Metaheuristics with Deep Transfer Learning Enabled Detection and classification model for industrial waste management

Neelakandan, S.; Prakash, M.; Geetha, B.T.; Nanda, Ashok Kumar; Metwally, Ahmed Mohammed; Moorthy, Madhappan Santha; Gupta, Monika

doi:10.1016/j.chemosphere.2022.136046

Cited by 29 publications

(16 citation statements)

References 14 publications

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“…The IF has a similar idea of utilising the tree algorithm as the RF model. It processes data points to recurrent random splits that are based on feature selection [20]. The major benefit of the IF technique is how, it processes information.…”

Section: Isolation Forest Based Outlier Detectionmentioning

confidence: 99%

Evolutionary Algorithm Based Feature Subset Selection for Students Academic Performance Analysis

Babu¹,

MathuSoothana²,

Kumar³

2023

Intelligent Automation &Amp; Soft Computing

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Educational Data Mining (EDM) is an emergent discipline that concentrates on the design of self-learning and adaptive approaches. Higher education institutions have started to utilize analytical tools to improve students' grades and retention. Prediction of students' performance is a difficult process owing to the massive quantity of educational data. Therefore, Artificial Intelligence (AI) techniques can be used for educational data mining in a big data environment. At the same time, in EDM, the feature selection process becomes necessary in creation of feature subsets. Since the feature selection performance affects the predictive performance of any model, it is important to elaborately investigate the outcome of students' performance model related to the feature selection techniques. With this motivation, this paper presents a new Metaheuristic Optimization-based Feature Subset Selection with an Optimal Deep Learning model (MOFSS-ODL) for predicting students' performance. In addition, the proposed model uses an isolation forest-based outlier detection approach to eliminate the existence of outliers. Besides, the Chaotic Monarch Butterfly Optimization Algorithm (CBOA) is used for the selection of highly related features with low complexity and high performance. Then, a sailfish optimizer with stacked sparse autoencoder (SFO-SSAE) approach is utilized for the classification of educational data. The MOFSS-ODL model is tested against a benchmark student's performance data set from the UCI repository. A wide-ranging simulation analysis portrayed the improved predictive performance of the MOFSS-ODL technique over recent approaches in terms of different measures. Compared to other methods, experimental results prove that the proposed (MOFSS-ODL) classification model does a great job of predicting students' academic progress, with an accuracy of 96.49%.

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Section: Isolation Forest Based Outlier Detectionmentioning

confidence: 99%

Evolutionary Algorithm Based Feature Subset Selection for Students Academic Performance Analysis

Babu¹,

MathuSoothana²,

Kumar³

2023

Intelligent Automation &Amp; Soft Computing

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“…31 Individuals with an eGFR less than 60 mL/min per 173 m 2 (stage 3 and above) were judged to have chronic renal illness, while those with a greater eGFR were regarded to be controls (corresponding to chronic kidney disease stage 0-2). Neelakandan et al 6,7 ecology of chronic kidney disease eGFR was determined using serum creatinine and a collaboration equation. 32 A study conducted in Australia5 discovered that around half of diabetic patients who saw general practitioners had not had their chronic renal illness examined in the previous 18 months.…”

Section: Related Workmentioning

confidence: 99%

“…Individuals with an eGFR less than 60 mL/min per 173 m 2 (stage 3 and above) were judged to have chronic renal illness, while those with a greater eGFR were regarded to be controls (corresponding to chronic kidney disease stage 0–2). Neelakandan et al 6,7 ecology of chronic kidney disease eGFR was determined using serum creatinine and a collaboration equation 32 …”

Section: Related Workmentioning

confidence: 99%

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An explainable deep learning model for prediction of early‐stage chronic kidney disease

Arumugham,

Sankaralingam,

Jayachandran

et al. 2023

Computational Intelligence

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Chronic kidney disease (CKD) is a major public health concern with rising prevalence and huge costs associated with dialysis and transplantation. Early prediction of CKD can reduce the patient's risk of CKD progression to end‐stage kidney failure. Artificial intelligence offers more intelligent and expert healthcare services in disease diagnosis. In this work, a deep learning model is built using deep neural networks (DNN) with an adaptive moment estimation optimization function to predict early‐stage CKD. The health care applications require interpretability over the predictions of the black‐box model to build conviction towards the model's prediction. Hence, the predictions of the DNN‐CKD model are explained by the local interpretable model‐agnostic explainer (LIME). The diagnostic patient data is trained on five layered DNN with three hidden layers. Over the unseen data, the DNN‐CKD model yields an accuracy of 98.75% and a roc_auc score of 98.86% in detecting CKD risk. The explanation revealed by the LIME algorithm echoes the influence of each feature on the prediction made by the DNN‐CKD model over the given CKD data. With its interpretability and accuracy, the proposed system may effectively help medical experts in the early diagnosis of CKD.

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“…Proses yang dijelaskan di atas juga menggunakan sejumlah teknologi pengelolaan sampah yang sudah ada di fasilitas pengelolaan sampah [6]- [8]. Dari beberapa proses di atas, proses pemisahan sampah merupakan proses awal yang sangat penting untuk dilakukan karena dapat berpengaruh pada proses-proses yang lain [9].…”

Section: Pendahuluanunclassified

Implementasi Artificial Intelligence Pada Pengembangan Sistem Deteksi Limbah Industri Produk Kulit Otomatis

Frannita

Putri

Hidayahtullah

2022

bptkspk

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Rapid globalization has led to the growth of a significant amount of waste, including in the industry. Due to the considerable amount of growth of waste every year, effective and efficient waste management is needed to protect our environment. In the leather product industry, waste management is strongly important since it may have a significant impact to the employee and production process. Regarding those issues, waste management technology is considered proposed in order to solve those problems. Current research reported the outstanding work of implementing artificial intelligence for detecting and recognizing industrial waste. Artificial intelligence was proven to be a highly recommended approach that is able to classify several waste types with outstanding performance. Regardless of those facts, artificial intelligence still remains several hurdles, such as the high computational demands, especially for deep learning networks. Regarding the mentioned issue, we proposed a more proper deep learning network for recognizing industrial waste. In this research work, we use Single Shot Detector (SSD) to recognize and classify industrial waste. Our proposed solution was performed in the TrashNet dataset and Waste Picture dataset. Our proposed solution successfully achieve mAP of 0.8813, accuracy of 0,9795, precision of 0,9985 and recall of 0,9693 in the training process. Whereas, in the testing process we achieve average accuracy of 0,8254. According to those results, we can conclude that our proposed solution is suitable for industrial waste detection and has potential to be implemented as an embedded system for recognizing industrial waste automatically.

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Metaheuristics with Deep Transfer Learning Enabled Detection and classification model for industrial waste management

Cited by 29 publications

References 14 publications

Evolutionary Algorithm Based Feature Subset Selection for Students Academic Performance Analysis

Evolutionary Algorithm Based Feature Subset Selection for Students Academic Performance Analysis

An explainable deep learning model for prediction of early‐stage chronic kidney disease

Implementasi Artificial Intelligence Pada Pengembangan Sistem Deteksi Limbah Industri Produk Kulit Otomatis

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