna Department of Computer Science and Engineering Patna801106 India Indian Institute of Technology Kharagpur Department of Computer Science and Engineering Kharagpur721302 India

Crop yield prediction is a crucial task in agricultural science, involving the classification of potential yield into various levels. This is vital for both farmers and policymakers. The features considered for this task are diverse, including weather, soil, and historical yield data. Recently, plant images captured in different modalities, such as red-green-blue, infrared, and multispectral bands, have also been utilized. Most of these data are inherently temporal. Integrating such multimodal and temporal data is advantageous for yield classification. In this work, a deep learning framework based on meta-transformers and temporal graph neural networks has been proposed to achieve this goal. Meta-Transformers allow the modeling of multimodal interactions, while temporayel graph neural networks enable the utilization of time sequences. Experimental results on the publicly available EPFL multimodal dataset demonstrate that the proposed framework achieves a high classification accuracy of nearly 97%, surpassing other state-of-the-art models, such as long short-term memory networks, 1-D convolutional neural networks, and Transformers. In addition, the proposed model excels in accuracy metrics, with a precision of approximately 98%, an F1-Score of 91%, and a recall of 94% in crop yield prediction. © 2024 IEEE.

关键词： Distribution transformers

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Improved UNET Architecture for Effective COVID-19 Severity Prediction from Pulmonary CT Images 1

Improved UNET Architecture for Effective COVID-19 Severity P...

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1st International Conference on Innovations in Communications, Electrical and Computer engineering, ICICEC 2024

作者： Ramla, M. Jasmine, R. Anita Ramesh, M. Bai, Femilda Josephin Joseph Shobana SRM Institute of Science and Technology Department of Computer Applications Chennai India SRM Institute of Science and Technology Department of Data Science and Business Systems Chennai India SRM Institute of Science and Technology Department of Computer Science Chennai India Istinye University Department of Computer Engineering Istanbul Turkey

ISBN: (纸本)9798350376517

COVID-19, is one of the deadliest pandemic hit globally crises in the recent years, continues to persist with emerging variants of concern. Due to the virus's high transmissibility, rapid and precise detection of COVID-19 is crucial for timely medical intervention. This research presents an advanced deep transfer learning approach for quantifying the severity of COVID-19 infection from CT lung images. Using computer vision techniques, lung regions were extracted from CT scans through semantic segmentation using SegNet. An enhanced U-Net architecture, integrated with the Atrous Spatial Pyramid Pooling (ASPP) technique, was employed to semantically segment the infected regions of the lungs. Based on the segmented infection areas, the severity of the infection was classified into mild, moderate, and severe. The proposed framework, which incorporates lung extraction followed by Ground-Glass Opacity (GGO) segmentation, achieves the segmentation accuracy of 96% and a prediction accuracy of 98.9%. © 2024 IEEE.

关键词： COVID-19

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Effortless learning: leveraging summarization and tabulation for automated note making

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Multimedia Tools and Applications 2024年 1-26页

作者： Arivarasi, A. Srinivasan, R. Rajeswari, D. Govindasamy, Alagiri School of Electronics Engineering Vellore Institute of Technology Tamil Nadu Chennai600127 India Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur603203 India Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur603203 India Future Connect Technologies Pvt. Ltd Tamil Nadu Kanchipuram600034 India

In this fast-paced Digital Age, Natural Language Processing (NLP) can prove beneficial in consuming quality information efficiently. With the ever-growing number of learning resources, it is becoming onerous for students to choose the best-suited learning resource. Furthermore, many students find it difficult to understand and retain highly factual information. To overcome these issues, this paper proposes a novel Automated TExt Summarization of scholastic text in Table format (Auto-TEST) to provide information at a glance by mimicking the student’s style of note-making. The Auto-TEST framework is achieved through two steps which is Summarization and Tabulation. Initially, the Extractive Single Document Text Summarization (ESDTS) method is adopted to identify the salient information from the non-fictional (scholastic) text which is extracted and grouped together to form a concise summary. A Named Entity Recognition (NER) method is implemented to extract noun chunks and then arrange them in a tabulation format that aids in memorizing essential facts and figures by enabling effortless retrieval. The performance of the Auto-TEST framework is gauged by using scholastic domain-specific dataset using ROUGE as an evaluation metric. The extracted entities gives an average Recall of 71.40%, an average Precision score of 75.80% and an average F-score of 73.54% for the dataset respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Students

