Reviewerly/exHarmony huggingface dataset:
A Conceptual Framework to Measure the Relationship between Social Media and the Decision-Making Process of Plastic Surgery Consumers in Jordan

Aims: This article describes a unifying conceptual framework to measure the impact of social media on the decision-making process of plastic surgery consumers.&#x0D; Methodology: A conceptual framework for the plastic surgery consumers was developed based on the work of (Blakey, 2011; Schwarz et al., 2013; and Roberts &amp; Lilien, 1993), and a conceptual model was previously developed by Akar et al. (2015).&#x0D; Results: The conceptual framework consists of three components that can be considered in relationship to each other: Social Media, Social Influence Process, and Decision-Making. Although the availability of measures for each of these components varies, the framework can be used to examine the relationship between Social Media and the Decision-Making Process of Plastic Surgery Consumers in Jordan.&#x0D; Conclusion: A conceptual framework that illustrates the relationships between social media and the decision-making process is an essential step toward providing a scientific base for studying plastic surgery consumers in Jordan.
Object Tracking in Crowd Environment Using Deep Learning

Abstract: Object tracking is fetching a primary set of object identification, allocating a unique ID to each one of them, and then ensuing each of the objects as they move around frames in a movie while safe keeping the ID assignment. Person tracking is a difficult task in video surveillance. In recent years, many computer vision, deep and machine learning have been developed. Convolutional Neural Networks (CNN) are transforming target tracking. The project's purpose is to recognize and track images utilizing object identification techniques such as Region based Convolutional Neural Networks (RCNN), Faster RCNN, Single Shot Detector (SSD), and You Only Look Once (YOLO). Faster-RCNN and SSD have superior accuracy among them, whereas YOLO performs better when speed is prioritized above accuracy. Deep learning combines SSD and Mobile Nets for efficient identification and tracking implementation. This technique detects objects quickly and efficiently without sacrificing performance. Convolutionally default boxex are passed over several feature maps. If a detected object matches one of the object classifiers during prediction, a score is produced..
Modelling the power draw of tumbling mills: A comprehensive review

Optimizing power consumption in grinding, the most consumed stage in the mining industry, plays an influential role in reducing operating costs.Obtaining an efficient model to predict tumbling mills' power consumption accurately took the attention of researchers, mineral processing engineers, and tumbling mill manufacturers.This article comprehensively reviews the published mill power models and the most critical studies on this topic since 1919.Furthermore, the employed approaches for modelling the tumbling mills' power draw, the incorporated parameters into the developed models, the models' performances in predicting the industrial mills' power draw, and the potential gaps in the available literature are discussed.Moreover, based on the shortages identified in this review, some recommendations have been made to enhance the modelling mill power draw.
ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI KEMATIAN PASIEN COVID-19 MENGGUNAKAN KLASIFIKASI BERSTRUKTUR POHON BINER DENGAN ALGORITMA QUEST

COVID-19 is an infectious disease caused by a type of coronavirus that was only discovered in December 2019. Patients with underlying medical conditions, or comorbidities, have a higher risk of developing severe illness due to COVID-19. The purpose of this study is to classify and analyze the factors which mostly affect the death in COVID-19 patients using QUEST algorithm. The main strengths of QUEST algorithm are unbiased selection of variables and high computational speed. Data used in this study are primary data of 14 variables on 460 COVID-19 patients taken from Dr. M. Goenawan Partowidigdo Lung Hospital in Cisarua, West Java, from March 2020 to January 2021. Results show that there are three significant factors that affected the death in COVID-19 patients. The first factor is the status of COVID-19 patients. The second and third factors are comorbidities, i.e. hypertension and kidney failure, respectively. The factor which mostly affected the death in COVID-19 patients is patient with probable status, with a mortality rate of 95%. The second most factor affecting the death in COVID-19 is patients with under-surveillance, suspected and confirmed status with kidney failure, where the mortality rate is 60%. The accuracy of the classification tree is 80.6%, which is quite optimal.
An Intelligent TLDR Software for Summarization

