Reviewerly/exHarmony huggingface dataset:
Robust Gesture Recognition and Classification for Visually Impaired Persons Using Growth Optimizer with Deep Stacked Autoencoder

Visual impairment affects the major population of the world, and impaired vision people need assistance for their day-to-day activities. With the enormous growth and usage of new technologies, various devices were developed to help them with object identification in addition to navigation in the indoor and outdoor surroundings. Gesture detection and classification for blind people aims to develop technologies to assist those people to navigate their surroundings more easily. To achieve this goal, using machine learning and computer vision techniques is a better solution to classify and detect hand gestures. Such methods are utilized for finding the shape, position, and movement of the hands in real-time. With this motivation, this article presents a robust gesture recognition and classification using growth optimizer with deep stacked autoencoder (RGRC-GODSAE) model for visually impaired persons. The goal of the RGRC-GODSAE technique lies in the accurate recognition and classification of gestures to assist visually impaired persons. The RGRC-GODSAE technique follows the Gabor filter approach at the initial stage to remove noise. In addition, the RGRC-GODSAE technique uses the ShuffleNet model as a feature extractor and the GO algorithm as a hyperparameter optimizer. Finally, the deep stacked autoencoder model is exploited for the automated recognition and classification of gestures. The experimental validation of the RGRC-GODSAE technique is carried out on the benchmark dataset. The extensive comparison study showed better gesture recognition performance of the RGRC-GODSAE technique over other deep learning models.
A Supervised Learning Algorithm for Disease Prediction Using Complex Network and Machine Learning

The availability of advanced tools to store and process huge volumes of data has propelled the healthcare domain to adopt a more data-driven approach to disease detection and prevention. Data mining and machine learning are major contributors to designing prediction models that can accurately predict diseases but they overlook the connection between diseases and symptoms. These relationships can be represented and understood using network analysis. In this study, we have proposed a novel complex network-based approach that classifies a patient based on their medical test data and symptoms. Each node in the network represents a patient, and a similarity score is calculated for each patient pair in the network using the vector representation of their diagnosis data. Connections between the nodes are made using this similarity score such that patients with similar diagnoses and symptoms are connected to each other. Using this Patient Similarity Network an instance-based learning approach to classify new instances has been built. Every node in the network has a weighted contribution toward classifying the new instance. For evaluating the proposed method, the prediction accuracy is calculated which is found to be good. We perform a comparative study with existing prediction algorithms like KNN and SVM for validation purposes.
A Coupled System for Simultaneous Image Despeckling and Segmentation

Real-world images are inherently degraded by noise and intensity heterogeneity, posing significant challenges for image segmentation. To tackle these issues, we proposed a new active contour model that can effectively and accurately segment images which are affected by speckle noise and heterogeneity in intensity. The variational formulation of the present model consists of two key components: image despeckling and segmentation terms. The segmentation term is guiding the initial contour toward precise target boundaries, while the despeckling term is designed to minimize the impact of noise. To check the efficiency of the present model, we apply it on natural and medical images that exhibit high noise levels and intensity heterogeneity. Moreover, we utilize various assessment parameters to assess the effectiveness of the newly presented model. The numerical experiments reveal that the present model outperforms most existing active contour models.
Deep Learning-Based Real-Time Hand Gesture Recognition Using Histogram of Oriented Gradient

The main social issues facing the deaf-dumb population that keep them from using the most basic and necessary services of life are the communication gap and the hearing majority. Even though the issue has been addressed by advancements in automatic sign language identification, an effective solution has still not been found as a result of a number of difficult issues. The majority of works currently in existence attempt to create vision-based recognition system using the traditional pattern analysis approach by deriving intricately designed feature descriptors from the gesture’s images. However, the effectiveness of those techniques is severely constrained when dealing with a substantial sign vocabulary recorded in chaotic and complicated backdrop settings. This paper introduces an algorithm based on histogram of oriented gradient (HOG) and support vector machine (SVM) for detecting the hand gestures. Further, we have also used convolution neural network to classify different types of hand gestures. Visual hand gesture recognition is becoming more and more desired for user interfaces for human–computer interaction. The experimental outcomes demonstrate that the proposed approach shows its effectiveness in terms of an accuracy of 80%, precision of 0.84, recall of 0.81, and F1-score of 0.83 compared with other existing hand gesture recognition techniques. Hence, this proposed technique could be employed as a viable route for hand gesture detection, while lowering computational cost and time.
The Impact of E-Business on SME’s Productivity (Case Study on “Kiddy Zone” Company in Qatar)

