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2024 international Conference on intelligent Algorithms for Computational intelligence Systems, iACiS 2024

作者： Pillai, M.R. Tadepalli, L N Teja Bhamre, S.R. Gajbhiye, Shreya M Uplaonkar, Deepak I2IT Department of Computer Engineering Pune India

ISBN: (纸本)9798350360660

The research utilizes YOLOv5 for symbol detection, EasyOCR for text recognition, and the Progressive Probabilistic Hough Transform (PPHT) for identifying straight lines in Piping and instrumentation Diagrams (P&iDs), pivotal blueprints depicting industrial plant piping, instrumentation, and control systems. During the training phase, symbols are meticulously cropped and labeled using an open-source tool. Performance evaluation reveals that early stopping applied to the Group Shuffle Split method achieves an outstanding precision of 93%, surpassing alternative approaches. These results underscore the effectiveness of our sampling and model refinement strategies in optimizing P&iD analysis. identified texts and symbols are cataloged based on unique iDs, occurrence frequency, and labels, enhancing comprehension crucial for efficient industrial facility operation and maintenance. Additionally, PPHT aids in detecting lines between symbols identified by the YOLOv5 algorithm, further enriching the analysis of P&iDs. © 2024 iEEE.

关键词： Hough transforms

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Advancing P&iD Digitization with YOLOv5

Advancing P&ID Digitization with YOLOv5

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2023 international Conference on integrated intelligence and Communication Systems, iCiiCS 2023

作者： Gajbhiye, Shreya M. Bhamre, S.R. Tadepalli, L. N. Teja Pillai, M.R. Uplaonkar, Deepak I2IT Department of Computer Engineering Hinjawadi Maharashtra Pune411057 India

ISBN: (纸本)9798350315455

in the realm of engineering diagram recognition and digitization, significant progress is evident across various domains, including the handling of intricate diagram categories like Piping and instrumentation Diagrams (P&iDs), scene text in images, and point symbols on scanned topographic maps. These advancements result from diverse methodologies, such as deep learning, neural networks, digital image processing, and the implementation of the YOLOv5 algorithm for symbol detection and recognition. These approaches substantially improve efficiency and automation in applications ranging from plant design to information extraction. They not only surmount challenges in processing complex visual data but also showcase how advanced technology like YOLOv5 is transforming industries, from manufacturing to geographic information systems. in the field of engineering diagram recognition and digitization, YOLOv5 achieves a 93% accuracy rate, demonstrating its significant impact. The model excelled in precision (92%), recall (94%), F1 score (93%), AUC score (0.97), nd ROC score (0.92), advancing document digitization and automation. These innovations pave the way for further enhancements in engineering and document digitization. © 2023 iEEE.

关键词： Deep Learning information Extraction Diagrams (P&iDs) Digitization, Piping and instrumentation engineering Diagram Recognition

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Online Examination and Evaluation System

Online Examination and Evaluation System

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2022 iEEE Pune Section international Conference, PuneCon 2022

作者： Kumar Dandage, Harshad Uplaonkar, Deepak S. Shete, Ankita Shete, Avani Bodele, Lavanya Jadhav, Shruti Southern Taiwan University of Science and Technology Department of Computer Engineering Taiwan Department of Computer Engineering I2IT Pune India Department of It I2IT Pune India

ISBN: (纸本)9781665498975

Currently, online test systems have adapted easily to today's technologically advanced world. Examinations are an intrinsic part of the educational process. Even though the test are conducted online the teacher has to do manual evaluation. The examinations can be classified into two main types of evaluation, objective answer and subjective answer. As of now, online evaluation is available for the objective questions, hence the manual assessment of the theory answer, is a tedious task for the teacher. The teacher checks the answer manually and gives the marks. in this paper, the literature survey of existing solution is analyzed. © 2022 iEEE.

关键词： Online systems

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Exploring Speech Disorder Detection in Laryngeal Diseases: A Comprehensive Examination 7th

Exploring Speech Disorder Detection in Laryngeal Diseases: A...

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7th international Conference on intelligent Computing and Optimization, iCO 2023

作者： Gharde, Manisha B. Patil Principal, V.V. AISSMS IOIT Pune India Department of E&TC Engineering I2IT Hinjewadi Pune India

ISBN: (纸本)9783031733239

Speech disorders due to laryngeal diseases can significantly impact an individual’s quality of life, leading to difficulties in communication and social interaction. Early detection and diagnosis of these disorders is crucial for effective treatment and management. Deep learning algorithms have shown promise in detecting speech disorders using acoustic features extracted from speech signals in recent years. This paper presents a review of the current state-of-the-art in the detection of speech disorders due to laryngeal diseases using deep learning algorithms. The review covers recent research on various deep learning techniques, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and their variants. The paper also discusses the challenges and limitations of using deep learning to detect speech disorders, such as the need for large training data and the potential for overfitting. Strategies for addressing these challenges, such as transfer learning and data augmentation, are also discussed. The review concludes by highlighting the potential of deep learning algorithms for the early detection and diagnosis of speech disorders due to laryngeal diseases. The study suggests that further research is needed to validate the effectiveness of deep learning algorithms in real-world clinical settings and to develop robust and reliable diagnostic tools for detecting speech disorders. The paper highlights the importance of collaboration between researchers and clinicians to translate deep learning research into clinical practice successfully. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Convolutional neural networks

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Visualizing Vitality: Decoding P&iD Connectivity via Symbol Detection and Line Analysis

Visualizing Vitality: Decoding P&ID Connectivity via Symbol ...

