Using Long Short-Term Memory Networks to Make and Train Neural Network Based Pseudo Random Number Generator

Neural Networks have been used in many decision-making models and been employed in computer vision, and natural language processing. Several works have also used Neural Networks for developing Pseudo-Random Number Generators [2, 4, 5, 7, 8]. However, despite great performance in the National Institute of Standards and Technology (NIST) statistical test suite for randomness, they fail to discuss how the complexity of a neural network affects such statistical results. This work introduces: 1) a series of new Long Short- Term Memory Network (LSTM) based and Fully Connected Neural Network (FCNN – baseline [2] + variations) Pseudo Random Number Generators (PRNG) and 2) an LSTMbased predictor. The thesis also performs adversarial training to determine two things: 1) How the use of sequence models such as LSTMs after adversarial training affects the performance on NIST tests. 2) To study how the complexity of the fully connected network-based generator in [2] and the LSTM-based generator affects NIST results. Experiments were done on four different sets of generators and predictors, i) Fully Connected Neural Network Generator (FC NN Gen) – Convolutional Neural Network Predictor (CNN Pred), ii) FC NN Gen - LSTM Pred, iii) LSTM-based Gen – CNN. Pred, iv) LSTM-based Gen – LSTM Pred, where FC NN Gen and CNN Pred were taken as the baseline from [2] while LSTM-based Gen and LSTM Pred were proposed. Based on the experiments, LSTM Predictor overall gave much consistent and even better results on the NIST test suite than the CNN Predictor from [2]. It was observed that using LSTM generator showed a higher pass rate for NIST test on average when paired with LSTM Predictor but a very low fluctuating trend. On the other hand, an increasing trend was observed for the average NIST test passing rate when the same generator was trained with CNN Predictor in an adversarial environment. The baseline [2] and its variations however only displayed a fluctuating trend, but with better results with the adversarial training with the LSTM-based Predictor than the CNN Predictor

Thermophysical, excess and transport properties of organic solvents with imidazolium based ionic liquids

Ultrasonic velocity and refractive index have been evaluated for eight binary mixtures comprising imidazolium based ([BMIM][PF6], [HMIM][PF6], [OMIM][PF6] and [MMIM][CH3SO4]) ionic liquids with three organic solvents of varying nature, viz., 2-propanol, 2-butanone and ethylacetate, at three different temperatures (293.15, 298.15 and 303.15 K). Evaluation of refractive index has been carried out by eight approaches, whereas five methods have been employed for computation of ultrasonic velocity. Molecular interaction studies have been carried out with the help of intermolecular free length, and interaction parameter. Furthermore, the excess counterpart of the coefficient of thermal expansion has been determined to get a deeper understanding on the behavior in terms of nature and extent of interactions present in these systems

Thermophysical, excess and transport properties of organic solvents with imidazolium based ionic liquids

21-35Ultrasonic velocity and refractive index have been evaluated for eight binary mixtures comprising imidazolium based ([BMIM][PF6], [HMIM][PF6], [OMIM][PF6] and [MMIM] CH3SO4]) ionic liquids with three organic solvents of varying nature, viz., 2-propanol, 2-butanone and ethylacetate, at three different temperatures (293.15, 298.15 and 303.15 K). Evaluation of refractive index has been carried out by eight approaches, whereas five methods have been employed for computation of ultrasonic velocity. Molecular interaction studies have been carried out with the help of intermolecular free length, and interaction parameter. Furthermore, the excess counterpart of the coefficient of thermal expansion has been determined to get a deeper understanding on the behavior in terms of nature and extent of interactions present in these systems

TEQIP - III Sponsored First International Conference on Innovations and Challenges in Computing, Analytics and Security

This book contains abstracts of the various research papers of the academic & research community presented at the International Conference on Innovations and Challenges in Computing, Analytics and Security (ICICCAS-2020). ICICCAS-2020 has served as a platform for researchers, professionals to meet and exchange ideas on computing, data analytics, and security. The conference has invited papers in seven main tracks of Data Science, Networking Technologies, Sequential, Parallel, Distributed and Cloud Computing, Advances in Software Engineering, Multimedia, Image Processing, and Embedded Systems, Security and Privacy, Special Track (IoT, Smart Technologies and Green Engineering). The Technical and Advisory Committee Members were from various countries that have rich Research and Academic experience. Conference Title: TEQIP - III Sponsored First International Conference on Innovations and Challenges in Computing, Analytics and SecurityConference Acronym: ICICCAS-2020Conference Date: 29-30 July 2020Conference Location: Pondicherry Engineering College, Puducherry – 605014, India (Virtual Mode)Conference Organizer: Department of Computer Science and Engineering, Pondicherry Engineering College, Puducherry, India.Conference Sponsor: TEQIP-III NPIU (A Unit of the Ministry of Human Resource Development, India)