International Journal of Computer Engineering and Data Science (IJCEDS)

Healthcare Professional's Lifelong Learning Automation by Adapting Pedagogical Currents and Bloom's Taxonomy to Artificial Intelligence

Tue, 18 Feb 2025 00:00:00 +0000

Background: This study focuses on the integration of artificial intelligence (AI), pedagogical techniques and Bloom's taxonomy in health sciences education. AI plays a key role in this field, changing educational paradigms through personalized learning experiences. Methodology: The study examines how AI enables personalized educational progression based on individual needs, promoting continuous lifelong learning. It examines the potential of AI to provide rapid feedback on tasks and assessments, improving conceptual understanding. In addition, AI helps trainers discover learning trends through data analysis, and creates dynamic learning environments. Results: Research shows that AI-based education systems boost students' grasp of complicated subjects, problem-solving ability, and writing capabilities. Furthermore, AI's flexible capabilities enhance educational inclusion by tailoring learning approaches to various individual problems. Discussion: The findings highlight AI's transformative impact on health sciences education, stressing the transition from traditional models to adaptive, learner-centered approaches. AI's ability to accommodate different learning styles and facilitate continual skill development demonstrates its promise to transform professional education in the health sciences. Conclusion: Integrating AI into health sciences education not only improves learning outcomes but also fosters a culture of lifelong learning among students and practitioners. As AI advances, its integration with pedagogical frameworks such as Bloom's taxonomy opens up new possibilities for improving educational procedures and preparing future healthcare practitioners for dynamic professional challenges.

Comparative Study of Technical Provisioning Methods in Health Insurance: An Analytical Approach Using the Chain Ladder Method, the Mack Method and Generalized Linear Models

Ayyoub SAOUDI, Ghita Hajraoui, Jamal Zahi — Tue, 18 Feb 2025 00:00:00 +0000

This paper presents an in-depth analysis of technical reserving in health insurance, using three different methods: the Chain Ladder method (deterministic), the Mack stochastic method (stochastic) and Generalized Linear Models (GLM). The aim of this study is to compare these methods in terms of forecast accuracy, robustness and ability to consider the uncertainty inherent in health insurance claims. The scientific contribution of this research lies in the proposal of methods for improving technical reserving, thereby improving the financial management of health insurance companies.

Security Considerations in the Internet of Medical Things: COVID-19 IoMT Gadgets

Thu, 19 Oct 2023 00:00:00 +0000

The COVID-19 pandemic has underscored the significance of the Internet of Medical Things (IoMT) in healthcare. During this crisis, IoMT devices played a crucial role in remotely monitoring patients, tracking virus transmission, and supporting healthcare professionals. However, the increased adoption of IoMT devices has also raised concerns regarding security and privacy vulnerabilities. This paper presents a comprehensive analysis of the vulnerabilities found in IoMT devices designed and rapidly deployed during the pandemic, such as remote patient monitoring devices and telemedicine platforms. The study identifies common hardware, software, and communication vulnerabilities that pose potential threats to patient safety and the integrity and confidentiality of healthcare data. Furthermore, the paper examines proposed security mechanisms, including blockchain-based frameworks and lightweight authentication schemes, all of which aim to address these challenges. By raising awareness of these vulnerabilities and risks, this research aims to encourage the development of more secure and resilient IoMT devices in the future.

Artificial Neural Network model for Call Options Pricing Using Market Data

Georgios Rigopoulos — Mon, 18 Nov 2024 00:00:00 +0000

Accurate option pricing is of key importance for markets and traders. This work explores the feasibility of using artificial neural network model in call option pricing, using the traditional Black-Scholes model as a benchmark. We used a multilayer perceptron model trained to learn Black-Scholes function and tested in real option data from thirty-five S&P100 stocks. In our approach testing data is not oriented from the same distribution as training and this is a unique contribution to existing research. Findings demonstrate that artificial neural networks performs well in actual market data. Although further exploration and experimentation is required to reach required robustness and become less ad hoc and data sensitive, it is a promising approach and can play a substantial role in option pricing,.

Improving sustainable management through an AI-based strategic transition: A policy that promotes firms to use AI advancements in the Industry 4.0 era

Hanane ALLIOUI, Youssef Mourdi — Mon, 18 Nov 2024 00:00:00 +0000

Due to the severe effects of the Fourth Industrial Revolution and environmental disasters or subsequent pandemics, the digital infrastructures of businesses need drastic and constant changes. As a result, many companies are actively implementing innovative digital strategies to accelerate digital adjustments throughout the scale of their organizational structures. Since artificial intelligence (AI) has demonstrated its success in a wide range of fields, including both business processes and daily activities, it has gained extreme interest in corporate ecosystems. Business management techniques could be transformed by the integration of AI for more productivity, cost-effectiveness, and overall efficiency. AI is strategically incorporated into businesses to help them engage with their target consumers more effectively, giving them an edge over their digital competitors. Additionally, AI has the potential to revolutionize corporate operations, enabling the creation of novel ideas, the completion of complex tasks, and the acceleration of significant economic growth. To achieve thorough optimization, it is necessary to carefully adjust AI integration tactics to the unique requirements at each stage of development. In this study, we present a strong strategy to hasten the conception, alignment, and prioritizing of developing activities, supporting a smooth transition to more effective and sustainable management methods.

