An Effective Algorithm for Correlation Attribute Subset Selection by Using Genetic Algorithm Based On Naive Bays Classifier

In recent years, application of feature selection methods in various datasets has greatly increased. Feature selection is an important topic in data mining, especially for high dimensional datasets. Feature selection (also known as subset selection) is a process commonly used in machine learning, wherein subsets of the features available from the data are selected for application of a learning algorithm. The main idea of feature selection is to choose a subset of input variables by eliminating features with little or no predictive information. The challenging task in feature selection is how to obtain an optimal subset of relevant and non redundant features which will give an optimal solution without increasing the complexity of the modeling task. Feature selection that selects a subset of most salient features and removes irrelevant, redundant and noisy features is a process commonly employed in machine learning to solve the high dimensionality problem. It focuses learning algorithms on most useful aspects of data, thereby making learning task faster and more accurate. A data warehouse is designed to consolidate and maintain all features that are relevant for the analysis processes

Indian and Chinese Oncologists’ ASCO Conference-based Derivative Articles: A Comparative Study

Introduction: Conference-based derivative article is a welcome step in medical fraternity because it overcomes limited peer review, enhances validation of the results, avoids duplication and reduces publication bias to some extent. Further, such work helps in the furthering visibility, archival and accessibility issues of the content. With this background the scope of the present work is restricted to comparing oncology publication output of India and China in terms of derivative articles based on American Society of Clinical Oncology (ASCO) conference papers. Objectives: The significance of the present study lies in the fact that it aims to compare the conversion rate of derivative articles for both the countries, understand some of the factors such as, authorship pattern, identify the bibliometric pattern of derivative articles, and attendance for a conference and number of papers presented and converted. Methods: ASCO conference papers for the period of 2005-14 are investigated for derivative articles in PubMed. At least one of the authors is from India or China in the conference paper. While, the criteria to determine the derivative article includes important keywords, reasonably content-wise similarity and at least one of the authors is common between conference paper and corresponding journal article. Results: It is revealed that in terms of attendance by oncologists, their contribution in conference papers and conversion into derivative articles, China is ahead of India. Conclusions: It is concluded that Chinese oncologists have surpassed Indian in terms of higher (i) conversion ratio, (ii) average number of authors, (iii) significantly higher presence of first and last author in corresponding derivative articles, (iv) citation impact including h- Index, (v) higher attendance at ASCO conference, and (vi) phase wise trials studies

Modified Golomb-Rice Algorithm for Color Image Compression

The memory required to store the color image is more. We have reduced the memory requirements using Golomb-rice algorithm. Golomb-rice algorithm consists of the following two steps. In Golomb-Rice algorithm the first step is to compress the image using discrete wavelet transform. By using DWT compression the 8 × 8 image is converted into m × n sub-windows and it is converted into raster file format for producing m × n-1 differential data. Encoding is done by using Golomb-Rice coding.  After encoding, the process length, code word and size are calculated by using GR coding.In the second step decoding is done by GR coding based on the obtained length and code word. After that decoded image is decompressed in order to get the original image by using the inverse discrete wavelet transform.&nbsp

A GLOBAL ATM LOCATOR AND A METHOD THEREOF

The present disclosure is related to a global Automated Teller Machine (ATM) locator and a method thereof. The method includes receiving, by a global ATM locator, a request message from an electronic device of a user for details related to a nearest ATM. The global ATM locator initiates an API call to a payment network provider associated with the global network provider to fetch the information related to the nearest ATM for the user, as per the information given in the request message and current location of the user. Thereafter, the global ATM locator receives the requested details related to the nearest ATM such as balance amount, denominations of the balance amount, location of the ATM, wheelchair accessibility at the ATM, depositing capability at the ATM, access fee of the ATM and the like. The received information is provided to the user such that can make an informed decision about which ATM to visit for performing desired financial operations. Further, the present disclosure enables the user to query a global ATM locator to receive information related to not just the nearest ATM, but also other details such as balance amount in the ATM, working status of the ATM, wheelchair accessibility at the ATM, depositing capability at the ATM, Access fee/transaction fee of the ATM, and the like without the need to physically visit the ATM center. Therefore, the present disclosure enhances user experience, eliminates the time and resources involved in the tedious process of searching for an ATM center for performing certain financial operations, and also reduces delays in the financial operations that can be performed through the ATM

Recognition of Anthracnose Injuries on Apple Surfaces using YOLOV 3-Dense

Plant ailment is one of the essential drivers of harvest yield decrease. With the advancement of PC vision and profound learning innovation, independent discovery of plant surface sore pictures gathered by optical sensors has become a significant research bearing for convenient yield ailment analysis. Right now, anthracnose injury identification strategy dependent on profound learning is proposed.  Right  off the bat, for  the  issue  of  lacking  picture  information brought about by the irregular event of apple illnesses, notwithstanding conventional picture expansion strategies, Cycle-Consistent Adversarial Network (CycleGAN) profound learning model is utilized right now achieve information  increase. These strategies adequately  enhance  the  decent  variety of preparing information and give  a  strong  establishment to  preparing  the  identification  model.  Right now, the premise of picture information increase, thickly associated neural system (DenseNet) is used to streamline highlight layers of the YOLO-V3 model which have lower goals. DenseNet extraordinarily improves the  usage  of  highlights in  the  neural  system  and  upgrades  the identification consequence of the YOLO-V3  model.  It  is  checked in tests that the improved model surpasses Faster  R-CNN with VGG16 NET, the  first  YOLO-V3  model,  and  other  three  cutting  edge  arranges  in  discovery   execution,  and it can understand continuous recognition. The proposed technique can be all around applied to the recognition of anthracnose injuries on apple surfaces in-plantations

