Metaheuristic Algorithms for Convolution Neural Network

A typical modern optimization technique is usually either heuristic or metaheuristic. This technique has managed to solve some optimization problems in the research area of science, engineering, and industry. However, implementation strategy of metaheuristic for accuracy improvement on convolution neural networks (CNN), a famous deep learning method, is still rarely investigated. Deep learning relates to a type of machine learning technique, where its aim is to move closer to the goal of artificial intelligence of creating a machine that could successfully perform any intellectual tasks that can be carried out by a human. In this paper, we propose the implementation strategy of three popular metaheuristic approaches, that is, simulated annealing, differential evolution, and harmony search, to optimize CNN. The performances of these metaheuristic methods in optimizing CNN on classifying MNIST and CIFAR dataset were evaluated and compared. Furthermore, the proposed methods are also compared with the original CNN. Although the proposed methods show an increase in the computation time, their accuracy has also been improved (up to 7.14 percent).Comment: Article ID 1537325, 13 pages. Received 29 January 2016; Revised 15 April 2016; Accepted 10 May 2016. Academic Editor: Martin Hagan. in Hindawi Publishing. Computational Intelligence and Neuroscience Volume 2016 (2016

Random Adjustment - Based Chaotic Metaheuristic Algorithms for Image Contrast Enhancement

Metaheuristic algorithm is a powerful optimization method, in which it can solve problemsby exploring the ordinarily large solution search space of these instances, that are believed tobe hard in general. However, the performances of these algorithms signicantly depend onthe setting of their parameter, while is not easy to set them accurately as well as completelyrelying on the problem\u27s characteristic. To ne-tune the parameters automatically, manymethods have been proposed to address this challenge, including fuzzy logic, chaos, randomadjustment and others. All of these methods for many years have been developed indepen-dently for automatic setting of metaheuristic parameters, and integration of two or more ofthese methods has not yet much conducted. Thus, a method that provides advantage fromcombining chaos and random adjustment is proposed. Some popular metaheuristic algo-rithms are used to test the performance of the proposed method, i.e. simulated annealing,particle swarm optimization, dierential evolution, and harmony search. As a case study ofthis research is contrast enhancement for images of Cameraman, Lena, Boat and Rice. Ingeneral, the simulation results show that the proposed methods are better than the originalmetaheuristic, chaotic metaheuristic, and metaheuristic by random adjustment

Optimasi Deep Belief Network Menggunakan Simulated Annealing

Dalam beberapa tahun terakhir, deep learning (DL) merupakan area penelitian yang sangat penting dalam pemelajaran mesin. Metode ini dapat mempelajari beragam tingkat abstraksi dan representasi pada berbagai macam data seperti teks, suara dan citra. Meskipun metode DL telah sukses dipergunakan untuk aplikasi seperti pemrosesan suara, pengenalan fonetik, robotika, pencarian informasi, bahkan sampai analisa molekul, namun untuk melatih metode ini tidaklah mudah. Sejumlah teknik telah diusulkan untuk membuat pelatihan dalam DL menjadi lebih optimal, seperti menambahkan proses pra-training, mengganti fungsi aktivasi maupun metode gradien standar yang dipergunakan, ataupun memutuskan sebagian dari jaringan pada lapisan. Dalam penelitian ini, diusulkan optimasi deep belief network, yang merupakan salah satu metode popular dalam DL, dengan menambahkan simulated annealing pada lapisan terakhir. Hasil eksperimen yang dilakukan menggunakan data set MNIST menunjukkan bahwa, meskipun ada penambahan waktu komputasi, akan tetapi secara umum ada peningkatan akurasi dari DBN asli

Studi Pengenalan Ekspresi Wajah Berbasis Convolutional Neural Network

Pengenalan ekspresi wajah adalah topik penelitian yang aktif dilakukan dalam beberapa dekade terakhir. Aplikasinya dapat ditemukan dalam biometric security, robotika, kesehatan, neuromarketing sampai animasi avatar. Mengenal ekspresi pada wajah bukanlah merupakan masalah yang mudah dalam pembelajaran mesin, karena setiap orang dapat memiliki cara yang berbeda untuk memperlihatkan ekspresinya. Bahkan citra pada orang yang sama dalam satu ekspresi, dapat menghasilkan fitur yangbervariasi, tergantung pencahayaan, posisi maupun latarbelakangcitra. Karena itu pengenalan ekspresi wajah masih merupakan masalah yang menantang dalam bidang computer vision. Pendekatan tradisional untuk pengenalan ekspresi wajah sangat bergantung pada hand-crafted features seperti SIFT, HOG maupun LBP, yang kemudian dilanjutkan dengan training data pada citra. Dalam beberapa tahun terakhir, metode deep learning mulai dipergunakan dalam pengenalan ekspresi wajah dengan hasil yang secara umum lebih baik. Penelitian ini menggunakan salah satu metode deep learning yang umum dipakai pada citra, yaitu Convolutional Neural Network untuk pengenalan ekspressi wajah, dengan sejumlah variasi parameter menggunakan dataset JAFFE. Hasil terbaik yang diperoleh adalah nilai akurasi sebesar 87,5% untuk arsitektur hanya dengan 3 lapisan training pada jaringan

Nature inspired meta-heuristic algorithms for deep learning: recent progress and novel perspective

Deep learning is presently attracting extra ordinary attention from both the industry and the academia. The application of deep learning in computer vision has recently gain popularity. The optimization of deep learning models through nature inspired algorithms is a subject of debate in computer science. The application areas of the hybrid of natured inspired algorithms and deep learning architecture includes: machine vision and learning, image processing, data science, autonomous vehicles, medical image analysis, biometrics, etc. In this paper, we present recent progress on the application of nature inspired algorithms in deep learning. The survey pointed out recent development issues, strengths, weaknesses and prospects for future research. A new taxonomy is created based on natured inspired algorithms for deep learning. The trend of the publications in this domain is depicted; it shows the research area is growing but slowly. The deep learning architectures not exploit by the nature inspired algorithms for optimization are unveiled. We believed that the survey can facilitate synergy between the nature inspired algorithms and deep learning research communities. As such, massive attention can be expected in a near future

Algoritma Genetik dan Aplikasinya dalam Desain Coating thin-film Optik

Algoritma Genetik (AG) diperkenalkan sebagai salah satu metode sintesis untuk memecahkan optimasi parameter sistem. Tahapan desain coating optik diuraikan dengan memperhatikan parameter GA seperti ukuran populasi, maksimum generasi serta pemilihan tipe operator genetik: Seleksi, Rekombinasi dan Mutasi. Dua tipe substrate (TaFd30 dan FK5) serta material coating (MgF2  dan OH5) dipergunakan sebagai model. Dua desain dikembangkan untuk coating antirefleksi 5 layer dan 8 layer dengan hasil sesuai target yang ditentukan