Reviewerly/exHarmony huggingface dataset:
A Self Diagnosis Medical Chatbot Using Sklearn

Artificial intelligence and machine learning are expanding day by day virtual assistant bots, also known as chatbots are one of the many new technologies that will continue to touch our daily lives. A healthy lifestyle depends heavily on proper healthcare. Nevertheless, it might be exceedingly challenging to schedule a doctor’s appointment for every health issue. The goal is to develop a medical chatbot utilizing artificial intelligence that can diagnose the disease and provide precaution about it. It helps you to reduce the healthcare cost, also saves your time, and improves accessibility to medical knowledge. In recent years, numerous businesses and organizations have partnered with hospitals to offer assistance that can help doctors and medical personnels deal with patients better and expend less energy thanks to technology, chatbots have a lot of potential to transform the healthcare sector. With the aid of a text diagnosis bot, patients can participate in drug reviews and give a unique analysis report based on their symptoms. People will therefore be aware of their health and be properly protected. Certain chatbots serve as medical encyclopedias, educating patients about their conditions and promoting better health. A chatbot based on Sklearn is where you can give a symptom and it will ask you questions and will tell you the details and give some advice. This research study utilized “train test split” (split arrays into random train and test subsets) and “cross Val score” (evaluate a score by cross-validation) modules from Sklearn, Sklearn SVC (support vector classification). In this paper, authors have trained our model based on given data we gathered from the Internet (check data folder) and used Sklearn decision tree classifier (DTC). In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set is correctly predicted.
A Machine Learning Approach to Predict Software Faults

A software fault prediction system ensures that software is bug-free during the development and testing phases before it is put into production (Azeem et al. in Inf Softw Technol 108:115–138, 2019). Predicting software breakdown during software development is essential to ensuring quality and reliability. The paper categorizes the National Aeronautics and Space Administration (NASA) Promise datasets, namely CM1, JM1, and PC1. A component, class, or module should be tested for faults at an early stage of the development process. As a result, costs and time will be reduced. Findings, as measured by the F1-score three defect datasets, yielded a classification performance of 99–100% with the most successful execution using the Support Vector matrix.
Mining Frequent Patterns and Association Rules Using Similarity Measures

Association rule mining (ARM) discover interesting associations and connections between sets of data items. ARM algorithms are divided into two steps, step one: locate all frequent patterns (FPs) occurring in itemsets and step two: create strong association rules (ARs) using the frequently used itemsets. In this paper, for the first step, we utilize the similarity function (SF) to find FPs to crop the search space and improve the processing speed. In addition, in the process of installation, the paper proposes an improvement by using a convolutional array. For the second step, to generate interesting association rules we use the GenRules algorithm. Experiments indicates show that the proposed algorithm is faster and achieves better pattern than previous similar algorithms.
Punctuation Prediction for the Arabic Language

Missing punctuation makes texts difficult to understand and confusing to read, be it in formal or casual writing. What punctuation marking does is that it defines sentence constituents and sentence boundaries, which is critical to such Natural Language Processing (NLP) downstream tasks as machine translation, automatic speech analysis and synthesis. Although there is a rising amount of accessible Arabic data on the Web, the majority of it either doesn’t contain any punctuation or it uses punctuation inappropriately. To the best of our knowledge, very few people in the Arabic NLP community have considered this issue. This paper aims to present a method for automatically punctuating Arabic text using a model based on a pre-trained transformer. The goal of the Arabic punctuation prediction is to identify and insert the appropriate punctuation marks in punctuation-stripped Arabic texts. Using a robust dataset with a large number of punctuated sentences, we trained and tested the system. The experimental results show that AraBERT v0.2 (Antoun et al. in Arabert: transformer-based model for Arabic language understanding, [1]) and AraELECTRA discriminator (Antoun et al. in AraELECTRA: pre-training text discriminators for Arabic language understanding, [2]) achieved the highest accuracies, 95.88% and 95.86% respectively, and overall F1-scores.
3D Application Development Using Unity Real Time Platform

Unity has a sincerely amazing feature set for fast and effortlessly building games. These video games will commonly export easily across platforms and run impressively. In this paper, we’ll discover the Unity feature set by building a FPS game. While many papers give attention to game creation with Unity, the very best market for an independent developer additionally called indie dev is video games. Games are typically less expensive to produce, extra viable for small teams, and much more likely to be completed. While there are a limitless amount of choices for choosing a game genre and game type to make a complete game like Plat former, Story-Driven, Puzzle, RPGs, we choose a FPS game. FPS is video games wherein gamers in general use weapons to keep away from or conquer their opponents. In this type of video game, players may generally move undetected by hiding, creeping, or disguising themselves. In some games, the player can choose between a secret approach and an adversary who attacks directly but rewards the player by using more kills. This style makes use of unique forces, unique agents, spies, thieves, ninjas, or spies with the protagonist who’s an assassin, counterterrorism, and fraudulent themes. Some video games integrate shooting factors with different genres along with first-man or woman shooters and plat formers.
A Scientometric Study of Research Development on Cloud Computing-Based Data Management Technique

