Reviewerly/exHarmony huggingface dataset:
A Novel Machine Learning Classification Model to Analyze the COVID-19 Pandemic’s Impact on Academics: Sentiment Analysis Approach

The COVID-19 Pandemic is considered as the worst situation for human beings; it affected people’s lives worldwide. Due to this pandemic, the respective government authority announced the lockdown to break the coronavirus chain. The lockdown impacted people’s mental health, leading to many psychological issues as well as hampered students’ academics. In this chapter we have studied the impacts on students’ academics due to lockdown effect. The data has been collected via a google form questionnaire circulated to various educational institutes. Further, we have developed a novel machine learning classifier model called Naïve Bayes-Support Vector Machine for analyzing the data, which utilizes the properties of both classifiers by using a deep learning framework. We have used natural language processing (TextBlob, Stanza and Vader) libraries to label the dataset and applied in the proposed NBSVM method and other machine learning models and classified the sentiments into two categories (Positive vs Negative). We also applied the natural language processing libraries used a topic-modelling technique called Latent Dirichlet Allocation to know the essential topics words of both classes from students’ feedback data. The study revealed 83% and 86% accuracy for unigram and bigram, respectively, whereas the precision was 79% and recall 81%. According to NLP libraries’ result, approximately 71% of the feedback’s sentiment is negative, and only 16% of feedbacks are positive. The proposed model shown that (Naïve Bayes-Support Vector Machine) outperforms the other variants of the Naïve Bayes and support vector machine.
Design of Historical Mechanisms in 3D Printer

Advanced engineering design approaches have long been used in the field of engineering design and production, which encompasses the processes required to design, manufacture, assemble, and maintain products and systems. Following a typical method that includes developing conceptual ideas based on the product’s purpose and use, modeling it with 3D software, and finally processing and printing it. Traditional engineering transforms logical, mathematical, and abstract concepts into tangible objects. It progresses from the creation of physical objects or systems to the creation of digital models. From manual measurements, many ways have evolved to utilize the possibilities given by 3D technologies. This paper examines a reverse engineering case study to see what the opportunities and problems are for reverse engineering and production control utilizing 3D design methodology and technologies. This project focuses on Juanelo Turriano’s mechanism study and redesign. Once the design has been created, the pieces are printed and assembled. This allows to detect design failures and possible problems while setting parameters in the corresponding manufacturing machines. Solving this problems improve the accuracy and functionality of future construction. This article details a case study that investigated the benefits and drawbacks of using 3D design technologies for reverse engineering and structural redesign.
Cyber Attack Detection and Implementation of Prevention Methods For Web Application

The internet and web applications are the only things that run the modern world. Today, the biggest concern facing businesses is web security. It is seen as serving as the fundamental framework for the global data society. Security breaches can happen to web applications. Web security is merely protecting a layer of a web application from attacks by attackers or unauthorized users. A large number of problems with web based applications are mostly the result of incorrect client input. The various facets of web security are covered in this paper, along with its flaws. This paper also discusses the key components of web security strategies, including encryption, authentication, passwords, and integrity. Additionally described in detail are the attack methods and the anatomy of a web based application attack. This paper explores a number of methods for detection and prevention of vulnerabilities in the web application. This research suggests a more effective method for reducing this category of web vulnerabilities. Additionally, it offers the finest defence against the a for mentioned threats.
A clonal selection mechanism for load balancing in the cloud computing system

Clients demand more services and better results as cloud computing expand quickly. As a result, the load balance management component in cloud computing systems should be able to redistribute resources by user needs. The massive increase in users using cloud services increases the load on the cloud, making its management difficult. Load balancing is therefore suggested as a way to handle the excess load on the cloud. Algorithms for load balancing are the basis of load balancing. Effective load balancing and job scheduling techniques have been proposed for this reason. The main requirement of cloud computing is to share and supply computing resources, such as virtual machines, based on user demand. In the cloud, load-balancing algorithms can be used to allocate virtual machines to user requests. Requests are planned as the number of users grows, and as the number of requests grows, some scheduling algorithms in cloud computing struggle to serve users' needs effectively. As a result, effective algorithms are required to reduce processing expenses, calculation time, and energy consumption. To address these objectives, hybrid algorithms were developed. For the clonal selection algorithm, load-balancing techniques are used in this research. This novel strategy is also tested using a cloud Analyst simulator. Finally, the findings are examined and contrasted with prior research.
An Improved Drone Detection Method Using Deep Learning for Augmentation Detection Speed