Efficient Cluster Formation Protocol in WSN

WSN which is called as Wireless Sensor Networks plays vital role in many applications. Most WSNs exploit clustering method for data communication from sensor destination nodes to the sink. So, Clustering should be made as efficient as possible. In most of the existing clustering protocols, residual nodes (non-cluster nodes) may be formed during clustering. Though these nodes can send their data directly to the base station, it needs large amount of energy. In the proposed method, PSO algorithm which is termed as Particle Swarm Optimization is used for cluster configuration which evades the formation of residual nodes. The base station performs cluster formation. Network Simulator-2 (NS-2) tool is used to achieve simulation. Simulation outcomes reveal enhanced operation of the proposed protocol than existing LEACH and OEERP protocols

Simulation Software Based Analysis of Automotive radiators: a Review

The thermal execution of a vehicle radiator assumes an essential role in the execution of a vehicles-cooling system and all other connected system. For various years, this element has experienced little consideration with almost no changing in its built-up cost, geometry and operation. Compared with the longstanding tubular heat exchangers at present frame the foundation of the present procedure industry with their innovative performance reading levels tubular heat exchangers they are commonly utilized. Present review focuses on the different research with respect to CFD Analysis to increase vehicle radiator efficiency. present paper is an examination about the impact of different parameters like size of radiator core, way flow of working fluid, frontal zone of radiator, space in fins, pitch area of tube, tube and fin shape, mass flow rate of coolant, fins material, velocity of fluid, pitch area of tube, inlet temperature of air and different parameters to develop vehicle radiator determination and efficiency of advanced geometry from above parametric investigation. The Computational fluid elements (CFD) simulations were comparing the pressure drop and heat transfer of heat exchanger with various parameters for ideal performance. CFD results have high correspondence level by authentic experimental outcomes. Several results of review recommend that CFD have been shown very effective in decreasing production cost and testing time

Monitoring of Drainage System in Urban Using Device Free Localization Neural Networks and Cloud computing

Wireless Sensor Network is a Wi-Fi community consisting of spatially propagated and self-sufficient devices using sensors to detect physical or environmental conditions. During heavy rainfall, the urban drainage system cannot drain the water. A wireless sensor with many interconnected wireless sensor nodes captures real-time data from the network environment and transmits this data to a base station for analysis and operation. With wireless sensor nodes, it is possible to capture and monitor the amount of water in drainages and the difference in water flow between the two points in the drainage system. Nevertheless, the majority localization techniques aims on device based localization, which can find target with festinated devices. It is not suitable for applications such as terrain, drainage flow and flooding. Here device free wireless localization system using artificial neural networks and a cluster based wireless sensor network system to monitor urban drainage is proposed. There are two stages in the system. During the off-line preparation stage, Acceptable Signal Strength (RSS) differential metrics are calculated between the RSS metrics together while the monitor area is empty and calculated by a specialized in the region. Some RSS dissimilarity values ??are selected in the RSS Difference Matrix. The RSS dissimilarity standards ??and associated matrix indices are taken as the inputs of the ANN representation in addition to the identified position coordinate are in its outputs. The real-time data collected from the wireless sensor network is used to detect overflow and provide alarms before disturbances arise

Understanding the Concept of Data Encryption in Network Security: Review of Types, Algorithms and Methodologies

Data encryption is the process of security method which is also used to provide security in network. There are many security methods available. Data encryption is the latest and most commonly used security method in order to protect the data and sensitive information of the user. It depends upon the technical team to choose the best network security method so that they are able to protect the data and information of the organisation. The technical team need to understand the business goals and objectives and based upon the type of data and information of the organisation, should select the best and efficient network security methods which is also one of the major challenges of the team. The objective of data encryption is to encrypt the data in such a way that it is not easily understood by the anyone else other than the person who is authorised to have access and also have the key to access it. It is one of the best methods of data encryption. The paper will discuss the importance of data encryption, the methodologies available and also its benefits as well as challenges

STRUCTURAL ANALYSIS OF SPUR GEAR USING FEM

To achieve improved static gear drives and enhanced load carrying capacity and reliability, a complete studyabout gear drive design and analysis is carried out. The structural analysis is examined for spur gear drive. Geardrives transmit motion and power by tooth mesh mostly in the form of involute profiles, gear tooth mesh is acomplex process involving multi tooth engagement, multipoint contact and varying load conditions. The contactstresses were examined using 3-D FEM model. The gears are modeled by using AUTO- CAD and analyzed byANSYS 16.0. In the present work an attempt is proposed to find the structural analysis at the point of gear toothengagement under static loading conditions through finite element softwar