Privacy Considerations Research Articles

Febrile disease modeling and diagnosis system for optimizing medical decisions in resource-scarce settings

Febrile diseases are highly prevalent in tropical regions due to elevated humidity and high temperatures. These regions, mainly comprising low- and middle-income countries, often face challenges related to inadequate medical infrastructure and a lack of skilled personnel for accurately diagnosing febrile diseases. Distinguishing one febrile illness from another posed a significant challenge, adding to the complexity of accurate diagnoses. This study developed a multi-symptom multi-disease model to address this challenge, leveraging exploratory data analysis of patient datasets from field studies and the expertise of medical practitioners specializing in tropical diseases. The research investigated the most effective modeling approach for differentiating among 11 febrile illnesses that are prevalent in Nigeria using three intelligent techniques: Extreme Gradient Boost (XGBoost), Fuzzy Cognitive Map (FCM), and Analytic Hierarchy Process (AHP). Comparative analysis demonstrates that AHP surpassed the others, achieving a precision of 84%, recall of 83%, and an F1-score of 84%. Consequently, the AHP technique was integrated into the development of “Febra Diagnostica,” an app aimed at enhancing febrile disease diagnosis in resource-constrained settings. The app was then deployed and utilized in select Nigerian states, offering scalability and empowering frontline health workers in primary health facilities. Febra Diagnostica featured user-friendly interfaces, automated diagnosis and treatment suggestions, streamlined referrals, and provisions for further investigations. Encryption, access control, and multi-factor authentication were some of the security and privacy considerations in the app which gained acceptance from medical experts and adapted to regulatory and ethical policies for smart healthcare systems.

Voting System using Face Recognition

This introduction explores the idea of using face recognition technology in college student voting systems. The aim is to make voting more secure, efficient, and user-friendly for students. Face recognition can enhance security by preventing fraud and speed up the voting process. It also ensures accessibility for all students. However, concerns about privacy and fairness need careful consideration. Striking a balance between innovation and protecting students' rights is crucial for successfully implementing face recognition in college voting systems. This approach promises a modern and trustworthy voting experience tailored to the needs of college students

Active Chat Monitoring and Suspicious Chat Detection Over Internet

In the digital age, the proliferation of online communication platforms has facilitated unprecedented levels of connectivity and interaction. However, this interconnectedness has also introduced new challenges, particularly in ensuring the safety and security of users in online environments. One critical area of concern is the monitoring and detection of suspicious activities within chat platforms, where malicious actors may engage in harmful behaviors such as cyberbullying, harassment, or illicit activities. This research paper focuses on the development and implementation of active chat monitoring techniques for the detection of suspicious behavior over the internet. By leveraging advancements in natural language processing (NLP), machine learning, and data analytics, this study aims to explore effective methodologies for real-time monitoring and analysis of chat conversations to identify potentially harmful content. The paper will delve into various approaches for detecting suspicious patterns, including keyword analysis, sentiment analysis, and anomaly detection algorithms. Additionally, considerations for privacy, ethics, and legal implications surrounding chat monitoring will be discussed. Ultimately, this research endeavors to contribute to the enhancement of online safety measures by providing insights into the design and implementation of proactive monitoring systems capable of identifying and mitigating risks in virtual communication spaces. Keywords— Active Chat Monitoring, cybersecurity, user experience, digital era, security, user-controlled, effectiveness, evolving threat, Chat content analysis, Threat intelligence.

The Impact of Telerehabilitation on Physical Therapy Services in Rural Communities

Telerehabilitation is a transformative approach to physical therapy, revolutionizing the accessibility of healthcare in rural communities through the strategic use of Telecommunications technology. This novel approach has the potential to significantly enhance the efficacy of healthcare delivery, particularly considering the critical challenges posed by geographical isolation and resource scarcity. This paper explores the multifaceted benefits of Telerehabilitation, including increased access to care and reduced costs, alongside the challenges of technological barriers and privacy considerations. It provides a comprehensive overview of Telerehabilitation’s impact on rural healthcare, emphasizing its capacity to optimize patient outcomes and proposing strategies for effective implementation. The findings of this study suggest that the use of technology to deliver telecare is a key means of delivering equitable healthcare to underserved populations, a promising way to improve access to rural physiotherapy services address the challenge of telehealth resources, and promote the long-term sustainability of rural Telerehabilitation practices.

