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Advanced technology in agriculture help to know about suitable environmental conditions, soil health status, water and fertilizer requirements, and crop monitoring at every plant growth stage, resulting in higher yield. In the past few decades, many countries have witnessed different rain and temperature patterns due to change in environmental conditions. The plantation schedule imparts mark-able effects on the crop yield. The correct and well-planned schedule can result in getting maximum productivity with limited resources. This study presents rule-based fuzzy classification method, for predicting the sowing fuzziness based on environmental conditions. The proposed study is a three-step procedure that identifies the sowing time of Cotton, Maize, and Groundnut. First, the knowledge and rule base of the fuzzy inference system is designed. In the second step rule base of the fuzzy inference system is optimized using multi-objective evolutionary algorithm NSGA-2, which helps maximize the accuracy and minimize the number of fuzzy rules taken for classification. Finally, the fuzziness of crop sowing in different slots is determined. Set of solutions in NSGA-2 are validated through a cross-validation approach. Further, the fuzziness of the sowing slot of Cotton, Maize, and Groundnut is correlated to yield in a given year to measure the model's effectiveness.

The detection of disease development in plants becomes very crucial because of its adverse effect on the quality and productivity of agriculture. The automatic disease detection in plants using image processing and machine learning is beneficial due to its fast computing and practicability for continuous monitoring of a large farm. This paper presents an automatic disease detection system using image segmentation, feature extraction, optimization, and classification algorithms. This paper proposes a memetic salp swarm optimization algorithm (MSSOA), which is transformed into binary MSSOA to search for the optimal number of features that give the best classification accuracy. The performance of the proposed algorithm for feature selection is compared with five metaheuristic feature selection (BSSA, BPSO, BMFO, BCOA, IBHHO) algorithms against the UCI benchmark datasets. The obtained results indicate the proposed algorithm outperforms the other algorithms in obtaining good classification accuracy and reducing the feature size. The proposed algorithm is implemented for automatic disease detection of maize, rice, and grape plant and achieved a classification accuracy of 90.6%, 67.9%, and 91.6% and best classification accuracy of 93.6%, 79.1%, and 95%, respectively.

The weather forecasting process is used to predict the future atmospheric condition of a specified location. The evolution of the big data era gives the chances to significantly increase the prediction accuracy of weather conditions. In this paper, a deep learning-based stacked sparse autoencoder (DSSAE) has been proposed for predicting the weather condition of a particular area. This model requires a pre-processing approach to obtain essential data from big weather data and increase the prediction model’s speed. For this, the principal component analysis (PCA) is utilized to reduce dimensionality and extraction of features with more significant variance. Also, it integrates the feature selection algorithm based on Binary Butterfly Optimization Algorithm (BBOA) along with a deep stack autoencoder to improve prediction accuracy. The proposed model is validated using the weather data taken from the division of weather underground for short term and long term weather prediction. The simulation consequences illustrate that the proposed model overtakes the existing models in terms of computation time, accuracy and error rate.

Wireless sensor network (WSN) is a communication network with several sensor nodes connected without any wired connection. Sensor nodes in the WSN are battery operated; therefore, recharging or replacement of the sensor node is a crucial task. Efficient energy consumption of sensor nodes is one of the significant challenges in WSN. In cluster based WSN, sensor nodes are grouped to form clusters. Gateway collects data from sensor nodes, aggregates data and transmits to the base station directly or through other gateways using the routing path. This causes heavy traffic load at the sink and forms an energy hole around the sink. In this paper, an energy efficient routing structure using the multi-layer network model is proposed to avoid the energy hole around the sink. The routing structure considers the one-to-one connection for all gateways to balance the overall load on the network. In the designed network model, the number of gateways increases in each layer towards the sink to minimize the traffic load around the sink. The extensive simulations on four different scenarios are performed in terms of network parameters such as the first gateway dies, first sensor node dies, half of the sensor nodes alive over the number of rounds, energy consumption rate and residual energy of sensor nodes and gateways. The experimental results are significant and proven the competency of the proposed algorithm.

