Reviewerly/exHarmony huggingface dataset:
Strategic Evaluation of Implementing Artificial Intelligence Towards Shaping Entrepreneurial Development During Covid- 19 Outbreaks

Artificial intelligence (AI) shape the business enterprises and enable in realizing enhanced growth and development. It has been observed that the application of AI enables in influencing the organizational culture in an effective manner thereby enhancing the working environment which will lead to better productivity and output. The implementation of AI support in enhancing the entrepreneurial aspects through enhancing the cultural and ethical aspects and influence better decision making, create new business practices which will harness human capital and effective leadership capabilities. It has been regarded that the implementation of AI possesses the overall ability to quickly understand the business process and address the issues efficiently for superior performance. The companies which apply AI enable in enhancing the service delivery, understand customer requirements, forecast the business patterns for realizing growth and development. It has been regarded that the current organizational environment is focusing in implementing effective and dynamic capabilities which will influence in attaining growth and success in an effective manner Therefore, the overall success to these initiatives is mainly based on the changes in the internal and external environment, and culture & ethical aspects are being applied in the organizational context. The application of effective leadership support in enhancing the organizational culture and which will create better entrepreneurial aspect for effective performance. This study is focused in addressing the critical strategic evaluation of implementing Artificial Intelligence towards shaping Entrepreneurial Development during COVID- 19 Outbreaks. In its pure essence it is focused in understanding the critical nature of culture and ethical aspect which will enable in creating better programs and support entrepreneurial spirit.
Software reusability: A product transition Perspective

Practitioners highlight the want of getting a prepared and green technique for software program reusability assessment. For this purpose there may be a want to increase an extra rational answer for reusability assessment at blueprint segment. Software reusability has changed into one of the maximum good sized worries of the software program industry. Value-added reusability manual to decreased software program improvement efforts in addition to decreased charge besides time also. Reusability assessment at the initial periods at blueprint segment is particularly underlined in my planned study; henceforth, taken into consideration crucial for the transport of fine software programs. Aforementioned records stimulated the researcher to make the effort on this path and to increase a complete reusability assessment outline as well as model to degree item oriented software program reusability at blueprint segment of improvement lifestyles cycle. The paper highlights the significance of Reusability in standard in addition to as a thing of software program fine. Two predominant charities are made in this paper. A relation among Reusability elements and item orientated traits has been mounted as a primary contribution. In the 2nd contribution, a model has been planned for assessing Reusability of item orientated software program the usage of blueprint metrics. In addition, the planned model has been authenticated by means of investigational try-out.
OCSK-SMOTE: An improved minority oversampling technique for unbalanced dataset using One-Class SVM and K-means

Classification of dataset that have an imbalance in there classes still a challenge to the real world problem and a lot of research has been done and continuously being done to get a resolution to this problem, SMOTE has achieved tremendous triumph in this regard and in this paper we have focused on extension of SMOTE and developed an algorithm to overcome its shortcomings, The Highlight of the algorithm is that it removes the outliers from the Dataset and then proceeds to oversampling by first dividing into clusters of majority and minority and then oversampling only in clusters where there is more minority data so to also tackle data distribution problem of class imbalance, to test the proposed method it has been tested on 10 UCI datasets with varying degree of class imbalance ratio.
Challenges in Music Generation Using Deep Learning

Owing to its massive potential, the field of Artificial Intelligence is booming at an unprecedented rate. With the increase in efficiency and accuracy in prediction, classification, translation, and other traditional tasks, deep learning has also witnessed a surge in applications in various other sectors, one of them being music generation. This paper provides a brief introduction to the process of creating fresh new music from a collection of existing music as the sample, using deep learning architectures and training techniques. The creation of music using automated processes in the absence of human intelligence is not a fresh proposal and has been influencing human curiosity for a long time. Being an innovative and complex venture, it poses certain unorthodox challenges which are needed to be addressed. This paper focuses on the analysis of these challenges and explores the scope of this fascinating pursuit.
Dashboard for Crime Analytics Using R &amp; Shiny

