Reviewerly/exHarmony huggingface dataset:
Improved Slow Feature Analysis Algorithm and Its Application in Abnormal Human Behavior Recognition

With the development of social economy and the increasing population, human activities are becoming more and more complex, and various abnormal events have also increased. Therefore, abnormal human behavior recognition has gradually become more popular. Because of the complexity of human movement and the diversity of the external environment, it is difficult for us to obtain effective features. In order to solve the problem, an algorithm based on improved slow feature analysis for abnormal human behavior is present. Firstly, the video is preprocessed using the three-frame difference method to obtain human motion foreground. Then, the discriminant slow feature analysis algorithm is adopted to extract the slow features that can represent the nature of the behaviors. The slow features have the invariance of translation, rotation, zoom, illumination, etc., and have the characteristics of direction selectivity and edge direction selectivity. Finally, the cross-validation is used for classification and recognition. The experiments are performed on the KTH public database and the results demonstrate that the proposed algorithm can effectively identify various human behaviors, with an average recognition rate of 92.16%.
3D Realistic Animation of Greek Sign Language’s Fingerspelled Signs

Signing avatars make it possible for deaf people to access information in their preferred language. This paper describes the implementation of a new 3D high-realistic avatar, for Greek Sign Language (GSL) fingerspelling and numbering signs. This research study explores open-source 3D platforms such as MakeHuman, Blender, and Unity’s game engine as 3D rendering architectures. Creating signing virtual characters is important for training and communicating in virtual environments or further applications. This research looks at how to use the power of C# unity script code and layered motion game technology to achieve programmable transitions of animation segments and to create dynamical, real-time (on the fly), natural, and understandable outputs with controllable playback speed and playback views. Another novelty of the proposed scheme focuses on the precision of the transition time and how it blends between the last sign posture (first sign) and first sign posture animation (second sign).
Dim Recognition in Images, Videos and Real-Time Video Streams

This paper proposes an obscured image identification and examination system for naturally recognizing video transfers and images. This research study has developed few haze highlight models by using image tone, slope and range data, and a quick fourier transformation is performed to identify the obscure in recordings and images. Furthermore, the proposed research study has utilized a fast fourier transform calculation, which is worked into NumPy as the reason for the proposed procedure. Extensive examinations show that the proposed strategy works acceptably on testing the image information, which lays out a specialized starting point by taking care of a few PC vision issues, like movement investigation and picture rebuilding, utilizing the haze data.
Distributed Deep Learning for Content-Based Image Retrieval

In content-based image retrieval (CBIR), the main objective is to obtain the best possible feature of an image. Traditionally, color, texture and shape were used to extract the features of image. But as the deep learning era has started, researchers started using different deep learning models for extracting the features. The main problem with these deep learning models is the time that it takes for training the model. To overcome this difficulty, the distributed deep learning (DDL) can be used to reduce the total training time by training the model in a distributed environment. In this paper, we performed DDL using AlexNet architecture for CBIR. The three modes in DDL which are used in this paper are synchronous, asynchronous and Hogwild. The execution is done on these modes along with the deep learning version with no distribution. These modes are executed on the self-made in-house ‘Hadoop and Spark’ clusters which are 1 + 4 node cluster and 1 + 15 node cluster. The time and model accuracies of the different modes are compared. To further analyze the model, the five performance measures: average precision ratio (APR), F-score, average recall ratio (ARR), total minimum retrieval epoch (TMRE), and average normalized modified retrieval rank (ANMRR) are evaluated and compared.
Human–Machine Interface for Wheelchair Control Using sEMG Signals

Electromyogram (EMG) signal is the electrical activity that is generated by alpha motor neurons in response to the impulse from the brain. EMG is divided into two types: surface EMG and intramuscular EMG. Surface EMG signals provide information about the intensity of muscle activation. EMG signals are used to make myoelectric control system-powered upper-limb prostheses, and electric-powered wheelchairs are the major applications of the myoelectric control system. Tremendous growth has been seen in human–machine interface (HMI) wheelchairs over the last few decades. The manual wheelchair that can be moved by pushing the wheels with hands is replaced by Joystick and a voice-controlled wheelchair. However, even with the advances in technology elderly and paralyzed people have difficulties in intuitive control and navigation of wheelchairs. Therefore, a smart wheelchair based on surface EMG signals and an accelerometer are proposed. The signals from EMG sensors act as input signals which get processed by Arduino Uno, and the output signal based on detected hand gesture is wirelessly transmitted. Proper training is performed to determine the threshold value for each gesture identification. The RF receiver sends the received signal to Arduino mega to process the signal, and Arduino mega sends a command to motor drive to move the wheelchair. The smart wheelchair is controlled in left, right, forward and backward directions. The ultrasonic sensor is being used in the wheelchair to detect obstacles. The hardware design is properly tested and validated; thereby smart wheelchair is cost-effective, easy to use, and safety is ensured.
Simulation Analysis of Robot Obstacle Avoidance Path Control Based on RGV Mobile Platform Design

