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Efficient Machine Learning and Factional Calculus Based Mathematical Model for Early COVID Prediction

Human-Centric Intelligent Systems 2023年第4期3卷 508-520页

作者： Chandra, Saroj Kumar Bajpai, Manish Kumar Center of Excellence in Industry 4.0 Computer Science and Engineering OP Jindal University Raigarh India Computer Science and Engineering PDPM-Indian Institute of Information Technology Design and Manufacturing Jabalpur India

Diseases are increasing with exponential rate worldwide. Its detection is challenging task due to unavailability of the experts. Machine learning models provide automated mechanism to detect diseases once trained. It has been used to predict and detect many diseases such as cancer, heart attack, liver infections, kidney infections. The new coronavirus has become one of the deadliest diseases. Its case escalated in unexpected ways. In the literature, many machine learning models such as Extreme Gradient Boosting (XGBoosting), Support Vector Machine (SVM), regression, and Logistic regression have been used. It has been observed that these models can predict COVID cases early but are unable to find the peak point and deadline of the disease. Hence, mathematical models have been designed to early predict and find peak point and dead-line in disease prediction. These mathematical models use integral calculus-based Ordinary Differential Equations (ODEs) to predict COVID cases. Governments are dependent on these models’ pre- diction for early preparation of hospitalization, medicines, and many more. Hence, higher prediction accuracy is required. It has been found in the literature that fractional calculus-based models are more accurate in disease prediction and detection. Fractional models provides to choose order of derivative with fractional value due to which information processing capability increases. In the present work, mathematical model using fractional calculus has been devised for prediction of COVID cases. In the model, quarantine, symptomatic and asymptomatic cases have been incorporated for accurate prediction. It is found that the proposed fractional model not only predicts COVID cases more accurately but also gives peak point and dead-line of the disease.

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Facemask Detection System using CNN Model

Facemask Detection System using CNN Model

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Recent Advances in science and engineering Technology (ICRASET), International Conference on

作者： Senbagavalli M Saswati Debnath Rajagopal R Kishkind Ghildial Computer Science & Engineering and IT Department Alliance College of Engineering & Design Alliance University Bangalore Karnataka India Computer Science & Engineering Department Alliance College of Engineering & Design Alliance University Bangalore Karnataka India

Global trade and transportation have been impacted by the COVID-19 epidemic, which has quickly affected our everyday activities. One of the best ways to stop the Covid-19 virus from transmitting is to wear a face mask. As a result, the World Health Organization (WHO) advised wearing masks as a precaution in crowded areas. Not only COVID-19, wearing mask can reduce the risk of many infectious diseases. In certain places, diseases caused by bacteria, viruses, fungi, or parasites spread quickly due to the inappropriate usage of face masks. Many public services providers demand that their clientele participate in their services while suitably dressed in masks. Therefore, identifying face masks has become a crucial duty in supporting global civilization. This paper develops face mask detection system with an alert that detects the presence of a mask in real-time. The proposed system accurately examines the face from the picture and then determines whether it is covered by a mask or not. A notification can be issued to the administrator if the camera records an unrecognizable face. After that, the administrator will be able to track down the infringer.

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A Comprehensive Review of Sensor-Based Smart Packaging Technology

A Comprehensive Review of Sensor-Based Smart Packaging Techn...

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International Conference on Power engineering and Intelligent Systems, PEIS 2023

作者： Aniruddha Prabhu, B.P. Dani, Rakesh Abdullah, Khairul Hafezad Sharma, Tushar Bhatt, Chandradeep Chauhan, Rahul Alliance College of Engineering and Design Alliance University Bangalore India University Dehradun India Department of Academic Affair UniversitiTeknologi MARA Perlis Branch Arau Campus Shah Alam Malaysia Department of computer Science and Engineering Graphic Era Hill University Dehradun India

