Combined Economic Emission Dispatch of Microgrid with the Incorporation of Renewable Energy Sources Using Improved Mayfly Optimization Algorithm

Karthik, N.; Rajagopalan, Arul; Angalaeswari, S.; Natrayan, L.; Mammo, Wubishet Degife

doi:10.1155/2022/6461690

Cited by 75 publications

(54 citation statements)

References 41 publications

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“…At these low noise values, the proposed system (SLBP) is also a little less affected and clearly shown as a little less fluctuating [41]. Figure 11 represents the time series evaluation of nodes for the values of example 1 in the presence of stochastic parameters.…”

Section: Numerical Observationsmentioning

confidence: 99%

An Epidemic Patch-Enabled Delayed Model for Virus Propagation: Towards Evaluating Bifurcation and White Noise

Liu

Madhusudanan²,

Srinivas

et al. 2022

Mathematical Problems in Engineering

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The massive disruptions caused by malware, such as a virus in computer networks and other aspects of information and communication technology, have generated attention, making it a hot research topic. While antivirus and firewalls can be effective, there is also a need to understand the spread patterns of viral infection using epidemic models to curb its incidences. Many previous research attempts have produced analytical models for computer viruses under various infectiousness situations. As a result, we suggested the SLBS model, which considers infection latency and transient immunity in patched nodes. Under certain conditions, the local stability of all equilibrium points is investigated. By setting the delay parameter, we established the occurrence of a Hopf bifurcation (HB) as it crossed a crucial point by several analyses. We also used the centre manifold theorem and normal form theory to examine the attributes of the HB. While the former was used to study the time delay and direction of Hopf bifurcation, the latter was used to investigate external noise and its intensities. Finally, numerical simulations two dimensional and three-dimensional graphs were used to depict the perturbations of the model, thus bolstering the essentiality of the study.

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Section: Numerical Observationsmentioning

confidence: 99%

An Epidemic Patch-Enabled Delayed Model for Virus Propagation: Towards Evaluating Bifurcation and White Noise

Liu

Madhusudanan²,

Srinivas

et al. 2022

Mathematical Problems in Engineering

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show abstract

“…Buzzers are frequently utilized for timers, alarms, and the affirmation of human input, such as a keystroke or mouse click. ingSpeak is a cloud-oriented IoT analytics platform that enables users to gather, view, and analyze real-time data streams [34]. Data may be supplied to ingSpeak from our devices, and web services can be used to build rapid representations of live data and deliver warnings/alerts.…”

Section: Proposed Systemmentioning

confidence: 99%

IoT-Based Harmful Toxic Gases Monitoring and Fault Detection on the Sensor Dataset Using Deep Learning Techniques

Praveenchandar

Vetrithangam

Kaliappan³

et al. 2022

Scientific Programming

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One of the main reasons for accidents among workers is harmful gas leakage. Many people die in chemical industries and their surrounding areas. The present invention is responsible for monitoring and controlling hazardous toxic gases like nitrogen dioxide (NO2), carbon monoxide, ozone (O3), sulfur dioxide (SO2), LPG, hydrocarbon gases, silicones, hydrocarbons, alcohol, CH4, hexane, benzine, as well as environmental conditions, such as temperature and relative humidity to prevent industrial accidents. The Arduino UNO R3 board is used as the central microcontroller. It is connected to the Cloud via AQ3 sensor, Minipid 2 HS PID sensor, IR5500 open path infrared gas detector, DHT11 Temperature and Humidity Sensor, MQ3 sensor, and ESP8266 and WIFI Module, which can store real-time sensor data and send alert messages to the industry’s safety control board. Machine learning and artificial intelligence will be used to make an intelligent prediction (AI). The information gathered will be examined in real-time. The real-time data provided through the sensor can be accessed worldwide. Sensor data quality is critical in the Internet of Things (IoT) applications because poor data quality renders them useless. Error detection in sensor data improves the IoT-based toxic gas monitoring, controlling, and prediction system. Live data from sensors or datasets should be analyzed properly using appropriate techniques. Hence, hybrid hidden Markov and artificial intelligence models are applied as an error detection technique in the sensor dataset. This technique outperformed the dataset gas sensor array under dynamic gas mixtures and lived data. Our method outperformed harmful gas monitoring and error detection in sensor datasets compared to other existing technologies. The hybrid HMM and ANN fault detection methods performed well on the datasets and produced 0.01% false positive rate.

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“…en, codebooks, descriptors were developed that gave AN approach by that a vector of fixed length will be planned in bar graph area, and therefore the multiclass order of the component histograms utilizing support vector machines. Besides, HOG descriptors were applied orderly [32]. e gauge classifier approach of highest neighbor was used and contrasted with SVM-based classifiers.…”

Section: Related Workmentioning

confidence: 99%

Rice Disease Detection Using Artificial Intelligence and Machine Learning Techniques to Improvise Agro-Business

Aggarwal

Suchithra

Chandramouli³

et al. 2022

Scientific Programming

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Agro-business is highly dependent on rice quality and its protection from diseases. There are several prerequisites for the procedures and the strategies that are productive and efficient for expanding the harvest yield. The advancement in computer science has supported various domains; agricultural innovation is one of them. The apparatuses which utilize the strategies of advanced artificial intelligence and machine learning have been featured in this paper. These techniques attain abnormally productive outcomes for the recognition of infections engrossing the images of leaves, fields of harvest, or seeds. In this context, this work presents a survey that focuses on accuracy agribusiness for expanding the conception of rice, which is one of the main harvests on the planet. In this paper, the overview and examination of various papers distributed in the most recent eight years with various methodologies identified with crop diseases identification, the health of seedlings, and quality of grain have been introduced. Experiments are performed for knowledge extraction using Web of Science and Scopus databases to analyze research trends in the domain of rice disease identification using artificial intelligence using global analysis, year-wise and country-wise citations, and so on to support various researchers working in this domain.

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Combined Economic Emission Dispatch of Microgrid with the Incorporation of Renewable Energy Sources Using Improved Mayfly Optimization Algorithm

Cited by 75 publications

References 41 publications

An Epidemic Patch-Enabled Delayed Model for Virus Propagation: Towards Evaluating Bifurcation and White Noise

An Epidemic Patch-Enabled Delayed Model for Virus Propagation: Towards Evaluating Bifurcation and White Noise

IoT-Based Harmful Toxic Gases Monitoring and Fault Detection on the Sensor Dataset Using Deep Learning Techniques

Rice Disease Detection Using Artificial Intelligence and Machine Learning Techniques to Improvise Agro-Business

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