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Sarcasm Detection Using Stacked Bi-Directional LSTM Model 3

Sarcasm Detection Using Stacked Bi-Directional LSTM Model

引用

3rd international conference on Advances in Computing, Communication control and Networking, ICAC3N 2021

作者： Kumar, Dr. Manoj Patidar, Ashish Delhi Technological University Department of Computer Science and Engineering Delhi India

ISBN: (纸本)9781665438117

Sarcasm is one of the sentiments which is being used to communicate a negative opinion utilizing positive words. The world is full of social media and many kinds of the web-based portal and this media stores a huge amount of textual data which contains sentiments, and sarcasm is one of the sentiments which is being used nowadays in many of this platform, using sarcasm someone can communicate their negative words in a positive way which is we can call a sarcastic way of communication. In opinion mining, the field of natural language processing detection of sarcasm from a given data is an important task. It is a binary classification task for which model proposed a system which classifies whether a given set of word is sarcastic or not-sarcastic. In this research work, we proposed the work based on the Stacked Bi-Directional Long Short-Term Memory (Stk-BLSTM) network which enhances the overall result in terms of performance matrix. © 2021 IEEE.

关键词： Sentiment analysis

来源：评论

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Fake News Detection using Hybrid of Deep Neural Network and Stacked LSTM 3

Fake News Detection using Hybrid of Deep Neural Network and ...

引用

3rd international conference on Advances in Computing, Communication control and Networking, ICAC3N 2021

作者： Narayan, Udit Kumar, Ajad Kumar, Kuldeep Dr. B R Ambedkar National Institute of Technology Jalandhar Department of Computer Science and Engineering Punjab Jalandhar India

ISBN: (纸本)9781665438117

Fake News is one of the major concerns for the world and it could cause many problems like misleading people, the potential to influence people's opinions, etc. Hence distinguishing fake news is very crucial. Fake news can be detected using artificial intelligence methodologies using a title, body of the news, and other attributes such as credibility of the authors, location, media such as images, audio, and video, etc. In this paper, a hybrid of deep neural networks and stacked LSTM (Long Short-Term Memory) is used to detect fake news. We used Glove 300d as a word embedding layer and implemented the concept of stacked LSTM. The model has been applied to two different datasets. The results are compared with previous models and have found with good performance in comparison to the selected previously existing models. © 2021 IEEE.

关键词： Deep neural networks

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CNN Implementation for IDS 3

CNN Implementation for IDS

引用

3rd international conference on Advances in Computing, Communication control and Networking, ICAC3N 2021

作者： Varanasi, Venkata Ramani Razia, Shaik Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram India

ISBN: (纸本)9781665438117

Real world data is more complex in nature and requires proper handling to use with machine learning techniques. Handling large volumes of data is the key aspect in deep learning (DL). As new attacks are evolving, traditional Intrusion detection systems (IDS) are unable to handle those complex attacks. With successful applications of convolutional neural networks (CNN) in other fields like object detection, face recognition, healthcare, this paper proposes CNN based IDS using CIC-IDS-2017 dataset. The performance of the model is compared with that of a deep neural network (DNN). The numerical features are converted into gray scale images and fed to the model. The model is repeated with different values of hyper parameters and results are presented. CNN model can be considered as useful for transfer learning as it reduces training time and gives better accuracy by considering the correlation between the features. The authors implemented CNN model with the objective of applying it to homogeneous and heterogeneous transfer learning in the future. CNN model attained an accuracy of 99% as compared to DNN with 98%. © 2021 IEEE.

关键词： Deep neural networks

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Comparative Study between Leading Transfer Learning Architectures for Source Camera Identification 2

Comparative Study between Leading Transfer Learning Architec...

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2nd international conference on Power, control and Computing Technologies, ICPC2T 2022

作者： Chakravarty, Shreya Fating, Shardul Jain, Ishita Varun, Ishika Khandelwal, Richa R. Shri Ramdeobaba College of Engineering and Management Department of Electronics Engineering Nagpur India

ISBN: (纸本)9781665458580

The all-embracing use of digital images has revamped the quality of life and security to a great extent. Right from finding an item on online shopping websites through a clicked picture, to CCTV cameras being used for road traffic control, the users have learnt to appreciate the existence of technology being as advanced. However, one cannot overlook the gravity of this technology being misused. Although, the digitization has incorporated advanced concepts like computer vision and Deep Learning for security-check and crowd control, this has encouraged the advancement of courtroom discussions. Framing people for wrongdoings they are not involved with, on the basis of a fake 'digital proof,' is one of the newly faced muddles. False allegations on a person, on the basis of a picture or a video, can potentially put a question on the existence of a person. The need to find the legitimacy of a produced image is therefore, of utmost importance. There have been various studies over the years, wherein a lot of methods were proposed to develop a system that identifies the camera model. Through this paper, we aim to produce a comparative study between four leading architectures, DenseNet, Inception V3, MobileNetV2 and Exception(XCeption), and suggest a the most competent architecture for commercialization of this system. © 2022 IEEE.

