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Word Game Prediction and Difficulty Classification based on machine learning 6

Word Game Prediction and Difficulty Classification based on ...

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6th international conference on computer Information Science and application Technology, CISAT 2023

作者： Zhang, Wenzhe College of Engineering Yanbian University Yanji133002 China

ISBN: (纸本)9781510668546

this article focuses on the Wordle game, a word puzzle game that has become extremely popular on social media. Based on the research objectives, three models have been proposed to solve the problem: a model for predicting the number of players, a model for predicting the number of attempts, and a model for classifying word difficulty. For the problem of predicting the number of players, the (Autoregressive Integrated Moving Average-Support Vector machine) ARIMA-SVM model was proposed. the results showed that the ARIMA-SVM model performed about 17% better than the ARIMA model. To address the problem of predicting the number of attempts, the Random Forest Regression model was proposed. In regards to the problem of classifying word difficulty, the GA-FCM clustering model was proposed. Finally, this paper contributes to the difficulty classification of words based on different word attributes, and the experimental results showed that (Genetic Algorithm-Fuzzy C-Means) GA-FCM performed significantly better than the FCM model in all indicators. © 2023 SPIE.

关键词： Genetic algorithms

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Development of Secure Hardware Architectures for Preventing Side-Channel Attacks and Protecting Sensitive Data in VLSI Systems 4

Development of Secure Hardware Architectures for Preventing ...

引用

4th international conference on Smart Electronics and Communication, ICOSEC 2023

作者： Kumara, Varuna Jayakumar, S. Rani, D. Leela Maranan, Ramya Ramesh, D. Raja Deivakani, M. Moodlakatte Institute of Technology Kundapura Department of Artificial Intelligence and Machine Learning Karnataka Kundapura576217 India Sri Sairam College of Engineering Anekal Department of Electronics and Communication Engineering Bengaluru562106 India School of Engineering Mohan Babu University Department of Electronics and Communication Engineering Andhra Pradesh Tirupati517102 India Saveetha School of Engineering Department of Research and Innovation Tamil Nadu Chennai602105 India Chintalavalasa Department of Electronics and Communication Engineering Vizianagaram535005 India PSNA College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Dindigul624622 India

ISBN: (纸本)9798350300888

this research paper has been developed by targeting to develop the secure hardware architecture for preventing the potential threats like side channel attack and protecting sensitive data in VLSI systems. In this context, major of the key security frameworks like information theory, cryptography, online threats protector has been implemented in this study. It has been found, on the basis of extensive simulations, that the innovative design effectively protects against side-channel assaults and cuts the chance of mistakes by half, from 16 to 1. this concept has the potential to deliver considerable advantages for a variety of different sectors, including the Internet of things, the military, and aerospace. In order to improve the design's overall performance and investigate the ways in which it could be applicable to other VLSI systems, more study could be carried out. © 2023 IEEE.

关键词： computer software

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An Effective Approach for Image Denoising Using Wavelet Transform Involving Deep learning Techniques 4

An Effective Approach for Image Denoising Using Wavelet Tran...

引用

4th international conference on Smart Electronics and Communication, ICOSEC 2023

作者： Shukla, Abhay Seethalakshmi, K. Hema, P. Musale, Jitendra Chandrakant Axis Institute of Technology and Management Department of Computer Science and Engineering Uttar Pradesh Kanpur208024 India Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Department Computer Science and Engineering Tamilnadu Chennai62 India R.M.K. College of Engineering and Technology Department of Mathematics Tamil Nadu Thiruvallur601206 India Shri Jagdishprasad Jhabarmal Tibrewala University University in Jhunjhunu Department of Computer Science and Engineering Rajasthan India

ISBN: (纸本)9798350300888

In contemporary years, image denoising has developed as an important investigation topic in the arena of image processing due to the increasing demand for high-quality images. Deep learning approaches have recently demonstrated outstanding results in a variety of image processing applications. In this research, a wavelet transform and deep learning-based method is suggested for image denoising. To successfully remove noise from an image, the suggested method is based on a hybrid network that combines wavelet transform with convolutional neural networks (CNNs). the suggested method is divided into two steps, one based on wavelet transforms and the other on deep learning. the input image is divided into wavelet coefficients at various scales and orientations in the wavelet transform-based stage. the noise is subsequently filtered out of the wavelet coefficients using a thresholding method. A CNN is taught to acquire the plotting among the noisy image and the denoised image during the deep learning-based step. the experimental results show that, in terms of both quantitative metrics and visual quality of the denoised images, the suggested strategy outperforms a number of state-of-the-art image denoising methods. the suggested method is also independent of the prior knowledge of the noise statistics and resilient to various types of noise. Overall, the suggested method successfully tackles image denoising by fusing the compensations of deep learning and wavelet transform. the suggested method could be used for a variety of computer vision applications that call for high-quality image denoising. © 2023 IEEE.

