A. V. Senthil Kumar

Architecture for mobile control functions in supplier deliveries for distributed integrated ERP modules

The consistency of data in central databases is normally implemented by using the ACID (Atomicity, Consistency, Isolation and Durability) properties of a DBMS (Data Base Management System). However, it is not possible to use the ACID properties of a DBMS if distributed and/or mobile databases are involved and at the same time, the availability of data also has to be optimized. To overcome this problem we will in this paper use so called relaxed ACID properties across different locations/DBMS systems. The objective of designing relaxed ACID properties across different database locations is that the users can trust the data they use even if the distributed database is temporarily inconsistent. The objective of this paper is to use the latest theory in system integration with relaxed ACID properties to describe how mobile control functions/workflows may be used as a control in the supplier module of an ERP system. This is illustrated by designing a new architecture for mobile registrations and control functions in distributed integrated mobile ERP modules. By using mobile registration and control systems, it is possible to move the registrations to the location where the data is created and thus improve the quality of the data. It may even be possible to operate in surroundings that are not as clean as normal for computer systems.

CTCHABC- hybrid online sequential fuzzy Extreme Kernel learning method for detection of Breast Cancer with hierarchical Artificial Bee

Circulating Tumor Cells (CTCs) in Peripheral Blood (PB) testing is measured as significant investigative and promising microarray technology for breast cancer examination. Few numbers of the work have been proposed in earlier for the role of CTCs detection in breast cancer; but still the development of novel method for identification of CTC becomes difficult because of hundreds and thousands of indicative genes is presented. The main intention of the work is to the identification of CTC in PB during Breast Cancer (BC) regarding to Metastatic (MS), Non-Metastatic (NMS) and hybrid MS and NMS. The proposed method is not only the identification of CTC in BC, in addition it solves gene selection by proposing hybrid fuzzy online sequential Particle Swarm Genetic (PSG) kernel extreme learning machine finally named as (FOP-KELM) classification. The proposed FOP-KELM method calculates the mean values for each gene features and it is compared objective function of KELM to select and remove unimportant gene features. In order to reduce the fuzzy membership assumption value in ELM, it is optimized using PSG algorithm. The impact of selected features from FOP-KELM has been investigated using clustering method. To perform classification task for selected gene features, a novel Hierarchical Artificial Bee clustering algorithm (HABCA) is proposed. It capably distinguishes the CTC through the separation of tumor samples into a hierarchical tree structure in a top-down manner, where the distance between two gene tumor samples is determined by using ABC. Clustering results are classified into MS, NMS, MS and NMS.

High speed efficient FPGA implementation of pipelined AES S-Box

Smart City is becoming a commonly-used term to describe the concept of utilizing information and communication technologies (ICT) to enhance urban services and improve the quality of life for citizens. All communications should be fast and properly protected against unauthorized eavesdropping, interception, and modification. Therefore high speed and strong cryptography is required. Advanced Encryption Standard (AES) is the most widely and secure symmetric key encryption algorithms today. S-box substitution is the only non-linear step in this algorithm. It is one of the most complicated and costly part of the system. In this article, we present high speed efficient S-box AES architecture using combinational logic. We have used 5-stage pipeline design in order to increase the speed and the maximum operation frequency. Therefore registers are inserted in optimal placements. The implementation has been successfully done by virtex-6 (xc6vlx240t) FPGA device using Xilinx ISE 14.7. Our proposed design achieves a frequency of 842.744 MHz and occupied 20 slices, whereas the highest operation frequency reported in the literature is 696.37 MHz using 32 slices.

A novel disengagement detection strategy for online learning using quasi framework

The online learning gains more popularity in recent days; its key success is delivering content over internet and can be accessed by students from anywhere and anytime. In general, attraction is the quality of arousing interest. Similarly, motivation is the other hand to support for learning. Since, the online learning has less control over students compared to the conventional teaching method. Therefore engagement of student gets more importance on online learning. Most of the learning systems stores learners activities in log files and their profile related informations in database. Usually log file analysis alone could not have enough data to find out disengagement. Thus we integrate the log file information with database and develop a novel disengagement detection strategy using quasi framework. This study result reveals that quasi framework is effective in term of quality compared to previous proposals.

Communications in Computer and Information Science: Diagnosis of Diabetes Using Intensified Fuzzy Verdict Mechanism

The use of Fuzzy Expert System has highly increased in the field of medicine, to diagnosis the illness of patient pursuit. By applying the intensified fuzzy verdict mechanism the diagnosis of diabetes becomes simple for medical practitioners. The intensified fuzzy verdict mechanism consists of fuzzy inference, implication and aggregation. For the diagnosis of diabetes, knowledge are represented in the form of fuzzification to convert crisp values into fuzzy values. This mechanism, contains set of rules with fuzzy operators. Defuzzification method is adopted to convert the fuzzy values into crisp values. In this paper, intensified fuzzy verdict mechanism is proposed to complete the knowledge representation and the inference model for diabetes data. The result of the proposed methods is compared with earlier method using accuracy as metrics. This mechanism is focused on increasing the accuracy and quality of knowledge for diabetes application.