Journal of Management and Service Science (JMSS)

Cloud Security for Healthcare Services

Subhanshu Mohan Gupta, Dr. Syed Wajahat Abbas Rizvi — Tue, 25 Apr 2023 00:00:00 +0000

The primary goal of this paper is to give the audience a set of principles to follow when purchasing cloud services to deliver healthcare services in order to ensure cybersecurity and the security of personal data processing, as well as a clear awareness of the ac-companying obligations. The objectives are to provide a landscape of the applicable EU legislative instruments relevant to cloud services in the healthcare sector as well as an overview of the major cybersecurity and data protection challenges related to the secu-rity of personal data processing for cloud customers in the healthcare sector.

Fake News Detection Using Naive Bayes Classifier: A Comparative Study

Abhinandan Yadav, Dr. Devaraju Venkata Rao — Tue, 25 Apr 2023 00:00:00 +0000

Machine learning is a subfield of artificial intelligence (AI) and computer science that utilizes data and algorithms to imitate how people learn, progressively improving its accuracy. Machine learning is an important component of the growing field of data science. Through the use of statistical methods, algorithms are trained to make classifications or predictions, uncovering key insights. Detecting fake news comes under a classification problem. Fake news is false or misleading information presented as news. The initial stage in classification is dataset collection, which is followed by preprocessing, feature selection, dataset training and testing, and finally executing the classifier. There is a large amount of written text in the news. This text is processed using NLP. NLP can perform an intelligent analysis of large amounts of plain written text and generate insights from it. It involves methods like data preprocessing and feature selection. Data pre-processing involves data cleaning, removing any incorrect, duplicate, or incomplete data within a dataset. Feature selection is done using the CountVectorizer and TF-IDF Vectorizer. Then comes dataset training and testing and the use of similar data for training and testing reduces the impact of data inconsistencies. After processing the model using the training set, the model is tested by making predictions against the test set. Then, to assess the performance of the classification model for the provided set of test data confusion matrix is used. The primary purpose is to use the Naive Bayes (NB) Classifier technique to generate two classification models one using CountVectorizer and other using TF-IDF Vectorizer and compare their accuracy.

Random Effect Model for Ethical Stewardship Mediation among Ethiopia's SMEs based on Marketing Proficiency and Orientation

Dereje Dinsa Negeri, Gada Gizachew Wakjira, Shashi Kant — Tue, 25 Apr 2023 00:00:00 +0000

As the world economy continues to move towards deeper integration, MSE will benefit most from its involvement in the global economy. With ethical stewardship acting as a mediating factor, this study will investigate the effect of marketing talent and direction on venture success: the state of a select group of Ethiopian SME sectors. Using surveys and questionnaires to gather data, the study used a quantitative research design method. SME owners and leaders of the job opportunity creation and proficiency offices in both zones and towns are purposefully chosen to participate in this study through random sampling and purposeful sampling, and they are asked to complete questionnaires. Given the extent of the Zone, the researcher will pick five towns to study: Nagele, Adola, Shakiso, Bore, and Haro Wachu. The sample size was determined to be 382 responders in total. Due to Yamane's (1967) simplicity of use, the sample size will be determined by taking into account the anticipated total population of 2,322 business owners, job creators, and competent office executives in 5 towns. For the data analysis in the meta analysis, the random effect model was employe. The impact of marketing talent and direction on venture success was examined in this case using mata analysis, which also used forest plot, funnel plot, moderation analysis, publishing bias based on effect size, and ethical stewardship as a mediating factor. The results indicated that every factor had a positive and notable impact on a company's ability to sustain itself.

