Reviewerly/exHarmony huggingface dataset:
Analysis And Comparison Of Ventricular Cardiac Arrhythmia Classification Using Potassium Channel Parameters With ANN And KNN Classifier

Aim: The Motive of this research is to analyze, compare ventricular Cardiac Arrhythmia (CA) classification using potassium channel (k+) parameters with Artificial Neural Network (ANN) and K-Nearest Neighbor (KNN) classifiers. Materials and Methods: D Noble Model For Human Ventricular Tissue (DNFHVT) is used for our classification. The DNFHVT is a mathematical model of action potential focusing on major ionic currents like K+,Na+ and Ca+.. Size of the sample was calculated by keeping threshold 0.05, G Power 80%, confidence interval 95% and enrolment ratio as 1. Number of samples considered is 20. These data are imported to KNN and ANN classifiers to find better accuracy among them. The accuracy of novel ANN and KNN classifiers for 20 samples is obtained by alternating the cross fold validation. These results will be imported to Statistical Package for the Social Science (SPSS) software to identify the overall accuracy for each classifier. Results: The results are obtained from SPSS for novel ANN and KNN classifiers. ANN shows accuracy of 13.14% with standard deviation (1.6800) and Standard error mean (0.3757). Similarly KNN produces an accuracy value of 7.19% with standard deviation (1.6902) and Standard error mean (0.377). Conclusion: As of the results, it clearly shows that ANN has better accuracy for classification than KNN.
Analysis and Comparison of Image Enhancement Technique for Improving PSNR of Lung Images by Median Filtering over Histogram Equalization Technique

Aim: The main goal of this project is image enhancement to improve interpretability or perception of information in images for human viewers and also to provide better input for other automated image processing techniques. Materials and Methods: In this research different sources of lung images collected from Kaggle website were used. Samples were considered as (N=30) for median filtering and (N=30) for novel histogram equalization technique with total sample size calculated using clinical.com. As a result the total number of sample was calculated to be 60.Using SPSS Software and a standard data set,the PSNR was obtained. Both median filter and novel histogram technique image enhancement were implemented on Lung images through Matlab coding and also extracting PSNR values of each image. Then through SPSS software comparison and analysis has been made Results: In an image enhancement of the image processing pathway, novel histogram equalization technique shows the best performance by removing noise to improve PSNR of lung images than median filtering. Comparison of PSNR values are done by independent sample test using IBM-SPSS software. There is a statistical difference between histogram technique and median filtering. The novel histogram equalization technique showed higher results of PSNR (69.6557dB) with (p=0.04) in comparison with median filtering (37.6427dB). Conclusion: Histogram equalization image enhancement technique provides high PSNR values for different sources of lung images than median filtering Technique.
Analysis and Comparison of Kidney Stone Detection using Parallel Piped Classifier and Bayesian Classifier with Improved Classification Accuracy

Aim: The goal of this research is to use parallel piped classifiers and bayesian classifiers to predict and detect kidney stones. Materials and Methods: This investigation made use of a collection of data from Kaggle website. Samples were considered as (N=10) for parallel piped classifiers and (N=10) for bayesian classifiers according to clinicalc.com, total sample size calculated. The accuracy was calculated by using MATLAB with a standard data set. Pretest G power taken as 85 in sample size calculation can be done through clinical.com. Results: The accuracy (%) of both classification techniques are compared using SPSS software by independent sample t-tests. There is a significant difference between the two classification techniques. Comparison results show that innovative parallel piped classifiers give better classification with an accuracy of (83.5410%) than bayesian classifiers (71.1314%).There is a statistical significant difference between parallelepiped classifiers and bayesian classifiers. The parallel piped classifiers with p=0.007, p&lt;0.05 significant accuracy (83.54%) showed better results in comparison to bayesian classifiers. Conclusion: The parallel piped classifiers appear to give better classification accuracy than the bayesian classifiers.
Based on feature reconstruction and feature intersection, the Deep CTR Prediction Model

