Thermal power plants play a major role in power production. The impacts of the various pollutants such as sulphur dioxide (SO2), Nitrogen oxide (NOx) and carbon dioxide (CO2) affects the environmental issues. The fuel cost of the generator in an economic dispatch problem can be presented by any order polynomial. The literature review reported that most of the papers consider the single pollutant using the second order polynomial function. The paper formulates the dispatch problem using a cubic function for both fuel cost and emission values. The impacts of various pollutants and price penalty factors such as Max-Max, MinMax, Average, and Common are considered in the multi-objective dispatch problem. The dispatch problem is solved using Lagrange's method and the simulation results are provided for IEEE 30 bus system. It concludes that Min-Max Price penalty factor provides minimum fuel cost and minimum emission values in comparison to the other price penalty factors.

http://digitalknowledge.cput.ac.za/bitstream/11189/8079/1/Impact%20of%20Price%20Penalty%20Factors%20on%20the%20Solution.pdf

Impact of price penalty factors on the solution of the combined economic emission dispatch problem using cubic criterion functions

The analysis of sentiments is essential in identifying and classifying opinions regarding a source material that is, a product or service. The analysis of these sentiments finds a variety of applications like product reviews, opinion polls, movie reviews on YouTube, news video analysis, and health care applications including stress and depression analysis. The traditional approach of sentiment analysis which is based on text involves the collection of large textual data and different algorithms to extract the sentiment information from it. But multimodal sentimental analysis provides methods to carry out opinion analysis based on the combination of video, audio, and text which goes a way beyond the conventional text‐based sentimental analysis in understanding human behaviors. The remarkable increase in the use of social media provides a large collection of multimodal data that reflects the user's sentiment on certain aspects. This multimodal sentimental analysis approach helps in classifying the polarity (positive, negative, and neutral) of the individual sentiments. Our work aims to present a survey of recent developments in analyzing the multimodal sentiments (involving text, audio, and video/image) which involve human–machine interaction and challenges involved in analyzing them. A detailed survey on sentimental dataset, feature extraction algorithms, data fusion methods, and efficiency of different classification techniques are presented in this work.

Multimodal sentimental analysis for social media applications: A comprehensive review

A Quantum-Like multimodal network framework for modeling interaction dynamics in multiparty conversational sentiment analysis

Learning interaction dynamics with an interactive LSTM for conversational sentiment analysis.

Handwriting recognition remains a challenge in the machine vision field, especially in optical character recognition (OCR). The OCR has various applications such as the detection of handwritten Farsi digits and the diagnosis of biomedical science. In expanding and improving quality of the subject, this research focus on the recognition of Farsi Handwriting Digits and illustration applications in biomedical science. The detection of handwritten Farsi digits is being widely used in most contexts involving the collection of generic digital numerical information, such as reading checks or digits of postcodes. Selecting an appropriate classifier has become an issue highlighted in the recognition of handwritten digits. The paper aims at identifying handwritten Farsi digits written with different handwritten styles. Digits are classified using several traditional methods, including K-nearest neighbor, artificial neural network (ANN), and support vector machine (SVM) classifiers. New features of digits, namely, geometric and correlation-based features, have demonstrated to achieve better recognition performance. A noble class of methods, known as deep neural networks (DNNs), is also used to identify handwritten digits through machine vision. Here, two types of introduce its expansion form, a convolutional neural network (CNN) and an auto-encoder, are implemented. Moreover, by using a new combination of CNN layers one can obtain improved results in classifying Farsi digits. The performances of the DNN-based and traditional classifiers are compared to investigate the improvements in accuracy and calculation time. The SVM shows the best results among the traditional classifiers, whereas the CNN achieves the best results among the investigated techniques. The ANN offers better execution time than the SVM, but its accuracy is lower. The best accuracy among the traditional classifiers based on all investigated features is 99.3% accuracy obtained by the SVM, and the CNN achieves the best overall accuracy of 99.45%.

Analysis and comparison of machine learning classifiers and deep neural networks techniques for recognition of Farsi handwritten digits

Brain tumor is a hazardous disease. It has to be treated rightly because the patient cannot survive for even a year. This research work is to design a mechanized system that discerns benign and malignant tumor images and improves the classification accuracy. In this system, histogram equalization is used to raise the intensity of tumor so that it can be detected more precisely. In the projected system, segmentation is performed by k-means clustering and Gaussian mixture model (GMM). Along with them, the authors are extracting the features from discrete wavelet transform (DWT) and feature reduction from principal component analysis (PCA). The classifiers support vector machine (SVM) and artificial neural networks (ANN) are used to classify benign and malignant tumor from brain images.

M Rajesh Kumar

Papers

Brain Tumor Detection and Classification Based on Histogram Equalization Using Machine Learning