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— In this paper, ML strategy for detecting phoney reviews is proposed. Using a range of feature engineering methodologies, this paper extends beyond the review's feature extraction technique to identify various examiner behaviours. The paper compares and contrasts the outcomes of various programmes. We compared the effectiveness of several classifiers in both cases using an actual dataset of restaurant ratings with and without factors derived from the user's activitiesWhen it comes to the f-score, the remaining classifiers have the highest f-score of 74.79 percent. According to the results. The f-score has grown by 14.14 percent after factoring behavioural characteristics of the reviewers.

— In today's web context, where participation and collaboration are the foundation for the development of common knowledge, the usage of Metaverses as a platform for human interaction is a worldwide trend. It's a virtual or augmented reality universe that allows you to create virtual social interactions. Neal Stephenson coined the term "metaverse" in his 1992 science fiction novel "Snow Crash," which envisioned lifelike avatars meeting in realistic 3D buildings and other virtual reality scenarios. However, the term "metaverse" was coined by Facebook, now known as Meta, to define its goal of working and playing in a virtual environment. Metaverse is all about 3D models and virtually created environment. In this paper I am going to discuss about sample implementation of metaverse by building an application based on augmented reality. It is an android application which is developed with unity framework. &nbsp

Cortical Visual Impairment (CVI) is resultant from neurological injury and damage to visual pathways or vision centers in the brain. CVI is sometimes undiagnosed in individuals with brain injuries due to the complexity of the human visual system. The International Institute for the Brain (iBrain) is a specialized school for students ages 5 to 21 who have a brain disorder or an acquired brain injury. They often present with noticeable CVI. While there are various metrics and interventions for the pediatric population, the adequacy has been lacking in terms of the vulnerability of this non-verbal population. Assessing the safety and effectiveness of rehabilitative interventions for this fragile student population can be challenging as most traditional metrics cannot be used. In this methodological review paper, available metrics were investigated and their applicability for this specific population is discussed with the end goal of identifying the best metrics that could be used to determine treatment effectiveness and providing a way for monitoring adverse effects. Combining pulse oximetry, cortisol response sensor, and galvanic skin response as biometrics theoretically offers a comprehensive assessment of autonomic activity and responses and establishes objective measures to identify treatment outcomes and adverse reactions. However, future experimental studies are needed to verify if the proposed protocol is feasible and if it is well tolerated by the iBrain students before it can be implemented to monitor adverse reaction to intervention and as a potential treatment outcome measure for children affected by CVI.

primary sector operations, such as farming, are dependent this weather for productivity. Because the climate is changing at such a rapid rate these days, traditional weather forecast systems are becoming less effective and more time consuming. Improved and dependable weather forecast technologies are necessary to solve these challenges. These forecasts have an impact on a country's economy and people's lives. The major goal of this project is to create a weather forecasting system that can be used in remote places. To forecast weather conditions, data analytics and the machine learning techniques like as random forest classifications is mainly used here. The study recomends a low--cost, portable of weather forecasting systems.

Cloud computing is a community-focused environ-ment that relies on sharing informations, storage, provider, programs & different vital computing sources.In modern-day generation of cloud computing era very helpful for centralized the all-agricultural related statistics financialinstitution (Soil&associated, weather, research,Crop, Farmers, Agriculture advertising and marketing, fertilizers and pesti- cide statistics) inside the cloud. Lots of cloud computing companies have emerged up and there is a large boom within the usage of this provider.. Google, Microsoft, IBM and Amazon have began imparting cloud computing services. The future development of agriculture de- pends how the new technologies consisting of cloud computing are followed with a focal point on farmer wishes. The use of this technologies should help a farmer in terms of accessibility. Latest technological development has through a drastic change in every field and agriculture is no other practices to it. Cloud computing generation impacted sincerely onagriculture area and related services they offer for customers on this paper, also talk Computing model, characteristics, deployment model, cloud agro model, cloud advantages and assignment of cloud computing in agriculture field.

To provide increased data security, image steganography algorithms can be used with encryption methods.This paper proposes using the Least Significant Bit algorithm in conjunction with the OpenSSL Base64 encryption approach to create a two-layer password security system.To hide data, the suggested approach exploits one of the three colours contained in each image pixel (RED, GREEN, or BLUE). This approach successfully conceals data in an image that contains the LSB of BLUE colour pixel value with no major changes in the picture colors.This approach for concealing information in photos improves on the Least Significant Bit (LSB) approach.