关键词： Image denoising

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Implementation of machine learning Classification Techniques for Intrusion Detection System 4

Implementation of Machine Learning Classification Techniques...

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4th international conference on Intelligent Engineering and Management, ICIEM 2023

作者： William, P. Inukollu, Venkata Narasimha Rao Ramasamy, Viswanathan Madan, Parul Shrivastava, Anurag Srivastava, Aparna Sanjivani College of Engineering Savitribai Phule Pune University Department of Information Technology Pune India Purdue University Fortwayne Fort WayneIN United States Koneru Lakshmaiah Education Foundation Department of CSE Guntur India Graphic Era Deemed to be University Depatment of CSE Dehradun India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Lloyd Institute of Engineering and Technology Department of MCA Greater Noida India

ISBN: (纸本)9798350341126

In the region internet including service, flaws of software design that is present on the device on its own, protocol, and also in control. these vulnerabilities of the community can be very easily hacked by the community hackers and to the net put the potential intrusion attacks. With the time as there is an increase in cases of personal computers security attacks and also increase in the number, fundamental security attacks are most in the number of cases than before. No long these issues of community safety can be ignored. the protection of these types of assets is entailed by computer protection. Information's Availability, integrity, and confidentiality to the users are legal characteristics this may be represented. Properties of privacy and secrecy are deal with confidentiality. On the negative chose set of rules finds attacks and intrusion. this machine of intrusion detection is a machine sturdily gaining knowledge of capability and excellent adaptive capacity. Using it may stumble on numerous unknown kinds of intrusions of attacks and intrusions. © 2023 IEEE.

关键词： Software design

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BSGD: A behavior and social based group discovery method 6

BSGD: A behavior and social based group discovery method

引用

6th international conference on computer Information Science and application Technology, CISAT 2023

作者： Han, Qiqi Wang, Daocheng Zhang, Hongbiao Xu, Pengyao Shandong Artificial Intelligence Institute Qilu University of Technology Shandong Academy of Sciences Jinan250014 China

ISBN: (纸本)9781510668546

With the rise of social networks and online communities, group activities become increasingly common, which promotes the development of group recommendations. As the first step of group recommendation, group discovery aims to identify potential groups from users. Current group discovery methods often fail to consider behavior preferences and social connections simultaneously and may not be trained in a group-discovery-oriented manner. To address the above problems, we propose a novel group discovery method called BSGD based on users' behaviors and socialization, which introduces three tasks and implements group-discovery-oriented end-to-end training through multi-task joint training. the experiment results show that the proposed method has an excellent performance in both group discovery and gains in group recommendation on two real-world datasets. © 2023 SPIE.

关键词： machine learning Social networks Matrices Computing systems Deep learning Neural networks Intelligence systems

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A Method for Wireless Communication Interference Signal Identification Based on Extreme learning machine 5

A Method for Wireless Communication Interference Signal Iden...

引用

5th international conference on computer Information Science and Artificial Intelligence, CISAI 2022

作者： Liu, XiaoZheng Wang, Yue Wang, XiaoFei Geng, Jian Beijing Institute of Space Long March Vehicle China Academy of Launch Vehicle Technology Beijing100076 China

ISBN: (纸本)9781510662520

Intelligent anti-jam communication is a new generation of anti-interference technology combined with artificial intelligence, and the identification of interference signals is the basis of the technology. It is required to achieve better identification results with lower computational complexity in engineering applications. However, previous research has shown that they cannot balance these two sides. Here, we report an interference signal identification algorithm based on Extreme learning machine (ELM). Five typical oppressive interference signals were recognized based on ELM which is based on feature extraction. the overall correct identification rate is more than 96% under the condition of 40 neurons in a single hidden layer, and it has certain generalization ability. this study objectively promotes the engineering application of this technology. © 2023 SPIE.

关键词： Signal interference

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machine learning Techniques and Routing Protocols in 5G and 6G Mobile Network Communication System - An Overview 7

Machine Learning Techniques and Routing Protocols in 5G and ...

引用

7th international conference on Trends in Electronics and Informatics, ICOEI 2023

作者： Goswami, Mitul Panda, Namita Mohanty, Suneeta Pattnaik, Prasant Kumar School of Computer Engineering Kiit DU Bhubaneswar India

ISBN: (纸本)9798350397284

In the surge of demand for communication systems in the recent two decades starting from 1G networks, there was only an analog mode of communication, 2G saw the introduction of digital communication systems. In subsequent generations like 3G & 4G, interventions were done to improve the speed and introduce security features like encryption. In the modern 5G and 6G communication systems, more enhanced features are being introduced, the main differentiators being higher frequency band operation, extremely low air latency, and tremendous high data rate of the order of 1000 GBPS. this study, reviews various machine learning techniques and routing protocols used in 5G and 6G mobile network communication changes. © 2023 IEEE.

