Energy Consumption Optimization and User Comfort Maximization in Smart Buildings Using a Hybrid of the Firefly and Genetic Algorithms

Wahid, Fazli; Fayaz, Muhammad; Aljarbouh, Ayman; Mir, M. Afzal; Muhammad, Atta; Imran,

doi:10.3390/en13174363

Cited by 42 publications

(32 citation statements)

References 29 publications

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“…Each optimization algorithm has its own merits and demerits due which these several algorithms are made hybrid with others for optimum solutions. In [17] proposes a hybrid approach based on Genetic Algorithm and firefly algorithms for optimization problems. The simplest technique for choosing the best CNN architecture is cross-validation [18].…”

Section: Related Workmentioning

confidence: 99%

An Optimized Convolution Neural Network Architecture for Paddy Disease Classification

Saleem¹,

Aamir²,

Ibrahim³

et al. 2022

Computers, Materials &Amp; Continua

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Plant disease classification based on digital pictures is challenging. Machine learning approaches and plant image categorization technologies such as deep learning have been utilized to recognize, identify, and diagnose plant diseases in the previous decade. Increasing the yield quantity and quality of rice forming is an important cause for the paddy production countries. However, some diseases that are blocking the improvement in paddy production are considered as an ominous threat. Convolution Neural Network (CNN) has shown a remarkable performance in solving the early detection of paddy leaf diseases based on its images in the fast-growing era of science and technology. Nevertheless, the significant CNN architectures construction is dependent on expertise in a neural network and domain knowledge. This approach is time-consuming, and high computational resources are mandatory. In this research, we propose a novel method based on Mutant Particle swarm optimization (MUT-PSO) Algorithms to search for an optimum CNN architecture for Paddy leaf disease classification. Experimentation results show that Mutant Particle swarm optimization Convolution Neural Network (MUTPSO-CNN ) can find optimum CNN architecture that offers better performance than existing hand-crafted CNN architectures in terms of accuracy, precision/recall, and execution time.

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Section: Related Workmentioning

confidence: 99%

An Optimized Convolution Neural Network Architecture for Paddy Disease Classification

Saleem¹,

Aamir²,

Ibrahim³

et al. 2022

Computers, Materials &Amp; Continua

Self Cite

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“…Artificial intelligence is taking over the current era and is changing the current era into a revolutionary practical world. Data analysis, predictive analytics and optimization models are used for many real-life applications [30][31][32]. Anomaly detection is a type of data analysis used to identify irregular and abnormal data from a given data set.…”

Section: Related Workmentioning

confidence: 99%

An Ensemble of Prediction and Learning Mechanism for Improving Accuracy of Anomaly Detection in Network Intrusion Environments

2021

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The connectivity of our surrounding objects to the internet plays a tremendous role in our daily lives. Many network applications have been developed in every domain of life, including business, healthcare, smart homes, and smart cities, to name a few. As these network applications provide a wide range of services for large user groups, the network intruders are prone to developing intrusion skills for attack and malicious compliance. Therefore, safeguarding network applications and things connected to the internet has always been a point of interest for researchers. Many studies propose solutions for intrusion detection systems and intrusion prevention systems. Network communities have produced benchmark datasets available for researchers to improve the accuracy of intrusion detection systems. The scientific community has presented data mining and machine learning-based mechanisms to detect intrusion with high classification accuracy. This paper presents an intrusion detection system based on the ensemble of prediction and learning mechanisms to improve anomaly detection accuracy in a network intrusion environment. The learning mechanism is based on automated machine learning, and the prediction model is based on the Kalman filter. Performance analysis of the proposed intrusion detection system is evaluated using publicly available intrusion datasets UNSW-NB15 and CICIDS2017. The proposed model-based intrusion detection accuracy for the UNSW-NB15 dataset is 98.801 percent, and the CICIDS2017 dataset is 97.02 percent. The performance comparison results show that the proposed ensemble model-based intrusion detection significantly improves the intrusion detection accuracy.

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“…The innovations and discovery of new data processing, data analysis, and predictive analysis enable the development of intelligent applications [24][25][26]. Many smart architectures are proposed and based on the Internet of Things, blockchain, machine learning, and their integrated mechanisms [26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Survey of IoT, Machine Learning, and Blockchain for Health Care Applications: A Topical Assessment for Pandemic Preparedness, Challenges, and Solutions

Imran

Zaman²,

Imran

et al. 2021

Electronics

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Internet of Things (IoT) communication technologies have brought immense revolutions in various domains, especially in health monitoring systems. Machine learning techniques coupled with advanced artificial intelligence techniques detect patterns associated with diseases and health conditions. Presently, the scientific community is focused on enhancing IoT-enabled applications by integrating blockchain technology with machine learning models to benefit medical report management, drug traceability, tracking infectious diseases, etc. To date, contemporary state-of-the-art techniques have presented various efforts on the adaptability of blockchain and machine learning in IoT applications; however, there exist various essential aspects that must also be incorporated to achieve more robust performance. This study presents a comprehensive survey of emerging IoT technologies, machine learning, and blockchain for healthcare applications. The reviewed articles comprise a plethora of research articles published in the web of science. The analysis is focused on research articles related to keywords such as `machine learning’, blockchain, `Internet of Things or IoT’, and keywords conjoined with `healthcare’ and `health application’ in six famous publisher databases, namely IEEEXplore, Nature, ScienceDirect, MDPI, SpringerLink, and Google Scholar. We selected and reviewed 263 articles in total. The topical survey of the contemporary IoT-based models is presented in healthcare domains in three steps. Firstly, a detailed analysis of healthcare applications of IoT, blockchain, and machine learning demonstrates the importance of the discussed fields. Secondly, the adaptation mechanism of machine learning and blockchain in IoT for healthcare applications are discussed to delineate the scope of the mentioned techniques in IoT domains. Finally, the challenges and issues of healthcare applications based on machine learning, blockchain, and IoT are discussed. The presented future directions in this domain can significantly help the scholarly community determine research gaps to address.

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Energy Consumption Optimization and User Comfort Maximization in Smart Buildings Using a Hybrid of the Firefly and Genetic Algorithms

Cited by 42 publications

References 29 publications

An Optimized Convolution Neural Network Architecture for Paddy Disease Classification

An Optimized Convolution Neural Network Architecture for Paddy Disease Classification

An Ensemble of Prediction and Learning Mechanism for Improving Accuracy of Anomaly Detection in Network Intrusion Environments

Comprehensive Survey of IoT, Machine Learning, and Blockchain for Health Care Applications: A Topical Assessment for Pandemic Preparedness, Challenges, and Solutions

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