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Enhancing Online Learning: A Comparative Analysis of Machine Learning Models and Data Visualization Techniques for Predicting Learner Flexibility

Enhancing Online Learning: A Comparative Analysis of Machine...

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2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Priya, P. Kanaga Suguna, R.K. Sujitha, S. Devi, G. Nandhini, D. Kalyan, G. Ramesh KPR Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Kongu Engineering College Department of Computer Science and Engineering Erode India SNS College of Technology Department of Computer Science and Engineering Coimbatore India SNS College of Engineering Department of Computer Science and Technology Coimbatore India

ISBN: (纸本)9798350319200

The fast growth of online learning has increased the demand for efficient tools and approaches to measure and improve student's capacity to adapt to online learning. This paper gives a thorough comparative comparison of Machine Learning (ML) models and data visualization techniques for predicting the flexibility of learners in the realm of online education. In this study, various data visualization techniques are investigated to learn more about how learners behave, engage with, and perform in online courses. Understanding patterns and trends through data visualization is essential for educators and institutions to make well-informed decisions to enhance learner's learning journeys. To find out how effective different visualization techniques are in revealing useful insights, IBM Cognos and Python are used. The suitability of several ML algorithms for predicting learners' flexibility levels is also examined in this study. The predictive accuracy or mean accuracy score of several ML models, including XGBoost, Random Forests, CatBoost, Logistic Regression, and K Nearest Neighbour, is compared. The dataset used for this investigation contains a variety of learner-related variables, such as demographic data, past academic achievement, and interactions with online courses. For educators and educational institutions looking to use data-driven techniques to improve online learning experiences, the findings of this comparative study offer invaluable advice to them. This study contributes to the ongoing attempts to raise the caliber and efficacy of online education by identifying the most efficient data visualization strategies and ML models for forecasting learners' flexibility. The study's conclusions enable educators to proactively identify learners who are at risk of having difficulty with online learning, enabling prompt interventions and individualized support for academic success. © 2023 IEEE.

关键词： accuracy and flexibility IBM Cognos machine learning online learning visualization

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A survey of intrusion detection from the perspective of intrusion datasets and machine learning techniques

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International Journal of computers and Applications 2022年第7期44卷 659-669页

作者： Singh, Geeta Khare, Neelu School of Computer Science and Engineering Vellore Institute of Technology Vellore India School of Information Technology and Engineering Vellore Institute of Technology Vellore India

The evolution in the attack scenarios has been such that finding efficient and optimal Network Intrusion Detection Systems (NIDS) with frequent updates has become a big challenge. NIDS implementation using machine learning (ML) techniques and updated intrusion datasets is one of the solutions for effective modeling of NIDS. This article presents a brief description of publicly available labeled intrusion datasets and ML techniques. Later a brief explanation of the literary works is given in which machine learning techniques are applied for NIDS implementation in different networking scenarios, such as traditional networks, cloud networks, Ad-Hoc, WSNs, and IoT networks. Hence, this article brings together publicly available intrusion datasets and machine learning techniques utilized in recent intrusion detection systems to reveal present-day challenges and future directions. This article also explains problems associated with NIDS. This will help researchers to enhance the existing NIDS models as well as to develop new effective models. © 2021 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Intrusion detection

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Accelerating matrix/boundary precipitations to explore high-strength and high-ductile Co_(34)Cr_(32)Ni_(27)Al_(3.5)Ti_(3.5) multicomponent alloys through hot extrusion and annealing

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Journal of Materials science & technology 2023年第12期143卷 62-83页

作者： Xiaoming Liu Zongde Kou Ruitao Qu Weidong Song Yijia Gu Changshan Zhou Qingwei Gao Jiyao Zhang Chongde Cao Kaikai Song Vladislav Zadorozhnyy Zequn Zhang Jürgen Eckert Shenzhen Research Institute of Shandong University Shenzhen 518057China State Key Laboratory of Explosion Science and Technology Beijing Institute of TechnologyBeijing 100081China School of Mechanical Electrical&Information EngineeringShandong UniversityWeihai 264209China School of Material Science and Engineering Nanjing University of Science and TechnologyNanjing 210094China School of Materials Science and Engineering Northwestern Polytechnical UniversityXi’an 710072China Department of Materials Science and Engineering Missouri University of Science and TechnologyRollaMO 65409USA Research and Development Institute of Northwestern Polytechnical University in Shenzhen Shenzhen 518057China National University of Science and Technology«MISIS» Moscow 119049Russia Erich Schmid Institute of Materials Science Austrian Academy of SciencesLeoben A-8700Austria Department of Materials Science Montanuniversität LeobenLeoben A-8700Austria

