Validation of Expert Systems: Personal Choice Expert -- A Flexible Employee Benefit System

A method for validating expert systems, based on psychological validation literature and Turing\u27s imitation game, is applied to a flexible benefits expert system. Expert system validation entails determining if a difference exists between expert and novice decisions (construct validity), if the system uses the same inputs and processes to make its decisions as experts (content validity), and if the system produces the same results as experts (criterionrelated validity). If these criteria are satisfied, then the system is indistinguishable from experts for its domain and satisfies Turing\u27s imitation game. The methods developed in this paper are applied to a human resource expert system, Personal Choice Expert (PCE), designed to help employees choose a benefits package in a flexible benefits system. Expert and novice recommendations are compared to those generated by PCE. PCE\u27s recommendations do not significantly differ from those given by experts. High inter-expert agreement exists for some benefit recommendations (e.g. Dental Care and Long-Term Disability) but not for others (e.g. Short-Term Disability and Life Insurance). Insights offered by this method are illustrated and examined

What is usability in the context of the digital library and how can it be measured?

This paper reviews how usability has been defined in the context of the digital library, what methods have been applied and their applicability, and proposes an evaluation model and a suite of instruments for evaluating usability for academic digital libraries. The model examines effectiveness, efficiency, satisfaction, and learnability. It is found that there exists an interlocking relationship among effectiveness, efficiency, and satisfaction. It also examines how learnability interacts with these three attributes

Drowsy Eyes and Face Mask Detection for Car Drivers using the Embedded System

Efforts to prevent the spread of the COVID-19 virus have underscored the critical importance of mask-wearing as a preventive measure. Concurrently, road traffic accidents, often resulting from human error, have emerged as a significant contributor to global mortality rates. This study endeavors to address these pressing issues by employing advanced Deep Learning techniques to detect mask usage and identify drowsy eyes, thus contributing to the prevention of COVID-19 and accidents due to driver fatigue. To achieve this objective, an embedded system was developed, utilizing the integration of hardware and software components. The system effectively utilizes MobileNetV2 for face mask detection and employs HOG and SVM algorithms for drowsy eye detection. By seamlessly integrating these detection systems into a single embedded device, the simultaneous detection of both mask usage and drowsy eyes is made possible. The results demonstrates a commendable accuracy rate of 80% for face mask detection and 75% for drowsy eye detection. Furthermore, the mask detection component exhibits a remarkable training accuracy of 99%, while the drowsy eye detection component demonstrates an 80% training accuracy, affirming the system's efficacy in precisely identifying masks and drowsy eyes. The proposed embedded system offers potential applications in enhancing road safety. Its capability to effectively detect drowsy eyes and mask usage in car drivers contributes significantly to preventing accidents due to driver fatigue. Additionally, it plays a vital role in mitigating COVID-19 transmission by promoting widespread mask-wearing among individuals. This study exemplifies the potential of integrating Deep Learning methodologies with embedded systems, thus paving the way for future research and development in the realm of driver safety and virus prevention

Peer-to-Peer File Sharing WebApp: Enhancing Data Security and Privacy through Peer-to-Peer File Transfer in a Web Application

Peer-to-peer (P2P) networking has emerged as a promising technology that enables distributed systems to operate in a decentralized manner. P2P networks are based on a model where each node in the network can act as both a client and a server, thereby enabling data and resource sharing without relying on centralized servers. The P2P model has gained considerable attention in recent years due to its potential to provide a scalable, fault-tolerant, and resilient architecture for various applications such as file sharing, content distribution, and social networks.In recent years, researchers have also proposed hybrid architectures that combine the benefits of both structured and unstructured P2P networks. For example, the Distributed Hash Table (DHT) is a popular hybrid architecture that provides efficient lookup and search algorithms while maintaining the flexibility and adaptability of the unstructured network.To demonstrate the feasibility of P2P systems, several prototypes have been developed, such as the BitTorrent file-sharing protocol and the Skype voice-over-IP (VoIP) service. These prototypes have demonstrated the potential of P2P systems for large-scale applications and have paved the way for the development of new P2P-based systems

