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QML Based Community Detection in the realm of Social Network Analysis

QML Based Community Detection in the realm of Social Network...

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international conference on computing and networking Technology (ICCNT)

作者： Sana Akbar Sri Khetwat Saritha MANIT Bhopal

ISBN: (数字)9781728168517

ISBN: (纸本)9781728168524

Social network analysis (SNA) has been the topic of interest for researchers and analysts for quite a while as social networks have become the indispensable part of the semantic web. SNA has now forked itself into community detection to cater to the needs of business analytics, health monitoring, military, academia, recommendation systems, etc.; thus utilizing and processing huge amount of data. In this regard, quantum computing (QC) has has come in time to augment the classical systems with quantum characteristics ensuring unprecedented data storage and manipulation capabilities. It thus becomes necessary to facilitate effective data utility in SNA by incorporating emerging technologies like QML (Quantum machine learning) in the domain of community detection. the paper aims to present a brief insight into social network analysis and community detection highlighting the limitations prevalent in the current technologies. A systematic analysis of evolution of QML from QC has been presented whilst reviewing and discussing the relevance of QML based community detection algorithms in resolving the issues associated with the current approaches. Lastly, QML has been proposed as a novel approach to harness the power of ML (Machine learning) in quantum empowered systems by illustrating its supremacy to classical ML based community detection algorithms.

关键词： Quantum computing Social networking (online) Machine learning algorithms Machine learning Qubit Heuristic algorithms Clustering algorithms

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Analysis Of Bangladeshi People's Emotion During Covid-19 In Social Media Using Deep Learning

Analysis Of Bangladeshi People's Emotion During Covid-19 In ...

引用

international conference on computing and networking Technology (ICCNT)

作者： Md. Sabbir Alam Pran Md. Rafiuzzaman Bhuiyan Syed Akhter Hossain Sheikh Abujar Daffodil International University Dhaka Bangladesh

ISBN: (数字)9781728168517

ISBN: (纸本)9781728168524

World is passing through a very uncertain circumstance as Coronavirus becoming a great threat. Staying in home is the best solution now to be safe. People are now passing their most of the time in social platform. they're reacting in public posts, news, articles and also commenting there. And a persons comment can talk about his sentiment. Emotion exploration is a very famous topic in the field of data mining. Lots of work have been done yet. In this piece of research, Bangladeshi people's comments on several Facebook news post related to coronavirus have been analyzed to observe the sentiment of them toward this situation. Using three classes investigation have been done on their emotions. which are Analytical, Depressed, Angry. the data set was developed in Bangla language. Several deep learning algorithms have been applied and found the maximum accuracy in CNN 97.24% and in LSTM 95.33%. Result shows that most people commented analytically. the outcome draw up the public psychology of Bangladesh toward the pandemic.

关键词： Social networking (online) COVID-19 Training Pandemics Deep learning Data mining Government

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A Machine Learning Approach for Predicting the Sunspot of Solar Cycle

A Machine Learning Approach for Predicting the Sunspot of So...

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international conference on computing and networking Technology (ICCNT)

作者： thaharim Khan Faisal Arafat Mayen Uddin Mojumdar Aditya Rajbongshi Shah Md Tanvir Siddiquee Narayan Ranjan Chakraborty Daffodil International University

ISBN: (数字)9781728168517

ISBN: (纸本)9781728168524

Sunspots are the fascinating things on the periphery which is the reason it would be all the more captivating if sunspots become predictable. Sunspot number (SSN) is used in this regard to predicting the Solar Cycle (SC) 25 using the data set containing data from the year of 1818. this is work mainly a representation of Artificial Neural Network (ANN) for predicting the Solar Cycle (SC). For time series related data set as well as continuous data set the main issue is gap length of the data set. Long Short Term Memory (LSTM) network can handle this type of continuous dataset also capable of learning long term dependencies as well. this work mainly detaches various sunspot numbers (SSN) for measuring the Solar Cycle (SC) 25. Like other sunspot numbers (SSN) prediction method this work is not splitting the data set into many parts for analyzing. this result is propulsion for disclosing various differences as well as influence. this model is one of the most effective time series model for measuring the Solar Cycle (SC) 25 compared with other predicted models of time series.

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Multi-attribute Decision Making Approach for Waste Bin Site Selection Problem: A case study in Dhaka City

Multi-attribute Decision Making Approach for Waste Bin Site ...

