Search results for: data mining.

Authors: Wuttigrai Ngamsirijit

Abstract:

Talent management in today’s modern organizations has become data-driven due to a demand for objective human resource decision making and development of analytics technologies. HR managers have been faced with some obstacles in exploiting data and information to obtain their effective talent management decisions. These include process-based data and records; insufficient human capital-related measures and metrics; lack of capabilities in data modeling in strategic manners; and, time consuming to add up numbers and make decisions. This paper proposes a framework of talent management through integration of talent value chain and human capital analytics approaches. It encompasses key data, measures, and metrics regarding strategic talent management decisions along the organizational and talent value chain. Moreover, specific predictive and prescriptive models incorporating these data and information are recommended to help managers in understanding the state of talent, gaps in managing talent and the organization, and the ways to develop optimized talent strategies.    

Keywords: Decision making, human capital analytics, talent management, talent value chain.

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Authors: S. Deelers, S. Auwatanamongkol

Abstract:

In this paper, we propose an algorithm to compute initial cluster centers for K-means clustering. Data in a cell is partitioned using a cutting plane that divides cell in two smaller cells. The plane is perpendicular to the data axis with the highest variance and is designed to reduce the sum squared errors of the two cells as much as possible, while at the same time keep the two cells far apart as possible. Cells are partitioned one at a time until the number of cells equals to the predefined number of clusters, K. The centers of the K cells become the initial cluster centers for K-means. The experimental results suggest that the proposed algorithm is effective, converge to better clustering results than those of the random initialization method. The research also indicated the proposed algorithm would greatly improve the likelihood of every cluster containing some data in it.

Keywords: Clustering algorithm, K-means algorithm, Datapartitioning, Initial cluster centers.

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Authors: Jindong Gu, Matthias Schubert, Volker Tresp

Abstract:

In many outlier detection tasks, only training data belonging to one class, i.e., the positive class, is available. The task is then to predict a new data point as belonging either to the positive class or to the negative class, in which case the data point is considered an outlier. For this task, we propose a novel corrupted Generative Adversarial Network (CorGAN). In the adversarial process of training CorGAN, the Generator generates outlier samples for the negative class, and the Discriminator is trained to distinguish the positive training data from the generated negative data. The proposed framework is evaluated using an image dataset and a real-world network intrusion dataset. Our outlier-detection method achieves state-of-the-art performance on both tasks.

Keywords: Outlier detection, generative adversary networks, semi-supervised learning.

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Authors: Buse A. Ataç, Doğan K. Cenan, Arda Çetinkaya, Naz D. Şahin, Köksal Sanlı, Zeynep Koç, Akın Kısa

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With its spatial data reliability, interpretation and questioning capabilities, Geographical Information Systems make significant contributions to scientists, planners and practitioners. Geographic information systems have received great attention in today's digital world, growing rapidly, and increasing the efficiency of use. Access to and use of current and accurate geographical data, which are the most important components of the Geographical Information System, has become a necessity rather than a need for sustainable and economic development. This project aims to enable sharing of data collected by public institutions and organizations on a web-based platform. Within the scope of the project, INSPIRE (Infrastructure for Spatial Information in the European Community) data specifications are considered as a road-map. In this context, Turkey's National Geographic Information System (TUCBS) Integration Project supports sharing spatial data within 61 pilot public institutions as complied with defined national standards. In this paper, which is prepared by the project team members in the TUCBS Integration Project, the technical process with a detailed methodology is explained. In this context, the main technical processes of the Project consist of Geographic Data Analysis, Geographic Data Harmonization (Standardization), Web Service Creation (WMS, WFS) and Metadata Creation-Publication. In this paper, the integration process carried out to provide the data produced by 61 institutions to be shared from the National Geographic Data Portal (GEOPORTAL), have been trying to be conveyed with a detailed methodology.

Keywords: Data specification, geoportal, GIS, INSPIRE, TUCBS, Turkey’s National Geographic Information System.

