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data Mining Technology-Based Algorithms for Evaluating English Language Teaching Indicators 3rd

Data Mining Technology-Based Algorithms for Evaluating Engli...

引用

3rd EAI international conference on Application of Big data, Blockchain, and Internet of Things for Education Informatization, BigIoT-EDU 2023

作者： Qian, Wang Jiaxin, Wang Huixing, Zhou Beijing Polytechnic Beijing100176 China

ISBN: (纸本)9783031631290

TE is a judgement on the value of teachers’ teaching (TT) and students’ learning (SL), and has become an important part of the teaching management and teaching process in universities. There are many common TE systems, most of which evaluate the behavioural performance of teachers, while the learning process and effectiveness of students are rarely mentioned. At the same time, the workflow of implementing TE is tedious and often requires the completion of a large number of data calculation tasks. Therefore how to use modern science and technology to establish a sound, objective and feasible classroom TE system and optimise the evaluation process is an important issue that needs to be addressed urgently. The main objective of this paper is to conduct a study on the evaluation algorithm of English teaching indicators based on data mining (DM) technology. Starting from the construction of a learning-centred university TE system, this paper optimises student TE indicators by using data correlation analysis and association rules. At the same time, machine learning algorithms are introduced into the TE process to build TE models and automate the TE process. Through clustering, the experiment can divide all teachers into corresponding categories, analyse the overall characteristics of teachers in each category, and obtain the performance of teachers in different categories in each indicator, and teachers can focus on their lower level indicators according to the performance of each indicator in their respective categories. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering 2024.

关键词： data mining

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Secure Multi-Party Multi-Key Adversarial Cryptography Using GAN 3

Secure Multi-Party Multi-Key Adversarial Cryptography Using ...

引用

3rd international conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Anushruthika Justus, J.Jean St. Joseph's College of Engineering Computer Science and Engineering India

ISBN: (数字)9798350372120

ISBN: (纸本)9798350372120

The realm of cryptography, rooted in neural networks, has experienced advancements following the inception of adversarial cryptography. This approach employs Generative Adversarial Networks (GANs) to construct neural networks capable of autonomously acquiring encryption skills. Our primary focus lies in ensuring the confidentiality of information within a multiagent system, with properties articulated in terms of an adversary. Our approach consists of a neural network named 'Alice' to create a ciphertext from plaintext and multiple keys, for data security. Simultaneously, we introduce several neural networks, collectively called 'Bob,' designed to identify and decrypt the ciphertext back to plaintext. Our system is designed to withstand various threats, including intruders possessing knowledge about the encryption process, and potentially having access to leaked ciphertext or a subset of keys. Our approach addresses the challenges posed by attackers engaging in activities like Chosen-Plaintext and Chosen-Ciphertext Attacks. Our system trains 'Alice' and 'Bob' collaboratively to counteract the impact of both intruders and attackers. Over the training process, Alice and Bob reach a state of equilibrium, enabling them to decrypt ciphers in the presence of intruders. Our implementation reveals that nearly 800 training epochs are necessary for model synchronization. Intruders are left with only random guesses about the plaintext, leading to increased errors. Even CPA and CCA attackers fail to strongly predict a probability of either 0 or 1, yielding probabilities close to 0.5. In contrast, Bob decrypts with minimal errors. Moreover, the study illustrates how increasing the number of keys results in faster learning for Bob. © 2024 IEEE.

关键词： Cryptography

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Hybrid Machine learning based Intrusion Detection System for IoT 3

Hybrid Machine Learning based Intrusion Detection System for...

引用

3rd IEEE international conference on Computing, Communication, and intelligent Systems, ICCCIS 2022

作者： Mittal, Kajol Batra, Payal Khurana Jaypee Institute of Information Technology Department of Computer Science and Engineering Noida India

ISBN: (纸本)9781665462006

The Internet of Things (IoT) is susceptible to several attacks by insiders and outsiders. IoT requires an effective Intrusion Detection System (IDS). The development of IDSs that can quickly and automatically identify and classify cyberattacks at the network and host level commonly uses machine learning techniques. This research proposes a hybrid machine learning model that combines Naive Bayes and K-NN. The UNSW-NB15 dataset, open to the public, is used to test the suggested model. Accuracy, precision, recall, and f1-Score are four well-known performance evaluation metrics used to analyze the model's performance. The experimental results show that the hybrid approach performs better than the individual ML model, with the accuracy of 91.8% in the case of multiclass classification and 96.2% in the case of binary classification. © 2022 IEEE.

