Delhi Technological University

About: Delhi Technological University is a education organization based out in New Delhi, India. It is known for research contribution in the topics: Computer science & Control theory. The organization has 4427 authors who have published 6761 publications receiving 71035 citations. The organization is also known as: Delhi College of Engineering & DTU.

Organic–Inorganic Hybrid Nanocomposite-Based Gas Sensors for Environmental Monitoring

Abstract: Environmental Monitoring Ajeet Kaushik,*,†,‡ Rajesh Kumar,*,‡,§ Sunil K. Arya, Madhavan Nair,† B. D. Malhotra, and Shekhar Bhansali‡ †Center for Personalized Nanomedicine, Institute of Neuroimmune Pharmacology, Department of Immunology, Herbert Wertheim College of Medicine, Florida International University, Miami, Florida 33199, United States ‡Bio-MEMS Microsystems Laboratory, Department of Electrical and Computer Engineering, College of Engineering, Florida International University, Miami, Florida 33174, United States Department of Physics, Panjab University, Chandigarh 160014, India Bioelectronics Program, Institute of Microelectronics, A*Star, 11 Science Park Road, Singapore Science Park II, Singapore Department of Biotechnology, Delhi Technological University, Shahbad Daulatpur, Delhi 110042, India

A literature review and perspectives in reverse logistics

Abstract: In recent years, reverse logistics (RL) has become a field of importance for all organizations due to growing environmental concerns, legislation, corporate social responsibility and sustainable competitiveness. RL refers to the sequence of activities required to collect the used product from the customers for the purpose of either reuse or repair or re-manufacture or recycle or dispose of it. Perusal of the literature shows that research in the field of RL is in evolving phase and issues pertaining to adoption and implementation, forecasting product returns, outsourcing, RL networks from secondary market perspective, and disposition decisions have not been reviewed extensively. This study attempts to fill the existing gap through literature review on these issues, and outline the future directions for research based on research gap analysis. Total 242 published articles were selected, categorized, analyzed, and gaps in literature were identified to suggest for future research opportunities. The review may be useful for academicians, researchers and practitioners for better understanding of RL and guidance for future research.

Elliptic curve cryptography

Abstract: This paper describes the Elliptic Curve Cryptography algorithm and its suitability for smart cards.

Sentiment analysis using deep learning architectures: a review

Abstract: Social media is a powerful source of communication among people to share their sentiments in the form of opinions and views about any topic or article, which results in an enormous amount of unstructured information. Business organizations need to process and study these sentiments to investigate data and to gain business insights. Hence, to analyze these sentiments, various machine learning, and natural language processing-based approaches have been used in the past. However, deep learning-based methods are becoming very popular due to their high performance in recent times. This paper provides a detailed survey of popular deep learning models that are increasingly applied in sentiment analysis. We present a taxonomy of sentiment analysis and discuss the implications of popular deep learning architectures. The key contributions of various researchers are highlighted with the prime focus on deep learning approaches. The crucial sentiment analysis tasks are presented, and multiple languages are identified on which sentiment analysis is done. The survey also summarizes the popular datasets, key features of the datasets, deep learning model applied on them, accuracy obtained from them, and the comparison of various deep learning models. The primary purpose of this survey is to highlight the power of deep learning architectures for solving sentiment analysis problems.