In the present world, cloud computing-based data management mechanisms have become the most modernized problem-solving method for the business area due to their low cost, scalability, lightness, and ease of access to data, which is basically a grid computing-oriented technique. Cloud computing is an updated network-based computational technique that can perform its functionalities over the internet using its intra-connections among hardware, application software, and the internet’s topological design system. In a cloud-based network-based database, versatile data base operations like effective storage, searching, access, query processing, and optimization of data can be performed using the data management mechanism. Due to the development and harmonization of customer data throughout the system successfully and proficiently, any commercial association can benefit from the capacity of adopting the cloud system, implementation area, and functionalities. Essentially, it is a scheme to systematize data across the cloud system that is unquestionably useful for data storage because of some specific functionality such as disaster recovery, backup, and data achievement. The problem statement of this paper is: why and how research in this field developed. To clarify this problem statement, we use Scopus and the Web of Science database to provide a scientometric overview of a variety of aspects of the research track as introduced in the cloud computing-based data management field. We have carried out different experiments for data analysis considering different parameters like publication time, authorship, citation quantity, source, publishing organization, and documents separately for these two types of databases (Scopus and Web of Science) in a global perspective. We have observed that there has been momentous progress in research on cloud computing-based data management techniques due to the growing recognition of this research area.
Review on Depression Detection on Social Media Using Machine Learning

Today, it's common to use depression detection to understand how people are feeling in different situations in their daily lives. Social media data, including text data, emoticons, emojis, and other symbols, is used throughout the process, including the analysis and classification phases. In previous studies, binomial and trinomial classifications were widely used, but multiclass classification provides more accurate analysis. In multiclass classification, polarity is used to separate data into various subclasses. Deep learning and machine learning techniques are used in the categorization process. Depression levels can be guided or analyzed through social media. The purpose of this research is to present an overview of depression identification in social media data using various artificial intelligence algorithms to detect anxiety and sadness. Text, emoticons, and emojis from social media were visually recorded and used for sentiment identification using a variety of artificial intelligence approaches in this study. In sentiment analysis, multiclass categorization utilizing deep learning algorithms yields higher efficiency ratings.
CG-Transmission: A New Encrypted Transmission Method for the Data Middle Platform

As one of the major hubs of the Industrial Internet of Things, the Data Middle Platform is under immense pressure to preserve the security and privacy of the Industrial Internet. A critical challenge is how to ensure that data reaching the Data Middle Platform from external devices or systems is safe and secured against unauthorized data. Through a literature review of the Data Middle Platform and blockchain applications in the industry, the authors propose a theory of encrypted data transfer from the blockchain as a depository device to the Data Middle Platform, known as the CG-Transmission Theory. The theory involves seven objects in total and is divided into two processes: encryption and decryption. Based on the theory, the research team constructed a test case to test the feasibility of the theory and verified its encryption and decryption times for data files of different sizes. By performing 100 encryption and decryption tests on each of three different file sizes and types, the team concluded that firstly, the theory is valid. Secondly, the theory copes well with small and medium-sized files, but the encryption and decryption speed need to be enhanced for individual larger files. This is an area that needs attention in subsequent research.
“Comparison of Different Cloud Computing Platforms for Data Analytics”

Cloud Computing is a method of providing IT computing resources over Internet, ranging from data storage and processing of software’s to provisioning of an operating platform. With the advent of Amazon Web Services, Google Cloud Platform, Microsoft Azure, the cloud computing services are now affordable and businesses can transform their existing server infrastructures into dynamic environments where they can expand and reduce their server capacity depending on their requirements. The advent of technology is leading to the big data cloud computing which is an on-demand delivery of IT resource over Internet. Different cloud computing platforms like AWS, GCP, IBM Cloud, MS Azure, provide pay as per need services. Cloud computing provides different models as services like Infrastructure as Service, Platform as a Service and Software as a Service to the users. In big data cloud computing, platforms are very commonly used for data analysis. In the paper, services offered by various cloud computing platforms like AWS, GCP, IBM Cloud and MS Azure for building a data analytics platform are discussed.
Review of Deep-Learning Based User-Identification by Typing Behavior