ACCOUNTING AND FINANCIAL STATEMENTS AUTO ANALYSIS SYSTEM

This project was motivated by the need to revolutionize the generation of financial statements and financial analysis process thus speeding up business decision making. The research questions were: 1) How can machine learning increase the speed of financial statement preparation and automate financial statements analysis? 2) How can businesses balance the benefits of automating financial analysis with potential concerns around privacy, data security, and bias? 3) Can the Java J2EE framework provide a reliable running environment for machine learning? The findings were: 1) Machine learning can significantly increase the accuracy and speed of financial analysis. Using machine learning algorithms, financial data can be processed and analyzed in real-time, allowing for quicker and more precise financial analysis. Machine learning models can identify patterns and trends in financial data that may not be easily detectable by humans, leading to more accurate financial statements and analysis. Additionally, machine learning can automate repetitive tasks in the financial analysis process, saving time and resources for businesses. 2) Businesses need to carefully balance the benefits of automating financial analysis with potential concerns around privacy, data security, and bias. While machine learning can offer significant advantages in terms of accuracy and speed, it also requires handling sensitive financial data. Therefore, it is crucial for businesses to implement robust data security measures to protect against potential data breaches and ensure compliance with privacy regulations. Additionally, businesses need to be mindful of potential biases in machine learning algorithms, as biased algorithms can result in biased financial analysis. Regular audits and monitoring of machine learning models should be conducted to address and mitigate any potential biases. 3) The Java J2EE framework can provide a reliable running environment for machine learning. Java J2EE (Java 2 Platform, Enterprise Edition) is a widely used and mature framework for developing enterprise applications, including machine learning applications. It offers scalability, reliability, and security features that are essential for running machine learning algorithms in a production environment. Java J2EE provides robust support for distributed computing, allowing for efficient processing of large financial datasets. Furthermore, it offers a wide range of libraries and tools for implementing machine learning algorithms, making it a viable choice for running machine learning applications in the financial industry. The conclusions were: 1) Machine learning has the potential to significantly increase the accuracy and speed of financial analysis, thereby revolutionizing the generation of financial statements and the financial analysis process. Various machine learning algorithms, such as decision trees, random forests, and deep learning algorithms, can be utilized to identify patterns, trends, and hidden risks in financial data, leading to more informed and efficient business decision making. 2) Businesses need to carefully balance the benefits of automating financial analysis with potential concerns around privacy, data security, and bias. While machine learning can offer significant advantages in terms of accuracy and speed, there are ethical considerations that need to be addressed, such as ensuring data privacy, implementing effective data security measures, and mitigating biases in machine learning algorithms used in financial analysis. Businesses should adopt a responsible approach to machine learning implementation, considering the potential risks and benefits. 3) The Java J2EE framework can provide a reliable running environment for machine learning applications, but further research is needed to evaluate the performance and scalability of machine learning models in this framework. Identifying potential optimizations for running machine learning applications at scale in the Java J2EE framework can lead to more efficient and effective implementation of machine learning in financial analysis and decision-making processes. Further research in this area can contribute to the development of robust and scalable machine learning applications for financial analysis in the business domain. Areas for further study include: 1) Exploring different machine learning algorithms and techniques to further improve the accuracy and speed of financial analysis. 2) Conducting research on the impact of machine learning on financial decision making and business performance. 3) Investigating methods for addressing and mitigating biases in machine learning algorithms used in financial analysis. 4) Evaluating the effectiveness of different data security measures in protecting sensitive financial data in machine learning applications. 5) Studying the performance and scalability of machine learning models in the Java J2EE framework and identifying potential optimizations for running machine learning applications at scale

Tram-tastic Cloud Computing

This master’s thesis evaluates the scalability and cost-effectiveness of the AWS cloud platform used to collect and utilize data generated by the 87 digitally equipped trams. The SL-18 Cloud Platform was developed before the trams arrived, and resource configuration estimates were made to handle the data generated by the trams. However, with a few trams currently operational, it is crucial to evaluate the allocation of resources to the services based on actual data. Thus, the thesis's objective is to estimate the data generated by all 87 trams and evaluate the current resource provisioning on the AWS Cloud Platform in terms of scalability and cost. By doing so, this study will provide insights into the optimal resource allocation required for the AWS Cloud Platform to accommodate the data generated by the trams. In this study, we use an existing Digital Twin tool for the trams to evaluate the scalability of the platform, ensuring that it can handle the load while keeping the cost low. To achieve this, the existing Digital Twin is modified to run 87 or more instances concurrently. Using this modified tool, the SL-18 IT platform, which processes real-time data from all 87 trams simultaneously, is evaluated. We monitored the metrics of AWS services to identify any issues. Then based on measurements, we make recommendations for each service's upgrading, downgrading, or keeping the current configuration. Most services are recommended to scale down to reduce costs, while three services require scaling up to be operational. Although our process is well-defined and could be replicated by other studies, it is crucial to have in-depth discussions with the relevant teams for each service and perform repeated validations and evaluations. This is also a necessary protocol in Sporveien to present the results to the various stakeholders and implement the recommended changes. With these changes, Sporveien can save costs and most importantly have a platform capable of handling the data load of 87 SL-18 trams

A Mobile-Based Skin Disease Identification System Using Convolutional Neural Networks

Skin diseases pose significant challenges in the field of dermatology. In recent years, Convolutional Neural Networks (CNNs) have emerged as a powerful tool for image recognition and analysis tasks. This research paper presents a comprehensive study on the application of CNNs for skin disease diagnosis. We propose a CNN-based framework for skin disease diagnosis, which utilizes a large dataset of dermatological images to accurately identify various skin diseases. The proposed model leverages the deep learning capabilities of CNNs to learn discriminative features from input images, enabling accurate and efficient diagnosis. We demonstrate improved accuracy and efficiency in skin disease diagnosis by employing pre-trained models. Our proposed model enables accurate classification of skin diseases into high, medium, and low severity categories by leveraging a large dataset of annotated images, assisting healthcare professionals in prioritizing treatment strategies. In conclusion, this research paper presents a comprehensive study on the application of CNNs for skin disease diagnosis, skin lesion classification, melanoma skin cancer classification, and skin disease severity classification. The proposed models showcase significant advancements in the field of dermatology, providing accurate and efficient tools for dermatologists and healthcare professionals. The findings of this research contribute to improving the diagnosis, classification, and severity assessment of skin diseases, ultimately enhancing patient care and treatment outcomes