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 paper presents adaptive Noise Cancelation (ANC) by Normalized Least Mean Square (NLMS) algorithm and also showcases its efficiency over other algorithms like Recursive Least Square (RLS) algorithm and the Least Mean Squares (LMS). In this paper, we have improved and also optimized the step size value (µ) which determines the convergence in the mean. Improper value can cause sharp changes in variance which will be misleading and is one of the major problems of least mean squares algorithm. The aim of this paper is to highlight and statistically compare the algorithms and also demonstrate the effectiveness of normalized least mean square algorithms with various additive noises.

NLMS-LOESS Algorithm for Adaptive Noise Cancelation

This paper proposes a method to track human figures in physical spaces and then utilizes this data to generate several data points such as footfall distribution, demographic analysis, heat maps as well as gender distribution. The proposed framework aims to establish this while utilizing minimum computational resources while remaining real time. It is often useful to have information such as what kind of people visit a certain place or what hour of the day experiences maximum activity, Such analysis can be used improve sales, manage huge number of people as well as predict future behaviour. The proposed framework is designed in a way such that it can take input streams from IP cameras and use that to generate relevant data points using open source tools such as OpenCV and raspberryPi. The system uses the AdaBoost Algorithm, HOG detectors and Fisher Faces and just like any other e-commerce platform can generate data points useful in several situations.

Generating analytic insights on human behavior using image processing

The recent devastations due to fire accidents that have occurred around the world are a result of erroneous decisions of fire systems and humans. Fire detection and proper response for extinguishing the fire are of utter importance for any industry operating at a large scale, therefore, the industry requires fast and reliable technology to be employed as a fire response system. The systems in the past and quite a few in present involve sensors like a smoke detector (MQ2) as input transducers which trigger the respective course of action if the input is detected such as smoke. While these are erroneous in the presence of cigar smoke or incense stick smoke, they also lack fast detection mechanism which can be very critical in reducing financial and human life loss. Our proposed work achieves a low-cost, user-friendly, add-on smart system to detect fire. The mechanism uses a faster computation to trigger a suitable response system to extinguish the fire. We propose a system that uses digital image processing at its core which monitors video of the region of interest through CCTV and a cutting-edge IoT based fire response system that is autonomous not involving human intervention at any point in time. The paper also discusses the installments of different response systems such as high-pressure water sprinklers, carbon dioxide diffuser. OpenCV library is a computer vision programming functionality that is employed in this work to detect flame with better accuracy.

Computer Vision Based Autonomous Fire Detection and IoT Based Fire Response System

The research work is to enhance the classification accuracy of the pulmonary nodules with the limited number of features extracted using Gray level co-occurrence matrix and linear binary pattern. The classification is done using the machine learning algorithm such as artificial neural network (ANN) and the random forest classifier (RF). In present, lung cancer seems to be the most deadly disease in the world which can be detected only after the computerized tomography (i.e., CT scan images of the person). Detecting the infected portion at the early period is the challenging task. Hence, the recent researchers where under the detection of pulmonary nodules to categorize it either as benign nodules which named as non-cancerous or as malignant nodules which are named as cancerous. When associated the results with the recent papers, the accuracy has been improved in classifying the lung nodules.

Pulomonary Nodule Classification From CT Scan Images Using Machine Learning Method

In this chapter, the authors make use of signal processing techniques and machine learning models to analyze the EEG signal. First, the EEG signal is broken down into the frequency sub-bands using a discrete wavelet transform (DWT). Then the kernel principle component analysis (KPCA) method is used to reduce the dimension of data. They input these extracted features into a neural network to find if the patient has an epileptic seizure or not. The results of the classification process due to artificial neural networks (ANN) are studied and analyzed. Also, to recognize the abnormal activities in the EEG signal, caused by changes in neuronal electrochemical activity in epileptic patients, the EEG signal is processed using the Hilbert Huang transform (HHT). Given the wide array of epilepsy, we need to make use of intelligent devices in the treatment of epilepsy by using the patient's neurophysiology for better diagnosis before the clinical operation.

M Rajesh Kumar

Papers

NLMS-LOESS Algorithm for Adaptive Noise Cancelation

Generating analytic insights on human behavior using image processing

Computer Vision Based Autonomous Fire Detection and IoT Based Fire Response System

Pulomonary Nodule Classification From CT Scan Images Using Machine Learning Method

Investigation of Epileptic Seizures and Sleep Disturbance