Annealing-regulated precipitation strengthening combined with cold-working is one of the most efficient strategies for resolving the conflict between strength and ductility in metals and ***,precipitation control and grain refinement are mutually contradictory due to the excellent phase stability of multicomponent *** work utilizes the high-temperature extrusion and annealing to optimize the microstructures and mechanical properties of the Co_(34)Cr_(32)Ni_(27)Al_(3.5)Ti_(3.5) multicomponent *** extrusion effectively reduces grain sizes and simultaneously accelerates the precipitation of coherent L12 nanoparticles inside the face-centered cubic(FCC)matrix and grain boundary precipitations(i.e.,submicron Cr-rich particles and L12-Ni 3(Ti,Al)precipitates),resulting in strongly reciprocal interaction between dislocation slip and hierarchical-scale *** annealing regulates grain sizes,dislocations,twins,and precipitates,further allowing to tailor mechanical *** high yield strength is attributed to the coupled precipitation strengthening effects from nanoscale coherent L12 particles inside grains and submicron grain boundary precipitates under the support of pre-existing *** excellent ductility results from the synergistic activation of dislocations,stacking faults,and twins during plastic *** present study provides a promising approach for regulat-ing microstructures,especially defects,and enhancing the mechanical properties of multicomponent alloys.

关键词： Multicomponent alloys Hot extrusion Microstructure Mechanical properties Precipitation strengthening

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Machine learning based cardiovascular disease prediction

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Materials Today: Proceedings 2022年 64卷 459-463页

作者： Chinnasamy, P. Arun Kumar, S. Navya, V. Lakshmi Priya, K. Sruthi Boddu, Siva Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India Department of Computer Science and Engineering Bethesda Institute of Technology and Science Gwalior India Department of Electronics and Communication Engineering East Point College of Engineering & Technology Bangalore India Department of Electronics and Communication Engineering PERI Institute of Technology Chennai India UG Student Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

In past few years, one of the really tough situations in medicine is already making predictions cardiac dysfunction. Almost any moment, roughly one patient dies through myocardial infarction with in present era. Due to the difficulty of forecasting cardiac sickness, it is vital to systematize the procedure in place to minimize the risk associated with and to educate the patient well enough in preparation. The risk of cardiovascular disease in the World is tremendous. The accurate and thorough assessment of a physician's cardiovascular risk is critical for reducing the incidence and severity attack and stroke and improving cardiovascular protection. In order to solve this issue, we are proposing method to discover these heart abnormalities as soon as reasonably practicable to avert fatal consequences. The next step is to determine whether or not the user is at risk of developing cardiovascular disease. It also answers difficult problems concerning heart detection of diseases, allowing clinicians to make better clinical decisions. Medical specialists have accumulated a large number of medical data that can be analysed and important fact retrieved. Machine learning approaches can improve in the prognosis and early identification of heart illness after hypothesis testing. © 2022

关键词： Machine learning

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CNN-Based Rice Nitrogen Status Estimation Using UAV Integrated Multispectral Camera Images 3

CNN-Based Rice Nitrogen Status Estimation Using UAV Integrat...

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3rd IEEE Applied Signal Processing Conference, ASPCON 2023

作者： Saha, Rituparna Misra, Sudip Chatterjee, Chandranath Biswas, Amit Haldia Institute of Technology Department of Computer Science and Engineering India Indian Institute of Technology Department of Computer Science and Engineering Kharagpur India Indian Institute of Technology Department of Agricultural and Food Engineering Kharagpur India Haldia Institute of Technology Department of Agricultural Engineering India

ISBN: (纸本)9798350325881

Nitrogen (N) plays an important role in rice growth and productivity. The convolutional neural network (CNN) model already proved its efficiency in N deficiency estimation for rice crops. However, the latest researches still lack the rice N status estimation at an early growth stages. In this study, we devise a rice N status estimation system using the CNN model from the UAV-based multispectral camera images of rice crop at different growth stages. We also perform a comparative study across different CNN model architectures, such as AlexNet, VGG-16, and ResNet-18. The CNN models are trained through two training-testing datasets, which consists of UAV-based multispectral camera-captured field images of two different growth stages of rice crop, tillering and booting. Moreover, an extensive study is carried out by training the CNN models with varying the number of classes related to N applications. Further, the performance of the classifier is measured in terms of accuracy, recall, precision, and F1 score. From the experimental result, we conclude that our proposed system shows better results for the AlexNet model than the other two CNN models. We also conclude that our system shows better validation accuracy with the N applications with three classes than the six classes for the both tillering and booting stage of rice crop. © 2023 IEEE.

