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Fuzzy validation of Taguchi-based convolutional fuzzy neural classifier for lung cancer imaging

Multimedia Tools and Applications 2024年第41期83卷 89415-89437页

作者： Chang, Tsang-Chuan Lin, Cheng-Jian Yang, Tang-Yun Department of Intelligent Production Engineering National Taichung University of Science and Technology Taichung404336 Taiwan Department of Computer Science and Information Engineering National Chin-Yi University of Technology Taichung411030 Taiwan

While deep learning technology is widely used in the field of image classification and recognition, parameter setting for convolutional neural networks is complex, and a high number of parameters make the technology difficult to apply in practice. Therefore, this study proposes a Taguchi-based convolutional fuzzy neural classifier (T-CFNC) to classify computed tomography (CT) images. Two layers of convolution and pooling are used to extract features of the input images, and a fuzzy neural network is used to replace fully-connected neural networks to reduce the number of model parameters. To reduce cost and time, the Taguchi experimental design method determines the optimal combination of model parameters with the minimal number of experiments. The SPIE-AAPM lung CT challenge dataset was used to validate the proposed T-CFNC model. Experimental results indicate accuracy of 99.95%, a true positive rate of 99.97%, and a true negative rate of 99.94%. While the confusion matrix is commonly applied to evaluate model performance, its accuracy varies with the quality of model training, and it is easily affected by extreme values. Single and average values also incur the possibility of misjudgment. We therefore further propose fuzzy validation for performance evaluation. Results confirm the superiority of the proposed T-CFNC model in terms of lung cancer image classification over the traditional CFNC model. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Lung cancer

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Automated face recognition using deep learning technique and center symmetric multivariant local binary pattern

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Neural Computing and Applications 2025年第1期37卷 263-281页

作者： Sekhar, J.C. Josephson, P. Joel Chinnasamy, A. Maheswari, M. Sankar, S. Kalangi, Ruth Ramya Department of Computer Science and Engineering NRI Institute of Technology Andhra Pradesh Guntur India Department of Electronics and Communication Engineering Malla Reddy Engineering College Telangana Hyderabad India Department of Data Science and Business Systems School of Computing SRMIST Kattankulathur Chennai India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vijayawada India

Researchers have recently created several deep learning strategies for various tasks, and facial recognition has made remarkable progress in employing these techniques. Face recognition is a noncontact, nonobligatory, acceptable, and harmonious biometric recognition method with a promising national and social security future. The purpose of this paper is to improve the existing face recognition algorithm, investigate extensive data-driven face recognition methods, and propose a unique automated face recognition methodology based on generative adversarial networks (GANs) and the center symmetric multivariable local binary pattern (CS-MLBP). To begin, this paper employs the center symmetric multivariant local binary pattern (CS-MLBP) algorithm to extract the texture features of the face, addressing the issue that C2DPCA (column-based two-dimensional principle component analysis) does an excellent job of removing the global characteristics of the face but struggles to process the local features of the face under large samples. The extracted texture features are combined with the international features retrieved using C2DPCA to generate a multifeatured face. The proposed method, GAN-CS-MLBP, syndicates the power of GAN with the robustness of CS-MLBP, resulting in an accurate and efficient face recognition system. Deep learning algorithms, mainly neural networks, automatically extract discriminative properties from facial images. The learned features capture low-level information and high-level meanings, permitting the model to distinguish among dissimilar persons more successfully. To assess the proposed technique’s GAN-CS-MLBP performance, extensive experiments are performed on benchmark face recognition datasets such as LFW, YTF, and CASIA-WebFace. Giving to the findings, our method exceeds state-of-the-art facial recognition systems in terms of recognition accuracy and resilience. The proposed automatic face recognition system GAN-CS-MLBP provides a solid basis for a

关键词： Principal component analysis

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Anonymous and robust biometric authentication scheme for secure social IoT healthcare applications

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Journal of engineering and Applied Science 2024年第1期71卷 1-23页

作者： Arpitha, T. Chouhan, Dharamendra Shreyas, J. Department of Computer Science and Engineering University Visvesvaraya College of Engineering Bangalore University Bengaluru India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India