ISBN: (纸本)9789819972159

Economic growth for good quality food contributed to packaging advancements. The topic of discussion in this research paper is intelligent packaging technologies, their applications in food packaging, as well as research and breakthroughs in the packaging industry. Food products with more intelligent and active packaging are both healthier and of higher quality. Active packaging makes use of additives to maintain or extend the quality and freshness of the food inside. During the entire process of storage and transportation, intelligent systems check packed foods for correct information. These advances satisfy the demand for foods that are healthier and can be stored for longer. It is anticipated that the market would expand as a result of the incorporation of active and intelligent packaging technologies. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Additives

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Interactions beyond the Pandemic: Lessons Learned from Large-scale Emergency Remote Teaching in Higher Education 25

Interactions beyond the Pandemic: Lessons Learned from Large...

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2025 CHI Conference on Human Factors in Computing Systems, CHI 2025

作者： Yarmand, Matin Li, Haowei Weibel, Nadir Computer Science and Engineering The Design Lab UC San Diego San DiegoCA United States Graduate School of Education University of Pennsylvania PhiladelphiaPA United States

ISBN: (纸本)9798400713941

Online education - given the enhanced access for diverse populations and flexible participation - has been a topic of interest for many computer science and learning science researchers. The sudden shift to online settings during the COVID-19 Emergency Remote Teaching (ERT) provided a valuable opportunity to examine the use of educational technologies on a global scale with various digital readiness skills, beyond many past works that relied on small lab studies. Following a PRISMA-inspired methodology grounded on Moore's three types of classroom interaction, this descriptive review investigates 22 empirical research papers published during the COVID-19 ERT era focused on higher-education online classrooms. We explore the empirical evidence reported in the collected corpus, and given how ERT remains a likely future occurrence, we suggest key directions for future research, including a new learning paradigm that centralizes and augments Learner-Content interaction to balance between flexibility and structure of online learning. © 2025 Copyright held by the owner/author(s).

关键词： Teaching

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Temporal-aware Mechanism with Bidirectional Complementarity for Video Q&A

Temporal-aware Mechanism with Bidirectional Complementarity ...

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2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Luo, Yuanmao Wang, Ruomei Zhang, Fuwei Zhou, Fan Lin, Shujin School of Computer Science and Engineering National Engineering Research Center of Digital Life Sun Yat-sen University School of Communication and Design Guangzhou China

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

Video question answering (Video Q&A) is a challenging task as it requires a sufficient understanding of the video and question information. Video is composed of frame sequence, which contains multi-scale temporal relationships and corresponding contextual information. A model competently tackle Video Q&A task that needs to be able to: 1) construct long-term and neighborhood dependencies in frame sequences to extract global and local contextual features that can reflect multi-scale temporal dependencies, and deduce the temporal-aware refined features, and 2) identify static and dynamic features from pertinent moments of a video, while filtering away question-irrelated dependencies of feature sequences, to yield the most precise and reasonable temporal-aware overall contextual features. In response to the above requirements, we propose a novel Video Q&A mechanism which consists of Bidirectional Complementary Attention(BCA) module and Adaptive Temporal-aware(ATA) module. Bidirectional complementary attention module stacks multi-head self-attention layer and convolutional layer in different orders to designed two kinds of attention units, which is able to make bidirectional multi-step reasoning based on complete global information and accurate local information to obtain temporal-aware refined features. Adaptive temporal-aware module is used to filter away question-irrelated dependencies in the feature sequence to yield the most precise and reasonable temporal-aware overall contextual features. Comprehensive comparative experiments are conducted on publicly available benchmark datasets. An extended ablation study is further conducted to show the usefulness of each module of the solution in acquiring its computational Q&A capabilities. © 2022 IEEE.

