Wireless local area networks (WLANs) are in a period of great expansion and there is a strong need for them to support multimedia applications. With the increasing demand and penetration of wireless services, users of wireless networks now expect Quality of Service (QoS) and performance comparable to what is available from fixed networks. Providing QoS requirements like good throughput and minimum access delay are challenging tasks with regard to 802.11 WLAN protocols and Medium Access Control (MAC) functions.
This research is done to study, the presently implemented schemes (the Point Coordination Function (PCF) of IEEE 802.11, the Enhanced Distributed Coordination Function (EDCF) of the proposed IEEE 802.11e extension to IEEE 802.11), solves these issues and what can be done to improve them further.  The metrics used were Throughput, Data Drop, Retransmission and Medium Access Delay, to analyze the performance of various MAC protocols in providing QoS to users of WLAN.
Two scenarios, with same Physical and MAC parameters, one implementing the DCF and other EDCF, were created in the network simulation tool (OPNET MODELER) to obtain the results. The results showed that the performance of EDCF was better in providing QoS for real-time interactive services (like video conferencing) as compared to DCF, because of its ability to differentiate and prioritize various services. Index Terms - Wireless local area networks (WLANs),

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This paper aims to investigate the effect of cell phone radiation on human brain. It is known that the cell phone emits electromagnetic (EM) radiation which could be harmful to the human brain. In this research the electroencephalogram (EEG) signal has been acquired from 24 healthy subjects using a 16 channel EEG headset under different experimental conditions. The signal is decomposed into different brain rhythms using Daubechies Discrete Wavelet Transform up to 5th-level of the decomposition. Quantitative analysis has been carried out using two statistical parameters (Energy, Entropy) and Absolute Power. Special attention was focused on Temporal and Frontal lobes as these are near to the ear. Experimental results show higher values (for energy, entropy and absolute power) in the low-frequency bands (delta and theta) compared to the high frequency bands (alpha, beta and gamma) in both lobes. When the phone was placed 5cm away from the head there was less brain activation compared to when the cell phone was placed next to the ear/head on both sides. It was found that there was more effect on the right side compared to the left side from the cell phone’s radio waves. 

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Prediction of stock prices using various computer programs is on rise. Popularly known in the field of finance as algorithmic trading, a radical transformation has taken place in the field of stock markets for decision making through automated decision making agents. Machine learning techniques can be applied for predicting stock prices. This paper attempts to study the various stock market forecasting processes available in the forecasting plugin of the WEKA tool. Twenty experiments have been conducted on twenty different stocks to analyse the prediction capacity of the tool.

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This paper introduces a preliminary agent-based simulation model that seeks to analyze individual-level behaviors. The model is guided by theory and previous empirical studies on riot simulation. Data on the evolution of the contagion processamong assembled civilian agents is collected to better understand how the ratio of various civilian groups affectsriot development. Numerous variables including the severity of punishment in the form of increased jail sentences for activist civilian agents, the ratio of police to civilian agents, and various contagion thresholds among civilian agents are analyzed. Results from the simulation suggest among other thingsthat thegreaterthe density of activistcivilian agents attending public demonstrationsthe more powerful the contagion and the more quicklyapeaceful protest can be transformed into to a riotous mob. Additionally, increasing levels of guardianship in the form of policedecreases the likelihood of a riot occurring even when group emotions escalate.Limitations of the current model are discussed in addition to the findings, and the future direction of agent-based models on riot simulation. 

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Social networking website creates new ways for engaging people belonging to different communities, moral and social values to communicate and share valuable knowledge, therefore creating a large amount of data. The importance of mining social media cannot be over emphasized, due to significant information that are revealed which can be applied in different areas. In this paper, a systematic approach for traversing the content of weblog, considering location and time (spatiotemporal) is proposed. The proposed model is capable of searching for subjects in social media using Boyer Moore Horspool (BMH) algorithm with respect to location and time. BMH is an efficient string searching algorithm, where the search is done in such a way that every character in the text needs not to be checked and some characters can be skipped without missing the subject occurrence.  Semantic analysis was carried out on the subject by computing the mean occurrence of the subject with the corresponding predicate and object from the total occurrence of the subject. Experiments were carried out on two datasets: the first category was crawled from twitter website from September to October 2014 and the second category was obtained from spinn3r data set made available through the International AAAI (Association for the Advancement of Artificial Intelligence) Conference on Web and Social Media (ICWSM). The results obtained from tracking some subjects such as Islam and Obama shows that the mean occurrence of the analysis of the subject successfully reveals the pattern of the subject over a period of time for a specific location. Evaluation of the system which is based on performance and functionality reveals that the model performs better than some baseline models. The proposed model is capable of revealing spatiotemporal pattern for a subject, and can be applied in any area where spatiotemporal factor is to be considered.

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The application of data mining (DM) in healthcare is increasing. Healthcare organizations generate and collect large voluminous and heterogeneous information daily and DM helps to uncover some interesting patterns, which leads to the manual tasks elimination, easy data extraction directly from records, to save lives, to reduce the cost of medical services and to enable early detection of diseases.   These patterns can help healthcare specialists to make forecasts, put diagnoses, and set treatments for patients in health facilities. This work overviews DM methods and main issues. Three case studies illustrate DM in healthcare applications: (i) In-Vitro Fertilization; (ii) Content-Based Image Retrieval (CBIR); and (iii) Organ transplantation.

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This report focuses on the execution of multithreaded  programs and finding bugs and errors in those programs. Testing is done to determine if the code written  runs correctly or not. The report also covers comparison of traditional testing tools with  the new and efficient  systematic testing tool called CHESS. The repost explains in detail about  the testing technique of CHESS including  how it identifies and handles bugs in multithreaded programs. The various experiments performed using different outputs have also been discussed and their respective results have also been shown in order to determine the behavior of CHESS tool when it is provided random inputs. Using this input did not lead to non-deterministic test and the execution time increases exponentially.

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Powdery mildew disease of beans in India causes major economic losses in agriculture. For sustainable agriculture detection and identification of diseases in plants is very important.  In this review, we are trying to identify the powdery mildew disease of beans crop by using some image processing and pattern recognition techniques and comparing with molecular and spectroscopic techniques. Early information on crop health and disease detection can facilitate the control of diseases through proper management strategies. The present review recognizes the need for developing a rapid, cost-effective, and reliable health monitoring techniques that would facilitate advancements in agriculture. These technologies include image processing and pattern recognition based plant disease detection methods

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