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An improved density peaks clustering based on a new local density

An improved density peaks clustering based on a new local de...

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international conference on Machine Learning, Big data and Business Intelligence (MLBDBI)

作者： Jing Lu School of Computer Science and Engineering Northeastern University Shenyang China

Density Peaks(DP) clustering algorithm is shown to be a novel and effective clustering approach. It has been widely used in a range of applications. DP requires computing density $\rho$ and distance $\delta$ of each point. the clustering result is determined by $\rho$ and $\delta$. the $\delta$ of each point is relative to its $\rho$. the local density $\rho$ is computed by counting the number of points within a cutoff distance $d c$. Finally, the clustering result of DP depends on the value of $d c$, i.e., DP is sensitive to the cut-off distance $d c$. We found that a tiny change in $d c$ always results in different clustering results, which decreases the applicability of DP. this is because simply counting the number of points within the $d c$ range is not able to describe the local density of each point very well. To solve the above problem, in this paper, we proposed DP-NLD, an improved density peaks clustering algorithm based on a new local density measurement method. In DP-NLD, we not only count the number of points within $d c$, but also take the distribution of neighbors into account when computing the density of each point. Compared with the original DP, DP-NLD has better robustness. Our experimental results show that DP-NLD has better clustering results than DP and other clustering algorithms.

关键词： Machine learning algorithms Density measurement Clustering algorithms Machine learning Big data Robustness Business intelligence

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Intuitionistic fuzzy multi-attribute decision-making method based on improved score function

Intuitionistic fuzzy multi-attribute decision-making method ...

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science and Technology Innovation (IAECST), international Academic Exchange conference on

作者： Nan Zhou Fuchuan Li Institute 706 Second Academy of China Aerospace Science and Industry Corporation Beijing China

Aiming at the problem that attribute weight information in the classic TOPSIS algorithm is given by experts and is too subjective due to personal preference, a method for determining attribute weight based on improved score function is proposed, which allows experts to give the importance information of attributes through intuitionistic fuzzy numbers rather than accurate values, and increases the flexibility of decision assignment. the related properties of the improved score function are discussed and compared with other score functions to verify the rationality of the score function. Combined with intuitionistic fuzzy mixed average operator, the evaluation information of each expert is integrated from the perspective of group experts, and the attribute weight is obtained by using the normalization principle to process the score value. An improved TOPSIS method for multi-attribute decision-making is proposed by using the score function to define the attribute importance, and then modifying the closeness function between the evaluation objective and the optimal objective. It is applied to the decision-making of the evaluation scheme of the software reliability growth model. the decision-making results show that the method is feasible and effective, which provides a new solution for determining the attribute weight and making multi-attribute decision-making.

关键词： Technological innovation Cloud computing Computational modeling Decision making Big data Software reliability Artificial intelligence

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

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

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Advancements in computing (ICAC), international conference on

作者： W.M.S.K. Wijesinghe Muditha Tissera 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)9798350398106

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.

关键词： Machine learning algorithms Annotations Computational modeling Stochastic processes Machine learning Approximation algorithms Natural language processing

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A Study of LSTM in Stock Price Prediction

A Study of LSTM in Stock Price Prediction

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Advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： Ishan Adhikari Bairagi A. Daniel Anchal Sharma School of Computer Science and Engineering Galgotias University Greater Noida India

Investing into stocks has become a trend in the new generation and everyone shows keen interest in putting their money and trying their luck into the stock game. But it requires deep analysis and thorough knowledge to get you through the risk and failures. there are multiple platforms which let you invest money into stocks nationally and internationally and with the boom in cryptocurrency, it has become evident that investments are an efficient way to earn and give us an upper hand over others as far as income parameters are concerned. this project aims to predict the price of the selected stock and give useful and near reliable insights so that investor can have an idea of how the stock may perform in the future. this can really help assess the performance of a stock and make calculated decisions. We intend to predict the stock using the LSTM neural network and then at a later stage provide the user with a dashboard using plotly dash for stock analysis. However stock prices are subject to market conditions and may change abruptly depending on multiple factors. thus the project aims to give supposed prices to the investor on an approximate basis. the future of this project can be made in a mobile based app which plugged in with previous performance of stock data will help users take advantage of the prediction at their hands.

