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data science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： Sudheer Reddy Bandi M Anbarasan G Merlin Linda Department of Artificial Intelligence and Data Science Panimalar Engineering College Chennai India Department of Computer Science and Engineering Swarna Bharathi Institute of Science and Technology Khammam India

Synthetic Aperture Radar (SAR) and Optical sensors are very popular depending on their strengths and application needs. The ocean surface will be covered with clouds most of the time and optical sensors cannot capture the information as they cannot operate in dust and cloudy conditions. So, SAR sensors come into existence to capture the atmospheric phenomena in those regions. There is a wide range of texture extraction techniques such as traditional-based wavelet analysis, statistical analysis, local pixel-based approaches, and dimensional reduction approaches. However, these approaches have limitations in boundary extraction, high-dimensional feature extraction, and uniformity representation. In recent times, deep learning (DL) approaches to extract features which varies based on the applications, image interpretation, and so on. At the same time, DL approaches are limited in transparency, computation requirements, and hyperparameter tuning. In contrast, the proposed S5ELBP i.e., Supervised SAR image classification using the integration of Support Vector Machine (SVM) and Scale Selective Extended Local Binary Pattern (SSELBP) extraction techniques shall overcome various challenges of existing traditional and DL approaches such as adaptive thresholding, less computational resources, and noise robustness. Hence, the objective of the proposed methodology is to extract the image features from the Scale Selective Extended Local Binary Pattern (SSELBP). Also, the SSELBP features are integrated with SVM to show the strength of the proposed approach in SAR image classification. Additionally, the performance of the present work is enhanced by comparing the extracted results with various classifiers. Finally, the work is concluded by recommending the scope of future work.

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Classifying Tweet Sentiment Analysis on Airlines using Random Forest Classifier, Naïve Bayes Classifier and K-Nearest Neighbors Classifier Algorithm

Classifying Tweet Sentiment Analysis on Airlines using Rando...

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2023 International Conference on Artificial Intelligence Robotics, Signal and Image Processing, AIRoSIP 2023

作者： Alexander, Ivan Heryadi, Yaya Lukas Suparta, Wayan Sonata, Ilvico Bina Nusantara University Binus Graduate Program Computer Science Computer Science Departement Jakarta11480 Indonesia Universitas Katolik Indonesia Atma Jaya Cognitive Engineering Research Group Faculty of Engineering Jakarta12930 Indonesia Institut Teknologi Nasional Yogyakarta Faculty of Industrial Technology Department of Electrical Engineering Yogyakarta55281 Indonesia

ISBN: (纸本)9798350327472

Opinion has always affected businesses and individuals especially from the Public. People react through social media and spread it incompletely. The situation was then accepted as public opinion. There are three categories of opinion i.e., positive, negative, and neutral. To investigate public opinion on a particular issue of interest, sentiment analysis might be employed. By offering a solution to the text classification challenge, this experiment focuses on the perception of airline tweets among the American public. We offer a method to classify tweets as positive, negative, or neutral using three machine learning classifiers: Random Forest Classifier, Naive Bayes Classifier, and K-Nearest Neighbors Classifier. We examined the performance of three machine learning classifiers in terms of accuracy, average precision, average recall, and average f-1 score. The results demonstrate that the Random Forest Classifier, which produces 82% accuracy, 80% average precision, 82% average recall, and 80% average f1-score, performs the best among the machine learning classifiers. © 2023 IEEE.

关键词： Tweets

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Privacy-Enhanced data Deduplication Computational Intelligence Technique for Secure Healthcare Applications

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computers, Materials & Continua 2022年第2期70卷 4169-4184页

作者： Jinsu Kim Sungwook Ryu Namje Park Department of Convergence Information Security Graduate SchoolJeju National University63294Korea Master’s Program in Future Strategy Korea Advanced Institute of Science and Technology34141Korea Department of Computer Education Teachers CollegeJeju National University63294Korea

