International Journal of Data Structures
International Journal of Data Structures (IJDS) Editor-in-Chief Dr. R. Rajasekkar Professor, Department of Computer Science and Engineering, St.Peter’s Engineering College, Maisammaguda, R.R.District, Hyderabad, Telangana. Email: rajasekaratr@gmail.com Click here for complete Editorial Board International Journal of Data Structures (IJDS) is a journal focused towards the rapid publication of fundamental research papers on all areas of data structures. It's a biannual journal, started in 2015. Journal DOI No: 10.37628/IJDS Readership: Graduates, Postgraduates, Research Scholars, in Institutions, and IT Companies Indexing: The Journal is indexed in Google Scholar Focus and Scope Fundamental data structures Recursive algorithms Dynamic information structures Language structures and compilers ASCII character set Pascal syntax diagrams Advanced data structures and algorithms B-trees Array data structure Submission of Paper: All contributions to the journal are rigorously refereed and are selected on the basis of quality and originality of the work. The journal publishes the most significant new research papers or any other original contribution in the form of reviews and reports on new concepts in all areas pertaining to its scope and research being done in the world, thus ensuring its scientific priority and significance. Manuscripts are invited from academicians, students, research scholars and faculties for publication consideration. Papers are accepted for editorial consideration through email info@journalspub.com or nikita@stmjournals.com Abbreviation: IJDS Frequency: Two issues per year Editorial Board Instructions to Authors
Recent Advancements in B-tree Based Data Structures
When many new keys need to be added to a B-tree, it is advantageous to insert them as a batch rather than individually, as it allows for all edges of the tree to be traversed only once instead of repeatedly traversing the path from the root to a leaf for each key. The information used for searching in the indexset is updated exclusively by a specialized maintenance process, which also takes care of maintaining the load factor of all levels in the tree structure. This load factor can be chosen freely and can differ between levels. When compared to Sagiv's method, it is clear that this approach provides additional benefits. Additionally, this approach can be more easily scaled to handle larger data volumes or more complex data structures, since the maintenance process can be optimized independently of the normal data processing pipeline. Overall, the use of a specialized maintenance process for updating the search index-set and load factor in a tree structure offers numerous benefits for improving the efficiency, reliability, and scalability of data retrieval operations.
Copyright (c) 2023 International Journal of Data Structures
Song Recommendation System Based on Data Science Technology: A Review
In this paper, we first study how data science is not well understood, which might leave people disappointed if the idea devolves into meaningless hype. A collection of data known as a data set is the subject of data science. There are numerous components of data science that were created in domains, like machine learning and data mining. After removing the confusion about data science, we see what a song recommendation is and on which technique it is based. Song recommendations are generally based on collaborative and content-based filtering. The more advanced and fascinating filtering mechanism focuses on user similarities and prior interactions between people and things. Contrarily, content-based systems consider characteristics or attributes of things. This paper shows that collaborative filtering is much better than content-based filtering.
ERP (Enterprise Resource Planning) systems have grown to be an essential part of managing corporate operations, but SMEs (Small and Midsize Enterprises) have found it challenging to embrace them due to their complexity and high cost. Easy Manage ERP provides MSMEs with real-time visibility into their sales and administration, allowing them to track orders, monitor product availability, and optimize their supply chain management processes. Companies also gain access to useful information and insights that can be used to increase sales and organizational effectiveness. This research work suggests Easy Manage ERP, a system created specially to meet the difficulties SMEs encounter in running their businesses on a daily basis. It is a cloud-based ERP system that helps Micro, Small, and Medium-Sized Businesses (MSMEs) increase their income and save time. The system is designed to make managing orders, supplies, and their products straightforward and effective, giving businesses more time to focus on expansion and growth. Complete flexible and secure cloud-based ERP solution improves the efficiency and effectiveness of order for businesses of all sizes. Because of its simplicity, adaptability, and affordability, it is the ideal choice for businesses looking to enhance their order, products and material administration processes. It delivers an effective and user-friendly experience, allowing businesses to concentrate on growing their core businesses while the system takes care of the rest.
Humans were formed by the Almighty with a variety of needs and desires, which connects them to their own information, preferences, and choices. Knowing a customer's wishes or needs based on data is crucial for any company or organization to grow and thrive. Data is becoming increasingly important in all organizations and is used to support effective operations. In this work, we will provide a study of a meteorology system utilising Data Science. Weather analysis is a process of using technology as a tool to anticipate the climate in a specific location. It ranks among the largest global issues. This research made weather forecasts using data modelling. Realizing that many industries, including business, agriculture, aviation, and others are directly impacted by weather, in this research, we developed weather forecasting predictive model using ANN (Artificial Neural Networks) method that is widely employed in weather forecasting studies.
Nowadays, people are very cautious once they need to buy new residence within their budgets. The main purpose of this study is to predict the house price whatever location you want in the Bengaluru city. This study analyses various features like number of bathrooms in the house, number of bedrooms in the house, total area of the house etc. This research work has applied the dataset from Kaggle. An evaluation is performed with the aid of making use of superior system mastering regression techniques along with Linear regression, Decision Tree Regression, Bagging Regression, XGBoost Regression, Gradient Boosting Regression etc. The representation of comparing all the regression models is presented in the graph format.
Mapping the Research Productivity on Android Technology: A Scientometric Analysis
An enormous number of papers containing the collected knowledge of this field of research have been produced because of the popularity and quick development of android technology in recent years. It is becoming more challenging to evaluate the overall structure and evolution of this field without analysis methods due to the interdisciplinary character and significant relevance of android technology research. We identified with the analysis of the related literature, a lack of a comprehensive scientometric study in android technology research. Based on a large bibliographic database, viz., web of science, this study applied scientometric means of empirical study that evaluate the progress and state of research on android technology. Thereby, we were able to provide in-depth explanations of publication patterns, year-by-year distribution, form-by-form distribution, subject-by- subject distribution, the authors with the most publications, the top institutes, the rankings oF periodicals, and the impact and productivity of research. The study’s results offer a better understanding of patterns, trends, and other significant elements that can be used to guide future research, share expertise, and collaborate on projects pertaining to android technology.
Copyright (c) 2022 International Journal of Data Structures
For many computer vision applications, including visual surveillance, picture retrieval, and driver assistance systems, human detection is crucial. The Project Real Time Human Detection addresses the issues of face recognition, face detection, and human detection. In a given photograph or video, the project is capable of identifying a human and its face. A crucial component of video surveillance is person detection, a technique that predetermines the human forms in images and videos and ignores everything else which plays an irreplaceable role in video Surveillance. The Histogram of Oriented Gradient (HOG) based Descriptor significantly perform better than any other gradient based descriptor. This field is one of the most popular fields of study for many researchers nowadays as it has been already seen its implementation in self-driving car and for security purpose. The use of machine learning and image processing, using Convolution Neural Networks (CNN) and OpenCV, an open-source computer vision library, is the foundation of the entire effort.
CROP, FERTILIZER AND DISEASE PREDICTION USING ML
Agricultural development and expansion in India will be backed by E-Governance of Agriculture. Applications of E-governance incorporates Determining the suitable crops to grow in specific region, Recognizing the plant Disease, determining soil fertility. Government of India takes due measures to procure and provide the essential food items and provide it too the needed ones. Despite of the efforts of government, there is a huge gap between demand and supply of essential food items to needed one, due to less number of farmers aware of what and how to grow according to soil conditions, farmersm have a tendency to grow the similar crops using excessive fertilizer in process. The essence of the solution lies in the ability that can a) Decide the crops to be grown in specific reason depending on the Weather, Soil Structure and Water Supply. b) Recognizing the disease of the plant and then coming up with the correct reason for that disease and coming up with all the possible cures. c) Fertilizer Recommendation. The project revolves around providing hassle free experience to the farmers of the nation.