关键词： Routing protocols

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Breast Cancer Detection in Deep learning based Architectures using Mammogram Images 4

Breast Cancer Detection in Deep Learning based Architectures...

引用

4th international conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Bilal, Ghaith Kumar, Dinesh Shah, Sejal thanki, Rohit Marwadi University Computer Engineering Rajkot India Marwadi University Computer Engineering - Artificial Intelligence Rajkot India Marwadi University Bioinformatics Rajkot India Krian GmbH Germany

ISBN: (纸本)9798350300093

Breast cancer is a very devastating type of cancer that typically develops in breast cells and in the past few decades, the incidence rate of breast cancer has risen steadily. the diagnosis mostly depends on the radiologists' experience, questionable diagnoses are common, and there are worries about lawsuits due to incorrect diagnoses or missed lesions. therefore, developing deep learning models for breast cancer identification is urgently needed, despite substantial advancements in the treatment of primary breast cancer during the past ten years. In Deep learning, the convolutional neural network can be a useful technique for classification *** this study, some of the Transfer learning models are applied, which are pre-trained models that are trained on huge datasets such as the ImageNet dataset, which contains about 1.2 million images. However, these models are VGG16 VGG19 Mobilenet V2, and it is known that these models are used for classification problems, and therefore it will be used in this study in addition to developing a Convolutional Neural Network, which achieved the highest accuracy and best performance, compared to the applied models. these models were applied to the presented Mammogram Images dataset, which contains eight classes, each of which represents a form of mass that can be cancerous or benign. And since the dataset is unbalanced in terms of the distribution of samples for the classes, the Oversampling technique was applied, which had a key role in improving the performance of the models, especially for the CNN network, which obtained the highest accuracy of 96%. © 2023 IEEE.

关键词： Convolutional neural networks

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Skin Cancer Classification Using Transfer learning-Based Pre-Trained VGG 16 Model 4

Skin Cancer Classification Using Transfer Learning-Based Pre...

引用

4th IEEE international conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： Saini, Archana Guleria, Kalpna Sharma, Shagun Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140401 India

ISBN: (纸本)9798350306118

Skin is one of the most dangerous types of cancer which affect, millions of people every year. the identification and treatment of skin cancer is challenging and expensive due to the requirements of advanced technologies. According to recent studies, dermatologists can categorise medical images with the aid of machine learning and deep learning-based methods. Skin cancer is currently one of the most prevalent and deadliest forms of cancer if it reaches to the malignant stage. Melanoma is a skin cancer which occurs when the skin undergoes through various abnormal changes. To reduce the effort, time, and risk involved with skin cancer, an accurate automated system for skin lesion classification is required. Due to the complexity of the skin's texture and the visual proximity of the disease, it becomes very difficult to accurately identify this type of cancer. In this work, a pre-Trained VGG16 model has been used for the prediction of skin cancer. the results of the proposed model have been identified in terms of AUC and loss at the validation and training phases. these results have been identified by performing fine-Tuning of the pre-Trained VGG16 model and keeping the epoch values as 2, 10, and 20. the performance of the model has been analyzed which identified that the best results of AUC have been identified at epoch 20 with the ROC value of 0.841 and loss value of 0.034. In future, this work can be expanded with the implementation of transfer learning models to achieve the performance in terms of accuracy. © 2023 IEEE.

关键词： Diseases

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Simulation-Based machine learning Training Data Generation System 4

Simulation-Based Machine Learning Training Data Generation S...

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4th international conference on Artificial Intelligence and Electromechanical Automation, AIEA 2023

作者： Liang, Mu Xu, Yu School of Information Science Beijing University of Technology Beijing100124 China

ISBN: (纸本)9781510666412

In recent years, machine learning has made considerable progress in various fields. the training of deep learning algorithm model requires a large amount of data, and a large amount of data annotation is very costly. In this case, the use of simulation data can provide great convenience. Using the three-dimensional reconstruction algorithm to construct a three-dimensional model of the real scene can speed up the construction of simulation data. the model has problems such as model damage and model fragmentation. the repair model takes a long time and the repair effect is poor. To solve these problems, we designed and developed a simulation training data generation system. the system uses Blender as a development platform, introduces a three-dimensional reconstruction algorithm to initialize the model, transforms the model into a point cloud for repair operation, and proposes a point cloud region adjustment method and a point cloud fast repair method. Finally, the refined model is used to generate a data set with true values. the experimental results show that the system we designed can improve the efficiency and accuracy of data generation and efficiently generate simulation training data for machine learning. © 2023 SPIE.

关键词： learning algorithms

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