Machine Learning Based Approaches for Prediction of Parkinson's Disease mlaij
The prediction of Parkinson’s disease is most important and challenging problem for biomedical engineering researchers and doctors. The symptoms of disease are investigated in middle and late middle age. In this paper, minimum redundancy maximum relevance feature selection algorithms is used to select the most important feature among all the features to predict the Parkinson diseases. Here, it is observed that the random forest with 20 number of features selected by minimum redundancy maximum relevance feature selection algorithms provide the overall accuracy 90.3%, precision 90.2%, Mathews correlation coefficient values of 0.73 and ROC values 0.96 which is better in comparison to all other machine learning based approaches such as bagging, boosting, random forest, rotation forest, random subspace, support vector machine, multilayer perceptron, and decision tree based methods.
Analysis of Opinionated Text for Opinion Miningmlaij
In sentiment analysis, the polarities of the opinions expressed on an object/feature are determined to assess the sentiment of a sentence or document whether it is positive/negative/neutral. Naturally, the object/feature is a noun representation which refers to a product or a component of a product, let’s say, the "lens" in a camera and opinions emanating on it are captured in adjectives, verbs, adverbs and noun words themselves. Apart from such words, other meta-information and diverse effective features are also going to play an important role in influencing the sentiment polarity and contribute significantly to the performance of the system. In this paper, some of the associated information/meta-data are explored and investigated in the sentiment text. Based on the analysis results presented here, there is scope for further assessment and utilization of the meta-information as features in text categorization, ranking text document, identification of spam documents and polarity classification problems.
An Ensemble of Filters and Wrappers for Microarray Data Classification mlaij
The development of microarray technology has supplied a large volume of data to many fields. The gene microarray analysis and classification have demonstrated an effective way for the effective diagnosis of diseases and cancers. In as much as the data achieving from microarray technology is very noisy and also has thousands of features, feature selection plays an important role in removing irrelevant and redundant features and also reducing computational complexity. There are two important approaches for gene selection in microarray data analysis, the filters and the wrappers. To select a concise subset of informative genes, we introduce a hybrid feature selection which combines two approaches. The fact of the matter is that candidate’s features are first selected from the original set via several effective filters. The candidate feature set is further refined by more accurate wrappers. Thus, we can take advantage of both the filters and wrappers. Experimental results based on 11 microarray datasets show that our mechanism can be effected with a smaller feature set. Moreover, these feature subsets can be obtained in a reasonable time.
In early days the main emphases were on the cognitive aspects of learning and traditional instructions of teaching in the classroom using outdated and conventional techniques. But today in this world of constant innovations and discoveries, scientists and gadget-experts are continuously searching for one or the two technological devices a day. Nodoubt technology has made our life much easier and better in many aspects. In developed countries, technology facilitates and helps students and teacher to learn things in more effective ways. But in the country like India, the development in technology is not upto that mark. We still are moving towards the path of progress. Thus, this paper will best describes about the conceptual framework regarding futuristic studies related to future technologies such as M-Learning, E-Learning, , iPod, I-Pad self-efficacy learning, Virtual Learning Environment (VLE ) etc. In this paper investigator highlighted some of the studies related to trends in futurology and innovations that could prove an important aspect of education technology.
An New Attractive Mage Technique Using L-Diversity mlaij
Data that is published or shared between organizations contain private information about an individual. The concept of Privacy Preservation aims to preserve this sensitive information from various privacy threats that violate the privacy of an individual. Analysis of this private information could reveal information that can be used for malicious purposes by the attackers. Anonymization is a privacy preservation approach suitable for mixed data that contains both numerical and categorical attributes. In this paper a novel method called Micro-aggregation Generalization (MAGE) is used for anonymization of microdata that can retain more semantics of the original data. Here the Micro-aggregation is applied over the numerical data and Generalization is applied over the categorical data. Even though the MAGE approach preserves privacy it fails to address the homogeneity and background knowledge attacks. Later the l-diversity approach is applied to deal with homogeneity attack. In l-diversity, the anonymized records are reordered to satisfy a new privacy principle that removes homogeneity of sensitive information. The result shows that the MAGE approach suffers from homogeneity attack and applying l-diversity over MAGE prevents homogeneity attack and also provides better privacy and data utility.