Mobile robot, as an important part of the current research in the field of robotics in China, integrates image processing, intelligent control, artificial intelligence and other professional technologies, and integrates many kinds of disciplines. It is an important achievement of the current intelligent robot research institute. In essence, the biggest feature of mobile robots is that they can plan their own walking routes on the basis of feeling and processing the information of the external environment, so as to achieve the purpose of people using mobile robots to deal with a task. Therefore, on the basis of understanding the current situation of robot obstacle avoidance path control simulation based on RGV mobile platform design, this paper carries out obstacle avoidance simulation analysis according to the motion control of the robot, and finally proves that the current intelligent mobile robot has application value.
A Novel Study on Tools and Frameworks for Mitigating Bias in Multimodal Datasets

Bias in the data always affects the performance of the system. Our need of finding the accurate results in a system using any Machine Learning algorithms will be degraded when bias is present in the data that we choose to analyze. Mitigating bias in Artificial Intelligence, mainly deals with the dataset development from an existing datasets which reduces the bias by comparing and validating different samples of training data and algorithm development with an established policies and practices to enhance the performance of the algorithms at later stages of the development of the framework. To detect the bias from the given datasets, and mitigate the bias, various tools and technologies are proposed and in consequences of this work shows that not only bias, but also helps to decrease without forfeiting model performance rate and improves the performance of the model. This paper focuses on exhibiting various tools and frameworks associated to mitigate the bias present in various databases choose to different applications intended to verify and validate.
Chrome Extension for Text Sentiment Analysis

Sentiment analysis has been one of the trending research subjects in the field of natural language processing over the last decade. The main reason behind the interest in sentiment analysis is the wide range of its applications and the amount of data or the information available at the fingertips for processing these applications. This motivated us to develop a new sentiment analysis application with a new perspective of sentiment analysis utility. We built a sentiment analysis application for chrome, which analyzes the text used for Google search. Thus, our target is to extract sentiment from every text used in search engines. In contemporary times, the core theme is that every person uses search engines in daily life, and they turned out to be a necessity. People search for news, ask questions, and learn new things and techniques, but they are not always on the right track. So, based on observation of how high the usage of search engines is, we propose an application that can detect potentially harmful, self-harming or anomalous search texts. Hence, this chrome extension can potentially act as parental monitoring of an individual’s digital web life, which helps in the detection of the person’s extreme psychological condition in some suicidal, abusive cases. With the advantage of prior identification, we can use it to notify or warn people about this. Machine learning algorithms and JavaScript were used in developing this application to create models and the chrome extension, respectively.
Performance of RSA Algorithm Using Game Theory for Aadhaar Card

Data security is ensured by the use of cryptography. Data security refers to the protection of data and privacy to prevent hackers from gaining unauthorized access to applications, computers, and data servers. Cryptography is a process of encrypting data and storing it in databases in a manner that anyone who gains access to it by accident is rendered useless. To encrypt and decrypt data, the RSA algorithm, the ECC algorithm, and other encryption methods are routinely employed. Game Theory—Rivest–Shamir–Adleman (GT-RSA) is a new hybrid algorithm that combines Game Theory and RSA to improve the efficiency of the RSA algorithm by altering the function. By improving the speed, throughput, avalanche effect, and power consumption performance of the GT-RSA algorithm, it is proposed. The performance of the GT-RSA algorithm has been enhanced, and experimental results have been shown.
Wearable Sensor-Based Framework for the Detection of Daily Living Activities Utilizing In-Depth Features

