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Handwritten Character Recognition for South Indian Languages Using Deep Learning 1

引用

5th international conference on computer and Communication Technologies, IC3T 2023

作者： Arjun, C.S. Rani, N. Shobha Prabhu, Akshatha Department of Computer Science School of Computing Mysuru Campus Amrita Vishwa Vidyapeetham Mysuru India

ISBN: (数字)9789819997046

ISBN: (纸本)9789819997039

Character recognition is a crucial component of many applications, including document processing, handwriting recognition, and optical character recognition (OCR). We present an OCR model-based character recognition workflow in this research that involves feature extraction from the input images, model comparison, and performance assessment. We compared the performance of four OCR models, including FAST R-CNN, Efficient Net B7, VGG16, and VGG19, on Kannada and Malayalam handwritten character datasets. the results showed that Efficient Net B7 outperformed the other models with the highest accuracy. We also analysed the performance of each model in terms of precision, recall, and F1-score. Our results showed that FAST R-CNN was second with accuracy of 94.55%, Efficient Net B7 was first with accuracy of 96.66%, VGG19 was third with accuracy of 92.23%, and VGG16 was last with accuracy of 91.38%. the workflow provides a structured approach for developing and evaluating OCR models for character recognition tasks in Indian scripts. the findings can be useful for various applications that require accurate and efficient character recognition. Future work could focus on exploring other deep learning architectures and datasets for character recognition in Indian scripts. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Optical character recognition

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A Scalable Approach for Improving IoT Healthcare systems with Privacy and Permissioned Blockchain 25th

A Scalable Approach for Improving IoT Healthcare Systems wi...

引用

25th international conference on Web Information systems Engineering, WISE 2024

作者： Yahyaoui, Arije Kotel, Sonia Sbiaa, Fatma Hamel, Lazhar Lahouij, Aida Kamoun, Raouda Maraoui Département d’informatique ISIMM Université de Monastir Monastir Tunisia Laboratoire RIADI École Nationale des Sciences de l’Informatique Université de la Manouba Manouba Tunisia ESME École d’ingénieurs Paris France Efrei Research Lab Paris Panthéon Assas University Villejuif France

ISBN: (纸本)9789819605699

In the context of increasingly complex and resource-constrained healthcare ecosystems, the need for efficient and secure management of patient data is paramount. While traditional approaches involving health record management systems and insurance frameworks offer a foundation, the integration of Internet-of-things (IoT) devices presents novel challenges in data security and access control. this research addresses these concerns by proposing a blockchain-based architecture specifically designed to secure IoT-enabled healthcare systems while balancing the blockchain trilemma of scalability, security, and decentralization. We propose a robust permissioned blockchain architecture based on Hyperledger Fabric that ensures personal data in the Healthcare Record System (HRS) remains confidential and accessible only to authorized users. this architecture integrates smart contracts for data control, guaranteeing compliance with the GDPR. We used the Caliper benchmarking tool to evaluate the performance of our architecture, carrying out different scenarios with various configurations. Results demonstrate the scalability and effectiveness of our solution, optimizing performance metrics such as latency and throughput. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electronic health record

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A Study of the Neuro Learning Model to Diagnosis of the (COVID-19) 7th

A Study of the Neuro Learning Model to Diagnosis of the (COV...

引用

7th international conference on Information and Communication Technology for Competitive Strategies , ICTCS 2022

作者： Sivasakthi, S. Radha, V. Department of Computer Science Avinashilingam Institute for Home Science and Higher Education for Women Tamil Nadu Coimbatore641043 India

ISBN: (纸本)9789811996375

Coronavirus disease (COVID-19) is an ongoing chaos that was declared as the global pandemic that had affected many human lives and created an extensive impact on the global economy as well. this pandemic has also challenged the researchers and the medical practitioners to develop a novel and automatic algorithm to diagnosis the disease with less cost. the first step in the diagnosis is the chest X-ray imaging. there are many analysing techniques using the deep learning algorithms to detect the presence of COVID-19 analysing the CT lung imaging. this article is a survey study that provides a detailed insight about the most hopeful and promising regions of the investigation using deep learning algorithms. A novel bio-inspired neuro-analysis framework for COVID-19 analysis through cross-class modelling and pseudo-medication suggestions for early diagnosis was proposed with the workflow architecture and workflow. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： COVID-19

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Towards Understanding the Distribution and Evolution of Code Smells: An Empirical Study on Java and Python Projects 36

Towards Understanding the Distribution and Evolution of Code...

