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Spectral-Spatial Deep learning for Multispectral Enhanced Image Classification and Analysis 4

Spectral-Spatial Deep Learning for Multispectral Enhanced Im...

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4th international conference on Computation, Automation and Knowledge Management, ICCAKM 2023

作者： Jagtap, Varunraje J. Ahire, Nilesh B. Chaudhari, Sarthak A. Shinde, Prathamesh S. William, P. Sanjivani College of Engineering Savitribai Phule Pune University Department of Electronics and Computer Engineering Pune India Sanjivani College of Engineering Savitribai Phule Pune University Department of Computer Engineering Pune India Sanjivani College of Engineering Savitribai Phule Pune University Department of Information Technology Pune India

ISBN: (纸本)9798350393248

In this study, we present a framework for spectral-spatial feature-based classification (SSFC), which employs deep learning techniques and dimension reduction to simultaneously extract spectral and spatial characteristics. the term 'spectral-spatial feature-based classification' is abbreviated as 'SSFC.' the acronym SSFC (Spectral-Spatial Feature-Based Categorization) stands for this method of categorization. It is suggested here to apply a method called balanced local discriminant embedding to extract spectral attributes from high-dimensional hyperspectral data sets. these features may be used to better understand the data. During the course of this experiment, multispectral image analysis (385-1050 nm) was used to take a recording of a 20-wavelength reflection of the pellets. Principal component analysis (PCA), support vector machine (SVM), and linear discriminant analysis (LDA) were used in the statistical analysis that was conducted. A novel nonparametric supervised spectral-spatial classification technique for multispectral images is introduced and discussed in this study. the technique was created with the idea of applying it to the world of data imaging. the posterior probability for a multispectral image's unknown pixel will be higher if it possesses digital number (DN) vectors that are comparable to those of other pixels in the training class. this is because distinct kinds of DN vectors are thought to follow various statistical distributions in their own particular manners. the proposed method assumes that the vectors in each class have a Gaussian mixed distribution based on the statistical features of DN vectors. this is done to make the analysis simpler. For better understanding, a nonparametric adaptively Bayesian method has been devised to estimate the most precise values for the distribution model's parameters. © 2023 IEEE.

关键词： Vectors

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application of Feedforward Neural Network in Portfolio Optimization and Geometric Brownian Motion in Stock Price Prediction 4

Application of Feedforward Neural Network in Portfolio Optim...

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4th international conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Aheer, Anirudh Kumar Pradhan, Ashish Kumar Srivastava, R. Delhi Technological University New Delhi India

ISBN: (纸本)9798350300093

this research study intends to predict the stock price movement of a set of stocks using the Geometric Brownian Motion and optimize the portfolio built from selected stocks using Feed Forward [1] Neural Network. the onset of the current global pandemic has led to major negative implications for the overall economy and personal financial situations. With many people losing jobs, getting reduced salaries, and exhausting their savings, they have started to invest heavily. the Securities and Exchange Board of India (SEBI) reports that there were 4.9 million new Demat accounts opened in the fiscal year 2020, which was the biggest in at least ten years. thus, the study of the Stock Exchange is very relevant to the current times. We shall explain the importance of learning about investing in the Stock Market and derive an efficient model to make real-time predictions of a stock price and build an optimized portfolio. In the finance sector, portfolio optimization is a critical responsibility. the goal of portfolio optimization is to increase return on investment while lowering portfolio risk. Due to its capacity to understand intricate correlations between input and output variables, feed-forward neural networks have been successfully used to solve portfolio optimization issues. We explore the research on portfolio optimization using feed-forward neural networks in this paper. © 2023 IEEE.

关键词： Financial markets

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Analyzing Customer Churn: A Comparative Study of machine learning Models on Pay-TV Subscribers in Turkey 4

Analyzing Customer Churn: A Comparative Study of Machine Lea...