One of the popular and most used Direction and web Mapping tool which is customer friendly is Google Maps. It maps the routes and directions into map and serves as an online/offline mapping tool. Google develop and maintains the Google map technology. Some of the features which it provides are Street view, Satellite imagery, direction, roads, speed, real-time traffic, re-routes to shorter routes, driving, flying (in beta), and taking public transportation are all accessible. In this the accident prone areas are analyzed, studied. We get the accident prone are from the official website of Kerala Road Safety Authority (KRSA) The data is then visualized onto the custom map using Google My Maps. The Mapping is done based on the latitude and longitude information obtained from the data. There is Starting point and ending point on the plotted map which indicates the accident prone areas. The Location marker gives info about the number of accidents occurred in the place, number of causalities and many more information regarding the place From the plotted map when a route is set between two places we can see the blackspot areas in the map. Providing an early information will help to reduce the number of accidents to a good extend.

A password is a string of characters used to protect access to specified resources from unauthorised users. Anyone with access to the resources should be able to keep the password secret in order to prevent resources from being stolen or misused by those who do not have permission. The system that serves as a resource storage and is also in charge of granting access to those resources should be secure enough to handle access verification. The computer should have a means to validate the password in order to authenticate a person using a password. A security issue arises when a plain password is saved somewhere to be used for verification. Anyone who can identify the location where the password is kept and read it can have access to all of the system's resources. The authentication system should be able to store and validate passwords in a secure manner. With the advancement of computer hardware, the chances of cracking the security of hashing algorithms, such as Bcrypt, are increasing. Certain gear has been developed particularly to execute certain cracking algorithms at their best. To avoid the chance of cracking utilising high-end computer hardware, a more secure hashing technique is required. Argon2 is yet another hashing function that can outperform current hardware in terms of compute power. Argon2 addresses several important flaws in existing algorithms by being designed for the fastest memory filling rate and the most efficient usage of numerous processing units while still offering protection against tradeoff attacks.

Security of server in all contexts is dominating in every field of computing, while working on the servers numerous threats and attacks like cracking of passwords, knowing the root of machine, giving privilege to unauthorized users are common attacks that can harm the system and take access of servers. The most prevalent commands like Hydra and Medusa, Ncrack are there which can be used for cracking the passwords of server and unauthorized users can take the access of serverby applying these commands. In this paper we will consider brute force attack and its tools with its implementation and prevention ways or techniques to avoid these types of attack.

— The Model-View-Controller (MVC) architectural pattern divides an application into three logical components: model, view, and controller. Each of these components is designed to handle unique aspects of application creation. MVC is a common industry-standard web development platform for developing scalable and extensible projects. This pattern has become common for developing web applications and mobile applications after being traditionally used for desktop graphical user interfaces (GUIs). Popular programming languages such as Javascript, Python, Perl, C#, Java, Swift, and Elixr have MVC frameworks that can be used to quickly build mobile or web applications.

Since the volume of the medical data is increasing due to the presence of vast amount of features, the conventional rule mining technique is not competent to handle the data and to perform ...

Graph grammars are capable of modelling the generation of various families of graphs. Graph rewriting has basically two different approaches namely, node replacement and edge replacement rewriting. A variant of node replacement graph grammar called $nc-eNCE$ graph grammars was introduced recently. Recently, a special kind of graph, called Parikh word representable graph was introduced and its properties were studied. The problem of generation of graph structures using graph grammars has been considered in many studies. Here we generate the Parikh word representable graphs using $nc-eNCE$ graph grammars.

Big data is one of the fastest-growing technologies, which can handle huge amounts of data from various sources, such as social media, weblogs, banking and business sectors, etc. A Hadoop MapReduce job can be delayed if one of its many tasks is being assigned to an unreliable or congested machine. To solve this straggler problem, a novel algorithm design of speculative execution schemes for parallel processing clusters, from an optimization perspective, under different loading conditions is proposed. For the lightly loaded case, a task cloning scheme, namely the combined file task cloning algorithm, which is based on maximizing the overall system utility, and a straggler-detection algorithm is proposed based on a workload threshold. The detection and cloning of tasks assigned with the stragglers only will not be enough to enhance the performance unless cloning of tasks is allocated in a resource-aware method. So, a method is proposed which identifies and optimizes the resource allocation by considering all possible aspects of cluster performance balancing. One of the main issues appears due to the pre-configuration of the distinct map and reduces slots based on the number of files in the input folder. This can cause severe under-utilization of the slot as map slots might not be fully utilized to the input splits. To solve this issue, an alternative technique of Hadoop slot allocation is introduced in this paper by keeping the efficient management of the slots model. The combined file task cloning algorithm combines the files which are less than the size of a single data block and executes them in the highest performing data node. On implementing these efficient cloning and combining techniques on a heavily loaded cluster after detecting the straggler, the machine is found to reduce the elapsed time of execution to an average of 40%. The detection algorithm improves the overall performance of the heavily loaded cluster by 20% of the total elapsed time in comparison with the native Hadoop algorithm.