It is normally seen that algorithms are more important than array but in reality, it's inversely important to break computation al problems efficiently.in this article, we've explored Array Data Structure in dept. We explore crucial ideas in Array and how we develop our own custom array with different way sofit sutilisation. This work attempts to provide insight on the operation of data structures in the context of information organisation. Information storage and retrieval is a field of research that is rising in prominence as the necessity and desire for sharing and studying information grows. Data structures have been the area of interest for many researchers for a long period in the era of data management and retrieval. The necessity for appropriate data structures has become increasingly crucial as data expands at an exponential rate. In today’s era, people are so addicted with the devices such as mobile phones or computers which produce large amount of the data difficult for storage. Data structures are essential components of computers that are used to organise, process, access, and store data in order for it to be used efficiently. This document
It is one of the bio-inspired algorithms and finding the best solution in the available space is straightforward. It differs from ordinary optimization methods in that it does not depend on the gradient or any differential forms of the objective function, except the objective function itself. It is a heuristic approach because we can never verify the actual global optimal solution can be found and it typically isn't. However, we frequently see that the PSO solution feels pretty darn close to being the overall best. It makes use of a non-dominated trees order on members of non-dominated sets to make it easier to select the optimal global individual for each swarm member according to their velocities. The advantages of affecting particle flight are also discussed in the new PSO methods. Even though particle swarm optimization has few hyper-parameters and is very forgiving on the objective function, it can be used to solve a wide range of problems. We provide numerous designs with varied degrees of simplicity and optimality, as well as lower bounds to demonstrate the optimality of our constructs. We provide numerous designs with varied degrees of simplicity and optimality, as well as lower bounds to demonstrate the optimality of our constructs. These researchers first examined computer models of flocking birds before working to enhance the algorithm based on their findings. Particle swarm optimization, which is based on the premise that keeping track of the various particles for example, components of a peer-to-peer network, can yield useful insights, can now assist engineers in solving a variety of machine learning challenges. The technique is demonstrated to be much superior to two current MOAs using several test functions.
An Ensemble based Machine Learning Approach for Analyzing the Sentiments of Scientific
The study of Sentiment analysis (SA) and opinion mining (OM) is a field which has developed significantly in the past 10 years. The main intend of this scientific analysis is to decide objectively perspective of the critic and also to equate this with evaluation of the author. This district of study attempt to conclude approach, opinions as well as emotions of people on something or somebody else. This would enable scientists to identify, analyze and analytical evaluation of a scientific report across the board. The interpretation of review is provided by an ensemble method that combines the machine learning (ML) algos with the processing of natural languages. This approach uses part-of-speech (POS) to get syntactic sentence structure. A variety of experiments have been performed to evaluate strengths and efficiency of proposed approach virtual to measurements, utilizing standard metrics, like consistency, precision, recall & F1. This syntactic structure or vocabulary usage will help us assess semantic direction of the analysis by means of an algo. The findings reveal that developments in the binary, ternary or 5-point classification of classic ML algos like bagging and boosting are also difficult to enhance the classification of multi classification in this area.
Data Query Processing Approach in Apache Spark
In the current scenario of Big Data the Spark tool is widely being used to analyse, maintain, and transform the data into information. Spark has finished up the usage of Apache Hadoop. Apache Hadoop uses Map-Reduce frame for data processing while Spark has come with the in-processing feature that increases the speed of processing the large amount of data. Data Science is a current stream of development and technology, so Data Query Processing as part of Data Science is need to be evaluate. The main objective of the research is to provide valuable study about Data Science and Data Query Processing. This paper will help in analyse the efficiency and data transformation, data analysis and data manipulation features of Apache Spark that makes it differ from Apache Hadoop. The optimization of interactive queries is done based on two parameters, the first one is cost of evaluation and second one is rule-based optimization.
Comparative Study of Process Scheduling Algorithm
In computing, Scheduling is the strategy where the work is appointed to resources that finishes the work., there are various algorithm but this paper mainly focuses on FCFS (First Come First Serve), RR (Round Robin), Priority Scheduling and SJF (Shortest Job First). The comparison between these algorithm is based on listed parameters such as: Throughput, waiting time, Turnaround Time and Response time. From the analysis made in this paper, there is no single algorithm that can satisfy the condition of an ideal algorithm.
This review paper comprises of the outlook on different methods which are used for clustering datasets. clustering is the way toward sorting out datasets into different groups whose objects are equable here and there or the other. A group is a mixture of datasets which are like one another and are different to the datasets having a place with different clusters. clustering includes utilizing of different algorithms like k-implies algorithm, BIRCH algorithm, CLIQUE algorithm, DBSCAN algorithms. This paper presents the review of these method. Clustering is an undirected data mining technique that identifies several hidden patterns in data without generating any specific hypothesis. The goal of clustering is to find similarities between objects and group them together.
System of NULL Convention Logic for Ultra-Low Power Applications
Leakage power is becoming a major design challenge for circuit designers as transistor feature sizes shrink into the ultra-deep submicron range. This review article focuses on the application of Multi- Threshold NULL Convention Logic (MTNCL), an asynchronous design paradigm, to adaptive beamforming circuits MTNCL, also known as Sleep Convention Logic (SCL), is a type of logic that combines Multi-Threshold CMOS (MTCMOS) and NULL Convention Logic (NCL) to achieve significant power savings while avoiding the limitations of synchronous circuits. The MTNCL and synchronous designs were constructed using IBM's 130 nm bulk CMOS technology for power and area comparison. The MTNCL design outperformed the synchronous design in terms of active energy and leakage power. The MTNCL circuit enables delay-insensitive logic operation while minimizing leakage power and active energy consumption. For even more power savings, the MTNCL circuit can function successfully under extreme supply voltage scaling down to the sub-threshold zone. Four MTNCL topologies and four MTNCL threshold gate designs enable asynchronous logic design, allowing for glitch-free, ultra-low-power, and speedier circuits.
Data and Program-flows in Data Analytics Framework and Cloud Environment
Data generates at various sources. Data also have various formats. Organizing and managing data are at the cloud. Cloud stores and hosts number of applications and services. Cloud usage ensures availability. The scaling up and scaling out are at minimum cost. Cloud manages user’s requests at large scale efficiently. Big data are large data sets above 10 M byte and extend to Peta Bytes. Storage and analysis of big data need unconventional data storage and processing approaches. Data analytics extract hidden information or patterns in existing data sets. The analysis improves understanding, improve decision making process and discover new knowledge. The present work describes the architecture layers for data and program flows in Data Analytics Framework and Cloud Environment.
Copyright (c) 2021 International Journal of Data Structures
Heart Disease Prediction Using Machine Learning
Heart disease also known as cardiovascular disease is even being highlighted as a silent killer that results in death of individual without obvious symptoms. This nature of sickness is that explanation for growing anxiety regarding this sickness and its consequences. Thus continuing efforts in field getting done to predict the chance of this deadly disease in its early phase so that various tools and techniques are frequently being tested to suit the current day healthcare system needs. By collecting the data related to heart diseases from various source and classifying data under fit headings and finally analyzing to extract the required knowledge we are able to determine that for prediction of Heart diseases machine learning techniques are most reliable and efficient, Machine Learning techniques can be a relevant in this regard.
