Bio: R. Anand is an academic researcher from Sona College of Technology. The author has contributed to research in topics: Convolutional neural network & Deep learning. The author has an hindex of 7, co-authored 39 publications receiving 179 citations. Previous affiliations of R. Anand include Indian Institute of Technology Madras & Amrita Vishwa Vidyapeetham.
08 Apr 2016
TL;DR: The goal of proposed work is to diagnose the disease of brinjal leaf using image processing and artificial neural techniques and the proposed detection model based artiifical neural networks are very effective in recognizing leaf diseases.
Abstract: This work presents a method for identifying plant leaf disease and an approach for careful detection of diseases. The goal of proposed work is to diagnose the disease of brinjal leaf using image processing and artificial neural techniques. The diseases on the brinjal are critical issue which makes the sharp decrease in the production of brinjal. The study of interest is the leaf rather than whole brinjal plant because about 85–95 % of diseases occurred on the brinjal leaf like, Bacterial Wilt, Cercospora Leaf Spot, Tobacco mosaic virus (TMV). The methodology to detect brinjal leaf disease in this work includes K-means clustering algorithm for segmentation and Neural-network for classification. The proposed detection model based artiifical neural networks are very effective in recognizing leaf diseases.
TL;DR: The method proposed in this work detects four types of skin diseases using computer vision using Convolutional Neural Networks with specific focus on skin disease.
Abstract: Skin diseases are becoming a most common health issues among all the countries worldwide. The method proposed in this work detects four types of skin diseases using computer vision. The proposed approach involves Convolutional Neural Networks with specific focus on skin disease. The Convolutional Neural Network (CNN) used in this paper has utilized around 11 layers viz., Convolution Layer, Activation Layer, Pooling Layer, Fully Connected Layer and Soft-Max Classifier. Images from the DermNet database are used for validating the architecture. The database comprises all types of skin diseases out of which we have considered four different types of skin diseases like Acne, Keratosis, Eczema herpeticum, Urticaria with each class containing around 30 to 60 different samples. The challenges in automating the process includes the variation of skin tones, location of the disease, specifications of the image acquisition system etc., The proposed CNN Classifier results in an accuracy of 98.6% to 99.04%.
••01 Jan 2020
TL;DR: A glimpse of deep learning is given, from creation of dataset to training and deploying the models, and the method can be applied for dataset corresponding to any field, be it medicine, agriculture or manufacturing, reducing the human effort and thus triggering the revolution of automation.
Abstract: Face recognition is the most important tool in computer vision and an inevitable technology finding applications in robotics, security, and mobile devices. Though it is a technology of the past, state-of-the-art machine learning (ML) techniques have made this technology game-changing and even surpass human counterparts in terms of accuracy. This paper focuses on applying one of the advanced machine learning tools in face recognition to achieve higher accuracy. We created our own dataset and trained it on the GoogleNet (inception) deep learning model using the Caffe and Nvidia DIGITS framework. We achieved an overall accuracy of 91.43% which was fairly high enough to recognize the faces better than the conventional ML techniques. The scope of the application of deep learning is enormous and by training a huge volume of data with massive computational power, accuracy greater than 99% can be achieved. This paper will give a glimpse of deep learning, from creation of dataset to training and deploying the models, and the method can be applied for dataset corresponding to any field, be it medicine, agriculture or manufacturing, reducing the human effort and thus triggering the revolution of automation.
TL;DR: In this article, the 3D-DWT features are extracted and fed to the following classifiers (i) random forest (ii) KNN and (iii) support vector machine (SVM) for hyperspectral image classification.
Abstract: Hyperspectral image classification is an emerging and interesting research area that has attracted several researchers to contribute to this field. Hyperspectral images have multiple narrow bands for a single image that enable the development of algorithms to extract diverse features. Three-dimensional discrete wavelet transform (3D-DWT) has the advantage of extracting the spatial and spectral information simultaneously. Decomposing an image into a set of spatial–spectral components is an important characteristic of 3D-DWT. It has motivated us to perform the proposed research work. The novelty of this work is to bring out the features of 3D-DWT applicable to hyperspectral images classification using Haar, Fejer-Korovkin and Coiflet filters. Three-dimensional-DWT is implemented with the help of three stages of 1D-DWT. The first two stages of 3D-DWT are extracting spatial resolution, and the third stage is extracting the spectral content. In this work, the 3D-DWT features are extracted and fed to the following classifiers (i) random forest (ii) K-nearest neighbor (KNN) and (iii) support vector machine (SVM). Exploiting both spectral and spatial features help the classifiers to provide a better classification accuracy. A comparison of results was performed with the same classifiers without DWT features. The experiments were performed using Salinas Scene and Indian Pines hyperspectral datasets. From the experiments, it has been observed that the SVM with 3D-DWT features performs better in terms of the performance metrics such as overall accuracy, average accuracy and kappa coefficient. It has shown significant improvement compared to the state of art techniques. The overall accuracy of 3D-DWT+SVM is 88.3%, which is 14.5% larger than that of traditional SVM (77.1%) for the Indian Pines dataset. The classification map of 3D-DWT + SVM is more closely related to the ground truth map.
