scholarly journals Waste Management and Prediction of Air Pollutants Using IoT and Machine Learning Approach

Energies ◽  
2020 ◽  
Vol 13 (15) ◽  
pp. 3930 ◽  
Author(s):  
Ayaz Hussain ◽  
Umar Draz ◽  
Tariq Ali ◽  
Saman Tariq ◽  
Muhammad Irfan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Real Time ◽  
Air Pollutants ◽  
Garbage Collection ◽  
Air Pollutant ◽  
Real Time Monitoring ◽  
Collection System ◽  
The Real ◽  
The Status

Increasing waste generation has become a significant issue over the globe due to the rapid increase in urbanization and industrialization. In the literature, many issues that have a direct impact on the increase of waste and the improper disposal of waste have been investigated. Most of the existing work in the literature has focused on providing a cost-efficient solution for the monitoring of garbage collection system using the Internet of Things (IoT). Though an IoT-based solution provides the real-time monitoring of a garbage collection system, it is limited to control the spreading of overspill and bad odor blowout gasses. The poor and inadequate disposal of waste produces toxic gases, and radiation in the environment has adverse effects on human health, the greenhouse system, and global warming. While considering the importance of air pollutants, it is imperative to monitor and forecast the concentration of air pollutants in addition to the management of the waste. In this paper, we present and IoT-based smart bin using a machine and deep learning model to manage the disposal of garbage and to forecast the air pollutant present in the surrounding bin environment. The smart bin is connected to an IoT-based server, the Google Cloud Server (GCP), which performs the computation necessary for predicting the status of the bin and for forecasting air quality based on real-time data. We experimented with a traditional model (k-nearest neighbors algorithm (k-NN) and logistic reg) and a non-traditional (long short term memory (LSTM) network-based deep learning) algorithm for the creation of alert messages regarding bin status and forecasting the amount of air pollutant carbon monoxide (CO) present in the air at a specific instance. The recalls of logistic regression and k-NN algorithm is 79% and 83%, respectively, in a real-time testing environment for predicting the status of the bin. The accuracy of modified LSTM and simple LSTM models is 90% and 88%, respectively, to predict the future concentration of gases present in the air. The system resulted in a delay of 4 s in the creation and transmission of the alert message to a sanitary worker. The system provided the real-time monitoring of garbage levels along with notifications from the alert mechanism. The proposed works provide improved accuracy by utilizing machine learning as compared to existing solutions based on simple approaches.

scholarly journals SmartBark: An Automated Dog Barking Detection and Monitoring System using AI and Deep Learning

2021 ◽  
Author(s):  
Xiangjian Liu ◽  
Yishan Zou ◽  
Yu Sun
Keyword(s):  
Quality Of Life ◽  
Machine Learning ◽  
Deep Learning ◽  
Everyday Life ◽  
Real Time ◽  
Monitoring System ◽  
Audio Clip ◽  
The Everyday ◽  
The Status

Dogs have the tendency to bark at loud noises that they perceive as an intruder or a threat, and the hostile barking can often last up to hours depending on the duration of such noise. These barking sessions are unnecessary and negatively impact the quality of life of the others in your community, causing annoyance to your neighbors [1]. Having the rights to file noise complaints to the Home Owners Association, potentially resulting in fines or even the removal of the pet [2]. In this paper, we will discuss the development of an algorithm that takes in audio inputs through a microphone, then processes the audio and identifies that the audio clip is dog barks through machine learning, and ultimately sends the notification to the user. By implementing our application to the everyday life of dog owners, it allows them to accurately determine the status of their dog in real-time with minimal false reports.

scholarly journals Implementation of Real-Time Monitoring of Working Conditions to Improve the Monitoring Level of VOCs Pollution

2020 ◽  
Vol 145 ◽  
pp. 02071
Author(s):  
Zhipeng Zhuang ◽  
Bing Hong ◽  
Weiming Liu ◽  
Tongsheng Chen ◽  
Baoyuan Huang
Keyword(s):  
Real Time ◽  
Working Conditions ◽  
Organic Waste ◽  
National Policy ◽  
Real Time Monitoring ◽  
Time Data ◽  
The Real ◽  
Waste Gas ◽  
The Status ◽  
The Government