Crime analysis is a systematic method of identifying &#x0026; analyzing crime trends. By using computerized systems, crime data analysis can assist Law enforcement officers in solving crimes more quickly. The Dashboard is built on Crime Big Data. It enables us to examine how crimes are escalating in various states across the country. This paper is primarily devoted to presenting a graphic representation of the many crimes that are committed in the country. Through data analysis and visual representation, many factors can be represented, such as crime areas and the rate at which crime is increasing. A dynamic dashboard will be used to visualize the crime scene area which is a big data extracted from several sources. It can determine whether a crime trend is evolving, identify areas with a high crime rate, analyze temporal trends of crime, and identify possible causes of crime. For this, R, R Shiny, and Google Maps API are being used. It allows for the display of crime patterns across regions.
Systematic review of methods used in text summarization

Context: with the improvement in technology large amount of text data is available on internet on different platforms like twitter, micro blog, Facebook, e- Newspapers, magazines and different information providing websites. Summary is the need of hour as people do not have enough time to go through all the documents manually and get accurate and allusive information. Textual summarization is the process of condensing size of a text document in such a manner that its original meaning is maintained. In time bound situations it is not possible for humans to extract important info manually from bulky volumes of text data that is available on internet. To extract and process such magnanimous data Text summarizers are being developed. Over the period of time Summaries generated by these text summarizers are improved to produce more human oriented summaries.
Task Migrations in a Cloud Computing Virtual Machine Using Cauchy Swarm Optimization Techniques

By offering virtual resources to end users, cloud computing model is significant to address the difficulty of management of memory. To determine the cloud workloads and subsequently apply the migration algorithm for efficient resource use, several techniques were applied. The procedure is reliant on previous results and just a few steps ahead projections. As a result, the allocation takes longer to process and an essential element allocation of resources is provided in order to allocate and use resources efficiently in response to user requests. Virtualization aims to reduce the expense of running programmes on separate hardware sets. The attribute selection is done using Independent Component Analysis (ICA) and relief. The hybrid Cauchy particle swarm technique is then used to allocate resources based on the selected attribute. The Cluster dataset is used to assess the proposed technique's migrant count and power consumption. The optimal strategic outperforms both the present methodology and the existing method by reducing the energy consumption and the frequency of virtual machine migrations. As a result, for dynamically assigning cloud resources, the proposed ICA and relief-based MCPSO are the best options.
A Comparative Study in Understanding the Emerging Field of Blockchain Toward Effective Accounting Information Systems in Large Corporations

Block chain technology is now becoming a demanding technology for large corporations since it benefited the companies more in their accounting and finance fields. This technology could provide a platform for large corporations to store their financial data and track their business assets efficiently. Therefore, by using the technology the firm could make an immense effort in improving its accounting information system which could directly improve the business performance of the large corporation.
A Comparative Study of Implementing Intelligent Internet of Things for Creating Better Surveillance and Security in Developing Nations

In current history, rapid digitization has opened up new technical possibilities, which have already begun to alter the major safety and security sectors and society in general. The digital revolution opened up new opportunities for progress and more effective use of scarce resources, technologies, and operations in several sectors of the safety measurements. The Internet of Things (IoT) reflects the growth of mobile phones, houses, and integrated technologies. Because the items surrounding will have a greater understanding of our interests, ambitions, and requirements, such a development will aid in the creation of a smart environment for humans. Since IoT technologies grow more common, security challenges in IoT have gotten a lot of attention from academic and industrial. IoT systems, in comparison to traditional computing environments, have greater inherent problems and, as a result, may have increased security procedures. The existing IoT design, on the other hand, does not adequately handle the heightened security statutory responsibilities by those weaknesses. Mixed methods are used for this paper to collect statistical as well as theory-based information from different sources. Primary quantitative and secondary qualitative methods are used to collect various information.
Fake News Detection on Social Media

Fake articles, misleading news and lies are there since before the appearance of the web. The comprehensively identified explanation of web fake or we can say wrong news is: misleading articles intentionally built to hoodwink peruses. There is such a great deal spread of the fake news that adversely affects the general public and in person too. Above all else fake news are piece of work which is composed and spread with wrong intensions. They misdirect individuals to trust the helper data just to meet the eagerness. Also it is the most moving part to remove the typical individuals from such deceptive fake news, which is exceptionally enormous in number. The reason for the work is to return up with a response which will be used by clients to identify and channel locales containing bogus and misdirecting data. We utilize straightforward and carefully chose highlights of the title and post to precisely distinguish fake posts. All through the paper we have shown the examination related zones, open inconveniences, and future bearings for the exploration on fake news identification on social media. We end up the report by lift discernment about look at and opportunity for organizations that are by and by on the chase to help suddenly identifying fake news by giving web administrations.
Reimaging Indian Healthcare using block chain technology and disease detection