Abstract: The amount of textual data available from diverse resources is increasing dramatically in the substantial data age. This textual volume has a wealth of information and expertise that must be skilfully summarised in order to be useful. Because billions of articles are published every day, it takes a long time to look through and keep up with all of the information available. Much of this text material has to be reduced to shorter, focused summaries that capture the most important aspects, both so we can explore it more efficiently and to ensure that the bigger papers include the information we need. Because manual text summarising is a time-consuming and typically difficult activity, automating it is expanding in popularity and thus provides an ideal impetus for academic study. The growing availability of documents has necessitated much study in the field of natural language processing (NLP) for automatic text summarization. "Is there any software that can assist us digest the facts more efficiently and in less time?" is the genuine question. As a result, the major goal of the summarization system is to extract the most important information from the data and deliver it to the consumers. In NLP, summarization is the act of condensing text information in huge texts to make it easier to understand and consume. We suggest a solution by developing a text summary programme that uses Natural Language Processing and accepts an input (plain text or text scrapped from a website). The output is the outlined text. Natural language processing, along with machine learning, makes it easier to condense large quantities of information into a coherent and fluent summary that only incorporates the article's most important points.
Performance Metric Estimation of Fast RCNN with VGG-16 Architecture for Emotional Recognition

Faster R-CNN is a state-of-the-art universal object detection approach based on a convolutional neural network that offers object limits and objectness scores at each location in an image at the same time. To hypothesis object locations, state-of-the-art object detection networks rely on region proposal techniques. The accuracy of ML/DL models has been shown to be limited in the past due to a range of issues, including wavelength selection, spatial resolution, and hyper parameter selection and tuning. The goal of this study is to create a new automated emotional detection system based on the CK+ database. Fast R-CNN has lowered the detection network’s operating time, revealing region proposal computation as a bottleneck. We develop a Region Proposal Network (RPN) in this paper that shares full-image convolutional features with the detection network, allowing for almost cost-free region suggestions. The suggested VGG-16 Fast RCNN model obtained user accuracy close to 100 percent in the emotion class, followed by VGG-16 (99.79 percent), Alexnet (98.58 percent), and Googlenet (98.58 percent) (98.32 percent). After extensive hyper parameter tuning for emotional recognition, the generated Fast RCNN VGG-16 model showed an overall accuracy of 99.79 percent, far higher than previously published results.
IMPLEMENTASI MODEL PEMBELAJARAN KOOPERATIF TIPE TAI (TEAM ASISTENDINDIVIDUALIZATIONS) DALAM MENINGKATKAN HASIL BELAJAR SISWA PADA TEMA PERTUMBUHAN DAN PERKEMBANGAN MAHLUK HIDUP DI KELAS III MI NW TUMPANG SARI

Abstract: The low student learning out comes because teachers are less varied in applying learning models, and there are often less effective learning methods, so that the result thing value does’nt incluede the KKN (minimum completeness criteria) value that has been determined. To improve the quality of student learning, learning does’nt only us the lecture model in the teaching and learning process but also needs to apply the model other learning models such as cooperative learning model TAI (Team Assistand Individualization). This research is a class room action research (CAR). Class room action research a consists of several cycles, but the researchers here use only cycle II, because the subject in this study where all third grade student, totalling 37 students. The instruments as in used in collecting data are observation sheets and tests that have passed the validation stage of 2 falidators. The results showed that the applycation of the cooperative leraning model tipe TAI (Team Assistand Individualization) can improve student cycle II, where the first cycle was 43% to 100% in the second cycle
Employee Engagement: A Key to Improve Performance – A Case Study on HCL

Purpose: Employee engagement may be defined as an employee's level of dedication and how enthusiastic they are about their work and the organisation. Compensation, awards and recognition, and job happiness are all elements that influence employee engagement Employee engagement, according to HCL, is a labour state of mind marked by qualities of energy, contentment, excitement, focus, and commitment. Their management philosophy is Employee First. HCL is looking for people who are passionate about their work. They want people to be enthralled by their pursuits. Their tasks piqued my interest. They're giddy with anticipation for what's to come. Engaged in developing new ideas and finding workable ideas. HCL discovered that the Employees First produces considerably more enthusiasm than any other inspirational or rewarding programme, according to HCL. The goal of this research is to investigate HCL's employee engagement practises. The main purpose of the research article is to investigate the concept of employee engagement, including how it can be implemented in the workplace and what factors influence employee engagement. Methodology: To meet the objective of the paper, secondary sources of data which included books, journals, articles, newspapers and websites are used. Findings: According to the study conducted by HCL, 55% of workers aim to hunt for a new job in August 2021. As per findings, the three crucial shifters managers make to increase employee engagement are to (a) empower employees to connect what they do to what they care about, (b) make work less exhausting and more enjoyable, and (c) reward employees with additional time off in addition to financial incentives. Originality/Value: Based on the secondary data available, this work makes an attempt to investigate the employee engagement practices at HCL. This study will help HCL's management to rebrand their plans and practices in boosting employee engagement and organizational productivity. Also, the study will enable the readers, researchers and practitioners to have a professional approach towards employee engagement. Paper Type: Case Study
Food Delivery Android Application

