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Identifying the Writing Style of Bangla Language Using Natural Language Processing 11

Identifying the Writing Style of Bangla Language Using Natur...

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11th international conference on computing, Communication and networking Technologies, ICCCNT 2020

作者： Shetu, Syeda Farjana Saifuzzaman, Mohd. Parvin, Masuma Moon, Nazmun Nessa Yousuf, Ridwanullah Sultana, Sharmin Daffodil International University Department of Computer Science and Engineering Dhaka Bangladesh Department of General Educational Development Daffodil International University Dhaka Bangladesh

ISBN: (纸本)9781728168517

Bangla is one of the 8th major spoken languages around the world and like other widely spoken languages, it is a very morphologically rich language. It has two styles, one is standard literary style, known as Sadhu Bhasha and the other one is a standard colloquial style which is known as Cholito Bhasha. Mixing both the styles in a written document is considered as a grammatical error in Bangla language known as Guruchondali Dosh. this research aims to develop an algorithm to identify the style of a Bangla paragraph i.e. whether it is in Sadhu Bhasha or Cholito Bhasha from a given Bangla paragraph input. It's a contribution towards finding the Goruchondali Dosh which is a common grammatical mistake in written Bangla language as it was observed that a number of research work for identifying Bangla grammar mistakes is not so notable whereas it is a common trend in other language researchers. © 2020 IEEE.

关键词： Morphology

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Proceedings of IA3 2020: 10th Workshop on Irregular Applications: Architectures and Algorithms, Held in conjunction with SC 2020: the international conference for High Performance computing, networking, Storage and Analysis

Proceedings of IA3 2020: 10th Workshop on Irregular Applicat...

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10th Workshop on Irregular Applications: Architectures and Algorithms, IA3 2020

ISBN: (纸本)9780738110905

the proceedings contain 8 papers. the topics discussed include: accelerating domain propagation: an efficient GPU-parallel algorithm over sparse matrices;parallelizing irregular computations for molecular docking;reducing queuing impact in irregular data streaming applications;supporting irregularity in throughput-oriented computing by SIMT-SIMD integration;DistDGL: distributed graph neural network training for billion-scale graphs;labeled triangle indexing for efficiency gains in distributed interactive subgraph search;distributed memory graph coloring algorithms for multiple GPU;and performance evaluation of the vectorizable binary search algorithms on an FPGA platform.

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A Deep Learning Approach Combining Autoencoder with One-class SVM for DDoS Attack Detection in SDNs 8

A Deep Learning Approach Combining Autoencoder with One-clas...

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8th international conference on Communications and networking, ComNet2020

作者： Mhamdi, Lotfi McLernon, Desmond El-Moussa, Fadi Raza Zaidi, Syed Ali Ghogho, Mounir Tang, Tuan University of Leeds United Kingdom BT Security Research United Kingdom International University of Rabat Morocco

ISBN: (纸本)9781728153209

Software Defined networking (SDN) provides us with the capability of collecting network traffic information and managing networks proactively. therefore, SDN facilitates the promotion of more robust and secure networks. Recently, several Machine Learning (ML)/Deep Learning (DL) intrusion detection approaches have been proposed to secure SDN networks. Currently, most of the proposed ML/DL intrusion detection approaches are based on supervised learning approach that required labelled and well-balanced datasets for training. However, this is time intensive and require significant human expertise to curate these datasets. these approaches cannot deal well with imbalanced and unlabeled datasets. In this paper, we propose a hybrid unsupervised DL approach using the stack autoencoder and One-class Support Vector Machine (SAE-1SVM) for distributed Denial of Service (DDoS) attack detection. the experimental results show that the proposed algorithm can achieve an average accuracy of 99.35 % with a small set of flow features. the SAE-1SVM shows that it can reduce the processing time significantly while maintaining a high detection rate. In summary, the SAE-1SVM can work well with imbalanced and unlabeled datasets and yield a high detection accuracy. © 2020 IEEE.

