EAI Endorsed Transactions on Smart Cities
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
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The Development and Evaluation of E-Learning for Professional Bus driver in Tanzania
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.
Copyright (c) 2024 Marwa Chacha, Ariane Cuenen, Prosper Nyaki, Ansar Yasar, Geert Wets https://creativecommons.org/licenses/by-nc-sa/4.0
Balancing Efficiency and Equity: Ethical Considerations for Automation in Urban Planning
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.
Smart Structural Health Monitoring: Building Resilient Smart Cities for the Future
Copyright (c) 2024 Abhishek Raisinghani, Vandit Mehta https://creativecommons.org/licenses/by-nc-sa/4.0
Enhancing precision agriculture: An IoT-based smart monitoring system integrated LoRaWAN, ML and AR
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.
Copyright (c) 2024 Do Thanh Huong, Nguyen Thi Hang Duy, Pham Vu Minh Tu, Huu Hoang Hanh, Kou Yamada https://creativecommons.org/licenses/by-nc-sa/4.0
Multimodal Sentiment Analysis in Natural Disaster Data on Social Media
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.
Copyright (c) 2024 Sefa Dursun, Süleyman Eken https://creativecommons.org/licenses/by-nc-sa/4.0
Development of a Smart Waste Management System with Automatic Bin Lid Control for Smart City Environment
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.
Copyright (c) 2023 Abbas Abdullahi, Ameer Mohammed, Mathias Usman Bonet, Abdussalam El-Suleiman, Rabiu B. Ahmad, Teng David Chollom https://creativecommons.org/licenses/by-nc-sa/4.0
Vehicle counting application utilizing background subtraction method with large-scale camera data
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
Copyright (c) 2023 Mien Puoc Doan, Vu The Tran, Sy Ngo Van https://creativecommons.org/licenses/by-nc-sa/4.0
A Contemporary Approach to Designing and Implementing Electronic Voting Systems (EVS)
This study delves into the potential of Electronic Voting Systems (EVS) in Ghana, to enhance transparent and trustworthy electoral processes. We presented a comprehensive framework highlighting trust, diaspora engagement, and human factors in voting. The study proposes a robust EVS framework for Ghana, emphasizing trust and accountability, preventing electoral fraud, and encouraging African governments to invest in IT and collaborate with experts in e-government and e-voting systems. We commence with a detailed systems analysis, identifying specific electoral challenges in Ghana. An artifact is designed and developed, and the artifact's effectiveness is demonstrated through Design Science Research Methodology (DSRM). We evaluated its alignment with the desired solution for Ghana's electoral issues. We emphasize the potential of EVS to address electoral challenges in Ghana and underscore the importance of proactive government policies, IT investments, and collaboration with IT experts. User acceptance testing achieved a remarkable 98% approval rate, showcasing the feasibility of implementing EVS at the national level. This research underlines the pivotal role of EVS in Ghana and advocates for visionary government policies and investments in IT. These measures can modernize electoral systems, align them with international standards, and promote democratic progress while preventing electoral fraud in Ghana and other African nations to avoid condemnation and punishment of unconstitutional transfers of power that are being challenged by meticulously planned takeovers.
Copyright (c) 2023 Adams Addison Kobla Azameti, Samuel Chris Quist, Godfred Koi-Akrofi, Benedict C. Nwachuku https://creativecommons.org/licenses/by-nc-sa/4.0
Evaluation of Frameworks for MLOps and Microservices
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.
Copyright (c) 2023 Igor Urias, Rogério Rossi https://creativecommons.org/licenses/by-nc-sa/4.0
Welcome to the second issue of Volume 7 of the EAI Endorsed Transactions on Smart Cities
Copyright (c) 2023 Dario Vieira https://creativecommons.org/licenses/by-nc-sa/4.0
Intelligent Aircraft Hangar Fire Detection and Location System Based on Wireless Sensor Network
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
Copyright (c) 2023 Abbas Abdullahi, Mathias Usman Bonet, Ubadike O. C., Ameer Muhammed, Ubadike O. A. https://creativecommons.org/licenses/by-nc-sa/4.0
Applied Design and Methodology of Delivery Robots Based on Human–Robot Interaction in Smart Cities
This paper proposes an optimised design of an autonomous delivery robot while adopting the latest technologies from the different branching fields of robotics, artificial intelligence, and tele-communication. As a prospective representation of a user-centric robot design, the proposal is design with the major focus on maximizing users’ satisfaction throughout every human–robot interaction (HRI) touchpoints. By the use of sensor fusion techniques along with the deployment of an image-detection-based technique accompanying the point-cloud-detection-based path-planning methodology, the robot delivery would be optimised with effective path-planning and obstacle avoidance capability. With the extension of 5G connectivity, it is proposed that the real-time status update and video stream would enable greater efficiency in terms of remote monitoring and centralised robot administration.
