[Retracted] Machine Learning and IoT‐Based Waste Management Model

Khan, Rijwan; Kumar, Santosh; Srivastava, Akhilesh Kumar; Dhingra, Niharika; Gupta, Mayank; Bhati, Neha; Kumari, P. Lalitha Surya

doi:10.1155/2021/5942574

Cited by 51 publications

(19 citation statements)

References 40 publications

(38 reference statements)

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“…Crops are distinguished by their seasonality, the derived nature of demand, and price fairly inelastic. Agricultural product price is influenced by the primary service and demand for the organic component of crop production [10,11]. Forecasting farm commodity prices, on the other hand, is a dangerous undertaking because price estimates might go awry owing to weather, economic conditions, or other unknown reasons, rendering forecasts useless [12].…”

Section: Introductionmentioning

confidence: 99%

Minimizing the Error Gap in Smart Framing by Forecasting Production and Demand Using ARIMA Model

Wawale

Jawarneh²,

et al. 2022

Journal of Food Quality

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Agribusiness employs more than 66 percent of India’s rural population and is the country’s economic backbone. Beat crop growth is essential for practical farming since it increases soil diversity and actual design, and it may be grown in blended frameworks. Crop growth rates, applicability, and yields have not improved significantly over time in the United States. Crops are defined by their seasonality, derived nature of demand, and relatively inelastic pricing. The general purpose of this research is to demonstrate the usefulness of price forecasting for agricultural prices and validate it for rice, which is consumed more in Indian states, for the year 2022, using time series data from 2016 to 2021. Every year, data for 50 days is collected and multiplied. The range of ten and its multiple is used for predicting. The results were obtained through the use of univariate analysis. To develop grain price estimates, researchers used Autoregressive Integrated Moving Average (ARIMA) methods, and the precision of the forecasts was examined using conventional mean square error (MSE) and mean absolute percentage error (MAPE) standards. As proven by the outcomes of ARIMA price predictions, the ARIMA model’s efficacy as a tool for price forecasting was effectively demonstrated by realistic models of projected prices for 2020. Because the MSA and MAPE values were lower, the forecast was more accurate. In addition, the price forecasting in this model is dependent on government incentives.

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Section: Introductionmentioning

confidence: 99%

Minimizing the Error Gap in Smart Framing by Forecasting Production and Demand Using ARIMA Model

Wawale

Jawarneh²,

et al. 2022

Journal of Food Quality

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“…In [16], an effective solution to smart and effective waste management utilizing ML and IoT was presented. During the presented solution, the researchers have utilized moisture sensor, Arduino UNO microcontroller, and ultrasonic sensor.…”

Section: Related Workmentioning

confidence: 99%

Deep Consensus Network for Recycling Waste Detection in Smart Cities

Hamza¹,

Mengash²,

Negm³

et al. 2023

Computers, Materials &Amp; Continua

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Recently, urbanization becomes a major concern for developing as well as developed countries. Owing to the increased urbanization, one of the important challenging issues in smart cities is waste management. So, automated waste detection and classification model becomes necessary for the smart city and to accomplish better recyclable waste management. Effective recycling of waste offers the chance of reducing the quantity of waste disposed to the land fill by minimizing the requirement of collecting raw materials. This study develops a novel Deep Consensus Network with Whale Optimization Algorithm for Recycling Waste Object Detection (DCNWO-RWOD) in Smart Cities. The goal of the DCNWO-RWOD technique intends to properly identify and classify the objects into recyclable and non-recyclable ones. The proposed DCNWO-RWOD technique involves the design of deep consensus network (DCN) to detect waste objects in the input image. For improving the overall object detection performance of the DCN model, the whale optimization algorithm (WOA) is exploited. Finally, Naïve Bayes (NB) classifier is used for the classification of detected waste objects into recyclable and non-recyclable ones. The performance validation of the DCNWO-RWOD technique takes place using the open access dataset. The extensive comparative study reported the enhanced performance of the DCNWO-RWOD technique interms of several measures.

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“…Apart from the number of vehicles and stops, priority should also be given to the level of trash in each bin. A system developed by Khan et al (2021) makes use of a mobile application that can track the truck movements and also provide an optimized route to efficiently collect trash from all the bins. The sensors attached to the bins help find the priority of one dustbin over the other which when synchronized with Google Map API can direct the collection trucks along the best route from a high priority location to a lower one.…”

Section: Route Optimizationmentioning

confidence: 99%

Technical solutions for waste classification and management: A mini-review

Shreya

Katyal

et al. 2022

Waste Manag Res

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The massive growth of population coupled with urbanization over the years has created a significant challenge of increase in waste generation. India has achieved massive developmental growth in economic and social areas but still lacks a proper waste management system. The lack of knowledge about segregation of waste into different categories and proper disposal techniques in a country like India with an accelerated population growth is a critical issue. Since trash has different disposal techniques, according to its type, segregating waste through an automated process at the point of collection will streamline the process and result in effective waste management and utilization. The mini-review article evaluates the recent literature on technologies used for municipal waste segregation and management, with the motive of providing critical information for advancement in current research. This article reviews the use of various convolutional neural network architectures for waste classification and describes in detail as to why image processing methods are preferred over sensors for segregation into respective categories. It is also important to have an efficient waste monitoring and management system for proper disposal. A comprehensive mini-review was undertaken to understand internet of things-based models proposing efficient waste handling, from the perspective of reduced costs, collection time and optimized routes. The proposed systems were compared and evaluated based on the sensors used monitoring, microcontrollers and communication protocols such as Long Range, Global System for Mobile Communication, Zigbee and Wi-Fi, which are employed for the secure and efficient data transmission.

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[Retracted] Machine Learning and IoT‐Based Waste Management Model

Cited by 51 publications

References 40 publications

Minimizing the Error Gap in Smart Framing by Forecasting Production and Demand Using ARIMA Model

Minimizing the Error Gap in Smart Framing by Forecasting Production and Demand Using ARIMA Model

Deep Consensus Network for Recycling Waste Detection in Smart Cities

Technical solutions for waste classification and management: A mini-review

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