Unique Endeavor in Business & Social Sciences
type: channel
publisher: Students Consultancy Home(R) (SCH)
language: en
publication name: Unique Endeavor in Business & Social Sciences
issn: 3006-0389
items: Recent Advancements in Machine Learning for Cybersecurity
items: Predicting Customer Churn in the USA: A Performance Assessment of Machine Learning Techniques
items: Cross-Platform Application Testing: AI-Driven Automation Strategies
items: Applying Natural Language Processing in Automating User Interface Testing
items: Machine Learning for Ensuring Data Integrity in Salesforce Applications
items: Reinforcement Learning for Optimizing Test Case Execution in Automated Testing
items: AI-Powered Automation in Salesforce Testing: Efficiency and Accuracy
items: Safeguarding Media Integrity: Cybersecurity Strategies for Resilient Broadcast Systems and Combatting Fake News
items: Fortifying Media Integrity: Cybersecurity Practices and Awareness in Bangladesh's Media Landscape
items: Student 360: Integrating and Analyzing Data for Enhanced Student Insights
items: Distributed Data Lake Architectures for Cloud-Based Big Data Integration
items: A Microservices Approach to Cloud Data Integration for Healthcare Applications
items: Leveraging Data Integration to Assess and Improve Teaching Effectiveness in Higher Education
items: Multi-Source Data Integration Using AI for Pandemic Contact Tracing
items: Exploring the Role of Food Security Technologies in Promoting Sustainable Agriculture and Enhancing Public Health
items: Impact of Medication Storage and Administration Errors in Aged Adult Care Homes
items: Cultural Competence in Safety Education for Aged Adults in Multicultural Societies
items: Customer Segmentation in E-commerce Using Big Data Clustering Techniques
items: Blockchain and AI Integration: A Novel Approach to Strengthening Cybersecurity Frameworks
items: Advanced Data Storage Techniques for E-commerce: From SSDs to Distributed File Systems
items: AI-Based Phishing Detection Techniques: A Comparative Analysis of Model Performance
items: Real-Time Data Analytics with AI: Improving Security Event Monitoring and Management
items: Emergency Evacuation Preparedness in Care Homes: Strategies for Protecting Vulnerable Populations
items: Developing a robust CRM Analytics strategy for Hedge Fund institutions to improve investment diversification
items: Integration of IoT and AI for Real-Time Monitoring and Autonomous Control in Food Engineering Systems
items: Real-Time AI and Blockchain for Traceability and Transparency in the U.S. Food Supply Chain
items: The Role of Technology in Enhancing Domiciliary Care: A Strategy for Reducing Healthcare Costs and Improving Safety for Aged Adults and Carers
items: Evaluating Reinforcement Learning Approaches for Supply Chain Optimization: A Comparative Study of Approximate SARSA and REINFORCE Algorithms
items: Exploring the Effectiveness of AI and Wearable Technology in Predicting Workplace Accidents in High-Risk Industries
items: Evaluating the Impact of Safety Culture on the Reduction of Healthcare-Associated Infections in Hospital Settings

Recent Advancements in Machine Learning for Cybersecurity
type: item
description: The rapid advancement of Machine Learning (ML) has profoundly impacted the field of cybersecurity, introducing innovative techniques and strategies to address emerging threats and vulnerabilities. Recent developments in ML have significantly enhanced the ability to detect, analyze, and respond to cyber threats with increased accuracy and efficiency. This article provides a comprehensive overview of recent advancements in ML applications within cybersecurity, focusing on novel algorithms, emerging trends, and practical implementations. We explore cutting-edge techniques such as deep learning for anomaly detection, reinforcement learning for adaptive security strategies, and adversarial training to enhance model robustness. Additionally, we examine the integration of ML with other technologies like blockchain and IoT to create more resilient security frameworks. By highlighting recent breakthroughs and their implications, this article aims to offer insights into how ML is shaping the future of cybersecurity and to identify areas for further research and development
subject: Articles
  topic: section
creator: Parameshwar Reddy Kothamali
creator: Subrata Banik
creator: Siddhartha Varma Nadimpalli
rights: Copyright (c) 2023 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2023-05-22
publication date: 2023-05-22
starting page: 142
ending page: 157

Predicting Customer Churn in the USA: A Performance Assessment of Machine Learning Techniques
type: item
description: Customer churn prediction is a critical challenge for businesses, particularly in competitive industries such as telecommunications, subscription services, and e-commerce. This study evaluates and compares the performance of several machine learning algorithms—Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), Gradient Boosting (GB), and Neural Networks (NN)—in predicting customer churn in a U.S.-based subscription service. The aim is to identify the most effective machine learning technique for churn prediction and assess its practical applicability for customer retention strategies. Using a dataset of customer behaviors and demographics, the models were evaluated based on accuracy, precision, recall, F1-score, and AUC-ROC. The results revealed that Gradient Boosting outperformed the other models in terms of overall accuracy and AUC-ROC, followed closely by Random Forest. Neural Networks demonstrated solid performance but faced challenges related to model interpretability and consistency. Logistic Regression and Support Vector Machine showed moderate performance, especially in scenarios requiring computational efficiency over raw accuracy. The study also highlighted the key features influencing churn, including customer tenure, payment history, and service usage, providing actionable insights for businesses. Based on the findings, the study concludes that while Gradient Boosting offers the highest predictive performance, Random Forest and Logistic Regression provide valuable alternatives depending on the specific needs of businesses.
subject: Articles
  topic: section
subject: Customer Churn Prediction
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Gradient Boosting
  topic: keywords
subject: Random Forest
  topic: keywords
subject: Neural Networks
  topic: keywords
subject: Support Vector Machine
  topic: keywords
subject: Logistic Regression
  topic: keywords
subject: Predictive Modeling
  topic: keywords
subject: Customer Retention
  topic: keywords
subject: AUC-ROC
  topic: keywords
subject: Model Performance
  topic: keywords
creator: Jordan Smith , Amelia Lucas
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2024-03-20
publication date: 2024-03-20
starting page: 61
ending page: 72