All web applications depend on the internet. Now a day’s web applications play an important role in everybody’s life. Thousands of transactions and confidential data are done through these applications, 80% out of which are vulnerable to malicious attacks according to the survey by the Open Web Application Security Projects. Website hacking exploits the web pages and database; thus, compromising the confidential and sensitive information in it. The highest security threat for web applications is SQL injections. Here we use two tools for finding the vulnerability in the websites and enables access to the database, viewing data in tables such as users, passwords, backups, phone numbers, credit cards, e-mail addresses, and other confidential and sensitive information. Grabber is a web application scanner which scans for the vulnerability. Sqlmap is a tool that provides penetration testing for the process of detecting and exploiting SQL injection flaws.

Password cracking has become one of the popular means of breaking into one’s privacy. Various methods have been adopted to store passwords securely from intruders. One such method is to keep the passwords hashed. The user passwords in Windows are stored as hashed codes in a registry hive. To obtain these codes secretly and remotely from the target system, the Remote Administration Tool (RAT) like PupyRAT is used. It can connect to a target system through a backdoor attack. Once a session is created, the attacker can bypass the User Account Control (UAC) to gain the admin privilege. Only then can the attacker download the registry hive. To be able to bypass the UAC, PupyRAT injects multiple PowerShell commands into the target system. A system that detects these PowerShell windows is developed to alert and terminate possible RAT attacks.

Liver resident-memory CD8+ T cells (TRM cells) can kill liver-stage Plasmodium-infected cells and prevent malaria, but simple vaccines for generating this important immune population are lacking. Here, we report the development of a fully synthetic self-adjuvanting glycolipid-peptide conjugate vaccine designed to efficiently induce liver TRM cells. Upon cleavage in vivo, the glycolipid-peptide conjugate vaccine releases an MHC I-restricted peptide epitope (to stimulate Plasmodium-specific CD8+ T cells) and an adjuvant component, the NKT cell agonist α-galactosylceramide (α-GalCer). A single dose of this vaccine in mice induced substantial numbers of intrahepatic malaria-specific CD8+ T cells expressing canonical markers of liver TRM cells (CD69, CXCR6, and CD101), and these cells could be further increased in number upon vaccine boosting. We show that modifications to the peptide, such as addition of proteasomal-cleavage sequences or epitope-flanking sequences, or the use of alternative conjugation methods to link the peptide to the glycolipid improved liver TRM cell generation and led to the development of a vaccine able to induce sterile protection in C57BL/6 mice against Plasmodium berghei sporozoite challenge after a single dose. Furthermore, this vaccine induced endogenous liver TRM cells that were long-lived (half-life of ~425 days) and were able to maintain >90% sterile protection to day 200. Our findings describe an ideal synthetic vaccine platform for generating large numbers of liver TRM cells for effective control of liver-stage malaria and, potentially, a variety of other hepatotropic infections.

