21 research outputs found

    A Brief Bibliometric Survey of Leukemia Detection by Machine Learning and Deep Learning Approaches

    Get PDF
    Background: This study aims to analyze the work done on leukemia detection and diagnosis using machine learning, deep learning and different image processing techniques from 2011 to 2020, using the bibliometric methods. Methods: different articles on leukemia detection were retrieved using one of the most popular database- Scopus. The research articles are considered between 2011 to 2020. Scopus analyzer is used for getting some analysis results such as documents by year, source, county and so on. VOSviewer Version 1.6.15 is used for the analysis of different units such as co-authorship, co-occurrences, citation analysis etc. Results: In our study, a database search outputs a total of 617 articles on leukemia detection from 2011 to 2020. Statistical analysis and network analysis shows the maximum articles are published in the years 2019 and 2020 with India contributed the largest number of documents. Network analysis of different parameters shows a good potential of the topic in terms of research. Conclusions: Scopus keyword search outcome has 617 articles with English language having the largest number. Authors, documents, country, affiliation etc are statically analyzed and indicates the potential of the topic. Network analysis of different parameters indicates that, there is a lot of scope to contribute in the further research in terms of advanced algorithms of computer vision, deep learning and machine learning

    A Brief Bibliometric Survey of Explainable AI in Medical Field

    Get PDF
    Background: This study aims to analyze the work done in the field of explainability related to artificial intelligence, especially in the medical field from 2004 onwards using the bibliometric methods. Methods: different articles based on the topic leukemia detection were retrieved using one of the most popular database- Scopus. The articles are considered from 2004 onwards. Scopus analyzer is used for different types of analysis including documents by year, source, county and so on. There are other different analysis tools such as VOSviewer Version 1.6.15. This is used for the analysis of different units such as co-authorship, co-occurrences, citation analysis etc. Results: In our study, the Scopus search has the outcome of a total of 91 articles on explainability of AI from 2004 onwards. The topic is so popular and is newly introduced. The maximum articles are published in the year 2020. Computer science area contributed the largest number of articles of 37% and United states contributed most of the articles in the field. Network analysis of different parameters shows a good potential of the topic in terms of research. Conclusions: Scopus keyword search outcome has 91 articles with English language having the largest number of 90 and one is contributed in German language. Authors, documents, country, affiliation etc are statically analyzed and indicates the potential of the topic. Network analysis of different parameters indicates that, there is a lot of scope to contribute in the further research in terms of explainability in medical fields including diagnosis in imaging. There are advanced algorithms of computer vision, deep learning and machine learning are utilized in medical diagnosis as far as imaging is concerned. Explainable AI frameworks will prove to increase the trustability in medical diagnosis

    A Brief Bibliometric Survey of Leukemia Detection by Machine Learning and Deep Learning Approaches

    Get PDF
    Background: This study aims to analyze the work done on leukemia detection and diagnosis using machine learning, deep learning and different image processing techniques from 2011 to 2020, using the bibliometric methods. Methods: different articles on leukemia detection were retrieved using one of the most popular database- Scopus. The research articles are considered between 2011 to 2020. Scopus analyzer is used for getting some analysis results such as documents by year, source, county and so on. VOSviewer Version 1.6.15 is used for the analysis of different units such as co-authorship, co-occurrences, citation analysis etc. Results: In our study, a database search outputs a total of 617 articles on leukemia detection from 2011 to 2020. Statistical analysis and network analysis shows the maximum articles are published in the years 2019 and 2020 with India contributed the largest number of documents. Network analysis of different parameters shows a good potential of the topic in terms of research. Conclusions: Scopus keyword search outcome has 617 articles with English language having the largest number. Authors, documents, country, affiliation etc are statically analyzed and indicates the potential of the topic. Network analysis of different parameters indicates that, there is a lot of scope to contribute in the further research in terms of advanced algorithms of computer vision, deep learning and machine learning

    Demystifying Artificial Intelligence based Behavior Prediction of Traffic Actors for Autonomous Vehicle- A Bibliometric Analysis of Trends and Techniques

