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Enhancing Computational Strategies to Decode ChatGPT’s Influence on the Critical thinking Abilities of University Students

Enhancing Computational Strategies to Decode ChatGPT’s Infl...

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Science and Information Technology in Smart Administration (ICSINTESA), international conference of

作者： Gunawan Shinta Palupi Richki Hardi Agung Sakti Pribadi Riski Zulkarnain M. Nurfalah Setiawan Nariza Wanti Wulan Sari Sulistiyanto Denny Trias Utomo Department of Informatics Universitas Mulia Balikpapan Indonesia Department of Information Systems Universitas Mulia Balikpapan Indonesia Department of Law Universitas Mulia Balikpapan Indonesia Department of Engineering Universitas Nurul Jadid Probolinggo Indonesia Department of Information Technology Politeknik Negeri Jember Jember Indonesia

ISBN: (数字)9798350376111

ISBN: (纸本)9798350376128

the integration of artificial intelligence (AI) tools such as ChatGPT into educational contexts is reshaping the landscape of student learning and critical thinking. this study investigates the influence of ChatGPT on the critical thinking abilities of university students by enhancing computational strategies for analysis. We developed an advanced computational framework that leverages natural language processing (NLP) and machine learning (ML) techniques to evaluate the interaction between students and ChatGPT. Our approach involves the collection and analysis of large-scale interaction data to identify patterns and impacts on critical thinking development. We employed sentiment analysis, topic modeling, and argumentation mining to decode the nature of student responses and their evolution over time. the results reveal significant improvements in critical thinking skills among students who actively engage with ChatGPT, suggesting that the AI can act as a catalyst for developing higher-order thinking skills. this study contributes to the growing body of research on AI in education by providing insights into the mechanisms through which AI tools can enhance critical thinking, and proposes practical implications for integrating such tools into academic curricula. the findings support the notion that computational analysis can provide a nuanced understanding of AI’s educational impacts, paving the way for more effective AI-assisted learning environments.

关键词： Sentiment analysis Analytical models Technological innovation Computational modeling Education machine learning learning (artificial intelligence) Chatbots data mining Information technology

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A Multichannel CNN-LSTM Network for Daily Activity recognition using Smartwatch Sensor data 6

A Multichannel CNN-LSTM Network for Daily Activity Recogniti...

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Joint 6th international conference on Digital Arts, Media and Technology with 4th ECTI Northern Section conference on Electrical, Electronics, Computer and Telecommunication Engineering, ECTI DAMT and NCON 2021

作者： Mekruksavanich, Sakorn Jitpattanakul, Anuchit University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (纸本)9781665415699

recognition of human behavior is recently an active and stimulating study the HAR can provide valuable information on human movement and the behavior of everyday life activities. In the last decade, a large range of HAR-based applications have been implemented, such as healthcare tracking, biometric user authentication, and so on. Previously, several deep learning approaches have been introduced to focus on the issue of conventional machine learning approaches with handcrafted features. So, a novel deep learning architecture to solve the HAR problem is proposed in this study the introduced architecture is a hybrid model called a multichannel CNN-LSTM network the model is evaluated by state-of-the-Art evaluation metrics;accuracy, precision, recall and F1-score, with a public dataset of smartwatch's accelerometer data called DHA dataset the proposed multichannel CNN-LSTM outperforms other deep learning methods in terms of accuracy, with a score of 96.87%. © 2021 IEEE.

关键词： Behavioral research

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Intelligent Concrete Surface Cracks Detection using Computer Vision, pattern recognition, and Artificial Neural Networks 4

Intelligent Concrete Surface Cracks Detection using Computer...

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4th international conference on Industry 4.0 and Smart Manufacturing, ISM 2022

作者： Mirbod, Majid Shoar, Maryam Department of Industrial Management Tehran North Branch Islamic Azad University Tehran Iran

this paper presents an intelligent concrete surface crack detection model, using machine vision, pattern recognition, artificial neural networks, and histogram of oriented gradients to feature extraction. this is an applied research category that so far, has been used in face recognition systems, and scaled conjugate gradient to training purpose multilayer neural networks, to achieve the monitoring of concrete surfaces to increase the protection that these are the innovations of this research. the main purpose of this research is to provide a reliable method for inspecting sensitive structures such as bridges using a continuous intelligent inspection system consisting of a camera and intelligent software. For this purpose, we used the Utah State University image dataset that contains over 56,000 images of cracked and non-cracked concrete bridge decks, walls, and pavements. Moreover, it includes data on cracks as narrow as 0.06 mm and a width of 25 mm with a variety of obstructions, shadows, surface roughness, scaling, edges, holes, and background debris. the ANN proposed in this study can recognize cracking images from non-cracking images. the efficiency of this network is 84.88% and it reduces network error from 0.631 to 0.199 and gradient index from 25.5 to 0.0801. this model will be useful for structural health monitoring and other sensitive surfaces such as aircraft engine shells and train moving rails. Based on the obtained results, the difference between the proposed method and other neural networks such as Convolutional Neural networks is that the former is simpler in coding and needs cheaper hardware. © 2022 Elsevier B.V.. All rights reserved.