Collecting a lot of feature data can enhance the prediction impact for the recommender system’s CTR prediction task. The second-order feature cross factorization machine model, which is the most popular, has the ability to automatically learn implicit features based on existing features. The factorization machine model, however, has the following issues: (1) The encoding is fixed, and the same feature represents the same in various contexts. (2) The encoding type is single, and the feature is represented by a single weight vector, which cannot completely express the information of the feature. To harvest possible feature information, recommender systems employ neural networks. A novel method of feature intersection is created by the Compressed Interaction Network (CIN) component suggested in xDeepFM. In order to address the issue of encoding fixation in the factorization machine, the original feature encoding is reconstructed in this paper using xDeepFM in conjunction with the feature refinement network (FRNet). Additionally, different feature representations are expressed in various contexts for the same feature. To address the issue of a single encoding type in factorization machines, the field-aware interactive machine (FIM) component provides feature information at the domain and feature levels, respectively. The model is known as the Field-aware Feature Refinement Depth Factorization Machine (FRDFFM). On the Criteo dataset and the Avazu dataset, respectively, the suggested model was evaluated, and it produced promising results.
Cross-sectional display system using AR (Augmented Reality), VR (Virtual Reality), and MR (Mixed Reality) for 3D x-ray CT inspection

Demand for 3D X-ray CT inspection (in the medical, nondestructive testing, and security fields) is increasing year by year, and AR (Augmented Reality), VR (Virtual Reality), and MR (Mixed Reality) are being used for displaying internal structure of object. Although, these applications can display surface-rendered or volume-rendered objects, but have not been developed to accurately represent and spatially help the observer understand the point he or she wants to see. In this research, the representation method and system were proposed that 2D image output from DICOM is superimposed on the cross-section of a surface-rendered model, and by using a motion sensor, an object imaged with a 3D X-ray CT can be freely manipulated in the virtual space using the human hand, just as if the object were actually moved in real space, so the cross-section of the point to be checked can be viewed in any direction. By using the stencil buffer function, a shader function on Unity, unnecessary areas other than the cross-section can be hidden, so the object itself is not hidden in the 2D image. This system can be performed with either spatial reality display, VR device, or AR glasses. The results show that the proposed method is effective as a useful 3D representation in 3D X-ray CT, which is voxel data containing internal information, and as a method that allows viewing tomographic images in CT images and easily representing specific locations.
Revolutionizing the Insurance Industry: A Blockchain based Claims Management System

Low efficiency and complex services are issues in the present medical insurance claims procedure.A patient must visit the hospital to request a diagnosis certificate and receipt before sending the necessary application materials to the insurance provider in order to submit a medical insurance claim.The patient won't get paid until the business has finished verifying everything with the hospital.However, blockchain technology has the potential to make the situation better.The new project is an integrated healthcare system in which all the hospitals and insurance companies will be able to do registration in the system.The patient's health record eg. medical bills, reports, admit cards, etc. will be maintained on blockchain servers in encrypted format and patient will be able to claim the insurance.The insurance company will be able to review all the bills and reports.As all data is maintained on blockchain servers there is no possibility of manipulation in bills and reports hence transparency will be maintained and security of the claims processing will be increased.
FusionTC: Encrypted App Traffic Classification Using Decision-Level Multimodal Fusion Learning of Flow Sequence

In recent years, an increasing number of mobile platforms and applications have adopted traffic encryption protocol technology to ensure privacy and security. Existing researches on encrypted traffic identification approaches often rely on a single-modal feature pattern (such as packet sequence and statistical features), which cannot fully represent the detail information of complex traffic features, and so their predictions are susceptible to anomalies. In order to improve the effect of classification on encrypted app traffic, we propose FusionTC, a novel app traffic classification framework based on feature fusion of flow sequence. FusionTC consists of two-level subclassifiers, which are used to perform decision-level fusion of multimodal features by an upgraded stacking method. The comprehensive capture and fusion of multimodal traffic details, coupled with the refined processing and segmentation of traffic, enables FusionTC to significantly promote classification accuracy and enhance robustness in challenging situations. Based on our self-built app traffic dataset, FusionTC improves the accuracy by at least 3.2% over the state-of-the-art approaches.
Midrash and sophism in the context of transformation scientific and educational traditions in the 21st century

