A Technique for Resolution of the Assignment Problem Containers in a Container Terminal

A container terminal is a complicated system made up of several components in interdependence. Several materials handle possible to move containers at the port to better meet the needs of ships awaiting loading or unloading. In order to effectively manage this area, it is necessary to know the location of each container. Containers search times can be considerable and lead to delays that cause financial penalties for terminal management operators. In this chapter, the authors propose an approach to solve the problem of placement of containers through the description of a model that optimizes available storage space to handle the distance travelled between the containers and the storage locations in a seaport. In other words, a model that minimizes the total number of unnecessary movement while respecting the constraints of space and time. This work develops a software tool enabling identification of the best location of a container using the methodological resolution Branch and Bound.

Multimodal Information Systems

The number of individuals using public transportation is increasing. Transport companies want to ensure, at best, the satisfaction of the travellers. Nevertheless, a significant number of these companies sometimes pushes the travellers to confusion to compose their itineraries and obtain the required information. The authors suggest in this chapter integrating several traveller information systems into the same global system. This chapter aims to provide information to the traveller without concern for their location and optimize processing by limiting the number of involved nodes. They opted for a multi-agent system associated with the Voronoï decomposition of the global network.

Big Data Analytics in Bioinformatics

Voluminous data are being generated by various means. The Internet of Things (IoT) has emerged recently to group all manmade artificial things around us. Due to intelligent devices, the annual growth of data generation has increased rapidly, and it is expected that by 2020, it will reach more than 40 trillion GB. Data generated through devices are in unstructured form. Traditional techniques of descriptive and predictive analysis are not enough for that. Big Data Analytics have emerged to perform descriptive and predictive analysis on such voluminous data. This chapter first deals with the introduction to Big Data Analytics. Big Data Analytics is very essential in Bioinformatics field as the size of human genome sometimes reaches 200 GB. The chapter next deals with different types of big data in Bioinformatics. The chapter describes several problems and challenges based on big data in Bioinformatics. Finally, the chapter deals with techniques of Big Data Analytics in the Bioinformatics field.

Bio-Inspired Algorithms for Medical Data Analysis

Medical data mining has great potential for exploring the hidden patterns in the data sets of the medical domain. These patterns can be utilized for clinical diagnosis. Bio-inspired algorithms is a new field of research. Its main advantage is knitting together subfields related to the topics of connectionism, social behavior, and emergence. Briefly put, it is the use of computers to model living phenomena and simultaneously the study of life to improve the usage of computers. In this chapter, the authors present an application of four bio-inspired algorithms and meta heuristics for classification of seven different real medical data sets. Two of these algorithms are based on similarity calculation between training and test data while the other two are based on random generation of population to construct classification rules. The results showed a very good efficiency of bio-inspired algorithms for supervised classification of medical data.

A Survey on Different Digital Authentication Systems Using Fuzzy Logic

The communication through wireless medium is very popular to the developed society. More specifically, the use of the internet as well as the use of social networking sites is increasing. Therefore, information security is an important factor during wireless communication. Three major components of it are confidentiality, integrity, and availability of information among authorized users. Integrity level is maintained through various digital authentication schemes. Fuzzy logic is an important soft computing tool that increases the digital watermarking system in various ways. In this chapter, different popular and high secured watermarking schemes using fuzzy logic are analyzed with their mathematical and experimental efficiency. A comparative analysis is developed here with corresponding different parameters.

A Discrete Black Hole Optimization Algorithm for Efficient Community Detection in Social Networks

In recent years, social networks analysis has attracted the attention of many researchers. Community detection is one of the highly studied problems in this field. It is considered an NP-hard problem, and several algorithms have been proposed to solve this problem. In this chapter, the authors present a new algorithm for community detection in social networks based on the Black Hole optimization algorithm. The authors use the modularity density evaluation measure as a function to maximize. They also propose the enhancement of the algorithm by using two new strategies: initialization and evolution. The proposed algorithm has been tested on famous synthetic and real-world networks; experimental results compared with three known algorithms show the effectiveness of using this algorithm for community detection in social networks.

New FastPFOR for Inverted File Compression

Inverted index is used in most Information Retrieval Systems (IRS) to achieve the fast query response time. In inverted index, compression schemes are used to improve the efficiency of IRS. In this chapter, the authors study and analyze various compression techniques that are used for indexing. They also present a new compression technique that is based on FastPFOR called New FastPFOR. The storage structure and the integers' representation of the proposed method can improve its performances both in compression and decompression. The study on existing works shows that the recent research works provide good results either in compression or in decoding, but not in both. Hence, their decompression performance is not fair. To achieve better performance in decompression, the authors propose New FastPFOR in this chapter. To evaluate the performance of the proposed method, they experiment with TREC collections. The results show that the proposed method could achieve better decompression performance than the existing techniques.

Methods for Gene Selection and Classification of Microarray Dataset

One of the problems that gene expression data resolved is feature selection. There is an important process for choosing which features are important for prediction; there are two general approaches for feature selection: filter approach and wrapper approach. In this chapter, the authors combine the filter approach with method ranked information gain and wrapper approach with a searching method of the genetic algorithm. The authors evaluate their approach on two data sets of gene expression data: Leukemia, and the Central Nervous System. The classifier Decision tree (C4.5) is used for improving the classification performance.

Efficient Implementation of Hadoop MapReduce-Based Dataflow

MapReduce is a solution for the treatment of large data. With it we can analyze and process data. It does this by distributing the computation in a large set of machines. Process mining provides an important bridge between data mining and business process analysis. This technique allows for the extraction of information from event logs. Firstly, the chapter mines small patterns from log traces. Those patterns are the representation of the traces execution from a business process. The authors use existing techniques; the patterns are represented by finite state automaton; the final model is the combination of only two types of patterns that are represented by the regular expressions. Secondly, the authors compute these patterns in parallel, and then combine those patterns using MapReduce. They have two parties. The first is the Map Step. The authors mine patterns from execution traces. The second is the combination of these small patterns as reduce step. The results are promising; they show that the approach is scalable, general, and precise. It minimizes the execution time by the use of MapReduce.

Comparative Study Between Two Swarm Intelligence Automatic Text Summaries

This chapter is a comparative study between two bio-inspired approach based on the swarm intelligence for automatic text summaries: Social Spiders and Social Bees. The authors use two techniques of extraction, one after the other: scoring of phrases and similarity that aims to eliminate redundant phrases without losing the theme of the text. While the optimization uses the bio-inspired approach to perform the results of the previous step, the objective function of the optimization is to maximize the sum of similarity between phrases of the candidate summary in order to keep the theme of the text and minimize the sum of scores in order to increase the summarization rate. This optimization will also give a candidate's summary where the order of the phrases changes compared to the original text. For the third and final step concerning choosing a best summary from all candidate summaries generated by optimization layer, the authors opted for the technique of voting with a simple majority.