Reviewerly/exHarmony huggingface dataset:
Human Action Recognition in Videos Using Deep Neural Network

Identifying the various actions in video is important in computer vision research. The present paper aims to develop a model that would take the video input from different sources like security cameras and recorded videos and detect actions that are being performed in them by the people also commonly referred to as HAR or human activity detection. Based on series of observations in a video from frame to frame, the action can be identified. In the present work, deep convolutional neural networks (CNNs) is used to implement the model for HAR. The model can be applicable in several applications like human computer interaction, surveillance, elder people assistance, and patient monitoring system.
Novel deep learning model for bitcoin price prediction by multiplicative LSTM with attention mechanism and technical indicators

Starting from the earlier civilisation till date, money plays a crucial part in the transfer of goods and services. With this digital world, the money also changes its faces from paper money to digital currency called cryptocurrency without any central bank, which runs on top of the technology called blockchain. The trendiest cryptocurrency is bitcoin. Forecasting the daily price is a challenging task due to its nonlinearity. Most of the researchers tried to predict using various statistical and machine learning models which were not satisfactory because of its large dataset with more noise. The intention is to design a deep learning multiplicative long short-term memory model to estimate the price of bitcoin with an attention mechanism using technical indicators which gives better accuracy and a very less error rate. The proposed model is compared with some existing models, say long short-term memory, peephole, gated recurrent unit and multiplicative long short-term memory on the presence and absence of technical indicators. The comparative result shows that the proposed model outperforms the existing models in terms of mean square error, root mean square error and mean absolute error when evaluated with two benchmark datasets.
Privacy-Preserving Preselection for Protected Biometric Identification Using Public-Key Encryption With Keyword Search

The efficiency of biometric systems, in particular efficient and accurate biometric identification, is one of the most challenging open problems in biometrics today. Adding to that, biometric data are sensitive data deserving adequate protection. As a solution, this work proposes an efficient privacy-preserving reduction of the computational workload of biometric identification systems using public-key encryption with keyword search (PEKS). For long-term protection of the biometric data, fully homomorphic encryption is applied for template protection. As all applied cryptographic schemes are lattice-based, they also offer post-quantum security. Throughout the system, the recognition accuracy of the unprotected system is preserved. In an evaluation on a public face database, the computational workload of an identification search in the encrypted domain is reduced down to 8.4&#x0025; compared to an exhaustive search, achieving identification on 1062 subjects in 210 milliseconds. Based on these results, an identification search on 1 million subjects can be estimated at under 3 minutes using off-the-shelf hardware.
Contrast Enhancement of CT-Scan Images of Lungs Using Morphological Filters

Lung cancer is one of the most fatal pulmonary diseases. Its screening can be done by multiple techniques such as X-ray imaging and computer tomography (CT) scan imaging. The CT-scan images have much better reliability and feature characteristics as compared to other screening techniques. Sometimes the traits and features of these images cannot be recognized due to the dire quality of the image attained. This paper proposes the use of combination of morphological operators of morphological filtering (MF), like open and bottom-hat function which can be helpful in reducing the noise of the image and also sharpens the image. The background details and visibility are also improved. The image quality assessment (IQA) of the enhanced image is done by the use of measure of enhancement (EME). The escalating values of the IQA convey that the noise from the image has been removed, the characteristics of the image appear to be sharper and retain the important features of the image.
Optimized Piano Music Education Model Based on Multimodal Information Fusion for Emotion Recognition in Multimedia Video Networks

Emotion is the important information that people transmit in the process of communication, and the change of emotional state affects people’s perception and decision-making, which introduces the emotional dimension into human-computer interaction. The modes of emotional expression include facial expressions, speech, posture, physiological signals, text, and so on. Emotion recognition is essentially a multimodal fusion problem. This paper investigates the different teaching modes of the teachers and students of our school, designs the load capacity through the K-means algorithm, builds a multimedia network sharing classroom, and creates a piano music situation to stimulate students’ learning interest, using audiovisual and other tools to mobilize students’ emotions, using multimedia guidance to extend students’ piano music knowledge, and comprehensively improve students’ aesthetic ability and autonomous learning ability. Comparing the changes of students after 3 months of teaching, the results of the study found that multimedia sharing classrooms can be up to 50% ahead of traditional teaching methods in enhancing students’ interest, and teachers’ acceptance of multimedia network sharing classrooms is also high.
Design of Distance English Teaching Application System Combined with Behavioral Cognitive Settlement

