Georgia Kontogianni
email: gkontogianni@eie.gr

Olga Papadodima

Ilias Maglogiannis

Konstantina Frangia-Tsivou

Aristotelis Chatziioannou

Integrative bioinformatic analysis of a Greek epidemiological cohort provides insight into the pathogenesis of primary cutaneous melanoma

Keywords: Data Integration, Next Generation Sequencing, Functional Analysis, Skin Cancer

Melanoma is the most lethal type of skin cancer. In this study for the first time we analyze a Greek cohort of primary cutaneous melanoma biopsies, subjected to whole exome sequencing, in order to derive their mutational profile landscape. Moreover, in the context of big data analytical methodologies, we integrated the results of the exome sequencing analysis with transcriptomic data of cutaneous melanoma from GEO, in an attempt to perform a multi-layered analysis and infer a tentative disease network for primary melanoma pathogenesis. The purpose of this research is to incorporate different levels of molecular data, so as to expand our understanding of cutaneous melanoma and the broader molecular network implicated with this type of cancer. Overall, we showed that the results of the integrative analysis offer deeper insight in the underlying mechanisms affected by melanoma and could potentially contribute to the valuable effective epidemiological characterization of this disease.

Introduction

Melanoma is the most dangerous form of skin cancer [START_REF]The Skin Cancer Foundation[END_REF]. Cutaneous melanoma (or melanoma of the skin), the most common type of melanoma, is a complex multi-factorial disease as both environmental and genetic factors are involved in its manifestation [START_REF] Rossi | Management of cutaneous melanoma M0: state of the art and trends[END_REF]. It is often a fatal neoplasm, derived from melanocytes, that accounts for most skin cancer deaths. In the advanced stages of this cancer, therapeutic intervention usually fails to improve survival despite recent advances in immunotherapy. According to the World Health Organization, 132,000 melanoma skin cancers occur globally each year and the global incidence of melanoma continues to increase, with a main predisposal factor; sun exposure.

The complexities of cellular metabolism and regulatory pathways involved have, until recently, obstructed the formulation of a unified description for melanoma [START_REF] Dummer | Human Melanoma: From Transcriptome to Tumor Biology[END_REF]. Thus, despite the descent of gene signatures for various cancers, e.g. breast or colon cancer, a similar progress remains elusive for malignant melanoma. This could be attributed to the intricate nature of the molecular basis of cutaneous melanoma, which needs neatly stratified epidemiological cohorts to effectively address the issue of the high heterogeneity of this disease. In any case, genomic studies are limited by the shortage of similar melanoma cohorts, collecting and maintaining frozen tumor tissue, therefore rendering gene expression profiling studies of melanoma relatively scarce [START_REF] Winnepenninckx | Gene expression profiling of primary cutaneous melanoma and clinical outcome[END_REF]. Still, efforts have been made to overcome any issues and shed some light on the underlying mechanisms involved with melanoma pathogenesis and metastases [START_REF] Winnepenninckx | Gene expression profiling of primary cutaneous melanoma and clinical outcome[END_REF][START_REF] Raskin | Transcriptome profiling identifies HMGA2 as a biomarker of melanoma progression and prognosis[END_REF]. A number of important emerging biological pathways and gene targets recently identified in melanoma are reported in [START_REF] Dutton-Regester | Reviewing the somatic genetics of melanoma: from current to future analytical approaches[END_REF]. Key biological pathways, where several significant genes (e.g. CDKN2A, CDK4, RB1) are involved, include proliferation, transcriptional control, extracellular matrix remodeling, glutamate signaling, and apoptosis.

In this study, we have focused on integrating different levels of molecular data through functional analysis to improve our understanding of the underlying mechanisms involved with melanoma. We incorporated established microarray datasets with next generation sequencing mutational data creating a potential disease network for melanoma.

Section 2 describes the techniques and methodology used in this study for the analysis of next generation sequencing and transcriptomic data. Then, in section 3 we present the results derived from the two datasets, in separate subsections, followed by another subsection for data integration. Finally, we conclude this study in section 4.

2

Materials & Methods

Analysis of Next Generation Exome Sequencing Data

The data analyzed in this section derived from Whole Exome Sequencing (WES) data of paired tumor and adjacent normal tissue from 9 patients with cutaneous melanoma (manuscript under preparation). The framework for the analysis of Next Generation Sequencing (NGS) data includes various state-of-the-art tools and has been previously presented by our team [START_REF] Maglogiannis | Redesigning EHRs and Clinical Decision Support Systems for the Precision Medicine Era[END_REF].

We first align the reads to the reference genome (hg19, version b37), using BWA (Burrows-Wheeler Aligner) [START_REF] Li | Fast and accurate long-read alignment with Burrows-Wheeler Transform[END_REF] for DNA reads, adjusted for paired-end sequencing and run in consecutive steps for finding the correct coordinates and generate the final alignment in proper format. Then, we preprocess reads using Picard [9], for marking duplicate reads and sorting sequences according to the reference, to allow further processing with GATK (Genome Analysis Toolkit) [START_REF] Mckenna | The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data[END_REF], so as to ensure the quality of reads (all reads are given quality scores and can be dismissed if needed) and perform realignments and recalibrations based on the scores and references, to optimize the output reads and permit the following variance and somatic mutation investigation. Inspection for variance is performed with MuTect [START_REF] Cibulskis | Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples[END_REF], which exploits statistical methodologies (Bayesian classifiers) and identifies sites of somatic mutations in paired datasets (tumor vs. normal). To annotate these sites, we use Oncotator [START_REF] Ramos | Oncotator: cancer variant annotation tool[END_REF], which utilizes several databases to link the sites to specific genes. Finally, we perform functional analysis to identify the molecular pathways affected by the specific mutations, and gene prioritization, so as to highlight genes with central role, implicated in diverse and major mechanisms in the Gene Ontology tree. These are performed using BioInfoMiner [START_REF]Nios BioInfoMiner tool[END_REF], which combines the StRAnGER2 [START_REF] Pilalis | Prioritized functional analysis of biological experiments using resampling and noise control methodologies[END_REF] and GOrevenge [START_REF] Moutselos | GOrevenge: a novel generic reverse engineering method for the identification of critical molecular players, through the use of ontologies[END_REF] algorithms. Figure 1 presents the workflow used here.

Fig. 1. Workflow of analysis for the identification of variance and somatic mutations

The complexity of NGS data is high, due to the high amount of information contained in each separate sample (compressed ~10Gb per sample/ 20Gb per patient/ ~150Gb for all) and the fact that several distinct parameters need to be adjusted at each step, so as to optimize the performance and the quality of the results (i.e., BWA needs to be adjusted for paired-end sequencing and run in consecutive steps for finding the correct coordinates and generate the final alignment in proper format).

Analysis of Transcriptomic Data

Transcriptomic analyses among different groups allow the exploration and identification of alterations in gene expression profiles between them. The data used in this section were previously analyzed in [START_REF] Moutselos | Integration of High-Volume Molecular and Imaging Data for Composite Biomarker Discovery in the Study of Melanoma[END_REF]. Briefly, the microarray dataset was taken from the Gene Expression Omnibus (GEO) [START_REF] Barrett | NCBI GEO: archive for functional genomics data sets -10 years on[END_REF], with accession number GDS1375. RNA was isolated from 45 primary melanoma, 18 benign skin nevi, and 7 normal skin tissue specimens and was analyzed for gene expression analysis, using the Affymetrix Hu133A microarray chip containing 22,000 probe sets. Following global normalization, gene expression values across all categories were log transformed, and the mean values of all genes in the normal skin were calculated. Afterwards, the mean gene vector concerning the normal skin categories was subtracted from all replicate vectors of the other two categories. The differentially expressed gene values of the melanoma versus skin, and nevi versus skin, were then analyzed. A false discovery rate for multiple testing adjustment, p-value 0.001 and a 2-fold change threshold for significant differential expression were applied and finally, 1425 unique genes were statistically selected, as being differentially expressed between melanoma and the normal state.

Results & Discussion

Mutational Data derived from Exome Sequencing

WES data derived from tumor and normal samples were aligned to the human genome, with an average sequence coverage of > 100x (number of reads aligning to known reference bases), ideal for achieving the mutational profile required. Overall, the individual samples have depth of coverage > 90, with only 3 samples achieving a lower score. Still this lower score is found only in normal samples, which does not affect further analysis, since high coverage is necessary mainly by the tumor samples to overcome endogenous heterogeneity. Table 1 contains the alignment rates and coverage for all samples that were examined. After the processing of individual samples for analysis of variance based on the reference genome, pairs of data from each patient (tumor vs. normal) are jointly analyzed, so as to identify somatic point mutations. Table 2 shows the number of putative sites of somatic mutations, after the MuTect analysis, as well as the count of missense and nonsense mutations for each patient. These mutations affect gene products, by amino acid substitutions or protein truncation, and require further analysis as candidate genetic biomarkers. It is worth noting that the complete workflow for a pair of samples (tumor and normal samples from one patient) needs approximately 35 hours running time on a 64Gb RAM/ 12 processor cluster server (finally, summarizing the results in ~10Mb). In order to discover the molecular pathways affected by the specific mutations, after annotating the mutations to specific genes, we performed functional analysis of the union of affected genes from all the patients (2685 unique genes), which revealed 40 statistically significant biological processes (pvalue < 0.05), shown in table 3.

