The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.

1. TOP 05 ARTIFICIAL INTELLIGENCE &
APPLICATIONS RESEARCH ARTICLES FROM
2016 ISSUE
International Journal of Artificial Intelligence
& Applications (IJAIA)
ISSN: 0975-900X (Online); 0976-2191 (Print)
http://www.airccse.org/journal/ijaia/ijaia.html

2. Citation Count – -10
PPRREEDDIICCTTIINNGG SSTTUUDDEENNTT AACCAADDEEMMIICC PPEERRFFOORRMMAANNCCEE IINN BBLLEENNDDEEDD
LLEEAARRNNIINNGG UUSSIINNGG AARRTTIIFFIICCIIAALL NNEEUURRAALL NNEETTWWOORRKKSS
NNiicckk ZZ.. ZZaacchhaarriiss
DDeeppaarrttmmeenntt ooff CCoommppuutteerr SSyysstteemmss EEnnggiinneeeerriinngg,, TTeecchhnnoollooggiiccaall EEdduuccaattiioonnaall IInnssttiittuuttee ooff PPiirraaeeuuss,,
AAtthheennss,, GGrreeeeccee
AABBSSTTRRAACCTT
Along with the spreading of online education, the importance of active support of students involved in
online learning processes has grown. The application of artificial intelligence in education allows
instructors to analyze data extracted from university servers, identify patterns of student behavior and
develop interventions for struggling students. This study used student data stored in a Moodle server and
predicted student success in course, based on four learning activities - communication via emails,
collaborative content creation with wiki, content interaction measured by files viewed and self-evaluation
through online quizzes. Next, a model based on the Multi-Layer Perceptron Neural Network was trained
to predict student performance on a blended learning course environment. The model predicted the
performance of students with correct classification rate, CCR, of 98.3%.
KKEEYYWWOORRDDSS
Artificial Neural Networks, Blended Learning, Student Achievement, Learning Analytics,
Moodle Data,
For More Details : http://aircconline.com/ijaia/V7N5/7516ijaia02.pdf
Volume Link : http://airccse.org/journal/ijaia/current2016.html

3. RREEFFEERREENNCCEESS
[[11]] MMaaccffaaddyyeenn,, LL.. PP..,, && DDaawwssoonn,, SS.. ((22001100)).. MMiinniinngg LLMMSS ddaattaa ttoo ddeevveelloopp aann ““eeaarrllyy wwaarrnniinngg ssyysstteemm”” ffoorr
eedduuccaattoorrss:: AA pprrooooff ooff ccoonncceepptt.. CCoommppuutteerrss && EEdduuccaattiioonn,, 5544((22)),, 558888––559999..
[[22]] ZZaacchhaarriiss,, NN.. ZZ.. ((22001155)).. AA mmuullttiivvaarriiaattee aapppprrooaacchh ttoo pprreeddiiccttiinngg ssttuuddeenntt oouuttccoommeess iinn wweebb--eennaabblleedd bblleennddeedd
lleeaarrnniinngg ccoouurrsseess.. IInntteerrnneett aanndd HHiigghheerr EEdduuccaattiioonn,, 2277,, 4444––5533..
[[33]] SSttrraanngg,, DD.. KK.. ((22001166)).. CCaann oonnlliinnee ssttuuddeenntt ppeerrffoorrmmaannccee bbee ffoorreeccaasstteedd bbyy lleeaarrnniinngg aannaallyyttiiccss?? IInntteerrnnaattiioonnaall
JJoouurrnnaall ooff TTeecchhnnoollooggyy EEnnhhaanncceedd LLeeaarrnniinngg,, 88((11)),, 2266--4477..
[[44]] SSaabboouurriinn,, JJ..,, RRoowwee,, JJ..,, MMootttt,, BB..,, LLeesstteerr,, JJ.. ((22001111)).. WWhheenn OOffff--TTaasskk iinn OOnn--TTaasskk:: TThhee AAffffeeccttiivvee RRoollee ooff OOffff--TTaasskk
BBeehhaavviioorr iinn NNaarrrraattiivvee--CCeenntteerreedd LLeeaarrnniinngg EEnnvviirroonnmmeennttss.. PPrroocceeeeddiinnggss ooff tthhee 1155tthh IInntteerrnnaattiioonnaall CCoonnffeerreennccee oonn
AArrttiiffiicciiaall IInntteelllliiggeennccee iinn EEdduuccaattiioonn,, 553344--553366..
[[55]] BBaakkeerr,, RR..SS..JJ..dd..,, YYaacceeff,, KK.. ((22000099)).. TThhee SSttaattee ooff EEdduuccaattiioonnaall DDaattaa MMiinniinngg iinn 22000099:: AA RReevviieeww aanndd FFuuttuurree
VViissiioonnss.. JJoouurrnnaall ooff EEdduuccaattiioonnaall DDaattaa MMiinniinngg,, 11((11)),, 33--1177..