Global risk management has been affected by the SARS-CoV2 epidemic Blockchain is quickly being utilized in medical services the board as an essential instrument to work on functional principles and lay the preparation for a more proficient and successful proof based dynamic cycle. We need to demonstrate that blockchain can be utilized in medical services and deal a way to a COVID19-agreeable clinical practice. The utilization of blockchain related to man-made brainpower frameworks takes into account the foundation of a generalizable expectation framework that could assist with pandemic gamble a regulation on public area. To feature valuable open doors and constraints, SWOT investigation based on execution of ablockchain made forecast prototype in medical care and SARS- CoV-2contamination was performed. Blockchain can possibly assume a critical part in store for computerized medical care, especially in further developing COVID19-safe clinical practice. The major principles accessible from several blockchain-based models, particularly those connected to clinical workflow, have been documented and critically explored in this paper.
Secure Key Exchange and Authentication Mechanism for XML Document using Lagrange's Interpolation

The confidentiality and integrity of the data must be ensured over any communication channel so that users can trust the received data. Lower layers of TCP/IP network could ensure the security of data but not securing data at application level would cause the data leakage. One common key used for the encryption of the messages in the entire session could be compromised if an intruder gains an access to the common key. The key exchange algorithm proposed in this paper utilizes Lagrange's interpolation for formulating the polynomial and deriving the new encryption key for every message sent in a session. The key exchange mechanism doesn't use explicit handshake messages for sharing the new key instead it uses piggybacked key exchange mechanism in which the information regarding the new key is embedded in the response message. The performance analysis of Lagrange's interpolation-based algorithm is performed w.r.t the Newton forward interpolationbased method.
An Empirical Investigation in Applying Reliable Industry 4.0 Based Machine Learning (ML) Approaches in Analysing and Monitoring Smart Meters using Multivariate Analysis of Variance (Manova)

Conventional power systems have been evolving towards prospective smart grids in recent decades as market needs have changed and independent innovations, such as the “Internet of Things” (IoT), have developed. The further automation of traditional development of industrial operations with a variety of current advanced techniques is termed as Industry 4.0. The most efficient strategy to boost the productivity of the enterprise environment, such as smart grids, is to use contemporary control technology that regulates and supervises interaction between smart devices. Furthermore, the advancement of loT -enabled smart meters is aided by a separate data connection, such as “Bluetooth” or the “Global System for Mobiles” (GSM), for remotely inspecting and operating smart meters. In this context, SPSS software is used for analysing the role of Industry 4.0 based Machine Learning or ML in analysing Smart Meters and MANOV A (and correlation) approach is considered to get proper result. This study proposes a machine learning-based architecture for analysing and monitoring smart meter data. The advantages of loT integration into Industry 4.0 include the ability to exchange actual information across a wide range of smart meters for various applications, such as factories, hospitals and residences, which is critical for smart grids. Results suggested that Smart Grid is beneficial to the extent of reducing electricity cost; however, maintenance cost and security issues are the major challenging factors in the new technology.
Sensitive information hiding using machine learning based stegnographic technique

Steganography, also known as the science of invisible communication, is one of the ways for secretly transmitting information., which generally focuses on ways to conceal the presence of the conveyed message. If this is accomplished effectively, the message will not draw the attention of eavesdroppers or attackers. Information may be buried in various embedding materials, known as carriers, using steganography. Images, music, video, and text files can all be utilised as carriers. The research&#x0027;s goal is to employ a picture file as a carrier. As a result, a taxonomy of extant picture steganographic techniques has been defined. These techniques are analyzed and graded not just in order to optimally to conceal information in image files, but also in terms of the amount of information that can be hidden and their resilience to various image processing assaults.
CardioVascular Disease Detection uing Power Transforamtion and GradientBoosting Classifier