引用

36th international conference on Software Engineering and Knowledge Engineering, SEKE 2024

作者： Mi, Qing Zhang, Tengyuan Cai, Keru Wang, Yuhe School of Computer Science Beijing University of Technology Beijing China

ISBN: (纸本)1891706594

Code smells are usually associated with poor coding practices, design issues, and potential bugs. Studies on code smells are important. However, existing studies lack research on the distribution and evolution of code smells in different programming languages. To fill this gap, we collected 9 Java projects and 9 Python projects from Github and analyzed them with existing tools. We found that Java’s code smell rate (the percentage of the number of code smells in total lines of code) of 11.11% is higher than Python’s code smell rate of 1.31%. "Magic Number", "Long Statement", and "Unutilized Abstraction" are the top three code smells in Java, while "Design Smells", "Common Weakness Enumeration", and "Unused Code" are the top three code smells in Python. the code smell aggregation level (the percentage of code smells in the top five modules in the total number of code smells) in Java is 44.07% while in Python is 59.18%, indicating that both languages are likely to accumulate code smells in certain modules. During the project’s progress, the code smell rate remained steady in both languages, while the aggregation level showed a downward trend. Our findings can help developers understand common code smells and their trends in projects, thus guiding them to improve software quality. © 2024 Knowledge systems Institute Graduate School. All rights reserved.

关键词： Python

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Speech Method for Caregiving Robots Considering Uncertainty 16th

Speech Method for Caregiving Robots Considering Uncertainty

引用

16th international conference on Social Robotics, ICSR + AI 2024

作者： Shindo, Arisa Kawasaki, Yosuke Takahashi, Masaki School of Science for Open and Environmental Systems Graduate School of Science and Technology Keio University Kanagawa Japan Department of System Design Engineering Faculty of Science and Technology Keio University Kanagawa Japan

ISBN: (纸本)9789819635214

Recent studies have explored the integration of large language models (LLMs) into caregiving robots. the use of LLMs facilitates the generation of human-like natural dialogues and diverse, varied expressions. However, in case of a lack of direct and specific instructions from a user, gaining an accurate understanding of the user's intent is challenging, which may result in miscommunication. thus, this study proposed a method to achieve robust communication even in the absence of direct instructions by considering the uncertainty in destination prediction probabilities. the proposed system leveraged GPT-4 to generate various Japanese speech content candidates and employed neighborhood components analysis to facilitate dimensionality reduction. Consequently, notable features of the rich linguistic embeddings were preserved while transforming them into manageable data. the system selected appropriate utterances based on the destination prediction probabilities. Furthermore, this study investigated changes in the speech content according to the variations in these probabilities, and the incorporation of uncertainty in communication was validated. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Dimensionality reduction

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A Survey of Computational Intelligence Techniques Used for Cyber-Attack Detection 6th

A Survey of Computational Intelligence Techniques Used for C...

引用

6th international conference on Information and Communication Technology for Intelligent systems, ICTIS 2021

作者： Rajan, S. Deepa Karthika, R.A. Vels Institute of Science Technology and Advanced Studies Tamil Nadu Chennai India

ISBN: (纸本)9789811935701

In recent times, the number of computer systems and networks has grown dramatically. these computer systems and networks are now more vulnerable to cyber-attacks than ever before. Due to the complexity and dynamic characteristics of cyber-attacks, computer systems require many cyber-protecting mechanisms. In this article, a review of computational intelligence-based cyber-attack detection methods is presented. the fundamental issues in cybersecurity and attack detection were described before introducing various computational intelligence-based attack detection applications. this review is focused on the cyber-attack detection approaches based on machine learning, deep learning and reinforcement learning techniques. the benchmark datasets used in cyber-attack detection research are first described and the performance of several computational intelligence-based cyber-attack detection methods was compared to validate the attack detection efficiency. Finally, a multi-agent reinforcement learning-based cyber-attack detection method was proposed to improve the attack detection performance when using reinforcement learning. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Reinforcement learning

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Intelligent systems for Early Malaria Disease Detection in Patient Cells Using Transfer Learning Approaches 4

Intelligent Systems for Early Malaria Disease Detection in P...

引用

4th international conference on Computing, Mathematics and Engineering Technologies, iCoMET 2023

作者： Zaman Khan, Gul Ali Shah, Ibrar Farhatullah Hassan, Muhammad Abul Junaid, Hazrat Sardar, Fouzia Uet Department of Computer Software Engineering Mardan Pakistan School of Automation China University of Geosciences Wuhan430074 China University of Trento Department of Information Engineering and Computer Science Italy University of Malakand Department of Computer Science and Information Technology Pakistan University F Malakand Department of Zoology Dir Lower Pakistan

ISBN: (纸本)9798350335316

Malaria is an infectious disease spread by mosquitoes that effect humans and other animals. It is a massive threat to humanity, with instances growing each year. It is essential to prevent and diagnose malaria immediately and efficiently. For the time being, conventional methods are used for diagnosing malaria in which the patient's blood sample is examined by microscope or by using malaria RTD kits. this approach has several limitations because it requires medical expertise, is expensive, takes a long time, and the results are unsatisfactory. Artificial intelligence-based systems can prevent and help in diagnose of this infectious disease. Because of these limitations, the proposed work has proposed an AI-based diagnosis system that can detect malaria parasites immediately and efficiently. In the proposed experiment, we have applied four different pre-trained deep learning models on the image dataset with some preprocessing and optimization techniques for malaria parasite detection. After investigations, evaluation matrices such as precision, Recall, F1-score, sensitivity, and specificity are used to measure the performance of the proposed models. the Inception-Resnet outperformed by achieving 95% accuracy, VGG16 achieved 92% accuracy, inception achieved 93% accuracy, and VGG19 achieved 91% accuracy. the positive outcomes of this study show that this approach performs much better than the approaches currently used. Furthermore, the proposed method is relevant to health experts for screening purposes. © 2023 IEEE.