[[66]] LLyykkoouurreennttzzoouu,, II..,, GGiiaannnnoouukkooss,, II..,, MMppaarrddiiss,, GG..,, NNiikkoollooppoouullooss,, VV.. aanndd LLoouummooss,, VV.. ((22000099)),, EEaarrllyy aanndd ddyynnaammiicc
ssttuuddeenntt aacchhiieevveemmeenntt pprreeddiiccttiioonn iinn ee--lleeaarrnniinngg ccoouurrsseess uussiinngg nneeuurraall nneettwwoorrkkss.. JJ.. AAmm.. SSoocc.. IInnff.. SSccii..,, 6600::
337722––338800.. ddooii:: 1100..11000022//aassii..2200997700
[[77]] PPaalliiwwaall,, MM..,, && KKuummaarr,, UU.. AA.. ((22000099)).. AA ssttuuddyy ooff aaccaaddeemmiicc ppeerrffoorrmmaannccee ooff bbuussiinneessss sscchhooooll ggrraadduuaatteess uussiinngg
nneeuurraall nneettwwoorrkk aanndd ssttaattiissttiiccaall tteecchhnniiqquueess.. EExxppeerrtt SSyysstteemmss wwiitthh AApppplliiccaattiioonnss,, 3366((44)),, 77886655––77887722..
[[88]] JJaayynnee CC,, LLaanniittiiss AA,, CChhrriissttooddoouulloouu CC ((22001111)).. NNeeuurraall nneettwwoorrkk mmeetthhooddss ffoorr oonnee--ttoo--mmaannyy mmuullttii--vvaalluueedd mmaappppiinngg
pprroobblleemmss.. NNeeuurraall CCoommppuutt AAppppll 2200((66))::777755––778855
[[99]] KKaannaakkaannaa,, GG..MM..,, OOllaannrreewwaajjuu,, AA..OO.. ((22001111)) PPrreeddiiccttiinngg ssttuuddeenntt ppeerrffoorrmmaannccee iinn eennggiinneeeerriinngg eedduuccaattiioonn uussiinngg
aann aarrttiiffiicciiaall nneeuurraall nneettwwoorrkk aatt TTsshhwwaannee uunniivveerrssiittyy ooff tteecchhnnoollooggyy.. PPrroocceeeeddiinnggss ooff tthhee IInntteerrnnaattiioonnaall
CCoonnffeerreennccee oonn IInndduussttrriiaall EEnnggiinneeeerriinngg,, SSyysstteemmss EEnnggiinneeeerriinngg aanndd EEnnggiinneeeerriinngg MMaannaaggeemmeenntt ffoorr SSuussttaaiinnaabbllee
GGlloobbaall DDeevveellooppmmeenntt,, SStteelllleennbboosscchh,, SSoouutthh AAffrriiccaa,, pppp.. 11––77..
[[1100]] SShhaahhiirrii,, AA..MM..,, HHuussaaiinn,, WW..,, RRaasshhiidd,, AA..NN.. ((22001155)).. AA rreevviieeww oonn pprreeddiiccttiinngg ssttuuddeenntt''ss ppeerrffoorrmmaannccee uussiinngg ddaattaa
mmiinniinngg tteecchhnniiqquueess.. PPrroocceeddiiaa CCoommppuutteerr SScciieennccee,, 7722,, 441144--442222..
[[1111]] MMccCClleellllaanndd,, JJ..LL..,, RRuummeellhhaarrtt,, DD..EE..,, aanndd HHiinnttoonn,, GG..EE.. ((11998866)).. TThhee aappppeeaall ooff ppaarraalllleell ddiissttrriibbuutteedd pprroocceessssiinngg,, iinn
PPaarraalllleell DDiissttrriibbuutteedd PPrroocceessssiinngg:: EExxpplloorraattiioonnss iinn tthhee MMiiccrroossttrruuccttuurree ooff CCooggnniittiioonn -- FFoouunnddaattiioonnss,, VVooll..11,, MMIITT
PPrreessss,, CCaammbbrriiddggee,, pppp..33--4444..
[[1122]] LLeevveerriinnggttoonn,, DD.. ((22000099)).. AA BBaassiicc IInnttrroodduuccttiioonn ttoo FFeeeeddffoorrwwaarrdd BBaacckkpprrooppaaggaattiioonn NNeeuurraall NNeettwwoorrkkss..
hhttttpp::////wwwwww..wweebbppaaggeess..ttttuu..eedduu//ddlleevveerriinn//nneeuurraall__nneettwwoorrkk//nneeuurraall__nneettwwoorrkkss..hhttmmll
[[1133]] RRoojjaass RRaaúúll ((11999966)).. NNeeuurraall NNeettwwoorrkkss:: AA SSyysstteemmaattiicc IInnttrroodduuccttiioonn,, SSpprriinnggeerr--VVeerrllaagg,, BBeerrlliinn,, NNeeww--YYoorrkk..
[[1144]] MMaarrwwaallaa,, TT.. ((22001100)).. FFiinniittee EElleemmeenntt MMooddeell UUppddaattiinngg UUssiinngg CCoommppuuttaattiioonnaall IInntteelllliiggeennccee TTeecchhnniiqquueess::
AApppplliiccaattiioonnss ttoo SSttrruuccttuurraall DDyynnaammiiccss,, SSpprriinnggeerr PPuubblliisshhiinngg CCoommppaannyy,, IInncc ..