In recent years, remotely piloted birds (drones) have become significantly more accessible to the general public. Affordable prices economical, being equipped with advanced technologies, small sizes, easy portability and setup, create many concerns. For example, drones can be used for destructive activities, spying on private properties, monitoring vital places, carrying dangerous objects which is a big threat to the society. For this reason, the identification of drones is considered important has been in order to solve the above challenges that the university and the industry have provided several solutions in recent years. In this paper, an improved method is introduced to detect drones based on deep learning. This system is designed based on camera recognition. Based on the camera images, the system determines the location of the drone on the image by dragging the box around it. For our methods OpenCV library and YOLO algorithm are used. The simulation results show that the drone can be detected in 1 7 milliseconds and the detection is done with 85% accuracy.
Methods for Handling Spontaneous Health Arabic Queries using unsupervised machine learning

The goal of this work is to demonstrate that using mixed sublanguage and linguistic processing techniques, is both essential and possible to create a robust NL-based systems. The merging of accurate language processing with the analysis of the sublanguage will undoubtedly improve the processing's correctness and resilience. As a proof-of-concept, we created an experimental system (HASE) to test this hypothesis. The system is a search system for Arabic documents in the health and medical domain. To study the sublanguage we employed machine learning techniques. The initial corpus consists of 40 thousands unedited queries. HASE is built on top of SOLR with the integration of Arabic linguistic processing Component. Responses are generated using IR approach. Altibby is actively deploying HASE in Jordan (the largest health content). The IR component achieves a 90% f-measure when tested with actual noisy free text.
A Deep Learning Approach for Minimizing False Negatives in Predicting Receipt Emails

Businesses generate receipts for their customers that include information such as the products purchased, their cost, the date and time of purchase, the store id etc. After an online purchase of item/s is made, a receipt is often emailed to the buyer's email address. For this evaluation, a classified database with receipt and non-receipt emails was available. Previously, Machine Learning (ML) algorithms for determining receipt validity had been implemented on this test database. The results showed that the Random Forest technique performed better than Naive Bayes and Support Vector Machine. In this paper, a Deep Learning algorithm named Long Short-Term Memory [LSTM] is implemented and its results compared with the previous implementation. The capacity of this recurrent network to handle the exploding/vanishing gradient problem, which is a challenge when training recurrent or very deep neural networks, is one factor in its success. It was found that LSTM is more effective in terms of accuracy compared to the previous ML approach. Also, the false negative values predicted by LSTM were fewer that those predicted by the ML approach. In the classification of receipt emails, processing an email without receipt data incurs a relatively low cost, yet failing to detect a receipt email results in the loss of important data. As a result, the system needs to be tuned to minimize false negatives while permitting a wider tolerance for false positives since the cost of false negatives in this situation is substantially higher than that of false positives.
Web Service Ranking based on QoS and Use Preference

One of the most important issues of ranking is thousands of web service available. Finding and implementing algorithms that do ranking well, is very essential. The important issue is that after ranking and choosing their best by different algorithms and suggesting them to user he is satisfied. Different algorithms presented so far for this purpose have focused on processing the content of user application and they have tried to do this selection based on word key or meaning or quality features, yet psychological features of users has impact on their satisfaction of suggested services of algorithms which hasn't been noted to this dependency. The purpose of this research is ranking web services and suggesting their best considering psychological features of persons. We review 2 ways of ranking. At first using qualitative model we consider qualitative features and then psychological features in qualitative and psychological combined model besides qualitative features. In qualitative and psychological combined model psychological valid test NEO-FFI has been used to get people psychological features and put them in related clusters given persons psychological features, on one hand, we obtain the amount of people importance to qualitative features using “prioritizing to web services features” form and then the relationships between persons psychological features and qualitative features of services are computed and Correlation coefficients indicating the importance of the qualitative feature in terms of each cluster persons are obtained and we can obtain the amount of general quality of each web service by its impact on qualitative features of data base of real life of QWS consisting 2500 web services and rank them and suggest the best to user. Above forms (given kokran formula) were distributed among 66 IT experts from 5 universities. At last 10 services which were suggested by qualitative and qualitative and psychological combined models, gave to 20 users for 14 days and with revising again of them we concluded that in the first case, users were satisfied 83.15% and in the second case 93.03% which is an optimal result.
Predicting chronic diseases using clinical notes and fine-tuned transformers

