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Issue 1

Visit the new journal website to submit and consult our contents: https://publications.eai.eu/index.php/sc/index

Editor(s)-in-Chief: Hiep Xuan Huynh and Fabien Ferrero

EAI Endorsed Transactions on Smart Cities is open access, peer-reviewed scholarly journal focused on applications for Smart Cities with leverage on big-data applications, ICT devices used in the factory of the future, HPC, industrial processes, energy efficiency systems, social platforms, and more. The journal publishes research articles, review articles, commentaries, editorials, technical articles, and short communications with a quarterly frequency (four issues per year). Authors are not charged for article submission and processing.

INDEXING: DOAJ, CrossRef, Google Scholar, ProQuest, EBSCO, CNKI, Dimensions

Topics include, but are not limited to, the following:

  • Applications for Smart Cities able to leverage Big-Data Applications, ICT devices used in Factory of the Future, HPC, Industrial Processes, Energy Efficiency Systems, Social Platforms, etc.
  • Building Trust-based communication and computing systems for IoT and Clouds applications being Secure for Citizens
  • eHealth applications for Citizens.
  • Connecting in City Buildings for optimizing resources and utilities.
  • Software engineering methodology for the design of Smart Cities Systems
  • Energy Efficient and Green Software-defined Infrastructures aimed at Smart Cities facilities.
  • Re-allocation of resources and services (independently of their location) across distributed computing and geographically separated data storage and processing infrastructures. Inter-Cities along with Federation Services: availability, Disaster recovery, etc.
  • Federation of Services (IaaS, PaaS and SaaS) aimed at Public Administrations
  • Service discovery in Smart Cities - device Discovery and Data Discovery.
  • Easy and Large Deployment and autonomic management of densely interconnected and decentralised Cloud, Networks and IoTs infrastructures aimed at Smart Cities.
  • Control Architectures/Network Programmability in Smart Cities aimed at mobility, traffic and road networks, transport systems, energy, economy, and so on.
  • ICT applications enabling Energy-aware, smart and sustainable mobility solutions for Smart Cities.
  • Verification/Debugging/ Auditing Tools for all Applications in Smart Cities Domains
  • Energy-aware and sustainable computing solutions for Smart Cities applications
  • Editors-in-Chief
  • Hiep Xuan Huynh, Can Tho University, Vietnam
  • Fabien Ferrero, Université de Côte d'Azur (UCA), France
  • Associate Editors
  • Vincent Rodin, Université de Bretagne Occidentale (UBO), France
  • Serge Stinckwich, United Nations University (UNU), Macau
  • Thanh-Nghi Do, Can Tho University (CTU), Vietnam
  • Jean-François Dorville, The University of the West Indies, Jamaica / Université des Antilles Guyanes, Guadeloupe
  • Mahamadou Traore, Université Gaston Berger Saint-Louis, Senegal
  • Onil Goubier, CIRELA, Indonesia
  • Tsimitomby Briand, Institut Supérieur de Technologie d'Antsiranana (IST-ANTSIRANANA), Madagascar
  • Nghia Duong-Trung, German Research Center for Artificial Intelligence (DFKI), Germany
  • Mansha Swami, Morgan State University, USA
  • Area Editors
  • Sergei Gorlatch, University of Muenster, Germany
  • Bao Hoai Lam, Can Tho University, Vietnam
  • Dung Van Hoang, Ho Chi Minh City University of Technology and Education (HCMUTE), Vietnam
  • Nguyen Nhat Vo, University of Michigan, USA
  • Nhat Minh Viet Vo, Hue University, Vietnam
  • Angelo Martella, University of Salento, Italy
  • Khoi Tan Nguyen, University of Science and Technology - The University of Da Nang (DUT-UDN), Vietnam
  • Marco Zappatore, University of Salento, Italy
  • Thang Cong Pham, University of Science and Technology - The University of Da Nang (DUT-UDN), Vietnam
  • Duy Khanh Ninh, University of Science and Technology - The University of Da Nang (DUT-UDN), Vietnam
  • Thuy Thu Thi Pham, Nha Trang University (NTU), Vietnam
  • Yen-Wen Chen, National Central University, Taiwan
  • Anh Viet Nguyen, Institute of Information Technology, Vietnam Academy of Science and Technology, Vietnam
  • Nghia Quoc Phan, Tra Vinh University, Vietnam
  • Vu Tran Pham, Ho Chi Minh City University of Technology, Vietnam National University (Ho Chi Minh City), Vietnam
  • Xia Wang, German Research Center for Artificial Intelligence (DFKI), Germany
  • Anand Nayyar, Duy Tan University, Vietnam
  • Sheikh Faisal Rashid, German Research Center for Artificial Intelligence (DFKI), Germany
  • Tung Kieu, Aalborg University, Denmark
  • Chinh Quoc Bui, Elsevier, Netherlands
  • Binh Thanh Nguyen, Monash University, Australia
  • Viet Xuan Le, Quy Nhon University, Vietnam
  • Lang Van Tran, Vietnam Academy of Science and Technology, Vietnam
  • Son Hoang Le, Information Technology Institute, Vietnam National University (Hanoi), Vietnam
  • Vinh Quoc Tran Nguyen, University of Science and Education, The University of Da Nang (DUT-UDN), Vietnam
  • Toan Nang Do, Vietnam National University - Hanoi, Vietnam
  • Son Xuan Ha, RMIT University, Vietnam
  • Hanh Hong Thi Nguyen, Can Tho University of Technology, Vietnam
  • Eloi Keita, Université de Bretagne Occidentale (UBO), France
  • Congduc Pham, Université de Pau et des Pays de l’Adour (UPPA), France
  • An Cong Tran, Can Tho University (CTU), Vietnam
  • Duong Ngoc Vo, University of Science and Technology - The University of Da Nang (DUT-UDN), Vietnam
  • Hanh Huu Hoang, Ministry of Information and Communications, Vietnam

