Hasil Pencarian  ::  Simpan CSV :: Kembali

"This chapter describes some advances in modern pattern classification techniques, and new classes of information systems dedicated for image analysis, interpretation and semantic classification. In this book we present some new solutions for the development of modern pattern recognition techniques for processing and analysis of several classes of visual patterns, as well as some theoretical foundations for modern pattern interpretation approaches. In particular this monograph presents selected areas of application of pattern recognition and classification approaches including handwriting recognition, medical image analysis and interpretation, development of cognitive systems for image computer understanding, moving object detection, advanced image filtration and intelligent multi-object labeling and classification."

"This book describes hybrid intelligent systems using type-2 fuzzy logic and modular neural networks for pattern recognition applications. Hybrid intelligent systems combine several intelligent computing paradigms, including fuzzy logic, neural networks, and bio-inspired optimization algorithms, which can be used to produce powerful pattern recognition systems. Type-2 fuzzy logic is an extension of traditional type-1 fuzzy logic that enables managing higher levels of uncertainty in complex real world problems, which are of particular importance in the area of pattern recognition. The book is organized in three main parts, each containing a group of chapters built around a similar subject. The first part consists of chapters with the main theme of theory and design algorithms, which are basically chapters that propose new models and concepts, which are the basis for achieving intelligent pattern recognition. The second part contains chapters with the main theme of using type-2 fuzzy models and modular neural networks with the aim of designing intelligent systems for complex pattern recognition problems, including iris, ear, face and voice recognition. The third part contains chapters with the theme of evolutionary optimization of type-2 fuzzy systems and modular neural networks in the area of intelligent pattern recognition, which includes the application of genetic algorithms for obtaining optimal type-2 fuzzy integration systems and ideal neural network architectures for solving problems in this area."

"Dengan memperhatikan serta menyusun pola makan, kesehatan tubuh dapat meningkat dikarenakan nutrisi yang didapatkan oleh tubuh. Pemanfaatan machine learning, melalui model deteksi multiobjek, dapat membantu pendeteksian berbagai jenis makanan hanya dengan input sebuah gambar. Dengan terdeteksinya jenis makanan digabungkan dengan output berupa nutrisi yang terkandung dalam makanan dapat membantu dalam mengatur pola makan. Pengaturan pola makan dengan memanfaatkan deteksi objek dapat dilakukan dengan pelatihan sebuah dataset dengan menggunakan algoritma YOLO. Pendeteksian makanan yang dilakukan dengan menggunakan algoritma YOLO memerlukan acuan evaluasi dengan tujuan meningkatkan akurasi dari deteksi yang dilakukan, yang mana merupakan alasan dari pengukuran mAP. Penggunaan arsitektur YOLOv7 terlihat dapat menghasilkan model yang lebih baik dibandingkan YOLOv5 dengan mAP 0,947. Penggabungan YOLOv7 dengan dataset yang berisikan multiclass single image juga berhasil dalam melakukan deteksi multi-object makanan sesuai dengan kategori yang telah ditentukan. Dengan tujuan penggunaan model oleh masyarakat luas, model deteksi jenis makanan diimplementasikan dalam bentuk aplikasi mobile dengan basis Java. Implementasi dalam bentuk aplikasi membuat masyarakat luas dapat memanfaatkan model deteksi objek sebagai salah satu acuan pemilihan pola makan yang lebih sehat.

---

By paying attention to and compiling a diet, body health can improve due to the nutrients the body gets. Utilization of machine learning, through a multi-object detection model, can help detect various types of food only by inputting an image. Diet adjustment using object detection can be done by training a dataset using the YOLO algorithm. Food detection carried out using the YOLO algorithm requires an evaluation reference with the aim of increasing the accuracy of the detection carried out, which is the reason for using mAP.. The use of the YOLOv7 architecture seems to produce a better model than YOLOv5 with a mAP of 0.947. Merging YOLOv7 with a dataset containing multiclass single images was also successful in detecting multi-object food according to predetermined categories. With the aim of using the model by the wider community, a food type detection model is implemented in the form of a mobile application based on Java. Implementation in the form of an application allows the general public to utilize the object detection model as a reference for choosing a healthier diet."

"Suatu sistem biometrik sangat penting untuk identifikasi dan verifikasi suatu individu dengan berbagai tujuan. Biometrik iris merupakan salah satu tipe biometrik dengan tingkat akurasi yang tinggi tetapi banyak pemakaian memori. Tahap pencocokan merupakan salah satu bagian dari sistem biometik iris yang memakai banyak memori sehingga berpengaruh pada waktu proses.Dalam skripsi ini, akan disimulasikan algoritma Incremental Dissimilarity Approximation (IDA) yang akan dibandinkan dengan algoritma vector quantization (VQ). IDA merupakan algoritma pencocokan cepat berdasarkan ketidaksamaan fungsi norm Lp dimana akan menjadi syarat untuk batas pencarian pencocokan. Hasil simulasi menunjukkan bahwa IDA tidak cocok untuk diaplikasikan pada sistem biometrik iris.Performa yang ditunjukkan sama dengan VQ karena variasi vektor pada citra iris masih memenuhi batas pada algoritma IDA. Namun, secara eksperimental telah didapat nilai batas yang optimal untuk sistem biometrik iris sehingga mempersingkat waktu proses. Kode untuk algoritma VQ dan IDA dikembangkan dengan program MATLAB.

---

A biometric system is really important for identification and verification of a person for a lot of purposes. Biometric iris is one of biometric types that has high accuracy but use lot of memory. The pattern matching is part of iris biometric system that required a lot of memory that affect to the time process.In this paper, Incremental Dissimilarity Approximation (IDA) algorithm will be simulated and compared with vector quantization (VQ) algorithm. IDA is a fast pattern matching based on dissimilarity functions derived from Lp norm for becoming the bounding criterion of pattern matching. The simulation result show that IDA is not suitable to implement for iris biometric system. It has the same performance as VQ because the variety of the vector still satisfy the bounding criterion. However, the experiment has determined the optimal bound for pattern matching in iris biometric system that decrease the time process. The code for VQ and IDA are developed with MATLAB."