[[1155]] IIBBMM ((22001166)).. KKnnoowwlleeddggee CCeenntteerr.. hhttttpp::////ggoooo..ggll//SSuuuuMMHHuu
[[1166]] MMøølllleerr,, MM..FF..,, 11999933.. AA ssccaalleedd ccoonnjjuuggaattee ggrraaddiieenntt aallggoorriitthhmm ffoorr ffaasstt ssuuppeerrvviisseedd lleeaarrnniinngg.. NNeeuurraall NNeettwwoorrkkss,,
66 ((44)),,552255––553333..

4. Citation Count –04
AARRAABBIICC OONNLLIINNEE HHAANNDDWWRRIITTIINNGG RREECCOOGGNNIITTIIOONN
UUSSIINNGG NNEEUURRAALL NNEETTWWOORRKK
Abdelkarim Mars1 and Georges Antoniadis2
1 Laboratory LIDILEM, Alpes University, Grenoble, French
2 Laboratory LIDILEM, Alpes University, Grenoble, French
AABBSSTTRRAACCTT
This article presents the development of an Arabic online handwriting recognition system. To
develop our system, we have chosen the neural network approach. It offers solutions for most of
the difficulties linked to Arabic script recognition. We test the approach with our collected
databases. This system shows a good result and it has a high accuracy (98.50% for characters,
96.90% for words).
KKEEYYWWOORRDDSS
Neural Network, Handwriting recognition, Online, Arabic Script
For More Details : http://aircconline.com/ijaia/V7N5/7516ijaia04.pdf
Volume Link : http://airccse.org/journal/ijaia/current2016.html

5. RREEFFEERREENNCCEESS
[1] Essoukhri, Ben Amara, (2002) "Problématique et orientations en reconnaissance de l’écriture arabe", Colloque
International Francophone sur l’Ecrit et le Document, pp.1.-10, Hammamet, Tunisie, Octobre 2002
[2] Essoukhri, Ben Amara, A, Belaïd. & A, Ellouze., (2000), "Utilisation des modèles markoviens en
reconnaissance de l’écriture arabe: Etat de l'art", CIFED’2000, Colloque International Francophone sur
l’Ecrit et le Document, pp.181-191, Lyon, France, 2000.
[3] G, Lorette., (2013), "Handwriting Recognition or Reading Situation". At the Dawn Of The 3rd Millenium”,
in Advances in Handwriting Recognition, World Scientific Publications, pp. 3-13.
[4] M, El-Wakil. & A, Shoukry, (1989), “On-line recognition of handwritten isolated arabic characters”.
Pattern Recognition 22, 97–105 1989.
[5] N, Mezghani. A, Mitiche and M, Cheriet (2002) “On-line recognition of handwritten arabic characters
using a kohonen neural network, in: Frontiers in Handwriting Recognition”, 2002. Proceedings. Eighth
International Workshop on, IEEE. pp. 490–495.
[6] A, T, Al-Taani (2005) “An efficient feature extraction algorithm for the recognition of handwritten
arabic digits”. International journal of computational intelligence 2, 107–111 2005.
[7] R, I, Elanwar. M,A, Rashwan & S, A, Mashali (2007) “Simultaneous segmentation and recognition of
arabic characters in an unconstrained on-line cursive handwritten document”, in: Proceedings of world
academy of science, engineering and technology, pp. 288–291 2007.
[8] K, Daifallah. N, Zarka & H, Jamous (2009) “Recognition-based segmentation algorithm for on-line arabic
handwriting”, in: Document Analysis and Recognition, 2009. ICDAR’09. 10th International Conference on,
IEEE. pp. 886–890 2009.
[9] R, I, Elanwar. M, A, Rashwan.& S, A, Mashali (2007). "Simultaneous segmentation and recognition of Arabic
characters in an unconstrained on-line cursive handwritten document". Proceedings of World Academy of
Science, Engineering and Technology (WASET), International conference on Machine learning and Pattern
Recognition MLPR2007, vol. 23, pp. 288–291, Germany (2007).
[10] H, Boubaker. A, Chaabouni. M, Kherallah. A, M, Alimi & H, El Abed (2010). "Fuzzy segmentation and
graphemes modeling for online Arabic handwriting recognition". Proceedings of ICFHR 2010, pp. 695–700
(2010).
[11] R, Halavati. M, Jamzad. & M, Soleymani (2005) "A novel approach to Persian online hand writing
recognition". Trans. Eng. Comput. Technol. 6, 232–236.
[12] F, Bouslama & A, Amin (1998) “Pen-based recognition system of Arabic character utilizing structural and
fuzzy techniques”. In: Proceedings of Second International Conference on Knowledge-Based Intelligent
Electronic Systems, pp. 76–85 (1998).
[13] A, Mars. & G, Antoniadis (2015) "Handwriting recognition system for Arabic language learning"
WCITCA’2014 World Congress on Information Technology and Computer Application, HAMMAMET 2015
– International Journal N&N Global technology.
[14] A, H, Ganapathiraju.& J, Picone (2004) . "Applications of support vector machines to speech recognition".
IEEE Transactions on Signal Processing, 52, 2348 - 2355.

6. [15] J, S, Bridle (1990) "Training Stochastic Model Recognition Algorithms as Networks Can Lead to Maximum
Mutual Information Estimation of Parameters", in Advances in Neural Information Processing Sys-tems 2,
D.S. Touretzky, ed., pages 211-21 7, 1990.
[16] D, Kim, (1999), Normalization Methods for Input and Output Vectors in Backpropagation Neural Networks,
International Journal of Computer Mathematics, Vol. 71, No. 2, 161-171.
