Stevens Institute of Technology

About: Stevens Institute of Technology is a education organization based out in Hoboken, New Jersey, United States. It is known for research contribution in the topics: Computer science & Cognitive radio. The organization has 5440 authors who have published 12684 publications receiving 296875 citations. The organization is also known as: Stevens & Stevens Tech.

Multilayer pulsed-current electrodeposition process

Abstract: A process for electrodepositing a multilayer deposit on an electrically-conductive substrate from a single electrodeposition bath yields a deposit which includes a sequence of essentially repeating groups of layers. Each group of layers comprises a layer of a first electrodeposited material and a layer of a second electrodeposited layer. The process includes the steps of immersing the substrate in an electrodeposition bath and repeatedly passing a charge burst of a first electric current and a second electric current through the electrodeposition bath to the substrate. The first electric current is a pulsed current with a first pulsed-on/off waveform and a first peak current density which is effective to electrodeposit the first electrodeposited material. The second electric current has a second waveform and a second current density which is effective to electrodeposit the second electrodeposited material. The duration of the charge bursts of the first and second electric currents is effective to cause layers of the first and second electrodeposited material of desired thicknesses to be deposited. Electrodeposits produced by preferred process of the invention can have outstanding mechanical and other properties.

Iterative extensions of the sturm/triggs algorithm: convergence and nonconvergence

Abstract: We show that SIESTA, the simplest iterative extension of the Sturm/Triggs algorithm, descends an error function. However, we prove that SIESTA does not converge to usable results. The iterative extension of Mahamud et al. has similar problems, and experiments with “balanced” iterations show that they can fail to converge. We present CIESTA, an algorithm which avoids these problems. It is identical to SIESTA except for one extra, simple stage of computation. We prove that CIESTA descends an error and approaches fixed points. Under weak assumptions, it converges. The CIESTA error can be minimized using a standard descent method such as Gauss–Newton, combining quadratic convergence with the advantage of minimizing in the projective depths.

Risk Tuning with Generalized Linear Regression

Abstract: A framework is set up in which linear regression, as a way of approximating a random variable by other random variables, can be carried out in a variety of ways, which, moreover, can be tuned to the needs of a particular model in finance, or operations research more broadly. Although the idea of adapting the form of regression to the circumstances at hand has already found advocates in promoting quantile regression as an alternative to classical least-squares approaches, it is carried here much farther than that. Axiomatic concepts of error measure, deviation measure, and risk measure are coordinated with certain “statistics” that likewise say something about a random variable. Problems of regression utilizing these concepts are analyzed and the character of their solutions is explored in a range of examples. Special attention is paid to parametric forms of regression which arise in connection with factor models. It is argued that when different aspects of risk enter an optimization problem, different forms of regression ought to be invoked for each of those aspects.

Classification of stroke disease using machine learning algorithms

Abstract: This paper presents a prototype to classify stroke that combines text mining tools and machine learning algorithms. Machine learning can be portrayed as a significant tracker in areas like surveillance, medicine, data management with the aid of suitably trained machine learning algorithms. Data mining techniques applied in this work give an overall review about the tracking of information with respect to semantic as well as syntactic perspectives. The proposed idea is to mine patients’ symptoms from the case sheets and train the system with the acquired data. In the data collection phase, the case sheets of 507 patients were collected from Sugam Multispecialty Hospital, Kumbakonam, Tamil Nadu, India. Next, the case sheets were mined using tagging and maximum entropy methodologies, and the proposed stemmer extracts the common and unique set of attributes to classify the strokes. Then, the processed data were fed into various machine learning algorithms such as artificial neural networks, support vector machine, boosting and bagging and random forests. Among these algorithms, artificial neural networks trained with a stochastic gradient descent algorithm outperformed the other algorithms with a higher classification accuracy of 95% and a smaller standard deviation of 14.69.