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Bloom’s Taxonomy Based Question Analysis for Personalized Learning 7th

Bloom’s Taxonomy Based Question Analysis for Personalized L...

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7th IFIP TC 12 international conference on Computational Intelligence in Data science, ICCIDS 2024

作者： Jeslin Shanthamalar, J. Sheelam, Dinesh Bodla, Shiva Raj Gowri Manohari, V. Nancy Noella, R.S. Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Tamil Nadu Chennai600119 India

ISBN: (纸本)9783031699856

In the 1950s, Benjamin Bloom and his associates introduced Bloom’s Taxonomy, a foundational framework for categorizing learning objectives and cognitive skills. While question classification typically operates at a fine level, such as sentences and phrases, and text classification focuses on the document level, past research has explored the intersection of question classification and Bloom’s Taxonomy to assess learners’ cognitive levels in higher education. However, existing feature types from previous studies may excel in datasets with narrowly focused queries, necessitating the development of multiple classifiers for diverse fields or areas. To address this, A new kind of feature called "taxonomy-based" is suggested to improve the accuracy of question classification in datasets from different fields. Utilizing datasets comprising questions from distinct topics, the study evaluates the effectiveness of taxonomy-based features. Support vector machines (SVMs) are chosen as the classifier for their reputed text classification accuracy. the research reveals that taxonomy-based features significantly improve classifier performance when applied to question sets from different domains. this effort is driven by the need to improve question categorization accuracy over a variety of datasets and the realization of Bloom’s Taxonomy’s continuing use in educational settings. Although Bloom’s Taxonomy offers an extensive framework for comprehending cognitive abilities and learning objectives, current question classification techniques frequently do not have the granularity necessary to fully utilize this taxonomy. Furthermore, prior work has mostly concentrated on fine-level categorization, ignoring the advantages of integrating Bloom’s Taxonomy into the classification procedure. the highest accuracy achieved using SVM with TF-IDF vectorizer is 74.17% for BCL’s Data set and 95.23% for BT Dataset. Employing the KNN algorithm with the TF-IDF vectorizer yields 59.17% accuracy for

关键词： Support vector machines

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MANAGE: A Novel Malware Evolution Model based on Digital Genes 7

MANAGE: A Novel Malware Evolution Model based on Digital Gen...

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7th IEEE international conference on Data science in Cyberspace, DSC 2022

作者： Zhang, Yu Liu, Qingzhong Shi, Yuanquan Polytechinc Normal University School of Cybersecurity Guangdong Guangzhou China Sam Houston State University Department of Computer Science Houston United States Huaihua University School of Computer Science and Engineering Huaihua China

ISBN: (数字)9781665474801

ISBN: (纸本)9781665474801

Malwares have been plaguing individuals, organizations and government agencies for almost 40 years. Evolving over time, these malicious codes have kept pace with the newest advances in defense technology that works to make computer systems and networks more safety. Identifying how malwares have evolved over time helps the computer users protect their information systems from infecting malicious codes and keep their data safe. Inspired by the gene theory, we propose a novel malware evolution model based on digital genes (MANAGE). In the MANAGE, we make a peer-to-peer mapping between malwares and gene theory from simple to complex: program code segments vs Genes, program modules vs DNAs, and program procedures or functions vs Chromosomes. the mathematical models of Malwares evolution in these three levels of Genes, DNAs, and Chromosomes are established in the MANAGE. the experiments of 10000 VBscript malware samples on these three levels are carried on to evaluate the MANAGE model. theoretical analysis and experimental results show that malwares in the MANAGE have powerful ability to adapt themselves to the changing external environments with interactions between Genes DNAs and Chromosomes even in face of harsh external pressure. © 2022 IEEE.

关键词： Genes

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A Comparative Analysis of Child Activity Recognition Using Machine Learning 5th

A Comparative Analysis of Child Activity Recognition Using M...

引用

5th international conference on advances in Information Communication technology and Computing, AICTC 2024

作者： Kumar, Narendra Arora, Shefali Aggrawal, Lakshay Computer Science and Engineering National Institute of Technology Punjab Jalandhar144008 India Computer Science and Engineering DAV Institute of Engineering and Technology Punjab Jalandhar144008 India

ISBN: (纸本)9789819761050

this research study aims to create a smart child activity recognition device combines a Raspberry Pi-pico, sensors for Internet of things (IoT). the system includes several sensors for monitoring human motion, pulse, and location parameters. Different types of machine learning and deep learning algorithm is used for analyzing data to identify various kinds of activities in children including sitting, standing, running, and climbing the stairs. this study utilizes a Raspberry Pi-pico, which is a cost-effective as well as tiny single-board computer central processing unit for an infant monitoring system. It can be easily integrated with various sensors and devices, which make it a suitable foundation for developing an IoT-based tracking system. It is able to link to the Internet through Wi-Fi, allowing for surveillance remotely. the dataset collected from 22 different children based on their features like sitting, standing, running, and climbing the stairs. the model was trained using a data of child mobility and behavior, distinguishing activities such as sitting, standing, running, and stair climbing. there are six different classifiers: LSTM, SVM, ANN, KNN, AdaBoost, and Logistic Regression. When applied to pre-processed data, SVM outperformed other classification methods. the classification accuracy reached 98.81%. the experimental results indicate that the proposed system may identify behavior and it will keep parents remotely. the technology continuously monitors and analyzes the child’s environment and behavior to ensure their safety and well-being. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Support vector machines

