Conference Proceeding - Big Data and Intelligent Computing June 2019
Volume 6 | Issue 2

 Abstract

The term Big Data refers to large-scale information management and analysis technologies that exceed the capability of traditional data processing technologies. The acceleration in the production of information has created a need for new technologies to analyze massive data sets. Data is growing because of use of internet, smart phone and social network. Big data is a collection of data sets which is very large in size as well as complex. Generally size of the data is Petabyte and Exabyte. Big data analytics provide new ways for businesses and government to analyze unstructured data. Now a day, Big data is one of the most talked topic in IT industry. It is going to play important role in future. Big data changes the way that data is managed and used. Some of the applications are in areas such as healthcare, traffic management, banking, retail, education and so on. Organizations are becoming more flexible and more open. The present paper highlights important concepts of Big Data. In this write up we discuss various aspects of big data. We define Big Data and discuss the parameters along which Big Data is defined.

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 Keywords

Exabyte, Petabyte, Veracity, Database, Velocity, Data Analytics, Big data, Traditional approach, Collaborative approach, Structured and Unstructured data.

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 Abstract

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Sentiment Analysis , Opinion Mining,Polarity

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Data Mining in Healthcare has become a present trend for obtaining accurate results of medical diagnosis. Chronic Kidney Disease (CKD) has become an international fitness problem and is a place of concern. It is a situation where kidneys turn out to be damaged and cannot filter toxic wastes within the frame. By using Data Mining Techniques, researchers have the scope to predict the Chronic Kidney Disease. Enormous data mining techniques are existing for predicting diseases namely classification, clustering, association rules, summarizations, regression and etc. The main objective of this research work is to predict kidney diseases using classification algorithms such as Associative classification. This helps doctors to diagnose and suggest the treatment at an early stage. It also helps the patients to know about their health condition at an earlier stage and follow necessary diet and prescriptions.

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 Keywords

Data mining, Associative Classification, WAC

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Development cannot happen in isolation whether it may be in micro or macro level. So there is a need for a strong tool to connect people instantly so that the knowledge transfer is never ending and instant. Media for development uses to convey messages on issues such as health care, poverty reduction, good governance, environmental protection and community development, socio-economic and cultural development. Today the world is becoming a small place to live in and share knowledge, ideas and passing over valuable culture to the next generation, all the above said things is possible through social networking and media. Every aspect in the society has positive and negative externalities so we need to see how these negative externalities can be balanced and surpassed through positive externalities to reach development in all dimensions.

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TRANSFORMATION OF EMOTIONAL MINING IN THE HEALTH CARE SYSTEM: A Study

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Nano things, Graphene, antennas, FET

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Selection of relevant features of a dataset is necessary in high dimensional datasets in order to avoid the curse of dimensionality. Feature Selection is performed to reduce overfitting, to improve accuracy and to reduce the training time of the algorithms. In this paper a nature inspired meta heuristic algorithm called Whale Swarm Algorithm devised by Bing Zeng, Liang Gao and Xinyu Li is used for feature subset selection (Bing Zeng, 2017). It focuses on selecting a features subset using Whale Swarm Algorithm (WSA) where Logistic Regression (LR), Random Forest (RF) and k-Nearest Neighbor (KNN) are used as fitness functions. These WSA-LR,WSA-RF and WSA-KNN combinations generate different feature subsets for different number of iterations. Then training and testing is done on the dataset with the subset of selected features using LR, RF, Support Vector Classifier (SVC) and Gaussian Naive Bayes (GNB) and prediction accuracies generated are analyzed.

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 Keywords

Feature Selection, Whale Swarm Algorithm, Logistic Regression (LR), Random Forest (RF) and k-Nearest Neighbor (KNN)

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UBER SUPPLY AND DEMAND GAP ANALYSIS USING PYTHON

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Question paper, blueprint, examination

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Cloud computing is becoming very popular technology in the computer field. Its services are preferred in various fields. Many service composition processes are enacted and executed in this environment. The resource selection and minimizing the time to complete the work are being the main consideration to provide the quality of service. There are number of resources are used to serve the tasks and their utilization efficiency. Tasks are competing with other task in order to access the resource first, to complete the work, but the task may face problem sometimes. One of the problems in accessing resource in distributed environments is dead lock. This is similar to cloud environment also. The objective of this work, analyzing the situations for sharing the resource and its techniques for the cloud environment and suggesting the solution for it. This paper provides four algorithms for resource allocation in cloud environment with its merits and demerits

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Cloud Computing, Service Provider, Resource Allocation, Algorithm

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The AI and the Robotics gives the similar trouble. The two fields interact profitably in the area of building intelligent agents and this inter action has resulted in important developments in the area of vision and phased action. Recent advancements of technologies, including computation, robotics, machine learning statement, and miniaturization technologies, brings us earlier to futuristic vision of considerate intelligent device. The missing component is a basic understanding of how to relate human functions (physiological, physical, and cognitive) to the design of intelligent devices and systems that aid and interact with people. Robotics is an branch of technology that deals with the designs, constructions, operations and applications of robots and the computer systems for their control sensory feedback, and information processing.