Electronic health records(EHR) have been used extensively by researchers lately to gain insights and use them as clinical informatics. EHR data contains structured data, as a result of having information systems in-place, and also unstructured data like clinical notes. These unstructured data have a huge scope of exploration and can derive meaningful insights. Challenges exists like the heterogeneous and multi modal nature of such data. This work provides insights into the EHR data, the datasets available for research, the tasks that can be performed on them, the methods that can be applied on them, and then demonstrates how BERT and DistilBERT can be fine-tuned on the medical datasets to predict chronic diseases like asthma, renal diseases, heart diseases and arthritis and how DISTILBERT can be a preferred option over BERT. Both the models BERT and DISTILBERT have been pre-trained and then fine tuned to predict the chronic diseases from the clinical notes.
Improve Data Mining Techniques with a High-Performance Cluster

People's reliance on computers and the computing power they provide is growing by the minute. An ever-increasing amount of data is being created each day, and the power to analyze this data requires the use of cluster computers to process and calculate data. It has been discovered that data clustering is a beneficial data mining approach. There have been a number of recent attempts to cluster data mining methods. Using a Raspberry Pi cluster, this study employs the Apriori algorithm, which is the most generally used algorithm, to extract frequent itemsets from large data sets. The fundamental aim is to build a cluster and provide data analysis capabilities based on an examination of the major clustering phases in order to illustrate the power of cluster computing and the applications of data analytics. Each Raspberry Pi uses the MPI standard and Python multiprocessing to share a large task and then coordinate their findings among a group of four or more MPICH systems at the conclusion of the processing. At the data partitioning stage, the issue of load balancing must be taken into account. According to our testing results, clustering accelerates sequential classification by a factor of 10. There is a noticeable increase in performance when there are additional processors installed. Additionally, we discovered that item count had a bigger effect on clustering performance than transaction count.
Investigating the impact of management information systems (MIS) on organizational transparency with an emphasis on work ethics

The present study was conducted to investigate the impact of management information systems (MIS) on organizational transparency with an emphasis on work ethics. The current research is practical in terms of purpose, and in terms of numerical data analysis in order to express and explain the reasons for changes in social phenomena, it is a quantitative research, and in terms of data collection, it is a descriptive-causal and comparative type of research. is counted The statistical population of the current research is all principals of primary schools in Chabahar city, whose number is about 700 people. In the present study, the sample size was determined based on Cochran's formula of 248 people, and simple random sampling method was used in this study. In order to collect data in this research, standard questionnaires of management information systems (MIS), organizational transparency and work ethics were used, and the reliability of the questionnaires was calculated by Cronbach's Alpha method as 0.79, 0.79 and 0.84 respectively. became. Based on the results of this research, it showed that there was a direct and significant impact between management information systems on their work ethics in the organization; Also, based on the results of information analysis, we came to the conclusion that there was a direct and significant impact between management information systems on their organizational transparency in the organization; The results also showed that there was a direct and significant effect between managers' work ethics on their organizational transparency in the organization; In the end, we came to the conclusion that management information systems in the organization have a positive and meaningful indirect effect through the mediation of work ethics with the organizational transparency of managers in the organization.
Digital Educational Platforms in Modern Russia: Technologies, Solutions, Risks and Services in The Context of Smartization

The problems of digitalization of education, e-learning and artificial intelligence tools implementation are extremely relevant and require a comprehensive academic solution. The purpose of the presented article is to examine the main digital platforms and services in education and the grounds for the choice of modern educational strategies by Russian youth based on the introduction of the “platform approach”. To analyze the efficiency of the development of digital educational services and identify the specifics of political values that deter-mine the criteria for their choice, theauthors conducted focus groups with students of St. Petersburg State University and a program regional survey of representatives of the younger generation in the online questionnaire format (1111 respondents aged 16 to 35 years). In the course of the study, various assessments of digital platforms available in the field of education were obtained, in particular: aggregator platforms; platforms aimed at providing services; platforms that contribute to the implementation of state educational and cultural trajectories.
Gradient Boosting Approach for Sentiment Analysis for Job Recommendation and Candidate Profiling