In order to improve the quality of Distance English teaching, this study studies the teaching application system combined with behavioral cognition. In the research, relevant technologies such as settlement system integration, automatic scoring, data encryption, authority control and problem matching are introduced, and the system is improved. This process promotes the social and economic benefits brought by this kind of system with the help of various verification methods. At the same time, it also summarizes the advantages of Distance English teaching, and finally realizes the effective analysis of this kind of system. The design of the Distance English teaching application system is of great help to the follow-up teaching work. Therefore, we need to actively design such a system to optimize the quality of English teaching and lay a good foundation for teachers’ follow-up teaching.
Enhancement of frequency scanning interferometry signal for non-cooperative target based on generative adversarial network

Abstract In non-cooperative target frequency scanning interferometry, the return optical power is low, the quality of the interferometric signal is poor, and the signal-to-noise ratio (SNR) is low. Moreover, the power change accompanying the use of the frequency scanning laser modulates the interferometric signal’s amplitude and shifts the amplitude centre. Traditional signal enhancement techniques, such as filtering, can only solve some of the problems affecting the measurement accuracy, and the full-factor processing of such signals is difficult. This paper proposes a non-cooperative target frequency scanning interferometry signal enhancement method based on a generative adversarial network. By learning the sample dataset, the SNR of the signal can be improved within a certain range, and the signal waveform can be corrected simultaneously. The simulation results reveal that the SNR of the non-cooperative target signal is improved and the signal waveform is satisfactorily corrected. Finally, the effectiveness of the enhancement method was experimentally confirmed.
An Overview of New Generation Bio-Inspired Algorithms for Portfolio Optimization

Bio-inspired computing is one of the foremost subfields of artificial intelligence, which aims to tackle complex optimization problems. The main advantage of bio-inspired algorithms over traditional methods is their searching ability. Portfolio selection is a popular optimization problem in economics and finance. It aims to find an optimal allocation of capital among a set of assets by maximization of return with simultaneous minimization of risk. Since the portfolio optimization problem is NP-hard, a large number of researchers have resorted to bio-inspired algorithms to deal with the computational complexity. This study provides an overview of the new generation bio-inspired algorithms from the recently published literature for portfolio optimization. Besides, opportunities for future research within this area discussed.
Remote Sensing Studies

Since the field of door measurement may be different from the expertise of many readers of this book, this chapter first introduces the basic definitions of this science and finally some applications of this field in the study of alluvial fans, especially for mineral applications and Environmental paid.
A Hierarchical Children’s Dance Movement Pose Estimation Method Based on Sequence Multiscale Feature Fusion Representation

Aiming at the problem that traditional human motion pose estimation methods cannot accurately capture and estimate the movement changes of children dancers, a hierarchical dance pose estimation method for children based on sequence multiscale feature fusion representation is proposed. By comparing the pose feature extraction algorithm with the actual recognition effect, the recognition rates of the dancer’s upper body, infiltration, and whole body have increased by 14.2, 10.6, and 12.6, respectively. The experimental results show that the proposed pose estimation algorithm achieves good pose estimation results on both the standard human pose estimation dataset and the self-built dance dataset.
Machine Learning Applications for Fraud Detection in Finance Sector

Due to advances in information technology, instantaneous accessibility to financial services through digital channels has increased. Although digital platforms' usage makes an individual's life more comfortable, it may also cause some critical consequences like financial fraud which causes critical losses for companies in the industrial sector, investors, and governments. Identification of frauds can be challenging task for a human because it may be necessary to analyse high volume data during long time periods. An alternative is to use financial data as a fraud detection tool to automatically classify fraudulent activities. Currently, there are many practical solutions for automatically detect frauds in the finance domain. In this chapter, we examined on three different fraud types (bank fraud, insurance fraud, and corporate fraud) in finance sector and reviewed the studies using machine learning methods to detect financial fraud in a detailed manner. The findings from this review show that most commonly applied algorithms for financial fraud detection are Decision Tree, Support Vector Machine (SVM), Artificial Neural Networks (ANN), and Random Forest and most of machine learning-based studies were performed in bank fraud field. This chapter also reveals that deep learning and ensemble-based machine learning applications has been frequently preferred in recent years to improve detection performance of the frauds in finance sector.
Retinex Algorithm and Mathematical Methods Based Texture Detail Enhancement Method for Panoramic Images

