10th International Conference on ICT for Intelligent Systems in New York

Authors - Nelson Herrera Herrera, Estevan Gomez-Torres, Paul Baldeon-Egas, Renato Toasa
Abstract - The Context-Aware Mobile Systems (CAMS) framework simplifies the development of context-aware mobile applications using Model-Driven Development (MDD). CAMS incorporates a Domain-Specific Language (DSL) built with Xtext, enabling developers to model contextual information, define business rules, and establish event-driven behaviors. The generation of Android applications is automated via Acceleo, supporting the seamless integration of cloud services such as Azure Maps for geolocation, IoT Hub for sensor data management, and Twilio for contextual notifications. To enhance scalability and deployment efficiency, CAMS leverages Infrastructure-as-Code (IaC) solutions through Pulumi and Terraform, automating cloud resource provisioning. This approach reduces development complexity, minimizes manual configurations, and accelerates deployment cycles. A case study on package tracking showcases CAMS' ability to dynamically adjust application behavior in real time based on contextual data from IoT sensors and geolocation services. Evaluation results demonstrate a 40% reduction in development time compared to traditional methods, alongside improved scalability, supporting up to 10,000 IoT devices simultaneously. CAMS offers an innovative framework for developing intelligent mobile applications across various sectors, including logistics, smart cities, and real-time monitoring. Its modular architecture and automated deployment processes enable rapid prototyping and efficient scaling, making it adaptable to diverse application scenarios. Future enhancements include expanding support for iOS applications, integrating additional IoT capabilities, and incorporating AI-driven decision-making tools to enhance real-time responsiveness. CAMS positions itself as a robust and flexible solution for next-generation mobile applications, bridging the gap between contextual awareness and scalable cloud-based infrastructures.

Authors - Thavaprakash Arulsivakumar, Senthil Veerasamy
Abstract - The perspectives and insights of sentiments on destinations are important for tourism stakeholders. Purpose of this research work is to understand tourist behaviour with the help of Electronic Word of Mouth (eWOM) and to bring out the cognitive insights using TripAdvisor for Asian destinations. A well-established Stimulus Organism Response (SOR) theory is used as a thematic foundation in this research and ChatGPT based content analysis is conducted using traveler reviews. Sample size of 151872 TripAdvisor review comments from 90 Asian destinations are analyzed in this research. Logistic regression and Structural equation modelling are used to examine the sample data. Empirical findings highlighted that travelers have unique sentiments on each destination and moderate positive sentiments influence the intention to visit. Macro and micro analysis is used in this research, its findings are re-shaping the demand and supply and is helpful for the decision makers to promote destinations.

Authors - Tihomir Dovramadjiev, Petya Manolova, Rozalina Dimova, Dimo Dimov, Vasil Gatev
Abstract - This article investigates the interplay between human factors and criminology in identifying both the techniques and motivations behind cyber threats. By analyzing the psychological, social, and environmental drivers of cybercriminals, as well as the methods they employ, this study proposes a comprehensive framework for detecting and mitigating cyber threats. The integration of criminological theories and human factors research offers a novel approach to understanding and countering the evolving tactics of cyber attackers. In the era of Society 5.0, where human-centric technologies and artificial intelligence (AI) are deeply integrated into daily life, the role of human factors becomes even more critical. Human factors research helps uncover how cognitive biases, emotional states, and social influences shape both the behavior of cybercriminals and the effectiveness of defenders. For instance, AI-driven cybersecurity systems, while powerful, still rely on human oversight and decision-making, making it essential to address human vulnerabilities such as fatigue, stress, and misjudgment. Furthermore, as Society 5.0 emphasizes the fusion of cyber and physical spaces, understanding human behavior is key to designing systems that are resilient to social engineering, phishing, and other human-exploitative techniques. By combining main cybersecurity layers insights with human factors, this study not only addresses the technical aspects of cyber threats but also highlights the importance of human-centered design in cybersecurity of data assets.

Authors - Yuki Shirahama, Kayoko Yamamoto
Abstract - In order to help yourself, it is necessary for the general public to keep adequate stockpiles in their homes that will be useful at the disaster outbreak times. Additionally, in order to provide mutual aid from the normal times, it is important for local residents to accumulate and share necessary information in a local community without relying on the government to provide information. Against such a backdrop, the purpose of the present study is to design, develop, operate and evaluate an original disaster information system to support self-help and mutual aid from the normal times to the disaster outbreak times, by integrating a stockpiling improvement support system, a local social network service (SNS) and web-geographic information systems (Web-GIS). Chofu City, Tokyo, was selected as the operation target area for the system. During the operation period of the system, the number of users was 57, and the number of information submitted by users was 25. From the result of the online questionnaire survey for users, it is evident that the system was useful in terms of improving the stockpiling situations in their homes at normal times, and in supporting the accumulation and sharing of disaster information among them from the normal times to the disaster outbreak times.