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international conference on computing and networking Technology (ICCNT)

作者： Nusrat Zahan Zahid Hasan Daffodil International University Dhaka Bangladesh

ISBN: (数字)9781728168517

ISBN: (纸本)9781728168524

Site selection for the waste bin in a metropolitan city is one of the essential and critical decision problems taken by policymakers. An inappropriate site selection imposes excessive effects on the environment and health issues of city inhabitants. Nowadays, a multi-attribute decision-making approach is recommended to apply to progress exactness of decision making and lessen additional adverse. this work proposes a decision support framework for location selection of waste bin using the analytic hierarchy process (AHP) model. the major objective of this work is to determine the appropriate location for waste bin using this process in Dhaka city of Bangladesh. In this study, we have considered 7 attributes among 3 different locations of Dhaka city as alternatives. the alternatives relative importance has been calculated by using AHP. the ultimate decision is prepared based on the arithmetical scores snatched by the alternatives. A numerical study is presented to demonstrate the possible alternative of the proposed methodology.

关键词： Urban areas Analytic hierarchy process Organizations Statistics Sociology Production Monte Carlo methods

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Real-Time Vehicle Classification Using CNN

Real-Time Vehicle Classification Using CNN

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international conference on computing and networking Technology (ICCNT)

作者： Nusrat Jahan Saiful Islam Md. Ferdouse Ahmed Foysal CSE Daffodil International University Dhaka Bangladesh

ISBN: (数字)9781728168517

ISBN: (纸本)9781728168524

Convolutional Neural Network (CNN) is a model of artificial neural networks that has grown to be most well known in computer vision assignment. In this paper work, we presented convolutional neural network for classifying four types of common vehicle in our country. Vehicle classification plays a vital role of various application such as surveillance security system, traffic control system. We addressed these issues and fixed an aim to find a solution to reduce road accident due to traffic related cases. the greatest challenge of computer vision is to achieve effective results to implement a system due to variation in shapes and colors of data. To classify the vehicle we used two methods feature extraction and classification. these two methods can straightforwardly performed by convolutional neural network. the method shows quite good performance on real-time standard dataset. Our mentioned method able to reach 97% accuracy in case of vehicle classification.

关键词： Road accidents Feature extraction Convolutional neural networks Classification algorithms Real-time systems Neurons Deep learning

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A Deep Learning Approach for Recognizing Bengali Character Sign Langauage

A Deep Learning Approach for Recognizing Bengali Character S...

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international conference on computing and networking Technology (ICCNT)

作者： Devjoyti Aich Abdulla Al Zubair K. M. Zubair Hasan Antora Deb Nath Zahid Hasan Daffodil International University Dhaka Bangladesh

ISBN: (数字)9781728168517

ISBN: (纸本)9781728168524

For many years, researchers are trying to recognize Bengali sign language for helping deaf-mute people which is very challenging task. Every research has its own margins and is still incapable to be used commercially. For that reason, device interpreter is obligate to accommodate that deaf and hard-of-hearing community to communicate with normal people. In this paper, the main aim to construct a model to recognize Bengali Character Sign Language using deep leaning approach. For that reason, we use Convolutional Neural Network (CNN) to train individual signs. For those individual signs, a data set is constructed named Bengali Ishara-Lipi to achieve our goal. this model is trained by 5760 preprocessed images and tested by 1440 images. the quantitative relation of the trained and tested images was 80% and 20% severely. Finally, our model gained 92.7% accuracy to recognize Bangla alphabetical sign language. this model will avail for commencing to make Bengali sign language device interpreter.

关键词： Assistive technology Gesture recognition Convolution Character recognition Optical filters Kernel Training

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Neural Network Based Bengali News Headline Multi Classification System: Selection of Features describes Comparative Performance

Neural Network Based Bengali News Headline Multi Classificat...

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international conference on computing and networking Technology (ICCNT)

作者： Sharun Akter Khushbu Abu Kaisar Mohammad Masum Sheikh Abujar Syed Akhter Hossain Daffodil International University Dhaka Bangladesh

ISBN: (数字)9781728168517

ISBN: (纸本)9781728168524

the modern era is gradually developing in all sectors. Moreover, development is necessary to research. Accordingly natural language processing is the expanding area of process text. At present year, memorizing the data is very tough due to the rapidly growing volume of data. Newspapers are a great habit to the whole world of all ages. To acquire a variety of knowledge from different segments is entertaining by themselves. According to this work using bengali news headline may be more specific to others by defining its news type. therefore the machine can smartly review the sequence of sentences within reach output to find newstype. With experiment, we connect our approach by Neural Network of adoption with 90% accuracy performance. Coming with a momentous outcome we've done Multi Classification reached at SVM, NB, Logistic Regression, Neural Network, Random forest applying bengali dataset.

