Informatics Publishing Limited

E. Thirupavai ¹, S. Veni ¹, S. Sitta Djody ¹, R. Ilangovan ¹, M. Balaganesh ¹, N. Srinivasan ¹

Affiliations
1 Department of Endocrinology, Dr. ALM Post Graduate Institute of Basic Medical Sciences, University of Madras, Taramani Campus, Chennai-600113, IN

Abstract

Bone marrow is a complex tissue, which contains stem cells for haematopoietic and mesenchymal lineage. Among various growth factors that regulate bone remodeling, TGF-bl is the major growth fator, which is synthesized and stored in bone in large amount. But, the exact role of TGF-bl on the differentiation and mineralization of bone marrow stem cells (BMSCs) is still not clear. Hence, the present study is designed to delineate the role of rhTGF-bl on the differentiation and mineralization of rat BMSC in vitro. Bone marrow stem cells were isolated from the femur bones of healthy rats and cultured with osteogenic medium (OM).

Full Text

     

S. Veni ¹, E. Thirupavai ¹, S. Sitta Djody ¹, R. Ilangovan ¹, N. Srinivasan ¹

Affiliations
1 Department of Endocrinology, Dr. ALM Post Graduate Institute of Basic Medical Sciences, University of Madras, Taramani Campus, Chennai-600113, IN

Abstract

Bone marrow stem cells (BMSC) develop into pre-osteoblast, mature into osteoblast and attain sensitivity as osteocytes. Among the potential regulators of bone formation, insulin like growth factor-I (IGF-I) occupies an important position because of its ability to stimulate osteoblast proliferation and differentiation. Although, it is well known that osteoblasts are important target cell types, little is known about the effect of rhIGF-I on osteoprogenitors.

Full Text

     

S. Veni ¹, G. M. Kadhar Nawaz ², P. Praba ³

Affiliations
1 Department of Computer Applications, Karpagam University, Coimbatore, IN
2 Department of Applications, Sona College of Technology, Salem, IN
3 Department of Computer Science, Karpagam University, Coimbatore, IN

Abstract

Multiprotocol Label Switching (MPLS) is an Internet Engineering Task Force (IETF)-specified framework that provides for the designation, routing, forwarding and switching of traffic flows through the network. MPLS is a versatile solution to address the problems faced by present-day networks-speed, scalability, quality-of-service (QoS) management, and traffic engineering. Constraint-based Routing Label Distribution Protocol (CR-LDP) and  Resource Reservation Protocol  - Traffic Extension(RSVP-TE) are two label distribution protocols that provide support for Traffic Engineering. The operation of two protocols is different although the two protocols provide a similar level of service. The choice of signaling protocol is crucial for the success of MPLS performance.

Keywords

CR-LDP, MPLS, QoS, RSVP-TE.

Full Text

     

P. Tamil Selvan ¹, S. Veni ¹

Affiliations
1 Department of Computer Science, Karpagam Academy of Higher Education, Coimbatore - 641021, Tamil Nadu, IN

Abstract

Background/Objectives: This paper proposes an Optimized Social Ant Based Sensitive Item Hiding (OSA-SIH) technique and expands the scope of quality privacy preservation for distributed data mining with optimal side effects on the original dataset. Methods/Statistical Analysis: in OSA-SIH technique, initially sensitive items for the given distributed dataset are evaluated using the social ant based relative item set distribution. Based on the evaluated dataset, optimal hiding of sensitive item is arrived with social ant based relative item set distribution even for larger item sets, ensuring time for optimal hiding. Next, sensitive item hiding is performed through multiplicative and transformational data perturbation. This data perturbation is based on socially cohesive relational rate between sensitive and non sensitive item sets, ensuring privacy preservation accuracy. The side effects on the modified dataset are checked for several users' requested item set distribution. Findings: The experimental results demonstrated that proposed technique out performed than the existing state of the art works in terms of privacy preservation accuracy, rate of side effects on the modified dataset, and time for optimal hiding. Improvement/Application: Experiments revealed that the proposed OSA-SIH technique

Keywords

Perturbation, Privacy Preserving Data Mining, Social Ant, Sensitive Item Hiding, Transformational Data Perturbation, Multiplicative Data Perturbation

Full Text

   

K. A. Rafidha Rehiman ¹, S. Veni ¹

Affiliations
1 Department of Computer Science, Karpagam Academy of Higher Education, Coimbatore - 641021, Tamil Nadu, IN

Abstract

Background/Objectives: Internet of Things (IoT) has made significant changes in the real world and penetrates all aspects of human life. The user acceptance of IoT is enormously high and its widespread usage is because of the availability of smart phones and tablets. Wide adoption of IoT in the applications of each field always collecting sensitive information and provide a larger surface for intruders. So privacy preserved authentication and access controls are big challenges in its research area. Methods/Statistical Analysis: In this paper we introduced a novel algorithm based on Zero Knowledge Protocol and Accumulated Hashing to provide secure authentication to sensor enabled mobile devices in IoT. Also for ensuring confidentiality in communication proposed a new method for key exchange using current time. Findings: The proposed method fulfills the requirements of resource and battery constrained mobile devices in IoT when compared with traditional authentication and access control mechanisms for other applications.