Authors - Marwan Babiker, Eda Merisalu, Zenija Roja, Henrijs Kalkis
Abstract - A Health Information System (HIS) is an electronic database of managerial and clinical information, which allows physicians to utilize quality improvement processes in clinical practice and support multiple services in healthcare organizations. According to research, physician satisfaction varies greatly and is frequently correlated with particular features and the ease of exchanging information. The study aimed to identify the traits of doctors' acceptability of using a hospital information system (HIS) and their degree of satisfaction with it. It is a cross-sectional study that used an online self-administered survey, which was conducted among all 56 physicians working in family medicine centers in the Royal Commission health service program, Saudi Arabia. The questionnaire was used to assess the physician’s satisfaction level about the two structures that will be examined: system quality and satisfaction level. The findings showed that the physicians in the family medicine centers have a high agreement proportion about the use and satisfaction of the use of HIS, which indicates a high quality of the HIS. The outcomes measure system excellence and user satisfaction when assessing the effectiveness of HIS in several domains. The participating physicians expressed satisfaction with the way HIS improved their clinical processes and how reliable it was for patient care. More longitudinal studies are required to understand the indicator of physician satisfaction with the HIS use.

Authors - Rehab Hassan Bader, Issa A. Abed, Bayadir A.Isaa
Abstract - Multi-Robot Systems (MRS) have developed as a disruptive technology in robotics, allowing multiple autonomous robots to work together to execute complex tasks. Multi-Robot Task Allocation (MRTA) is an important component of MRS since it involves distributing tasks to robots in a way that maximizes performance, efficiency, and resource use. Effective scheduling is crucial in MRTA because it determines the order and timing of task execution, which has a significant impact on overall system performance. This paper investigates current MRTA and scheduling strategies and algorithms, with an emphasis on the challenges raised by dynamic environments and the requirement for flexibility in task allocation. They discuss numerous approaches, including centralized and decentralized methods, constraint-based models, and reinforcement learning techniques, emphasizing the benefits and drawbacks. The combination of reallocation and rescheduling approaches is also being researched as a means of boosting system responsiveness to unanticipated changes. The findings suggest that future research should focus on developing hybrid models that take advantage of existing approaches to create more resilient and efficient multi-robot systems capable of operating in real-world scenarios.

Authors - Victoria Orozco Arias, Ernesto Rivera Alvarado
Abstract - Data protection on websites is a common objective for entities and organizations, so new security strategies are constantly being sought. This research evaluates the effectiveness of using security metadata in HTML structures to protect sensitive data. To achieve this, a website scraping experiment will be conducted on sites that include authentication, to verify on which sites this type of fraud can be performed and to identify the use of security metadata. Our results show that 25% of the websites selected for the experiment could be extracted from the database, and the remaining sites have different settings, such as metadata, robots.txt file, and others, to protect and prevent data extraction. This proves that metadata is the first step of website security.

Authors - Ossman Lopez, Joan Alvarado, Juan Diaz-Gonzalez, Elisa Rendon-Cadavid, Sara Lopez, Mikel Maiza, Juan Felipe Restrepo-Arias, David Velasquez
Abstract - This study presents the design and development of a non-invasive sensor for measuring the capacitance of cocoa fruits to detect ripening stages and the onset of fungal disease caused by Moniliophthora roreri. Capacitance is recognized in the state of the art as an effective physiological indicator due to its correlation with moisture content, cell development, and glucose levels in fruit. Based on this premise, the hypothesis was that capacitance measurements are directly related to fruit ripening and fungal infection. The sensor system features an electronic circuit that generates a 10V peak output at a frequency of 1 kHz, integrated with a Sauty Bridge for real-time capacitance measurements without harming the fruit. The device is lightweight, user-friendly, and energy-efficient. The experiments were carried out under controlled conditions using the CNCH13 cocoa variety to standardize the measurements. The preliminary results confirm a strong correlation between capacitance levels, ripening stages in healthy fruits, and the presence of fungal disease. The prototype developed effectively measures capacitance, providing a reliable method for assessing the physiological state of cocoa fruits.