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YOLOv8 Kidney Guard: Smart Imaging for Stone Detection Using Deep Learning 2

YOLOv8 Kidney Guard: Smart Imaging for Stone Detection Using...

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2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Anantha Sivaprakasam, S. Rajalakshmi, D. Muthu Kumar, S. Senthil Pandi, S. Department of CSE Rajalakshmi Engineering College Chennai India Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur Chennai India Department of CSE St. Joseph's Institute of Technology Chennai India

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383867

A common urological condition that can lead to severe pain and complications is kidney stones. Kidney stones must be identified and diagnosed as soon as possible in order to provide appropriate care. This study uses the YOLO (You Only Look Once) object detection framework to propose a novel kidney stone detection method. Yolo is a well-known object detection technology that works well in medical imaging because of its accurate and real-time capabilities. We make use of a dataset of medical photos that show kidney stones in both positive and negative cases. Using this dataset, the YOLO model is trained to identify the traits and features of kidney stones. The trained model performance is then measured using Mean Average Precision (mAP), a commonly used metric in object detection tasks, on a different test dataset. Significant progress in artificial intelligence has been made with the help of the deep learning algorithms which suggests the programmed recognition of kidney stones (whether or not they contain stones) using captured medical images. Kidney stone diagnosis can be recommended automatically with this *** addition to identifying kidney stones, this model also displays the stone's location in the output image. The outcomes show how effective the suggested YOLO-based kidney stone detection method is. The model locates and classifies kidney stones in medical images with accuracy, as evidenced by its high mAP value of 0.92. Because YOLO is real-time, it is a useful tool for quick screening and diagnosis, allowing patients with kidney stones to receive treatment on time. © 2024 IEEE.

关键词： Deep learning

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Public Decision-Making Analysis in Urban Heat Islands Based on Socioeconomic Modelling by Deep Learning Techniques

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Remote Sensing in Earth systems sciences 2024年第1期8卷 108-117页

作者： Prakash, S. Wilson Kiruthigha, R. Yadav, Ajay Kumar Kanna, S. K. Rajesh Shaik, Khasimbee Krishna, B. Murali Department of Data Science and Business Systems SRM Institute of Science and Technology Chennai Kattankulathur India Department of Information Technology SRM Institute of Science and Technology Chennai Ramapuram India Department of Computer Application United Institute of Management UP Prayagraj 211010 India Department of Mechanical Engineering Rajalakshmi Institute of Technology Chennai India Department of Computer Science and Engineering Aditya University Andhra Pradesh Surampalem India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

Urban heat island (UHI) effects, especially in highly urbanised areas, and greenhouse gas emissions from human activity are two elements that accelerate global climate change (GCC). Sustainable city planning and modification may benefit from the quantification of the surface UHI (SUHI) effect utilising land surface temperature (LST), local climate zones (LCZ), and deep learning methods like convolutional neural networks (CNN) and pix2pix. The findings show that socioeconomic variables account for 12–20% of variability in UHI intensity. In general, the contribution rate of urban economic scale is higher than that of population and industrial structure variables. The increase in urban economic expansion increased summertime heat discomfort. This research propose novel method in socioeconomic analysis based on urban heat pattern analysis with public healthcare system using decision-making with DL (deep learning) model. Input is collected as urban heat pattern dataset and processed for noise removal and normalisation. This data has been features are extracted and classified using convolutional fuzzy Q-Gaussian neural network and variational K-C-means regressive learning. The simulation analysis has been carried out based on training accuracy, average precision, recall, and F-measure. The proposed model attained average precision was 94%, recall was 90%, training accuracy was 97%, and F1-score was 92%. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Decision-making Deep learning model Public healthcare system Socioeconomic analysis Urban heat pattern

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Deriving Pipeline for Emergency Services Using Natural Language Processing Techniques 11th

Deriving Pipeline for Emergency Services Using Natural Langu...