A significant number of cloud storage environments are already implementing deduplication *** to the nature of the cloud environment,a storage server capable of accommodating large-capacity storage is *** storage capacity increases,additional storage solutions are *** leveraging deduplication,you can fundamentally solve the cost ***,deduplication poses privacy concerns due to the structure *** this paper,we point out the privacy infringement problemand propose a new deduplication technique to solve *** the proposed technique,since the user’s map structure and files are not stored on the server,the file uploader list cannot be obtained through the server’s meta-information analysis,so the user’s privacy is *** addition,the personal identification number(PIN)can be used to solve the file ownership problemand provides advantages such as safety against insider breaches and sniffing *** proposed mechanism required an additional time of approximately 100 ms to add a IDRef to distinguish user-file during typical deduplication,and for smaller file sizes,the time required for additional operations is similar to the operation time,but relatively less time as the file’s capacity grows.

关键词： Computational intelligence cloud multimedia data deduplication

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Fake News Detection using Machine Learning Algorithms

Fake News Detection using Machine Learning Algorithms

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New Frontiers in Communication, Automation, Management and Security (ICCAMS), International Conference on

作者： Tina Babu Rekha R Nair Adithya Challa Rahul Srikanth Sri Sai Aravindan Suhas S Department of Computer Science and Engineering Alliance Univerity Bengaluru India Department of Computer Science & Engineering (Data Science) School of Engineering Dayananda Sagar Univerity Bengaluru India Department of Computer Science & Engineering (AI & ML) School of Engineering Dayananda Sagar Univerity Bengaluru India

ISBN: (数字)9798350317060

ISBN: (纸本)9798350317077

The spread of false news threatens news and information sources, especially in Indian politics. We present a machine learning-based strategy to identify false news by vectorizing and tokenizing news headlines using a pre-defined dataset of authentic and fraudulent news items. We want to create a model that can properly identify news stories by textual content, separating propaganda from real news. Our technique is tested on a benchmark dataset of news items. Our suggested technique outperforms various state-of-the-art false news detection algorithms in the literature. Our methodology can prevent false news and safeguard news and information sources in Indian politics. This technique is versatile and successful for identifying and reducing the impact of false news across several topics and languages.

关键词： Machine learning algorithms Limiting Detectors Forestry Graph theory Security Labeling

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Prediction of Bankruptcy of a company using machine learning techniques 3

Prediction of Bankruptcy of a company using machine learning...

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3rd International Conference on Electronics and Sustainable Communication Systems, ICESC 2022

作者： Kothuru, Vivek Jha, Raman Kumar Ranjit, Sasanapuri Kumar Reddy, Bommireddy Vijay Roy, Sagnik Sudheer, Sachin Niit University Department of Computer Science Engineering Rajasthan India D y Patil College of Engineering Department of Computer Engineering Pune India Pes University Department of Electronics and Communication Engineering Bangalore India National Institute of Technology Srinagar Department of Civil Engineering Jammu and Kashmir India Srm Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur India Amrita Vishwa Vidyapeetham Kollam Amrita School of Engineering Department of Computer Science and Engineering Kerala India

ISBN: (数字)9781665479714

ISBN: (纸本)9781665479714

Bankruptcy prediction is an important modern technique because it helps not only the policy makers but the investors and all concerned people so they can take all necessary steps to prevent or to reduce the after effects of bankruptcy. Companies take a large quantity of loan and when they become bankrupt the entire system of the bank is shattered as it is really tough to get the given money bank. Hence, it is must needed to develop a system that could predict it the company's bankruptcy and stop the loan. It is observed that the newer machine learning prediction model is outperforming the models of past thirteen years, using relatively small data but this data is still very big and balanced. Some of the machine learning models or techniques are Random Forest, XGBoost, Vector Machines. The main motive of the paper aims to compare the machine learning models which are support vector machine, random forest and XGBoost and give which one performs the best amongst them and give a novel accuracy. © 2022 IEEE.

