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Detecting Laryngeal Cancer Lesions from Endoscopy Images Using Deep Ensemble Model

Detecting Laryngeal Cancer Lesions from Endoscopy Images Usi...

2023 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2023

作者： Bhattacharjee, Ramanuj Suganya Devi, K. Vijaykanth, S. National Institute of Technology Silchar Medical Imaging Laboratory Department of Computer Science and Engineering Silchar India Cachar Cancer Hospital and Research Centre Department of Medical Oncology Silchar India

ISBN: (纸本)9798350312126

To improve the chances of survival for a patient with laryngeal cancer, early detection is crucial. Currently, the standard diagnostic method involves an endoscopic examination of the larynx, followed by a biopsy and histological analysis by an oncologist, which can be subject to variability due to subjective evaluation. Therefore, there is a need for a faster and more accurate detection system that can replace the current manual examination. Recent research has shown that Deep Learning technology can assist in identifying laryngeal cancer, including precancerous and cancerous tumors, from endoscopic pictures. However, endoscopic image processing is a challenging task due to the highly dynamic nature of the endoscopic video, spectrum fluctuations, and numerous image interferences. To address this challenge, a Deep Ensemble Learning approach using convolutional neural networks (CNNs) and an effective image segmentation technique has been proposed. The suggested model has an overall accuracy of 98.12%. © 2023 IEEE.

关键词： Image segmentation

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Estimation Analysis of Edge and Line Detection Methods in Digital Image Processing 3

Estimation Analysis of Edge and Line Detection Methods in Di...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Garg, Prateek Yadav, Rakesh Kumar Devaraj Verma, C. Nirmala, D. Sable, Nilesh P. Murari, Krishn Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Computer Science and Engineering Karnataka Ramnagar562112 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Information Technology Pune India Vivekananda Global University Department of Civil Engineering Jaipur India

ISBN: (纸本)9798350319125

This paper provides an estimation evaluation of the area and line detection methods in virtual photo processing. This analysis evaluates the performance of numerous facet and line detection algorithms in phrases of facet/line first-rate, accuracy, computational complexity, and robustness to noise in the context of artificial and actualinternational snapshots. Mainly, it discusses the overall performance of 4 edge detection methods: Roberts, Prewitt, Sobel, and Canny;and 3 line detection techniques: Hough remodel, Linear Estimation, and Probabilistic Hough remodel. as compared to the classical algorithms, the Probabilistic Hough rework algorithm is determined to have the excellent accuracy and robustness to noise. Furthermore, a contrast of computational complexities shows that the Hough transform has the bottom complexity and computational time, even as the Linear Estimation algorithm has the best complexity and computational time. The primary outcome of this study is that the brink/line first-rate, accuracy, computational complexity, and robustness are very dependent on the sort of entered snapshots and the respective selected parameters. © 2023 IEEE.

关键词： comparison computational performance Probabilistic Transform

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A Double-Side Self-Tuning IPT Converter Immune to Parameters Variation Under Misalignment Condition 50

A Double-Side Self-Tuning IPT Converter Immune to Parameters...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Zou, Bowei Huang, Zhicong Iam, Io-Wa Chi-Seng, Lam Shien-Ming Wu School of Intelligent Engineering South China University of Technology Guangzhou China State Key Laboratory of Analog and Mixed-Signal VLSI University of Macau China Institute of Microelectronics University of Macau China Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau China

ISBN: (纸本)9781665464543

For inductive power transfer (IPT) systems, the loosely coupled transformer (LCT) is a crucial component. Variations in the air gap can lead to fluctuations in the parameters of the LCT, such as self-inductance and mutual inductance, caused by positional deviations of the ferrite cores on both sides. However, such variable LCT parameters can damage the resonant tank, thus impacting transmission efficiency and output stability. To address this issue, this paper proposes a self-tuning S/S IPT system based on switch-controlled capacitors (SCCs). The system utilizes gradient descent methods independently on double-side to achieve decoupled tuning control. This approach effectively mitigates the impact of LCT parameter fluctuations without requiring wireless communication. Furthermore, all switches in the system operate at fixed frequencies and implement soft switching. Finally, a prototype is constructed to validate the effectiveness of the proposed system. © 2024 IEEE.

关键词： Energy transfer

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Evaluation of Deep Learning-Based Segmentation Models for Forest Cover Detection in Iran: A Study on Sentinel-2 Imagery

Evaluation of Deep Learning-Based Segmentation Models for Fo...

