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 - Muhammad Sufyan Akbar, Guo Jiandong, Muhammad Irfan Khan, Asif Iqbal, Salim Abstract - This paper introduces a deep learning-based approach for point cloud classification, leveraging the PointNet architecture to optimize 3D object recognition. The method effectively addresses the challenges associated with unordered point cloud data, achieving superior classification performance with 92% accuracy, 91% precision and recall, 89% F1-score, and 96% sensitivity and specificity. The proposed model captures spatial features directly from raw point cloud data, demonstrating its potential for real-world applications in 3D object recognition and scene understanding. Comprehensive experiments on benchmark datasets validate the model’s effectiveness in classifying complex 3D structures, highlighting its robustness and efficiency. Future research will focus on advancing feature extraction techniques and refining the model to enhance classification performance under more demanding conditions.
Authors - Timothy T Adeliyi, Funmi Adebesin, Edidiong R Umoh Abstract - Unemployment remains a persistent challenge for both developed and developing countries, leading to the underutilisation of resources. Many Sub-Saharan countries experience high unemployment rates due to weak economic indicators. This study adopts a data-driven approach to investigate women's unemployment in Sub-Saharan Africa, with a focus on the factors contributing to employment disparities and advancing gender equality, for Sustainable Development Goal 5 (SDG 5). Using Multiple Correspondence Analysis (MCA), the research identifies and analyses key factors that contributes to the high unemployment rates among women in the region. The findings reveal significant links between unemployment and factors such as age, region, and wealth index. By shedding light on these disparities, the study offers a comprehensive understanding of the structural barriers faced by women in the labour market. The results emphasise the need for specific policies and interventions to combat gender inequality and boost women's economic participation to achieve SDG 5. This research enriches the broader dialogue on sustainable development and gender equality, providing crucial insights for policymakers and stakeholders working towards more inclusive labour markets in Sub-Saharan Africa.
Authors - Salimah Saeid, Tahani Almabruk, Muetaz Abdulsamad Abstract - The diagnosis of ovarian tumors remains a challenging task due to the inherent variability and complexity of imaging data. This study evaluates the efficacy of transfer learning and fine-tuning techniques in convolutional neural net-works to enhance the classification accuracy of ovarian tumors in ultrasound images. The performance of YOLOv8 and VGG16 models were compared, including a modified VGG16 architecture optimized for this application. YOLOv8 models were evaluated both from scratch and with pre-trained weights, while VGG16 was employed for feature extraction and fine-tuning. The Modified VGG16 outperformed all other models, achieving the highest classification accuracy (%90) and the shortest training time (8.63 hours). Advanced data augmentation strategies and architectural optimizations effectively addressed issues such as class imbalance and overfitting. These results highlight the potential of customized CNN architectures and transfer learning to improve diagnostic accuracy and efficiency, advancing the development of reliable tools for ovarian tumor classification in clinical imaging.
Authors - Mohammad Anwar Rahman, Rafiul Hassan Abstract - Accurate prediction of lithiumion batteries' state of health (SOH) is crucial for preventing catastrophic system failures. This study investigates the application of ensemble modeling to characterize capacity degradation and fore-cast remaining charge-discharge cycles. Leveraging NASA's battery charge/discharge dataset, we developed and compared feed-forward neural network (FNN) and random forest (RF) regression models. To enhance predictive accuracy, we constructed an ensemble model that combines the strengths of both individual models. A key aspect of our methodology was the accurate evaluation of model performance across different battery datasets. Rather than using a single dataset for training and testing, we adopted a cross-validation approach to assess model generalization capabilities. This strategy allowed us to identify the robustness of the models for predicting SOH and estimating remaining battery life. Our findings indicate comparable performance among the FNN, RF, and ensemble models. While all models demonstrated effective capacity degradation prediction, the ensemble model exhibited slightly superior performance in a few scenarios. These findings emphasize the advantages of ensemble modeling in enhancing the accuracy and reliability of lithiumion battery prognostics.
Thursday February 20, 2025 4:15pm - 5:45pm GMT
Virtual Room DLondon, United Kingdom
Authors - Dattatraya Adane, Lakshya Agrawal, Shradha Wangota, Sharvari Inamdar Abstract - In order to overcome issues with traditional voting, such as voter impersonation, booth capturing, and logistical inefficiencies, this study proposes a biometric voting method. The technology guarantees that only authorised individuals can cast ballots while maintaining anonymity by combining fingerprint authentication with secure digital platforms. It features a web-based interface for election managers to manage candidates, track votes, and display real-time results, as well as a mobile app for voter registration and remote voting, improving accessibility. The solution lowers costs while increasing security, transparency, and engagement by utilising technologies like Firebase Firestore, the Mantra MFS100 fingerprint scanner, and QR code integration. This essay examines its design, use, and effects on modernising elections to promote efficiency, inclusivity, and trust.
