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A reliable solution to detect deepfakes using Deep Learning 4

A reliable solution to detect deepfakes using Deep Learning

引用

4th international conference on Cognitive computing and Information Processing, CCIP 2022

作者： Vedamurthy, H.K. Ravi, V. Gururaj, S.P. Siddaganga Institute of Technology Dept. Computer Science Tumkur India

ISBN: (纸本)9781665456487

Recently, it has become simple to produce trustworthy face video exchanges that leave a few signs of deception thanks to in-depth free reading software tools (DF). Despite decades of effective use of visual effects in digital video deception, recent developments in in-depth learning have significantly improved the genuine nature of misleading content and the accessibility that can be achieved with it. this is referred to as AI-synthesized media or DF in short. Making DF is a simple task that uses practical tools. However, it is a significant difficulty if these DFs are discovered, because it is hard to train the algorithm for identifying DF. CNNs and RNNs have helped us come closer to DF. the Convolutional Neural Network (CNN) is used by the system to extract features at the individual level. the continuous neural network (RNN) states learn to recognize whether or not a video is being deceived and be able to spot temporary anomalies among the frames given by DF's creative tools thanks to these capabilities. An extensive collection of pseudo-videos gathered from a common data source is the anticipated outcome. We demonstrate how our method can produce a competitive outcome in this work that is simple to utilize. © 2022 IEEE.

关键词： Computer graphics

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An Investigation on Sentiment Analysis merged with CNN and Deep BI-GRU Model 4

An Investigation on Sentiment Analysis merged with CNN and D...

引用

4th international conference on Emerging Research in Electronics, Computer science and Technology, ICERECT 2022

作者： Khan, Gulista Kanniga, Devi Teerthanker Mahaveer University Department of Computing Science and IT Moradabad India Jain University Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9781665456357

Convolutional neural networks (CNN) and recurrent neural networks (RNN) have been utilised in message sentiment analysis, and both have produced promising results. CNN overlooks the coherent semantic information between words despite its ability to separate nearby information between successive articulations of a sentence. Many of the current sentiment analysis methods are seeking to extract the complicated semantic also, linguistic data from the message, however they are not especially applicable to the examination of understudy opinions. For the study of student message sentiment, the CNN-BiGRU-AT model - often referred to as a novel convolutional neural network with the bidirectional gated recurrent unit and an attention mechanism - is developed. the removal of n-gram information from each sentence is subsequently accomplished using a convolutional neural network (CNN). the sentences are then consecutively condensed through the bidirectional gated intermittent unit to dispose of the message's significant semantic data parts (BiGRU). the CNN-BiGRU model is then given a thinking mechanism, and by including the attention score, different learning loads are introduced to the model. the disclosures display that its structure's accuracy rate and audit rate dependably outperform 0.9, and the F1 regard isn't lower than 0.8. these figures are superior to those of various models. © 2022 IEEE.

关键词： Sentiment analysis

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Automatic Detection of Rice Diseases using Deep Convolutional Neural Networks with SGD and ADAM 4

Automatic Detection of Rice Diseases using Deep Convolutiona...

引用

4th international conference on Advances in computing, Communication Control and Networking, ICAC3N 2022

作者： Seelwal, Pardeep Sharma, Alok Baba Mastnath University Department of Computer Science and Applications Rohtak India

ISBN: (纸本)9781665474368

Rice diseases are the major problem in all over the world of agriculture sector. the early detection of this disease will prevent the huge economic loss for the farmer. this paper proposes a deep learning algorithm to classify the disease in the rice plant. Images of healthy and blast disease affected leaves are taken for the proposed system. the features are extracted for the healthy and brown spot, leaf blast, hispa disease of the rice leaf. the total data set is divided for training and testing purposes. these images are processed with the proposed multi-classification method and the leaf is categorized into brown spot, leaf blast, hispa disease and healthy. In this work CNN model is employed using two different optimizers i.e. SGD and ADAM. It is found that SGD performed well as compared to ADAM for our application as the training accuracy is obtained from proposed model with ADAM optimizer is 92.04 % while with SGD produced the training accuracy 96.32 %.the proposed model capable of getting promising accuracy for healthy and brown spot, leaf blast, hispa disease of the rice using multi-classification model. © 2022 IEEE.

关键词： Convolutional neural networks

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Hindi and Kannada Twitter Sentiment Analysis Using Machine Learning Algorithm 4

Hindi and Kannada Twitter Sentiment Analysis Using Machine L...

