10th International Congress on Information and Communication Technology in concurrent with ICT Excellence Awards (ICICT 2025) will be held at London, United Kingdom | February 18 - 21 2025.
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Authors - Md Fahim Afridi Ani, Abdullah Al Hasib, Munima Haque Abstract - This research explores the possibility of improving insect farming by integrating Artificial Intelligence (AI) unlocking the complicated relationship between butterflies and plants they pollinate to reconsider the way species are classified and helping to redraw farming practices for the butterflies. Traditional methods of butterfly classification are morphologically and behaviorally intensive, thus mostly very time-consuming to conduct considering that most of them have a high level of subjective interpretation. We therefore apply our approach to ecological interactions involving butterfly species and their respective plants for efficient data-driven solutions. This also focuses on the application of AI in making full benefits from butterfly farming, trying to determine where each species will be best located. The system will, therefore, classify and manage butterflies with much more ease, saving time and energy usually used in conventional classification methods hence on to the farmer or industrial client. The research deepens the understanding of insect-plant relationships for better forecasting of butterfly behavior and, therefore, healthier ecosystems through optimized pollination and habitat balance. For that purpose, a dataset of butterfly species and related plants was developed, on which machine learning models were applied, including decision trees, random forests, and neural networks. It tuned out that the neural network outperformed the others with an accuracy of 93%. Apart from classification, it helps in the identification of a habitat to provide the best conditions possible for the rearing of butterflies. Application of AI in this field simplifies the work of butterfly farming hence being an important tool to be used in improving growth and conservation of biodiversity. Integrating machine learning into ecological research and industry provides scalable, time-efficient solutions for the classification of species toward the sustainable farming of butterflies.
Authors - Zachary Matthew Alabastro, Stephen Daeniel Mansueto, Joseph Benjamin Ilagan Abstract - Product innovation is critical in strategizing business decisions in highly-competitive markets. For product enhancements, the entrepreneur must garner data from a target demographic through research. A solution to this involves qualitative customer feedback. The study proposes the viability of artificial intelligence (AI) as a co-pilot model to simulate synthetic customer feedback with agentic systems. Prompting with ChatGPT-4o’s homo silicus attribute can generate feedback on certain business contexts. Results show that large language models (LLM) can generate qualitative insights to utilize in product innovation. Results seem to generate human-like responses through few-shot techniques and Chain-of-Thought (CoT) prompting. Data was validated with a Python script. Cosine similarity tested the similarity of datasets to quantify the juxtaposition of synthetic and actual customer feedback. This model can be essential in reducing the total resources needed for product evaluation through preliminary analysis, which can help in sustainable competitive advantage.
Authors - Jeehaan Algaraady, Mohammad Mahyoob Albuhairy Abstract - Sarcasm, a sentiment often used to express disdain, is the focus of our comprehensive research. We aim to explore the effectiveness of various machine learning and deep learning models, such as Support Vector Machine (SVM), Recurrent Neural Networks (RNN), Bidirectional Long Short-Term Memory (BiLSTM), and fine-tuning pre-trained transformer-based mode (BERT) models, for detecting sarcasm using the News Headlines dataset. Our thorough framework investigates the impact of the DistilBert method for text embeddings on enhancing the accuracy of the DL models (RNN and LSTM) for training and classification. To assess the highest values of the proposed models, the authors utilized the four-performance metrics: F1 score, recall, precision, and accuracy. The outcomes revealed that incorporating the BERT model achieves outstanding performance and outperforms other models for an impressive sarcasm classification with a state-of-the-art F1 score of 98%. The outcomes revealed that the F1 scores for SVM, BiSLTM, and RNN are 93%, 95.05%, and 95.52%, respectively. Our experiment on the News Headlines dataset demonstrates that incorporating Distil-Bert to process the word vector enhances the performance of RNN, and BiLSTM notably improves their accuracy. The accuracy of the BiLSTM and RNN models when incorporating FT-IDT, Word2Vec, and GLoVe embeddings scored 93.9% and 93.8%, respectively. In contrast, these scores increased to 95.05% and 95.52% when these models incorporated Distil-Bert for text embedding. This augmentation can be recognized to the capability of Distil-Bert to acquire contextual information and semantic relationships between words, thereby enriching the word vector representation.
Authors - Lois Abigail To, Zachary Matthew Alabastro, Joseph Benjamin Ilagan Abstract - Customer development (CD) is a Lean Startup (LS) methodology for startups to validate their business hypotheses and refine their business model based on customer feedback. This paper proposes designing a large language model-based multi-agent system (LLM MAS) to enhance the customer development process by simulating customer feedback. Using LLMs’ natural language understanding (NLU) and synthetic multi-agent capabilities, startups can conduct faster validation while obtaining preliminary insights that may help refine their business model before engaging with the real market. The study presents a model in which the LLM MAS simulates customer discovery interactions between a startup founder and potential customers, together with design considerations to ensure real-world accuracy and alignment with CD. If carefully designed and implemented, the model may serve as a useful co-pilot that accelerates the customer development process.
Authors - Prince Kelvin Owusu, George Oppong Ampong, Joseph Akwetey Djossou, Gibson Afriyie Owusu, Thomas Henaku, Bless Ababio, Jean Michel Koffel Abstract - In today's dynamic digital landscape, understanding customer opinions and sentiments has become paramount for businesses striving to maintain competitiveness and foster customer loyalty. However, the banking sector in Ghana faces challenges in effectively harnessing innovative technologies to grasp and respond to customer sentiments. This study aims to address this gap by investigating the application of ChatGPT technology within Ghanaian banks to augment customer service and refine sentiment analysis in real-time. Employing a mixed-method approach, the study engaged (40) representatives including IT specialists, data analysts, and customer service managers from (4) banks in Ghana through interviews. Additionally, (160) customers, (40) from each bank, participated in a survey. The findings revealed a significant misalignment between customer expectations and current service provisions. To bridge this gap, the integration of ChatGPT technology is proposed, offering enhanced sentiment analysis capabilities. This approach holds promise for elevating customer satisfaction and fostering loyalty within Ghana's competitive banking landscape.
Authors - Japheth Otieno Ondiek, Kennedy Ogada, Tobias Mwalili Abstract - This experiment models the implementation of distance metrics and three-way decisions for K-Nearest Neighbor classification (KNN). KNN as a machine learning method has inherent classification deficits due to high computing power, outliers and the curse of dimensionality. Many researchers have experimented and found that a combination of various algorithmic methods can lead to better results in prediction and forecasting fields. In this experimentation, we used the combination and strengths of the Euclidean metric distance to develop and evaluate computing query distance for nearest neighbors using weighted three-way decision to model a highly adaptable and accurate KNN technique for classification. The implementation is based on experimental design method to ascertain the improved computed Euclidean distance and weighted three-way decisions classification to achieve better computing power and predictability through classification in the KNN model. Our experimental results revealed that distance metrics significantly affects the performance of KNN classifier through the choice of K-Values. We found that K-Value on the applied datasets tolerate noise levels to ascertain degree while some distance metrics are less affected by the noise levels. This experiment primarily focused on the findings that best K-value from distance metrics measure guarantees three way KNN classification accuracy and performance. The combination of best distance metrics and three-way decision model for KNN classification algorithm has shown improved performance as compared with other conventional algorithm set-ups making in more ideal for classification in the context of this experiment. It outperforms KNN, ANN, DT, NB and the SVM from the crop yielding datasets applied in the experiment.