关键词： Diseases

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System for Prioritizing Covid-19 Patients Based on Certain Parameters 3

System for Prioritizing Covid-19 Patients Based on Certain P...

引用

3rd international conference on Power, Energy, control and Transmission systems, ICPECTS 2022

作者： Hemavathi, S. Sumesh, Lohith Sai Krishnan, R. Latha, K. Jayasakthi Velmurugan, K. Kavitha, D. Sri Sairam Engineering College Department of Computer Science and Engineering TamilNadu Chennai India Jeppiar College of Engineering Department of Computer Science and Engineering India

ISBN: (纸本)9781665462754

Over numerous nations on earth, a brand-new virus known as the corona virus has been spreading like wildfire. Hospitals are exposed to many people, and it might be difficult to follow COVID-19 and take into consideration all patients. We have created a decision-making method based on a few parameters and X-rays to identify the priority of patients. the Mask R- CNN technique is used to train and test on the dataset to classify patients as having COVID infection or not. On chest X-ray pictures, the mask R CNN technique enhances COVID - 19 detection performance. © 2022 IEEE.

关键词： Viruses

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Using V2X-Information for Trajectory Prediction at Urban Intersections 10

Using V2X-Information for Trajectory Prediction at Urban Int...

引用

10th international conference on Vehicle Technology and Intelligent Transport systems, VEHITS 2024

作者： Klöppel-Gersdorf, Michael Otto, thomas Fraunhofer IVI Fraunhofer Institute for Transportation and Infrastructure Systems Dresden Germany

ISBN: (纸本)9789897587030

Crossing an urban intersection is one of the major challenges in automated/autonomous driving. this is due to a manifold of possible interactions with other traffic participants. In this paper, we propose a trajectory prediction service based on historical V2X-information gathered from Cooperative Awareness Messages (CAMs)/Basic Safety Messages (BSMs). the service allows connected vehicles to more easily navigate the intersection by identifying possibly critical encounters, especially with traffic participants which are not covered by the vehicle's sensors. In comparison with approaches relying on video or other sensor data sources, this has the advantage that Road-Side Units (RSUs), which are used for Vehicle-to-Everything (V2X) communication, are more and more available at public intersections, e.g., due to equipment rollouts all over Europe in projects like C-Roads. the prediction service introduced in this paper is a first step in ongoing research and will act as a baseline for further projects, where additional sensors and also more involved prediction algorithms, e.g., based on neural networks, will be considered. Copyright © 2024 by SCITEPRESS - science and Technology Publications, Lda.

关键词： Vehicle to Everything

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Don’t Train, Just Prompt: Towards a Prompt Engineering Approach for a More Generative Container Orchestration Management 14

Don’t Train, Just Prompt: Towards a Prompt Engineering Appr...

引用

14th international conference on Cloud Computing and Services science, CLOSER 2024

作者： Kratzke, Nane Drews, André Department of Electrical Engineering and Computer Science Lübeck University of Applied Sciences Germany Expert Group Artificial Intelligence in Applications Lübeck University of Applied Sciences Germany

ISBN: (纸本)9789897587016

Background: the intricate architecture of container orchestration systems like Kubernetes relies on the critical role of declarative manifest files that serve as the blueprints for orchestration. However, managing these manifest files often presents complex challenges requiring significant DevOps expertise. Methodology: this position paper explores using Large Language Models (LLMs) to automate the generation of Kubernetes manifest files through natural language specifications and prompt engineering, aiming to simplify Kubernetes management. the study evaluates these LLMs using Zero-Shot, Few-Shot, and Prompt-Chaining techniques against DevOps requirements and the ability to support fully automated deployment pipelines. Results show that LLMs can produce Kubernetes manifests with varying degrees of manual intervention, with GPT-4 and GPT-3.5 showing potential for fully automated deployments. Interestingly, smaller models sometimes outperform larger ones, questioning the assumption that bigger is always better. Conclusion: the study emphasizes that prompt engineering is critical to optimizing LLM outputs for Kubernetes. It suggests further research into prompt strategies and LLM comparisons and highlights a promising research direction for integrating LLMs into automatic deployment pipelines. © 2024 by SCITEPRESS – science and Technology Publications, Lda.

关键词： Automation

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