关键词： Internet of things

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ICU Mortality Prediction Using Long Short-Term Memory Networks 1

引用

23rd international conference on intelligent data engineering and automated learning (IDEAL)

作者： Mili, Manel Kerkeni, Asma Ben Abdallah, Asma Bedoui, Mohamed Hedi Univ Monastir Fac Med Monastir Tunisia Univ Monastir Higher Inst Comp Sci & Math Monastir Tunisia Univ Monastir Lab Technol & Med Imaging Fac Med Monastir Tunisia

ISBN: (数字)9783031217531

ISBN: (纸本)9783031217524;9783031217531

Extensive bedside monitoring in Intensive Care Units (ICUs) has resulted in complex temporal data regarding patient physiology, which presents an upscale context for clinical data analysis. In the other hand, identifying the time-series patterns within these data may provide a high aptitude to predict clinical events. Hence, we investigate, during this work, the implementation of an automatic data-driven system, which analyzes large amounts of multivariate temporal data derived from Electronic Health Records (EHRs), and extracts high-level information so as to predict in-hospital mortality and Length of Stay (LOS) early. Practically, we investigate the applicability of LSTM network by reducing the time-frame to 6-hour so as to enhance clinical tasks. The experimental results highlight the efficiency of LSTM model with rigorous multivariate time-series measurements for building real-world prediction engines.

关键词： Electronic health record Multivariate time-series data MIMIC-III

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Design of Pest Infestation Early Warning System Based on Deepsort Algorithm 3

Design of Pest Infestation Early Warning System Based on Dee...

引用

3rd IEEE international conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Wang, Lifen Wang, Shuyan Electrical and Information Engineering College Jilin Agricultural Science and Technology University Jilin Jilin China

ISBN: (纸本)9798350317022

This paper uses DeepSORT as a framework to design an efficient and convenient early warning system for insect infestation [1]. The system uses the multi-object detection algorithm DeepSORT to track, identify, and count the pests in the video, and uses STM32 single-chip microcomputer with GY30, DHT11, ATGM332D and other modules to collect and upload environmental data, which can comprehensively generate pest alarm levels according to the number of pests and environmental data [2]. This design has good identification ability for different types, different speeds, and different numbers of pests, and can accurately generate alarm levels based on environmental data. Therefore, this design has a good application ability of insect plague early warning, and provides reference and reference for the design and optimization of insect plague early warning system. © 2023 IEEE.

关键词： Alarm Deepsort Multi-target tracking Stm32

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Adam Optimizer's effects on a Sequential Convolutional Network for Classifying Planets 3

Adam Optimizer's effects on a Sequential Convolutional Netwo...

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3rd international conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2023

作者： Singh Gill, Kanwarpartap Sharma, Avinash Anand, Vatsala Gupta, Rupesh Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Haryana Mullana-Ambala133207 India

ISBN: (纸本)9781665494007

The accessibility of fawning and observational information over the NASA database has made a wide extend of exercises conceivable, counting the distinguishing proof of planets and the forecast of their directions for the elucidation and improvement of machine learning models. We require photographs with higher picture quality for ideal representation. Pictures of a geological range so grant the watcher a establishment for understanding how characteristics can alter both spatially and transiently. The required data almost the target regularly impacts the imaging innovation determination. Conventional imaging strategies regularly make utilize of well characterized signals, such as enthusiastic impartial molecules or photons with a restricted range run. In any case, a number of viable imaging strategies have been made for analyzing different planetary and space situations utilizing long-wavelength electromagnetic radio waves. The categorization and show of planets employing a consecutive demonstrate are hence finished in this think about exertion with the help of the dataset, which is vital for investigate purposes. Profound learning and machine learning methods are utilized to achieve this. The proposed sequential model offers the highest degree of precision for further investigations. © 2023 IEEE.

关键词： Convolutional neural networks

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Recognition of Scanned Handwritten digits using Deep learning 3

Recognition of Scanned Handwritten digits using Deep Learnin...

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3rd IEEE Mysore Sub Section international conference, MysuruCon 2023

作者： Singh, Paras Nath Babu T S, Kiran CMR Institute of Technology Dept. of CSE Bengaluru560037 India MVJ College of Engineering Dept. of CSE Bengaluru560067 India

ISBN: (纸本)9798350340358

The capability of ML (Machine learning) algorithms to recognize images of handwritten numerals is known as HDR (Handwritten Digit Recognition). Because handwritten numerals are imperfect and can be generated with a variety of flavors, it is a difficult work for the machine. The answer to this issue is handwritten digit recognition, which uses an image of a digit to identify the digit that is contained in the image. Additionally, there may be various visual artifacts including intensity changes, blurred and noisy effects makes this process more difficult. A CNN (Convolutional Neural Network)-based deep learning technique for HDR is proposed to get beyond the aforementioned restrictions. The input photos are initially transformed to a grayscale image for the area of interest using a pixel resolution ratio of 0-255. For classification of written digits is trained for recognition. The proposed model has been tested using MNIST dataset to show its effectiveness, and it achieved higher accuracy value of 99%. The suggested method can recognize HDR images with diverse writing styles and variations of sizes numbers, with prediction in plots and predicted digits in results. Tensorflow with Keras module of Python was imported for training and implementation. Scaling the training data it gives 98.7% of accuracy. © 2023 IEEE.