The big data is one of the fastest growing technologies, which can to handle huge amounts of data from various sources, such as social media, web logs, banking and business sectors etc. In order to pace with the changes in the data patterns and to accommodate the requirements of big data analytics, the platform for storage and processing such as Hadoop, also requires great advancements. Hadoop, an open source project executes the big data processing job in map and reduce phases and follows master-slave architecture. A Hadoop MapReduce job can be delayed if one of its many tasks is being assigned to an unreliable or congested machine. To solve this straggler problem, a novel algorithm design of speculative execution schemes for parallel processing clusters, from an optimization perspective, under different loading conditions is proposed. For the lightly loaded case, a task cloning scheme, namely, the combined file task cloning algorithm, which is based on maximizing the overall system utility, a straggler detection algorithm is proposed based on a workload threshold. The detection and cloning of tasks assigned with the stragglers only will not be enough to enhance the performance unless cloning of tasks is allocated in a resource aware method. So, a method is proposed which identifies and optimizes the resource allocation by considering all possible aspects of cluster performance balancing. One main issue arises due to the pre configuration of distinct map and reduce slots based on the number of files in the input folder. This can cause severe under-utilization of slot as map slots might not be fully utilized with respect to the input splits. To solve this issue, an alternative technique of Hadoop Slot Allocation is introduced in this paper by keeping the efficient management of slots model. The combine file task cloning algorithm combines the files which are less than the size of a single data block and executes them in the highly performing data node. On implementing these efficient cloning and combining techniques on a heavily loaded cluster after detecting the straggler, machine is found to reduce the elapsed time of execution to an average of 40%. The detection algorithm improves the overall performance of the heavily loaded cluster by 20% of the total elapsed time in comparison with the native Hadoop algorithm.

Liver resident-memory CD8

GPS place remainder and Accident Detection system is used to remind the user about a particular product and to detect accident occurrence. The GPS place remainder system is used to notify the user when a location reaches with product availability. For this the system make use of fastest searching algorithm called Boyer-Moore Algorithm. This system also got the feature of accident detection. When an accident occurs, the user's phone automatically detects the accident and notify the nearest hospital and friends. Both this feature is developed in the android application for better user interface.

The string-searching algorithms, also called string-matching algorithms, are a significant class of string algorithms. The objective of these algorithms is to discover a place where one or a few strings (likewise called patterns) are found inside a larger string or text. Nowadays there are numerous algorithms accessible for string looking with changed seeking speed. The larger the move of the pattern with respect to the string if there should be an occurrence of pattern and string characters' confound is, the higher is the algorithm running speed. This paper offers an algorithm, which has been created based on the Kunth-Morris-Pratt string looking algorithm. This algorithm depends on different fundamental standards of pattern matching. Knuth-Morris-Pratt algorithm depends on forwarding pattern and the pattern is completely contrasted and the selected text window (STW) of the text string and show the beginning index position.

Image inpainting is a process that tries to fill in missing parts of a degraded image or remove objects from an image, but maintain the realistic content in the image. Various methods can be employed to perform inpainting. Here three benchmark methods of which two are conventional and other GAN based are evaluated to check the effectives of the inpainting process, for different class of images. The visual quality evaluation as well as quantification parameter namely the SSIM score was computed. Based on the test score it was seen that the GAN based method was capable of providing visually good restored images with an average SSIM score of 0.9516, which is an indicator on the quality of the image. The GAN based methods can be further enhanced to provide more realistic images by incorporating functions to extract both local and global contextual content.

Delivering a software is not a simple process, even if it is a completely new software created from the scratch, the enhancement of an existing software or fixing the bugs of an existing application. As per the new industrial standard most of the companies follow the DevOps methodologies to deliver the software products, which leverage the flexibilities of the software delivery with expected quality on time with minimal errors. One of the most important factors of the DevOps methodology is Continuous Integration and Continuous Deployment. As the entire software industry is moving towards Cloud Computing one of the most powerful, cost effective and easily maintainable environment to host the software applications. There are various Cloud computing providers available in the industry now. Most of them have its own advantage and dis-advantages. There are many researches going on various topics on these areas and the importance of these topics are getting more relevant in the industry now. There are lot of gaps on the Continuous Integration and Continuous Deployment concepts and more improvement options have to be identified and certified throughout the various researches. To understand the importance of this platform, we decided to concentrate the research on the Continuous Integration and Continuous Deployment in Azure DevOps for a controlled Microsoft .NET environment using different techniques and practices. Here our intention is to identify the various factors which helps the industry to do a better Continuous Integration and Continuous Deployment in the Azure DevOps for a controlled Microsoft .NET environment using different techniques and practices. Among the “n” number of factors identified on this subject, only couple of factors are considered as part of this research.

Faster release of features to the production environment is the need of the hour of every IT organizations. This faster and continuous software delivery is made possible through DevOps. DevOps is a set of practices that bridges the gap between development team and operations team. The different aspects of DevOps came from agile methodology. Continuous integration is the paramount of DevOps life cycle. In a continuous integration development practice, the development code and the applications code are continuously integrated several times a day. Developers follow the practice of committing changes frequently into the version control system such as git. A change in the version control system triggers the continuous integration system such as Jenkins. This exploratory study presents the ways that support the developers during the build break in continuous integration environment and thereby improve the efficiency of continuous integration process. Our findings contribute that the selected list of plugins in the Jenkins support the developers during the build break, thus developers can come up with faster recovery of build failures which in turn increase the efficiency of continuous integration environment.

