Asian Journal of Research in Computer Science

One of the main factors that assist to increase the growth of any country is Agriculture. The detection of diseases from plant leaf images is one of the most important fields of agricultural research. To identify disease factors, this field requires a reliable prediction approach. Data Mining (DM) is the process of analyzing data from different aspects and summarizing it into valuable information. It helps users to categorize and identify relationships between data from various dimensions. As there are many plants on the farm, detecting and classifying the diseases of each plant on the farm is extremely difficult for the human eye. And diagnosing each plant is very critical since these diseases may spread. DM classification is an important method that has a wide range of applications. It classifies each item in a set of data into one of a set of predefined classes. In this paper, a comparison of different DM classification methods such as Naive Bayes, Decision trees, SVM, and Random Forest algorithms has been illustrated by using of Weka Tool.

In the presentage, online paymentsystem is a very simple practice.But manypeople use this system to manipulate people’s money. Many are trying for finding a variety of solutions. Butthereis no way to stopthatcrime.Blockchain’syoke is a blessing.Usingblockchainisaveryeasywaytocompletea paymentwithoutmakingany mistakes.Hackerwill never find a way to do theirworkin this kindof system. OurSystem is full workedwith Blockchain.Basically, we choose blockchainas our projectbecauseit is the most secureway to do a transaction in everyonlinesystem.Thecentralbusinessmodelis basedon a database management system. Once accomplishedthe security of the transaction can no longer be guaranteed. On the otherhand, itis really expensivetoresolvepossiblefraudtransactionsby a middle man.Aiming at solving issues concerningsecurityand worthlessness,there is a proposalof a model which is completely madeofblockchainsystem.InOursystemtherearemany blocksof information of eachandeverytransaction. Wehave proposedan algorithm.The algorithm will make consumersable to transact through cryptocurrency in blockchainnetworks.It is totally different from the fiat system where consumers will be able to transact without the help of thirdpartiesand vendors can also be relievedwith theirtransaction. Thistypeof transaction will be very comfortablefor both consumersand vendors. Consumers along with vendorscan see the whole transaction date, time and everythingthatthey dealtwith when the transaction was held.

As Grid computing continues to make inroads into different spheres of our lives and multicore computers become ubiquitous, the need to leverage the gains of multicore computers for the scheduling of Grid jobs becomes a necessity. Most Grid schedulers remain sequential in nature and are inadequate in meeting up with the growing data and processing need of the Grid. Also, the leakage of Moore’s dividend continues as most computing platforms still depend on the underlying hardware for increased performance. Leveraging the Grid for the data challenge of the future requires a shift away from the traditional sequential method. This work extends the work of [1] on a quadcore system. A random method was used to group machines and the total processing power of machines in each group was computed, a size proportional to speed method is then used to estimates the size of jobs for allocation to machine groups. The MinMin scheduling algorithm was implemented within the groups to schedule a range of jobs while varying the number of groups and threads. The experiment was executed on a single processor system and on a quadcore system. Significant improvement was achieved using the group method on the quadcore system compared to the ordinary MinMin on the quadcore. We also find significant performance improvement with increasing groups. Thirdly, we find that the MinMin algorithm also gained marginally from the quadcore system meaning that it is also scalable.

The mass dispersal of digital communication requires the special measures of safety. The need for safe communication is greater than ever before, with computer networks now managing almost all of our business and personal affairs. Information security has become a major concern in our digital lives. The creation of new transmission technologies forces a specific protection mechanisms strategy particularly in data communication state.

 We proposed a steganography method in this paper, which reads the message, converting it into its Residue Number System equivalent using the Chinese Remainder Theorem (CRT), encrypting it using the Rivest Shamir Adleman (RSA) algorithm before embedding it in a digital image using the Least Significant Bit algorithm of steganography and then transmitting it through to the appropriate destination and from which the information required to reconstruct the original message is extracted. These techniques will enhance the ability to hide data and the hiding of ciphers in steganographic image and the implementation of CRT will make the device more efficient and stronger. It reduces complexity problems and improved execution speed and reduced the time taken for processing the encryption and embedding competencies.

Breast cancer is one of the most common diseases among women, accounting for many deaths each year. Even though cancer can be treated and cured in its early stages, many patients are diagnosed at a late stage. Data mining is the method of finding or extracting information from massive databases or datasets, and it is a field of computer science with a lot of potentials. It covers a wide range of areas, one of which is classification. Classification may also be accomplished using a variety of methods or algorithms. With the aid of MATLAB, five classification algorithms were compared. This paper presents a performance comparison among the classifiers: Support Vector Machine (SVM), Logistics Regression (LR), K-Nearest Neighbors (K-NN), Weighted K-Nearest Neighbors (Weighted K-NN), and Gaussian Naïve Bayes (Gaussian NB). The data set was taken from UCI Machine learning Repository. The main objective of this study is to classify breast cancer women using the application of machine learning algorithms based on their accuracy. The results have revealed that Weighted K-NN (96.7%) has the highest accuracy among all the classifiers.