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Machine learning: Fisher fund classiﬁcation using neural network and particle swarm optimization

(Conference Paper)

, ,

Master's Dept. of Information System, Diponegoro University, Semarang, Indonesia Department of Physics, Diponegoro University, Semarang, Indonesia

Department of Informatics, Diponegoro University, Semarang, Indonesia

Abstract

Assistance fishery can increase the income of fishermen and contribute to the country. Required classification awarding kind of help to improve the utilization of fund. In this research the ML to learn the features in a dataset of combinations Fishermen Card database and data Monitoring and Evaluation fisheries fund. ML process performed by NN MLP models combined with PSO with performance models such as Precision qualitative reached 0.75, sensitivity is reduced to 0.44, with the quantitative achievements reached 0.66 Accuracy and Error 0.34 with network model and small parameters. © 2018 IEEE.

SciVal Topic Prominence

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Prominence percentile: 98.158

Author keywords

Fishers Fund Classiﬁcation Machine Learning Multilayer Perceptron Neural Network Particle Swarm Optimization

Indexed keywords

Engineering controlled terms:

Fisheries Learning systems Swarm intelligence

Engineering uncontrolled terms

Data monitoring MLP model Multi-layer perceptron neural networks Network modeling Neural network and particle swarm optimizations Performance Model

Engineering main heading:

Particle swarm optimization (PSO)

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2018 International Conference on Information and Communications Technology, ICOIACT 2018 Volume 2018-January, 26 April 2018, Pages 315-320

1st International Conference on Information and Communications Technology, ICOIACT 2018;

Grand ZuriYogyakarta; Indonesia; 6 March 2018 through 7 March 2018; Category numberCFP18L86-ART; Code 136213

Tindi, A.P.^a  Gernowo, R.^b  Nurhayati, O.D.^c 

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References (16)

Fishing, J.

Technical assistance facility fishing fishing at the directorate general of TA. 2016 (2016) The Ministry of Maritime Affairs and Fisheries

Fishing, L.Ka.S.

(2016) Report Distribution, Monitoring, and Evaluation Help Fishermen Fishing District Kepl. Sangihe 2016

Arnawa, I.K., Purnama, I.B., Mekse, G., Arisena, K.

Impact assistance facility ﬁshing ﬁshermen against increased revenue in gianyar bali province (2016) J. Manaj. Agribusiness IPB, 4 (1), pp. 47-55.

Pakpahan, H.T., Lumintang, R., Susanto, D.

The relationship between work motivation and behavior of ﬁshermen in the ﬁshery business (2006) J. Penyul. 2006 Inst. Pertan. Bogor, 2 (1), pp. 26-34.

Rasmussen, Williams

(2006) Gaussian Processes for Machine Learning, 14 (2), pp. 69-106. .

Chandiok, A., Chaturvedi, D.K.

(2015) 2015 IEEE Workshop on Computational Intelligence: Theories, Applications and Future Directions, WCI 2015, art. no. 7495529. .

ISBN: 978-146738215-1 doi: 10.1109/WCI.2015.7495529

Wang, L., Chen, Y., Zhao, Y., Meng, Q., Zhang, Y.

(2016) Proceedings - 2016 8th International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2016, 1, art. no. 7783661, pp. 497-500. .

ISBN: 978-150900768-4 doi: 10.1109/IHMSC.2016.165 ISBN: 978-153860954-5

Source Type: Conference Proceeding Original language: English

DOI: 10.1109/ICOIACT.2018.8350787 Document Type: Conference Paper Sponsors: GIT Solution,Time Excelindo

Publisher: Institute of Electrical and Electronics Engineers Inc.

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%96-(,-962313 278-8983*)',2303+=)4909,34)1&)6 2(32)7-%

-0%974-8%7%6- 2-:)67-8%7#3+=%/%68% 2(32)7-%

#9%2732+-%3 8,032)278-898)3*)',2303+= 6)0%2(

%7-896)7,- 2-:)67-8=36%(36( 2-8)(-2+(316)%8

6-8%-2

(13)

0-%-)- 2-:)67-8=3)',2303+==(2)= 9786%0-%

%62-%,-1 2-:)67-8-)/2-/%0%0%=7-%)0%/% %0%=7-%

)1%2891%6%8, %8%327908%2'=)6:-')7 2(-%

.-8)((= 3/-%

6-')2%908 278-898-2)7)0)'31)0)'319(%6-7 6%2')