The base of a typing-based identification system (TBIS) is uniqueness in keystroke-based typing manner of a person. Through deep training and learning process, a machine can be empowered to respond after minutely observing some events or changes in a better way than a human. On account of this, any sensitive digital machine can acquire one level up security when deep learning joins its hands with keystroke dynamics. Numerous deep TBISs have been cited, though the methodical investigation is still rarely available on recent studies on machine learning. Prevailing surveys, reviews and comparative analysis-based research works are considered here in this paper to cover keystroke typing manner with deep neural models. A perceptive description of latest contributions to grow a TBIS with deep-knowledge approaches is provided here broadly. In addition, almost all the already utilized standard datasets (Benchmark datasets) and their feature illustration methods (on the basis of number-counting, picture and time-series) are likewise reviewed in this paper. This paper finally sums up by providing a relative scrutiny of all TBISs developed by using deep learning neural network models. It eventually enlightens the future researchers to invent and apply their ideas with novelty to fill the gap.
Wavelet Tree Compression in Legal Documents

The wavelet tree is a highly efficient data structure that enables quick storage and retrieval of information from large datasets. It is particularly useful for indexing and summarizing data, as it allows grouping of characters from single to multicharacter strings. In this paper, we used wavelet trees to compress online data from the Bombay and Uttarakhand high courts. To evaluate the performance of these wavelet trees, we conducted experiments on two different processors: the I7 and the I5. Our results showed that the I7 processor outperformed the I5 processor in terms of speed and efficiency when using wavelet trees to compress data. In general, the wavelet tree is a useful tool for organizing and accessing information, as it allows for the compact representation and quick search of large datasets.
A Comparative Analysis of Multiclass Human Activity Recognition Using LSTM-Based Model

Human Activity Recognition (HAR) is a process of identification of physical activities for various human motions. This human motion has found its applicability in many activities like indoor, outdoor, static, dynamic, and elderly working movements. HAR can be monitored either stationary or dynamically from any location using IoT and cloud-based environments. Several studies were presented in the past that used 2D image-based analysis or sensory textual data for HAR. But the analysis of HAR using real-time videos is still an open challenge. This paper presents the comparative analysis of two-time series models using videos of human actions for real-time analysis. The study presents an analysis of the applicability of the models for remote monitoring.
A Systematic Study of Breast Cancer Detection by Using Algorithms of Machine Intelligence

The most common malignancy among women is breast cancer. The identification of the breast cancer in primary stage of its formation is highly desirable as the mortality rate of such cancers is always a concern for the society. However, there exists many such solutions that prove their applicability in the cure of the cancer types but all such methods are on clinical aspects. In addition, research was conducted on Computer Aided Diagnosis (CAD) systems that can assist in cancer prediction utilizing AI-based approaches. Such an intelligent systems can help the radiologists to make the better predictions based on the generated reports using CAD systems. These systems are always considered a beneficial tool to the radiologists to get faster, more precise and approximate results about abnormalities and thus considered as a second opinion aspect for them. This paper presents the detailed systematic study about all such works that uses the CAD systems incorporated with the fast AI and machine learning algorithms for making decisions on tasks of classification of cancer cells using the histology imaging and segmentation of the abnormality regions from the input histology images. The collective study, as presented in this work, help in understanding the latest works on the above said domain with the summarized results on the approaches used, dataset types, and their performance metrics. Thus the objective of work to summarize the overall result on histology imaging is fulfilled in this article.
Exploring Gestural Agency in Collaborative and Embodied Rates of Change Simulations

This chapter describes the theories, design, and analysis applied to a pair of collaborative mixed-reality simulations addressing different scientific content but linked by the crosscutting concept of “rates of change”. Body tracking technologies allowed users to interact with these simulations using one of two conventions: through gestures chosen by participants, or a prescribed gesture representing the slope of a line using one’s arms. The two simulations explore the relationship between inputs and outputs in the context of population ecology and climate change systems. Participant pairs were randomly assigned to one of 4 treatment groups for gestural convention (learner-defined or prescribed) and simulation order (population ecology or climate change first). Knowledge and engagement surveys found a significant overall increase in individual knowledge, but no significant difference across the four treatments. This motivated deeper analysis of the dialog and actions of the participant pairs as they engaged with the system, where it was found that the order of simulation exposure produced a shift in the ratio between collaborative directives and simulation behavior dialog. In addition, the gestures recorded in the learner-defined gesture group were also leveraged in un-expected ways, such as in facilitating synchronization between partners.
Integration of Wearable Devices in VR-Enhanced Aircraft Maintenance Training