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Authors: Jae Young Park, Hwansu Jung, Jong Tae Kim

Abstract:

In the Solid-State-Drive (SSD) performance, whether the data has been well parallelized is an important factor. SSD parallelization is affected by allocation scheme and it is directly connected to SSD performance. There are dynamic allocation and static allocation in representative allocation schemes. Dynamic allocation is more adaptive in exploiting write operation parallelism, while static allocation is better in read operation parallelism. Therefore, it is hard to select the appropriate allocation scheme when the workload is mixed read and write operations. We simulated conditions on a few mixed data patterns and analyzed the results to help the right choice for better performance. As the results, if data arrival interval is long enough prior operations to be finished and continuous read intensive data environment static allocation is more suitable. Dynamic allocation performs the best on write performance and random data patterns.

Keywords: Dynamic allocation, NAND Flash based SSD, SSD parallelism, static allocation.

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Authors: Ahmed Abdulla Ashlam, Atta Badii, Frederic Stahl

Abstract:

In recent years, SQL injection attacks have been identified as being prevalent against web applications. They affect network security and user data, which leads to a considerable loss of money and data every year. This paper presents the use of classification algorithms in machine learning using a method to classify the login data filtering inputs into "SQLi" or "Non-SQLi,” thus increasing the reliability and accuracy of results in terms of deciding whether an operation is an attack or a valid operation. A method as a Web-App is developed for auto-generated data replication to provide a twin of the targeted data structure. Shielding against SQLi attacks (WebAppShield) that verifies all users and prevents attackers (SQLi attacks) from entering and or accessing the database, which the machine learning module predicts as "Non-SQLi", has been developed. A special login form has been developed with a special instance of the data validation; this verification process secures the web application from its early stages. The system has been tested and validated, and up to 99% of SQLi attacks have been prevented.

Keywords: SQL injection, attacks, web application, accuracy, database, WebAppShield.

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Authors: Mingtao Ding, Zheng Tian, Haixia Xu

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The batch nature limits the standard kernel principal component analysis (KPCA) methods in numerous applications, especially for dynamic or large-scale data. In this paper, an efficient adaptive approach is presented for online extraction of the kernel principal components (KPC). The contribution of this paper may be divided into two parts. First, kernel covariance matrix is correctly updated to adapt to the changing characteristics of data. Second, KPC are recursively formulated to overcome the batch nature of standard KPCA.This formulation is derived from the recursive eigen-decomposition of kernel covariance matrix and indicates the KPC variation caused by the new data. The proposed method not only alleviates sub-optimality of the KPCA method for non-stationary data, but also maintains constant update speed and memory usage as the data-size increases. Experiments for simulation data and real applications demonstrate that our approach yields improvements in terms of both computational speed and approximation accuracy.

Keywords: adaptive method, kernel principal component analysis, online extraction, recursive algorithm

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Authors: Anjum Iqbal, Mohd Aizaini Maarof

Abstract:

Artificial Immune System (AIS) is relatively naive paradigm for intelligent computations. The inspiration for AIS is derived from natural Immune System (IS). Classically it is believed that IS strives to discriminate between self and non-self. Most of the existing AIS research is based on this approach. Danger Theory (DT) argues this approach and proposes that IS fights against danger producing elements and tolerates others. We, the computational researchers, are not concerned with the arguments among immunologists but try to extract from it novel abstractions for intelligent computation. This paper aims to follow DT inspiration for intelligent data processing. The approach may introduce new avenue in intelligent processing. The data used is system calls data that is potentially significant in intrusion detection applications.