Copyright (c) 2023 Wing Ting LAW, Kam Wah Fan, Ki Sing Li, Tiande Mo https://creativecommons.org/licenses/by-nc-sa/4.0
Detection of Cyber Attacks using Machine Learning ‎based Intrusion Detection System for IoT Based Smart ‎Cities
The world’s dynamics is evolving with artificial intelligence (AI) and the results are smart products. A smart city has smart city is collection of smart innovations powered with AI and internet of things (IoTs). Along with the ease and comfort that the concept of a smart city pointed at, many security concerns are being raised that hinders the path of its flourishment. An Intrusion Detection System (IDS) monitors the whole network traffic and alerts in case of any anomaly. A Machine Learning-based IDS intelligently senses the network threats, takes decisions about data packet legibility and alarm the user. Researchers have deployed various ML techniques to IDS to improve the detection accuracy. This work presents a comparative analysis of various ML algorithms trained over UNSW-NB15 dataset. ADA Boost, Linear Support Vector Machine (LSVM), Auto Encoder Classifier, ‎Quadratic Support Vector Machine (QSVM) and Multi-Layer Perceptron algorithms are being employed in the stimulation. ADA Boost showed an excellent accuracy of 98.3% in the results.
Copyright (c) 2023 Maria Nawaz Chohan, Usman Haider, Muhammad Yaseen Ayub, Hina Shoukat, Tarandeep Kaur Bhatia, Muhammad Furqan Ul Hassan https://creativecommons.org/licenses/by-nc-sa/4.0
An Intelligent Machine Learning based Intrusion Detection System (IDS) for Smart cities networks
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.
Copyright (c) 2023 Muhammad Ayub, Usman Haider, Ali Haider, Muhammad Tehmasib Ali Tashfeen, Hina Shoukat, Abdul Basit https://creativecommons.org/licenses/by-nc-sa/4.0
Propaganda Detection And Challenges Managing Smart Cities Information On Social Media
Misinformation, false news, and various forms of propaganda have increased as a consequence of the rapid spread of information on social media. The Covid-19 spread deeply transformed citizens' day-to-day lives due to the overview of new methods of effort and access to facilities based on smart technologies. Social media propagandistic data and high-quality information on smart cities are the most challenging elements of this study. As a result of a natural language processing perspective, we have developed a system that automatically extracts information from bi-lingual sources. This information is either in Urdu or English (Ur or Eng), and we apply machine translation to obtain the target language. We explore different neural architectures and extract linguistic layout and relevant features in the bi-lingual corpus. Moreover, we fine-tune RoBERTa and ensemble BiLSM, CRF and BiRNN model. Our solution uses fine-tuned RoBERTa, a pretrained language model, to perform word-level classification. This paper provides insight into the model's learning abilities by analyzing its attention heads and the model's evaluation results.
Copyright (c) 2023 Pir Noman Ahmad, Khalid Khan https://creativecommons.org/licenses/by-nc-sa/4.0
Editorial: Welcome to the first issue of Volume 7 of the EAI Endorsed Transactions on Smart Cities
Copyright (c) 2023 Mohammad Derawi https://creativecommons.org/licenses/by-nc-sa/4.0
Performance Evaluation of ARIMA and FB-Prophet Forecasting Methods in the Context of Endemic Diseases: A Case Study of Gedaref State in Sudan
Today, artificial intelligence is a key tool for turning a city into a smart city, and advances in information and communication technology (ICT) have led to the development of smart cities with many different parts. Smart Health is one of these components and is used to improve healthcare by providing services such as disease forecasting, early diagnosis, and others. There are various machine learning algorithms available now that can help with S-Health services, but which is better for disease forecasting? Gedaref State, for example, has some of Sudan's heaviest rains, and malaria and pneumonia are widespread throughout the year. Predicting future trends for these diseases has been a major focus for researchers in order for Gedaref's administration and the state's ministry of health to design effective ways to prevent and control the development of these diseases, as well as to prepare an adequate stock of medicine. As a result, it is necessary to establish a trustworthy and accurate forecasting model to aid Gedaref's government in developing economic and medical strategies for dealing with these diseases, as well as taking action on medical resource allocation. This study uses a time series dataset collected from the state's ministry of health to estimate malaria and pneumonia as common diseases in Gedaref state, Sudan, five months later. To comprehend the overall number of cases of diseases, two forecasting methodologies, namely the ARIMA and Prophet models, are applied to the disease's dataset. The performance of the ARIMA and FB-Prophet forecasting systems in predicting malaria and pneumonia diseases in Gedaref State is compared in this study. The data was collected from the state's ministry of health between January 2017 and December 2021. The results reveal that the ARIMA technique outperforms the FB-Prophet forecasting method in both malaria (RMSE: 182.8, MAE: 141.6, MAPE: 0.0057, and MASE: 0.0537) and pneumonia (RMSE: 1400.3, MAE: 1001.4, MAPE: 0.0513, and MASE: 0.9136).