The end of the 20th century and the beginning of the 21st cen-tury is characterized by multiple crises in most aspects of human existence, among which the education crisis is one of the most noticeable. The usual logic of problematizing and seeking ways to overcome the education crisis is linked to changes in the nor-mative-legal and economic conditions of educational activities, and much less attention is paid to the ontological problems of education. In this regard, the first task of this study was to con-ceptually formulate a request regarding the resources and tools for overcoming the ontological crisis in education. In the context of studying the main stages and results of three decades of re-forms in the field of Russian education, three interconnected re-quests have been identified: 1) Individualization and differentia-tion of the content of education while maintaining basic value-semantic unity; 2) Ensuring the social and economic relevance of the content of education, ensuring its demand and feasibility; 3) Rooting the content of education in social and humanitarian tra-ditions that are capable of influencing and transforming modern social and humanitarian practices. The categories of “midrash” and “sophism” emerged at roughly the same time (around the 5th century BCE) and represent complex phenomena of oral cultures that developed in the Eastern Mediterranean: midrash in the con-text of the Jewish religious tradition, and sophism (as well as the syllogism that gave rise to it) in the ancient city-state tradition. Moreover, each of these phenomena fully corresponds to the re-quirements listed above. 1) Reconceptualization of the educa-tional attitude as a means of ensuring intergenerational connec-tion and continuity; 2) Inherent duality of cultural and education-al traditions in their relationship with “text” and “language” (to which modern culture adds “visible image”); 3) The need to modernize the system of educational standards based on dia-chronic traditions and synchronic cultural-anthropological mod-els; 4) Actualization of issues and tools for ensuring dialogue and "openness" of traditions, identifying their contextual meanings and requests addressed by current socio-humanitarian practices; 5) Integration of projects, socio-educational initiatives and prac-tices around identified scientific and educational traditions and cultural-educational models underlying them.
Indian education through a G20 lens-Ensuring continuity of sustainable development

Beginning with a global inquiry for increasing technology-enabled learning and education, this article analyses Indian education policy responses in G20 nations. Educators and students in India are generally well-prepared to use best practices in tech-related education. Therefore, the G20 partnership must think about how to best encourage the development of educational systems and practices that are robust, efficient, equitable, and adaptive in order to better respond to and lessen the effects of current and future crises (United Nations 2020a; Reimers and Schleicher 2020). In this context, it is currently necessary to make provisions to improve access to the technology infrastructures for remote education within and outside the G20 countries. Additionally, to facilitate safer and more fluid transitions to remote modes of EduTech during school education in India is disrupted, educators, students, and parents require help and direction. Finally, the G20 must immediately look into more sustainable growth models in case the education sector experiences long-term difficulties.
AUDIT SISTEM INFORMASI MENGGUNAKAN KERANGKA KERJA COBIT 4.1 PADA PT. CINDYANI TIWI LESTARI

The development of information systems has had a significant impact on various companies. Information systems that have been implemented are used to support business activities. This is also done by PT. Cindyani Tiwi Lestari, a company that has been established since 2014, is a company engaged in procurement services, quality improvement and problem solving for various heavy equipment. In this study the authors wanted to know whether the information system governance set by PT. Cindyani Tiwi Lestari is in accordance with the company's vision, mission, goals and objectives. The audit that has been carried out uses the Cobit 4.1 framework, especially in the delivery and support (DS) domain, because this domain includes security, data integrity, operational facilities and smooth system management. The technique of collecting data was carried out by observation, interviews and questionnaires. From the results of this study, the antidote will be obtained at the level of maturity (maturity level) at PT. Cindyani Tiwi Lestari, especially in the DS domain, which on average is at 1,510, this means that the process of the procedures applied has been developed in such a way and provides recommendations to the company to carry out regular checks. periodically and continuously on data in information systems related to company business processes.
Network Lossless Information Hiding Communication Method Based on Big Data

With the rapid development of China’s economy, information technology came into being under the economic development. The shadow of the information technology gradually emerged in the China’s communication technology. Lossless information has become the most important part of communication technology. How to realize the lossless network information, realize the encryption of information content, and realize the hidden communication without affecting the original content of information. Through the ages, people have various ways to hide information, but the principle is basically the same, that is, the information is stored in the “beyond the power” of vision, hearing and other senses, so as to achieve the purpose of “muddling through”. For multimedia information hiding (IH), this paper mainly introduces the network lossless IH communication method based on big date (BD). Thus, the confidential information can be transmitted without being noticed, which greatly improves the security of the information security system. Based on the study of the dissemination of large data information, this paper reasonably analyses the feasibility of transmitting information without loss from two aspects: information and electronic and offline. It can perform a column integer wavelet transform on the image after entering the information, and then enter the data according to the same method. Through such processing, the amount of embedded data can be increased by about twice. The experimental results show that the network lossless IH communication method based on BD improves the communication confidentiality by 16%. The limitations of network lossless IH communication are analyzed, discussed and summarized, so as to enrich the academic research results.
Anomaly Recognition Method of Network Media Large Data Stream Based on Feature Learning