Cross-Platform Application Testing: AI-Driven Automation Strategies
type: item
description: This study investigates the impact of AI-powered automation on Salesforce testing, focusing on improvements in efficiency and accuracy compared to traditional methods. The research addresses the challenge of ensuring robust testing processes in complex CRM environments, where conventional methods often fall short. A comparative analysis was conducted using both traditional and AI-powered testing tools, with metrics including test execution time, accuracy rates, and error detection rates. The results reveal that AI-powered tools significantly enhance testing efficiency, reducing execution time by 40% and increasing accuracy by 15%, with a 20% improvement in error detection. These findings suggest that AI can substantially optimize Salesforce testing by automating repetitive tasks and providing advanced analytical capabilities. However, challenges such as initial setup costs and integration with existing frameworks were also identified. The study concludes that AI-powered testing offers considerable benefits, but organizations must weigh these against practical considerations for effective implementation.
subject: Articles
  topic: section
subject: AI-driven testing
  topic: keywords
subject: cross-platform applications
  topic: keywords
subject: automated testing
  topic: keywords
subject: defect detection
  topic: keywords
subject: resource optimization
  topic: keywords
creator: Noone Srinivas
creator: Vinod kumar Karne
creator: Nagaraj Mandaloju
creator: Siddhartha Varma Nadimpalli
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2023-06-18
publication date: 2023-06-18
starting page: 120
ending page: 132

Applying Natural Language Processing in Automating User Interface Testing
type: item
description: This study investigates the application of Natural Language Processing (NLP) in automating user interface (UI) testing for web and mobile applications. The primary research problem addressed is how NLP can effectively interpret and execute test cases from natural language descriptions, aiming to streamline and enhance the testing process. The study employs a design that includes developing an NLP-based framework, generating test cases from natural language inputs, and evaluating the framework's performance and accuracy. Key findings reveal that the NLP framework successfully converts natural language descriptions into actionable test cases with high accuracy. It also performs efficiently in executing these test cases and demonstrates effective error detection and reporting. These results support the hypothesis that NLP can significantly improve UI testing by making test case creation more intuitive and automation more effective. The study concludes that NLP-driven automation offers a valuable advancement in UI testing methodologies, suggesting further exploration of advanced NLP techniques and broader application scenarios.
subject: Articles
  topic: section
subject: Natural Language Processing
  topic: keywords
subject: UI Testing Automation
  topic: keywords
subject: Test Case Generation
  topic: keywords
subject: Execution Performance
  topic: keywords
subject: Error Detection
  topic: keywords
creator: Vinod kumar Karne
creator: Nagaraj Mandaloju
creator: Noone Srinivas
creator: Siddhartha Varma Nadimpalli
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2024-02-12
publication date: 2024-02-12
starting page: 30
ending page: 44

Machine Learning for Ensuring Data Integrity in Salesforce Applications
type: item
description: This study investigates the application of machine learning algorithms to enhance data integrity within Salesforce applications, addressing the challenge of detecting anomalies in complex CRM datasets. The research aims to evaluate the effectiveness of various machine learning models—specifically Isolation Forest, One-Class SVM, and Autoencoders—in identifying data irregularities and improving overall data quality. Utilizing a dataset of 10,000 records from Salesforce, the study involved preprocessing the data, implementing the ML models, and assessing their performance using metrics such as Precision, Recall, and F1 Score. Major findings indicate that machine learning models significantly outperform traditional anomaly detection methods, with Autoencoders demonstrating superior performance in handling high-dimensional data. The implementation of these models resulted in notable improvements in data accuracy and reduced error rates. The study concludes that integrating machine learning into CRM systems can substantially enhance data integrity, offering valuable insights for both theoretical research and practical applications. Future research should explore additional algorithms and real-world deployment challenges.
subject: Articles
  topic: section
subject: Machine Learning
  topic: keywords
subject: Data Integrity
  topic: keywords
subject: Salesforce
  topic: keywords
subject: Anomaly Detection
  topic: keywords
subject: Autoencoders
  topic: keywords
creator: Nagaraj Mandaloju
creator: Siddhartha Varma Nadimpalli
creator: Vinod kumar Karne
creator: Noone Srinivas
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2023-01-16
publication date: 2023-01-16
starting page: 133
ending page: 149

Reinforcement Learning for Optimizing Test Case Execution in Automated Testing
type: item
description: This study explores the use of reinforcement learning (RL) techniques to optimize test case execution in automated testing frameworks, addressing the inefficiencies of traditional testing methods. The primary research problem involves enhancing testing efficiency, improving coverage, and reducing redundancy through intelligent RL-based optimization. The study employed a design that integrated RL algorithms into automated testing frameworks, involving the development and training of an RL model, followed by empirical evaluation. ajor findings indicate that RL-based optimization significantly reduced test case execution time, improved test coverage, and minimized redundancy compared to conventional methods. The RL model dynamically adjusted test case sequences based on real-time feedback, leading to enhanced efficiency and more comprehensive testing. The study concludes that RL techniques offer a promising approach to overcoming traditional testing limitations, demonstrating tangible benefits in real-world scenarios. To put in a nutshell, RL-based optimization effectively addresses key challenges in automated testing, offering a more adaptive and efficient strategy for test case execution.
subject: Articles
  topic: section
subject: Reinforcement Learning
  topic: keywords
subject: Automated Testing
  topic: keywords
subject: Test Case Optimization
  topic: keywords
subject: Test Coverage
  topic: keywords
subject: Redundancy Reduction
  topic: keywords
creator: Vinod kumar Karne
creator: Nagaraj Mandaloju
creator: Noone Srinivas
creator: Siddhartha Varma Nadimpalli
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2024-03-10
publication date: 2024-03-10
starting page: 13
ending page: 29

