Volume 12 | Issue 4 |

 Abstract

This traditional approach, which seems reasonable at first look but may really result in biases, unforeseen errors, and higher expenses, may endanger patients' QoS (Quality-of-Service). Almost all of the hospitals in Bangladesh do not give exercise bikes since there aren't any intelligent technologies that are both scalable and simple to install. The main goal of this project is to help hospitals treat severely sick patients by providing a feedback system that works in as it happens. we provide a standardized architecture, associated nomenclature, and a categorization model for evaluating the critical patient's health status. Predicting patients' total fitness using machine learning (ML) is the fundamental principle of this work. Our data and ML models may be saved and accessed using IBM Watson Studio and the IBM Cloud. Naive Bayes, Logistic Regression, K-Neighbors, Decision Tree, Random Forest, Gradient Boosting, and MLP were the Base Predictors used by our machine learning models. To increase the model's accuracy, the bagging approach of ensemble learning was used. Collectively, ensemble learning techniques include bagging ridge, bagging support vector machines, bagging additional trees, and bagging random forest.

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 Keywords

Critical Patient Management System - CPMS, MLP, Gradient Boosting, Naive Bayes

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

In today's digital world, when sensitive data is frequently in danger of being read by unauthorized parties, secure file storage is a crucial concern. Hybrid cryptography, which combines the advantages of many encryption methods in both a vertical and horizontal way, is one approach to solving this issue. In hybrid cryptography, the original symmetric key is encrypted using a different symmetric key while the real data is encrypted using a different symmetric key. This permits the data to be encrypted and decrypted quickly and effectively, while also adding the extra protection of several encryption methods for authentication. Organizations may guarantee the safe storage of their critical data while utilizing hybrid cryptography, shielding it from illegal access and any data breaches.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Hybrid Cryptography, Symmetric Encryption, Cipher Block Chaining, Data Security, Data Protection, Information Security, Cryptographic Techniques, Cloud.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

To protect privacy, users prefer to store encrypted data on cloud servers. Cloud servers reduce storage costs and network bandwidth by eliminating duplicate copies. To solve the problem of possible internal information flow, the concept of cluster deviation was proposed for the first time. We improve the DBSCAN algorithm to tolerate cluster change. The data duplication model is built on a new algorithm that treats users as instances of clusters. Instead of immediately regrouping new users, certain deviations are allowed when assigning users to existing categories. We determine the popularity of data based on user grouping results and use different encryption methods to protect the security of unpopular data more effectively. The performance of the algorithm is analyzed and compared with experiments with other methods, and the results confirm the feasibility and effectiveness of the proposed duplication method.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Text Recognition, Character Recognition, Document Image Analysis.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Currently, numerous types of cybercrime are organized through the internet. Hence, this study mainly focuses on phishing attacks. Although phishing was first used in 1996, it hasbecome the most severe and dangerous cybercrime on the internet. Phishing utilizes email distortion as its underlying mechanism for tricky correspondences, followed by mock sites, to obtain the required data from people in question. Different studies have presentedtheir work on the precaution, identification, and knowledge of phishing attacks; however, there is currently no complete and proper solution for frustrating them. The proposed project is based on the phishing URL-based dataset extracted from the famous dataset repository, Which contains phishing and legal URL features collected from 11000+ website datasets in vector form. After preprocessing, several machine learning algorithms are implemented and designed to block phishing URLs and provide security to the user. This project uses machine learning models like Decision Tree (DT), Linear Regression (LR), Random Forest (RF), Naive Bayes (NB), Gradiant Boosting Classifier (GBM), K-Neighbors Classifier (KNN), Support Vector Classifier. Does. (SVC), and the proposed hybrid LSD model, which is a combination of logistic regression, support vector machine and decision tree (LR + SVC + DT) with soft and hard voting to protect against phishing attacks with high accuracy and efficiency.

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 Keywords

Voting classifier,Ensemble classifier,Machine learning,Uniform Resource Locator(URL),Logistic Regression,Support Vector Machine,and Decision Tree(LST),Protocol,Cyber security,Social networks

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This examination paper approaches the strategies involved with fostering a portable mobile application pointed toward advancing psychological well-being mindfulness and offering help to clients for standing up mental health wellness challenges. The paper examines the plan contemplations, elements, and usefulness of the application, as well as the execution cycle. Moreover, the paper inspects the viability of the application in accomplishing it. In addition the paper examines potential areas for development.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Mental Health, Physiological Health, Fitness, Wellness, Wellbeing

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

As research introduces a novel decentralized e-Government architecture fortified with threat detection capabilities to tackle security and privacy issues prevalent in centralized systems. By harnessing Blockchain's encryption and validation mechanisms, the proposed framework ensures stringent privacy and security measures. Integrating an artificial immune system further bolsters the system's resilience against potential threats to Blockchain integrity. Validation conducted via the eVIBES simulator using publicly available datasets affirms the framework's efficacy in mitigating both insider and external threats while upholding data privacy standards. This decentralized approach not only addresses vulnerabilities inherent in traditional e-Government systems but also demonstrates marked improvements in security measures and a resilient privacy framework, essential for optimizing service delivery in the digital governance domain. Additionally, to bolster accuracy, we have implemented the XGBOOST algorithm with genetic algorithm features, leveraging its ensemble approach to refine model performance and enhance accuracy.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Blockchain, E-Government