Background Diabetes is a pervasive disease, with a mounting prevalence and burden on health care systems. Under this collective term of diabetes falls diabetes after diseases of the exocrine pancreas, a condition which was previously under-recognised and often mislabeled as type 2 diabetes mellitus and is now increasingly acknowledged as a stand-alone entity. However, there is a paucity of clinical studies investigating the underlying pathophysiology of diabetes after acute pancreatitis, the most frequent disease of the pancreas. This study aimed to investigate the role of adipocytokines in glucose metabolism after acute pancreatitis. Methods This was a cross-sectional follow-up study of a patient cohort diagnosed with acute pancreatitis. Fasting venous blood samples were collected to analyse markers of glucose metabolism (fasting blood glucose, haemoglobin A1c, homeostasis model assessment (HOMA-IR) as a measure of insulin resistance) and adypocytokines (adiponectin, interleukin-6, leptin, monocyte chemoattractant protein-1, retinol binding protein-4, resistin, and tumor necrosis factor-α). Participants were categorized into two groups: normoglycemia after acute pancreatitis and chronic hyperglycemia after acute pancreatitis (CHAP). Binary logistic regression and linear regression analyses were used to investigate the association between each of the adipocytokines and markers of glucose metabolism. Potential confounders were adjusted for in multivariate analyses. Results A total of 83 patients with acute pancreatitis were included, of whom 19 developed CHAP. Interleukin-6 was significantly associated with CHAP in both unadjusted and adjusted models ( p = 0.030 and p = 0.018, respectively). Further, it was also significantly associated with HOMA-IR in both unadjusted and adjusted models ( p = 0.029 and p = 0.037, respectively). Other adipocytokines were not significantly associated with markers of glucose metabolism. Conclusion Interleukin-6 appears to be implicated in the development of chronic hyperglycemia and insulin resistance in patients after acute pancreatitis. It may become a potential target in the prevention and early treatment of diabetes after diseases of the exocrine pancreas.

The challenging issue in microarray technique is to analyze and interpret the large volume of data. This can be achieved by clustering techniques in data mining. In hard clustering like hierarchical and k-means clustering techniques, data is divided into distinct clusters, where each data element belongs to exactly one cluster so that the outcome of the clustering may not be correct in many times. The problems addressed in hard clustering could be solved in fuzzy clustering technique. Among fuzzy based clustering, fuzzy c means (FCM) is the most suitable for microarray gene expression data. The problem associated with fuzzy c-means is the number of clusters to be generated for the given dataset needs to be specified in prior. The main objective of this proposed Possibilistic fuzzy c-means method is to determine the precise number of clusters and interpret the same efficiently. The PFCM is a good clustering algorithm to perform classification tests because it possesses capabilities to give more importance to topicalities or membership values. PFCM is a hybridization of PCM and FCM that often avoids various problems of PCM, FCM and FPCM. Based on the sample dataset „lung‟ the entire research has been developed. The available research works already developed in this area are not exclusively working with cancer genes.At this juncture, using of the Modified Possibilitistic fuzzy cmeans algorithm could be found matching with cancer genes in a better fashion. “Matlab” is used for the algorithm.The accuracy of the dataset may be identified with the usage of different training sets.Possibilistic fuzzy c means algorithm has provided better results while identifying the cancer gene. For evaluating the feasibility of the Possibilistic Fuzzy C-Means (PFCM) clustering approach, the researcher has carried out the experimental analysis.

