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Enhancing Security and Privacy of Machine Learning Algorithms in Agricultural Automation 15

Enhancing Security and Privacy of Machine Learning Algorithm...

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Singh, Amit Kumar Kharbas, Vikas Kumar Vinay Kumar, S.B. Arka Jain University Department of Journalisim & Mass Communication Jharkhand Jamshedpur India Vivekananda Global University Department of Computer Science & Application Jaipur India School of Engineering and Technology Department of Electronics and Communication Engineering Karnataka Bangalore India

ISBN: (纸本)9798350370249

This paper specializes in enhancing the safety and privacy of machine-mastering algorithms in agricultural automation. With the emergence of digital generation in agricultural production, system mastering algorithms have emerged as essential to offer precision control in farm control operations. However, their security and privacy may be compromised due to assault vectors inclusive of opposed inputs, facts leakage assaults, and facts poisoning attacks. Several ability solutions are studied, which include the introduction of statistics anonymization strategies, getting the right of entry to manage mechanisms, cryptographic strategies, and software program and hardware protection tactics. This paper analyses the one-of-a-kind security and privacy enhancement techniques and affords a comprehensive discussion of their effectiveness. Sooner or later, the authors advise a complete solution that encompasses all the security and privacy enhancement approaches to shield the gadget studying algorithms in agricultural automation effectively. © 2024 IEEE.

关键词： Differential privacy

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Extracting Aspect Sentiment from Users’ Book Reviews for Recommendation

Extracting Aspect Sentiment from Users’ Book Reviews for Re...

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Computational Linguistics and Natural Language Processing (CLNLP), International Conference on

作者： Yachao Cui Peng Zhou Kang Li Bingfeng Seng Han Cao School of Computer Science Shaanxi Normal University Xi’an China Department of Computer Technology and Application Qinghai University Xining China

ISBN: (数字)9798350392098

ISBN: (纸本)9798350392104

When shopping for books on e-commerce platforms, other users’ evaluations of books contain a large number of users’ intuitive feelings, from which useful information can be mined to provide buyers with ideas for shopping. In terms of recommendation algorithms, although the existing research work can use various methods to model fine-grained user preferences to improve the recommendation effect. However, we believe there are still shortcomings. First, existing methods are overly dependent on the accuracy of external sentiment analysis tools and are not necessarily applicable to the recommendation domain. Second, the features extracted from reviews are still potential features when incorporated into recommendation algorithms, and they cannot be fully utilized to characterize user embeddings. For this reason, this paper proposes a new user comment extraction scheme for personalized recommendation. We first use text summarization techniques in the field of NLP to preprocess and refine the comment text to reduce data noise and obtain high-quality text information. Subsequently, in order to more accurately characterize user embeddings and item features, we use advanced aspect-based sentiment analysis techniques to extract fine-grained aspectual items and corresponding sentiment polarity from the review text. Finally, experimental validation on a real book dataset is conducted to demonstrate the effectiveness of our model.

关键词： Sentiment analysis Accuracy Reviews Computational modeling Noise Text summarization Feature extraction Book reviews Electronic commerce

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Evaluation of an Educational Game Based on Compilation Techniques to Enhance Students' Understanding of Programming Concept

Evaluation of an Educational Game Based on Compilation Techn...

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International Conference on New Trends in Computing Sciences (ICTCS)

作者： Steven Andrew Halim Raevel Philander Christopher Angrico Muhamad Fajar Computer Science Department Game Application & Technology School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331523411

ISBN: (纸本)9798331523428

Compilers play a major role in software development, as an understanding of compiling concepts is fundamental to writing effective programs. While understanding this concept may be challenging, creating an innovative and accessible way to learn is also crucial. This paper explores the use of serious games when implemented for learning compilation technique concepts. We aim to help learners understand the concept of compilation techniques and compare how serious games stand up to more conventional methods of learning. Through participatory approaches, we asked volunteers to test out one of the 3 methods of learning and have them complete a questionnaire. The results state that serious games are better than video-based learning and conventional teaching methods (p < 0.05).

关键词： Learning systems Program processors Education Games Writing Market research Programming profession Software development management

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Music Generation using Time Distributed Dense Stateful Char-RNNs 7

Music Generation using Time Distributed Dense Stateful Char-...