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4th international Informatics and Software Engineering conference, IISEC 2023

作者： Hatas, Tugce Aydin Obali, Emir Yildiz, Aysenur Caliskan, Sibel Kirmizigul Karani Yilmaz, Veysel Kara, Erkan Mese, Yasemin Kurtcu Oz, Ebru Cakar, Tuna MEF University Information Technologies Istanbul Turkey Andromeda TV Digital Plat. Op. Inc. Subscriber Retention Manager Istanbul Turkey Andromeda TV Digital Plat. Op. Inc. RandD/Design Center Specialist Istanbul Turkey Andromeda TV Digital Plat. Op. Inc. Business Intelligence Manager Istanbul Turkey Andromeda TV Digital Plat. Op. Inc. Inf. Tech. and System Network CTO Istanbul Turkey Andromeda TV Digital Plat. Op. Inc. RandD Center Manager Istanbul Turkey Andromeda TV Digital Plat. Op. Inc. CVM and Loyalty Director Istanbul Turkey MEF University Computer Engineering Istanbul Turkey

ISBN: (纸本)9798350318036

Understanding the reasons for customer churn provides added value in terms of retaining existing customers, as customer attrition leads to revenue loss for companies and incurs marketing costs for acquiring new customers. In this study, the 6-month historical data of a Pay-TV company operating in Turkey was used, and due to the imbalanced nature of the dataset on a label basis, the oversampling method was applied. During the model development phase, various artificial learning algorithms (Random Forest, Logistic Regression, KNearest Neighbors, Decision Tree, AdaBoost, XGBoost, Extra Tree Classifier) were utilized, and their performances were compared. Based on the evaluation of success criteria for each model, it was observed that the tree-based Random Forest, Extra Tree Classifier and XGBoost achieved the highest performance for this dataset. © 2023 IEEE.

关键词： Decision trees

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Sustainable Coffee Production: A Federated learning Framework with CNN for Disease Detection and Classification 4

Sustainable Coffee Production: A Federated Learning Framewor...

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4th IEEE international conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： Jindal, Varun Kukreja, Vinay Bhattacherjee, Abhishek Garg, Umang Mehta, Shiva Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Computer Science and Engineering Lovely Professional University Punjab Phagwara India Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350306118

Using federated learning with Convolutional Neural Networks (CNN), this research study provides a novel technique for identifying and categorizing coffee leaf illnesses. the research focuses on five categories of coffee leaf diseases and includes information from five clients, combining local knowledge with global comprehension. the research uses three averaging techniques-Macro, Micro, and Weighted averages-in the Result Analysis section, each offering significant insights into the illness categorization. Precision was 83.53%, recall was 87.04%, F1-score was 91.23%, Support was 90.46%, and accuracy was 92.79%, according to the study of the macro Average. A precision of 85.73%, recall of 88.10%, F1-score of 91.74%, Support of 90.62%, and accuracy of 92.79% were obtained from the weighted Average. the Micro average's final statistics were accuracy at 92.78%, recall at 88.12%, F1-score at 91.73%, and Support at 90.62%. Additionally, federated averaging, a technique that harmonizes local data and transforms it into useful global insights, was used in the research's part on converting local data to global data. the values for the five clients were Cta-1: 83.69% precision, 83.41 % recall, 83.49% F1-score, 430.40 support, and 0.94 accuracy;Cta-2: 87.18%, 86.90%, 87.03%, 518.40, and 0.95;Cta-3: 91.30%, 91.16%, 91.21 %, 638.60, and 0.97;Cta-4: 90.68%, 90.25%, 90.44%, 757.00, and 0.96;Cta-5: 92.85%, 92.74%, 92.78%, 949.80, and 0.97. this research pioneers a unique approach to disease identification in coffee leaves by the synergistic use of various approaches, possibly revolutionizing agricultural practices and resulting in more sustainable, data-driven choices in the coffee sector. © 2023 IEEE.

关键词： Diseases

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8th international conference on Innovative Technologies in Intelligent Systems and Industrial applications, CITISIA 2023

8th International Conference on Innovative Technologies in I...

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8th international conference on Innovative Technologies in Intelligent Systems and Industrial applications, CITISIA 2023