01 Mar 2021
TL;DR: A standard deep learning architecture, VGGNet, is modified for classifying chest X-ray images under four categories, namely COVID, bacterial, normal, and viral images, and the performance matrices of the planned model are compared with five deep learning architectures.
Abstract: The world encountered a deadly disease by the beginning of 2020, known as the coronavirus disease (COVID-19). Among the different screening techniques available for COVID-19, chest radiography is an efficient method for disease detection. Whereas other disease detection techniques are time consuming, radiography requires less time to identify abnormalities caused by the disease in the lungs. In this study, one of the standard deep learning architectures, VGGNet, is modified for classifying chest X-ray images under four categories. The planned model uses images of four classes, namely COVID, bacterial, normal, and viral images. The performance matrices of the planned model are compared with five deep learning architectures, namely VGGNet, AlexNET, GoogLeNET, Inception-v4, and DenseNet-201.
TL;DR: A comprehensive review of the current-state-of-the-art in DL for HSI classification, analyzing the strengths and weaknesses of the most widely used classifiers in the literature is provided, providing an exhaustive comparison of the discussed techniques.
Abstract: Advances in computing technology have fostered the development of new and powerful deep learning (DL) techniques, which have demonstrated promising results in a wide range of applications. Particularly, DL methods have been successfully used to classify remotely sensed data collected by Earth Observation (EO) instruments. Hyperspectral imaging (HSI) is a hot topic in remote sensing data analysis due to the vast amount of information comprised by this kind of images, which allows for a better characterization and exploitation of the Earth surface by combining rich spectral and spatial information. However, HSI poses major challenges for supervised classification methods due to the high dimensionality of the data and the limited availability of training samples. These issues, together with the high intraclass variability (and interclass similarity) –often present in HSI data– may hamper the effectiveness of classifiers. In order to solve these limitations, several DL-based architectures have been recently developed, exhibiting great potential in HSI data interpretation. This paper provides a comprehensive review of the current-state-of-the-art in DL for HSI classification, analyzing the strengths and weaknesses of the most widely used classifiers in the literature. For each discussed method, we provide quantitative results using several well-known and widely used HSI scenes, thus providing an exhaustive comparison of the discussed techniques. The paper concludes with some remarks and hints about future challenges in the application of DL techniques to HSI classification. The source codes of the methods discussed in this paper are available from: https://github.com/mhaut/hyperspectral_deeplearning_review .
TL;DR: A comprehensive review of recent studies carried out in the area of crop pest and disease recognition using image processing and machine learning techniques and reports that recent efforts have focused on the use of deep learning instead of training shallow classifiers using hand-crafted features.
Abstract: Fast and accurate plant disease detection is critical to increasing agricultural productivity in a sustainable way. Traditionally, human experts have been relied upon to diagnose anomalies in plants caused by diseases, pests, nutritional deficiencies or extreme weather. However, this is expensive, time consuming and in some cases impractical. To counter these challenges, research into the use of image processing techniques for plant disease recognition has become a hot research topic. In this paper, we provide a comprehensive review of recent studies carried out in the area of crop pest and disease recognition using image processing and machine learning techniques. We hope that this work will be a valuable resource for researchers in this area of crop pest and disease recognition using image processing techniques. In particular, we concentrate on the use of RGB images owing to the low cost and high availability of digital RGB cameras. We report that recent efforts have focused on the use of deep learning instead of training shallow classifiers using hand-crafted features. Researchers have reported high recognition accuracies on particular datasets but in many cases, the performance of those systems deteriorated significantly when tested on different datasets or in field conditions. Nevertheless, progress made so far has been encouraging. Experimental results showing the leaf disease recognition performance of ten CNN architectures in terms of recognition accuracy, recall, precision, specificity, F1-score, training duration and storage requirements are also presented. Subsequently, recommendations are made on the most suitable architectures to deploy in conventional as well as mobile/embedded computing environments. We also discuss some of the unresolved challenges that need to be addressed in order to develop practical automatic plant disease recognition systems for use in field conditions.