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11th International Conference on Recent Trends in Computing, ICRTC 2022

作者： Anand, Akshat Rajeswari, D. Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Chennai603203 India

ISBN: (纸本)9789811988240

Communications have a crucial role in times of crisis, particularly in times of emergency. Whenever a region is affected by any disaster, social media sites such as Twitter, etc., are a great way to get information out to those who need it the most. Our paper suggests that in times of crisis, when people are worried about finding the right emergency contacts or are experiencing a transformation in their psychological response, they should manually enter their experience into an app to obtain contact information for the nearest SOS. Alternatively, the proposed application suggests scraping hashtags from around the world with web scraping scripts to retrieve geo-referenced tweets in real time. In order to gather accurate data, Twitter's API or some third-party application can be used in the development of this software. When there is a major crisis, social media user behavior plays a vital role, owing to its efficiency and high reliability with swift data dissemination. Anxiety over finding the right emergency team contact may cause people to lose their cool and become unclear about which authority they should call when an emergency occurs. Eventually, our application bridges the gap between clients in need of help and the approved agency that can really provide that support in different circumstances. In the case of, for example, a lost child or a flooded area, our application helps you get in touch with an organization that can assist you in dealing with the situation. We have used CNN-1-D + LSTM, CNN-1-D + Bi-LSTMs, and CNN-1-D + LSTM + Glove-100-D to get maximum accuracy for the classification model. It is possible to use the prototype solution in a novel way to organize immediate assistance with authorities in comprehensive administration, while integrating Twitter or other social platforms into the application can make it a feasible platform for preventive intervention and catastrophe management. © 2023, The Author(s), under exclusive license to Springer Nature

关键词： Emergency services

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Hybrid feature-based neural network regression method for load profiles forecasting

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Energy Informatics 2025年第1期8卷 1-20页

作者： Satan, Aidos Zhakiyev, Nurkhat Nugumanova, Aliya Friedrich, Daniel Department of Science and Innovation Astana IT University Astana010000 Kazakhstan Big Data and Blockchain technologies Research Center Astana IT University Astana Kazakhstan School of Engineering Institute for Energy Systems University of Edinburgh EdinburghEH93DW United Kingdom Department of Energy Engineering University of Genova Savona17100 Italy

This study addresses the critical need for improved demand forecasting models that can accurately predict energy consumption, particularly in the context of varying geographical and climatic conditions. The work introduces a novel demand forecasting model that integrates clustering techniques and feature engineering into neural network regression, with a specific focus on incorporating correlations with air temperature. Evaluation of the model’s efficacy utilized a benchmark dataset from Tetouan, Morocco, where existing forecasting methods yielded RMSE values ranging from 6429 to 10,220 [MWh]. In contrast, the proposed approach achieved a significantly lower RMSE of 5168, indicating its superiority. Subsequent application of the model to forecast demand in Astana, Kazakhstan, as a case study, showcased its efficacy further. Comparative analysis against a baseline neural network method revealed a notable improvement, with the proposed model exhibiting a MAPE of 5.19% compared to the baseline’s 17.36%. These findings highlight the potential of the proposed approach to enhance demand forecasting accuracy, particularly across diverse geographical contexts, by leveraging climate-related inputs, the methodology also demonstrates potential for broader applications, such as flood forecasting, agricultural yield prediction, or water resource management. © The Author(s) 2025.

关键词： Demand response

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