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Research on Attribute-Based Privacy-Preserving computing Technologies 4th

Research on Attribute-Based Privacy-Preserving Computing Tec...

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4th international conference on Big data and Security, ICBDS 2022

作者： Qiu, Shuo Ni, Wenhui Shi, Yanfeng Xu, Wanni School of Software Engineering Jinling Institute of Technology Nanjing211169 China School of Computer Engineering Nanjing Institute of Technology Nanjing211167 China School of Internet of Things Engineering Hohai University Changzhou213022 China

ISBN: (纸本)9789819932993

Privacy-preserving computing is an important topic in epidemic prevention, credit report and education resource sharing. data analysis is the basis for realizing the overall speed increase of returning to work and production. this paper comprehensively summarizes the current academic research on privacy-preserving computing. In this work, we systematically summarize the four aspects of the related schemes on the attribute-based encryption schemes: keyword search, symmetric search, PSI, homomorphic encryption. We classify the schemes and compare the same-type schemes accordingly. Finally, we present the directions and challenges for our future work. this paper is intended to provide convenience for the subsequent research on privacy-preserving computing. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Privacy-preserving techniques

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Smart Device and Tracer to Overcome COVID-19 Using Digital Technology for Better Protection

Smart Device and Tracer to Overcome COVID-19 Using Digital T...

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Advancements in computing (ICAC), international conference on

作者： Kulana Avinash Chethika Dithmal Pathum Wijerathne Nipuna Kaushan Hansi De Silva Dharshana Kasthurirathna Department of Information Technology Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer Science and Software Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350398106

A number of nations have experienced challenging circumstances as a result of the coronavirus disease (COVID-19), which has turned into a global pandemic. As a result of the social changes it has caused, this crisis will also have an impact on future generations. With the help of this technology, health organizations can quickly locate individuals who are infected with COVID-19 and provide them with medical care. the objective of this work is to develop a COVID-19 Tracer that is capable of COVID-19 detection and mitigation. the goal of this research is to reduce the number of COVID-19-related fatalities in Sri Lanka while also enabling users who are infected with the disease to access appropriate care and hospitalization. this software uses digital technologies to acquire accurate data and provide precise interpretations based on that data. through the proposed method, patients can be treated using the application to get a precise diagnosis of their disease, maintaining social distance, stabilizing the mental level of the patient through AI, predicting the epidemic, providing COVID-19 vaccinations, as well as ambulance services through this application. Using every preventative measure available, this mobile application has now been developed to safeguard against COVID-19.

关键词： COVID-19 Pandemics Organizations Human factors Software Social factors Vaccines

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Internet of things (IoT) based Health Monitor Device

Internet of Things (IoT) based Health Monitor Device

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international conference on Electrical, Computer & Telecommunication Engineering (ICECTE)

作者： Rezwan Zakaria Afif Hassan Choudhury Md. Razin Mohammad Sajjad Hossain Naymul Hasan Md. Ishfak Tahmid Department of Electrical and Electronic Engineering Shahjalal University of Science and Technology Sylhet Bangladesh

Health is the centre of human enlightenment. Due to the recent Covid outbreak and several environmental pollutions, checking one's vitals regularly has become a necessity. Ours is an IoT-based device that measures a user's heart rate, blood oxygen level and body temperature. the device is compact and portable, making it easy for users to wear. the readings are measured and shown on an OLED display with the help of sensors. the data is also available on the cloud. A webpage and a mobile application were developed to view the data from the cloud. Individual graphs of the vitals with time are available on the mobile application. this can be used for progress measurement and statistical analyses. Authorized personnel can access the patient's vitals. this creates a scope for Tele-medication in rural and underdeveloped regions. Besides, one can also view his/her vitals for personal health routine.

关键词： Temperature measurement Temperature sensors Heart rate Cloud computing Medical services Pollution measurement Mobile applications

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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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Text Classification Using Feature Extraction and Classification Model

Text Classification Using Feature Extraction and Classificat...

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

作者： Anirudh Rana Vishal Bharti Department Of Computer Science and Engineering Chandigarh University Gharuan

Sentiment analysis represents context-based text mining that classifies and abstracts particular information from the source data, and helps to interpret people's feelings. It adopts the NLP approach to classify people's opinions regarding goods or feedback. Sentiment analysis handles people's opinion and behavior in the context of feelings and attitudes concerning an episode or incidence. there are many domains that use opinion mining widely such as commercial goods review, social media study and film evaluation and so on. Semantic analysis provides major support in building recommendation frameworks. User provides textual feedback such as web-based reviews, remarks or responses on social networking and business sites. Machine learning is a common approach used for classification. this project devises a nascent methodology through the integration of dissimilar frameworks. this work applies Random Forest as a feature extraction method for taking out crucial features of the data suite. the K-mean clustering is enforced which can cluster similar type of features and in the last voting classifier is applied. Voting classifier consists of LR, KNN, SVM models. Python is the tool used in this work for the implementation of the devised methodology and evaluates new methodology with respect to some global performance indices.

关键词： Support vector machines Text mining Sentiment analysis Social networking (online) Text categorization Semantics Transforms

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

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

引用

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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