An Ensemble of Filters and Wrappers for Microarray Data Classification mlaij
The development of microarray technology has suppli
ed a large volume of data to many fields. The gene
microarray analysis and classification have demonst
rated an effective way for the effective diagnosis
of
diseases and cancers. In as much as the data achiev
ing from microarray technology is very noisy and al
so
has thousands of features, feature selection plays
an important role in removing irrelevant and redund
ant
features and also reducing computational complexity
. There are two important approaches for gene
selection in microarray data analysis, the filters
and the wrappers. To select a concise subset of inf
ormative
genes, we introduce a hybrid feature selection whic
h combines two approaches. The fact of the matter i
s
that candidate’s features are first selected from t
he original set via several effective filters. The
candidate
feature set is further refined by more accurate wra
ppers. Thus, we can take advantage of both the filt
ers
and wrappers. Experimental results based on 11 micr
oarray datasets show that our mechanism can be
effected with a smaller feature set. Moreover, thes
e feature subsets can be obtained in a reasonable t
ime
Machine Learning Based Approaches for Cancer Classification Using Gene Expres...mlaij
The classification of different types of tumor is of great importance in cancer diagnosis and drug discovery.
Earlier studies on cancer classification have limited diagnostic ability. The recent development of DNA
microarray technology has made monitoring of thousands of gene expression simultaneously. By using this
abundance of gene expression data researchers are exploring the possibilities of cancer classification.
There are number of methods proposed with good results, but lot of issues still need to be addressed. This
paper present an overview of various cancer classification methods and evaluate these proposed methods
based on their classification accuracy, computational time and ability to reveal gene information. We have
also evaluated and introduced various proposed gene selection method. In this paper, several issues
related to cancer classification have also been discussed.
Machine Learning Based Approaches for Prediction of Parkinson's Disease mlaij
The prediction of Parkinson’s disease is most important and challenging problem for biomedical engineering researchers and doctors. The symptoms of disease are investigated in middle and late middle age. In this paper, minimum redundancy maximum relevance feature selection algorithms is used to select the most important feature among all the features to predict the Parkinson diseases. Here, it is observed that the random forest with 20 number of features selected by minimum redundancy maximum relevance feature selection algorithms provide the overall accuracy 90.3%, precision 90.2%, Mathews correlation coefficient values of 0.73 and ROC values 0.96 which is better in comparison to all other machine learning based approaches such as bagging, boosting, random forest, rotation forest, random subspace, support vector machine, multilayer perceptron, and decision tree based methods.
Analysis of Opinionated Text for Opinion Miningmlaij
In sentiment analysis, the polarities of the opinions expressed on an object/feature are determined to assess the sentiment of a sentence or document whether it is positive/negative/neutral. Naturally, the object/feature is a noun representation which refers to a product or a component of a product, let’s say, the "lens" in a camera and opinions emanating on it are captured in adjectives, verbs, adverbs and noun words themselves. Apart from such words, other meta-information and diverse effective features are also going to play an important role in influencing the sentiment polarity and contribute significantly to the performance of the system. In this paper, some of the associated information/meta-data are explored and investigated in the sentiment text. Based on the analysis results presented here, there is scope for further assessment and utilization of the meta-information as features in text categorization, ranking text document, identification of spam documents and polarity classification problems.
