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Evaluation of Individual Innovation and Entrepreneurship Effect Based on Linear Space Model and Grey Correlation

Journal of Combinatorial Mathematics and Combinatorial Computing 2023年 118卷 3-17页

作者： Ning, Xiaohao College of Computer Science and Engineering Jishou University Hunan Zhangjiajie422700 China

This paper analyzed the components of the connotation of innovation ability, then constructs a linear spatial model of innovation and entrepreneurship ability, proposes a multi-objective function model of the utilization efficiency and allocation efficiency of education resources, and uses the grey correlation algorithm The experimental simulation and model solution are carried out. The simulation results show that, through the optimization, the utilization efficiency and allocation efficiency of the educational resources for innovation and entrepreneurship for all are increased by 18.72% and 20.98% respectively, and tend to be in equilibrium, which can achieve the optimization of educational resources allocation. Among all people, the correlation value with ideal entrepreneurship is 0.3177, achieving the most excellent innovation and entrepreneurship education. © 2023 Charles Babbage Research Centre. All rights reserved.

关键词： Multiobjective optimization

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Continuous Monitoring of Web Server Assaults Using Machine Learning 2

Continuous Monitoring of Web Server Assaults Using Machine L...

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2nd International Conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Vishali, M. Mirudhula, A. Priya, A. Iswarya, M. Subramanian, Kavitha Panimalar Engineering College M.E Computer Science and Engineering Tamil Nadu Chennai India Panimalar Engineering College Dept of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350328202

Because of the widespread accessibility of the weband its ease of use, every organisation nowadays uses web apps to expand its company. With the growth of Due to growing online usage, attack riskhas grown as well. An effective monitoring tools that can recognise these threats Timely is necessary. Java script, PHP, and HTML are utilisedfor website development and database use of SQL management in depth. most typical web server assaults include XSS, DOS (Denial of Service), and SQL injection Cascading Style Sheets. IDS with rules work with keywords and patterns but fail to recognise unidentified assaults. In this article, ML based model is used for leveraging web server logsto identify intrusions. It identifies the sort of assault as well as if a given log is normal or malicious. By establishing a private network with the aid of an Apache WAMP server, web server logs are produced and gathered. © 2024 IEEE.

关键词： Machine learning

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Attention Contours: A Comparative Study on 2D and Virtual Reality Attention Profile Assessment Tests 5

Attention Contours: A Comparative Study on 2D and Virtual Re...

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5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Rajarajeswari, P. Madhubala, R. Gomathi, V. Ramco Institute of Technology Department of Computer Science and Engineering Rajapalayam India National Engineering College Department of Computer Science and Engineering Kovilpatti India

ISBN: (纸本)9798350389609

Virtual Reality (VR) technology in health-care has emerged as a valuable tool for advancing diagnostic techniques and enhancing patient care. This research explores the application of VR in attention profile assessments, with a particular focus on diagnosing Attention Deficit/Hyperactivity Disorder (ADHD). ADHD poses challenges in maintaining attention, and traditional diagnostic approaches often lack real-world relevance. This study examines the efficacy of Attention Contours - a VR-based Continuous Performance Task (CPT) - in comparison to its conventional 2D (two-dimensional) counterpart for assessing attention profiles. Attention Contours VR incorporates varied virtual settings and language-neutral stimuli to improve ecological validity. User trials reveal greater engagement and fewer distractions in the VR environment, highlighting its potential for more accurate attention assessment. Participants showed a strong preference for the VR format, praising its immersive experience and enhanced usability. The findings emphasize the impact of VR in advancing attention assessment tools and suggest its promising role in ADHD assessment. © 2024 IEEE.

关键词： Virtual environments

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Childhood Asthma Disease Prediction Using Classification Algorithms of Supervised Machine Learning 1

Childhood Asthma Disease Prediction Using Classification Alg...

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1st International Conference on Advanced Computing and Emerging Technologies, ACET 2024

作者： Kumar, Pawan Jain, Jaishree Jaiswal, Updesh Kumar Yadav, Deepika Solanki, Beerbal Ajay Kumar Garg Engineering College Department of Computer Science & Engineering UP Ghaziabad India Bharati Vidyapeeth's College of Engineering Department of Computer Science & Engineering New Delhi India