[17] N, Mezghani. A, Mitiche & M, Cheriet (2008) "Bayes classification of online Arabic characters by Gibbs
modeling of class conditional densities". IEEE Trans. Pattern Anal. Mach. Intell. 30(7), 1121– 11.
[18] N, Intrator, (1992) "Feature Extraction Using an Unsupervised Neural Network", Neural Computation,
vol. 4, no. 1, pp. 98-107, 1992
[19] S, Izadi. M, Haji and C,Y, Suen, (2008), A new segmentation algorithm for online handwritten word
recognition in Persian script, 11th International Conference on Frontiers in Handwriting Recognition (CFHR
2008), Montreal, Canada, 2008, 598-603.
[20] V, Peddinti. D, Povey and S, Khudanpur, (2015), "A Time Delay Neural Network Architecture for Efficient
Modeling of Long Temporal Contexts", Proceedings of Interspeech.
[21] A, Mars. & G, Antoniadis (2015) "Handwriting recognition system for Arabic language learning".
International Journal of Engineering and Advanced Technology Studies Vol.3, No.7, pp.55-63, September
2015.

7. Citation Count –05
SSEELLFF LLEEAARRNNIINNGG CCOOMMPPUUTTEERR TTRROOUUBBLLEESSHHOOOOTTIINNGG EEXXPPEERRTT
SSYYSSTTEEMM
Amanuel Ayde Ergado
Department of Information science, Jimma University, Jimma, Ethiopia.
AABBSSTTRRAACCTT
IInn ccoommppuutteerr ddoommaaiinn tthhee pprrooffeessssiioonnaallss wweerree lliimmiitteedd iinn nnuummbbeerr bbuutt tthhee nnuummbbeerrss ooff iinnssttiittuuttiioonnss llooookkiinngg ffoorr
ccoommppuutteerr pprrooffeessssiioonnaallss wweerree hhiigghh.. TThhee aaiimm ooff tthhiiss ssttuuddyy iiss ddeevveellooppiinngg sseellff lleeaarrnniinngg eexxppeerrtt ssyysstteemm wwhhiicchh
iiss pprroovviiddiinngg ttrroouubblleesshhoooottiinngg iinnffoorrmmaattiioonn aabboouutt pprroobblleemmss ooccccuurrrreedd iinn tthhee ccoommppuutteerr ssyysstteemm ffoorr tthhee
iinnffoorrmmaattiioonn aanndd ccoommmmuunniiccaattiioonn tteecchhnnoollooggyy tteecchhnniicciiaannss aanndd ccoommppuutteerr uusseerrss ttoo ssoollvvee pprroobblleemmss eeffffeeccttiivveellyy
aanndd eeffffiicciieennttllyy ttoo uuttiilliizzee ccoommppuutteerr aanndd ccoommppuutteerr rreellaatteedd rreessoouurrcceess.. DDoommaaiinn kknnoowwlleeddggee wwaass aaccqquuiirreedd
uussiinngg sseemmii ssttrruuccttuurreedd iinntteerrvviieeww tteecchhnniiqquuee,, oobbsseerrvvaattiioonn aanndd ddooccuummeenntt aannaallyyssiiss.. DDoommaaiinn eexxppeerrttss wweerree
ppuurrppoossiivveellyy sseelleecctteedd ffoorr tthhee iinntteerrvviieeww qquueessttiioonn.. TThhee ccoonncceeppttuuaall mmooddeell ooff tthhee eexxppeerrtt ssyysstteemm wwaass ddeessiiggnneedd
bbyy uussiinngg aa ddeecciissiioonn ttrreeee ssttrruuccttuurree wwhhiicchh iiss eeaassyy ttoo uunnddeerrssttaanndd aanndd iinntteerrpprreett tthhee ccaauusseess iinnvvoollvveedd iinn
ccoommppuutteerr ttrroouubblleesshhoooottiinngg.. BBaasseedd oonn tthhee ccoonncceeppttuuaall mmooddeell,, tthhee eexxppeerrtt ssyysstteemm wwaass ddeevveellooppeedd bbyy uussiinngg ‘‘iiff
–– tthheenn’’ rruulleess.. TThhee ddeevveellooppeedd ssyysstteemm uusseedd bbaacckkwwaarrdd cchhaaiinniinngg ttoo iinnffeerr tthhee rruulleess aanndd pprroovviiddee aapppprroopprriiaattee
rreeccoommmmeennddaattiioonnss.. AAccccoorrddiinngg ttoo tthhee ssyysstteemm eevvaalluuaattoorrss 8833..66%% ooff tthhee uusseerrss wweerree ssaattiissffiieedd wwiitthh tthhee
pprroottoottyyppee..
KKEEYYWWOORRDDSS
expert system, computer troubleshooting, self learning, knowledge based system
For More Details : http://aircconline.com/ijaia/V7N1/7116ijaia05.pdf
Volume Link : http://airccse.org/journal/ijaia/current2016.html

8. RREEFFEERREENNCCEESS
[[11]] AA.. DD.. MM.. AAffrriiccaa.. ““AAnn EExxppeerrtt SSyysstteemm AAllggoorriitthhmm ffoorr CCoommppuutteerr SSyysstteemm DDiiaaggnnoossttiiccss..”” IInntteerrnnaattiioonnaall JJoouurrnnaall
ooff EEnnggiinneeeerriinngg ((IIJJEE)),, 55((55)),, PPPP.. 443355 --446677,, 22001111..