The purpose of this study was to investigate the effect of e-business on the productivity of small and medium-sized enterprises (SMEs). The study focused on two variables: the independent variable was e-business, which consisted of four dimensions - purchasing and warehousing cycle, information systems, human resources capabilities, and e-marketing; while the dependent variable was productivity. The study employed a comprehensive sample and distributed 120 questionnaires to all employees using Google forms. The study received 108 completed questionnaires, representing a response rate of 90%.The researcher used the descriptive-analytical method and appropriate statistical techniques in SPSS to test the hypotheses and achieve the study objectives. The main finding of the study was that implementing e-business dimensions, including purchasing and warehousing cycle, information systems, human resources capabilities, and e-marketing, had a significant impact on productivity. The e-business dimensions contributed to approximately 69.9% of productivity, with information systems having the highest contribution to productivity. Additionally, the company demonstrated a strong perspective towards purchasing and warehousing systems as one of the e-business dimensions.The study concluded with several recommendations, including conducting a needs assessment to identify the company’s e-business requirements and using high-tech tools to manage information without the need for office work.
Homomorphic Routing

We propose a new private routing and packet forwarding scheme for the Internet---Homomorphic Routing (HR)---that enables endpoints to communicate with one another without divulging source or destination addresses to the routers or service providers along the path. This is achieved via homomorphic encryption, whereby domains can match encrypted address ranges with encrypted destinations of packets without the need of decryption. Compared to approaches such as source or onion routing, HR is a hop-by-hop solution that allows current BGP-like decisions and traffic engineering techniques to remain largely unchanged, while per-flow state need not be maintained by routers. Preliminary performance evaluation shows that HR implies a tolerable computational overhead compared to plain text operations. Through aggregation we can compress inter-domain routing rules to around 5% of those required for current IPv6 and we can organize encrypted forwarding rules so that matching can be achieved in logarithmic time.
Role of Social Media in Changing the Social Life Patterns of Youth at UAE

During the changing world of information communication, the role of social media is of greater significance. Particularly, during the current Covid-19 crisis, we have seen social media incorporated in many fields of life and has several impacts. This research also focused on examining the impacts of Social Media on the life of the young Emirati generation in terms of their communication with family and other relevant people. The researcher used the descriptive method and gathered data by using close-ended questionnaires from university-level students. Findings indicated an overall positive impact of Social Media on the social life of individuals during the Covid-19 outbreak. The respondents expressed their dependency on social media, and also indicated social media as a source to communicate with family members. Participants say connected with their family, take more interest in their domestic matters, and also interact more with their parents and relatives. Thus, we conclude that the mutual consensus of study respondents lean towards the role of social media as an agent to sustain socialization even during one of the most challenging times in human history.
SDinIWTrack: A Novel Database for Training Self-driving Vehicles

Object tracking is an emerging research topic in the computer vision field and overwhelming research progress has been achieved in this field in recent years because of its real-life significance. Designing efficient self-driving vehicles is one of the most popular applications in this research domain. Although commendable research advancements have been achieved in this research field but still there are various unexplored areas in which further research can be conducted. The performance efficiencies of the automated tracking methods are largely dependant on the databases which are used to train them apart from the methods’ architectures. Owing due to the importance of databases in this research domain, here we have conducted a comprehensive survey on existing object tracking databases. The main contribution of this paper lies in the creation of a novel database namely, SDinIWTrack for training self-driving vehicles to drive safely during challenging conditions and to avoid undesirable fatal accidents. This database includes thousands of annotated sequences of road scenes captured during inclement weather conditions for, e.g., sunny, rainy, foggy, etc. and under varied illumination conditions for, e.g., uniform illumination (during daytime) and spatially variant illumination (during night). To the best of our knowledge, SDinIWTrack is the first database which is created with this objective. The sequences of road scenes included in this database are collected from various publicly available sources and are annotated by a group of annotators.
Binary Classification of Kidney Glomeruli Using Deep Neural Networks

Glomeruli is a collection of blood vessels present in kidneys of the human body. Since kidneys are one of the most important human body organs, diagnosis of any abnormality becomes very important. This work focuses on the binary classification of normal and scleroses glomeruli using convolutional neural networks from whole slide images (WSI). These images are periodic acid-Schiff (PAS) stained and the glomerulus can be seen as circular areas of dark stains on the slide. The main purpose of this work is to make the diagnoses of the kidney glomeruli fast and accurate since manual detection is quite time-consuming and has many human errors as well. In our work, we have performed the classification of microscopic images to detect the scleroses glomerulus from the kidney. This study deployed, four different types of CNN models and subsequently evaluated, they are AlexNet, Visual Geometry Group-19, GoogleNet, and a customized model. The comparative analysis has been made by considering several parameters such as the number of epochs, optimizers, batch size, and learning rate in which the customized model achieved the accuracy of 97.86%. The results of the proposed work are quite promising. The performances of the models used in this work are compared using various metrics such as accuracy, recall, precision, and F1-score, and the results from each of the models are noted.
The Role of Artificial Intelligence Techniques in the Digital Transformation of Jordanian Banking System

