Browsing by Author "Lossan Bonde"

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A Conceptual Design of a Generative Artificial Intelligence System for Education
(International journal of research and innovation in applied science (IJRIAS), 2024-05-24) Lossan Bonde
Artificial Intelligence (AI) and, more specifically, Generative Artificial Intelligence (GenAI) are among the most trending disruptive technologies, offering unprecedented possibilities for innovation in education by customising learning paths, fostering creativity, and bridging knowledge gaps. Gen AI is a subfield of AI which generates content (text, image, audio or video) that is comparable to human-generated content. It uses complex Machine Learning (ML) models and Neural Networks (NN) trained on large datasets. The launch of ChatGPT in November 2022 has significantly raised the standards for generative artificial intelligence capabilities. Since then, researchers and practitioners have extensively investigated the potential applications of this technology. Consequently, GenAI has been applied in various sectors, including business and marketing, media and entertainment, arts and design, research and innovation, and education. While it is evident that GenAI has a high potential transformative power on education, its actual application in classical educational settings such as schools and universities remains limited to individual (student or teacher) trials without institutional guidance and support. The aim of this is expressed in two points: (1) to explore the advances in GenAI and provide guidelines on leveraging the technology in the context of learning and teaching. (2) to identify the main barriers to adopting GenAI in education and how to address them when building a Gen AI application for education. The paper’s main contribution is conceptualising a Gen AI system for learning and teaching. Such a system produces course content, exercises, and supplementary materials tailored to the curriculum and each student’s learning pace and style. This system will enhance comprehension and retention, making education more accessible and effective. Secondarily, the research addresses the ethical issues and other barriers to adopting GenAI in education.
A Generative Artificial Intelligence Based Tutor for Personalized Learning
(IEEE Xplore, 2024) Lossan Bonde
Personalized learning refers to a system of teaching and learning where the content, methods, and assessment are tailored to each learner’s needs, capacities/skills/competencies, and pace. There is abundant literature about personalized learning, describing its advantages, challenges, and approaches to implementing it. This study has explored the abundant literature and realized that while there are substantial proposed ways to achieve personalized learning, these proposals are not implemented into actionable products. This research aims to construct a Generative Artificial Intelligence (Gen AI) tutor that implements personalized learning and teaching in a higher education level course. The research methodology involved exploring literature to determine the requirements for personalized learning and designing a tutor system. The resultant system uses Custom GPT technology, Application Programming Interface (API), a repository for storing content, learners’ profile information, and performance metrics. Out of the six components that make the proposed system, only the “Custom GPT” component has been implemented and tested on a course named “Applied Cryptography” in a postgraduate program. Though the preliminary results are promising, an effective assessment of the system will be made when the full implementation is completed. Further, issues related to ethics, scaling and integration with learning management systems should also be considered. Subsequent work to this study will focus on those issues.
A Machine Learning Based Framework forCollecting and Using Social Media for Real-timeTerrorist Attacks Prediction
(International Conference on Artificial Intelligence and its Applications, 2023) Lossan Bonde; Severin Dembele
Terrorism has become a global plague causing insecurity and jeopardizing the development of many countries. In the past few years, terrorism has exploded in Burkina Faso, affecting education, national security, health, and the economy. There is a great need for solutions to detect and stop terrorist attacks before they occur. This research project seeks to use Artificial Intelligence (AI) to mine social media and detect probable future terrorist attacks. This article describes the design of a framework, its partial implementation, and an experiment to validate the technique. The system consists of five steps: taking social media as input, converting it to text, validating it, extracting essential information, predicting its class, and storing it in a dataset. The modest size of the manually produced dataset utilized in the original experiment is a key drawback of the work discussed in this research. The modest size proved inconvenient for Deep Learning algorithms, which operate best with massive datasets. When we complete the entire system, inserting increased data from social media into the dataset will resolve this limitation. The other limitation is the partial implementation of the framework, which does not provide a comprehensive picture of the proposed approach. Our future works will address the remainder parts of the proposed framework.
A Unified Generative Artificial Intelligence Approach for Converting Social Media Content
(IEEE Explore, 2024) Lossan Bonde; Severin Dembele
Social media content is relevant for many applications, including applications that assist in fighting the plague of terrorism through Artificial Intelligence (AI). However, social media content is diverse in its form - text, image, audio, and video. Depending on the nature of the applications, it may be desirable to convert all these forms into a unique format to ease processing. Once the data is converted into text, it is then possible to organize it into structured tabular data to feed Machine Learning (ML) algorithms for real-time terrorist attack detections. This paper explores using the emerging Generative Artificial Intelligence (Gen AI) tools for converting social media content (text, image, audio or video) into text format suitable for applying machine learning algorithms. The methodology of this research consisted of studying existing Gen AI tools, evaluating and selecting the best among those that offer API or code integration to implement a tool for converting all forms of social media content into text. The main limitation of this work is the small size of the datasets used in the tools’ evaluation. The design and implementation of the proposed solution have been completed, and the tool is ready for use and integration into a framework for collecting and analysing social media content to fight against terrorism.
