The study "Performance Revision of a Wearable Antenna Sensor for Glucose Detection Utilizing Artificial Neural Networks" explores machine learning techniques to improve the accuracy and responsiveness of a wearable antenna sensor for glucose detection. The results demonstrate significant improvements in the sensor's performance, particularly in real-time glucose monitoring. By fine-tuning the neural network architecture, the researchers achieved a higher degree of precision while minimizing false readings, enhancing the accuracy of glucose measurements and paving the way for more customized diabetes management strategies. The findings suggest that integrating additional data sources, such as patient activity levels and dietary habits, could further refine the predictive capabilities of the system. Future work will focus on integrating these advancements into a compact, wearable format, ensuring user comfort and accessibility. Additionally, exploring the potential for remote monitoring features could further empower individuals in managing their health proactively. The study also addresses the issue of non-linearity due to diffraction effects from different layers in microwave resonators. The proposed antenna designs are based on a low-cost, highly-sensitive microwave antenna for identifying different liquid samples through monitoring the variation in S21 magnitude. The antenna's accuracy is improved through a back loop trace technique, eliminating nonlinear effects due to multi-layer diffractions. An analytical model based on circuit theory is suggested for the proposed antenna operation, and the results are validated numerically using the Computer Simulation Technology Microwave studio package. DOI : https://doi.org/10.52783/pst.1871

Electrical equipment dependability is essential to operational efficiency and cost-effectiveness across power and energy sectors. The development of an up-to-date Fault Detection and Diagnosis (FDD) method is essential to improve electrical equipment dependability in current modern applications. Advanced FDD approaches include signal processing, statistical modelling, and machine learning algorithms to analyze vibrations and monitor temperatures of the machine. This study examines essential concepts and situations related to electrical machines problems including rotor and stator breakdowns using numerical simulations. Furthermore, case studies and computer simulations demonstrate how these strategies enhance predictive maintenance and problem diagnosis. In fact, the presented work explains the advancements in FDD utilizing hybrid model-based data-driven methods. The findings show that AI, sensor technologies, and condition monitoring systems improve problem detection accuracy and efficiency, lowering downtime and maintenance costs. This paper advance’s reliability engineering by providing a solid foundation for FDD system enhancements, encouraging the utilization of more advanced techniques such as machine learning and AI to enhance the reliability of electric machines in modern power system applications. DOI : https://doi.org/10.52783/pst.1824

The current state of healthcare developments has a significant impact on our lives and well-being. The number of start-ups pursuing healthcare innovations is steadily increasing. To put it another way, we would be able to utilize AI as an extension of our brain. Increasing the bandwidth between the digital world and ourselves is the primary objective of the project. We are already using our mobile gadgets to extend ourselves at the moment. The bandwidth is quite limited because we may interact with our devices with our thumbs. This present study evaluates the research question in the light of secondary data, as conceived from a number of previous studies. The researcher pinned some of the keywords and selected or included the studies on the basis of the same. Total 132 studies were touched and 89 were selected finally for the study, A questionnaire was also conducted for health security personnel to determine the impact of using artificial intelligence and nanotechnology in maintaining the security of health facilities. DOI : https://doi.org/10.52783/pst.1753

This study presents an advanced methodology for multiple pulmonary disease detection including COVID-19 from CT images, combining a preprocessing normalization function for luminosity enhancement and a Convolutional Tubular Neighborhood (CNN) model. The proposed luminosity enhancement ensures consistent normalization, mitigating the impact of varied illumination conditions in CT scans and accentuating subtle features. The Convolutional Tubular Neighborhood CNN model, integrated into the feature extraction module, strategically focuses on tubular structures for precise information capture. Fine-tuned through 600 epochs with 256 neurons and a 5-Fold training scenario, our model achieved outstanding results, boasting an accuracy, specificity, sensitivity, and F1 Score of 0.99, 0.993, 0.99, and 0.99, respectively. The synergistic combination of luminosity enhancement and the specialized CNN architecture contributes to enhanced feature extraction, resulting in a robust and effective system for multiple pulmonary disease detection including COVID-19 detection. This methodology holds promise for diverse applications, particularly in healthcare, where accurate and efficient identification of COVID-19 from CT images is imperative. DOI : https://doi.org/10.52783/pst.1740

This study examines the relationship between financial literacy and investment behavior among 133 working women in Davangere District, Karnataka. Stratified Random Sampling is used. Using a structured questionnaire and SPSS for analysis, the findings reveal significant gaps in financial literacy, particularly regarding advanced products and risk management. However, a positive correlation between financial literacy and informed investment decisions underscores the need for targeted financial education to enhance women’s economic independence and investment confidence.