[[22]] WWiikkiippeeddiiaa.. ““EExxppeerrtt SSyysstteemm..”” hhttttpp::////eenn..wwiikkiippeeddiiaa..oorrgg//wwiikkii//EExxppeerrtt__ssyysstteemm,, NNoovv.. 2222,, 22001144 [[DDeecc.. 0055,, 22001144]]..
[[33]] JJ.. KKiinngg.. ““KKnnoowwlleeddggee bbaasseedd ssyysstteemm ddeevveellooppmmeenntt ttoooollss..”” AArrttiiffiicciiaall IInntteelllliiggeennccee,, SSccoottllaanndd:: UUnniivveerrssiittyy ooff
EEddiinnbbuurrgghh,, pppp 11--1100..
[[44]] SS.. MMaannddaall ,, SS.. CChhaatttteerrjjeeee aanndd BB.. NNeeooggii.. ((22001133)) ““DDiiaaggnnoossiiss aanndd TTrroouubblleesshhoooottiinngg ooff CCoommppuutteerr FFaauullttss BBaasseedd
oonn EExxppeerrtt SSyysstteemm AAnndd AArrttiiffiicciiaall IInntteelllliiggeennccee..”” IInntteerrnnaattiioonnaall JJoouurrnnaall ooff PPuurree aanndd AApppplliieedd
MMaatthheemmaattiiccss..[[oonnlliinnee]].. 8833((55)),, pppp.. 771177--772299.. AAvvaaiillaabbllee:: hhttttpp::////wwwwww..iijjppaamm..eeuu [[JJuunnee,, 22001155]]..
[[55]] II.. CC.. CCaammeerroonn.. ““AA CCoommppuutteerr TTrroouubblleesshhoooottiinngg EExxppeerrtt SSyysstteemm TToo AAiidd TTeecchhnniiccaall SSuuppppoorrtt
RReepprreesseennttaattiivveess..”” MMaasstteerr TThheesseess,, AAllggoommaa UUnniivveerrssiittyy CCoolllleeggee,, CCaannaaddaa,, 22000055..
[[66]] EEiinnoollllaahh ppiirraa,, MMoohhaammmmaadd RReezzaa MMiirraallvvaanndd aanndd FFaakkhhtteehh SSoollttaannii,,((22001144)) ““vveerriiffiiccaattiioonn ooff ccoonnfflliiccttiioonn aanndd
UUnnrreeaacchhaabbiilliittyy iinn rruullee--bbaasseedd eexxppeerrtt SSyysstteemmss wwiitthh mmooddeell cchheecckkiinngg..”” IInntteerrnnaattiioonnaall JJoouurrnnaall ooff AArrttiiffiicciiaall
IInntteelllliiggeennccee && AApppplliiccaattiioonnss ((IIJJAAIIAA)),, 55((22)),, pppp.. 2211--2288..
[[77]] SS.. MMaannddaa,, SS.. CChhaatttteerrjjeeee,, BB.. NNeeooggii.. ((22001122)) ““DDiiaaggnnoossiiss aanndd TTrroouubblleesshhoooottiinngg ooff CCoommppuutteerr FFaauullttss BBaasseedd OOnn
EExxppeerrtt SSyysstteemm aanndd AArrttiiffiicciiaall IInntteelllliiggeennccee..”” IInntteerrnnaattiioonnaall JJoouurrnnaall ooff PPuurree aanndd AApppplliieedd MMaatthheemmaattiiccss,, 8833((55)),,
pppp..771177--772299..
[[88]] DD.. ZZmmaarraannddaa,, HH.. SSiillaagghhii,, GG.. GGaabboorr,, CC.. VVaanncceeaa.. ((FFeebbrruuaarryy,, 22001133)).. ““IIssssuueess oonn AAppppllyyiinngg KKnnoowwlleeddggeeBBaasseedd
TTeecchhnniiqquueess iinn RReeaall--TTiimmee CCoonnttrrooll SSyysstteemmss..”” IINNTT JJ CCOOMMPPUUTT CCOOMMMMUUNN..[[oonnlliinnee]].. 88((11)):: pppp..116666--117755..
AAvvaaiillaabbllee::hhttttpp::////uunniivvaaggoorraa..rroo//jjoouurr//iinnddeexx..pphhpp//iijjcccccc//aarrttiiccllee//vviieewwFFiillee//118811//ppddff [[DDeecc..,, 22,, 22001144]]..
[[99]] SS.. RRuusssseellll aanndd PP.. NNoorrvviigg..((22001100)).. AArrttiiffiicciiaall IInntteelllliiggeennccee MMooddeerrnn AApppprrooaacchh..((33rrdd eeddiittiioonn)).. [[oonnlliinnee]].. AAvvaaiillaabbllee::
wwwwww..ppeeaarrssoonnhhiigghheerreedd..ccoomm [[OOcctt..,, 22001155]]..