Abstract: A food delivery application that consists of modules for the chef, the customer, and the delivery person will be developed by us. It is an software with elements consisting of a sign-in page, registration chores with to be had meals items, details about the chef and the delivery driver, to add dishes to the cart and the status of deliveries. All of these features are made by us, and the code is written in a variety of languages, such as Java, XML, and Kotlin. We utilise Google's cloud platform Firebase for our backend work. A set of comprehensive development tools are featured in a software development kit (SDK). These help you develop your application efficiently.. Android Studio is the name of Google's official Integrated Development Environment (IDE) for building Android applications. The User Interface (UI) layouts for this app are achieved using Extensible Markup Language (XML).
A study of the Somaskanda Murtham in the Hindu cultural tradition

Semaskanda Murtham is a form of expression of elements of the Hindu cultural tradition such as religion, art, science, philosophy and faith. The sages who are special to Lord Shiva are one of the three sages Somaskanda Murtham is one of the sixty-four Shiva deities worshiped by vegetarians. This study is titled Somaskanda Murtham a Study in the Hindu Cultural Tradition, with the aim of highlighting the importance of Somaskanda Murtham in the Hindu Cultural Tradition. This study is carried out with the aim of exploring the theory. Somaskanda Moortham Historical and theoretical ideas have been the basis of this study since antiquity. This study forms a descriptive study. Performed with analysis, comparison and historical analysis when appropriate. This study clarifies the philosophy of Somaskandar Murtham, Somaskandar Murtham in literature, Somaskandar Murtham in Agama, Somaskanda Murtha portrait structure, Somaskanda sculptures. It is hoped that such research and their findings will help to illustrate the present and future generations.
College English Teaching Evaluation with Neural Network

The notion of abilities in colleges and universities is undergoing a substantial transition, and the accompanying curricular view is also evolving in response to the demands of social and economic development. Students who are not English majors in college or university play a critical role in growing their knowledge of foreign languages, improving the quality of foreign languages, and fostering their capacity to use the language in real-world situations. As a result, one of the most important methods to assess the quality of a college's curriculum is to look at how well it teaches English. Consequently, how to evaluate collegiate English instruction has become a major concern. This research offers a neural network (NNs) for evaluating collegiate English education based on the BP network’s application principle. The main work is as follows: (1) based on the peculiarities of college English teaching assessment, the weights and thresholds of the BP network are tuned using the global optimization ability of the ant colony algorithm. (2) To improve optimization ability of ACO, an update of pheromone is realized by combining global as well as local methods. In formula of the global update pheromone, a function is added to adjust the information residual coefficient according to the distribution of the solution. The residual coefficient of the local pheromone is adjusted according to the way of the minimum error judgment. (3) Optimize the BP algorithm with the improved ant colony optimization (IACO), build the IACO-BP network, and comprehend the optimal selection of weights and thresholds. Optimized BP algorithm is applied to the English teaching evaluation.
Secure Analysis for IIOT Systems Using Hyperchaotic Image Encryption

Like edge computing, intelligent cameras and image sensors are widely used in the Industrial Internet of Things (IIOT), including design and finished product quality inspection. However, the images generated by these sensors are constantly at risk of information leakage and privacy violations in the IIOT. Due to the involvement of third parties, traditional encryption algorithms are no longer adapted to image encryption for IIOT. In the context of the IIOT, an image encryption technology based on hyperchaotic systems and dynamic DNA coding is proposed. First, the image pixel position is scrambled by the hyperchaotic mapping index sequence, so that the image pixel matrix is dynamically DNA coded, and the base operation is performed on the given DNA sequence. Then, Keccak is used to calculate the hash value of the given DNA sequence as the initial value of the chaotic system and a certain number of base substitutions are performed on the DNA encoded pixel value according to the quaternary hyperchaotic sequence generated by the hyperchaotic system. Finally, ciphertext feedback and chaotic system iteration are used to further enhance the confusion and diffusion characteristics of the algorithm. The test results show that the algorithm not only has a large key space, strong sensitivity to keys, but also has strong resistance to exhaustive analysis attacks.
Collaborative Artifical Intelligence Concept: Federated Learning Review

Artificial intelligence (AI) draws its power from big data. However, accessing and processing big data may not always be possible due to both confidentiality and hardware requirements for high computational performance. Federated learning (FL) is a new concept proposed to solve the aforementioned privacy &amp; big data dilemma. FL is also a framework that performs updating of the parameters of a common AI model trained by the different participants and then combining the updated parameters through the coordinator while protecting data privacy. Due to the modular design of the FL concept, the workload is shared among the participants while protecting data privacy. It also provides advantages like scalability in terms of collaborator count and higher performance and lower execution time for some sort of problems. Depending on the similarity of the feature and sample spaces of the collaborators, there are some FL approaches such as horizontal, vertical and transfer. FL is applicable to any field in which machine learning methods are utilized and the data privacy is an important issue. Healthcare services, transportation sector, financial technologies and natural language processing are the prominent fields where horizontal FL concept is applied. On the other hand, AI-based collaborations between the sectors can be developed with vertical and transfer FL concepts.
Introduction to Cryptocurrency: An Overview

