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 - Lillian Mzyece, Jackson Phiri, Mayumbo Nyirenda Abstract - Accurate rainfall forecasts are critical for various sectors, yet traditional methods struggle due to evolving and non-linear weather patterns. This study evaluates four machine learning algorithms—Support Vector Machines (SVM), Random Forest (RF), Neural Prophet (NP), and Long Short-Term Memory (LSTM)—to determine the most effective algorithm for rainfall forecasting in Zambia. Results show that Neural Prophet outperformed others, achieving the lowest RMSE (4.67), MAE (16.75), and MAPE (13.40%). Its autoregressive capabilities, interpretability, and reduced parameter complexity make Neural Prophet the preferred choice for forecasting rainfall trends in Zambia.
Authors - Nailah Al-Madi Abstract - The diagnosis of appendicitis is a challenge especially for children, as its symptoms overlap other diseases and children are unable to express their pain well. The misdiagnosis rate ranges from 28% to 57% in children. Machine learning is efficient in building models that can help predict diseases. XGBoost is one of the best machine learning models since it is based on ensemble learning approach. XGBoost has hyper-parameters that should be tuned well in order to achieve high performance. These parameters could be optimized to find the optimal or near optimal performance of XGBoost. In this paper, an Optimized- XGBoost model is proposed, which uses Genetic Algorithm to optimize seven parameters of XGBoost to achieve high performance. This Optimized-XGBoost is used to predict three class labels of pediatric Appendicitis, including diagnosis (appendicitis or no appendicitis), Severity (complicated or not complicated), and management(conservative or surgical). The experiments were implemented on Pediatric Appendicitis with 38 features and 780 records, and compared optimized-XGBoost with original XGBoost, and other well-known classifiers, such as DT, SVM, NB, KNN, RF, and Adaboost. Results show that optimized-XGBoost achieved highest results for accuracy, precision, recall and F1-Score. For example, the F1 score results for the prediction of severity is 96.15%, for the prediction of diagnosis is 99.36%, and for treatment is 99.36%.
Authors - Andre Viviers, Bertram Haskins, Reinhardt A Botha Abstract - Tracking gastropod chemical trails is time-consuming and error-prone. This paper argues that computer vision provides a viable alternative. Using selected image manipulation and segmentation techniques, an unlabeled dataset was generated. A simple K-Means clustering algorithm and manual labelling created a labelled dataset. Thereafter, a best-effort model was trained to detect gastropods within images using this dataset. Using the model, a prototype was created to locate gastropods in a video feed and draw trace lines based on their movement. Five evaluation runs serve to gauge the prototype’s effectiveness. Videos with varying properties from the original dataset were purposefully chosen for each run. The prototype’s trace lines were compared to the original dataset’s human-drawn pathways. The versatility of the prototype is demonstrated in the final evaluation by generating fine-grained trace lines post-processing. This enables the plot to be adjusted to different parameters based on the characteristics that the resulting plot should have. This research demonstrated that a gastropod tracking solution based on computer vision can alleviate human effort.
Authors - Maya Dimitrova, Nina Valchkova Abstract - The paper presents the concept of a ‘cyber-physical nurse’ from a feasibility perspective for wider inclusion in healthcare, in particular in relation to empathic communication with the patient. The results of a pilot study on user perception of two robotic and one human faces are presented and discussed in this context. Users attributed positive features to neutral agents’ facial expressions, but not negative, which increases the feasibility of introducing social robots in healthcare. Some guidelines for cyber-physical nurse design are discussed, addressing challenges to its possible implementation in hospitals, rehabilitation centers, and home care settings.
Authors - Tagnon Adechina Geoffroy Zannou, Semevo Arnaud Roland Martial Ahouandjinou, Manhougbe Probus Aymard Farel Kiki, Adote Francois-Xavier Ametepe Abstract - Sensor-based gas analysis has been the subject of much research, particularly in the development of electronic nose (e-nose) systems. E-noses are based on chemical sensors to detect and analyze volatile organic compounds, and thus play an important role in a variety of fields. In this paper, based on three research strings, we have performed a bibliometric analysis to examine current trends and scientific contributions in the field of sensors for detecting odors and volatile organic compounds (VOCs), their use in electronic nose systems, work to improve their performance and their optimization. Using the Scopus database and English-language documents published between 2014 and 2024, we identify the most prolific authors, countries and journals in these fields. After that, a short literature review provides a detailed overview of the strategies to improve e-noses selectivity and reduce their drift. The results of the bibliometric analysis show a growing intercontinental interest, with strong scientific activity in China, the United States, India and Italy, with a particularly strong focus on performance improvement and sensor optimization. The short survey reveals the existence of a wide range of gas sensors with their advantages and disadvantages, significant advances in improving the performance of sensors and electronic noses, as well as new challenges that deserve attention.
Authors - Adibah Alawiah Osman, Azwan Abdullah, Sharul Shahida Shakrein Safian, Nor Zawani Ibrahim, Nik Rozila Nik Mohd Masdek, Norhasimah Shaharuddin, Nur Athirah Sumardi Abstract - The process of using various data methods within one study to con-firm that the results are firmly supported by the predictions made is called triangulation. Several methodological debates have highlighted the limitations of quantitative research compared to qualitative research. This paper hunts to explore the triangulation research approach in the background of Islamic marketing at fully established Islamic banks in Malaysia. Islamic marketing of Islamic banks is defined as the application of Islamic banking knowledge, Islamic advertising ethics, and the augmentation of learning and instruction by Islamic bank employees in this study. The present research clarifies the basis links between the quantitative data of Islamic bank’s staffs at fully established Islamic banks and the qualitative insights of Islamic financial experts. The amalgamation of both qualitative and quantitative approaches in data collection and evaluation significantly improves the quality of the research outcomes. Further studies ought to explore the application of the triangulation method in other domains.