This study examines the role of artificial intelligence (AI) in the digital transformation of the Jordanian listed banks. It explores the current state of AI adoption in the Jordanian financial sector, as well as investigating the drivers and challenges of AI adoption in this industry. Additionally, through using the deductive research approach, the study examined the impact of AI on various financial functions such as risk management, fraud detection, and customer service. Consequently, the findings suggest that the Jordanian financial industry is at an early stage of AI adoption, with limited awareness of its potential benefits. The study highlights the need for financial institutions to invest in AI and develop a strategic plan for its implementation to fully realize its benefits. The study had revealed that Rob advisor, block chain, and artificial intelligence approaches are all contributing to the digital transformation of Jordan's banking system, with the second null hypothesis rejected due to its lower level of significance.
An Overview of Segmentation Models for the Extraction of Brain Tissues from Magnetic Resonance Images

Image segmentation plays an inevitable role in biomedical image processing for the delineation of anatomical organs and affected tissues. The main focus of this research work is the analysis of algorithms to segment the tissues in MR brain images, which aids in the diagnosis of neuro disorders. The widely used segmentation algorithms for the extraction of brain tissues are thresholding, clustering, atlas-guided and deep learning models. A qualitative study of widely used segmentation approaches for the MR brain images is discussed in this chapter. In the current scenario, hybrid segmentation approach gains prominence in the extraction of brain tissues from MR images. The simulation results of the expectation–maximization algorithm for the segmentation of brain tissues from MR images are also furnished in this chapter and validated by performance metrics.
The Impact of Technological Change on Organizational Productivity and Customer Satisfaction: A Case Study of a Number of Factories Operating in Jordan

This study aims to determine the impact of technological change on organizational productivity and customer satisfaction. The sample of the study is 220 employees who worked in some factories operating in Jordan. A questionnaire designed by researchers was used, and the Cronbach’s value was (0.803). The study results found that that there is an impact of technological change on organizational productivity and customer satisfaction. The employees feel strongly that both the organizational productivity and the customer satisfaction affected by the impact of technological change. It was found that there is a statistically significant impact due to the age, job and place of the employees of technological change on organizational productivity. Furthermore, results indicate that there is statistical significance due to the experience of the employees. The employees with less than 10 years of experience feel more than others that there is an impact on productivity and satisfaction.
A New Technique of Cipher Type Identification Using Convolutional Neural Networks

This paper focuses on modern cipher types classification mechanism using convolutional neural networks (CNNs). In case of ciphertext-only-attack, it is momentous to a cryptanalyst to recognize the cipher type first for further cryptanalysis work, usually when the messages are transmitted from an unknown source. In this research paper, two modern ciphers named as AES-128 and RC-4 have been considered for the classification problem. At first, a corpus is created. Messages from the corpus are encrypted using these two encryption algorithms, and the ciphertexts are recorded into punched tape. We have taken the images of punched tapes and put those images into the dataset. These images are provided to different pretrained CNN architecture models to train the network models. For the training purpose, we have used transfer learning method. The results obtained from two different CNN architectures such as ResNet50V2 and MobileNetV2 have been compared and plotted. The primary success of this research is that a very much complex problem is solved in a much simpler way. The experimental results prove the validity of our proposed work.
A Comparative Study of Deep Learning-Based Face Recognition and Emotion Detection Techniques Using Social Media Customized Cartoon Post

Cartoon posts regarding many renowned Indian personalities have increased alarmingly in different social media platforms. Consequently, it throws challenges for recognizing and identifying the personalities and analyzing the features of their faces for understanding the general emotion conveyed by the post. In this work, we have created a manually labeled dataset using Indian cartoon posts and investigating different approaches using deep learning models to recognize the face of the person and understand the mood of the person from the post. In addition, we proposed a pipeline called “Haar base ViT fully connected face recognition and person identification along with emotion detection” using vision transformer. The performance of the proposed pipeline is compared with three pretrained CNN based models (MobileNetV2, VGG-16, and ResNet-50). Our approach exhibits competitive results (in terms of accuracy, it is 90% for face recognition and 88% for emotion detection) when evaluated on the individual tasks.
Addressing Class Imbalance in Fake News Detection with Latent Space Resampling