引用

4th international conference on Advances in computing, Communication Control and Networking, ICAC3N 2022

作者： Sumana Kanchan, Pradeep Dept. of Computer Science and Engineering Karkala India

ISBN: (纸本)9781665474368

Sentiment analysis is an important task in natural language processing. Twitter sentiment analysis (SA) is the most frequently researched discipline today. Only a few researchers have concentrated on multilingual twitter sentiment analysis, while the majority of scholars have worked on English twitter sentiment analysis. Recently, there seems to be a large increase in the quantity of data sets transmitted via social media in a variety of languages. this work examines the semantic nature of a text in order to classify positive and negative tweets using Hindi and Kannada language models, respectively. Machine learning models such as Naïve Bayes Classifier, KNeighbors' Classifier, Decision Tree Classifier and Random Forest Classifier are used to tackle the classification task. © 2022 IEEE.

关键词： Sentiment analysis

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Sinhala Named Entity Recognition Model: Domain-Specific Classes in Sports 4

Sinhala Named Entity Recognition Model: Domain-Specific Clas...

引用

4th international conference on Advancements in computing, ICAC 2022

作者： Wijesinghe, W.M.S.K. Tissera, Muditha Department of Statistics and Computer Science Faculty of Science University of Kelaniya Sri Lanka Department of Software Engineering Faculty of Computing & Technology University of Kelaniya Sri Lanka

ISBN: (纸本)9798350398090

Named Entity Recognition (NER) is one of the crucial and vital subtasks that must be solved in most Natural Language Processing (NLP) tasks. However, constructing a NER system for the Sinhala Language is challenging. Because it comes under the category of low-resource languages. therefore, the proposed approach attempted designing a mechanism to identify specific named entities in the sports domain. Firstly, a domain-specific corpus was built using Sinhala sport e-News articles. then a semi-automated, rule-based component named as "Class_Label_Suggester" was built to annotate pre-defined named entities. After auto annotation, the outcome was further validated manually with a little effort. Finally, it was trained using the annotated data. Linear Perceptron, Stochastic Gradient Descent (SGD), Multinomial Naive Bayes (MNB), and Passive Aggressive classifiers were used to train the NER model. though, the above Machine Learning (ML) algorithms showed approximately 98% accuracy, the MNB model demonstrated highest accuracy for the identified class labels of which, 99.76% for 'Ground', 99.53% for 'School', 98.55% for 'Tournament', and 97.87% for 'Other' classes. Additionally, high precision values of the above classes were 81%, 72%, 62%, and 98% respectively. An accurately annotated Sinhala dataset and the trained Sinhala NER model are main contributions of the study. © 2022 IEEE.

关键词： Sports

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A data-driven distributed control method for performance optimization of interconnected industrial processes 4

A data-driven distributed control method for performance opt...

引用

4th international conference on Industrial Artificial Intelligence, IAI 2022

作者： Wang, Hao Luo, Hao Jiang, Yuchen Xu, Xiaoyi Li, Xiang Harbin Institute of Technology Department of Control Science and Engineering Harbin150001 China

ISBN: (数字)9781665451208

ISBN: (纸本)9781665451208

In this paper, distributed plug-and-play (PnP) optimization control method for interconnected industrial processes is studied. Due to the influence of information interaction between subsystems, the performance of each subsystems are affected by their neighbor subsystem. Decentralized control approaches face difficulty in achieving satisfactory performance where the impact of information interaction between subsystems. Centralized optimal control method can achieve good results, but it burden on communication and computing. Compared with centralized and decentralized controller, distributed approaches has less online computational load and more flexible implementation schemes in interconnected industrial processes. In this work, a residual-driven distributed PnP optimization control approach is further developed, in which local residuals and residuals from neighbor subsystems are used to drive PnP optimization controllers. the influence between adjacent subsystems is fully considered in this design method. Experimental results on a three tank benchmark system show the effectiveness of the proposed algorithm. © 2022 IEEE.

关键词： Decentralized control

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Hybrid model for detection of frauds in credit cards* 4

Hybrid model for detection of frauds in credit cards*

引用

4th international conference on Advances in computing, Communication Control and Networking, ICAC3N 2022

作者： Sharma, Divya Singh Kang, Sandeep Chandigarh University Department of Computer Science & Engg Mohali India

ISBN: (纸本)9781665474368

the goal of this study is to decide on the correct model that can predict a credit card holder's likelihood of missing their next payment based on their demographic characteristics and payment history. Our goal is to reduce the number of defaulters that are labelled as non-defaulters. that's because our company can afford to have non-defaulters who do default but not non-defaulters who do not default, as it is implied that the bank has a stringent no-NPA policy (more or less). When putting machine learning algorithms into practice, supervised learning must be used since we have known upstream and downstream values and anticipate a prediction model at the conclusion. the correlation amid attributes and target sets is established in the first stage. Classification stage classifies input data into certain categories. In order to examine the performance of recommended approach, the comparison of recommended approach and other available approaches has been conducted in this work along with this achieving the 85% accuracy *** accurately categorizing credit card delinquencies and non-defaulters, the mortgage lenders will be able to utilize the accuracy of the model to lower the high delinquent rate. © 2022 IEEE.