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intelligent Algorithms for Computational intelligence Systems (iACiS), international Conference on

作者： M R Pillai L N Teja Tadepalli S R Bhamre Shreya M Gajbhiye Deepak Uplaonkar Department of Computer Engineering I2IT Pune India

ISBN: (数字)9798350360660

ISBN: (纸本)9798350360677

关键词： YOLO Training Productivity Visualization Text recognition instruments Symbols Transforms Probabilistic logic Stakeholders

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Research Challenges and Opportunities for Trust Management Systems in ioT Networks 15

Research Challenges and Opportunities for Trust Management S...

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15th international Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Kamble, Satish Mahajan, Surendra Sapkal, Deepak D. Ramteke, Kimi Department of Computer Engineering SKNCOE SPPU Pune India Department of Information Technology PVG'S COET & GKPWIOM SPPU Pune India Department of Computer Engineering PVG'S COET & GKPWIOM SPPU Pune India Department of Computer Engineering I2IT SPPU Pune India

ISBN: (纸本)9798331300579

ioT device applications are rapidly expanding in the current environment. Today, we can find ioT devices in a variety of industries, including agriculture, home security, entertainment, health, transportation, and education. Today, users of ioT devices and applications come from large cities, small towns, and rural areas. Security and trust are vital areas for the success of ioT implementation in ioT applications. When ioT devices engage with one another, trust grows between them. ioT device communications may be made secure by implementing trust-featured systems. Algorithms based on Machine learning, Fuzzy logic, Bayesian, and Entropy can be utilized for trust management systems. Trust management systems are surveyed in this study. Further, this paper highlights research challenges and directions in this area. © Grenze Scientific Society, 2024.

关键词： Fertilizers

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Advancing P&iD Digitization with YOLOv5

Advancing P&ID Digitization with YOLOv5

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integrated intelligence and Communication Systems (iCiiCS), international Conference on

作者： Shreya M Gajbhiye S R Bhamre L N Teja Tadepalli M R Pillai Deepak Uplaonkar Department of Computer Engineering I2IT Hinjawadi Pune Maharashtra

关键词：

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improving Word Sense Disambiguation By Adopting Refined Algorithms

Improving Word Sense Disambiguation By Adopting Refined Algo...

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Trends in Electrical, Electronics, computer engineering Conference (TEECCON)

作者： Anirudh S Nair Aditi Vasmatkar Sagar Pande Shruti Kolhe Pallavi Yevale Mukesh More Department of Computer Engineering International Institute of Information Technology (I2IT) Hinjawadi Pune Maharashtra India

ISBN: (数字)9798331505714

ISBN: (纸本)9798331505721

Word sense disambiguation (WSD) is an important subfield of Natural Language Processing (NLP) that helps in determining the correct sense of a word in a sentence based on the context. Certain words have different meanings depending on their context, and misinterpretations of these words can lead to ambiguity. Ambiguity can create uncertainty about the meaning of a sentence, leading to misunderstandings in the communication. This paper explores different WSD approaches, focusing their efficiency, specifically in terms of accuracy and computational resources. While significant progress has been made, there are still some challenges including shortage of labeled datasets, especially in low resource languages. This highlights the necessity for advancements in WSD techniques to improve their effectiveness.

关键词： Uncertainty Accuracy Computational modeling Focusing Transformers Market research Natural language processing Computational efficiency Context modeling

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Online Examination and Evaluation System

Online Examination and Evaluation System

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iEEE Punecon

作者： Harshad Kumar Dandage Deepak S. Uplaonkar Ankita Shete Avani Shete Lavanya Bodele Shruti Jadhav Department of Computer Engineering Southern Taiwan University of Science and Technology Taiwan Department of Computer Engineering I2IT Pune India Department of IT I2IT Pune India Department of IT I2IT Pune India

ISBN: (纸本)9781665498982

关键词： iEEE Sections Manuals Task analysis

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High-fidelity model to predict heat transfer enhancement for liquid film boiling on uniform micro-structured wicking surfaces

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National Science Review 2024年第6期11卷 93-95页

作者： Vishwanath Ganesan Mohammad Jalal inanlu Nenad Miljkovic Department of Mechanical Science and Engineering University of Illinois Urbana-Champaign Department of Electrical and Computer Engineering University of Illinois Urbana-Champaign Materials Research Laboratory University of Illinois Urbana-Champaign International Institute for Carbon Neutral Energy Research (WPI-I2CNER) Kyushu University Institute for Sustainability Energy and Environment (iSEE)University of Illinois Urbana-Champaign

The field of thermal management in highpower electronic devices is a critical area of study, focusing on the efficient dissipation of heat to prevent device overheating and failure [1]. A key aspect of this field is understanding the dynamics of thin liquid film boiling, both on uniform and non-uniform micro-structured wicking surfaces, which can significantly enhance the heat transfer effectiveness[2].

关键词： structured predict micro liquid model boiling enhancement film heat high-fidelity

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