%+97-28=%62% )4909,34)1&)6278-898)3*)',2303+= 2(32)7-%

-132-)86331%23 2-:)67-8=3*%430-)()6-'3 8%0=

#%28-971%;%8- )0/312-:)67-8= 2(32)7-%

39%6-%&-6-2 )7)%6',2' %4%2

%-=%2%-=3( ,32%)22-:)67-8= ,%-0%2(

1-%0%1%, )&)0%7%6)82-:)67-8= 2(32)7-%

%=%28%1%6/%6 -.%=% -88%0%278-898)3*)',2303+= 2(-%

-=%2%683%623 278-898)/2303+-)4909,34)1&)6 2(32)7-%

-8,-0)=7,%8,-=%2%6%=%2%2 -8=2-:)67-8=3*32(32 2-8)(-2+(316)%8 6-8%-2

-%2%;-86- 2(32)7-%

391=%)2 2-:)67-8=3*%0'988%30/%8% 2(-%

2-2(-8%)48-%6-2- 2-:)6-78%790%;%61%2 2(32)7-%

1)0)66%8 0+)6-%

!%;%2)8-%;%2 2-:)67-8%7)2(-(-/%22(32)7-% 2(32)7-%

6-)*)8=%283 2-:)67-8%7#3+=%/%68% 2(32)7-%

;%2)8=%;%2 %8=%!%'%2%,6-78-%22-:)67-8= 2(32)7-%

=%6-*%,%>0-2)=)(%(>-6 2-:)67-8-)/2303+-%0%=7-% %0%=7-%

)26-%283)26-%283 -2972-:)67-8= 2(32)7-%

(-8-,%61% 2+-2))6-2+300)+)3(,496 2(-%

9/90,%61% %.%78,%2)',2-'%02-:)67-8= 2(-%

)7,%.,%61%%2.%() 28)036436%8-32

%2++=9,-2 (:%2')(278-898)32(9786-%0)',2303+= %4%2 1%1,3- 2-:)67-8%770%1)+)6-=%6-*-(%=%8900%,%/%68% 2(32)7-%

,%2%2.%=-2+, %2/9/2-:)67-8=3*36)-+289(-)7 36)%

)6--71363 2-:)67-8%71-/31#3+=%/%68% 2(32)7-%

,-2%32%+-6- =()6%&%( 2(-%

39&-2+32+ 1&6=-((0))632%98-'%02-:)67-8=

-'/,332+ 36)%(:%2')(278-898)3*'-)2')%2()',2303+= 36)%

#-)29 ,9)2-:)67-8= %-;%2

3)=9&% 2-:)67-8=3*8,)779148-32 ,-0-44-2)7

9(%61%;%29(%61%;%2 #3+=%/%68%2-:)67-8= 2(32)7-%

&&%9+%2(%-67%2+ -2%97%28%6%2-:)67-8= 2(32)7-%

%61%29/%623 )0/312-:)67-8= 2(32)7-%

2(-92=383 2-:)67-8%7#3+=%/%68% 2(32)7-%

-'396%28,% -2%97%28%6%2-:)67-8= 2(32)7-%

3:-2(96=%;%27,- 2-:)67-8=3*92)92) 2(-%

6-)797%283 =%6-*-(%=%8900%,%/%68% 2(32)7-%

9=%2839=%283 )0/312-:)67-8= 2(32)7-%

-63236-9>9/- -4432278-898)3*)',2303+= %4%2

%/9.-%',-&%2% 2-:)67-8=3*9/9- %4%2

6-2-:%7909%(-7)88= %/%8-=%2-:)67-8=300)+)3*2+-2))6-2+%2(

)',2303+= 2(-%

-632%3%/%,%7,- 9**)62-:)67-8= %4%2

97,-0,%0) 63(6-+9)7278-898)3*)',2303+= 2(-%

:%22%-138-97 %8=%!%'%2%,6-78-%22-:)67-8= 2(32)7-%

%(-%28%6-%81%(.% 2-:)67-8%7%(.%,%(% 2(32)7-%

(14)

