Smart IoT and Machine Learning-based Framework for Water Quality Assessment and Device Component Monitoring

Bhardwaj, Akashdeep; Dagar, Vishal; Khan, Muhammad Owais; Aggarwal, Akarsh; Alvarado, Rafael; Kumar, Manoj; Irfan, Muhammad; Proshad, Ram

doi:10.1007/s11356-022-19014-3

Cited by 39 publications

(14 citation statements)

References 22 publications

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“…It incorporates environmental considerations into financial decision-making. These environmental and sustainability considerations will be enhanced through the use of green finance to fund climate-neutral as well as resource-efficient technologies ( Bhardwaj et al, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Does Green Financing Develop a Cleaner Environment for Environmental Sustainability: Empirical Insights From Association of Southeast Asian Nations Economies

Irfan²

2022

Front. Psychol.

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One of the most frequently used terms in climate change discussions is environmental sustainability. With economic growth and foreign direct investment as moderator factors, this study investigates the influence of green finance and financial development on environmental sustainability and growth in ASEAN economies from 2012 to 2019. ADF and Phillip-Peron (PP) unit root tests, fully modified least square (FMOLS), were employed for long-run empirical estimates. A substantial body of evidence supports the study’s findings using VECM technology. Green financing was negatively associated with CO2 emissions. However, environmental sustainability in ASEAN is favorably associated with green financing. It is also worth noting that green financing promotes environmental sustainability at the expenditure of economic growth. Financial development, foreign direct investment, R&D investment, and green technology foster economic expansion at the price of environmental sustainability. There are still many fences to green finance that need to be addressed, including pricing CO2 emissions and reforming inefficient nonrenewable fossil fuel subsidies. Local governments play a vital role in eliminating these barriers and addressing disincentives. It is recommended that policymakers push the financial sector to adopt a green finance strategy to further the goals of long-term sustainable development. Industry must integrate multiple objectives, such as inclusive growth and environmental protection and productivity, through an even broader range of legislative frameworks ideal for decoupling growth from social and ecological unsustainability, at the heart of the green manufacturing process.

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Section: Introductionmentioning

confidence: 99%

Does Green Financing Develop a Cleaner Environment for Environmental Sustainability: Empirical Insights From Association of Southeast Asian Nations Economies

Irfan²

2022

Front. Psychol.

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“…In this process, the dataset is partitioned into 10 random folds, and each fold is representing a miniature version of the overall dataset, every time the training is done on the 9 samples and evaluation is done upon the rest samples, it changes iteratively. When the extracted features are capable of discriminating shaky and non-shaky videos, motivated by the conventional machine learning algorithms which do not require large number of samples for successful classification, we propose to use Random Forest classifier for classification in this work [19][20][21][22][23][24]. The Random Forest Classifier is a well-known technique for classification, which can handle imbalanced features and noise features and it can avoid overfitting problems.…”

Section: Proposed Methodsmentioning

confidence: 99%

Spatio-Temporal FFT Based Approach for Arbitrarily Moving Object Classification in Videos of Protected and Sensitive Scenes

Asadzadehkaljahi

Halder

Shivakumara

et al. 2023

AIA

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Arbitrary moving object detection including vehicles and human beings in the real environment, such as protected and sensitive areas, is challenging due to arbitrary deformation and directions caused by Shakey camera and wind. This work aims at adopting a Spatio-temporal approach for classifying arbitrarily moving objects. The proposed method segments foreground objects from the background using the frame difference between the median frame and individual frames. This step outputs several different foreground information. The mean of foreground images is computed, which is referred to as the mean activation map. For the mean activation map, the method employs the Fast Fourier Transform (FFT), which outputs amplitude and frequencies. The mean of frequencies is computed for moving objects in using activation maps of temporal frames, which is considered as a frequency feature vector. The features are normalized to avoid the problems of imbalanced features and class sizes. For classification, the work uses 10-fold cross-validation to choose the number of training and testing samples and the random forest classifier is used for the final classification of arbitrary moving and static videos. For evaluating the proposed method, we construct our dataset, which contains videos of static and arbitrarily moving objects caused by shaky cameras and wind. The results on the video dataset show that the proposed method achieves the state-of-the-art performance.

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“…Since our goal is to develop simple and effective model which can fit for realtime environment, the proposed works adapt conventional machine learning models (Chhabra et al, 2022;Chithaluru et al, 2022;Kaushik et al, 2022) as classifier rather than deep learning models. For the final classification, the proposed work uses random forest (RF) classifier, which is a well-known technique for classification (Bharadwaj et al, 2022;Solanki et al, 2021). The reason to use random forest classifier is that this has the ability to handle imbalanced features and noise features, and it can avoid overfitting problems.…”

Section: Proposed Methodsmentioning

confidence: 99%

Spatiotemporal Edges for Arbitrarily Moving Video Classification in Protected and Sensitive Scenes

Asadzadehkaljahi

Halder

Pal

et al. 2023

AIA

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Classification of arbitrary moving objects including vehicles and human beings in a real environment (such as protected and sensitive areas) is challenging due to arbitrary deformation and directions caused by shaky camera and wind. This work aims at adopting a spatiotemporal approach for classifying arbitrarily moving objects. The intuition to propose the approach is that the behavior of the arbitrary moving objects caused by wind and shaky camera is inconsistent and unstable, while, for static objects, the behavior is consistent and stable. The proposed method segments foreground objects from background using the frame difference between median frame and individual frame. This step outputs several different foreground information. The method finds static and dynamic edges by subtracting Canny of foreground information from the Canny edges of respective input frames. The ratio of the number of static and dynamic edges of each frame is considered as features. The features are normalized to avoid the problems of imbalanced feature size and irrelevant features. For classification, the work uses 10-fold cross-validation to choose the number of training and testing samples, and the random forest classifier is used for the final classification of frames with static objects and arbitrarily moving objects. For evaluating the proposed method, we construct our own dataset, which contains video of static and arbitrarily moving objects caused by shaky camera and wind. The results on the video dataset show that the proposed method achieves the state-of-the-art performance (76% classification rate) which is 14% better than the best existing method.

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Smart IoT and Machine Learning-based Framework for Water Quality Assessment and Device Component Monitoring

Cited by 39 publications

References 22 publications

Does Green Financing Develop a Cleaner Environment for Environmental Sustainability: Empirical Insights From Association of Southeast Asian Nations Economies

Does Green Financing Develop a Cleaner Environment for Environmental Sustainability: Empirical Insights From Association of Southeast Asian Nations Economies

Spatio-Temporal FFT Based Approach for Arbitrarily Moving Object Classification in Videos of Protected and Sensitive Scenes

Spatiotemporal Edges for Arbitrarily Moving Video Classification in Protected and Sensitive Scenes

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