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A Grid-Based DAP Placement Approach for Smart Grids 6th

A Grid-Based DAP Placement Approach for Smart Grids

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6th international conference on Advanced Machine Learning technologies and Applications, AMLTA 2021

作者： Xu, Yuntao Sung, Tien-Wen Chang, Kuo-Chi College of Computer Science and Mathematics Fujian University of Technology Fuzhou China

ISBN: (纸本)9783030697167

Smart meters are the basic equipment for data collection in a smart grid system and responsible for collecting, measuring, and transmitting original electricity data. The smart meter data is first transmitted to the data aggregation point (DAP) through wireless communication and then transmitted to the control center. Each DAP installation incurs costs, but the number and locations of DAPs have an impact on the quality of communication services due to the distance between DAP and smart meters and the related transmission routes. Therefore, it is important to choose the best positions for installing DAPs in a smart meter network. This study investigates the DAP placement problem and proposes a solution to reduce the number of DAPs and the distance between DAPs and smart meters by using a grid-based model. Without loss of generality, simulations for performance evaluation are conducted based on random data. The simulation results show that the proposed solution can reduce the number of DAPs in a smart meter network. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Smart power grids

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A Comparative Statistical Analysis Between ML Algorithms & DNN Techniques Using MNIST dataset

A Comparative Statistical Analysis Between ML Algorithms & D...

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

作者： Akshit Rajan Rastogi Chelsea Verma Divya Sharma Puneet Kumar Goyal Computer Science & Engineering ABES Engineering College Ghaziabad India

Handwritten digit recognition has been around since a very long time and it has always been a topic of attention among the students and researchers. This job of recognizing handwritten content has great importance as it helps in recognizing various type of important information which are filled by hand but are not clearly understood. While solving this problem, the most common challenge is that the digits written by hands are of different size, orientation or thickness. Inspite of all these challenges, various Machine Learning Algorithms & Deep Neural Network Techniques are trying harder to achieve higher accuracy. In this report, we have tried to compare the accuracies of most common and popular ML Algorithms which includes SVM (Support Vector Machine), Random Forest Classifier (RFC) & KNN (K-Nearest Neighbour) with DNN Techniques (Deep Neural Network Technique) also termed as CNN (Convolutional Neural Network) using Keras. We have applied these algorithms and techniques on MNIST dataset. With the help of the results obtained, we have plotted some graphs to show differences between ML (Machine Learning Algorithms) & DNN (Deep Neural Network Techniques). In this research work, we got the accuracy of approx 97.86% using SVM, 97.10% using KNN, 97.05% using RFC while on the other hand, by using CNN, we got 99.98% accuracy on validation image data. Also, we were able to achieve accuracy approx , 96.85% using RFC, 97.73% using SVM, 96.80% using KNN while on the other hand, by using CNN, we got 98.72% accuracy on test image data.

关键词： Support vector machines Deep learning Handwriting recognition Machine learning algorithms Statistical analysis Neural networks Prediction algorithms

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Collaborative Learning in data science Education: A data Expedition as a Formative Assessment Tool 21st

Collaborative Learning in Data Science Education: A Data Exp...

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21st international conference on Interactive Collaborative Learning (ICL) / 47th IGIP international conference on Engineering Pedagogy - Teaching and Learning in a Digital World

作者： Maksimenkova, Olga Neznanov, Alexey Radchenko, Irina Natl Res Univ Higher Sch Econ Moscow Russia ITMO Univ St Petersburg Russia

ISBN: (纸本)9783030119324;9783030119317

The paper addresses the questions of data science education of current importance. It aims to introduce and justify the framework that allows flexibly evaluate the processes of a data expedition and a digital media created during it. For these purposes, the authors explore features of digital media artefacts which are specific to data expeditions and are essential to accurate evaluation. The rubrics as a power but hardly formalizable evaluation method in application to digital media artefacts are also discussed. Moreover, the paper documents the experience of rubrics creation according to the suggested framework. The rubrics were successfully adopted to two data-driven journalism courses. The authors also formulate recommendations on data expedition evaluation which should take into consideration structural features of a data expedition, distinctive features of digital media, etc.