Achieving Organizational Performance in Profit-Oriented Businesses Through the Use of Artificial Intelligence
(Pan-African Journal of Education and Social Sciences (PAJES), 2023) Lossan Bonde; Prof Musa Nyakora
Artificial Intelligence (AI) is undoubtedly one of the most evolving technologies, finding its application in almost every aspect of human activity. At the individual, business, and corporate organization levels, AI transforms lives and business processes and affects how people work. There is extensive literature on AI’s potential positive contribution to organizational performance. However, there is a lack of research on how to leverage AI to effectively achieve organizational performance. This paper seeks to fill that gap by 1) identifying the AI components driving organizational performance and then 2) designing a conceptual framework for integrating AI into an organization to achieve higher performance. A significant limitation of this work is the lack of implementation and testing of the framework, which we will address in future research.
An Evaluation of Machine Learning Techniques for Crop Detection from Garden Images
(IEEE Xplore, 2023) Samson O. Ooko; Grace Kwagalakwe; Lossan Bonde
Agriculture is a major driver for different economies across the globe. With the continued advancement in technology, there is a move towards precision agriculture. A major area of research interest is the detection of crops by use of computer vision. Such can help in controlling pests and diseases and thus increase yields. However, given the wide range of techniques applied, there is a need to evaluate them to recommend the most suitable for the detection of different crops. This study was thus aimed at evaluating both traditional Machine Learning (ML) and Deep Learning (DL) techniques for the detection of banana and cassava crops from a set of garden images. First, a crop classification model was built using the traditional machine learning model with feature extraction algorithms being applied before training using K-Nearest Neighbor (KNN) and Naïve Bayes. Another crop classification model was also built using deep learning (CNN) on an annotated dataset. The performance of both models was evaluated with deep learning giving the best result. Deep learning is thus recommended as the best model for crop detection.
From Email-based to Chatbot-based IT Support: A Conceptual Design
(Journal of Emerging Technologies and Innovative Research (JETIR), 2019-06) Lossan Bonde
Email-based IT support refers to IT support systems whereby an email address is set to receive requests from users. Upon receipt of an email at that address, a ticketing system triggers an incident ticket generation. Then the ticket is assigned to an IT support person who processes it manually and responds to the user. This process is subject to long delays since the assignment and processing require human intervention. The manual operation is not only time-consuming but also leads to a lot of workloads to IT support personnel and produces frustrations on the users’ side. As chatbots technology gets more and more reliable, IT support is one of the areas where their use is much needed and appreciated. This research proposes to elaborate on a conceptual design of a framework for the production of chatbots for IT support. The solution operates in three steps: (1) mails in the mailbox are read, analysed and incorporated into a knowledge base (KB). (2) A machine learning algorithm (MLA) is applied to enrich it and make it ready for natural language processing. (3) The last step of the process produces the chatbot. The design and implementation of the proposed framework is a heavy project that is just starting. The scope of this paper is about the overall conceptual design of the framework. Our future work will address the details and implementations of the components
High Accuracy Location Information Extraction From Social Network Texts Using Natural Language Processing.
(International Journal on Natural Language Computing (IJNLC), 2023-08) Lossan Bonde; Severin Dembele
Terrorism has become a worldwide plague with severe consequences for the development of nations. Besides killing innocent people daily and preventing educational activities from taking place, terrorism is also hindering economic growth. Machine Learning (ML) and Natural Language Processing (NLP) can contribute to fighting terrorism by predicting in real-time future terrorist attacks if accurate data is available. This paper is part of a research project that uses text from social networks to extract necessary information to build an adequate dataset for terrorist attack prediction. We collected a set of 3000 social network texts about terrorism in Burkina Faso and used a subset to experiment with existing NLP solutions. The experiment reveals that existing solutions have poor accuracy for location recognition, which our solution resolves. We will extend the solution to extract dates and action information to achieve the project's goal.
Innovative M-Learning with Automatic Feedback: Enhancing Language Acquisition for Level 2 Indonesian Foreign Speakers (BIPA)
(JOLLT Journal of Languages and Language Teaching, 2024-10) Helmi Muzaki; Gatut Susanto; Didin Widyartono; Lossan Bonde; Thilip Kumar Moorthy; Ilham Akhsani
Indonesian online. However, online learning creates obstacles such as time differences between teachers and BIPA students, internet connections, and providing less than optimal feedback. This research aims to develop m-learning with automatic feedback for BIPA level 2 learners. This research uses a 4D development model: define, design, develop and deploy. This study's instrument is a questionnaire distributed to 2 validators and 18 BIPA learners. The results of this study are m-learning products that minimize internet connections; once installed, students only need to be connected to the internet when working on questions. In addition, m-learning is also equipped with automatic feedback that appears immediately after students answer questions. The results of the product trial show that students can use m-learning to learn anytime and anywhere, including in areas with minimal internet access. Automatic feedback in m-learning also helps students learn independently because they do not need to wait for feedback from teachers. The automatic feedback in M-learning is only for listening and reading questions while speaking and writing questions are still in the form of answer keywords or assessment rubrics that the teacher must correct. Based on expert validation and product trials with an average score of 88.8, we conclude that the development of m-learning is suitable for BIPA level 2 students.