Effective Features for Efficient Detection of Digestive Sys. Pathologies using Endoscopic Images
Digestive System Pathology is gaining awareness now-a-days because of its growth rate. Currently working systems are not capable to detect the pathology at early stage. So, to overcome this problem we proposed a novel technique. The center capacity of the human stomach is a guide to processing. The four key segments of gastric stomach related capacity are its anything but a supply, corrosive emission, chemical discharge and its part in gastrointestinal motility. The supply limit of the stomach permits it to build its volume essentially while inward pressing factor increments just somewhat. Corrosive emission is a vital non-immunological safeguard against attacking microorganisms just as being a significant component for vertebrates to have more perplexing eating regimens. Discriminative features are obligatory for the accurately detection and classification of Gastrointestinal Track cancers. Other authors' observations and literature work is important to know for correctly detection of pathogenesis of the hemorrhagic infarct of the digestive system, mesenteric vein apoplexy, dangerous a ruptured appendix and cholecystitis. Discriminative features are obligatory for the accurately detection and classification of Gastrointestinal Track cancers. In this work, we perform the union of structural (Curvelet and Wavelet Transform) and textural (Local Binary Patterns LBP) feature descriptors. Purposed technique is executed on the CH-dataset. This dataset contains 56 normal and 120 abnormal endoscopic images. For classification we sent our model to the SVM classifier and SVM classifier came up with an accuracy of 89.2 %.
This software scheme is a customary supermarket billing scheme among a quantity of additional functionality. This arrangement is built meant for fast data giving out along with bill production in favor of supermarket clientele. The billing system consists of a sql database and effective front end designed in Asp.net. The invoice database is a immense collected works of item for consumption name, charge along with further product explicit data. A product when billed is look for as of the database with its price is added to the bill support upon the invention capacity. The organizations to be full of money off on a variety of creations so that the invention is proffered at reduced price while billing. The supermarket billing arrangement is build to facilitate supermarkets estimate with show bills as well as provide the purchaser in a quicker furthermore capable behavior. This software development consists of a useful along with simple GUI to facilitate the employees in simple bill computation moreover provided that a capable customer examination.
Hand Tracking by using Python and Machine Learning
Hand tracking is an important component in most practical gesture interaction. In this work, we present a real-time method for hand gesture recognition. We present another constant hand global positioning framework dependent on a solitary profundity camera. In our Project, the hand region is extracted from the background with the background removing method. Then, the palm and fingers are segmented so as to detect and recognize the fingers. In this paper, an automatic hand tracking and segmentation method based on depth information is proposed. Hand depth is determined and then exact hand region is obtained. In this manner, exact hand tracking is realized with very less time and regardless of the complex background. From the tracking process we extract several hand features that are fed to a finite state classifier which identifies the hand configuration. Experiments show the effectiveness of the hand tracking and dynamic gesture recognition. A research was based on a number of algorithms that could best explained a hand gesture. In the pre-processing state, a selfdeveloped algorithm eliminate the background of each training gesture. After that the image is transform into a binary image and the sums of all diagonal segments of the pictures are taken. A completely powerful hand gesture recognition system is still under development.
Fake News Analysis Using Machine Learning
This research paper thinks of the uses of NLP (Natural Language Processing) methods for recognizing the 'Fake news', that is, deceiving reports that comes from the non-respectable sources. Simply by building a model dependent on a tally vectorizer (utilizing word counts) or a (Term Frequency Inverse Document Frequency) tfidf framework, (word counts comparative with how regularly they're utilized in different articles in your dataset) can just get you up until this point. Be that as it may, these models don't consider the significant characteristics like word requesting and setting. It is entirely conceivable that two articles that are comparable in their promise include will be totally extraordinary in their significance. The information science local area has reacted by making moves against the issue. There is a contest called as the "Fake News Challenge" and Facebook is utilizing AI to sift counterfeit reports through of clients' channels. Combatting the phony news is an exemplary book arrangement project with a straight forward recommendation. Is it feasible for you to assemble a model that can separate between "Genuine "news and "Fake" news? So a proposed work on amassing a dataset of both Fake and genuine news and utilize a Naive Bayes classifier to make a model to characterize an article into fake or genuine dependent on its words and expressions.
Comparative Study of Spread of COVID-19 Pandemic in India
ABSTRACT The COVID-19 pandemic which has brought the whole world to a standstill stays a matter of grave concern even after months of its upsurge. The deadly disease needs to be addressed at a sustainable level. The pandemic still poses a great threat to people all over the world belonging to different age groups Even after unprecedented measures taken by different countries including quarantine, social distancing etc., the pandemic seems far from over. The present study aims at the evaluation of plots based on number of reported positive COVID-19 cases, and number of recovered and death cases for different states of India. The study clearly recognizes the failure to contain the disease at a considerable rate due a number of possible reasons. The lack of medical facilities to cope up with the needs of large population in India can be one of the major reasons for the rate of infection doubling after a couple of days. Keywords: COVID-19, uncontrollable breakout, catalyst, lockdown, social distancing
Copyright (c) 2020 International Journal of Data Structures
Multiple Imputation of Industrial Missing Data Using Chernoff Bound Techniques
ABSTRACT Data mining requires a pre-processing task in which the data are prepared and cleaned for ensuring the quality. Missing data value occurs when no data value is stored for a variable in an observation. Imputation is popular method because it is conceptually simple and because the resulting sample has the same number of observations as the full data set. Its trust for the industry to forbid the qualifies of handling enormous data. Multiple Imputations is a popular technique for analyzing incomplete data. Missing at random mechanism is often assumed when multiple imputation is performed, assuming that the response mechanism does not depend on the missing variable. However, the assumption of ignorable nonresponse may lead to largely biased estimates when in fact the missingness is nonignorable. In the research methodology of this research, we take the selection model approach and specify the response model and the respondents’ outcome model to capture the joint model of the study variable and the response indicator. This article deals with several algorithms in supervised and Unsupervised machine learning techniques like Mean, Median, Standard Deviation, Regression and Naïve Bayesian classifier. The performance of above method has been compared by using correlation statistics analysis gives the imputed values are positively related or negatively related or not related with each other. To evaluate the performance of missing values can be measured by using Chernoff Bounded Theorem, which set the bounds infimum and Supremum to the data imputation. Based upon the performance of Monotonic sequence and subsequence, the imputed missing values are increasing or decreasing or a bounded monotonic sequence of finite limit and also analyzing that every bounded sequence of missing values has a convergent subsequence. To evaluate the performance, the standard machine learning repository dataset has been used. This article focuses primarily on how to implement Chernoff Bound theorem to perform imputation of missing values. The proposed data augmentation algorithm uses the respondents’ outcome model and incorporates a semi parametric estimation of the respondents’ outcome model. The proposed multiple imputation method performs well if the specified response model is correct. In this paper, we propose a multiple imputation method in the presence of nonignorable nonresponse Data Augmentation Techniques-An effective imputation for datasets where the proportion of missing data is high. The performances of existing imputation approaches of missing value cannot satisfy the analysis requirements due to its low accuracy and poor stability, especially the rapid decreasing imputation accuracy with the increasing rate of missing data. Patterns of missing data can be with respect to cases or attributes. The global impact assessment of the imputed data is performed by several statistical tests. It is found that the imputation value is high with DarbouX variate, which fixes the infimum and supremum of the missing data. Keywords: Imputation, Knowledge Transfer, Missing data, Data patterns, Multiple imputation. Chernoff Bound, Infimum and Supremum.
