Metode Image Recognation pada Aplikasi Pengenalan Alat Musik Tradisional

Alat musik tradisional merupakan salah satu identitas kesenian setiap daerah di Indonesia. Provinsi DKI Jakarta memiliki alat musik tradisional yang beraneka ragam. Namun seiring perkembangan zaman sudah jarang generasi muda yang memainkan alat musik tradisional. Semua ini terjadi karena adanya perubahan alat musik tradisional menjadi yang lebih modern. Penelitian ini menggunakan dataset public melalui pencarian google image sebanyak 1200. Selanjutnya, dilakukan pengembangan struktur jaringan CNN dengan menggunakan Bahasa pemrograman Dart dan text editor VisualStudio Code. Pembuatan aplikasi menggunakan salah satu teknologi machine learning yaitu Image Recognation diharapkan dapat membantu masyarakat mengetahui jenis alat musik tradisional DKI Jakarta. Metode yang digunakan dalam pembuatan aplikasi adalah CRISP-DM yaitu Business Understanding, Data Understanding, Data Preparation, Modelling, Evaluation, dan Deployment. Model yang sudah dibuat dan dievaluasi, diimplementasikan menjadi sebuah aplikasi berbasis android sehingga dapat digunakan untuk membantu pengenalan alat musik tradisional DKI Jakarta agar tetap terjaga kelestariannya. Hasil pengujian menunjukan bahwa system dapat mendeteksi alat musik dengan akurasi sebesar 94%, presisi sebesar 79%, dan sensitifitas sebesar 83%

Unveiling the Power of Mixup for Stronger Classifiers

Mixup-based data augmentations have achieved great success as regularizers for deep neural networks. However, existing methods rely on deliberately handcrafted mixup policies, which ignore or oversell the semantic matching between mixed samples and labels. Driven by their prior assumptions, early methods attempt to smooth decision boundaries by random linear interpolation while others focus on maximizing class-related information via offline saliency optimization. As a result, the issue of label mismatch has not been well addressed. Additionally, the optimization stability of mixup training is constantly troubled by the label mismatch. To address these challenges, we first reformulate mixup for supervised classification as two sub-tasks, mixup sample generation and classification, then propose Automatic Mixup (AutoMix), a revolutionary mixup framework. Specifically, a learnable lightweight Mix Block (MB) with a cross-attention mechanism is proposed to generate a mixed sample by modeling a fair relationship between the pair of samples under direct supervision of the corresponding mixed label. Moreover, the proposed Momentum Pipeline (MP) enhances training stability and accelerates convergence on top of making the Mix Block fully trained end-to-end. Extensive experiments on five popular classification benchmarks show that the proposed approach consistently outperforms leading methods by a large margin.Comment: The second version of AutoMix. 12 pages, 7 figure

COMPARISON OF MOBILENET AND CNN METHODS FOR IDENTIFYING TOMATO LEAF DISEASES

Tomato plants are usually easily attacked by diseases, either viruses or fungi, resulting in a significant reduction in the quality and quantity of crop production. Tomato production is at risk from various diseases affecting the leaves. Early diagnosis of these diseases allows farmers to take preventive action and protect their crops. The use of artificial intelligence, especially deep learning, has greatly improved plant disease detection systems. Advances in computer vision, particularly Convolutional Neural Networks (CNN), have shown reliable results in image classification and identification. Below is previous research on identifying tomato leaf diseases

LKCell: Efficient Cell Nuclei Instance Segmentation with Large Convolution Kernels

The segmentation of cell nuclei in tissue images stained with the blood dye hematoxylin and eosin (H$\&$E) is essential for various clinical applications and analyses. Due to the complex characteristics of cellular morphology, a large receptive field is considered crucial for generating high-quality segmentation. However, previous methods face challenges in achieving a balance between the receptive field and computational burden. To address this issue, we propose LKCell, a high-accuracy and efficient cell segmentation method. Its core insight lies in unleashing the potential of large convolution kernels to achieve computationally efficient large receptive fields. Specifically, (1) We transfer pre-trained large convolution kernel models to the medical domain for the first time, demonstrating their effectiveness in cell segmentation. (2) We analyze the redundancy of previous methods and design a new segmentation decoder based on large convolution kernels. It achieves higher performance while significantly reducing the number of parameters. We evaluate our method on the most challenging benchmark and achieve state-of-the-art results (0.5080 mPQ) in cell nuclei instance segmentation with only 21.6% FLOPs compared with the previous leading method. Our source code and models are available at https://github.com/hustvl/LKCell