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

India is expected to overtake China as the world's most populous country between 2023 and 2050. India has seen tremendous urban progress since liberalization in 1991. As a result, more and more people migrated to cities in search of jobs and income. This lead to a shift from agriculture and manufacturing to the service sector. This shift has resulted in a decline in formal employment opportunities, pushing more people into the informal sector. People with lesser qualification find more opportunities in the informal sector. This lowers poverty though it is low paying and workers may face exclusion and marginalization. People in the informal sector are vulnerable due to lack of formal systems / bureaucratic systems. This was visible during the pandemic when many employers and employees in the informal sector were suddenly without any source of income or protection. Since it is unorganized tax evasion, is a challenge faced by the Indian government. So the objective of the study was to understand the income earning patterns in the informal sector, the level of education of people in the informal sector as also gender dynamics of the informal sector. A sample of 259 informal sector entrepreneurs were interviewed and the random sampling method was utilized for selecting the sample. It was observed that a huge gender gap was prevailing in the sector along with a large number of educated citizens being a part of this ecosystem. The data showed that the number of people employed in businesses like street food, groceries and agriculture are among the highest. But the income earned through street food business and agriculture is among the lowest. Many businesses in the informal sector have also started using UPI enabled digital transactions, which leave a traceable electronic trail which makes it more difficult for informal businesses to hide their income and evade taxes, as transactions are recorded and can be monitored by tax authorities. Still there is enormous scope to study the challenges faced by people in the informal sector and how they can be initiated into the formal sector thereby helping to establish the economic balance in the country.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Informal Sector, Unorganized Sector, Tax Evasion, Covid-19

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Creative Commons Attribution 4.0 and The Open Definition

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This study concentrates on the livestock farming in India in relationship with sustainable development. Livestock farming plays a crucial role in the overall development of the economy as it adds huge money to the state exchequer, but on the other hand there is always a threat to all the stake holders of the environment. Livestock farming contributes to the environmental disaster to a large extent which causes an irreparable damage to the environment.

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Livestock, farming, sustainable development etc.

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Creative Commons Attribution 4.0 and The Open Definition

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Employee turnover is a critical concern for organizations across industries, impacting productivity, morale, and ultimately, the bottom line. This report investigates the relationship between employee evolution and turnover rates within organizations. Employee evolution encompasses factors such as skill development, career advancement opportunities, and organizational culture. The report begins by examining the theoretical framework surrounding employee evolution and turnover, drawing on established models and empirical research. It then delves into various dimensions of employee evolution, including training programs, leadership development initiatives, and the fostering of a supportive work environment. Through a comprehensive review of existing literature and case studies, the report identifies key drivers and inhibitors of employee evolution and their subsequent impact on turnover rates. Additionally, it explores the role of technology and remote work in shaping employee evolution and retention strategies in the modern workplace. Furthermore, the report analyzes the implications of demographic shifts, such as the influx of millennials and Generation Z into the workforce, on employee expectations and organizational dynamics. It also considers the influence of external factors, such as economic conditions and industry trends, on turnover rates. Finally, the report offers practical recommendations for organizations seeking to optimize their employee evolution strategies to mitigate turnover and foster a more engaged and loyal workforce. These recommendations encompass areas such as talent development programs, performance management systems, and the cultivation of a positive organizational culture conducive to growth and retention. By understanding the nuanced interplay between employee evolution and turnover, organizations can proactively address retention challenges and cultivate a workforce that is resilient, adaptive, and committed to achieving shared goals.

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 Keywords

"Effect of Employee Evolution on Organization Turnover Rates"

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The use of artificial intelligence (AI) technology is spreading throughout many facets of daily life. Even though it is not yet widely used, the healthcare sector is concerned about it. The usage of AI technology in their daily work, whether directly through its application or indirectly through related medical fields like radiology, pathology, and respiratory medicine, is something that thoracic surgeons should be aware of. This article aims to examine the uses of AI in thoracic surgery and to talk about the boundaries of its use in the EU. Clinical routes will be used to build key parts of AI, starting with lung cancer diagnostics, moving on to a prognostic-aided decision-making program, robotic surgery, and concluding with the limitations of AI. This research paper explores the transformative integration of Artificial Intelligence (AI) in thoracic surgery, a field where precision and decision-making are paramount. It delves into the current state of AI applications in thoracic procedures, highlighting advancements in diagnostic imaging, surgical robotics, and postoperative care. The paper also projects future developments, envisioning AI's potential in enhancing surgical accuracy, patient-specific treatment, and predictive health analytics. Key challenges, including ethical considerations, data security, and algorithmic reliability, are critically examined. Through a comprehensive analysis, this paper underscores AI's role in revolutionizing thoracic surgery, offering insights into a future where technology augments human expertise to improve patient outcomes.

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 Keywords

Robotic-Assisted Thoracic Surgery, AI-Powered Diagnostic Imaging, Predictive Analytics in Surgery, Postoperative AI Monitoring, Ethical Considerations of AI in Healthcare

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Creative Commons Attribution 4.0 and The Open Definition