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 - Deborah Prasetya Kusuma, Honey Paramitha Soetioso, Nurul Sukma Lestari Abstract - The aim is to examine how employee engagement influences performance, considering the roles of technology and work-life balance. Furthermore, this research also evaluates job engagement as a mediating variable between digital engagement and work-life balance on job performance. This study utilizes quantitative methods, gathering data through both online and in-person questionnaire surveys. The data analyzed using partial least squares structural equation modeling (PLS SEM) and Smart PLS software. The participants are Generation Z hotel employees in Jakarta, such as contract or permanent staff, daily workers, and part-timers who are influenced by technology, referred to digital engagement. A total of 240 respondents successfully completed the survey. The results are digital engagement has influence on job performance, work life balance has not significant influence on job performance, digital engagement, work-life balance, mediated by job engagement has influence on job performance. This research presents a novel conceptual framework for analyzing hotel performance. It also provides valuable insights for hotel management to develop strategies that enhance generation z employees’ performance by improving digital engagement and work-life balance while simultaneously supporting the hotel’s sustainability. For further research can examine variables that were not included in this study, such as digital addiction, job stress, management support, job environment, and motivation with a broader reach local hotel or comparing even until international.
Authors - Rayner Henry Pailus, Rayner Alfred Abstract - Pose face recognition systems often struggle with the variability of illumination and face poses, especially when images are captured in uncontrolled environments. This paper addresses these challenges by proposing a novel face recognition approach: Multiple Adaptive Derivative Face Recognition (MADFR). Our method focuses on optimizing face recognition at every processing level to enhance overall accuracy. By incorporating multiple illumination training samples and diverse training data, including both controlled and wild images, our approach improves the robustness of face recognition models. Our analysis highlights the limitations of existing models like FaceNet, particularly in handling images with multiple face poses and varying background illuminations. We propose pose estimation landmarking and localization with multiple landmarks, which significantly enhances discriminant features. The effectiveness of our approach is demonstrated through extensive experiments on three datasets: LFW, Pointing 04, and Carl Dataset. Our results show that the proposed MADFR system, combined with the ensemble method MADBOOST, consistently outperforms other models. Specifically, MFRF 10 emerged as the top-performing model across all datasets, exhibiting high accuracy and low error rates. This research makes a significant contribution to the eld of face recognition by providing a robust solution that effectively handles the complexities of real-world scenarios. In conclusion, the MADFR system, with its optimized processing and decision-making capabilities, demonstrates substantial improvements in face recognition accuracy, paving the way for more reliable and effective face recognition technologies.
Authors - Sayaka Matsumoto, Kunihiko Takamatsu, Shotaro Imai, Tsunenori Inakura, Masao Mori Abstract - In the context of higher education, Institutional Research (IR) has increasingly emphasized the use of data-driven tools such as student surveys to enhance educational practices and university operations. This study addresses challenges in managing and improving student surveys through advanced visualization techniques. We propose a third visualization method—a stacked bar graph—alongside two existing methods, the heatmap and bar graph with line overlay. This third method visually represents the progression of respondent dropout across questions, offering a detailed view of response continuity. The three visualization methods were used to compare pre- and post-improvement survey data, highlighting key factors such as question design and response behavior. The results indicate that reducing the number of questions and providing clear instructions significantly improve response rates, especially in the later sections of the surveys. The third visualization method effectively highlights these improvements by enabling precise monitoring of dropout trends and response continuity. This study situates its contributions within the interdisciplinary framework of Eduinformatics, integrating education and informatics to optimize educational processes. The proposed visualization methods offer practical tools for evaluating the quality of student surveys and ensuring the validity of collected data. While primarily aimed at student surveys, these methods have broader applicability to other survey-based research contexts.
Authors - Asmaa Berdigh, Kenza Oufaska, Khalid El Yassini Abstract - This study proposes a gradual transition from cabled to wireless communication in vehicles as a means of reducing weight and meeting regulatory requirements related to CO2 emissions, maintenance costs, and time to market. However, the study recognizes that different network domains and compartments in the vehicle have varying requirements and constraints. Therefore, a hybrid architecture between classical wired and wireless networks using Ultra-Wideband (UWB) was proposed as a starting point for testing the feasibility and obtaining feedback. We selected the Headlamp Control Module (HCM) as an application domain since it represents a reduced network consisting of a microcontroller unit (MCU) that operates as a slave to another electronic control unit (ECU) and sensors. This allowed the study to apply the proposed approach to a representative unit scenario. The study outlines the system architectural description for the selected system, the HCM. It describes the Controller Area Network (CAN) and UWB communication and analyzes the requirements that must be fulfilled to interchange both communication technologies. This paper proposes a CAN-UWB gateway system architecture and simulates it to evaluate its ability to meet communication requirements.
Authors - Eyad Mamdouh, Mohamed Gabr, Marvy Badr Monir Mansour, Amr Aboshousha, Wassim Alexan, Dina Reda El-Damak Abstract - This study presents an encryption algorithm for picture cubes that is based on complex differential equation-derived hyperchaotic systems. In order to enable efficient multidimensional encryption, the sensitivity to beginning conditions—a key component of chaos theory—has been extended into the hyperchaotic realm. The combination of DNA coding sequences with Linear Feedback Shift Registers (LFSRs) has increased the complexity of the method. The utilization of LFSRs provides secure pseudo-random sequences, whereas DNA coding adds more cryptographic depth. This combination has produced a strong encryption system that guarantees data security and resistance to sophisticated cryptanalysis attacks. The suggested encryption method has proven to be suitable for protecting volumetric picture data due to its superior performance in entropy, key sensitivity, and resilience to statistical attacks.
Authors - Berliana Tadjudin, Elencia, Davy Jivan Parmono, Tiurida Lily Anita Abstract - There are factors that indirectly influence consumer purchasing decisions in the restaurant industry. As consumer awareness toward environmental issues grows, the implementation of eco-friendly packaging, environmentally friendly visual appeal and adopting sustainable business model are a growing trend in the restaurant industry. In other hand, elements such as the aesthetics of the menu, food packaging, the design of the restaurant room, and the general brand awareness are also one of the factors that play an important role in influencing purchasing decisions, which could also be a factor toward consumer trust in the restaurants and loyalty toward the business. With both ideas in mind, this research was conducted to answer and analyze the impact of various elements on consumer buying decisions toward a restaurant adapting sustainability model. The research is conducted in Greater Jakarta Region and manages to gather 250 samples of respondents which are analyzed statistically, to investigate the validity of the hypothesis. The data gathered from the analysis shows that there are significant relationships between the variables.
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.