ISBN: (纸本)9783031717727

the proceedings contain 38 papers. the special focus in this conference is on Innovative Technologies in Intelligent Systems and Industrial applications. the topics include: Privacy Techniques in Health Data Mining;network Segmentation and Its Advancement in Containing Insider threats;constructing a Proactive Cyber Resilient Culture for Better Business Outcomes;the application of Zero Trust Architecture to Mitigate Password Compromise in Accessing Cloud Services;enhancing Banking Security: A Collaborative Framework to Combat Cybercrime and Secure Financial Transactions;host-Based Intrusion Detection Systems for Resource Constrained Environments: A Review;access Control for Secure Remote Patient Monitoring;advanced Persistent threats—Techniques, Detection and Defences;Edge-AI Addresses the Big Data Challenges of IoT in the Cloud Edge;analysis of the Complexity and Risk to the Government eHealth System When Adopting the Cloud Services;unified Medical Data Management on the Cloud: Leveraging Blockchain for Multi-source Integration;blockchain and Cloud Security—A Review;machine learning-Powered Blockchain in Vehicular Ad-Hoc Networks;evaluation of the Next Generation Firewall with Breach and Attack Simulation;implementing a Usable Hybrid Project Management Model for Local Government;organisational Challenges, Risks, and Success Factors When Implementing Agile Methodologies Post Covid-19;Exploring Security and Data Privacy Issues in Industrial Internet of things (IIoT)—A Review;agile Implementations in Cyber Security: Issues Arising and Recommendations;using Artificial Intelligence to Defend Internet of things for Smart City Networks;smart Home IoT Devices Influence or Impact on Elderly’s Life: A Review;analysis and Detection of IoT Botnets Using machine learning;helmet Detection for Motorcycle Ignition: A computer Vision and Android-Based Approach.

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the Research on the application Modes of Intelligent Instructional Design Generation in Primary and Secondary Schools Supported by Large Models 24

The Research on the Application Modes of Intelligent Instruc...

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7th international conference on Educational Technology Management, ICETM 2024

作者： Lin, Jian Wang, Xiaoyi Li, Bingjun He, Musheng Luo, Yufei School of Computer and Intelligent Education Lingnan Normal University Guangdong Zhanjiang China Guangdong Provincial Key Laboratory of Development and Education for Special Needs Children Lingnan Normal University Guangdong Zhanjiang China School of Electronic and Electrical Engineering Lingnan Normal University Guangdong Zhanjiang China

ISBN: (纸本)9798400717468

As the development of large language models and generative artificial intelligence technologies continues to advance, the application of digital teaching resources is exhibiting a new trend of human-machine collaboration and co-creation. As an important digital teaching resource for the implementation of smart teaching, the application of instructional design documents has been significantly influenced by the development of intelligent technologies, especially in the field of primary and secondary education, which holds significant research value. However, there is a clear gap between the value claims and actual effects of traditional instructional design, which limits the professional development of teachers and hinders the comprehensive development of students. this research aims to combine ChatGPT-4 models and generative artificial intelligence technology to construct an intelligent instructional design system platform, achieving the intelligent transformation and upgrading of instructional design. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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Medicinal Plants Attribute Detection by Deep learning Image Processing Techniques 4

Medicinal Plants Attribute Detection by Deep learning Image ...

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4th international conference on Communication, Computing and Industry 6.0, C216 2023

作者： Chamanth, S. Begam, M. Farida Chandan, N.S. Girish, G.S. Department of Information Science and Engineering CMR Institute of Technology Bengaluru India

ISBN: (纸本)9798350327328

the identification of medicinal plant species leads to many benefits to wide range of stakeholders such as, forestry services, taxonomists, doctors, and pharmaceutical labs, organizations that protect endangered species, the government, and the general public. Important plant species are disappearing around the world on a daily basis. Steps include plant identification, restoration, and protection in order to control the phenomenon. Typically, leaves are used to identify plants. Convolutional Neural Network which is the widely used architecture for image base classification motivated us to creating automated methods for classifying various plant species and identify medicinal attributes of the plants. In this paper, using computer vision and machine learning approaches, a fully automated front end based software system has been designed and described for achieving this. Each leaf's length, width, perimeter, area, number of vertices, color, perimeter, and hull area were all just a few of the many features that were taken out. then, from these properties, a number of derived features were calculated. the native people are expected to get more knowledge about medicinal leaves with the use of this work for the automatic recognition of medicinal leaves. Additionally, it helps taxonomists in the development of more effective methods for identifying species, which will greatly help in the preservation of endangered species. the utilization of various plant traits ranges from the most basic, such as leaf color and form, to the most complicated, such as tissue and cell structure are applied. the results obtained with these analytics gives 98% accuracy in 100 epochs. © 2023 IEEE.

关键词： Convolution

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Predictive Analysis Techniques of Disease using machine learning 4

Predictive Analysis Techniques of Disease using Machine Lear...