-,%-0=%+923: %8-32%0)7)%6',2-:)67-8=37'3;3;)6

2+-2))6-2+ 977-%

9,%1%((%,%11%62+ 2-:)67-8-%0%=7-%%,%2+ %0%=7-%

7%/36%/- )-32-:)67-8= %4%2

((=!%,=9(-) 2-:)67-8= 2-8)(6%&1-6%8)7

92'363!%789;-&3;3 )0/312(32)7-% 2(32)7-%

90-%2!)&&)6 7%/%2-:)67-8= %4%2

)66=!%,=9!-&3;3 2-:)67-8%7#3+=%/%68% 2(32)7-%

/-!-'%/7323 2-:)67-8%7%(.%,%(% 2(32)7-%

)(=!-.%=% )0/312-:)67-8= 2(32)7-%

-2+2"9) 7-2+,9%2-:)67-8= ,-2%

!%697-%#%77-2 2-:)67-8-)/2-/%0%0%=7-%)0%/% %0%=7-%

),1)8/-*#%>-'- 78%2&90)',2-'%02-:)67-8= 96/)=

,%;))7%/#-2+8,%;36279/ -2+32+/9872-:)67-8=3*)',2303+=,32&96- ,%-0%2(

#9=%#3/3=%1% =3836)*)'896%02-:)67-8= %4%2

,%9#9)2 -2+%436)2-:)67-8=3*)',2303+=%2()7-+2 -2+%436)

3#92 )6-38!%882-:)67-8=%0%=7-% %0%=7-%

%9>-%,$%-29((-2 2-:)67-8-%0%=7-%%,%2+ %0%=7-%

/6%1$)/- 28)62%8-32%070%1-'2-:)67-8=%0%=7-% %0%=7-%

!)-;)2$,%2+ 278-898)3-+,)6361%2')31498-2+ -2+%436)

6-$90-%2% 92%2%0-.%+% 2(32)7-%

(15)

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(16)

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:35:11>5:3:0;:1?5-

(17)

4/22/2020 IEEE Xplore - Conference Table of Contents

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(18)

4/22/2020 IEEE Xplore - Conference Table of Contents Publication Year: 2018, Page(s): 56 - 61

Abstract (490 Kb)

Leaf morphological feature extraction based on K- Nearest Neighbor

Muhamad Hardi ; Muhammad Nuur Firdaus ; Bayu Putra Pamungkas ; Usman Sudibyo ; Christy Atika Sari ; Yani Parti Astuti ; Eko Hari Rachmawanto

Year: 2018

Reduction of catastrophic forgetting for multilayer neural networks trained by no-prop algorithm

Motonobu Hattori ; Hideto Tsuboi

Abstract (1555 Kb)

Reduction of catastrophic forgetting for multilayer neural networks trained by no-prop algorithm

Motonobu Hattori ; Hideto Tsuboi

Year: 2018

Complex-valued support vector machines based on multi-valued neurons

Hokuto Shinoda ; Motonobu Hattori Publication Year: 2018, Page(s): 208 - 213

Abstract (157 Kb)

Complex-valued support vector machines based on multi-valued neurons

Hokuto Shinoda ; Motonobu Hattori

Year: 2018

Pathloss modeling based on measurement at 3 Ghz for on body area network application

Kurnia Paranita Kartika ; Gamantyo Hendrantoro ; Achmad Mauludiyanto

Publication Year: 2018, Page(s): 905 - 910 Cited by: Papers (1)

Abstract (459 Kb)

Pathloss modeling based on measurement at 3 Ghz for on body area network application

Kurnia Paranita Kartika ; Gamantyo Hendrantoro ; Achmad Mauludiyanto

Year: 2018

Multi document summarization for the Indonesian language based on latent dirichlet allocation and significance sentence Agus Widjanarko ; Retno Kusumaningrum ; Bayu Surarso Publication Year: 2018, Page(s): 520 - 524

Cited by: Papers (1)

Abstract (139 Kb)

Multi document summarization for the Indonesian language based on latent dirichlet allocation and significance sentence

(19)

4/22/2020 IEEE Xplore - Conference Table of Contents Mochammad Apriyadi Hadi Sirad ; Muhammad Rais ; Muhammad Ruswandi Djalal ; Andi Nur Putri

Year: 2018

Model development of students' scholarship status at first asia institute of technology and humanities (Faith)

Jonalyn Joy B. Labayne ; Lester L. Mercado ; Jheanel Espiritu Estrada

Abstract (260 Kb)

Model development of students' scholarship status at first asia institute of technology and humanities (Faith)

Jonalyn Joy B. Labayne ; Lester L. Mercado ; Jheanel Espiritu Estrada

Year: 2018

Design of land optical fiber backbone communication network in North Sumatera

Yudiansyah ; Prita Dewi Mariyam ; Arie Pangesti Aji ; Novietasari Chisnariandini ; Catur Apriono