Liver resident-memory CD8+ T cells (TRM cells) can kill liver-stage Plasmodium-infected cells and prevent malaria, but simple vaccines for generating this important immune population are lacking. Here, we report the development of a fully synthetic self-adjuvanting glycolipid-peptide conjugate vaccine designed to efficiently induce liver TRM cells. Upon cleavage in vivo, the glycolipid-peptide conjugate vaccine releases an MHC I-restricted peptide epitope (to stimulate Plasmodium-specific CD8+ T cells) and an adjuvant component, the NKT cell agonist a-galactosylceramide (a-GalCer). Following transfer of a transgenic CD8+ T cell population, a single dose of this vaccine induced substantial numbers of intrahepatic CD8+ T cells expressing canonical markers of liver TRM cells (CD69, CXCR6 and CD101), and these cells could be further increased in number upon vaccine boosting. We show that modifications to the peptide, such as addition of proteosomal-cleavage sequences or epitope-flanking sequences, or the use of alternative conjugation methods to link the peptide to the glycolipid, improved liver TRM cell generation and led to the development of a vaccine able to induce sterile protection in C57BL/6 mice against P. berghei sporozoite challenge after a single dose. Incorporation of a cognate malaria antigen into the vaccine resulted in the generation of large numbers of long-lived liver TRM cells derived from the endogenous T cell population that were capable of providing sterile immunity. Our findings describe an ideal synthetic vaccine platform for generating large numbers of liver TRM cells for effective control of liver-stage malaria and, potentially, a variety of other hepatotropic infections

Mucopolysaccharidosis VI (MPS VI) is an autosomal recessive inborn error of metabolism caused by mutations in the arylsulfatase B gene (ARSB) and consequent deficient activity of ARSB, a lysosomal enzyme. We present here the results of a study undertaken to identify the mutations in ARSB in MPS VI patients in India. Around 160 ARSB mutations, of which just 4 are from India, have been reported in the literature. Our study covered nine MPS VI patients from eight families. Both familial mutations were found in seven families, and only one mutation was found in one family. Seven mutations were found - four novel (p.G38_G40del3, p.C91R, p.L98R and p.R315P), two previously reported from India (p.D53N and p.W450C), and one reported from outside India (p.R160Q). One mutation, p.W450C, was present in two families, and the other six mutations were present in one family each. Analysis of the molecular structure of the enzyme revealed that most of these mutations either cause loss of an active site residue or destabilize the structure of the enzyme. The only previous study on mutations in ARSB in Indian MPS VI patients, by Kantaputra et al. 2014 [1], reported four novel mutations of which two (p.D53N and p.W450C) were found in our study as well. Till date, nine mutations have been reported from India, through our study and the Kantaputra study. Eight out of these nine mutations have been found only in India. This suggests that the population studied by us might have its own typical set of mutations, with other populations equally likely to have their own set of mutations.

To determine the incidence of acute pancreatitis (AP), chronic pancreatitis (CP), and post-pancreatitis diabetes mellitus (DP) in New Zealand, and the effect of ethnic and geographic variations.Data were collected from all district health boards in New Zealand by the Ministry of Health (Manatū Hauora). Diagnosis of AP, CP and DP was determined by the International Classification of Diseases-10 codes. Incidence rates per 100,000 population per year were calculated using incident AP, CP and DP cases as the numerator, and the adult resident population of New Zealand as the denominator. Poisson distribution was used to estimate 95% confidence intervals. The district health board domicile codes and corresponding incidence rates were used to map geographical variations for AP, CP and DP.On average, 2,072 new cases of AP, CP and DP were diagnosed in New Zealand every year. The crude incidence of AP was 58.42 [57.55, 59.30], CP - 3.97 [3.74, 4.20], and DP - 7.95 [7.62, 8.27] per 100,000 population per year. Māori had the highest incidence of AP (95.21 [91.74, 98.68] per 100,000 population per year), CP (6.27 [5.37, 7.16] per 100,000 population per year), and DP (18.23 [16.71, 19.76] per 100,000 population per year). Incidence of AP and DP was at least 1.8 and 2.6 times higher in Māori than New Zealand Europeans in every age group, and incidence of DP was at least 1.9 times higher in Pacific people than New Zealand Europeans in every age group. Auckland/Northland had the highest incidence of AP (135.25 [134.82, 135.68] per 100,000 population), and CP (9.03 [8.60, 9.46] per 100,000 population), while Lakes/Waikato had the highest incidence of DP (20.64 [20.21, 21.07] per 100,000 population) in New Zealand.New Zealanders have a very high incidence rate of AP, with Māori having the highest reported incidence of AP worldwide. There is a significant geographic variation in incidence of pancreatic diseases, with the Upper North Island having the highest incidence rates of AP, CP and DP in the country. Future high-quality studies are required to understand the mechanisms of pancreatitis and DP in order to develop preventive and therapeutic strategies that would benefit New Zealanders in general and Māori in particular.

