Intelligent Model Of Ecosystem For Smart Cities Using Artificial Neural Networks

Batool, Tooba; Abbas, Sagheer; Alhwaiti, Yousef; Saleem, Muhammad; Ahmad, Munir; Asif, Muhammad; Elmitwally, Nouh Sabri

doi:10.32604/iasc.2021.018770

Cited by 39 publications

(12 citation statements)

References 12 publications

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“…However, Batool et al and others believe that when two medium and strong volumes are combined, strong sound will produce strong sound effect, and strong sound will have a greater impact on sound in hearing. Just as in listening to symphony works, weak sound will produce synesthesia effect of distance feeling, and strong sound will produce sound impact feeling of close distance [11]. erefore, in the cutting and integration of the strength treatment of chamber music performance, we need to first consider the constant volume of the ensemble of musical instruments, split each sound part on this basis, reasonably plan the strength of each sound part, and then integrate to form a complete constant volume structure.…”

Section: Literature Reviewmentioning

confidence: 99%

Pitch Evaluation of Matouqin Chamber Music Performance Based on Artificial Neural Network

2022

Mobile Information Systems

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In order to study the pitch evaluation of Matouqin chamber music performance based on artificial neural network, this paper puts forward the relevant theories in the fields of human ear auditory perception system, auditory psychology, music theory knowledge, and pattern recognition. This paper extracts the auditory image features of chords and then establishes a sparse representation classifier model for chord recognition and classification. Scale-invariant feature transformation (SIFT) and spatial pyramid matching (SPM) are used to extract the detailed features of chord auditory images. The experimental results show that the highest correct recognition rate of the chord recognition algorithm based on the auditory image proposed in this paper is 76.2%, which is 20.4% higher than that of MFCC feature based on human auditory characteristics.

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Section: Literature Reviewmentioning

confidence: 99%

Pitch Evaluation of Matouqin Chamber Music Performance Based on Artificial Neural Network

2022

Mobile Information Systems

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“…The latest outbreak in West Africa, which occurred from 2014 to 2016, was the most widespread, killing more than 11,000 people and resulting in a case fatality of over 40%. As of August 18, 2020, researchers had identified over 3,470 cases and 2,287 deaths in the Democratic Republic of Congo [9]. AI can be used to detect infrequent symptoms and other red flags for disease, which can alert healthcare administration and patients, allowing for a quicker decision-making process and lower costs.…”

Section: Literature Reviewmentioning

confidence: 99%

AI-Based Intelligent Model to Predict Epidemics Using Machine Learning Technique

Ali¹,

Alnawayseh²,

Salahat³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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The immediate international spread of severe acute respiratory syndrome revealed the potential threat of infectious diseases in a closely integrated and interdependent world. When an outbreak occurs, each country must have a well-coordinated and preventative plan to address the situation. Information and Communication Technologies have provided innovative approaches to dealing with numerous facets of daily living. Although intelligent devices and applications have become a vital part of our everyday lives, smart gadgets have also led to several physical and psychological health problems in modern society. Here, we used an artificial intelligence AI-based system for disease prediction using an Artificial Neural Network (ANN). The ANN improved the regularization of the classification model, hence increasing its accuracy. The unconstrained optimization model reduced the classifier's cost function to obtain the lowest possible cost. To verify the performance of the intelligent system, we compared the outcomes of the suggested scheme with the results of previously proposed models. The proposed intelligent system achieved an accuracy of 0.89, and the miss rate 0.11 was higher than in previously proposed models.

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“…Saleem et al [15] developed a paradigm for managing electricity loads in smart home control, which included three parts: the user had to describe the load kind and load description, control reserve loads, and reveal a board for load management. Batool et al [16] built a dynamic pricing system using the multiple knapsack technique, which saves money on power; consumer appliances were put on a variable peak pricing schedule day ahead. Based on load scheduling, several more techniques have been developed.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Energy Consumption For Smart Homes Using Fused Machine-Learning Technique

AlZaabi¹,

Shaalan²,

Ghazal³

et al. 2023

Computers, Materials &Amp; Continua

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Energy is essential to practically all exercises and is imperative for the development of personal satisfaction. So, valuable energy has been in great demand for many years, especially for using smart homes and structures, as individuals quickly improve their way of life depending on current innovations. However, there is a shortage of energy, as the energy required is higher than that produced. Many new plans are being designed to meet the consumer's energy requirements. In many regions, energy utilization in the housing area is 30%-40%. The growth of smart homes has raised the requirement for intelligence in applications such as asset management, energy-efficient automation, security, and healthcare monitoring to learn about residents' actions and forecast their future demands. To overcome the challenges of energy consumption optimization, in this study, we apply an energy management technique. Data fusion has recently attracted much energy efficiency in buildings, where numerous types of information are processed. The proposed research developed a data fusion model to predict energy consumption for accuracy and miss rate. The results of the proposed approach are compared with those of the previously published techniques and found that the prediction accuracy of the proposed method is 92%, which is higher than the previously published approaches.

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Intelligent Model Of Ecosystem For Smart Cities Using Artificial Neural Networks

Cited by 39 publications

References 12 publications

Pitch Evaluation of Matouqin Chamber Music Performance Based on Artificial Neural Network

Pitch Evaluation of Matouqin Chamber Music Performance Based on Artificial Neural Network

AI-Based Intelligent Model to Predict Epidemics Using Machine Learning Technique

Intelligent Energy Consumption For Smart Homes Using Fused Machine-Learning Technique

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