Keywords: artificial immune system, danger theory, intelligent processing, system calls

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Authors: Zahra Ghadampour, Gholamreza Rakhshandehroo

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A concern that researchers usually face in different applications of Artificial Neural Network (ANN) is determination of the size of effective domain in time series. In this paper, trial and error method was used on groundwater depth time series to determine the size of effective domain in the series in an observation well in Union County, New Jersey, U.S. different domains of 20, 40, 60, 80, 100, and 120 preceding day were examined and the 80 days was considered as effective length of the domain. Data sets in different domains were fed to a Feed Forward Back Propagation ANN with one hidden layer and the groundwater depths were forecasted. Root Mean Square Error (RMSE) and the correlation factor (R2) of estimated and observed groundwater depths for all domains were determined. In general, groundwater depth forecast improved, as evidenced by lower RMSEs and higher R2s, when the domain length increased from 20 to 120. However, 80 days was selected as the effective domain because the improvement was less than 1% beyond that. Forecasted ground water depths utilizing measured daily data (set #1) and data averaged over the effective domain (set #2) were compared. It was postulated that more accurate nature of measured daily data was the reason for a better forecast with lower RMSE (0.1027 m compared to 0.255 m) in set #1. However, the size of input data in this set was 80 times the size of input data in set #2; a factor that may increase the computational effort unpredictably. It was concluded that 80 daily data may be successfully utilized to lower the size of input data sets considerably, while maintaining the effective information in the data set.

Keywords: Neural networks, groundwater depth, forecast.

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Authors: B. Ferdousi, J. Bari

Abstract:

With advancement of mobile computing, employees are increasingly doing their job-related works using personally owned mobile devices or organization owned devices. The Bring Your Own Device (BYOD) model allows employees to use their own mobile devices for job-related works, while Corporate Owned, Personally Enabled (COPE) model allows both organizations and employees to install applications onto organization-owned mobile devices used for job-related works. While there are many benefits of using mobile computing for job-related works, there are also serious concerns of different levels of threats to the organizational data security. Consequently, it is crucial to know the level of threat to the organizational data security in the BOYD and COPE models. It is also important to ensure that employees comply with the organizational data security policy. This paper discusses the organizational data security issues in perspective of ownership of mobile devices used by employees, especially in BYOD and COPE models. It appears that while the BYOD model has many benefits, there are relatively more data security risks in this model than in the COPE model. The findings also showed that in both BYOD and COPE environments, a more practical approach towards achieving secure mobile computing in organizational setting is through the development of comprehensive cybersecurity policies balancing employees’ need for convenience with organizational data security. The study helps to figure out the compliance and the risks of security breach in BYOD and COPE models.

Keywords: Data security, mobile computing, BYOD, COPE, cybersecurity policy, cybersecurity compliance.

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Authors: Haythem Yahyaoui, Samir Moalla, Mounir Bouden, Skander Ghorbel

Abstract:

Nowadays, cloud environments are becoming a need for companies, this new technology gives the opportunities to access to the data anywhere and anytime. It also provides an optimized and secured access to the resources and gives more security for the data which is stored in the platform. However, some companies do not trust Cloud providers, they think that providers can access and modify some confidential data such as bank accounts. Many works have been done in this context, they conclude that encryption methods realized by providers ensure the confidentiality, but, they forgot that Cloud providers can decrypt the confidential resources. The best solution here is to apply some operations on the data before sending them to the provider Cloud in the objective to make them unreadable. The principal idea is to allow user how it can protect his data with his own methods. In this paper, we are going to demonstrate our approach and prove that is more efficient in term of execution time than some existing methods. This work aims at enhancing the quality of service of providers and ensuring the trust of the customers. 

Keywords: Confidentiality, cryptography, security issues, trust issues.

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Authors: Radek Malinský, Ivan Jelínek

Abstract:

Web 2.0 (social networking, blogging and online forums) can serve as a data source for social science research because it contains vast amount of information from many different users. The volume of that information has been growing at a very high rate and becoming a network of heterogeneous data; this makes things difficult to find and is therefore not almost useful. We have proposed a novel theoretical model for gathering and processing data from Web 2.0, which would reflect semantic content of web pages in better way. This article deals with the analysis part of the model and its usage for content analysis of blogs. The introductory part of the article describes methodology for the gathering and processing data from blogs. The next part of the article is focused on the evaluation and content analysis of blogs, which write about specific trend.

