Search

Big data analytics involve systematic approach to find hidden patterns to help the organization grow from large volume and variety of data. In recent years big data analytics is widely used in the agricultural domain to improve yield. Viticulture (the cultivation of grapes) is one of the most lucrative farming in India. It is a subdivision of horticulture and is the study of wine growing. The demand for Indian Wine is increasing at about 27% each year since the 21st century and thus more and more ways are being developed to improve the quality and quantity of the wine products. In this paper, we focus on a specific agricultural practice as viticulture. Weather forecasting and disease detection are the two main research areas in precision viticulture. Leaf disease detection as a part of plant pathology is the key research area in this paper. It can be applied on vineyards of India where farmers are bereft of the latest technologies. Proposed system architecture comprises four modules: Data collection, data preprocessing, classification and visualization. Database module involve grape leaf dataset, consists of healthy images combined with disease leaves such as Black measles, Black rot, and Leaf blight. Models have been implemented on Apache Hadoop using map reduce programming framework. It apply feature extraction to extract various features of the live images and classification algorithm with reduced computational complexity. Gray Level Co-occurrence Matrix (GLCM) followed by K-Nearest Neighborhood (KNN) algorithm. System also recommends the necessary steps and remedies that the viticulturists can take to assure that the grapes can be salvaged at the right time and in the right manner based on classification results. Overall system will help Indian viticulturists to improve the harvesting process. Accuracy of the model is 72% and it can be increased as a future work by including deep learning with time series grape leaf images.

Abstract: Alzheimer’s disease (AD) is prevalent dementia that can cause neurological brain disorders, poor decision making, impaired memory, mood swings, unstable emotions, and personality change. Deep neural networks are proficient in classifying Alzheimer's disease based on MRI images. This classification assists human experts in diagnosing AD and predicts its future progression. The paper proposes various Deep Neural Networks (DNN) for early AD detection to save cost and time for doctors, radiologists, and caregivers. A 3330-image-based Kaggle dataset is used to train the DNN, including 52 images of AD, 717 images of Mild Cognitive Impairment (MCI), and the remaining images of Cognitive Normal (CN). Stratified partitioning splits the dataset into 80% and 20% proportions for training and validation datasets. Proposed models include DenseNet169, DenseNet201, and ResNet152 DNNs with additional three fully-connected layers and softmax and Kullback Leibler Divergence (KLD) loss function. These models are trained considering pre-trained, partially pre-trained, and fully re-trained extended base models. The KLD loss function reduces the error and increases accuracy for all models. The partially pre-trained DenseNet201 model outperformed all the other models. DenseNet201 gives the highest accuracy of 99.98% for training, 99.07% for validation, and 95.66% for test datasets. The DenseNet201 model has the highest accuracy in comparison to other state-of-art-methods.

Time series forecasting is a method that predicts future values by analyzing past values. Temperature alarms are valuable predictions because they are used to safeguard life and property and to increase operational performance. Here 2m air temperature refers to the temperature of air recorded at 2 meters from the ground. Paper consists of time-series prediction of temperature data that has been taken from automatic weather stations(AWS) installed by the Indian Space Research Organisation. Given paper presents the applicability of different machine learning(ML) algorithms like convolutional neural network(CNN), long short term memory(LSTM), and autoregressive integrated moving average(ARIMA) algorithms for the validity of temperature prediction over four different stations which are Ahmedabad, Balasore, Coimbatore, and Udaipur. These stations record the hourly-based temperatures. Based on different datasets, the prediction accuracy of algorithms is compared. The paper discusses the results showing that by applying different algorithms to the different datasets with different characteristics, it is observed that the various algorithms behave distinctly with numerous 1-dimensional datasets based on the variation in recorded values, location, or type of the input data i.e hourly input data or daily input data. This analysis shows that different machine learning algorithms have a different performance ratio while applied to various data sets.

Extensive use of tools and technology in the healthcare field results in big data. Indexing helps in the faster retrieval of intended data from very big datasets. Indexing of big data becomes a major concern when the querying field is present in very few of the input files, but the number of input files is more. In this paper, the authors have built an index structure for the datasets for the faster-querying purposes by reducing the number of files to be scanned for querying the database. A focused literature survey imparts different indexing methodology with a suitable application area. The inverted index is the most suitable indexing for the COVID-19 dataset, including the details of the patients of India. The inverted index is created on the column with state names in the dataset. MapReduce programming framework is used for the implementation with the Apache Hadoop platform. Algorithms for grouping the data, creation of an index, and querying are exemplified with the explanation. Test cases are designed on the COVID-19 dataset with indexing and without indexing. Time taken to create an index and to fetch the records is recorded for various volumetric input files. Results and discussion part of this paper indicates that the use of an index enhances the performance of querying on big data. The performance that is achieved while querying the database after index construction is much better than normal querying.

Referring to reviews, checking online comments and, visiting different websites before buying any product is a call of the day. Online reviews are an excellent source of information both for users and organisations alike. In this article, a hybrid model, named as aspect and context-based latent factor model (ACMF), is proposed to predict user rating on an item based on star ratings provided by users, feature-opinion information, and context information. ACMF mainly consists of three phases: the first phase extracts spam reviews, the second phase extracts features and opinions from written reviews and calculates the polarity score of opinions. In the last phase, reviews and context information are aggregated to predict the unknown rating of a user for better recommendations. The proposed model is tested on ratings and reviews downloaded from the Amazon website. Experiment results show RMSE of ACMF has been achieved significantly less than other relevant methods.

Various Solutions for Vertex Cover algorithms are available in the world which are from NP-Complete class. There are several real world applications of Vertex Cover algorithm like different networks as Terrorist communication n/w, Wireless communication n/w, Airline communication n/w. We are representing the comparative analysis of various subsisting algorithms like Approximation algorithm, List(s) algorithm, Greedy technique and Alom's algorithm for the Vertex Cover problem in this paper. From the analysis, we came to know that the Alom's algorithm is giving optimized result among all Vertex Cover algorithms of every graph having large number of nodes where the Approximation algorithm gives the worst response of the execution for huge graphs.