In the era of rapid technological advancement, the Internet of Things (IoT) has revolutionised healthcare through systems like the Telecare Medicine information System (TMIS), designed to streamline patient-doctor interactions and enhance medical treatment. However, the transmission of sensitive patient data over inherently insecure Internet channels exposes it to a spectrum of security risks. Protecting patient medical privacy and ensuring system reliability necessitate mutual authentication between both patients and medical servers. TMIS relies on robust authentication mechanisms, and combining passwords and smart cards has been a recognised approach for mutual authentication. This research introduces an innovative three-factor authentication technique with perfect forward secrecy by leveraging the power of Elliptic Curve Cryptography (ECC) in tandem with smart cards. Additionally, we have incorporated biometric authentication with a Fuzzy Extractor technology to enhance the security and reliability of the system, setting a new standard for user authentication within the realm of Social IoT healthcare. The use of ECC in the method is justified due to its compact key size and robust security measures, making the solution both efficient and secure. The proposed method safeguards user privacy by permitting registered users to change their passwords without divulging their identity to the server. The Burrows–Abadi–Needham logic (also known as the BAN logic) serves as a proof-of-concept for the proposed scheme’s security. Our system provides privacy protection along with mutual authentication and session key negotiation at a considerably low computation cost and communication cost of up to 71.03% compared to the other four relevant techniques, making it more useful in real-world scenarios. © 2023, The Author(s).

关键词： Smart cards

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Deep Learning-based Environmental Sound Classification Using Feature Fusion and Data Enhancement

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computers, Materials & Continua 2023年第1期74卷 1069-1091页

作者： Rashid Jahangir Muhammad Asif Nauman Roobaea Alroobaea Jasem Almotiri Muhammad Mohsin Malik Sabah M.Alzahrani Department of Computer Science COMSATS University IslamabadVehari CampusPakistan Department of Computer Science University of Engineering and Technology LahorePakistan Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21974Saudi Arabia

Environmental sound classification(ESC)involves the process of distinguishing an audio stream associated with numerous environmental *** common aspects such as the framework difference,overlapping of different sound events,and the presence of various sound sources during recording make the ESC task much more complicated and *** research is to propose a deep learning model to improve the recognition rate of environmental sounds and reduce the model training time under limited computation *** this research,the performance of transformer and convolutional neural networks(CNN)are *** audio features,chromagram,Mel-spectrogram,tonnetz,Mel-Frequency Cepstral Coefficients(MFCCs),delta MFCCs,delta-delta MFCCs and spectral contrast,are extracted fromtheUrbanSound8K,ESC-50,and ESC-10,***,this research also employed three data enhancement methods,namely,white noise,pitch tuning,and time stretch to reduce the risk of overfitting issue due to the limited audio *** evaluation of various experiments demonstrates that the best performance was achieved by the proposed transformer model using seven audio features on enhanced *** UrbanSound8K,ESC-50,and ESC-10,the highest attained accuracies are 0.98,0.94,and 0.97 *** experimental results reveal that the proposed technique can achieve the best performance for ESC problems.

关键词： Environmental sound classification convolutional neural network deep learning transformer data augmentation

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Exploring IoT and Machine Learning Integration for Soil Nutrients Monitoring and Crop Recommendation: A Survey 3

Exploring IoT and Machine Learning Integration for Soil Nutr...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Khemnar, Vijaya R. Bendre, Mininath R. Kolekar, Vikas K. Pravara Rural Engineering College Department of Computer Engineering Loni India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798331509675

The integration of machine learning (ML) and Internet of Things (IoT) technologies has a scope of improvement in precision farming techniques and revolutionise the agriculture sector. This research paper examines the integration of IoT and ML in soil nutrient monitoring and crop recommendation systems. The aim is to provide a thorough overview of the most recent developments, applications, challenges, and potential future directions. IoT sensors collect and give immediate soil health information, while ML algorithms analyses these data to suggest nutrient deficits and recommend the most suitable crops. This research work evaluates advancements in IoT and Machine Learning applications, emphasising their benefits for precision agriculture. This integration aims to improve agricultural production and sustainability via data driven decision making and resource optimisation. At the end research gaps are discussed, along with future research directions. © 2025 IEEE.