关键词： Software defined networking

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Fault-Tolerant Covering Points by UAVs 21

Fault-Tolerant Covering Points by UAVs

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8th international conference on networking, Systems and Security, NSysS 2021

作者： Samanta, Supravat Pramanick, Subhajit Mandal, Partha Sarathi Department of Mathematics Indian Institute of Technology Guwahati India

ISBN: (纸本)9781450387378

Unmanned Aerial vehicles (UAVs) or Drones crash due to power failure, communication failure, mechanical malfunction, or Cyber-Physical attacks. this paper aims to study fault-tolerant covering by UAVs, i.e., in case of failure of some UAVs, other existing non-faulty UAVs cover up the job for the faulty UAVs. We consider two communication techniques to replace the faulty UAVs: (a) with local knowledge, i.e., non-faulty neighbors of the faulty UAVs communicate between themselves to find a suitable replacement and (b) with global knowledge, where the rest of the non-faulty UAVs communicate themselves via a satellite to find a suitable replacement. Here the objective of a suitable replacement is to minimize the cost of replacement. Let P be a set of n non-negative weighted points (ground users such as fire fighters or soldiers). Using a given set of m UAVs with equal covering capacities, the objective of the covering problem is to cover a subset of points on the 2D plane such that the total weight of the covered points is maximized. the objective of our fault-tolerant covering problem is to maximize the total weight covered by the UAVs in the presence of failure and replacement of UAVs at any point of time under the two communication techniques mentioned above. In the case of globally connected, we are using a variation of Hungarian algorithm for assignment problem while in locally connected, we are using a variation of Depth-First Search. We implement both the techniques and compare the performance of the two methods through a simulation study. © 2021 ACM.

关键词： UAV Surveillance Fault tolerance distributed Algorithm Coverage

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Selective Combination and Management of distributed Machine Learning Models 8th

Selective Combination and Management of Distributed Machine ...

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8th international conference on Future Data and Security Engineering , FDSE 2021

作者： Tsuchiya, Takeshi Mochizuki, Ryuichi Hirose, Hiroo Yamada, Tetsuyasu Koyanagi, Keiichi Minh, Quang Tran Suwa University of Science Chino Japan Institute for Data Science Education Tokyo International University Kawagoe Japan Waseda University Tokyo Japan Faculty of Computer Science and Engineering Ho Chi Minh City University of Technology 268 Ly Thuong Kiet District 10 Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh City Linh Trung Ward Thu Duc District Ho Chi Minh City Viet Nam

ISBN: (纸本)9783030913861

this study presents a method for selecting and combining feature models constructed by the machine learning on the processing task capability. the evaluation of combining the feature models shows that the processing task capability can be improved by selecting and reaching feature models based on their similarity to the vector of queries without combining all feature models. then, we discuss a method for constructing logical the R-Tree algorithm on the distributed fog nodes. For future work, we will implement the proposed method on various types of data. © 2021, Springer Nature Switzerland AG.

关键词： Fog computing

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Social Synchrony: An Analytical Contemplation of Contemporary State of Art Frameworks

Social Synchrony: An Analytical Contemplation of Contemporar...

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international conference on computing for Sustainable Global Development (INDIACom)

作者： Shabana Nargis Rasool Prem Kumar Singh Ayaz Hassan Moon Amity Inst. of Information Technology Amity University Noida India Islamic University of Science and Technology Awantipora India

An appraisal of recent studies conducted under the broad spectrum of social synchrony to measure the information flow and its analytics. there is indeed requirement for social network analysis in static as well as dynamic environment. Social synchrony arises when large number of users mimic for the specific topic in specific time on the given online social networks. Subsequently, time and geographic incorporates dynamicity in social networks which affect the social synchrony. this paper focuses on social synchrony and its pattern using online social networks. the paper reports a systematic review on requirement of social synchrony, its effects, open challenges and future work for further analysis.

关键词： Deep learning Systematics Art Social networking (online) Computational modeling Market research Feature extraction

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Depression Detection on Social Media with the Aid of Machine Learning Platform: A Comprehensive Survey

Depression Detection on Social Media with the Aid of Machine...

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international conference on computing for Sustainable Global Development (INDIACom)

作者： Gaurav Kumar Gupta Dilip Kumar Sharma GLA University Mathura India

Depression is a group of mental disorders associated with certain factors which can affect the mood, feelings, negativity, losing interest, and sadness in human participants. To maintain the quality of life, people tend to experience fewer mental health issues. Today social media is a major part of our daily life and these social media sites offer an important platform to share their emotions, feelings in day-to-day routine and life events. In recent years, automatic depression detection on social media-related studies has improved. the objective of this paper is to identify the different machine learning algorithm methods, techniques, and approaches used by various studies related to depression detection on social media platforms by conducting a comprehensive review. Various studies of from year 2013 to 2020 are reviewed to explore the research gaps and future directions.

