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data Analysis in Shopping Mall data using K - Means Clustering

Data Analysis in Shopping Mall data using K - Means Clusteri...

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

作者： Meghna Patel Shital Patel Sonal Patel A.M.Patel Institute of Computer Studies Ganpat University Mehsana India Department of Computer Science Ganpat University Mehsana India

Now a days in modern era, a shopping mall takes up large part of people's lives. For the mall operator, store design is of paramount importance as it increases customer satisfaction and business profit. Finding a sensible layout for a shopping center, however, remains a challenging and long-term challenge. Based on this scrutiny, a machine learning approach is proposed to evaluate preferred arrangement of a shopping mall. With this scrutiny, information about transactions is collected from banks as input. Many shopping malls have basic analysis system for analyzing data of customer, data of each and every transaction including demo-graphic data, gender, age and income. they include additional features to shop-ping malls to maintain loyalty of customer. In this paper, we have a shopping center analyze customer buying behavior. We rank customers based on how often they shop, how much they buy, and what the value of the item purchased is. Applied the K-Means algorithm to group based on the customer's annual income and spending money, the customer's age, and the money spent at the mall, spending money, customer age and spending money in shopping mall.

关键词： Measurement Machine learning algorithms data analysis IEEE merchandise Layout Clustering algorithms Machine learning

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data analysis of Global Perceived Stress Scores During Covid-19

Data analysis of Global Perceived Stress Scores During Covid...

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international conference on Circuits, Communication, Control and computing

作者： Chakita Muttaraju Anusha Kabber S. Parimala Prajwal Anagani Gowri Srinivasa Department of Computer Science and Engineering PES University Bengaluru India Dept. of CSE PES Center for Pattern Recognition PES University Bengaluru India

the Covid-19 Pandemic that broke out in late December 2019 has had a widespread negative effect on the mental health of people around the world. this work aims to elicit features that had a major influence on mental health during the pandemic to better understand preventive measures and remedial actions that can be taken to help individuals in need. Along with factors such as demographic age, gender, marital status, and employment status, additional information such as the effect of media used as a source of information, coping methods, trust in the country's government, and healthcare organizations was analyzed to find their correlation (if any) to the perceived stress of the individual. Machine Learning techniques such as XGBoost, AdaBoost, Decision Trees, Ordinal regression, k-Nearest Neighbors, Lasso and Ridge regression were used to arrive at a relationship between the perceived stress scores and the features considered. On interpreting results from the different models, we conclude that the main factor influencing stress scores was loneliness followed by features indicating trust in government, compliance with Covid-19 preventive measures and concerns regarding the pandemic.

关键词： COVID-19 Video games Pandemics Government Sociology Mental health Medical services

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An Scrupulous Framework to Forecast the Weather using CNN with Back Propagation Method

An Scrupulous Framework to Forecast the Weather using CNN wi...

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

作者： Vaibhav Raj Ravi Kumar N.Suresh Kumar School of Computing Science and Engineering Galgotias University Greater Noida India

Weather estimation has turned into a significant field of research over the most recent couple of many years. In a large portion of the cases, the specialist had analyzed to build up a straight connection between the climate information and the relating objective information. Yet, with the disclosure of non-linearity in the idea of climate information, the center has moved towards the nonlinear forecasting of climate information. Our work mainly focuses on the data structure. Even though, there are numerous written works in nonlinear insights for the climate anticipating, the majority of them required that the nonlinear model be indicated before the assessment is finished. Be that as it may; since the climate information is non-linear and pursues an extremely sporadic direction, Artificial Neural Network (ANN) has advanced out to be a superior method to draw out the underlying connection between the different elements. Here mainly data mining and statistical methods are used to make future weather conditions & other climatic changes. Solid air temperature expectations increment the exactness of energy utilization. Among them, air temperature, relative dampness, precipitation, and wind speed are considered the ordinary commitments for air temperature gauges. While various meteorological elements have been dealt with into different sorts of NN approaches as information sources, the geological wellsprings of data.

关键词： Temperature Precipitation Statistical analysis Wind speed Geology Artificial neural networks Organizations

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RLPassGAN: Password Guessing Model Based on GAN with Policy Gradient 1

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4th EAI international conference on Security and Privacy in New computing Environments, SPNCE 2021

作者： Huang, Deng Wang, Yufei Chen, Wen School of Cyber Science and Engineering Sichuan University Chengdu610065 China