ISBN: (纸本)9798350367720

Asthma is a very dangerous lung disease in which our airways are swollen and become narrow and blocked. Air does not flow through narrow airways properly. It is an ongoing disease and does not go away, regular treatment is needed lifelong. According to the survey report, 25 million people in the United States are living with this disease and out of that 5 million are children who are asthma patients. Asthma is a life-threatening disease if don't get treatment timely, can lose your life. Early prediction of this disease among children not only provides a recovery solution for the disease but also helps to increase their lifetime. The proposed designed Intelligent system provides useful resources to the healthcare system to make accurate decisions for preventing asthma in childhood. The research paper aims to design an intelligent artificial-based machine learning classification model to predict asthma disease among children aged less than 10 years. Research also included a comparative analysis of different classification methods of machine learning like Linear regression, KNN Model, Decision Trees, Random Forests and Support Vector machines. It also helps to select the best model by analyzing the performances of these algorithms based on efficiency and accuracy. The outcomes of the research paper to study asthma prediction using different classification models and help doctors and patients to take the right decision timely. © 2024 IEEE.

关键词： Lung cancer

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Generalized halftone classification approach using stochastic analysis

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Journal of Ambient Intelligence and Humanized Computing 2023年第11期14卷 14907-14919页

作者： Guo, Jing Ming Seshathiri, Sankarasrinivasan College of Electrical Engineering and Computer Science National Taiwan University of Science and Technology Taipei Taiwan

Halftone classification is a primary requisite for the perfect reconstruction of binary patterns during inverse halftone process. Majority of the halftone classification techniques are either limited to error diffused halftone or to limited categories, and cannot be generalized to all halftone versions. In this scenario, a new classification approach is proposed based on the premise that the stochastic analysis can uniquely characterize the halftone patterns. The proposed scheme exploits inherent association of halftone patterns and stochastic geometry, and utilizes its spatial and spectral parameters for feature vector construction. Extreme learning machine based multi-classifier model is adopted resulting in rapid and accurate classification. A digital halftone database comprises of 96 reference images along with the 21 varieties of halftone and multitone class is developed. From extensive analysis, it has been validated that the proposed scheme can achieve a 100% classification rate on many halftone versions and exhibit superior performance over the exiting classification techniques. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Classification (of information)

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Feature Adaptive Developmental Mechanisms for Mobile Apps Recommendations System using the Nearest Centroid Classification Algorithm 7

Feature Adaptive Developmental Mechanisms for Mobile Apps Re...

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7th International Conference on Trends in Electronics and Informatics, ICOEI 2023

作者： Twinkle Geojini, G. Ramnath, M. Rubavathi, C. Yesubai Thamirabharani Engineering College Department of Computer Science and Engineering India Department of Computer Science and Engineering Francis Xavier Engineering College India

ISBN: (纸本)9798350397284

Reviews and ratings play an important part in modern technology since they provide insights that may be used to enhance the functionality and performance of apps. Some customers give app reviews that don't provide developers with useful feedback for improving the app. Developers can't fix bugs or release new versions of their software if they don't catch them as soon as possible. With this method, we extract the most helpful user comments from existing app evaluations and compare them to the apps' overall star ratings. We present a Nearest Centroid Classification (NCC) approach based on supervised machine learning with the goal of spotting new app problems via review analysis. To successfully illustrate resilience to the extreme values that are beyond the range, the suggested framework contains mean vectors of k to evaluate both distance closeness and geographical extent of k-neighbors in each class. After a long time of waiting, developers may at last quickly correct bugs and fine-tune the app's features to enhance the user experience. © 2023 IEEE.

关键词： Supervised learning

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Reversible data hiding in encrypted images with block-based bit-plane reallocation

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Multimedia Tools and Applications 2024年第37期83卷 84911-84932页

作者： Liu, Li Chen, Chaofan Wu, Yingchun Chang, Chin-Chen Wang, Anhong College of Electronic Information and Engineering Taiyuan University of Science and Technology Taiyuan030024 China Department of Information Engineering and Computer Science Feng Chia University Taichung40724 Taiwan

As cloud storage and multimedia communication continue to evolve, the preservation of image privacy is becoming increasingly important. Reversible data hiding in encrypted images (RDHEI) is an effective method for enhancing the protection of personal privacy. To improve the embedding capacity, this paper introduces a novel scheme for RDHEI using block-based bit-plane reallocation. First, an effective optimized prediction method is utilized to predict the value of each pixel, followed by the calculation of prediction errors (PEs). Then, block-based bit-planes of PEs are extracted, and ten different bit-plane types are categorized to facilitate bit-plane reallocation, thereby freeing up redundant space. Finally, an adaptive encryption procedure is employed to encrypt the reallocated bit-plane, excluding the flag-bits. Experimental results demonstrate that the proposed scheme achieves average embedding rates (ER) of 3.584 bpp, 3.501 bpp, and 2.985 bpp on the three experimental datasets, all of which are higher than those of other state-of-the-art RDHEI schemes while ensuring both reversibility and security. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Cryptography