  • An Integrated Framework for Virtual Testing of Autonomous Vehicles in Mixed Urban Traffic

    Appears in:
    sc 26(1):

    Authors:
    Javad Sadeghi, Shadi Nikneshan, Cristiana Botta, Brunella Caroleo, Maurizio Arnone

    Published:
    27th Apr 2026

    Abstract:
    INTRODUCTION: As cities gradually begin integrating autonomous vehicles into existing transport systems, it becomes essential to assess their potential impacts on traffic dynamics and safety in a comprehensive and systematic manner — particularly through tools that can anticipate impacts before actual on-road deployment. OBJECTIVES: This paper aims to develop a data-driven and modular framework to evaluate the integration of autonomous mobility solutions in mixed traffic conditions. METHODS: A data-driven approach combining sensor data collected during autonomous shuttle trials with video-based behavioural analysis of road users and calibrated traffic microsimulation is employed to perform ex-ante assessment of different deployment scenarios. RESULTS: The framework enables the evaluation of the impacts of autonomous mobility solutions on traffic performance and safety, providing insights across multiple scenarios. CONCLUSION: The framework supports informed decision-making and enhances the understanding of how autonomous mobility can be effectively integrated into urban environments.

  • Ultra-Low Latency V2X Systems with AI-Driven Resource Optimization

    Appears in:
    sc 25(1):

    Authors:
    Milad Rahmati

    Published:
    18th Nov 2025

    Abstract:
    Achieving ultra-low latency in Vehicle-to-Everything (V2X) communication is essential for ensuring the safety and effectiveness of autonomous vehicles (AVs). However, existing systems often struggle to meet the stringent latency demands, particularly in complex and rapidly changing urban environments. This study introduces an innovative framework that utilizes artificial intelligence (AI) for dynamic resource allocation in V2X networks. By integrating real-time data analysis, edge computing, and 5G capabilities, the proposed approach effectively minimizes latency. Simulation results indicate up to a 35% reduction in latency compared to conventional models, underscoring the potential of AI in enhancing the responsiveness and reliability of V2X systems. These findings offer a significant step toward making autonomous vehicle deployments more viable in smart cities.