There is a growing demand for using VR for educational purposes due to the realistic and immersive experience that it provides. For hands-on tasks, like aircraft maintenance procedures, several researchers have studied various methods of enhancing this learning experience. While performing the interactive learning of the aircraft hydraulics system for example, it is highly important to incorporate safety elements during the hands-on tasks. This way students have a chance to develop habits for safety hazards control while learning the knowledge of hydraulics systems. This chapter reports wearable devices we designed providing interactive alerts or warnings to avoid hitting in a constrained working area. In this study, custom-made tactile gloves and jackets are developed to improve the learning experience of a VR aircraft hydraulics system learning application available in-house.
An Immersive Learning Environment to Improve User Experience in Science Museums

Science museums are not only places for scientific research but also places for the public to access various aspects of science fields. With the increasing inquiries and interest of the public to science, traditional exhibitions cannot fulfill the needs of their audiences to acquire science knowledge nowadays. Diversities and various inquiries of visitors should be paid attention to for science museum administrators. Thus, new formats of learning are necessary to be implemented into the construction of science museums. Among various types of learning means, the immersive learning supported by Virtual Reality (VR) technologies can be a good candidate to build immersive and interactive virtual learning environments so that visitor’s experience can be enhanced. Guided by the sensory perceptual system introduced by James Gibson, this study proposes a construction framework for science museums to systematically build immersive learning environments for visitors. We also embedded force feedback technology for enhancing the haptic perception of system users to increase their level of immersion. A pilot study of thirty senior year high school students from Beijing Experimental Middle School was conducted to evaluate the usability and user experience of the developed virtual immersive learning environment. Results showed that students reported comparatively higher feeling of presence and engagement than those in the control group. But overall, students reported satisfying user experience through the developed learning system.
A Systematic Review of Pedagogy Related to Mixed Reality in K-12 Education

The recent development of applications for virtual reality (VR), augmented reality (AR) and mixed reality (MR) in education has stimulated interest in how these technologies can support teaching and learning at the school level. Of particular interest, is what pedagogies could be applied such that the affordances of these technologies are optimally exploited in supporting content understanding, long-term memory retention, improved task performance, and increased motivation and engagement for learners. This chapter is a systematic review of 18 peer-reviewed journal articles from the last decade starting 2011–2021 September related to MR pedagogy in K-12 education. The main aim of the review is to establish pedagogical approaches, theories, and the impact of integrating MR in K-12 education. Data from selected articles analysed by means of content analysis revealed that though most studies adopt interactive learner-centered pedagogies like inquiry-based, activity-based, discovery, and collaborative learning for MR interventions, they are quite inexplicit about the pedagogical dimensions of the MR-enhanced learning interventions. Based on the findings of this review, we propose a guiding framework for pedagogical processes to be followed in integrating MR technologies for K-12 teaching and make recommendations on future research that can be pursued in this area.
Exploring the Mozilla® HUBS® Platform for Virtual Final Year Project Exhibition

In the current Covid era where all forms of physical engagement with students are severely restricted or stopped, many conventional activities and assessments have been hampered. One particularly important course in any BS(CS) 4 year undergraduate degree program is the final year project. Students are asked to solve a local or global problem under guidance of a faculty member as their final year project supervisor. The final year projects are evaluated multiple times through mid and final presentations and a final year projects exhibition. In these evaluations, the projects are evaluated by both internal and external experts. Based on mid, final presentation and exhibition scores, an aggregate score is generated for the course. Due to strict Covid SOPs, we are unable to host the final year exhibition on campus. So we decided to develop the first virtual final year exhibition at Habib University on the Mozilla® Hubs® platform. This was achieved by creating a virtual replica of the Habib University design thinking and exhibition space (The Habib University Playground). We provided virtual spaces for final year student groups to showcase their posters and presentations. Thanks to the support provided by the Mozilla® Hubs® platform and its built-in Spoke® editor, we were able to do streaming of YouTube® presentations for each group. The students, participants and evaluators had their own virtual avatars in the virtual space. Moreover, distance based sound attenuation enabled talking among participants as if they were meeting physically. This allowed students and evaluators to cross question during the exhibition. Based on our own experience of using Mozilla® Hubs®, we can say with confidence that it is a promising tool for creating engaging virtual events like online exhibitions giving everyone a much more refreshing virtual experience unlike the kind of stereotypic presentations possible through Zoom® or Google Meet®.