Ensemble human movement sequence prediction model with Apriori based Probability Tree Classifier (APTC) and Bagged J48 on Machine learning

Raj, Suman; Nandhini, M.

doi:10.1016/j.jksuci.2018.04.002

Cited by 12 publications

(3 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…J48, an ML method, is used to identify and indicate statistical classifiers in categorical and continuous data using a tree-based decision technique [32]. J48 operates on the basis of decision trees or recommendations derived from them.…”

Section: J48mentioning

confidence: 99%

Image-Based Classical Features and Machine Learning Analysis of Skin Cancer Instances

Almutairi

Khan

2023

Applied Sciences

View full text Add to dashboard Cite

Skin conditions influence people of all ages and genders and impose an enormous strain on worldwide public health. For efficient management and medical treatment, skin disorders must be accurately categorized. However, the conventional method of classifying skin conditions can be arbitrary and time-consuming, delaying diagnosis and treatment. In this research, we examine the application of traditional machine learning models and conventional image characteristics for the classification of skin cancer based on picture features. Specifically, we employ six feature extraction approaches, which we model using six classical classifiers. To evaluate our approach, we address skin cancer detection as both a seven-class problem and a two-class problem comprising 21 permutations of skin cancer instances. Our experimental results demonstrate that Random Forest achieves the highest performance, followed by Support Vector Machines. Additionally, our analysis reveals that the Edge Histogram and Fuzzy Opponent Histogram feature sets perform best in learning the skin cancer model. Our comprehensive evaluation of various models provides practitioners with valuable insights when selecting appropriate models for similar problems. Our findings demonstrate that acceptable detection performance can be achieved even with simple feature extraction and non-deep classifiers. We argue that classical features are not only easier and faster to extract than deep features but can also be combined with classical machine learning models to save time and valuable resources.

show abstract

Section: J48mentioning

confidence: 99%

Image-Based Classical Features and Machine Learning Analysis of Skin Cancer Instances

Almutairi

Khan

2023

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…4. Ensemble ML ( Raj S. and M., 2021 ) ( Luca et al, 2021 ) are DL topologies with one ANN that compress the input feature vector in a latent space and one ANN to decode the latent vector to the output feature vector. Various types of DL models have been used in the literature for the encoder and decoder such as simple LSTM for both of them (LSTM-ED), bidirectional and simple LSTM (BD-LSTM-ED), unidirectional and bidirectional LSTM (Uni-BD LSTM ED, also named HB ED) and CNN encoder and LSTM decoder (CNN-LSTM ED).…”

Section: Time-series Prediction Mechanismmentioning

confidence: 99%

Self-Attention based encoder-Decoder for multistep human density prediction

Violos

Θεοδωρόπουλος

Maroudis

et al. 2022

Journal of Urban Mobility

View full text Add to dashboard Cite

“…It is used for recognizing frequently occurring attribute-value relationships in the dataset. Example of IoT use-case that utilized apriori is human sequential movement prediction [74].…”

Section: Machine Learning Techniquesmentioning

confidence: 99%

Data Analytics in the Internet of Things: A Survey

Saleem

Chishti

2019

SCPE

View full text Add to dashboard Cite

The plethora of sensors deployed in Internet of Things (IoT) environments generate unprecedented volumes of data, thereby creating a data deluge. Data collected from these sensors can be used to comprehend, examine and control intricate environments around us, facilitating greater intelligence, smarter decision-making, and better performance. The key challenge here is how to mine out proﬁcient information from such immense data. Copious solutions have been put forth to obtain valuable inferences and insights, however, these solutions are still in their developing stages. Moreover, conventional procedures do not address the surging analytical demands of IoT systems. Motivated to resolve this concern, this work investigates the key enablers for performing desired data analytics in IoT applications. A comprehensive survey on the identiﬁed key enablers including their role in IoT data analytics, use cases in which they have been applied and the corresponding IoT applications for the use cases is presented. Furthermore, open research challenges and future research opportunities are also discussed. This article can be used as a basis to foster advanced research in the arena of IoT data analytics.

show abstract

Ensemble human movement sequence prediction model with Apriori based Probability Tree Classifier (APTC) and Bagged J48 on Machine learning

Cited by 12 publications

References 18 publications

Image-Based Classical Features and Machine Learning Analysis of Skin Cancer Instances

Image-Based Classical Features and Machine Learning Analysis of Skin Cancer Instances

Self-Attention based encoder-Decoder for multistep human density prediction

Data Analytics in the Internet of Things: A Survey

Contact Info

Product

Resources

About