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Intelligent Deep Learning Based Disease Diagnosis Using Biomedical Tongue Images

computers, Materials & Continua 2022年第3期70卷 5667-5681页

作者： V.Thanikachalam S.Shanthi K.Kalirajan Sayed Abdel-Khalek Mohamed Omri Lotfi M.Ladhar Sri Sivasubramaniya Nadar College of Engineering Chennai603110India Department of Information Technology Department of Electronics and Communication EngineeringCARE College of EngineeringTiruchirappalli620009India Department of Electronics and Communication Engineering KPR Institute of Engineering and TechnologyCoimbatore641407India Department of Mathematics and Statistics College of ScienceTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia Mathematics Department Faculty of ScienceSohag UniversitySohagEgypt Deanship of Scientific Research King Abdulaziz UniversityJeddahSaudi Arabia Department of Electrical and Computer Engineering Faculty of EngineeringKing Abdulaziz UniversityJeddahSaudi Arabia

The rapid development of biomedical imaging modalities led to its wide application in disease ***-based diagnostic procedures are proficient and non-invasive in nature to carry out secondary diagnostic processes ***,physicians examine the characteristics of tongue prior to *** this scenario,to get rid of qualitative aspects,tongue images can be quantitatively inspected for which a new disease diagnosis model is *** model can reduce the physical harm made to the *** tongue image analytical methodologies have been proposed ***,there is a need exists to design an intelligent Deep Learning(DL)based disease diagnosis *** this motivation,the current research article designs an Intelligent DL-basedDisease Diagnosis method using Biomedical Tongue Images called IDLDD-BTI *** proposed IDLDD-BTI model incorporates Fuzzy-based Adaptive Median Filtering(FADM)technique for noise removal ***,SqueezeNet model is employed as a feature extractor in which the hyperparameters of SqueezeNet are tuned using Oppositional Glowworm Swarm Optimization(OGSO)*** last,Weighted Extreme Learning Machine(WELM)classifier is applied to allocate proper class labels for input tongue color *** design of OGSO algorithm for SqueezeNet model shows the novelty of the *** assess the enhanced diagnostic performance of the presented IDLDD-BTI technique,a series of simulations was conducted on benchmark dataset and the results were examined in terms of several *** resultant experimental values highlighted the supremacy of IDLDD-BTI model over other state-of-the-art methods.

关键词： Biomedical images image processing tongue color image deep learning squeezenet disease diagnosis

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A Novel Multiple Dependent State Sampling Plan Based on Time Truncated Life Tests Using Mean Lifetime

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computers, Materials & Continua 2022年第12期73卷 4611-4626页

作者： Pramote Charongrattanasakul Wimonmas Bamrungsetthapong Poom Kumam Division of Mathematics Department of Mathematics and Computer ScienceFaculty of Science and TechnologyRajamangala University of Technology KrungthepBangkok10120Thailand Division of Applied Statistics Department of Mathematics and Computer ScienceFaculty of Science and TechnologyRajamangala University of Technology ThanyaburiPathum Thani12110Thailand Center of Excellence in Theoretical and Computational Science(TaCS-CoE)&KMUTT Fixed Point Research Laboratory Room SCL 802 Fixed Point LaboratoryScience Laboratory BuildingDepartments of MathematicsFaculty of ScienceKing Mongkut’s University of Technology Thonburi(KMUTT)Bangkok10140Thailand

The design of a new adaptive version of the multiple dependent state(AMDS)sampling plan is presented based on the time truncated life test under the Weibull *** achieved the proposed sampling plan by applying the concept of the double sampling plan and existing multiple dependent state sampling plans.A warning sign for acceptance number was proposed to increase the probability of current lot *** optimal plan parameters were determined simultaneously with nonlinear optimization problems under the producer’s risk and consumer’s risk.A simulation study was presented to support the proposed sampling plan.A comparison between the proposed and existing sampling plans,namely multiple dependent state(MDS)sampling plans and a modified multiple dependent state(MMDS)sampling plan,was considered under the average sampling number and operating characteristic curve *** addition,the use of two real datasets demonstrated the practicality and usefulness of the proposed sampling *** results indicated that the proposed plan is more flexible and efficient in terms of the average sample number compared to the existing MDS and MMDS sampling plans.