Transcriptomic Data

The transcriptomic analysis from [START_REF] Moutselos | Integration of High-Volume Molecular and Imaging Data for Composite Biomarker Discovery in the Study of Melanoma[END_REF] revealed 1425 unique differentially expressed genes. Enrichment analysis showed 36 statistically significant biological processes (p-value < 0.05), which are presented in Table 4. Table 4. Table of the significant biological processes influenced by the differentially expressed genes. Enrichment represents the ratio of the number of genes in the input list annotated with a GO term to the total number of genes annotated to this specific term

Data Integration

To facilitate a deeper examination of our datasets, we compared the gene lists from the mutational and transcriptomic analyses. Figure 2 illustrates the total unique and common genes, from the two types of datasets. Only 5% of the total genes were common between the two sets. Nevertheless, among the highly ranked processes, presented in tables 3 and 4, cell adhesion, extracellular matrix organization and extracellular matrix disassembly, containing a large number of genes, are found as significantly affected in both cases.

Fig. 2. Venn diagram for the significant gene lists from the two analyses

In order to create a feasible disease network for melanoma, we merged the previous results, and carried out an additional functional analysis. This enrichment analysis revealed 45 statistically significant biological processes (p-value < 0.05), presented in Figure 3, ranked according to their corrected p-values. Fig. 3. Bar plot of significant terms with the number of associated genes (x-axis). Terms are ranked using the corrected p-value. The colors of the genes specify their expression fold change, green -on the left-for underexpressed genes and red -on the right-for over-expressed genes, with neutral indicating somatic mutation This potential Disease Network revealed several mechanisms with known significance, consistent with melanoma. Enrichment of GO terms, such as epithelial tube branching involved in lung morphogenesis, morphogenesis of an epithelium, epithelial cell differentiation, and regulation of mammary gland epithelial cell proliferation reflects the topological origin of cutaneous melanoma [START_REF] Martin-Belmonte | Epithelial cell polarity, stem cells and cancer[END_REF][START_REF] Jögi | Cancer cell differentiation heterogeneity and aggressive behavior in solid tumors[END_REF]. Furthermore, cell-matrix procedures (organization, adhesion) have been previously reported as significantly altered in tumors [START_REF] Saladi | Modulation of extracellular matrix/adhesion molecule expression by BRG1 is associated with increased melanoma invasiveness[END_REF][START_REF] Hart | Cell adhesion receptor expression during melanoma progression and metastasis[END_REF], as well as lamellipodium assembly, an essential structure for cell migration, which plays an important part in cell invasion and metastasis of cancer [START_REF] Machesky | Lamellipodia and filopodia in metastasis and invasion[END_REF][START_REF] Kato | Rac1-dependent lamellipodial motility in prostate cancer PC-3 cells revealed by optogenetic control of Rac1 activity[END_REF]. In relation to the ephrin receptor and Rho protein signaling pathways, the Eph receptor tyrosine kinases and their ephrin ligands have specific expression patterns in cancer cells [START_REF] Pasquale | Eph receptors and ephrins in cancer: bidirectional signalling and beyond[END_REF], while Rho-like GTPase have been identified as key regulators of epithelial architecture and cell migration, both correlated to cancer development [START_REF] Ridley | Rho proteins and cancer[END_REF][START_REF] Sander | Rho-like GTPases: their role in epithelial cell-cell adhesion and invasion[END_REF].

As expected, the previously discussed significant pathways from tables 3 and 4 are complemented by the additional data, incorporating an increased number of genes, with considerable implication in melanoma manifestation and progression. Among the significant processes are several previously highlighted by the distinct datasets, but also a number of newly generated, after data integration. Figure 4 indicates the unique and common pathways in each case. In this study, we sought to export the broader molecular network implicated with cutaneous melanoma. We integrated molecular data of different levels in order to identify the important mechanisms that are involved in this type of cancer. This integration advanced our understanding about the mechanisms implicated with melanoma, by observing the correlation between different sets and levels of data. More importantly, it allowed the manifestation of additional mechanisms previously concealed by the statistical cut-offs, thus enhancing the disease network and our general understanding of the phenomenon.

Our future aim is to expand our current dataset, including data from more patients. Apart from that, we want explore additional methodologies for data integration. Furthermore, our goal is to integrate the molecular data with imaging data from dermoscopy, to improve feature selection and classification techniques, concerning melanoma.

Fig. 4 .

 4 Fig. 4. Venn diagram for the significant pathway lists from the two distinct analyses, as well as their integration

Table 1 .

 1 Alignment rates and coverage

	patient	Normal alignment rate	coverage	Tumor alignment rate	coverage
	1	96	129.8	95.4	118.9
	2	84.2	70.5	89.5	91
	3	66.7	35.9	93.6	102.2
	4	93.8	103.9	92.6	111.5
	5	96.8	101.5	96.7	111.4
	6	96.8	123.3	96.2	104.8
	7	97.4	117.4	96.8	111.9
	8	87.9	88.7	88.8	92.3
	9	95.6	128.4	92	120.7

Table 2 .

 2 Number of somatic mutations, missense/nonsense mutations, and unique genes affected per patient

Patient Sites of somatic mutations Missense/Nonsense mutations

				Unique genes
				affected
	1	855	224	214
	2	1134	309	295
	3	826	281	265
	4	73	10	10
	5	944	275	265
	6	5985	1811	1474
	7	812	226	200
	8	922	224	219
	9	1111	224	214

Table 3 .

 3 Table of the significant biological processes influenced by the mutated genes. Enrichment represents the ratio of the number of genes in the input list annotated with a GO term to the total number of genes annotated to this specific term

	Term id	Term Definition	Enrichment	Hypergeometric	Corrected
				p-value	p-value
	GO:0007156	homophilic cell adhesion via plasma	69/150	4.33E-20	0.0014
		membrane adhesion molecules
	GO:0007155	cell adhesion	148/531	2.17E-15	0.0027
	GO:0050911	detection of chemical stimulus involved	105/389	1.68E-10	0.0037
		in sensory perception of smell
	GO:0030198	extracellular matrix organization	84/313	1.38E-08	0.0048
	GO:0086010	membrane depolarization during action	17/30	1.10E-07	0.0063
		potential
	GO:0007411	axon guidance	95/375	3.64E-08	0.0068
	GO:0006811	ion transport	82/319	1.53E-07	0.0101
	GO:0022617	extracellular matrix disassembly	39/117	2.98E-07	0.0108
	GO:0006814	sodium ion transport	37/106	1.59E-07	0.0115
	GO:0055085	transmembrane transport	162/767	7.19E-07	0.012
	GO:0007608	sensory perception of smell	61/224	7.06E-07	0.0125
	GO:0019228	neuronal action potential	16/31	1.42E-06	0.0144
	GO:0035725	sodium ion transmembrane transport	30/89	5.11E-06	0.0145
	GO:0007268	synaptic transmission	97/428	5.75E-06	0.0178
	GO:0042391	regulation of membrane potential	36/117	6.79E-06	0.0195
	GO:0007186	G-protein coupled receptor signaling	192/976	9.61E-06	0.0198
		pathway
	GO:0030574	collagen catabolic process	26/74	9.12E-06	0.0203
	GO:0007605	sensory perception of sound	39/133	1.03E-05	0.0223
	GO:0034765	regulation of ion transmembrane	35/118	2.18E-05	0.0257
		transport
	GO:0060080	inhibitory postsynaptic potential	8/11	2.26E-05	0.0257
	GO:0070588	calcium ion transmembrane transport	38/129	1.19E-05	0.0258
	GO:0018108	peptidyl-tyrosine phosphorylation	37/130	3.53E-05	0.0287
	GO:0016339	calcium-dependent cell-cell adhesion via	13/27	3.64E-05	0.0306
		plasma membrane cell adhesion
		molecules
	GO:0070509	calcium ion import	13/28	5.89E-05	0.0323
	GO:0007018	microtubule-based movement	24/74	8.72E-05	0.0331
	GO:0001539	cilium or flagellum-dependent cell	6/7	5.96E-05	0.034
		motility
	GO:0032228	regulation of synaptic transmission,	7/10	0.0001	0.0353
		GABAergic
	GO:0007399	nervous system development	72/322	0.0001	0.0376
	GO:0007169	transmembrane receptor protein tyrosine	33/119	0.0002	0.0382
		kinase signaling pathway
	GO:0034220	ion transmembrane transport	65/286	0.0002	0.0395
	GO:0001964	startle response	10/20	0.0002	0.0399
	GO:0050907	detection of chemical stimulus involved	28/96	0.0002	0.0405
		in sensory perception
	GO:0007416	synapse assembly	17/47	0.0002	0.0445
	GO:0071625	vocalization behavior	7/12	0.0006	0.0447
	GO:2000821	regulation of grooming behavior	4/4	0.0005	0.0455
	GO:0016337	single organismal cell-cell adhesion	30/109	0.0003	0.0465
	GO:0030534	adult behavior	12/29	0.0004	0.0468
	GO:0034332	adherens junction organization	14/38	0.0006	0.0476
	GO:0034329	cell junction assembly	22/76	0.001	0.0492
	GO:0015721	bile acid and bile salt transport	11/27	0.0009	0.0493

Acknowledgements

This work has been supported by the 12CHN 204 Bilateral Greece-China Research Program of the Hellenic General Secretariat of Research and Technology and the Chinese Ministry of Research and Technology entitled "Personalization of melanoma therapeutic management through the fusion of systems biology and intelligent data mining methodologies-PROMISE," sponsored by the Program "Competitiveness and Entrepreneurship," Priority Health of the Peripheral Entrepreneurial Program of Attiki.


