Data Mining Practical Machine Learning Tools and Techniques

Nature-Inspired Optimization Algorithms provides a systematic introduction to all major nature-inspired algorithms for optimization. The book's unified approach, balancing algorithm introduction, theoretical background and practical implementation, complements extensive literature with well-chosen case studies to illustrate how these algorithms work. Topics include particle swarm optimization, ant and bee algorithms, simulated annealing, cuckoo search, firefly algorithm, bat algorithm, flower algorithm, harmony search, algorithm analysis, constraint handling, hybrid methods, parameter tuning and control, as well as multi-objective optimization. This book can serve as an introductory book for graduates, doctoral students and lecturers in computer science, engineering and natural sciences. It can also serve a source of inspiration for new applications. Researchers and engineers as well as experienced experts will also find it a handy reference.Discusses and summarizes the latest developments in nature-inspired algorithms with comprehensive, timely literatureProvides a theoretical understanding as well as practical implementation hintsProvides a step-by-step introduction to each algorithm

Nature-Inspired Optimization Algorithms

Nature-inspired metaheuristic algorithms, especially those based on swarm intelligence, have attracted much attention in the last ten years. Firefly algorithm appeared in about five years ago, its literature has expanded dramatically with diverse applications. In this paper, we will briefly review the fundamentals of firefly algorithm together with a selection of recent publications. Then, we discuss the optimality associated with balancing exploration and exploitation, which is essential for all metaheuristic algorithms. By comparing with intermittent search strategy, we conclude that metaheuristics such as firefly algorithm are better than the optimal intermittent search strategy. We also analyse algorithms and their implications for higherdimensional optimisation problems.

Firefly algorithm: recent advances and applications

The IEEE 80211 MAC protocol provides shared access to a wireless channel This paper uses an analytic model to study the channel capacity - ie, maximum throughput - when using the basic access (two-way handshaking) method in this protocol It provides closed-form approximations for the probability of collision p, the maximum throughput S and the limit on the number of stations in a wireless cell The analysis also shows that: p does not depend on the packet length, the latency in crossing the MAC and physical layers, the acknowledgment timeout, the interframe spaces and the slot size; p and S (and other performance measures) depend on the minimum window size W and the number of stations n only through a gap g D W=n 1/ - consequently, halving W is like doubling n ;t he maximum contention window size has minimal effect onp and S; the choice of W that maximizes S is proportional to the square root of the packet length; S is maximum when transmission rate (including collisions) equals the reciprocal of transmission time, and this happens when channel wastage due to collisions balances idle bandwidth caused by backoffs The results suggest guidelines on when and howW can be adjusted to suit measured traffic, thus making the protocol adaptive

/pdf/a-capacity-analysis-for-the-ieee-802-11-mac-protocol-13697mqqcu.pdf

A capacity analysis for the IEEE 802.11 MAC protocol

Network Simulator NS2

Scheduling jobs on computational grids is identified as NP-complete problem due to the heterogeneity of resources; the resources belong to different administrative domains and apply different management policies. This paper presents a novel metaheuristics method based on Firefly Algorithm (FA) for scheduling jobs on grid computing. The proposed method is to dynamically create an optimal schedule to complete the jobs within minimum makespan. The proposed method is compared with other heuristic methods using simple and different simulation scenarios. Each firefly represents a candidate solution of the grid scheduling problem in a vector form, with n elements; where n is the number of jobs to be scheduled. Firefly[i] specifies the resource to which the job number i is allocated. Therefore, the vector values are natural numbers. Also we note that the vector values are the resource IDs and hence the resource ID may appear more than one time in the firefly vector. This comes about because more than one job may be allocated to the same resource. To evaluate the effectiveness and the efficiency of job scheduling algorithms on computational grid, it is difficult and impractical to achieve performance assessment experimentally in such large scale heterogeneous system and to repeat and control the experiments to perform different scenarios. To encounter this limitation this research used mathematical modeling and simulation to model and evaluate the proposed mechanism. The results demonstrated that, the firefly scheduling mechanism achieved less makespan time than Min-Min and Max- Min heuristics in several scheduling scenarios. The results in this paper showed that the FA is promising method that can be used to optimize scheduling jobs on grid computing.

Scheduling jobs on grid computing using firefly algorithm

Malware is a computer security problem that can morph to evade traditional detection methods based on known signature matching. Since new malware variants contain patterns that are similar to those in observed malware, machine learning techniques can be used to identify new malware. This work presents a comparative study of several feature selection methods with four different machine learning classifiers in the context of static malware detection based on n-grams analysis. The result shows that the use of Principal Component Analysis (PCA) feature selection and Support Vector Machines (SVM) classification gives the best classification accuracy using a minimum number of features.

Feature selection and machine learning classification for malware detection

Carrier sense multiple access with collision avoidance (CSMA-CA) will be used as the basic medium access control (MAC) protocol for wireless LANs. Wireless LAN will be supporting two types of services, time bounded and asynchronous data transmission. In this paper we present simulation results for throughput and delays for a distributed radio LAN for different values of inter frame spacing periods, transmission speeds and number of nodes. Depending on the application, the results obtained suggest that proper inter frame space values have to be chosen for optimal network performance.

Performance of CSMA-CA MAC protocol for distributed radio local area networks

Worms are self-contained programs that spread over the Internet. Worms cause problems such as lost of information, information theft and denial-of-service attacks. The first part of the paper evaluates the detection of worms based on content classification by using all machine learning techniques available in WEKA data mining tools. Four most accurate and quite fast classifiers are identified for further analysisâ€“Naive Bayes, J48, SMO and Winnow. Results show that classification using machine learning techniques could classify worms to 99% accuracy. From the accuracy perspective, J48 performs better than other algorithms meanwhile Naive Bayes and Winnow show the best performances in terms of speed. The second part of the paper analyzes the accuracy these four classifiers under the presence of class noise in learning corpora. By injecting class noise ranging between 0% and 50% into positive and negative corpora, results from the simulation show gradual decrease in accuracy and increase in false positive and false negative for all analyzed techniques. The presence of the classes noise affects false positive more significantly compared to false negative. The results show that worm detection with classification algorithms could not tolerate the presence of classes noise in learning corpora.

Detecting Worms Using Data Mining Techniques: Learning in the Presence of Class Noise

Scheduling jobs on computational grids is identified as NP-complete problem due to the heterogeneity of resources; the resources belong to different administrative domains and apply different management policies. This paper conducted an extensive and wide literature review to study the state of the art of grid scheduling algorithms. This review starts with an overview of the grid technologies and a description of the grid resource management systems. The evolution of the grid scheduling mechanisms is illustrated in this paper started from basic scheduling mechanisms such as Min-Min and Max-Min approaches ending with the swarm intelligence optimization methods. The swarm intelligence and evolutionary mechanisms are also presented and critically analyzed.

http://www.sersc.org/journals/IJGDC/vol8_no6/13.pdf

Sulaiman Mohd Nor

Papers

Scheduling jobs on grid computing using firefly algorithm

Feature selection and machine learning classification for malware detection

Performance of CSMA-CA MAC protocol for distributed radio local area networks

Detecting Worms Using Data Mining Techniques: Learning in the Presence of Class Noise

Job Scheduling Algorithms on Grid Computing: State-of- the Art