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Diagnosis, Treatment and Containment of Epilepsy Disease 7

Diagnosis, Treatment and Containment of Epilepsy Disease

引用

7th international conference on Parallel, Distributed and Grid Computing, PDGC 2022

作者： Dhondiyal, Shiv Ashish Dimri, Sushil Chandra Graphic Era Deemed to Be University Dept. of Computer Science and Engineering Dehradun India

ISBN: (纸本)9781665454018

the research work includes a thorough examination of the evolution of Epilepsy Disease, its symptoms, therapies, and progression, as well as algorithmic techniques for diagnosis. the majority of research on this condition is on identifying Epilepsy condition using EEG data and machine learning, and also on assessing the influence of medications on its development. Recent advances show that selection of features and optimization computation are often used to extract a group of best-performing characteristics that deliver high accuracy rates while rejecting other comparable parameters. Several research gaps have also been found in this work that might be investigated further in the future, including (i) identification of initial biomarkers of epilepsy disease from various types of available datasets, using Machine Learning techniques is required. (ii) Need to design a machine learning classifier based on the various data sources and the user inputs (symptoms) to fast and more accurate detection of epilepsy diseases. (iii) Selection of appropriate feature sets for classification of the epilepsy disease.(iv) case study of different medicines required to control and treat the epilepsy disease as early as possible. © 2022 IEEE.

关键词： Feature extraction

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An Effective Framework for Sentiment Analysis Using RNN and LSTM-Based Deep Learning Approaches 7th

An Effective Framework for Sentiment Analysis Using RNN and ...

引用

Proceedings of the 7th international conference on advances in Computing and Data sciences, ICACDS 2023

作者： Shrivash, Brajesh Kumar Verma, Dinesh Kumar Pandey, Prateek Jaypee University of Engineering and Technology A-B Road M.P. Distt. - Guna Raghogarh India

ISBN: (纸本)9783031379390

Sentiment Analysis (SA) is an active research field in modern times by studying people’s sentiments posted on different web platforms over the internet. To study these sentiments, different machine and deep learning techniques are in use. Deep learning is an efficient approach for SA. It gives more accurate results by learning text features from sentiments using a deep neural network. In this paper, we proposed a framework for sentiment classification;. the model was, trained, and tested on Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Deep Learning (DL) algorithms for SA. the performance of proposed framework was evaluated using accuracy, precision, recall, and F-1 score values. To calculate the performance, the data set was split in the ratio of 70:30, 80:20, and 90:10 for training and testing the model and observed that LSTM performs well with 88% accuracy and precision value, 87% recall value, and 87% F-1 measures. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Sentiment analysis

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Energy-Aware Reliable Routing with Blockchain Security for Heterogeneous Wireless Sensor Networks 5th

Energy-Aware Reliable Routing with Blockchain Security for H...

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5th international conference on advances in Information Communication technology and Computing, AICTC 2024

作者： Swapna, M.P. Ramkumar, J. Karthikeyan, R. Center for Cyber Security Systems and Networks Amrita Vishwa Vidyapeetham Kollam Amritapuri India Department of Information Technology & Cognitive Systems Sri Krishna Arts & Science College Coimbatore India Department of Computer Technology Sri Krishna Adithya College of Arts and Science Coimbatore India

ISBN: (纸本)9789819761050

Wireless sensor networks, the dynamic networks with spatially dispersed sensors are used indispensably in a wide range of applications. Heterogeneous wireless sensor networks represent a collection of sensor nodes of varying capabilities. However, if the energy in the network is not handled effectively, the resource-constrained sensors may reach an early death, affecting the network lifetime. Security and routing are the two vital phenomena in wireless sensor networks that need careful and effective algorithmic execution. the proposed approach is an energy-aware routing algorithm combined with secure data transmission. TDDSRP is an innovative protocol to ensure security while reducing energy consumption. the residual energy and the direct trust value of nodes are crucial parameters to evaluate the energy trust value. the effectiveness of the protocol is tested against the existing counterparts using MATLAB simulations. the experimental results illustrate the performance of the protocol in energy consumption rate, throughput, and security measures. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： MATLAB

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Deep Friendly Embedding Space for Clustering 13th

Deep Friendly Embedding Space for Clustering

引用

13th IFIP TC 12 international conference on Intelligent Information Processing, IIP 2024

