The Democratization of Innovation in Africa - A Perspective Driven by Artificial Intelligence Trends
Survey on Enhancing Dialogue Agent Alignment through MiniLLM with Targeted Human Assessments
Coffee Leaf Disease Detection using Deep Learning
The Impact of Artificial Intelligence in Education
Non-Invasive Prediction of Bone Disorder using Machine Learning
Youtube Transcript Summarizer: A Survey
Coffee Leaf Disease Detection using Deep Learning
Advancing Autism Spectrum Disorder Diagnosis through Ensemble Learning
Enhancing Chronic Kidney Disease Prediction Performance through Algorithm Fusion: A Combined KNN - SVM Approach
A Comparative Exploration of Activation Functions for Image Classification in Convolutional Neural Networks
Machine Learning Solutions for the Healthcare Industry: A Review
This paper provides a unique perspective on the democratization of innovation in Africa through Artificial Intelligence (AI) trends. This work avoids a problem statement and methodology, instead providing a holistic view of Africa's evolving innovation landscape, particularly in relation to AI. This unconventional approach serves as a preface to future study in the growing field of AI in Africa. By examining the intersection of AI trends and democratization of innovation, this paper offers insights into the transformative potential of AI technologies in addressing societal challenges, empowering local communities, and driving inclusive growth across the continent. As Africa embraces AI as a catalyst for progress, this abstract sets the stage for further exploration of the implications, opportunities, and challenges that lie ahead in harnessing the power of AI to unlock Africa's vast potential for innovation and development.
This paper presents the development of a compact and effective language model inspired by the LLaMA architecture. The model's design is based on the fundamental principles of LLaMA, which influenced the architectural decisions and training methods. This study explores innovative approaches and expands the possibilities achievable with limited resources. By leveraging open-source datasets and advanced training techniques, significant progress was made without relying on extensive computational power or proprietary data. However, due to resource constraints, the model remains a work in progress. Individuals with access to greater computational capabilities could build upon this foundation to enhance its performance. This investigation aims to promote further contributions to the advancement of more robust and accessible language models. Key training parameters include context window size, number of layers, batch size, and model dimensions. Model evaluation is based on epoch count, execution time, model parameters, and validation loss.
Coffee is one of the most widely consumed beverages globally, and its production is significantly threatened by various leaf diseases, leading to substantial economic losses for farmers. To reduce this a deep learning-based approach for the detection of coffee leaf diseases utilizing Convolutional Neural Networks (CNNs) and transfer learning techniques are used. A diverse dataset of coffee leaf images are collected, representing healthy leaves and those affected by common diseases, including coffee leaf rust, bacterial blight, and leaf spot. The dataset was augmented through techniques such as rotation, flipping, and scaling to enhance model robustness. Transfer learning with pre-trained models, specifically DenseNet and ResNet were fine-tuned on the dataset to leverage their powerful feature extraction capabilities. The suggested model was examined and achieving an 82.3% accuracy and primary objective is to enhance the model's accuracy in detecting leaf-based diseases by leveraging advanced deep learning techniques and this is crucial for agricultural practices.
Artificial intelligence in education is one of the most promising areas of educational technology. Artificial intelligence (AI) has grown to unprecedented proportions in recent decades, infiltrating numerous fields, including education. The modern educational period is mostly focused on artificial intelligence and outcome-based education. The current educational era is focusing on Artificial Intelligence and outcome based education predominantly. Artificial intelligence and associated tools and technologies are becoming more widely available, allowing them to be used in a variety of fields. The application of AI in education is also on the rise; however, its extent and associated challenges are not fully understood. The aim of this study is to explore the impact of artificial intelligence in education. This paper also addresses the challenges of AI in education, as well as the potential risks of such an endeavor. Participants were selected for the study from the Kanyakumari district, ensuring an adequate sample size for statistical analysis. The responses were collected, coded, and analyzed. The Garrett Ranking technique was applied for the study. The results emphasize that cost and over-reliance on technology were identified as the major challenges of using artificial intelligence in education. Finally, the study proposed some recommendations for AI in education, with an emphasis on starting conversations about the opportunities and hazards of AI in education for sustainable development.
Osteoarthritis, a prevalent degenerative joint disease, significantly impairs quality of life, particularly among the elderly. Traditional diagnostic methods frequently involve invasive and expensive imaging techniques. This study aims to develop a non-invasive, real-time prediction system for osteoarthritis using the K-Nearest Neighbors (KNN) algorithm, a robust machine learning approach. The core of this system is its ability to accurately and comprehensively collect sensor data from the user's joints. The system integrates a variety of non-invasive sensors, including flex sensors, MPU6050 sensors, and piezoelectric sensors, interfaced with a Node MCU microcontroller. The data from these sensors is transmitted to the cloud and analyzed using the KNN algorithm to predict the likelihood of osteoarthritis. The dataset, sourced from Kaggle, is split into 70% for training and 30% for testing. The KNN algorithm is applied to classify individuals into different osteoarthritis risk categories. This non-invasive, portable, and efficient solution offers a promising alternative to traditional diagnostic methods, making osteoarthritis prediction more accessible and cost-effective.
YouTube has increasingly become the preferred platform for consuming educational content. To learn complex and intricate concepts, students frequently need to watch several hours of YouTube videos, with an average video length of about 20 minutes. To help users quickly determine whether a video's content is relevant to their needs, the YouTube Video Summarizer was conceptualized. This Chrome extension efficiently generates a summary of a YouTube video using its English-language transcript. By automating this process, the tool enables users to obtain a concise synopsis without spending hours watching the video to assess its relevance.
