Cascade Feed Forward Neural Network-based Model for Air Pollutants Evaluation of Single Monitoring Stations in Urban Areas

Capizzi, Giacomo; Sciuto, Grazia Lo; Monforte, Pietro; Napoli, Christian

doi:10.1515/eletel-2015-0042

Cited by 23 publications

(9 citation statements)

References 20 publications

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“…In this regards, the use of machine learning techniques, which are increasingly being developed in a wide variety of areas [30][31][32][33][34] including the field of hearing [35], would allow us the extraction of additional information with respect to the mere count of occurrences from the spike patterns, leading us to an improvement of the detection performances. Hereof, since today's machine learning systems are frequently based on neural networks [36][37][38][39][40] (among which the SNNs [41]) a direction could be that of using nature-inspired recognition systems, which would allow us to better mimic the subsequent stages of PAS (i.e., SNC part of fig. 1) and to expand the system to model the complete HAS.…”

Section: Discussionmentioning

confidence: 99%

A Computational Approach for the Understanding of Stochastic Resonance Phenomena in the Human Auditory System

Susi

Bartolacci

Massarelli

2019

Int. J. Adv. Sci. Eng. Inf. Technol.

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Stochastic resonance (SR) is a nonlinear phenomenon by which the introduction of noise in a system causes a counterintuitive increase in levels of detection performance of a signal. SR has been extensively studied in different physical and biological systems, including the human auditory system (HAS), where a positive role for noise has been recognized both at the level of peripheral auditory system (PAS) and central nervous system (CNS). This dualism regarding the mechanistic underpinnings of the RS phenomenon in the HAS is confirmed by discrepancies among different experimental studies and reflects on a disagreement about how this phenomenon can be exploited for the improvement of prosthesis and aids devoted to hypoacusic people. HAS is one of the human body's most complex sensory system. On the other hand, SR involves system nonlinearities. Then, the characterization of SR in the HAS is very challenging and many efforts are being made to characterize this mechanism as a whole. Current computational modelling tools make possible to investigate the phenomena separately in the CNS and in the PAS, then simplifying the analysis of the involved mechanisms. In this work we present a computational model of PAS supporting SR, that shows improved detection of sounds when input noise is added. As preparatory step we provided a test signal to the system, at the edge of the hearing threshold. As next step we repeated the experiment adding background noise at different intensities. We found an increase of relative spike count in the frequency bands of the test signal when input noise is added, confirming that the maximum value is obtained under a specific range of added noise, whereas further increase in noise intensity only degrades signal detection or information content.

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Section: Discussionmentioning

confidence: 99%

A Computational Approach for the Understanding of Stochastic Resonance Phenomena in the Human Auditory System

Susi

Bartolacci

Massarelli

2019

Int. J. Adv. Sci. Eng. Inf. Technol.

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“…Capizzi et al ( 2015 ) showed that particulate matter (PM) is the most dispersed pollutant in major cities. PM concentrations can impact public health and environmental systems and they represent a big concern in urban regions because of their hazards (Aryal et al, 2012 ).…”

Section: Dust Entrainment and Transportmentioning

confidence: 99%

An extensive dust storm impact on air quality on 22 November 2018 in Sydney, Australia, using satellite remote sensing and ground data

Attiya

Jones

2022

Environ Monit Assess

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Recurrent dust storms represent a significant concern in Australia because of their related hazards and damages since particulate matter (PM) has harmful impacts on the environmental, health and economic sectors. The particulate matter may be released from natural sources and human activities. The major part of natural particulate matter is emitted into the air by wind erosion processes from desert and semi-desert areas at the world scale. A huge dust storm crossed over several areas of New South Wales (NSW), Australia, including the Sydney region on 21–22 November 2018 and decreased the horizontal visibility to less than 1 km for 22 h. This study examined the synoptic weather conditions, and assessed the air quality and identified the source and transport trajectory of the dust storm over Sydney using ground and satellite remote sensing data. PM10 (< 10 μm) concentrations were obtained from selected air quality monitoring sites operated by the Environmental Protection Agency in NSW. The highest hourly concentration of PM10 (578.7 μg/m3) was recorded at Singleton in the Hunter Valley, while concentrations in Sydney ranged from 480 to 385 μg/m3, well above the standard air quality level in Australia (50 μg/m3 per 24 h). The HYSPLIT back trajectories of air parcels suggest that the potential sources of the dust episode originated from the Lake Eyre Basin and northeast South Australia, the Mundi Mundi plains west of Broken Hill, Cobar and the grazing lands and the red sandplains in northwestern NSW. It then travelled towards the east coast. These long-range airflows transported suspended dust particles, raising air quality to hazardous levels (elevated PM10 levels) over most areas of NSW. The results from the HYSPLIT model for dust movement are confirmed by MODIS satellite images. Many areas of NSW experienced this intense dust storm due to northwest wind generated by the low-pressure systems and cold fronts over South Australia and many parts of western NSW as it moved eastward.

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“…• Cascade feed-forward (CFF):is a feed-forward neural network with a direct connection from input to each layer, and from each layer to successive layers [31].…”

Section: B Deep Spatio-temporal Feature Evaluationmentioning

confidence: 99%

Compound Emotion Recognition of Autistic Children During Meltdown Crisis Based on Deep Spatio-Temporal Analysis of Facial Geometric Features

2020

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An important contribution to computer vision applications has been made by recognizing human emotion. Although it is very significant, this work considers the security of autistic people while in meltdown crisis by introducing a new system to warn caregivers through facial expressions detection. A precautionary approach has been taken to deal with meltdown crisis. Certainly, the indications of Meltdown are linked to abnormal facial expressions related to compound emotions. Actually, researchers thought long ago that Human Facial Expressions (HFE) are not able to express more than the seven basics emotions. HFE have been considered by psychologists as very complicated one, which can indicate two or even more emotions known as compound or mixed ones. A few studies have been done concerning Compound Emotion (CE). As well as, many difficult tasks to detect Compound Emotion Recognition (CER). In this paper, we empirically assess a group of deep spatio-temporal geometric features of microexpressions of autistic children during a meltdown crisis. To achieve this goal, we make a comparison of the CER performance and diverse collections of micro-expressions features to select the features which best differentiates autistic children CE in meltdown crisis from normal state, and the best classifier performance. We record autistic children videos in normal and meltdown crisis using Kinect camera in serious circumstances. The experimental evaluation shows that the deep spatio-temporal geometric features and Recurrent Neural Network RNN with 3 hidden layer using Information Gain Feature Selection methods provide best performance (85.8%). INDEX TERMS Autism, deep spatio-temporal features, meltdown crisis, facial expressions, compound emotions.

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Cascade Feed Forward Neural Network-based Model for Air Pollutants Evaluation of Single Monitoring Stations in Urban Areas

Cited by 23 publications

References 20 publications

A Computational Approach for the Understanding of Stochastic Resonance Phenomena in the Human Auditory System

A Computational Approach for the Understanding of Stochastic Resonance Phenomena in the Human Auditory System

An extensive dust storm impact on air quality on 22 November 2018 in Sydney, Australia, using satellite remote sensing and ground data

Compound Emotion Recognition of Autistic Children During Meltdown Crisis Based on Deep Spatio-Temporal Analysis of Facial Geometric Features

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