关键词： Neural networks Games Market research Cryptocurrency Reliability Investment

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An Integrated Approach for Suicidal Tendency Detection

An Integrated Approach for Suicidal Tendency Detection

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Advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： V. Baby Dama Sudheshna Desu Sarvani Sahithi Vesangi Saketh Reddy Regatte Department of Computer Science and Engineering VNR Vignana Jyothi Institute of Engineering and Technology Hyderabad India

Suicidal Tendency or the intention to kill oneself and end their life is a catastrophic situation which is mostly unknown by any person in the victim’s life. In many studies it is evident that, victims tend to kill themselves either to end their pain, pressure or to have a relief that they are not going to live in this world anymore. Current suicidal tendency detection methods include numerous machine learning and deep learning approaches using clinical data or online social media, provide a solution to this existing problem. this paper aims to present a multi-modal approach that could help the victim’s close ones to know if he/she is suffering from suicidal tendency. Providing a multi-faceted method that can detect this tendency and intimate the family, friends or the close ones beforehand can prove to be a boon for the invention. three different approaches like Facial Gesture Recognition, Voice Pattern Recognition and Text Pattern Recognition are imbibed in the Android Mobile Application and used as modules to determine suicidal intent in a complementary manner. Finally, the limitations and future scope are extrapolated to provide an overview for the future research.

关键词： Deep learning Technological innovation Social networking (online) Pain Process control Gesture recognition Computer architecture

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Identification of Fake News Using Machine Learning Approach

Identification of Fake News Using Machine Learning Approach

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Advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： Gurupraksh Singh Brijendra Kumar Yadav Bhuvnesh Pratap Singh Shelja Sharama Galgotias College of Engineering And Technology Greater Noida UP India Department of Computer Science and Engineering Galgotias College of Engineering and Technology Greater Noida UP India

In today’s world everyone is using internet and Social media platforms like Instagram, Twitter, Facebook etc. In such scenario fake news spreads very rapidly and reaches to millions of user in a short span of time. Riots during election, riots between different religious groups are consequences of these fake news. Many political parties utilize false information to boost their vote totals. Machine learning is important for classification of data, but it has limitations. On the basis of the kaggle dataset, a model has been suggested to classify fake and authentic news in this project. Our technology will be programmed to discern between fake and legitimate news from various social media platforms. It also aims to distinguish genuine news from a variety of sources. Our research looks into several textual qualities that can be used to tell the difference between phoney and real content. We use machine learning algorithms, such as the Passive Aggressive classifier using TF-IDF, to train our model utilizing those properties.

关键词： Machine learning algorithms Social networking (online) Voting Multimedia Web sites Blogs Machine learning Partitioning algorithms

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Application of machine learning algorithm for prediction of heart disease

Application of machine learning algorithm for prediction of ...

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Advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： Ankit Kumar Upadhyay Sharzeel Saleem Adarsh Anubhav Shrddha Sagar Department of Computer Science and Engineering Galgotias University Greater Noida India

Healthcare is the one the domain in which every organization is working on it globally. It need to make it better and better via different researches and the innovations in this sector. Machine learning is the most used algorithm in every sector and organization. It not merely transforming the field of healthcare but also able to emulate the various human intelligence from the surrounding in order to solve various problems. Machine learning in health care sector has shown its various application in it. Disease prediction is one of the applications of it. this study helps to predict heart diseases with the help of its algorithm to determine the risk and treat the patient in the healthcare system. In this paper we have taken out the data from the kaggle and implemented it using the Jupiter notebook tool. the three algorithm we have implemented (naïve bayes, decision tree and random forest) out of which random forest is giving. Hence, with the help of the following algorithms it is possible for us to predict the heart disease with more accuracy. this research will able to predict various disease over its various symptoms more easily and accurately.

