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 - Pablo Salamea, Remigio Hurtado, Rodolfo Bojorque Abstract - Recent advancements in deep learning have enabled the development of convolutional neural network (CNN) architectures, which have proven to be valuable tools in computer-aided diagnosis (CAD) systems. These systems assist radiologists in identifying regions of interest associated with pathologies in chest X-ray images, a diagnostic tool recognized as essential by the World Health Organization (WHO). The WHO highlights that chest X-rays are an accessible and cost-effective method, crucial for evaluating respiratory and thoracic diseases, particularly in resource-limited settings and during global health emergencies. In this study, the Vindr-CXR dataset was used, known for providing labeled chest X-ray images suitable for multi-label classification tasks. The process began with data preparation, where images and labels were grouped in a binary format and split into training and validation sets. Subsequently, pre-trained neural network architectures, such as VGG16, InceptionV3, ResNet50, and EfficientNetB0, were utilized with weights initialized from ImageNet. The initial layers of these architectures were frozen, and dense layers with sigmoid activation were added for multilabel classification. During training, the binary crossentropy loss function and the Adam optimizer were employed. The models were trained for a fixed number of epochs, with validation conducted at the end of each epoch to evaluate metrics such as accuracy and loss. Finally, predictions were generated on the validation set, and key metrics such as the ROC curve, precision, recall, and F1-Score were calculated. The models achieved a promising performance, with an accuracy of 0.72 in detecting thoracic pathologies. These findings highlight the potential of deep learning to enhance diagnostic precision and support clinical decision-making, reaffirming the critical role of chest X-rays
Authors - Pei-Jung Lin, Meng-Chian Wu,Jen-Wei Chang Abstract - To compare the effects of music and meditation on brainwave patterns and attention, this study designed a series of EEG-based experiments. Participants were instructed to either listen to music or engage in meditation, while their attention levels were assessed using a Rapid Serial Visual Presentation (RSVP) paradigm to validate brainwave differences under varying attentional states. EEG data were collected to analyze changes in attention during exposure to different types of music. Subsequently, mathematical computations were applied to quantify and summarize the pre- and post-intervention differences. The experimental results revealed significant differences in the impact of various music genres on attention. Listening to classical music effectively enhanced attention, whereas listening to popular music demonstrated a notable effect on emotional relaxation. Deep meditation yielded the greatest improvement in concentration, and its brainwave patterns closely resembled those observed when listening to classical music. An analysis of Arousal and Valence metrics indicated that meditation led to positive emotional changes. These findings suggest that both music and meditation can influence attention and emotional states.
Authors - John Khoo, Rayner Alfred, Khalifa Chekima, Rayner Pailus, Chin Kim On, Ervin Gubin Moung, Raymond Alfred, Oliver Valentine Eboy, Normah Awang Besar Raffie, Ashraf Osman Ibrahim, Nosius Luaran Abstract - Carbon stock serves as a crucial metric for assessing the quantity of carbon stored within terrestrial and aquatic ecosystems, exerting signi cant in uence on global carbon dynamics and climate change mitigation eorts. Eective management of carbon stocks is vital for regulating atmospheric carbon dioxide (CO2) levels and mitigating the adverse impacts of climate change. The study delves into the estimation of carbon stocks, particularly focusing on above-ground biomass (AGB) as a key component of carbon storage in forests. In addition, explores methods for estimating above-ground biomass (AGB) of carbon storage in forests. Traditional eld-based approaches, statistical methods like regression, and machine learning techniques such as deep learning oer varied strategies for AGB estimation. These methods leverage a variety of data to enhance accuracy and scalability. Through empirical examples, the study presents their eectiveness in informing conservation strategies and fostering sustainable development amidst environmental challenges.
Authors - Juan Dominguez, Carlos Carranco, Remigio Hurtado, Rodolfo Bojorque Abstract - Driver fatigue is one of the leading causes of road accidents worldwide, affecting concentration, reaction time, and vehicle control. Sleep deprivation, long driving hours, and monotonous conditions increase the risk, particularly among professional drivers and shift workers. Identifying early signs of fatigue is essential for improving road safety and preventing accidents. This study introduces a structured framework for detecting fatigue based on EEG and EOG signal analysis. Using the SEED-VIG dataset, the methodology integrates multiple stages, including data processing, feature selection, model training, and performance optimization. Various machine learning models were tested, with particular emphasis on Random Forest, LSTM networks, and ensemble techniques such as Gradient Boosting, XGBoost, and LightGBM. Additionally, explainability techniques like SHAP and LIME were applied to highlight critical fatigue indicators, such as variations in blink frequency, saccadic movements, and brainwave activity in the theta and delta frequency bands. Among the tested models, the optimized Random Forest approach yielded the highest accuracy, with an RMSE of 0.0257. These findings contribute to the advancement of fatigue monitoring technologies, offering practical solutions for real-time driver assessment and accident prevention.
Authors - Madhwendra Nath, Subodh Srivastava Abstract - Denoising of the heart sound signal is crucial part of the heart sound signal analysis, as it reduces the interfering noise such as respiration noise, gastric noise, speech, motion artifacts, and power-line interference from the signal. The In-band noise in a phonocardiogram (PCG) signal refers to noise or artifacts that overlap with the frequency range of interest for major heart sounds which is typically 20–100 Hz. To reduce this in-band noise; a Daubechies-wavelets based approach is proposed. The parameters of Dabuchies-wavelets are revamped. To judge the proficiency of the proposed method, a novel performance-metric-index, Noise-area-difference (NAD) has been introduced. It evaluates the Denoising performance. The proposed method is compared with three other existing methods. The comparison results reveal that the proposed method outperforms existing state-of-the-art Denoising of Heart sound signals.