ISBN: (数字)9783030967918

ISBN: (纸本)9783030967901

the unsupervised neural network GAN can automatically generate synthetic samples conform to the distribution of learned samples. therefore, password guessing models based on GAN, e.g. PassGAN are widely studied in recent years. However, there are two problems when dealing with discrete password data using GAN-based models. On the one hand, the non-differentiability of discrete password data may result in the failure of the backward of gradients;on the other hand, the outputs of the intermediate layers of the generator are incomplete password sequences, which cannot be directly evaluated by the discriminator until they reached the output layers, resulting in many redundant synthesized passwords. therefore, a new password guessing method RLPassGAN based on SeqGAN with policy gradient are proposed in this paper. Policy gradient is applied to the proposed model to ensure that the model parameters can be continuously optimized. Furthermore, the incomplete password sequences of the output of the intermediate layers are evaluated by Monte Carlo search. the results show that in terms of the quality of the generated samples, the synthesized samples of RLPassGAN can cover more than 99% of the real passwords in the training set, while PassGAN and RNNPassGAN can only cover less than 30% of the real passwords in the training set;in terms of cracking on the specified site, RLPassGAN outperforms the two models by 16.4%–84.1%;in terms of cross-site cracking, RLPassGAN raised the cracking rate by 30.5%–84.9%. © 2022, ICST Institute for Computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Authentication

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Bug Recognition using Hybrid Fuzzy Logic algorithm and Support Vector Machine classification

Bug Recognition using Hybrid Fuzzy Logic algorithm and Suppo...

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

作者： K. Ananthi M. Balakrishnan M. Pandi N. Senthil Madasamy Artificial Intelligence &Data Science Dr.Mahalingam College of Engineering and Technology Coimbatore Tamil Nadu India Computer Science and Engineering Dr.Mahalingam College of Engineering and Technology Coimbatore Tamil Nadu India

In data mining and pattern recognition, the Support Vector Machine is an extensively used classification technique. the tuning weight methods and the distance of the metrics used in a Support Vector based classification system are important. the problem of determining the best feature weight values and measuring the distance between neighbors, which affects the Support vector machine's classification accuracy, is a key obstacle. For parameter optimization, a hybrid approach is applied in this paper. To quantify the equalities between thetraining data set and the test data set are observed and the similarity measuring approach known as the fuzzy distance metric is used. Additional work, such as creating a unique fuzzy membership function and membership degrees for each dataset, adds time to the design process for these hybrid systems. To improve the classification model's precision, this automated approach will heuristically explore the optimal bounds of fuzzy membership functions for each dataset. Additionally, looking into the optimal boundaries may decrease error rates and improve accuracy in the proposed system. the experimental results will be implemented into a real-world classification challenge sourced from the UCI machine-learning benchmark library.

关键词： Measurement Fuzzy logic Weight measurement Training Computer bugs Support vector machine classification Machine learning

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Early Prediction of Complex Business Processes Using Association Rule Based Mining 4th

Early Prediction of Complex Business Processes Using Associ...

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4th international conference on Recent Trends in Image Processing and Pattern Recognition, RTIP2R 2021

作者： Khan, Naveed Tariq, Zeeshan Ali, Aftab McClean, Sally Taylor, Paul Nauck, Detlef School of Computing Ulster University JordanstownBT37 0QB United Kingdom Applied Research BT Ipswich United Kingdom

ISBN: (纸本)9783031070044

Complex business processes are challenging and hard to analyse. the objective here is to enhance delivery of processes in terms of improving quality of service and customer satisfaction. therefore, an automated process prediction system is desirable to monitor and evaluate complex business processes and forecast process outcome during execution time. the analysis of such processes would help domain experts to make in-time decisions to improve the process. the in-time response greatly effects the quality of service and customer satisfaction. therefore, in this paper, the early process prediction framework using Classification Based on Association rules (CBA) has been proposed to predict outcomes for such incomplete processes. the essential part of the proposed system is to extract association rules from the process data up to a certain point in time (i.e. the cut-off time) at which the prediction needs to be made;in an live process this would usually be the current time. the CBA algorithm generates rules with user specified support and confidence which are then utilised for early process prediction. the experimental results based on real business process data are presented for on-time and delayed processes. the proposed early process prediction system is evaluated using different metrics such as accuracy, precision, recall and the F-measure. Moreover, the proposed system is also compared with our prior published work in terms of accuracy, recall and F-measure. the analysis shows that the performance of proposed system outperforms schemes in the literature. © 2022, Springer Nature Switzerland AG.

关键词： Association rules

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Construction of a Sharing Mode Based on VR Technology in the Context of Big data 2

Construction of a Sharing Mode Based on VR Technology in the...

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2nd international conference on computing and data science, conf-cds 2021

作者： Chen, Fei School of Big Data Chongqing Vocational College of Transportation Chongqing China

With the advancement of science and technology in recent years, the era of big data has gradually matured, and the era of intelligence has gradually penetrated into ordinary life. the improvement of technology around the world not only promotes the improvement of the quality of human life, but also has a direct impact on the operating mode of companies in all walks of life. Intelligent products are gradually replacing simple manual work projects. As the core technology of computers, VR technology has been widely used in various fields of social life. As the most imaginative and promising industry in the 21st century, virtual reality technology will completely change people's lifestyles. Virtual reality technology (English name: Virtual Reality, abbreviated as VR) is a new practical technology developed in the 20th century, including computers, electronic information and simulation. Including all technologies, the basic realization method is the computer VR environment to make people feel immersed in the environment. With the development of social productivity and science and technology, the demand for VR technology in various fields also increases. VR technology has made considerable progress and has become a new field of science and technology, and a proposal to establish a shared model system has been proposed. It aims to improve the state of the technology research and development industry and solve its pain. By combining VR technology with sharing, we will promote the reform of the sharing model, open public scientific and technological knowledge resources to those in need, expand the depth of access to technical knowledge, and increase the difficulty of current Internet knowledge. It aims to solve and improve the quality and satisfaction of people's knowledge needs. © Published under licence by IOP Publishing Ltd.