Accurate detection of malignant tumor on lung computed tomography scans is crucial for early diagnosis of lung cancer and hence the faster recovery of patients. Several deep learning methodologies have been proposed for lung tumor detection, especially the convolution neural network (CNN). However, as CNN may lose some of the spatial relationships between features, we plan to combine texture features such as fractal features and gray-level co-occurrence matrix (GLCM) features along with the CNN features to improve the accuracy of tumor detection. Our framework has two advantages. First it fuses the advantage of CNN features with hand-crafted features such as fractal and GLCM features to gather the spatial information. Second, we reduce the overfitting effect by replacing the softmax layer with the support vector machine classifier. Experiments have shown that texture features such as fractal and GLCM when concatenated with deep features extracted from DenseNet architecture have a better accuracy of 95.42%, sensitivity of 97.49%, and specificity of 93.97%, and a positive predictive value of 95.96% with area under curve score of 0.95.

Communities in social networks are the essential feature which may be considered as a potential parameter in modeling the behavior of the social entities. Detection of communities has attracted a lot of attention in research in social network analysis. It is one of the major challenging problems as it involves high complexity in processing complex web structure. In fact, this problem can be considered as a NP-complete problem in large-scale networks, as this problem is somewhat reducible to the clique problem in graph theory. A number of meta-heuristic algorithms have been proposed to explore the hidden communities. Most of these algorithms have considered the modularity of the network as their objective function. But, the aspect of optimizing the value of modularity is associated with a problem known as resolution limit, where the size of the detected communities depends on the number of edges existing in the network. In this paper, a genetic algorithm-based community detection has been proposed where an efficient single objective function based on similarity matrix has been devised. The similarity index between each pair of nodes has been calculated in a distributed manner over multiple computing nodes. Similarity index proposed in this paper is based on the topological structure of the network. The effectiveness of the proposed approach is examined by comparing the performance with other state-of-the-art community detection algorithms applied over some real-world network datasets.

At the present time, one of the most important sources of survival as well as the most crucial factor in the growth of Indian economy is agriculture. More than 70% of the Indian population is involved in agricultural activities. The crop yield prediction is one of the most desirable yet challenging tasks for every nation. Nowadays, due to the unpredictable climatic changes, farmers are struggling to obtain a good amount of yield from the crops. To feed the increasing population of India, there is a need to incorporate the latest technology and tools in the agricultural sector. This study focuses on the prediction of major kharif crops in Andhra Pradesh’s one of the largest costal districts: Visakhapatnam. As rainfall is the main factor in determining amount of kharif crop production, in this study, first we predict the amount of monsoon rainfall by using modular artificial neural networks (MANNs), and then, we predict the amount of major kharif crops that can be yielded by using the rainfall data and area given to that particular crop by using support vector regression (SVR). By using the methodology of MANNs-SVR, proper agricultural strategies can be made in order to increase the yield of the crops. Comparison with other machine learning algorithms has been done which shows that the proposed methodology outperforms in predicting the instances for kharif crop production.

The increasing population in Asia brings up the need for integration of healthcare for efficient and timely manageable treatment for different diseases. Healthcare domain is one of the most important and challenging fields in terms of data collection and analysis. This domain always provide lots of opportunities to explore the hidden knowledge in accessing health records. With the growth of unstructured data in large volume that leads towards the solution by the NoSQL data management tool to manage the huge amount of data. This framework proposes a MapReduce Approach (MRA) for data management in healthcare industry with join based expectation maximization algorithm for NoSQL data management solution, which scales the data with accurate modality. This approach also simplifies the way to integrate healthcare data from different models in the distributed environment from different health hubs. Experimental results show that the proposed approach works in a scalable manner to integrate and match the unstructured data of different health data sources. Examples are illustrated with suitable methodology and further research scope is pinpointed.