关键词： Multilayer neural networks

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A Novel Approach to Enhance the Productivity and thinking of User-MANGO Framework

A Novel Approach to Enhance the Productivity and Thinking of...

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international conference on ICT in Business Industry & Government (ICTBIG)

作者： Karnam Akhil M. Ravikanth Tanmayi Challa Harshadha Bollineni Reshma Mulpuri Punnamaraju JV Gopala Krishna Pranav Department of CSE VNR Vignana Jyothi Institute Of Engineering and Technology Hyderabad India TATA Consultancy Services Hyderabad India

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

Sequential data mining (SDM) is an extension of association data mining approach which discovers and identifies hidden patterns or relationships in sequential data where events and items occur in a sequence of time or a specific order. SDM plays an important role in identifying complex relations that lead to the enhancement of the industry and aid in strategic decision-making. Its applications extend across a wide range of domains like health care, telecommunications, retail, and many. SDM focuses more on the order of events and finding relations among the events while Market Basket Analysis focuses on identifying the product associations in the same event. the need to find hidden patterns in the data gave rise to the evolution of new algorithms called Apriori and FP-growth to generate strong association rules. these algorithms made mining processes much easier and helped users find unusual relationships in huge volumes of data. Many Multinational companies have applied these algorithms to find insights that enabled them to enhance their sales and profits by making the right decisions. In this work, we propose a new architecture, the Market Basket Analysis and Cognitive Approach for Analyzing (Customer) and thinking (Library)-MANGO, designed for two key applications—library management and supermarket optimization. the Mango tool kit offers new suggestions and optimization techniques that could be implemented in libraries and supermarkets to enhance productivity, attract customers and make jobs easier. We also believe this framework can be extended to other fields, offering solutions for various industries that rely on sequential pattern analysis. the focus is on addressing the challenges specific to library management and supermarkets, where sequential pattern mining plays a critical role in optimizing operations and decision-making.

关键词： Productivity Industries Decision making Medical services Libraries Association rule learning Telecommunications pattern analysis Optimization Recommender systems

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False data Injection Attack Prediction Using Federated Deep learning Approach 4th

False Data Injection Attack Prediction Using Federated Deep ...

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4th international Research conference on machine learning, Deep learning, and Blockchain, IRCICD 2023

作者： Mouli, K. Arun, P. Vaitheeswari, R. Sridhar, S. Department of CSE SRM Institute of Science and Technology Vadapalani Chennai India

ISBN: (纸本)9783031882364

A unique data-driven methodology for system state estimation against attacks from bogus data injection that cannot be observed is provided in this study. the proposed system continuously detects and classifies attacks involving false data injection. Using the knowledge that has been learned, the control signal is then retrieved. this method is carried out by three main modules that implement state-of-the-art detection categorization and retrieval of control signals. the detecting module keeps track of any deviation pattern brought on by a complicated plane strike by tracking historical phasor measurement changes. the direction, amount, and ratio of the fake data injection are among the assault elements revealed by this method. With the use of this knowledge, the signal recovery module can swiftly obtain the actual control signal and get rid of the false data that was purposefully added. the classifier module can be used to learn more details about the attack type. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image reconstruction

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To Ameliorate Classification Accuracy using Ensemble Distributed Decision Tree (DDT) Vote Approach: An Empirical discourse of Geographical data mining

To Ameliorate Classification Accuracy using Ensemble Distrib...

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12th international conference on Ambient Systems, Networks and Technologies (ANT) / 4th international conference on Emerging data and Industry 4.0 (EDI40)

作者： Fayaz, Sheikh Amir Zaman, Majid Butt, Muheet Ahmed Univ Kashmir Dept Comp Sci Srinagar India Univ Kashmir Directorate IT & SS Srinagar India

Weather data of Kashmir province has 6 attributes recorded at three different substations. this paper proposes a distributed decision tree algorithm and its implementation on Historical Geographical data of Kashmir province. the machine learning Decision tree algorithm applied on the Kashmir province dataset generates the accuracy of 81.54%. the distributed decision tree generates multiple trees based on the partitions of the original dataset in which the data is segregated according to the substations (42026, 42027 and 42044). the ratio between generated data sets was distributed in 32.38%, 34.19% and 33.42% respectively which is appropriate for the parallelism. Its distributed implementation, i.e. Distributed Decision Tree produces a specified number of sub-trees (depending upon number of partitions of input dataset) and at the end collects votes or averages the prediction or classification. In this paper, we have implemented the hard- voting approach to calculate the overall performance of the n-number of trees in distributed environment. the empirical results demonstrate that distributed decision trees approach has not improved the overall accuracy as compared to the original dataset without partitioning. (C) 2021 the Authors. Published by Elsevier B.V.

关键词： Decision trees Distributed Decision trees Information Gain Geographical data Hard Voting

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A machine learning and Deep learning Integrated Model to Detect Criminal Activities

A Machine Learning and Deep Learning Integrated Model to Det...

