An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

Market demand is highly unpredictable after transportation disruption.It designs an improved prediction model of grey neural networks.It determines the number of neurons in the input layer of BP neural networks.It tests the feasibility of the prediction model through case studies.It helps optimize inventory and production after transportation disruption. Transportation disruption is the direct result of various accidents in supply chains, which have multiple negative impacts on supply chains and member enterprises. After transportation disruption, market demand becomes highly unpredictable and thus it is necessary for enterprises to better predict market demand and optimize purchase, inventory and production. As such, this article endeavors to design an improved model of grey neural networks to help enterprises better predict market demand after transportation disruption and then the empirical study tests its feasibility. This improved model of grey neural networks exceeds the conventional grey model GM(1,1) with respect to the fact that the raw data tend to show exponential growth and data variation is required to be moderate, demonstrating the good attribute of nonlinear approximation in terms of neural networks, setting up standards for selecting the number of neurons in the input layer of BP neural networks, increasing the fitting degree and prediction accuracy and enhancing the stability and reliability of prediction. It can be applied to sequential data prediction in transportation disruption or mutation, contributing to the prediction of transportation disruption. The forecasting results can provide scientific evidence for demand prediction, inventory management and production of supply chain enterprises.

An improved grey neural network model for predicting transportation disruptions

COVID-19 has crippled the world’s healthcare systems, setting back the economy and taking the lives of several people. Although potential vaccines are being tested and supplied around the world, it will take a long time to reach every human being, more so with new variants of the virus emerging, enforcing a lockdown-like situation on parts of the world. Thus, there is a dire need for early and accurate detection of COVID-19 to prevent the spread of the disease, even more. The current gold-standard RT-PCR test is only 71% sensitive and is a laborious test to perform, leading to the incapability of conducting the population-wide screening. To this end, in this paper, we propose an automated COVID-19 detection system that uses CT-scan images of the lungs for classifying the same into COVID and Non-COVID cases. The proposed method applies an ensemble strategy that generates fuzzy ranks of the base classification models using the Gompertz function and fuses the decision scores of the base models adaptively to make the final predictions on the test cases. Three transfer learning-based convolutional neural network models are used, namely VGG-11, Wide ResNet-50-2, and Inception v3, to generate the decision scores to be fused by the proposed ensemble model. The framework has been evaluated on two publicly available chest CT scan datasets achieving state-of-the-art performance, justifying the reliability of the model. The relevant source codes related to the present work is available in: GitHub.

Fuzzy rank-based fusion of CNN models using Gompertz function for screening COVID-19 CT-scans.

Selection of meteorological parameters affecting rainfall estimation using neuro-fuzzy computing methodology

COVID-19 detection from lung CT-Scans using a fuzzy integral-based CNN ensemble.

Since the onset of 2020, the spread of coronavirus disease (COVID-19) has rapidly accelerated worldwide into a state of severe pandemic. COVID-19 has infected more than 29 million people and caused more than 900 thousand deaths at the time of writing. Since it is highly contagious, it causes explosive community transmission. Thus, health care delivery has been disrupted and compromised by the lack of testing kits. COVID-19-infected patients show severe acute respiratory syndrome. Meanwhile, the scientific community has been involved in the implementation of deep learning (DL) techniques to diagnose COVID-19 using computed tomography (CT) lung scans, since CT is a pertinent screening tool due to its higher sensitivity in recognizing early pneumonic changes. However, large datasets of CT-scan images are not publicly available due to privacy concerns and obtaining very accurate models has become difficult. Thus, to overcome this drawback, transfer-learning pre-trained models are used in the proposed methodology to classify COVID-19 (positive) and COVID-19 (negative) patients. We describe the development of a DL framework that includes pre-trained models (DenseNet201, VGG16, ResNet50V2, and MobileNet) as its backbone, known as KarNet. To extensively test and analyze the framework, each model was trained on original (i.e. unaugmented) and manipulated (i.e. augmented) datasets. Among the four pre-trained models of KarNet, the one that used DenseNet201 demonstrated excellent diagnostic ability, with AUC scores of 1.00 and 0.99 for models trained on unaugmented and augmented data sets, respectively. Even after considerable distortion of the images (i.e. the augmented dataset) DenseNet201 achieved an accuracy of 97% for the test dataset, followed by ResNet50V2, MobileNet, and VGG16 (which achieved accuracies of 96%, 95%, and 94%, respectively).