01 Jan 2014
TL;DR: A taxonomy of mathematics education can be found in this paper, where Bloom's Taxonomy of Mathematics Education is used to describe the main issues in mathematics education, including the following: "Actions, Processes, Projects, Schema (APOS) in Mathematics Education."
Abstract: Ability Grouping in Mathematics Classrooms.- Abstraction in Mathematics Education.- Actions, Processes, Projects, Schema (APOS) in Mathematics Education.- Activity Theory in Mathematics Education.- Adults Learning Mathematics.- Affect in Mathematics Education.- Algebra Teaching and Learning.- Algorithmics.- Algorithms.- Anthropological Approaches in Mathematics Education, French Perspective.- Argumentation in Mathematics.- Argumentation in Mathematics Education.- Assessment Frameworks in Mathematics Education.- Assessment of Mathematics Teacher Knowledge.- Authority in Mathematics Education.- Autism, Special Needs and Mathematics Learning.- Behaviorism in Mathematics Education.- Bilingual and Multilingual Issues in Mathematics Education.- Blind Children, Special Needs and Mathematics Learning.- Bloom's Taxonomy in Mathematics Education.- Calculus Teaching and Learning.- Cognitive Acceleration in Mathematics Education.- Collaborative Learning in Mathematics Education.- Communities of Inquiry in Mathematics Teacher Education.- Communities of Practice in Mathematics Education.- Communities of Practice in Mathematics Teacher Education.- Competency Frameworks in Mathematics Education.- Complexity in Mathematics Education.- Concept Development in Mathematics Education.- Concept Maps in Mathematics Education.- Constructivism and Radical Constructivism in Mathematics Education.- Constructivist Teaching Experiment Creativity in Mathematics Education.- Critical Mathematics.- Critical Thinking in Mathematics Education.- Cultural Anthropological Approaches in Mathematics Education.- Cultural Diversity in Mathematics Education.- Cultural influences in Mathematics Education.- Cultural Traditions of Mathematics Teaching.- Curricular Resources and Textbooks.- Data Handling and Statistics Teaching and Learning.- Deaf Children, Special Needs and Mathematics Learning.- Deductive Reasoning in Mathematics Education.- Design research in mathematics education.- Dialogic teaching and learning in mathematics education.- Didactic Contract in Mathematics Education.- Didactic engineering in mathematics education.- Didactic Situations in Mathematics Education.- Didactic Transposition in Mathematics Education.- Didactical Phenomenology (Freudenthal).- Discourse Analytic Approaches in Mathematics Education.- Discrete Mathematics Teaching and Learning.- Discursive Approaches to Learning Mathematics.- Down Syndrome, Special Needs and Mathematics Learning.- Dyscalculia.- Early Algebra Teaching and Learning.- Early Childhood Mathematics Education.- Education of Mathematics Teacher Educators.- Elkonin and Davydov Curriculum in Mathematics Education.- Embodied Cognition.- Enactivist Theories.- Epistemological Obstacles in Mathematics Education.- Equity and Access in Mathematics Education.- Ethnicity and Race in Mathematics Education.- Ethno-mathematics.- External Assessment in Mathematics Education.- Fieldwork/ practicum in mathematics education.- Frameworks for Conceptualizing Mathematics Teacher Knowledge.- Gender in Mathematics Education.- Giftedness and high ability in mathematics.- Goals of Mathematics Education.- Heuristics in Mathematics Education.- Historical Overview of Mathematics Teaching Materials.- History of Mathematics and Education.- History of Mathematics Teaching and Learning.- History of Research in Mathematics Education.- Hypothetical Learning Trajectories in Mathematics Education.- Immigrant Students in Mathematics Education.- Immigrant Teachers in Mathematics Education.- Inclusive Mathematics Classrooms.- Indigenous Students in Mathematics Education.- Informal Learning in Mathematics Education.- Information and Communication Technology (ICT) Affordances in Mathematics Education.- Inquiry Based Mathematics Education.- Instrumental and Relational Understanding in Mathematics Education.- Instrumentation in Mathematics Education.- Interactionist and Ethnomethodological Approaches in Mathematics Education.- Interdisciplinary approaches in mathematics education.- International Comparative Studies in Mathematics: An Overview.- Intuition in Mathematics Education.- Language Background in Mathematics Education.- Language Disorders, Special Needs and Mathematics Learning.- Learner Centred Teaching in Mathematics Education.- Learning Difficulties, Special Needs and Mathematics Learning.- Learning Environments in Mathematics Education.- Learning Practices in Digital Environments.