关键词： Neural network models

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Metal size effects over metal/zeolite bifunctional catalysts in the selective hydroalkylation of benzene

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Frontiers of Chemical science and engineering 2024年第4期18卷 107-114页

作者： Junjie Li Chuang Liu Zhenlong Jia Yingchun Ye Dawei Lan Wei Meng Jianqiang Wang Zhendong Wang Yongfeng Hu Weimin Yang State Key Laboratory of Green Chemical Engineering and Industrial Catalysis Sinopec Shanghai Research Institute of Petrochemical Technology Co.Ltd.Shanghai 201208China School of Chemical Engineering East China University of Science and TechnologyShanghai 200237China

Bifunctional metal/zeolite materials are some of the most suitable catalysts for the direct hydroalkylation of benzene to *** overall catalytic performance of this reaction is strongly influenced by the hydrogenation,which is dependent on the metal ***,systematically investigating the metal size effects in the hydroalkylation of benzene is *** this work,we successfully synthesized Ru and Pd nanoparticles on Sinopec Composition Materials No.1 zeolite with various metal *** demonstrated the size-dependent catalytic activity of zeolite-supported Ru and Pd catalysts in the hydroalkylation of benzene,which can be attributed to the size-induced hydrogen spillover capability *** work presents new insights into the hydroalkylation reaction and may open up a new avenue for the smart design of advanced metal/zeolite bi-functional catalysts.

关键词： size effects bifunctional catalysts metal/zeolite hydroalkylation

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Global video object segmentation with spatial constraint module

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Computational Visual Media 2023年第2期9卷 385-400页

作者： Yadang Chen Duolin Wang Zhiguo Chen Zhi-Xin Yang Enhua Wu Engineering Research Center of Digital Forensics Ministry of EducationSchool of Computer and SoftwareNanjing University of Information Science and TechnologyNanjing 210044China State Key Laboratory of Internet of Things for Smart City Department of Electromechanical EngineeringUniversity of MacaoMacao 999078China State Key Laboratory of Computer Science Institute of SoftwareUniversity of Chinese Academy of SciencesBeijing 100190China Faculty of Science and Technology University of MacaoMacao 999078China

We present a lightweight and efficient semisupervised video object segmentation network based on the space-time memory *** some extent,our method solves the two difficulties encountered in traditional video object segmentation:one is that the single frame calculation time is too long,and the other is that the current frame’s segmentation should use more information from past *** algorithm uses a global context(GC)module to achieve highperformance,real-time *** GC module can effectively integrate multi-frame image information without increased memory and can process each frame in real ***,the prediction mask of the previous frame is helpful for the segmentation of the current frame,so we input it into a spatial constraint module(SCM),which constrains the areas of segments in the current *** SCM effectively alleviates mismatching of similar targets yet consumes few additional *** added a refinement module to the decoder to improve boundary *** model achieves state-of-the-art results on various datasets,scoring 80.1%on YouTube-VOS 2018 and a J&F score of 78.0%on DAVIS 2017,while taking 0.05 s per frame on the DAVIS 2016 validation dataset.

关键词： video object segmentation semantic segmentation global context(GC)module spatial constraint

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Towards high-efficiency of hydrogen purification in metal hydride

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Progress in Natural science:Materials International 2023年第3期33卷 301-308页

作者： Koua Alain Jesus Koua Jiahe Peng Arramel Arramel Neng Li State Key Laboratory of Silicate Materials for Architectures School of Materials Science and Engineering Wuhan University of Technology Shenzhen Research Institute of Wuhan University of Technology Nano Center Indonesia Jalen Raya PUSPIPTEK South Tangerang School of Chemistry and Environmental Engineering School of Environmental Ecology and Biological Engineering Novel Catalytic Materials of Hubei Engineering Research Center Wuhan Institute of Technology

Recent advancement on hydrogen（H2） retrieval in a mixture gas of H2/CO2based on the metal hydride（MH）technique has attracted numerous interests. In this work, we implement the lumped element model simulation to conduct a theoretical approach on the hydrogen purification process. The influence of orifice geometry and temperature of the tank, and inflow gas rate(fin) is investigated. The results show that the discharge coefficient（DC） is affected by the tank's orifice geometry. The orifice shape is increasing the value of DC, as a result, the CO2（impurity） outflow rate heightens during the venting process and thereby promoting to obtain a high hydrogen purity. By overcoming the exothermic/endothermic effect in the absorption/desorption step and a proper finselection, we find an improvement in the hydrogen storage capacity（wt%） into MH and consequently the hydrogen recovery rate increases. A sharp improvement in orifice geometry and thermal conditions of the tank,and finenhances the main targeted results（hydrogen purity and recovery rate） and ensures the proper operation of the MH technique.

