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Automatic liver-vessel examination from CT slice using Kapur’s thresholding and watershed algorithm

Procedia computer Science 2024年 235卷 1824-1831页

作者： Seifedine Kadry Laith Abualigah Rubén González Crespo Elena Verdú Robertas Damasevicius Vijendra Singh Venkatesan Rajinikanth Department of Applied Data Science Noroff University College Kristiansand Norway Artificial Intelligence Research Center (AIRC) Ajman University Ajman 346 United Arab Emirates Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Computer Science Department Al al-Bayt University Mafraq 25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan MEU Research Unit Middle East University Amman 11831 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang 11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya 27500 Malaysia Higher School of Engineering and Technology Universidad Internacional de La Rioja Logroño 26006 Spain Department of Applied Informatics Vytautas Magnus University 53361 Kaunas Lithuania School of Computer Science University of Petroleum and Energy Studies Dehradun 248007 India Department of Computer Science and Engineering Division of Research and Innovation Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai 602105 India

computerized medical image examination (CMIE) plays a significant role in modern hospitals to achieve the necessary tasks, like segmentation and classification. By segmenting an image, we can extract a particular section for examination. A two-dimensional computed tomography (CT) slice was used for liver-vessel examination (LiVE). A simple automatic technique for supporting LiVE is being developed in this research. A CT slice is collected, a 3D to 2D conversion is done, (ii) Kapur’s tri-level thresholding and Hummingbird-Optimizer is used to enhance the CT slice, (iii) the watershed algorithm (WA) is used to extract the vessel, and (iv) the WA is compared and verified against the segmentation methods chosen. WA provides better segmentation results than other methods because it is an automatic approach. Using the chosen image database, the proposed technique achieves an overall segmentation accuracy of >97%. Other segmentation problems can be used in the future to verify the merit of this scheme.

关键词： liver CT liver-vessel Kapur’s thresholding hummingbird-optimizer watershed-algorithm

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Machine Learning Advances aiding Recognition and Classification of Indian Monuments and Landmarks 8

Machine Learning Advances aiding Recognition and Classificat...

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8th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and computer engineering, UPCON 2021

作者： Paul, Aditya Jyoti Ghose, Smaranjit Aggarwal, Kanishka Nethaji, Niketha Pal, Shivam Dutta Purkayastha, Arnab Department of Computer Science and Engineering Bharati Vidyapeeth's College of Engineering New Delhi India Cognitive Applications Research Lab India Department of Computer Science and Engineering Srm Institute of Science and Technology Chennai India Department of Electronics and Communication Engineering Srm Institute of Science and Technology Chennai India

ISBN: (纸本)9781665409629

Tourism in India plays a quintessential role in the country's economy with an estimated 9.2% GDP share for the year 2018. With a yearly growth rate of 6.2%, the industry holds a huge potential for being the primary driver of the economy as observed in the nations of the Middle East like the United Arab Emirates. The historical and cultural diversity exhibited throughout the geography of the nation is a unique spectacle for people around the world and therefore serves to attract tourists in tens of millions in number every year. Traditionally, tour guides or academic professionals who study these heritage monuments were responsible for providing information to the visitors regarding their architectural and historical significance. However, unfortunately this system has several caveats when considered on a large scale such as unavailability of sufficient trained people, lack of accurate information, failure to convey the richness of details in an attractive format etc. Recently, machine learning approaches revolving around the usage of monument pictures have been shown to be useful for rudimentary analysis of heritage sights. This paper serves as a survey of the research endeavors undertaken in this direction which would eventually provide insights for building an automated decision system that could be utilized to make the experience of tourism in India more modernized for visitors. © 2021 IEEE.

关键词： computer vision

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Supply Chain Management Using Blockchain: Opportunities, Challenges, and Future Directions 2

Supply Chain Management Using Blockchain: Opportunities, Cha...