  • Integrating Digital Transformation and AI in Civil Engineering: A Multidisciplinary Approach to Disaster Management and Sustainable Urban Development

    Appears in:
    sc 25(1):

    Authors:
    Dimitrios Sargiotis

    Published:
    13rd Nov 2025

    Abstract:
    The rapid advancement of digital technologies, particularly Artificial Intelligence (AI) and Machine Learning (ML), is ushering in a transformative era for civil engineering and disaster management. This paper outlines a multidisciplinary approach that harnesses the power of digital transformation to augment disaster preparedness, response mechanisms, and sustainable urban development. By integrating AI and ML innovations, Information and Communication Technology (ICT) advancements, and emerging technologies like quantum computing and blockchain, this study explores innovative digital solutions aimed at addressing critical challenges within the sector. Key focal points include the application of AI for early prediction and management of natural disasters, the strategic use of ICT in enhancing urban resilience, and the adoption of virtual simulation tools in education to bridge the theoretical-practical gap. Through an in-depth analysis of case studies—ranging from AI-powered mobile applications in smart city ecosystems to advanced materials engineering—this research highlights the potential of digital technologies to build resilient infrastructures, improve public health outcomes, and promote sustainable urban planning. This paper contributes to the ongoing discourse on the role of digital innovation in civil engineering, providing insights into the benefits and challenges of integrating technology into traditional practices, with the ultimate goal of achieving a more sustainable and resilient future.

  • Planning for Integrated Command and Control Centre in Indian Cities

    Appears in:
    sc 25(4):

    Authors:
    Saoni Sanyal, Nilanjana Dasgupta Sur Dasgupta Sur

    Published:
    6th Nov 2025

    Abstract:
    The Integrated Control and Command Centre (ICCC) is instrumental in enhancing global cities' efficiency, resilience, and sustainability as the central hub for urban management. By integrating diverse technological solutions and data sources, the ICCC enables real-time monitoring, analysis, and control of urban systems, aiding informed decision-making in city planning. Urban planners leverage the ICCC to assimilate data from various sectors such as traffic management, public safety, utilities, and environmental monitoring, fostering a holistic understanding of urban dynamics. This data-driven approach empowers planners to anticipate challenges, identify trends, and devise effective strategies for sustainable development, including optimizing traffic flow and implementing targeted interventions for environmental sustainability. Moreover, the ICCC serves as a centralized platform for emergency response and fosters citizen engagement through smart governance tools, enabling residents to access real-time information and provide feedback. However, challenges like lack of infrastructure, governance, funding, and skills hinder its holistic implementation. As technology continues to advance, the role of the ICCC will evolve, presenting new opportunities for innovation and sustainable urban development. Proposed in all Smart Cities of India under the Smart Cities Mission, Integrated Control and Command Centres (ICCCs) are operational in 100 cities, with Agartala, Indore, and Vadodara highlighted for sustainable business models. Yet, challenges persist in creating comprehensive ICCC models. Therefore, the main objective of this paper is to investigate and strategize the implementation of ICCCs in Indian cities for improved governance and urban resilience.

  • Development and Evaluation of E-Learning for Professional Bus driver in Tanzania

    Appears in:
    sc 25(4):

    Authors:
    Marwa Chacha, Ariane Cuenen, Geert Wets, Ansar Yasar, Prosper Nyaki

    Published:
    5th May 2025

    Abstract:
    INTRODUCTION:Commercial buses serve as the predominant mode of public transportation in Tanzania, with 90% of travellers opting for them, especially for inter-regional and urban-rural journeys. Despite their widespread use, the affordability of this mode has led to a rise in road safety issues, resulting in an alarming surge in accidents, injuries, and fatalities. This paper explores the potential of eLearning as an alternative approach to enhance road safety in Tanzania's commercial bus sector. OBJECTIVES:The primary aim of this study is to assess bus drivers' perceptions of eLearning deployment and develop a tailored course to improve road safety practices. The overarching goal is to contribute to existing knowledge by creating a training course addressing identified gaps in the context of changing driver behaviours in Tanzania. METHODS:Employing a quantitative approach, data for this study were collected through an online survey on Qualtrics and semi-structured interviews over three weeks. Participants included individuals from the National Institute of Transport (NIT), Vocational Education Training Authority (VETA), road safety NGOs, and bus drivers in Dar es Salaam, in collaboration with the Land Transport Regulatory Authority (LATRA). The study involved the development of an eLearning package tailored for professional bus drivers, utilizing social network analysis techniques. RESULTS:The survey, comprising 153 participants, provided insights into bus drivers' preferences for a 35-hour theory and practical training program. Findings indicated a high willingness among drivers to adopt eLearning, with smartphones being the preferred device. The study also proposed a comprehensive eLearning package, incorporating six modules derived from research findings, to enhance road safety awareness among professional bus drivers. CONCLUSION:This research advocates for developing and implementing eLearning as a viable strategy to enhance road safety awareness and skills among commercial bus drivers in Tanzania. The proposed eLearning modules and the learning management system (LMS) aim to address the limitations of traditional in-person training, providing a flexible and accessible alternative. Future efforts should involve stakeholders, policy discussions, and integration with GPS tracking for targeted feedback and continuous improvement in driving behaviours. Overall, the introduction of eLearning has the potential to impact safety cultures within companies positively and contribute to reducing road traffic accidents.