A panoramic image texture detail enhancement method based on the Retinex algorithm is proposed to work on the nature of panoramic images. Firstly, the panoramic image is collected, then the panoramic image is preprocessed through brightness enhancement, the brightness of the preprocessed panoramic image is normalized, and the panoramic image is optimized and increased by the improved Retinex algorithm. Finally, the simulation test of the panoramic image was carried out. The results show that the improved Retinex algorithm works on the sign to-commotion proportion of the panoramic image. Furthermore, the time required to enhance the panoramic texture detail is short, which can meet the practical requirements of panoramic image subsequent processing.
A Comparative Study on Sentiment Analysis of Uber and Ola Customer Reviews Based on Machine Learning Approaches

Sentimental analysis decides the perspectives on the users from the online media. It is utilized to classify the content into neutral, negative, and positive classes. Different analysts have utilized various strategies to prepare and arrange Twitter dataset with various outcomes when algorithm plays an important part in calculating precision, accuracy, and recall which are considered as important requirements with respect to time. In this paper, a comparative study of different machine learning algorithm is processed to arrange the Twitter data based on reviews on Ola and Uber. Uber and Ola are major stakeholders in cab services providers. Sympathetic requirements and problems of cab service can help in boosting and solving a variety of problems related to customers that are tweeted. The process is done in three phases. In the principal stage, information is gathered and preprocessed; in the second stage, the information is enhanced by separating important highlights, and in the third stage, the refreshed dataset is ordered into various classes by applying machine learning algorithms such as Naive Bayes (NB), support vector machine (SVM), and K-nearest neighbors (KNN). Its purpose is to analyze sentiments more effectively on the basis of their functionality recall, precision, and accuracy. We implemented Python programming language to generate outputs. Every calculation gives various outcomes. It is seen that the proposed technique, i.e., Naive Bayes algorithm, gives great exactness of 86.45% contrasted with other machine learning algorithms.
Voting Classification Approach for Breast Cancer Detection

The cancer of breast is the most frequent type of cancer among ladies worldwide following by the lung cancer. A cycle of regeneration procedures in body is maintained with the cells. In general, the natural functioning of the body is maintained by balancing the development and death rate of cells. The pipeline of breast cancer detection includes many tasks such as preprocessing, segmentation, feature extraction and classification. In this research work, voting classification approach is applied for the breast cancer detection. The proposed approach improve performance in comparison to existing models in compliance with precision, recall and accuracy.
Assessment of Weight Factor in Genetic Programming Fitness Function for Imbalanced Data Classification

In real-world data classification, applications often have an imbalanced distribution of data over various classes. This imbalanced distribution imposes intense challenges, and because of this, traditional classification methods are not effective in this case. This problem also influences genetic programming (GP). One approach to resolve this issue is to assign a custom high weight to the classes during training. This custom weight assignment may nullify the impact of higher counts of any classes during the learning phase of the classifier. The GP fitness function may introduce the custom weight assignment for the minority class samples. The fitness function performs an essential role in GP and influences each building block of GP. This research work assesses the impact of weight factors in GP’s fitness function for imbalanced data classification. For this assessment, eight imbalanced classification problems are taken from the UCI repository, and intensive experimentation is done on the different weight factors.
Breast Cancer Patient Classification from Risk Factor Analysis Using Machine Learning Classifiers

Breast cancer-related mortality in females is a rising global health problem. The fact that for the greater duration of its course of spread, it shows no clinical symptoms, further makes it more challenging to control. Earlier diagnosis has deciding role on prognosis of disease. Innovative diagnostic techniques have provided a large database of disease. These databases, with support of machine learning, provide us a framework to arrive at a decision. This paper aims to find machine learning usefulness, its techniques and algorithms for breast cancer prediction. In this work, classifiers such as naïve Bayes, random forest, sequential minimum optimization (SMO) and logistic regression are used for classification of breast cancer patients based on different risk factors. Performance of these machine learning algorithms is analyzed based on accuracy, F-measure, recall and precision using Waikato Environment for Knowledge Analysis (WEKA). Naïve Bayes classifier has given the accurate classification of patients at high risk and low risk of breast cancer.
Learning, Technology, and the Instrumentalisation of Critique

