Advances On Machine And Deep Learning Techniques In Modern Applications

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Advances on Machine and Deep Learning Techniques in Modern Applications

Dr. T. Arumuga Maria Devi ,Mrs.Ajitha S Raj,Mr.A.Chockalingam,MrsSunitha S,S.Gnana Sophia
Advances on Machine and Deep Learning Techniques in Modern Applications Book PDF

Author : Dr. T. Arumuga Maria Devi ,Mrs.Ajitha S Raj,Mr.A.Chockalingam,MrsSunitha S,S.Gnana Sophia
Publisher : SK Research Group of Companies
Page : 304 pages
File Size : 50,6 Mb
Release : 2022-11-08
Category : Computers
ISBN : 9789391077921

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Advances on Machine and Deep Learning Techniques in Modern Applications by Dr. T. Arumuga Maria Devi ,Mrs.Ajitha S Raj,Mr.A.Chockalingam,MrsSunitha S,S.Gnana Sophia Pdf

Dr.T.Arumuga Maria Devi, Assistant Professor, Centre for Information Technology and Engineering, Manonmaniam Sundaranar University, Tirunelveli, Tamil Nadu, India. Mrs.Ajitha S Raj, Assistant Professor, Department of Computer Science, Womens Christian College, Nagercoil, Tamil Nadu, India and Researcher, Centre for Information Technology and Engineering, Manonmaniam Sundaranar University, Tirunelveli, Tamil Nadu, India. Mr.A.Chockalingam, Assistant Professor Temp and Researcher, Centre for Information Technology and Engineering, Manonmaniam Sundaranar University, Tirunelveli, Tamilnadu, India. Mrs.S.SUNITHA, Assistant Professor, Department of Computer Science, Womens Christian College, Nagercoil, Tamil Nadu, India. Mrs.S.GNANA SOPHIA, Assistant Professor, Department of Computer Applications, Scott Christian College Autonomous , Nagercoil, Tamil Nadu, India.

Advances on Machine and Deep Learning Techniques in Modern Strategies

Mr.Chitra Sabapathy Ranganathan
Advances on Machine and Deep Learning Techniques in Modern Strategies Book PDF

Author : Mr.Chitra Sabapathy Ranganathan
Publisher : Leilani Katie Publication
Page : 155 pages
File Size : 45,8 Mb
Release : 2024-04-02
Category : Computers
ISBN : 9788197213885

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Advances on Machine and Deep Learning Techniques in Modern Strategies by Mr.Chitra Sabapathy Ranganathan Pdf

Mr.Chitra Sabapathy Ranganathan, Associate Vice President, Mphasis Corporation, Arizona, USA

Advances and Applications in Deep Learning

Anonim
Advances and Applications in Deep Learning Book PDF

Author : Anonim
Publisher : BoD – Books on Demand
Page : 124 pages
File Size : 48,6 Mb
Release : 2020-12-09
Category : Computers
ISBN : 9781839628788

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Advances and Applications in Deep Learning by Anonim Pdf

Artificial Intelligence (AI) has attracted the attention of researchers and users alike and is taking an increasingly crucial role in our modern society. From cars, smartphones, and airplanes to medical equipment, consumer applications, and industrial machines, the impact of AI is notoriously changing the world we live in. In this context, Deep Learning (DL) is one of the techniques that has taken the lead for cognitive processes, pattern recognition, object detection, and machine learning, all of which have played a crucial role in the growth of AI. As such, this book examines DL applications and future trends in the field. It is a useful resource for researchers and students alike.

Fundamentals and Methods of Machine and Deep Learning

Pradeep Singh
Fundamentals and Methods of Machine and Deep Learning Book PDF

Author : Pradeep Singh
Publisher : John Wiley & Sons
Page : 484 pages
File Size : 52,8 Mb
Release : 2022-03-02
Category : Computers
ISBN : 9781119821250

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Fundamentals and Methods of Machine and Deep Learning by Pradeep Singh Pdf