关键词： Logistic regression

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An Exploration on Verification of Educational Assets through Blockchain Technology 4

An Exploration on Verification of Educational Assets Through...

引用

4th international conference on Emerging Research in Electronics, Computer science and Technology, ICERECT 2022

作者： Rastogi, Ajay Sinha, Deepak Kumar Teerthanker Mahaveer University Department of Computing Science and IT Moradabad India Jain University Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9781665456357

Emergence and development of the blockchain technology, which is able to transform into 'a most powerful disruptive innovation', shall definitely concern universities. Moreover, nowadays the blockchain technology meets the challenges that both the system of higher education and the entire society are currently facing. Advantages of the blockchain technology are decentralized open data, absence of forgeries, safe storage of information, and reduction of transaction expenses related to data checkup, control, and verification. As of right moment, blockchain is considered to be a widely applicable technology with huge potential. In addition, information stored on a Blockchain record can be accessed from anywhere at any time. the potential for blockchain to manage and maintain student records is enormous. Blockchain is the key piece of technology used to create digital currencies like bit coins. Blockchain technology is a component of the fourth modern revolution after the development of the steam engine, the electric motor, and the computer. It has been used in many fields, including finance, law, and trade. the current article focused on its potential educational applications and looked into how blockchain technology might be used to address various challenges with education. © 2022 IEEE.

关键词： Blockchain

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Grant-Free Random Access in Smart-Grid Networks with Power Control and data Retransmission 4

Grant-Free Random Access in Smart-Grid Networks with Power C...

引用

4th international conference on Advances in Computer Technology, Information science and Communications, CTISC 2022

作者： Zhou, Hao Liu, Nan Pan, Zhiwen Wang, Cheng-Xiang Southeast University The National Mobile Communications Research Laboratory Nanjing China Southeast University Purple Mountain Laboratories The National Mobile Communications Research Laboratory Nanjing China

ISBN: (数字)9781665458726

ISBN: (纸本)9781665458726

With the increasing scale and intelligence of the smart-grid network, a significant quantity of smart meters will be deployed. How to support the information transfer from more and more devices is an interesting and pressing problem. In this paper, we study a grant-free random access model for the devices with two types of data transmissions, i.e., periodic data and event-driven data, using the compressive sensing technology. We introduce power control and data retransmission to our model to reduce the impact of devices with weak signals. We derive approximate closed-form expressions of the probability of successful transmission to evaluate the performance of power control and/or data retransmission. Furthermore, we propose an algorithm to optimally find the maximum number of devices that can be supported given that both the probability of successful transmission and energy efficiency is above their corresponding thresholds. through numerical results, we demonstrate that a significant number of devices can be supported by employing power control and data retransmission. © 2022 IEEE.

关键词： Compressed sensing

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Effectiveness of SVM kernel for YOUTUBE Comment Filtering 4

Effectiveness of SVM kernel for YOUTUBE Comment Filtering

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4th international conference on Advances in computing, Communication Control and Networking, ICAC3N 2022

作者： Kumar, Shailender Gupta, Shweta Delhi Technological University Department of Computer Science & Engineering Delhi India

ISBN: (纸本)9781665474368

the impact of Social networking platform is evident in each generation and almost all fields. It made many tasks more convenient that was not possible earlier. YouTube is one such platform. It can be utilized as a source of entertainment, discussion portal, information sharing, knowledge gathering. Various newborn and popularly known organizations display their advertisements over the platform as short video or links in comment section that help content creators of the video through some financial gains. these advertisements can be greatly helpful to visitors and firm. Also the comment section may contain some cogitative discussions. With all such activities found on the platform some spiteful ones are also lurking over it. Spammers used to suffuse malicious links and data over the platform. In this study, we directed towards the comment section of the platform and executed a comparative analysis of different SVM kernels for the classification of Spam or ham comments over the YouTube application. We also studied the impact of feature extraction incorporating two feature extraction techniques Tf-Idf and BOW. © 2022 IEEE.

关键词： Feature extraction

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