It is imperative to improve the monitoring level of organic waste gas pollution by carrying out real-time monitoring of working conditions, which is in line with national policy and social development. Due to the complex composition, widespread distribution of pollution sources and unorganized emission, it is difficult to control organic waste gas effectively only by current emission regulation. The government should follow up continuously and in real time the status of environmental protection equipment opened by polluting enterprises when the technical conditions permits. It is realized the real-time data collection, transmission, storage and form various reports to monitor, check and judge the working status. Through the implementation of the real-time monitoring of working conditions, the supervision efficiency is improved, the management means is enriched, and a great deal of manpower and management costs are saved.

Emergent Advancements in M.tb Detection: An Artificial Intelligence Approach (Preprint)

2021 ◽  
Author(s):  
Jasleen Kaur ◽  
Shruti Kapoor ◽  
Maninder Singh ◽  
Parvinderjit Singh Kohli ◽  
Urvinder Singh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Early Detection ◽  
Real Time ◽  
Automatic Detection ◽  
Machine Learning Algorithms ◽  
Real Time Monitoring ◽  
Time Data ◽  
The Real ◽  
Handheld Device ◽  
Real Time Data

BACKGROUND Infectious diseases are the major cause of mortality across the globe. Tuberculosis is one such infectious disease which is in the top 10 deaths causing diseases in developing as well as developed countries. The biosensors have emerged as a promising approach to attain the early detection of the pathogenic infection with accuracy and precision. However, the main challenge with biosensors is real time data monitoring preferentially reversible and label free measurements of certain analytes. Integration of biosensor and Artificial Intelligence (AI) approach would enable better acquisition of patient’s data in real time manner enabling automatic detection and monitoring of Mycobacterium tuberculosis (M.tb.) at an early stage. Here we propose a biosensor based smart handheld device that can be designed for automatic detection and real time monitoring of M.tb from varied analytic sources including DNA, proteins and biochemical metabolites. The collected data would be continuously transferred to the connected cloud integrated with AI based clinical decision support systems (CDSS) which may consist of the machine learning based analysis model useful in studying the patterns of disease infestation, progression, early detection and treatment. The proposed system may get deployed in different collaborating centres for validation and collecting the real time data. OBJECTIVE To propose a biosensor based smart handheld device that can be designed for automatic detection and real time monitoring of M.tb from varied analytic sources including DNA, proteins and biochemical metabolites. METHODS The Major challenges for control and early detection of the Mycobacterium tuberculosis were studied based upon the literature survey. Based upon the observed challenges, the biosensor based smart handheld device has been proposed for automatic detection and real time monitoring of M.tb from varied analytic sources including DNA, proteins and biochemical metabolites. RESULTS In this viewpoint, we propose an application based novel approach of combining AI based machine learning algorithms on the real time data collected with the use of biosensor technology which can serve as a point of care system for early diagnosis of the disease which would be low cost, simple, responsive, measurable, can diagnose and distinguish between active and passive cases, include single patient visits, cause considerable inconvenience, can evaluate the cough sample, require minimum material aid and experienced staff, and is user-friendly. CONCLUSIONS In this viewpoint, we propose an application based novel approach of combining AI based machine learning algorithms on the real time data collected with the use of biosensor technology which can serve as a point of care system for early diagnosis of the disease which would be low cost, simple, responsive, measurable, can diagnose and distinguish between active and passive cases, include single patient visits, cause considerable inconvenience, can evaluate the cough sample, require minimum material aid and experienced staff, and is user-friendly.