关键词： data expedition data science Collaborative technologies Education

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A Holistic Neural Networks Classification for Wangiri Fraud Detection in Telecommunications Regulatory Authorities 6th

A Holistic Neural Networks Classification for Wangiri Fraud ...

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6th international conference on Advanced Machine Learning technologies and Applications, AMLTA 2021

作者： Mawgoud, Ahmed A. Abu-Talleb, Amr Tawfik, Benbella S. Information Technology Department Cairo University Giza Egypt Computer Science Department German University in Cairo Cairo Egypt Computer Science Department Suez Canal University Ismailia Egypt

ISBN: (纸本)9783030697167

Cybercrimes and fraud techniques are major threats to telecommunications sectors in the last decade, one of those fraud approaches called Wangiri fraud. Wangiri is a common type of fraud techniques in telecommunications sector, the definition is originated from a Japanese word that means (one & cut);as the fraudsters depend on a single ring method to gain illegal money from the subscribers. Consequently, the approaches that are used to detect fraud cases are used to classify subscribers based on their behaviors such as data extraction that identifies patterns in large datasets through a combination of statistical methods, artificial intelligence and databases. Neural networks are used to process & evaluate the given datasets;in order to uncover ambiguous communication and secret data patterns. This paper proposes the usage of AI neural networks to overcome the highly predictive wangiri fraud in a telecom dataset and to make an effective and convenient classification. ISTAT was used to test both accuracy and efficiency of the proposed method. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Classification (of information)

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Deep Anime Recommendation System: Recommending Anime Using Collaborative and Content-based Filtering

Deep Anime Recommendation System: Recommending Anime Using C...

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

作者： Varun Prakash Sagar Raghav Shubham Sood Mrinal Pandey Mamta Arora Dept. of Computer Science & Technology Manav Rachna University Faridabad India

In the vast multitude of products and services sprawling in our world, it is often daunting to make a reasonable choice backed with statistical evidence addressing the various aspects that governs the reliability and durability of the things we opt for. As such Recommendation Systems that facilitates personalization, and always up to date filtering of products or services based on the user historical track are often celebrated as one of the most visible applications of Machine Learning and Deep Learning. It thus, usually does not come in a surprise that recommendation systems have become pivotal aspect of almost all the applications dealing with E-Business, leaving no grounds unexplored. One of such relatively uncharted examples for recommendation system, however, involves recommending anime based on user’s historical choices. The sheer potential of market sizing anime, and the long-tail nature of product or services served by online businesses thus, compelled us to propose a solution for collaborative filtering, and content-based filtering. We thus, proposed a model-based architecture that employs Gated Recurrent Neural Network along with a trivial memory-based recommendation employing Cosine Similarity to make prediction for the top K rated anime by an anonymous set of users. Moreover, since content-based filtering for recommendations tends to be inherently susceptible from loss in performance with sparse data or the data with missing values, we noticed that our Neural Network based architecture turns out far superior to Cosine Similarity based approach. Thus, our proposed research could be used by different authentic or aggregator service alike that provide anime streaming services to implement a reliable and efficient recommendation system into their existing products.

关键词： Deep learning Recurrent neural networks Filtering Collaborative filtering Optimized production technology Computer architecture Predictive models

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An Efficient Techniques For Disease Prediction From Medical data Using data Mining And Machine Learning

An Efficient Techniques For Disease Prediction From Medical ...