关键词： Convolutional neural networks

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ECG data Mining Approach for Detection of Arrhythmia Using Machine learning 3

ECG Data Mining Approach for Detection of Arrhythmia Using M...

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3rd IEEE international conference on Device Intelligence, Computing and Communication Technologies, DICCT 2025

作者： Naaz, Rahema Ahmad, Shafeeq Azad Institute of Engineering and Technology Department of Computer Science & Engineering Lucknow India

ISBN: (纸本)9798331543358

The Electrocardiogram (ECG) is very important signal generated from human body that provides information for assessment of variation in cardiac activities. These ECG data are generally distorted due to high power line or magnetic field associated noise that results as a problem in error during diagnosis of heart related disorders on analysis through biomedical analysis software. This work contributes for very significant stage that is introduced to reduce the anomaly in the ECG signals to perform accurate automated diagnosis. This work follows a methodology of an automated machine learning algorithm is developed for accurate estimation of ECG data segment cycle using clustering approach integrated with Discrete Wavelet Transform (DWT). The key findings of this works cover the development and analysis of an approach to overcome the errors in detection of T-wave using Implantable Cardioverter Defibrillator (ICD). The work reports the solution of issues using fuzzy C-means clustering behind prediction of fatal Ventricular Arrhythmias (VA) for performing standard classifications. © 2025 IEEE.

关键词： Electrocardiography

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Analysis and Classification of Restaurants Based on Rating with XGBoost Model 3

Analysis and Classification of Restaurants Based on Rating w...

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3rd international conference on Issues and Challenges in intelligent Computing Techniques, ICICT 2022

作者： Dixit, Anuj Kumar Nair, Rekha R Babu, Tina School of Engineering Dayananda Sagar University Department of Computer Applications Bengaluru India School of Engineering Dayananda Sagar University Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9781665482684

The restaurant business is one of the most competitive and the need for restaurants is growing daily. Bangalore is a foodie's paradise, boasting cuisines from all over the world. Hence, this research work focuses the classification of restaurants on the basis of rate with XGBoost model. The Exploratory data Analysis(EDA) with different graphs provides an analysis of the data before classification. Prior to EDA the data sets are cleaned with various steps to increase the accuracy of the visualization and classification. The research was performed using Zomato data set for restaurants in a specific locality(Bangalore). data Visualization techniques helped to analyze food culture, trends and patterns. This research describes a model for understanding the elements that influence restaurant ratings. Predictive analytics and machine learning together with a variety of tools and methodologies, help in predicting restaurant ratings. The model in this research is developed using multiple supervised techniques, with the most efficient algorithm being evaluated. The classification XGBoost model provided an accuracy of 98.07 %. The outcome of the work assists new restaurants in selecting on their menu, cuisine, theme, prices, geographic location, and so on, consequently enhancing business. The rate and location details helps people to select the restaurants for the dining. © 2022 IEEE.

关键词： Predictive analytics

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Artificial intelligence in Quality4.0 3

Artificial intelligence in Quality4.0

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3rd international conference on Embedded Systems and Artificial Intelligence, ESAI 2024

作者： Lahmine, Souad Bennouna, Fatima ENSA of Fez LISA Department of Industrial Engineering Fez Morocco

ISBN: (纸本)9798331522056

Many studies, mostly based on different sectors applying Industry 4.0, do not highlight the role of artificial intelligence in those sectors and particularly in quality management. Though an appropriate amount of research regarding the implementation of Quality 4.0 has advanced, which is the evolution of quality management systems pertaining to technological alignment with Industry 4.0 technologies, it is mostly within the confined applications of a single sector. It has narrowed the general knowledge of how AI could really improvise processes of quality across different industries. This broad paper takes such a scope in aiming to assess the incorporation of AI in Quality 4.0 from a cross-sectoral view with a perspective to learn how advanced machine learning, predictive analytics, and real-Time data analytics can help polish quality management systems. The emphasis is placed upon the manner in which AI could assist an industry to manage large amounts of data, improve production efficiency, and forestall issue through proactive steps such as anomaly detection and predictive maintenance. With this study, by considering the prospect of AI in varied Eeconomic sectors, new doorways are unlocked for creative quality management practices. Our study attempts to establish that transformative potential of AI for process optimization under Quality 4.0 approaches that make them skillful, effective, and adaptive to rapidly evolving requirements in today's industrial setup. Therefore, it offers an insight into how AI can complement Quality 4.0 toward leading the future of industrial operations with the wider vision of Industry 4.0 while ensuring that the industries are coming up with even better solutions to cope with the complexities of the new production setups. © 2024 IEEE.

关键词： Artificial Intelligence (AI) Industry 4.0 Quality 4.0 quality management

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