FUNDAMENTALS AND METHODS OF MACHINE AND DEEP LEARNING The book provides a practical approach by explaining the concepts of machine learning and deep learning algorithms, evaluation of methodology advances, and algorithm demonstrations with applications. Over the past two decades, the field of machine learning and its subfield deep learning have played a main role in software applications development. Also, in recent research studies, they are regarded as one of the disruptive technologies that will transform our future life, business, and the global economy. The recent explosion of digital data in a wide variety of domains, including science, engineering, Internet of Things, biomedical, healthcare, and many business sectors, has declared the era of big data, which cannot be analysed by classical statistics but by the more modern, robust machine learning and deep learning techniques. Since machine learning learns from data rather than by programming hard-coded decision rules, an attempt is being made to use machine learning to make computers that are able to solve problems like human experts in the field. The goal of this book is to present a??practical approach by explaining the concepts of machine learning and deep learning algorithms with applications. Supervised machine learning algorithms, ensemble machine learning algorithms, feature selection, deep learning techniques, and their applications are discussed. Also included in the eighteen chapters is unique information which provides a clear understanding of concepts by using algorithms and case studies illustrated with applications of machine learning and deep learning in different domains, including disease prediction, software defect prediction, online television analysis, medical image processing, etc. Each of the chapters briefly described below provides both a chosen approach and its implementation. Audience Researchers and engineers in artificial intelligence, computer scientists as well as software developers.

Advances on Machine and Deep Learning Techniques in Modern Era

Dr.T.Arumuga Maria Devi,Mrs.V.S.Jeyalakshmi,Mrs.S.Kowsalya,Mrs.V.Bhavani
Advances on Machine and Deep Learning Techniques in Modern Era Book PDF

Author : Dr.T.Arumuga Maria Devi,Mrs.V.S.Jeyalakshmi,Mrs.S.Kowsalya,Mrs.V.Bhavani
Publisher : SK Research Group of Companies
Page : 239 pages
File Size : 41,7 Mb
Release : 2023-05-17
Category : Computers
ISBN : 9789395341714

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Advances on Machine and Deep Learning Techniques in Modern Era by Dr.T.Arumuga Maria Devi,Mrs.V.S.Jeyalakshmi,Mrs.S.Kowsalya,Mrs.V.Bhavani Pdf

Dr.T.Arumuga Maria Devi, Assistant Professor, Centre for Information Technology and Engineering, Manonmaniam Sundaranar University, Tirunelveli, Tamil Nadu, India. Mrs.V.S.Jeyalakshmi, Researcher, Centre for Information Technology and Engineering, Manonmaniam Sundaranar University, Tirunelveli, Tamil Nadu, India. Mrs.S.Kowsalya, Researcher, Centre for Information Technology and Engineering, Manonmaniam Sundaranar University, Tirunelveli, Tamil Nadu, India. Mrs.V.Bhavani, Assistant Professor, Department of Computer Applications, Mannar Thirumalai Naicker College (Autonomous), Madurai, Tamil Nadu, India.

Concepts and Real-Time Applications of Deep Learning

Smriti Srivastava,Manju Khari,Ruben Gonzalez Crespo,Gopal Chaudhary,Parul Arora
Concepts and Real Time Applications of Deep Learning Book PDF

Author : Smriti Srivastava,Manju Khari,Ruben Gonzalez Crespo,Gopal Chaudhary,Parul Arora
Publisher : Springer Nature
Page : 212 pages
File Size : 54,5 Mb
Release : 2021-09-23
Category : Technology & Engineering
ISBN : 9783030761677

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Concepts and Real-Time Applications of Deep Learning by Smriti Srivastava,Manju Khari,Ruben Gonzalez Crespo,Gopal Chaudhary,Parul Arora Pdf

This book provides readers with a comprehensive and recent exposition in deep learning and its multidisciplinary applications, with a concentration on advances of deep learning architectures. The book discusses various artificial intelligence (AI) techniques based on deep learning architecture with applications in natural language processing, semantic knowledge, forecasting and many more. The authors shed light on various applications that can benefit from the use of deep learning in pattern recognition, person re-identification in surveillance videos, action recognition in videos, image and video captioning. The book also highlights how deep learning concepts can be interwoven with more modern concepts to yield applications in multidisciplinary fields. Presents a comprehensive look at deep learning and its multidisciplinary applications, concentrating on advances of deep learning architectures; Includes a survey of deep learning problems and solutions, identifying the main open issues, innovations and latest technologies; Shows industrial deep learning in practice with examples/cases, efforts, challenges, and strategic approaches.