Cryptocurrency is one of the safest digital money. The currency has broader usage. Bitcoin and different sorts of tokens in addition to coins with all forms of cryptocurrency were utilized in India for a long term now. Cryptocurrency has no fund transfer limit, account of the people cannot be hacked and also there is no central point of failure. The transactions of these sites cannot be handled by a third party. Therefore this paper mainly focuses on secondary data to understand the concept, types, advantages, disadvantages, and legal status of cryptocurrency in India. The study found that cryptocurrency brought out an effective advancement to the sectors like e-business and e-payment. Nevertheless, the cryptocurrency had lack of trust yet. It is must to take adequate steps by the India Government to administer for making it safer, transparent and more trustworthy.
Optimal path planning for mobile robot using Preference based Evolutionary Qlearning algorithm

An efficient Preference based Evolutionary Qlearning algorithm (PEQL) for mobile robot path planning method is proposed that finds optimal path between source to destination using an improved policy evaluation with adaptive reward and policy switching process, the proposed mobile robot path planning algorithm is unique and novel since it finds the optimal path using policy switching and better success index than the conventional methods. Keywords: mobile robot, path planning, adaptive reward, policy switching
An Analytical Study to Design and Produce "non-official" Pop-up Double Pages for Children's Book at the Faculty of Applied Arts, October 6 University in Egypt

One of the most crucial suggestions of this paper is to study is an appeal to the officials of the Ministry of Education in Egypt to use the techniques of the pop-up pages in textbooks at primary schools to improve the quality of the textbook and reach higher levels of comprehension among students.It is a study of an experiment taught by the researcher with his 3rd-grade students, At the Department of Advertising, Faculty of Applied Arts, October 6 University, Book Art 1 Course for the Academic Year 2021/2022, to design and produce non-official pop-up pages for various children's books.The researcher Chose the best work for analysis and study; he did a detailed analysis.Moreover, explained the stages of quantitative production, then studied its statistical impact on the students of the third grade of primary school "Nour Al Salam Private Language School", Al-Qanater Al-Khairia Educational Administration, in Egypt, to reach the results and the recommendations, most notably: "The pop-up movement mechanism: is attractive on the study sample, where the percentage reached (94.9%) of the study sample", but it comes in seventh place in the used Questionnaire, It should also be modified to be more attractive on other pages, as well as the ease of reading the font used in the sample came in the sixth place with a relative weight of (2.97) -(97.4%) approval from the study sample, which is indicative Good, however, should be modified to be more accessible in the reading process.
Cloud Computing and Security

Following personal computers, one of the most important innovations in the IT industry is cloud computing ad it appeared with the advent of a major revolution in the IT industry. Cloud computing allows organizations to reduce or increase their resources according to operational needs. In fact, cloud computing means the development and deployment of computer technology to provide computational resources through the Internet. Cloud computing is an information space architecture that allows network-based and demand-driven access to a large array of common computing resources at very low cost, high innovation, and development, as well as without spatial and temporal constraints. Cloud computing is one of the most important and powerful tools for securing and managing massive data and allocating resources in the infinity of the Internet.
A General Approach for Meeting Summarization: From Speech to Extractive Summarization

Developing technologies and techniques have increased the amount of information and enabled easier access to information resources. However, due to the ever-growing amount of information sources, it has become difficult to access the information needed in a limited time. Consequently, the need for summary information has become important. This research is focused on the extraction of inferential written summaries of communications that occur in oral environments such as meetings, lectures and conferences. However, since this type of problem requires conversion from audio to text, it also includes issues such as the human factor, sound recording environments, and language-specific problems. This study aimed to take the audio recordings of the meetings, especially the IT sector, to process and summarize. Spontaneous conversations were converted into audio recordings and the obtained texts were summarized using extractive summarization techniques. The motivation of the study is to catch the important points that may escape the attention of the individuals at the meeting and to summarize the main agenda items for the personnel who could not attend the meeting. The experimentally generated dataset (converted from audio recordings to text) was summarized by three different human summarizers and compared with the summaries obtained from the developed inferential summative model. The results obtained are remarkable and it is seen that approximately 71% success was achieved.