Fuzzy Subalgebras and Fuzzy Ideals of BCI-Algebras with Operators

The aim of this paper is to introduce the concepts of fuzzy subalgebras, fuzzy ideals and fuzzy quotient algebras of BCI-algebras with operators, and to investigate their basic properties

Spectral radius and k-factor-critical graphs

For a nonnegative integer $k$, a graph $G$ is said to be $k$-factor-critical if $G-Q$ admits a perfect matching for any $Q\subseteq V(G)$ with $|Q|=k$. In this article, we prove spectral radius conditions for the existence of $k$-factor-critical graphs. Our result generalises one previous result on perfect matchings of graphs. Furthermore, we claim that the bounds on spectral radius in Theorem 3.1 are sharp.Comment: 12 page

Breast Cancer Detection in Mammography Image using Convolutional Neural Network

Breast cancer is one of the non-contagious diseases that tends to increase every year. This disease occurs almost entirely in women, but can also occur in men. One way to detect this disease is by observing mammography images. However, mammography images often tend to be blurry with low quality so that it is possible to detect them incorrectly. Therefore, in this study, automatic classification of breast cancer on mammographic images was carried out using the Convolutional Neural Network (CNN). This proposed system uses the VGG16 architecture with a transfer learning system. The proposed system is then optimized using Adam optimizers and RMSprop optimizers. The results of system testing for normal, benign, and malignant classifications obtained an accuracy value of 80% - 90% with the highest accuracy achieved using Adam's optimizers. With this proposed system, it is hoped that it can help in the clinical diagnosis of breast cancer.

Classification of ECG signal-based cardiac abnormalities using fluctuation-based dispersion entropy and first-order statistics

The heart is one of the most important organs in the human body. The presence of abnormalities in the heart can be fatal for a person. One way to detect heart abnormalities is an Electrocardiogram (EKG) signal examination. To facilitate the detection of ECG signal abnormalities, an automatic classification method is needed. Therefore, in this study, a method for classifying ECG signals using FdispEn (Fluctuation-based dispersion Entropy) and first-order statistics is proposed. FdispEn measures the uncertainty in the signal and is expected to be able to distinguish the physiological state of the ECG signal time series. In this study FdispEn and statistical computing were used as feature extraction of the ECG signal and combined with the Support Vector Machine (SVM) for the classification process of Normal ECG, AFIB (Atrial Fibrilation), and CHF (Congestive Heart Failure). The method proposed in this study generates an accuracy of 91.5%. The system proposed in this study is expected to assist in the clinical diagnosis of abnormalities in the heart. &nbsp

PENGENALAN PLAT NOMOR KENDARAAN SECARA OFFLINE DENGAN TEKNIK OPTIMASI ALGORITMA GENETIKA

ABSTRAKSI: Setiap kendaraan telah memiliki identitas berupa plat kendaraan yang berisi nomor polisi. Identitas inilah yang membedakan antara kendaraan yang satu dengan yang lainnya. Pengenalan plat kendaraan dapat digunakan di berbagai sistem seperti sistem keamanan, sistem jalan tol dan sistem parkir tanpa harus membuat identitas baru sehingga topik ini menarik untuk diteliti. Setiap negara memiliki standar plat yang berbeda, tak terkecuali Indonesia. Beberapa penelitian dengan menggunakan system yang berbeda telah dilakukan, namun plat nomor yangdigunakan berbeda dengan karakteristik plat Indonesia. Oleh karena itu padaTesis ini diteliti system deteksi nomor plat yang sesuai dengan karakteristik plat Indonesia. Sistem deteksi plat yang dibuat menggunakan operasimorfologi dan karakteristik plat. Ekstraksi ciri menggunakan sistem pembagian grid, dan dilakukan pembandingan menggunakan algoritma klasifikasi KNN dan Jaringan SyarafTiruan. Optimasi dilakukanterhadap algoritma klasifikasi menggunakan algoritma genetika . Dari hasil pengujiandidapatkan akurasi sistem keseluruhan 92.31% untuk KNN dan88.46% untuk JST-BP dengan masukan berupavideo. Kata Kunci : plat, KNN, Jaringan Syaraf Tiruan, Algoritma GenetikaABSTRACT: Each vehicle has a vehicle identification plate that contains a number i dentity which distinguishes between one vehicle to another. Vehicle license plate recognition c an be used in various systems such as security systems, highway systems and parking systems without having to create a new identity. Each state has different license plate standards, including Indonesia. Several studies using different systems have been ma de, but the number plate is used in contrast to the characteristics of Indonesian plate. Therefore be studied in this thesis number plate detection system in accordance with the characteristics of the Indonesian plate . Plate detection system made using morphological operations and the characteristics of the plate. Feature extraction using grid distribution system . C omparisons were made between KNN classification algorithm and Neural Networks . Optimization is done on the classification algorithms using genetic algorithms. From the test results obtained the overall system accuracy 92.31% for KNN and 88.46% for ANN - BP with a video input .Keyword: license plate, KNN, Neural Networks, Genetic Algorithm