Advances in Deep Learning Applications for Smart Cities

Kumar, Rajeev,Dwivedi, Rakesh Kumar
Advances in Deep Learning Applications for Smart Cities Book PDF

Author : Kumar, Rajeev,Dwivedi, Rakesh Kumar
Publisher : IGI Global
Page : 335 pages
File Size : 55,6 Mb
Release : 2022-05-13
Category : Political Science
ISBN : 9781799897125

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Advances in Deep Learning Applications for Smart Cities by Kumar, Rajeev,Dwivedi, Rakesh Kumar Pdf

Within the past decade, technology has grown exponentially, and governments have promoted smart cities. Emerging smart cities have become both crucibles and showrooms for the practical application of the internet of things (IoT), cloud computing, and the integration of big data into everyday life. This complex concoction requires new thinking of the synergistic utilization of deep learning and blockchain methods and data-driven decision making with automation infrastructure, autonomous transportation, and more. Advances in Deep Learning Applications for Smart Cities provides a global perspective on current and future trends concerning the integration of deep learning and blockchain for smart cities. It provides valuable insights on the best practices and success factors for smart cities. Covering topics such as digital healthcare, object detection methods, and power consumption, this book is an excellent reference for researchers, scientists, libraries, industry experts, government organizations, students and educators of higher education, business professionals, communication and marketing agencies, entrepreneurs, and academicians.

Advances in Machine Learning/Deep Learning-based Technologies

George A. Tsihrintzis,Maria Virvou,Lakhmi C. Jain
Advances in Machine Learning Deep Learning based Technologies Book PDF

Author : George A. Tsihrintzis,Maria Virvou,Lakhmi C. Jain
Publisher : Springer Nature
Page : 237 pages
File Size : 49,9 Mb
Release : 2021-08-05
Category : Technology & Engineering
ISBN : 9783030767945

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Advances in Machine Learning/Deep Learning-based Technologies by George A. Tsihrintzis,Maria Virvou,Lakhmi C. Jain Pdf

As the 4th Industrial Revolution is restructuring human societal organization into, so-called, “Society 5.0”, the field of Machine Learning (and its sub-field of Deep Learning) and related technologies is growing continuously and rapidly, developing in both itself and towards applications in many other disciplines. Researchers worldwide aim at incorporating cognitive abilities into machines, such as learning and problem solving. When machines and software systems have been enhanced with Machine Learning/Deep Learning components, they become better and more efficient at performing specific tasks. Consequently, Machine Learning/Deep Learning stands out as a research discipline due to its worldwide pace of growth in both theoretical advances and areas of application, while achieving very high rates of success and promising major impact in science, technology and society. The book at hand aims at exposing its readers to some of the most significant Advances in Machine Learning/Deep Learning-based Technologies. The book consists of an editorial note and an additional ten (10) chapters, all invited from authors who work on the corresponding chapter theme and are recognized for their significant research contributions. In more detail, the chapters in the book are organized into five parts, namely (i) Machine Learning/Deep Learning in Socializing and Entertainment, (ii) Machine Learning/Deep Learning in Education, (iii) Machine Learning/Deep Learning in Security, (iv) Machine Learning/Deep Learning in Time Series Forecasting, and (v) Machine Learning in Video Coding and Information Extraction. This research book is directed towards professors, researchers, scientists, engineers and students in Machine Learning/Deep Learning-related disciplines. It is also directed towards readers who come from other disciplines and are interested in becoming versed in some of the most recent Machine Learning/Deep Learning-based technologies. An extensive list of bibliographic references at the end of each chapter guides the readers to probe further into the application areas of interest to them.

Machine Learning Paradigms

George A. Tsihrintzis,Lakhmi C. Jain
Machine Learning Paradigms Book PDF

Author : George A. Tsihrintzis,Lakhmi C. Jain
Publisher : Springer Nature
Page : 429 pages
File Size : 54,5 Mb
Release : 2020-07-23
Category : Computers
ISBN : 9783030497248

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Machine Learning Paradigms by George A. Tsihrintzis,Lakhmi C. Jain Pdf