关键词： Support vector machines

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Deep Stereo Video Inpainting

Deep Stereo Video Inpainting

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Zhiliang Wu Changchang Sun Hanyu Xuan Yan Yan School of Computer Science and Engineering Nanjing University of Science and Technology China Department of Computer Science Illinois Institute of Technology USA School of Big Data and Statistics Anhui University China

Stereo video inpainting aims to fill the missing regions on the left and right views of the stereo video with plausible content simultaneously. Compared with the single video inpainting that has achieved promising results using deep convolutional neural networks, inpainting the missing regions of stereo video has not been thoroughly explored. In essence, apart from the spatial and temporal consistency that single video inpainting needs to achieve, another key challenge for stereo video inpainting is to maintain the stereo consistency between left and right views and hence alleviate the 3D fatigue for viewers. In this paper, we propose a novel deep stereo video inpainting network named SVINet, which is the first attempt for stereo video inpainting task utilizing deep convolutional neural networks. SVINet first utilizes a self-supervised flow-guided deformable temporal alignment module to align the features on the left and right view branches, respectively. Then, the aligned features are fed into a shared adaptive feature aggregation module to generate missing contents of their respective branches. Finally, the parallax attention module (PAM) that uses the cross-view information to consider the significant stereo correlation is introduced to fuse the completed features of left and right views. Furthermore, we develop a stereo consistency loss to regularize the trained parameters, so that our model is able to yield high-quality stereo video inpainting results with better stereo consistency. Experimental results demonstrate that our SVINet outperforms state-of-the-art single video inpainting models.

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Learning Patterns from Biological Networks: A Compounded Burr Probability Model

arXiv

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arXiv 2024年

作者： Chakraborty, Tanujit Naik, Shraddha M. Chattopadhyay, Swarup Das, Suchismita Department of Science and Engineering Sorbonne University Abu Dhabi United Arab Emirates Department of Computer Science & Engineering XIM University Odisha Bhubaneswar India Department of Data Science SP Jain School of Global Management Mumbai India

Complex biological networks, comprising metabolic reactions, gene interactions, and protein interactions, often exhibit scale-free characteristics with power-law degree distributions. However, empirical studies have revealed discrepancies between observed biological network data and ideal power-law fits, highlighting the need for improved modeling approaches. To address this challenge, we propose a novel family of distributions, building upon the baseline Burr distribution. Specifically, we introduce the compounded Burr (CBurr) distribution, derived from a continuous probability distribution family, enabling flexible and efficient modeling of node degree distributions in biological networks. This study comprehensively investigates the general properties of the CBurr distribution, focusing on parameter estimation using the maximum likelihood method. Subsequently, we apply the CBurr distribution model to large-scale biological network data, aiming to evaluate its efficacy in fitting the entire range of node degree distributions, surpassing conventional power-law distributions and other benchmarks. Through extensive data analysis and graphical illustrations, we demonstrate that the CBurr distribution exhibits superior modeling capabilities compared to traditional power-law distributions. This novel distribution model holds great promise for accurately capturing the complex nature of biological networks and advancing our understanding of their underlying mechanisms. © 2024, CC0.

关键词： Complex networks

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MLCardiovascularPrediction: Machine Learning-Based Cardiovascular Disease Prediction 3

MLCardiovascularPrediction: Machine Learning-Based Cardiovas...

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3rd International Mobile, Intelligent, and Ubiquitous Computing Conference, MIUCC 2023

作者： Abdelminaam, Diaa Salama Madbouly, Magda M. Zeid, Magdy Abd-Elghany Elatif Gomaa, Ibrahim Abd Abualigah, Laith Misr International University Faculty of Computer Science Cairo Egypt Benha University Faculty of Computers and Artificial Intelligence Benha Egypt Alexandria University Faculty of Computer and Data Science Alexandria Egypt Alobour High Institute for Computer and Informatics Computer Science Department Cairo Egypt Lebanese American University Department of Electrical and Computer Engineering Byblos13-5053 Lebanon Al-Ahliyya Amman University Hourani Center for Applied Scientific Research Amman19328 Jordan Middle East University Meu Research Unit Amman11831 Jordan School of Computer Sciences Universiti Sains Malaysia Pinang Pulau11800 Malaysia