关键词： Support vector machines Machine learning algorithms Social networking (online) Mood Supervised learning Mental health Broadcasting

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Energy Optimization-based Optimal Trade-off Scheme for Job Scheduling in Fog computing

Energy Optimization-based Optimal Trade-off Scheme for Job S...

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international conference on computing for Sustainable Global Development (INDIACom)

作者： Harwant Singh Arri Ramandeep Singh Lovely Professional University Punjab India

Fog computing is designed to expand the cloud-based storage, computing, and networking features that involve the concept of data processing on the end devices of the network and not in the cloud. In this article, an energy optimization based optimal tradeoff scheme for job scheduling in fog computing platforms using Artificial Bee Colony (ABC) as an optimization technique with Artificial Neural Network (ANN) is proposed. this research is designed to solve the challenges faced by researchers in the field of fog computing during job scheduling in terms of energy consumption, job completion time and successfully task allocation to a Virtual Machine (VM). the experimental results demonstrate that the proposed scheme using ABC with ANN achieved a far better result as compare to the other existing work based on the job completion time. Where we find out the task completion time is reduced by 40.67% as compare to the work with a Genetic Algorithm based model. Also, the successful task allocation and energy computing rate are improved by using the concept of ABC-ANN and other Qualities of Service (QoS) parameters are also contrasted in terms of task completion time and successfully task allocation.

关键词： Support vector machines Cloud computing Processor scheduling Artificial neural networks Quality of service Virtual machining Resource management

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A Survey on Video-Based Fake News Detection Techniques

A Survey on Video-Based Fake News Detection Techniques

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international conference on computing for Sustainable Global Development (INDIACom)

作者： Ronak Agrawal Dilip Kumar Sharma GLA University Mathura India

In today's world, fake news identification is a critical problem. Fake news may exist in form of text, images and videos also. there are several techniques exist for fake news detection including forgery detection techniques. this paper discussed the existing forgery techniques used for the fake video detection. In this study, we addressed the existing issues and challenges which make the forgery detection task cumbersome. We have discussed the use of deep neural network, convolutional neural network, biological signal and spatio-temporal neural network for fake video identification. A comparative study of existing techniques, used for forgery detection, is also provided. this exhaustive survey will help the other researchers to combat deep fake problem.

关键词： Social networking (online) Neural networks Streaming media Forgery Biology Floods Convolutional neural networks

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Domestic Violence Crisis Recognition Method based on Bi-LSTM+Attention

Domestic Violence Crisis Recognition Method based on Bi-LSTM...

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international conference on Network and Information Systems for Computers (ICNISC)

作者： Zhixiao Wang Wenyao Yan Zhuochun Li Min Huang Qinyuan Fan Xin Wang Xi'an University of Technology Xi'an China Shaanxi Key Laboratory of Network Computing and Security Xi'an China College of Xi'an Innovation Yan'an University Xi'an China University of Pittsburgh Pittsburgh PA USA Art and Design Xi'an University of Technology Xi'an China Fakultät Verkehrs- und Maschinensysteme Technische Universität Berlin Berlin Germany Computer Science and Engineering Xi'an University of Technology Xi'an China

ISBN: (纸本)9781665453523

Domestic violence (DV) is getting more and more attention due to the critical trouble and danger that pose great challenge to human rights and health. the popularity of various social medias can help these victims to share their experiences and obtain more possible support from numerous open communities. However, it is not only inefficient but also laborious and time-consuming to manually access and browse through a great deal of available posts on the Internet and social media space. therefore, considering the advantages of Deep Learning (DL) technology in Natural Language Processing, adopting DL as an efficient strategy for automatic recognition and evaluation of DV victims is in the urgent requirements. this paper takes social media Facebook posts about domestic violence as the research object and uses Word2Vec to build word vector model. the paper uses Bi-LSTM+self-Attention deep learning models to accomplish DV crisis recognition task and compares it with CNN, RNN and LSTM. the assessment of experimental results show that the accuracy of CNN and LSTM are both above 90% which is better than RNN; And the accuracy of Bi-LSTM is the highest after using the Attention mechanism. the research results of this article will provide reference and help for public DVCS to rescue DV victims.

关键词： Deep learning Social networking (online) Computational modeling Blogs Manuals Predictive models Data models

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