Copyright (c) 2023 Hussein Ali Hussein, Mukhtar M. E. Mahmoud, Haroun A. Eisa https://creativecommons.org/licenses/by-nc-sa/4.0
IoT-based Hybrid Wireless Network for Tourist Boat Tracking towards Smart Cities
Moving and transporting by canoe and boat on rivers and canals is a cultural feature of the Mekong Delta and plays an important role in the economy and society. However, the management and use of equipment to support the monitoring of waterway transport vehicles in this area has yet to receive adequate investment and attention. Given the complicated evolution of the COVID-19 epidemic, it is critical to strengthen oversight of inland waterway management, as well as freight and passenger transportation. This paper presents the design and implementation of an IoT-based support system for managing and monitoring passenger ships and tourism activities in smart cities. This study proposes a hybrid wireless communication network solution that takes advantage of the strengths of LoRa and Zigbee wireless communication technologies, as well as telecommunication networks, to ensure that the system has a wide operating range of several kilometers, low power consumption, and can be deployed in areas where telecommunications are not available. Aside from tracking the journey and managing information about vehicles, drivers, and passengers, the system also aids in the collection of environmental parameters along river routes according to the travel route. An experimental evaluation of the system's operation was carried out for the tourist boat route between two famous tourist sites, Ninh Kieu Key and Cai Rang floating market in Can Tho city, Vietnam.
Copyright (c) 2023 Tuyen Truong, Phong Vu Truong , Viet Quoc Tran https://creativecommons.org/licenses/by-nc-sa/4.0
Investigation of Blockchain for COVID-19: A Systematic Review, Applications and Possible Challenges
Smart city is emerging application in which many Internet of Things (IoT) devices are embedded to perform overall monitoring and perform processing automatically. In smart city the authenticity is key problem and many users in the in smart city has faced challenges during COVID-19. The COVID-19 epidemic, a deadly virus, first appeared in the globe in 2019. The World Health Organization (WHO) states that it is almost certainly feasible to contain this virus in its early phases if some precautions are taken. To contain the infection, most nations declared emergencies both inside and outside their borders and prohibited travel. Artificial intelligence and blockchain are being used in smart city applications to monitor the general condition in the nation and reduce the mortality rate. Blockchain has also made it possible to safeguard patient medical histories and provide epidemic tracking. AI also offers the ideal, wanted answer for correctly identifying the signs. The primary goal of this study is to fully investigate blockchain technology and artificial intelligence (AI) in relation to COVID-19. A case study that was recently developed to identify and networked pathogens acquired important knowledge and data. Additionally, AI that can handle massive quantities of medical data and perform difficult jobs will be able to reduce the likelihood of intricacy in data analysis. Lastly, we highlight the present difficulties and suggest potential paths for addressing the 19 diseases in future circumstances.
Copyright (c) 2023 Shah Hussain Badshah, Muhammad Imad, Muhammad Abul Hassan, Naimullah, Shabir khan, Farhatullah, Sana Ullah, Syed Haider Ali https://creativecommons.org/licenses/by-nc-sa/4.0
An Efficient Substitution Box design with a chaotic logistic map and Linear Congruential Generator for secure communication in Smart cities
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.