An Ensemble of Filters and Wrappers for Microarray Data Classification mlaij
The development of microarray technology has supplied a large volume of data to many fields. The gene microarray analysis and classification have demonstrated an effective way for the effective diagnosis of diseases and cancers. In as much as the data achieving from microarray technology is very noisy and also has thousands of features, feature selection plays an important role in removing irrelevant and redundant features and also reducing computational complexity. There are two important approaches for gene selection in microarray data analysis, the filters and the wrappers. To select a concise subset of informative genes, we introduce a hybrid feature selection which combines two approaches. The fact of the matter is that candidate’s features are first selected from the original set via several effective filters. The candidate feature set is further refined by more accurate wrappers. Thus, we can take advantage of both the filters and wrappers. Experimental results based on 11 microarray datasets show that our mechanism can be effected with a smaller feature set. Moreover, these feature subsets can be obtained in a reasonable time.
In early days the main emphases were on the cognitive aspects of learning and traditional instructions of teaching in the classroom using outdated and conventional techniques. But today in this world of constant innovations and discoveries, scientists and gadget-experts are continuously searching for one or the two technological devices a day. Nodoubt technology has made our life much easier and better in many aspects. In developed countries, technology facilitates and helps students and teacher to learn things in more effective ways. But in the country like India, the development in technology is not upto that mark. We still are moving towards the path of progress. Thus, this paper will best describes about the conceptual framework regarding futuristic studies related to future technologies such as M-Learning, E-Learning, , iPod, I-Pad self-efficacy learning, Virtual Learning Environment (VLE ) etc. In this paper investigator highlighted some of the studies related to trends in futurology and innovations that could prove an important aspect of education technology.
An New Attractive Mage Technique Using L-Diversity mlaij
Data that is published or shared between organizations contain private information about an individual. The concept of Privacy Preservation aims to preserve this sensitive information from various privacy threats that violate the privacy of an individual. Analysis of this private information could reveal information that can be used for malicious purposes by the attackers. Anonymization is a privacy preservation approach suitable for mixed data that contains both numerical and categorical attributes. In this paper a novel method called Micro-aggregation Generalization (MAGE) is used for anonymization of microdata that can retain more semantics of the original data. Here the Micro-aggregation is applied over the numerical data and Generalization is applied over the categorical data. Even though the MAGE approach preserves privacy it fails to address the homogeneity and background knowledge attacks. Later the l-diversity approach is applied to deal with homogeneity attack. In l-diversity, the anonymized records are reordered to satisfy a new privacy principle that removes homogeneity of sensitive information. The result shows that the MAGE approach suffers from homogeneity attack and applying l-diversity over MAGE prevents homogeneity attack and also provides better privacy and data utility.
An Ensemble of Filters and Wrappers for Microarray Data Classification mlaij
The development of microarray technology has suppli
ed a large volume of data to many fields. The gene
microarray analysis and classification have demonst
rated an effective way for the effective diagnosis
of
diseases and cancers. In as much as the data achiev
ing from microarray technology is very noisy and al
so
has thousands of features, feature selection plays
an important role in removing irrelevant and redund
ant
features and also reducing computational complexity
. There are two important approaches for gene
selection in microarray data analysis, the filters
and the wrappers. To select a concise subset of inf
ormative
genes, we introduce a hybrid feature selection whic
h combines two approaches. The fact of the matter i
s
that candidate’s features are first selected from t
he original set via several effective filters. The
candidate
feature set is further refined by more accurate wra
ppers. Thus, we can take advantage of both the filt
ers
and wrappers. Experimental results based on 11 micr
oarray datasets show that our mechanism can be
effected with a smaller feature set. Moreover, thes
e feature subsets can be obtained in a reasonable t
ime
Machine Learning Based Approaches for Cancer Classification Using Gene Expres...mlaij
The classification of different types of tumor is of great importance in cancer diagnosis and drug discovery.
Earlier studies on cancer classification have limited diagnostic ability. The recent development of DNA
microarray technology has made monitoring of thousands of gene expression simultaneously. By using this
abundance of gene expression data researchers are exploring the possibilities of cancer classification.
There are number of methods proposed with good results, but lot of issues still need to be addressed. This
paper present an overview of various cancer classification methods and evaluate these proposed methods
based on their classification accuracy, computational time and ability to reveal gene information. We have
also evaluated and introduced various proposed gene selection method. In this paper, several issues
related to cancer classification have also been discussed.