ISBN: (纸本)9798350306231

Like most diseases that affect a human's life, car-diovascular disease is dangerous and unwanted for anyone who has heard of it as it brings a high risk of death. In today's world, most deaths are caused by heart disease as it could come from age, heritage, smoking, and many factors that could seem normal, and it doesn't matter what part of the world you are in. There must be a way to know or predict if this person is at high risk of cardiovascular disease. This paper and project talk about six algorithms: K-Nearest-Neighbour (KNN), Decision Tree (DT), Naive Bayes, Neural Network, Logistic Regression, and Support Vector Machine (SVM). The results came between 60% to 70% in one dataset and from 80 % to 90 % in the second. Still, they mostly favor the Neural Network algorithm as it can learn and model around complex relationships. The SVM showed different outputs between the two datasets as one is significantly larger than the other, possibly due to the outliers in both datasets being different and more complex in the larger one. Cardiovascular disease can be very dangerous, but finding and discovering it soon can be a lifesaver. These algorithms also can help find and predict it, as it was shown with not just decent, but good and respectable success rates for each one. © 2023 IEEE.

关键词： Decision trees

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Spatio-Temporal Analysis of Rates Derived from Count data Using Generalized Fused Lasso Poisson Model 15th

Spatio-Temporal Analysis of Rates Derived from Count Data ...

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15th International KES Conference on Intelligent Decision Technologies, KESIDT 2023

作者： Yamamura, Mariko Ohishi, Mineaki Yanagihara, Hirokazu Department of Statistics Radiation Effects Research Foundation Hiroshima732-0815 Japan Center for Data-driven Science and Artificial Intelligence Tohoku University Sendai980-8576 Japan Mathematics Program Graduate School of Advanced Science and Engineering Hiroshima University Hiroshima Higashi-Hiroshima739-8526 Japan

ISBN: (纸本)9789819929689

We propose a method to statistically analyze rates obtained from count data in spatio-temporal terms, allowing for regional and temporal comparisons. Generalized fused Lasso Poisson model is used to estimate the spatio-temporal effects of the rates;the coordinate descent algorithm is used for estimation. The results of an analysis using data on crime rates in Japan’s Osaka Prefecture from 1995 to 2008 confirm the validity of the approach. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Adaptive fused lasso Coordinate descent algorithm Spatio-temporal proportion data

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Purchase intention of Amazon sustainable brands: a hybrid approach of structural equation modeling and Rasch model

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SN Business and Economics 2023年第12期3卷 1-29页

作者： Santos, Luiz Diego Vidal Holanda, Francisco Sandro Rodrigues de Santana Oliveira, Catuxe Varjão Pedrotti, Alceu de Menezes, Pedro Vinícius Bertulino Villwock, Ana Paula Schervinski Graduate Program in Intellectual Property Science Universidade Federal de Sergipe-UFS Sergipe São Cristóvão Brazil Agronomy Engineering Department Universidade Federal de Sergipe-UFS Sergipe São Cristóvão Brazil Computer Science Department Instituto Federal de Sergipe-UFS Sergipe Lagarto Brazil

This article conducts a comprehensive examination of brand-based equity closely linked to environmental sustainability in the Amazon Rainforest. The research is primarily dedicated to identifying the factors that influence consumers’ willingness to purchase products from sustainable brands within six categories: cosmetics, tourism, clothing, accessories, and food. The central aim of this study is to investigate how brand equity constructs, rooted in environmental sustainability within the Amazon Rainforest, impact consumer loyalty and purchase intention. To accomplish this objective, a mixed-methodology approach was employed, combining structural equation modeling with item response theory. This study involved 535 potential consumers, and data from eight selected brands served as the basis for the analysis. Within the framework of Purchase Intention (PI), the following variables were regarded as formative constructs: Strategic Brand Positioning (SBP), Perceived Brand Quality (PBQ), Retail Innovation (RI), and Brand Authenticity (BA). The results obtained from the structural equation models underscore the statistical significance and substantial impact of all pathways. Notably, SBP emerged as the most significant contributor to the effect on PI, as hypothesized. Brand equity and environmental sustainability were found to indirectly affect behavioral PI. Consumers exhibited a high level of latent trait (θ), with items consistently reflecting elevated values, confirming their acceptance of products, except brand differentiation. Overall, the findings highlight the effectiveness of branding strategies rooted in environmental sustainability in enhancing the brand equity of small forest companies. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023.

关键词： Amazon Rainforest Brands Entrepreneurship Microenterprises Sustainability performance Sustainable development

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