EAI Endorsed Transactions on Smart Cities

Performance Evaluation of ARIMA and FB-Prophet Forecasting Methods in the Context of Endemic Diseases: A Case Study of Gedaref State in Sudan

Hussein Ali Hussein, Mukhtar M. E. Mahmoud, Haroun A. Eisa — Thu, 30 Mar 2023 00:00:00 +0000

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).

Propaganda Detection And Challenges Managing Smart Cities Information On Social Media

Pir Noman Ahmad, Khalid Khan — Thu, 30 Mar 2023 00:00:00 +0000

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.

Applied Design and Methodology of Delivery Robots Based on Human–Robot Interaction in Smart Cities

Wing Ting LAW, Kam Wah Fan, Ki Sing Li, Tiande Mo — Mon, 26 Jun 2023 00:00:00 +0000

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.

Detection of Cyber Attacks using Machine Learning ‎based Intrusion Detection System for IoT Based Smart ‎Cities

Wed, 28 Jun 2023 00:00:00 +0000

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.