Background and objective Malaria is one of the most prevalent diseases in urban areas. Malaria flourishes in sub-tropical countries and affect the public health. The impact is very high, where health monitoring facilities are very limited. To minimize the impact of malaria population in sub-tropical domains, a suitable disease prediction model is required. The objective of this study is to determine the malaria abundances using clinical and environmental variables with Big Data on the geographical location of Khammam district, Telanagana, India. Methods Prediction model is based on the data collected from primary health centres of department of vector borne diseases (DVBD) of Khammam district and satellite data such as rain fall, relative humidity, temperature and vegetation taken for the time period of 1995–2014. In this study, we test the efficacy of the artificial neural network (ANN) for mosquito abundance prediction. Prediction model was developed for the period of 2015 using a feed forward neural network and compared with the observed values. Results and conclusions The results vary from area to area based on clinical variables and rainfall in the prediction model corresponding to areas. The average error of the prediction model ranges from 18% to 117%. Clinical data such as number of patients treated with symptoms and without symptoms can improve the prediction level when combined with environmental variables. We perform preliminary findings of malaria abundances by collecting clinical big data across different seasons. Further, more exploration is required in prediction of malaria using big data to improve the accuracy in real practice. In this manuscript, we perform some preliminary findings of malaria abundances by collecting larger data across different seasons. Till today, many models have been developed to examine the malaria prediction with different approaches, but malaria prediction with environmental and clinical data is a new approach with big data analysis.

NoSQL database technology has been doing rounds since the early 1990s, but it was the exponential growth of internet and the rise of web applications that lead to a dynamic surge in the popularity of NoSQL databases. The BigTable research by Google (2006) and the Dynamo research by Amazon (2007) paved the way for databases which could develop with agility and operate at any scale. Cassandra and MongoDB have emerged as the two most widely used NoSQL database and hence either of the two is preferred depending on the data problem user is attempting to solve. This paper describes the underlying principles as well as the differences between both the databases. We focus on showing the anomaly in performance of Cassandra as the data volume increases and at the same time we compare its performance with that of MongoDB. We establish how important factor is data volume in choosing either of the databases for an application. Extensive experiments have been carried out to scale the performance in terms of anomaly similarities, and the future scope is pinpointed.

This paper systematically reviews association rules mining using crowd data in different sectors. Various sectors like health care, education, and tourism require human thinking to be harnessed to answer the queries that can be difficult for computers to answer. Traditional techniques, like Delphi, require experts in the domain which can make the system expensive, and results can be time taking. Crowdsourcing has emerged as a popular approach out of open innovation which can be used for problem solving, ideation, and even in software development because it enables us to make use of experience and background knowledge of crowd using any platform or system or generated forms. It involves a group of experts and non-experts with certain knowledge toward the problem which they solve together, and the solution is shared further. In association rule mining using crowd data, we outsource work to the crowd and mine association rules from their answers using different methods of aggregation. Crowdsourcing systems are beneficial when information required is not in a systematic manner or it is complex to aggregate. These systems can be utilized for many commercial and non-commercial platforms like Amazon Mechanical Turk (AMT) or Kaggle and they can be structured in different forms like crowdfunding or micro-tasking. We first review the current work of mining association rules using crowd data in different sectors. Then we propose suggestions in other sectors and problems which can be solved in those sectors. Finally, we conclude the limitations of the above framework in different sectors that have been covered in this paper.

Visualization of data which is enormously generated at present has become one of the major steps in machine learning before applying the classifier on related domain problems. This paper inculcates the visualization and recognition of human activity from n-dimensional space to low-dimensional space by implementing an algorithm and sometimes best termed as a tool called as t-SNE (t-distributed stochastic neighborhood embedding), which is a non-linear algorithm and has been inspired by SNE (stochastic neighbor embedding). This algorithm is applied to build the relationship between various actions done by human, where class label information has been introduced to well represent the actions from similar action class. This comes under a machine learning methodology called manifold learning, where the class label information helps to classify the action done by human. This learning technique uses probabilistic embedding of neighbors from n-dimensional space with low-dimension to find a mapping of data points from one distribution to another distribution. Experimental results and their comparison with other visualization tools like PCA shows the effectiveness of t-SNE algorithm with human activity recognition and MNIST dataset. The results produced by t-SNE algorithm in terms of visualization are always better than many other algorithms.