Ben Mehdi

Morten Lazreg
email: mehdi.ben.lazreg@uia.no

Ole-Christoffer Goodwin
email: morten.goodwin@uia.no

Granmo
email: ole.granmo@uia.no

Information Abstraction from Crises Related Tweets Using Recurrent Neural Network

Keywords: information abstraction, recurrent neural network, twitter data, crisis management

Social media has become an important open communication medium during crises. The information shared about a crisis in social media is massive, complex, informal and heterogeneous, which makes extracting useful information a difficult task. This papers presents a first step towards an approach for information extraction from large Twitter data. In brief, we propose a Recurrent Neural Network based model for text generation able to produce a unique text capturing the general consensus of a large collection of twitter messages. The generated text is able to capture information about different crises from tens of thousand of tweets summarized only in a 2000 characters text.

Introduction

Social media has become the de facto open crises communication medium [START_REF] Zielinski | Social media text mining and network analysis for decision support in natural crisis management[END_REF]. It plays a pivotal role in most crises today, from getting life signs from people affected to communicating with responders [START_REF] Imran | Processing social media messages in mass emergency: a survey[END_REF]. However, processing and extracting useful information and inferring valuable knowledge from such social media messages is difficult for several reasons. The messages are typically brief, informal, and heterogeneous (mix of languages, acronyms, and misspellings) with varying quality, and it may be required to know the context of a message to understand its meaning. Moreover, people also post information on other mundane events, which introduces additional noise into the data.

The state-of-the-art in the area of information discovery using machine learning mostly centres on supervised learning techniques. Those techniques are based on training an algorithm on sets of text from each topic to learn a predictive function, which in turn is used to classify new texts into a previously learnt topic [START_REF] Sakaki | Earthquake shakes twitter users: real-time event detection by social sensors[END_REF]. A limitation of this approach is the scope of the topics: If a new text about an unforeseen topic is presented to the algorithm, such as a new crisis, it will wrongly classify it as one of the existing topics. Another challenge is that crises are diverse and the number of topics discussed in social media during a single crisis is large, dynamic, and changing from crisis to crisis. Moreover, applying a classifier trained on data from previous disasters on the next disaster may not perform well in practise. This can be explained by the fact that the next disaster will typically be more or less unique compared to the previous ones. Accordingly, a loss of accuracy occurs even if the crises have many similarities. Alternatively, unsupervised techniques try to look for co-occurrences of terms in the text as a metric of similarity [START_REF] Dumais | Latent semantic analysis[END_REF] or infer the word distribution of a set of words the text contains and use it for document clustering [START_REF] Blei | Latent dirichlet allocation[END_REF]. Moreover, different methods based on graph theory has been used to extract information from a document. As an example, a graph based ranking model for document processing was adopted to extract key words from a text document [START_REF] Mihalcea | Textrank: Bringing order into texts[END_REF]. In addition, a stochastic graph based method has also been employed to extract the most important sentence in a text document [START_REF] Erkan | Lexrank: Graph-based lexical centrality as salience in text summarization[END_REF].

Recurrent Neural Networks and its long-short term memory variant have emerged as an efficient model in a variety of application involving sequential data [START_REF] Lecun | Deep learning[END_REF]. This includes handwriting recognition [START_REF] Graves | A novel connectionist system for unconstrained handwriting recognition[END_REF], speech recognition [START_REF] Farahat | Noise robust speech recognition using deep belief networks[END_REF], and video analysis [START_REF] Kahou | Recurrent neural networks for emotion recognition in video[END_REF]. As an example, RNN was trained on Wikipedia articles for text generation with great success. The power of recurrent neural network comes from their high dimensional hidden state with non-linear dynamics which has the ability to remember and process past input information [START_REF] Karpathy | Visualizing and understanding recurrent networks[END_REF]. The goal of this paper is to make a model that summarises and reproduces content from massive Twitter streams. The model is based on recurrent neural network to predict the next character in a stream of text. The approach allows to generate a text that compresses the information contained in the text that the network has been trained on.

This paper is organised as follow. Section 2 gives an overview of the state-ofthe-art twitter analysis in crises situations. Section 3 introduces recurrent neural network and illustrates its basic features. Section 4 proposes a recurrent network based model for topic discovery in crisis related twitter data. Section 5 presents tests and results of the model. Finally, Section 6 concludes and provides pointers to further work.

Twitter analysis for crisis situations

There is no doubt that valuable, high throughput data is produced on social media only seconds after a crisis occurs [START_REF] Zielinski | Social media text mining and network analysis for decision support in natural crisis management[END_REF]. To cope with the complexity of the social media data, and extract information from crisis related messages, machine learning techniques have been applied [START_REF] Imran | Processing social media messages in mass emergency: a survey[END_REF]. Two main approaches were investigated: supervised and unsupervised learning.

In a supervised approach the goal is to classify a social media message as part of one particular crisis event. To achieve this, the algorithm learns a predictive function so that it can classify any new unknown message as part of one of the categories of crises. A number of approaches have been investigated including Naïve Bayes, Support Vector Machine (SVM) [START_REF] Yin | Using social media to enhance emergency situation awareness[END_REF], Random Forests [START_REF] Imran | Aidr: Artificial intelligence for disaster response[END_REF], and Logistic Regression [START_REF] Ashktorab | Tweedr: Mining twitter to inform disaster response[END_REF]. Further, some research focus on only analysing tweets containing certain keywords [START_REF] Imran | Processing social media messages in mass emergency: a survey[END_REF]. In this way, the tags can replace manual labelling for training. As an example, SVM was used to classify tweets related to earthquakes and landslides [START_REF] Sakaki | Earthquake shakes twitter users: real-time event detection by social sensors[END_REF][START_REF] Musaev | Litmus: Landslide detection by integrating multiple sources[END_REF]. In a supervised approach, labels are necessary for training the classifiers, but they might be highly difficult to obtain especially in the case of multi-language messages or context knowledge [START_REF] Imran | Processing social media messages in mass emergency: a survey[END_REF]. To address this problem, unsupervised learning techniques are used.

Unsupervised methods are used to identify patterns in unlabelled data. They are most useful when the information seekers do not know specifically what information to look for in the data -which is the case in many crises situations. An example is grouping tweets into stories (clusters of tweets) after a keyword filter [START_REF] Rogstadius | Crisistracker: Crowdsourced social media curation for disaster awareness[END_REF]. This method reduces the number of social media messages to be handled by humans since it groups equivalent messages together. Another application using unsupervised learning identifies events related to public and safety using a spatio-temporal clustering approach [START_REF] Berlingerio | Safercity: a system for detecting and analyzing incidents from social media[END_REF]. In addition to strictly clustering elements into groups, soft clusters have been used to allow items to simultaneously belong to several clusters with variant degrees. In this approach, the tweets similarity is based on words they contain and the length of the tweets [START_REF] Kireyev | Applications of topics models to analysis of disaster-related twitter data[END_REF]. The approach was applied on the Indonesia earthquake (2009) data and detected different aspects related to the crisis (relief, deaths, missing persons, and so on).

Artificial Neural Networks

An Artificial Neural Network (ANN) is a machine learning algorithm developed to mimic the human brain and reach its information processing capabilities [START_REF] Graves | Supervised sequence labelling[END_REF]. An ANN is a network of processing units (analogous to neurons) joined by weighted connections (analogous to synapse). The network is activated by giving an input to some or all of the units. This activation is then spread throughout the hidden layers of the network until it reaches the output layer (see Figure 1).

Many varieties of ANN exist all with different sets of properties [START_REF] Graves | Supervised sequence labelling[END_REF]. One major distinction is between networks where the connections are acyclic called Feedforwad Neural Networks (FNN) and networks where the connections form cycles called Recurrent Neural Networks (RNN). RNN is most suited for tasks that involve sequential input such as speech and language. A RNN processes an input sequence one element at a time, and maintains information about the history of the past elements in the sequence. This ability makes it suitable for learning patterns form text since a text is a series of correlated characters. RNN is successfully used to predict the next word in a sequence of semantically related words [START_REF] Lecun | Deep learning[END_REF]. It also has some success in predicting the next character in a sequence of characters which is used to generate text, and in machine translation [START_REF] Lecun | Deep learning[END_REF].

Recurrent Neural Networks

A Recurrent neural network (RNN) is an ANN where the connections between neurons are allowed to form a circle (see Figure 1) [START_REF] Graves | Supervised sequence labelling[END_REF]. As Figure 1 shows, the connections between units on the same layer allow mapping the history of previous inputs to the output vectors. For each unit k in the RNN, the activation a k of that unit depends on the inputs {x 1 , x 2 , ..., x n } of the unit and the weights {w 1 , w 2 , ..., w n } of their respective connections as shown in Equation 1.

a k = f ( n i=1 w i x i + b k ) (1)
The most widely used activation functions are sigmoid, hyperbolic tangent (in this case the unit is called a logistic unit) and linear functions (in this case the unit is called linear unit) [START_REF] Graves | Supervised sequence labelling[END_REF]. b k is a bias term that represents the expected mean value of the activation when all the inputs are zeroes.

During the training phase of an RNN, the aim is to update the weights so that for a given input, the output produced minimises a loss function that measures the similarity between the output of the network and the desired output [START_REF] Graves | Supervised sequence labelling[END_REF].

The training of a RNN goes through three major steps [START_REF] Graves | Supervised sequence labelling[END_REF]:

1. Initialise the weights w i to a generally small value (in the range [-0.1, 0.1]). 2. Forward pass: Computes the activations a k of all the unit in the RNN. 3. Backward pass: Updates the weights of the network in a manner that minimises the loss function between the output of the RNN and the desired output. This is performed using gradient descent. Backpropagation is used to efficiently compute the gradient and update the weights.

The three steps are repeated until a minimum of the loss function is reached. Note that the solution converged to may actually represent a local minimum.