AI-Powered Automation in Salesforce Testing: Efficiency and Accuracy
type: item
description: This study investigates the impact of AI-powered automation on Salesforce testing, focusing on improvements in efficiency and accuracy compared to traditional methods. The research addresses the challenge of ensuring robust testing processes in complex CRM environments, where conventional methods often fall short. A comparative analysis was conducted using both traditional and AI-powered testing tools, with metrics including test execution time, accuracy rates, and error detection rates. The results reveal that AI-powered tools significantly enhance testing efficiency, reducing execution time by 40% and increasing accuracy by 15%, with a 20% improvement in error detection. These findings suggest that AI can substantially optimize Salesforce testing by automating repetitive tasks and providing advanced analytical capabilities. However, challenges such as initial setup costs and integration with existing frameworks were also identified. The study concludes that AI-powered testing offers considerable benefits, but organizations must weigh these against practical considerations for effective implementation.
subject: Articles
  topic: section
subject: AI-powered automation
  topic: keywords
subject: Salesforce testing
  topic: keywords
subject: efficiency
  topic: keywords
subject: accuracy
  topic: keywords
subject: error detection
  topic: keywords
creator: Nagaraj Mandaloju ,
creator: Vinod kumar Karne,
creator: Noone Srinivas,
creator: Siddhartha Varma Nadimpalli, n
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2024-02-10
publication date: 2024-02-10
starting page: 45
ending page: 60

Safeguarding Media Integrity: Cybersecurity Strategies for Resilient Broadcast Systems and Combatting Fake News
type: item
description: In an era where digital media reigns supreme, safeguarding the integrity of broadcast systems is paramount to combatting the proliferation of fake news. This paper explores cybersecurity strategies tailored to resilient broadcast systems, aiming to fortify media integrity amidst the onslaught of digital threats. Drawing upon a synthesis of literature, case studies, and expert insights, the research elucidates proactive measures to protect against cyber attacks, mitigate misinformation campaigns, and uphold journalistic standards. By addressing the intersection of cybersecurity and media integrity, this study offers actionable recommendations for broadcasters, policymakers, and cybersecurity professionals in their collective efforts to preserve the credibility and trustworthiness of broadcast media.
subject: Articles
  topic: section
subject: Media Integrity
  topic: keywords
subject: Cybersecurity Strategies
  topic: keywords
subject: Broadcast Systems
  topic: keywords
subject: Fake News
  topic: keywords
subject: Cyber Threats
  topic: keywords
subject: Misinformation
  topic: keywords
subject: Resilience
  topic: keywords
subject: Journalistic Standards
  topic: keywords
subject: Policy Recommendations
  topic: keywords
subject: Trustworthiness
  topic: keywords
creator: Md Rasel,
creator: Md Abdus Salam,
creator: Ashif Mohammad,
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2023-06-30
publication date: 2023-06-30
starting page: 72
ending page: 93

Fortifying Media Integrity: Cybersecurity Practices and Awareness in Bangladesh's Media Landscape
type: item
description: The proliferation of digital technologies has transformed the media landscape, offering unprecedented opportunities for information dissemination and audience engagement. However, this digital revolution has also exposed media organizations to a myriad of cybersecurity threats, ranging from data breaches to disinformation campaigns. This paper investigates cybersecurity practices and awareness within Bangladesh's media landscape, aiming to assess the readiness of media organizations in fortifying media integrity. Through a mixed-methods approach encompassing surveys, interviews, and case studies, this research examines the cybersecurity challenges faced by media organizations, identifies best practices for mitigating cyber risks, and proposes strategies to enhance cybersecurity awareness and resilience. The findings of this study provide valuable insights for policymakers, media professionals, and cybersecurity practitioners seeking to safeguard media integrity in an increasingly digitized world.
subject: Articles
  topic: section
subject: Media Integrity
  topic: keywords
subject: Cybersecurity Practices
  topic: keywords
subject: Cyber Risks
  topic: keywords
subject: Awareness
  topic: keywords
subject: Media Landscape
  topic: keywords
subject: Bangladesh
  topic: keywords
subject: Data Breaches
  topic: keywords
subject: Disinformation
  topic: keywords
subject: Resilience
  topic: keywords
subject: Best Practices
  topic: keywords
creator: Md Rasel,
creator: Md Abdus Salam,
creator: Reduanul Bari Shovon,
creator: Md Aminul Islam, n
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2023-06-30
publication date: 2023-06-30
starting page: 94
ending page: 119

Student 360: Integrating and Analyzing Data for Enhanced Student Insights
type: item
description: In the rapidly evolving landscape of higher education, gaining a comprehensive understanding of students’ diverse needs and behaviors is essential for fostering academic success, personal growth, and institutional effectiveness. Yet, data related to students is frequently fragmented across various systems, including academic records, learning management systems, attendance logs, and records of extracurricular engagement. This paper introduces the student 360 data integration and analytics platform, designed to unify these disparate data sources into a single system, creating a comprehensive view of each student’s academic journey and engagement across multiple touchpoints. Through advanced data integration techniques, the platform provides educators and administrators with actionable insights into student behaviors, academic progress, and areas requiring support, enabling a more personalized and impactful educational experience. The Student 360 platform leverages robust data integration processes, including Extract, Transform, Load (ETL) methods and cloud-based storage, to consolidate data from both academic and non-academic sources. Key data sources include academic performance metrics, attendance records, participation in extracurricular activities, and engagement data from learning management systems (LMS) such as Blackboard, Canvas, and Moodle. By bringing together diverse datasets, the platform provides a holistic view of each student’s engagement, from classroom achievements to involvement in student organizations. This unified student profile allows educators to detect patterns and correlations between academic performance and factors like attendance and extracurricular participation, providing a richer perspective on the overall student experience. One of the distinctive features of the Student 360 platform is its predictive analytics capabilities, which empower institutions to forecast student outcomes based on historical and real-time data. The platform ap…
subject: Articles
  topic: section
creator: Vamshi Bharath Munagandla¹,
creator: Sai Rama Krishna Nersu²,
creator: Srikanth Reddy Kathram³,
creator: Sandeep Pochu,
rights: Copyright (c) 2024
date: 2020-03-17
publication date: 2020-03-17
starting page: 17
ending page: 29

