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Hybrid Color Texture Features Classification Through ANN for Melanoma

Intelligent Automation & Soft Computing 2023年第2期35卷 2205-2218页

作者： Saleem Mustafa Arfan Jaffar Muhammad Waseem Iqbal Asma Abubakar Abdullah S.Alshahrani Ahmed Alghamdi Faculty of Computer Science&Information Technology Superior UniversityLahore54600Pakistan Department of Software Engineering Superior UniversityLahore54600Pakistan Department of Computer Science&Artificial Intelligence College of Computer Science&EngineeringUniversity of Jeddah21493Saudi Arabia Department of Software Engineering College of Computer Science and EngineeringUniversity of Jeddah21493Saudi Arabia

Melanoma is of the lethal and rare types of skin *** is curable at an initial stage and the patient can survive *** is very difﬁcult to screen all skin lesion patients due to costly *** are requiring a correct method for the right treatment for dermoscopic clinical features such as lesion borders,pigment networks,and the color of *** challenges are required an automated system to classify the clinical features of melanoma and non-melanoma *** trained clinicians can overcome the issues such as low contrast,lesions varying in size,color,and the existence of several objects like hair,reﬂections,air bubbles,and oils on almost all *** contour is one of the suitable methods with some drawbacks for the segmentation of irre-gular *** entropy and morphology-based automated mask selection is pro-posed for the active contour *** proposed method can improve the overall segmentation along with the boundary of melanoma *** this study,features have been extracted to perform the classiﬁcation on different texture scales like Gray level co-occurrence matrix(GLCM)and Local binary pattern(LBP).When four different moments pull out in six different color spaces like HSV,Lin RGB,YIQ,YCbCr,XYZ,and CIE L*a*b then global information from different colors channels have been ***,hybrid fused texture features;such as local,color feature as global,shape features,and Artiﬁcial neural network(ANN)as classiﬁers have been proposed for the categorization of the malignant and *** had been carried out on datasets Dermis,DermQuest,and *** results of our advanced method showed super-iority and contrast with the existing state-of-the-art techniques.

关键词： Gray level co-occurrence matrix local binary pattern artiﬁcial neural networks support vector machines color skin cancer dermoscopic

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Cyber intrusion detection using dual interactive Wasserstein generative adversarial network with war strategy optimization in wireless sensor networks

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Multimedia Tools and Applications 2025年第18期84卷 19223-19253页

作者： Anusha, N. Tapas Bapu, B.R. S, Selvakumaran Vijayaraj, A. Ramesh Kumar, C. P, Raji Department of IoT School of Computer science and Engineering VIT Tamil Nadu Vellore India Department of Electronics and Communication Engineering S.A.Engineering College Tamil Nadu Chennai India Department of Artificial Intelligence and Data Science Rajalakshmi Institute of Technology Tamil Nadu Chennai India Department of Information Technology R.M.K Engineering College R.S.M Nagar Tamil Nadu Kavaraipettai India Department of Electronics and Communication Engineering Panimalar Engineering College Tamil Nadu Chennai India Department of Electronics and Communication Engineering Saveetha Engineering College Tamil Nadu Chennai India

Wireless sensor network (WSN) is one of the essential components of a multi-hop cyber-physical system comprising many fixed or moving sensors. There are many common attacks in WSN, which can quickly harm a WSN system. In this manuscript, Dual Interactive Wasserstein Generative Adversarial Network (DIWGAN) with war strategy optimization (WSO) algorithm is introduced for detecting intrusion in WSN. Initially, the input data is attained from the NSL-KDD and CICIDS2017 datasets. The data is then passed onto Morphological filtering and Extended Empirical wavelet transformation (MFEEWT) based pre-processing. After that, the filtered data is sent to the DIWGAN, which detects the intrusion and classifies the fraud data in the network. Additionally, the WSO algorithm is introduced to enhance the classification parameters of DIWGAN. By then the performance of the proposed DIWGAN-WSO-IDS methodology is evaluated using Python Platform. Also, the performance of the proposed methodology is calculated using various metrics like accuracy, precision, recall, f-measure, mean square error, and attack detection rate. Thus, the proposed technique has attained 29.87%, 31.7%, and 26.8% higher accuracy, 23.54%, 20.09%, and 26.98% higher recall, 12.76%, 10.96%, and 6.7% higher precision. And 6.85%, 4.56%, and 8.56% lower MSE than the existing techniques like BFSGSRF, MHICA-SSA, and CNN-LSTM methods. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Wireless sensor networks

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Transformer based Hybrid System for Speech Transcription and Disorder Detection 4

Transformer based Hybrid System for Speech Transcription and...