The Power of Artificial Intelligence in Digital Marketing

In the dynamic landscape of digital marketing, businesses are constantly challenged to engage their target audience effectively amidst a plethora of information and choices. Artificial Intelligence (AI) emerges as a pivotal solution in addressing these challenges, revolutionizing traditional marketing approaches through its diverse applications and capabilities. This paper explores the multifaceted role of AI in digital marketing, drawing upon insights from existing literature and practical examples. AI empowers marketers with enhanced customer insights, predictive analytics, and automated campaign optimization, enabling them to tailor marketing strategies to individual preferences and behaviors. Moreover, AI-driven tools such as chatbots and virtual assistants streamline customer interactions, while also offering cost efficiencies and improved return on investment (ROI). Despite the undeniable benefits, the integration of AI in digital marketing raises concerns regarding data privacy, algorithmic bias, and ethical considerations. Nevertheless, by navigating these challenges responsibly and adopting ethical frameworks, businesses can leverage AI to unlock new opportunities for growth, differentiation, and sustainable success in the digital era

Research on Step-Down Aviation Three-Phase Power Factor Correction based on Machine Learning and Optimization Algorithm

Objectives: The study looks at power quality issues, such as phase imbalance, voltage sags, and power factor issues, in aircraft systems. It highlights how immediate power factor correction steps are required to avoid equipment longevity problems and boost system efficiency, which will ultimately improve the performance of the entire aviation system. Methodology: Using a variety of data sources, real-time data gathering, and machine learning approaches, this study focuses on data collecting and real-time monitoring in aviation systems. Predictive modeling, testing methods for data preparation and analysis, and optimization algorithms for capacitor bank size and placement are all included. Considerations for privacy and security are covered. Result: Salp Swarm Optimization and the Dragonfly Algorithm are used in the study to assess the ideal capacitor sizes and positions, which results in better voltage profiles and lower active power losses. Aviation systems benefit from improved power factor correction through the application of integrated machine learning and optimization, which promotes sustainability and energy efficiency. Conclusion: The study demonstrated the significance of power factor correction in aviation by successfully addressing power quality issues in aircraft electrical systems through the use of optimization algorithms for capacitor sizing and location.

Prompt engineering: The next big skill in rheumatology research.

Large language models (LLMs) like GPT-4 and Claude are catalyzing transformation across medical research including rheumatology. This review examines their applications, highlighting the pivotal role of prompt engineering in effectively guiding LLMs. Key aspects explored include literature synthesis, data analysis, manuscript drafting, coding assistance, privacy considerations, and generative artificial intelligence integrations. While LLMs accelerate workflows, reliance without apt prompting jeopardizes accuracy. By methodically constructing prompts and gauging model outputs, researchers can maximize relevance and utility. Locally run open-source models also offer data privacy protections. As LLMs permeate rheumatology research, developing expertise in strategic prompting and assessing model limitations is critical. With proper oversight, LLMs markedly boost scholarly productivity.

Navigating privacy and security in telemedicine for primary care.

To examine patient and provider perspectives on privacy and security considerations in telemedicine during the COVID-19 pandemic. Qualitative study with patients and providers from primary care practices in 3 National Patient-Centered Clinical Research Network sites in New York, New York; North Carolina; and Florida. Semistructured interviews were conducted, audio recorded, transcribed verbatim, and coded using an inductive process. Data related to privacy and information security were analyzed. Sixty-five patients and 21 providers participated. Patients and providers faced technology-related security concerns as well as difficulties ensuring privacy in the transformed shared space of telemedicine. Patients expressed increased comfort doing telemedicine from home but often did not like their providers to offer virtual visits from outside an office setting. Providers initially struggled to find secure and Health Insurance Portability and Accountability Act-compliant platforms and devices to host the software. Whereas some patients preferred familiar platforms such as FaceTime, others recognized potential security concerns. Audio-only encounters sometimes raised patient concerns that they would not be able to confirm the identity of the provider. Telemedicine led to novel concerns about privacy because patients and providers were often at home or in public spaces, and they shared concerns about software and hardware security. In addition to technological safeguards, our study emphasizes the critical role of physical infrastructure in ensuring privacy and security. As telemedicine continues to evolve, it is important to address and mitigate concerns around privacy and security to ensure high-quality and safe delivery of care to patients in remote settings.