At the dawn of the 4th Industrial Revolution, the field of Deep Learning (a sub-field of Artificial Intelligence and Machine Learning) is growing continuously and rapidly, developing both theoretically and towards applications in increasingly many and diverse other disciplines. The book at hand aims at exposing its reader to some of the most significant recent advances in deep learning-based technological applications and consists of an editorial note and an additional fifteen (15) chapters. All chapters in the book were invited from authors who work in the corresponding chapter theme and are recognized for their significant research contributions. In more detail, the chapters in the book are organized into six parts, namely (1) Deep Learning in Sensing, (2) Deep Learning in Social Media and IOT, (3) Deep Learning in the Medical Field, (4) Deep Learning in Systems Control, (5) Deep Learning in Feature Vector Processing, and (6) Evaluation of Algorithm Performance. This research book is directed towards professors, researchers, scientists, engineers and students in computer science-related disciplines. It is also directed towards readers who come from other disciplines and are interested in becoming versed in some of the most recent deep learning-based technological applications. An extensive list of bibliographic references at the end of each chapter guides the readers to probe deeper into their application areas of interest.

Deep Learning for Computer Vision

Rajalingappaa Shanmugamani
Deep Learning for Computer Vision Book PDF

Author : Rajalingappaa Shanmugamani
Publisher : Packt Publishing Ltd
Page : 304 pages
File Size : 45,8 Mb
Release : 2018-01-23
Category : Computers
ISBN : 9781788293358

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Deep Learning for Computer Vision by Rajalingappaa Shanmugamani Pdf

Learn how to model and train advanced neural networks to implement a variety of Computer Vision tasks Key Features Train different kinds of deep learning model from scratch to solve specific problems in Computer Vision Combine the power of Python, Keras, and TensorFlow to build deep learning models for object detection, image classification, similarity learning, image captioning, and more Includes tips on optimizing and improving the performance of your models under various constraints Book Description Deep learning has shown its power in several application areas of Artificial Intelligence, especially in Computer Vision. Computer Vision is the science of understanding and manipulating images, and finds enormous applications in the areas of robotics, automation, and so on. This book will also show you, with practical examples, how to develop Computer Vision applications by leveraging the power of deep learning. In this book, you will learn different techniques related to object classification, object detection, image segmentation, captioning, image generation, face analysis, and more. You will also explore their applications using popular Python libraries such as TensorFlow and Keras. This book will help you master state-of-the-art, deep learning algorithms and their implementation. What you will learn Set up an environment for deep learning with Python, TensorFlow, and Keras Define and train a model for image and video classification Use features from a pre-trained Convolutional Neural Network model for image retrieval Understand and implement object detection using the real-world Pedestrian Detection scenario Learn about various problems in image captioning and how to overcome them by training images and text together Implement similarity matching and train a model for face recognition Understand the concept of generative models and use them for image generation Deploy your deep learning models and optimize them for high performance Who this book is for This book is targeted at data scientists and Computer Vision practitioners who wish to apply the concepts of Deep Learning to overcome any problem related to Computer Vision. A basic knowledge of programming in Python—and some understanding of machine learning concepts—is required to get the best out of this book.

Advanced Deep Learning with R

Bharatendra Rai
Advanced Deep Learning with R Book PDF

Author : Bharatendra Rai
Publisher : Packt Publishing Ltd
Page : 339 pages
File Size : 49,9 Mb
Release : 2019-12-17
Category : Computers
ISBN : 9781789534986

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Advanced Deep Learning with R by Bharatendra Rai Pdf

Discover best practices for choosing, building, training, and improving deep learning models using Keras-R, and TensorFlow-R libraries Key FeaturesImplement deep learning algorithms to build AI models with the help of tips and tricksUnderstand how deep learning models operate using expert techniquesApply reinforcement learning, computer vision, GANs, and NLP using a range of datasetsBook Description Deep learning is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data. Advanced Deep Learning with R will help you understand popular deep learning architectures and their variants in R, along with providing real-life examples for them. This deep learning book starts by covering the essential deep learning techniques and concepts for prediction and classification. You will learn about neural networks, deep learning architectures, and the fundamentals for implementing deep learning with R. The book will also take you through using important deep learning libraries such as Keras-R and TensorFlow-R to implement deep learning algorithms within applications. You will get up to speed with artificial neural networks, recurrent neural networks, convolutional neural networks, long short-term memory networks, and more using advanced examples. Later, you'll discover how to apply generative adversarial networks (GANs) to generate new images; autoencoder neural networks for image dimension reduction, image de-noising and image correction and transfer learning to prepare, define, train, and model a deep neural network. By the end of this book, you will be ready to implement your knowledge and newly acquired skills for applying deep learning algorithms in R through real-world examples. What you will learnLearn how to create binary and multi-class deep neural network modelsImplement GANs for generating new imagesCreate autoencoder neural networks for image dimension reduction, image de-noising and image correctionImplement deep neural networks for performing efficient text classificationLearn to define a recurrent convolutional network model for classification in KerasExplore best practices and tips for performance optimization of various deep learning modelsWho this book is for This book is for data scientists, machine learning practitioners, deep learning researchers and AI enthusiasts who want to develop their skills and knowledge to implement deep learning techniques and algorithms using the power of R. A solid understanding of machine learning and working knowledge of the R programming language are required.

