Cognitive cloud framework for waste dumping analysis using deep learning vision computing in healthy environment

Liu, J.; Prabuwono, Anton Satria; Abulfaraj, Anas W.; Miniaoui, Sami; Taheri, Nedasadat

doi:10.1016/j.compeleceng.2023.108814

Cited by 9 publications

(3 citation statements)

References 23 publications

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“…The method uses behavioral data to facilitate personalized patient care and digital healthcare within the I-fog-cloud network [116]. Also, incorporating advanced deep learning vision-computing techniques within a cognitive cloud framework, this research contributes to the intersection of AI, ML, and IoT in waste management, promoting a healthier environment and sustainable practices [117].…”

Section: Inferring Personal Health Conditions By Wearable Devicesmentioning

confidence: 99%

The Personal Health Applications of Machine Learning Techniques in the Internet of Behaviors

Amiri,

Heidari,

Darbandi

et al. 2023

Sustainability

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With the swift pace of the development of artificial intelligence (AI) in diverse spheres, the medical and healthcare fields are utilizing machine learning (ML) methodologies in numerous inventive ways. ML techniques have outstripped formerly state-of-the-art techniques in medical and healthcare practices, yielding faster and more precise outcomes. Healthcare practitioners are increasingly drawn to this technology in their initiatives relating to the Internet of Behavior (IoB). This area of research scrutinizes the rationales, approaches, and timing of human technology adoption, encompassing the domains of the Internet of Things (IoT), behavioral science, and edge analytics. The significance of ML in medical and healthcare applications based on the IoB stems from its ability to analyze and interpret copious amounts of complex data instantly, providing innovative perspectives that can enhance healthcare outcomes and boost the efficiency of IoB-based medical and healthcare procedures and thus aid in diagnoses, treatment protocols, and clinical decision making. As a result of the inadequacy of thorough inquiry into the employment of ML-based approaches in the context of using IoB for healthcare applications, we conducted a study on this subject matter, introducing a novel taxonomy that underscores the need to employ each ML method distinctively. With this objective in mind, we have classified the cutting-edge ML solutions for IoB-based healthcare challenges into five categories, which are convolutional neural networks (CNNs), recurrent neural networks (RNNs), deep neural networks (DNNs), multilayer perceptions (MLPs), and hybrid methods. In order to delve deeper, we conducted a systematic literature review (SLR) that examined critical factors, such as the primary concept, benefits, drawbacks, simulation environment, and datasets. Subsequently, we highlighted pioneering studies on ML methodologies for IoB-based medical issues. Moreover, several challenges related to the implementation of ML in healthcare and medicine have been tackled, thereby gradually fostering further research endeavors that can enhance IoB-based health and medical studies. Our findings indicated that Tensorflow was the most commonly utilized simulation setting, accounting for 24% of the proposed methodologies by researchers. Additionally, accuracy was deemed to be the most crucial parameter in the majority of the examined papers.

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Section: Inferring Personal Health Conditions By Wearable Devicesmentioning

confidence: 99%

The Personal Health Applications of Machine Learning Techniques in the Internet of Behaviors

Amiri,

Heidari,

Darbandi

et al. 2023

Sustainability

View full text Add to dashboard Cite

show abstract

“…It is an essential direction of related research to delegate the tasks of feature mining and feature screening in traditional feature engineering to deep learning algorithms [19]. This method is widely used in popular research fields with more complex features, such as economics [20,21], medicine [22], and electronic information field [23,24]. These studies using artificial intelligence methods for automated feature engineering all belong to category (3).…”

Section: High Segmentation Accuracymentioning

confidence: 99%

Method of Attention-Based CNN for Weighing Pleurotus eryngii

Jia,

Hu,

Zhang

et al. 2023

Agriculture

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Automatic weight detection is an essential step in the factory production of Pleurotus eryngii. In this study, a data set containing 1154 Pleurotus eryngii images was created, and then machine vision technology was used to extract eight two-dimensional features from the images. Because the fruiting bodies of Pleurotus eryngii have different shapes, these features were less correlated with weight. This paper proposed a multidimensional feature derivation method and an Attention-Based CNN model to solve this problem. This study aimed to realize the traditional feature screening task by deep learning algorithms and built an estimation model. Compared with different regression algorithms, the R2, RMSE, MAE, and MAPE of the Attention-Based CNN were 0.971, 7.77, 5.69, and 5.87%, respectively, and showed the best performance. Therefore, it can be used as an accurate, objective, and effective method for automatic weight measurements of Pleurotus eryngii.

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“…The seamless integration of these two sophisticated technologies allows the system to independently identify and classify waste items. Computer vision is an academic field that lets computers view and understand visual data like images and movies like humans [23][24][25][26][27]. Computer vision systems can recognize trash items' unique traits.…”

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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Cognitive cloud framework for waste dumping analysis using deep learning vision computing in healthy environment

Cited by 9 publications

References 23 publications

The Personal Health Applications of Machine Learning Techniques in the Internet of Behaviors

The Personal Health Applications of Machine Learning Techniques in the Internet of Behaviors

Method of Attention-Based CNN for Weighing Pleurotus eryngii

Recyclable Waste Classification using SquezeeNet and XGBoost

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