College of Engineering, Pune

About: College of Engineering, Pune is a based out in . It is known for research contribution in the topics: Computer science & Sliding mode control. The organization has 4264 authors who have published 3492 publications receiving 19371 citations. The organization is also known as: COEP.

A fuzzy approach to trust based access control in internet of things

Abstract: In the Internet of thing (IoT), the activities of daily life are supported by a multitude of heterogeneous, loosely coupled ubiquitous devices. Traditional access control model are not suitable to the nomadic, decentralized and dynamic scenarios in the IoT where identities are not known in advance. This makes the trust management in IoT more promising to address the access control issues .This paper present a Fuzzy approach to the Trust Based Access Control (FBAC) with the notion of trust levels for identity management. The presented fuzzy approach for trust calculations deals with the linguistic information of devices to address access control in the IoT. The simulation result shows that the fuzzy approach for trust based access control guarantees scalability ad it is energy efficient. This paper also proposes FTBAC framework or trust based dynamic access control in distributed IoT. FTBAC framework i a flexible and scalable a increasing number o devices do not affect the functioning and performance.

Artificial Intelligence and Machine Learning in Computational Nanotoxicology: Unlocking and Empowering Nanomedicine.

Abstract: Advances in nanomedicine, coupled with novel methods of creating advanced materials at the nanoscale, have opened new perspectives for the development of healthcare and medical products Special attention must be paid toward safe design approaches for nanomaterial-based products Recently, artificial intelligence (AI) and machine learning (ML) gifted the computational tool for enhancing and improving the simulation and modeling process for nanotoxicology and nanotherapeutics In particular, the correlation of in vitro generated pharmacokinetics and pharmacodynamics to in vivo application scenarios is an important step toward the development of safe nanomedicinal products This review portrays how in vitro and in vivo datasets are used in in silico models to unlock and empower nanomedicine Physiologically based pharmacokinetic (PBPK) modeling and absorption, distribution, metabolism, and excretion (ADME)-based in silico methods along with dosimetry models as a focus area for nanomedicine are mainly described The computational OMICS, colloidal particle determination, and algorithms to establish dosimetry for inhalation toxicology, and quantitative structure-activity relationships at nanoscale (nano-QSAR) are revisited The challenges and opportunities facing the blind spots in nanotoxicology in this computationally dominated era are highlighted as the future to accelerate nanomedicine clinical translation

A review of machine learning techniques using decision tree and support vector machine

Abstract: In this paper, the brief survey of data mining classification by using the machine learning techniques is presented. The machine learning techniques like decision tree and support vector machine play the important role in all the applications of artificial intelligence. Decision tree works efficiently with discrete data and SVM is capable of building the nonlinear boundaries among the classes. Both of these techniques have their own set of strengths which makes them suitable in almost all classification tasks.