A Machine Learning Based Framework for Intelligent High Density Garbage Area Classification

Ghanshala, Tejasvi; Tripathi, Vikas; Pant, Bhaskar

doi:10.1007/978-3-030-63128-4_12

Cited by 4 publications

(3 citation statements)

References 7 publications

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“…Ghanshala et al [11] presented ML based structure for classifying the regions that are free in garbage and regions containing maximum density garbage. Baker et al [12] examined the developing requirement for automatic e-waste recycling as a vital condition for coping with fast-developing e-waste streaming and it can be shed the light on effect of AI from supportive the recycling procedure with smart classifier of devices, in which the smartphone was analysis.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

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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Section: Related Workmentioning

confidence: 99%

“…If |A| < 1, upgrade the position of individual is implemented by Eqs. ( 9) or (11). Based on a β probabilities value that is 0.5 to all the WOA processes switch among encircling prey or bubble-net attack model.…”

Section: Hyperparameter Tuning Using Woamentioning

confidence: 99%

Deep Consensus Network for Recycling Waste Detection in Smart Cities

Hamza¹,

Mengash²,

Negm³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…Optical identification, convolutional neural networks, and Naive Bayes are crucial garbage categorization technologies. Ghanshala et al use machine learning to categorize areas into two categories: garbage-free and high-garbage-filled [36]. The study used four algorithms, achieving 98.6% accuracy with kNN and Naïve Bayes, 85.4% with Decision Tree, and 98.4% with Random Forest.…”

Section: Introductionmentioning

confidence: 99%

Recyclable Waste Classification using SquezeeNet and XGBoost

Yulita,

Ardiansyah,

Prabuwono

et al. 2023

IJACSA

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The unregulated buildup of waste results in the occurrence of flames. This phenomenon poses a substantial threat to both the ecological system and human welfare. To tackle this problem, the current study proposes the implementation of Machine Learning technology to automate the sorting of waste. The methodology being examined incorporates the utilization of SqueezeNet as an image embedding method in conjunction with XGBoost as the final classifier. This work examines the efficacy of the aforementioned technique by doing a comparative analysis with many alternative final classifiers, including LightGBM, XGBoost, CatBoost, Random Forest, SVM, Naïve Bayes, KNN, and Decision Tree. The experimental results indicate that the integration of SqueezeNet and XGBoost produces the highest level of performance in the field of garbage categorization, as supported by an F1-score of 0.931. SqueezeNet is a method employed for image embedding that enables the extraction of salient features from images. This procedure enables the recognition of unique characteristics linked to different classes. Therefore, XGBoost may be utilized to enhance classification tasks. XGBoost has the ability to generate a feature importance score. Therefore, enabling the recognition of the most prominent attributes. This methodology possesses the capacity to alleviate the risk of fire that arises due to the accumulation of unregulated trash. This work makes a substantial contribution to environmental conservation and the improvement of public safety.

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