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2nd International Conference on informatics, ICI 2023

作者： Rajchandar, K. Shameem, A. Biswas, Piali Geetha, B.T. Arunkumar, J.R. Lakineni, Prasanna Kumar SR University Department of CS and AI Warangal Telangana India AMET University AMET Business School India Kolhan University Department of Commerce Jamshedpur Co-Operative College Jamshedpur Jharkhand India SIMATS Saveetha School of Engineering Department of ECE Tamil Nadu India Modern Institute of Technology and Research Centre Department of Computer Science and Engineering Alwar Rajasthan India GITAM University GITAM School of Technology Department of CSE Visakhapatnam India

ISBN: (纸本)9798350343830

Blockchain innovation is being proclaimed as a unique advantage. Numerous examiners see blockchain innovation as a troublesome creation. Blockchain innovation has grabbed the eye of scholastics, chiefs, and specialists the whole way across the world. Blockchain is widely regarded by these entities as a highly disruptive technology that is already reshaping businesses and their supply chain models. Blockchain has the potential to transform supply chain (SC) processes in various ways, such as enhancing SC provenance, improving security, and reengineering business operations. An increasing number of recent studies have explored the use of blockchain in supply chains. Some have shown significant awareness of blockchain's role in functional transparency across various sectors, including e-commerce, agriculture, and public administration. We emphasize the identification of valuable opportunities, potential cultural implications, the current state of the art in advancements, as well as significant trends and challenges. We research a few modern areas, including transportation, fabricating, and auto, flight, finance, innovation, energy, medical care, horticulture and food, online business, and schooling, among others, that can be effectively overhauled with blockchain. The main aim of the study presents a complete evaluation of 178 examination distributions and examines different components of blockchain innovation in SC and coordinated operations the board. © 2023 IEEE.

关键词： Blockchain

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An Exploratory Study of Diverse Models and Datasets for Transfer Learning based Image Classification on Sparse Data

An Exploratory Study of Diverse Models and Datasets for Tran...

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Moratuwa engineering research Conference (MERCon)

作者： Jathurshan Pathmarasa Udaya Wijenayake Ruchire Eranga Wijesinghe Bhagya Nathali Silva Department of Computer Engineering Faculty of Engineering University of Sri Jayewardenepura Nugegoda Sri Lanka Department of Electrical and Electronic Engineering Faculty of Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka Center for Excellence in Informatics Electronics & Transmission (CIET) Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Information Technology Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (数字)9798331529048

ISBN: (纸本)9798331529055

Transfer learning is a powerful technique for image classification, especially when dealing with limited data. However, selection of the best transfer learning approach and model remains challenging, since the strategy is highly influenced by the scenario. This work conducted a thorough analysis of four transfer learning approaches for binary classification, and multiclass classification using four pretrained models i.e. MobileNetV2, MobileNetV3Large, EfficientNetB0, and EfficientNetB5. The test accuracy was measured with respect to the sample size considering the effects of data augmentation and fine tuning. The key finding of the study revealed that data augmentation has improved the test accuracy by up to $11.8 \%$. Moreover, the lightweight models marked a breakthrough by outperforming large scale models’ accuracy for sparse data settings when fine tuning (FT) and data augmentation (DA) are applied appropriately. Hence, this work provides a comprehensive overview on selecting different transfer learning approaches for sparse data classification by exploring diverse models and datasets, while discussing the consequences and challenges for future real-world applications.

关键词： Knowledge engineering Accuracy Transfer learning Optical wavelength conversion Data augmentation Feature extraction Size measurement Data models Tuning Image classification

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The Effectiveness of a Simplified Model Structure for Crowd Counting

arXiv

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arXiv 2024年

作者： Chen, Lei Gao, Xinghang Chao, Fei Chang, Xiang Lin, Chih-Min Gao, Xingen Lin, Shaopeng Zhang, Hongyi Lin, Juqiang The School of Opto-Electronic and Communication Engineering Xiamen University of Technology Xiamen China The Department of Artificial Intelligence School of Informatics Xiamen University Xiamen China The Department of Computer Science Institute of Mathematics Physics and Computer Science Aberystwyth University Aberystwyth United Kingdom The Department of Electrical Engineering Yuan Ze University Taoyuan City320 Taiwan

In the field of crowd counting research, many recent deep learning based methods have demonstrated robust capabilities for accurately estimating crowd sizes. However, the enhancement in their performance often arises from an increase in the complexity of the model structure. This paper discusses how to construct high-performance crowd counting models using only simple structures. We proposes the Fuss-Free Network (FFNet) that is characterized by its simple and efficieny structure, consisting of only a backbone network and a multi-scale feature fusion structure. The multi-scale feature fusion structure is a simple structure consisting of three branches, each only equipped with a focus transition module, and combines the features from these branches through the concatenation operation. Our proposed crowd counting model is trained and evaluated on four widely used public datasets, and it achieves accuracy that is comparable to that of existing complex models. Furthermore, we conduct a comprehensive evaluation by replacing the existing backbones of various models such as FFNet and CCTrans with different networks, including MobileNet-v3, ConvNeXt-Tiny, and Swin-Transformer-Small. The experimental results further indicate that excellent crowd counting performance can be achieved with the simplied structure proposed by us. Copyright © 2024, The Authors. All rights reserved.