  • Balancing Efficiency and Equity: Ethical Considerations for Automation in Urban Planning

    Appears in:
    sc 25(4):

    Authors:
    Vandit Mehta, Abhishek Raisinghani

    Published:
    21st Feb 2025

    Abstract:
    The integration of automation into urban planning introduces a complex dynamic where efficiency often clashes with equity, especially for marginalized communities. This necessitates a delicate balance between these two aspects. This article investigates the ethical principles and equity considerations in urban planning decisions, revealing a historical and contemporary bias towards efficiency, marginalizing certain groups. Automation, while beneficial in sectors like transportation, land use, and infrastructure, can perpetuate existing inequities and pose ethical challenges such as algorithmic bias and data privacy concerns. The article explores the impacts of automation on plan execution and monitoring, highlighting the need for current best practices to address these challenges. It provides an overview of automation in urban planning and calls for continuous research, collaboration, and improvement to ensure efficiency and equity are mutually reinforced.      

  • Enhancing precision agriculture: An IoT-based smart monitoring system integrated LoRaWAN, ML and AR

    Appears in:
    sc 24(4):

    Authors:
    Nguyen Thi Hang Duy, Do Thanh Huong, Kou Yamada, Pham Vu Minh Tu, Huu Hoang Hanh

    Published:
    20th Nov 2024

    Abstract:
    Effective crop production and harvesting decisions rely on proper farm monitoring and management. Each region has distinct needs for farm oversight, but the primary focus remains on collecting and evaluating environmental data such as temperature, soil moisture, air humidity, all of which are vital to plant growth. Gathering this data on a large scale requires significant effort and is often based on intuition or simple measurement tools. This paper proposes a novel solution for farming data collection using an IoT platform integrated Long-Range Wide Area Networks (LoRaWAN) network application with Augmented Reality (AR) technology and Machine Learning (ML) algorithms to predict key environmental daily indexes. In a pilot study in Quang Tho, Vietnam, the system accurately predicted environmental conditions, reduced the risk of crop failure, and improved farm management efficiency. This approach enhances real-time data interaction and offers predictive analytics, supporting sustainable agriculture.

  • Multimodal Sentiment Analysis in Natural Disaster Data on Social Media

    Appears in:
    sc 24(4):

    Authors:
    Sefa Dursun, Süleyman Eken

    Published:
    14th Nov 2024

    Abstract:
    INTRODUCTION: With the development of the Internet, users tend to express their opinions and emotions through text, visual and/or audio content. This has increased the interest in multimodal analysis methods. OBJECTIVES: This study addresses multimodal sentiment analysis on tweets related to natural disasters by combining textual and visual embeddings. METHODS: The use of textual representations together with the emotional expressions of the visual content provides a more comprehensive analysis. To investigate the impact of high-level visual and texual features, a three-layer neural network is used in the study, where the first two layers collect features from different modalities and the third layer is used to analyze sentiments. RESULTS: According to experimental tests on our dataset, the highest performance values (77% Accuracy, 71% F1-score) are achieved by using the CLIP model in the image and the RoBERTa model in the text. CONCLUSION: Such analyzes can be used in different application areas such as agencies, advertising, social/digital media content producers, humanitarian aid organizations and can provide important information in terms of social awareness.

  • Development of a Smart Waste Management System with Automatic Bin Lid Control for Smart City Environment

    Appears in:
    sc 24(3):

    Authors:
    Abdussalam El-Suleiman, Teng David Chollom, Rabiu B. Ahmad, Ameer Mohammed, Mathias Usman Bonet, Abbas Abdullahi