A Survey Paper on Big Data Storage in NoSQL
ABSTRACT In this highly competitive and strategic world, data analysis is very much noteworthy for helping in better decision-making process. Because of the variety, velocity and volume of data, traditional data management tools are insufficient to handle the need of hour. These tools are inexpert to manage unstructured and semi-structured data. To overcome these limitations various new database tools are introduced over the time which lead to the category of NoSQL. The NoSQL databases provide a feasible query processing tool for data collections which are not structured. In this paper authors emphasizes on what is NoSQL and why NoSQL gained popularity in the recent time, the review of various categories of NoSQL databases and will accomplish that which database has the best performance under the specific requirements and scenario. Aim of the present study was to literature review on the NoSQL Database for Big Data processing including the structural issues and the real-time data mining techniques to extract the estimated valuable information. Storage which is efficient to process this large scale of data to extract important information in less response time is the need of current competitive time. Keywords: NoSQL, Key Value, Document Oriented, Graph based, Column Oriented, Unstructured data, Big Data, Data Processing, Hadoop, Mongo DB
A Systematic Review on Opinion Mining and Sentiment Analysis
ABSTRACT Online reviews have become an important consideration as purchasing and business decisions are made by individuals. For service providers, customer ratings and reviews are very critical. Sellers who deal products on the web also ask or collect consumer feedback of the products they have purchased. As e-commerce is rising and becoming popular day by day, the number of product reviews received by consumers is increasing rapidly. The ratings can go up to thousands for a famous product. The growing popularity of online reviews is also stimulating the fake review writing industry. E-commerce companies and manufacturers may gain consumer opinion by mining customer feedback to enhance service and merchandise. There are several views on a product on the internet, so it can be difficult to make decisions. To classify the reviews according to their polarity, opinion mining is then used. Opinion mining is a mechanism of the review’s mining opinion. For any person, reviewing consumer review is more relevant in making the right buying product and organization decision. Opinion mining is often referred to as evaluating thoughts. In this paper we survey on Opinion mining with respect to their different levels, need of opinion mining, applications, challenges and future directions. Keywords: Data mining, opinion mining, data base, policy making, sentiment analysis, decision making
Green Data Mining (G-DM) Based Energy Consumption on Internet of Things: A Review
ABSTRACT The vast Internet of Things (IoT) data is considered to be of high business value, and data mining algorithms can be applied to the IoT to extract secret information from data. A very large and enormous amount of data is generated or accessed using IoT. The amount of data analysed is increasing as devices are increasingly connected. Enabling smart interactions with users is one of the important aspects of IoT (such as automation of services). In order to make the IoT system smart enough to provide suitable services and environments, data mining plays a crucial role. The ability of devices, applications and services to implement data mining is the most important factor in IoT. When we apply data mining to IoT, IoT-collected data is transformed into serviceable information, and then this data is converted into user-needed knowledge. Green information and communication technology are an emerging field of research and development, and energy-efficient designs for IT systems are a major factor in this area. The accuracy of outcomes with current consumption efficiency and good latency, commonly known as inaccurate or estimated computing, is one of the most sought-after solutions. In this regard, some approximation techniques used to achieve energy-efficient data mining or green data mining with the best possible results for a given permissible deviation are used. The majority of businesses currently have data centers to store their data; high-end processing machines and devices are what data centers need. The implementation of green database techniques, however, helps to reduce the amount of energy that data centers consume. Patients and costs are required for implementing data warehouses and data centers. The installation of a data warehouse can be very expensive without proper planning, particularly when it comes to electricity. The idea of reducing energy consumption when analyzing data collected by IoT devices and making the world secure is envisaged by Green Data Mining. This paper addressed the techniques used to reduce data center energy usage, thus moving data from IoT devices to data centers and analyzing data using data mining on data center servers. Keywords: Green data mining, Internet of Things, energy consumption, network, server, data center, smart city.
A Review: Web Application for Smart City
Smart city initiative is trending nowadays in India. The government of India is also focusing on smart city initiatives. This initiative aims to give its citizens a better life and the overall improvement of the city. When the city develops, it is difficult to manage the various establishment of the city. So, smart city approach focuses on the demand and different convenience for citizens. Thus, the implementation of projects in the field of information and communication technology (ICT) plays a successful and effective role. We can provide a web application for a particular smart city. By using this technology, citizens can directly communicate with city infrastructure. This web application is composed of everything specific to the city and all the e-services of government as well as private. Also, citizens can directly access the latest information and updates from the city. By using this smart city web application, citizens can explore the city in less time and with greater accuracy. This ICT tool will produce different opportunities for the service providers as well as for the citizens. Keywords: Citizen, e-services, ICT (Information and communication technology) smart cities, web-based application Cite this Article: Amit Bhosale, Ashpak Zari, Sanket Tilekar. A review: web application for smart city. International Journal of Data Structures. 2020; 6(1): 6–10p.
Implementation of Blockchain in Current Transaction Systems
The blockchain technology is being deployed in many areas and the banking system is not an exception. It is a new technology and should be introduced in the modern banking system. The aim of this project is to give an overview of blockchain technology with its benefits and emphasizing on the application of this technology in the banking sector. We are going to generate our own cryptocurrency along with a banking system with number of nodes between which we can perform transactions using blockchain. And we are using Ethereum (Online Transaction Management System) for our ongoing project. Now, blockchain is better than the conventional banking systems as, combining shared databases and cryptography, blockchain technology allows multiple parties that may not know each other from different geographical locations to have simultaneous access to a constantly updated digital ledger that cannot be altered. So, we will generate our own virtual nodes (Users who want to transfer money) which will transfer our own cryptocurrency to each other. The blockchain is a ground-breaking innovation that empowers Bitcoin, Litecoin, Dogecoin, and other virtual monetary standards to be open, mysterious, and secure. The blockchain basically is a database about each Bitcoin exchange in detail. Typically known as an "open record," the log contains metadata about when and how every exchange occurred. The record is freely available through APIs and deluge destinations. To forestall messing with current and furthermore past exchanges, the database is cryptographically made sure about. Because of Cryptography can edit only the parts of the blockchain that they “own” - by possessing the private keys required to write to the file. It also keeps everyone’s copy of the distributed blockchain is kept in sync. Using blockchain rather than conventional banking system would help in fraud reduction and that too by quiet a lot of margin. Financial institutions spend millions of money per year to keep up with Know your Customer (KYC) and customer due diligence regulations according to a Thomson Reuters Survey. These regulations are meant to help reduce money laundering and terrorism activities by having requirements for businesses to verify and identify their clients. Blockchain would allow an organization to access the verification details of a client by another organization, thus avoiding repetition of the KYC process. Blockchain disruption could be highly transformative in the payments process. It would allow banks higher security with minimal lower costs to process payment between organizations and their clients and even between banks themselves. Blockchain would get rid of all the intermediaries in the payment processing system. Keywords: Blockchain, current transaction system, KYC, online transaction management system, processing system Cite this Article: Dhruv Rawat, Gaurav Rana, Jaskaran Singh Bindra, Amit Kumar. Implementation of blockchain in current transaction systems. International Journal of Data Structures. 2020; 6(1): 31–59p.
This paper includes productive AI calculations utilized in anticipating illness through indications. As the health industry has a gigantic measure of information for different fields along these lines, we need to make a system where we too can utilize different utilizations of AI on health industry. This all had been done to settle on the better clinical choices and furthermore to ascend in the exactness. As accurate analysis of the early prediction of disease helps in the patient care and the society services. These all challenges can be easier by the help of various tools, algorithms and framework provided by the machine learning. In addition to all these predictions, we are making a chatbot for all that where patients can add the symptoms that are helpful to predict the disease and also check the risk of heart disease status through the various information provided to system by the patients and the database obtained from UCI repository. Keywords: Decision tree classification, logistic regression, Naive Bayes, NLTK, UCI repository Cite this Article: Megha Gupta, Ruchika Gupta. Disease prediction using ML. International Journal of Data Structures. 2020; 6(1): 11–17p.