Enhancing Trust and Security in E-Voting: A Comprehensive Review of Blockchain and NFC Integration

Abstract: This paper provides a thorough review of how the integration of blockchain and Near Field Communication (NFC) enhances trust and security in electronic voting (E-Voting) systems. Examining the strengths of blockchain's transparency and NFC's secure authentication, the study explores real-world applications, revealing practical advantages and challenges. The synergy between these technologies is emphasized, showcasing their collective potential to reinforce the integrity of E-Voting. Security measures and privacy considerations are addressed, concluding with insights into current challenges, future prospects, and the overall impact on trustworthy electronic voting systems. This concise review aims to inform researchers, policymakers, and practitioners in the dynamic intersection of blockchain, NFC, and electoral processes.

Design and Development of Android App Malware Detector API Using Androguard and Catboost

Abstract: Android has been constant target of attacks through the use of malicious applications (malware) that can harm users, for instance, by leaking sensitive data (e.g., bank account details), blocking access to information and demanding monetary compensation for the ransom, or even leveraging social engineering scams. A large number of malicious applications is distributed in a daily basis through different distribution vectors, such as application markets, including official and third party stores, or untrusted sources like Web repositories. The increasing prevalence of Android malware necessitates advanced detection mechanisms to safeguard users and their devices. The aim of the project is to presents an innovative approach to Android malware detection, integrating machine learning, blockchain technology, and web development tools. The proposed system is to develop a Andriod App Malware Detector API capable of identifying and mitigating malicious applications effectively. The system begins with the collection of diverse datasets comprising both benign and malicious Android applications, ensuring the representation of various threat vectors and scenarios. Androguard, a powerful tool for Android app analysis, the system extracts relevant features such as permissions, API calls, and intents, providing valuable insights into the behaviour and characteristics of each application. The AndroMal Model is designed and trained using CatBoost Algorithm and integrated with a blockchain framework for secure metadata storage. Consensus mechanisms and smart contracts enhance transparency and reliability. Privacy considerations, continuous monitoring, and user feedback mechanisms further fortify the system. The proposed design promises an effective, scalable, and user-centric solution for Android malware detection.

The Use of AI in Personalized Marketing: Balancing Benefits and Privacy Concerns

In general, the integration of Artificial Intelligence into personalized marketing has revolutionized the mode in which companies engage with their consumers, enabling them to deliver tailor-made experiences and targeted ads dependent on consumers’ individual preferences and activities. The above analysis gets driven by the fact that the utility of AI in personalized marketing enhances customer satisfaction, increases sales, and improves the overall efficiency of marketing. However, the vast application of Artificial Intelligence in personalized marketing usage has raised significant privacy concerns centring on the aspect of data collection, profiling, as well as the use of targeted ad measures for strategies. For this reason, it is imperative that while the benefits of personalized marketing via AI are maximized, privacy considerations should also be taken into account to build consumers’ trust and compliance with relevant laws.