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International Conference on Web Research (ICWR)

作者： Erfan Khoshnevisan Sina Khoshnevisan Saeid Gharechelou Seyede Fatemeh Khakzad Faculty of Computer Science Shahrood University of Technology Shahrood Iran Faculty of Civil Engineering Shahrood University of Technology Shahrood Iran

ISBN: (数字)9798331508913

ISBN: (纸本)9798331508920

Forests, as critical pillars of ecosystems, play an essential role in stabilizing the climate and preserving biodiversity. Industrial development, urban sprawl, and unsustainable exploitation have led to a reduction in forest cover and an increase in wildfire occurrences, particularly in regions of Iran where the invaluable Hyrcanian and Alborz forests are under threat. In this research, Sentinel-2 satellite images with a 10-meter spatial resolution were meticulously annotated and partitioned into training, validation, and test sets to assess deep learning-based image segmentation techniques for detecting forested areas. For this purpose, three deep learning models—Nested U-Net, SegNet, and FCN—were compared in terms of their ability to accurately differentiate forest cover from other land cover types. The results indicate that the Nested U-Net model, achieving an IoU of 81.36% and a Recall of 99.54%, outperforms the other models, demonstrating a high capacity for multi-scale feature extraction and precise delineation of forest boundaries. These findings underscore the significance of integrating remote sensing technologies with advanced segmentation algorithms in the management and conservation of natural resources.

关键词： Deep learning Natural resources Training Image segmentation Forests Thermal sensors Transformers Satellite images Spatial resolution Remote sensing

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Characterization of interaction aggregating operators setting interval-valued Pythogorean neutrosophic set

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Neutrosophic Sets and Systems 2025年 81卷 285-305页

作者： Hatamleh, Raed Heilat, Ahmed Salem Palanikumar, M. Al-Husban, Abdallah Department of Mathematics Faculty of Science Jadara University P.O. Box 733 Irbid21110 Jordan Department of Mathematics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602105 India Department of Mathematics Faculty of Science and Technology Irbid National University P.O. Box: 2600 Irbid Jordan Jadara Research Center Jadara University Irbid21110 Jordan

In this work, we present novel techniques for the interval-valued Pythogorean neutrosophic interaction aggregating operator. A hybrid of the neutrosophic set and the interval-valued Pythogorean fuzzy set. The innovative averaging and geometric operations of interval-valued Pythogorean neutrosophic interaction numbers are studied using aggregation operator. The interval-valued Pythogorean neutrosophic interaction is boundedness compatible, idempotent, associative, and commutative. Four new aggregating operators are introduced: IPNI weighted averaging operator, interval-valued Pythogorean neutrosophic interaction weighted geometric operator, generalized interval-valued Pythogorean neutrosophic interaction weighted averaging and generalized interval-valued Pythogorean neutrosophic interaction weighted geometric. © 2025, University of New Mexico. All rights reserved.

关键词： Phytochemistry

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An efficient scheduling mechanism for IoT-based home automation system

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International Journal of Electronic Business 2021年第2期16卷 147-156页

作者： Potluri, Sirisha Mohanty, Sachi Nandan Department of Computer Science and Engineering Faculty of Science and Technology ICFAI Foundation for Higher Education Telangana Hyderabad501203 India

Using internet of things (IoT), we can connect and enable every object to send or receive data through the internet. When it comes to real life applications we can make use of this technology for easy communication through home automation. Smart homes, smart agriculture, smart automobiles, and smart electronic gadgets are the examples of IoT-based applications. When more number of objects is connected in distributed environment, controlling and managing the device is much difficult. Due to IoT enabled framework, the number of requests coming to the cloud computing environment is increasing exponentially. The scheduling algorithm which is used in such cases should be efficient to handle various requests coming from these connected devices. The objective of this paper is to develop efficient scheduling mechanism which controls home appliances remotely by using any android or smart phone. Since the devices are connected over cloud computing environment, the proposed method can efficiently schedule the tasks. Copyright © 2021 Inderscience Enterprises Ltd.

关键词： Cloud computing

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Space Decomposition for Sentence Embedding

arXiv

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arXiv 2024年

作者： Ponwitayarat, Wuttikorn Limkonchotiwat, Peerat Chuangsuwanich, Ekapol Nutanong, Sarana School of Information Science and Technology VISTEC Thailand Department of Computer Engineering Faculty of Engineering Chulalongkorn University Thailand

Determining sentence pair similarity is crucial for various NLP tasks. A common technique to address this is typically evaluated on a continuous semantic textual similarity scale from 0 to 5. However, based on a linguistic observation in STS annotation guidelines, we found that the score in the range [4,5] indicates an upper-range sample, while the rest are lower-range samples. This necessitates a new approach to treating the upper-range and lower-range classes separately. In this paper, we introduce a novel embedding space decomposition method called MixSP utilizing a Mixture of Specialized Projectors, designed to distinguish and rank upper-range and lower-range samples accurately. The experimental results demonstrate that MixSP decreased the overlap representation between upper-range and lower-range classes significantly while outperforming competitors on STS and zero-shot benchmarks. 1 Copyright © 2024, The Authors. All rights reserved.