1. ÖNÉLETRAJZ
Telefonszám: (06) 70 452-51-76
E-mail: cardpeter@hotmail.com
1081 Budapest Népszínház utca 16. I./10.
KÁRTYÁS PÉTER
SZAKMAI TAPASZTALAT
2005. január – 2015. június
Óbudai Egyetem – belső ellenőrzési vezető
Az intézmény 12.000 hallgatót oktató, közel 1000 fős szervezet. A
rektor közvetlen irányítása alatt feladataim közé tartozott az éves
ellenőrzési munkaterv összeállítása; az ellenőrzések programjának
tervezése; az ellenőrzések lefolytatása; a jelentések összeállítása;
és a beszámolás a fenntartó minisztérium felé. Főként gazdasági-
pénzügyi elemzéseket készítettem és szabályszerűségi
ellenőrzéseket vezettem.
2013. október – 2014. január
At Work Kft. – HR tanácsadó gyakornok
Feladataim közé tartozott jelöltek felkutatása adatbázisokból, jelöltek
megkeresése, és az adott pozíciók megajánlása, kapcsolattartás a
jelöltekkel. A kompetenciamérést támogató Vienna
Tesztrendszerben a tesztfelvételek intézése, a kiértékelő riportok
összeállítása magyar és angol nyelven, a teszteredmények
értékelése. Különböző tesztek felhasználói kézikönyveinek angolról
magyarra fordítása.
2. 2001. január – 2008. október
Family Business Kft. – marketing igazgató; tréner és oktató
A cég magyar kereskedelmi vállalkozás, mely a több mint 300 féle
termékének értékesítését 5000 fős üzletkötői hálózaton keresztül
végezte. Feladatom volt a kft. marketing stratégiájának és az
értékesítők ösztönzési rendszerének kialakítása; az üzletkötők
toborzása, oktatása; csapatépítő, kommunikációs, értékesítési és
motivációs tréningek szervezése és megtartása.
VÉGZETTSÉG
2012. szeptember – 2014. július Pécsi Tudományegyetem
Bölcsészettudományi Kar; Szociál- és szervezetpszichológia
mesterszak; pszichológus
2009. szeptember – 2012. július Pécsi Tudományegyetem
Bölcsészettudományi Kar; Pszichológia alapszak; viselkedéselemző
2007. szeptember – 2008. június Budapesti Műszaki Főiskola
Keleti Károly Gazdasági Kar; Üzleti tanácsadó szak, coach
2002. szeptember – 2004. július Budapesti Közgazdaságtudományi
és Államigazgatási Egyetem
Gazdálkodástudományi Kar, Számvitel szak, közgazdász
1998. szeptember – 2002. február Budapesti Gazdasági Főiskola
Pénzügyi és Számviteli Főiskolai Kar, Számvitel szak, közgazdász
NYELVTUDÁS
Angol nyelv – tárgyalási szint, C1 (gazdasági felsőfokú C típusú
nyelvvizsga és általános középfokú C típusú nyelvvizsga)
Német nyelv – középfokú értésszintű ismeret, B2 (gazdasági középfokú
C típusú nyelvvizsga)
Orosz nyelv – középfokú értésszintű ismeret, A1 (jeles érettségi vizsga)
EGYÉB KÉPZÉS, TRÉNING, SZAKMAI TAPSZTALAT
Erős számítógép felhasználói ismeretek (MS Office, Adobe, Internet)
3. A Delta Vision Kiadónál 4 regény és 4 antológia lektorálása
Delta Vision - Mesterművek fordítói műhelytagság (3 novellafordítás)
A Menedzserpraxis Kiadó Munkajogi Tanácsadó folyóiratában 16
munkapszichológiai/munkahelyi mentálhigiéné szakcikk publikálása
2013 novemberétől
SZEMÉLYI ADATOK
Születési hely, idő: Budapest, 1980. március 20.
Budapest, 2016. január 4.