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Bhargavi, N.M. Bharadwaj, M. Shashank Koushik, M. Alva, Khushi U. Vedavathi, N. Vidyavardhaka College of Engineering Department of Computer Science & Engineering Mysuru India

ISBN: (纸本)9798331529635

Stuttering is a speech disorder accompanied by disruptions in the fluency of speaking and is characterized by involuntary repetitions, prolongations, or blocks in speech. The early detection and recommendation for personalized therapies are considered to be very important in effective treatment. Deep learning and machine learning-based techniques like ANN's and CNN's have held much promise as solutions in the implementation of an automatic stuttering detection and therapy suggestion system. The paper is interested in a number of methods followed for detection of stuttering and focuses on the integration of wav2vec for learning speech representation and BERT for classification based on text for stuttering events. We review existing models and techniques;compare the performances, and an experimental evaluation on the approach presented, using wav2vec and BERT. Our results show that deep learning models, especially language and speech pre-trained models, significantly improve the stuttering detection accuracy. © 2024 IEEE.

关键词： Contrastive Learning

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Learning continuous network emerging dynamics from scarce observations via data-adaptive stochastic processes

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science China(Information sciences) 2024年第12期67卷 240-255页

作者： Jiaxu CUI Qipeng WANG Bingyi SUN Jiming LIU Bo YANG College of Computer Science and Technology Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Public Computer Education and Research Center Jilin University Department of Computer Science Hong Kong Baptist University

Learning network dynamics from the empirical structure and spatio-temporal observation data is crucial to revealing the interaction mechanisms of complex networks in a wide range of domains. However,most existing methods only aim at learning network dynamic behaviors generated by a specific ordinary differential equation instance, resulting in ineffectiveness for new ones, and generally require dense *** observed data, especially from network emerging dynamics, are usually difficult to obtain, which brings trouble to model learning. Therefore, learning accurate network dynamics with sparse, irregularly-sampled,partial, and noisy observations remains a fundamental challenge. We introduce a new concept of the stochastic skeleton and its neural implementation, i.e., neural ODE processes for network dynamics(NDP4ND), a new class of stochastic processes governed by stochastic data-adaptive network dynamics, to overcome the challenge and learn continuous network dynamics from scarce observations. Intensive experiments conducted on various network dynamics in ecological population evolution, phototaxis movement, brain activity, epidemic spreading, and real-world empirical systems, demonstrate that the proposed method has excellent data adaptability and computational efficiency, and can adapt to unseen network emerging dynamics, producing accurate interpolation and extrapolation with reducing the ratio of required observation data to only about 6% and improving the learning speed for new dynamics by three orders of magnitude.

关键词： complex networks network dynamics emerging spatio-temporal dynamics neural processes

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License Number Plate Recognition(LNPR) 4

License Number Plate Recognition(LNPR)

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4th International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2022

作者： Garg, Khushi Gautam, Kapil Singh, Karamveer Gaur, Madhvi Abes Engineering College Ghaziabad Department of Computer Science and Engineering Ghaziabad India

ISBN: (纸本)9781665474368

License number plate recognition is a technology which is used to detect the number of vehicles;it can be achieved by the OCR (optical character recognition) and OpenCV which is a computer vision technology. Most of the time the accuracy depends on hardware which is used to collect the data and the environment also like whether, Lighting. LNPR can be used in for traffic system where police can easily identify the vehicle number with the help of LNPR and it can be used in toll-tax areas where road authority can used it to get the tax from the public. It can also be used in the parking areas to get the data which vehicle is present at which time.. ANPR is a fascinating subject for academics because of its unreliability, tough surroundings, and other complications. © 2022 IEEE.

关键词： Optical character recognition

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Congruent Feature Selection Method to Improve the Efficacy of Machine Learning-Based Classification in Medical Image Processing

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computer Modeling in engineering & sciences 2025年第1期142卷 357-384页

作者： Mohd Anjum Naoufel Kraiem Hong Min Ashit Kumar Dutta Yousef Ibrahim Daradkeh Department of Computer Engineering Aligarh Muslim UniversityAligarh202002India College of Computer Science King Khalid UniversityAbha61413Saudi Arabia School of Computing Gachon UniversitySeongnam13120Republic of Korea Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityRiyadh13713Saudi Arabia Department of Computer Engineering and Information College of Engineering in Wadi AlddawasirPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia

Machine learning(ML)is increasingly applied for medical image processing with appropriate learning *** applications include analyzing images of various organs,such as the brain,lung,eye,etc.,to identify specific flaws/diseases for *** primary concern of ML applications is the precise selection of flexible image features for pattern detection and region *** of the extracted image features are irrelevant and lead to an increase in computation ***,this article uses an analytical learning paradigm to design a Congruent Feature Selection Method to select the most relevant image *** process trains the learning paradigm using similarity and correlation-based features over different textural intensities and pixel *** similarity between the pixels over the various distribution patterns with high indexes is recommended for disease ***,the correlation based on intensity and distribution is analyzed to improve the feature selection ***,the more congruent pixels are sorted in the descending order of the selection,which identifies better regions than the ***,the learning paradigm is trained using intensity and region-based similarity to maximize the chances of ***,the probability of feature selection,regardless of the textures and medical image patterns,is *** process enhances the performance of ML applications for different medical image *** proposed method improves the accuracy,precision,and training rate by 13.19%,10.69%,and 11.06%,respectively,compared to other models for the selected *** mean error and selection time is also reduced by 12.56%and 13.56%,respectively,compared to the same models and dataset.