关键词： Adaptive version of multiple dependent state sampling plan time truncated life test quality level weibull distribution mean lifetime

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research on Traffic Sign Detection Based on Improved YOLOv8

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Journal of computer and Communications 2023年第7期11卷 226-232页

作者： Zhongjie Huang Lintao Li Gerd Christian Krizek Linhao Sun School of Computer and Information Engineering Xinxiang University Xinxiang China Department of Applied Mathematics and Physics UAS Technikum Wien Vienna Austria

Aiming at solving the problem of missed detection and low accuracy in detecting traffic signs in the wild, an improved method of YOLOv8 is proposed. Firstly, combined with the characteristics of small target objects in the actual scene, this paper further adds blur and noise operation. Then, the asymptotic feature pyramid network (AFPN) is introduced to highlight the influence of key layer features after feature fusion, and simultaneously solve the direct interaction of non-adjacent layers. Experimental results on the TT100K dataset show that compared with the YOLOv8, the detection accuracy and recall are higher. .

关键词： Traffic Sign Detection Small Object Detection YOLOv8 Feature Fusion

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Automated Fracture Detection and Classification Using Siamese Neural Networks

Automated Fracture Detection and Classification Using Siames...

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2024 International Conference on Decision Aid Sciences and Applications, DASA 2024

作者： Anderson, K.L. Engelbrecht, A.P. Birkholtz, F.F. Stellenbosch University Department of Computer Science Stellenbosch South Africa Stellenbosch University Computer Science Division Department of Industrial Engineering Stellenbosch South Africa Gulf University of Science and Technology GUST Engineering and Applied Innovation Research Centre Kuwait Institute of Orthopaedics and Rheumatology Mediclinic Winelands Orthopaedic Hospital Stellenbosch University Stellenbosch Division of Orthopaedic Surgery Department of Surgical Sciences Stellenbosch South Africa

ISBN: (纸本)9798350369106

This study aims to address the critical need for accurate and efficient fracture detection and classification in medical radiography, by leveraging recent developments in deep learning techniques. A two-stage pipeline is proposed combining a cutting-edge convolutional neural network (CNN), You only look once (YOLOv9), for wrist fracture detection using a Siamese neural network (SNN) for fracture type classification. The proposed methods aim to overcome the limitations of a single-stage approach by optimizing detection and classification stages independently. The results obtained demonstrate the efficacy of the two-stage pipeline in improving accuracy and reducing class imbalance issues, highlighting the potential of artificial intelligence (AI) driven solutions in enhancing patient care and reducing diagnosis time. © 2024 IEEE.

关键词： Convolutional neural networks

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Selection of Electrical Distribution System using SVAS based Einstein Averaging Operator 4

Selection of Electrical Distribution System using SVAS based...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Joshi, B.P. Sachdeva, Shweta Pant, Sangeeta Goyal, Shweta Kumar, Anuj Kharayat, Pushpendra Singh Department of Mathematics Graphic Era Hill University Bhimtal263136 India Department of Computer Science & Enggineering Graphic Era Hill University Bhimtal263136 India Dev Bhoomi Uttarakhand University School of Engineering and Computing Uttarakhand Dehradun India Department of Applied Sciences Pune Maharashtra Lavale412115 India Graphic Era Deemed to Be University Department of Electrical Engineering Dehradun248002 India School of Computer Science Engg. & Applications Akrudi Maharashtra Pune411044 India

ISBN: (纸本)9798350360165

The contribution of this study is to present the Einstein Weighted Averaging Operator (EWAO) based on the Einstein Sum (ES) under the Single-Valued Ambiguous Set (SVAS) information, which is termed as 'SVASEA'. The SVAS theory (SVAST) was offered to address ambiguity as the generalization of fuzzy set (FS). It is powerful than the FS and Intuitionistic FS due to four different degrees of memberships. Then, to apply the SVASea operator to real life problems, a framework is presented to include it in the information fusion of decision making (DM) process. Moreover, the selection of Electrical Distribution System (EDS) is carried out using the proposed operator and proposed framework to show its applicability. The obtained results and comparative analysis show the feasibility and validity of the SVASEA operator. © 2024 IEEE.

