Predicting soil moisture quantity could directly help the people engaged in sustainable agriculture and associated socio-economic structures. Recently researchers have engaged traditional and machine learning based models to predict soil moisture quantity. In the current study a modified Flower Pollination Algorithm (MFPA) has been employed to train Artificial Neural Network (ANN) to predict soil moisture quantity. The proposed method is compared with well known PSO (Particle Swarm optimization) supported ANN and Cuckoo Search (CS) supported ANN along with MLP-FFN classifier. The stability of the proposed model in presence of varying weather conditions has been established by performing a stability analysis using data level perturbation. Experimental results have indicated that NN-MFPA achieved an average RMSE of 0.0019 and outperformed other models. The ingenuity of the proposed model is further established by performing Wilcoxon rank test with 5% level of significance. © 2018 Elsevier Inc.

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University of Calcutta (informally known as Calcutta University or CU) is a collegiate public state university located in Kolkata, West Bengal, India.&nbsp;Within India it is recognized as a &quot;Five-Star University&quot; and accredited &quot;A&quot; Grade by National Assessment and Accreditation Council.

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The University of Calcutta has secured the Fifth position among the Universities of India in the prestigious &quot;Indian University Ranking 2019&quot; list, released by the NIRF of the Ministry of Human Resource Development, Government of India.

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It was established on 24 January 1857, and was one of the first institutions in Asia to be established as a multidisciplinary and Western-style university. Its alumni and faculty include several heads of state, heads of government, social reformers, prominent artists, only academy award winner and Dirac medal winner in India, many Fellows of the Royal Society and five Nobel laureates as of 2019 which is highest in South Asia.

University Of Calcutta

Sustainable Computing: Informatics and Systems

The world population is expected to grow by another two billion in 2050, according to the survey taken by the Food and Agriculture Organization, while the arable area is likely to grow only by 5%. Therefore, smart and efficient farming techniques are necessary to improve agriculture productivity. Agriculture land suitability assessment is one of the essential tools for agriculture development. Several new technologies and innovations are being implemented in agriculture as an alternative to collect and process farm information. The rapid development of wireless sensor networks has triggered the design of low-cost and small sensor devices with the Internet of Things (IoT) empowered as a feasible tool for automating and decision-making in the domain of agriculture. This research proposes an expert system by integrating sensor networks with Artificial Intelligence systems such as neural networks and Multi-Layer Perceptron (MLP) for the assessment of agriculture land suitability. This proposed system will help the farmers to assess the agriculture land for cultivation in terms of four decision classes, namely more suitable, suitable, moderately suitable, and unsuitable. This assessment is determined based on the input collected from the various sensor devices, which are used for training the system. The results obtained using MLP with four hidden layers is found to be effective for the multiclass classification system when compared to the other existing model. This trained model will be used for evaluating future assessments and classifying the land after every cultivation.

Fulltext

Sensors

Sensors Driven AI-Based Agriculture Recommendation Model for Assessing Land Suitability

The advancement in science and technology has led to a substantial amount of data from various fields of agriculture to be incremented in the public domain. Hence a desideratum arises from the investigation of the available data and integrating them with a process like a crop improvement, yield prediction, crop disease analysis, identifying water stress, and so on. Computing techniques like Machine learning is a new advent for the analysis and resoluteness of these intricate issues. Various analytical models like Decision Trees, Random Forests, Support Vector Machines, Bayesian Networks, and Artificial Neural Networks, and so on, have been utilized for engendering the models and analyze the results. These methods enable to analyze soil, climate, and water regime which are significantly involved in crop growth and precision farming. This survey incorporates an overview of some of the existing supervised and unsupervised machine learning models associated with the crop yield in literature. Moreover, this survey compares one approach with other using various error measures like Root Mean Square Error (RMSE), Relative Root Mean Square Error (RRMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R2). © 2018 Elsevier B.V.

Computers and Electronics in Agriculture

Forecasting yield by integrating agrarian factors and machine learning models: A survey

The environmental ever demanding improvement along with the increasing demand of electricity attracted researchers in designing efficient, accurate and robust models. Such models are used mainly to predict the energy output of combined steam and gas turbine mechanisms. The applicability of these systems depends on their sustainability. It is inevitable to predict the combined mechanisms output energy in order to produce more trustworthy mechanisms. Since the acceptability of the aforesaid turbine systems is judged in terms of their profitability, the output energy prediction plays a vital role. In machine learning, the neural network (NN) based models has been proven to be a trustworthy in critical prediction tasks. However, the traditional learning algorithms in the NNs suffer from premature convergence to local optima while finding the optimum weight vectors. Consequently, the present work proposed a Cuckoo Search (CS) supported NN (NN-CS) and a Particle Swarm Optimization (PSO) supported NN (NN-PSO) to efficiently predict the electrical energy output of the combined cycle gas turbines. In the current study, five features are extracted, namely the ambient temperature, relative humidity and ambient pressure in gas turbines and exhaust vacuum from a steam turbine. The results established the improved performance of the CS based NN compared to the multilayer perceptron feed-forward neural network (MLP-FFN) and the NN-PSO (particle swarm optimization) in terms of root mean squared error. Proposed NN-CS achieved an average of 2.58% the mean square error (RMSE). © 2018, Springer Nature Singapore Pte Ltd.