Sentiment Analysis has increasingly been used nowadays in many applications to evaluate opinion of public about products, policies, movies, politics. It is also used by government and law enforcement to understand behavior of people. One of the potential applications of sentiment analysis is candidate profiling and job recommendation. In the proposed research work, we evaluated the performance of supervised machine learning algorithms on dataset generated by us from twitter and indeed. We illustrated the steps involved in preproccesing the dataset generated through web scraping and making it ready for feeding into supervised algorithms. From our experimental study it is observed that Gradient Boosting Classifier gave the highest classification accuracy of 78.08 percent and AUC score of 0.819 on the test dataset.
Securing the Internet of Things via Blockchain-Aided Smart Contracts

The Internet of Things (IoT) paradigm is a relatively novel concept that is rapidly and steadily growing in modern wireless communication models. In IoT, many of the objects in the surrounding environment of us are networked in one or more forms. Sensor network technologies are increasing to meet this new challenge where invisible information and communication systems are embedded in our surroundings. This produces a large amount of information that must be acquired, transferred, stored, sorted, processed, and presented in an integrated, efficient, sensible, and easily interpretable format. Therefore, this interaction and cooperation between humans and smart devices create new challenges for exchanging information security. On the other hand, the lack of appropriate and strong security services, arrangements, and measures can have a negative or even detrimental effect on the objective acceptance of the IoT paradigm. This paper delves into the impacts of the message queuing telemetry transport (MQTT) protocol, which is a widely-applied protocol in the implemented IoT schemes. This protocol does not guarantee secure and reliable native authentication mechanisms requested by developers. Thus, this paper proposes an authentication scheme via blockchain-based smart contracts for MQTT. This proposal enables the authentication of devices in the IoT network by preserving client security and privacy to ensure trust and accountability through Ethereum-based smart contracts. This review article intends to express the security challenges of this issue as well as its proposed solution in the form of a general description of the work.
BADR: A Talking Face To Teach Arabic Vocabulary Under COVID-19

We propose an Arabic talking face called Badr to teach the language vocabulary for young students under COVID-19 pandemic. Badr is built on the previous talking face Baldi (e.g., an American English talking face) with many enhancements in interaction and words pronunciation. It can complement the work of instructors who can get tired and bored when teaching online for long hours. It can pronounce standard Arabic vocabulary accurately and interact with learners in natural manner. Listening and observing Badr improve the learners' ability to understand speech in a noisy background. We tested Badr with Qatar University students, and we show its effectiveness and usefulness in introducing smoothly new vocabulary. Badr can act as an independent tutor for different categories of learners including those with learning difficulties, slow learners, and non-Arabic speaking learners.
VGG FaceNet Based Sketch to Face Recognition with Morphable Model

Sketch to face recognition automation can play important role in forensic operations. The forensic departments can generate sketches with the help of drawing artists. The resulting sketch images may have difference compared to actual faces in terms of facial parts and expressions. The convolutional neural network (CNN) based method proposed in this paper shows augmentation based sketch and facial expression dataset generation by modifying the public dataset. The generated dataset is thus used to train the VGGFaceNet CNN model and performance is evaluated. The performance of VGGFaceNet model is tested with reference to parameters like accuracy, specificity and sensitivity. The proposed system indicates accuracy of 88% over to other conventional methods such as Local Binary Pattern, Support Vector Machine.
Depth Estimation of Monocular Images using Transfer Learning based Unet Model

The problem of depth estimation using monocular images is very challenging contrasted to methods for estimating depth that involve several pictures, like stereo depth perception. Previous studies in this field have usually focused on utilizing geometrical priors or relied on other data collection techniques. Various machine learning methods, notably deep convolutional neural networks (CNN) integrated with artificial intelligence (AI) approaches, have recently produced new marks for a variety of visual applications. In this paper, a convolution neural network is used for estimating high-resolution depth image. We have used a pretrained model DenseNet-169 which is trained on ImageNet. The encoder-decoder model used is a simple Unet model, used in biomedical image analysis. The proposed model is more accurate and efficient with reduced complexity in terms of the fewer parameters used for training. This model is also noteworthy when comparing a state of art and qualitatively it performs well and captures better edges and corners of the depth map, which is the most important factor in the depth estimation.