Jawaharlal Nehru Technological University, Hyderabad

About: Jawaharlal Nehru Technological University, Hyderabad is a education organization based out in Hyderabad, India. It is known for research contribution in the topics: Cluster analysis & Control theory. The organization has 3956 authors who have published 5277 publications receiving 48435 citations. The organization is also known as: JNTU Hyderabad & Nagarjuna Sagar Engineering College.

Particle swarm optimized multiple regression linear model for data classification

Abstract: This paper presents a new data classification method based on particle swarm optimization (PSO) techniques. The paper discusses the building of a classifier model based on multiple regression linear approach. The coefficients of multiple regression linear models (MRLMs) are estimated using least square estimation technique and PSO techniques for percentage of correct classification performance comparisons. The mathematical models are developed for many real world datasets collected from UCI machine repository. The mathematical models give the user an insight into how the attributes are interrelated to predict the class membership. The proposed approach is illustrated on many real data sets for classification purposes. The comparison results on the illustrative examples show that the PSO based approach is superior to traditional least square approach in classifying multi-class data sets.

Nanostructure ZnFe2O4 with Bacillus subtilis for Detection of LPG at Low Temperature

Abstract: The present study deals with the development of a chemical sensor for the detection of liquefied petroleum gas (LPG) at a low operating temperature using Zinc ferrite (ZnFe2O4)/Bacillus subtilis (B. subtilis) hybrid nanostructures. The nanostructure ZnFe2O4 and B. subtilis powder, taken in equal proportion was made into films using the spin coating technique. X-ray diffraction, thermal analysis, scanning electron microscopy, and transmission electron microscopy were used to study morphology, structure and crystallite size. The sensing properties of the hybrid structure were studied and excellent response was observed in the temperature range of 50–55°C for 400 ppm LPG, when compared to the individual components of the hybrid. The signal output of the proposed sensor were extremely stable for more than 30 days. This method proposes the usage of the biomolecule/metal oxide composites in electronics and helps to reduce the metal oxide usage.

Emerging contaminant (triclosan) identification and its treatment: a review

Abstract: Pollutants from personal care products (PCPs), pharmaceuticals, industrial wastes, food additives, pesticides and fertilisers are classified as emerging contaminants (ECs). These ECs have been given much attention due to their deleterious effects on human life, plants and animals. Triclosan (TCS), a broad spectrum antibiotic, is under the category of emerging contaminants; it is shown to have an eco-toxicity. It is a ubiquitous contaminant due to its wide range of applications in PCPs as an antibacterial agent. Inefficacy in the conventional treatment of wastewater which is being discharged into natural streams has led to the bioaccumulation of TCS. Concentrations of TCS have been detected in several wastewater treatment plants and urinary samples of humans and streams. In surface waters, TCS was detected in Korea, USA, Europe, China, Japan and India. In order to overcome the astringent effects of TCS, there is a need for its treatment. This paper addresses studies conducted on methods of treating TCS, and among all the methods, membrane technology (MT) was found to be effective and this is mainly due to hydrophilic nature of TCS, high log Kow value (4.8), and a removal efficiency > 95% was observed when powdered activated carbon of 100 mg/l was combined with MT.

Study of fiber optic sugar sensor

Abstract: Over the last two decades, the fiber optic technology has passed through many analytical stages. Some commercially available fiber optic sensors, though in a small way, are being used for automation in mechanical and industrial environments. They are also used for instrumentation and controls.

Improved partial differential equation-based total variation approach to non-subsampled contourlet transform for medical image denoising

Abstract: This article proposes an improved partial differential equation (PDE)-based total variation (TV) model that enhances grey and coloured brain tumour images obtained by magnetic resonance imaging. A nonsubsampled contourlet transform was applied to images from standard databases that converted into lowpass and highpass (or bandpass) contourlet coefficients. An improved version of the power-law transform method was used on the lowpass contourlet coefficients, and an adaptive threshold method was applied to the highpass (or bandpass) contourlet coefficients. The inverse contourlet transform was performed on all the enhanced contourlet coefficients to generate a complete brain tumour image. Finally, the PDE-based TV model was applied to this image to produce the denoised image. The performance of the suggested method was calculated in terms of the peak signal-to-noise ratio, mean square error, and structural similarity index. This method achieved the best peak signal-to-noise ratio, mean square error, and structural similarity index of 77.9846 dB, 0.00012612, and 97.895%, respectively, compared to the conventional PDE+modified transform-based gamma correction, adaptive PDE+generalized cross-validation, parallel magnetic resonance imaging, and Berkeley wavelet transform+support vector machine methods.