- Learning Study in Mathematics Education.- Lesson Study in Mathematics Education.- Logic in Mathematics Education.- Manipulatives in Mathematics Education.- Mathematical Ability.- Mathematical Approaches.- Mathematical Functions Teaching and Learning.- Mathematical Games in Learning and Teaching.- Mathematical Knowledge for Teaching.- Mathematical Language.- Mathematical Literacy.- Mathematical Modeling and Applications in Education.- Mathematical Proof, Argumentation and Reasoning.- Mathematical Representations.- Mathematics Classroom Assessment.- Mathematics Curriculum Evaluation.- Mathematics Teacher as Learner.- Mathematics Teacher Education Organization, Curriculum and Outcomes.- Mathematics Teacher Educator as Learner.- Mathematics Teacher Identity.- Mathematics Teacher Roles.- Mathematics Teachers and Curricula.- Mathematisation as Social Process.- Metacognition in Mathematics Education.- Metaphors in Mathematics Education.- Misconceptions and Alternative Conceptions in Mathematics Education.- Models of In-service Mathematics Teacher Education Professional Development.- Models of Preservice Mathematics Teacher Education.- Motivation in Mathematics Learning.- Multiple Representations in Mathematics Education.- Neuroscience and Mathematics Education.- Noticing of Mathematics Teachers.- Number Lines in Mathematics Education.- Number Teaching and Learning.- Pedagogical Content Knowledge in Mathematics Education.- Philosophy, Mathematics, and Education.- Policy Debates in Mathematics Education.- Political Perspectives in Mathematics Education.- Poststructuralist and Psychoanalytic Approaches in Mathematics Education.- Probability Teaching and Learning.- Problem Solving in Mathematics Education.- Professional Learning Communities in Mathematics Education.- Psychological Approaches in Mathematics Education.- Quasi-empirical Reasoning (Lakatos).- Questioning in Mathematics Education.- Realistic Mathematics Education.- Recontextualisation in Mathematics Education.- Reflective Practitioner in Mathematics Education.- Rural and Remote Mathematics Education.- Scaffolding in Mathematics Education.- Semiotics in Mathematics Education.- Shape and Spaceometry Teaching and Learning.- Single Sex Mathematics Classrooms.- Situated Cognition in Mathematics Education.- Socio-economic Class in Mathematics Education.- Socio-mathematical Norms in Mathematics Education.- Sociological Approaches in Mathematics Education.- Stoffdidaktik in Mathematics Education.- Structure of the Observed Learning Outcome (SOLO) Model.- Student Attitudes in Mathematics Education.- Task-based Interviews in Mathematics Education.- Teacher as Researcher in Mathematics Education.- Teacher Beliefs, Attitudes and Self-Efficacy in Mathematics Education.- Teacher Centred Teaching in Mathematics Education.- Teacher Education Development Study - Mathematics (TEDS-M).- Teacher Supply and Retention in Mathematics Education.- Teaching Practices in Digital Environments.- Technology and Curricula in Mathematics Education.- Technology Design in Mathematics Education.- The van Hiele theory.- Theories of Learning Mathematics.- Types of Technology in Mathematics Education.- Urban Mathematics Education.- Values in Mathematics Education.- Visualisation and Learning in Mathematics Education.- Wait Time in Mathematics Teaching.- Word Problems in Mathematics Education.- Zone of Proximal Development in Mathematics Education.
01 Jan 2009
TL;DR: This paper gives a tutorial overview of OFDM highlighting the aspects that are likely to be important in optical applications and the constraints imposed by single mode optical fiber, multimode optical fiber and optical wireless.
Abstract: Orthogonal frequency division multiplexing (OFDM) is a modulation technique which is now used in most new and emerging broadband wired and wireless communication systems because it is an effective solution to intersymbol interference caused by a dispersive channel. Very recently a number of researchers have shown that OFDM is also a promising technology for optical communications. This paper gives a tutorial overview of OFDM highlighting the aspects that are likely to be important in optical applications. To achieve good performance in optical systems OFDM must be adapted in various ways. The constraints imposed by single mode optical fiber, multimode optical fiber and optical wireless are discussed and the new forms of optical OFDM which have been developed are outlined. The main drawbacks of OFDM are its high peak to average power ratio and its sensitivity to phase noise and frequency offset. The impairments that these cause are described and their implications for optical systems discussed.