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international conference on Contemporary computing and Informatics (IC3I)

作者： Anjani Kumar Bhatnagar Parul Madan Ajay Rana Sandeep Sharma Sameer Sonawane C. Vimala Josphine Research Scholar Quantum University Roorkee Uttarakhand India Department of CSE Graphic Era Deemed to be University Dehradun Uttarakhand India Amity University Greater Noida Uttar Pradesh India Department of EC Lloyd Institute of Engineering and Technology Greater Noida Uttar Pradesh Department of Postgraduate Studies Bharati Vidyapeeth’s Institute of Management Studies & Research Navi-Mumbai Department of Artificial Intelligence and Data Science R. M. K Engineering College Kavaraipettai Tamilnadu India

Individuals are susceptible to numerous ailments in today’s global setting, with people living highly automated lives under tremendous job pressure, both at home and at work. Such disorders have recently been on the rise at an alarming pace. As a result, the healthcare business must assume a prominent position soon, accountable for people’s health, a better society, and a successful country at large. Healthcare prices are rising as the demand for health amenities grows. Healthcare facilities with improved detection, diagnostics, and treatment procedures are desperately needed. With growing digitization and computing techniques in place, a massive amount of data is being generated and used for diagnostic and detection approaches. This data might be used to obtain data for anticipating disease illnesses, commencing preventative measures, and improving treatment procedures long before the diseases progressed. Powerful computing technologies must be used to build the finest intelligence of forecasting and decision-making abilities evaluated by an expert. Modern data mining methods are used to the obtained to analyze such characteristics that may reveal crucial and critical indications regarding the existence and progression of the medical condition.

关键词： Support vector machines Radio frequency Training Machine learning algorithms Computational modeling Predictive models Prediction algorithms

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Evaluating Serverless Architecture for Big data Enterprise Applications 21

Evaluating Serverless Architecture for Big Data Enterprise A...

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2021 IEEE/ACM 8th international conference on Big data computing, Applications and technologies (BDCAT '21)

作者： Aimer Bhat Heeki Park Madhumonti Roy Computer Science University of Illinois Urbana Champaign USA

ISBN: (纸本)9781450391641

Migration of enterprise applications to the cloud has been driven by a myriad of benefits ranging from availability of infinite computing resources to the elimination of upfront CapEx cost. However, many users still face the burden of complex framework knowledge requirement to efficiently deploy applications in the cloud. In this paper, we investigate serverless computing for performing large scale data processing with cloud-native primitives. Serverless computing environment abstracts all infrastructure handling, simplifying developers’ work who aspire to deploy applications on the cloud. With dynamic input load on the system, serverless architecture has promise to provide better resource utilization and lower costs.

关键词： Serverless computing cloud computing Function as a Service Big data processing MapReduce Enterprise applications AWS Lambda load variations

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Deep Learning in Lung and Colon Cancer classifications

Deep Learning in Lung and Colon Cancer classifications

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advances in computing, Communication & Materials (ICACCM), 2020 international conference on

作者： Krishna Mridha MD. Iftekhar Islam Shamin Ashfaq Masrur Ahsan Priyok Dipayan Barua Computer Engineering Marwadi University Rajkot India Department of Electrical and Electronic Engineering American International University Bangladesh Dhaka Bangladesh Department of Computer Science and Engineering Southeast University Software Engineering Tianjin University China Informaation Technology Marwadi Univeristy Rajkot India

ISBN: (纸本)9781665474405

The past few decades have seen revolutionary advancements in the field of Healthcare and Medicine. During this period, the underlying reasons for many fatal diseases have been unveiled, novel diagnosis techniques were invented, and medical solutions were developed. Instead of these breakthroughs, we are still vulnerable and haunted by certain diseases like cancer. Globally, cancer is the second most common cause of mortality which accounts for the death of one in every six affected individuals. Among many other types, the colon and lung are the most prevailing and life-threatening ones. Only these two variants are responsible for 25% of all cancer cases around the world. However, early lung and colon cancer detection can significantly enhance the possibility of survival. Currently, Deep Learning (DL) has facilitated the automated identification of cancer cells which is allowing us to assess more patients in a minimum time and without the help of a medical professional. The study has put forward a DL framework to classify and differentiate five types of colon and lung cancer cells. Digital Image processing (DIP) and Deep Learning (DL) algorithms are used to process and evaluate histopathological images of cancer cells. The proposed framework has obtained a maximum testing accuracy of 98.3% in identifying and classifying cancer cells. By employing the proposed model in practice, medical experts will be able to integrate an accurate and automated system for diagnosing different forms of colon and lung cancers.