Abstract (197 Kb)

Design of land optical fiber backbone communication network in North Sumatera

Yudiansyah ; Prita Dewi Mariyam ; Arie Pangesti Aji ; Novietasari Chisnariandini ; Catur Apriono

Year: 2018

LINGO-based optimization problem of cloud computing of bandwidth consumption in the Internet

Indrawati ; Fitri Maya Puspita ; Sri Erlita ; Inosensius Nadeak ; Bella Arisha

Abstract (221 Kb)

LINGO-based optimization problem of cloud computing of bandwidth consumption in the Internet

Indrawati ; Fitri Maya Puspita ; Sri Erlita ; Inosensius Nadeak ; Bella Arisha

Year: 2018

LINGO-based on robust counterpart open capacitated vehicle routing problem (RC-OCVRP) model of waste transportation in Palembang

Yusuf Hartono ; Fitri Maya Puspita ; Desi Indah Permatasari ; Bella Arisha

Abstract (235 Kb)

LINGO-based on robust counterpart open capacitated vehicle routing problem (RC-OCVRP) model of waste transportation in Palembang

(20)

4/22/2020 IEEE Xplore - Conference Table of Contents Yusuf Hartono ; Fitri Maya Puspita ; Desi Indah Permatasari ; Bella Arisha

Year: 2018

Failover mechanism during upgrading process for software- defined networking

Siew-Hoon Lim ; Yung-Wey Chong ; Qi-Guan Ng ; Khong-Lim Yap Publication Year: 2018, Page(s): 591 - 596

Abstract (330 Kb)

Failover mechanism during upgrading process for software-defined networking

Siew-Hoon Lim ; Yung-Wey Chong ; Qi-Guan Ng ; Khong-Lim Yap 2018 International Conference on Information and

Communications Technology (ICOIACT) Year: 2018

Civil servant behaviors performance evaluation: Combining DEAHP and 360-degree feedback

Irfani Zuhrufillah ; Farikhin ; Rizal Isnanto Publication Year: 2018, Page(s): 280 - 285

Abstract (811 Kb)

Civil servant behaviors performance evaluation:

Combining DEAHP and 360-degree feedback

Irfani Zuhrufillah ; Farikhin ; Rizal Isnanto

Year: 2018

Management of fault tolerance and traffic congestion in cloud data center

Humphrey Emesowum ; Athanasios Paraskelidis ; Mo Adda Publication Year: 2018, Page(s): 10 - 15

Cited by: Papers (1)

Abstract (716 Kb)

Management of fault tolerance and traffic congestion in cloud data center

Humphrey Emesowum ; Athanasios Paraskelidis ; Mo Adda 2018 International Conference on Information and

Effect of stator slot geometry on high speed spindle motor performance

Wawan Purwanto ; Risfendra ; Dwi Sudarno Putra Publication Year: 2018, Page(s): 561 - 565 Cited by: Papers (2)

Abstract (319 Kb)

Effect of stator slot geometry on high speed spindle motor performance

Wawan Purwanto ; Risfendra ; Dwi Sudarno Putra 2018 International Conference on Information and Communications Technology (ICOIACT)

Year: 2018

CEW-DTW: A new time series model for text mining GuanDong Zhang ; Hao Yu ; Lu Xiao

(21)

4/22/2020 IEEE Xplore - Conference Table of Contents

Abstract (297 Kb)

CEW-DTW: A new time series model for text mining

GuanDong Zhang ; Hao Yu ; Lu Xiao

Year: 2018

Optimization of light tracker movement using fuzzy logic control Lutfi Mahardika ; Anik Nur Handayani ; Heru Wahyu Herwanto ; Kohei Arai

Abstract (521 Kb)

Optimization of light tracker movement using fuzzy logic control

Lutfi Mahardika ; Anik Nur Handayani ; Heru Wahyu Herwanto ; Kohei Arai

Year: 2018

Combined economic emission dispatch with cubic criterion function using cuckoo search algorithm

Muhammad Khalil ; Rony Seto Wibowo ; Ontoseno Penangsang Publication Year: 2018, Page(s): 36 - 40

Abstract (227 Kb)

Combined economic emission dispatch with cubic criterion function using cuckoo search algorithm

Muhammad Khalil ; Rony Seto Wibowo ; Ontoseno Penangsang 2018 International Conference on Information and