关键词： Information fusion

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An integrated framework based deep learning for cancer classification using microarray datasets

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Journal of Ambient Intelligence and Humanized Computing 2023年第3期14卷 2249-2260页

作者： Alrefai, Nashat Ibrahim, Othman Shehzad, Hafiz Muhammad Faisal Altigani, Abdelrahman Abu-ulbeh, Waheeb Alzaqebah, Malek Alsmadi, Mutasem K. Johor Johor Bahru81310 Malaysia Johor Skudai81310 Malaysia Department of Computer Science and IT University of Sargodha Sargodha40100 Pakistan Computer Science Department College of Computer Science and Information Technology Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia Faculty of Information Technology Al Istiqlal University Jericho4728 Palestine Department of Mathematics College of Science Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia Basic and Applied Scientific Research Center Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia Department of MIS College of Applied Studies and Community Service Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia

Around the world, cancer is one of the leading reasons of mortality. The importance of earlier detection and prognosis of cancer types is highly significant for patients’ health. In recent research, deep neural networks were trained using gene expression microarray, to classify cancer. Biologists are able to monitor thousands of genes in one experiment using microarray technology. Microarray datasets are considered high-dimensional data, as they are cluttered with irrelevant, redundant, and noisy genes that contribute insignificantly to classification. The most informative genes contributing to cancer classification have been identified using computational intelligence algorithms. In this paper, we propose an integrated framework for cancer classification. This framework is divided into three tasks. Firstly, particle swarm optimization with ensemble learning (PSO-ensemble) reduces the microarray dataset's high dimensionality. Secondly, The Adaptive self-training method (ASTM) is used to solve low-size issues. Finally, a Convolutional Neural Network (CNN) was employed for classification. CNN has the ability to discover the complex non-linear relationships between features and select the most informative. Transfer learning was used sequentially with CNN to integrate the classification procedure because it can reduce the training time and computational complexity. Six microarray datasets are used, namely liver, breast, colon, prostate, central nervous system, and lung. The proposed CNN architecture with transfer learning provided 100% classification accuracy for colon, prostate, CNS and lung microarray datasets, and 97.62%, 95.45% accuracy for liver and breast cancer respectively. Experiments show that our proposed method delivers the highest classification accuracy and reduces training time with the smallest gene subset. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Convolutional neural networks

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An Enhanced Authentication Protocol Suitable for Constrained RFID Systems

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IEEE Access 2024年 12卷 61610-61628页

作者： Hosseinzadeh, Mehdi Servati, Mohammad Reza Rahmani, Amir Masoud Safkhani, Masoumeh Lansky, Jan Janoscova, Renata Ahmed, Omed Hassan Tanveer, Jawad Lee, Sang-Woong Duy Tan University Institute of Research and Development Da Nang550000 Viet Nam Duy Tan University School of Medicine and Pharmacy Da Nang550000 Viet Nam Shahid Rajaee Teacher Training University Department of Computer Engineering Tehran16788-15811 Iran National Yunlin University of Science and Technology Future Technology Research Center Douliou Yunlin64002 Taiwan School of Computer Science Tehran19395-5746 Iran University of Finance and Administration Faculty of Economic Studies Department of Computer Science and Mathematics Prague101 00 Czech Republic University of Human Development Department of Information Technology Sulaymaniyah0778-6 Iraq Sejong University Department of Computer Science and Engineering Seoul05006 Korea Republic of Gachon University Pattern Recognition and Machine Learning Laboratory Department of Ai Software Seongnam-si13557 Korea Republic of

RFID technology offers an affordable and user-friendly solution for contactless identification of objects and individuals. However, the widespread adoption of RFID systems raises concerns regarding security and privacy. Vulnerabilities such as message tampering, interception, and eavesdropping pose significant risks to the integrity of the system. This study examines the effectiveness of two recently proposed ultra-lightweight RFID authentication protocols, URASP and KUAJB, in addressing these security challenges. Surprisingly, our findings reveal that both URASP and KUAJB protocols are susceptible to secret disclosure attacks, despite their claims of providing robust security. Consequently, the development of a truly ultra-lightweight protocol that ensures adequate security becomes a formidable task. As a potential solution, the enhancement of the URASP protocol through the integration of a simple cryptographic primitive is suggested to bolster its security measures. © 2013 IEEE.

关键词： Hash functions

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Efficient Chest X-Ray Investigation Using Firefly Algorithm Optimized Deep and Handcrafted Features 9th

Efficient Chest X-Ray Investigation Using Firefly Algorithm ...

