The Dual Impact of Artificial Intelligence in Healthcare: Balancing Advancements with Ethical and Operational Challenges (Published)
The synchronic and diachronic study of the evolution of Artificial Intelligence (AI) unveils one prominent fact that its effect can be traced in almost all fields such as healthcare industry. The growth is perceived holistically in software, hardware implementation, or application in these various fields. As the title suggests, the review will highlight the impact of AI on healthcare possibly in all dimensions including precision medicine, diagnostics, drug development, automation of the process, etc., explicating whether AI is a blessing or a curse or both. With the availability of enough data and analysis to examine the topic at hand, however, its application is still functioning in quite early stages in many fields, the present work will endeavour to provide an answer to the question. This paper takes a close look at how AI is transforming areas such as diagnostics, precision medicine, and drug discovery, while also addressing some of the key ethical challenges it brings. Issues like patient privacy, safety, and the fairness of AI decisions are explored to understand whether AI in healthcare is a positive force, a potential risk, or perhaps both
Keywords: Artificial Intelligence, Diagnostics, drug development, healthcare applications, precision medicine
Advancements in Robotics Process Automation: A Novel Model with Enhanced Empirical Validation and Theoretical Insights (Published)
Robotics Process Automation (RPA) is revolutionizing business operations by significantly enhancing efficiency, productivity, and operational excellence across various industries. This manuscript delivers a comprehensive review of recent advancements in RPA technologies and proposes a novel model designed to elevate RPA capabilities. Incorporating cutting-edge artificial intelligence (AI) techniques, advanced machine learning algorithms, and strategic integration frameworks, the proposed model aims to push RPA’s boundaries. The paper includes a detailed analysis of functionalities, implementation strategies, and expanded empirical validation through rigorous testing across multiple industries. Theoretical insights underpin the model’s design, offering a robust framework for its application. Limitations of current models are critically discussed, and future research directions are outlined to guide the next wave of RPA innovation. This study offers valuable guidance for practitioners and researchers aiming to advance RPA technology and its applications.
Keywords: Artificial Intelligence, RPA, data integration, machine learning
Teaching Science Education in an Era of Artificial Intelligence (Published)
School science instruction builds the groundwork for a generation of scientifically literate people who are equipped to handle and navigate the complex issues of the twenty-first century. If artificial intelligence (AI) is included into science courses, it could have a profound impact on how science education is provided. Teachers may develop more dynamic and interesting lessons that are tailored to the needs of each individual student by utilising artificial intelligence (AI) solutions like intelligent tutoring systems, virtual reality simulations, and personalised learning platforms. The main goal of the study is to establish a framework for investigating the possibilities of science education in the age of artificial intelligence (AI). This paper aims to examine the history of artificial intelligence (AI) and its application in science education, curriculum development, and classroom instruction in the current day. Furthermore, it will expand on the current corpus of information and offer insights into the possible advantages of integrating artificial intelligence (AI) into science education to improve teaching methods and speed up student learning.
Keywords: Artificial Intelligence, Science Education, Science Teaching, Utilization of AI
The Role of Artificial Intelligence in Teaching of Science Education in Secondary Schools in Nigeria (Published)
This study aims to examine and evaluate the influence of incorporating Artificial Intelligence (AI) into the instruction of scientific education in secondary schools in Nigeria. The primary objective is to investigate how AI technologies might improve the overall quality and efficacy of scientific teaching, leading to enhanced learning outcomes for secondary school students. The study employs a retrospective research approach, analyzing past data to gain insights into the development and impact of AI in scientific teaching in Nigerian secondary schools. The research design involves a comprehensive collection and analysis of secondary data from educational databases, government papers, academic journals, and other relevant repositories. Results from the study highlight the role of AI in teaching science, emphasizing Adaptive Learning Systems (ALS), Intelligent Tutoring Systems (ITS), and Virtual Laboratories and Simulations. ALS personalizes the learning process, ITS provides interactive and individualized instruction, and virtual laboratories offer immersive digital experiments. Challenges and barriers to the effective adoption of AI in scientific education include infrastructural limitations, teacher preparation and competence, and ethical considerations. Opportunities for successful integration involve government support, teacher training, and industry partnerships. Future prospects anticipate developments in personalized learning environments, improved data analytics, integration of virtual and augmented reality, enhanced natural language processing, and global cooperation in education. In conclusion, the study recommends the integration of AI into the national curriculum, adequate funding and resources, ongoing professional development for teachers, and a strategic curriculum development that fosters a blended learning environment.
Keywords: Artificial Intelligence, Science Education, Tailored Learning, Teaching
Analysis on Deep Learning Performance with Low Complexity (Published)
In this article, presenting deep learning to the LTE-A uplink channel estimation system. This work involved creating two SC-FDMA databases, one for training and one for testing, based on three different channel propagation models. The first part of this work consists in applying artificial neural networks to estimate the channel of the SC-FDMA link. Neural network training is an iterative process consisting of adapting the values (weights and biases) of its parameters. After training, the neural network is tested and implemented in the recipient. The second part of this work addresses the same experiment, but uses deep learning, not traditional neural networks. The simulation results show that deep learning has improved significantly compared to traditional methods for bit error rate and processing speed. The third part of this work is devoted to complexity research. Deep learning has been shown to provide better performance than less complex MMSE estimators.
Keywords: ANN, Artificial Intelligence, LTE-A, SC-FDMA, and portable, channel estimation, communications systems, deep learning, mobile
Artificial Intelligence in the Management of the Firm (Published)
Artificial Intelligence has changed from inquire about research facilities to business works. . current studies refers to that several companies in the last two years has developed AI applications till the present time in most of these applications represents as systems only but the others are identified with conventional data frameworks like as information preparing and administration data frameworks and notwithstanding learning based Expert Systems (ES) applications and a numerous number of AI applications, for example, neural systems , information based arranging , booking frameworks , discourse amalgamation frameworks , voice-acknowledgment frameworks chiefs and designers see minimal about the commonsense issues related with the association of fake keen , administration and associations likewise this theme may consider an essential one in light of the fact that the accomplishment of AI framework relies upon solving different technical, managerial and organizational issues.
Keywords: Artificial Intelligence, Firms, Initial Data, Network Architecture