Long-short term memory

The main benefit of a RNN is its ability to use the input at previous time steps to produce an output. Nevertheless, in a standard RNN the range of past inputs that can influence output is quite limited because gradients can either decay or blow up exponentially as feedback cycles around the network recurrent connections [START_REF] Graves | Supervised sequence labelling[END_REF]. This problem is known as the vanishing/exploding gradient problem [START_REF] Pascanu | On the difficulty of training recurrent neural networks[END_REF]. To address this problem a Long-Short Term Memory (LSTM) architecture was proposed [START_REF] Graves | A novel connectionist system for unconstrained handwriting recognition[END_REF]. In a LSTM architecture, the unit in the hidden layer of a RNN is replaced by a block (see Figure 2) analogous to a memory block. The block is composed of :

-A linear unit "c t " presenting the state of the block a time t.

-A logistic input unit "i t ": analogous to a write gate that updates the value in "c t " when on (outputs a value close to 1) -A logistic output unit "o t ": analog to a read gate that retrieves the value in "c t " when on. -A logistic forget unit "f t ": analog to a keep gate that maintains the value in "c t " when on.

The state of units "i t ", "o t " and "f t " are updated based on the input x t , the output of the block at the previous time step h t-1 , and the output of other blocks in the LSTM network. This architecture is proved successful at a range of tasks that require long range memory including text generation, speech recognition and handwriting recognition [START_REF] Lecun | Deep learning[END_REF].

Approach

Our approach aims at learning patterns form crises related tweets, and later generate a compressed text deducing the main topic (see Figure 3). This means that the model learns characteristics of tweets, understands the context and is able to reproduce similar tweets. Note that this is very different from reciting tweets in that the model has learnt concepts. It does not copy Twitter text. It is designed to capture the main concept even with a noisy set of tweets i.e. a collection of tweets about different topics. However, the model does not aim at presenting a comprehensive assessment of the crisis at this point. It only present fragments of the crisis present in the generated text. To achieve this goal, we train a character based LSTM architecture on crises related tweets. The used LSTM architecture contains multiple hidden layers, each containing multiple LSTM blocks presented in Figure 2. The input of the network is a vector representing the current character x t where the character can be a letter or a special character. The output node of the network h t is a softmax distribution over characters [START_REF] Graves | Supervised sequence labelling[END_REF]. The softmax function produces an output in the [0, 1] interval that represents the probability of the next character given the input of the node. For a training set {(x 1 , y 1 ), (x 2 , y 2 ), .., (x n , y n )}, the softmax distribution is defined by Equation 2.

p(y i = k|x i ) = exp(θ i x i ) K j=1 exp(θ j x i ) (2)
Where x i ∈ IN K is vector coding of the input of the softmax (note that in our case the input of the softmax is the output of the hidden layers of the LSTM architecture), y i = {1, 2, .., K} is the index of the output character, K is the number of possible characters. θ i is the parameter of the softmax to be determined during the training phase to minimize the loss function in Equation 3. The loss function represents the sum of the negative log-likelihood of y i knowing x i . By minimizing the loss function, the probabilities that the correct character is predicted approaches one.

J(θ) = - 1 n n i=1 K j=1 1{y i = j} log(p(y i = j|x i )) (3)
1{y i = j} = 1 if y i = j ; 0 otherwise ( 4
)
The hypothesis is that the generated text should provide a description of the tweets the network has been trained on. Hence, a practitioner can easily get an overview of the underlying topics of the tweets.

Tests and results

The model described in the previous section was used to learn patterns from Twitter data related to several crises and then generate a unique text containing information present in the initial tweets.

The data

The crises data used to train our model is provided by CrisisLex [START_REF] Olteanu | Crisislex: A lexicon for collecting and filtering microblogged communications in crises[END_REF] a platform for collecting and filtering communications during a crisis. Table 1 refers to more than fifty four thousand tweets about several crises. The data is a mix of tweets where some are related to the respective crisis and some are not. The percentage of unrelated tweets for each crisis ranges from 38% to 44% of the whole set of Twitter messages. Moreover, in the case of the Alberta flood, only 30% out of the related tweets gave concrete useful information about the crisis. The remaining tweets include other mundane topics. The percentage of informative tweets out of the related tweets goes up to a maximum 48% in the Queensland flood data.

Experiments

The network was trained on a Twitter data collect from several crises (see Section 5.1). Table 2 summarises the empirical results of the model tested with different setups. The performance of the model can be measured with the training loss indicating the difference between the predicted and true value during the training period (Equation 3), and the validation loss indicating the difference between the predicted and true value over a validation data (additional data over which the model is applied after training). The first conducted experiment was to show the improvement in training and validation loss the LSTM brings over a simple RNN. Table 2 shows that the validation loss drops from 1.81 for a simple RNN to 1.5 for LSTM. Similarly, by showing the evolution of the training loss over the amount of train data for RNN and LSTM architectures, Figure 4 illustrates the margin in training loss between LSTM and RNN even for small amount of training data. The training loss ends at a value of 1.97 for RNN and 1.48 for LSTM.

The LSTM architecture possesses different parameters that can be tuned to improve the model's ability to learn patterns from the training set and predict the next character in a sequence. An important parameter is the number of units (or nodes) in the network. Table 2 shows that increasing the number of nodes in the network improves the validation loss from 1.65 with 128 nodes to 1.47 with 512 nodes. Figure 5 displays the same trend: A 512 units network reaches a training loss of 1.17 while the loss for 128 units network remains at 1.81. However, with 512 nodes the validation loss is significantly higher then training loss strongly indicating that the network is over-fitting the data. Over-fitting will cause the generated text later on to be a copy of the tweets existing in the training set which will present no value added. Moreover, using 512 nodes will require more processing time for little gain in validation loss over 256 nodes (1.5 for 256 units and 1.47 for 512 units). An equilibrium is reached at 265 units where the validation loss 1.5 is slightly higher then the training loss 1.48. Another parameter of LSTM is the number of hidden layers in the network. Table 2 suggests that increasing the number of hidden layers slightly improves the validation loss from 1.57 for one layer to 1.5 and 1.49 for 2 and 3 layers respectively. Nevertheless, the validation loss barely changes between 2 and 3 hidden layer. Likewise, the training loss is not greatly influenced by the number of layers going from 1.53 to 1.48 and 1.56 for 1, 2 and 3 layers respectively.

The dropout intends to avoid over-fitting by dropping out each node in the network with a certain probability at each training step [START_REF] Srivastava | Dropout: A simple way to prevent neural networks from overfitting[END_REF]. Table 2 shows that 0 dropout improves the training loss to 0.8 compared to 2 for a dropout of 0.9. Nevertheless, the validation loss remains high, which again indicates over-fitting. In contrast, a high dropout causes both losses to be high (training loss of 2 and a validation loss of 1.9) which suggest under-fitting. Under-fitting means that the model cannot capture data patterns and fails to fit the data well enough.

The batch size determines how many examples the model looks at before making a weight update [START_REF] Lecun | Efficient backprop[END_REF]. Lower batch sizes should intuitively improve the validation and training loss. However, the change is no longer compelling after reaching an batch size of 100. As Table 2 shows, the training loss goes from 1.85 to 1.48 for a change of batch size from 1000 to 100 and stays at 1.48 for a batch size of 50. Similarly, the validation loss goes from 1.79 to 1.5 for a change of batch size from 1000 to 100 but then increases to 1.61 for a batch size of 50.

The sequence length is the maximum number of characters that remains in the network memory to perform a prediction. Our experiments tested 3 values: the most frequent length of tweets (30 characters), the average length of tweets (50 characters) and the maximum length of tweets (140 characters). The results presented in Table 2 show an improved validation loss with shorter sequence moving from 1.51 to 1.48 for sequence lengths of 140 and 30 respectively. The model starts degrading in fitting the training data for shorter sequence shown by an increase of training loss from 1.49 to 1.52. The best combination of validation and training loss for 50 sequences.

We will in the remainder of the paper apply the configuration and parameters that provide the best performance above. The best setup consists of an LSTM architecture with 2 hidden layers and 256 hidden nodes, which represents approximately 400 thousand parameters to train. We used a dropout of 0.5, a batch size of 100, and the network keeps a memory of last 50 characters to use in its predictions.

Results

The model was successful in generating a 2000 character text for each crisis. A sample of the generated text is presented in Table 3. The aim is to generate tweets which are concise, explanatory, and extract the main topic of the big twitter data. The generated text is unique and only generated by the model. This means that all generated text is different and is not contained in the training data. Hence, from a structural point of view, the model was able to learn the basic component of a tweet: RT (retweets), hashtags, the "@" to address a specific person and the hypertext links. It was also able to predict an open bracket following two points, :(, for a sad smiley face in the first hurricane Sandy related text.

From a content point of view, the text contains misspelling and the sentences are unstructured. However, they clearly present valuable information that exists in the training data. An example is the Boston bombing, the first tweets clearly indicates the event of a bomb at the Boston marathon and presents a name: "Jeff Bauran". The name is actually a misspelling of "Jeff Bauman", a witness who identified one of the attackers [25]. The second text indicates that the FBI released a video about the Bombing. What actually happened, and present in the training data, is that the FBI released pictures and videos of the attackers [25]. An arrest was also made as the training data suggest and this was also captured by the model in the third tweet.