The Design of Data Acquisition Storage and Real-Time Monitoring System Based on FPGA

2011 ◽  
Vol 341-342 ◽  
pp. 803-806
Author(s):  
Su Hua Liu ◽  
San Min Shen ◽  
Yong Ye ◽  
Ji Jun Xiong
Keyword(s):  
Data Acquisition ◽  
Real Time ◽  
Digital Signal ◽  
Test System ◽  
Real Time Monitoring ◽  
Collection System ◽  
The Real ◽  
Overall Design ◽  
The Stability ◽  
And Storage

In the test system, all kinds of signals need to be processed. In order to meet the need of test system,we propose a kind of method of overall design of the real-time monitoring and storage of analog signal and digital signal. And we descript that how the data is sent to PC to real-time monitor and how we read out the data of the storage, and at the same time, the effect of the real-time monitoring is mentioned. After the application of the practice, the stability and the reliability of the collection system get confirmed.

scholarly journals Deep Transfer Learning Based Intrusion Detection System for Electric Vehicular Networks

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4736
Author(s):  
Sk. Tanzir Mehedi ◽  
Adnan Anwar ◽  
Ziaur Rahman ◽  
Kawsar Ahmed
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Intrusion Detection ◽  
Real Time ◽  
Transfer Learning ◽  
Security Requirements ◽  
Detection Accuracy ◽  
Area Network ◽  
Complex Data ◽  
Network Intrusion

The Controller Area Network (CAN) bus works as an important protocol in the real-time In-Vehicle Network (IVN) systems for its simple, suitable, and robust architecture. The risk of IVN devices has still been insecure and vulnerable due to the complex data-intensive architectures which greatly increase the accessibility to unauthorized networks and the possibility of various types of cyberattacks. Therefore, the detection of cyberattacks in IVN devices has become a growing interest. With the rapid development of IVNs and evolving threat types, the traditional machine learning-based IDS has to update to cope with the security requirements of the current environment. Nowadays, the progression of deep learning, deep transfer learning, and its impactful outcome in several areas has guided as an effective solution for network intrusion detection. This manuscript proposes a deep transfer learning-based IDS model for IVN along with improved performance in comparison to several other existing models. The unique contributions include effective attribute selection which is best suited to identify malicious CAN messages and accurately detect the normal and abnormal activities, designing a deep transfer learning-based LeNet model, and evaluating considering real-world data. To this end, an extensive experimental performance evaluation has been conducted. The architecture along with empirical analyses shows that the proposed IDS greatly improves the detection accuracy over the mainstream machine learning, deep learning, and benchmark deep transfer learning models and has demonstrated better performance for real-time IVN security.

scholarly journals Comparison of Forecasting Models for Real-Time Monitoring of Water Quality Parameters Based on Hybrid Deep Learning Neural Networks

Water ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 1547
Author(s):  
Jian Sha ◽  
Xue Li ◽  
Man Zhang ◽  
Zhong-Liang Wang
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Deep Learning ◽  
Real Time ◽  
Best Management Practices ◽  
Input Data ◽  
Management Practices ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Real Time Monitoring

Accurate real-time water quality prediction is of great significance for local environmental managers to deal with upcoming events and emergencies to develop best management practices. In this study, the performances in real-time water quality forecasting based on different deep learning (DL) models with different input data pre-processing methods were compared. There were three popular DL models concerned, including the convolutional neural network (CNN), long short-term memory neural network (LSTM), and hybrid CNN–LSTM. Two types of input data were applied, including the original one-dimensional time series and the two-dimensional grey image based on the complete ensemble empirical mode decomposition algorithm with adaptive noise (CEEMDAN) decomposition. Each type of input data was used in each DL model to forecast the real-time monitoring water quality parameters of dissolved oxygen (DO) and total nitrogen (TN). The results showed that (1) the performances of CNN–LSTM were superior to the standalone model CNN and LSTM; (2) the models used CEEMDAN-based input data performed much better than the models used the original input data, while the improvements for non-periodic parameter TN were much greater than that for periodic parameter DO; and (3) the model accuracies gradually decreased with the increase of prediction steps, while the original input data decayed faster than the CEEMDAN-based input data and the non-periodic parameter TN decayed faster than the periodic parameter DO. Overall, the input data preprocessed by the CEEMDAN method could effectively improve the forecasting performances of deep learning models, and this improvement was especially significant for non-periodic parameters of TN.

scholarly journals On-Device Deep Learning Inference for System-on-Chip (SoC) Architectures