SOCIAL IMPACT OF AUTOMATED ACCOUNTING SYSTEMS: A REVIEW: ANALYZING THE SOCIETAL AND EMPLOYMENT IMPLICATIONS OF THE RAPID DIGITIZATION IN THE ACCOUNTING INDUSTRY

This study examines the social impact of automated accounting systems, focusing on the implications for employment, ethical considerations, and broader societal effects. Employing a systematic literature review combined with content analysis, the research draws on recent academic articles, industry reports, and regulatory documents published between 2015 and 2024. The methodology aims to capture a comprehensive view of how automation and artificial intelligence (AI) are reshaping the accounting profession. Key findings reveal that automation acts as a dual-edged sword: it enhances efficiency and accuracy, enabling accountants to focus on higher-value strategic roles, but also raises concerns about job displacement for roles centered on manual tasks. Ethical and privacy considerations emerge as critical issues, necessitating robust frameworks to ensure data protection and maintain trust. The future landscape of accounting is poised to be significantly influenced by ongoing technological advancements, with a transformation in employment patterns and a heightened focus on ethical standards. To navigate this transition, the study recommends strategic initiatives for stakeholders, including updating educational curricula to include relevant digital age skills, developing regulations to address ethical and privacy concerns, and fostering industry collaboration to establish best practices. Lastly, the study underscores the transformative potential of automated accounting systems, highlighting the need for a balanced approach that leverages technological advancements while addressing associated challenges. Future research is encouraged to explore the long-term impacts of automation on the profession, focusing on employment patterns, ethical standards evolution, and societal implications. Keywords: Automated Accounting Systems, Employment, Accounting Industry, Digital Transformation.

How Can Non-Intrusive Load Monitoring Contribute to the Assessment of the Smart Readiness Indicator?

The Smart Readiness Indicator (SRI) is a framework introduced by the EU in 2018 to assess smart buildings in various aspects. However, the SRI has been criticized for several limitations, including its ambiguous service definitions. This paper proposes the application of Non-Intrusive-Load Monitoring (NILM) technology to enhance SRI evaluation on the example of SRI service E-12. NILM can be used to disaggregate energy consumption data to end use levels and allows for granular non-intrusive energy consumption measurement. The study involves a rigorous methodology using open sensor data and NILM algorithms to evaluate device-specific energy consumption We evaluate the IDEAL dataset and three different frequencies (5s, 15min, 1h), three different algorithms (CO, RNN, Seq2Point) and one data imputation strategies (forward filling). The results show that with a higher frequency, the performance metrics (F-score, normalized absolute error) increase. Regarding further considerations, we identify a trade-off between resource and energy efficiency, as well as privacy considerations with increasing measurement frequency. To achieve its aims for awareness, the SRI needs to consider interoperability and appropriate aggregations (frequency and spatial).

I-CHECKIN@PKB: The acceptance among Politeknik Kota Bharu staff

I-Checkin@PKB has been utilized at Politeknik Kota Bharu (PKB) for nearly six months, replacing the thumbprint attendance monitoring system. With I-Checkin@PKB, users can eliminate the need to wait in line at the centralized terminal for attendance registration, making it more efficient and hygienic. This system helps avoid potential germ transmission through contact with the same terminal, particularly amidst the challenges posed by Covid-19 and viral fever outbreaks. While the management of PKB has received verbal feedback from users, the actual level of user acceptance regarding I-Checkin@PKB remains unknown. Therefore, this study aims to investigate the acceptance level among I-Checkin@PKB users, focusing on perceived ease of use, perceived usefulness, attention to use, and behavioral intention. The study involved 100 respondents selected through random sampling. Utilizing 12 items with a Cronbach Alpha reliability coefficient of α=0.97, representing the constructs, the results indicated a high level of acceptance among users regarding I-Checkin@PKB, including its ease of use, usefulness, attention, and behavioral intention. Furthermore, the positive perceptions among I-Checkin@PKB users are likely to garner continuous support due to its ability to reduce the risk of human error associated with manual methods, such as paper-based attendance sheets. It is crucial to emphasize that while I-Checkin@PKB systems offer numerous benefits, their successful implementation necessitates clear communication, employee training, and consideration of privacy and security concerns to ensure a positive experience for both employees and the organization.