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4th international conference on Emerging Research in Electronics, computer Science and Technology, ICERECT 2022

作者： Naaz, Rohaila Karunakaran, V. Teerthanker Mahaveer University Department of Computing Science and IT Moradabad India Jain University Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9781665456357

Electronic data have collected because of the health care area's boundless reception of PC based innovation. the primary goal of this essay is to discuss data mining's application to the provision of medical care. these data mining methods can also be applied in a number of examination and educational settings. the quickest arising area of clinical science is the Smart Health Prediction System. One area of computer science called data mining uses the information already available in the clinical field to estimate the event of diseases. We can learn from groups of large datasets by extracting new patterns using database management and machine learning tools. In this research paper, we examine different algorithmic data mining strategies that have been applied to the prediction of illness. Health organizations frequently use data mining to categorize diseases like diabetes and cancer in bioinformatics research. the review on how data mining methods are joined with machine learning to anticipate diseases in light of client side effects is made in the accompanying paper. © 2022 IEEE.

关键词： Health care

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learning Structural Similarities from Handwriting on Papyri - An application to Scribe Characterization 20

Learning Structural Similarities from Handwriting on Papyri ...

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20th international conference on Frontiers of Information Technology, FIT 2023

作者： Sajjad, Hassan Siddiqi, Imran Moetesum, Momina Marthot-Santaniello, Isabelle Bahria University Department of Computer Science Islamabad Pakistan Xynoptik Pty Ltd. VIC Australia Islamabad Pakistan Basel University Department of Ancient Civilizations Basel Switzerland

ISBN: (纸本)9798350395785

Computational analysis of historical manuscripts has remained an interesting as well as a challenging research problem for the pattern recognition community. the recent advancements in different areas of machine learning and the successful completion of joint projects between computer scientists and paleographers have resulted in increased research interest in 'digital paleography'. Among various tasks the rich collections of historical documents offer, this study focuses on characterizing of writer from handwriting on papyri. We formulate the problem in a similarity learning framework and exploit Siamese neural networks to compare an input pair of writing samples. the highly degraded images of handwriting on papyri are binarized using a deep learning-based binarization technique. Patches of handwriting are subsequently extracted by sampling the complete image and pairs of samples from the same and different writers are produced. these positive and negative pairs are employed to fine-tune a Siamese neural network with different popular architectures as the backbone model. An experimental study on the samples in the GRK-Papyri dataset with 97 writing samples reports a test accuracy of 62% on DenseNet-121 and 69% with omniglot-trained CNN. the preliminary findings are quite promising and can be employed in writer identification, verification, and retrieval frameworks. © 2023 IEEE.

关键词： Deep learning

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Ransomware Attack Detection by Applying machine learning Techniques 4th

Ransomware Attack Detection by Applying Machine Learning Tec...

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4th international conference on machine Intelligence and Signal Processing, MISP 2022

作者： Routray, Siddharth Prusti, Debachudamani Rath, Santanu Kumar Department of Computer Science and Engineering National Institute of Technology Rourkela Odisha Rourkela India

ISBN: (纸本)9789819900848

the world faces various perilous threats due to computer security breaches in the present era. It is proliferating at such a fast rate that it hampers the integrity and confidentiality of people as well as organizations, resulting in a substantial monetary loss. Among different threats, it has been observed that ransomware is one of its types that results in data loss and makes victims by paying huge ransoms. In this study, a research attempt has been made to detect the attack by applying various machine learning techniques with the dataset. First, the data were trained directly using different machine learning techniques such as k-nearest neighbor, support vector machine (SVM) with different kernel functions (SVM-linear, SVM-polynomial, SVM-radial basis function, SVM-sigmoid), random forest, decision tree, and multilayer perceptron without incorporating any feature selection techniques to detect whether the attack is ransomware or benign. Further to optimize the results, feature selection methods based on the filter (Chi-square test, correlation coefficient), wrapper (forward feature selection, backward feature elimination), and embedded methods (Lasso regularization (L1)) are applied to select the prominent features, and redundant features are discarded. Finally, all the results obtained from different experiments are analyzed with critical assessment. By investigating the performance measures of various classifiers, it has been observed that significant improvement in the result is being achieved by the machine learning techniques when the feature selection techniques are considered. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Feature Selection

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