With the rise and rapid development of mobile communication, intelligent terminals and feature learning technologies, we are entering the era of mobile Internet. When we meet developers in the web, we have no control over the progress of various specific applications. Therefore, feature learning needs to provide a high degree of flexibility for anomaly detection. This paper firstly proposes a method for external identification of big data stream network media based on feature theory. In this paper, an applied research on optimization problems based on feature theory is carried out. The integration and distribution of high-performance evolutionary algorithms also need to be improved, and great success has been achieved in the field of recognition. This kind of feature takes the region with significant gray change (such as inflection point intersection) as the point of interest, and designs the corresponding feature description vector to quantify the detected region. These local features are robust to background and attitude changes, and are usually used in target detection, target classification and other fields. Therefore, the study of outlier coevolution feature learning is of great significance. The experimental results show that the outlier detection method based on feature learning in big data network streaming media improves the optimization rate of outlier problem by 14%. The classification of external optimization problems based on feature learning provides a good method for the application of big data analysis and genetic algorithms, thereby improving the results of learning research.
ASSISTING THE INTEGRATION OF TECHNOLOGY AND ITS EFFECT ON STUDENTS' ATTITUDES IN DISTANCE LEARNING

Teaching and learning process can be easily conducted due to technological developments. The development and application of information technology is also beneficial for the world of education. The purpose of this study is to assist in the integration of technology and its influence on students' attitudes in distance learning during the Covid 19 pandemic. The method of conducting this report is qualitatively descriptive study and the implementation of the service is carried out with two stages, that is the first is mentoring and the second is data retrieval through interviews and questionnaires addressed to students. The results of the implementation of this assessment show that there are many technological concerns about student attitudes, and technology can affect students' attitudes positively or negatively. Among the positive influences of technology on students are that it can increase creativity in using technology, feel more curious about technology and can affect the attitude of cooperation in student self-development. As for the negative impact that affects students, students find it quite difficult to understand the material provided by the teacher. The results of the assistances showed that the positive impact was more dominant than the negative impact experienced by students.
Aspect-based Sentiment Analysis on Car Reviews Using SpaCy Dependency Parsing and VADER

All businesses, including car manufacturers, need to understand what aspects of their products are perceived as positive and negative based on user reviews so that they can make improvements for the negative aspects and maintain the already positive aspects of their products. One of the available tools for this task is Sentiment Analysis. The traditional document-level and sentence-level sentiment analysis will only classify each document / sentence into a class. This approach is incapable of finding the more fine-grained sentiment for a specific aspect of interest, for example, comfort, price, engine, paint, etc. Therefore, in this case, Aspect-based Sentiment Analysis is used. A total of 22.702 rows of car review data are scraped from the Edmunds website (www.edmunds.com) for a specific car manufacturer. Dependency Parsing and noun phrase extraction were carried out using the SpaCy module in Python, and VADER sentiment analysis was used to determine the polarity of the sentiment for each noun phrase. Results showed that the vast majority of the sentiments are on the positive aspects: comfortable to drive, good fuel economy / mileage, reliability, spaciousness, value for money, helpful rear camera, quiet ride, good acceleration, well-designed, good sound system, and solid build. The results for the negative aspects have some similar aspects with those in the positive class but has a very low frequency. This finding means that the vast majority of the users are satisfied with multiple aspects of the produced cars. The limitation of this research and future research direction are discussed.
Development of electronic courses for training employees in business organizations

Introduction. Intensive changes in the information space necessitate employee's regular knowledge and skills improvement, which is promoted by most organizations. The creation of an electronic course involves a complete re-evaluation, decomposition of available information, training programs in the organization, which were created for training with the personal participation of training specialists. Aim. Based on the author's experience in creating electronic training courses, to analyze the process, main stages and model of creating electronic training courses for business organization employees. Results. Creation of an electronic course (E-learning course) in a business organization has a project format. The principles and models of Instructional Design, ID are used to create electronic courses. The model for creating an electronic course consists of five stages: 1. Request; 2. Designing; 3. Creation+pilot; 4. Implementation; 5. Assessment of learning outcomes. The role of each specialist involved in the electronic course creation is analyzed in detail: electronic course customer; expert in the field of knowledge which the course will be created (Subject Matter Experts - SME); instructional designer (or methodologist); typewriter; graphic designer. The author discusses the importance of the Matrix of Competences as a tool for request clarifying and educational material structuring, as well as describes in detail the Authoring Tools used in course creation. An e-course focus group and electronic education platforms (Learning Management System - LMS) were shown to play an important role in effective business organization personnel's training. Conclusions: The author analyzes his experience in creating electronic courses, which can be used in training business organization personnel.