The Necessity of Artificial Intelligence for Smart Environment: Future Perspective and Research Challenges

Kumari, Shabnam; Muthulakshmi, P.

doi:10.1109/iccci56745.2023.10128362

Cited by 2 publications

(1 citation statement)

References 24 publications

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“…The smart city itself is part of community collaboration with public administration in developing smart cities, using IT support to create smart and inclusive communities [14]. Smart environments use digital technology such as Artificial Intelligence (AI) to help human life [15]. Developing the Internet of Things (IoT) using sensors and computational devices to help environmental protection by monitoring water quality, air quality, pollution, etc [16].…”

Section: Introductionmentioning

confidence: 99%

Comparative Study of Indonesia and Denmark in Creating a Smart Environment

Hanif,

Utomo,

Husein

2024

JPSL

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This study aims to compare the efforts of the Governments of Indonesia and Denmark in realizing a smart environment SDG's agenda. This urgency is motivated by the poor environmental conditions in Indonesia while the government is targeting a reduction in emission production by 2030. This research uses a qualitative method. The results of this study indicate that the Government of Indonesia in pursuing a smart environment has not been optimal. This can be seen from the several indicators used in this study: (1) the Attraction of Natural Conditions indicator, the Indonesian Government has not enforced regulations or programs aimed at maintaining the balance of green open spaces. Meanwhile, the Danish Government has launched a city greening program through construction of green buildings. (2) Air Pollution Integrated Index indicator, the Indonesian government has not been able to minimize the number of vehicles through the use of electromobility-based vehicles as has been done by the Danish government. (3) Indicators of Sustainable resource management, the Indonesian Government has not been able to optimize the development of technology for processing waste and waste into residues that can be reused. While in Denmark, which is able to turn waste into residue that can be reused.

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Section: Introductionmentioning

confidence: 99%

Comparative Study of Indonesia and Denmark in Creating a Smart Environment

Hanif,

Utomo,

Husein

2024

JPSL

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Cloud computing-based framework for heart disease classification using quantum machine learning approach

Enad,

Mohammed

2024

Journal of Intelligent Systems

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Accurate early identification and treatment of cardiovascular diseases can prevent heart failure problems and reduce mortality rates. This study aims to use quantum learning to predict heart problems to increase the accuracy of traditional prediction and classification methods. Machine learning (ML) and deep learning (DL) techniques need quantum learning to quickly and accurately analyze massive volumes of complex data. With quantum computing, the suggested DL and ML algorithms can change their predictions on the basis of changes in the dataset. This approach could help with the early and accurate detection of chronic diseases. The Cleveland heart disease dataset is undergoing preliminary processing to validate missing values to increase the precision rate and prevent incorrect forecasts. This study examined the feasibility of employing and deploying a quantum ML (QML) framework via cloud computing to categorize cardiac conditions. The research was divided into four sections. First, the principal component analysis was used to preprocess the Cleveland dataset, recursive feature elimination was used to select features, and min–max normalization was used to give the dataset a high-dimensional value. Second, we compared traditional classifiers, such as support vector machine (SVM) and artificial neural network, with the quantum approach to verify the quantum approach’s efficiency. Third, we examined two unique QML classification methods: quantum neural networks (QNNs) and quantum SVM (QSVM). Fourth, bagging-QSVM was developed and deployed as an ensemble learning model. Experimental results using the QNN show an accuracy of 77%, a precision of 76%, a recall of 73%, and an F1 score of 75%. With an accuracy of 85%, a precision of 79%, a recall of 90%, and an F1-score of 84%, the QSVM method demonstrated a much better performance than the QNN. Particularly, the Bagging_QSVM model exhibited an outstanding performance, with a flawless score of 100% across all critical performance measures. The study shows that the bagging method for ensemble learning is a solid way of increasing the accuracy of quantum method predictions.

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The Necessity of Artificial Intelligence for Smart Environment: Future Perspective and Research Challenges

Cited by 2 publications

References 24 publications

Comparative Study of Indonesia and Denmark in Creating a Smart Environment

Comparative Study of Indonesia and Denmark in Creating a Smart Environment

Cloud computing-based framework for heart disease classification using quantum machine learning approach

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