Energy Efficient Topology Selection for 6LoWPAN Based Applications
The objective of this research is to analyze the impact of IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL) in extending the lifetime of IPv6 over Low Power Wireless Personal Area Networks (6LoWPAN) based applications. The analysis of RPL protocol is carried out for different topologies namely linear, ellipse, random, manual and for varied network sizes. The performance metrics evaluated are Packet Delivery Ratio (PDR), Round Trip Time (RTT) and energy consumption in the network. The Cooja simulator of Contiki operating system is used for the simulation. From the results, it is observed that random topology spends lesser energy and offers reduced RTT as the position of nodes in the topology is unsystematic and placed around the border router. Enhancement in PDR is observed in manual topology as the position of nodes is user-defined. Keywords: 6LoWPAN, RPL, lifetime, topology, PDR, RTT, energy and Cooja Cite this Article: V. Gokilapriya, P.T.V. Bhuvaneswari. Energy efficient topology selection for 6LoWPAN based applications. International Journal of Data Structures. 2020; 6(1): 18–30p.
Krishi Portal: Web Based Farmer Assistance
Information and communication technologies (ICTs) have made our lives easy. ICTs are playing valuable roles in variety of technical fields such as education, banking, health, E-commerce and many more. ICTs can also be used for farmers to help them to get more production and profit. This paper describes a web based system - “Krishi-Portal” which will assist farmer to get information about various crops, diseases on crops, crops’ rates, government schemes for farmers and weather forecasting. The system is also a good platform for selling farmer’s production and buying material from vendors which is necessary for farm. Farmer can also buy or sell new or old agricultural machineries that are used in farms like cultivators, disc harrows, etc. This system is specifically useful for farmers but other business sectors will definitely get benefit of it; those are agricultural agencies (such as tractor sellers), hotels (by getting raw materials- vegetables, milk, etc.), and fertilizer sellers. Keywords: E-commerce, fertilizer, information and communication technologies (ICTs), weather forecasting, web Cite this Article: Komal Pawar, Sonali Londhe, Vaibhav Kakade, Vaibhav Shelar. Krishi Portal: web based farmer assistance. International Journal of Data Structures. 2020; 6(1): 1–5p.
Utilization of Cloud Services for IoT Device and Data Management
Cloud storage services are valuable and widely used resources that are widely used in various applications. In addition to being used in many academic research fields, industrial companies are rapidly adopting cloud storage. Cloud computing is a common and appropriate model for on-demand network access to implement a shared pool of configurable computing resources (networks, servers, storage, applications, services, etc.). Set up and publish quickly with minimal administrative effort or interaction with a service provider. With built-in security, you can easily use the cloud anywhere. Virtualization is a technology that can use services as a reality, but it is not a reality. Like many computer systems in the cloud. Scheduling is a set of instructions that define the execution of a task based on specific instructions. It follows the priority and importance of the data. IoT devices are electronic devices that operate without physical access to the device and obtain responses from the device. This paper shows how to protect these data transfers using existing cloud services. Keywords: IoT devices, cloud services, personal cloud storage, application domain for IoT devices, data management, cloud, IoT, virtualization, simulator Cite this Article: Sujeet Kumar, Surjit Singh. Utilization of cloud services for IoT device and data management. International Journal of Data Structures. 2019; 5(2): 26–34p.
With days in and out, we are facing a problem in managing our local network architecture whether it’s for schools/colleges/organizations. Network Management System is only the solution for this issue which helps one to monitor and manage their network system. In this research study, we are proposing a JAVA Based Model of NMS (Network Management System) which is “CloudConnect: Your Own Private Space” with the introduction and deep linking with SNMPv3 due to its numerous security measures. Keywords: JAVA, network management system, security, simple network management protocol (SNMP), OID Cite this Article: Ramneek Kalra, Yashita Sharma, Namrata Sukhija. CloudConnect: Your Own Private Space. International Journal of Data Structures. 2019; 5(2): 8–14p.
Audience Behaviour Mining Using Data Analysis
Each person has different views, different opinions, different interest’s and different ideology. From the current variety of options to know what suits the best is a tough task. In the present situation we have to rate and comment such as to see whether the product is good or bad. But through our project, we are trying to analyze by just the comments; we rate the show and recommend according to the viewers mindset. We are implementing a new way of methodology where videos are ranked based on its quality of theme. The emotional nature of every video is being analyzed and a certain ranking is being specified to each one, based on the algorithmic intelligence. The intelligence works based on the positive comments and reviews held on a video, which makes the qualitative prediction in a much better and unambiguous way. The typical way of channel ranking based on previous views and advertising power is being avoided here. Videos are analyzed on its quality in nature as well as the positive reviews and responses. Keywords: data mining, audience behaviour mining, TV, integrated development environment (IDE), PHP, MySQL Cite this Article: Arathi Nair, Alex Mathew, Sterin, Jeswin Roy Decouth. Audience Behaviour Mining Using Data Analysis. International Journal of Data Structures. 2019; 5(2): 1–7p.
Sensitive Data Protection in Cloud Education System Using Security Layers
Abstract: Security is an issue for storing data in any platform when we work in large scale then we carry large amount of data it contain some sensitive information. If we are failing of securing data then our application management is also fail so security is a most important part of any planning. This paper present an small idea for securing data in initial level in terms of layer of security in education system. Keywords: cloud computing, SAAS, IAAS, PAAS, Security Layers Cite this Article: Seema Gusain. Sensitive data protection in cloud education system using security layers. International Journal of Data Structures. 2019; 5(2): 22–25.
Enhancement of Data Security in Cloud Computing Environment: A Study
Cloud computing is an internet-based computing model which provides several resources through Cloud Service providers to cloud users on-demand basis without buying the underlying infrastructure and follows pay-per-use basis. Cloud computing usage has increased rapidly in many companies. Cloud computing is the well-known technology for scaling of extensive data and complex computation. Cloud computing offers many benefits in terms of low cost and accessibility of data. Data security is the major issue in cloud computing. This paper covers various security algorithms related to cloud computing and shows a comparative study on data security algorithms. This mix profits by the productive security of deviated encryption and the quick execution of symmetric encryption while moderating the privileges of clients to get information by a secured and authorized way. Evaluation results prove that the processing time of our lightweight algorithm is faster than state-of-the-art cryptographic algorithms. Keywords: cloud computing, security algorithm, cryptographic algorithm, data security, cloud public key, owner’s public key Cite this Article: Darshan Deepak Manjrekar. Audience Enhancement of Data Security in Cloud Computing Environment: A Study. International Journal of Data Structures. 2019; 5(2): 15–21p.
Abstract—As the number of road users constantly increases and resources provided limited, intelligent traffic signal controller is a very major requirement. Some limitations to the usage of intelligent traffic control exist. In traffic control system an RF transmitter on the ambulance will communicate with the RF receiver mounted on the signal post. An algorithm is used to control the traffic signals automatically based on the key pressed by the driver from keyboard in the ambulance. With a large population and large amount of vehicles there is also a big trouble of car accidents or road accidents and with these overcrowded roads there is a problem of delay in first aid service. To overcome this delay in first aid service this paper describes a solution that is “Intelligent Ambulance with Automatic Traffic Control” which includes the accident detecting, alerting and tracking mechanism with automatic traffic light controlling system such that the ambulance can achieve a freeway in order to provide the first aid to patient as fast as possible. In traffic control system an RF transmitter on the ambulance will communicate with the RF receiver mounted on the signal post. An algorithm is used to control the traffic signals automatically and moving toward the location or while taking the patient to the hospital there may or may not occur some traffic so in order to free or release the traffic the intelligent ambulance controls the traffic light itself such that the traffic light converts in a manner so that it could receive the freeway to the hospital. Keywords: RF transmitter, RF receiver, arduino Cite this Article: S. Niviyadharshini, G. Pearlstone Emmanuel, M. Yasar Arafath, R. Mouleeshuwarapprabu. Make Way. International Journal of Data Structures. 2019; 5 (1): 21-23p.