关键词： Embeddings

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Design Strategies for Machine Learning Algorithms for Text Classification 3

Design Strategies for Machine Learning Algorithms for Text C...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Junnarkar, Aparna A. Jakhar, Sunil Kumar Nagpal, Manish Yadav, Rakesh Kumar Moger, Vinod Sindhu, M. Vishwakarma Institute of Information Technology Department of Information Technology Pune India Vivekananda Global University Department of Mechanical Engineering Jaipur India Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Mechanical Engineering Karnataka Ramnagar562112 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai127 India

ISBN: (纸本)9798350319125

The text type is a vital undertaking within the realm of gadget-gaining knowledge of algorithms and can contain classifying text files into predefined classes. Text category is used in many herbal language processing obligations, sentiment evaluation, topic modeling, and record summarization. Design techniques for device mastering algorithms for textual content categories contain information preprocessing, feature engineering, model choice, and evaluation. Records preprocessing requires expertise in text information and includes the stairs of tokenizing the text records, removing stopwords, acting stemming and lemmatization, part-of-speech tagging, and vectorization. Feature engineering, which is a step that requires domain expertise, applies strategies consisting of feature selection, function extraction, and feature transformation. Version choice and optimization techniques, including hyperparameter tuning and version choice, are used to choose the most appropriate studying algorithm for the task. Lastly, assessment metrics evaluate the model's effectiveness, including accuracy, precision, remembering, and f-ratings. On average, these layout techniques are critical components while growing a successful text-type model. © 2023 IEEE.

关键词： Classification (of information)

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Brain Tumor Classification and Segmentation Using Dual-Outputs for U-Net Architecture: O2U-Net

Brain Tumor Classification and Segmentation Using Dual-Outpu...

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21st International Conference on Informatics, Management, and technology in Healthcare, ICIMTH 2023

作者： Zargari, Seyed Aman Kia, Zahra Sadat Nickfarjam, Ali Mohammad Hieber, Daniel Holl, Felix Electrical Engineering Department Faculty of Electrical and Computer Engineering Semnan University Semnan Iran Department of Health Information Management and Technology School of Allied Medical Sciences Kashan University of Medical Science Kashan Iran Health Information Management Research Center Kashan University of Medical Science Kashan Iran Institute DigiHealth Neu-Ulm University of Applied Sciences Neu-Ulm Germany

ISBN: (纸本)9781643684000

We propose a modified version of the U-Net architecture for segmenting and classifying brain tumors, introducing another output between down- and up-sampling. Our proposed architecture utilizes two outputs, adding a classification output beside the segmentation output. The central idea is to use fully connected layers to classify each image before applying U-Net's up-sampling operations. This is achieved by utilizing the features extracted during the down-sampling procedure and combining them with fully connected layers for classification. Afterward, the segmented image is generated by U-Net's up-sampling process. Initial tests show competitive results against comparable models with 80.83%, 99.34%, and 77.39% for the dice coefficient, accuracy, and sensitivity, respectively. The tests were conducted on the well-established dataset from Nanfang Hospital, Guangzhou, China, and General Hospital, Tianjin medical University, China, from 2005 to 2010 containing MRI images of 3064 brain tumors. © 2023 The authors and IOS Press.

关键词： Tumors

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Improving Real-Time Data Analysis Accuracy with Machine Learning Tools 3

Improving Real-Time Data Analysis Accuracy with Machine Lear...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Singla, Abhishek Divya, K. Manohar, K. Kantak, Ganesh Kumar Singh, Akhilendra Pratap Rajesh, A. Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Information Technology Pune India Vivekananda Global University Department of Mechanical Engineering Jaipur India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India

ISBN: (纸本)9798350319125

Gadget studying can be used to enhance the accuracy of actual-time records evaluation. System-gaining knowledge can discover patterns and tendencies in information extra quickly and accurately while not having to manually analyze the data, allowing for decisions to be made with extra self-belief and accuracy. By means of incorporating capabilities that tune user interaction, machines gaining knowledge can use supervised and unsupervised studying to regulate the models to beautify accuracy. This paper explores the use of machine learning tools to improve the accuracy of real-time data analysis. With the constant influx of data in various industries, accurate and timely analysis is crucial for making informed decisions. However, traditional data analysis methods may fall short in handling the complexity and volume of real-time data. Machine learning algorithms offer the potential to overcome these challenges by autonomously identifying patterns and trends in real-time data. This paper discusses the benefits of incorporating machine learning in real-time data analysis and provides a comprehensive review of existing tools and techniques. Through case studies and experiments, we demonstrate the effectiveness of machine learning in improving the accuracy of real-time data analysis. The results of our study highlight the potential of machine learning tools in enhancing decision-making processes and driving better outcomes in various industries. Furthermore, we discuss the challenges and limitations of using machine learning in real-time data analysis and provide recommendations for future research in this area. This method of the usage of system gaining knowledge of tools will increase the efficiency and accuracy of real-time records evaluation and allows to make knowledgeable choices which can result in better effects. © 2023 IEEE.

关键词： Anomaly detection

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