Distributed Data Lake Architectures for Cloud-Based Big Data Integration
type: item
description: The proliferation of Big Data across sectors such as healthcare and higher education has highlighted the need for scalable, efficient data storage and integration solutions. Data lakes, which allow the storage of structured and unstructured data in its native format, have emerged as a powerful model for handling diverse data sources in cloud environments. This paper examines distributed data lake architectures specifically designed to support cloud-based Big Data integration, with a focus on creating an infrastructure that enables seamless data ingestion, storage, and retrieval across multiple sources. The proposed architecture leverages cloud-native tools such as Amazon S3, Azure Data Lake, and Google Cloud Storage, as well as distributed processing frameworks like Apache Spark and Apache Hadoop, to provide an efficient and scalable solution for storing and analyzing vast datasets. A key advantage of distributed data lake architectures is their ability to handle heterogeneous data from various sources, including databases, Internet of Things (IoT) sensors, social media, and transaction logs. In sectors like healthcare, where data is generated from EHR systems, patient monitoring devices, and diagnostic imaging, and in higher education, where data comes from student information systems, learning management platforms, and research databases, integrating and analyzing this data is critical. The proposed architecture enables these institutions to consolidate data from multiple silos into a unified, cloud-based repository, allowing for advanced analytics that can enhance decision-making, improve operational efficiency, and support innovative research. By storing data in a distributed architecture on the cloud, organizations can eliminate data redundancy, improve accessibility, and reduce storage costs, while also enabling real-time insights through parallel processing capabilities. The paper details the structure of a distributed data lake architecture, focusing on four…
subject: Articles
  topic: section
creator: Vamshi Bharath Munagandla¹,
creator: Sai Rama Krishna Nersu²,
creator: Sandeep Pochu,
creator: Srikanth Reddy Kathram,
rights: Copyright (c) 2024
date: 2020-03-17
publication date: 2020-03-17
starting page: 1
ending page: 16

A Microservices Approach to Cloud Data Integration for Healthcare Applications
type: item
description: As the healthcare sector grows increasingly complex, the need for robust, seamless data integration becomes vital to improving patient care, optimizing operations, and meeting regulatory compliance. However, healthcare data is often scattered across disparate systems such as electronic health records (EHRs), laboratory information systems, imaging platforms, and insurance providers, making integration a considerable challenge. This paper presents an innovative microservices-based architecture for cloud data integration tailored to healthcare applications, enabling real-time data exchange and enhancing data accessibility across healthcare organizations. By leveraging a microservices approach, the architecture provides modular, scalable, and resilient integration solutions that improve data interoperability, support personalized patient care, and streamline healthcare workflows. The core of the proposed framework is built on microservices principles, which decompose monolithic applications into smaller, independent services. Each microservice is dedicated to a specific function, such as patient data retrieval, laboratory data processing, or insurance claim verification, and communicates with other services through lightweight RESTful APIs. This architecture allows each microservice to be independently deployed, scaled, and updated, offering a high degree of flexibility and adaptability within complex healthcare systems. Additionally, the microservices-based architecture supports rapid deployment of new integrations without impacting existing functionality, making it highly responsive to evolving healthcare needs. The cloud-based infrastructure supporting this microservices architecture leverages tools such as Kubernetes for container orchestration, ensuring reliable and scalable service delivery across distributed environments. Containers encapsulate each microservice along with its dependencies, enabling seamless deployment and efficient resource utilization within t…
subject: Articles
  topic: section
creator: Vamshi Bharath Munagandla¹,
creator: Sandeep Pochu,
creator: Sai Rama Krishna Nersu,
creator: Srikanth Reddy Kathram,
rights: Copyright (c) 2024
date: 2019-03-18
publication date: 2019-03-18
starting page: 14
ending page: 29

Leveraging Data Integration to Assess and Improve Teaching Effectiveness in Higher Education
type: item
description: The pursuit of teaching effectiveness is central to the mission of higher education institutions. Yet, assessing and improving teaching effectiveness remains a complex task, often hampered by fragmented data and limited insights into the multifaceted factors influencing learning outcomes. This paper presents a data integration framework designed to consolidate disparate data sources across higher education institutions, enabling comprehensive assessment and targeted improvements in teaching effectiveness. By integrating data from student evaluations, learning management systems (LMS), attendance records, academic performance, and classroom engagement metrics, the framework provides a holistic view of teaching practices and their impact on student success. With real-time access to these integrated data sources, educators and administrators can make data-informed decisions that enhance instructional strategies, improve student engagement, and promote overall academic achievement. The proposed framework employs advanced data integration techniques to combine structured and unstructured data from multiple sources, creating a unified repository of teaching-related data. Data integration tools, such as Extract, Transform, Load (ETL) processes and cloud-based data warehouses, serve as the backbone of the system, facilitating the seamless consolidation of data from LMS platforms (e.g., Canvas, Blackboard), student feedback forms, and institutional databases. Additionally, the framework incorporates machine learning algorithms to analyze patterns in teaching effectiveness, identifying correlations between instructional practices and student outcomes. For instance, the model can detect teaching behaviors associated with higher student performance, providing valuable insights for faculty professional development and pedagogical enhancement. The real power of this data integration framework lies in its ability to perform longitudinal analyses of teaching effectiveness, tracking…
subject: Articles
  topic: section
creator: Vamshi Bharath Munagandla¹,
creator: Sai Rama Krishna Nersu²,
creator: Srikanth Reddy Kathram³,
creator: Sandeep Pochu,
rights: Copyright (c) 2024
date: 2019-03-18
publication date: 2019-03-18
starting page: 1
ending page: 13