Application of IoT, AI and Big Data in Agriculture
ABSTRACT Agriculture has played a significant role in the improvement of the Indian economy. It represents about 19% of gross domestic product. This segment contributes near a quarter of India's national income, and workforce occupied with agriculture is about 60%. About 70% of the people live in country regions and a greater part of them rely on farming as their essential wellspring of salary. Green revolution started from 1965 onwards in India. The expanded utilization of fertilizer and irrigation, referred to overall as the Green Revolution, expanded creation expected to make India independent in food grains. The farmers additionally get low cost for their yield on account of a few reasons like predetermined number of Mandis, absence of value data, absence of innovative information and so on. Online portals are likewise accessible to help famers limitedly. However, the help of such research, innovation and extension of sectors could be taken and make new technologies that make it possible to the farmers at right place and time to increase farmer’s productivity and profitability. Keywords: AI (Artificial Intelligence), IoT (Internet of Things), IT (Information Technology) Cite this Article: Sanket Dhumal, Pritish Kadam. Application of IoT, AI and Big Data in Agriculture. International Journal of Data Structures. 2019; 5 (1): 5-8p.
A Study of Recent Advancements of Information Technology in Waste Management World
Abstract - We live in a world where everything can be controlled and operated automatically, but there are still a few important sectors in our country where automation has not been adopted or not been put to a full-fledged use, perhaps because of several reasons one such reason is cost. One such field is that of waste management. In this paper I have tried to summarize the use of technology in the way to help the waste management sector which can help towards a sustainable development. Keywords - information and communications technologies (ICT), Waste Management Cite this Article: Arjun Varma, Sandeep Vishwakarma. A Study of Recent Advancements of Information Technology in Waste Management World. International Journal of Data Structures. 2019; 5 (1): 1-4p.
Blend of Cloud Computing and Internet of Things in agriculture sector
Abstract- The emergence of the turbulent technologies like cloud computing, IoT (Internet of Things), machine learning and data analytics has created its mark in several sectors like transportation, agriculture, healthcare, environment monitoring, renewable energy systems, retail, and industry. The IoT (Internet of Things) provides connectivity of things dynamically to the network whereas cloud computing provides virtualization in storage and processing. In Cloud IoT paradigm, cloud and IoT are merged together to supply complementary features in smart applications/services. Cloud IoT solutions build it possible to envisage ubiquitous and pervasive connectivity to the users. The source of livelihood for a majority of the population is agriculture; it mantles a dominant role in the economy of the country. The projected work focuses on Cloud IoT architecture for providing any smart solutions in several sectors. A case study of a smart irrigation system is discussed in this paper. The cost-effective and efficient solution is demonstrated and results speak the strength and performance of the system. Keywords- Amazon Cloud, Raspberry, MQTT, sensor, data analysis. Cite this Article: Amit Vishwakarma, Aarti Vishwakarma. Blend of Cloud Computing and Internet of Things in Agriculture Sector. International Journal of Data Structures. 2019; 5 (1): 9-14p.
Abstract—SEO stands for Search Engine Optimization. SEO is a process which enables online businesses to be seen on the internet. This means it gives online business to show their presence on the internet and allowing them to use various marketing strategies. SEO may target different types of data and shows relevant data to the target audience. The phrase “search engine optimization" became popular around 1997 according to Danny Sullivan – an industry analyst. Sullivan has mentioned the name of Bruce Clay crediting him as the first people to popularize this term. Jason Gambert tried to trademark SEO by explaining that SEO is not a marketing service but manipulation of keywords. Knowing the importance of SEO in I.T industry, various methodologies are being applied so that the online search results are suitable and relevant to the audience. Keywords—SEO, Search engines, digital marketing, keywords, optimizations. Cite this Article: Shah Charmi, Shah Kaushal. SEO: Challenges and Characteristics. International Journal of Data Structures. 2019; 5 (1): 15-20p.
Dual Carrier Modulation using Improved Data Allocation Scheme
Purpose: The increasing need for high speed low power data transmissions over frequency selec-tive fading channels has drawn attention to suggest dual carrier modulation (DCM) for multiband orthogonal frequency division multiplexing (OFDM) transceiver for ultra wideband (UWB) wireless personal area network (WPAN). Design/Methodology/Approach: Under frequency selective fading channel conditions, the de-coder is not su_cient enough to decode the transmission bits of severely attenuated data tones hence,we suggest DCM for a multiband OFDM transceiver due to its multiple capability of providing both frequency diversity and coding gain. It also resulted in low bit-error-rate (BER) at a given signal-to-noise ratio (SNR) when compared to conventional multiband OFDM system. In order to achieve an optimised BER, DCM transforms four re-ordered bits into two quaternary phase shift keying (QPSK) symbols and further transforms to two 16-quadrature amplitude modulation (16-QAM) like symbols with a suitable mapping technique and at the receiver end they are decoded with maximum likelihood (ML) decision rule. After performing the transformation, the outage probability and average BER expressions are derived to analyse the system performance. Findings: DCM is suitable for high data rate transmission and is immune to frequency selective fading. The outage and BER performance outstands over conventional multiband OFDM transceiver due to the inclusion of DCM mapping. Practical Implications: It is widely used in WPANs like high de_nition multimedia interface and wireless universal serial bus (WUSB). Originality/Value: This paper derives novel closed-form outage probability and a tight upper bound on average BER expressions for DCM based multiband OFDM UWB transceiver over frequency selective Nakagami-m fading channels, for any arbitrary value of m. For this, moment generating function (MGF) of sum of squared, identical independently distributed, Nakagami-m random variables are used. Further the system performance is also validated for the case of exponential decaying PDP and the simulation results are provided to check the accuracy of the derived expressions.
Smart Buildings Power Management with Monitoring and Controlling Using WSNS
The design and development of a smart monitoring and controlling system for household electrical appliances in real time. In this system the sensors gives the values like voltage, current of electrical appliances and calculates the power consumed. The developed system is economical and flexible. the main aim of system is power management by controlling mechanism of household appliances and it can saves power. By this we can control of the electrical appliances through application. It can be extended for monitoring the whole intelligent building. We aim to determine the areas of daily peak hours of electricity usage levels and come with a solution by which we can lower the consumption and enhance better utilization of already limited resources during peak hours.
A Review of Distributed QOS Modeling Analysis
ABSTRACT: Model-driven engineering (MDE), the Domain-Specific Languages (DSLs), specifically, are progressively being utilized to deal with the intricacy of creating applications in different areas. Many DSL benefits are subjective, there is a need to quantitatively show the advantages of DSLs to simplify correlation and assessment. This paper portrays how we directed profitability examination for the Distributed Quality-of-Service (QoS) Modeling Language (DQML). Keywords: MDE (Model Driven Engineering), DSLs (Domain Specific Languages), QoS (Quality of Service), DQML (Distributes QoS Modeling Language).
A Review of Spatial Deployment Optimization
ABSTRACT: Distributed real-time and embedded (DRE) frameworks can be made out of several product segments running crosswise over tens or many arranged processorsthat are physically isolated from each other. A key worry in DRE frameworks is deciding the spatial sending topology, which is the manner by which the product segments guide to the fundamental equipment parts. Enhancements, for example, setting programming parts with high-recurrence correspondences on processors that are nearer together, can yield various critical advantages, for example, diminished power utilization because of diminished remote transmission control required to impart between the preparing hubs. Keywords: Distributed real-time and embedded (DRE), ScatterD, earliest-deadline-first (EDF), rate monotonic (RM).
A Novel Traffic Management System Using IOT and Raspberry PI 3 Microcontroller
The existing Traffic control system is based on the “time” which is already assigned in the system. According to these times the signals are working in each lane. But in these system condition is occurs as all vehicles in lane( L1) are passed and vehicles in another lane (L2) still in waiting state because time is not over and hence signal is still red. These systems are very inefficient because they are unable to handle various simple situations which are occurs throughout the day. Major drawback is it has unnecessary waiting time and there is no facility to handle emergency vehicles. The project is designed to develop a system which performs execution based on density of vehicles (Vehicle Count). After calculating the number of vehicles we will came to know in which side the density is high based on which signals will be allotted for a particular side. Raspberry pi is used as a microcontroller which provides the signal timing based on the traffic density. Raspberry pi directly uploads the Traffic status to the server by using Ethernet connection or Wi-Fi connection. The end user access this data by using GUI designed for specific application. By using webcam it captures the traffic Congestion and emails it to the predefined user.
