Proceedings of Tenth International Congress on Information and Communication Technology

Table of Contents

1. Frontmatter

2. Adoption Intentions of Mobility As A Service (MaaS) in the UAE: A Market Segmentation Study

   Sulafa Badi, Salam Khoury, Kholoud Yasin, Khalid Al Marri

   Abstract

   This study investigates consumer attitudes towards Mobility as a Service (MaaS) in the context of the UAE's diverse population, focusing on the factors influencing adoption intentions. A survey of 744 participants was conducted to assess public perceptions, employing hierarchical and non-hierarchical clustering methods to identify distinct consumer segments. The analysis reveals five clusters characterised by varying demographics, travel lifestyles, and attitudes towards MaaS, highlighting the influence of UTAUT2 variables, including performance expectancy, social influence, hedonic motivation, price value, and perceived risk. Among the clusters, ‘Enthusiastic Adopters’ and ‘Convenience-Driven Adopters’ emerge as key segments with a strong reliance on public transport and a willingness to adopt innovative transportation solutions. The findings indicate a shared recognition of the potential benefits of MaaS despite differing opinions on its implementation. This research contributes to the theoretical understanding of MaaS adoption by offering an analytical typology relevant to a developing economy while also providing practical insights for policymakers and transport providers. By tailoring services to meet the unique needs of various consumer segments, stakeholders can enhance the integration of MaaS technologies into the UAE's transportation system. Future research should explore the dynamic nature of public sentiment regarding MaaS to inform ongoing development and implementation efforts.

3. Modeling and Simulation of Mushroom Cultivation in a Protected Environment Using Fuzzy Logic

   Honorato Ccalli Pacco

   Abstract

   Mushrooms are important in human nutrition due to their nutritional value in terms of protein, vitamin, and mineral content. The volume of mushroom cultivation is currently increasing. This research focuses in the modeling and simulation of temperature, humidity, and irrigation time controlling in mushroom cultivation in a protected environment. Using fuzzy logic in an intelligent system that allows process control and the LabVIEW software that facilitates graphic programming by means of virtual instruments, the irrigation time program was obtained as an output variable or an input variable-dependent response (input variables were temperature and humidity) in the intelligent system. The result was a program that shows how to act in different situations of temperature and humidity in mushroom cultivation in a protected environment. The fuzzy logic program in LabVIEW allowed the simulation of the system in terms of irrigation time in mushroom cultivation in a protected environment to achieve the expected results. In experimental results, it can be observed that at low temperatures (15 °C) and low humidity (35%) the irrigation time is an average value (44.03). With the high temperature (35 °C) and high humidity (95%) in the protected environment, the irrigation time will be with a low value (22.32). And it could be simulated by varying the input variables.

4. Comparison of Machine Learning Algorithms in Water Quality Index Prediction: A Case Study in Juiz De Fora, Brazil

   Larissa de Lima, Priscila Capriles, Nathan Oliveira

   Abstract

   This paper explores the use of machine learning (ML) with various physical, chemical, and biological parameter combinations to predict water quality, focusing on the Water Quality Index (WQI). We assess the performance of several regression algorithms across five different data combinations and examine the impact of inference and class balancing techniques on model outcomes. Our analysis reveals that LightGBM achieved the highest accuracy in WQI regression at 93%. This research introduces a novel approach to calculating WQI by automating the traditional manual and complex parameter collection and calculation process. By streamlining water quality monitoring, our ML-based method offers a more efficient and innovative solution. Additionally, the study provides practical insights into handling data scarcity and using statistical inference for skewed sampling distributions.

5. An Analysis of Cross-Lingual Natural Language Processing for Low-Resource Languages

   Varsha Naik, K. Rajeswari, Kshitij Jadhav, Aniket Rahalkar

   Abstract

   This study examines cross-lingual natural language processing (NLP) techniques to address the challenges of developing conversational AI systems for low-resource languages. These languages often lack extensive linguistic resources such as large-scale corpora, annotated datasets, and language-specific tools, making it difficult to capture the linguistic distinctions and contextual meaning essential for high-quality dialogue systems. This language gap restricts accessibility and inclusivity, preventing speakers of these underrepresented languages from fully benefiting from advancements in technology. The study compares various factors that affect model performance, including transformer model architecture, cross-lingual embeddings, fine-tuning strategies, and transfer learning approaches. Despite these challenges, the research shows that cross-lingual models offer promising solutions, especially when utilizing techniques like transfer learning and multilingual pre-training. By transferring knowledge from high-resource languages, these models can compensate for the scarcity of data in low-resource languages, enabling the development of more accurate, culturally sensitive, and inclusive AI systems. The findings highlight the importance of bridging linguistic divides to foster greater language diversity, accessibility, and technological inclusivity, ultimately supporting cultural preservation and revitalization.

