Data Mining Practical Machine Learning Tools and Techniques

I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

In this book, Andrew Harvey sets out to provide a unified and comprehensive theory of structural time series models. Unlike the traditional ARIMA models, structural time series models consist explicitly of unobserved components, such as trends and seasonals, which have a direct interpretation. As a result the model selection methodology associated with structural models is much closer to econometric methodology. The link with econometrics is made even closer by the natural way in which the models can be extended to include explanatory variables and to cope with multivariate time series. From the technical point of view, state space models and the Kalman filter play a key role in the statistical treatment of structural time series models. The book includes a detailed treatment of the Kalman filter. This technique was originally developed in control engineering, but is becoming increasingly important in fields such as economics and operations research. This book is concerned primarily with modelling economic and social time series, and with addressing the special problems which the treatment of such series poses. The properties of the models and the methodological techniques used to select them are illustrated with various applications. These range from the modellling of trends and cycles in US macroeconomic time series to to an evaluation of the effects of seat belt legislation in the UK.

Forecasting, Structural Time Series Models and the Kalman Filter

Leu-M

Fast Fourier Transform

With the advent of multiple radio interfaces on a single device, wireless mesh networks start to achieve significant improvement in network capacity, latency, and fault tolerance. The improvement is achieved through concurrent transmissions over different channels utilizing the multiple radio interfaces. However, the introduction of different channels over multiple radios on single mesh node compels to retrospect different issues such as interference, channel diversity, and channel switching from novel perspectives. Due to these novel perspectives, conventional channel assignment techniques proposed for single-radio wireless mesh networks are not generally applicable to the multi-radio cases. Consequently, we have to reconsider the different issues while making a tradeoff among all the available channel assignment options to extract the best performance from a multi-radio wireless mesh network. There are a number of research studies that propose various channel assignment techniques to extract the best performance. In this paper, we present a comprehensive survey on these studies. First, we point out various design issues pertinent to the techniques presented in the studies, and adopt the issues as the basis of our further discussion. Second, we briefly describe several important already-proposed channel assignment techniques. Third, we present a number of channel assignment metrics that are exploited by the already-proposed techniques. Then, depending on the considerations in these techniques, we categorize the techniques and present an exhaustive comparison among them. Nevertheless, we point out a number of real deployments and applications of these techniques in real scenarios. Finally, we identify several open issues for future research with their current status in the literature.

Channel Assignment Techniques for Multi-Radio Wireless Mesh Networks: A Survey

Maintaining the performance of reliable transport protocols, such as transmission control protocol (TCP), over wireless mesh networks (WMNs) is a challenging problem due to the unique characteristics of data transmission over WMNs. The unique characteristics include multi-hop communication over lossy and non-deterministic wireless mediums, data transmission in the absence of a base station, similar traffic patterns over neighboring mesh nodes, etc. One of the reasons for the poor performance of conventional TCP variants over WMNs is that the congestion control mechanisms in conventional TCP variants do not explicitly account for these unique characteristics. To address this problem, this paper proposes a novel artificial intelligence based congestion control technique for reliable data transfer over WMNs. The synergy with artificial intelligence is established by exploiting a carefully designed neural network (NN) in the congestion control mechanism. We analyze the proposed NN based congestion control technique in detail and incorporate it into TCP to create a new variant that we name as intelligent TCP or iTCP. We evaluate the performance of iTCP using both ns-2 simulations and real testbed experiments. Our evaluation results demonstrate that our proposed congestion control technique exhibits a significant improvement in total network throughput and average energy consumption per transmitted bit compared to the congestion control techniques used in other TCP variants.

iTCP: an intelligent TCP with neural network based end-to-end congestion control for ad-hoc multi-hop wireless mesh networks

Network lifetime is one of the important metrics that indicate the performance of a sensor network. Different techniques are used to elongate network lifetime. Among them, clustering is one of the popular techniques. LEACH (Low-Energy Adaptive Clustering Hierarchy) is one of the most widely cited clustering solutions due to its simplicity and effectiveness. LEACH has several parameters that can be tuned to get better performance. Percentage of cluster heads is one such important parameter which affects the network lifetime significantly. At present it is hard to find the optimum value for the percentage of cluster head parameter due to the absence of a complete mathematical model on LEACH. A complete mathematical model on LEACH can be used to tune other LEACH parameters in order to get better performance. In this paper, we formulate a new and complete mathematical model on LEACH. From this new mathematical model, we compute the value for the optimal percentage of cluster heads in order to increase the network lifetime. Simulation results verify both the correctness of our mathematical model and the effectiveness of computing the optimal percentage of cluster heads to increase the network lifetime.

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Finding the Optimal Percentage of Cluster Heads from a New and Complete Mathematical Model on LEACH

Emotions are primarily thought of as mental experiences of body states, which are mostly shown in the face with precise and specific muscle patterns. It is, perhaps, the most critical attribute of living beings, and is extremely difficult to detect and generate artificially. Its detection always remains a well-explored classical problem. Existing approaches for detecting human emotions generally demand significant infrastructural overheads. Excluding these overheads, in this paper, we propose a much simpler way of emotion detection. To do so, We have induced different states of emotion through different multimedia components, and then collected participants' keystrokes (free text) and mouse usage data through a custom-developed survey. We have used several existing classifiers (KNN, KStar, RandomCommittee and RandomForest) and a newly proposed light-weight classifier namely Bounded K-means Clustering, to analyze those usage data for different emotional states. Our analysis demonstrates that emotion can be detected from the usage data up to a certain level. Moreover, our proposed classifier enables the best detection of five emotional states namely happiness, inspiration, sympathy, disgust, and fear compared to other existing classifiers. Besides, the analysis also reveals that user identification through usage dynamics does not result in a good level of accuracy when usage gets influenced by different emotional states.

A. B. M. Alim Al Islam

Papers

Channel Assignment Techniques for Multi-Radio Wireless Mesh Networks: A Survey

iTCP: an intelligent TCP with neural network based end-to-end congestion control for ad-hoc multi-hop wireless mesh networks

Finding the Optimal Percentage of Cluster Heads from a New and Complete Mathematical Model on LEACH

Keystroke/mouse usage based emotion detection and user identification

A hybrid IoT-based approach for emergency evacuation