Keywords: Blog, Sentiment Analysis, Web 2.0, Webometrics

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Authors: Hamed Qahri Saremi, Gholam Ali Montazer

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With the proliferation of World Wide Web, development of web-based technologies and the growth in web content, the structure of a website becomes more complex and web navigation becomes a critical issue to both web designers and users. In this paper we define the content and web pages as two important and influential factors in website navigation and paraphrase the enhancement in the website navigation as making some useful changes in the link structure of the website based on the aforementioned factors. Then we suggest a new method for proposing the changes using fuzzy approach to optimize the website architecture. Applying the proposed method to a real case of Iranian Civil Aviation Organization (CAO) website, we discuss the results of the novel approach at the final section.

Keywords: Web content, Web navigation, Website system, Webusage mining.

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Authors: Ladan Darougaran, Hossein Shahinzadeh, Hajar Ghotb, Leila Ramezanpour

Abstract:

In ad hoc networks, the main issue about designing of protocols is quality of service, so that in wireless sensor networks the main constraint in designing protocols is limited energy of sensors. In fact, protocols which minimize the power consumption in sensors are more considered in wireless sensor networks. One approach of reducing energy consumption in wireless sensor networks is to reduce the number of packages that are transmitted in network. The technique of collecting data that combines related data and prevent transmission of additional packages in network can be effective in the reducing of transmitted packages- number. According to this fact that information processing consumes less power than information transmitting, Data Aggregation has great importance and because of this fact this technique is used in many protocols [5]. One of the Data Aggregation techniques is to use Data Aggregation tree. But finding one optimum Data Aggregation tree to collect data in networks with one sink is a NP-hard problem. In the Data Aggregation technique, related information packages are combined in intermediate nodes and form one package. So the number of packages which are transmitted in network reduces and therefore, less energy will be consumed that at last results in improvement of longevity of network. Heuristic methods are used in order to solve the NP-hard problem that one of these optimization methods is to solve Simulated Annealing problems. In this article, we will propose new method in order to build data collection tree in wireless sensor networks by using Simulated Annealing algorithm and we will evaluate its efficiency whit Genetic Algorithm.

Keywords: Data aggregation, wireless sensor networks, energy efficiency, simulated annealing algorithm, genetic algorithm.

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Authors: Mohammad Ali Jabraeil Jamali, Ahmad Khademzadeh, Hasan Asil, Amir Asil

Abstract:

This method decrease usage power (expenditure) in networks on chips (NOC). This method data coding for data transferring in order to reduces expenditure. This method uses data compression reduces the size. Expenditure calculation in NOC occurs inside of NOC based on grown models and transitive activities in entry ports. The goal of simulating is to weigh expenditure for encoding, decoding and compressing in Baseline networks and reduction of switches in this type of networks. KeywordsNetworks on chip, Compression, Encoding, Baseline networks, Banyan networks.

Keywords: Networks on chip, Compression, Encoding, Baseline networks, Banyan networks

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Authors: H. Y. Jung, Ju H. Park, S. M. Lee

Abstract:

Sampled-data controller is presented for solid oxide fuel cell systems which is expressed by a sector bounded nonlinear model. The proposed control law is obtained by solving a convex problem satisfying several linear matrix inequalities. Simulation results are given to show the effectiveness of the proposed design method.

Keywords: Sampled-data control, Sector bound, Solid oxide fuel cell, Time-delay.

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Authors: Yuki. Akiyama, Hiroaki. Sengoku, Ryosuke. Shibasaki

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In this study, the locations and areas of commercial accumulations were detected by using digital yellow page data. An original buffering method that can accurately create polygons of commercial accumulations is proposed in this paper.; by using this method, distribution of commercial accumulations can be easily created and monitored over a wide area. The locations, areas, and time-series changes of commercial accumulations in the South Kanto region can be monitored by integrating polygons of commercial accumulations with the time-series data of digital yellow page data. The circumstances of commercial accumulations were shown to vary according to areas, that is, highly- urbanized regions such as the city center of Tokyo and prefectural capitals, suburban areas near large cities, and suburban and rural areas.