Advanced Deep Learning with Python

Ivan Vasilev
Advanced Deep Learning with Python Book PDF

Author : Ivan Vasilev
Publisher : Packt Publishing Ltd
Page : 456 pages
File Size : 51,6 Mb
Release : 2019-12-12
Category : Computers
ISBN : 9781789952711

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Advanced Deep Learning with Python by Ivan Vasilev Pdf

Gain expertise in advanced deep learning domains such as neural networks, meta-learning, graph neural networks, and memory augmented neural networks using the Python ecosystem Key FeaturesGet to grips with building faster and more robust deep learning architecturesInvestigate and train convolutional neural network (CNN) models with GPU-accelerated libraries such as TensorFlow and PyTorchApply deep neural networks (DNNs) to computer vision problems, NLP, and GANsBook Description In order to build robust deep learning systems, you’ll need to understand everything from how neural networks work to training CNN models. In this book, you’ll discover newly developed deep learning models, methodologies used in the domain, and their implementation based on areas of application. You’ll start by understanding the building blocks and the math behind neural networks, and then move on to CNNs and their advanced applications in computer vision. You'll also learn to apply the most popular CNN architectures in object detection and image segmentation. Further on, you’ll focus on variational autoencoders and GANs. You’ll then use neural networks to extract sophisticated vector representations of words, before going on to cover various types of recurrent networks, such as LSTM and GRU. You’ll even explore the attention mechanism to process sequential data without the help of recurrent neural networks (RNNs). Later, you’ll use graph neural networks for processing structured data, along with covering meta-learning, which allows you to train neural networks with fewer training samples. Finally, you’ll understand how to apply deep learning to autonomous vehicles. By the end of this book, you’ll have mastered key deep learning concepts and the different applications of deep learning models in the real world. What you will learnCover advanced and state-of-the-art neural network architecturesUnderstand the theory and math behind neural networksTrain DNNs and apply them to modern deep learning problemsUse CNNs for object detection and image segmentationImplement generative adversarial networks (GANs) and variational autoencoders to generate new imagesSolve natural language processing (NLP) tasks, such as machine translation, using sequence-to-sequence modelsUnderstand DL techniques, such as meta-learning and graph neural networksWho this book is for This book is for data scientists, deep learning engineers and researchers, and AI developers who want to further their knowledge of deep learning and build innovative and unique deep learning projects. Anyone looking to get to grips with advanced use cases and methodologies adopted in the deep learning domain using real-world examples will also find this book useful. Basic understanding of deep learning concepts and working knowledge of the Python programming language is assumed.

Advanced Methods and Deep Learning in Computer Vision

E. R. Davies,Matthew Turk
Advanced Methods and Deep Learning in Computer Vision Book PDF

Author : E. R. Davies,Matthew Turk
Publisher : Academic Press
Page : 584 pages
File Size : 50,8 Mb
Release : 2021-11-09
Category : Computers
ISBN : 9780128221495

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Advanced Methods and Deep Learning in Computer Vision by E. R. Davies,Matthew Turk Pdf

Advanced Methods and Deep Learning in Computer Vision presents advanced computer vision methods, emphasizing machine and deep learning techniques that have emerged during the past 5–10 years. The book provides clear explanations of principles and algorithms supported with applications. Topics covered include machine learning, deep learning networks, generative adversarial networks, deep reinforcement learning, self-supervised learning, extraction of robust features, object detection, semantic segmentation, linguistic descriptions of images, visual search, visual tracking, 3D shape retrieval, image inpainting, novelty and anomaly detection. This book provides easy learning for researchers and practitioners of advanced computer vision methods, but it is also suitable as a textbook for a second course on computer vision and deep learning for advanced undergraduates and graduate students. Provides an important reference on deep learning and advanced computer methods that was created by leaders in the field Illustrates principles with modern, real-world applications Suitable for self-learning or as a text for graduate courses