Multi-Source Data Integration Using AI for Pandemic Contact Tracing
type: item
description: During pandemics, the ability to track and contain infectious disease spread relies heavily on efficient and accurate contact tracing. However, traditional contact tracing methods face limitations in handling large volumes of data from diverse sources, resulting in incomplete or delayed insights. This paper introduces an AI-driven multi-source data integration model designed specifically for pandemic contact tracing, enabling comprehensive and dynamic mapping of contact networks. By consolidating data from various sources—including healthcare records, mobile location data, public transportation logs, and social media interactions—the model provides a robust framework for identifying high-risk interactions and effectively supporting public health interventions. The proposed AI-based integration model tackles the complexity of disparate data by leveraging advanced machine learning algorithms, such as natural language processing (NLP) for text analysis and clustering algorithms for network mapping. The system continuously processes and integrates structured and unstructured data from real-time data streams and historical records, offering an up-to-date picture of infection pathways. Healthcare records provide essential baseline information on confirmed cases and testing data, while mobile data and public transportation logs help track individual movements and identify potential exposures. Social media interactions, meanwhile, offer contextual insights into gatherings, reported symptoms, and public sentiment, which can complement more structured data sources. The integration of these sources into a unified platform provides a more comprehensive and timely assessment of the contact network, supporting targeted interventions by public health agencies. To ensure scalability and responsiveness, the model uses cloud-based infrastructure, including data storage solutions and distributed computing frameworks like Apache Kafka and Apache Spark. This architecture enables the sys…
subject: Articles
  topic: section
creator: Sandeep Pochu,
creator: Vamshi Bharath Munagandla,
creator: Sai Rama Krishna Nersu,
creator: Srikanth Reddy Kathram,
rights: Copyright (c) 2024
date: 2021-03-25
publication date: 2021-03-25
starting page: 1
ending page: 15

Exploring the Role of Food Security Technologies in Promoting Sustainable Agriculture and Enhancing Public Health
type: item
description: Food security remains a pressing global issue, intricately linked to sustainable agriculture and public health. The role of food security technologies has become increasingly significant in addressing these interdependent challenges. This study explores the relationship between food security technologies, sustainable agriculture, and public health enhancement. It examines current technological advancements, their impact on sustainable farming practices, and the broader implications for community health outcomes. By analyzing case studies, reviewing relevant literature, and discussing policy frameworks, the research underscores the critical role of innovation in creating resilient agricultural systems and healthier populations.
subject: Articles
  topic: section
subject: Food Security
  topic: keywords
subject: Public Health
  topic: keywords
subject: Sustainable Agriculture
  topic: keywords
subject: Technological Innovations
  topic: keywords
creator: Juba Jeremiah Alaba
creator: Racheal Efem Paul
rights: Copyright (c) 2024
date: 2024-09-10
publication date: 2024-09-10
starting page: 68
ending page: 73

Impact of Medication Storage and Administration Errors in Aged Adult Care Homes
type: item
description: Abstract Medication errors in aged adult care homes represent a significant threat to the health and safety of residents. These errors, ranging from improper storage to incorrect administration, can lead to adverse drug reactions, hospitalizations, and even fatalities. This research examines the prevalence of medication errors in long-term care facilities and evaluates how the implementation of rigorous medication safety protocols influences health outcomes for aged adults. Drawing from existing literature and evidence-based practices, the study identifies critical challenges, discusses the role of staff training, technology, and regulatory frameworks, and proposes actionable solutions to mitigate risks.
subject: Articles
  topic: section
creator: Ndofor Atud Aburo
rights: Copyright (c) 2024
date: 2024-09-10
publication date: 2024-09-10
starting page: 64
ending page: 67

Cultural Competence in Safety Education for Aged Adults in Multicultural Societies
type: item
description: Abstract As aging populations grow worldwide, ensuring the safety and wellbeing of aged adults in multicultural societies presents unique challenges. Safety education, when adapted to the cultural and linguistic needs of diverse communities, fosters better outcomes for aged adults. This article examines the critical role of cultural competence in safety education programs for aged adults, emphasizing the necessity of culturally sensitive approaches to mitigate risks. Drawing insights from existing research, including contributions from Juba et al., this paper explores language barriers, cultural taboos, and the importance of inclusivity in educational strategies. Practical strategies for implementing culturally competent safety education and recommendations for policy development are provided to support practitioners, caregivers, and policymakers.
subject: Articles
  topic: section
creator: Boluwatife F. Olumide
rights: Copyright (c) 2024
date: 2024-09-10
publication date: 2024-09-10
starting page: 60
ending page: 63

Customer Segmentation in E-commerce Using Big Data Clustering Techniques
type: item
description: Customer segmentation is a pivotal strategy in e-commerce, enabling personalized marketing efforts and tailored product recommendations. Leveraging big data clustering techniques, this study aims to segment customers effectively based on their purchasing behaviors, preferences, and demographic attributes. By analyzing large volumes of transactional data, we identify distinct customer segments, each with unique characteristics and buying patterns. We employ advanced clustering algorithms such as k-means, hierarchical clustering, and DBSCAN to partition customers into homogeneous groups. Through this segmentation, e-commerce businesses can gain profound insights into their customer base, optimize marketing strategies, enhance customer satisfaction, and ultimately drive sales and revenue growth. This paper presents a comprehensive overview of customer segmentation in e-commerce, emphasizing the significance of big data analytics and clustering techniques in understanding consumer behavior and fostering sustainable business growth.
subject: Articles
  topic: section
subject:
  topic: keywords
  value: Customer Segmentation, E-commerce, Big Data Analytics, Clustering Techniques, Personalization, Marketing Strategy, Purchasing Behavior, K-means, Hierarchical Clustering, DBSCAN, Consumer Insights, Revenue Growth
creator: 1Vijay Mallik Reddy, 2Lakshmi Nivas Nalla
rights: Copyright (c) 2024
date: 2022-06-30
publication date: 2022-06-30
starting page: 66
ending page: 78