Keywords: Commercial accumulations, Spatio-temporal analysis, Urban monitoring, Yellow page data

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Authors: Maan M. Shaker

Abstract:

The electroencephalograph (EEG) signal is one of the most widely signal used in the bioinformatics field due to its rich information about human tasks. In this work EEG waves classification is achieved using the Discrete Wavelet Transform DWT with Fast Fourier Transform (FFT) by adopting the normalized EEG data. The DWT is used as a classifier of the EEG wave's frequencies, while FFT is implemented to visualize the EEG waves in multi-resolution of DWT. Several real EEG data sets (real EEG data for both normal and abnormal persons) have been tested and the results improve the validity of the proposed technique.

Keywords: Bioinformatics, DWT, EEG waves, FFT.

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Authors: Moohyun Lee, Soojung Hur, Yongwan Park

Abstract:

We propose obstacle classification method based on 2D LIDAR Database. The existing obstacle classification method based on 2D LIDAR, has an advantage in terms of accuracy and shorter calculation time. However, it was difficult to classifier the type of obstacle and therefore accurate path planning was not possible. In order to overcome this problem, a method of classifying obstacle type based on width data of obstacle was proposed. However, width data was not sufficient to improve accuracy. In this paper, database was established by width and intensity data; the first classification was processed by the width data; the second classification was processed by the intensity data; classification was processed by comparing to database; result of obstacle classification was determined by finding the one with highest similarity values. An experiment using an actual autonomous vehicle under real environment shows that calculation time declined in comparison to 3D LIDAR and it was possible to classify obstacle using single 2D LIDAR.

Keywords: Obstacle, Classification, LIDAR, Segmentation, Width, Intensity, Database.

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Authors: Sajjad Farashi, Mohammadjavad Abolhassani, Mostafa Taghavi Kani

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Information in the nervous system is coded as firing patterns of electrical signals called action potential or spike so an essential step in analysis of neural mechanism is detection of action potentials embedded in the neural data. There are several methods proposed in the literature for such a purpose. In this paper a novel method based on empirical mode decomposition (EMD) has been developed. EMD is a decomposition method that extracts oscillations with different frequency range in a waveform. The method is adaptive and no a-priori knowledge about data or parameter adjusting is needed in it. The results for simulated data indicate that proposed method is comparable with wavelet based methods for spike detection. For neural signals with signal-to-noise ratio near 3 proposed methods is capable to detect more than 95% of action potentials accurately.

Keywords: EMD, neural data processing, spike detection, wavelet decomposition.

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Authors: Maha Shamseddine, Amjad Nusayr, Wassim Itani

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In this paper, we present a Platform-as-a-Service (PaaS) model for controlling the privacy enforcement mechanisms applied on user data when stored and processed in Cloud data centers. The proposed architecture consists of establishing user configurable ‘sticky’ policies on the Graphical User Interface (GUI) data-bound components during the application development phase to specify the details of privacy enforcement on the contents of these components. Various privacy classification classes on the data components are formally defined to give the user full control on the degree and scope of privacy enforcement including the type of execution containers to process the data in the Cloud. This not only enhances the privacy-awareness of the developed Cloud services, but also results in major savings in performance and energy efficiency due to the fact that the privacy mechanisms are solely applied on sensitive data units and not on all the user content. The proposed design is implemented in a real PaaS cloud computing environment on the Microsoft Azure platform.

Keywords: Privacy enforcement, Platform-as-a-Service privacy awareness, cloud computing privacy.

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Authors: Thashmee Karunaratne, Henrik Böstrom

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Logic based methods for learning from structured data is limited w.r.t. handling large search spaces, preventing large-sized substructures from being considered by the resulting classifiers. A novel approach to learning from structured data is introduced that employs a structure transformation method, called finger printing, for addressing these limitations. The method, which generates features corresponding to arbitrarily complex substructures, is implemented in a system, called DIFFER. The method is demonstrated to perform comparably to an existing state-of-art method on some benchmark data sets without requiring restrictions on the search space. Furthermore, learning from the union of features generated by finger printing and the previous method outperforms learning from each individual set of features on all benchmark data sets, demonstrating the benefit of developing complementary, rather than competing, methods for structure classification.

