Design and Testing of Automated Smoke Monitoring Sensors in Vehicles

Wastewater is created by pharma firms and has become a huge worry for the ecosystem. There is a significant amount of toxins that are being dropped continuously from numerous pharmaceutical companies that causes serious damages to the environment and public health because of its comprising high organics as well as inorganic loadings and thus requirements appropriate treatment before final disposal to the ecosystem. Goal of this approach is to treat the wastewater treatment model with industrial data. Algorithms of the artificial neural network (ANN) were employed progressively to predict parameters for wastewater plants. This provision assists users to take remedial measures and function the process by the standards. It is proven as beneficial technology because of its complicated mechanism, dynamic and inconsistent changes in aspects, to overcome some of the limitations of common mathematical models for the wastewater treatment plant. The target is to achieve better prediction accuracy in wastewater treatment model. In this paper, ANN approaches are relevant to the prediction of input and effluent chemical oxygen demand (COD) for effluent treatment procedures. Artificial neural networks (ANNs) offer accurate technique modeling for complex systems using an artificial intelligence technique. Three distinct types of back-propagation ANN were devised to avoid the concentration of wastewater treatment facilities in the concentration of COD, suspended particles, and mixed liquid solids in an epidermal water treatment tank (MLSS). To anticipate COD levels in influential and effluent areas, two ANN-based techniques have been presented. The proper structure for the neural network models was identified via a variety of training and model testing methods. An efficient and robust forecasting tool has been created for the ANN model.

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“…e AI hybrid model includes neural and specialist technologies [22]. e AI hybrid controlling model WWTP is shown in Figure 1.…”

Section: Hybrid Ai Techniquesmentioning

confidence: 99%

IOT Based Smart Wastewater Treatment Model for Industry 4.0 Using Artificial Intelligence

Murugamani²,

Kshirsagar

et al. 2022

Scientific Programming

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“…While dynamic analysis is advantageous, it is inefficient due to the malware's need to watch for many minutes. Additionally, some modern malware is intended to be “Virtual Machine” and will refrain from doing harmful actions if a virtual machine is identified [ 24 ]. The execution of an assigned model in a sandboxed environment is required for behavior-based methods, and run-time practices are observed and recorded.…”

Section: Malware Detection Using Artificial Intelligence Approachesmentioning

confidence: 99%

An Attribute Extraction for Automated Malware Attack Classification and Detection Using Soft Computing Techniques

Albishry

AlGhamdi

Almalawi

et al. 2022

Computational Intelligence and Neuroscience

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Malware has grown in popularity as a method of conducting cyber assaults in former decades as a result of numerous new deception methods employed by malware. To preserve networks, information, and intelligence, malware must be detected as soon as feasible. This article compares various attribute extraction techniques with distinct machine learning algorithms for static malware classification and detection. The findings indicated that merging PCA attribute extraction and SVM classifier results in the highest correct rate with the fewest possible attributes, and this paper discusses sophisticated malware, their detection techniques, and how and where to defend systems and data from malware attacks. Overall, 96% the proposed method determines the malware more accurately than the existing methods.

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“…Further, logical reasoning is also presented that determines the widespread feature of application process. Another application is emphasized on healthcare technologies which are used to detect the presence of coronavirus in many conflict zones [12][13][14]. However, only challenges have been discussed, but diagnosis and early treatment methods are not projected which leads to high state of confusion in application sequence.…”

Section: Literature Surveymentioning

confidence: 99%

“…Furthermore, Vitabile et al [15] suggest a natural method using artificial intelligence for all healthcare applications where the perception of locating affected individuals has been examined with big data storage [16,17]. In these case studies, it is possible to evaluate the presence of diseases using wireless sensors, but if the same has been changed to nanosensors, then, accuracy of identical process will increase to a higher extent [13,14].…”

Section: Literature Surveymentioning

confidence: 99%

Impact of Big Data Analysis on Nanosensors for Applied Sciences Using Neural Networks

Shitharth¹,

Meshram

Kshirsagar³

et al. 2021

Journal of Nanomaterials

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In the current-generation wireless systems, there is a huge requirement on integrating big data which can able to predict the market trends of all application systems. Therefore, the proposed method emphasizes on the integration of nanosensors with big data analysis which will be used in healthcare applications. Also, safety precautions are considered when this nanosensor is integrated where depth and reflection of signals are also observed using different time samples. In addition, to analyze the effect of nanosensors, six fundamental scenarios that provide good impact on real-time applications are also deliberated. Moreover, for proving the adeptness of the proposed method, the results are equipped in both online and offline analyses for investigating error measurement, sensitivity, and permeability parameters. Since nanosensors are introduced, the efficiency of the projected technique is increased by implementing media access control (MAC) protocol with recurrent neural network (RNN). Further, after observing the simulation results, it is proved that the proposed method is more effective for an average percentile of 67% when compared to the existing methods.

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Design and Testing of Automated Smoke Monitoring Sensors in Vehicles

Cited by 23 publications

References 20 publications

IOT Based Smart Wastewater Treatment Model for Industry 4.0 Using Artificial Intelligence

IOT Based Smart Wastewater Treatment Model for Industry 4.0 Using Artificial Intelligence

An Attribute Extraction for Automated Malware Attack Classification and Detection Using Soft Computing Techniques

Impact of Big Data Analysis on Nanosensors for Applied Sciences Using Neural Networks

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