关键词： Deep learning Lung Lung cancer Cancer detection data models Colon Classification algorithms

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Analysis of Arabic Songs: Abdel ElHalim as a Case Study 6th

Analysis of Arabic Songs: Abdel ElHalim as a Case Study

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6th international conference on Advanced Machine Learning technologies and Applications, AMLTA 2021

作者： Gomaa, Walid Egypt Japan University of Science and Technology Alexandria21934 Egypt Faculty of Engineering Alexandria University Alexandria11432 Egypt

ISBN: (纸本)9783030697167

Recently there has been a great deal of interest in computationally studying and investigating musicals. This has been fostered by recent advances in computational power, computational paradigms, analytics, and algorithms. Arabic is spoken by more than 400 million speakers and it is one of the most used languages over the Internet. Over the last decade, Arabic, its varieties, and dialects have begun to gain ground in the area of research within the Natural Language Processing (NLP) communities. In this work we initiate the study of Arabic songs and their lyrics. We start by studying the career work of one of the most famous and influential singers in Egypt and the Arab World during the 20th century, namely, Abdel ElHalim Hafez (). We use data analysis tools in order to shed light into the artist’s career. The work of Abdel ElHalim is very diverse including a wide range of genres from romanticism, nationalism, spiritual religion, etc. In order to celebrate this inspiring and diverse body of work, we will explore and analyze the general characteristics of his work including composers and lyricists with whom he had been working. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： data Analytics

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Development of Flexible, Sharing and Leasing a Car Using MERN Stack

Development of Flexible, Sharing and Leasing a Car Using MER...

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Advanced technologies in Intelligent Control, Environment, computing & Communication Engineering (ICATIECE), international conference on

作者： P. Hemalatha M. Dhavavarshini Dhivya. N Department of Artificial Intelligence and Data Science IFET College of Engineering (An Autonomous Institution) Villupuram India Department of Computer Science and Engineering IFET College of Engineering (An Autonomous Institution) Villupuram India

ISBN: (纸本)9781665493970

The proposed venture will include an analysis of the key decisions and challenges encountered along the way, as well as lessons learned and potential directions for future development. The objective of this project is to build a real-world, scalable, online car reservation system that makes use of the whole MERN (MongoDB, Express, React, and ***) stack. Business transactions will be simplified and improved by implementing the suggested MERN stack automobile rental management system. The development system will enable users to efficiently manage transactions, scheduling, and automobile inventory in the car rental sector. The article introduces a peer-to-peer vehicle webservice without the need for a centralized power, allowing in cheaper rates and more open sharing of information in the system. This is the key unique aspect of the paper. In addition to the physical application and associated development environment that created the working MVP, this paper also analyses prospective improvements that, when coupled with the practical application, could serve as the foundation for the project's further phases. In many countries, renting a car is increasingly becoming the most popular form of mobility, particularly among employees and students. Users who are accustomed to sophisticated technology benefit from this convenience. The workflow and types of resources retained in the car rental industry are two areas where many revolutions have switched from traditional to online systems. Clients will be able to make reservations for their cars from everywhere in the world thanks to the Car Rental System. For the purpose of submitting information to this application, users fill out their personal information. A customer who registers on the website will have access to booking a vehicle. The suggested system is an online platform that is entirely integrated. It effectively and successfully automates manual procedures. Customers can fill up the details based on their needs th

关键词： Industries Technological innovation Job shop scheduling Switches Manuals Software Peer-to-peer computing

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