    Get PDF
    Background: The purpose of this study is to examine, using bibliometric methods, the work done on behavior prediction of traffic actors for autonomous vehicles using various artificial intelligence algorithms from 2011 to 2020. Methods: Using one of the most common databases, Scopus, numerous papers on behavior prediction of traffic actors for autonomous vehicles were retrieved. The research papers are being considered for the period from 2011 to 2020. The Scopus analyzer is used to obtain some results of the study, such as documents by year, source, and country and so on. VOSviewer Version 1.6.16 is used for the analysis of different units such as co-authorship, co-occurrences, citation analysis etc. Results: In our study, a database search outputs a total of 275 articles on behavior prediction for autonomous vehicle from 2011 to 2020. Statistical analysis and network analysis shows the maximum articles are published in the years 2019 and 2020 with United State contributed the largest number of documents. Network analysis of different parameters shows a good potential of the topic in terms of research. Conclusions: Scopus keyword search outcome has 272 articles with English language having the largest number. Authors, documents, country, affiliation etc are statically analyzed and indicates the potential of the topic. Network analysis of different parameters indicates that, there is a lot of scope to contribute in the further research in terms of advanced algorithms of computer vision, deep learning, machine learning and explainable artificial intelligence

    Iris Liveness Detection Using Multiple Deep Convolution Networks

    No full text
    In the recent decade, comprehensive research has been carried out in terms of promising biometrics modalities regarding humans’ physical features for person recognition. This work focuses on iris characteristics and traits for person identification and iris liveness detection. This study used five pre-trained networks, including VGG-16, Inceptionv3, Resnet50, Densenet121, and EfficientNetB7, to recognize iris liveness using transfer learning techniques. These models are compared using three state-of-the-art biometric databases: the LivDet-Iris 2015 dataset, IIITD contact dataset, and ND Iris3D 2020 dataset. Validation accuracy, loss, precision, recall, and f1-score, APCER (attack presentation classification error rate), NPCER (normal presentation classification error rate), and ACER (average classification error rate) were used to evaluate the performance of all pre-trained models. According to the observational data, these models have a considerable ability to transfer their experience to the field of iris recognition and to recognize the nanostructures within the iris region. Using the ND Iris 3D 2020 dataset, the EfficeintNetB7 model has achieved 99.97% identification accuracy. Experiments show that pre-trained models outperform other current iris biometrics variants

    Iris Liveness Detection Using Multiple Deep Convolution Networks

    No full text
    In the recent decade, comprehensive research has been carried out in terms of promising biometrics modalities regarding humans’ physical features for person recognition. This work focuses on iris characteristics and traits for person identification and iris liveness detection. This study used five pre-trained networks, including VGG-16, Inceptionv3, Resnet50, Densenet121, and EfficientNetB7, to recognize iris liveness using transfer learning techniques. These models are compared using three state-of-the-art biometric databases: the LivDet-Iris 2015 dataset, IIITD contact dataset, and ND Iris3D 2020 dataset. Validation accuracy, loss, precision, recall, and f1-score, APCER (attack presentation classification error rate), NPCER (normal presentation classification error rate), and ACER (average classification error rate) were used to evaluate the performance of all pre-trained models. According to the observational data, these models have a considerable ability to transfer their experience to the field of iris recognition and to recognize the nanostructures within the iris region. Using the ND Iris 3D 2020 dataset, the EfficeintNetB7 model has achieved 99.97% identification accuracy. Experiments show that pre-trained models outperform other current iris biometrics variants

    A Bibliometric Analysis of Face Presentation Attacks based on Domain Adaptation

    Get PDF
    Background: This research aims to look into the work that has been done on Face Presentation attacks and detection with domain adaptation techniques from 2011 to 2021 utilizing bibliography methods. Approaches: Diverse research articles on Face Presentation Attacks were retrieved using the two most popular databases Web of Science & Scopus. Research articles consider between 2011-2021. Some research results, such as documents by year, documents by source, documents by funding agencies, countries, etc are obtained using Web of Science and Scopus Analyzers. The analysis is performed using Vos Viewer version 1.6.16 and various parameters such as keywords, co-authorship, co-occurrences, citation analysis, and so on. Results: We present findings for both the Web of Science and Scopus datasets in this paper. As a result of primary keywords as Face Presentation attacks and secondary keywords as domain adaptation-based Face anti-spoofing, the total no of documents is 117 and 151 respectively retrieved. The maximum number of documents are published in the year 2020, most of the research is carried out by China as it has maximum funding research agencies. Conclusions: The purpose of using two databases for analysis in this study is to reduce the efforts of research scholars in analyzing the two most popular databases separately. Research documents are analyzed based on various parameters indicates that the research topic has a very good potential. The network study of various parameters shows that there is a lot of space for contribution in terms of domain adaptation, generalization, adversarial attacks, GAN, machine learning, and deep learning in future research

    Machine Learning Driven Mental Stress Detection on Reddit Posts Using Natural Language Processing