Pengenalan Plat Kendaraan Berbasis Pengolahan Citra Digital dan Jaringan Syaraf Tiruan Self-Organizing Maps(SOM)

ABSTRAKSI: Setiap kendaraan memiliki identitas berupa plat nomor kendaraan yang secara resmi dikeluarkan oleh negara. Nomor kendaraan yang berbeda itulah yang membuat plat banyak digunakan sebagai identitas pada berbagai sistem seperti sistem pakir, sistem keamanan bangunan dan sistem tol. Dan salah satu sub sistem yang penting dari sistem tersebut adalah pengenalan plat kendaraan.Pada tugas akhir ini dibuat sistem pengenalan plat kendaraan yang akan membaca karakter pada plat kendaraan dari masukan berupa video yang telah disimpan terlebih dahulu.Pada prosesnya dilakukan 6 tahapan. Pada tahap preprocessing dilakukan peningkatan kualitas citra dengan mengunakan transformasi top-hat dan bottom-hat, pada tahap deteksi plat dilakukan deteksi daerah plat dan cropping plat, pada tahap segmentasi dilakukan pemisahan tiap karakter yang akan dideteksi, dan pada tahap normalisasi dilakukan proses perubahan dimensi karakter. Selanjutnya, pada tahap ekstraksi ciri dilakukan pengambilan ciri-ciri tertentu dari tiap karakter. Dan pada tahap pengenalan karakter yang menggunakan JST Self Organizing Map (SOM) dilakukan penentuan vektor penyusun garis karakter dengan cara menghubungkan titik-titik neuron yang ada pada suatu region karakter.Pengujian dilakukan dengan mengambil sampel-sampel video untuk menguji akurasi sistem yang dibuat. Hasil akurasi terbaik adalah 89,05% dengan menggunakan topologi randtop dengan fungsi manhattan.Kata Kunci : Plat Kendaraan, cropping, ekstraksi ciri, Jaringan Syaraf Tiruan SOM,ABSTRACT: Every vehicle has license plate as identity that is given by country. This difference make license plate usually used as identity for several system such as parking system, security system in building and toll system. License plate recognition is one of important sub system from those systems which makes this topic is interested to research.This final assessment is designed vehicle plate recognition system which is used to recognize character of plate from video that is saved before.Its process is done in 6 steps. In preprocessing, the system will enhance frame by using top hat and bottom hat transformation, detection step will detect and crop plate by using integral protection, segmentation step will separate every character, and normalization process will modify character dimension. Then, feature extraction will remove certain features of each character. And the last step is character recognizing use Self Organizing Map (SOM) artificial neural network. This step will determine vector character line by connecting the dots of neuron in a character region.The testing process is done by taking video samples to recognize every character at plate. The best accuracy result is 89.05% by using random topology and manhattan distance function.Keyword: License plate, cropping, feature extraction, SOM artificial neural network