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