Keywords

Authentication, Accumulated Hashing, Internet of Things, Mobile Security, Zero Knowledge Protocol

Full Text

   

S. Abhishek ¹, S. Veni ², K. A. Narayanankutty ¹

Affiliations
1 Department of Electronics and Communication Engineering, Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, Amrita University, Coimbatore – 641112, Tamil Nadu, IN
2 Department of Electronics and Communication Engineering, Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, Amrita University, Coimbatore – 641112, Tamil Nadu

Abstract

Background/Objectives: Compressed Sensing (CS) is an efficient sensing paradigm which guarantees reasonable reconstruction with less number of samples. We aim to increase the reconstruction quality of signals in CS. Methods/ Statistical Analysis: The behavior of random matrices is analyzed and an efficient method for improving the reconstruction quality is developed in CS based ECG reconstruction applications. The method is compared against Biorthogonal wavelet based approaches. Findings: Our analysis reveals that introduction of a modified column vector in the reconstruction matrix, which contains the sum of all columns of random matrix increases the reconstruction quality in CS applications. This idea was applied to different sparsifying domains and the results are very encouraging. We studied the effect of doing this on the singular values and both unitary matrices U and V. The first singular value (Σ) shot up making the condition number high, however there was not much change in the other singular values. The matrix U seems to remain random unitary matrix, where as matrix V has one value becoming unity in its rank space. Application/Improvements: Compared to wavelet based approaches the method shows reasonable improvement in Percentage Root Square Deviation (PRD).

Keywords

Compressed Sensing, ECG, PRD, Singular Values, Splines

Full Text

   

A. P. Nirmala ¹, S. Veni ²

Affiliations
1 New Horizon College of Engineering, Bangalore 560103, Karnataka, IN
2 Department of Computer Science, Karpagam University, Coimbatore − 641021, Tamil Nadu, IN

Abstract

Virtualization plays a vital role in cloud computing. It provides better manageability, availability, optimistic provisioning, scalability and resource utilization in current cloud computing environments. However the performance issue is a major concern in virtualization. The performance of the application running inside the virtual machine gets affected by the interference of the co-virtual machines. This approach provides a novel task scheduling mechanism that allocates the suitable resources to virtual machines which are running in parallel. An interference prediction scheme is proposed to utilize characteristics that are collected when an application running on virtual machines to maintain less system overhead. Nelder-mead method is employed in prediction to create relationship model from the observed response and control variables. A hybrid algorithm: Ant Colony Optimization and Cuckoo search algorithm with K-means++ is adopted for task scheduling process. This approach shows effective improvements in terms of throughput and execution time.

Keywords

Ant Colony Optimization, Cuckoo Search, K-means++ Algorithm, Performance Interference, Throughput, Virtualization.

Full Text

   

V. Lakshmana Narayanan ¹, G. Ramesh Pandi ¹, K. Kaleeswari ², S. Veni ²

Affiliations
1 Department of Management Studies, Kalasalingam Academy of Research and Education, IN
2 PG and Research Department of Commerce, Pasumpon Muthuramalinga Thevar College, IN

Abstract

The rapid growth of Fintech innovation and the widespread adoption of blockchain technologies have indeed had a transformative impact on the financial industry. In this paper, the focus is on the application of machine learning algorithms, specifically the Random Forest Regression algorithm, within the context of Fintech and blockchain. This research contributes to the advancement of machine learning techniques in the field of Fintech and blockchain. The Random Forest Regression algorithm utilizes ensemble learning, combining multiple decision trees to analyze complex financial data and make predictions on various outcomes. This algorithm has proven to be effective in addressing key challenges within the industry, such as predicting loan defaults, detecting fraud, and assessing risks. Through experimental evaluations and case studies, the paper demonstrates the effectiveness of the Random Forest Regression algorithm in enhancing Fintech innovation in blockchain applications. The algorithm improved accuracy, scalability, and interpretability enable financial institutions to make data-driven decisions and optimize their operations.

Keywords

Fintech, Blockchain, Innovations, Random Forest Regression, Machine Learning, Industry.

Full Text