Kiran Jyoti

Bio: Kiran Jyoti is an academic researcher from Guru Nanak Dev Engineering College, Ludhiana. The author has contributed to research in topics: Drug delivery & Wireless sensor network. The author has an hindex of 12, co-authored 47 publications receiving 603 citations. Previous affiliations of Kiran Jyoti include Punjab Technical University & Guru Nanak Dev Engineering College, Bidar.

An Analysis of Heart Disease Prediction using Different Data Mining Techniques

Abstract: Heart disease is a term that assigns to a large number of medical conditions related to heart. These medical conditions describe the abnormal health conditions that directly influence the heart and all its parts. Heart disease is a major health problem in today’s time. This paper aims at analyzing the various data mining techniques introduced in recent years for heart disease prediction. The observations reveal that Neural networks with 15 attributes has outperformed over all other data mining techniques. Another conclusion from the analysis is that decision tree has also shown good accuracy with the help of genetic algorithm and feature subset selection.

Research on Auto-Scaling of Web Applications in Cloud: Survey, Trends and Future Directions

Abstract: Cloud computing emerging environment attracts many applications providers to deploy web applications on cloud data centers. The primary area of attraction is elasticity, which allows to auto-scale the resources on-demand. However, web applications usually have dynamic workload and hard to predict. Cloud service providers and researchers are working to reduce the cost while maintaining the Quality of Service (QoS). One of the key challenges for web application in cloud computing is auto-scaling. The auto-scaling in cloud computing is still in infancy and required detail investigation of taxonomy, approach and types of resources mapped to the current research. In this article, we presented the literature survey for auto-scaling techniques of web applications in cloud computing. This survey supports the research community to find the requirements in auto-scaling techniques. We present a taxonomy of reviewed articles with parameters such as auto-scaling techniques, approach, resources, monitoring tool, experiment, workload, and metric, etc. Based on the analysis, we proposed the new areas of research in this direction.

A Novel Approach for Heart Disease Diagnosis using Data Mining and Fuzzy Logic

Abstract: disease is a term used to describe a variety of heart diseases, illnesses, and events that impact the heart and circulatory system. A clinician uses several sources of data and tests to make a diagnostic impression but it is not necessary that all the tests are useful for the diagnosis of a heart disease. The objective of our work is to reduce the number of attributes used in heart disease diagnosis that will automatically reduce the number of tests which are required to be taken by a patient. Our work also aims at increasing the efficiency of the proposed system. The observations illustrated that Decision Tree and Naive Bayes using fuzzy logic has outplayed over other data mining techniques.

Inhalable nanostructured lipid particles of 9-bromo-noscapine, a tubulin-binding cytotoxic agent: in vitro and in vivo studies.

Abstract: 9-Bromo-noscapine (9-Br-Nos) alters tubulin polymerization in non-small cell lung cancer cells differently from noscapine. However, clinical applications of 9-Br-Nos are limited owing to poor aqueous solubility and high lipophilicity that eventually lead to suboptimal therapeutic efficacy at the site of action. Hence, 9-Br-Nos loaded nanostructured lipid particles (9-Br-Nos-NLPs) were prepared by nanoemulsion method to reduce the particle size below 100 nm. To impart the inhalable and rapid release (RR) attributes, 9-Br-Nos-NLPs were treated with spray dried lactose and effervescent excipients to generate, 9-Br-Nos-RR-NLPs. The mean particle and aerodynamic size of 9-Br-Nos-NLPs were measured to be 13.4±3.2 nm and 2.3±1.5 μm, significantly (P<0.05) lower than 19.4±6.1 nm and 3.1±1.8 μm of 9-Br-Nos-RR-NLPs. In addition, zeta-potential of 9-Br-Nos-NLPs was examined to be -9.54±0.16 mV, significantly (P<0.05) lower than -7.23±0.10 mV of 9-Br-Nos-RR-NLPs. Hence, both formulations were found to be optimum for pulmonary delivery through inhalation route of administration. Next, 9-Br-Nos-RR-NLPs exhibited enhanced cytotoxicity, apoptosis and cellular uptake in A549, lung cancer cells, as compared to 9-Br-Nos-NLPs and 9-Br-Nos suspension. This may be attributed to enhanced drug delivery and internalization character of 9-Br-Nos-RR-NLPs by energy-dependent endocytosis and passive diffusion mechanism. Pharmacokinetic and distribution analysis demonstrated the superiority of 9-Br-Nos-RR-NLPs that exhibited ∼1.12 and ∼1.75-folds enhancement in half-life of the drug as compared to 9-Br-Nos-NLPs and 9-Br-Nos powder following inhalation route. Continuation to this, 9-Br-Nos-RR-NLPs also displayed ∼3.75-fold increment in half-life of the drug in lungs, as compared to 9-Br-Nos suspension following intravenous route of administration. Furthermore, enhanced drug exposure was measured in terms of AUC(last) in lungs following administration of 9-Br-Nos-RR-NLPs, as compared to 9-Br-Nos-NLPs, 9-Br-Nos powder and 9-Br-Nos suspension. This may be attributed to rapid dispersion, enhanced dissolution and deep lung deposition of nanoparticles following inhalation route. Therefore, inhalable 9-Br-Nos-RR-NLPs claims further in depth in vivo tumor regression study to scale up the technology for clinical applications.