Authors - Ernesto Rivera-Alvarado, Saul Guadamuz
Abstract - Predatory journals are characterized by publishing research articles without the proper peer-review process. They are mainly focused on getting money from authors, regardless of the quality of the article being submitted, in a scenario where academics and researchers are usually incentivized to create a high volume of research to advance their career paths. While publishing in a predatory journal is easier than in a reputable publisher, the author’s credibility could be tarnished. The rise of large language models has provided a new danger for this scenario. It is now easier to create low-quality research papers using IA tools that could be sent to predatory publishers that fail to perform a rigorous peer-review process. A bad combination of fast and poorly created research papers with highly available pay-to-publish journals provides an unsafe scenario for science publishing and unaware scientists. This paper aims to warn researchers about the dangers of generating low-quality research and publishing in predatory journals.

Authors - Mohammadmahdi Ghobadi, Renee Bryce
Abstract - Modern applications are built from many small services that work together. In these systems, an API gateway plays a key role in routing requests, balancing the load, and processing events. Most API gateways use fixed rules that do not change with shifting workloads, causing delays as high-priority events may be blocked by less critical tasks. In this paper, we propose a new method that uses reinforcement learning to create adaptive API gateways. Our system learns from real-time performance data such as CPU usage, memory usage, event latency, arrival rates of high-priority events, and throughput of low-priority events. By using multiple metrics, the system adjusts its settings to respond to changing conditions. It processes high-priority events immediately while grouping low-priority events into batches for efficient processing. We use the Proximal Policy Optimization (PPO) algorithm because it is stable and effective for learning the best settings. We evaluated our method using simulations that mimic real-world conditions. The results show that our approach reduces the delay for highpriority events by about 41% and significantly lowers the delay for lowpriority events. The system also uses fewer resources than rule-based gateways. These improvements demonstrate that considering multiple performance metrics can lead to smarter, more adaptive API gateways. Our approach adapts very quickly to varying workloads, ensuring remarkably reliable operation during sudden spikes. Our work shows that reinforcement learning can improve API gateway performance for modern applications. This is important for fields such as finance, healthcare, and emergency systems, where fast and reliable responses are critical.

Authors - Paula Poveda-Sotomayor, Henry N. Roa, Edison Loza-Aguirre, Javier Guana-Moya, Nelson Salgado-Reyes
Abstract - Water treatment facilities require efficient and continuous monitoring to ensure safe drinking water. Traditional Supervisory Control and Data Acquisition (SCADA) systems often face limitations in scalability, cost-effectiveness, and real-time data accessibility, prompting interest in alternative technologies such as the Internet of Things (IoT). This study proposes a cloud-integrated IoT framework explicitly designed for real-time monitoring of key water quality parameters, including pH, turbidity, and flow rate. The framework securely transmits data using MQTT protocol to AWS IoT Core, leveraging AWS Timestream and Grafana dashboards for advanced analytics, visualization, and proactive anomaly detection. The system's feasibility and performance were validated through simulation, guided by the Design Science Research (DSR) methodology. Results indicate significant potential benefits, including improved real-time responsiveness, reduced manual monitoring requirements and enhanced operational decision-making. This framework provides a scalable and secure solution that addresses existing SCADA limitations, laying the groundwork for future integration into smart water management practices.

Authors - S. M. Mizanoor Rahman
Abstract - We proposed and investigated a bidirectional trust-triggered cyber-physical-human (CPH) system framework for human-robot collaborative assembly in flexible manufacturing. For this purpose, we developed a one human-one robot hybrid cell where the human and the robot collaborated with each other to perform the assembly operation of different manufacturing components in a flexible manufacturing setup. In the proposed framework, we configured the human-robot collaborative system in three interconnected components of a CPH system: cyber system (software system), physical system (the robot, sensors and necessary hardware), and human system (the human co-worker, supervisor and work environment). We divided the functions of the CPH framework into three interconnected modules: computing or computation, communication and control. We used a model to compute the human and robot’s bidirectional trust in real-time to monitor the performance of the CPH framework. We evaluated the performance of the CPH framework implementing it on an actual human-robot collaborative assembly setup considering systematic variations in the complexity levels of the computing module. The overall results revealed that variations in computing complexity significantly impacted the performance of the CPH framework in terms of human-robot interactions and task efficiency and quality. The results thus proved that it became easy and effective to monitor the performance and interactional effectiveness of a human-robot collaborative system modularly when the system was configured in the form of a CPH system. The results can transform the design, development, analysis and control of human-robot collaborative systems for various applications such as industrial manufacturing and assembly, military operations, transportation, healthcare and rehabilitation, construction, social services, etc.