Clustering is the process of assigning data objects into a set of disjoint groups called clusters so that objects in each cluster are more similar to each other than objects from different clusters. We try to exploit computational power from the multicore processors. We need a new design on existing algorithms and software. Firefly algorithm is one of the metaheuristic algorithms which are used for solving optimization problems. The existing clustering algorithms either handle different data types with inefficiency in handling large data or handle large data with limitations in considering numeric attributes. Hence, parallel clustering has come into picture to provide crucial contribution towards clustering large data. In this paper, we have developed a scalable parallel clustering algorithm using FA and genetic algorithm to cluster large data. Modified FA algorithm does not handle the large data effectively. So, our ultimate aim is to design and develops an algorithm in parallel way by considering data. The experimental analysis will be carried out to evaluate the feasibility of the new combined clustering approach. The experimental analysis showed that the proposed approach obtained upper head over existing method in terms of accuracy and time. Most of the programming languages doesn't provide multiprocessing facilities and hence wastage of processing resources. In order to utilize the intrinsic capabilities of a multi-core processor the software application must be able to execute tasks in parallel using all available CPUs. To achieve this we can use fork/join method in java programming. It is the most effective design method for achieve good parallel performance.

Join and Fork model has been shown to be a powerful approach for boosting a system performance. The programming language Java supports the multithreading programming as part of the language itself instead of treating threads through the operating system. We tested several clustering algorithms implemented with Java language using multithreading approach and Join Fork approach on multi-core CPU. We try to exploit computational power from the multicore processors. Performance is increased on single core and multiple cores CPU in different ways in complexity of the algorithm and the platform. In order to utilize the intrinsic capabilities of a multi-core processor the software application must be able to execute tasks in parallel using all available CPUs. This paper analyzes about the performance of Modified Parallel K Means algorithm and Parallel Genetic K Means algorithm using Java Join and Fork Method. Fork/join method overcomes deficiencies of multithreaded execution.

This paper mainly focuses on identifying the limitations of the K-Means algorithm and to propose the parallelization of the K-Means using Firefly based clustering method. The new parallel architecture can handle large number of clusters. Modified Firefly algorithm can be used to find initial optimal cluster centroid and then K-Means algorithm with optimized centroid can be used to refine them and improve clustering accuracy. The final convergence issue is also addressed and solved to a great extent. The design methodology is explained in the subsequent sections. Finally, modified algorithm is compared with Parallel K-Means. It is demonstrated with experiments and it has been found that the performance of modified algorithm is better than that of the existing algorithm. Four typical benchmark data sets from the UCI machine learning repository are used to demonstrate the results of the techniques

This paper mainly focuses in identifying the limitations of the k means algorithm and to propose the parallelization of the k-means using firefly based clustering method. The new parallel architecture can handle large number of clusters. Firefly algorithm to find initial optimal cluster centroid and then k-means algorithm with optimized centroid to refined them and improve clustering accuracy. The final convergence issue is also addressed and solved to a great extent. Finally modified algorithm is compared with parallel k means is demonstrated with experiments and it has been found that the performance of modified algorithm is better than the existing algorithm. Four typical benchmark data sets from the UCI machine learning repository are used to demonstrate the results of the techniques. To achieve this we can use fork/join method in java programming. It is the most effective design method for achieve good parallel performance

In various domains, big data play crucial and related processes because of the latest developments in the digital planet. Such irrepressible data growth has led to bring clustering algorithms to segment the data into small sets to perform associated processes with them. However, the challenge continues in dealing with large data, because most of the algorithms are compatible only with small data. However, the existing clustering algorithms either handle different data types with inefficiency in handling large data or handle large data with limitations in considering numeric attributes. Hence, parallel clustering has come into the picture to provide crucial contribution towards clustering large data. This insists the need of having scalable parallel clustering to solve the aforesaid problems. In this paper, we have developed a scalable parallel clustering algorithm called Possibilistic Fuzzy C-Means (PFCM) clustering to cluster large data. So, our ultimate aim is to design and develop an algorithm in parallel way by considering data. The parallel architecture includes, splitting the input data and clustering each set of data using PFCM. Then the genetic firefly algorithm applied to the merged cluster data, which will provide better clustering accuracy in merge data. The experimental analysis will be carried out to evaluate the feasibility of the scalable Possibilistic Fuzzy C-Means (PFCM) clustering approach. The experimental analysis showed that the proposed approach obtained upper head over existing method in terms of accuracy and time.