Narula Institute of Technology

About: Narula Institute of Technology is a based out in . It is known for research contribution in the topics: Quantum dot cellular automaton & Cognitive radio. The organization has 288 authors who have published 490 publications receiving 2258 citations. The organization is also known as: NiT.

The effect of country of origin on brand equity: an empirical study on generic drugs

Abstract: Purpose – The purpose of this paper is to explore the impact of country of origin image on brand equity of branded generic drugs.Design/methodology/approach – Brand equity of branded generics is examined through an analytical review. Country of origin image is hypothesised to influence components of brand equity, i.e. brand strength and brand awareness, which in turn influence brand equity. An empirical investigation was carried out among professionally similar respondents, i.e. doctors of different categories in Kolkata megapolis, India.Findings – Results showed that country of origin image had a positive and significant effect on components of brand equity, i.e. brand strength and brand awareness, derived from factor analysis conducted on brand equity components. The result also showed that country of origin image of branded generics significantly, but indirectly, affected brand equity through the mediating variables, brand strength and brand awareness.Research limitations/implications – Different varia...

IoT Based Intelligent Agriculture Field Monitoring System

Abstract: Agriculture is becoming an important growing sector throughout the world due to increasing population. Major challenge in agriculture sector is to improve farm productivity and quality of farming without continuous manual monitoring to meet the rapidly growing demand for food. Apart from increasing population, the climate change is also a big concern in agricultural sector. The purpose of this research work is to propose a smart farming method based on Internet of Things (IoT) to deal with the adverse situations. The smart farming can be adopted which offer high precision crop control, collection of useful data and automated farming technique. This work presents an intelligent agriculture field monitoring system which monitors soil humidity and temperature. After processing the sensed data it takes necessary action based on these values without human intervention. Here temperature and moisture of the soil are measured and these sensed values are stored in ThingSpeak [11] cloud for future data analysis.

A review on fabrication, characterization and implementation of wearable strain sensors

Abstract: The paper presents a substantial review on the different kinds of strain sensors that have been employed as wearable sensing prototypes. The importance of strain sensors lies in their low cost, high sensitivity and multifunctional applications. The flexible strain sensors have been developed with printing techniques that have generated prototypes with varied electrical, mechanical and thermal characteristics. These types of devices have been primarily used for biomedical applications, where a small amount of deflection holds a crucial worth to monitor acute and chronic anomalies in human beings. Among the major areas in healthcare applications where strain sensors have been utilized, wearable sensing holds a pivotal role due to their capability of ubiquitous monitoring. The wearable sensors have been designed and fabricated with a range of processing materials, based on their respective applications. Along with the significant research related to the fabrication and implementation of wearable strain sensors, explanation related to the challenges of the current sensors and their futuristic possibilities have also been showcased in the paper.

Melanoma Diagnosis Using Deep Learning and Fuzzy Logic.

Abstract: Melanoma or malignant melanoma is a type of skin cancer that develops when melanocyte cells, damaged by excessive exposure to harmful UV radiations, start to grow out of control. Though less common than some other kinds of skin cancers, it is more dangerous because it rapidly metastasizes if not diagnosed and treated at an early stage. The distinction between benign and melanocytic lesions could at times be perplexing, but the manifestations of the disease could fairly be distinguished by a skilled study of its histopathological and clinical features. In recent years, deep convolutional neural networks (DCNNs) have succeeded in achieving more encouraging results yet faster and computationally effective systems for detection of the fatal disease are the need of the hour. This paper presents a deep learning-based ‘You Only Look Once (YOLO)’ algorithm, which is based on the application of DCNNs to detect melanoma from dermoscopic and digital images and offer faster and more precise output as compared to conventional CNNs. In terms with the location of the identified object in the cell, this network predicts the bounding box of the detected object and the class confidence score. The highlight of the paper, however, lies in its infusion of certain resourceful concepts like two phase segmentation done by a combination of the graph theory using minimal spanning tree concept and L-type fuzzy number based approximations and mathematical extraction of the actual affected area of the lesion region during feature extraction process. Experimented on a total of 20250 images from three publicly accessible datasets—PH2, International Symposium on Biomedical Imaging (ISBI) 2017 and The International Skin Imaging Collaboration (ISIC) 2019, encouraging results have been obtained. It achieved a Jac score of 79.84% on ISIC 2019 dataset and 86.99% and 88.64% on ISBI 2017 and PH2 datasets, respectively. Upon comparison of the pre-defined parameters with recent works in this area yielded comparatively superior output in most cases.