关键词： Complex networks

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Development of AI Chatbot for Tourism Promotion: A Case Study in Ranong and Chumphon, Thailand

Development of AI Chatbot for Tourism Promotion: A Case Stud...

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International Joint Conference on computer Science and Software engineering (JCSSE)

作者： Nasith Laosen Kanjana Laosen Piyawat Sriprasert Department of Digital Technology Faculty of Science and Technology Phuket Rajabhat University Phuket Thailand Andaman Intelligent Tourism and Service Informatics Center College of Computing Prince of Songkla University Phuket Thailand Computer Engineering Institute of Engineering Suranaree University of Technology Nakhon Ratchasima Thailand

ISBN: (数字)9798350381764

ISBN: (纸本)9798350381771

Ranong and Chumphon provinces in Thailand pos-sess significant tourism potential due to their natural beauty and archaeological heritage. However, tourism development in the region remains limited due to inadequate information infrastructure and limited technology integration. This research presents a chatbot designed to promote tourism in these locations. The chatbot leverages a graph database built using data from the University to Tambon (U2T) project. Functioning as a virtual travel companion, it provides information on tourist attractions, accommodations, and dining options. Advanced natural language processing and machine learning enable the chatbot to under-stand user inquiries and effectively retrieve relevant information. Evaluation demonstrates strong performance with an F1-score of 99.56 for intent classification and 99.96 for named entity recognition. Testing with user-designed test cases confirmed the chatbot's functionality.

关键词： Adaptation models Databases Training data Prototypes Machine learning Chatbots Data models

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Machine Learning-Based Security Mechanism for Detecting Phishing Attacks

Machine Learning-Based Security Mechanism for Detecting Phis...

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2024 IEEE International Conference on Communications, Computing, Cybersecurity and informatics, CCCI 2024

作者： Bhatt, Pragya Obaidat, Mohammad S. Dangwal, Gagan Das, Ashok Kumar Wazid, Mohammad Sadoun, Balqies Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun248 002 India University of Science and Technology Beijing Beijing100083 China SRM University Kattankulathur603203 India Amity University Noida201301 India Center for Security Theory and Algorithmic Research International Institute of Information Technology Hyderabad500 032 India College of Engineering Al-Balqa' Applied University Al-Salt19117 Jordan

ISBN: (纸本)9798350349832

Phishing attacks pose a serious threat to online users by impersonatina trustworthy entities and stealing sensitive data. This can result in reputational damage, financial loss, ransomware, or additional spyware outbreaks. Phishing scams take into consideration roughly 22% of all data violations, making it one of the most prominent cybercrimes, in accordance with the FBI's 2021 IC3 report. According to phishing email statistics, roughly 1.2% of all emails received are fraudulent, resulting in 3.4 billion phishing emails per day. One out of every 4,200 emails sent is almost certainly a phishing attempt. Cognitive methods, such as machine learning (ML) and deep learning (DL), are widely used in cybersecurity to extract information and detect possible intrusions and phishing attempts. This research investigates the procedure of employing machine learning methods for recognizing URLs that are used for phishing attacks. We have used the Pristine and Malicious URLs Dataset to train, test, and evaluate the performance of a set of algorithms to develop a robust binary class URL phishing attack detection mechanism. The obtained results show that the proposed mechanism outperforms the other existing competing mechanisms with 99.6% accuracy. © 2024 IEEE.

关键词： Phishing

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Hybrid Heuristic Optimization of Deep Learning Models for Robust Plant Disease Detection

Hybrid Heuristic Optimization of Deep Learning Models for Ro...