Lecture Notes in Networks and Systems

Electrical Energy Output Prediction Using Cuckoo Search Based Artificial Neural Network

Water is one of the most important necessities for human survival. In municipal corporation areas, water quality affects a large part of the population. Good quality water supply is an imperative parameter that influences individuals' health. Automated accurate water quality determination becomes an urgent necessity. Detecting the drinking water quality can prevent such scenarios prior to the critical stage. Recent research works have achieved reasonable success in predicting the water quality by deploying several machine learning-based techniques and utilising different aspects to analyse water quality. The accuracy levels of already proposed models are to be improved, keeping in mind the sensitivity of the problem domain. In the current work, Non-dominated Sorting Genetic Algorithm-II (NN-NSGA-II) was employed to train the artificial neural network (ANN) to improve its performance over its traditional counterparts. The proposed model gradually minimises two different objective functions, namely the root mean square error (RMSE) and Maximum Error (ME) in order to find the optimal weight vector for the ANN. The proposed model was compared with another two well-established models namely ANN trained with Genetic Algorithm (NN-GA) and ANN trained with Particle Swarm Optimisation (NN-PSO) in terms of accuracy, precision, recall, F-Measure, Matthews correlation coefficient (MCC) and Fowlkes-Mallows (FM) index. Furthermore, a data perturbation-based stability analysis is proposed to test the stability of the proposed method. The simulation results established superior accuracy of NN-NSGA-II over the other models. © 2018 World Scientific Publishing Co.

Journal of Information and Knowledge Management

Non-dominated sorting genetic algorithm-II-induced neural-supported prediction of water quality with stability analysis

Dengue fever detection and classification have a vital role due to the recent outbreaks of different kinds of dengue fever. Recently, the advancement in the microarray technology can be employed for such classification process. Several studies have established that the gene selection phase takes a significant role in the classifier performance. Subsequently, the current study focused on detecting two different variations, namely, dengue fever (DF) and dengue hemorrhagic fever (DHF). A modified bag-of-features method has been proposed to select the most promising genes in the classification process. Afterward, a modified cuckoo search optimization algorithm has been engaged to support the artificial neural (ANN-MCS) to classify the unknown subjects into three different classes namely, DF, DHF, and another class containing convalescent and normal cases. The proposed method has been compared with other three well-known classifiers, namely, multilayer perceptron feed-forward network (MLP-FFN), artificial neural network (ANN) trained with cuckoo search (ANN-CS), and ANN trained with PSO (ANN-PSO). Experiments have been carried out with different number of clusters for the initial bag-of-features-based feature selection phase. After obtaining the reduced dataset, the hybrid ANN-MCS model has been employed for the classification process. The results have been compared in terms of the confusion matrix-based performance measuring metrics. The experimental results indicated a highly statistically significant improvement with the proposed classifier over the traditional ANN-CS model. © 2017, International Federation for Medical and Biological Engineering.

Medical and Biological Engineering and Computing

Clinical application of modified bag-of-features coupled with hybrid neural-based classifier in dengue fever classification using gene expression data

In the present work a Cuckoo Search (CS) trained Neural Network (NN) or NN-CS based model has been proposed to detect Chronic Kidney Disease (CKD) which has become one of the newest threats to the developing and undeveloped countries. Studies and surveys in different parts of India have suggested that CKD is becoming a major concern day by day. The financial burden of the treatment and future consequences of CKD could be unaffordable to many if not detected at an earlier stage. Motivated by this, the NN-CS model has been proposed which significantly overcomes the problem of using local search based learning algorithms to train NNs. The input weight vector of the NN is gradually optimized by using CS to train the NN. The model has been compared with well-known classifiers like Multilayer Perceptron Feedforward Network (MLP-FFN) (trained with scaled conjugate gradient descent) and also with NN supported by Genetic Algorithm (NN-GA). The performance of the classifiers has been measured in terms of accuracy, precision, recall and F-Measure. The experimental results suggest that NN-CS based model is capable of detecting CKD more efficiently than any other existing model. © 2017 IEEE.

2017 1st International Conference on Electronics, Materials Engineering and Nano-Technology, IEMENTech 2017

Cuckoo search coupled artificial neural network in detection of chronic kidney disease

A mosquito borne pathogen called Dengue virus (DENV) has been emerged as one of the most fatal threats in the recent time. Infections can be in two main forms, namely the DF (Dengue Fever), and DHF (Dengue Hemorrhagic Fever). An efficient detection method for both fever types turns out to be a significant task. Thus, in the present work, a novel application of Particle Swarm Optimization (PSO) trained Artificial Neural Network (ANN) has been employed to separate the patients having Dengue fevers from those who are recovering from it or do not have DF. The ANN’s input weight vector are optimized using PSO to achieve the expected accuracy and to avoid premature convergence toward the local optima. Therefore, a gene expression data (GDS5093 dataset) available publicly is used. The dataset contains gene expression data for DF, DHF, convalescent and healthy control patients of total 56 subjects. Greedy forward selection method has been applied to select most promising genes to identify the DF, DHF and normal (either convalescent or healthy controlled) patients. The proposed system performance was compared to the multilayer perceptron feed-forward neural network (MLP-FFN) classifier. Results proved the dominance of the proposed method with achieved accuracy of 90.91 %. © Springer Nature Singapore Pte Ltd. 2017.

Journal	Data powered by TypesetSustainable Computing: Informatics and Systems
Publisher	Data powered by TypesetElsevier Inc.
ISSN	2210-5379
Open Access	No