Tahar Kechadi

Application of fuzzy logic to online recognition of handwritten symbols

Fuzzy logic is highly suitable for dealing with uncertainty and variation. Therefore it is seems reasonable to apply this technique to the recognition of handwritten symbols. This paper presents an approach to the task in which fuzzy logic is used extensively. We present a three-phase process, the central phase being feature extraction. Firstly a pre-processing phase generates a chord vector for each handwritten stroke, thereby eliminating noise and greatly reducing the number of sections of the input which need to be assessed as potential features. In the feature extraction phase fuzzy rules are used to determine membership values of chord sequences in fuzzy sets corresponding to feature types, and subsequently the most likely set of features is determined. In the final phase, fuzzy classification rules are used to determine the most likely identity of the symbol according to the feature extraction result. The approach has achieved high recognition rates in experiments on isolated symbols from the UNIPEN database.

DIALOG: A framework for modeling, analysis and reuse of digital forensic knowledge

This paper presents DIALOG (Digital Investigation Ontology); a framework for the management, reuse, and analysis of Digital Investigation knowledge. DIALOG provides a general, application independent vocabulary that can be used to describe an investigation at different levels of detail. DIALOG is defined to encapsulate all concepts of the digital forensics field and the relationships between them. In particular, we concentrate on the Windows Registry, where registry keys are modeled in terms of both their structure and function. Registry analysis software tools are modeled in a similar manner and we illustrate how the interpretation of their results can be done using the reasoning capabilities of ontology.

Performance study of distributed Apriori-like frequent itemsets mining

In this article, we focus on distributed Apriori-based frequent itemsets mining. We present a new distributed approach which takes into account inherent characteristics of this algorithm. We study the distribution aspect of this algorithm and give a comparison of the proposed approach with a classical Apriori-like distributed algorithm, using both analytical and experimental studies. We find that under a wide range of conditions and datasets, the performance of a distributed Apriori-like algorithm is not related to global strategies of pruning since the performance of the local Apriori generation is usually characterized by relatively high success rates of candidate sets frequency at low levels which switch to very low rates at some stage, and often drops to zero. This means that the intermediate communication steps and remote support counts computation and collection in classical distributed schemes are computationally inefficient locally, and then constrains the global performance. Our performance evaluation is done on a large cluster of workstations using the Condor system and its workflow manager DAGMan. The results show that the presented approach greatly enhances the performance and achieves good scalability compared to a typical distributed Apriori founded algorithm.

Lightweight clustering technique for distributed data mining applications

Many parallel and distributed clustering algorithms have already been proposed. Most of them are based on the aggregation of local models according to some collected local statistics. In this paper, we propose a lightweight distributed clustering algorithm based on minimum variance increases criterion which requires a very limited communication overhead. We also introduce the notion of distributed perturbation to improve the globally generated clustering. We show that this algorithm improves the quality of the overall clustering and manage to find the real structure and number of clusters of the global dataset.

Grid-Based Approaches for Distributed Data Mining Applications

The data mining field is an important source of large-scale applications and datasets which are getting more and more common. In this paper, we present grid-based approaches for two basic data mining applications, and a performance evaluation on an experimental grid environment that provides interesting monitoring capabilities and configuration tools. We propose a new distributed clustering approach and a distributed frequent itemsets generation well-adapted for grid environments. Performance evaluation is done using the Condor system and its workflow manager DAGMan. We also compare this performance analysis to a simple analytical model to evaluate the overheads related to the workflow engine and the underlying grid system. This will specifically show that realistic performance expectations are currently difficult to achieve on the grid.

The State of the Art Forensic Techniques in Mobile Cloud Environment: A Survey, Challenges and Current Trends

Smartphones have become popular in recent days due to the accessibility of a wide range of applications. These sophisticated applications demand more computing resources in a resource constraint smartphone. Cloud computing is the motivating factor for the progress of these applications. The emerging mobile cloud computing introduces a new architecture to offload smartphone and utilize cloud computing technology to solve resource requirements. The popularity of mobile cloud computing is an opportunity for misuse and unlawful activities. Therefore, it is a challenging platform for digital forensic investigations due to the non-availability of methodologies, tools and techniques. The aim of this work is to analyze the forensic tools and methodologies for crime investigation in a mobile cloud platform as it poses challenges in proving the evidence.

A comparative evaluation of filter-based feature selection methods for hyper-spectral band selection

Band selection (dimensionality reduction) plays an essential role in hyper-spectral image processing and applications. This article presents a unified comparison framework for systematic performance comparison of filter-based feature selection models and conducts a comparative evaluation of four methods: maximal minimal associated index (MMAIQ), mutual information-based max-dependency criterion (mRMR), relief feature selection (Relief-F), and correlation-based feature selection (CFS) for hyper-spectral band selection. The evaluation is based on the performance of effectiveness, robustness, and classification accuracy, which involves five measuring indices: class separability, feature entropy, feature stability, feature redundancy, and classification accuracy. Three images acquired by different sensors were used to investigate the performance of the metrics. Experimental results show the best results for MMAIQ for all data sets in terms of used measurements, except for feature stability where mRMR and Relief-F exhibit their superiority.

Cufres : clustering using fuzzy representative eventsselection for the fault recognition problem intelecommunication networks

In this paper we introduce an efficient clustering algorithm embedded in a novel approach for solving the problem of faults identification in large telecommunication networks. Our algorithm is especially designed for the event correlation problem taking into account comprehensive information about the system behaviour. Although alarms are usually useful for identifying faults in such systems, their large number overloads the current management systems, making it extremely difficult to provide an answer within a reasonable response time. The alarm flow presents some interesting characteristics like alarm storm and alarm cascade. For instance, a single fault may result in a large number of alarms, and it is often very difficult to isolate the true cause of the fault. One way of overcoming this problem is to analyze, interpret and reduce the number of these alarms before trying to localize the faults. In this paper, we present a new original algorithm, and compare it with some available clustering algorithms by experimenting them with some samples of both simulated and real data from Ericssons network.

Semantic Modelling of Digital Forensic Evidence

The reporting of digital investigation results are traditionally carried out in prose and in a large investigation may require successive communication of findings between different parties. Popular forensic suites aid in the reporting process by storing provenance and positional data but do not automatically encode why the evidence is considered important. In this paper we introduce an evidence management methodology to encode the semantic information of evidence. A structured vocabulary of terms, ontology, is used to model the results in a logical and predefined manner. The descriptions are application independent and automatically organised. The encoded descriptions aim to help the investigation in the task of report writing and evidence communication and can be used in addition to existing evidence management techniques.

A New Method for Estimation of Missing Data Based on Sampling Methods for Data Mining

Today we collect large amounts of data and we receive more than we can handle, the accumulated data are often raw and far from being of good quality they contain Missing Values and noise.