This paper implements the physical activity recognition model on 13 (sitting, standing, ironing, etc.) daily living activities using a 2D convolutional neural network (CNN). The model has outperformed the existing methods for the PAMAP2 and WISDM datasets. The various machine learning models are also implemented. These are SVM, Naïve Bayes, decision tree, and random forest. Average accuracy, F1 score, precision, and recall are the performance metrics used to evaluate the results. It is found in overall findings that convolutional neural networks have detected all the activities very precisely and accurately where machine learning models felt difficulty in classifying more accurately a particular activity. This difficulty of these machine learning models is addressed in this research by introducing CNN features in the training of machine learning models. Mean accuracy of 99.13 and 99.18% is achieved with convolutional neural network and Naïve Bayes with CNN features, respectively, for the PAMAP2 dataset. Mean accuracy of 98.01 and 98.09% is achieved with convolutional neural network and Naïve Bayes with CNN features, respectively, for the WISDM dataset.
Research on Image Processing Method and Image Classification Model Based on Artificial Intelligence

In the development of social economy and scientific and technological innovation, the image processing mode and classification model chosen by network technology platform is becoming more and more changeable, but in essence, it is necessary to obtain characteristic information in effective image recognition and choose high-quality network algorithm and processing technology to complete image processing and image classification. Therefore, on the basis of understanding the current research trend of computer image processing and image classification model methods, this paper conducts in-depth discussion on the image processing methods and image classification model training design with artificial intelligence as the core and takes the image classification model of transfer learning as an example for practical exploration. The final results show that the image processing method and image classification model based on artificial intelligence have strong performance advantages in practical application.
Enhanced Object Detection in Floor Plan Through Super-Resolution

Building information modelling (BIM) software uses scalable vector formats to enable flexible designing of floor plans in the industry. Floor plans in the architectural domain can come from many sources that may or may not be in scalable vector format. The conversion of floor plan images to fully annotated vector images is a process that can now be realized by computer vision. Novel data sets in this field have been used to train convolutional neural network (CNN) architectures for object detection. Image enhancement through super-resolution (SR) is also an established CNN-based network in computer vision that is used for converting low-resolution images to high-resolution ones. This work focuses on creating a multi-component module that stacks a SR model on a floor plan object detection model. The proposed stacked model shows greater performance than the corresponding vanilla object detection model. For the best case, the inclusion of SR showed an improvement of 39.47% in object detection over the vanilla network.
Hybrid Sign Language Learning Approach Using Multi-scale Hierarchical Deep Convolutional Neural Network (MDCnn)

Sign language is now the only means of communication for deaf or hard-of-hearing people. SLR has been the trendiest research area in recent years. Determining an accurate result from many photo data sets is crucial for an automatic sign language recognition system. Regardless, convolutional neural networks (CNNs) outperform traditional neural networks in a wide range of visual tasks. Improved results in numerous measures, including as accuracy, recall, and FI score, are required for many resources and methodologies. In this context, we present convolutional neural network model approach with multi-scale layers and various filters that serve as an additional layer in the design. This model could be used on a variety of data sets and is also useful for real-time picture streaming. Two self-created data sets were utilized to test the performance of the proposed model: ASL and ISL data sets. Transfer learning models like as VGG16, MobileNet, VGG19, MobileNetV2, ResNet50, and InceptionV3 are also used to evaluate these data sets. The experimental findings reveal that our suggested model performs better than the basic CNN model, with an accuracy of 98% using ISL data set and 95% using ASL data set.
A Survey of Few-Shot Learning for Image Classification of Aerial Objects

The aircraft has high requirements for effective detection and rapid support in different situations, and in this process, the image classification of aerial objects plays a very important role. In view of the situation that many tasks in the field of image classification of aerial objects cannot get enough samples to learn, it is necessary to study few-shot learning technology, which is mainly divided into three aspects: data based, model based and optimization based. Considering the advantages and disadvantages of various few-shot learning methods and their contribution to the accuracy of few-shot image classification, the developing trend of few-shot learning technology in the field of image classification of aerial objects is summarized. The three research directions of data, model and optimization are not independent of each other, and it is the mainstream to combine them. The application of few-shot learning technology in aviation can realize the deployment of high-precision and high-stability models for aerial tasks with little available data, and reduce the high cost of data tags to a certain extent, which is meaningful. It is hoped that this paper can provide ideas for the application of few-shot learning in the field of aerial image, and then solve practical problems.
Extenics in Face Recognition

