Journal on Intelligent Systems Engineering and Applied Data Science

The evolution of image processing-powered hot-stage microscopy in pharmaceutical characterization

2024-07-08T08:24:15+00:00

Hot-stage microscopy is essential for pharmaceutical characterization, revealing compounds' physical and chemical properties. It helps understand drug stability, purity, and formulation efficacy by observing melting points, polymorphic transformations, and crystallization events in real time in controlled heating samples. To analyze and interpret images, hot-stage microscopy relies on image processing. The process includes enhancement, filtering, segmentation, quantitative analysis, feature extraction, temperature mapping, real-time monitoring, image registration and alignment, and data visualization. As it evolves, hot-stage microscopy can improve drug development and patient outcomes. Hot-stage microscopy has revolutionized pharmaceutical characterization by directly observing the physical and chemical changes of drug substances during heating. This method illuminates melting points, phase transitions, polymorphism, and crystallinity, which affect drug stability, bioavailability, and efficacy. Future studies should explore more applications beyond crystalline phase analysis, amorphous phases, polymorphism, drug-excipient compatibility, and methodology optimization. Combining hot-stage microscopy with other analytical methods could lead to a more holistic approach to pharmaceutical characterization. The development of hot-stage microscopy presents promising opportunities for research and innovation in pharmaceutical science.

A review of Naive Bayes and decision tree methods for predicting particle size distribution in pharmaceutical manufacturing

2024-07-08T08:35:18+00:00

Pharmaceutical manufacturing relies heavily on accurate particle size distribution prediction for drug efficacy, bioavailability, and patient safety. Machine learning algorithms like Naïve Bayes and Decision Tree have gained popularity for their ability to forecast complex data patterns and make informed predictions. However, Naïve Bayes assumes all features are independent, which may compromise the accuracy of predictions in certain scenarios. Researchers have explored hybrid approaches that combine Naïve Bayes with other machine learning algorithms, such as decision trees. The Decision Tree method, which is based on strong data mining methods like multivariate data analysis (MVDA), could help predict important quality factors like particle size distribution. By integrating innovative technologies like nanoelectrodes, the Decision Tree method can enhance efficiency and precision in predicting particle size distribution within pharmaceutical formulations. Accurate particle size distribution prediction is crucial for ensuring the quality and efficacy of pharmaceutical products. Future research should focus on combining Naïve Bayes and Decision Tree methods with advanced machine learning techniques, focusing on feature selection techniques and real-time monitoring and control systems within pharmaceutical manufacturing processes.

Integrating novel sensors and machine learning for predictive maintenance of medium voltage switchgear in LNG plants using failure mode and effects analysis

2024-07-09T03:58:29+00:00

LNG plants are increasingly utilizing machine learning and predictive maintenance to enhance efficiency, safety, and cost-effectiveness. By integrating advanced sensors and machine learning algorithms, operators can collect real-time data on the health and performance of medium-voltage switchgear, enabling proactive scheduling of maintenance tasks before breakdowns occur. One key tool in this process is Failure Mode and Effects Analysis (FMEA), which allows for the systematic identification and mitigation of potential failure modes. This approach is particularly beneficial for medium-voltage switchgear, which plays a critical role in ensuring the safe and efficient operation of the plant. The use of FMEA is critical in implementing predictive maintenance strategies for medium-voltage switchgear in LNG plants. By analyzing the likelihood and consequences of failures, maintenance teams can proactively address issues before they escalate, reducing downtime and minimizing unexpected breakdowns. The successful implementation of these innovative technologies marks a crucial step forward in ensuring the reliability and sustainability of LNG plants in the face of increasing operational demands and environmental concerns. Future research should focus on the application of advanced machine learning algorithms, such as deep learning, in conjunction with novel sensors for predictive maintenance in LNG plants. Additionally, we should develop more comprehensive risk assessment methods specifically tailored to LNG plants.

Electricity-assisted cancer therapies: nanotechnology, electrochemotherapy, and machine learning

2024-07-09T06:18:04+00:00

Electricity-assisted cancer therapies, including nanotechnology and electrochemotherapy (ECT), have emerged as promising approaches in oncology. Nanotechnology delivers anticancer drugs to tumor sites precisely, improving efficacy and reducing side effects. ECT uses electric pulses to increase cancer cell drug uptake, making them more treatable. Electricity-assisted cancer therapies increasingly employ machine learning algorithms to analyze complex data, optimize treatment protocols, and predict patient outcomes. Nanotechnology has shown promise in improving therapy efficacy and targeting. Researchers can precisely deliver drugs to cancerous cells using nanoparticles, minimizing tissue damage. Nanoparticles' unique properties enable customization to meet treatment needs, improving patient outcomes and treatment success. With fewer side effects, ECT kills cancer cells more effectively. It uses electric pulses to increase cancer cell uptake of chemotherapy drugs, improving treatment outcomes. More research is necessary to optimize electric pulses and chemotherapy drugs to maximize the therapeutic potential of ECT. Machine learning algorithms for electricity-assisted cancer therapies have the potential to revolutionize cancer treatment. Researchers can improve existing therapies and develop patient-specific approaches by using artificial intelligence to analyze massive amounts of data and optimize treatment protocols.

Fatigue and drowsiness detection using a support vector machine for traffic accident reduction

2024-07-10T10:31:09+00:00

Fatigue and drowsiness are major contributors to road safety issues, causing slower reactions, poor decision-making, and increased accidents. Support vector machine (SVM) can improve road safety by analyzing complex data sets and patterns related to driver behavior. When using features extracted from electrooculography signals to determine driver fatigue, SVM demonstrated high classification accuracy. This shows that it could be a useful tool in real-time fatigue detection systems. SVM's successful application in traffic accident reduction demonstrates its potential for improving road safety through predictive modeling and early warning systems. Integrating SVM algorithms into traffic accident prediction models enables the analysis of a wide range of factors, including road conditions, driver behavior, and vehicle characteristics, in order to identify potential risk factors and take proactive measures to avoid accidents. Studies have shown that SVM-based systems can predict accidents with high accuracy, resulting in timely interventions and, ultimately, fewer road fatalities and injuries. In conclusion, using SVM to detect driver fatigue and drowsiness is critical for increasing road safety. Future research should focus on improving the system's accuracy and real-time capabilities, incorporating advanced machine learning algorithms, and developing adaptive SVM models that constantly learn and update their parameters based on real-time data.