With the exponential growth of the Internet, the need of handling the sort of unstructured, messy and unpredictable data became so urgent. This makes NoSQL databases to be introduced to compensate for the inefficiency of traditional relational databases in dealing with these kind of data. Among those NoSQL databases, Cassandra gained popularity than the rest and today it is at use in over 1500 companies like Reddit, Netflix, Instagram, GitHub, eBay. The EQUALS and IN operator are the two most basic tools used in Cassandra Query Language (CQL) to compare two or more data items. However, the two operators are not capable of dealing with non-traditional data types like images as instead of looking for similarity between two images, they use identity (=) which is not always the correct method. Thus, this paper introduces similarity-aware EQUALS and IN operator which is very much capable of dealing with modern complex data types like images. These operators based on similarity can be used in various fields like Agriculture for finding regions capable of growing a particular crop. In this paper, we demonstrate the application of the two operators in Agriculture.

Inflation has come up as a leading concern of India's economic policymakers and citizens over the last decade. The prices of various commodities is getting higher day by day in various parts of India like Bangalore, hence it is high time that something must be done in order to decrease the price of commodities so that each and every citizen of India could afford to purchase the products he or she desires. To accommodate these, in this paper, an attempt has been made in order to analyze the prices of fruits and vegetables in the region of Bengaluru, Karnataka. To exemplify the price prediction, seasonal ARIMA is used to forecast the prices of fruits and vegetable. This will certainly help the people if the predicted prices are getting higher in the upcoming months then proper strategies can be made in order to decrease the prices of fruits and vegetables.

Fuzzy classification rules are more interpretable and cope better with pervasive uncertainty and vagueness with respect to crisp rules. Because of this fact, fuzzy classification rules are extensively used in classification and decision support systems for disease diagnosis. But, most of the rule mining techniques failed to generate accurate and comprehensive fuzzy rules. This paper presents a hybrid decision support system based on Rough Set Theory (RST) and Bat optimization Algorithm (BA) called RST-BatMiner. It consists of two stages. In the first stage, redundant features have been removed from the data set through RST-based QUICK-REDUCT approach. In the second stage, for each class BA is invoked to generate fuzzy rules by minimizing proposed fitness function. Further, an Ada-Boosting technique is applied to the rules generated by BA to increase the accuracy rate of generated fuzzy rules. Moreover, to generate comprehensive fuzzy rules, a new ≠ (not equal) operator along with = (equal) operator is introduced into BA encoding scheme. The proposed RST-BatMiner builds consolidated fuzzy ruleset by learning the rules associated with each class separately. The proposed RST-BatMiner is experimented on six bench-mark datasets namely Pima Indians Diabetes, Wisconsin Breast Cancer, Cleveland Heart disease, iris, wine and glass, in order to validate its generalization capability. These experimental results show that except for wine dataset the proposed RST-BatMiner yields high accuracy and comprehensible ruleset when compared to other state-of-the-art bio-inspired based fuzzy rule miners and Fuzzy Rule Based Classification Systems (FRBCS) in the literature. In the case of wine dataset, the proposed RST-BatMiner yields second highest accuracy along with comprehensible ruleset.

The researcher's predilection towards the concerned infinite resources and the dynamic provisioning on rental premises encourages the scheduling of complex scientific applications in the cloud. The scheduling of workflows in the cloud is constrained to QoS parameters. Many heuristic and meta-heuristic algorithms are widely investigated for the QoS constrained workflow scheduling problem. However, it is still an open area of research, as most of the existing techniques concentrate on minimization of either cost or time and ignores the optimization of multiple QoS constraints simultaneously. To address this problem, in this paper, a Hybrid Spider Monkey Optimization (HSMO) algorithm has been proposed. The proposed algorithm optimizes the makespan and the cost while satisfying the budget and deadline constraints. The proposed algorithm is the hybridization of recently developed SMO and the other popular heuristic BDSD algorithm. BDSD is a budget and deadline constrained algorithm, which guides HSMO in generating a feasible schedule. Moreover, the proposed strategy involves the penalty function to restrict selecting those solutions that fail to satisfy the QoS constraints. Experimental results demonstrate the effectiveness of HSMO over existing ABC, Bi-Criteria PSO, and BDSD algorithms.