For the Texas explosion in West Fertilizer Company, the model was able to capture the number 60 surrounded by "killed" and "injured" in the first tweet. Actually, in the training data, this number appears sometimes as the number of killed in the explosion and other times as the number of injured. It is worth noticing that the number of deaths declared by the authorities was 14 [START_REF] Gillam | Ammonium nitrate stores exploded at texas plant: state agency[END_REF]. However, this number was not part of the training Twitter data. This is an example of misleading information that can be present in the generated text cause by people spreading rumors through Twitter. The second tweet is unrelated to the crises and handles mundane topic present in the training data. The same applies to the third tweet about Alberta flood. Moreover, the generated tweets about the Texas explosion do not explicitly state the nature of the crises. This might be due to the fact that the training data related the crisis present the highest percentage of unrelated tweets which influence the generated text. Therefore, we tried to remove the unrelated tweets from the train data related to the Texas explosion, retrain the model and generate a new text. The last tweet about the Texas explosion represents a sample of what we obtain. Even though the nature of the crisis is explicitly stated in the tweet, the tweet contains much more misspellings and unstructured. This is caused by reducing the training data (eliminating unrelated tweets), the model had not enough data to capture the structure and learn words.

For the remaining crises in the data, the tweets indicate the type of crisis, its location and provide some update on it status like the school destroyed during hurricane sandy, and the deepening of the Queensland flood. Note that some tweets are related to the crises but do not provide value added information about its status, like the firefighters mission in the Alberta flood second tweet. Nevertheless, these tweets present a significant chunk of the training data (see Section 5.1) linked to the crisis but not presenting useful information about it.

When we tried to generate the text anew, the information present in the new text was similar to previously discussed text. It is also worth noticing that some valuable information was present in the training data that was not extracted by the generated text which could be an area of further improvement in addition to automatically displaying the text in a manner that improves situational awareness further.

Conclusion

During a crises, valuable and substantial information about the crisis is shared on social media. However, the complexity and heterogeneity of tweeted text render extraction of useful information a difficult task for machine learning techniques. This papers presents a first step towards an approach for information extraction from large Twitter data collections. We propose training an LSTM architecture on crises related tweets and then use the trained model to generate a novel text. The model is able to capture valuable information from tens of thousands of tweets summarized only in an adjustable 2000 character text. Work is still to be made to filter useful crises related information and present it automatically in a more intuitive manner. Another future direction is to adopt a diversification mechanism that uses scores to rank the generated text based on their similarities and the information they bring.

Fig. 1 .

 1 Fig. 1. Example of a RNN.

Fig. 2 .

 2 Fig. 2. A block of LSTM architecture.

Fig. 3 .

 3 Fig. 3. A hight level overview of the proposed model.

Fig. 4 .

 4 Fig. 4. Training loss for RNN and LSTM architectures.

Fig. 5 .

 5 Fig. 5. Training loss for different number of nodes.

Table 1 .

 1 Crises related twitter data

	Crisis	Year Number of tweets Percentage of unrelated tweets
	Boston bombing 2013	11012	40%
	Texas explosion 2013	11006	44%
	Alberta flood 2013	11036	44%
	Hurricane Sandy 2012	10008	38%
	Queensland flood 2011	11233	43%

Table 2 .

 2 Experiments results

	Parameter		Training loss Validation loss
	Architecture	RNN LSTM	1.97 1.48	1.81 1.50
		128	1.81	1.65
	Number of nodes	256	1,48	1.50
		512	1.17	1.47
		1	1.53	1.57
	Number of layers	2	1.48	1.50
		3	1.56	1.49
		0	0.80	1.90
	Dropout	0.50	1.48	1.50
		0.90	2.00	1.90
		50	1.48	1.61
	Batch size	100 500	1.48 1.66	1.50 1.62
		1000	1.85	1.79
		30	1.52	1.48
	Sequence length	50	1.48	1.50
		140	1.49	1.51

Table 3 .

 3 Generated text

	Crisis	Generated tweets
	Boston bombing	1. Jeff Bauran, was bomb at #Boston Marathon and rew for the Boston marathon. My thoughts and prayers go out to everyone
		affected by today.
		2. RT @CSDiggren: Wook, suspect in Boston Marathon bombings:
		FBI releases video of the #BostonMarathon bombing: Any are
		peosled of sole. Same name gun...
		3. RT @SportsCenter: BREAKING: An arrest has been made in
		the Boston Marathon bombings, CNN reports.
	Texas explosion	1. ?earcharliess killed 60 injured) http://t.co/jaKhPJfNND 2. he's saying we won't knight.
		3. Eeproike for ouf the texas #PrayForWest PHOTO: Fertilizer
		plant explosion httexas. I gagrings! Thoughts To fertilizer plant
		explosionor the mesting, #WestTXClr4MwWUp
	Alberta flood	1. now video all the stay safe. They work to help the flood victims to help. Everyone pock the floods to my content for calgary floods:
		RT @nenshi:
		2. This pic of an awesome firefighter in Mission needs to go viral.
		Please share! #abflood http://t.co/x1Y
		3. #job #hiring http://t.co/MDEIzdl5lN
	Hurricane Sandy	1. nice in plays of Hurricane Sandy election to some going hurricane sandy. Tomorrow :(
		2. school destroyed http://t.co/nIsP2NJK
	Queensland flood	1. Queensland's flood crisis deepens -The Australian: Sydney Morning
		2. Photos: Flood water rises in Australia: http://t.co/eFEOl


S Mohssen Ghafari
email: m.ghafari@ihu.edu.gr

Christos Tjortjis
email: c.tjortjis@ihu.edu.gr

Association Rules Mining by improving the Imperialism Competitive Algorithm (ARMICA)

Keywords: Association Rules Mining, Data Mining, Knowledge Engineering

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

INTRODUCTION

With the dramatic increase in the amount of available data, searching for information in databases can be done manually with queries, but takes a long time and is not always efficient [START_REF] Bhandari | Improvised apriori algorithm using frequent pattern tree for real time applications in data mining[END_REF]. Alternatively, Data mining and particularly Association Rules Mining (ARM) have been proposed to analyze databases and extract frequent patterns and rules. A well-known method for ARM algorithms employs a two-step approach: find the Frequent List and extract Rules from that list [START_REF] Qodmanan | Multi objective association rule mining with genetic algorithm without specifying minimum support and minimum confidence[END_REF].

However, there are still challenges faced by many ARM algorithms, as they require the user to define two parameters named Minimum Support and Minimum Confidence [START_REF] Tjortjis | Experiences of Using a Quantitative Approach for Mining Association Rules[END_REF][START_REF] Tjortjis | PRICES: An Efficient Algorithm for Mining Association Rules[END_REF][START_REF] Agrawal | Fast Algorithms for Mining Association Rules in Large Databases[END_REF]. Another challenge for Apriori style ARM algorithms is that they use a time and resource consuming process, named Pruning which checks the feasibility of a rule and removes weak rules [START_REF] Agrawal | Fast Algorithms for Mining Association Rules in Large Databases[END_REF]. ARM algorithms could improve by approaches that automatically determine the value for parameters like minimum support and confidence [START_REF] Djenouri | A Hybrid Bees Swarm Optimization and Tabu Search Algorithm for Association[END_REF][START_REF] Kuo | Application of particle swarm optimization to association rule mining[END_REF][START_REF] Nandhini | Association rule mining using swarm intelligence and domain ontology[END_REF], as well as by replacing pruning with more efficient methods. Although there have been researches addressing this topic, more needs to be done.

In this paper, we propose Association Rules Mining by improving the Imperialism Competitive Algorithm (ARMICA), a novel dynamic method based on the heuristic Imperialism Competitive Algorithm (ICA) [START_REF] Atashpaz-Gargari | Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition[END_REF]. ARMICA customizes itself according to the database that it operates on, and eliminates the dependability on initial parameters like minimum support and confidence. It automatically selects the strongest rules from the database. Moreover, in contrast with algorithms like Apriori, it does not involve time-consuming pruning. Because it eliminates all the weak items, it results in eliminating the need for pruning. ARMICA improves ICA in that it selects imperialists among the most powerful countries, rather than randomly, thus alleviating the need for a revolution part. In addition, having found the minimum support, ARMICA disqualifies and removes countries that have less cost than this minimum support. Finally, ARMICA has a different fitness function and method for creating initial countries. It was evaluated on publicly available datasets against Apriori. Experimental results show that it produces accurate results, up to 3 times faster than Apriori.

The rest of the paper is organized as follows: Section 2 discusses previous related work. Section 3 introduces and details the Imperialism Competitive Algorithm (ICA). Section 4 presents our proposed method, ARMICA. Section 5 presents the evaluation process and Section 6 discusses this work, its contribution and how it complements the state of the art. The paper concludes with directions for future work in Section 7.

Related Work

Association Rules Mining (ARM) is a well-known data mining method [START_REF] Qodmanan | Multi objective association rule mining with genetic algorithm without specifying minimum support and minimum confidence[END_REF]. It aims at extracting frequent patterns and structure from data. Given T, a set of transactions in a transactional database: {t1, t2,…, tn }and I, a set of items in this database: {i1, i2,…, in }, association rules mining goal is to find all {X, Y} where X and Y are both sets of some items (itemsets). In other words, ARM finds all the rules of the form: X → Y, where X and Y are both subsets of I and X ∩ Y = Φ. This rule indicates that whenever X occurs then, Y may occur with certain probability. As a result, extracting such rules could help enrich our knowledge about a database, which supports solving real world problems.