关键词： Neural networks Support vector machines Text categorization Data models Numerical models Training Logistics

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Ascertaining the Fluctuation of Rice Price in Bangladesh Using Machine Learning Approach

Ascertaining the Fluctuation of Rice Price in Bangladesh Usi...

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international conference on computing and networking Technology (ICCNT)

作者： Md. Mehedi Hasan Muslima Tuz Zahara Md. Mahamudunnobi Sykot Arafat Ullah Nur Mohd. Saifuzzaman Rubaiya Hafiz Daffodil International University Dhaka Bangladesh

ISBN: (数字)9781728168517

ISBN: (纸本)9781728168524

Rice is the most grown crop in Bangladesh. It is consumed as the main food course in Bangladesh. the price of rice makes a difference in whether people will eat or starve. To know what's going to happen in the rice market using pen and paper is a far cry as well as time-consuming. Machine Learning (ML) provides the facilities to predict the price of any products to prevent a future collapse in the market. the goal of this paper is to predict the price of rice using Machine learning approach. Data collected from the Ministry of Agriculture website, Bangladesh was used to predict the price. Several machine learning algorithms were used to make this prediction i.e. Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Naïve Bayes, Decision Tree and Random Forest. All these algorithms are analyzed to find out which algorithm provides the best performance. Now, we can predict the price of rice, whether it is reasonable, low, or high based on the results achieved by the mentioned algorithms.

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A Trust computing Model for Future Generation Networks

A Trust Computing Model for Future Generation Networks

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international conference on computing and networking Technology (ICCNT)

作者： Prodipto Das Somen Debnath Assam University Silchar Assam INDIA Mizoram University Aizawl Mizoram INDIA

ISBN: (数字)9781728168517

ISBN: (纸本)9781728168524

Future generation networks are heterogenous in nature due to variety of nodes, terminals, small-sized networks, private networks, virtual networks, IoTs etc. When number of nodes and terminal machines take part in a network, authentication become a major issue. Authentication of node is associated with trust. Trust of a node is a primary behaviour of the node in the network operations. Networks with fewer restrictions in case of user registration may have more trust related issues. A node may exhibit random behaviour at the time of data transmission and forwarding of packets. the major concern is about the assessment of trust value of a node before actual data communication. Trust computing model is the model which gives trust values of nodes in a network. Different trust computing models are designed and developed for different scenarios. In this paper, a model of trust computing is proposed with maximum possible factors. A simulation program in NS3 is written and executed to check the trust values and its effect in the network operations. the results are analysed with and without risk factors to check the accuracy of network performances. It is observed in the experimental work that trust with risk factors is more authenticated solution for the proposed trust model.

关键词： Computational modeling Security Reliability Uncertainty Data models Data communication Cloud computing

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Wireless Virtual Embedding Algorithm Considering Inter-cell Interference in 5G Ultra-dense Network 11

Wireless Virtual Embedding Algorithm Considering Inter-cell ...

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IEEE 11th international conference on Communication Software and Networks (ICCSN)

作者： Zhang, Hui Liu, Yangbo Zhao, Haitao Zhu, Hongbo Sun, Yanfei Soochow Univ NJUPT Prov Key Lab Comp Informat Proc Technol NJUPT Natl Engn Res Ctr Commun & Networking Jiangsu Key Lab Wireless Commun Nanjing Peoples R China NJUPT Minist Educ Engn Res Ctr Hlth Serv Syst Based Ubiquitous Wire Nanjing Peoples R China

ISBN: (纸本)9781728121840

Ultra-dense networking is an effective solution to the problem of 5G hotspot high-capacity network scenarios. However, its densely distributed base stations make the interference in the network much more serious than the traditional cellular network. In this paper, wireless virtual embedding is used to virtualize wireless resource in ultra-dense networks and dynamically allocate resource to reduce inter-cell interference in the network. First, a dynamic model of services in an ultra-dense network is established by stochastic process to predict the number of service arrivals for the next time period. then, a channel allocation matrix is established according to the predicted number. Finally, establish an optimization model for the channel assignment matrix. By solving the 0-1 planning problem step by step, the optimal solution of the model is obtained, and the effectiveness of the algorithm is verified by simulation. Compared with the two spectrum allocation algorithms, the simulation results of this algorithm show superior performance.

关键词： 5G ultra-dense network inter-cell interference wireless virtual embedding

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