Intelligent Health Monitoring in Smart Homes

Intelligent Health Monitoring in Smart Homes is a burgeoning field that integrates advanced technologies to revolutionize healthcare delivery within residential environments. This paper explores the concept of smart homes as proactive health monitoring ecosystems, leveraging interconnected devices, sensors, and data analytics to monitor residents' health parameters and behaviors in real-time. Through a comprehensive review of existing literature and case studies, this study elucidates the potential benefits, challenges, and emerging trends in intelligent health monitoring within smart home environments.Key components of intelligent health monitoring systems, including wearable devices, ambient sensors, and machine learning algorithms, are examined in detail, highlighting their roles in detecting early signs of health deterioration, predicting health-related events, and facilitating timely interventions. Furthermore, the integration of telemedicine platforms and communication technologies enhances remote patient monitoring and enables seamless interaction between residents and healthcare providers.The paper also addresses critical considerations such as privacy, data security, and interoperability standards to ensure the ethical and responsible implementation of smart health monitoring solutions. By fostering collaboration among healthcare professionals, technology developers, and policymakers, intelligent health monitoring in smart homes has the potential to improve health outcomes, enhance quality of life, and reduce healthcare costs. The integration of artificial intelligence, Internet of Things (IoT) devices, and cloud computing enables seamless communication and coordination among various components of the smart home ecosystem. Through real-time monitoring and analysis, potential health risks can be identified early, enabling timely interventions and proactive healthcare management. Additionally, the ability to remotely access and share health data facilitates collaborative care and empowers individuals to take control of their own health and well-being. However, the widespread adoption of intelligent health monitoring in smart homes also raises important ethical, privacy, and regulatory considerations. Safeguarding the security and privacy of sensitive health data, ensuring transparency and accountability in algorithmic decision-making, and promoting equitable access to technology are essential aspects that require careful attention and proactive measures. In conclusion, Intelligent Health Monitoring in Smart Homes holds tremendous potential to revolutionize healthcare delivery, promoting preventive care, empowering individuals, and ultimately improving health outcomes. By addressing technical, ethical, and regulatory challenges, this transformative approach can pave the way for a more connected, proactive, and personalized healthcare experience.This paper studies recent state-of-the-art research on the field of IoT for health monitoring and smart homes, examines several potential use-cases of blending the technology, and proposes integration with an existing smart home test bed for further study. Challenges of adoption and future research on the topic are also discussed, Keywords: Intelligent Health Monitoring, Smart Homes, Healthcare, IoT, Machine Learning, Data Analytics, Remote Monitoring, Proactive Healthcare, Privacy, Ethics, Regulatory Compliance.

Automatic Traffic E-challan Generation

Utilizing state-of-the-art technology, Automated Traffic E-challan Generation systems are employed to promptly detect and document traffic violations as they occur. By seamlessly integrating cameras, sensors, and sophisticated algorithms, these systems independently identify transgressions including but not limited to speeding and unauthorized parking. After detection, electronic citations (e-challans) are swiftly produced and delivered to vehicle proprietors, thereby optimizing enforcement procedures and bolstering overall road safety. However, notwithstanding their efficacy, the deployment of such systems mandates the meticulous consideration of privacy and equity issues, necessitating the enactment of suitable regulatory protocols. The Automatic recognition of license plate is the basis of effective management in traffic, the automatic detection and localization of license plate is an important part. Keywords- Python, Raspberry Pi, Convolutional Neural Network

Unfulfilled Promises of Child Safety and Privacy: Portrayals and Use of Children in Smart Home Marketing

Smart home technologies are making their way into families. Parents' and children's shared use of smart home technologies has received growing attention in CSCW and related research communities. Families and children are also frequently featured as target audiences in smart home product marketing. However, there is limited knowledge of how exactly children and family interactions are portrayed in smart home product marketing, and to what extent those portrayals align with the actual consideration of children and families in product features and resources for child safety and privacy. We conducted a content analysis of product websites and online resources of 102 smart home products, as these materials constitute a main marketing channel and information source about products for consumers. We found that despite featuring children in smart home marketing, most analyzed product websites did not mention child safety features and lacked sufficient information on how children's data is collected and used. Specifically, our findings highlight misalignments in three aspects: (1) children are depicted as users of smart home products but there are insufficient child-friendly product features; (2) harmonious child-product co-presence is portrayed but potential child safety issues are neglected; and (3) children are shown as the subject of monitoring and datafication but there is limited information on child data collection and use. We discuss how parent-child relationships and parenting may be negatively impacted by such marketing depictions, and we provide design and policy recommendations for better incorporating child safety and privacy considerations into smart home products.