Methodologies, Frameworks, and Applications of Machine Learning

Srivastava, Pramod Kumar,Yadav, Ashok Kumar
Methodologies Frameworks and Applications of Machine Learning Book PDF

Author : Srivastava, Pramod Kumar,Yadav, Ashok Kumar
Publisher : IGI Global
Page : 315 pages
File Size : 54,9 Mb
Release : 2024-03-22
Category : Computers
ISBN : 9798369310632

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Methodologies, Frameworks, and Applications of Machine Learning by Srivastava, Pramod Kumar,Yadav, Ashok Kumar Pdf

Technology is constantly evolving, and machine learning is positioned to become a pivotal tool with the power to transform industries and revolutionize everyday life. This book underscores the urgency of leveraging the latest machine learning methodologies and theoretical advancements, all while harnessing a wealth of realistic data and affordable computational resources. Machine learning is no longer confined to theoretical domains; it is now a vital component in healthcare, manufacturing, education, finance, law enforcement, and marketing, ushering in an era of data-driven decision-making. Academic scholars seeking to unlock the potential of machine learning in the context of Industry 5.0 and advanced IoT applications will find that the groundbreaking book, Methodologies, Frameworks, and Applications of Machine Learning, introduces an unmissable opportunity to delve into the forefront of modern research and application. This book offers a wealth of knowledge and practical insights across a wide array of topics, ranging from conceptual frameworks and methodological approaches to the application of probability theory, statistical techniques, and machine learning in domains as diverse as e-government, healthcare, cyber-physical systems, and sustainable development, this comprehensive guide equips you with the tools to navigate the complexities of Industry 5.0 and the Internet of Things (IoT).

Deep Learning in Biomedical and Health Informatics

M. A. Jabbar,Ajith Abraham,Onur Dogan,Ana Maria Madureira,Sanju Tiwari
Deep Learning in Biomedical and Health Informatics Book PDF

Author : M. A. Jabbar,Ajith Abraham,Onur Dogan,Ana Maria Madureira,Sanju Tiwari
Publisher : CRC Press
Page : 224 pages
File Size : 53,6 Mb
Release : 2021-09-26
Category : Computers
ISBN : 9781000429084

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Deep Learning in Biomedical and Health Informatics by M. A. Jabbar,Ajith Abraham,Onur Dogan,Ana Maria Madureira,Sanju Tiwari Pdf

This book provides a proficient guide on the relationship between Artificial Intelligence (AI) and healthcare and how AI is changing all aspects of the healthcare industry. It also covers how deep learning will help in diagnosis and the prediction of disease spread. The editors present a comprehensive review of research applying deep learning in health informatics in the fields of medical imaging, electronic health records, genomics, and sensing, and highlights various challenges in applying deep learning in health care. This book also includes applications and case studies across all areas of AI in healthcare data. The editors also aim to provide new theories, techniques, developments, and applications of deep learning, and to solve emerging problems in healthcare and other domains. This book is intended for computer scientists, biomedical engineers, and healthcare professionals researching and developing deep learning techniques. In short, the volume : Discusses the relationship between AI and healthcare, and how AI is changing the health care industry. Considers uses of deep learning in diagnosis and prediction of disease spread. Presents a comprehensive review of research applying deep learning in health informatics across multiple fields. Highlights challenges in applying deep learning in the field. Promotes research in ddeep llearning application in understanding the biomedical process. Dr.. M.A. Jabbar is a professor and Head of the Department AI&ML, Vardhaman College of Engineering, Hyderabad, Telangana, India. Prof. (Dr.) Ajith Abraham is the Director of Machine Intelligence Research Labs (MIR Labs), Auburn, Washington, USA. Dr.. Onur Dogan is an assistant professor at İzmir Bakırçay University, Turkey. Prof. Dr. Ana Madureira is the Director of The Interdisciplinary Studies Research Center at Instituto Superior de Engenharia do Porto (ISEP), Portugal. Dr.. Sanju Tiwari is a senior researcher at Universidad Autonoma de Tamaulipas, Mexico.