关键词： Deep learning Digital images Transfer learning Power control computer architecture Cameras Security

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Blockchain-Internet of things-Machine Learning: Development of Traceable System for Multi Purposes

Blockchain-Internet of things-Machine Learning: Development ...

引用

Advance Computing and Innovative Technologies in Engineering (ICACITE), international conference on

作者： K. Kowsalya R. Paritala Jhansi Rani Ritu Malika Bhiyana Manisha Saini Pravin P Patil Computer Science and Applications SRM Institute of Science and Technology India Computer Science and Engineering Department Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Computer Science Kurukshetra University Computer Science Govt PG College Ambala Cantt Kurukshetra University Kurukshetra Computer Science Govt PG College Ambala Cantt Kurukshetra University Department of Mechanical Engineering Graphic Era Deemed To Be University Dehradun Uttarakhand India

The Iot devices (IoT) and cryptocurrencies may be combined to create a platform that promotes improved and sustainable and credibility. The resulting innovations have been used in a number of industries, most notably in the monitoring of farm products. Particularly Ai devices (such as RFID, GIS, Geolocation, and others) have the capacity to streamline the collection of data pertaining to crucial traceable features. To handle, store, and search for information, data is acquired and put into the public ledger. The data security that enters the system may be ensured via a dispersed, randomized, and noninflatable cryptocurrency. Nonetheless, IoT devices could produce aberrant data as they gather *** study evaluates the entire beverage distribution network from sowing to sales, develops the network topology and each operate effectively, and patterns and enforces a computer vision (Fluid ounces) proof - of - stake tea reputable monitoring system, which considers literally the entire record keeping chain of food goods (MBITTS). This article presents a unique approach based on Internet of Things (IoT) technologies, such as RFID sensors, for enhancing the precision of cryptocurrency source data.

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Trust based Mechanism for Detection and Isolation of DDOS Attack in Software Defined Networks 3

Trust based Mechanism for Detection and Isolation of DDOS At...

引用

3rd international conference on Advances in Computing, Communication control and Networking, ICAC3N 2021

作者： Saini, Chiman Kang, Sandeep Singh Chandigarh University Department of Computer Science and Engineering Mohali India

ISBN: (纸本)9781665438117

SDN networks are different from the classic networks in terms of features. Different from SDN, in classic networks, networking tool stake decision regarding the handling of an arriving packet just on the address of its IP destination. The attacking nodes after making entry in the network may activate a DDoS attack. This research work is an attempt of detecting these compromised nodes by implementing a trust-based approach. The trust-assisted approach involves computing the trust level of the allnetwork nodes. The node with minimal trust is labelled as the malevolent node. The trust is calculated on the basis of the number of packets as transmitted node during the time period. The node which has transmitted maximum packets and consumes least resources will be marked as malicious node from the network. The proposed model is implemented in NS2 and the results are analysed in terms of certain parameters such as throughput, delay and overhead. To compute trust, the number of packets forwarded by the nodes in a certain period are considered. © 2021 IEEE.

关键词： Software defined networking

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Integrating Smart Contracts in IoT Networks Powered by Blockchain Technology

Integrating Smart Contracts in IoT Networks Powered by Block...

引用

Innovation in Technology (INOCON), IEEE international conference for

作者： Pompi Das Sengupta Aakriti Chauhan Raja Praveen K N Department of Management ARKA JAIN University Jamshedpur Jharkhand India Department of Agri-Business Management Vivekananda Global University Jaipur India Department of Computer Science and Engineering School of Engineering and Technology JAIN Bangalore Karnataka

integrating smart contracts into the Internet of Factors (Iota) networks powered by using block chain technology presents a unique set of challenges and possibilities. Smart contracts offer an automatic technique to facilitate transaction processing and facilitate cozy and reliable statistics sharing across the dispensed Iota community. At the same time, understanding the safety implications of this technology, techniques for addressing such implications, and sensible answers for integration remains an ongoing region of research. Block chain technology is revolutionizing the structure of the Iota. By utilizing clever contracts, block chain can enable trustless, relaxed verbal exchange channels and automatic validation of transactions. It presents a unique method for safely storing, authorizing, and sharing information throughout numerous distributed nodes. Smart contracts also allow the cost-powerful and cozy execution of multiple clever contracts at the identical Iota network. The security of any Iota gadget is substantially impacted by using the integration of clever contracts. This integration calls for the addressing of troubles related to scaling, failure control, and machine integrity. It additionally calls for the robust encryption of records and the authentication of requests through virtual signatures.

