Developing Diabetes Disease Classification Model using Sequential Forward Selection Algorithm

Nowadays, speed up development and use of digital devices such as smartphones have put people at risk of internet crimes. The evidence of present crimes in a computer file can be easily unreachable by changing the prefix of a file or other algorithms. In more complex cases, either file divided into different parts or the parts of a file that has information about the file type are deleted, where the file fragment recognition issue is discussed. The known files are divided into different fragments, and different classification algorithms to solve the problems of file fragment recognition. A confusion matrix measures the accuracy of type recognition. In the present study, first, the file is divided into different fragments. Then, the file fragment features, which are obtained from Binary Frequency Distribution (BFD), are reduced by 2 feature reduction algorithms; Sequential Forward Selection algorithm (SFS) as well as Sequential Floating Forward Selection algorithm (SFFS) to delete sparse features that result in increased accuracy and speed. Finally, the reduced features are given to 3 classifier algorithms, Multilayer Perceptron (MLP), Support Vector Machines (SVM), and K-Nearest Neighbor (KNN) for classification and comparison of the results. In this paper, we proposed the algorithm of file type recognition that can recognize 6 types of useful files ( pdf, txt, jpg, doc, html, exe), which may distinguish a type of file fragments with higher accuracy than the similar works done.

Download Full-text

File Fragment Recognition Based on Content and Statistical Features

10.20944/preprints202001.0305.v1 ◽

2020 ◽

Author(s):

Marzieh Masoumi ◽

Ahmad Keshavarz

Keyword(s):

Nearest Neighbor ◽

Confusion Matrix ◽

Feature Reduction ◽

Support Vector ◽

Selection Algorithm ◽

K Nearest Neighbor ◽

Forward Selection ◽

Sequential Floating Forward Selection ◽

Comparison Of The Results ◽

Sequential Forward Selection

Nowadays, speed up development and use of digital devices such as smartphones have put people at risk of internet crimes. The evidence of present crimes in a computer file can be easily unreachable by changing the prefix of a file or other algorithms. In more complex cases, either file divided into different parts or the parts of a file that has information about the file type are deleted, where the file fragment recognition issue is discussed. The known files are divided into different fragments, and different classification algorithms to solve the problems of file fragment recognition. A confusion matrix measures the accuracy of type recognition. In the present study, first, the file is divided into different fragments. Then, the file fragment features, which are obtained from Binary Frequency Distribution (BFD), are reduced by 2 feature reduction algorithms; Sequential Forward Selection algorithm (SFS) as well as Sequential Floating Forward Selection algorithm (SFFS) to delete sparse features that result in increased accuracy and speed. Finally, the reduced features are given to 3 classifier algorithms, Multilayer Perceptron (MLP), Support Vector Machines (SVM), and K-Nearest Neighbor (KNN) for classification and comparison of the results. In this paper, we proposed the algorithm of file type recognition that can recognize 6 types of useful files ( pdf, txt, jpg, doc, html, exe), which may distinguish a type of file fragments with higher accuracy than the similar works done.

Download Full-text

Round-Robin sequential forward selection algorithm for prostate cancer classification and diagnosis using multispectral imagery

Machine Vision and Applications ◽

10.1007/s00138-010-0292-x ◽

2010 ◽

Vol 22 (5) ◽

pp. 865-878 ◽

Cited By ~ 13

Author(s):

Sabrina Bouatmane ◽

Mohamed Ali Roula ◽

Ahmed Bouridane ◽

Somaya Al-Maadeed

Keyword(s):

Prostate Cancer ◽

Round Robin ◽

Cancer Classification ◽

Multispectral Imagery ◽

Selection Algorithm ◽

Forward Selection ◽

Sequential Forward Selection ◽

Prostate Cancer Classification

Download Full-text

A Leaf Disease Classification Model in Betel Vine Using Machine Learning Techniques

2021 2nd International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST) ◽

10.1109/icrest51555.2021.9331142 ◽

2021 ◽

Author(s):

Md Zahid Hasan ◽

Nahid Zeba ◽

Md. Abdul Malek ◽

Sanjida Sultana Reya

Keyword(s):

Machine Learning ◽

Disease Classification ◽

Classification Model ◽

Machine Learning Techniques ◽

Leaf Disease ◽

Learning Techniques

Download Full-text

Pulmonary Lesion Subtypes Recognition of COVID-19 From Radiomics Data With Three Dimensional Texture Characterization in CT Images

10.21203/rs.3.rs-532131/v1 ◽

2021 ◽

Author(s):

Wei Li ◽

Yangyong Cao ◽

Kun Yu ◽

Yibo Cai ◽

Feng Huang ◽

...