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Emerging Smart Computing and informatics (ESCI), Conference on

作者： K. Ananthajothi Pravin R. Kshirsagar Tan Kuan Tak Kailash Kumar Department of Computer Science and Engineering Rajalakshmi Engineering College Chennai India Department of Electronics & Telecommunication Engineering J D College of Engineering & Management Nagpur India Electrical Power Engineering Programme Singapore Institute of Technology Singapore College of Computing and Informatics Saudi Electronic University Riyadh Kingdom of Saudi Arabia

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

Plant diseases directly affect farm output by lowering grain, fruit, and vegetable quality, threatening global food security. Thus, farmers inspect plant leaves with their eyes. Plant leaf monitoring is unreliable and error-prone. A multitude of deep learning algorithms have been developed to detect plant leaf diseases; however, most rely on low-resolution photos utilizing convolutional neural nets (CNNs). This research seeks to create an improved nine-layer the CNN network model for the accurate classification of leaf-related illnesses. A contrast enhancement approach is used to pre-process plant leaf pictures. Following binary thresholding, the pre-processed pictures are separated into leaf images and abnormality segmented using the "Enhanced U-Net (EU-Net)" approach. A Multilevel Feature Fusion Network Convolutional Neural Network (MFFN-CNN) is employed to categorize illnesses of leaves based on segmented images. The "Hybrid Leader Cat Swarm Optimization(HLCSO)" method improves U-Net parameter optimization. The effectiveness of the leaf diagnostic model is demonstrated experimentally employing a variety of factors through the use of numerous baseline approaches.

关键词： Deep learning Plant diseases Image segmentation Computational modeling Neural networks Convolutional neural networks Particle swarm optimization informatics Optimization Monitoring

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Statistical Analyses for Key Risk Factor Identification and Prediction of Chronic Kidney Disease *

Statistical Analyses for Key Risk Factor Identification and ...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Ananya Samanta Soham Bandyopadhyay Debasis Samanta Sponsored Research and Institute Consultancy Indian Institute of Technology Kharagpur India Advanced Technology Development Center Indian Institute of Technology Kharagpur India Department of Computer Science & Engineering Indian Institute of Technology Kharagpur India

Chronic renal disease, also known as chronic kidney disease (CKD) is a common disease and is a concern of public health management. Effective techniques for early CKD prediction are desirable. Given a set of biomarkers, Machine learning techniques are known for predicting CKD. This work aims of predicting CKD given clinical data. The proposed work suggests a methodology that includes data prepossessing (i.e. data cleaning, addressing null values and normalizing), applying statistical methodologies for finding key risk factors. Finally, using the most significant risk factors, machine learning techniques is applied to prognosticate the onset of CKD. The proposed approach has been tested with two data sets and proves to be fast, cost-effective and accurate compared to the existing state of the art *** relevance— Prognosticating CKD with a higher accuracy using a minimum number of risk factors is a significant aspects of healthcare informatics, where the treatment cost and predictive results both are optimized towards the betterment of patients.

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Light Weight Cryptography for Cloud-Based E-Health Records 7

Light Weight Cryptography for Cloud-Based E-Health Records

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7th International Conference on Communication and Electronics Systems, ICCES 2022

作者： Mittal, Shikha Monga, Chetna Upreti, Kamal Kumar, Nishant Raut, Ranjana Dinkar Alam, Mohammad Shabbir Chitkara University Institute of Engineering and Technology Punjab India Dr. Akhilesh das Gupta Institute of Technology and Management Department of Computer Science and Engineering India Amity University Amity School of Business Uttar Pradesh Noida India Sant Gadge Baba Amravati University Department of Applied Electronics Maharashtra India Universiti Teknologi Malaysia Razak Faculty of Technology and Informatics Kuala Lumpur Malaysia

ISBN: (数字)9781665496346

ISBN: (纸本)9781665496346

Cloud computing is currently a popular research topic among academics. It is an internet-based resource pool with a wide range of resources. The cloud environment is extremely dependable in terms of making resources available to internet users when they are needed. Reliable computing services can be provided without the need for any additional infrastructure, making it a cost-effective alternative. The majority of the businesses relied on cloud computing to host their apps. The health-care unit service is the most important service for the population. It is critical to keep sensitive information about a patient's medical history in a secure location. As a result, the most secure encryption and decryption should be used. The maintenance of Electronic Health Records is one of the advanced technologies in cloud computing. There is very little research and development in the areas of personal health records and electronic health records. The ultimate goal of this work is to offer a system for exchanging personal health information in a safe cloud setting. The medical information about a patient is likewise kept in this EHR by cloud environment in a dispersed manner. The saved information of user can be collected, shared, exchanged, and organised by other users. In addition, this research study demonstrates an effective way for securing an e-health cloud system in a cloud computing environment utilising identity-based cryptographic technique. © 2022 IEEE.

关键词： Cryptography

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