Analysis of evaluation quality website from developers perspective for build website

Dwi Rahayu ; Ema Utami ; Emha Taufiq Luthfi Publication Year: 2018, Page(s): 120 - 124

Abstract (301 Kb)

Analysis of evaluation quality website from developers perspective for build website

Dwi Rahayu ; Ema Utami ; Emha Taufiq Luthfi 2018 International Conference on Information and Communications Technology (ICOIACT)

Year: 2018

Control system based on fuzzy logic in nutmeg oil distillation process for energy optimization

Syamsul ; Rudi Syahputra ; Suherman Publication Year: 2018, Page(s): 679 - 683

Abstract (233 Kb)

Control system based on fuzzy logic in nutmeg oil distillation process for energy optimization

Syamsul ; Rudi Syahputra ; Suherman

Year: 2018

Twitter data transformation for network visualization based context analysis

Hani Nurrahmi ; Rini Wijayanti ; Andri Fachrur Rozie ; Andria Arisal

(22)

4/22/2020 IEEE Xplore - Conference Table of Contents Spatial probit regression model: Recursive importance sampling approach

Taufiq Fajar Dewanto ; Vita Ratnasari ; Purhadi Publication Year: 2018, Page(s): 759 - 764

Abstract (445 Kb)

Spatial probit regression model: Recursive importance sampling approach

Taufiq Fajar Dewanto ; Vita Ratnasari ; Purhadi 2018 International Conference on Information and Communications Technology (ICOIACT)

Year: 2018

Wireless service at Public University: A survey of users perception on security aspects

Arif Ridho Lubis ; Ferry Fachrizal ; Muharman Lubis ; Hatim Mohamad Tahir

Abstract (531 Kb)

Wireless service at Public University: A survey of users perception on security aspects

Arif Ridho Lubis ; Ferry Fachrizal ; Muharman Lubis ; Hatim Mohamad Tahir

Year: 2018

Machine learning: Fisher fund classification using neural Machine learning

network and particle swarm optimization

Arifin Paulus Tindi ; Rahmat Gernowo ; Oky Dwi Nurhayati Publication Year: 2018, Page(s): 315 - 320

Abstract (536 Kb)

Machine learning: Fisher fund classification using neural network and particle swarm optimization

Arifin Paulus Tindi ; Rahmat Gernowo ; Oky Dwi Nurhayati 2018 International Conference on Information and Communications Technology (ICOIACT)

Year: 2018

A double stage micro-inverter for optimal power flow control in grid connected PV system

Ferdian Ronilaya ; Achsanul Khabib ; Mohammad Noor Hidayat ; Indrazno Siradjudin ; Erfan Rohadi ; Rosa Andri Asmara ; Erni Yudaningtyas

Abstract (749 Kb)

A double stage micro-inverter for optimal power flow control in grid connected PV system

Ferdian Ronilaya ; Achsanul Khabib ; Mohammad Noor Hidayat ; Indrazno Siradjudin ; Erfan Rohadi ; Rosa Andri Asmara ; Erni Yudaningtyas

Year: 2018

River body extraction and classification using enhanced models of modified normalized water difference index at Yeh Unda River Bali

Putu Virga Nanta Nugraha ; Sunu Wibirama ; Risanuri Hidayat

(23)

4/22/2020 Machine learning: Fisher fund classification using neural network and particle swarm optimization - IEEE Conference Publication

Conferences > 2018 International Conference...

3Author(s) Arifin Paulus Tindi ; Rahmat Gernowo ; Oky Dwi Nurhayati View All Authors

Machine learning: Fisher fund classification using neural network and particle swarm optimization

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Abstract Document Sections I. Introduction II. Related Work III. Background

Theory IV. Implementation,

Result and Discussion V. Conclusion

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Abstract: Assistance fishery can increase the income of fishermen and contribute to the country. Required classification awarding kind of help to improve the utilization of fund.

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Published in: 2018 International Conference on Information and Communications Technology (ICOIACT)

Abstract:

Assistance fishery can increase the income of fishermen and contribute to the country.

Required classification awarding kind of help to improve the utilization of fund. In this research the ML to learn the features in a dataset of combinations Fishermen Card database and data Monitoring and Evaluation fisheries fund. ML process performed by NN MLP models combined with PSO with performance models such as Precision qualitative reached 0.75, sensitivity is reduced to 0.44, with the quantitative achievements reached 0.66 Accuracy and Error 0.34 with network model and small parameters.