Most of ARM approaches consist of two stages: finding the Frequent Itemset List and generating rules. One of the best-known ARM algorithms is Apriori [START_REF] Agrawal | Fast Algorithms for Mining Association Rules in Large Databases[END_REF]. It starts with finding the candidate Itemset list. Then, with a technique, named pruning and joining, it iteratively creates a candidate list and eliminates weak itemsets, employing the concept of Minimum Support. Support is the percentage of itemset occurrences in the transactions. Therefore, Apriori uses a user-defined threshold to check itemsets in the candidate list. If there is an itemset that has a support value less than the minimum, it removes it. Moreover, at each level there is a procedure, named pruning, which checks that every itemset in the candidate list satisfies this condition: "all the non-empty subsets of each itemset should be in the previous candidate list". If there is an itemset which does not meet this condition, Apriori removes it from the list. Eventually the candidate list becomes the frequent list. At that point, every non-empty subset of each item in the frequent list can be extracted. The algorithm removes the weaker rules based on a parameter, named Minimum Confidence. The confidence of a rule is calculated by:

Support (X ∪ Y) /Support (X) (1)
Therefore, Apriori uses a user-defined threshold for min. confidence removing rules with lower confidence. Apriori is known for its efficiency in extracting frequent itemsets [START_REF] Agrawal | Fast Algorithms for Mining Association Rules in Large Databases[END_REF], [START_REF] Agrawal | Mining association rules between sets of items in large databases[END_REF]. Its main concept is the Apriori principle: any superset of an infrequent itemset must be an infrequent itemset. There were many algorithms that were proposed based on Apriori, which can be classified as follows [START_REF] Yang | An Efficient Hash-Based Method for Discovering the Maximal Frequent Set[END_REF]:

 Candidate itemsets reduction:
A smaller number of candidate itemsets incurs reduced computational cost. The hash-based DHP is such example [START_REF] Park | An Effective Hash Based Algorithm for Mining Association Rules[END_REF]. Its main advantages are: efficient generation of frequent itemsets, effective declining in transaction database size and reduction in number of database scans. Before the next database scan, it uses a hash table to reduce the size of the candidate k-itemsets (that is Ck, for k>1). The number of candidate k-itemsets may be reduced substantially, especially when the length of itemsets is equal to 2. However, DHP has a similar to Apriori behavior, as it needs to scan the database as many times as the length of the longest frequent itemset in it.

 Database scans reduction

A large number of database scans increases the cost of time-consuming disk I/O. In order to tackle this problem, Savasere et al. proposed the Partition algorithm [START_REF] Savasere | An Efficient Algorithm for Mining Association Rules in Large Databases[END_REF], which produces all frequent itemsets with two database scans. The Partition algorithm divides the database into several chunks in such a way that each portion can fit in memory and can be processed by Apriori. However, one of its drawbacks is that the number of candidate itemsets examined is much bigger than that of Apriori. The Dynamic Itemset Counting (DIC) algorithm [START_REF] Brin | Dynamic Itemset Counting and Implication Rules for Market Basket Data[END_REF], also divides the database into several parts. Once some frequent itemsets are produced, DIC can generate new candidate itemsets and immediately starts counting them. It should also be noted that the data distribution of a database has great impact on the performance of DIC [START_REF] Brin | Dynamic Itemset Counting and Implication Rules for Market Basket Data[END_REF].

 Combination of bottom-up and top-down search

This algorithm also employs the Apriori principle to identify frequent itemsets in the bottom-up search, while in each round the infrequent itemsets found in the same direction are used to separate the maximal frequent candidate itemsets in the top-down direction. The advantage is that all subsets of the maximal frequent itemsets are ready as long as the maximal frequent itemsets are identified. As a result, there is no need to extract subsets of the maximal frequent itemsets again from the bottom-up direction. The Pincer-Search algorithm [START_REF] Lin | Pincer-Search: A New Algorithm for Discovering the Maximum Frequent Set[END_REF] and the MaxMiner algorithm [START_REF] Park | An Effective Hash Based Algorithm for Mining Association Rules[END_REF] are two examples of this concept, but they are not efficient in finding the maximal frequent candidate itemsets. Meanwhile, the advance made by this approach is not remarkable when the length of the longest frequent itemset is relatively short [START_REF] Yang | An Efficient Hash-Based Method for Discovering the Maximal Frequent Set[END_REF].

 Proposing Dynamic ARM Algorithms

Researchers proposed dynamic algorithms to deal with the drawbacks of setting the minimum support and confidence manually. For instance, Djenouri et al. proposed HBSO-TS, a method that uses two heuristic algorithms: Artificial Bee Colony (ABC) for the main processing and Tabu Search (TS) for searching the neighbors of each food source for each bee [START_REF] Djenouri | A Hybrid Bees Swarm Optimization and Tabu Search Algorithm for Association[END_REF]. The authors concede that setting many parameters required by ABC and TS is hard. In addition, they do not mention anything about possible advantages of their method concerning pruning.

Kuo et al. [START_REF] Kuo | Application of particle swarm optimization to association rule mining[END_REF] propose an algorithm that tries to determine minimum support and confidence automatically. It uses a heuristic algorithm, named Particle Swarm Optimization (PSO). First, they apply particle swarm encoding, which they claim to be similar to chromosome encoding of genetic algorithms. Next, they calculate the fitness value, and, based on that, they generate a population of particle swarms. Finally, PSO searching is applied until it satisfies the stop condition that is finding the best particle. The support and confidence of the best particle can represent the minimal support and minimal confidence. It should be noted that setting the best solution as minimum support may not be efficient. Besides, they use minimum confidence to evaluate their final rules, like most ARM algorithms.

Nandhini et al. [START_REF] Nandhini | Association rule mining using swarm intelligence and domain ontology[END_REF] propose an algorithm also based on PSO and a domain ontology. Like PSO, it determines min. support automatically. It also considers support and confidence of the best particle as minimum support and confidence. However, it also needs to set the minimum confidence in order to evaluate rules. Next, we detail the Imperialism Competitive Algorithm (ICA).

Imperialism Competitive Algorithm (ICA)

ICA is a powerful heuristic algorithm based on the notion of the relation between imperialistic countries and their competition for acquiring colonies [START_REF] Atashpaz-Gargari | Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition[END_REF]. ICA involves key concepts that we present and discuss here:

 Country: the variables that we want to optimize  Imperialist: A powerful country that can control other countries in the form of colonies.  Colony: less powerful country that imperialists try to control.  Empire: a group of countries headed by an imperialist.  Cost: a parameter depending on the nature of the problem at hand. In this paper, we consider it the number of occupancies of item in all the transactions.  Normalized cost: the cost of each country calculated using formula (3) below.  Power: a factor calculated based on the cost of each country. A country could be considered as more powerful than another, depending on their costs.  Total empire Power: The sum of the powers of the imperialist and its colonies in one empire.  NCountry: the number of all countries (items)  Nimp: the number of Imperialists, which is a parameter [START_REF] Talatahari | Imperialist competitive algorithm combined with chaos for global optimization[END_REF].  Ncol: the number of colonies given by: (Number of all countries -number of Imperialists)

ICA firstly considers all the variables that we want to optimize, as a concept named country. Then it randomly selects some countries as Imperialists. Then it calculates the cost of imperialists and calculates their powers. The cost of each country could be different depending on the case. For instance, in this paper we consider each item as a country and the number of occurrences of each item in all the transactions as the cost of each country. Then, based on the power of imperialists, it divides other countries (colonies) among imperialists. In this step, the algorithm assumes that each imperialist and its colonies are part of an Empire.

Next, ICA calculates the power of each empire based on the power of its colonies and imperialist and establishes a competition between empires. In this step, the most powerful empires remove colonies from the weakest empires. If there is only one colony left for the weakest empire, then the imperialist and the last colony of the weakest empire become colonies of the strongest empire. This competition continues until there is only one empire left, as shown in Table I.

Creating the Initial Empires

ICA considers an array of variables (countries) which should be optimized. We calculate the cost of each country by a function f at variables ( ) ( , , ,..., )

i i N cost f country f p p p p   (2)
At the beginning, ICA initializes countries of size Npop and randomly selects Nimp the countries as initial imperialists. It also considers all the remaining countries (Ncol) as potential colonies. They should be distributed across the imperialists, based on their power. The definition of normalized cost of an imperialist is defined in [START_REF] Talatahari | Imperialist competitive algorithm combined with chaos for global optimization[END_REF]. We apply this formula to calculate the costs of Items:

max{ } n i n i C c c   ( 3
)
where cn is the cost of the n th imperialist and Cn is its normalized cost. After having calculated the normalized cost of all imperialists, the normalized power of each imperialist can be computed as follows [START_REF] Talatahari | Imperialist competitive algorithm combined with chaos for global optimization[END_REF]:

1 imp n n N i i C p C    (4)

Total Empire Power

We sum up the power of the imperialist of an empire and the mean power of its colonies as the power of an empire with formula (5) [START_REF] Atashpaz-Gargari | Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition[END_REF].

. . = + ( ′ ) (5)

TABLE I. IMPERIALISM COMPETITVE ALGORITHM

1) Initialize the empires 2) Calculate the total Power empires.

3) Competition between empires. Select a colony from the weakest empires and assign it to the most powerful one. 4) Eliminate the weakest empires. 5) If there is only one empire left, stop the process. Otherwise, go to step2

Proposed Method

Section 3 introduced the original ICA algorithm. This section discusses our proposed method, ARMICA, which employs ICA, suitably modified for association rules mining. Some parts of original ICA are not suitable for ARM, thus we had to change them. For that reason, we modified ICA to adapt it with the characteristics of an ARM algorithm. Firstly, we select imperialists among the most powerful countries, in contrast to ICA, which selects them randomly. Next, we remove the revolution part of ICA. Revolution is about checking if a colony becomes more powerful than the imperialist of its empire is. In that situation, a revolution takes place and ICA exchanges that colony with the imperialist. However, in ARMICA, since we select the weakest colonies and imperialists are the most powerful countries, there is no chance for a colony to become more powerful than its imperialist. As a result, ARMICA does not need incorporate revolution. In addition, having found the minimum support in the first run of ICA, we are using this value to disqualify countries that have less cost than this minimum support. This helps to remove many unusable countries; there is no such mechanism in the original ICA. Finally, there are other modifications, which were necessary for ARMICA in order to improve ICA, like changing the fitness function and creating the initial countries. Table II illustrates pseudo code for ARMICA.

