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

The sensor pattern noise is associated with digital images, due to imperfection in the chip of image sensor manufacturing process and it causes pixel sensitivity variation in the imaging sensor. The distinct property of these pattern noises makes it unique to that image sensor. Therefore, it acts as ‘fingerprint’ of that particular imaging sensor. The main contributor of sensor pattern noise is Photo Response Non-Uniformity noise (PRNU). In this proposed work, we analyse the PRNU estimation and enhance the content of the PRNU for better accurate identification of the source camera. The PRNU extraction consists of three stages: filtering, estimation and enhancement stage. Each stage consists of various techniques incorporated for the PRNU extraction. The experiments were conducted on natural images taken from the different camera models. For our experiment, 300 images from 6 different camera models are used and identification of source camera of a given image is done by correlating the PRNU reference pattern with the noise residual model obtained from the test image.

Source camera identification using SPN with PRNU estimation and enhancement

With the increase in interchange of data, there is a growing necessity of security. Considering the volumes of digital data that is transmitted, they are in need to be secure. Among the many forms of tampering possible, one widespread technique is Copy Move Forgery (CMF). This forgery occurs when parts of the image are copied and duplicated elsewhere in the same image. There exist a number of algorithms to detect such a forgery in which the primary step involved is feature extraction. The feature extraction techniques employed must have lesser time and space complexity involved for an efficient and faster processing of media. Also, majority of the existing state of art techniques often tend to falsely match similar genuine objects as copy move forged during the detection process. To tackle these problems, the paper proposes a novel algorithm that recognizes a unique approach of using Hu's Invariant Moments and Log-polar Transformations to reduce feature vector dimension to one feature per block simultaneously detecting CMF among genuine similar objects in an image. The qualitative and quantitative results obtained demonstrate the effectiveness of this algorithm.

Copy Move Forgery using Hu's Invariant Moments and Log-Polar Transformations

This paper presents an efficient approach for distinguishing ECG signals based on certain diseases by implementing Pan Tompkins algorithm and neural networks. Pan Tompkins algorithm is used for feature extraction on electrocardiography (ECG) signals, while neural networks help in detection and classification of the signal into four cardiac diseases: Sleep Apnea, Arrhythmia, Supraventricular Arrhythmia and Long-Term Atrial Fibrillation (AF) and normal heart beat. The paper also presents a new approach towards signal classification using the existing neural networks classifiers.

Implementation of Neural Network and Feature Extraction to Classify ECG Signals

One of the most arduous and captivating domains under image processing is handwritten character recognition. In this paper we have proposed a feature extraction technique which is a combination of unique features of geometric, zone-based hybrid, gradient features extraction approaches and three different neural networks namely the Multilayer Perceptron network using Backpropagation algorithm (MLP BP), the Multilayer Perceptron network using Levenberg-Marquardt algorithm (MLP LM) and the Convolutional neural network (CNN) which have been implemented along with the Minimum Distance Classifier (MDC). The procedures lead to the conclusion that the proposed feature extraction algorithm is more accurate than its individual counterparts and also that Convolutional Neural Network is the most efficient neural network of the three in consideration.

Handwritten Character Recognition Using Unique Feature Extraction Technique

The rising need of secret image sharing with high security has led to much advancement in lucrative exchange of important images which contain vital and confidential information. Multi secret image sharing system (MSIS) is an efficient and robust method for transmitting one or more secret images securely. In recent research, n secret images are encrypted into n or n+ 1 shared image's and stored in different database servers. Decoder has to receive all n or n+1 encrypted images to reproduce the secret image. One can recover partial secret information from n-1 or fewer shared images, which poses risk for the confidential information encrypted. In this proposed paper we developed a novel algorithm to increase the sharing capacity by using (n, n/8) multi secret sharing scheme with increased security by generating unique security key. An unrevealed comparison image is used to produce shares which makes the secret image invulnerable to the hackers.

M Rajesh Kumar

Papers

Source camera identification using SPN with PRNU estimation and enhancement

Copy Move Forgery using Hu's Invariant Moments and Log-Polar Transformations

Implementation of Neural Network and Feature Extraction to Classify ECG Signals

Handwritten Character Recognition Using Unique Feature Extraction Technique

High capacity, secure (n, n/8) multi secret image sharing scheme with security key