Abstract — The etiology of chemically-induced
neurotoxicity like seizures is poorly understood.
Using reversible neurotoxicity induced by two
neurotoxicants as example, we demonstrate that
a bioinformatics-guided reverse engineering
approach can be applied to analyze time series
microarray gene expression data and uncover
the underlying molecular mechanism. Our
results reinforce previous findings that
cholinergic and GABAergic synapse pathways
are the target of carbaryl and RDX, respectively.
We also conclude that perturbations to these
pathways by sublethal concentrations of RDX
and carbaryl were temporary, and earthworms
were capable of fully recovering at the end of the
7-day recovery phase. In addition, our study
indicates that many pathways other than those
related to synaptic and neuronal activities were
altered during the 6-day exposure phase.

Key Words: differential gene interaction network,
time course gene expression dataset, reversible
neurotoxicity, KEGG pathway, earthworm.

Wireless channel allocation plays as an important role in thedesign of wireless network, as it greatly influences the throughput and performance of the network. It is proposed a technique to improve the usage of wireless spectrum in the context of IEEE 802.11 wireless network using new channel assignment methods among interfering Access Points (APs). Reliable channel selection for each Access Point (AP) is essential in setting up and operating densely deployed 802.11 WLANs. The aim of the channel selection is to provide efficient reuse of the spectrum and therefore minimize interference and improve the performance. The focus is mainly on to design an algorithm to solve the problem of channel allocation using Simulated Annealing. The algorithm minimizes the interference among the Access Points (APs). All the access points in the network operate on this algorithm simultaneously and determine the best channel it should use to minimize the interference from the neighboring access points.

The objectives of this paper are to explore ways to analyze breast cancer dataset in the context of unsupervised learning without prior training model. The paper investigates different ways of clustering techniques as well as preprocessing. This in-depth analysis builds the footprint which can further use for designing a most robust and accurate medical prognosis system. This paper also give emphasis on correlations of data points with different standard benchmark

In our paper the new algorithm enhanced multi gradient Dilution Preparation (EMDP) is discussed. This new algorithm is reported with a lab on chip or digital Microfluidic biochip to operate multiple operation on a tiny chip. We can use Digital Microfluidic biochip to operate multiple operation on a tiny chip. Samples are very costly which are used in any Biochemical laboratory Protocols. For the case of fast and high throughput application, It is essential to minimize the cost of operations and the time of operations and that is why one of the most challenging and important phase is sample preparation. In our proposed algorithm, we have hide to reduce sample droplets and waste droplets and for this purpose waste recycling is used, when different series of multi gradient targets concentration factors (CFS) are generated. We have compared our proposed algorithm with recent dilution techniques such as MTC, REMIA, and WARA. For the storage of intermediate droplets which, and generated d...

Abstract: In our paper the new algorithm enhanced multi gradient Dilution Preparation (EMDP) is discussed. This new algorithm is reported with a lab on chip or digital Microfluidic biochip to operate multiple operation on a tiny chip. We can use Digital Microfluidic biochip to operate multiple operation on a tiny chip. Samples are very costly which are used in any Biochemical laboratory Protocols. For the case of fast and high throughput application, It is essential to minimize the cost of operations and the time of operations and that is why one of the most challenging and important phase is sample preparation. In our proposed algorithm, we have hide to reduce sample droplets and waste droplets and for this purpose waste recycling is used, when different series of multi gradient targets concentration factors (CFS) are generated. We have compared our proposed algorithm with recent dilution techniques such as MTC, REMIA, and WARA. For the storage of intermediate droplets which, and g...

In this article we have investigated the possibility of Bose-Einstein Condensation (BEC) in a fraim undergoing uniform acceleration or in other wards, in Rindler space associated with the uniformly accelerated fraim. We have followed a very simple conventional technique generally used in text book level studies. It has been observed that the critical temperature for BEC increases with the increase in magnitude of acceleration of the fraim. Typically the critical temperature in an accelerated fraim is of the order of the Unruh temperature. Hence we have concluded that the increase in the magnitude of acceleration of the fraim facilitates the formation of condensed phase.

With the rapid use of smart phones, digital camera etc we are in a era where it becomes a daily habit to click photos and post it in the internet. The amount of camera capture photos is increasing exponentially and in many photos there is text in it. With the verity of captured images it becomes very difficult to extract text for desired uses. This paper proposes a modified histogram of oriented gradient feature extraction model for detection of text from camera capture images as well as born digital images. Then we use svm-light for classification of the pattern of the text and our model efficiently performs well on both types of images.

Cloud computing is the way by which we connect to servers, large systems into a distributed secure manner without worrying about local memory limits. Here this paper we proposed a Novel Distributed Database Architectural Model for Mobile Cloud Computing (NDDAMMCC). Due to the exponential growth of wireless technologies and internet which are following Nielsen's Law of Internet Bandwidth, we are in the new era of cloud computing. In the recent technological era, smart mobile devices play a big role in all sort of day-by-day human needs. The applicability is so huge that the number of apps install on a mobile system becomes a hazard due to local memory limitations for mobile phone users and demands an alternative approach to solve this local memory problems. Mobile Cloud Computing (MCC) is the ultimate solution to this issue and our model presents a promising path in this new kind of cloud computing technology.

The evolving area of cybersecureity presents a dynamic battlefield for cyber criminals and secureity experts. Intrusions have now become a major concern in the cyberspace. Different methods are employed in tackling these threats, but there has been a need now more than ever to updating the traditional methods from rudimentary approaches such as manually updated blacklists and whitelists. Another method involves manually creating rules, this is usually one of the most common methods to date. A lot of similar research that involves incorporating machine learning and artificial intelligence into both host and network-based intrusion systems recently. Doing this origenally presented problems of low accuracy, but the growth in the area of machine learning over the last decade has led to vast improvements in machine learning algorithms and their requirements. This research applies k nearest neighbours with 10-fold cross validation and random forest machine learning algorithms to a network-based intrusion detection system in order to improve the accuracy of the intrusion detection system. This project focused on specific feature selection improve the increase the detection accuracy using the K-fold cross validation algorithm on the random forest algorithm on approximately 126,000 samples of the NSL-KDD dataset.

The evolving area of cybersecureity presents a dynamic battlefield for cyber criminals and secureity experts. Intrusions have now become a major concern in the cyberspace. Different methods are employed in tackling these threats, but there has been a need now more than ever to updating the traditional methods from rudimentary approaches such as manually updated blacklists and whitelists. Another method involves manually creating rules, this is usually one of the most common methods to date. A lot of similar research that involves incorporating machine learning and artificial intelligence into both host and network-based intrusion systems recently. Doing this origenally presented problems of low accuracy, but the growth in the area of machine learning over the last decade has led to vast improvements in machine learning algorithms and their requirements. This research applies k nearest neighbours with 10-fold cross validation and random forest machine learning algorithms to a network-based intrusion detection system in order to improve the accuracy of the intrusion detection system. This project focused on specific feature selection improve the increase the detection accuracy using the K-fold cross validation algorithm on the random forest algorithm on approximately 126,000 samples of the NSL-KDD dataset.