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Observer-Based Adaptive Security control for Network control Systems Under TDS Actuator Attacks 3rd

Observer-Based Adaptive Security Control for Network Contro...

引用

3rd international conference on Neural Computing for Advanced Applications, NCAA 2022

作者： Wang, Liang Zhao, Ping School of Information Science and Engineering Shandong Normal University Jinan250358 China

ISBN: (纸本)9789811961410

Networked control systems (NCS) have been used widely in many practical fields. The communication connection in NCS allows agents to communicate with each other quickly, which can quickly respond to abrupt changes in the system. NCS depend on computers and multi-purpose networks for operation, rendering them vulnerable to attacks, especially cyber attacks. This paper focuses on time delay switch (TDS) attacks. A method based on Lyapunov theory is proposed to detect and estimate TDS attack in real time, so as to detect the unstable impact of TDS attack and recover from it. Simulation results illustrate the effectiveness of the proposed TDS attack estimator and security control strategy. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Time delay

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CompDNet-512: Hybrid Deep Learning Architecture for Prediction of COVID-19 3

CompDNet-512: Hybrid Deep Learning Architecture for Predicti...

引用

3rd international conference on Advances in Computing, Communication control and Networking, ICAC3N 2021

作者： Singh, Gurpreet Anand, Darpan Chandigarh University Computer Science and Engineering Mohali India

ISBN: (纸本)9781665438117

With a growing appearance of enormous cases of COVID-19 in people, computational solutions founded on deep learning can play a huge role in performing automated prediction of the corona virus disease. In this article a hybrid deep convolution neural network architecture named as CompDNet-512 is implemented to detect the presence of COVID disease in the chest radiographs of patients. Previously the researchers had only used deep transfer learning technique for train neural network model to detect the syndrome. They have succeeded to some extent, but none were able to developed generalized model which can be predicting various abnormalities present in clinical images. Herein, our model can be applied to range of medical application for abnormalities prediction. The proposed model is a combination of three techniques that is, lossless compression, dense network and progressive resizing, which when applied in a pipeline to the chest X-ray dataset consisting of positive corona virus patient studies and normal studies, outperformed the existing models in detecting COVID-19 and shown an accuracy of 98.3 %. © 2021 IEEE.

关键词： COVID-19

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D2D Underlay Cellular Network optimization Using GA Considering OFDMA-ICI

D2D Underlay Cellular Network optimization Using GA Consider...

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Power Electronics & IoT Applications in Renewable Energy and its control (PARC), 2020 international conference on

作者： Nagy Osman Khawla A. Alnajjar Ramesh C. Bansal Department of Electrical Engineering College of Engineering University of Sharjah Sharjah UAE Department of Electrical and Electronics Engineering Higher Colleges of Technology Sharjah UAE Department of Electric Electronic and Computer Engineering University of Pretoria Pretoria South Africa

Direct transmission between devices is now possible due to the emergence of device-to-device D2D technology. The D2D communication underlay cellular network intends to take advantage of device proximity and hence, boost consumer bitrates, as well as offer the ability to improve system capacity and energy efficiency while decreasing end-to-end latency. However, D2D devices that underlay cellular networks are vulnerable to co-channel interference, which negatively impacting network performance. Another factor that could also affect network performance and reduce the quality of service is inter-carrier interference in the orthogonal division multiple access (OFDMA) system. Most research has concentrated on co-channel interference only. This study aims to optimize network performance in the presence of both co-channel interference and OFDMA inter-carrier interference (ICI)-based multicast D2D underlay cellular networks. In comparison to different optimization techniques, the simulation results show that the proposed genetic algorithm (GA) achieves a cumulative distribution function of 0.5 at a capacity of 70 Mbps, compared to 5.5 and 6.5 for the greedy search algorithm (GSA) and binary power control (BPC), respectively. It shows the capability of successfully maintaining high network performance.

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