Copyright (c) 2023 Muhammad Asim Hashmi, Noshina Tariq https://creativecommons.org/licenses/by-nc-sa/4.0
Design a framework for IoT- Identification, Authentication and Anomaly detection using Deep Learning: A Review
The Internet of Things (IoT) connects billions of smart gadgets so that they may communicate with one another without the need for human intervention. With an expected 50 billion devices by the end of 2020, it is one of the fastest-growing industries in computer history. On the one hand, IoT technologies are critical in increasing a variety of real-world smart applications that can help people live better lives. The cross-cutting nature of IoT systems, on the other hand, has presented new security concerns due to the diverse components involved in their deployment. For IoT devices and their inherent weaknesses, security techniques such as encryption, authentication, permissions, network monitoring, \& application security are ineffective. To properly protect the IoT ecosystem, existing security solutions need to be strengthened. Machine learning and deep learning (ML/DL) have come a long way in recent years, and machine intelligence has gone from being a laboratory curiosity to being used in a variety of significant applications. The ability to intelligently monitor IoT devices is an important defense against new or negligible assaults. ML/DL are effective data exploration techniques for learning about 'normal' and 'bad' behavior in IoT devices and systems. Following a comprehensive literature analysis on Machine Learning methods as well as the importance of IoT security within the framework of different sorts of potential attacks, multiple DL algorithms have been evaluated in terms of detecting attacks as well as anomaly detection in this work. We propose a taxonomy of authorization and authentication systems in the Internet of Things based on the review, with a focus on DL-based schemes. The authentication security threats and problems for IoT are thoroughly examined using the taxonomy supplied. This article provides an overview of projects that involve the use of deep learning to efficiently and automatically provide IoT applications.
Copyright (c) 2023 Aimen Shoukat, Muhammad Abul Hassan, Muhammad Rizwan, Muhammad Imad, Farhatullah, Syed Haider Ali, Sana Ullah https://creativecommons.org/licenses/by-nc-sa/4.0
Securing Data using Audio Steganography for the Internet of Things
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.
Copyright (c) 2023 Anju Gera, Dr. Vaibhav Vyas https://creativecommons.org/licenses/by-nc-sa/4.0
Medical Data Analytics and Wearable Devices
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.
Copyright (c) 2022 Iswarya Manoharan, Jeslin Libisha J, Sowmiya E C, Harishma S., John Amose https://creativecommons.org/licenses/by-nc-sa/4.0
Intelligent Dashboards to Monitor the Occurrences in Smart Cities – A Portuguese Case Study
This article concerns the needed response by the Professional Fire Brigade Regiment (FBR) in the city of Lisbon. To solve and answer the question "How to improve FBR intervention requests when an emergency is detected?" the project aims to create a functional prototype containing interactive dashboards allowing the analysis of indicators that improve decision capacity. As results attest, 58% of false alarms are cancelled even after the emergency and rescue means have been activated to the location. About 97% of the suspended requests are not cancelled before the means are sent. The number of records of occurrences tends to increase over the 8 years of study. Sunday is the weekday with the highest number of associated records, with 23.33%, specifically at 9 am and 8 pm. Autumn is the season with more occurrences, with 26.51%. More than 50% of the occurrences are in the administrative services closing time and more than 50% of the registrations send only one vehicle to the place. These indicators aim to understand if these variables are probabilistically associated with requests for interventions to be able to anticipate these scenarios and help in decision-making whenever necessary.
Copyright (c) 2022 Rita Silva, Maria Silva, Gustavo Caldas, Filipe Portela, Henrique Santos https://creativecommons.org/licenses/by-nc-sa/4.0
Smart Tourism Ecosystem perspective on the Tourism Experience: A conceptual approach
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.
Copyright (c) 2022 Pedro Vaz Serra, Cláudia Seabra, Ana Caldeira https://creativecommons.org/licenses/by-nc-sa/4.0
AI for Healthy Meal Preparation in Smart Cities
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.
Copyright (c) 2022 Bhuvana Namasivayam https://creativecommons.org/licenses/by-nc-sa/4.0
Recent Trends and Challenges in Smart Cities
Smart systems are wanting for smart communities to adapt to restricted spaces and assets across the world. Thus, smart urban communities arose mostly because of exceptionally inventive ICT ventures and markets, and furthermore, they have begun to utilize novel arrangements exploiting the Internet of Things (IoT), huge information and distributed computing innovations to lay out a significant association between every part and layer of a city. Smarter solutions need to be executed to make digital services for economic and social advancement seamlessly reach the occupants in an easy and secure way and encourage them to continue using the amenities. A holistic development rather than just technological advancement is essential for the betterment of Smart lifestyle of the present and future population. This paper attempts to analyze advancements, and the challenges involved in implementing them in various sectors should be executed to make computerized administrations for monetary and social headway flawlessly arrive at the tenants in a simple and secure manner and urge them to keep utilizing the conveniences.
