scholarly journals Sofic measure entropy via finite partitions

2013 ◽  
Vol 7 (3) ◽  
pp. 617-632 ◽  
Author(s):  
David Kerr
Keyword(s):  
Sofic Measure ◽  
Measure Entropy

Application of Unascertained Measure-Entropy Evaluation Model for Program Selection of Shaft Construction under Complex Conditions

2011 ◽  
Vol 225-226 ◽  
pp. 407-410 ◽  
Author(s):  
Wan Qing Li ◽  
Mu Jie Chen ◽  
Wen Qing Meng
Keyword(s):  
Decision Making ◽  
Evaluation Model ◽  
Entropy Weight ◽  
Program Selection ◽  
Effective Decision ◽  
Effective Decision Making ◽  
Measure Entropy ◽  
Selection Of

An unascertained measure-entropy evaluation model for the program selection of shaft construction under complex conditions is established so that a scientific and effective decision making method is provided in this paper, the evaluation model of shaft construction is established based on unascertained measure and entropy weight theory, then, the model proposed in this paper is applied to evaluate three shaft construction program comprehensively, and the evaluation results show validity and applicability of the model.

Enhanced Bio-Inspired Algorithms for Detecting and Filtering Spam

2018 ◽  
pp. 179-215
Author(s):  
Hadj Ahmed Bouarara
Keyword(s):  
Electronic Communication ◽  
Distance Measure ◽  
Machine Learning Algorithms ◽  
Spam Detection ◽  
Social Bees ◽  
Representation Technique ◽  
Sensitive Parameters ◽  
Measure Entropy ◽  
Email Spam ◽  
F Measure

The internet era promotes electronic commerce and facilitates access to many services. In today's digital society, the explosion in communication has revolutionized the field of electronic communication. Unfortunately, this technology has become incontestably the original source of malicious activities, especially the plague called undesirables email (SPAM) that has grown tremendously in the last few years. This chapter unveils fresh bio-inspired techniques (artificial social cockroaches [ASC], artificial haemostasis system [AHS], and artificial heart lungs system [AHLS]) and their application for SPAM detection. For the experimentation, the authors used the benchmark SMS Spam corpus V.0.1 and the validation measures (recall, precision, f-measure, entropy, accuracy, and error). They optimize the sensitive parameters of each algorithm (text representation technique, distance measure, weightings, and threshold). The results are positive compared to the result of artificial social bees and machine-learning algorithms (decision tree C4.5 and K-means).

Enhanced Artificial Social Cockroaches (EASC) for Modern Information Retrieval

2016 ◽  
Vol 10 (2) ◽  
pp. 65-94
Author(s):  
Hadj Ahmed Bouarara ◽  
Reda Mohamed Hamou ◽  
Amine Abdelmalek
Keyword(s):  
Information Retrieval ◽  
Retrieval System ◽  
Relevant Information ◽  
Taboo Search ◽  
The Third ◽  
Naive Method ◽  
Substantial Problem ◽  
Simulating Annealing ◽  
Entropy Error ◽  
Measure Entropy

This article deals on an improved version of the recently developed Artificial Social Cockroaches (ASC) algorithm based on several modifications. The EASC has as input a set of artificial cockroaches and N selected shelters. It is based on a random displacement step and a set of operators (selection cockroaches, shelter attraction, congener's attraction, shelter permutation). Each cockroach must be hidden in the shelter where it feels safer (evaluation function). In the recent years with the coming of the world wide web, the amount of unstructured documents available in the digital society increases and becomes easily accessible, all this has led that satisfy the needs of users in terms of relevant information has become a substantial problem in the scientific community. The second component of the authors' study is to apply the algorithm (EASC) as an information retrieval system using multilingual pre-processing and thesaurus to solve the problems of multilingual query and searching with synonymy. The relevant documents will be rendered as a list of ranked and classified documents from the most relevant to the least relevant. Lastly the authors apply the benchmark Medline and a series of valuation measures (precision, recall, f-measure, entropy, error, accuracy, specificity, TCR, ROC) for the experimentation, also they have compared their results with the outcomes of set of existed systems (social worker bees, taboo search, genetic algorithm, simulating annealing, naïve method). The third component of the authors' system is the visualization step that ensures the presentation of the result in the form of a cobweb with some realism to be understandable by users.

scholarly journals Text documents clustering using modified multi-verse optimizer

2020 ◽  
Vol 10 (6) ◽  
pp. 6361
Author(s):  
Ammar Kamal Abasi ◽  
Ahamad Tajudin Khader ◽  
Mohammed Azmi Al-Betar ◽  
Syibrah Naim ◽  
Mohammed A. Awadallah ◽  
...  
Keyword(s):  
Euclidean Distance ◽  
Optimization Problems ◽  
Continuous Optimization ◽  
Search Space ◽  
Discrete Optimization Problem ◽  
Text Documents ◽  
Text Document ◽  
Continuous Optimization Problems ◽  
Measure Entropy

In this study, a multi-verse optimizer (MVO) is utilised for the text document clus- tering (TDC) problem. TDC is treated as a discrete optimization problem, and an objective function based on the Euclidean distance is applied as similarity measure. TDC is tackled by the division of the documents into clusters; documents belonging to the same cluster are similar, whereas those belonging to different clusters are dissimilar. MVO, which is a recent metaheuristic optimization algorithm established for continuous optimization problems, can intelligently navigate different areas in the search space and search deeply in each area using a particular learning mechanism. The proposed algorithm is called MVOTDC, and it adopts the convergence behaviour of MVO operators to deal with discrete, rather than continuous, optimization problems. For evaluating MVOTDC, a comprehensive comparative study is conducted on six text document datasets with various numbers of documents and clusters. The quality of the final results is assessed using precision, recall, F-measure, entropy accuracy, and purity measures. Experimental results reveal that the proposed method performs competitively in comparison with state-of-the-art algorithms. Statistical analysis is also conducted and shows that MVOTDC can produce significant results in comparison with three well-established methods.

New Bio Inspired Techniques in the Filtering of Spam

2020 ◽  
pp. 693-726
Author(s):  
Hadj Ahmed Bouarara ◽  
Reda Mohamed Hamou ◽  
Abdelmalek Amine
Keyword(s):  
Electronic Communication ◽  
Distance Measure ◽  
Machine Learning Algorithms ◽  
Spam Detection ◽  
Social Bees ◽  
Representation Technique ◽  
Sensitive Parameters ◽  
Measure Entropy ◽  
Email Spam ◽  
F Measure

The internet era promotes electronic commerce and facilitates access to many services. In today's digital society the explosion in communication has revolutionized the field of electronic communication. Unfortunately, this technology has become incontestably the original source of malicious activities, especially the plague called undesirables email (SPAM) that has grown tremendously in the last few years. This paper deals on the unveiling of fresh bio-inspired techniques (artificial social cockroaches (ASC), artificial haemostasis system (AHS) and artificial heart lungs system (AHLS)) and their application for SPAM detection. For the authors' experimentation, they have used the benchmark SMS Spam corpus V.0.1 and the validation measures (recall, precision, f-measure, entropy, accuracy and error). They have optimising the sensitive parameters of each algorithm (text representation technique, distance measure, weightings, and threshold). The results are positive compared to the result of artificial social bees and machine learning algorithms (decision tree C4.5 and K-means).

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