Kislay Raj - Academia.edu (original) (raw)

Papers by Kislay Raj

Research paper thumbnail of Image Data Augmentation Approaches: A Comprehensive Survey and Future directions

arXiv (Cornell University), Jan 7, 2023

Deep learning algorithms have demonstrated remarkable performance in various computer vision task... more Deep learning algorithms have demonstrated remarkable performance in various computer vision tasks, however, limited labeled data can lead to overfitting problems, hindering the network's performance on unseen data. To address this issue, various generalization techniques have been proposed, including dropout, normalization, and advanced data augmentation. Among these techniques, image data augmentationwhich increases the dataset size by incorporating sample diversity-has received significant attention in recent times. In this survey, we focus on advanced image data augmentation techniques. We provide an overview of data augmentation, present a novel and comprehensive taxonomy of the reviewed data augmentation techniques, and discuss their strengths and limitations. Furthermore, we provide comprehensive results of the impact of data augmentation on three popular computer vision tasks: image classification, object detection, and semantic segmentation. For results reproducibility, the available codes of all data augmentation techniques have been compiled. Finally, we discuss the challenges and difficulties, as well as possible future directions for the research community. This survey provides several benefits: i) readers will gain a deeper understanding of how data augmentation can help address overfitting problems, ii) researchers will save time searching for comparison results, iii) the codes for the data augmentation techniques are available for result reproducibility, and iv) the discussion of future work will spark interest in the research community.

Research paper thumbnail of Understanding EEG signals for subject-wise Definition of Armoni Activities

arXiv (Cornell University), Jan 3, 2023

In a growing world of technology, psychological disorders became a challenge to be solved. The me... more In a growing world of technology, psychological disorders became a challenge to be solved. The methods used for cognitive stimulation are very conventional and based on one-way communication, which only rely on the material or method used for training of an individual. It doesn't use any kind of feedback from the individual to analyze the progress of the training process. We have proposed a closed-loop methodology to improve the cognitive state of a person with ID (Intellectual disability). We have used a platform named 'Armoni', for providing training to the intellectually disabled individuals. The learning is performed in a closed-loop by using feedback in the form of change in affective state. For feedback to the Armoni, an EEG (Electroencephalograph) headband is used. All the changes in EEG are observed and classified against the change in the mean and standard deviation value of all frequency bands of signal. This comparison is being helpful in defining every activity with respect to change in brain signals. In this paper, we have discussed the process of treatment of EEG signal and its definition against the different activities of Armoni. We have tested it on 6 different systems with different age groups and cognitive levels.

Research paper thumbnail of Deep Learning-Based Cost-Effective and Responsive Robot for Autism Treatment

Drones

Recent studies state that, for a person with autism spectrum disorder, learning and improvement i... more Recent studies state that, for a person with autism spectrum disorder, learning and improvement is often seen in environments where technological tools are involved. A robot is an excellent tool to be used in therapy and teaching. It can transform teaching methods, not just in the classrooms but also in the in-house clinical practices. With the rapid advancement in deep learning techniques, robots became more capable of handling human behaviour. In this paper, we present a cost-efficient, socially designed robot called ‘Tinku’, developed to assist in teaching special needs children. ‘Tinku’ is low cost but is full of features and has the ability to produce human-like expressions. Its design is inspired by the widely accepted animated character ‘WALL-E’. Its capabilities include offline speech processing and computer vision—we used light object detection models, such as Yolo v3-tiny and single shot detector (SSD)—for obstacle avoidance, non-verbal communication, expressing emotions i...

Research paper thumbnail of A Computer Vision-Based Object Localization Model for Endangered Wildlife Detection

Research paper thumbnail of WilDect-YOLO: An efficient and robust computer vision-based accurate object localization model for automated endangered wildlife detection

Research paper thumbnail of SQL and NoSQL Databases Software architectures performance analysis and assessments -- A Systematic Literature review