关键词： Graph neural networks

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Test-Driven Feature Extraction of Web Components

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Journal of computer science & Technology 2022年第2期37卷 389-404页

作者： Yong-Hao Long Yan-Cheng Chen Xiang-Ping Chen Xiao-Hong Shi Fan Zhou School of Computer Science and Engineering National Engineering Research Center of Digital Life Sun Yat-sen UniversityGuangzhou 510006China School of Design The Hong Kong Polytechnic UniversityHong KongChina Guangdong Key Laboratory for Big Data Analysis and Simulation of Public Opinion The School of Communication and DesignSun Yat-sen UniversityGuangzhou 510006China School of Information Technology and Engineering Guangzhou College of CommerceGuangzhou 511363China

With the growing requirements of web applications,web components are developed to package the implementation of commonly-used features for *** some cases,the developer may want to reuse some features which cannot be customized by the component’s ***/she has to extract the implementation by *** is labor-intensive and *** the widely-used test cases which can be useful to specify the software features,a test-driven approach is proposed to extract the implementation of the desired features in web *** satisfaction of the user^requirements is transformed into the passing rate of user-specified test *** this way,the quality of the extraction result can be evaluated ***,a record/replay-based GUI test generation method is proposed to ensure that the extraction result has the correct GUI *** extract the feature implementation,a hierarchical genetic algorithm is proposed to find the code snippet that can pass all the tests and has the approximate smallest *** compare our method with two existing feature extraction *** result shows that our method can extract the correct implementation with the minimum size.A human-subject study is conducted to show the effectiveness and weaknesses of our method in helping users extract the features.

关键词： feature extraction genetic algorithm graphical user interface software reuse

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ELU-GCN: EFFECTIVELY LABEL-UTILIZING GRAPH CONVOLUTIONAL NETWORK

arXiv

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arXiv 2024年

作者： Huang, Jincheng Mo, Yujie Shi, Xiaoshuang Feng, Lei Zhu, Xiaofeng School of Computer Science and Engineering University of Electronic Science and Technology of China China Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore

The message-passing mechanism of graph convolutional networks (i.e., GCNs) enables label information to be propagated to a broader range of neighbors, thereby increasing the utilization of labels. However, the label information is not always effectively utilized in the traditional GCN framework. To address this issue, we propose a new two-step framework called ELU-GCN. In the first stage, ELU-GCN conducts graph learning to learn a new graph structure (i.e., ELU-graph), which enables GCNs to effectively utilize label information. In the second stage, we design a new graph contrastive learning on the GCN framework for representation learning by exploring the consistency and mutually exclusive information between the learned ELU graph and the original graph. Moreover, we theoretically demonstrate that the proposed method can ensure the generalization ability of GCNs. Extensive experiments validate the superiority of the proposed method. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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Fig Leaf Disease Prediction and Severity Estimation Using Deep Learning

Fig Leaf Disease Prediction and Severity Estimation Using De...

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Computing and Intelligent Reality Technologies (ICCIRT), International Conference on

作者： K. R. Prasanna Kumar M. Gunasekar K. Logeswaran P Dhanya Sree P Malathi V Vignesh Dept. of Computer Science and Design Kongu Engineering College Erode Tamil Nadu India School of Computer Science and Engineering VIT Vellore Tamil Nadu India Dept. of Artificial Intelligence Kongu Engineering College Erode Tamil Nadu India Department of Computer Science and Design Kongu Engineering College Erode Tamil Nadu India

ISBN: (数字)9798331510299

ISBN: (纸本)9798331510305

Fig leaf diseases are among the major causes of crop loss. Better methods for detection and further increased accuracy in the estimation of the severity of the disease are demanded. In basis of what is previously researched, this project continues with not just the identification of the disease but also the identification of the level of infections. Having a large dataset of 1,871 images that comprises 971 healthy samples and 900 infected samples, the analysis will be more elaborate to determine the disease in comparison with previous estimates. We have used two deep learning models - GoogLeNet and AlexNet with outstanding performance in preceding research to make reliable predictions on leaf diseases. The novel contribution of our work is estimating the disease severity with the help of Convolutional Neural Networks that could extract features and classify disease. For the estimation of the disease severity, we will use the VGG16 model to be able to categorize the infection severity into three classes- healthy, mild, and high. The diseases addressed in this paper are Cerotelium fici (fig rust) and Fig mosaic *** goal is to offer a comprehensive tool designed to identify diseases in fig crops and assess their severity for fig farmers and the researcher so that timely disease management strategies could be undertaken. This integrated approach not only aids in taking prompt intervention but also contributes significantly toward sustainable agriculture by reducing adverse impacts on fig leaf diseases.