Blockchain and AI Integration: A Novel Approach to Strengthening Cybersecurity Frameworks
type: item
description: The integration of Blockchain technology and Artificial Intelligence (AI) represents a novel approach to enhancing cybersecurity frameworks in today's digital landscape. This paper explores the synergies between Blockchain and AI, elucidating their combined potential to address cybersecurity challenges. By leveraging Blockchain's immutable and decentralized ledger capabilities and AI's advanced analytics and decision-making prowess, organizations can establish robust and resilient cybersecurity infrastructures. This abstract provides an overview of the key concepts, methodologies, and findings discussed in the paper.
subject: Articles
  topic: section
subject: Blockchain, Artificial Intelligence, Cybersecurity, Integration, Decentralization, Advanced Analytics
  topic: keywords
creator: Bharat Reddy Maddireddy 1, Bhargava Reddy Maddireddy2
rights: Copyright (c) 2024
date: 2022-06-30
publication date: 2022-06-30
starting page: 46
ending page: 65

Advanced Data Storage Techniques for E-commerce: From SSDs to Distributed File Systems
type: item
description: In the realm of e-commerce, efficient data storage and retrieval mechanisms are imperative for ensuring seamless user experiences and robust backend operations. This paper explores advanced data storage techniques tailored to the unique demands of e-commerce, ranging from Solid-State Drives (SSDs) to Distributed File Systems (DFS). Through a comprehensive review of existing literature and case studies, this study elucidates the capabilities, advantages, and challenges associated with each storage solution. By examining the evolving landscape of data storage technologies, this paper offers insights into the optimal selection and implementation of storage solutions to enhance the performance, scalability, and reliability of e-commerce platforms.
subject: Articles
  topic: section
subject: E-commerce, Data Storage, Solid-State Drives (SSDs), Distributed File Systems (DFS), Scalability, Reliability.
  topic: keywords
creator: 1Lakshmi Nivas Nalla, 2Vijay Mallik Reddy
rights: Copyright (c) 2024
date: 2022-06-30
publication date: 2022-06-30
starting page: 32
ending page: 45

AI-Based Phishing Detection Techniques: A Comparative Analysis of Model Performance
type: item
description: Abstract: Phishing attacks continue to pose significant threats to cybersecurity, targeting individuals, businesses, and organizations worldwide. In response, researchers and practitioners have turned to artificial intelligence (AI) techniques to enhance phishing detection capabilities. This paper presents a comparative analysis of AI-based phishing detection techniques, evaluating the performance of various machine learning (ML) and deep learning (DL) models in identifying phishing attempts. The study explores a diverse range of features, including lexical, visual, and behavioral characteristics extracted from phishing emails and websites. Leveraging a dataset comprising real-world phishing instances, the performance metrics of different AI models are evaluated, including accuracy, precision, recall, and F1-score. Furthermore, the paper investigates the robustness of AI-based phishing detection techniques against adversarial attacks and examines the generalization capabilities of models across different phishing scenarios and attack vectors. The findings contribute to the understanding of the strengths and limitations of AI-based phishing detection approaches, offering insights into the most effective techniques for mitigating phishing threats in various contexts. Additionally, the study identifies areas for future research and development, such as the integration of ensemble learning methods and the incorporation of explainable AI techniques to enhance model interpretability and transparency. Overall, this comparative analysis provides valuable guidance for cybersecurity practitioners and decision-makers in selecting and deploying AI-based phishing detection solutions to bolster their defenses against evolving cyber threats.
subject: Articles
  topic: section
subject: Phishing detection, artificial intelligence, machine learning, deep learning, cybersecurity, adversarial attacks
  topic: keywords
creator: Bhargava Reddy Maddireddy1, Bharat Reddy Maddireddy 2
rights: Copyright (c) 2024
date: 2022-06-30
publication date: 2022-06-30
starting page: 17
ending page: 31

Real-Time Data Analytics with AI: Improving Security Event Monitoring and Management
type: item
description: Abstract: In an era where cyber threats are increasingly sophisticated and pervasive, the need for real-time data analytics and advanced security event monitoring is more critical than ever. This paper explores the integration of Artificial Intelligence (AI) with real-time data analytics to enhance security event monitoring and management systems. By leveraging machine learning algorithms and big data technologies, the proposed framework aims to provide a comprehensive and proactive approach to cybersecurity. The study focuses on the application of AI techniques, such as anomaly detection, predictive analytics, and automated incident response, to detect and mitigate security threats in real time. The methodology involves collecting and analyzing large volumes of network traffic data and system logs to identify patterns and anomalies indicative of potential security breaches. Key performance metrics, including detection accuracy, false positive rates, response times, and resource utilization, are evaluated to assess the effectiveness of the AI-driven system. Our findings demonstrate that the AI-enhanced system significantly improves the accuracy and speed of threat detection compared to traditional methods. The system achieves a detection accuracy of 94.5%, with a false positive rate of 2.1%, highlighting its reliability and efficiency. Moreover, the integration of real-time data analytics enables continuous monitoring and instant response to emerging threats, reducing the window of vulnerability. The study also explores the use of advanced machine learning models, such as deep learning and reinforcement learning, to further enhance the system's predictive capabilities and adaptability. In conclusion, the integration of AI with real-time data analytics offers a transformative approach to security event monitoring and management. This research provides valuable insights into the development of next-generation cybersecurity solutions that are capable of anticipating an…
subject: Articles
  topic: section
subject: Real-time analytics, AI security, anomaly detection, predictive analytics, automated incident response, cybersecurity management
  topic: keywords
creator: Bhargava Reddy Maddireddy1, Bharat Reddy Maddireddy 2
rights: Copyright (c) 2024
date: 2022-06-30
publication date: 2022-06-30
starting page: 1
ending page: 16