Face recognition is facing a new challenge, which was resulted from the contradiction between face recognition and the necessity to wear masks during the COVID-19. This article introduces extenics in face recognition (EFR) for solving the contradiction and constructs a facial model for psychology analysis to help the patients suffering from mental disorders. Moreover, a 2D-3D scene transformation model is integrated with EFR to fuse the virtual scene and real scene. Perspective fusions of EFR with EEG are also explained utilizing the five-layer intelligence theory for fighting against the COVID-19.
Real-Time Information Flow Across the Supply Chain—An Overview

The information flow is very important for proper coordination between the concerned stakeholders through the supply chain and for achieving the objectives of Industry 4.0 and the objectives of the ensuing industrial revolutions. Several new technologies are being introduced to gather real-time information about demand, stock level, product location, etc., on supply chain management. A basic overview of the three important techniques, namely Internet of Things, blockchain, and big data analytics that have been proposed by some researchers for obtaining real-time information across the supply chains is presented in this paper. Internet of Things, blockchain, and big data analytics can improve the functioning of the supply chain in all necessary aspects but, there are ample challenges to be addressed for the successful implementation of these new-age techniques.
Confiscate Boisterous from Color-Based Images Using Rule-Based Technique

This paper explains how noise removed from any motion videos using the new sorting technique. Removing an error or a corrupted pixel in a motion video is not an easy process. Identify the error indicator is not easy for the researchers. Using the sorting technique, finding the disturbance is one of the important steps, and this is achieved through a step-by-step process. Most of the existing techniques create problems because while removing the corrupted pixel, it also removes the originality of the content. The proposed technique finds the errors using a step-by-step process using a blurry ruling technique. Using this, noisy pixel is identified and removed, with a disturbance of the original. The experiments show the proposed method suitable for any type of input videos.
Efficacy of Autoencoders on Image Dataset

Autoencoder is an unsupervised learning technique widely used over different types of data that gets the output exactly like the input. The major usage of autoencoders lie in feature extraction, dimensionality reduction, image denoising, compression, etc. This paper concentrates on the implementation of undercomplete, sparse and variational autoencoder over the Modified National Institute of Standards and Technology (MNIST) dataset and analyse the efficiency using loss and activation function. The effect of the number of epochs in building the model is also analysed. For the dataset, sparse autoencoder performs better than the undercomplete when implemented with Adam as an activation function with mean square error as loss function. When compared with the variational autoencoder (VAE), VAE performs better when implemented using Adam function.
Combined Cryptography and Text Steganography for Enhanced Security Based on Number System

The whole world is being burdened with information each and every day. There is ample possibility of leaking of information as there is an arbitrary exchange or transfer of information throughout the day. Even standard algorithms are failing to protect data in spite of using various strategies. So, the first priority is to keep the information safe and to exchange information securely. The primary purpose of the strategy is to keep the information secret with the help of numerical system and mathematical formulas. It is a fancy combination of scientific mastery where the original information is hidden and displayed in a way that cannot be grasped by the naked eye. All digits, letters, and numbers will remain hidden from normal sight with the use of a visual process. If the message is to be sent through electronic media, it can remain unexposed by the message being sent in encipher form. An attempt to hide information with a different method with the help of steganography technique means that this work is not done as an improvisation on earlier work. But it is a different method which is a remarkable set/jumble of numbers, used for hiding information. Organizations that send information secretly, such as the defense forces, will be able to securely send highly confidential data using this application.
Avenues of Graph Theoretic Approach of Analysing the LIDAR Data for Point-To-Point Floor Exploration by Indoor AGV

Realizing point-to-point augmented path is one of the most challenging tasks for any mobile robot navigation. The challenge is much higher when it is GPS-denied indoor environment. This paper presents analysis of graph theoretic approaches including applicable heuristic algorithms for achieving optimal point-to-point navigation using LIDAR sensor data as source input, ensuring total workspace coverage and minimization of action performed by the robot in carrying out realistic applications. Sampling-based approaches which uses arbitrary information gain formulation and learning-based techniques, both are studied extensively. Uniform sampling-based techniques are found feasible in exploring the state space without any complexity in geometrically modelling the configuration area ensuring embedded intelligence into the mobile robots in finding optimal execution. The navigation over both static as well as dynamic obstacles are analysed, and the observations are presented in a comparative manner. The paper concludes with the graphical and numerical analysis of the accuracy achieved using mentioned algorithms. It would definitely help in obtaining individual navigational strategy by the mobile robot to reach a specific goal.