Keyword(s):

Data Augmentation ◽

Critical Role ◽

Three Dimensional ◽

Treatment Decision ◽

Ground Glass Opacity ◽

Ct Images ◽

Classification Model ◽

Volume Data ◽

Selection Algorithm ◽

Adaptive Selection

Abstract Background: The COVID-19 disease is putting unprecedented pressure on the global healthcare system. The CT examination as a auxiliary confirmed diagnostic method can help clinicians quickly detect lesions locations of COVID-19 once screening by PCR test. Furthermore, the lesion subtypes classification plays a critical role in the consequent treatment decision. Identifying the subtypes of lesions accurately can help doctors discover changes in lesions in time and better assess the severity of COVID-19. Method: The most four typical lesion subtypes of COVID-19 are discussed in this paper, which are ground-glass opacity (GGO), cord, solid and subsolid. A computer aided diagnosis approach of lesion subtype is proposed in this paper. The radiomics data of lesions are segmented from COVID-19 patients CT images with diagnosis and lesions annotations by radiologists. Then the three dimensional texture descriptors are applied on the volume data of lesions as well as shape and First order features. The massive feature data are selected by hybrid adaptive selection algorithm and a classification model is trained at the same time. The classifier is used to predict lesion subtypes as side decision information for radiologists. Results: There are 3734 lesions extracted from the dataset with 319 patients collection and then 189 radiomics features are obtained finally. The random forest classifier is trained with data augmentation that the number of different subtypes of lesions is imbalanced in initial dataset. The experimental results show that the accuracy of the four subtypes of lesions is (0.9306, 0.9684, 0.9958, and 0.9430), the recall is (0.9552, 0.9158, 0.9580 and 0.8075) and the f-score is (0.93.84, 0.92.37, 0.95.47, and 84.42). Conclusion: The method is evaluated in multiple sufficient experiments. The results show that the 3D radiomics features chosen by hybrid adaptive selection algorithm can better express the advanced information of the lesion data. The classification model obtains a good performance and is compared the models of COVID-19 in the stat of art, which can help clinicians more accurately identify the subtypes of COVID-19 lesions and provide help for further research.

Download Full-text

In-Vehicle Alcohol Detection Using Low-Cost Sensors and Genetic Algorithms to Aid in the Drinking and Driving Detection

Sensors ◽

10.3390/s21227752 ◽

2021 ◽

Vol 21 (22) ◽

pp. 7752

Author(s):

Jose M. Celaya-Padilla ◽

Jonathan S. Romero-González ◽

Carlos E. Galvan-Tejada ◽

Jorge I. Galvan-Tejada ◽

Huizilopoztli Luna-García ◽

...

Keyword(s):

Motor Vehicle ◽

Low Cost ◽

Child Death ◽

Classification Model ◽

Selection Strategy ◽

Drinking And Driving ◽

Forward Selection ◽

Vehicle Accidents ◽

Non Invasive ◽

Invasive Method

Worldwide, motor vehicle accidents are one of the leading causes of death, with alcohol-related accidents playing a significant role, particularly in child death. Aiming to aid in the prevention of this type of accidents, a novel non-invasive method capable of detecting the presence of alcohol inside a motor vehicle is presented. The proposed methodology uses a series of low-cost alcohol MQ3 sensors located inside the vehicle, whose signals are stored, standardized, time-adjusted, and transformed into 5 s window samples. Statistical features are extracted from each sample and a feature selection strategy is carried out using a genetic algorithm, and a forward selection and backwards elimination methodology. The four features derived from this process were used to construct an SVM classification model that detects presence of alcohol. The experiments yielded 7200 samples, 80% of which were used to train the model. The rest were used to evaluate the performance of the model, which obtained an area under the ROC curve of 0.98 and a sensitivity of 0.979. These results suggest that the proposed methodology can be used to detect the presence of alcohol and enforce prevention actions.

Download Full-text