关键词： Deep learning Training Adaptation models Accuracy Estimation Crops Predictive models Feature extraction Agriculture Diseases

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Predictive Modeling and Analysis of Campus Placements Using Machine Learning Techniques 5

Predictive Modeling and Analysis of Campus Placements Using ...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Gaurkhede, Sanskruti Burle, Rushikesh Gudadhe, Amit Chopra, PKP Gaitry Datta Meghe Institute of Higher Education and Research Computer science And Design Faculty of Engineering and Technology Mahartashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Mahartashtra Wardha442001 India

ISBN: (纸本)9798331530013

A prediction method for campus placements is designed to determine the likelihood that a student will be hired by a firm after completing a campus placement. Numerous parameters are taken into account by the model, which may be used to estimate the student's ability level. Some information is gathered from examinations administered through the placement management system, while other information is gathered from college-level sources such as academic performance, CGPA, pointers, projects, internships, HSC and SSC marks, etc. This placement prediction technique aims to assist students in enhancing their academic performance and outcomes while also helping them to acquire a soft skill that would increase their placement chances. The reputation and annual admissions of the institute are always contingent upon the student placements made by the institute. Thus, in an effort to enhance their organisation as a whole, the majority of schools diligently work to grow their placement department. The goal of this study is to evaluate student placement data from the previous year and use it to calculate the likelihood that current students will be placed on campus. Four distinct machine learning algorithms Logistic Regression, Decision Tree, Support Vector Machine, K Neighbour Classifier, and Random Forest have been tested for this purpose. © 2024 IEEE.

关键词： Self-supervised learning

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Enhancing Drone Surveillance with NeRF: Real-World Applications and Simulated Environments 43

Enhancing Drone Surveillance with NeRF: Real-World Applicati...

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43rd AIAA DATC/IEEE Digital Avionics Systems Conference, DASC 2024

作者： Lindén, Joakim Burresi, Giovanni Forsberg, Håkan Daneshtalab, Masoud Söderquis, Ingemar School of Innovation Design and Engineering Mälardalen University Division of Intelligent Future Technologies Västerås Sweden Saab Aeronautics Stockholm Sweden School of Electrical Engineering and Computer Science Royal Institute of Technology Division of Electronics and Embedded Systems Stockholm Sweden

ISBN: (纸本)9798350349610

Machine Learning (ML) systems require representative and diverse datasets to accurately learn the objective task. In supervised learning data needs to be accurately annotated, which is an expensive and error-prone process. We present a method for generating synthetic data tailored to the use-case achieving excellent performance in a real-world usecase. We provide a method for producing automatically annotated synthetic visual data of multi rotor unmanned aerial vehicles (UAV) and other airborne objects in a simulated environment with a high degree of scene diversity, from collection of 3D models to generation of annotated synthetic datasets (synthsets). In our data generation framework SynRender we introduce a novel method of using Neural Radiance Field (NeRF) methods to capture photo-realistic high-fidelity 3D-models of multirotor UAVs in order to automate data generation for an object detection task in diverse environments. By producing data tailored to the real-world setting, our NeRF-derived results show an advantage over generic 3D asset collection-based methods where the domain gap between the simulated and real-world is unacceptably large. In the spirit of keeping research open and accessible to the research community we release our dataset VISER DroneDiversity used in this project, where visual images, annotated boxes, instance segmentation and depth maps are all generated for each image sample. © 2024 IEEE.

关键词： Drones

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