关键词： Big data

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Fleet management systems in Logistics 4.0 era: a real time distributed and scalable architectural proposal 4

Fleet management systems in Logistics 4.0 era: a real time d...

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4th international conference on Industry 4.0 and Smart Manufacturing, ISM 2022

作者： Dintén, Ricardo García, Sebastián Zorrilla, Marta Istr Research Group Universidad de Cantabria Av. Los Castros s/n Santander39005 Spain

In an era marked by the big data paradigm and ubiquitous computing systems, it is increasingly common to see devices embedded in any type of object with the aim of collecting and sharing owned or read data from its environment. this type of interconnection between devices is known as the Internet of things (IoT). In particular in the logistics sector, vehicles are equipped with control units that are capable of monitoring a large number of parameters to ensure the correct operation of the vehicle. In addition, they are now able to share this data in near real time so that this information can be accessed and analysed at any time. However, due to the large amount of shared data, the frequency of data generation and delivery, and the high potential for growth in the number of devices, traditional technologies and architectures are not able to meet the performance demands of these real time decision making systems. In this article we describe and evaluate the benefits and potential trade-offs of implementing services based on a distributed and scalable architecture, called RAI4.0, in a truck fleet management company, which currently has 20,000 on-board devices and expects to grow to 80,000 devices in the next 2 years. With the change of architecture, the company expects to be able to implement near real-time services to monitor and notify its drivers of driving tips and diagnose possible vehicle failures in advance, among others. © 2022 the Authors. Published by ELSEVIER B.V.

关键词： Big data

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Moringa Oleifera As An Alternative to Conventional Coagulants in Big data and data science Analytics

Moringa Oleifera As An Alternative to Conventional Coagulant...

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

作者： Tanmay Singh Harishchander Anandaram Sunil Shukla Vinod Balmiki Awadhesh Chandramauli Kapil Joshi Department of Civil Engineering Uttaranchal Institute of Technology Uttaranchal University Dehradun Uttarakhand India Amrita Vishwa Vidyapeetham Coimbatore Tamil Nadu India University of Engineering and Technology Roorkee India Department of CSE Uttaranchal Institute of Technology Uttaranchal University Dehradun Uttarakhand India

Ever since the population explosion began postmodern industrialization since 1950 the population has grown more than threefold. With the global population crossing the Benchmark of 8.0 billion in June 2022, while some nations reap the benefits of progress, there are still nations who face the challenges of a pre-industrialized world, even today there is a quarter of world Population that does not have access to safe and clean drinking water. In this paper, conventionally, treatment of water is carried out with the help of Chemical Coagulants (Primarily Alum), several research have linked the usage of Alum with Neurodegenerative Diseases like Alzheimer's. Hence the need arises to develop a suitable alternative, Seeds of Moringa Oleifera also known as miracle tree due to its Phytochemical Properties contain Mo-CBP3-4 a protein that causes adsorption and neutralization of the colloidal charges thereby clarifying the water, For high initial Turbidity waters the optimal Dosage was the same for Moringa Oleifera (50mg/l) and the sludge produced post treatment is not only innocuous but also three to four times less by volume when compared with Alum and specially we use big data for data analysis.

关键词： Proteins Oxygen Sociology Water quality Big data Fault location Rendering (computer graphics)

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data-driven modeling method based on hybrid neural network

Data-driven modeling method based on hybrid neural network

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

作者： Xiaolin Qiu Zhikai Gan Cheng Qiu Wenzhan Zhang Dan Liao Nanchang Institute of Technology Jiangxi

In recent years, with the rapid progress of computer computing power and the easier acquisition and storage of data, data-driven non-parametric models have been applied by more and more scholars in the study of option pricing, which includes neural network models. the emergence of neural network option pricing model undoubtedly promotes the development of option pricing theory, but most of the existing neural network option pricing models are based on the fully connected neural network framework, and only innovate in the selection of input variables and output variables, and few studies innovate in the neural network framework. this paper discusses the photovoltaic module output characteristics of hybrid neural network, and proposes some solutions to its problems, aiming to speed up the progress of photovoltaic module output characteristics of hybrid neural network. Photovoltaic power generation technology has certain advantages in sustainability, energy conversion efficiency, technology maturity and other aspects, and has become one of the very favored clean energy sources. As China takes energy Internet construction as a long-term strategic goal, photovoltaic power generation technology will present great potential in the future energy system.

关键词： Photovoltaic systems Training Technological innovation Computational modeling Neural networks Pricing Predictive models

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