6. Data Envelopment Analysis Approach for Smart City Performance Evaluation

   Mehtap Dursun, Mert Unal, Nazli Goker

   Abstract

   Smart cities are city concepts that aim to optimize the infrastructure, quality, and services of cities’ life of cities using communication and information technologies and the IoTs (Internet of Things). Smart cities aim to create more sustainable, efficient, and livable cities by integrating technology in the fields named as energy, transportation, water management, public services, and security. Smart cities will continue to be one of the cornerstones of urbanization in the future, in line with the aims of enhancing sustainability, efficiency, and quality of life. International Institute for Management Development (IMD) provides the index of smart city at each year. The IMD Smart City Index (SCI) evaluates citizens’ opinions about the structure and technology applications. The final score for each city is calculated employing the perceptions of the previous 3 years, with the weight of 3:2:1. The objective of this study is to provide an alternative performance=ranking method for smart cities. Data envelopment analysis (DEA) technique is used and the performance score of each country is determined considering structure and technology pillars and the results are analyzed.

7. Developing a Photovoltaic Fuel-Less Power-Generating System from Mechanical Waste: Implications for Clean Energy Generation

   Williams A. Ayara, Adenike O. Boyo, Mustapha O. Adewusi, Razaq O. Kesinro, Mojisola R. Usikalu, Kehinde D. Oyeyemi

   Abstract

   The search for enhancing green electricity generation and the constant increase in the price of crude oil and its products propelled the choice of this research. Hence, a photovoltaic fuel-less power-generating system using locally available materials. The input and output characteristics are analyzed to determine the efficiency, and the power generated by the photovoltaic-powered fuel-less generator is used to power an external load. The photovoltaic used is oriented to face in a direction with optimum tilt for maximum yield (to face southward) of solar power. This orientation and angle of tilt were determined using the Garmin Oregon450 GPS in conjunction with a Seaward Solar Survey 200R meter. Thus, the photovoltaic fuel-less generator was successfully developed. The driving component of this power-generating system is the 1 HP Direct Current (DC) motor, powered by two (2) 250 W mono-crystalline solar panels via a 12 V battery connected to a 30 A charge controller to maintain the charge level of the battery which helps to spin the 650 W Alternating Current (AC) alternator to deliver electricity. The device efficiently delivered power by lighting three (3) incandescent bulbs and a standing fan with total power between 100 and 220 W, and an efficiency of 70–75%. This generator is eco-friendly since it does not emit any contaminants to the environment.

8. Predicting Breast Cancer Recurrence Using Hybrid Machine Learning Algorithms: A Study on Mizoram State Cancer Institute Data

   M. S. Dawngliani, H. Thangkhanhau, Lalhruaitluanga

   Abstract

   Breast cancer continues to pose a major public health challenge worldwide, necessitating the development of accurate prediction algorithms to improve patient outcomes. This study aimed to devise a predictive model for breast cancer recurrence using machine learning techniques, with data sourced from the Mizoram State Cancer Institute. Utilizing the Weka machine learning toolkit, a hybrid approach incorporating classifiers such as K-Nearest Neighbors (KNN) and Random Forest was explored. Additionally, individual classifiers including J48, Naïve Bayes, Multilayer Perceptron, and SMO were employed to evaluate their predictive efficacy. Voting ensembles are utilized to augment performance accuracy. The hybridization of Random Forest and KNN classifiers, along with other base classifiers, demonstrated notable improvements in predictive performance across most classifiers. In particular, the combination of Random Forest with J48 yielded the highest performance accuracy at 82.807%. However, the J48 classifier alone achieved a superior accuracy rate of 84.2105%, signifying its efficacy in this context. Thus, drawing upon the analysis of the breast cancer dataset from the Mizoram State Cancer Institute, Aizawl, it was concluded that J48 exhibits the highest predictive accuracy compared to alternative classifiers.

9. A Novel Design of Rectangular Patch Antenna with UWB Characteristics

   Angel R. Tenemea-Vele, Cinthya A. Lojano-Lojano, Luis F. Guerrero-Vásquez, Jean M. Romero-Romero, Bryan P. Vasquez-Luna, Paul A. Chasi-Pesantez, Jorge O. Ordoñez-Ordoñez

   Abstract

   This document presents the design, simulation, and measurement of a UWB antenna, starting from a rectangular patch antenna. The initial modifications included alterations to the ground plane and the addition of slots, yielding promising results for an ultra-wideband antenna. Advanced experiments, such as the addition of a central slot and the insertion of tips in the patch, led to the development of a unique antenna with a bandwidth ranging from 2.89 to 15 GHz. These adjustments significantly improved the antenna’s ability to efficiently transmit signals across a wide range of frequencies, making it highly effective and versatile for various applications.

