Machine Learning Techniques to Predict Autism Spectrum Disorder
Autism Spectrum Disorder (ASD) is a serious developmental abnormality that seriously affects the ... more Autism Spectrum Disorder (ASD) is a serious developmental abnormality that seriously affects the behavior and communication of an individual. It limits the use of communicative, social and cognitive skills as well as abilities of the affected personality whereas its symptoms may vary from person to person. Artificial Intelligence’s branch i.e Machine learning is applied to diagnose ASD problem as a classification task in which prediction models were built based on chronological dataset, and then used those patterns to predict that the person is suffering from ASD or not. So it can be used for decision making under ambiguity. Here in this paper we have applied machine learning techniques and validate their performance on a Autism Spectrum Disorder dataset. In our result, we have shown comparison of the performance of different algorithms to diagnose ASD.
In this paper, we approach with four different CNN-based models i.e., VGG-19, VGG-16, InceptionV3... more In this paper, we approach with four different CNN-based models i.e., VGG-19, VGG-16, InceptionV3 and MobileNetV3 with an improved version of the previous models for violence detection and recognition from videos. The proposed models use the pre-trained models as the base model for feature extraction and for classification after freezing the rest of the layer, the head model is prepared with averagepooling2D of (5, 5), and after flattening only one dense layer having 512 nodes with ‘ReLU’ activation function, dropout layer of 0.5 and last output layer with only 2 classes and ‘softmax’ activation function. This head model of fully connected layers was used in the proposed models. These models are trained and evaluated on the Hockey fight dataset and Real life violence situations detection datasets. The experimental results are far better in terms of accuracy and other performance metrics and the models have reduced parameters and less computational time than previous models.
Security Analytics: Challenges and Future Directions
The frequency and type of cyber attacks are increasing day by day. However, well-known cyber secu... more The frequency and type of cyber attacks are increasing day by day. However, well-known cyber security solutions are not able to cope with the increasing volume of data that is generated for providing security solutions. Therefore, current trend in research on cyber security is to apply Big Data Analytics (BDA) techniques to cyber security. This field, called security analytics (SA), can help network managers in the monitoring and surveillance of real-time network streams and real-time detection of malicious and/or suspicious patterns. Researchers believe that an SA system can assist in enhancing all traditional security mechanisms. Nonetheless, there are certain issues related to incorporating big data analytics to cyber security. This paper presents an analysis on the issues and challenges faced by Security Analytics, and further provides future directions in the field.
Machine Learning Techniques to Predict Autism Spectrum Disorder
2018 IEEE Punecon, 2018
Autism Spectrum Disorder (ASD) is a serious developmental abnormality that seriously affects the ... more Autism Spectrum Disorder (ASD) is a serious developmental abnormality that seriously affects the behavior and communication of an individual. It limits the use of communicative, social and cognitive skills as well as abilities of the affected personality whereas its symptoms may vary from person to person. Artificial Intelligence’s branch i.e Machine learning is applied to diagnose ASD problem as a classification task in which prediction models were built based on chronological dataset, and then used those patterns to predict that the person is suffering from ASD or not. So it can be used for decision making under ambiguity. Here in this paper we have applied machine learning techniques and validate their performance on a Autism Spectrum Disorder dataset. In our result, we have shown comparison of the performance of different algorithms to diagnose ASD.
Machine Learning Techniques to Predict Autism Spectrum Disorder
Autism Spectrum Disorder (ASD) is a serious developmental abnormality that seriously affects the ... more Autism Spectrum Disorder (ASD) is a serious developmental abnormality that seriously affects the behavior and communication of an individual. It limits the use of communicative, social and cognitive skills as well as abilities of the affected personality whereas its symptoms may vary from person to person. Artificial Intelligence’s branch i.e Machine learning is applied to diagnose ASD problem as a classification task in which prediction models were built based on chronological dataset, and then used those patterns to predict that the person is suffering from ASD or not. So it can be used for decision making under ambiguity. Here in this paper we have applied machine learning techniques and validate their performance on a Autism Spectrum Disorder dataset. In our result, we have shown comparison of the performance of different algorithms to diagnose ASD.
In this paper, we approach with four different CNN-based models i.e., VGG-19, VGG-16, InceptionV3... more In this paper, we approach with four different CNN-based models i.e., VGG-19, VGG-16, InceptionV3 and MobileNetV3 with an improved version of the previous models for violence detection and recognition from videos. The proposed models use the pre-trained models as the base model for feature extraction and for classification after freezing the rest of the layer, the head model is prepared with averagepooling2D of (5, 5), and after flattening only one dense layer having 512 nodes with ‘ReLU’ activation function, dropout layer of 0.5 and last output layer with only 2 classes and ‘softmax’ activation function. This head model of fully connected layers was used in the proposed models. These models are trained and evaluated on the Hockey fight dataset and Real life violence situations detection datasets. The experimental results are far better in terms of accuracy and other performance metrics and the models have reduced parameters and less computational time than previous models.
Security Analytics: Challenges and Future Directions
The frequency and type of cyber attacks are increasing day by day. However, well-known cyber secu... more The frequency and type of cyber attacks are increasing day by day. However, well-known cyber security solutions are not able to cope with the increasing volume of data that is generated for providing security solutions. Therefore, current trend in research on cyber security is to apply Big Data Analytics (BDA) techniques to cyber security. This field, called security analytics (SA), can help network managers in the monitoring and surveillance of real-time network streams and real-time detection of malicious and/or suspicious patterns. Researchers believe that an SA system can assist in enhancing all traditional security mechanisms. Nonetheless, there are certain issues related to incorporating big data analytics to cyber security. This paper presents an analysis on the issues and challenges faced by Security Analytics, and further provides future directions in the field.
Machine Learning Techniques to Predict Autism Spectrum Disorder
2018 IEEE Punecon, 2018
Autism Spectrum Disorder (ASD) is a serious developmental abnormality that seriously affects the ... more Autism Spectrum Disorder (ASD) is a serious developmental abnormality that seriously affects the behavior and communication of an individual. It limits the use of communicative, social and cognitive skills as well as abilities of the affected personality whereas its symptoms may vary from person to person. Artificial Intelligence’s branch i.e Machine learning is applied to diagnose ASD problem as a classification task in which prediction models were built based on chronological dataset, and then used those patterns to predict that the person is suffering from ASD or not. So it can be used for decision making under ambiguity. Here in this paper we have applied machine learning techniques and validate their performance on a Autism Spectrum Disorder dataset. In our result, we have shown comparison of the performance of different algorithms to diagnose ASD.
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Papers by bhawana tyagi