Classification of Human Activity Recognition Utilizing  Smartphone Data of CNN-LSTM

Fadilah, Widya Rizka Ulul; Kusuma, Wahyu Andhyka; Minarno, Agus Eko; Munarko, Yuda

doi:10.22219/kinetik.v6i2.1319

Cited by 4 publications

(7 citation statements)

References 13 publications

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“…The dataset is imported into the array using NumPy, available in Python [24]. Labels containing waste categories based on type are converted into numbers so that the machine can understand the categories that have been determined [25]. The original image uses a red, green, and blue (RGB) color scale.…”

Section: Data Collectionmentioning

confidence: 99%

Image Processing Using Morphology on Support Vector Machine Classification Model for Waste Image

Fahmi,

Yudhana,

Sunardi

2023

matrik

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Sorting waste has always been an important part of managing waste. The primary issue with the waste sorting process has been the discomfort caused by prolonged contact with waste odor. A machinelearning method for identifying waste types was created to address this issue. The study’s goal was to create machine learning to solve waste management challenges by applying the most accurate categorization model available. The research approach was the quantitative analysis of the classification model accuracy. The Kaggle dataset was used to collect and curate data, which was subsequently preprocessed using the morphology approach. Based on picture sources, the data was trained and used to classify waste. The Support Vector Machine model was used in this investigation and feature extraction via the Convolutional Neural Network. The results showed that the system categorized waste successfully, with an accuracy of 99.30% and a loss of 2.47% across all categories. According to the findings of this study, SVM combined with morphological image processing functioned as a strong classification model, with a remarkable accuracy rate of 99.30%. This study’s outcomes contributed to waste management by giving an efficient and dependable waste classification solution compared to many previous studies.

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Section: Data Collectionmentioning

confidence: 99%

Image Processing Using Morphology on Support Vector Machine Classification Model for Waste Image

Fahmi,

Yudhana,

Sunardi

2023

matrik

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“…The data is normalized using min-max normalization. Available labels are converted to numbers so the system can read them more efficiently [22]. The image is grayed out by adding a grayscale feature using cv2 [22].…”

Section: Preprocessingmentioning

confidence: 99%

“…Available labels are converted to numbers so the system can read them more efficiently [22]. The image is grayed out by adding a grayscale feature using cv2 [22]. The pixels are 50x50x1 because all images are resized to 50x50 pixels [22].…”

Section: Preprocessingmentioning

confidence: 99%

“…The image is grayed out by adding a grayscale feature using cv2 [22]. The pixels are 50x50x1 because all images are resized to 50x50 pixels [22]. Variable 1 represents the image turned gray based on RGB to grayscale conversion formula explained in Eq.…”

Section: Preprocessingmentioning

confidence: 99%

“…(1) The size of all images in the dataset is changed to 50x50x1 pixels, and the image matrix is converted to one dimension array of 2500 8-bit pixels (flatten) [22]. The data is then shuffled to vary the categories [22]. Figure 2 shows the preprocessing of a dataset containing the waste image.…”

Section: Preprocessingmentioning

confidence: 99%

See 2 more Smart Citations

Improving Waste Classification Using Convolutional Neural Networks: An Application of Machine Learning for Effective Environmental Management

Sunardi,

Yudhana,

Fahmi

2023

RIA

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Waste management, particularly waste sorting, constitutes a critical global challenge. The integration of advanced technology, specifically machine learning, offers potential solutions to this pressing issue. In this study, a convolutional neural network (CNN) model was employed to devise an efficient waste classification system. The model achieved notable results, attaining an accuracy rate of 98.92% and a loss percentage of only 4.03% in overall performance on the test set, utilizing the Kaggle dataset. To further improve the CNN model's performance, advanced preprocessing techniques were implemented alongside a stream lined CNN model, yielding substantial effectiveness. This investigation demonstrates that the application of machine learning techniques can result in highly accurate and efficient waste classification, presenting promising solutions for waste management challenges. By accurately identifying and sorting waste materials, this technology has the potential to significantly reduce the volume of waste directed to landfills, safeguard the environment, and conserve valuable resources.

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SVM-CNN Hybrid Classification for Waste Image Using Morphology and HSV Color Model Image Processing

Sunardi,

Yudhana,

Fahmi

2023

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Waste is a significant problem that is around us. The problem occurs because waste volume speed could be faster. This problem can be solved by implementing machine learning in the waste sorting process based on two categories which are organic and inorganic. Knowing the most efficient image processing and classification machine learning model is necessary. This research uses the Support Vector Machine classification model hybridized with the Convolutional Neural Network, image processing morphology, and the HSV color model. The dataset is collected from the images available on the Kaggle website and executed using Python. The data used amounted to 25,077 with a training and test data ratio of 85:15. The data is processed using the proposed method, namely the morphology and HSV color model, to determine the performance between using the image process and those that do not. The data that has been processed is classified using the SVM-CNN Hybrid classification model. The performance results are an accuracy rate of 99.34% and a loss of 1.67% without overfitting.

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Classification of Human Activity Recognition Utilizing Smartphone Data of CNN-LSTM

Cited by 4 publications

References 13 publications

Image Processing Using Morphology on Support Vector Machine Classification Model for Waste Image

Image Processing Using Morphology on Support Vector Machine Classification Model for Waste Image

Improving Waste Classification Using Convolutional Neural Networks: An Application of Machine Learning for Effective Environmental Management

SVM-CNN Hybrid Classification for Waste Image Using Morphology and HSV Color Model Image Processing

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