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

This chapter gives an effective and efficient technique that can detect epilepsy in real time. It is low cost, low power, and real-time devices that can easily detect epilepsy. Along with EEG device, one can upgrade with GSM module to alert the doctors and parents of patients about its occurrence to prevent a sudden fall, which may cause injury and death. The accuracy of this EEG device depends on the quality of feature extraction technique and classification algorithm. In this chapter, support vector machine (SVM) is used as a classifier. Wavelet transform gives feature extraction, which helps to train data and to detect normal or seizure patients. Discrete wavelet transform (DWT) decomposes the signals into three decomposition levels. In this detection, mean, median, and non-linear parameter entropy were calculated for every sub-band as key parameters. The extracted features are then applied to SVM classifier for the classification. Better accuracy of classification is obtained using wavelet and SVM classifier.

Real-Time EEG Device for Epilepsy Detection Using Wavelet Transform and Support Vector Machine

Mobile phones are becoming increasingly advanced and the latest ones are equipped with many diverse and powerful sensors These sensors can be used to study different positions and orientations of the phone which can help smartphone manufacturers to track the handling of their customer’s phones from the recorded log The inbuilt sensors such as the accelerometer and gyroscope present in our phones are used to obtain data for acceleration and orientation of the phone in the three axes for different phone vulnerable position From the data obtained appropriate features are extracted using various feature extraction techniques which are given to classifiers such as the neural network to classify them and decide whether the phone is in a vulnerable position to fall or it is in a safe position

Automatic Phone Slip Detection System

In this chapter, the authors use various signal processing techniques to analyze and gain insights on how ECG signals for patients suffering from sleep apnea (sleep apnea or obstructive sleep apnea occurs when the muscles that support the soft tissues in the throat, such as tongue and soft palate, relax temporarily) disease vary with respect to a normal person's ECG. The work has three stages: firstly, to identify waves, complexes, morphology in an ECG which reflect the presence of the disease; second, feature extraction techniques to extract features of ECG such as duration of the wave, amplitude distribution, and morphology classes; and third, detailed clustering (unsupervised) algorithm analysis of the extracted features with efficient feature reduction methodologies such as PCA and LDA. Finally, the authors use supervised machine learning algorithms (SVM, naive Bayes classifier, feed forward neural network, and decision tree) to distinguish between ECG signals with sleep apnea and normal ECG signals.

Classification of Sleep Apnea Using ECG Signals With Machine Learning Techniques

The advancement in power systems has led to the development of generation dispatch (GD) that is difficult to solve by classical optimisation method. The proposed paper work is to evolve simple and effective method for optimum generation dispatch to minimise the fuel cost, environmental cost and security requirement of power networks. The approach is based on the bi-criterion global optimisation and Particle Swarm Optimisation (PSO) technique. The proposed technique is tested on 3-area interconnected and longitudinal system. The effectiveness of the proposed optimisation is verified in simulation studies using MATLAB software. The PSO based approach has been extended to evaluate the trade- off curve between the fuel cost of power production and the environmental cost according to the bi-criterion objective function.

http://www.wseas.us/e-library/conferences/2006tenerife/papers/541-166.pdf

Using particle swarm optimisation with multiple fuel option

Human minds have the inherent capacity for emotions to be analyzed. This corporate content approach, however, it is time-consuming, inconsistent, and costly. Therefore, it is simply not possible to have individuals personally read tens of thousands of customer reviews individually and rate them for sentiment. Therefore, sentimental analysis is the process by which information is derived from the thoughts, evaluations and feelings of people with regard to individuals, events and their attributes. Sentiment analysis has been a hot-spot in the research sector in recent years. This is because the subjective text that appears on the internet is rising. We may characterize human life either positively or negatively. Through this article, we hope to strengthen the classification of feelings at a finer level, specifically at the level of sentences, where two categories involve the polarity of the sentence as positive and negative. We investigated text classification in this paper using a variety of techniques such as multinomial Naive Bayes, support vector machine (SVM), logistic regression, and recurrent neural networks (RNN).

M Rajesh Kumar

Papers

Real-Time EEG Device for Epilepsy Detection Using Wavelet Transform and Support Vector Machine

Automatic Phone Slip Detection System

Classification of Sleep Apnea Using ECG Signals With Machine Learning Techniques

Using particle swarm optimisation with multiple fuel option

Sentiment Analysis of Text Classification Using RNN Algorithm