Cardiovascular diseases are a set of blood vessel and heart problems. A setup of fatty plaques, one of the most major reasons is damage to the under cover of the blood vessels that serve the cardio and brain. So, given the current treatments, the best thing is to catch it early and get it underhand. Because there are so many people who suffer from this condition, there is a lot of data on the topic. This enables scientists to use information examination strategies to study this topic. This work data utilizes from IEEE data port, which has 70,000 cases with each having 12 features. There were three methods used: namely Logistic Regression, AdaBoosting, and GradientBoosting Classifier. Using these techniques, we were able to attain the maximum accuracy of 95 percent on all approaches.
Optimization of Bug Detection Model(OBDM):By Evaluating Performance Metric using Artificial Intelligence

Machine learning is a growing field of computational algorithms that aim to simulate human intelligence by learning from their surroundings. There are many machines learning Models to validate the work like the Regression model. The Regression model is implemented in the JAVA dataset of bug, where 21 data metrics are available different performance metrics are used like R-squared (R2), Root Mean Squared Error (RMSE), Residual Standard Error (RSE), Mean Absolute Error (MAE). Adding more variables to the model may increase R2 and reduce RMSE, so some strong methods should be there which evaluate the select the model with minimum variables dependency. Akaike's Information Criteria acronym as AIC and BIC means Bayesian Information Criteria. Mallows' Cp, named after Colin Lingwood Mallows, is a measure of a regression model's fit. AIC, BIC, and Mallows' Cp help find the significant data metrics that help to evaluate and select the right model. The lower these metrics the better the model. This paper explains various statistical regression metrics for the performance measure of a regression model and also evaluated the average prediction error rate to get the optimal model selection.
A Systematic Review on POS Tagging

A systematic and state-of-art review of Part-of-Speech (POS) taggers is presented in this work. This paper explains the strategies, followed by researchers, in the domain of text tagging to enhance the performance of existing POS taggers. Research trends in various natural language frameworks on POS taggers are provided with suitable evidence and a summary.
A Critical analysis on Neural Networks and Deep Learning Based Techniques for the Cloud Computing System and its Impact on Industrial Management

Cloud computing is stated as the critical aspect which has grown recently due to the increased usage of internet for communication, businesses financial transaction and others. This involves in offering dynamic expansion of various services through virtual networks. The cloud is mainly stated as the metaphor in describing various networks in the internet. The individuals tend to trust the cloud service providers in storing the large volume of data and information with the purpose of retrieving them in the future. This study is more focused in making a critical analysis on the application of neural networks and deep learning for cloud computing and its impact on the industrial management. Modern business enterprises are focusing in unleashing the power of technology in order to enhance their investments, gather large volume of data and store them in safe location so that they can be used for making organisational decision making. However, they are more focused on the cost aspect and the safety of storing the data, hence in order to support the customers various approaches were used by the cloud service providers some of them are Neural networks, Deep learning, machine learning etc. this enable in enhancing the trustworthiness of the users and achieve the desired goals through using the cloud computing. This study enables in using both primary and secondary data so as to apprehend the overall effectiveness of applying neural networks and deep learning mechanisms related to cloud computing. The researchers intend to apply correlation analysis and other non-parametric tools in order to measure the effectiveness. The conclusion is drawn based on the extensive analysis and interpretation of the data
Email Logging Interface using machine Learning techniques

Email, which is short for electronic mail, offers for all intents and purposes moment, one-way correspondence all over the planet by means of PC. It was one of the principal strategies for individual to-individual correspondence made accessible through the data expressway. In the beginning of email, straightforward instant messages were some of the time challenging to make due, and adding pictures or archives was conceivable provided that other programming was accessible to make transmission from email to PC conceivable. Current email programming by and large gives simple to-utilize choices to appending photographs, sounds, video cuts, complete records, and Hypertext Markup Language (HTML) code. Early email access was regularly given by government offices and colleges to representatives and exceptional gatherings who expected to speak with each other rapidly and straightforwardly. Analysts and researchers were among the primary reliable clients of email. Email addresses are presently accessible for an expense through business Internet Service Providers to anybody who has a home PC and a telephone line or different method for Internet access. Clients who don't want to contract with an ISP can secure email addresses through an assortment of Internet sites; notable instances of free email suppliers incorporate.com, Juno.com, and Yahoo.com. Despite the fact that there is no immediate expense for the client for these addresses, the administrations accompany a non-money related cost. Locales giving such email accounts normally are upheld by promoting, and the email accounts are regularly focuses of that publicizing.