[[1100]] HHeeiijjsstt.. ““CCoonncceeppttuuaall MMooddeelllliinngg ffoorr KKnnoowwlleeddggee--BBaasseedd SSyysstteemmss..”” EEnnccyyccllooppeeddiiaa ooff CCoommppuutteerr SScciieennccee aanndd
TTeecchhnnoollooggyy,, MMaarrccee DDeekkkkeerr IInncc..,, NNeeww YYoorrkk.. 22000066..
[[1111]] MM.. MMaallhhoottrraa..((jjuunnee,, 22001155)) ““EEvvoolluuttiioonn ooff KKnnoowwlleeddggee RReepprreesseennttaattiioonn aanndd RReettrriieevvaall TTeecchhnniiqquueess..”” II..JJ..
IInntteelllliiggeenntt SSyysstteemmss aanndd AApppplliiccaattiioonnss.. [[oonnlliinnee]].. 22001155((77)),, pppp.. 1188--2288.. AAvvaaiillaabbllee::
[[1122]] PP.. PP.. SSiinngghh TToommaarr aanndd PP.. KK.. SSaaxxeennaa.. ““AArrcchhiitteeccttuurree ffoorr MMeeddiiccaall DDiiaaggnnoossiiss UUssiinngg RRuullee--BBaasseedd TTeecchhnniiqquuee..””
TThhee FFiirrsstt IInntteerrnnaattiioonnaall CCoonnffeerreennccee oonn IInntteerrddiisscciipplliinnaarryy RReesseeaarrcchh aanndd DDeevveellooppmmeenntt,, DDaayyaallbbaagghh EEdduuccaattiioonnaall
IInnssttiittuuttee,, TThhaaiillaanndd,, 22001111..
[[1133]] JJ.. PPrreennttzzaass aanndd LL.. HHaattzziillyyggeerroouussddiiss..((MMaayy 22000077)).. ““CCaatteeggoorriizziinngg AApppprrooaacchheess CCoommbbiinniinngg RRuullee--BBaasseedd aanndd CCaassee--
BBaasseedd..”” EExxppeerrtt SSyysstteemm.. [[oonnlliinnee]].. 2244((22)),, pppp.. 9977--112222.. AAvvaaiillaabbllee::
[[1144]] II.. BBiicchhiinnddaarriittzz,, EE.. KKaannssuu aanndd KKeeiitthh MM.. SSuulllliivvaann.. ““IInntteeggrraattiinngg CCaassee--BBaasseedd RReeaassoonniinngg,, RRuullee--BBaasseedd
RReeaassoonniinngg aanndd IInntteelllliiggeenntt IInnffoorrmmaattiioonn RReettrriieevvaall ffoorr MMeeddiiccaall PPrroobblleemm--SSoollvviinngg,,”” AAAAAAII TTeecchhnniiccaall RReeppoorrtt,,
CClliinniiccaall RReesseeaarrcchh DDiivviissiioonn,, FFrreedd HHuuttcchhiinnssoonn CCaanncceerr RReesseeaarrcchh CCeenntteerr,, WWaasshhiinnggttoonn,, 11999988..
[[1155]] AA.. LLiiggeezzaa.. ((22000066)).. LLooggiiccaall FFoouunnddaattiioonn ffoorr RRuullee--BBaasseedd SSyysstteemmss..((22nndd eeddiittiioonn)).. [[oonnlliinnee]].. 22..
AAvvaaiillaabbllee::ffiillee:://////CC:://UUsseerrss//hhpp//DDoowwnnllooaaddss//99778833554400229911117766--tt11..ppddff.. [[JJuunn..,, 2233,, 22001155]]..

9. [[1166]] JJuuaann FFuueennttee,, AA.. AA.. ,, LLóóppeezz PPéérreezz,, BB.. ,, IInnffaannttee HHeerrnnáánnddeezz,, GG.. aanndd CCaasseess FFeerrnnáánnddeezz,, LL.. JJ.. ((22001133)).. ““UUssiinngg rruulleess
ttoo aaddaapptt aapppplliiccaattiioonnss ffoorr bbuussiinneessss mmooddeellss wwiitthh hhiigghh eevvoolluuttiioonnaarryy rraatteess..”” IInntteerrnnaattiioonnaall JJoouurrnnaall ooff AArrttiiffiicciiaall
IInntteelllliiggeennccee aanndd IInntteerraaccttiivvee MMuullttiimmeeddiiaa.. [[oonnlliinnee]].. 22((22)).. pppp.. 5566-- 6622 AAvvaaiillaabbllee:: DDOOII:: 1100..99778811//iijjiimmaaii..22001133..222277..
[[MMaayy,, 22,, 22001155]]..
[[1177]] MMaarryy LLoouu MMaahheerr,, ((11998844)) TToooollss aanndd tteecchhnniiqquueess ffoorr kknnoowwlleeddggee--bbaasseedd eexxppeerrtt ssyysstteemmss ffoorr eennggiinneeeerriinngg ddeessiiggnn,,
TTeecchhnniiccaall RReeppoorrtt [[oonnlliinnee]].. AAvvaaiillaabbllee::hhttttpp::////rreeppoossiittoorryy..ccmmuu..eedduu//ccggii//vviieewwccoonntteenntt..ccggii??aarrttiiccllee.. [[sseepp.. 2233,, 22001155]]..