关键词： Smart agriculture

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Deep learning-based classification,detection,and segmentation of tomato leaf diseases:A state-of-the-art review

Artificial Intelligence in Agriculture

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Artificial Intelligence in Agriculture 2025年第2期15卷 192-220页

作者： Aritra Das Fahad Pathan Jamin Rahman Jim Md Mohsin Kabir M.F.Mridha Department of Computer Science and Engineering American International University-BangladeshDhaka 1229Bangladesh Institute of Information and Communication Technology Shahjalal University of Science and TechnologySylhet 3114Bangladesh Advanced Machine Intelligence Research Lab Dhaka 1207Bangladesh Bangladesh University of Business and Technology DhakaBangladesh

The early identification and treatment of tomato leaf diseases are crucial for optimizing plant productivity,efficiency and *** by the farmers poses the risk of inadequate treatments,harming both tomato plants and *** of disease diagnosis is essential,necessitating a swift and accurate response to misdiagnosis for early *** regions are ideal for tomato plants,but there are inherent concerns,such as weather-related *** diseases largely cause financial losses in crop *** slow detection periods of conventional approaches are insufficient for the timely detection of tomato *** learning has emerged as a promising avenue for early disease *** study comprehensively analyzed techniques for classifying and detecting tomato leaf diseases and evaluating their strengths and *** study delves into various diagnostic procedures,including image pre-processing,localization and *** conclusion,applying deep learning algorithms holds great promise for enhancing the accuracy and efficiency of tomato leaf disease diagnosis by offering faster and more effective results.

关键词： Deep learning Tomato leaf disease Classification Detection Segmentation Imaging

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Deep Learning Based Underground Sewer Defect Classification Using a Modified RegNet

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computers, Materials & Continua 2023年第6期75卷 5451-5469页

作者： Yu Chen Sagar A.S.M.Sharifuzzaman Hangxiang Wang Yanfen Li L.Minh Dang Hyoung-Kyu Song Hyeonjoon Moon Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Department of Intelligent Mechatronics Engineering Sejong UniversitySeoul05006Korea Department of Information of Communication Engineering and Convergence Engineering for Intelligent DroneSejong UniversitySeoul05006Korea

The sewer system plays an important role in protecting rainfall and treating urban *** to the harsh internal environment and complex structure of the sewer,it is difficult to monitor the sewer *** are developing different methods,such as the Internet of Things and Artificial Intelligence,to monitor and detect the faults in the sewer *** learning is a promising artificial intelligence technology that can effectively identify and classify different sewer system ***,the existing deep learning based solution does not provide high accuracy prediction and the defect class considered for classification is very small,which can affect the robustness of the model in the constraint *** a result,this paper proposes a sewer condition monitoring framework based on deep learning,which can effectively detect and evaluate defects in sewer pipelines with high *** also introduce a large dataset of sewer defects with 20 different defect classes found in the sewer *** study modified the original RegNet model by modifying the squeeze excitation(SE)block and adding the dropout layer and Leaky Rectified Linear Units(LeakyReLU)activation function in the Block structure of RegNet *** study explored different deep learning methods such as RegNet,ResNet50,very deep convolutional networks(VGG),and GoogleNet to train on the sewer defect *** experimental results indicate that the proposed system framework based on the modified-RegNet(RegNet+)model achieves the highest accuracy of 99.5 compared with the commonly used deep learning *** proposed model provides a robust deep learning model that can effectively classify 20 different sewer defects and be utilized in real-world sewer condition monitoring applications.

关键词： Deep learning defect classification underground sewer computer vision convolutional neural network RegNet

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Machine Learning Models for Hotel Review: A Case Study of Hotels in Perak, Malaysia 14

Machine Learning Models for Hotel Review: A Case Study of Ho...

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14th International Conference on Electronics, communications and Networks, CECNet 2024

作者： Muslim, Norliana On, Wong Wai Salh, Adeb Samsamnun, Farina Saffa Mohamad Zainuddin, Zanariah Sabri, Nor Afifah Department of Computer and Communication Technology Faculty of Information and Communication Technology Universiti Tunku Abdul Rahman Perak Kampar Malaysia Department of Digital Economy Technology Faculty of Information and Communication Technology Universiti Tunku Abdul Rahman Perak Kampar Malaysia