10. Lifestyles and Stress Management of Families in Confinement

    Luis E. Quito-Calle, María E. Barros-Pontón, Dalila M. Gonzalez-González, Luis F. Guerrero-Vásquez, Jessica V. Quito-Calle

    Abstract

    The confinement of families, whether due to health emergencies or other quarantines, has caused lifestyle changes to cause changes in the behavior of population and cause stress among its members when facing confinement. Present study aimed to determine if there is an association between the lifestyles and parents’ coping with stress due to confinement due to the Health Emergency or quarantine due to COVID-19. This study methodology was quantitative, descriptive, correlational, and cross-sectional. Participants were made up of 75 representatives of Bilingual Educational Institute “Home and School” INEBHYE. Instruments used were Lifestyle Profile Questionnaire (PEPS-I, in Spanish) and Stress Coping Questionnaire (CAE, in Spanish) with which it was obtained as a result that a healthy lifestyle predominates because families have been facing their stress under problem-solving, positive reassessment, and religion in the face of confinement. As a conclusion, it is obtained that there is a statistically significant association between the subscales of coping with stress and families lifestyle, which would imply a change in lifestyle to face the stress caused by confinement due to COVID-19.

11. Experimental Determination of the Dielectric Permittivity of Materials: Resonant Rings and Far-Field Antennas

    Nathalia A. Chacón-Reino, Andres D. Auz-Cabrera, Luis F. Guerrero-Vásquez, Jorge O. Ordoñez-Ordoñez, Paul A. Chasi-Pesantez, Edgar E. Ochoa-Figeroa

    Abstract

    This study focuses on the precise measurement of dielectric permittivity, a crucial parameter in material characterization with extensive applications in materials science and electronic engineering. Addressing the inherent limitations of traditional methods such as transmission/reflection line techniques and resonant cavities, we introduce a novel approach that integrates ring resonator techniques with far-field measurements utilizing horn antennas. Our methodology, applied to 2 mm glass samples across a frequency range of 2–8 GHz, reveals a marked improvement in the accuracy and consistency of dielectric permittivity measurements. This approach effectively mitigates previous challenges, including inaccuracies and inconsistencies in specific frequency ranges and variations due to material properties. The advancement not only deepens the scientific understanding within material science but also holds substantial practical implications for electronic engineering and allied fields. The study underscores the necessity of continued innovation in developing precise, adaptable measurement techniques to meet the demands of both industrial and research applications.

12. Proposal for a PMBOK-Based Management Methodology for Continuous Education Process

    Elbia A. Morales-Almeida, Luis F. Guerrero Vásquez, Katherine C. Bustamante-Cacao, Jorge O. Ordoñez Ordoñez, Paul A. Chasi Pesantez, Luis J. Serpa Andrade

    Abstract

    This article presents a management methodology for Continuing Education (CE) processes based on PMBOK best practices. The study addresses the need for efficiency and quality in educational projects, proposing a management approach that enables a dynamic process while considering the essential particularities of CE. After reviewing the state of the art and analyzing PMBOK standards, a methodology adapted to the educational context was developed. The evaluation of the proposed methodology was conducted in two stages: (1) with project management experts who assessed the proposal using a structured rubric, showing a substantial level of agreement (\(\omega = 0.6009\)) according to Kendall’s Coefficient of Concordance; (2) by evaluating the perception of CE-related users through structured surveys using a Likert Scale, demonstrating high internal consistency (\(\alpha = 0.98\)) according to Cronbach’s alpha. The evaluations highlight strengths in conceptual integration and outcome measurement. Recommendations focus on adapting the methodology to evolving dynamics and improving stakeholder interaction. This study makes a significant contribution to the field of educational management by providing an applicable methodology with a solid theoretical foundation and practical orientation for professionals and those responsible for CE.

13. Cloud-Based Face-Swapping Application

    Rotimi Williams Bello, Pius A. Owolawi, Chunling Tu, Etienne A. van Wyk

    Abstract

    One of the mainstream methods for user identification has been by face. However, the vulnerability of face-swapping applications to security issues when swapping the faces between two different facial images has undermined the genuine aims of the technology, thereby threatening the security of certain applications and individual users when such action is performed without caution. To address this, we propose the development of scalable and safe cloud architecture for a face-swapping application that lets users upload two photos and get a face-swapped output. This is achieved by (1) creating a secure virtual private cloud (VPC) to hold all application resources, (2) using a Web Application Firewall (WAF) to filter and safeguard requests, (3) putting application programming interface (API) gateway into place to provide regulated access to the application's API, (4) processing and overseeing face-swapping operations with Lambda functions, (5) using VPC endpoint to store input and output photos in Simple Storage Service (S3) buckets for private access, and (6) configuring a Simple Notification Service (SNS) to inform users of the progress and completion of their requests. A face-swapping dataset derived from an open benchmark dataset was utilized for training and testing the proposed system. The experiment produced an effective solution with a 93% detection accuracy. The implications of this solution are (1) the provision of security and private access to Amazon Web Services (AWS) by VPC endpoints and WAF, (2) elimination of Network Address Translation (NAT) gateway costs by utilizing VPC endpoints for private S3 access, (3) offering of a scalable processing environment by Lambda functions without the need for server management, (4) delivering of real-time notifications by SNS to users regarding their request status, and (5) optimization of S3 storage ensures quick and efficient access to images.