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7th IEEE International conference for Convergence in technology, I2CT 2022

作者： Banerjee, Shobhan Rath, Manas Swain, Tanmaya Samant, Tapaswini Birla Institute of Technology and Science - Pilani Rajasthan India Kalinga Institute of Industrial Technology School of Computer Application Bhubaneswar India Kalinga Institute of Industrial Technology School of Electronics Engineering Bhubaneswar India

ISBN: (纸本)9781665421683

Sequence generation is one of the state-of-the-art topics in recent days, where given a sequence of inputs, the aim is to generate a similar sequence of outputs in a given context. The applications range from sentence autocompletion in mail bodies to text suggestions for automatic replies, etc. A similar idea can therefore be utilized to generate a sequence of musical notes using some LSTM/GRU based architecture, where we train our model based on given sequences of musical notes. If we look at music of a certain genre, it's simply a time series data where the data is in frequency domain. Fortunately for musicians these frequencies have a notation, which can serve as texts to train any given time series model. Hence, in this paper we propose a Char-RNN based model which can understand the patterns in each composition or a raga and generate new piece of music based on that. The model must not simply copy paste the sequence or generate any random note at a given instant of time but be capable enough to grasp the patterns in which the given piece of music is based upon and create a similar, new piece of music out of that. © 2022 IEEE.

关键词： Music

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A Method for Assigning Weight to the Assessment Indicators of Computational Thinking with XGBoost 5

A Method for Assigning Weight to the Assessment Indicators o...

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5th International Workshop on Artificial Intelligence and Education, WAIE 2023

作者： Zeng, Xinyao Zhang, Wei Chen, Yi Chen, Hao Central China Normal University Faculty of Artificial Intelligence in Education Wuhan China School of Computer Science Central China Normal University Wuhan China School of Information Technology Application Innovation Wuhan Polytechnic Wuhan China

ISBN: (纸本)9798350307061

Computational thinking (CT) assessment indicator assignment is an important strategy for optimizing assessment and an effective method for exploring differences in the importance of assessment indicators. However, the objectivity of the previous methods for assigning assessment indicators of CT is insufficient. Therefore, the paper proposed to use the XGBoost machine learning model to fit the assessment data, fully explore the data features and realize the weighting of CT assessment indicators. The CT assessment indicators are used as the features of XGBoost model training and the assessment levels are used as the training labels to obtain the feature weights through XGBoost model training. The feature weights are used as the CT assessment indicator weights to realize the weighting of assessment indicators of CT in reverse. From the analysis of the CT assessment indicator assignment results, the analysis of the importance of the assessment indicators and the comparative analysis of the assessment results before and after the indicator assignment, it is found that the CT assessment after the indicator assignment has higher robustness and credibility. © 2023 IEEE.

关键词： Assessment indicators Computational thinking Indicator weight XGBoost

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A range and radial velocity measurement method based on up-chirp and down-chirp LFM signal in pulsed radar

A range and radial velocity measurement method based on up-c...

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IET International Radar Conference 2023, IRC 2023

作者： Chen, Hao Lei, Yi Key Lab of Aperture Array and Space Application East China Research Institute of Electronic Engineering Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China

ISBN: (纸本)9781839539954

Range accuracy and radial velocity accuracy are important indexes in pulsed radar. However, in pulsed radar using linear frequency modulation (LFM) pulse, the range-Doppler coupling of LFM signal will lead to large range error in high-speed target measurement. The target's radial velocity is computed using range differentiation in traditional method, which is also not accurate. This paper presents a measurement method based on up-chirp and down-chirp LFM signal to get the target's accurate range and radial velocity simultaneously. Experiments confirm that the proposed method outperforms traditional method in both range accuracy and radial velocity accuracy. © The Institution of Engineering & technology 2023.

关键词： Chirp modulation

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Integrating Smart Farming with AI Techniques for Crop Disease Prediction 15

Integrating Smart Farming with AI Techniques for Crop Diseas...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Singla, Monika Gupta, Deepali Goyal, Deepam Dang, Rajeev Kumar Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Chandigarh group of colleges Chandigarh School of Business Department of Computer Application Jhanjeri140307 India UIET PUSSGRC Department of Mechanical Engineering Punjab Hoshiarpur India

ISBN: (纸本)9798350370249

Farming encompasses the act of cultivating land, growing crops, and raising animals, which is crucial for the growth of country's economy. For every country, agriculture contributes to approximately 70% of the main source of income. Historically, farmers have used traditional agricultural methods, which though time-consuming, often yielded imprecise results, leading to decreased output. Precision agriculture stands out as a viable solution to this issue, optimizing production by accurately identifying the necessary measures at an appropriate time. Elements of precision farming include meteorological prediction, soil assessment, crop recommendation for cultivation, and determination of optimal quantity of fertilizers and pesticides. Precise farming utilizes sophisticated techniques such as Machine learning, IoT, Data analytics and Data Mining to gather data, train systems, and forecast outcomes. Precision farming utilizes technology to minimize manual effort and enhance output. Recent agriculture challenges, such as crop failures because of low rainfall and soil sterility, raise the need for further exploration. This article aims to explore efficient methods used for crop management and harvesting in response to environmental changes. Several machine learning and deep learning models have come out with encouraging results in crop disease detection, thereby enhancing crop quality and productivity. © 2024 IEEE.