ISBN: (纸本)9781643685632

Online reviews significantly impact consumer behavior and shape perceptions of services in the digital marketplace. They serve as a vital source of information, influencing purchasing decisions and brand loyalty. As a result, businesses increasingly rely on managing their online reputation to attract and retain customers. With the growing reliance on online reviews in the hospitality industry, particularly for hotels, accurately analyzing customer feedback has become essential for improving service quality and enhancing guest satisfaction. However, processing and interpreting hotel reviews presents a significant challenge. This study addresses this issue by developing three widely used machine learning models: Naïve Bayes, Support Vector Machines (SVM), and Random Forest, to classify and evaluate hotel reviews gathered from the Google platform for hotels in Perak, Malaysia. Based on 2,993 samples, the analysis reveals that Naïve Bayes performs best overall with 76% accuracy and excels in detecting both positive and negative sentiments. SVM, with 70% accuracy, shows strong performance in detecting positive sentiments. On the other hand, Random Forest, with 74% accuracy, demonstrates high recall in detecting positive reviews but struggles with neutral sentiments. These results emphasize the necessity of choosing the right model according to specific evaluation metrics, especially in situations where customer feedback is diverse and complex. © 2024 The Authors.

关键词： Hotels

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Multi-Modal Feature Fusion with Fuzziness based Semi-Supervised Learning for Alzheimer's Disease Diagnosis

Multi-Modal Feature Fusion with Fuzziness based Semi-Supervi...

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2024 IEEE International Conference on Computing, Applications and Systems, COMPAS 2024

作者： Bushra, Umme Habiba Chowdhury Priya, Fahmida Patwary, M. Jamshed Alam International Islamic University Chittagong Department of Computer Science and Engineering Chattogram Bangladesh Chittagong University of Engineering and Technology Institute of Information and Communication Technology Chattogram Bangladesh

ISBN: (纸本)9798331529765

Alzheimer's disease is a prominent kind of dementia that is caused by a slow decline of cognitive abilities, including memory loss, impaired speech and diminished reactivity. The condition primarily affects brain regions responsible for cognition, memory and language and typically becomes apparent after the age of 60, with a greater risk among older individuals. To meet the requirement for early identification, researchers have explored various machine-learning techniques. To enhance the accuracy of diagnosing Alzheimer's disease, researchers have employed supervised and unsupervised learning approaches derived from machine learning. While supervised learning relies on labelled data, obtaining sufficient labelled samples can be time-consuming and requires specialized assistance. The focus is on multiclass classification, harnessing MRI and PET data. SGLDM and SIFT feature extraction algorithms are utilized to enable the fusion of features from diverse perspectives, resulting in a robust classification model. This research introduces a novel strategy for AD diagnosis, combining multimodal feature fusion with a fuzziness-based-semi-supervised learning method. The method leverages both labeled and mislabeled data to enhance the model's reliability. The proposed method demonstrated notable enhancements in the classifier's performance compared to existing approaches on the dataset. © 2024 IEEE.

关键词： Self-supervised learning

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DESIGN AND ANALYSIS OF FLEXIBLE MICROSTRIP PATCH ANTENNA ON rGO MATERIAL DEPOSITION FOR WIRELESS APPLICATIONS

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Telecommunications and Radio engineering (English translation of Elektrosvyaz and Radiotekhnika) 2025年第3期84卷 1-17页

作者： Madabhushi, Havishma Venkata De, Shounak Kumar, Pramod Ali, Tanweer Kumar, Praveen Kumar, Om Prakash Junjunwala, Vikas Kumar Vincent, Shweta Pathan, Sameena Department of Electronics & Communication Engineering Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India Department of Electrical & Electronics Engineering Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India Department of Mechatronics Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India Department of Information & Communication Technology Engineering Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India

This paper describes the design and analysis of a flexible microstrip patch antenna for wireless applications that uses reduced graphene oxide (rGO) material deposition on cotton substrates. Using a microstrip line-fed design, the proposed dual-band antenna resonates at 5.5 and 8 GHz. The study investigates the application of rGO as a conductive patch on cotton, emphasizing its durability, low energy gap, and strong conductivity. The design methodology uses a step-by-step optimization process to achieve an efficient performance. The antennas are simulated with the high-frequency structure simulator (HFSS), and prototype results are reported. © 2025 by Begell House, Inc.

关键词： Slot antennas

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