TABLE II. PSEUDO CODE FOR ARMICA

Input: Dataset Output: Rules 1) Run ICA with the dataset and create initial countries based on items (attributes) 2) Calculate the cost for each country 3) Create the empires and calculate the power of each empire 4) Establish competition among empires based on their power 5) The Candidate List consists of the imperialist of the last empire and its colonies 6) In the first candidate list, consider the cost of each country as its support and then sort the countries based on their support. median support as the primary min support for the next steps. 7) While there is at least one country that has support more than that primary min support do: Extract all the combinations of countries and store them in a list, send it again to ICA and get the next Candidate List 8) The last candidate list is the Frequent List 9) Extract all the subsets of items in the FrequentList and send them to ICA to get the best rules.

As II indicates, ARMICA uses the dataset as input and delivers association rules as output. In the first step, it creates a set of initial countries based on dataset attributes (initial countries). In other words, it defines each item as a country. As a result, there are as many items (attributes) in the dataset as there are countries for ICA. At this point, the algorithm calculates the cost for each country based on the number of occurrences of each item in the set of transactions. ICA considers countries with lower cost as more powerful.

However, in this paper, since we consider the cost of a country as the number of its occurrences in the set of transactions, it is more suitable that the countries with higher costs are items that are more desirable. As a result, in contrast to ICA, ARMICA assumes that the most powerful countries are those with the highest cost. After calculating the cost for all the empires, the algorithm selects the most powerful countries as imperialists and considers the remaining countries as colonies. Hence, imperialists are the most frequent items in the dataset. The number of Imperialists is a free parameter in the original ICA, but for ARMICA, after experimentation, we consider it the number of countries divided by 10. In addition, if the initial number of countries is NCountry, and we select Nimp countries as imperialists, we have NCol colonies.

In the next step, ARMICA calculates the power of each imperialist and distributes the colonies (other countries or other items) among them based on their power. As a result, by gathering imperialists and colonies together, the initial empires are built. ARMICA calculates the power of each empire based on the sum of powers of its colonies and their imperialist. For this purpose, the algorithm calculates the number of occurrences of each item in all the transactions, considers this value as the cost of that item (country), and calculates its power.

Next, ARMICA establishes a competition between empires, where the more powerful empires "steal" colonies from the weakest ones. Another difference between ARMICA and ICA is that in the original algorithm, each colony removed from the weakest empire is placed in the most powerful empire. However, ARMICA removes the colonies and stores them in a list, named Reserve List. If there is only one colony left in the weakest empire, the colony and its imperialist, become members (colonies) of the most powerful empire. This procedure continues until all the empires are eliminated and only the most powerful one remains. For this purpose, ARMICA removes the weakest colonies (items) of the weakest empire and keeps them in the Reserve List.

If the weakest empire has only one colony, then ARMICA removes that empire and considers its imperialist and its last colony as colonies of the most powerful empire. After several iterations, all the empires will be eliminated and only one empire remains. Now, if there is a member of the Reserve List which is more powerful, i.e. it has higher frequency than any colony in last empire, algorithm exchanges them. Hence, members of the final empire constitute our candidate list.

Overall, even though ARMICA uses a minimum support threshold, it sets it automatically and removes the weaker items. The procedure for setting the minimum support involves storing support values, which is the cost (frequency) of each country (item) in the dataset, for all the items of the first generated candidate list and sorting them. At that point, the algorithm considers the median support of all the countries as the universal minimum support. This value is also used to remove countries that have less cost than the minimum support in the next levels.

Then, ARMICA extracts all the combinations of items, stores them in a list, and sends them to ICA. This continues until there is at least one item with support higher than the minimum support. As a result, each time the algorithm produces an updated candidate list. It is noticeable that, here is another significant improvement compared to Apriori. In Apriori there is a step, named Pruning which eliminates itemsets with at least one of their subsets is not present in previous candidate lists. As it is clear, this process also needs to calculate all the subsets of the items and check their existence in the previous candidate list. In ARMICA, no such step is needed, as the algorithm selects the most powerful (frequent) items, thus eliminating the need for pruning.

Finally, the last candidate list becomes the Frequent List. At that point, ARMICA extracts all the non-empty subsets of all the items and sends them to ICA. If "I" is the item, then for every nonempty subset of s, we have a rule as: s→ (I-s), in the original Apriori, we have a condition: Support (I) / Support (s) >= Minimum Confidence (6) If a rule satisfies this condition, it is selected as a frequent rule. However, since the members of the last empire in ICA are the most powerful rules (have more confidence compared to other rules), ARMICA only selects the most powerful (frequent) subsets and returns the most powerful rules. Again, it is worth mentioning that, ARMICA does not need to have a minimum confidence parameter. Apriori and many other ARM algorithms need a user defined fixed threshold or even a dynamic threshold for minimum confidence. However, our approach only selects the most powerful rules among all the rules and removes all other rules.

Example

In this section, we explain our algorithm using a small example. Table III illustrates a set of six transactions and eight items. T2,…, T6 are 6 transactions and I1, I2, …, I8 are items in these transactions. First, the algorithm selects some of the most powerful countries as Imperialists. Since there are only 8 items, if we do not use just one imperialist (given by: number of countries / 10) as there would be no competition resulting in selecting all the items, but instead assume that there are 3 imperialists. Their power is calculated and the remaining countries are distributed among the imperialists based on their power:

Imperialist1: I3 Imperialist2: I2 Imperialist3: I8 I1 I7 I4 I6 I5
Initial empires are now built. ARMICA calculates the cost of countries and the power of each empire based on the sum of the powers of its colonies plus its imperialist. In this occasion, we have: Empire1's Power: 11(Powerful Empire), Empire2's Power: 10, Empire3's Power: 5 (Weakest Empire) Then, the algorithm establishes a competition between empires. As a result, a powerful empire tries to "steal" colonies from the weakest empire. Finally, if that weak empire has only one colony left, the imperialist and colony of that empire become members of the powerful empire and the algorithm eliminates that empire. As a result, Empire 3 is eliminated and I8 and I5 now belong to Empire 1.

Imperialist1: I3 Imperialist2: I2 I7 I1 I5 I8 I6 I4
The competition goes on. This time Empire 1 tries to get one of the colonies from Empire 2. I6 is removed from Empire 2 and saved in the Reserve List. Since Empire 2 has only 2 members, it joins Empire 1.

Imperialist 1: I3 Reserve List I7 I1 I2 I8 I4 I5 I6
Now the members of the Reserve List are compared with the Last Empire colonies. If there is a colony that has lower cost than the members of the reserve list do, then they are replaced. In this case, I6 has higher cost compared to I5, so the algorithm replaces them and removes I5. Clearly, ARMICA did not use minimum support and automatically eliminated weak countries. In this step, the algorithm considers the last empire as the Candidate List. At that point, it sorts the items of the candidate list based on their cost and selects the median cost of them as Minimum Support. As a result, the cost of the I7 becomes the minimum support, that is 4.

Next, the algorithm joins all the countries in a candidate list and calculates their costs. It is noticeable that the cost of a country like I1I2 is calculated by the number of occurrences of I1 and I2 in the same transaction, that is 2. The algorithm follows the same pattern that we mentioned before, repeatedly until there is no item left in the produced candidate list that has an equal or higher support (cost) than the minimum support. In this situation, the previous candidate list is the Frequent List. Clearly, ARMICA does not require pruning.

Frequent List: I1I3 Finally, it is time to extract all the non-empty subsets of the frequent list items and after that send them to ICA in order to find countries that are more powerful. Like previously, the last empire consists of the most powerful countries that represent the strongest rules. It is clear that we did not need to define Minimum Confidence. The last empire, whose members are the final rules, is here:

I1 → → I3 I3 I1

Evaluation

In this section, we evaluate ARMICA, the proposed method. We implemented it using Java 1.7 in Netbeans IDE 7.2 and ran it on an Intel (R) Core (TM) i5 CPU at 2.40 GHz and 4 GB RAM. We also used the implementation of Apriori from Weka 3.6 [START_REF] Witten | Data mining: practical machine learning tools and techniques[END_REF] along with the Supermarket, Breast Cancer and Nursery datasets from the UCI Machine Learning Repository [START_REF] Bache | UCI machine learning repository[END_REF] to benchmark our method. Supermarket has 217 attributes and 4627 transactions, Breast Cancer has 10 attributes and 286 transactions, whilst Nursery has 8 attributes and 12960 transactions. We considered two factors for this evaluation: accuracy and execution time. An improvement on Apriori should decrease the execution time without decreasing accuracy. ARMICA would be a good approach, if it produced accurate rules faster.

The minimum support that ARMICA determined automatically was 27.5%, 36.18% and 33.33% for the Supermarket, Breast Cancer and Nursery datasets, whilst minimum confidence was 71%, 47% and 1%, respectively. We used the same values for Apriori. Table IV illustrates the characteristics of the evaluation process. Apriori and ARMICA produced the same rules. In addition, we compared the two algorithms concerning their execution time. ARMICA took 6 sec compared to Apriori's 17 sec for Supermarket and 0,76 sec compared to Apriori's 0,78 for Breast Cancer. Finally, it required 1.15 sec for Nursery whereas Apriori required 1.22 sec. This means that ARMICA generated the same rules as Apriori, but in a shorter period.