Keywords: Machine learning, Structure classification, Propositionalization.

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Authors: Aigerim Uyzbayeva, Valeriya Tyo, Nurlan Ibrayev

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Kazakhstan is currently one of the dynamically developing states in its region. The stable growth in all sectors of the economy leads to a corresponding increase in energy consumption. Thus country consumes significant amount of energy due to the high level of industrialisation and the presence of energy-intensive manufacturing such as mining and metallurgy which in turn leads to low energy efficiency. With allowance for this the Government has set several priorities to adopt a transition of Republic of Kazakhstan to a “green economy”. This article provides an overview of Kazakhstan’s energy efficiency situation in for the period of 1991- 2014. First, the dynamics of production and consumption of conventional energy resources are given. Second, the potential of renewable energy sources is summarised followed by the description of GHG emissions trends in the country. Third, Kazakhstan’ national initiatives, policies and locally implemented projects in the field of energy efficiency are described.

Keywords: Energy efficiency in Kazakhstan, greenhouse gases, renewable energy, sustainable development.

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Authors: Kwangho Cha, Hyeyoung Cho, Sungho Kim

Abstract:

As many scientific applications require large data processing, the importance of parallel I/O has been increasingly recognized. Collective I/O is one of the considerable features of parallel I/O and enables application programmers to easily handle their large data volume. In this paper we measured and analyzed the performance of original collective I/O and the subgroup method, the way of using collective I/O of MPI effectively. From the experimental results, we found that the subgroup method showed good performance with small data size.

Keywords: Collective I/O, MPI, parallel file system.

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Authors: Y. N. Phang, E. F. Loh

Abstract:

Many researchers have suggested the use of zero inflated Poisson (ZIP) and zero inflated negative binomial (ZINB) models in modeling overdispersed medical count data with extra variations caused by extra zeros and unobserved heterogeneity. The studies indicate that ZIP and ZINB always provide better fit than using the normal Poisson and negative binomial models in modeling overdispersed medical count data. In this study, we proposed the use of Zero Inflated Inverse Trinomial (ZIIT), Zero Inflated Poisson Inverse Gaussian (ZIPIG) and zero inflated strict arcsine models in modeling overdispered medical count data. These proposed models are not widely used by many researchers especially in the medical field. The results show that these three suggested models can serve as alternative models in modeling overdispersed medical count data. This is supported by the application of these suggested models to a real life medical data set. Inverse trinomial, Poisson inverse Gaussian and strict arcsine are discrete distributions with cubic variance function of mean. Therefore, ZIIT, ZIPIG and ZISA are able to accommodate data with excess zeros and very heavy tailed. They are recommended to be used in modeling overdispersed medical count data when ZIP and ZINB are inadequate.

Keywords: Zero inflated, inverse trinomial distribution, Poisson inverse Gaussian distribution, strict arcsine distribution, Pearson’s goodness of fit.

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Authors: Rosie Borup, Hanifa Shah

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This paper aims to describe how student satisfaction is measured for work-based learners as these are non-traditional learners, conducting academic learning in the workplace, typically their curricula have a high degree of negotiation, and whose motivations are directly related to their employers- needs, as well as their own career ambitions. We argue that while increasing WBL participation, and use of SSD are both accepted as being of strategic importance to the HE agenda, the use of WBL SSD is rarely examined, and lessons can be learned from the comparison of SSD from a range of WBL programmes, and increased visibility of this type of data will provide insight into ways to improve and develop this type of delivery. The key themes that emerged from the analysis of the interview data were: learners profiles and needs, employers drivers, academic staff drivers, organizational approach, tools for collecting data and visibility of findings. The paper concludes with observations on best practice in the collection, analysis and use of WBL SSD, thus offering recommendations for both academic managers and practitioners.