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9th International Conference on Mining Intelligence and Knowledge Exploration, MIKE 2023

作者： Kadry, Seifedine Al-Betar, Mohammed Azmi Yassine, Sahar Mohan, Ramya Arunmozhi, Rama Rajinikanth, Venkatesan Department of Applied Data Science Noroff University College Kristiansand4612 Norway College of Engineering and Information Technology Ajman University Ajman United Arab Emirates Department of Computer Science and Engineering Division of Research and Innovation Saveetha School of Engineering SIMATS Chennai602105 India

ISBN: (纸本)9783031440830

Radiological imaging of the chest (X-ray) is a cost-effective, widely accepted method of examining the lungs and abnormalities. During this research, a clinically significant Convolutional-Neural-Network (CNN) framework will be proposed for the examination of lung abnormalities. The proposed scheme aims to achieve better detection accuracy from the selected chest X-ray data. The following stages are included in these techniques: (i) CNN segmentation of the lung section, (ii) Deep-feature extraction utilizing selected CNN schemes, (iii) Handcrafted-feature extraction, (iv) Optimizing features using Firefly Algorithms, and (v) Binary classification and cross-validation of fivefold cross-validation. By implementing the pre-trained VGG-UNet, this framework is capable of extracting lungs sections from X-ray images. Using this lung segment, handcrafted features such as Local Binary Patterns (LBP) and Pyramid Histograms of Oriented Gradients (PHOG) are obtained. DFs and HFs are obtained using the FA, and then a serial concatenation is performed in order to obtain a hybrid feature vector. This feature vector is used to classify X-ray images into healthy and diseased groups. For examination in this study, X-ray images of disease classes, such as tuberculosis, COVID19, pneumonia, lung masses, and effusions, are considered. Based on the experimental results of this study, >98% of disease detection accuracy was confirmed using the proposed scheme combined with the SoftMax classifier. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Feature extraction

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Cyberbullying Detection and Recognition with Type Determination Based on Machine Learning

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computers, Materials & Continua 2023年第6期75卷 5307-5319页

作者： Khalid M.O.Nahar Mohammad Alauthman Saud Yonbawi Ammar Almomani Computer Science Department Yarmouk UniversityIrbidJordan Department of Information Security Faculty of Information TechnologyUniversity of PetraAmmanJordan Software Engineering Department University of JeddahJeddahKSA Research and Innovation Department Skyline University CollegeP.O.Box 1797SharjahUAE IT Department-Al-Huson University College Al-Balqa Applied UniversityP.O.Box 50IrbidJordan

Social media networks are becoming essential to our daily activities,and many issues are due to this great involvement in our *** is a social media network issue,a global crisis affecting the victims and society as a *** results from a misunderstanding regarding freedom of *** this work,we proposed a methodology for detecting such behaviors(bullying,harassment,and hate-related texts)using supervised machine learning algo-rithms(SVM,Naïve Bayes,Logistic regression,and random forest)and for predicting a topic associated with these text data using unsupervised natural language processing,such as latent Dirichlet *** addition,we used accuracy,precision,recall,and F1 score to assess prior *** show that the use of logistic regression,support vector machine,random forest model,and Naïve Bayes has 95%,94.97%,94.66%,and 93.1%accuracy,respectively.

关键词： Cyberbullying social media naïve bayes support vector machine natural language processing LDA

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Insights into Numerical Solution of First-order Growth Models using Hybrid Block Methods with Four Collocation Points

Insights into Numerical Solution of First-order Growth Model...

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2024 International Conference on Science, engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Uwaifor, John Kehinde Olaoluwa Omole, Ezekiel Onu, Peter Abualigah, Laith Emadifar, Homan Oladimeji, Samuel Olawale University of Abuja Department of Mathematics Abuja Nigeria Landmark University Department of Physical Sciences Mathematics Programme Omu-Aran Nigeria Omu-Aran Nigeria Research Center Tabuk1491 Saudi Arabia Al-Alhiyya Amman University Hourani Center for Applied Scientific Research Amman19328 Jordan Middle East University MEU Research Unit Amman11831 Jordan Saveetha University Saveetha Institute of Medical and Technical Sciences Department of Mathematics Tamil Nadu India Landmark University Department of Mechanical Engineering Omu-Aran Nigeria Omu-Aran Nigeria

ISBN: (纸本)9798350358155

First-order growth models are fundamental in economics, finance, and various scientific disciplines for modeling dynamic processes. Solving these models numerically is often challenging due to their complex nature. Hybrid block methods have emerged as promising tools for efficiently and accurately solving such models. This article provides a comprehensive development, analysis and implementation of hybrid block method applied to the numerical solution of first-order growth models. We discuss the theoretical foundations, algorithmic implementations, and practical applications of these methods, highlighting their advantages and limitations. Additionally, we present numerical examples and comparisons with other numerical techniques to assess the performance of hybrid block method. Through these insights, we aim to provide insights into the state-of-the-art techniques for solving first-order growth models numerically. © 2024 IEEE.

关键词： Ordinary differential equations

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