TASM: technocrat ARIMA and SVR model for workload prediction of web applications in cloud

Abstract: Workload patterns of cloud applications are changing regularly. The workload prediction model is key for auto-scaling of resources in a cloud environment. It is helping with cost reduction and efficient resource utilization. The workload for the web applications is usually mixed for different application at different time span. The single prediction model is not able to predict different kinds of workload pattern of cloud applications. In this paper, an adaptive prediction model has been proposed using linear regression, ARIMA, and support vector regression for web applications. Workload classifier has been proposed to select the model as per workload features. Further the model parameters are selected through a heuristic approach. We have used real trace files to evaluate the proposed model with existing state-of-the-art models. The experiment results describe the significant improvement in root-mean-squared error and mean absolute percentage error metrics, and improve the quality of service of web applications in a cloud environment.

Comparing different supervised machine learning algorithms for disease prediction

Abstract: Supervised machine learning algorithms have been a dominant method in the data mining field. Disease prediction using health data has recently shown a potential application area for these methods. This study aims to identify the key trends among different types of supervised machine learning algorithms, and their performance and usage for disease risk prediction. In this study, extensive research efforts were made to identify those studies that applied more than one supervised machine learning algorithm on single disease prediction. Two databases (i.e., Scopus and PubMed) were searched for different types of search items. Thus, we selected 48 articles in total for the comparison among variants supervised machine learning algorithms for disease prediction. We found that the Support Vector Machine (SVM) algorithm is applied most frequently (in 29 studies) followed by the Naive Bayes algorithm (in 23 studies). However, the Random Forest (RF) algorithm showed superior accuracy comparatively. Of the 17 studies where it was applied, RF showed the highest accuracy in 9 of them, i.e., 53%. This was followed by SVM which topped in 41% of the studies it was considered. This study provides a wide overview of the relative performance of different variants of supervised machine learning algorithms for disease prediction. This important information of relative performance can be used to aid researchers in the selection of an appropriate supervised machine learning algorithm for their studies.

Medicinal Plants: Their Use in Anticancer Treatment.

Abstract: Globally cancer is a disease which severely effects the human population. There is a constant demand for new therapies to treat and prevent this life-threatening disease. Scientific and research interest is drawing its attention towards naturally-derived compounds as they are considered to have less toxic side effects compared to current treatments such as chemotherapy. The Plant Kingdom produces naturally occurring secondary metabolites which are being investigated for their anticancer activities leading to the development of new clinical drugs. With the success of these compounds that have been developed into staple drugs for cancer treatment new technologies are emerging to develop the area further. New technologies include nanoparticles for nano-medicines which aim to enhance anticancer activities of plant-derived drugs by controlling the release of the compound and investigating new methods for administration. This review discusses the demand for naturally-derived compounds from medicinal plants and their properties which make them targets for potential anticancer treatments.

From Model, Signal to Knowledge: A Data-Driven Perspective of Fault Detection and Diagnosis

Abstract: This review paper is to give a full picture of fault detection and diagnosis (FDD) in complex systems from the perspective of data processing. As a matter of fact, an FDD system is a data-processing system on the basis of information redundancy, in which the data and human's understanding of the data are two fundamental elements. Human's understanding may be an explicit input-output model representing the relationship among the system's variables. It may also be represented as knowledge implicitly (e.g., the connection weights of a neural network). Therefore, FDD is done through some kind of modeling, signal processing, and intelligence computation. In this paper, a variety of FDD techniques are reviewed within the unified data-processing framework to give a full picture of FDD and achieve a new level of understanding. According to the types of data and how the data are processed, the FDD methods are classified into three categories: model-based online data-driven methods, signal-based methods, and knowledge-based history data-driven methods. An outlook to the possible evolution of FDD in industrial automation, including the hybrid FDD and the emerging networked FDD, are also presented to reveal the future development direction in this field.