作者： Hou, Haiwei Ding, Shifei Xu, Xiao Guo, Lili School of Computer Science and Technology China University of Mining and Technology Xuzhou221116 China Mine Digitization Engineering Research Center of Ministry of Education of the People’s Republic of China Xuzhou221116 China

ISBN: (纸本)9783031578076

Deep clustering has powerful capabilities of dimensionality reduction and non-linear feature extraction, superior to conventional shallow clustering algorithms. Deep learning and clustering can be unified through one objective function, significantly improving clustering performance. However, the features of embedding space may have redundancy and ignore preserved manifold. Besides, the features lack discriminative, which hinders the clustering performance. To solve the above problems, the paper proposes a novel algorithm that improves the discrimination of features, filters redundant features and protects manifold structures for clustering. Firstly, it reduces the dimensionality in the embedding again to filter redundant and preserve the manifold for the features. then it improves the discriminative of the representation by reducing the intra-class distance. Performance evaluation is carried out on four benchmark datasets and a case study of engineering applications. Comparing with state-of-the-art algorithms indicates that our algorithm performs favorably and demonstrates good potential for real-world applications. © IFIP international Federation for Information Processing 2024.

关键词： Embeddings

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An investigation of malware and the systems used to detect and identify malware

An investigation of malware and the systems used to detect a...

引用

international conference on Advanced Technologies for Signal and Image Processing (ATSIP)

作者： Fahad Mira Department of Computer Science and Technology University of Bedfordshire UK

ISBN: (数字)9798350351484

ISBN: (纸本)9798350351491

Numerous malware has studied and fortifications throughout decades. New research shows that hazardous software is being created quickly. Frequent network and Internet use spreads and improves this software. To enhance computer security, researchers and manufacturers are developing effective anti-malware programs. this article thoroughly discusses malware infestation and anti-malware software advances. thus, it gives the current malware system developer detection reference.

关键词： Image processing Malware Internet computer security

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Enhancing Performance of Machine Learning Models in Healthcare: An Analytical Framework for Assessing and Improving Data Quality 5th

Enhancing Performance of Machine Learning Models in Healthca...

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5th international conference on advances in Information Communication technology and Computing, AICTC 2024

作者： Al-Hgaish, Areen Atan, Rodziah Yaakob, Razali Osman, Mohd Hafeez Department of Software Engineering and Information Systems Faculty of Computer Science and Information Technology Universiti Putra Malaysia Serdang44330 Malaysia Department of Computer Science Faculty of Computer Science and Information Technology Universiti Putra Malaysia Serdang44330 Malaysia

ISBN: (纸本)9789819761050

the utilization of Data-Driven Machine Learning (DDML) models in the healthcare sector poses unique challenges due to the crucial nature of clinical decision-making and its impact on patient outcomes. A primary concern in this domain is the data quality that forms the basis for these models, which is essential for their effectiveness. While numerous studies are dedicated to leveraging Machine Learning (ML) to enhance patient care, a substantial gap exists in understanding and evaluating the quality of training data before training ML models. this issue is exacerbated by the lack of well-defined standards to ensure that data characteristics align with the specific goals of ML in healthcare. To address this, our study introduces a conceptual three-dimensional framework focusing on data accuracy, completeness, and consistency aimed at assessing and improving the quality of training data. this framework is designed to enhance the performance, predictability, and interpretability of healthcare-specific ML models and reduce biases, thus bridging current research gaps and laying the groundwork for future developments in ML-driven healthcare systems. this paper presents the initial step for our proposed Data Quality Framework (DQF), which addresses the three dimensions mentioned above. It discusses its potential implications and applications in healthcare data, and its main goal is to provide a comprehensive overview of the proposed DQF, illustrating its significance in advancing healthcare data quality and ultimately improving patient outcomes. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Data accuracy

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Machine Learning-Based Temperature Monitoring and Prediction 7th

Machine Learning-Based Temperature Monitoring and Prediction

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Proceedings of the 7th international conference on advances in Computing and Data sciences, ICACDS 2023

作者： Bharti, Sonam Kumari Anand, Priyadarshi Kishore, Shradha Birla Institute of Technology Patna Mesra India

ISBN: (纸本)9783031379390

Critical monitoring of ambient temperature at all times is necessary for all areas of pharmaceutical storage inventory where a sudden temperature change can damage the medicine efficiency as sensitive medicines can only be stable when deviations from a required condition are minimal;otherwise, unexpected temperature changes might alter the physical-chemical and pharmacological properties of the medication, and potentially endangering the patient’s health. the temperature of a storage site is determined and data is produced from an LM35 temperature sensor, an analogue linear temperature sensor which has been interfaced to the Wi-Fi ESP8266 microcontroller. the generated data from the system is pushed to the cloud and can be fetched from any remote place where the internet is available, and by performing data analysis, the prediction of the temperature for the next instance of time is obtained. In this paper, the machine learning algorithms used for temperature prediction are linear regression and polynomial regression. To find the correct degree so it does not under-fit and over-fit with points and predict the temperature for the next instance of time. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Microcontrollers

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