[[1188]] SSyyllvveesstteerr II.. EEllee aanndd AAddeessoollaa,, WW..AA.. ((22001133)).. ““DDeessiiggnn ooff CCoommppuutteerr FFaauulltt DDiiaaggnnoossiiss aanndd TTrroouubblleesshhoooottiinngg
SSyysstteemm ((CCFFDDTTSS))..”” WWeesstt AAffrriiccaann JJoouurrnnaall ooff IInndduussttrriiaall aanndd AAccaaddeemmiicc RReesseeaarrcchh [[oonnlliinnee]].. 99((11)).. PPpp.. 4433--5533..
AAvvaaiillaabbllee:: ffiillee:://////CC:://UUsseerrss//hhpp//DDoowwnnllooaaddss//110055772255--228866773300--11--PPBB..ppddff [[JJaann..,, 55,, 22001155]]..
[[1199]] JJ.. SS.. BBeennnneett.. ““DDAARRTT::AAnn EExxppeerrtt SSyysstteemm ffoorr CCoommppuutteerr FFaauulltt DDiiaaggnnoossiiss..”” HHeeuurriissttiicc PPrrooggrraammmmiinngg PPrroojjeecctt,,
pppp..884433--88445544,, 11998800..
[[2200]] AAkkaannddee RRuutthh,, AAmmoossaa BBaabbaalloollaa,, SSoobboowwaallee AAddeeddaayyoo aanndd HHaammeeeedd MM..AA.. ((22001155)).. ““WWeebb bbaasseedd eexxppeerrtt ssyysstteemm
ffoorr ddiiaaggnnoossiiss aanndd mmaannaaggeemmeenntt ooff kkiiddnneeyy ddiisseeaasseess..”” IInntteerrnnaattiioonnaall JJoouurrnnaall ooff CCuurrrreenntt RReesseeaarrcchh aanndd AAccaaddeemmiicc
RReevviieeww.. [[oonnlliinnee]].. 33((22)).. PPpp.. 99--1199.. AAvvaaiillaabbllee:: hhttttpp::////wwwwww..iijjccrraarr..ccoomm//vvooll--33--22 [[JJuull..,, 11,, 22001155]]..
[[2211]] BBeenn KKhhaayyuutt ,, LLiinnaa FFaabbrrii aanndd MMaayyaa AAbbuukkhhaannaa,, ((22001144)) ““IInntteelllliiggeenntt uusseerr iinntteerrffaaccee iinn ffuuzzzzyy eennvviirroonnmmeenntt..””
IInntteerrnnaattiioonnaall JJoouurrnnaall ooff AArrttiiffiicciiaall IInntteelllliiggeennccee && AApppplliiccaattiioonnss ((IIJJAAIIAA)),, 55((11)),, pppp.. 6633--7788..
[[2222]] MMáárriiaa PPoohhrroonnsskkáá ((22001122)) ““IImmpplleemmeennttiinngg EEmmbbeeddddeedd EExxppeerrtt SSyysstteemmss vviiaa PPrrooggrraammmmaabbllee HHaarrddwwaarree..””
IInnffoorrmmaattiioonn SScciieenncceess aanndd TTeecchhnnoollooggiieess BBuulllleettiinn ooff tthhee AACCMM SSlloovvaakkiiaa,, 44((22)),, pppp.. 1100--1199..
[[2233]] PPiioottrr GGoollaańńsskkii11,, PPrrzzeemmyyssłłaaww MMąąddrrzzyycckkii,, ((22001155)) ““UUssee ooff tthhee eexxppeerrtt mmeetthhooddss iinn ccoommppuutteerr bbaasseedd
mmaaiinntteennaannccee ssuuppppoorrtt ooff tthhee mm--2288 aaiirrccrraafftt..”” ZZeesszzyyttyy nnaauukkoowwee aakkaaddeemmii ii mmaarryynnaarrkkii wwoojjeennnneejj sscciieennttiiffiicc
jjoouurrnnaall ooff ppoolliisshh nnaavvaall aaccaaddeemmyy,, 22 ((220011)),, pppp.. 55--1122..
[[2244]] YY.. BBaassssiill.. ((22001122)) ““EExxppeerrtt PPCC TTrroouubblleesshhooootteerr WWiitthh FFuuzzzzyy ––LLooggiicc..”” IInntteerrnnaattiioonnaall JJoouurrnnaall ooff AArrttiiffiicciiaall
IInntteelllliiggeennccee && AApppplliiccaattiioonnss ((IIJJAAIIAA)),, 33((22)),, pppp.. 1111--2211..
[[2255]] CChhuunnqquuaann LL..,, YYaanngg ZZhh.. aanndd QQuunn SS.. ““DDeecciissiioonn TTrreeee ffoorr DDyynnaammiicc aanndd UUnncceerrttaaiinn DDaattaa SSttrreeaammss..”” 22nndd AAssiiaann
CCoonnffeerreennccee oonn MMaacchhiinnee LLeeaarrnniinngg ((AACCMMLL22001100)),, NNoovv.. 88––1100,, 22001100,, pppp.. 220099--222244..