COVID-19 detection from lung CT-scan images using transfer learning approach

A novel rainfall prediction method has been proposed. In the present work rainfall prediction in Southern part of West Bengal (India) has been conducted. A two-step method has been employed. Greedy forward selection algorithm is used to reduce the feature set and to find the most promising features for rainfall prediction. First, in the training phase the data is clustered by applying k-means algorithm, then for each cluster a separate Neural Network (NN) is trained. The proposed two step prediction model (Hybrid Neural Network or HNN) has been compared with MLP-FFN classifier in terms of several statistical performance measuring metrics. The data for experimental purpose is collected by Dumdum meteorological station (West Bengal, India) over the period from 1989 to 1995. The experimental results have suggested a reasonable improvement over traditional methods in predicting rainfall. The proposed HNN model outperformed the compared models by achieving 84.26% accuracy without feature selection and 89.54% accuracy with feature selection.

Rainfall prediction using hybrid neural network approach

The present work proposes a hybrid neural network based model for rainfall prediction in the Southern part of the state West Bengal of India. The hybrid model is a multistep method. Initially, the data is clustered into a reasonable number of clusters by applying fuzzy c-means algorithm, then for every cluster a separate Neural Network (NN) is trained with the data points of that cluster using well known metaheuristic Flower Pollination Algorithm (FPA). In addition, as a preprocessing phase a feature selection phase is included. Greedy forward selection algorithm is employed to find the most suitable set of features for predicting rainfall. To establish the ingenuity of the proposed hybrid prediction model (Hybrid Neural Network or HNN) has been compared with two well-known models namely multilayer perceptron feed-forward network (MLP-FFN) using different performance metrics. The data set for simulating the model is collected from Dumdum meteorological station (West Bengal, India), recorded with in the 1989 to 1995. The simulation results have revealed that the proposed model is significantly better than traditional methods in predicting rainfall.

Hybrid neural network based rainfall prediction supported by flower pollination algorithm

Prediction of Atmospheric Pressure is one important and challenging task that needs lot of attention and study for analyzing atmospheric conditions. Advent of digital computers and development of data driven artificial intelligence approaches like Artificial Neural Networks (ANN) have helped in numerical prediction of pressure. However, very few works have been done till now in this area. The present study developed an ANN model based on the past observations of several meteorological parameters like temperature, humidity, air pressure and vapour pressure as an input for training the model. The novel architecture of the proposed model contains several multilayer perceptron network (MLP) to realize better performance. The model is enriched by analysis of alternative hybrid model of k-means clustering and MLP. The improvement of the performance in the prediction accuracy has been demonstrated by the automatic selection of the appropriate cluster
.

http://www.ijorcs.org/uploads/archive/Vol3-Issue-1-02-prediction-of-atmospheric-pressure-at-ground-level-using-artificial-neural-network.pdf

Prediction of Atmospheric Pressure at Ground Level using Artificial Neural Network

In this study, we demonstrate how connectionist models, in particular, multilayer perceptron network can be used for prediction of rainfall. Here we give a comparative study between conventional approach (using multivariate linear regression) and soft computing approach using artificial neural network (ANN). The basic idea is to identify a computational model to characterise the relation between the average monthly rainfalls of a region with that of different neighbouring regions. The model exploits both spatial as well as temporal information to achieve better prediction. Once the computational model is obtained, it is used to predict the average monthly rainfall. Early prediction of rainfall is expected to play a key role in economic planning.

Bimal Datta

Papers

COVID-19 detection from lung CT-scan images using transfer learning approach

Rainfall prediction using hybrid neural network approach

Hybrid neural network based rainfall prediction supported by flower pollination algorithm

Prediction of Atmospheric Pressure at Ground Level using Artificial Neural Network

Estimation of average monthly rainfall with neighbourhood values: comparative study between soft computing and statistical approach