关键词： Smart agriculture

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Multi-perspectives 2D Spine CT images Segmentation of 3D Fuse Algorithm 5

Multi-perspectives 2D Spine CT images Segmentation of 3D Fus...

引用

5th International Conference on Advanced Electronic Materials, computers and Software Engineering, AEMCSE 2022

作者： Cui, Jiali Gao, Dedong Zhao, Yan Wang, Linze Peng, Xin Qinghai University Department of Computer Technology and Application Xining810016 China Qinghai University School of Mechanical Engineering Xining810016 China

ISBN: (数字)9781665484749

ISBN: (纸本)9781665484749

Vertebrae segmentation is a key component of quantitative spine image analysis, which can directly detect and evaluate vertebrae fractures, and indirectly support modeling and monitoring of spinal aging process. There are two main segmentation methods in the past: one is to use 2D CNN for segmentation, and then stack along a certain axis;the other is to preform 3D CNN segmentation directly. However, the former does not utilize the correlation information between the upper and lower slices lead to low segmentation accuracy, the latter has significant shortcomings such as mass of parameters, dependent data and slow segmentation speed. In this paper, a 3D segmentation algorithm using multi-perspectives 2D spine images is proposed. The proposed algorithm does not require a large amount of data, which can segment the spine boundary more accurately. Specifically, the 2D slices from three perspectives are segmented respectively, and then the three segmentation results are fused. It is different from previous algorithms that ignore the information from different perspectives. In this paper, we adopt three perspectives for mutual supervision learning to obtain the 3D information of the spine. In particular, we add the shape flow information in order to obtain more accurate boundary segmentation in the network. The model obtains 83.4% PA on the standard spine data. Similarly, the proposed algorithm still has obvious advantages in BraTS 2018 brain tumor segmentation task, and the proposed algorithm still achieved 80.0% PA in the case of a small amount of data. © 2022 IEEE.

关键词： Image segmentation

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Detection of Arrhythmia from ECG Signal Using Bat Algorithm-Based Deep Neural Network 7th

Detection of Arrhythmia from ECG Signal Using Bat Algorithm-...

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7th International Conference on Advanced Computing and Intelligent Engineering, ICACIE 2022

作者： Singh, Kabrambam Rupabanta Dash, Sujata Atta-ur-Rahman Department of Computer Application Maharaja Srirama Chandra Bhanja Deo University Sriramachandra Vihar Odisha Baripada India DRIEMS School of Professional Studies Odisha Tangi Cuttack India College of Computer Science and Information Technology Imam Abdul Rahman Bin Faisal University Damian Saudi Arabia

ISBN: (纸本)9789819950140

Heart disease is one of the deathliest diseases in the world. As the ECG data is widely available, many machine learning and deep learning models have been developed for the automatic Arrhythmia diagnosis system. In this study, BiLSTM with the Bat algorithm has been proposed for the classification of the Normal ECG signal and the Arrhythmia ECG signal. It can detect the abnormality of the heart rhythm. The novelty of this study is the feature selection using the Bat algorithm that enhanced the performance of the classification model. The performance of this method is compared with the existing methods and it gives better performance than the other existing methods. With the proposed method, an accuracy of 99.75% has been achieved using tenfold cross-validation. It can help the risk patient in the early detection of the abnormality of the heart rhythm. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Diseases

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Design and Implementation of a Game Session Plugin based on Unreal Engine 23

Design and Implementation of a Game Session Plugin based on ...

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8th International Conference on Cyber Security and Information Engineering, ICCSIE 2023

作者： Wen, Siyao Li, Dandan Yuan, Junlong Huang, Jingbin Zhou, Long School of Computer and Information Engineering Hubei Normal University Huangshi China National Engineering Research Center of Educational Big Data Application Technology Central China Normal University Wuhan China

ISBN: (纸本)9798400708800

With the rise in popularity of multiplayer online games, efficient game network communication has become increasingly vital. This research paper presents a comprehensive design and implementation of a network session plug-in, focusing on framework construction, data module integration, intermediate module development, and plug-in functionality testing. The primary objective of the plug-in is to enhance the convenience and efficiency of game network communication. Extensive testing, including performance evaluation, stress testing, and functional testing, has been conducted to validate the effectiveness and feasibility of the plug-in. The results showcase the successful development of a robust network session plug-in for multiplayer online games, offering valuable insights for future plug-in advancements. This research contributes significantly to the enhancement of game network technology, ultimately improving the overall player experience in multiplayer online gaming environments. © 2023 ACM.

关键词： Integration testing

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