关键词： Heart Technological innovation Machine learning algorithms Jupiter Human intelligence Medical services Organizations

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Tweet Summarization Using Clustering Mechanisms

Tweet Summarization Using Clustering Mechanisms

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Advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： Karthikeyan Ampili Srinivas Kanakala Department of Computer Science and Engineering VNR VJIET Hyderabad India

Due to the growth of online sites like Twitter, this’s resulted in a rise in the quantity of user-generated content. It is difficult for users to absorb so enormous amounts of data. the outcome is creating automated technologies to summarise massive problematic. Twitter is a well-known platform for microblogging. Users post *** form of twitter posts. It serves as a framework for compiling tweet summaries in this article. Occasionally, in times of catastrophes or environmental hazards. Users tweet frequently. Just a few twitter posts will be beneficial. Displaced people find these twitter posts useful. the details can be gleaned out of those twitter posts. Emergency team can execute rescue efforts with an information gleaned from such twitter posts. the categorization of tweets serves as the preliminary step of the suggested strategy dividing twitter posts into relevant/irrelevant categories accompanied by a clustering mechanism. BERT paradigm, also referred to as the "transformers classifier," is utilized for categorization. this system considered clustering mechanism is DB SCAN. An innovative technique relying on neural networks is used to separate and simplify relevant Twitter posts. An unguided approach for categorizing Twitter posts as relevant/irrelevant will be used to build this model.

关键词： Social networking (online) Computational modeling Blogs User-generated content Neural networks Bit error rate Transformers

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An Ingenious Method to Detect COVID in X-Ray Images Using Machine Learning Techniques

An Ingenious Method to Detect COVID in X-Ray Images Using Ma...

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Advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： Palak Kumari Neha Rani N. Suresh Kumar School of Computing Science and Engineering Galgotias University Greater Noida India

Covid-19 is a fatal disease caused by the Covid-19 virus. It is very big problem for the whole world. the World Health Organization (WHO) has declared a pandemic. In May 2020, more people throughout the world had a favorable experience. the COVID illness is rapidly growing, and we are unable to stop it. We addressed the COVID-19 data science research initiatives employing a number of approaches, including statics, machine learning (ML), modelling, simulation, data visualization, and artificial intelligence (AI). We all suffering from COVID-19. in this case higher value of case comes from negative and lower false positive rate. the global impact of the COVID-19 outbreak was enormous. To tackle the pandemic, many projects have been launched, including those in the field of deep learning. this paper proposes a deep neural network modification based on the Xception model. the model is used to detect COVID-19 using chest X-ray images. Batch normalization and two stacks of two dense layers each are used in the proposed model. the layer addition is intended to avoid overfitting the proposed model. the proposed as a result, we compare the model’s loss, accuracy, and performance speed, and the results show that the quality of the machine learning model has higher prediction accuracy and loss, but it takes longer to execute than traditional machine learning languages. Machine learning algorithms in general, and convolutional neural networks (CNNs) in particular, have shown promise in medical picture analysis and categorization. the architecture of this study has been presented for the diagnosis of COVID-19.

关键词： COVID-19 Deep learning Machine learning algorithms Pandemics Computational modeling Predictive models data models

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A Systematic Study on methods of Spatiotemporal Hotspot Detection and Evaluation metrics

A Systematic Study on methods of Spatiotemporal Hotspot Dete...

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Advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： Rishabh Jain Aruna Bhat Department of Computer Science Delhi Technological University New Delhi India

A Spatial hotspot is a confined region/space inside a geographical area which has a higher concentration of activity points than outside the hotspot region within the study area. With technological breakthroughs in spatial data collection and increased computation capabilities, several research publications presented novel methods for Spatial Hotspot detection. the relationship between Spatial Hotspots and time (temporal effect) has also been investigated by various researchers, expanding the horizon of the spatial hotspot to spatiotemporal hotspot. the idea of Spatiotemporal hotspot detection gained considerable interest among researchers because of its practical utility in public health, public safety, traffic volume analysis, crime zone analysis, and other essential applications. However, it is also vital to analyze the research literature. Our research aims to organize the existing comprehensive literature into a well-organized hierarchical framework to comprehend better the various methodologies widely adopted around the world. We propose a framework that suitably categorizes the research effort of various scholars into several appropriate categories based on their differences in primary algorithmic approaches. Furthermore, we also present an extensive analysis of the widely utilized evaluation measures adopted in this research domain.

关键词： Systematics Gain measurement Extraterrestrial measurements Time measurement Spatial databases Spatiotemporal phenomena Safety

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