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SRC:Superior Robustness of COVID-19 Detection from Noisy Cough Data Using GFCC

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computer Systems science & engineering 2023年第8期46卷 2337-2349页

作者： Basanta Kumar Swain Mohammad Zubair Khan Chiranji Lal Chowdhary Abdullah Alsaeedi Department of Computer Science&Engineering Government College of EngineeringKalahandiBhawanipatna766002India Department of Computer Science and Information Taibah UniversityMedina42353Saudi Arabia School of Information Technology and Engineering VIT University Vellore632014Tamil NaduIndia Computer Science Department College of Computer Science and EngineeringTaibah UniversityMedina42353Saudi Arabia

This research is focused on a highly effective and untapped feature called gammatone frequency cepstral coefficients(GFCC)for the detection of COVID-19 by using the nature-inspired meta-heuristic algorithm of deer hunting optimization and artificial neural network(DHO-ANN).The noisy crowdsourced cough datasets were collected from the public *** research work claimed that the GFCC yielded better results in terms of COVID-19 detection as compared to the widely used Mel-frequency cepstral coefficient in noisy crowdsourced speech *** proposed algorithm's performance for detecting COVID-19 disease is rigorously validated using statistical measures,F1 score,confusion matrix,specificity,and sensitivity ***,it is found that the proposed algorithm using GFCC performs well in terms of detecting the COVID-19 disease from the noisy crowdsourced cough dataset,***,the proposed algorithm and undertaken feature parameters have improved the detection of COVID-19 by 5%compared to the existing methods.

关键词： COVID-19 GFCC DHO-ANN cough data

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An optimal detection of android malware using dynamic attention-based LSTM classifier

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Journal of Intelligent and Fuzzy Systems 2023年第1期44卷 1425-1438页

作者： Jebin Bose, S. Kalaiselvi, R. Department of Computer Science and Engineering Noorul Islam Centre for Higher Education Kumaracoil India Department of Computer Science and Engineering R.M.K College of Engineering and Technology Puduvoyal Gummidipoondi India

In today's world, Android has become the most significant and standard operating system for smartphones. The acceptance of the rapidly growing android system has outcome in a significant enhancement in the number of malware on comparing earlier days. There were several antimalware programs that are designed efficiently for protecting the sensitive data of the user in a mobile system from the occurrence of such attacks. Detection of malware system based on deep learning model along with the use of optimization technique is presented in this work. Initially, android malware dataset input is acquired and the normalization process is done. The feature selection is carried along with the optimization technique Recurrent Tuna Swarm Optimization. By this, an optimal selection of features can be attained. © 2023 - IOS Press. All rights reserved.

关键词： Android malware

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***: Learning-based Governance for Decentralized Finance (DeFi)

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IEEE Transactions on Services Computing 2025年第3期18卷 1278-1292页

作者： Xu, Jiahua Feng, Yebo Perez, Daniel Livshits, Benjamin University College London United Kingdom Exponential Science Foundation United Kingdom Nanyang Technological University School of Computer Science and Engineering Singapore Imperial College London Computer Science Department United Kingdom Imperial College London United Kingdom

Decentralized finance (DeFi) is an integral component of the blockchain ecosystem, enabling a range of financial activities through smart-contract-based protocols. Traditional DeFi governance typically involves manual parameter adjustments by protocol teams or token holder votes, and is thus prone to human bias and financial risks, undermining the system's integrity and security. While existing efforts aim to establish more adaptive parameter adjustment schemes, there remains a need for a governance model that is both more efficient and resilient to significant market manipulations. In this paper, we introduce "***", a learning-based governance framework that employs a deep Qnetwork (DQN) reinforcement learning (RL) strategy to perform semi-automated, data-driven parameter adjustments. We create a DeFi environment with an encoded action-state space akin to the Aave lending protocol for simulation and testing purposes, where *** has demonstrated the capability to retain funds that would have otherwise been lost to price oracle attacks. In tests with real-world data, *** outperforms the benchmark approaches by at least 14% and the static baseline model by tenfold, in terms of the preset performance metric–protocol profitability. Overall, the comprehensive evaluations confirm that *** is more efficient and effective than traditional governance methods, thereby enhancing the security, profitability, and ultimately, the sustainability of DeFi protocols. © 2025 IEEE. All rights reserved.

关键词： Decentralized finance

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