Measuring the readiness of Ecommerce Small and Medium Enterprises (SMEs) to use Artificial Intelligence: Example of Moroccan SMEs

Hanane ALLIOUI, Youssef Mourdi — Fri, 15 Mar 2024 00:00:00 +0000

success due to its increasing significance in facilitating large-scale data processing, producing insights, and automating repetitive processes. The implementation of AI technology has the potential to boost organizational effectiveness, save costs, and improve consumer experiences, resulting in increasing growth and competition. The adoption of AI in Small and Medium Enterprises (SMEs), especially in developing nations like Morocco, is still at a low level. This paper uses an expanded innovation-business ecosystem framework and human inventiveness to analyze the elements that influence AI adoption in Moroccan e-commerce SMEs. The study highlighted manager/ownership culture, monitoring environment, qualified advantages, maximal management support, and AI expertise as key predictors of AI adoption among Moroccan e-commerce SMEs. Although the study's sample size is small, additional research is required to investigate other issues that could hinder Moroccan e-commerce SMEs' adoption of AI and financial growth. The study's conclusions offer insightful information that might support e-commerce SMEs and the Moroccan government in spreading awareness of AI, a critical step towards technological advancement in a historically conservative corporate environment.

Advances in temperature control for microelectronic measurements

Malaoui Malaoui — Wed, 18 Oct 2023 00:00:00 +0000

This paper introduces an electronic temperature control system with the objective of delivering a functional prototype that is both cost-effective and capable of rapid and accurate performance. Intended for utilization in research laboratories for precise temperature control during electrical measurements, this device employs a 10-bit embedded system to implement the PID algorithm. The electronic schematics and results of this controller are discussed and compared to those of an earlier 8-bit controller and a standard laboratory regulator. Initial assessments focus on regulating the temperature of a photovoltaic junction to ascertain its intrinsic electrical characteristics. The results obtained vividly illustrate the extent and magnitude of the benefits conferred by this innovative device.

The governance of health information systems : a tool for improving the quality of care

fatima ezzahra salamate, Hasna ATTAR, Aadil BELHAJ, Jamal ZAHI — Sat, 03 Feb 2024 00:00:00 +0000

Computerization of the care process is essential. It facilitates the traceability of care activity, which is the founding regulatory element of care safety, and the operational element of continuous quality improvement initiatives. This is why the implementation of traceability in the field now requires the development of responsive information systems based on information technology governance, and specifically the ITIL best practice framework. The deployment of ITIL will enable care processes to be mastered in real time, eliminating malfunctions and improving SIS performance.

Unleashing the Potential of AI: Investigating Cutting-Edge Technologies That Are Transforming Businesses

Hanane ALLIOUI, Youssef Mourdi — Wed, 16 Aug 2023 00:00:00 +0000

The integration of AI has ushered in a new era of enhanced reliability in digital offerings, optimization of supply chain processes, and real-time access to invaluable data and analytics. Companies stand to benefit significantly as they employ AI to reduce lead times, uncover fresh customer insights, revolutionize customer service standards, and deliver unparalleled personalized experiences. This paper strives for excellence in its quest to bridge the knowledge gap and facilitate the successful assimilation of AI into business planning. By conducting a rigorous literature analysis and synthesizing contemporary methodologies and frameworks, it brings to the fore the potential advantages, challenges, and untapped possibilities. Moreover, this study delves into future research prospects, empowering businesses with the requisite knowledge and strategies to harness the full potential of AI and achieve unparalleled success in the dynamic and competitive world of business.

Does gender affect the identification of road crash occurrences? An Overview and a comparative study

Soukaina EL FEROUALI, zouhair Elamrani Abou Elassad, Abdelmounaîm ABDALI — Wed, 28 Jun 2023 00:00:00 +0000

Around the world, traffic accidents are regarded as a large and significant cause of injury and death. Nearly 3,700 people are killed and over 1.3 million individuals lose their lives in collisions involving trucks, vehicles, buses, motorcycles, or pedestrians. This article tries to identify the important causes of traffic accidents involving both men and women as well as the methods that have been suggested and put into practice based on the literature study. In order to determine how gender impacts the frequency of traffic accidents, a survey and a comparative study were conducted in this work. According to the findings, the factors that have been studied for accident causes in urban areas include speed, age, and gender. On rural roads, speed has been recognized as the primary cause of collisions, particularly among men, while age and lack of experience have been noted as influencing factors in women's traffic incidents. Because machine learning models are effective at predicting crashes, they have been utilized in the majority of research