This study presents a novel approach to PWM control in Electric Vehicle (EV) charging systems using a two-layered Long Short-Term Memory (LSTM) model. The proposed model leverages deep learning techniques to achieve precise and stable control of PWM signals during the charging process. By employing two LSTM layers, the model effectively captures temporal dependencies and nonlinear relationships within the charging system data, facilitating accurate prediction and regulation of PWM signals. Experimental results demonstrate the efficacy of the proposed approach, with minimal overshoot observed during system startup across various duty cycles. Additionally, comprehensive hardware details are provided, highlighting the integration of microcontrollers, sensors, and communication interfaces essential for implementing the LSTM-based PWM control system. This research contributes valuable insights into the development of intelligent charging solutions for electric vehicles, with implications for enhancing charging efficiency, stability, and overall performance in real-world applications. DOI : https://doi.org/10.52783/pst.1730

This study investigates various methods for video captioning, emphasizing the novel contributions of the proposed model. Existing approaches such as Vid2Seq, Positive-Augmented Contrastive Learning, GL-RG, and TextKG utilize diverse techniques to extract and interpret video features, yielding notable performance on datasets like MSR-VTT and MSVD. The proposed model, however, employs a CNN encoder with a combination of residual and bottleneck blocks to effectively capture temporal and spatial features, paired with an LSTM-based RNN for handling sequential data and long-range dependencies. Evaluated on MSR-VTT, MPII Cooking 2, and M-VAD datasets, the model achieves a peak BLEU score of 51, demonstrating its ability to generate high-quality video descriptions. The model maintains a streamlined yet powerful architecture, marking a significant advancement in video captioning. DOI : https://doi.org/10.52783/pst.1728

Influencer marketing has evolved from a numbers-driven approach, where follower counts dictated success, to a strategy centered on authenticity, engagement, and long-term brand partnerships. This paper examines the transformation of influencer marketing, tracing its progression from traditional celebrity endorsements to the rise of micro- and nano-influencers who foster deeper audience connections. The shift toward credibility and trust has led brands to prioritize meaningful collaborations over transactional relationships, focusing on storytelling, relatability, and community-driven marketing. The paper also explores the impact of algorithm changes, data analytics, and consumer skepticism on influencer marketing strategies. As audiences become more discerning, brands leverage influencer authenticity, user-generated content, and interactive campaigns to build trust and enhance engagement. Additionally, regulatory frameworks and ethical considerations, such as transparency in sponsored content, play a crucial role in shaping the industry's future. DOI:https://doi.org/10.52783/pst.1678

Generative AI is revolutionizing software development by providing intelligent programming assistance, automating code generation, debugging, and refactoring. This paper evaluates the impact of AI-powered coding assistants, such as GitHub Copilot and OpenAI Codex, on developer productivity and code quality. We analyze AI’s effectiveness in improving coding efficiency, reducing errors, and maintaining best practices while addressing challenges such as bias, over-reliance, and security risks. Our study compares AI-assisted and human-written code using empirical data and controlled experiments across multiple programming languages. Key metrics, including defect rates, maintainability, and adherence to industry standards, are used to assess AI’s influence on code quality. Additionally, we explore its implications for software engineering workflows, education, and future advancements in AI-driven development. Findings reveal that AI-assisted coding enhances productivity but requires human oversight to mitigate risks like code vulnerabilities and inconsistencies. This research provides valuable insights into AI’s evolving role in software engineering, offering guidance for developers, researchers, and industry practitioners. DOI:https://doi.org/10.52783/pst.1668

Voltage stability is an important consideration in power system operation, and the development of voltage stability indices is a key aspect of ensuring the reliability and security of the power grid. In this work, newly constructed voltage stability indices have been discussed that are designed to predict the closeness of a multibus system to voltage collapse. These stability indices are based on the analytical expression of complex power, which allows for a more accurate characterization of the system’s voltage stability. By using these indices, operators can gain a better understanding of the system’s stability and take appropriate actions to prevent collapse. To test the effectiveness of the newly developed index, it was applied to the IEEE bus system. The results showed that the index was able to accurately predict voltage instability or the proximity of a collapse. This is an important step in improving the reliability and security of the power grid, as it allows operators to take preemptive measures to avoid voltage collapse. Overall, the development of these new voltage stability indices is a positive step towards improving the performance of power systems. By providing a more accurate and reliable prediction of voltage instability, these indices can help prevent costly and potentially dangerous system failures.