Emergency Evacuation Preparedness in Care Homes: Strategies for Protecting Vulnerable Populations
type: item
description: Care homes house some of the most vulnerable populations, including elderly residents and individuals with disabilities or chronic illnesses, making emergency evacuation preparedness a critical issue. This study explores the strategies employed in care homes to ensure the safe evacuation of these at-risk individuals during emergencies such as fires, natural disasters, and other crisis events. The research focuses on identifying best practices, gaps, and challenges in current evacuation plans, particularly in facilities catering to elderly and disabled populations. Through a mixed-methods approach, including surveys of care home staff, interviews with emergency management professionals, and case studies of past evacuations, this study aims to provide a comprehensive understanding of effective preparedness protocols. The findings reveal that while many care homes have basic evacuation plans, there is often a lack of tailored solutions for residents with mobility impairments or cognitive limitations. Additionally, the research highlights the importance of regular training, multi-agency collaboration, and the need for adaptive equipment to facilitate swift evacuations. The study concludes by recommending enhanced training for care staff, investment in specialized evacuation tools, and improved coordination with local emergency services to protect the health and safety of care home residents during crises.
subject: Articles
  topic: section
creator: Ige Olabanji Isaac,
creator: Adekunle Ajasin
creator: Cynthia Gitau,
rights: Copyright (c) 2024
date: 2024-08-10
publication date: 2024-08-10
starting page: 49
ending page: 59

Developing a robust CRM Analytics strategy for Hedge Fund institutions to improve investment diversification
type: item
description: Abstract: In this paper, we explore how the international financial community can build a sustainable and adaptive financial sector that supports stable and efficient institutions of finance in both accelerating and declining global markets. The analysis, viewed from the side of public policy makers and private decisionmakers. We discuss systemic under provision of innovation driven by both homogenous competition in the financial industry and asymmetric requirement for heterogeneous innovations leading to two main problems: stagnant growth due to a lack of economic breakthrough, and unstable exposures caused by concentrated lowprobability highseverity risk events borne within opaque institutions. It describes the features of a properly created financial system, and it serves as a bullseye for those who build structures and institutions for the financial architecture. In order to address inherent risks and attain this ideal, the paper presents best practices that include a changemanagement framework allowing dynamic adaption to current market conditions. The paper suggests an enterprise risk management framework to enable sound financial service institutions. It is designed to reinforce value creation, curb opportunistic behavior, constantly manage riskreturn optimization and promote sustained improvement in the performance of institutions. The paper ends with policyrelevant reflections and words of caution around enacting the suggested framework for aligning dynamic outcomes between industry and investors, as well as enhancing institutional riskadjusted value.
subject: Articles
  topic: section
subject: global financial environments; instabilities exposures; stagnations growth; Basel Accords; systematic risks; incomplete risk spreading; imperfect information sharing.
  topic: keywords
creator: Venugopal Tamraparani,
creator: Aryendra Dalal,
rights: Copyright (c) 2022
date: 2022-04-20
publication date: 2022-04-20
starting page: 1
ending page: 10

Integration of IoT and AI for Real-Time Monitoring and Autonomous Control in Food Engineering Systems
type: item
description: Abstract: The integration of the Internet of Things (IoT) and Artificial Intelligence (AI) is revolutionizing food engineering systems, enabling real-time monitoring and autonomous control for enhanced efficiency, quality, and safety. IoT facilitates the seamless interconnection of devices, sensors, and machines, collecting vast amounts of data from various stages of food production, processing, and distribution. When paired with AI, this data can be analyzed in real time to enable predictive insights, optimize processes, and automate decision-making. This study explores the synergy between IoT and AI in food engineering, emphasizing their application in areas such as temperature and humidity monitoring, spoilage detection, and equipment maintenance. By employing machine learning algorithms, AI enhances the predictive capabilities of IoT networks, ensuring the timely detection of anomalies and reducing waste. Furthermore, autonomous control systems powered by AI enable smart factories to adapt dynamically to changing conditions, optimizing resource utilization and production efficiency. The findings highlight substantial improvements in operational efficiency, with case studies demonstrating a 35% reduction in energy consumption and a 25% increase in yield in automated food processing systems. Additionally, real-time monitoring enhanced food safety compliance by 40%, reducing the risk of contamination and recalls. Challenges such as data security, interoperability, and scalability remain, necessitating a multidisciplinary approach to fully realize the potential of IoT-AI integration. This research contributes to the evolving field of smart food systems, providing a framework for adopting IoT and AI technologies in food engineering. The outcomes underscore the potential for transforming the industry, aligning technological advancements with sustainability and food security goals.
subject: Articles
  topic: section
subject: IoT, AI, food engineering, real-time monitoring, autonomous control, smart food systems
  topic: keywords
creator: Khuram shehzad
creator: Umair Ali
creator: Akhtar Munir
rights: Copyright (c) 2024
date: 2024-06-10
publication date: 2024-06-10
starting page: 1
ending page: 12

Real-Time AI and Blockchain for Traceability and Transparency in the U.S. Food Supply Chain
type: item
description: Abstract: The integration of Artificial Intelligence (AI) and Blockchain technology is reshaping the U.S. food supply chain, addressing critical challenges in traceability, transparency, and safety. As consumer demands for ethically sourced and high-quality products grow, these technologies provide innovative solutions to monitor and verify the journey of food products from farm to fork. Blockchain ensures immutable, decentralized records, enabling stakeholders to trace origins, verify certifications, and identify contamination sources in real-time. Complementing this, AI-driven analytics process vast amounts of data to optimize supply chain operations, predict risks, and ensure compliance with safety regulations. This paper explores the synergistic application of AI and Blockchain in building a resilient and transparent food supply chain in the United States. By analyzing case studies and real-world implementations, the study highlights how AI enhances blockchain’s functionality through predictive algorithms and anomaly detection, while blockchain guarantees data integrity and accessibility. We discuss challenges such as interoperability, scalability, and the need for standardized protocols, offering practical solutions and policy recommendations. Our findings demonstrate that the fusion of these technologies not only mitigates risks like food fraud and contamination but also promotes sustainability by enabling precise tracking of carbon footprints and waste reduction efforts. The adoption of AI and Blockchain offers stakeholders—including producers, regulators, and consumers—a trustworthy ecosystem, fostering confidence in the safety and authenticity of food products. This paper underscores the transformative potential of these technologies in achieving a future-ready food supply chain, emphasizing the importance of innovation, collaboration, and regulatory alignment.
subject: Articles
  topic: section
subject: AI, Blockchain, Food Supply Chain, Traceability, Transparency, Sustainability
  topic: keywords
creator: Khuram shehzad
creator: Umair Ali
creator: Akhtar Munir
rights: Copyright (c) 2023
date: 2023-06-30
publication date: 2023-06-30
starting page: 1
ending page: 11

