Applied Science and Engineering Journal for Advanced Research
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Protocols and Techniques for End to End Congestion Avoidance on a Global Internet
Get PDFThe packet transmission congestion control at the network node is presented in this research study. It is increasing quickly due to the increased use of applications. It makes clogged flows unpredictable and erratic. There aren\u27t many outdated scheduling strategies for handling network node congestion, but the ones that do exist were only for end-to-end networks, thus they couldn\u27t prevent unfairness brought on by applications and congestion collapse. The switch-based packet transmission technique and the present packet transmission algorithm are similar. An increase in packets between nodes results in congestion control situations. in order to solve existing problems and develop a more flexible method for building a network\u27s improved leakage control scheme. The suggested Enhanced Leaky Bucket Index Algorithm can traverse the node and receive a large number of packets from it due to packet regulations. The proposed approach computes the full packet transmission. The suggested technique demonstrates the packet switching and congestion management mechanisms in the network node in order to identify and halt unresponsive congestion flows before they enter the network node and prevent congestion within the network node. Lastly, a comparison between the suggested and the current methods is made to control packet transmission overall
Evolution of Health Insurance in India Over a Decapod
Get PDFHealth insurance has seen a rapid growth in the Indian market over the last decade. The number of health insurance companies operating in India has increased from around 20 in 2014 to over 50 in 2023. Covid-19 pandemic has led to the integration of telemedicine into health insurance policies. Digitalization has played a major role in the evolution of the industry. On the one hand, it led to an increase in demand for health insurance products and higher premiums. On the other hand, it also led to an increase in claims and a greater emphasis on preventive care and mental health coverage. Profitability of the industry increased during the pandemic, as the higher premiums outweighed the increased claims. In addition, the pandemic led to several positive changes in the industry, such as an increased focus on telemedicine and preventive care. As healthcare and insurance industries further intertwine with technological advancements, the future holds the promise of even greater accessibility, customization, and affordability in health insurance coverage, ensuring that more Indians can access quality healthcare when they need it most
Security Considerations for the Application of Large Language Models in the Financial Sector
Get PDFWith the widespread application of large language models (LLMs) in the financial sector, their intelligent advantages have significantly enhanced the efficiency of business processes such as customer service, risk prediction, and compliance management. However, the security issues related to these models are becoming increasingly prominent, including data privacy and information leakage, bias and uncertainty in model output, and the risk of system attacks. This paper provides an in-depth analysis of these security concerns and proposes corresponding solutions, such as data encryption and protection technologies, model monitoring and validation mechanisms, as well as the strengthening of compliance and regulatory requirements. Finally, by examining a real-world case, the paper explores the future prospects and challenges of applying large language models in the financial sector, offering insights for further research and practice
Research on Optimizing Logistics Transportation Routes Using AI Large Models
Get PDFBackground: With the rapid development of global e-commerce, the logistics industry faces unprecedented challenges. The efficiency and cost control of logistics transportation have become critical factors affecting the competitiveness of enterprises. However, computational complexity and lack of flexibility limit traditional methods for optimizing transportation routes, making it difficult to meet the ever-changing and increasingly complex logistics demands. In recent years, large AI models have emerged with their powerful data processing capabilities and predictive accuracy, becoming an important application in optimizing logistics transportation routes.
Objective: This study explores how to utilize AI large models to optimize logistics transportation routes, enhancing the efficiency and accuracy of route planning to reduce transportation costs, shorten transportation time, and improve overall logistics service levels. Specifically, this research will address the gap in current studies on large-scale data processing and complex route optimization, providing an efficient and flexible route optimization solution.
Methods: This paper employs AI large models based on deep learning to train and test real logistics transportation data from open-source platforms such as Kaggle. The data includes transportation route data, transportation time, transportation costs, and other relevant logistics information. By building and training deep neural network models combined with reinforcement learning algorithms, transportation routes are optimized. Additionally, a series of comparative experiments were designed to verify the effectiveness and practicality of the models. Data processing and analysis were primarily conducted using Python and related data science libraries.
Findings: Experimental results show that the AI large model-based transportation route optimization methods exhibit significant advantages in various scenarios. Specifically, compared to traditional route optimization algorithms, AI large models not only significantly improve computation speed but also demonstrate higher accuracy in route selection and better control over transportation costs. The optimized route plans resulted in an average reduction of transportation time by approximately 15% and transportation costs by about 10%.
Discussion: The findings indicate that the application of AI large models in optimizing logistics transportation routes holds broad prospects and practical value. However, the models still have certain limitations when dealing with extremely complex transportation networks. Future research can further enhance the flexibility and adaptability of the models. Additionally, exploring the application of AI large models in other logistics segments (such as warehousing and sorting) by integrating more diversified data sources and more complex logistics scenarios is also an important research direction.