Design of Energy Efficient System for Classrooms Using Face Recognition
A novel face recognition based door access system was implemented to use in classrooms. This system uses a ARM7 with webcam to capture and process digital pictures of the students and recognize the faces to open the door, and automatically switch on the lights and fans based on number of students. The entire system uses two cameras with two GUI apps one for entry and one for exit. The algorithm runs continuously and detects registered faces and sends it to the microcontroller. Based on received data microcontroller open the door and also increments the number of students. If at exit side GUI detects the face then microcontroller decrements the number of students. The GUI designed especially for this system. The experimental results shows that the efficiency of this project is high when compared with IR based door access system.
Exposed infrastructures on the Internet signify a serious threat to countries with suppressed powers, which leads them to develop and deploy review mechanisms in their networks. Unfortunately, the existing suppression system does not offer guarantees of high availability for its users because censors can use the current advanced suppression technologies to easily identify and destroy the traffic that belongs to these systems. In this article, we propose to provide services (SWEET) through the use of email tunnels, which is a highly available suppression system. SWEET works by encapsulating the traffic of users who have been reviewed in emails transported by public email services such as Gmail and Yahoo Mail. Since the SWEET operation is not restricted by any particular email provider, we trust that the acceptor should block email communications to undermine SWEET, which is unlikely because email is an important part of the Internet today. Through experiments in our system pattern, we exposed that the SWEET performance is sufficient for web surfing.
Conceptual Graph: Shrewd method of attaining Semantic Exploration over Encrypted Data
Cloud Computing is an IT model that aids universal access to share lake of configurable system capitals. The benefit of cloud computing is its less cost and the limitation is its safety. Searchable encryption is an important area which has to be focused on Cloud Computing. The most efficient and reliable is cipher text search schemes which focuses on keyword, or shallow semantic parsing but also faces the problem of its inefficiency of satisfying user search intention. In order, to make semantic search smarter we have come up with an idea of context-aware search scheme. In this paper we introduced conceptual graphs as knowledge representation tool. Based on the demand we have introduced two schemes as PRSCG and PRSCH-TF. To avoid the problem with the single keyword we have come up with an idea of multi keyword ranked search over encrypted cloud data and by concentrating on PRSCG and PRSCG-TF to resolve the problem of privacy-preserving smart semantic search based on CGS. In this paper, we in deep analyzed the privacy and efficiency of proposed scheme and found it to be efficient enough.
The “World is moving Cloud” is the phrase where everybody (organization/business etc) are coming up with .The need of everybody in the present world is to have many costly resources, at low cost, use only those resources needed and don’t want to pay on those which remain idle, dynamic usage of resources, anytime, anywhere access to the resources flexible scaling of resources, this all needs is being answered by one and only one Technology called Cloud Computing. In this paper I have discussed in detail about cloud computing Technology. What was before this Technology? It gives fullest knowledge of cloud computing. Everywhere we go around we see Internet; is growing beyond our imagination and when Internet comes in our mind the very next world comes is the Cloud. In this paper we discuss about the various models, cloud Architecture and the growing need in cloud. The primary benefit to Cloud Computing is the ability to store and obtain data from any location, at any time, on multiple types of devices.
An approach to avoid intrusion and protection for Cloudlet-based Data Sharing
With the fame of wearable gadgets, alongside the improvement of mists and cloudlet innovation, there has beenincreasing need to give better therapeutic care. The preparing chain of therapeutic information principally incorporates information gathering, information stockpiling anddata sharing, and so forth. Customary medicinal services framework frequently requires the conveyance of restorative information to the cloud, which includes clients' sensitive information and causes correspondence vitality utilization. For all intents and purposes, therapeutic information sharing is a basic and testing issue. Thusin this paper, we develop a novel social insurance framework by using the adaptability of cloudlet. The elements of cloudlet incorporate privacyprotection, information sharing and interruption location. In the phase of information accumulation, we initially use Number Theory Research Unit (NTRU)method to scramble client's body information gathered by wearable gadgets. Those information will be transmitted to adjacent cloudlet in an energyefficient design. Also, we introduce another trust model to help clients to choose trustable accomplices who need to share put away information in thecloudlet. The trust display additionally encourages comparable patients to speak with each other about their maladies. Thirdly, we partition users'medical information put away in remote billow of healing facility into three sections, and give them legitimate security. At last, keeping in mind the end goal to shield thehealthcare framework from malevolent assaults, we build up a novel synergistic interruption location framework (IDS) strategy in view of cloudletmesh, which can adequately keep the remote medicinal services huge information cloud from assaults. Our examinations exhibit the effectivenessof the proposed plot.
A Review of Different Technologies of Cloud Computing
Cloud Computing delivers us a mode through which we can easily access various applications as efficacies, over the Internet. It also permits us to configure, produce, and modify applications online. Cloud computing is a valuable developing model of business computing. In the present paper, we discuss the concept of cloud architecture and equates cloud computing with grid computing. We also report the characteristics and applications of several popular cloud computing platforms. In this paper, we purpose to identify the challenges and issues of cloud computing.
Privacy Preserving and Data Publishing-Hybrid Approach
Information exchange and analyzing the data provides a much easier way to the researchers for doing research that is fundamental and involves various case studies. Data publishing may cause trouble for an individual in case one if suffering from a non-curable disease. In order to maintain the privacy and at the same time publishing data, various data anonymization techniques have been evolved. This paper involves study about these various types of Privacy Preserving and Data Publishing techniques.
Recovering Past Via Artificial Intelligence
This has always been a tough challenge for the mankind on this earth to recover the past. Human being has always been surrounded by “When”, “What”, ”How”, ”Where” and last “Why” questions. Discovery of past has been undergoing since ages. Ancient texts, scripts, techniques, lifestyle all have been discovered and deciphered by experts due to the advent of technology. Science has developed to the extent that nothing is beyond its scope. Computer science has broaden the scope and reach of mankind to the infinity. Artificial Intelligence is the field of computer science in which research is done involving human reactions and cognitive environment. The contribution of this science of Artificial Intelligence in recognizing the 4000 years old Indus script is included in this paper. Neural Network has played an important role in this work. Markov model and deep learning algorithm had been at the background to recognize the symbols in the Indus script. This algorithm is used in self driving cars and auto completion work of google. The process involves three stages: Extracting the symbol, Selective Search for the grapheme is done and lastly Region Grouping is done.
Cloud Computing has been information technologies services on developed to deliver demand to organizations like as individual users, this technology is still in its early stages of development because it suffers from different security threats that prevent users trust it. In this paper, we identify different security problems existing in cloud from several research papers and we show suggested solutions. This cross cloud speaks to next logical wave in processing, empowering complex mixture applications, cost and execution improvement, upgraded dependability, client adaptability and secure evasion. Providing testing as a service in cross clouds become hot topics in industry.