Link prediction in a given instance of a network topology is a crucial task for extracting and inspecting the evolution of social networks. It predicts missing links in existing community networks and new or terminating links in future systems. It also attracted much attention in many fields. In the past decade, many methodologies have been compiled to predict the suitable links in a given social network. Analyzing link prediction methods is difficult when the network is very complex due to restrictive computing cost. It is still a very challenging task to predict missing links efficiently and accurately in an incomplete complex network. Depending on the certainty, the nodes with an incredible number of normal neighbors will probably be connected. Numerous similarity indices have accomplished extensive exactness and efficiency that greatly optimized this task. To accommodate this instance, in this paper, we propose one such index, namely Clustering Coefficient Index, using triangle counting implemented on the component of Apache Spark’s GraphX methodology. The proposed index uses the property of formation of triangles in the given network topology and clustering coefficients. Experimental results show that the proposed methodology outperforms in linking the suitable communications compared to other existing methods.

Brain computer interface (BCI) provides communication between the computer and the brain. It is the combination of hardware and software which provides non-muscular channel to send the various messages to control the computer. BCI is useful in various medical applications such as patients with neuromuscular injuries, locked-in syndrome (LiS) etc. BCI is not only useful in medical applications, but also useful in lie detection, entertainment, etc. In this paper, spark and rule-KNN based scalable framework has been presented using BCI with the EEG data collected on 20 subjects in which 10 are acted as innocent and 10 are acted as guilty. Using BCI P300, Deceit identification Test (DIT) is performed. To perform DIT, we classify the P300 signals which have a positive peak of 300 ms–1000 ms in one stimulus start. Data processing is performed with band pass filter to cut the frequency ranges and features are extracted using non-parametric weighted feature extraction followed by rule based discriminant classification. For training and testing, the data ratio selected as 80:20 and achieved the accuracy 92.46 %. Proposed framework provides better results in comparison with existing models presented in literature. Hence this model is accurate, scalable and fault tolerant.

In the current digital era, huge data are being generated in a voluminous state from different sources. This lead towards a processing repository called Big Data. Managing and processing such data in parallel clusters is a big challenge. To capture this problem, in this paper, we propose a hybrid algorithm for cluster analysis using the Spark framework for analyzing the Big Data instances. The proposed algorithm is the combination of two machine learning techniques namely, K-Means (KM) and C5.0 Decision Tree (DT). As per the factor of cluster, euclidean distance is used to find the nearest cluster and the related DT is built for each cluster using C5.0 DT algorithm. The inferences of the DT are used to classify each anomaly and the normal instances of the large datasets. Experimental results show that the proposed hybrid algorithm outperforms with other existing algorithms and produces better classification accuracy for anomaly detection.

With the development of cloud computing and internet; e-Commerce, e-Business and corporate world revenue are increasing with high rate. These areas not only require scalable and consistent databases but also require inter database transaction support. In this paper, we present, a scalable three-tier architecture along with a distributed middle-ware protocol to support atomic transactions across heterogeneous NoSQL databases. Our methodology does not compromise on any assumption on the accuracy of failure modalities. Hence, it is suitable for a class of heterogeneous distributed systems. To achieve such a target, our architectural model exploits an innovative methodology to achieve distributed atomic transactions. We simulate this architectural setup with different latency tests under different environments to produce reliable impact and correctness. This study proposes a three-tier architecture to facilitate atomic transactions among heterogeneous NoSQL distributed database environment as HBase.The first tier called client tier, deals with user requests. The client tier wraps transaction request and deliver it to a corresponding global transaction manager (GTM), which is assigned by a manager node.The second tier called middle tier, contains a zookeeper, manager nodes, and labour nodes. Zookeeper assigns an active manager node to the client and the active manager node assigns a labour node to process the transaction.The third tier called distributed tier manages multiple heterogeneous NoSQL databases participating with different transactions.Experimental results show that the proposed architecture is scalable and fault tolerated.

Association rule mining is one of prominent techniques to discover the relation between data items of a transactional data. The process of mining has been simplified by considering only the frequent itemsets. Pincer search is one of the frequent itemset mining method which combines top-down and bottom-up search techniques to get the benefits of both. Top-down approach in Pincer search reduces the number of candidates in pass of iterations and saves a lot of computing resources. In this work, we present a Parallel Pincer Search (PPS) which is based on distributed implementation on Spark framework. We have converted the search algorithm according to the Spark framework to make it run in parallel. Spark provides a lot of features for the iterative algorithm such as in-memory execution, efficient data structure, better fault tolerant method, etc. We implemented the PPS on a Spark cluster with multiple datasets and analysed the performance.