Copyright (c) 2022 Pooja G, Sundar R, Harshini R, Arjuna S, Ram Kumar C https://creativecommons.org/licenses/by-nc-sa/4.0
Artificial Intelligence in Smart cities and Healthcare
In the era of the Internet of things IoT and Artificial knowledge (AI) continues to define the century.Artificial intelligence refers to a wide term that incorporates machine learning, normal language handling, rule based expert systems, actual robots, and robotic automation . The rise of computerized system and clinical gadgets in securely and productively diagnosing, treating, and planning care is an obvious sign that AI is digging in for the long haul and fill in significance. While AI holds a great deal of potential, the ramifications for essential consideration suppliers should be tended to as it might restrict execution. Since the Epidemic cities in 2019, the healthcare industry has escalated its AI-based advances by 60%. As indicated by the investigation, AI calculations like ANN, RNN/LSTM, CNN/R-CNN, DNN, and SVM/LS-SVMbhas a higher impact on the different smart city domains. Smart city advances the unification of conventional urban infrastructure and information technology (IT) to improve the quality of living and sustainable urban services in the city. To achieve this, smart cities require coordinated effort among the general public as well as private sectors to introduce IT stages to gather and examine massive amounts of information. Simultaneously, it is vital to design effective artificial intelligence (AI) based tools to deal with medical services emergency circumstances in smart urban communities. This paper reviews about the current technologies like artificial intelligence in the healthcare for smart cities.
Copyright (c) 2022 Sowmitha R., Shanmuga Raju S., Harshini R., Arjuna S., Ram Kumar C. https://creativecommons.org/licenses/by-nc-sa/4.0
Current practices and Limitations in E Health Informatics
E-health is a field which as seen tremendous growth in the recent times especially after the covid-19 outbreak. E-health offers the potential to provide patients with high-quality care at a reasonable cost in the ease of their own homes. E-health has a wide range of application that includes rehabilitation, cognitive disorder, behavioural therapy, defence application and many more. E-health as a technology is changing tremendously and always been evolving to meet the demands of the current practices. But still, it has certain challenges in implementing such as ethical issues, patient contest etc. Artificial intelligence framework capable of using non-consultancy, reinforcement learning, and all three. Controlling the processes within the m-Health application, choosing the best processes that can be used to alter the user's existing conditions, or selecting the best diagnosis-solution from an array of choices are all supported by intelligent optimization algorithms that can offer faster feedback.
Copyright (c) 2022 Umamaheshwari K., Manju Rathinam, Vivekitha V., Dinesh T. N. https://creativecommons.org/licenses/by-nc-sa/4.0
Artificial Intelligence is changing Health and eHealth care
Artificial Intelligence (AI) will be used more and more in the healthcare industry as a result of the complexity and growth of data in the sector. Payers, care providers, and life sciences organisations currently use a variety of AI technologies. The main application categories include recommendations for diagnosis and treatment, patient engagement and adherence, and administrative tasks. Although there are many situations in which AI can execute healthcare duties just as well as or better than humans, implementation issues will keep the jobs of healthcare professionals from becoming extensively automated for a substantial amount of time. The use of AI in healthcare and ethical concerns are also highlighted.
Copyright (c) 2022 Akshaya AVR, Vigneshwaran S., Ram Kumar C https://creativecommons.org/licenses/by-nc-sa/4.0
Challenges of eHealth and Current Developments in eHealth Applications: An Overview
Healthcare sector is moving towards digitalization in every aspect including e-consultations, surveillance of health,and all other services in healthcare industry. eHealth ends in the remodel of conventional methods of imparting specialist healthcare offerings digitally through the use of technology aimed toward both fee-effectiveness and patient satisfaction who are the customers of health offerings. Electronic health records has been maintained by developed countries which makes evaluating patient outcome easier. which makes evaluation of patient outcomes much easier. In the health sector, a variety of new ICTs are implemented to improve the efficiency of all levels of healthcare. eHealth—or digital health—is the use of ICT to improve the ability to treat patients, facilitate behaviour change, and improve health. Advances in information and communication technology (ICT) and the dissemination of network data processing created a new environment of universal access to information and globalization of communications, businesses, and services eHealth applications were analysed to determine the brand new developments in E-health programs. In this paper, the stakeholders are identified who're accountable for contributing to a selected eHealth challenge. By analysing the current scenario of E-health, we identified the challenges faced by eHealth technologies. The factors influencing the challenges were identified and classified. The emerging trends in the field of e-Health was studied and the applications and its benefits towards the patients was also analysed. The paper also elaborates on the role of mHealth in eHealth.
Copyright (c) 2022 Saikumari V., Arunraj A. https://creativecommons.org/licenses/by-nc-sa/4.0