The detection of fake news has become crucial with the popularity of social media as a primary medium of news consumption. However, real-world fake news datasets often suffer from the problem of class imbalance. These imbalanced datasets lead to unreliable and skewed classifier performance. Motivated by this, our paper addresses the class imbalance problem in fake news detection by employing a deep learning-based latent space resampling strategy. Firstly, a Bidirectional Variational Autoencoder is utilized to attain the most informative latent representations of the input text sequences. Next, the class-biased latent vectors are resampled using oversampling, undersampling, and hybrid-sampling techniques. The resampled latent vectors are then used to train several state-of-the-art classification algorithms and evaluated using a separate balanced testing set. Performance comparison with the imbalanced set serving as the baseline model and state-of-the-art methods, on the basis of the F1-score and Geometric mean, reveals that the proposed architecture is capable of alleviating the problem of class bias considerably.
A Transfer Learning Approach to Indian Currency Coin Recognition

Automatic coin recognition and identification system plays vital role in vending, slot machine and in several banking related equipments. Most people require the ability to recognize the value of various forms of currency in order to function in daily life. In order to automate financial transactions, computers must be capable of performing such recognition. The majority of current Indian currency coin denominations are of comparable size, making the identification process more difficult. In this work, we applied a transfer learning approach for recognizing Indian currency coins. The pretrained model used is VGG19. The model is tested on two datasets: our own dataset named “Mepco coin dataset” and Indian Coin Denomination dataset (ICDD) and report an accuracy of 92.8% and 95%.
The Effect of the Problem-Based Learning Model with the Help of Digital Comics on Students' Scientific Literacy Skills

Background: The low scientific literacy of students is caused by the selection of models and learning media that are less varied. This study aims to analyze the effect of the PBL model with digital comics on students’ scientific literacy on environmental pollution material. Methods: This type of research is a quasi-experimental nonequivalent control group design. The research sample used classes, namely class VII A and VII F, with 55 students selected using a purposive sampling technique. Results: The experimental class's average N-Gain of scientific literacy ability (0.42 ± 0.171) is moderate, while the control class (0.26 ± 0.17) is low. Conclusions: The results of this study indicate that Sig. (2-tailed) 0.001 &lt; 0.0, meaning that the PBL model using digital comics model cs influences students' scientific literacy skills.
Detection of Cipher Types Using Machine Learning Techniques

The identification of a cryptosystem has been a challenge for decades. This paper’s main objective is to identify the type of cryptosystem used to encrypt a particular text. We have explored the realm of machine learning to recognize a pattern among complex classical ciphertexts that generally have a simple representation in plaintext. We have modeled our objective as a sequence-to-sequence learning task that we have tried to solve using Convolution Neural Networks (CNNs) and state-of-the-art Transformer models. With only a tiny dataset (130 k) consisting of ciphertexts and the corresponding cryptosystem used to encrypt the same, our model has shown a good accuracy of 96.72 % which proves a significantly steep learning curve compared to other sequence-to-sequence models. Here we show the enormous potential of these models and how they can perform even better if the barrier of resources and computation time is lifted.
Improving a library workshop service: implementing change and enhancing the service based on data analysis

Purpose This paper aims to describe how one medical library implemented a new scheduling system, initiated data analysis and modified its regularly scheduled workshop program because of evidence-based decision-making. Academic libraries that struggle with workshop attendance may use this process as a model. Design/methodology/approach Workshop registration data analysis focused on registrants' affiliation, role and location, and how registrants learned of workshops. Workshop attendance data analysis focused on which workshops, days, times of the day and months had the highest attendance. The analysis led to changes in marketing and targeted scheduling of future workshops by the time of day, day of the week and month of the year. Findings Data collected for four years, fall 2018 – summer 2022 (12 semesters), shows a steady increase in the number of people attending library workshops. The increase in attendance and ROI experienced after the changes implemented at Ruth Lilly Medical Library (RLML) is significant as libraries often struggle with attendance, marketing and return on investment when offering ongoing educational workshops. Originality/value Many libraries offer ongoing workshops with low attendance. This article provides an example of how one library changed software and registration and implemented evidence-based decision-making related to scheduling which may have contributed to an increase in workshop attendance. Other academic libraries might consider adopting similar software and evidence-based decision-making to improve their library workshop service.