关键词： Hydrogen purification Metal hydride Discharge coefficient Lumped model simulation Carbon dioxide

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Tuning Stark effect by defect engineering on black titanium dioxide mesoporous spheres for enhanced hydrogen evolution

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Chinese Chemical Letters 2024年第11期35卷 254-259页

作者： Bingke Zhang Dongbo Wang Jiamu Cao Wen He Gang Liu Donghao Liu Chenchen Zhao Jingwen Pan Sihang Liu Weifeng Zhang Xuan Fang Liancheng Zhao Jinzhong Wang School of Materials Science and Engineering Harbin Institute of TechnologyHarbin 150001China School of Astronautics Harbin Institute of TechnologyHarbin 150001China Center for High Pressure Science and Technology Advanced Research Shanghai 201203China Qingdao University of Science and Technology Qingdao 266061China State Key Laboratory of High Power Semiconductor Lasers School of PhysicsChangchun University of Science and TechnologyChangchun 130022China

Defects can strongly affect the lattice,strain,and electronic structures of nanomaterials photocatalysts,like a double-edged sword of both positive significance and negative influence on photocatalytic *** date,most studies into defects only partially elucidated their beneficial or detrimental roles in ***,a quantitative understanding of the photocatalytic performances modulated by defect concentration still needs to be ***,a series of TiO_(2-X)mesoporous spheres(MS)with different oxygen vacancy concentrations for photocatalytic applications were prepared by hightemperature chemical *** link between oxygen vacancy concentration and photocatalytic performance was successfully *** localization of carriers dominated by the Stark effect is first enhanced and then weakened with increasing oxygen vacancy concentration,which is a crucial factor in explaining the double-edged sword role of defect concentration in *** the reduction temperature rises to 300℃,carrier localization dominated by the quantum-confined Stark effect maximizes the separation ability of photo generated electron hole pairs,thus exhibiting the best catalytic performance for photocatalytic hydrogen production and the degradation of organic pollutants,as demonstrated by a hydrogen evolution rate of 523.7μmol g^(-1)h^(-1)and a ninefold higher RhB photodegradation rate compared to TiO_(2)*** work offers excellent flexibility for precisely constructing high-performance photocatalysts by understanding vacancy engineering.

关键词： Photocatalysis Defect Carrier localization TiO_(2-X) Stark effect

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TRIESTE: translation based defense for text classifiers

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Journal of Ambient Intelligence and Humanized Computing 2023年第12期14卷 16385-16396页

作者： Gupta, Anup Kumar Paliwal, Vardhan Rastogi, Aryan Gupta, Puneet Department of Computer Science and Engineering Indian Institute of Technology Indore Madhya Pradesh Indore452020 India Department of Electrical Engineering Indian Institute of Technology Indore Madhya Pradesh Indore452020 India

The field of natural language processing (NLP) has significantly evolved with the advent of state-of-the-art models. The discovery of these models has entirely revolutionised how NLP tasks such as machine translation, sentiment analysis and many others are performed. However, despite their high efficacy and meticulous performance, these models are prone to adversarial attacks. Adversarial attacks involve the introduction of perturbations imperceptible to humans, which can severely impact the model’s learning and prediction accuracy. Current defenses on text data include approaches such as spell-checking and adversarial training, which have their limitations against state-of-the-art adversarial attacks. This paper put forward an effective transformation-based defense, TRIESTE (TRanslatIon basEd defenSe for Text classifiErs). The proposed defense overcomes the shortcomings of existing defenses by translating the input text from the source language to a target language and again back to the source language before providing it to the text classifier. Translation ensures that the sentiment of the translated text is similar to that of the input text by taking the entire text into consideration, which leads to the removal of adversarial perturbations. Rigorous evaluation on publicly available datasets showcases that TRIESTE is successful against state-of-the-art attacks without a significant drop in the classifier accuracy. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Classification (of information)

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