The democratisation of innovation in Africa - A perspective driven by Artificial Intelligence (AI) Trends
Non-Invasive Prediction of Bone Disorder using Machine Learning
YouTube Transcript Summarizer: A Survey
Survey on Enhancing Dialogue Agent Alignment via MiniLLM with Targeted Human Assessments
The Impact of Artificial Intelligence in Education
Coffee Leaf Disease Detection Using Deep Learning
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 abstract provides a unique perspective on the democratisation of innovation in Africa through Artificial Intelligence (AI) trends. Unlike traditional research papers, this study eschews a problem statement and methodology, opting to paint a holistic picture of the evolving landscape of innovation in Africa, particularly concerning AI. Authored by an established publisher, this unconventional approach serves as a preface to future research in the burgeoning field of AI in Africa. By examining the intersection of AI trends and democratisation of innovation, this study 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 abstract provides a unique perspective on the democratisation of innovation in Africa through Artificial Intelligence (AI) trends. Unlike traditional research papers, this study eschews a problem statement and methodology, opting to paint a holistic picture of the evolving landscape of innovation in Africa, particularly concerning AI. Authored by an established publisher, this unconventional approach serves as a preface to future research in the burgeoning field of AI in Africa. By examining the intersection of AI trends and democratisation of innovation, this study 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.
Osteoarthritis, a prevalent degenerative joint disease, significantly impairs quality of life, particularly among the elderly. Traditional diagnostic methods often involve invasive and expensive imaging techniques. This project 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—flex sensors, MPU6050 sensors, and piezoelectric sensors—interfaced with a NodeMCU microcontroller. These sensors offer high sensitivity, fast response time, and durability, making them ideal for capturing critical data relevant to osteoarthritis detection. The data from these sensors is transmitted to the cloud and analysed 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 become a go-to platform for consuming educational content. However, students often need to spend many hours watching videos, with an average length of about 20 minutes each, to grasp complex concepts. To address this, the YouTube Video Summarizer was developed. This Chrome extension quickly generates summaries of YouTube videos using their English-language transcripts. By automating this process, users can effortlessly obtain a synopsis of the video's content, helping them determine its relevance without having to watch the entire video.
This paper presents the development of a compact and effective language model inspired by the LLaMA architecture. Our focus was on constructing this model based on the fundamental principles of LLaMA, which have influenced our architectural decisions and training methods. We sought to explore innovative research avenues and expand the possibilities achievable with limited resources. By leveraging open-source datasets and sophisticated training techniques, we made notable advancements without relying on extensive computational power or proprietary data. Nevertheless, due to resource limitations, the model work in progress. Researchers with access to more substantial computational capabilities could build on this foundation to enhance the model’s performance. We hope this paper encourages others in the field to contribute to the development of more robust language models that are accessible to all. Key Parameters for training include context window size, number of layers, batch size, and model dimensions. Results are evaluated based on epoch count, execution duration, model parameters, and validation loss.
Artificial Intelligence in Education is one of the emerging fields in education technology. Artificial intelligence (AI) has expanded to unprecedented proportions in recent decades, penetrating vast areas, including education. The current educational era is focusing on Artificial Intelligence (AI) and outcome based education predominantly. The increasing availability of Artificial Intelligence and related tools and technologies has made it possible to use AI in various domains of life. The field of education is also witnessing an increasing use of AI; however, its scope and related challenges remain unclear. The aim of the study is to understand 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 endeavour. A total of 150 participants were selected for the study within Kanniya kumari district ensuring an adequate sample size for statistical analysis. The responses were collected, coded and analyzed. Garrett Ranking technique was implied for the study. The results emphasize that the cost and over reliance on technology were considered as the major challenge of using artificial intelligence in education. Finally, the study put forward some recommendations for AI in education, with a focus on establishing discussions around the possibilities and risks of AI in education for sustainable development.
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, fine-tuning them on our 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.