Authors - Oleksandr M. Khimich, Elena A. Nikolaevskaya, Pavlo S. Yershov
Abstract - Virtual assistants (VAs) are becoming an increasingly popular tool that helps users find information faster and more accurately. This paper proposes a new AI-powered Virtual Travel Guide (GuideAI) system. The concept, architecture, and implementation of a microservice for intelligent travel search are explored in detail. The study focuses on technical aspects, particularly the integration of GPT for analysis and personalization. It highlights how geolocation and personalization can be combined to create more adaptive, user-responsive systems. The proposed approach enhances travel experiences by providing real-time, tailored recommendations based on user preferences and location. The scientific contribution of this research lies in the development of a novel AI-driven approach to travel assistance, demonstrating how advanced natural language processing and contextual data analysis can improve recommendation accuracy. By leveraging machine learning techniques, GuideAI refines search results, enhances decision-making processes, and optimizes user interactions. This work contributes to the field of intelligent information retrieval and personalized AI systems, offering insights for future applications in tourism and beyond.

Authors - Alvaro A. Casanova, Elvis E. Gaona, Oscar Acosta
Abstract - This paper introduces a stochastic modeling framework for analyzing binaural urban noise in Bogotá, Colombia. A total of 4,200 five-minute audio recordings were collected using binaural sensors placed at seven urban locations over 47 days. To analyze the data, we utilized cloud-based tools, including Inferencer app for machine-learning-based sound classification and Soundmetrics app for extracting acoustic parameters such as LEQ (equivalent continuous sound level), IACC (inter-aural cross-correlation coefficient), and WIACC (weighted inter-aural cross-correlation coefficient). The probabilistic analysis revealed instances of extreme noise events exceeding 85 dB. We identified correlation patterns that informed the development of a predictive model based on discrete-time Markov chains. Which incorporated both noise intensity and sound source classification into composite acoustic states. The simulated trajectories effectively captured the temporal dynamics of urban acoustic conditions, successfully meeting the study’s objective of modeling future noise dynamics. This approach demonstrates a scalable solution for real-time monitoring, statistical characterization, and predictive modeling of complex urban soundscapes, providing actionable insights that support data-driven decision-making in smart city planning.

Authors - Ramiro Ramirez, Yasmany Garcia-Ramirez, Jandry Jaramillo
Abstract - This research explores the potential of mobile applications in improving road safety education and driver behavior. Road traffic crashes, particularly those involving vulnerable road users, continue to represent a significant global public health issue. Traditional road safety education methods often lack engagement and immediate feedback, limiting their effectiveness in promoting long-term behavioral change. This study introduces SafeJourney, a mobile application developed to enhance road safety learning through real-time feedback, location-based alerts, and personalized progress tracking. The application integrates GPS technology and mobile sensors to monitor driving, cycling, and pedestrian behavior, providing users with timely feedback to reinforce safe practices. The development of SafeJourney followed a modified SCRUM methodology, ensuring iterative and efficient progress. Key features include real-time alerts, integration with traffic law databases, and a user-friendly interface designed with Figma and React Native. Testing and validation confirmed the application's functionality, including low-latency tracking, device compatibility, and seamless integration with a cloud-based database. SafeJourney represents a promising innovation in road safety education, offering an interactive and scalable solution to reduce traffic-related injuries and fatalities.

Authors - Gery Prasetyo, Tanty Oktavia, Mohammad Ichsan
Abstract - This research presents the current state of development and implementation of AI predictive analytics for project management support through the lens of Indonesian financial projects, highlighting a practical implementation gap and its impacts towards the fulfilment of enhancements in the core project management aspects of risk prediction, budget and resource estimation, deliverables quality monitoring, and automation of project financial processes. A qualitative methodology is applied to explore the various field experiences and sentiments of five financial project professionals in individual financial firms through direct semi-structured interviews. Analysis of the findings affirms the implementation of machine learning predictive analytics in the four key project areas, with tasks in risk strategy implementation, budget estimation, and resource allocation management revealed as the prominent applications utilized by respondent financial companies. Benefits in streamlining tasks and improvements towards project efficiency are noted by the respondents, along with enhancing team awareness against unseen risks and resource issues, setting new standards for quality management, and reducing human errors in rapid financial processing. The findings further uncovered key barriers in predictive analytics implementation success stemming from system maturity and accuracy issues, and moreover the influence of intangible human factors towards the financial sector business environment and practices. Improving the synergy of the technical capabilities predictive AI systems with human subjectivity and intuition is paramount to ensure successful implementations of predictive project support tools in the Indonesian finance sector.