Conclusion: This study demonstrates through experiments that AI large models are effective in optimizing logistics transportation routes, providing logistics companies with an efficient and reliable route planning tool. In the future, as technology continues to advance, the application prospects of AI large models in the logistics industry will become even broader, with further potential to improve logistics efficiency and reduce costs
Artificial Intelligence in Flight Safety: Fatigue Monitoring and Risk Mitigation Technologies
Get PDFWith the improvement of computer, artificial intelligence, information technology and other technical levels, the relationship between man-machine environment systems is more complicated and diversified. The optimization, iteration and development of the new generation of intelligent equipment system and human-computer interaction interface put forward higher requirements for ensuring the safety of personnel, improving the efficiency of human-computer interaction and improving the efficiency of the system. Such as intelligent cabin adaptive cognitive decision aid system, how to adopt intelligent information display and human-computer interaction, optimize information processing, strengthen situational awareness; How to effectively present information and improve the efficiency of human-computer interaction, so that the system has good security, applicability and maximize its effectiveness; How to deal with man-machine matching and man-machine collaboration problems, so as to improve the efficiency of man-machine/unmanned collaborative work. Human factors throughout the life cycle of equipment systems must be fully considered. The human factor is considered in the system design, so that people, machines and the environment can work together and adapt to each other, so as to achieve benign interaction and feedback between people and equipment and interface and complete the full transmission and communication of human-machine intelligent interaction information. The development of new aircraft human-computer interaction systems combined with new technological methods has also gradually changed the role of pilots and staff. From the system operator gradually into the monitor and decision maker, especially with the improvement of the degree of intelligent flight, information technology, advanced complex airborne equipment is increasing, the amount of information that operators need to deal with is also increasing, and the allowed time for judgment and decision is very short, and the mental resources that pilots bear are gradually rising. As the mental load is a key factor affecting the allocation of cognitive tasks, when encountering emergency situations, the mental load overload caused by the increase of information processing tasks often occurs, which seriously affects the task performance of operators, physical and psychological comfort and flight safety, and thus affects the efficiency and safety of the entire aircraft man-machine system. This requires us to conduct real-time analysis of human-computer interaction situational awareness, especially the individual cognitive state as an uncontrollable factor
Present State and Future Challenges of Android Based Voice Controlled Power Devices
Get PDFAutomation of a modern person\u27s surroundings enables him to work more comfortably and efficiently. Routine chores performed by an individual can now be automated, which is a major advancement. Nowadays, the majority of individuals spend their entire day glued to their smartphones and other smart gadgets. Therefore, by personifying the usage of the cell phone, several everyday domestic duties can be completed with the assistance of his friend. According to a market analysis of smart phones, new customers are choosing Android-powered devices. In layman\u27s words, it has become a second name for a mobile phone. Voice Controlled Home Appliances (VCHA) automates an 8-bit Bluetooth-enabled microcontroller that regulates a variety of home appliances, including lights, fans, lamps, and many more, by means of an on/off relay. The device is designed for mobile phones running the Android operating system. This essay outlines an automated technique of home device control that could reduce the workload associated with utilizing the conventional switch method. In order to automate the system, Bluetooth—the most well-known and effective technology for short-range wireless communication—is used. With the ability to control up to 24 different appliances in any household setting, the VCHA system for Android users is a step in the right direction towards making duties easie
Password Complexity Prediction Based on RoBERTa Algorithm
Get PDFCorresponding author email: In the digital age, password security is a top priority for protecting personal information. Machine learning techniques provide us with intelligent and efficient means to enhance password security. In this paper, we adopt RoBERTa algorithm and use the password complexity text dataset for password complexity prediction, and the confusion matrix and accuracy rate of the three classifications are derived through two model trainings. The confusion matrix shows that the vast majority of the classification results are accurate, and the accuracy of the two classifications is over 99.741% and 99.11%, respectively. This indicates that the model is able to effectively predict password complexity, provide users with accurate feedback, and prompt users to enhance password security in a timely manner. Through this study, we can better understand how to use machine learning technology to improve password security and protect personal private information from malicious intrusion. In our daily life, we should pay attention to the complexity of password settings and realise the importance of password security for personal information protection. We look forward to the launch of more similar studies in the future to further strengthen cybersecurity protection measures and work together to build a more secure and reliable digital environment
Enterprise Supply Chain Risk Management and Decision Support Driven by Large Language Models
Get PDFThis paper explores the application and advantages of large-scale AI models in logistics and supply chains. Traditional enterprises need help with the timely detection of anomalies in the supply chain. At the same time, AI algorithms can quickly identify abnormal patterns in the data and issue alerts, helping enterprises adjust real-time strategies to ensure the supply chain\u27s stable operation. AI also reduces inventory costs and economic losses by predicting changes in market demand and optimizing inventory management. In addition, AI models perform well in intelligent scheduling and route planning, providing optimized solutions based on factors such as traffic flow, road conditions, and weather forecasts to improve transportation efficiency and accuracy. The article details the system architecture and functional modules designed to help enterprises meet the transformation challenges of the digital age
A Review Paper on Agile Project Management and Automation System in Software Development
Get PDFThe Agile Software Development Manifesto prioritizes people and relationships over procedures and equipment. However, managers can more effectively track the development of software projects by using software tools. An analysis of agile project management tools is presented in this report. This study on automation in agile projects could be beneficial to project managers. In the modern IT world, efficient software project management is typically the most crucial component in the success of numerous businesses, as well as their managers and engineers. Software managers oversee, direct, and oversee numerous projects at once. Agile project management insights are offered by the study. In order to comprehend the supported features that could affect their choice, the goal is to investigate and identify the top Agile project management solutions utilized by software project management teams and their managers. This paper examines a case study of a software project that automated software agile projects through the use of several software agile project management technologies. Automation of project planning, scheduling, and estimating can not only improve project success rates but also save project costs, completion times, and resource requirements. Agile project automation improves the likelihood that a project will be completed on time. Ultimately, the sum of these factors raises the software project success rate
Survey of Geothermal Heating and Cooling Technologies
Get PDFThe need to find new applications for renewable energy technology has grown as a result of the depletion of fossil resources and rising energy demand. In the Indian HVAC business, geothermal heating and cooling represents a recent development. Heat pump systems are used to utilize the heat from the ground to heat and cool areas, resulting in a 51% reduction in HVAC electricity consumption and a decrease in CO2 emissions. Lower operating costs and longer equipment life are geothermal energy\u27s primary future hopes. Recent developments in geothermal heating and cooling systems are reviewed in this article