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Big data Analytics and Practices (IBDAP), international conference on

作者： Kanij Fatema Arpa Tanni Mittra Tasnim Ferdous Nusrat Jahan Riffat Ara Khan Tayna Mahamudul Hasan Mohammad Rifat Ahmmad Rashid Md Sawkat Ali Department of Computer Science and Engineering East West University Dhaka Bangladesh

Crime is considered an intended execution of an act that is harmful to society and punishable under criminal law. Bangladesh as a developing country has experienced severe crime-related issues in recent years due to the constantly growing criminal activities. the law enforcement sector of Bangladesh is dedicated to implementing the law and reducing fear of offense to make Bangladesh a secure place to live. Despite this, the traditional ways of detecting crimes in Bangladesh are currently slow and less effective because they are not getting enough technological support. As a result, most of the time the crimes get reported after the occurrence. So to attain efficiency in policing activities it has become significant to develop such a system that can notify the police department before the occurrence of a crime. In our proposed model, we develop a system that can prevent crime by detecting and notifying real-time criminal activities beforehand. To carry out this concept, we build a central system consisting of three modules i.e. criminal activity identification module, weapon detection module, and criminal detection module. Each of the modules is centrally connected and can give an alert when any type of abnormality occurs. We use Deep learning and machine learning algorithms to perform criminal activity detection and face recognition of criminals. For each of the modules, we collect and prepare our own data set. We perform various experiments to investigate the effectiveness of the method. the evaluation result shows that our proposed method performs impressively in identifying criminal activities and criminals.

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MEFormer: Multi-Object Tracking with Multi-Scale Feature Enhanced Transformer

MEFormer: Multi-Object Tracking with Multi-Scale Feature Enh...

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international conference on machine learning, Big data and Business Intelligence (MLBDBI)

作者： Yufan Zhang Xinlong Liu Lei Deng Jianxi Yang Chongqing Jiaotong University Chongqing China

Track-by-attention model, such as TrackFormer, have been one of the most popular deep models for multi-object tracking (MOT). However, one of their core limitations is image recognition accuracy and local feature attention due to the long sequence attention mechanism. To remedy this problem, MEFormer, a multi-scale feature enhanced model is proposed, in which both the channel attention and spatial attention mechanisms are leveraged for feature enhanced. MEFormer can effectively capture discriminative representation for MOT with attention and aggregation. Experiments on the MOT17 dataset indicate that the proposed MEFormer is able to achieve competitive performance on the task of multi-object tracking with 56.0% MOTA and 2400 ID switches.

关键词： Micromechanical devices Graphics Target tracking Image recognition Video sequences Optimization methods machine learning

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Communication jamming signal recognition method based on fractal dimension and information entropy

Communication jamming signal recognition method based on fra...

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international conference on machine learning, Big data and Business Intelligence (MLBDBI)

作者： Guan Wang Yong Kang Hui Wang Jing Sun Jinyu Wang Zhiwei Zhang Air Force Communication NCO Academy Liaoning Dalian China

Aiming at the problem of low recognition accuracy of traditional communication jamming signals, a communication jamming signal recognition method combining fractal dimension and information entropy is proposed. Firstly, Fourier transform, singular value decomposition and wavelet packet decomposition are performed on the jamming signal in turn; then, the fractal dimension and information entropy are used to extract the spectrum box dimension, time-frequency image singular value entropy and wavelet packet energy entropy of the jamming signal respectively, and the three-dimensional feature vector is constructed; Finally, the feature vector is input into multi classification support vector machine for classification and recognition. the simulation results show that the proposed method can accurately identify five types of typical communication jamming signals under the condition of low jamming noise ratio, and the recognition rate can reach more than 90% when the jamming noise ratio is -10dB. It is a strong anti noise communication jamming recognition method with practical application value.

关键词： Support vector machines Solid modeling Feature extraction Fractals Entropy Wavelet packets data models

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Advancements and Trends in Student Prediction and Course Recommender Systems: A Review

Advancements and Trends in Student Prediction and Course Rec...

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Sustainable Expert Systems (ICSES), international conference on

作者： Syed Aamer Hashmi Yashpal Singh Harshit Bhardwaj Department of CSE ASET Amity University Jaipur Rajasthan India Department of CSE ASET Amity University Noida Uttar Pradesh India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

In the era of AI, one of the most important fields that can make best use of it is Education. Be it the student, teacher or the institution, each one of them can use it for their benefit. One important aspect in education is Personalized learning, it has evolved from being the suggestions given by teacher or mentor to AI based system which are giving suggestions to students according to individuals capabilities and Interest. this article gives an in-depth idea about AI based research done as of now for student's performance prediction and course recommendation systems. Article discusses various methods such as machine learning (ML), Deep learning (DL), Recommender Systems used by various researchers along with their comparison and accuracies. After studying and comparing the research articles, we have suggested our approach based on gap findings. We have suggested a framework which will use computational intelligence to classify students and courses and using these classifiers it will create a recommender system, the system will be used by students to get course recommendation.

关键词： Surveys Deep learning Reviews Education Market research data mining Expert systems Recommender systems Optimization Computational intelligence

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