Deep CNN: A Machine Learning Approach for Driver Drowsiness Detection Based on Eye State

Chirra, Venkata Rami Reddy; Reddy, U. Srinivasulu; Kolli, Venkata Krishna Kishore

doi:10.18280/ria.330609

Cited by 86 publications

(46 citation statements)

References 21 publications

(20 reference statements)

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“…The recent approaches in computer vision, especially in the fields of machine learning and deep learning have improved the efficiency of image classification tasks [1][2][3][4][5][6]. Detection of defected fruits and the classification of fresh and rotten fruits represent one of the major challenges in the agricultural fields.…”

Section: Introductionmentioning

confidence: 99%

Fresh and Rotten Fruits Classification Using CNN and Transfer Learning

Palakodati¹,

Chirra²,

Dasari³

et al. 2020

RIA

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Detecting the rotten fruits become significant in the agricultural industry. Usually, the classification of fresh and rotten fruits is carried by humans is not effectual for the fruit farmers. Human beings will become tired after doing the same task multiple times, but machines do not. Thus, the project proposes an approach to reduce human efforts, reduce the cost and time for production by identifying the defects in the fruits in the agricultural industry. If we do not detect those defects, those defected fruits may contaminate good fruits. Hence, we proposed a model to avoid the spread of rottenness. The proposed model classifies the fresh fruits and rotten fruits from the input fruit images. In this work, we have used three types of fruits, such as apple, banana, and oranges. A Convolutional Neural Network (CNN) is used for extracting the features from input fruit images, and Softmax is used to classify the images into fresh and rotten fruits. The performance of the proposed model is evaluated on a dataset that is downloaded from Kaggle and produces an accuracy of 97.82%. The results showed that the proposed CNN model can effectively classify the fresh fruits and rotten fruits. In the proposed work, we inspected the transfer learning methods in the classification of fresh and rotten fruits. The performance of the proposed CNN model outperforms the transfer learning models and the state of art methods.

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Section: Introductionmentioning

confidence: 99%

Fresh and Rotten Fruits Classification Using CNN and Transfer Learning

Palakodati¹,

Chirra²,

Dasari³

et al. 2020

RIA

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show abstract

“…Because all students face the same directly in class, all the faces in the video images must be directly forward. Thus, the Viola Jones (VJ) algorithm [23] was employed to detect faces in video images. This algorithm involves four stages: Haar feature extraction, integral image creation, AdaBoost training, and cascade classification.…”

Section: Face Tracking-based Standing Behavior Recognition Methodsmentioning

confidence: 99%

Recognition of Student Classroom Behaviors Based on Moving Target Detection

Wu¹,

Wang²,

Huang³

et al. 2021

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Classroom teaching, as the basic form of teaching, provides students with an important channel to acquire information and skills. The academic performance of students can be evaluated and predicted objectively based on the data on their classroom behaviors. Considering the complexity of classroom environment, this paper firstly envisages a moving target detection algorithm for student behavior recognition in class. Based on region of interest (ROI) and face tracking, the authors proposed two algorithms to recognize the standing behavior of students in class. Moreover, a recognition algorithm was developed for hand raising in class based on skin color detection. Through experiments, the proposed algorithms were proved as effective in recognition of student classroom behaviors.

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“…On the establishment of the evaluation model, credit evaluation has been widely researched with multidimensional multivariate model and some artificial intelligence (AI) methods. In the era of big data, these methods are often combined with modern financial theory, as well as the latest ML algorithms for data mining [12][13][14][15], namely, support vector machine (SVM) [16][17], and neural network (NN) [18][19], providing effective solutions to the problem of Internet finance fraud.…”

Section: Literature Reviewmentioning

confidence: 99%

A Big Data-Based Anti-Fraud Model for Internet Finance

Liu¹,

You²

2020

RIA

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Fraud has become a serious risk to the burgeoning industry of Internet finance. To predict and prevent the fraud, this paper develops a risk control model that accurately predicts the fraud behaviors of online loan borrowers, based on their social network data and consumption information. Firstly, statistical analysis was carried out to clarify the relationship between user behavior and fraud, and that between social network and fraud. Next, suitable variables were selected through big data analysis, and used to build the risk control model, in combination with random forest (RF) algorithm and modern financial theory. The effectiveness of the proposed model was confirmed through contrastive experiments. Our model provides peer-to-peer (P2P) online loan platforms with an effective tool to prevent fraud.

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Deep CNN: A Machine Learning Approach for Driver Drowsiness Detection Based on Eye State

Cited by 86 publications

References 21 publications

Fresh and Rotten Fruits Classification Using CNN and Transfer Learning

Fresh and Rotten Fruits Classification Using CNN and Transfer Learning

Recognition of Student Classroom Behaviors Based on Moving Target Detection

A Big Data-Based Anti-Fraud Model for Internet Finance

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