关键词： computer vision feature selection machine learning region detection texture analysis image classification medical images

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Computational Approaches for Identifying Potato Plant Pathogens 5

Computational Approaches for Identifying Potato Plant Pathog...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Julian, Anitha Vignesh, S. Saveetha Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350383522

Potato cultivation is a vital component of global agriculture, providing a significant source of nutrition and income. However, the threat of diseases affecting potato crops remains a persistent challenge for farmers. In our study, we present a novel approach to automatically classify diseases in potato plants using advanced deep learning methods. The proposed approach makes use of Convolutional Neural Networks (CNNs), that perform efficiently for image processing applications. The dataset utilized in our research comprises high-resolution Images depicting diverse disease symptoms in potato plants, including late blight, early blight, and the oomycete pathogen Phytophthora infestans, a destructive ailment that impacts potato crops. To automatically discern between healthy and diseased plants, we implement a multi-layered CNN architecture that learns discriminative characteristics of images. This research significantly contributes to precision agriculture by providing an automated and efficient solution for identifying potato diseases. The proposed method in disease classification not only facilitates prompt disease management but also enables precise treatments, thereby reducing reliance on chemical interventions. The practical implications of these findings extend to farmers, agronomists, and researchers involved in sustainable and resilient potato cultivation. © 2024 IEEE.

关键词： disease identification early blight late blight potato disease

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Implementation of Chat Bot Using Sequence to Sequence Model 1st

Implementation of Chat Bot Using Sequence to Sequence Model

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1st International Conference on Artificial Intelligence Towards Smart Sustainable Society, AISSS 2023

作者： Balarengadurai, C. Taranum, Ayesha Hamesaveni, M. Pruthvi, P.R. Department of Computer Science and Engineering Vidyavardhaka College of Engineering Mysuru India

ISBN: (纸本)9789819775910

A Twitter account was made with the efficient use of a conversation bot as its main goal, and it has since accumulated followers consistently and automatically. Software systems can be interfaced with using a wide variety of user interfaces, such as command line, GUI, menu-driven, form-based, natural language, and others. NLP and neural networks are two techniques that chat bots that are powered by AI may use to process human language. It has fruitful conversations with users and provides satisfactory answers to their inquiries. The result of its usefulness, chat bots are being used by more businesses to quickly communicate with their clients. Software with the ability to independently speak with a person is known as a chat bot. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Chatbots

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Income Prediction Based on Personal Attributes: Comparing Machine Learning Models 4

Income Prediction Based on Personal Attributes: Comparing Ma...

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Abisha, D. Dhanushree Narmatha, V. Ida Christy, J. Navedha Evanjalin, R. Swetha, S. National Engineering College Department of Computer Science and Engineering Kovilpatti India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529635

This research investigates the factors influencing income levels using machine learning techniques. Based on a dataset of 48,842 individuals, the study employs LightGBM, Random Forest, and K-Nearest Neighbors algorithms to predict annual income. The results demonstrate that LightGBM achieved the highest accuracy, exceeding 90%. Feature importance analysis revealed age as the most significant predictor of income. This study provides valuable insights into the factors that contribute to income inequality and has potential implications for policy development and social research. By understanding the key determinants of income, policymakers can develop targeted interventions to address income disparities and promote social equity. © 2024 IEEE.

关键词： Prediction models

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Nutrigrow Using Gradient Boosting Regressor 1

Nutrigrow Using Gradient Boosting Regressor

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1st International Conference on Trends in engineering Systems and Technologies, ICTEST 2024

作者： Ashwini, A. Loganathan, V. Saveetha Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798350307795

ISBN: (纸本)9798350307795

In modern agriculture, the quest for enhanced crop yield and nutrient efficiency is paramount. Leveraging the Gradient Boosting Regressor algorithm, this research proposes a novel method to address this challenge. By analyzing historical rainfall data and soil fertility levels, the system predicts crop nutrient requirements, aiding farmers in making informed decisions on fertilizer application. Moreover, real-time weather forecasts are integrated to issue timely alerts, mitigating risks associated with adverse weather conditions. This holistic approach not only enhances crop yields but also minimizes nutrient wastage, contributing to environmental sustainability and economic viability. Through the synergy of advanced algorithms and agricultural data, this work offers a promising avenue for optimizing crop production and resource management in modern farming practices. © 2024 IEEE.

关键词： Potassium

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