Low-power wearable devices for disease diagnosis are used at anytime and anywhere. These are non-invasive and pain-free for the better quality of life. However, these devices are resource constrained in terms of memory and processing capability. Memory constraint allows these devices to store a limited number of patterns and processing constraint provides delayed response. It is a challenging task to design a robust classification system under above constraints with high accuracy. In this Letter, to resolve this problem, a novel architecture for weightless neural networks (WNNs) has been proposed. It uses variable sized random access memories to optimise the memory usage and a modified binary TRIE data structure for reducing the test time. In addition, a bio-inspired-based genetic algorithm has been employed to improve the accuracy. The proposed architecture is experimented on various disease datasets using its software and hardware realisations. The experimental results prove that the proposed architecture achieves better performance in terms of accuracy, memory saving and test time as compared to standard WNNs. It also outperforms in terms of accuracy as compared to conventional neural network-based classifiers. The proposed architecture is a powerful part of most of the low-power wearable devices for the solution of memory, accuracy and time issues.

Frequent itemset mining is one of the popular techniques used to discover hidden knowledge from large-scale transactional datasets in a wide range of applications. Apriori algorithm is considered as a typical algorithm to find frequent itemsets in market basket analysis. Since its inception, many efforts have been made to enhance the efficiency of the original algorithm. MapReduce model is one of the efficient tools to implement parallel and distributed computing, so that large-scale data set algorithms such as Apriori algorithm can be made efficient in terms of speed up and other related parameters. One of the major drawbacks of the MapReduce model is that it is not suitable for iterative jobs/tasks due to overheads imposed. Now-a-days, Apache Spark is getting huge attention for iterative jobs because of its in-memory processing capabilities. Most of the frequent pattern mining algorithms consider only distinct items in a transaction. For transactional data analysis, multiple occurrences of an item or in other words “quantities” by which a particular item is purchased in the same transaction can be important to derive additional information about frequent itemsets. In this script, we propose a modified version of the Apriori algorithm based on Apache Spark framework that not only mines the frequent itemsets in the input transactional data but also analyzes related quantities of the items for a particular itemset to find the most frequent quantity being purchased for every frequent itemset. Experiments are conducted to gain insight in the form of effectiveness, efficiency, and scalability of the proposed approach.

The Radial Basis Function Neural Networks (RBFNNs) are non-iterative in nature so they are attractive for disease classification. These are four layer networks with input, hidden, output and decision layers. The RBFNNs require single iteration for training the network. On the other side, it suffers from growing hidden layer size on par with training dataset. Though various attempts have been made to solve this issue by clustering the input data. But, in a given dataset estimating the optimal number of clusters is unknown and also it involves more computational time. Hence, to address this problem in this paper, a Particle Swarm Optimization (PSO)-based clustering methodology has been proposed. In this context, we introduce a measure in the objective function of PSO, which allows us to measure the quality of wide range of clusters without prior information. Next, this PSO-based clustering methodology yields a set of High-Performance Cluster Centers (HPCCs). The proposed method experimented on three medical datasets. The experimental results indicate that the proposed method outperforms the competing approaches.

Many distributed applications have become very useful in these days, such as reservation enquiry in railways and airways, status of postal, and Internet banking. All these are related to common database, through which they are connected to provide the result of user’s query. But, the purpose of the above-distributed application will fail if we do not work cleverly on distributed database design. If we design the database with some motive to improve the performance of the application, the tolerance of the distributed database increases and it can handle several queries concurrently. So, improving the performance of a database system is a challenging research area. Earlier approaches have suggested fragmentation solution based on the data access and frequency of the queries. Previously distributed design used attribute matrix approach to generate a new matrix upon which they applied to their clustering algorithm to find out different fragments, which involves little bit complicated computation. In this paper, we present a vertical fragmentation technique, which uses attribute usage matrix instead of attribute affinity matrix by applying apriori algorithm on usage matrix for partitioning the relations. Our result comprises the proposed technique, which reduces the overhead of complicated computations.