Risk level analysis for diabetic nephropathy using data mining techniques
Diabetic nephropathy is a disease which affects the kidney leading to end stage renal disease. This problem occurs in patients affected by type2 diabetes. The earliest clinical manifestation is of microalbminuria which is a protein that is released in excess amount in patients affected by diabetic nephropathy. This research work aims at analyzing the risk factors involved in causing diabetic nephropathy using Bayesian classifier. The risk factors such as hypertension, hyperlipidemia, obesity, sedentary life style, urbanization and changing diets are analyzed and their probability in causing diabetic nephropathy is analyzed. Bayesian classifier is used to provide a decision support system to monitor the health status of the patient and and reduce the risk of creating nephropathy. The implementation is carried out using MATLAB and the risk is classified as low and high based on the probability. Keywords: Bayesian classifier; Nephropathy; Type2 diabetes; Probability; Decision Support System
Dynamic Ensemble Discovery and Analysis for Data Stream Classification
The classification techniques are used to assign transaction categories. The learning and testing tasks are carried out for the classification process. The classification operations are performed on the data values that are maintained under the databases. There is no limit for number of data scans and process time. The training set requires labeled transaction collection. The stream based classification uses data values collected from remote machines. The stream based classification model is restricted with time boundaries and data scan counts. The classification operation must be performed in minimum time intervals. Most existing data stream classification techniques ignore one important aspect of stream data with arrival of a novel class. A data stream classification technique is adapted to integrate a novel class detection mechanism into traditional classifiers. The system enables automatic detection of novel classes before the true labels of the novel class instances arrive. Novel class detection problem becomes more inspiring in the occurrence of concept-drift, when the original data distributions grow in streams. In order to determine whether an instance belongs to a novel class, the classification model sometimes needs to wait for more test instances to discover similarities among those instances. The novel class identification receipts more waiting time to evaluate the class instances. Data point identification is a time-consuming process. Stream based mining model collects data from streams from remote machines. Stream based classification model is used to fetch novel classes in concept drifting environment. The class ensembles are used to perform the transaction similarity measures. The Class Based ensembles for Class Evaluation (CBCE) scheme are applied to discover the classes in the streams. The class detection scheme is enhanced to assign class labels in dynamic feature set environment.
K-Means Clustering With Optimal Centroid Estimation Mechanism for Electricity Usage Pattern Discovery
Clustering techniques are used to group up the transactions based on the relevancy. Hierarchical and partition clustering techniques are used for the clustering process. Hierarchical clustering technique uses the structure and data values. The partition clustering technique uses the data similarity factors. Transactions are partitioned into small groups. K-means clustering (KMC) algorithm is one of the widely used clustering algorithm. Local cluster accuracy is high in the KMC algorithm. Inter cluster relationship is not focused in the K-means algorithm. KMC algorithm requires the cluster count as the major input. The transactions are compared with the centroid values in each cluster environment. The system chooses random transactions are initial centroid for each cluster. Cluster accuracy is related with the initial centroid estimation process. Because all the transactions are transferred to the relevant cluster based on the initial centroid comparison process. The random transaction based centroid selection model may select related transactions. In this case, the cluster accuracy is limited with respect to the distance between the centroid values. The K-means clusters with simulated annealing (KMC-SA) scheme is built to perform the data partitioning with centroid selection process. The centroid selection is carried out using the Simulated Annealing mechanism. The electricity usage pattern discovery system is designed to improve the KMC algorithm with optimal centroid estimation models. Cosine distance measure and Euclidean distance measure are used to estimate similarity between the transactions. Precision and recall and purity measure are used to test the cluster accuracy levels. Java language and Oracle database are selected for the system development.
Frequent Itemset Mining Using Normalized FP-Growth Algorithm
The growing interest in data storage has made the data size to be exponentially improved, hampering the process of knowledge discovery from these large volumes of high-dimensional and heterogeneous data. In recent years, many efficient algorithms for mining data associations have been proposed, facing up time and main memory requirements. However, this mining process could still become tough when the number of items and records is enormously high. In this paper, the goal is not to propose new efficient algorithms but a new data structure that could be used by a variety of existing algorithms without modifying its original schema. Thus, our goal is to speed up the association rule mining process regardless the algorithm used to this end, enabling the performance of efficient implementations to be enhanced. The structure simplifies, reorganizes, and speeds up the data access by sorting data by means of a shuffling strategy based on the hamming distance, which attain similar values to be closer, and considering both an inverted index mapping and a run length encoding compression. In the experimental study, we explore the bounds of the algorithms’ performance by using a wide number of data sets that comprise either thousands or millions of both items and records. The results demonstrate the utility of the proposed data structure in enhancing the algorithms’ runtime orders of magnitude, and substantially reducing both the auxiliary and the main memory requirements.
Privacy Ensured Big Data Processing With Map Reduce Framework
The cloud computing environment is deployed to share hardware and software resources to the users. Shared services are provided by the service providers. Cloud data centers are installed to provide the shared data values to the users. In privilege based manner the cloud environment divided into two major categories. They are public and private clouds. Services are provided under the public cloud environment and the data values are shared through the private cloud environment. The hybrid model integrates the public cloud services with private cloud data values.
An Effective Prediction of Diseases Using Significant Pattern Mining in Data Exploration
These tools are used for business analysis, scientific research, medical research and many other areas. In this project, the diseases can be predicted based on the analysis from their symptoms and the report is generated from the systematic analysis of a particular disease. Early detection and prevention of diseases plays a very important role in reducing the mortality rate caused by those diseases. It is a multilayered method which uses significant pattern mining using iterative search techniques to build a risk prediction system which predicts various diseases. It is user friendly, time and cost saving. This research uses data mining technology such as classification and prediction to identify potential treatments for patients according to their diseases. Data mining combines techniques including statistical analysis, visualization, decision trees, and neural networks to explore large amounts of data and discover relationships and patterns that shed light on business problems.
Review insight on Fingerprint Image Enhancement
Several algorithms for automatic fingerprint image quality assessment have been proposed in literature. Also, the benefits of incorporating automatic quality measures in fingerprint verification have been shown in recent studies.
Problem Identification and Objectives of Fingerprint Image Enhancement
Fingerprints have been used for over a century and are the most widely used form of biometric identification. Fingerprint identification is commonly employed in forensic science to support criminal investigations, and in biometric systems such as civilian and commercial identification devices.
Research Insight – Data Structures and Algorithms
Data structure is a procedure of storing and organizing data in a computer, to be retrieved later for use. They are important for the many reasons. Data structures are utilized in nearly every software system, they are essential constituents of many efficient algorithms, managing large integrated collection of databases, programming languages, and in software design. Algorithms too, are not far behind. They are step by step procedures to accomplish a task. By developing a good understanding of algorithms, we can choose the right decision for a problem and apply it properly. We use algorithms to find the shortest or the best path to travel from one location to another (Google Maps).There are many applications of algorithms in the modern world – weather forecasting, finding structural patterns and cure diseases, inventing games and so on. The latest example was developing a highly complex algorithm for a space probe that helped Indian scientists to reach Mars efficiently, at minimum costs.
In today’s scenario testing in software industries is a very hot topic to think .Corporate sector needs more and more testing for validating the data of users or clients. In this paper I would like to highlight some testing useful in industries.
System Development Methodology for Automobile Workshop
We have seen the process of the “Automobile Workshop” and get in problem to a great extent. All process is done manually. All the automobile industries doing their whole work on paper. It was a hectic task and it takes a lot of time and manpower. So they decided to do automation of their agency. This paper is based on the concept that how through computer applications of intranet we can help and improve automobile industry.
.NET Frame work and its Application Using Database Queries
The Internet revolution of the late 1990s represented a dramatic shift in the way individuals and organizations communicate with each other. Traditional applications, such as word processors and accounting packages, are modeled as stand-alone applications: they offer users the capability to perform tasks using data stored on the system the application resides and executes on. Most new software, in contrast, is modeled based on a distributed computing model where applications collaborate to provide services and expose functionality to each other. As a result, the primary role of most new software is changing into supporting information exchange (through Web servers and browsers), collaboration (through e-mail and instant messaging), and individual expression (through Web logs, also known as Blogs, and e-zines — Web based magazines). Essentially, the basic role of software is changing from providing discrete functionality to providing services.