Date of Conference: 6-7 March 2018 Date Added to IEEE Xplore: 30 April 2018

ISBN Information:

INSPEC Accession Number: 17750258 DOI: 10.1109/ICOIACT.2018.8350787 Publisher: IEEE

Conference Location: Yogyakarta, Indonesia

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4/22/2020 Machine learning: Fisher fund classification using neural network and particle swarm optimization - IEEE Conference Publication

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Arifin Paulus Tindi

Master's Dept. of Information System, Diponegoro University, Semarang, Indonesia

Rahmat Gernowo

Department of Physics, Diponegoro University, Semarang, Indonesia

Oky Dwi Nurhayati

I. Introduction

Fishing is a strategic sub-sectors in development. Efforts to improve the construction was done with the fund fisheries infrastructure. Utilization can help increase the income of fishermen and contribute to state revenues [1], Utilization of fund is still low due to the provision of fund that is not targeted[2], Utilization of fisheries fund is influenced by the characteristics and motivations of fishermen in an effort to increase revenue [3], [4]. To overcome these problems required the classification of the provision of the type of fund and beneficiary fishermen to learn the facts.

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Arifin Paulus Tindi

Master's Dept. of Information System, Diponegoro University, Semarang, Indonesia Rahmat Gernowo

Department of Physics, Diponegoro University, Semarang, Indonesia Oky Dwi Nurhayati

Reduction of catastrophic forgetting for multilayer neural networks trained by no-prop algorithm

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Abstract Document Sections I. Introduction II. No-Propagation

Algorithm III. Pseudorehearsal IV. Computer

Simulation Results V. Conclusions

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Abstract: Neural networks encounter sever fatal forgetting or fatal interference when information is learned sequentially. This is called catastrophic forgetting. One way to reduce... View more

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Neural networks encounter sever fatal forgetting or fatal interference when information is learned sequentially. This is called catastrophic forgetting. One way to reduce catastrophic forgetting is pseudorehearsal, in which pseudopatterns are learned with training patterns. This method has shown excellent performance in multilayered neural networks trained by the backpropagation algorithm. In recent years, a new learning method of multilayered neural networks called the no-propagation algorithm has been proposed. This algorithm is far easier to implement because only the weights of output layer neurons are trained, and convergence is much faster than backpropagation.

Nevertheless, its performance is essentially equivalent to that of backpropagation. Our previous research has shown that pseudorehearsal is effective even for networks trained by no-propagation, whereas it is less effective than networks trained by backpropagation. In this paper, we modify the no-propagation algorithm for pseudorehearsal so that it can much reduce catastrophic forgetting. Computer simulation results show that networks trained by the proposed algorithm can much improve the reduction of catastrophic forgetting in comparison with the conventional no- propagation algorithm and becomes comparable to those trained by backpropagation.

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4/22/2020 Reduction of catastrophic forgetting for multilayer neural networks trained by no-prop algorithm - IEEE Conference Publication

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Motonobu Hattori

Department of Computer Science and Engineering, Faculty of Interdisciplinary Research, University of Yamanashi, Kofu, Yamanashi, Japan

Hideto Tsuboi

Department of Computer Science and Media Engineering, Faculty of Engineering, University of Yamanashi, Kofu, Yamanashi, Japan

I. Introduction

If a neural network is trained on a set of patterns and later trying to add new patterns to its repertoire, catastrophic interference or complete loss of all of its previously learned information may occur. This type of radical forgetting is unacceptable for both human memory models and practical engineering applications. One typical solution to catastrophic forgetting is interleaved learning, that is, mixing the previous set of patterns during training for a new set of patterns. However, this solution requires the unrealistic assumption of permanent access to all patterns that the network has previously learned. Therefore, several methods have been proposed to reduce catastrophic forgetting without performing such straight rehearsal [1]–[5]. Among them, Robins has proposed

pseudorehearsal which does not require access to the information itself that was learned previously, and shown that it can greatly reduce catastrophic forgetting [4]. Moreover, several neural network models using pseudorehearsal have been proposed so far [6]–[8]. However, most of the studies so far were directed to multilayered neural networks trained by the back-propagation algorithm (Back-Prop).

