Evidence map›Paper›PMID 35237911›Full record

ReviewEnvironmental science and pollution research international2022

Industrial Internet of things-based solar photo voltaic cell waste management in next generation industries.

Parimala Devi Muthusamy, Gowrishankar Velusamy, Sathya Thandavan, Boopathi Raja Govindasamy, Nithya Savarimuthu

Abstract readReview
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In one paragraph

Review in Environmental science and pollution research international, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Parimala Devi MuthusamyDepartment of Electronics and Communication Engineering, Velalar College of Engineering and Technology, Erode - 638012, Tamil Nadu, India. parimaladevi.vlsi@gmail.com.ORCID http://orcid.org/0000-0003-3955-1339
Gowrishankar VelusamyDepartment of Electronics and Communication Engineering, Velalar College of Engineering and Technology, Erode - 638012, Tamil Nadu, India.
Sathya ThandavanDepartment of Electronics and Communication Engineering, Velalar College of Engineering and Technology, Erode - 638012, Tamil Nadu, India.
Boopathi Raja GovindasamyDepartment of Electronics and Communication Engineering, Velalar College of Engineering and Technology, Erode - 638012, Tamil Nadu, India.
Nithya SavarimuthuDepartment of EEE, SRMIST, Ramapuram, Chennai, Tamil Nadu, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Nowadays, modern industries generate their energy by using renewable solar. The rapid increase in photovoltaic (PV) module installations provides a better energy conversion, but their life cycle is a major concern. This research paper focuses on the recycling process for solar PV modules using the Internet of Things in industries. The smart bin with the Internet of Things (IoT) utilizes a machine learning approach to collect solar waste. The proposed smart bin uses k-Nearest Neighbor's algorithm (k-NN) and Long Short-Term Memory (LSTM), a network-based learning algorithm. These algorithms are useful in updating the level of the bin via alert messages. It also helps in identifying the type of waste material. The k-NN algorithm provides 83% accuracy in predicting the bin level in a real-time testing environment. The smart dust bin classifies the waste materials, and notifies its level to the collection center through the IoT platform when the level reaches a prescribed threshold, the signal corresponding to the level is passed to the common waste collection unit. IoT is connected to Cloud Server. It helps to predict the level of the smart bin. Delay is introduced in the order of 3-8 s while the alert message is sent to the common waste collection unit. The system monitors the smart bin levels and sends the notifications to alert and initiate the collection unit. Real-time mobile app monitors the bin's level and location. The cloud IoT analytics analyze the solar e-waste in a different locations in industries.The proposed system works better and provides accurate results by using machine learning approach.

Indexed as

Internet of ThingsWaste ManagementAlgorithmsMachine LearningRecyclingInternet of Things (IoT)Photovoltaic (PV) cellsRecyclingSmart binSolar waste

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.