    No full text
    Abstract People’s mental conditions are often reflected in their social media activity due to the internet's anonymity. Psychiatric issues are often detected through such activities and can be addressed in their early stages, potentially preventing the consequences of unattended mental disorders like depression and anxiety. In this paper, the authors have implemented machine learning models and used various embedding techniques to classify posts from the famous social media blog site Reddit as stressful and non-stressful. The dataset used contains user posts that can be analyzed to detect patterns in the social media activity of those diagnosed with mental disorders. This paper uses different NLP (Natural Language Processing) tools such as ELMo (Embeddings from Language Models) word embeddings, BERT (Bidirectional Encoder Representations from Transformers) tokenizers, and BoW (Bag of Words) approach to create word/sentence data that can be fed to machine learning models. The results of each method have been discussed. The results achieved a top F1 score of 0.76, a Precision score of 0.71, and a Recall of 0.74 using only the preprocessed texts and machine learning algorithms to classify the posts. The results achieved by this paper are significant and have the potential to be applied in real-world scenarios to analyze mental stress among social media users. Although this paper focuses on data from Reddit, the techniques used can be transferred to similar social media platforms and could help solve the growing mental health crisis

    Remote-Sensing Data and Deep-Learning Techniques in Crop Mapping and Yield Prediction: A Systematic Review

    No full text
    Reliable and timely crop-yield prediction and crop mapping are crucial for food security and decision making in the food industry and in agro-environmental management. The global coverage, rich spectral and spatial information and repetitive nature of remote sensing (RS) data have made them effective tools for mapping crop extent and predicting yield before harvesting. Advanced machine-learning methods, particularly deep learning (DL), can accurately represent the complex features essential for crop mapping and yield predictions by accounting for the nonlinear relationships between variables. The DL algorithm has attained remarkable success in different fields of RS and its use in crop monitoring is also increasing. Although a few reviews cover the use of DL techniques in broader RS and agricultural applications, only a small number of references are made to RS-based crop-mapping and yield-prediction studies. A few recently conducted reviews attempted to provide overviews of the applications of DL in crop-yield prediction. However, they did not cover crop mapping and did not consider some of the critical attributes that reveal the essential issues in the field. This study is one of the first in the literature to provide a thorough systematic review of the important scientific works related to state-of-the-art DL techniques and RS in crop mapping and yield estimation. This review systematically identified 90 papers from databases of peer-reviewed scientific publications and comprehensively reviewed the aspects related to the employed platforms, sensors, input features, architectures, frameworks, training data, spatial distributions of study sites, output scales, evaluation metrics and performances. The review suggests that multiple DL-based solutions using different RS data and DL architectures have been developed in recent years, thereby providing reliable solutions for crop mapping and yield prediction. However, challenges related to scarce training data, the development of effective, efficient and generalisable models and the transparency of predictions should be addressed to implement these solutions at scale for diverse locations and crops

    Explainable AI in Scene Understanding for Autonomous Vehicles in Unstructured Traffic Environments on Indian Roads Using the Inception U-Net Model with Grad-CAM Visualization

    No full text
    The intelligent transportation system, especially autonomous vehicles, has seen a lot of interest among researchers owing to the tremendous work in modern artificial intelligence (AI) techniques, especially deep neural learning. As a result of increased road accidents over the last few decades, significant industries are moving to design and develop autonomous vehicles. Understanding the surrounding environment is essential for understanding the behavior of nearby vehicles to enable the safe navigation of autonomous vehicles in crowded traffic environments. Several datasets are available for autonomous vehicles focusing only on structured driving environments. To develop an intelligent vehicle that drives in real-world traffic environments, which are unstructured by nature, there should be an availability of a dataset for an autonomous vehicle that focuses on unstructured traffic environments. Indian Driving Lite dataset (IDD-Lite), focused on an unstructured driving environment, was released as an online competition in NCPPRIPG 2019. This study proposed an explainable inception-based U-Net model with Grad-CAM visualization for semantic segmentation that combines an inception-based module as an encoder for automatic extraction of features and passes to a decoder for the reconstruction of the segmentation feature map. The black-box nature of deep neural networks failed to build trust within consumers. Grad-CAM is used to interpret the deep-learning-based inception U-Net model to increase consumer trust. The proposed inception U-net with Grad-CAM model achieves 0.622 intersection over union (IoU) on the Indian Driving Dataset (IDD-Lite), outperforming the state-of-the-art (SOTA) deep neural-network-based segmentation models
    corecore