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Cotton is the most important cash crop in India. It is also known as “White Gold” or “The King of fibers” among all cash crops in the country. About 80–90% of the diseases which occur on the leaves of cotton are Alternaria leaf spot, Cercospora leaf spot, Bacterial blight and Red spot. This paper presents a survey on detection and classification of cotton leaf diseases. It is difficult for human eyes...
Identifying disease from the images of the plant is one of the interesting research areas in computer and agriculture field. This paper presents a survey of different image processing and machine-learning techniques used in the identification of rice plant diseases based on images of disease infected rice plants. This paper presents not only survey of various techniques but also concisely discusses...
Document clustering is the application of cluster analysis to textual documents. It is commonly used technique in data mining, information retrieval, knowledge discovery from data, pattern recognition, etc. In traditional document clustering, a document is considered as a bag of words; where semantic meaning of word is not taken into consideration. However, to achieve accurate document clustering,...
Financial time series prediction is considered as a challenging task. The task becomes difficult due to inherent nonlinear and non-stationary characteristics of financial time series. This article proposes a combination of wavelet and Postfix-GP, a postfix notation based genetic programming system, for financial time series prediction. The discrete wavelet transform approach is used to smoothen the...
It is important for any MOEA (Multi Objective Evolutionary Algorithm) to improve convergence and diversity of solutions of Pareto front, which is obtained at the termination of MOEA. There are many MOEA available in the literature: NSGA-II, SPEA, SPEA2, PESAII and IBEA. This paper aims at improving solutions diversity of Pareto front of a well known multi-objective optimization algorithm, NSGA-II...
Multi-objective optimization aims at simultaneously optimizing two or more objectives of a problem. Multi-objective evolutionary algorithms (MOEAs) are widely accepted and useful for solving real world multi-objective problems. When we have two or more conflicting objectives of a problem then we can apply MOEA. MOEA generates a set of non-dominated solutions at the end of run, which is called Pareto...
Clustering is a widely used technique for finding the similar hidden patterns from a dataset. Many techniques are available for data clustering such as partition clustering, hierarchical clustering, density based clustering, and grid based clustering. This paper discusses various clustering techniques along with their benefits, drawbacks, characteristics, and applications. The paper also discusses...
Android is currently the fastest growing mobile platform. One of the fastest growing areas in Android applications is Location Based Service (LBS). LBS provides information services based on the current or a known location and is supported by the Mobile positioning system. Presently, MOSDAC (Meteorological and Oceanographic Satellite Data Archival Centre) disseminates the weather forecast information...
Classification of electrocardiogram (ECG) signals plays an important role in diagnoses of heart diseases. An accurate ECG classification is a challenging problem. This paper presents a survey of ECG classification into arrhythmia types. Early and accurate detection of arrhythmia types is important in detecting heart diseases and choosing appropriate treatment for a patient. Different classifiers are...
An accurate rainfall forecasting is very important for agriculture dependent countries like India. For analyzing the crop productivity, use of water resources and pre-planning of water resources, rainfall prediction is important. Statistical techniques for rainfall forecasting cannot perform well for long-term rainfall forecasting due to the dynamic nature of climate phenomena. Artificial Neural Networks...
The cutting stock problem (CSP) is an important problem in class of combinatorial optimization problems because of its NP-hard nature. Cutting the required material from available stock with minimum wastage is a challenging process in many manufacturing industries such as rod industry, paper industry, textile industry, wood industry, plastic and leather manufacturing industry etc. This objective of...
Clustering is the process of partitioning a set of data objects into subsets. It is commonly used technique in data mining, information retrieval, and knowledge discovery for finding hidden patterns or objects from a data of different category. Text clustering process deals with grouping of an unstructured collection of documents into semantically related groups. A document is considered as a bag...
Scheduling problem has been an active area of research in computing systems since their inception. The Apache Hadoop framework has emerged as most widely adopted framework for distributed data processing because of open source and allowing use of commodity hardware. Job scheduling has become an important factor to achieve high performance in Hadoop cluster. Several scheduling algorithms have been...
Research has substantially increased in modern times due to availability and search facility of literature sources through Internet. Carrying out research and proving that it is done is a necessity for excellent academic career and for satisfying requirements of Master or PhD degrees in most universities. There is no recipe book for producing the research with desired taste and quantity, however,...
Classification of imbalanced data set is a challenging problem as it is very difficult to achieve good classification accuracy for each class in case of imbalanced data sets. This problem arises in many real world applications like medical diagnosis of rare medical disease, fraud detection in financial domain, and faulty area detection in network troubleshooting etc. The imbalanced data set consists...
Empirical modeling, which is a process of developing a mathematical model of a system from experimental data, has attracted many researchers due to its wide applicability. Finding both the structure and appropriate numeric coefficients of the model is a real challenge. Genetic programming (GP) has been applied by many practitioners to solve this problem. However, there are a number of issues which...
The cutting stock problem (CSP) is an important problem in class of combinatorial optimization problems because of its NP-hard nature. Cutting of the required material from available stock with minimum wastage is a challenging process in many manufacturing industries such as rod industry, paper industry, textile industry, wood industry, plastic and leather manufacturing industry etc. The objective...
This paper presents implementation of load balancing mechanism using master-slave model and Berkeley Lab Checkpoint/Restart (BLCR) toolkit. The overall goal is to create a Master-Slave model through which we can migrate processes from highly loaded nodes to some dedicated lightly loaded nodes. The agent running on master node divides total work into equal sub tasks and delegates these sub-tasks to...
Traditional techniques for time series modeling can capture linear behavior of data and lack the ability to identify nonlinear patterns in time series. Therefore, machine learning techniques like Neural Network or Genetic Programming (GP) are used by practitioners for modeling nonlinear and irregular time series. GP is preferred over other techniques because it does not presume model structure a priori...
Multi-objective Optimization Problem (MOP) is an essential and challenging area for scientific research of real life problem. Multi-objective Optimization Problem (MOP) can be effectively solved by Multi-objective Evolutionary Algorithm (MOEA). In this paper, enhancements to a renowned Multi-objective Evolutionary algorithm SPEA2 are proposed. The proposed enhancements are useful to improve convergence...
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