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"Gupta, Deepak"
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Salts of Therapeutic Agents: Chemical, Physicochemical, and Biological Considerations
The physicochemical and biological properties of active pharmaceutical ingredients (APIs) are greatly affected by their salt forms. The choice of a particular salt formulation is based on numerous factors such as API chemistry, intended dosage form, pharmacokinetics, and pharmacodynamics. The appropriate salt can improve the overall therapeutic and pharmaceutical effects of an API. However, the incorrect salt form can have the opposite effect, and can be quite detrimental for overall drug development. This review summarizes several criteria for choosing the appropriate salt forms, along with the effects of salt forms on the pharmaceutical properties of APIs. In addition to a comprehensive review of the selection criteria, this review also gives a brief historic perspective of the salt selection processes.
Journal Article
Green computing in network security : energy efficient solutions for business and home
\"This book focuses on green computing-based network security techniques and addresses the challenges involved in practical implementation. It also explores the idea of energy-efficient computing for network and data security and covers the security threats involved in social networks, data centers, IoT, and biomedical applications. Green Computing in Network Security: Energy Efficient Solutions for Business and Home includes analysis of green-security mechanisms and explores the role of green computing for secured modern internet applications. It discusses green computing-based distributed learning approaches for security and emphasizes the development of green computing-based security systems for IoT devices. Written with researchers, academic libraries, and professionals in mind so they can get up to speed on network security, the challenges, and implementation processes\"-- Provided by publisher.
Book
Robotic technologies in biomedical and healthcare engineering
\"This book aims at exhibiting the latest research achievements, findings, and ideas in the field of robotics in biomedical and healthcare engineering, primarily focusing on the walking assistive robot, telerobotic surgery, upper/lower limb rehabilitation, and radiosurgery, etc\"-- Provided by publisher.
Book
Inducing and optimizing Markovian Mpemba effect with stochastic reset
A hot Markovian system can cool down faster than a colder one: this is known as the Mpemba effect. Here, we show that a non-equilibrium driving via stochastic reset can induce this phenomenon, when absent. Moreover, we derive an optimal driving protocol simultaneously optimizing the appearance time of the Mpemba effect, and the total energy dissipation into the environment, revealing the existence of a Pareto front. Building upon previous experimental results, our findings open up the avenue of possible experimental realizations of optimal cooling protocols in Markovian systems.
Journal Article
Work fluctuations for diffusion dynamics submitted to stochastic return
Returning a system to a desired state under a force field involves a thermodynamic cost, i.e.
work
. This cost fluctuates for a small-scale system from one experimental realization to another. We introduce a general framework to determine the work distribution for returning a system facilitated by a confining potential with its minimum at the restart location. The general strategy, based on average over
resetting pathways
, constitutes a robust method to gain access to the statistical information of observables from resetting systems. We exploit paradigmatic setups, where explicit computations are attainable, to illustrate the theory. Numerical simulations validate our theoretical predictions. For some of these examples, a non-trivial behavior of the work fluctuations opens a door to optimization problems. Specifically, work fluctuations can be minimized by an appropriate tuning of the return rate.
Journal Article
A Novel Spider Monkey Optimization for Reliable Data Dissemination in VANETs Based on Machine Learning
The growth in linked and autonomous vehicles has led to the emergence of vehicular ad hoc networks (VANETs) as a means to enhance road safety, traffic efficiency, and passenger comfort. However, VANETs face challenges in facilitating trustworthiness and high-quality services due to communication delays caused by traffic, dynamic topology changes, variable speeds, and other influencing factors. Hence, there is a need for a reliable data dissemination scheme capable of reducing communication delays among hops by identifying effective forwarder nodes. In this paper, we propose a novel, weighted, estimated, spider monkey-based, nature-inspired optimization (w-SMNO) method to generate a set of efficient relays. Additionally, we introduce a dynamic weight assignment and configuration model to enhance system accuracy using a neural network based on backpropagation with gradient descent optimization techniques to minimize errors in the machine learning model. The w-SMNO also incorporates a distinct algorithm for effective relay selection among multiple monkey spider groups. The simulation results demonstrate substantial improvements in w-SMNO, with a 35.7% increase in coverage, a 41.2% reduction in the end-to-end delay, a 36.4% improvement in the message delivery rate, and a 38.4% decrease in the collision rate compared to the state-of-the-art approaches.
Journal Article
Intelligent data analysis : from data gathering to data comprehension
This book focuses on methods and tools for intelligent data analysis, aimed at narrowing the increasing gap between data gathering and data comprehension, and emphasis will also be given to solving of problems which result from automated data collection, such as analysis of computer-based patient records, data warehousing tools, intelligent alarming, effective and efficient monitoring, and so on. This book aims to describe the different approaches of Intelligent Data Analysis from a practical point of view: solving common life problems with data analysis tools.
eBook
Density-weighted support vector machines for binary class imbalance learning
In real-world binary classification problems, the entirety of samples belonging to each class varies. These types of problems where the majority class is notably bigger than the minority class can be called as class imbalance learning (CIL) problem. Due to the CIL problem, model performance may degrade. This paper presents a new support vector machine (SVM) model based on density weight for binary CIL (DSVM-CIL) problem. Additionally, an improved 2-norm-based density-weighted least squares SVM for binary CIL (IDLSSVM-CIL) is also proposed to increase the training speed of DSVM-CIL. In IDLSSVM-CIL, the least squares solution is obtained by considering 2-norm of slack variables and solving the primal problem of DSVM-CIL with equality constraints instead of inequality constraints. The basic ideas behind the algorithms are that the training datapoints are given weights during the training phase based on their class distributions. The weights are generated by using a density-weighted technique (Cha et al. in Expert Syst Appl 41(7):3343–3350, 2014) to reduce the effects of CIL. Experimental analyses are performed on some interesting imbalanced artificial and real-world datasets, and their performances are measured using the area under the curve and geometric mean (G-mean). The results are compared with SVM, least squares SVM, fuzzy SVM, improved fuzzy least squares SVM, affinity and class probability-based fuzzy SVM and entropy-based fuzzy least squares SVM. Similar or better generalization results indicate the efficacy and applicability of the proposed algorithms.
Journal Article