arXiv (Cornell University), Sep 14, 2022

Context: The efficient processing of Big Data is a challenging task for SQL and NoSQL Databases, ... more Context: The efficient processing of Big Data is a challenging task for SQL and NoSQL Databases, where competent software architecture plays a vital role. The SQL Databases are designed for structuring data and supporting vertical scalability. In contrast, horizontal scalability is backed by NoSQL Databases and can process sizeable unstructured Data efficiently. One can choose the right paradigm according to the organisation's needs; however, making the correct choice can often be challenging. The SQL and NoSQL Databases follow different architectures. Also, the mixed model is followed by each category of NoSQL Databases. Hence, data movement becomes difficult for cloud consumers across multiple cloud service providers (CSPs). In addition, each cloud platform IaaS, PaaS, SaaS, and DBaaS also monitors various paradigms. Objective: This systematic literature review (SLR) aims to study the related articles associated with SQL and NoSQL Database software architectures and tackle data portability and Interoperability among various cloud platforms. State of the art presented many performance comparison studies of SQL and NoSQL Databases by observing scaling, performance, availability, consistency and sharding characteristics. According to the research studies, NoSQL Databasedesigned structures can be the right choice for big data analytics, while SQL Databases are suitable for OLTP Databases. The researcher proposes numerous approaches associated with data movement in the cloud. Platform-based APIs are developed, which makes users' data movement difficult. Therefore, data portability and Interoperability issues are noticed during data movement across multiple CSPs. To minimize developer efforts and Interoperability, Unified APIs are demanded to make data movement relatively more accessible among various cloud platforms. Method: The systematic literature review technique and approach are used in this paper to select the appropriate and related documents. Most of the articles investigated the technical reasons for both Databases and identified the scenarios when to use which Database. Data analysis, data collection process, and the required results are detailed in this paper. Results: A total of 142 papers have been selected associated with the topic in this systematic literature review. 35% are journal documents, 52% are conferences, and 11% are technical reports and thesis. We also performed a performance analysis between the SQL and NoSQL document Databases. Besides, DBaaS and unified APIs approaches are investigated in terms of data portability and Interoperability to extract the desired results. We evaluated and analyzed the research papers accordingly and identified the state-of-the-art gaps. Conclusion: According to our findings and analysis in this SLR, NoSQL Databases are not the alternative to SQL Databases. Each Database has its advantages in a particular scenario. The SQL and NoSQL Databases follow various data models and software architectures. In contrast, data movement is strenuous across multiple cloud platforms. DBaaS cloud architecture is used to transfer traditional Database architecture into cloud architecture. Different, unified APIs frameworks have been investigated to minimize data portability and Interoperability issues across various cloud platforms during data movement.

Research paper thumbnail of Introducing Urdu Digits Dataset with Demonstration of an Efficient and Robust Noisy Decoder-Based Pseudo Example Generator

Symmetry

In the present work, we propose a novel method utilizing only a decoder for generation of pseudo-... more In the present work, we propose a novel method utilizing only a decoder for generation of pseudo-examples, which has shown great success in image classification tasks. The proposed method is particularly constructive when the data are in a limited quantity used for semi-supervised learning (SSL) or few-shot learning (FSL). While most of the previous works have used an autoencoder to improve the classification performance for SSL, using a single autoencoder may generate confusing pseudo-examples that could degrade the classifier’s performance. On the other hand, various models that utilize encoder–decoder architecture for sample generation can significantly increase computational overhead. To address the issues mentioned above, we propose an efficient means of generating pseudo-examples by using only the generator (decoder) network separately for each class that has shown to be effective for both SSL and FSL. In our approach, the decoder is trained for each class sample using random ...

Research paper thumbnail of Image Data Augmentation Approaches: A Comprehensive Survey and Future directions

arXiv (Cornell University), Jan 7, 2023

Deep learning algorithms have demonstrated remarkable performance in various computer vision task... more Deep learning algorithms have demonstrated remarkable performance in various computer vision tasks, however, limited labeled data can lead to overfitting problems, hindering the network's performance on unseen data. To address this issue, various generalization techniques have been proposed, including dropout, normalization, and advanced data augmentation. Among these techniques, image data augmentationwhich increases the dataset size by incorporating sample diversity-has received significant attention in recent times. In this survey, we focus on advanced image data augmentation techniques. We provide an overview of data augmentation, present a novel and comprehensive taxonomy of the reviewed data augmentation techniques, and discuss their strengths and limitations. Furthermore, we provide comprehensive results of the impact of data augmentation on three popular computer vision tasks: image classification, object detection, and semantic segmentation. For results reproducibility, the available codes of all data augmentation techniques have been compiled. Finally, we discuss the challenges and difficulties, as well as possible future directions for the research community. This survey provides several benefits: i) readers will gain a deeper understanding of how data augmentation can help address overfitting problems, ii) researchers will save time searching for comparison results, iii) the codes for the data augmentation techniques are available for result reproducibility, and iv) the discussion of future work will spark interest in the research community.

Research paper thumbnail of Understanding EEG signals for subject-wise Definition of Armoni Activities

arXiv (Cornell University), Jan 3, 2023

In a growing world of technology, psychological disorders became a challenge to be solved. The me... more In a growing world of technology, psychological disorders became a challenge to be solved. The methods used for cognitive stimulation are very conventional and based on one-way communication, which only rely on the material or method used for training of an individual. It doesn't use any kind of feedback from the individual to analyze the progress of the training process. We have proposed a closed-loop methodology to improve the cognitive state of a person with ID (Intellectual disability). We have used a platform named 'Armoni', for providing training to the intellectually disabled individuals. The learning is performed in a closed-loop by using feedback in the form of change in affective state. For feedback to the Armoni, an EEG (Electroencephalograph) headband is used. All the changes in EEG are observed and classified against the change in the mean and standard deviation value of all frequency bands of signal. This comparison is being helpful in defining every activity with respect to change in brain signals. In this paper, we have discussed the process of treatment of EEG signal and its definition against the different activities of Armoni. We have tested it on 6 different systems with different age groups and cognitive levels.