A Comparative Study on customer satisfaction between Amazon and Flipkart

Sonu Anand — Tue, 25 Apr 2023 00:00:00 +0000

The best management programmers recognize the importance of combining theoretical knowledge with real-world application. Training is an integral part of the curriculum because it provides students with the hands-on experience and broad perspective they need to succeed in their chosen fields. Indicative of the state of the economy and the country as a whole, it serves as a useful gauge. It's essential for success in the business world. The average graduate student has more self-assurance, intelligence, and worldview than the average person. Marketing research is essential to the success of modern businesses. In my opinion, this methodical and illuminating approach to market preparation is the way to go. This method has applications beyond simple commerce. I've decided to zero in on this topic because I believe it's crucial to acquire as much information as possible about any given topic, be it an idea, an event, an organization, a location, or a person. The first step is to fully immerse oneself in the relevant market in order to study its dynamics and locate openings to meet the felt or felt requirements.

Enhancing Stock Market Predictability: A Comparative Analysis of RNN And LSTM Models for Retail Investors

Nevendra Kr Upadhyay, Vineet Singh, Dr. Shikha Singh, Dr. Pooja Khanna — Tue, 25 Apr 2023 00:00:00 +0000

The stock markets are important components of the global financial system and have a considerable impact on an economy's growth and stability. This research article uses algorithms, notably deep learning, to increase the prediction of stock values. The efficacy and precision of long short-term memory (LSTM) and recurrent neural networks (RNN) algorithms to estimate stock prices are compared in this study. The paper investigates the potential of deep learning algorithms in creating a more predictable and trustworthy environment for the stock market. The study utilizes historical market data obtained from the Alpha Vault API and evaluates the performance of the RNN and LSTM models in forecasting stock prices. The results indicate that LSTM exhibits superior precision and is better suited for stock price prediction, while RNN faces certain challenges. Overall, this research contributes to the understanding of the application of deep learning algorithms in stock market analysis, to make informed investment decisions, thereby reducing risks and maximizing returns.

Audio Management: Enhancing Wireless Sound Control through Hand Gestures

Nimesh Gupta, Dr. Sheenu Rizvi — Tue, 25 Apr 2023 00:00:00 +0000

TThis research paper presents research conducted to control the sound level of the system using hand gestures. The proposed system is designed using Python programming language and OpenCV library. The development of programs that can be controlled using natural gestures is an important area of research in the field of human - computer interaction. The system captures the video feed from the camera and analyzes it to de-tect the hand gestures. The captured frames are preprocessed to remove noise and sim-plify processing. The image is then converted to grayscale, thresholded to separate the hand from the background, and contours are found in the thresholded image. The de-tected gestures are then mapped to control the sound level of the system. The proposed system is implemented and tested on a laptop computer with satisfactory results. The program is tested on a range of operating systems and environments to ensure com-patibility and functionality. To optimize for speed and efficiency, the program can be parallelized and/or implemented using GPU acceleration.

Total Quality Management integration with Six Sigma for Operational Success of a Project

Lamesa Bulto, Dr. Shashi Kant — Tue, 25 Apr 2023 00:00:00 +0000

This study examines the connections between total quality management (TQM) and Six Sigma as perceived by a sample of 60 Ethiopian manufacturing firms. The goal is to determine whether TQM and Six Sigma are utilized in tandem in manufacturing firms or if TQM has been driven to the background by Six Sigma. Researchers used quality charts with moving averages. Interesting conclusions on how businesses feel about TQM and Six Sigma have been found, specifically, the connections between TQM, Six Sigma and improvement management. Industrial firms were used that use Six Sigma and TQM to-gether. Researchers used the moving average, quality charts to find the association be-tween TQM and six sigma. In this manner, the study seeks to close a void in the body of knowledge.

Improving the Utility of Web Surfing Using AI Techniques

Harsh Deep Keshari, Dr. Sheenu Rizvi — Tue, 25 Apr 2023 00:00:00 +0000

This paper aims to improve web surfing security and utility by leveraging artificial intelligence (AI) and machine learning (ML) algorithms. A more personalized, efficient, and secure online experience is achieved through improving the utility and security of web browsing. Web browsing has become an integral part of our daily lives in the digital age. Users often face information overload, irrelevant content, security threats, and privacy concerns. AI and machine learning are used in the proposed system to refine web browsing. A web browsing utility that uses user preferences, interests, and browsing behavior to provide personalized recommendations, filter out irrelevant content, and enhance overall utility is the objective of this paper.