    Published:
    18th Apr 2024

    Abstract:
    As cities worldwide transform into smart, interconnected urban environments, the management of municipal waste emerges as a pressing challenge. This paper, titled "Development of a Smart Waste Management System with Automatic Bin Lid Control," offers a sophisticated solution that integrates seamlessly within the concept of smart cities. This system harnesses the power of the Internet of Things (IoT) to optimize waste collection and enhance urban cleanliness. The primary aim of this project is to create a smart waste management system that extends beyond traditional waste bins. It introduces a network of intelligent waste containers equipped with automatic lid control mechanisms that operate based on real-time waste level data. When approaching these bins, the lid control mechanism automatically opens the bin only if the waste level is not full, thereby facilitating convenient waste disposal. In the context of smart cities, this innovative approach presents several advantages. It optimizes waste collection efficiency by prioritizing bins in need of immediate attention and ensures that waste containers are not prematurely emptied, reducing unnecessary waste disposal trips. Moreover, the system enables city authorities to gain insights into waste level trends, fostering data-driven and proactive waste management strategies for a cleaner, more sustainable urban environment.

  • Vehicle counting application utilizing background subtraction method with large-scale camera data

    Appears in:
    sc 24(3):

    Authors:
    Mien Puoc Doan, Vu The Tran, Sy Ngo Van

    Published:
    17th Apr 2024

    Abstract:
    In modern society, people are increasingly using cameras at home, in shops, and on the streets. Traffic systems have also invested in building more surveillance camera systems. The data collected by cameras contains valuable information for traffic regulation and recording traffic violations. The challenge is how to effectively use this data. In this article, we will discuss the use of real-time data from surveillance cameras on some roads in Da Nang City for vehicle counting using background subtraction methods. Additionally, we also tested the detection of red light violations to contribute to the development of a smart traffic system. So, the use of background subtraction in analyzing real-time data from surveillance cameras can greatly improve traffic management

  • Smart Tourism Ecosystem perspective on the Tourism Experience: A conceptual approach

    Appears in:
    sc 22(4): e3

    Authors:
    Cláudia Seabra, Pedro Vaz Serra, Ana Caldeira

    Downloads:
    2250

    Abstract:
    The smart tourism ecosystem concept, in addition to integrating various components, processes, and actions in the design of a place, advocates certain results through the convergence of technological resources, business environments, and value-inducing experiences. This conceptual paper should result in theoretical contributions regarding the specificity of the tourist experience within the framework of a smart tourism ecosystem, with a view to the competitiveness and sustainability of accommodation and destinations. From the perspective of a smart tourism ecosystem, the production and consumption of tourist value − which, hopefully, should be socially, culturally, environmentally, and economically sustainable −, is shared, and generate distinctive experiences, and the corresponding interactions are promoted by technology, through the collection, processing, and communication of data. The suggested approach has relevant implications at the management level, given the need to obtain differentiating factors, mediated by technology, with the incorporation of added value for the stakeholders.  

  • Medical Data Analytics and Wearable Devices

    Appears in:
    sc 22(4): e2

    Authors:
    John Amose , Sowmiya E C , Iswarya Manoharan, Jeslin Libisha J, Harishma S.

    Downloads:
    1601

    Abstract:
    Clinical decision-making may be directly impacted by wearable application. Some people think that wearable technologies, such as patient rehabilitation outside of hospitals, could boost patient care quality while lowering costs. The big data produced by wearable technology presents researchers with both a challenge and an opportunity to expand the use of artificial intelligence (AI) techniques on these data. By establishing new healthcare service systems, it is possible to organise diverse information and communications technologies into service linkages. This includes emerging smart systems, cloud computing, social networks, and enhanced sensing and data analysis techniques. The characteristics and features of big data, the significance of big data analytics in the healthcare industry, and a discussion of the effectiveness of several machine learning algorithms employed in big data analytics served as our conclusion.

  • Development of a Smart Waste Management System with Automatic Bin Lid Control for Smart City Environment

    Appears in:
    sc 24(3):

    Authors:
    Abdussalam El-Suleiman, Teng David Chollom, Rabiu B. Ahmad, Ameer Mohammed, Mathias Usman Bonet, Abbas Abdullahi

    Downloads:
    1494

    Abstract:
    As cities worldwide transform into smart, interconnected urban environments, the management of municipal waste emerges as a pressing challenge. This paper, titled "Development of a Smart Waste Management System with Automatic Bin Lid Control," offers a sophisticated solution that integrates seamlessly within the concept of smart cities. This system harnesses the power of the Internet of Things (IoT) to optimize waste collection and enhance urban cleanliness. The primary aim of this project is to create a smart waste management system that extends beyond traditional waste bins. It introduces a network of intelligent waste containers equipped with automatic lid control mechanisms that operate based on real-time waste level data. When approaching these bins, the lid control mechanism automatically opens the bin only if the waste level is not full, thereby facilitating convenient waste disposal. In the context of smart cities, this innovative approach presents several advantages. It optimizes waste collection efficiency by prioritizing bins in need of immediate attention and ensures that waste containers are not prematurely emptied, reducing unnecessary waste disposal trips. Moreover, the system enables city authorities to gain insights into waste level trends, fostering data-driven and proactive waste management strategies for a cleaner, more sustainable urban environment.