Motonobu Hattori

Hideto Tsuboi

Department of Computer Science and Media Engineering, Faculty of Engineering, University of Yamanashi, Kofu, Yamanashi, Japan

Complex-valued support vector machines based on multi-valued neurons

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Abstract Document Sections I. Introduction II. Multi-Valued

Neurons (MVNs) III. Complex-Valued Support Vector Machines (CVSVMs) IV. Implementation

Technique V. Computer

Simulation Results

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Abstract: In this paper, we propose complex-valued support vector machines (CVSVMs) which are a new type of support vector machines (SVMs) based on multi- valued neurons (MVNs). An ... View more

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In this paper, we propose complex-valued support vector machines (CVSVMs) which are a new type of support vector machines (SVMs) based on multi-valued neurons (MVNs). An MVN which is a type of complex-valued neurons is a component of the proposed CVSVM. The features of the proposed CVSVM are: 1) it has a multi-valued complex output; 2) it provides the generalization ability by a decision boundary with the maximal margin; 3) it can deal with non-linear classification by using a kernel function.

Experimental results for some famous benchmark problems show the effectiveness of the proposed CVSVM.

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4/22/2020 Complex-valued support vector machines based on multi-valued neurons - IEEE Conference Publication

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Hokuto Shinoda

Interdisciplinary Graduate School of Medicine and Engineering, University of Yamanashi, Kofu, Yamanashi, Japan

Motonobu Hattori

I. Introduction

Support vector machines (SVMs) [1] have attracted much attention in recent years. The principle of maximal margin method used in SVMs can optimize a decision boundary in the feature space for training examples. Moreover, by using a kernel function, SVMs can learn non- linear relations of training examples. Owing to these characteristics, SVMs have excellent generalization ability and show outstanding performance in pattern recognition, classification, prediction, regression, and so on. An SVM classifies the input pattern to positive or negative class, so it is called a binary classifier.

Hokuto Shinoda

Interdisciplinary Graduate School of Medicine and Engineering, University of Yamanashi, Kofu, Yamanashi, Japan

Motonobu Hattori

Model development of students' scholarship status at first asia institute of technology and humanities (Faith)

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Abstract Document Sections I. Introduction II. Methodology III. Result and

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Abstract: Data Mining nowadays is much known in the field of IT industry. It is a very powerful instrument and technique for many various fields such as education, IT and even in b... View more

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Data Mining nowadays is much known in the field of IT industry. It is a very powerful instrument and technique for many various fields such as education, IT and even in business industry. In this research, the researchers will predict if the students will retain or will not retain their scholarship based on the students' academic performance. On processing the data, the researchers will use different methods and techniques for data mining namely: Decision tree, Naïve Bayes and k-NN to provide more acceptable results on predicting the continuity of the scholarship of every students. Based on the result of the study, it shows that the students are having difficulties in Algebra and English subjects as shown by the results provided by the processed data on rapid miner. BAC with 17.58% and BSECE 15.37% has a high percentage of not retaining their scholarship because most of the students exceed on the allowable strikes of not maintaining their grades. The researchers found that the main cause why the students were not retaining their scholarship is because of their core subjects.

Date of Conference: 6-7 March 2018 INSPEC Accession Number: 17735619

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Date Added to IEEE Xplore: 30 April 2018 ISBN Information:

DOI: 10.1109/ICOIACT.2018.8350686 Publisher: IEEE

Jonalyn Joy B. Labayne

Technological Institute of the Philippines, Manila, Philippines

Lester L. Mercado

Jheanel Espiritu Estrada

I. Introduction

FAITH is an Institution of higher learning and research located in City of Tanauan in Batangas. Since its inception on September 8, 2000. Faith is envisioned to be a premier educational institution in the high-growth region, south of Metro Manila. It aims to contribute to the humane and holistic development of the Filipino nation and the individual by training and producing graduates who are technologically skilled, well-rounded and competent as well as grounded on Christian humanistic value. [1]

Jonalyn Joy B. Labayne

Technological Institute of the Philippines, Manila, Philippines Lester L. Mercado

Technological Institute of the Philippines, Manila, Philippines Jheanel Espiritu Estrada

Untitled - Universitas Diponegoro

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Machine learning: Fisher fund classiﬁcation using neural network and particle swarm optimization

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Credit management based on improved BP neural network

Particle swarm optimization modeling for solid waste collection problem with constraints

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Statistical control chart and neural network classiﬁcation for improving human fall detection

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A comparative study of metaheuristic optimization approaches for directional overcurrent relays coordination

Designing optimal architecture of neural network with particle swarm optimization techniques speciﬁcally for educational dataset

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