Keywords: Student satisfaction data, work based learning, employer engagement, NSS.

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Authors: Ghalem Belalem, Yahya Slimani

Abstract:

Large scale systems such as computational Grid is a distributed computing infrastructure that can provide globally available network resources. The evolution of information processing systems in Data Grid is characterized by a strong decentralization of data in several fields whose objective is to ensure the availability and the reliability of the data in the reason to provide a fault tolerance and scalability, which cannot be possible only with the use of the techniques of replication. Unfortunately the use of these techniques has a height cost, because it is necessary to maintain consistency between the distributed data. Nevertheless, to agree to live with certain imperfections can improve the performance of the system by improving competition. In this paper, we propose a multi-layer protocol combining the pessimistic and optimistic approaches conceived for the data consistency maintenance in large scale systems. Our approach is based on a hierarchical representation model with tree layers, whose objective is with double vocation, because it initially makes it possible to reduce response times compared to completely pessimistic approach and it the second time to improve the quality of service compared to an optimistic approach.

Keywords: Data Grid, replication, consistency, optimistic approach, pessimistic approach.

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Authors: Il-Do Jeong, Hang-Geun Jeong

Abstract:

This paper describes the design and fabrication of a clock and data recovery circuit (CDR). We propose a new clock and data recovery which is based on a 1/4-rate frequency detector (QRFD). The proposed frequency detector helps reduce the VCO frequency and is thus advantageous for high speed application. The proposed frequency detector can achieve low jitter operation and extend the pull-in range without using the reference clock. The proposed CDR was implemented using a 1/4-rate bang-bang type phase detector (PD) and a ring voltage controlled oscillator (VCO). The CDR circuit has been fabricated in a standard 0.18 CMOS technology. It occupies an active area of 1 x 1 and consumes 90 mW from a single 1.8V supply.

Keywords: Clock and data recovery, 1/4-rate frequency detector, 1/4-rate phase detector.

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Authors: Shobha Sharma, Amita Dev

Abstract:

Data driven dynamic logic is the high speed dynamic circuit with low area. The clock of the dynamic circuit is removed and data drives the circuit instead of clock for precharging purpose. This data driven dynamic nand gate is given static forward substrate biasing of Vsupply/2 as well as the substrate bias is connected to the input data, resulting in dynamic substrate bias. The dynamic substrate bias gives the shortest propagation delay with a penalty on the power dissipation. Propagation delay is reduced by 77.8% compared to the normal reverse substrate bias Data driven dynamic nand. Also dynamic substrate biased D3nand’s propagation delay is reduced by 31.26% compared to data driven dynamic nand gate with static forward substrate biasing of Vdd/2. This data driven dynamic nand gate with dynamic body biasing gives us the highest speed with no area penalty and finds its applications where power penalty is acceptable. Also combination of Dynamic and static Forward body bias can be used with reduced propagation delay compared to static forward biased circuit and with comparable increase in an average power. The simulations were done on hspice simulator with 22nm High-k metal gate strained Si technology HP models of Arizona State University, USA.

Keywords: Data driven nand gate, dynamic substrate biasing, nand gate, static substrate biasing.

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Authors: Pardeep Singh, Sanjay Sharma

Abstract:

With increasing data in medical databases, medical data retrieval is growing in popularity. Some of this analysis including inducing propositional rules from databases using many soft techniques, and then using these rules in an expert system. Diagnostic rules and information on features are extracted from clinical databases on diseases of congenital anomaly. This paper explain the latest soft computing techniques and some of the adaptive techniques encompasses an extensive group of methods that have been applied in the medical domain and that are used for the discovery of data dependencies, importance of features, patterns in sample data, and feature space dimensionality reduction. These approaches pave the way for new and interesting avenues of research in medical imaging and represent an important challenge for researchers.

Keywords: CBIR, GA, Rough sets, CBMIR, SVM.

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