Social determinants of health Z-code documentation practices in mental health settings: a scoping review.

Mental health remains an urgent global priority, alongside efforts to address underlying social determinants of health (SDoH) that contribute to the onset or exacerbate mental illness. SDoH factors can be captured in the form of International Classification of Disease, Tenth Revision, Clinical Modification (ICD-10-CM), SDoH Z codes. In this scoping review, we describe current SDoH Z-code documentation practices, with a focus on mental health care contexts. Among 2 743 061 374 health care encounters noted across 12 studies in the United States, SDoH Z-code documentation rates ranged from 0.5% to 2.4%. Documentation often involved patients under 64 years of age who are publicly insured and experience comorbidities, including depression, bipolar disorder and schizophrenia, chronic pulmonary disease, and substance abuse disorders. Documentation varied across hospital types, number of beds per facility, patient race/ethnicity, and geographic region. Variation was observed regarding patient sex/gender, although SDoH Z codes were more frequently documented for males. Documentation was most observed in government, nonfederal, and private not-for-profit hospitals. From these insights, we offer policy and practice recommendations, as well as considerations for patient data privacy, security, and confidentiality, to incentivize more routine documentation of Z codes to better assist patients with complex mental health care needs.

Unleashing Deep Learning: Transforming E-commerce Profit Prediction with CNNs

This research examines the potential of Convolutional Neural Networks (CNNs), including VGG16, ResNet50, and InceptionV3, in predicting ecommerce profits. Emphasizing the importance of high-quality datasets, the study showcases the superior performance of CNN models over traditional algorithms, particularly noting a notable accuracy rate of 92.55% with CNN (VGG16). These results highlight deep learning's capability to extract actionable insights from complex ecommerce data, offering significant opportunities for revenue optimization and operational efficiency improvement. The conclusion underscores the need for investment in infrastructure and expertise for successful CNN integration, alongside ethical and privacy considerations. This research contributes valuable insights to the discourse on deep learning in ecommerce, offering guidance to businesses navigating the competitive global market landscape.

Beyond Audio-Video Telehealth: Perspective on the Current State and Future Directions of Digital Neurological Care in the United States

Background The COVID-19 pandemic transformed neurological care by both requiring digital health modalities to reach patients and profoundly lowering barriers to digital health adoption. This combination of factors has given rise to a distinctive, emerging care model in neurology characterized by new technologies, care arrangements, and uncertainties. As the pandemic transitions to an endemic, there is a need to characterize the current and future states of this unique period in neurology. Objective We sought to describe the current state of the pandemic- and postpandemic-related changes in neurological care and offer a view of the possible future directions of the field. Methods We reviewed several themes across the “new digital normal” in neurology, including trends in technology adoption, barriers to technology access, newly available telehealth services, unresolved questions, and an outlook on the future of digital neurology. Results In this new era of neurological care, we emphasize that synchronous audio-video telehealth remains the predominant form of digital interaction between neurologists and patients, mainly due to pandemic-related regulatory changes and the preexisting, steady adoption of video platforms in the prepandemic era. We also identify a persistent digital divide, with audio-only telehealth remaining a necessity for preserving care access. Asynchronous telehealth methods and services, including care coordination, interprofessional consultations, remote patient monitoring, and teletreatment are becoming increasingly important for neurological care. Finally, we identify several unanswered questions regarding the future of this “new normal,” including the lasting effects of emergency regulatory changes, the value proposition of telehealth, the future of telehealth reimbursement in neurology, as well as privacy considerations and trade-offs in asynchronous neurological care models. Conclusions The COVID-19 pandemic has ushered in an era of digital adoption and innovation in neurological care, characterized by novel care models, services, and technologies, as well as numerous unresolved questions regarding the future.