Energy management in small and medium sized hotels on Mauritius Island
This paper sumarises several case studies on an energy audit in small and medium hotels in the island of Mauritius
A Transformer-Based Multimodal Framework for Enhanced Autism Spectrum Disorder Diagnosis
1) To improve identification performance, we proposed a multimodal framework that integrates medical imaging and clinical textual data.
2) Introduction of a classification token mechanism to enhance feature representation and determination with Vision Transformer.
3) Finally, we implement hyperparameter optimization techniques to improve model efficiency, generalization, and overall performance.
Improving Interpretability in Lung Cancer Prediction through Explainable Ensemble Voting Approach
• Presented an Explainable Ensemble Voting Approach (EEVA) which combines Random Forest (RF), Decision Tree (DT) and Multi-Layer Perceptron (MLP) using soft voting, to forecast lung cancer.
• Utilized LIME and SHAP to generate local and global explanations, enhancing model transparency and interpretability.
• Achieved an accuracy of 92.86%, outperforming baseline methods and demonstrating strong potential for clinical application.
Security Dilemma in Software Development: a Case Study on Understanding Developer Priorities and Practices in Stack Overflow
Applications have advanced rapidly in recent years, taking software development to a whole new level. These advancements have led to a growth in the complexity of applications, cloud computing, and the Internet of Things (IoT). As a result of this improvement, software security has become a paramount concern for developers, but it has traditionally been overlooked. But now, security issues are evolving day by day. In this paper, we explore developer priorities and practices in term of security in Stack Overflow website, whether they implement their software with security in mind or not. We utilize the Stack Exchange Data Dump to collect and compile a dataset of questions and their corresponding answers related to security vulnerabilities, specifically those that include user-submitted programming code snippets, our analysis concentrates on security-related topics. The experimental results indicate that Python emerged as the most commonly used language for security code snippets across these topics. From the expanded sample of code snippets, several vulnerabilities are flagged for security issues.
Deep Learning Based Bioimpedance Spectroscopy in Organic Communications: A Feasibility Analysis
The research contributions of this paper are:
-An SFD-BIS framework for vegetation-based OANs.
-A link established between channel response and sugarcane sucrose content using finite element analysis (FEA).
-A deep learning-based sucrose content estimator developed as a use case for the SFD-BIS framework.
AI -Based Tutor for Visually Impaired Students
Visually impaired students typically face intense
difficulties when handling electronic learning materials such as PDF
textbooks, academic papers, and study guides. Traditional assistive
technologies like screen readers have the basic text-to-speech
functionality without enabling smart interaction and understanding
document structure. This paper outlines the design and
implementation of an AI-Based Tutor system to enhance the
learning experience for visually impaired students. The system
integrates Optical Character Recognition (OCR) to extract
structured text from diverse PDF structures, including accurate line
and section detection. The system has high-quality Text-to-Speech
(TTS) capabilities for natural-sounding speech, and a Speech-toText (STT) functionality for real-time voice queries and
instructions. Essentially, the system employs leading-edge Natural
Language Processing (NLP) models such as GPT or Gemini to
provide intelligent question answering, context-based
conversations, and content summarization. A voice-enabled
interface affords hands-free and intuitive control of the learning
process. Preliminary evaluation with blind students shows increased
accessibility, comprehension, and interaction efficacy compared to
existing solutions. The current paper contributes a new, integrated
solution that provides blind students greater independence and
control over their studies
AI -Based Tutor for Visually Impaired Students
Visually impaired students typically face intense
difficulties when handling electronic learning materials such as PDF
textbooks, academic papers, and study guides. Traditional assistive
technologies like screen readers have the basic text-to-speech
functionality without enabling smart interaction and understanding
document structure. This paper outlines the design and
implementation of an AI-Based Tutor system to enhance the
learning experience for visually impaired students. The system
integrates Optical Character Recognition (OCR) to extract
structured text from diverse PDF structures, including accurate line
and section detection. The system has high-quality Text-to-Speech
(TTS) capabilities for natural-sounding speech, and a Speech-toText (STT) functionality for real-time voice queries and
instructions. Essentially, the system employs leading-edge Natural
Language Processing (NLP) models such as GPT or Gemini to
provide intelligent question answering, context-based
conversations, and content summarization. A voice-enabled
interface affords hands-free and intuitive control of the learning
process. Preliminary evaluation with blind students shows increased
accessibility, comprehension, and interaction efficacy compared to
existing solutions. The current paper contributes a new, integrated
solution that provides blind students greater independence and
control over their studies
Energy-Efficient IoT and Cloud Framework for Accurate and Scalable Hotel Occupancy Monitoring
The main contributions of this paper are summarized as follows:
• Development of a low-cost IoT-based hotel room occupancy detection system using radar sensing for both static and dynamic presence.
• Cloud-based storage and visualization through Firebase and a Django dashboard, enabling real-time and historical monitoring.
• Energy-efficient and sustainable operation using deep sleep, Wi-Fi Manager, and OTA-enabled remote maintenance.
• Demonstration of scalability across domains beyond hospitality, including smart homes, healthcare, and intelligent building systems.
An Explainable AI-Based Ensemble Machine Learning Framework for Early-Stage Diabetes Prediction
• Introduced the Explainable Ensemble Learning Framework (EELF) a Voting Classifier, that integrates Logistic Regression, Random Forest, and K-Nearest Neighbors with optimized hyperparameters to predict diabetes.
• Incorporated SHAP and LIME to enhance model interpretability by identifying key feature contributions, thereby improving clinician trust in the decision-making process.
• Conducted a comparative analysis, where the EELF achieved an accuracy of 81.16%, demonstrating strong potential for clinical application.
An Approach to Validate References in Scholarly Articles using RoBERTa
The significant research contribution of this paper lies in proposing a semi-automatic digital system for validating references in scholarly articles using RoBERTa-based semantic similarity analysis.
Key contributions include:
Introducing a novel framework that leverages RoBERTa embeddings with K-similar search to verify references against cited works.
Overcoming BERT’s input length limitations by applying document segmentation and preprocessing strategies for handling long research papers.
Achieving higher accuracy (F1-score: 0.777) compared to BERT and SBERT, demonstrating the effectiveness of RoBERTa for contextual similarity in reference validation.
Reducing reliance on manual cross-checking and peer reviewers, thereby streamlining the academic publication process while preserving reference authenticity.