Electronics ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 689
Author(s):  
Tom Springer ◽  
Elia Eiroa-Lledo ◽  
Elizabeth Stevens ◽  
Erik Linstead
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Real Time ◽  
Operating Systems ◽  
System On Chip ◽  
Low Latency ◽  
Management Framework ◽  
On Chip ◽  
Specialized Hardware ◽  
Deterministic Behavior

As machine learning becomes ubiquitous, the need to deploy models on real-time, embedded systems will become increasingly critical. This is especially true for deep learning solutions, whose large models pose interesting challenges for target architectures at the “edge” that are resource-constrained. The realization of machine learning, and deep learning, is being driven by the availability of specialized hardware, such as system-on-chip solutions, which provide some alleviation of constraints. Equally important, however, are the operating systems that run on this hardware, and specifically the ability to leverage commercial real-time operating systems which, unlike general purpose operating systems such as Linux, can provide the low-latency, deterministic execution required for embedded, and potentially safety-critical, applications at the edge. Despite this, studies considering the integration of real-time operating systems, specialized hardware, and machine learning/deep learning algorithms remain limited. In particular, better mechanisms for real-time scheduling in the context of machine learning applications will prove to be critical as these technologies move to the edge. In order to address some of these challenges, we present a resource management framework designed to provide a dynamic on-device approach to the allocation and scheduling of limited resources in a real-time processing environment. These types of mechanisms are necessary to support the deterministic behavior required by the control components contained in the edge nodes. To validate the effectiveness of our approach, we applied rigorous schedulability analysis to a large set of randomly generated simulated task sets and then verified the most time critical applications, such as the control tasks which maintained low-latency deterministic behavior even during off-nominal conditions. The practicality of our scheduling framework was demonstrated by integrating it into a commercial real-time operating system (VxWorks) then running a typical deep learning image processing application to perform simple object detection. The results indicate that our proposed resource management framework can be leveraged to facilitate integration of machine learning algorithms with real-time operating systems and embedded platforms, including widely-used, industry-standard real-time operating systems.

scholarly journals Deep Learning-Based Cattle Vocal Classification Model and Real-Time Livestock Monitoring System with Noise Filtering

Animals ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 357
Author(s):  
Dae-Hyun Jung ◽  
Na Yeon Kim ◽  
Sang Ho Moon ◽  
Changho Jhin ◽  
Hak-Jin Kim ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Model Development ◽  
Classification Model ◽  
Intelligence Analysis ◽  
Noise Filtering ◽  
Meat Industry ◽  
Mel Frequency Cepstral Coefficients ◽  
Web Based ◽  
The Status

The priority placed on animal welfare in the meat industry is increasing the importance of understanding livestock behavior. In this study, we developed a web-based monitoring and recording system based on artificial intelligence analysis for the classification of cattle sounds. The deep learning classification model of the system is a convolutional neural network (CNN) model that takes voice information converted to Mel-frequency cepstral coefficients (MFCCs) as input. The CNN model first achieved an accuracy of 91.38% in recognizing cattle sounds. Further, short-time Fourier transform-based noise filtering was applied to remove background noise, improving the classification model accuracy to 94.18%. Categorized cattle voices were then classified into four classes, and a total of 897 classification records were acquired for the classification model development. A final accuracy of 81.96% was obtained for the model. Our proposed web-based platform that provides information obtained from a total of 12 sound sensors provides cattle vocalization monitoring in real time, enabling farm owners to determine the status of their cattle.

Observing the real time formation of phosphine-ligated gold clusters by electrospray ionization mass spectrometry

2017 ◽  
Vol 19 (26) ◽  
pp. 17187-17198 ◽  
Author(s):  
Marshall R. Ligare ◽  
Grant E. Johnson ◽  
Julia Laskin
Keyword(s):  
Mass Spectrometry ◽  
Electrospray Ionization ◽  
Real Time ◽  
Electrospray Ionization Mass Spectrometry ◽  
Gold Cluster ◽  
Gold Clusters ◽  
Ionization Mass Spectrometry ◽  
Ionization Mass ◽  
Real Time Monitoring ◽  
The Real

Real-time monitoring of the gold cluster synthesis by electrospray ionization mass spectrometry reveals distinct formation pathways for Au8, Au9 and Au10 clusters.

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