The Role of Technology in Enhancing Domiciliary Care: A Strategy for Reducing Healthcare Costs and Improving Safety for Aged Adults and Carers
type: item
description: Abstract: Traditional healthcare system are overburdened with the rise in chronic diseases and age of global population. One such crucial alternative is domiciliary care (or homecare) which offered individuals to receive necessary care within the confined of their homes. This paper explores the use-case and functionality of technology supporting domiciliary care for older people, from increasing independent living capacity to reducing health system cost by improving safety both for carer as well as aged adults.
subject: Articles
  topic: section
subject: Domiciliary Care, Aged Adults, Carer Safety, Healthcare Costs, Technology Integration, Telehealth, Remote Monitoring, Smart Homes, Independent Living,
  topic: keywords
creator: Omolara Oluseun Juba
creator: Abimbola F. Olumide
creator: Juba Idowu David
creator: Kazeem Abiodun Adekunle
rights: Copyright (c) 2024 Unique Endeavor in Business & Social Sciences
date: 2024-06-30
publication date: 2024-06-30
starting page: 213
ending page: 233

Evaluating Reinforcement Learning Approaches for Supply Chain Optimization: A Comparative Study of Approximate SARSA and REINFORCE Algorithms
type: item
description: This paper presents a comparative analysis of re-inforcement learning (RL) algorithms applied to supply chain optimization problems. Specifically, I implement and evaluate three RL agents: an approximate SARSA agent, a REINFORCE agent, and a baseline heuristic based on the (,Q)-Policy within a supply chain environment. The supply chain model under consideration involves a factory and multiple warehouses with the goal of determining optimal production and inventory levels to manage seasonal demand fluctuations. I address the curse of di-mensionality by utilizing function approximation techniques and policy search methods. My results demonstrate that while both approximate SARSA and REINFORCE algorithms outperform the baseline heuristic, REINFORCE with linear feature-mapping consistently yields superior performance, particularly in complex scenarios with multiple warehouses. The approximate SARSA approach, though effective in simpler environments, exhibits limitations in handling more intricate setups. The findings suggest that feature engineering plays a critical role in the effectiveness of RL algorithms for supply chain optimization, and future work may benefit from exploring deep learning techniques to capture complex, non-linear dependencies in supply chain dynamics.
subject: Articles
  topic: section
creator: Paulami Bandyopadhyay
rights: Copyright (c) 2022
date: 2022-06-20
publication date: 2022-06-20
starting page: 1
ending page: 6

Exploring the Effectiveness of AI and Wearable Technology in Predicting Workplace Accidents in High-Risk Industries
type: item
description: Abstract: Workplace accidents pose significant threats to worker safety and productivity, especially in high-risk industries such as construction, manufacturing, and mining. This research explores the potential of Artificial Intelligence and wearable technology to predict and prevent workplace accidents. By analyzing data collected from wearable sensors, AI algorithms can identify patterns and anomalies indicative of potential hazards, enabling timely interventions. This paper reviews the existing literature on wearable sensors, AI algorithms, and their application in workplace safety. A proposed methodology outlines a framework for data collection, processing, and model training. The potential results of such a system are discussed, including improved hazard identification, personalized safety recommendations, and real-time alerts. The discussion section analyzes the challenges and limitations of implementing AI-powered wearable systems, such as data privacy, sensor reliability, and algorithm accuracy. Finally, the conclusion summarizes the potential benefits and future directions of this technology in revolutionizing workplace safety.
subject: Articles
  topic: section
creator: Albert Wayne
rights: Copyright (c) 2024
date: 2024-10-20
publication date: 2024-10-20
starting page: 36
ending page: 48

Evaluating the Impact of Safety Culture on the Reduction of Healthcare-Associated Infections in Hospital Settings
type: item
description: Healthcare-associated infections pose a significant threat to patient safety and contribute substantially to morbidity, mortality, and healthcare costs. A strong safety culture within hospitals is increasingly recognized as crucial for minimizing HAIs. This research investigates the impact of safety culture on HAI rates, exploring the mechanisms through which a positive safety climate influences infection prevention and control practices. A comprehensive literature review examines the conceptual underpinnings of safety culture and its relationship with HAIs. A mixed-methods methodology, incorporating both quantitative analysis of HAI rates and qualitative assessments of safety culture perceptions among healthcare professionals, is employed to provide a nuanced understanding of this complex relationship. The findings demonstrate a significant association between stronger safety cultures and lower HAI rates. Qualitative data reveals that hospitals with robust safety cultures exhibit enhanced communication, greater adherence to infection control protocols, and a more proactive approach to identifying and mitigating infection risks. The study concludes by emphasizing the importance of cultivating a positive safety culture as a cornerstone of effective HAI prevention strategies and offers practical recommendations for hospitals seeking to strengthen their safety climate.
subject: Articles
  topic: section
creator: Willie Wayne
rights: Copyright (c) 2024
date: 2024-10-19
publication date: 2024-10-19
starting page: 24
ending page: 35