14. Predicting Lithium-Ion Battery State of Health with Hybrid Ensemble Modeling

    Mohammad Anwar Rahman, Md Rafiul Hassan

    Abstract

    Accurate prediction of lithium-ion 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 forecast 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 lithium-ion battery prognostics.

15. Analysis of Reconfigurable Frequency-Selective Surface FSS Using Light-Dependent Resistor LDR

    Mariam Basim Al-Najjar, Khalil H. Sayidmarie

    Abstract

    This contribution investigates a proposed Frequency-Selective Surfaces (FSS) that can be reconfigured using light-dependent resistor LDR. The unit cell of the FSS comprises a split square ring equipped with a single LDR placed at its gap. The FSS is built on the FR4 substrate of 40X40 mm dimensions, and ring size of 29 × 29 mm to serve the WLAN application of 2.45 GHz frequency. When the LDR is adequately illuminated it exhibits a small resistance, and the ring behaves as a closed one, while in the dark condition, the resistance is high and the ring acts as a split ring. Therefore, the FSS works as a bandpass filter when illuminated, and as a bandstop filter without illumination. The LDR doesn’t need biasing wires that usually interfere with the structure of the FSS.

16. Unraveling Jingle-Jangle Fallacies in Digital Assistant Technologies: A Comprehensive Systematic Review and Research Agenda

    Niklas Preiss, Markus Westner

    Abstract

    Digital assistant technologies (DATs) such as chatbots, virtual assistants, and intelligent agents have gained widespread attention, yet inconsistent terminology remains a critical challenge. The fragmented nature of previous research has led to significant confusion due to overlapping and interchangeable use of terms across industries. This systematic literature review, following the PRISMA protocol, consolidates the current state of knowledge on DATs and addresses the prevalent jingle-jangle fallacies in their terminology. Analysis of 137 articles identified key characteristics, applications, and conceptual overlaps of various DATs, uncovering 39 distinct technologies categorized under three overarching concepts: assistants, chatbots, and agents. Despite shared functionalities, terminological inconsistencies persist across different sectors, presenting challenges for both academic research and practical implementation. This review emphasizes the need for standardized terminology and clearer classification frameworks to facilitate broader DAT adoption across organizational contexts.

17. Performance Evaluation of Deep Learning Approaches in In-Situ Nondestructive Testing

    Thi Tuyet Nga Phu, Hong Giang Nguyen

    Abstract

    Inspecting the compressive strength of buildings' concrete is essential for ensuring the safety of households. This paper examined the study samplers collected using the non-destructive testing (NDT) method combined with Ultrasonic Pulse Velocity (UPV) and Rebound Hammer (RH) tests to check the beams of some apartments over 30 years old. Firstly, research samples were deployed to analyze the level of data variation using the exploratory data analysis (EDA) method to assess the reliability and correlation of data samples. Next, the study focused on the prediction of concrete compressive strength deploying five functions of activation (AF) (tanhLU, tanh, leakyLU, reLU, and sigmoid) by using two deep learning models as long short-term memory (LSTM) and gated recurrent unit (GRU). Lastly, the experimental results showed that the GRU model combined with two kinds of hybrid AFs gave a fairly accurate prediction level; in contrast, the remaining AF showed acceptable results.

18. Factors and Prospects for the Development of Digital Educational Platforms in Uzbekistan

    Aziza Irmatova, Mukhabbatkhon Mirzakarimova, Dilafruz Iskandarova, Guli-ra’no Abdumalikova

    Abstract

    Nowadays, the development of digital education is playing an important role in radically changing the education system and making learning processes more innovative, interactive, and convenient. In particular, digital platforms are the main tools that can change the educational process. Through these platforms, students have the opportunity to study lessons anywhere and at any time, without being limited to traditional classrooms. From this point of view, the development and implementation of digital educational platforms in educational institutions is one of the urgent issues, and the success of this process largely depends on the Internet coverage in the country, investments in digital infrastructure, and the impact of government policy. This article empirically analyzes the impact of Internet coverage, investments in digital infrastructure, and government policy on the implementation of digital educational platforms in Uzbekistan. The measurement of government policy was carried out by assessing the public’s assessment of government policy.