This chapter sketches how anthropological critiques of dominant theories of learning and technology have a tendency to be absorbed and instrumentalised by the hegemonic projects they target. In particular, the chapter traces how anthropological critiques of mainstream cognitive theory and artificial intelligence research during the 1980s and early 1990s were adapted and deployed in the early 2000s as part of an effort to ‘reimagine’ learning institutions for the digital age. In tracing this history, the chapter argues for the importance of attending to the institutional relations that structure the production, circulation, and application of not just technologies and sociotechnical interventions but also anthropological critiques.
Enhanced Quality of Service (EQoS)-Enabled Load Balancing Approach for Cloud Environment

Cloud computing uses the “pay as you go model” to provide on-demand services to its users especially data storage, computing power, network, and others. These services are provided to users without their direct active participation in managing resources. Cloud computing relies upon resource sharing to acquire coherence and economies of scale. Task scheduling in such an environment is used for the task execution on a suitable resource by considering some parameters and constraints to achieve performance. During high demand for these virtualized resources, efficient task scheduling achieves the desired performance criteria by balancing the load in the system. This paper presents a balancing mechanism by practicing task scheduling to increase performance in the cloud environment. It perceives various load balancing approaches based on task scheduling and concludes that their optimization goals are multi-objective. The presented mechanism is an extension of the previous proposed work QoS-enabled JMLQ [1]. This proposed approach has been tested in the CloudSim simulator, and results show that the proposed approach achieves better results in comparison with QoS-enabled JMLQ and its other variants in the cloud environment.
Deep Learning-Based Face Mask Detecting System: An Initiative Against COVID-19

In this era of pandemic, wearing mask and taking precaution is a must, and this paper provides a way of detecting mask with the help of deep learning. Deep learning is an important part of machine learning. OpenCV, an important part of Python, is used for real-time image detection. This is a very decent system which can be applied at various platforms and can help in slowing down the transmission of coronavirus.
Comparative Analysis of Term Extraction and Selection Techniques for Query Reformulation Using PRF

Query reformulation (QR) is the process to make initial query exact by generating more significant terms. Term extraction and selection are one of the techniques of it. QR eliminates irrelevant and repetitive terms from the top ranked documents and upgrades the effectiveness of IR system. There are various term extraction and selection methods, and each method has its own strength and shortcoming. This paper does comparative analysis of various term extraction and selection techniques for QR using pseudo relevance feedback. Three standard datasets such as CACM, CISI, and Medline are utilized to conduct all the experiments. The outcomes are contrasted with each other in terms of average precision, average recall, and F-measure. It is found that BIM and Okapi BM25 outperform other techniques on both CACM and CISI datasets, while KLD and RSV outperform other techniques on Medline dataset.
Class Change Prediction by Incorporating Community Smell: An Empirical Study

To adapt to changing software requirements, developers need to maintain and modify software through code changes. Predicting change-prone code can help developers to reduce the cost of software maintenance in advance. Prior work confirmed code smell intensity is a reliable metric for predicting change-prone classes. Community smell is a derivation of the concept of code smell in open-source software development community, it refers to poor communication and collaboration problems among developers. We add community smell to existing change prediction models, and propose a software class change prediction model integrating process metrics, code smell intensity metrics, anti-pattern metrics and community smell metrics, which takes into account the technicality and organizational aspects of software development. Experimental results demonstrate that when Multilayer Perceptron is used to build a change prediction model, community smell improves the baseline model by 4.4% and 31.5% in terms of [Formula: see text]-Measure and Recall. In addition, community smell improves baseline model performance to a greater extent in terms of Recall and Precision than code smell-related information.
With Application of Agency Theory, Can Artificial Intelligence Eliminate Fraud Risk? A Conceptual Overview

Purpose of this study is to introduce conceptual framework to identify whether artificial intelligence can support public listed companies toward eliminating and mitigating fraud concerns or not by utilizing Agency Theory in this concept. In the current business environment, fraud is a major concern not only for the organizations but also for the shareholders and society as a whole. This study reviews the literature related to organizational governance, corporate fraud cases, and artificial intelligence supporting the business toward fraud elimination and mitigation. This study reviews the benefits related to the utilization of artificial intelligence within the corporate world and at organizational level toward mitigation and prevention of fraud. This study integrates significant empirical research and literature to broaden the potentials of artificial intelligence over fraud prevention and mitigation. This study provides reasoning that artificial intelligence can be utilized as an agent for the Agency Theory. This study is distinctive from the others because artificial intelligence’s support in the context of agency theory for the public listed companies has never been studied before; moreover, past studies have demonstrated artificial intelligence as control assistance only; whereas, this study will emphasize on artificial intelligence as support function and as agent working toward the support of management and shareholders for fraud mitigation and elimination.