Analisis Ekspresi Wajah Pengemudi Mobil Untuk Deteksi Kantuk Secara Real-Time Menggunakan Metode YOLOV5

Traffic accidents caused by driver drowsiness or fatigue are a serious problem in road transportation. This study proposes the use of a drowsiness detection application that uses the YOLOv5 method to detect signs of sleepiness on the driver's face and tests the accuracy and performance of the drowsiness detection application based on factors such as lighting level, user distance, user characteristics and delay time. The research method used in this study is the AI ​​Project Cycle method. The dataset is obtained through the Roboflow platform. This research involves the development of the YOLOv5 algorithm to detect signs of drowsiness such as closed eyes or tilted head of the driver through the front camera of a smartphone device. Furthermore, model evaluation is carried out using a confusion matrix and a precision-recall curve. In addition, the drowsiness detection application can provide a warning with an alarm when the driver is detected as drowsy and alert notifications based on the history of previous events. The results of this study indicate that a car driver's drowsiness detection system using the YOLOv5 Algorithm has been successfully developed and installed on a smartphone with high accuracy. The model produces a good level of accuracy, precision and recall, namely 95%, 94% and 96%. The drowsiness detection application is also able to provide a warning to drivers who are detected as drowsy with a threshold > 0.4. In addition, the ISO test best detects the driver's condition during the day with ISO Lux > 1000, measuring distance of 150 degrees and delay time of 2.4 seconds. Keywords: Traffic Accident, Driver's Drowsiness, YOLOV5 Algorithm, Drowsiness Detection Application, Roboflo

Generatiiviset mallit de novo -lääkekehityksessä : katsaus RNN-, AE- ja GAN-sovelluksiin

Lääkekehitys on kallis ja aikaa vievä prosessi. Lääkemäisiä molekyyliyhdisteitä arvellaan olevan 10³³. Prosessia voidaan helpottaa generatiivisten mallien avulla, joilla voidaan löytää sopivia molekyyliyhdisteitä halutuilla ominaisuuksilla. Generatiivisiin malleihin perustuvaa lääkekehitystä kutsutaan De Novo lääkekehitykseksi. Tässä työssä on tehty katsaus kolmeen generatiiviseen malliin: takaisinkytkeytyvät neuroverkot (RNN), generatiiviset kilpailevat verkot (GAN) ja autokooderit (AE). Työn tavoite on selvittää, kuinka malleja sovelletaan molekyylien löytämiseen. Työssä ensin esitellään generatiiviset mallit. Takaisinkytkeytyvistä neuroverkoista esitetään perusominaisuudet, sekä esitellään pitkäkestoinen lyhytkestomuisti -arkkitehtuuri (LSTM) ja GRU porttiyksikkö -arkkitehtuuri. Lisäksi esitetään autokooderin perusominaisuudet, sekä tästä kehittyneempi versio variaationaalinen autokooderi (VAE), joka hyödyntää Bayesilaista todennäköisyyttä. Lisäksi esitetään generatiivisten kilpailevien verkkojen toiminta. Työn toisessa osassa käydään läpi esiteltyjen mallien sovelluksia. Takaisinkytkeytyvistä neuroverkoista käytetyin arkkitehtuuri oli LSTM-arkkitehtuuri, joka oli myös tehokkain RNN, kun otetaan huomioon ominaisuudet kuten molekyylien uniikkius, kelvollisuus ja syntetisoitavuus. GAN-arkkitehtuurissa suurin ongelma on mode collapse, jossa neuroverkko ei tuota monimuotoisia molekyyliavaruuksia datamäärän kasvaessa. Tähän ongelmaan ei onnistuttu löytämään ratkaisua vaikkakin malli onnistui luomaan monimuotoisia molekyylejä. Autokoodereissa usein yhdisteltiin edellä esiteltyjä neuroverkkoja ja onnistuttiin luomaan molekyylejä tehokkaasti. Kaikissa sovelluksissa molekyylejä kehiteltiin SMILES-merkkijonoina mutta tämä merkintätapa osoittautui puutteelliseksi eikä sillä voida esittää molekyylien rakenteita täydellisesti. Lisäksi huomattiin, että tieteellisissä julkaisuissa ei ole yhtenäistä tapaa vertailla mallien luomien molekyylien soveltuvuutta tai tapaa vertailla malleja keskenään