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 Keywords

Intelligent Agent, Manipulators, Robotics, Effector

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Drawing the attention of student for the complete lecture session has always been a central concern of educators in almost all institutions. This is due to the lack of concentration of students in between the lectures for several reasons. An early prediction of student attention helps the educators to identify the student level of learning and to improve their delivering mechanism in the teaching-learning process. The identified set of input feature variables are classified into two groups such as student factors considered as primary parameters and teacher factors considered as secondary parameters. This paper presents a brief survey on BPNN and PNNin order to find out the perfect, efficient solutions for classifying the student attentiveness into different categories.

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Student attentiveness, Teaching-learning process, Back Propagation Neural Network(BPNN), Probabilistic Neural Network(PNN).

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Molecular dynamical simulation, localized modes, ferromagnetic spins chain, effect of impurity

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Cryptography, RSA, DES, AES, DSA, ECC and security tools.

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Systematic deployment of services within the cloud as well as resources to the cloud consumers via the Internet is known as Cloud computing. The IaaS (Infrastructure as a Service) facility is offered to the cloud user by allowing better access to storage, processing power, networks and basic computational services which comprise both operating systems as well as applications. The resources are made obtainable as Virtual Machines (VMs). Services within the cloud are offered to the cloud consumers on a pay-per-use model on demand and is the utilized services are billed appropriately. The VMs are found to run on several data centers within the cloud, which possess a multitude of resources making use of energy in abundance with the side-effect of alarming carbon emission in the atmosphere. Various research studies have suggested a variant of power-efficient techniques which are intended to minimizing power usage within the data centers. One amongst the popular solutions are algorithms that are inspired by nature. In this research work, a review of those algorithms inspired by nature for the ideal power utilization within the data centers of the cloud is exercised. Proper management of energy usage in the data centers and the Power-Efficient Nature inspired techniques for the data centers within the cloud are explored and a comparative evaluation of the different power-efficient methods is executed. This research work empowers both academic researchers as well as industry personnel in the data centers of the cloud to understand the literature development for ideal power-efficient methods for the data centers in the Cloud.

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Cloud computing, Power-efficiency within Data centers, Nature-Inspired techniques

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This paper outlines the challenges encountered by conventional toll collection systems in India and brings out the need for having an automated toll collection system such as FASTag, a program rolled out by National Highways Authority of India (NHAI) for Electronic Toll Collection on National Highways.

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Toll Collection System, RFID, FASTag, Traffic Management, Toll plaza

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The automobile companies make investments of millions in developing technologies to keep drivers safe and avoid accidents while driving their vehicles. These technologies are known as Advanced Driver Assistance Systems (ADAS). It incorporates frameworks dependent on sensors, such as Lane departure warning system, Electronic stability control, Active cruise control, Pre-crash systems. Lane Departure Warning System (LDWS) is technology in ADAS that alerts the driver if the vehicle tends to depart the lane unintentionally. To do so, this framework keeps track of the lane markings on the road. It takes input as a traffic video to measure the vehicle speed at various stages .For this process, the various image processing technique is proposed and an alert message is generated when the vehicle changes the lane. In this paper, the lane detection techniques and algorithms are discussed using lane related parameters and the performance of the different lane detection technique is also compared and contemplated.

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This study has been focused on the automatic detection of sensitive attributes using data mining technique in Hadoop environment. We establish Privacy Preserved Hadoop Environment (PPHE) that automatically detects sensitive attribute using data mining techniques. The proposed PPHE considers Twitter to enable user to post messages. The content of the posted tweets are wide ranging and contains private information such as email addresses, mobile numbers, physical addresses, and date of births. The major purpose of this study is in three folds. At First, we authenticate each twitter user using the combined algorithm RSA and Elgamal Algorithm. In the second, we classify the tweets into private and non-private attributes by using Type-2 Fuzzy Logic System. In the third, we pursue the data suppression technique to compress private tweets.

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Data mining, Hadoop environment, Twitter, RSA, Elgamal Algorithm, Type -2 Fuzzy Logic System.

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With the rapid development of electric and electronic technology, information technology and advanced manufacturing technology, the production mode of manufacturing enterprises is being transferred from digital to intelligent. Industry 4.0 is a combination of Cyber-Physical Systems linked in the supply chain and manufacturing processes with usage of the Internet of Things and Industrial developments. A definition for Industry 4.0 is that it is an (r)evolution towards digitalization.[1] The term Industry 4.0 stands for the fourth industrial revolution. It is understood better as a latest level of control and organization over the value chain of the product lifecycle, it is focused on individual customer requirements. The cycle starts from the product idea. Then order placement in made and product goes through the development and manufacturing. Then it makes it way to product delivery for the end customer where the cycle moves to the end phase, recycling. The availability of relevant information in real time by connecting all activities of the value chain forms the basis for the fourth industrial revolution. The connection of systems, things and people creates self-organizing, dynamic, real-time optimized value-added connections across companies. They are further optimised in accordance with different criteria like availability, costs and consumption of resources. Industry 4.0 is a reevaluated technique for manufacturing that makes use of the newest and latest technological creations, especially in incorporating operational and information technology

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 Keywords

Industry 4.0, smart factory, sensors, actuators, information channels, IoT, RFID tags, Business intelligence

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