Discussion

Many ARM algorithms need two parameters to be defined in advance by the user, who has no guidelines on selecting appropriate values, thus resulting in a trial and error exercise or guesswork. These parameters are used to eliminate infrequent itemsets and weak rules. With the increasing amounts of data available, it is not effective to use an algorithm that requires users to define these parameters. In this paper, we proposed an algorithm, named ARMICA, which applies the ICA algorithm, suitably modified, to find the most frequent rules of a dataset. In contrast to many algorithms, ARMICA sets minimum support automatically and does not need a minimum confidence parameter in order to eliminate the weakest rules.

ARMICA uses ICA, which is a heuristic algorithm to analyze a database. For this purpose, at the beginning, it produces the first candidate list. At that point, it considers the minimum support of itemsets in that list as the global Minimum Support. After that, it joins itemsets and produces a new candidate list. This process continues until there is no item left in the candidate list that has support that is equal or more than the Minimum Support. Next, ARMICA considers the last candidate list as Frequent List, extracts the non-empty subsets of the items, and sends them to ICA. ARMICA return the strongest rules without requiring minimum confidence setting. It is worth mentioning that, since ARMICA sends all the extracted subsets to ICA, ICA only returns subsets that according to formula (1) has higher confidence.

Another advantage of the proposed method is that instead of having a time consuming step, like Pruning, which involves extracting all the subsets of all the items, and trace their existence in the previous candidate list, ARMICA selects the most valuable items. Experimental results indicate that ARMICA has up to 3 times less execution time than Apriori in the Supermarket dataset and shorter execution time in Breast Cancer and Nursery datasets. However, since each improvement in execution time of Apriori may cause some decreasing in accuracy of generated rules, we also considered the accuracy of the ARMICA. The proposed method generates all the rules that Apriori generates. Hence, there is not any accuracy reduction in ARMICA.

Conclusion and Future Work

Data mining has attracted attention partly because of the enormous amount of data available. A well-established approach for extracting frequent rules and patterns from data is Association Rules Miming (ARM). Many algorithms have been proposed in this area [START_REF] Bhandari | Improvised apriori algorithm using frequent pattern tree for real time applications in data mining[END_REF][START_REF] Qodmanan | Multi objective association rule mining with genetic algorithm without specifying minimum support and minimum confidence[END_REF][START_REF] Tjortjis | Experiences of Using a Quantitative Approach for Mining Association Rules[END_REF][START_REF] Tjortjis | PRICES: An Efficient Algorithm for Mining Association Rules[END_REF][START_REF] Agrawal | Fast Algorithms for Mining Association Rules in Large Databases[END_REF][START_REF] Han | Mining frequent patterns without candidate generation[END_REF][START_REF] Scheffer | Finding Association Rules That Trade Support Optimally against Confidence[END_REF]. However, there are still areas that need to be investigated further.

In this paper, we proposed a new approach, named ARMICA, based on the Imperialism Competition Algorithm. It defines minimum support automatically and extracts the best rules without the need to define minimum confidence, either. In addition, it does not need a pruning stage, as the one used by many Apriori-like ARM algorithms. However, it requires setting of the number of imperialists, a free parameter in the original ICA. We explored ways to calculate it, and found empirically that the number of countries, divided by 10, is a sensible solution, which does not require domain knowledge nor details about the dataset. In the future, we envisage optimizing this selection, alleviating the need for the user to pre-define this parameter.

Finally, ARMICA produces accurate rules fast. In the other words, it generates all the rules that Apriori produces, in a shorter time. Initial experiments have indicated that it is also faster than FP-growth, but proper evaluation is required. We plan to focus on other potential capabilities of ARMICA such as dealing with massive and/or dynamic data sets, benchmark it against state of the art ARM algorithms and investigate performance related areas, such as memory usage and time consumption.

TABLE III

 III

	. TRANSACTION DETAILS
		I1 I2 I3 I4 I5 I6 I7 I8
	T1	t	t	t	t	t	t
	T2		t	t	t	t
	T3	t		t		t
	T4	t		t	t	t	t
	T5		t				t
	T6	t	t	t	t	t	t
	T1,

TABLE IV . EVALUATION CHARACTERISTICS Data set Items transac- tions Algorithm Predefined Min. Sup- port Predefined Min. Confi- dence Gen- erated Rules time (sec)

 IV

	Super-	217	4627	Apriori	27.5 %	71 %	104	17
	market			ARMICA	-	-	104	6
	Breast	10	286	Apriori	36.18%	47%	75	0,78
	Cancer			ARMICA	-	-	75	0,76
	Nursery	8	12960	Apriori	33.33%	1	2	1.22
				ARMICA	-	-	2	1.15

Acknowledgements

The authors thank the Hellenic Artificial Intelligence Society (EETN) for covering part of their expenses to participate in AIAI 16.


Dong Xue

Long Wu

Lian Xun

Enhancement of heat dissipation in an electronic chip cooling system using graphite fins

As electronic devices get smaller, cooling systems with higher thermal efficiency is demanding by fast growing electronic industry. Great amount of research has been performed on the cooling systems but research on the materials of the cooling systems needs more work. Graphite with high thermal conductivity and light weight is a great candidate to be used in electronic devices. The bottleneck of using graphene in the cooling systems is the thermal transport among the interface from the substrate to the graphene fin system. In this research finite element simulation of graphite fin cooling system has been investigated to study the effect of different applied pressure on the cooling system performance. Study of this cooling system showed good improvement in comparison with common copper fin cooling systems.

Introduction:

As the electronic devices shrink in size, cooling of the devices is more challenging and designing higher efficiency cooling systems is a must for the contemporary electronic devices (Lee and Chakrabarty; Mahabadipour and Ghaebi; Sabarou, Huang and Zhong). Various methods have been investigated by other researchers to improve cooling systems performance (Vasquez and Rastkar; Sahu, Joshi and Fedorov; Alfieri et al.; Dembla, Zhang and Bakir). Since the electronic industries (e.g. cell phone and laptop computer) demanding smaller size and lower power consumption, more fundamental research is required to improve the cooling systems from material engineering point of view alongside redesigning the available cooling systems (Gupta et al.; Sabarou and Zhong). Recently, researchers have focused on using different composite fins in electronic cooling systems to increase the heat dissipation characteristics of the cooling systems (Ellis and McDanels; Gerzeski et al.; Frazier et al.; Behfar, Ghiasvand and Yazdankhah; Putra and Septiadi; Mahabadipour and Ghaebi). The bottleneck of the graphite fin system is the thermal transport from the interface between the substrate and fins. Recently many research have been performed to improve and study the interfacial thermal transport.

Applying pressure to the interface is one of the most common methods of improving thermal transport in chip cooling systems (Reddy et al.; Nobakht, Shahsavan and Paykani).

In this paper, we have investigated graphite find performance on a sample electronic chip cooling system to improve the heat dissipation rate of a sample cooling system. We have employed finite element simulations to perform the numerical modeling in our simulations.

Simulation method:

We have considered a 10×20 mm graphite cooling system of an electronic chip (Figure 1) with heat flux of 250 W cm -2 (Sahu, Joshi and Fedorov) on silicon paste. We used home developed finite element code to simulate heat transfer in the considered cooling system. The fin cooling is considered to be force convection of 3000 Wm -2 (Alimohammadi et al.).

Figure1: cooling system schematic

The simulation is repeated for different interface pressure for the cooling system and interfacial thermal resistance value is considered an average value obtained from the available research in literature (Carlborg, Shiomi and Maruyama; Zhang; Ali and Seungha; Yousefzadi Nobakht and Shin Pressure Effects on in-Plane and Cross-Plane Thermal Transport within Graphene Heterostructures; Yousefzadi Nobakht and Shin Control of Thermal Transport in Graphene/Si Heterostructures). We used hexagonal mesh to improve model stability (Shaw et al.; Liu et al.; Diwan and Mahajan) the mesh was generated using G-Mesh (Geuzaine and Remacle) in a steady states simulation system. For the first few simulations we performed mesh size convergence test and after reaching a stable output for our simulation code, we used the same mesh size for all our simulations.

We used different pressures to evaluate the working temperature of the electronic chip in the different cooling system operational conditions.

The simulation model is a steady state thermal transport with constant heat flux in from the chip to the silicon layer and constant convective thermal transport from chip system to the ambient air.

Results and discussions:

Figure 2 shows a sample temperature profile of the simulation results. The resulted temperature is almost 17% lower than previous copper fin designs (Reddy et al.). Using graphite fins created higher efficiency fins and resulted in lower working temperature for the cooling system. The resulted working temperature for different applied pressure on the cooling system is illustrated in Figure 3. It can be seen that the efficiency of the cooling is enhancing dramatically as the applied pressure on the cooling system increases.

Conclusions:

Numerical simulation of graphite fins is investigated in this research. Different working conditions was simulated with different pressure applied to the cooling system. This research shows that using graphite fins can improve the cooling system efficiency in a dramatic way and enhance the working temperature of the electronic devices and yield in lower energy consumption and longer electronic devices life. The proposed cooling system can be used in the contemporary cooling systems in new electronic devices. Working temperature

Figure2

  Figure2: cooling system temperature profile

Fig 3 .

 3 Fig 3. Working temperature of the chip with respect to different applied pressures to the cooling system interface Figure 4 compares the graphite fins working temperature with previous research (Reddy et al.). It can be seen that the graphite fins are more effective in cooling systems.