  • Securing Data using Audio Steganography for the Internet of Things

    Appears in:
    sc 23(4): e5

    Authors:
    Anju Gera, Dr. Vaibhav Vyas

    Downloads:
    1443

    Abstract:
    The Internet of Things (IoT) is prevalent in today's world and is part of our everyday life. While the residential district gains in several respects, numerous problems are developed, such as data confidentiality and privacy. The community is worried, in reality, about what information might leak through IoT. Therefore, the need for a protected environment is necessary if data transmission from devices across the network is to be protected. As a consequence, this paper proposes a secure scheme for using audio steganography to secure data from Laptop, which is distributed as an IoT device to other devices, or on LAN or WAN networks, as an alternative protection strategy along with a home server. The outcome of the developed system shows that the amount of distortion exposed by the Signal to Noise Ratio (SNR) is low.  

  • An Intelligent Machine Learning based Intrusion Detection System (IDS) for Smart cities networks

    Appears in:
    sc 23(1): e4

    Authors:
    Abdul Basit, Muhammad Tehmasib Ali Tashfeen, Hina Shoukat, Ali Haider, Usman Haider, Muhammad Yaseen Ayub

    Downloads:
    1298

    Abstract:
    INTRODUCTION: Internet of Things (IoT) along with Cloud based systems are opening a new domain of development. They have several applications from smart homes, Smart farming, Smart cities, smart grid etc. Due to IoT sensors operating in such close proximity to humans and critical infrastructure, there arises privacy and security issues. Securing an IoT network is very essential and is a hot research topic. Different types of Intrusion Detection Systems (IDS) have been developed to detect and prevent an unauthorized intrusion into the network. OBJECTIVES: The paper presents a Machine Learning based light, fast and reliable Intrusion Detection System (IDS). METHODS: Multiple Supervised machine learning algorithms are applied and their results are compared. Algorithms applied include Linear Discriminant analysis, Quadratic Discriminant Analysis, XG Boost, KNN and Decision Tree. RESULTS: Simulation results showed that KNN Algorithm gives us the highest accuracy, followed by XG Boost and Decision Tree which are not far behind. CONCLUSION: A fast, secure and intelligent IDS is developed using machine learning algorithms. The resulting IDS can be used in various types of networks especially in IoT based networks.

  • Intelligent Aircraft Hangar Fire Detection and Location System Based on Wireless Sensor Network

    Appears in:
    sc 23(2): e5

    Authors:
    Ameer Muhammed, Ubadike Obunike Arinze, Ubadike Osichinaka Chiedu, Mathias Usman Bonet, Abbas Abdullahi

    Downloads:
    1273

    Abstract:
    Aircraft hangar fire detection systems are essential for protecting both the facility's assets and the contents of an aircraft. In terms of predicting a fire outbreak at an aircraft hangar, the Intelligent Aircraft Hangar Fire detection is considered as a high-performance system that is designed based on the principle of a wireless sensor network (WSN), which operates by employing three sensor nodes at different locations inside the aircraft hangar to transmit gas concentrations in the air to a base station (BS) and send the resulting data from the sensor nodes to a server for analysis and visualization of the risk level. The server uses Machine Learning (ML) techniques to analyze the acquired data along with the sample gas data and displays the report in real time. When the smoke (gas) concentration is high, the server predicts a fire outbreak by displaying a high concentration zone on the Graphic User Interface (GUI). By this, the server automatically issues a warning and identifies the potential fire location. The technology is built to protect aircraft assets, hangar buildings, and human (personnel) life. A crucial part in the early detection of fire is played by the intelligent system

  • Evaluation of Frameworks for MLOps and Microservices

    Appears in:
    sc 23(3):