AAUUTTHHOORRSS
AAmmaannuueell AAyyddee EErrggaaddoo,, rreecceeiivveedd hhiiss BBSScc iinn IInnffoorrmmaattiioonn SSttuuddiieess ffrroomm JJiimmmmaa UUnniivveerrssiittyy,,
EEtthhiiooppiiaa iinn 22001100 aanndd MMSScc iinn IInnffoorrmmaattiioonn SScciieennccee ffrroomm AAddddiiss AAbbaabbaa UUnniivveerrssiittyy,,
EEtthhiiooppiiaa iinn 22001144.. CCuurrrreennttllyy,, hhee iiss LLeeccttuurreerr iinn tthhee DDeeppaarrttmmeenntt ooff IInnffoorrmmaattiioonn SScciieennccee iinn
JJiimmmmaa UUnniivveerrssiittyy,, EEtthhiiooppiiaa.. HHiiss ccuurrrreenntt rreesseeaarrcchh iinntteerreessttss aarree iinn tthhee aarreeaass ooff aarrttiiffiicciiaall
iinntteelllliiggeenntt ssyysstteemmss,, iinnffoorrmmaattiioonn mmaannaaggeemmeenntt,, kknnoowwlleeddggee mmaannaaggeemmeenntt,, ddaattaa mmiinniinngg,,
ddaattaabbaassee mmaannaaggeemmeenntt ssyysstteemmss aanndd nnaattuurraall llaanngguuaaggee pprroocceessssiinngg..

10. Citation Count – 03
AA MMOODDIIFFIIEEDD VVOORRTTEEXX SSEEAARRCCHH AALLGGOORRIITTHHMM FFOORR NNUUMMEERRIICCAALL
FFUUNNCCTTIIOONN OOPPTTIIMMIIZZAATTIIOONN
Berat Doğan
Department of Biomedical Engineering, Inonu University, Malatya, Turkey
AABBSSTTRRAACCTT
The Vortex Search (VS) algorithm is one of the recently proposed metaheuristic algorithms which was
inspired from the vortical flow of the stirred fluids. Although the VS algorithm is shown to be a good
candidate for the solution of certain optimization problems, it also has some drawbacks. In the VS
algorithm, candidate solutions are generated around the current best solution by using a Gaussian
distribution at each iteration pass. This provides simplicity to the algorithm but it also leads to some
problems along. Especially, for the functions those have a number of local minimum points, to select a
single point to generate candidate solutions leads the algorithm to being trapped into a local minimum
point. Due to the adaptive step-size adjustment scheme used in the VS algorithm, the locality of the
created candidate solutions is increased at each iteration pass. Therefore, if the algorithm cannot escape a
local point as quickly as possible, it becomes much more difficult for the algorithm to escape from that
point in the latter iterations. In this study, a modified Vortex Search algorithm (MVS) is proposed to
overcome above mentioned drawback of the existing VS algorithm. In the MVS algorithm, the candidate
solutions are generated around a number of points at each iteration pass. Computational results showed
that with the help of this modification the global search ability of the existing VS algorithm is improved
and the MVS algorithm outperformed the existing VS algorithm, PSO2011 and ABC algorithms for the
benchmark numerical function set.
KKEEYYWWOORRDDSS
Metaheuristics, Numerical Function Optimization, Vortex Search Algorithm, Modified Vortex Search
Algorithm
For More Details : http://aircconline.com/ijaia/V7N3/7316ijaia04.pdf
Volume Link : http://airccse.org/journal/ijaia/current2016.html

11. RREEFFEERREENNCCEESS
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AAUUTTHHOORRSS
Dr. Berat Doğan received his BSc. degree in Electronics Engineering from rciyes University,
Turkey, 2006. He received his MSc. degree in Biomedical Engineering from Istanbul
Technical University, Turkey, 2009. He received his PhD. in Electronics Engineering at
Istanbul Technical University, Turkey, 2015. Between 2008-2009 he worked as a software
engineer at Nortel Networks Netas Telecommunication Inc. Then, from 2009 to July 2015 he
worked as a Research Assistant at Istanbul Technical University. Now he is working as an
Assistant Professor at Inonu University, Malatya, Turkey. His research interests include
optimization algorithms, pattern recognition, biomedical signal and image processing, and
bioinformatics

13. Citation Count – 02
AA RREEVVIIEEWW OONN OOPPTTIIMMIIZZAATTIIOONN OOFF LLEEAASSTT SSQQUUAARREESS SSUUPPPPOORRTT
VVEECCTTOORR MMAACCHHIINNEE FFOORR TTIIMMEE SSEERRIIEESS FFOORREECCAASSTTIINNGG
Yuhanis Yusof1
and Zuriani Mustaffa2
1 School of Computing, Universiti Utara Malaysia, Malaysia
2
Faculty of Computer Systems & Software Engineering, Universiti Malaysia Pahang, Malaysia
AABBSSTTRRAACCTT
Support Vector Machine has appeared as an active study in machine learning community and extensively
used in various fields including in prediction, pattern recognition and many more. However, the Least
Squares Support Vector Machine which is a variant of Support Vector Machine offers better solution
strategy. In order to utilize the LSSVM capability in data mining task such as prediction, there is a need to
optimize its hyper parameters. This paper presents a review on techniques used to optimize the parameters
based on two main classes; Evolutionary Computation and Cross Validation
KKEEYYWWOORRDDSS
Least Squares Support Vector Machine, Evolutionary Computation, Cross Validation, Swarm Intelligence
For More Details : http://aircconline.com/ijaia/V7N2/7216ijaia03.pdf
Volume Link : http://airccse.org/journal/ijaia/current2016.html

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