Research paper thumbnail of Deep Learning-Based Cost-Effective and Responsive Robot for Autism Treatment

Drones

Recent studies state that, for a person with autism spectrum disorder, learning and improvement i... more Recent studies state that, for a person with autism spectrum disorder, learning and improvement is often seen in environments where technological tools are involved. A robot is an excellent tool to be used in therapy and teaching. It can transform teaching methods, not just in the classrooms but also in the in-house clinical practices. With the rapid advancement in deep learning techniques, robots became more capable of handling human behaviour. In this paper, we present a cost-efficient, socially designed robot called ‘Tinku’, developed to assist in teaching special needs children. ‘Tinku’ is low cost but is full of features and has the ability to produce human-like expressions. Its design is inspired by the widely accepted animated character ‘WALL-E’. Its capabilities include offline speech processing and computer vision—we used light object detection models, such as Yolo v3-tiny and single shot detector (SSD)—for obstacle avoidance, non-verbal communication, expressing emotions i...

Research paper thumbnail of A Computer Vision-Based Object Localization Model for Endangered Wildlife Detection

Research paper thumbnail of WilDect-YOLO: An efficient and robust computer vision-based accurate object localization model for automated endangered wildlife detection

Research paper thumbnail of SQL and NoSQL Databases Software architectures performance analysis and assessments -- A Systematic Literature review

arXiv (Cornell University), Sep 14, 2022

Context: The efficient processing of Big Data is a challenging task for SQL and NoSQL Databases, ... more Context: The efficient processing of Big Data is a challenging task for SQL and NoSQL Databases, where competent software architecture plays a vital role. The SQL Databases are designed for structuring data and supporting vertical scalability. In contrast, horizontal scalability is backed by NoSQL Databases and can process sizeable unstructured Data efficiently. One can choose the right paradigm according to the organisation's needs; however, making the correct choice can often be challenging. The SQL and NoSQL Databases follow different architectures. Also, the mixed model is followed by each category of NoSQL Databases. Hence, data movement becomes difficult for cloud consumers across multiple cloud service providers (CSPs). In addition, each cloud platform IaaS, PaaS, SaaS, and DBaaS also monitors various paradigms. Objective: This systematic literature review (SLR) aims to study the related articles associated with SQL and NoSQL Database software architectures and tackle data portability and Interoperability among various cloud platforms. State of the art presented many performance comparison studies of SQL and NoSQL Databases by observing scaling, performance, availability, consistency and sharding characteristics. According to the research studies, NoSQL Databasedesigned structures can be the right choice for big data analytics, while SQL Databases are suitable for OLTP Databases. The researcher proposes numerous approaches associated with data movement in the cloud. Platform-based APIs are developed, which makes users' data movement difficult. Therefore, data portability and Interoperability issues are noticed during data movement across multiple CSPs. To minimize developer efforts and Interoperability, Unified APIs are demanded to make data movement relatively more accessible among various cloud platforms. Method: The systematic literature review technique and approach are used in this paper to select the appropriate and related documents. Most of the articles investigated the technical reasons for both Databases and identified the scenarios when to use which Database. Data analysis, data collection process, and the required results are detailed in this paper. Results: A total of 142 papers have been selected associated with the topic in this systematic literature review. 35% are journal documents, 52% are conferences, and 11% are technical reports and thesis. We also performed a performance analysis between the SQL and NoSQL document Databases. Besides, DBaaS and unified APIs approaches are investigated in terms of data portability and Interoperability to extract the desired results. We evaluated and analyzed the research papers accordingly and identified the state-of-the-art gaps. Conclusion: According to our findings and analysis in this SLR, NoSQL Databases are not the alternative to SQL Databases. Each Database has its advantages in a particular scenario. The SQL and NoSQL Databases follow various data models and software architectures. In contrast, data movement is strenuous across multiple cloud platforms. DBaaS cloud architecture is used to transfer traditional Database architecture into cloud architecture. Different, unified APIs frameworks have been investigated to minimize data portability and Interoperability issues across various cloud platforms during data movement.

Research paper thumbnail of Introducing Urdu Digits Dataset with Demonstration of an Efficient and Robust Noisy Decoder-Based Pseudo Example Generator

Symmetry

In the present work, we propose a novel method utilizing only a decoder for generation of pseudo-... more In the present work, we propose a novel method utilizing only a decoder for generation of pseudo-examples, which has shown great success in image classification tasks. The proposed method is particularly constructive when the data are in a limited quantity used for semi-supervised learning (SSL) or few-shot learning (FSL). While most of the previous works have used an autoencoder to improve the classification performance for SSL, using a single autoencoder may generate confusing pseudo-examples that could degrade the classifier’s performance. On the other hand, various models that utilize encoder–decoder architecture for sample generation can significantly increase computational overhead. To address the issues mentioned above, we propose an efficient means of generating pseudo-examples by using only the generator (decoder) network separately for each class that has shown to be effective for both SSL and FSL. In our approach, the decoder is trained for each class sample using random ...