    Authors:
    Rogério Rossi, Igor Urias

    Downloads:
    1216

    Abstract:
    Information Technology involves solutions for many kinds of industries and organizations, offering conditions for solving problems of different types and complexities. Artificial Intelligence, and more specifically applications that considers Machine Learning (ML) and Software Technology are part of these solutions for solving problems, including solutions for solving problems that involve smart cities approach. In order to present frameworks that deal with the operationalization of Machine Learning and Software technology, this article is based on the study and evaluation of frameworks that involve Machine Learning Operations (MLOps) and microservices. Specifically, three frameworks that integrate ML algorithms with microservices are evaluated based on a bibliographical review in scientific journals of relevance to the area. From an exploratory analysis of these frameworks, it was possible to highlight their main objectives, their benefits, and their ability to offer solutions that favor the large-scale use of Machine Learning algorithms in problem solving. The main results are highlighted in the article through a qualitative analysis that considers six evaluation criteria, such as: capacity for sharing resources, scope of use by users, and use in a cloud environment. The results achieved are satisfactory since the work allows, through a qualitative view of the evaluated frameworks, a perspective of how the integration of MLOps and microservices has been carried out, its benefits and possible results achieved through this integration.

  • An Efficient Substitution Box design with a chaotic logistic map and Linear Congruential Generator for secure communication in Smart cities

    Appears in:
    sc 23(1): e6

    Authors:
    Noshina Tariq , Muhammad Asim Hashmi

    Downloads:
    1206

    Abstract:
    The study provides a unique method for creating an efficient substitution box (S-box) for advanced encryption standards using a Chaotic Logistic Map (CLM) and a Linear Congruential Generator (LCG) (AES) for secure communications in a smart city. The Pseudo-Random Number Generator (PRNG), which is further examined, is constructed using an extensive search of reasonable possibilities for the initial seed and set parameters. Using statistical testing, the performance analysis of the new S-box is assessed. Additionally, the resilience of differential, as well as linear cryptanalysis, is shown. It is derived using other features, including nonlinearity, the Bit Independence Criterion (BIC), and the Strict Avalanche Criterion (SAC). The suggested S-box has good potential and is usable for symmetric key cryptography, according to the features of the new S-cryptographic box.

  • Multimodal Sentiment Analysis in Natural Disaster Data on Social Media

    Appears in:
    sc 24(4):

    Authors:
    Sefa Dursun, Süleyman Eken

    Downloads:
    1137

    Abstract:
    INTRODUCTION: With the development of the Internet, users tend to express their opinions and emotions through text, visual and/or audio content. This has increased the interest in multimodal analysis methods. OBJECTIVES: This study addresses multimodal sentiment analysis on tweets related to natural disasters by combining textual and visual embeddings. METHODS: The use of textual representations together with the emotional expressions of the visual content provides a more comprehensive analysis. To investigate the impact of high-level visual and texual features, a three-layer neural network is used in the study, where the first two layers collect features from different modalities and the third layer is used to analyze sentiments. RESULTS: According to experimental tests on our dataset, the highest performance values (77% Accuracy, 71% F1-score) are achieved by using the CLIP model in the image and the RoBERTa model in the text. CONCLUSION: Such analyzes can be used in different application areas such as agencies, advertising, social/digital media content producers, humanitarian aid organizations and can provide important information in terms of social awareness.

  • AI for Healthy Meal Preparation in Smart Cities

    Appears in:
    sc 22(4): e1

    Authors:
    Bhuvana Namasivayam

    Downloads:
    1126

    Abstract:
    INTRODUCTION: ‘Food is medicine’. Eating healthy fresh cooked foods is increasingly becoming a challenge, especially among working professionals, elderly people, people in care homes and those getting medical care, as they find it difficult to cook everyday meals and to make sure they take in all necessary nutrients regularly. OBJECTIVES: With the intervention of Robotics and AI, food preparation and delivery can be made efficient in a way it supports overall health and wellbeing. METHODS: The proposed idea is a smart city AI scheme with robots engaged in food preparation tasks such as chopping, grating etc, robotic kitchens assembled to prepare foods as per the dietary needs of various groups of people and delivery bots and drones to effectively deliver meals, fruits and necessary supplements on a daily basis and also pick up leftovers for effective waste management. This can also be extended to smart hospitals for providing nutritious meals to patients to aid in faster recovery and also avoid the carelessness and haste in food preparation when human workers are involved.
Publisher
EAI
ISSN
2518-3893
Number of Volumes
8
Last Published
2025-11-06
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