28 research outputs found
Comparing Computing Platforms for Deep Learning on a Humanoid Robot
The goal of this study is to test two different computing platforms with
respect to their suitability for running deep networks as part of a humanoid
robot software system. One of the platforms is the CPU-centered Intel NUC7i7BNH
and the other is a NVIDIA Jetson TX2 system that puts more emphasis on GPU
processing. The experiments addressed a number of benchmarking tasks including
pedestrian detection using deep neural networks. Some of the results were
unexpected but demonstrate that platforms exhibit both advantages and
disadvantages when taking computational performance and electrical power
requirements of such a system into account.Comment: 12 pages, 5 figure
PREDIKSI PENYAKIT STROKE MENGGUNAKAN SUPPORT VECTOR MACHINE (SVM)
Berdasarkan data dari Kementerian Kesehatan Indonesia, telah terjadi peningkatan jumlah pada kasuspenyakit stroke sebesar 3.9% mulai dari tahun 2013 sampai dengan tahun 2018. Secara nasional, jumlahkasus stroke sering terjadi pada kelompok yang memiliki rentang umur antara 55-64 tahun dan palingsedikit terjadi pada rentang umur 15-24. Stroke atau (Cerebrovascular Accidents) merupakan sebuahkeadaan dimana aliran darah ke otak mengalami gangguan mendadak atau berkurang. Hal tersebutdapat disebabkan oleh penyumbatan atau pecah pembuluh darah, sehingga sel-sel pada area otak tidakmendapatkan pasokan darah yang nutrisi dan oksigen. Diperlukan deteksi dini yang bertujuan untukmengurangi jumlah potensi kematian akibat stroke. Prediksi stroke masih menjadi tantang dalam bidangkedokteran, salah satu penyebabnya adalah volume data pada data medis yang memiliki heterogenitasdan kompleksitas yang tinggi. Teknik machine learning merupakan model analisis data yang dapatdigunakan untuk memprediksi penyakit stroke. Berbagai model pembelajaran machine learning telahdiusulkan oleh peneliti-peneliti sebelumnya, salah satunya Support Vector Machine. Penelitian inimencoba menerapkan kembali algoritma SVM dengan mendapatkan hasil kinerja lebih baik daripenelitian sebelumnya. Dalam penelitian ini didapatkan nilai accuracy sebesar 100% dan nilai ROC-AUC sebesar 100%. Perlu dilakukan pengkajian lagi terkait hasil yang didapatkan hingga mencapai100%
An Integrative Paradigm for Enhanced Stroke Prediction: Synergizing XGBoost and xDeepFM Algorithms
Stroke prediction plays a crucial role in preventing and managing this
debilitating condition. In this study, we address the challenge of stroke
prediction using a comprehensive dataset, and propose an ensemble model that
combines the power of XGBoost and xDeepFM algorithms. Our work aims to improve
upon existing stroke prediction models by achieving higher accuracy and
robustness. Through rigorous experimentation, we validate the effectiveness of
our ensemble model using the AUC metric. Through comparing our findings with
those of other models in the field, we gain valuable insights into the merits
and drawbacks of various approaches. This, in turn, contributes significantly
to the progress of machine learning and deep learning techniques specifically
in the domain of stroke prediction
Early Classifying Multimodal Sequences
Often pieces of information are received sequentially over time. When did one
collect enough such pieces to classify? Trading wait time for decision
certainty leads to early classification problems that have recently gained
attention as a means of adapting classification to more dynamic environments.
However, so far results have been limited to unimodal sequences. In this pilot
study, we expand into early classifying multimodal sequences by combining
existing methods. We show our new method yields experimental AUC advantages of
up to 8.7%.Comment: 7 pages, 5 figure
Spoken Language Identification System for English-Mandarin Code-Switching Child-Directed Speech
This work focuses on improving the Spoken Language Identification (LangId)
system for a challenge that focuses on developing robust language
identification systems that are reliable for non-standard, accented
(Singaporean accent), spontaneous code-switched, and child-directed speech
collected via Zoom. We propose a two-stage Encoder-Decoder-based E2E model. The
encoder module consists of 1D depth-wise separable convolutions with
Squeeze-and-Excitation (SE) layers with a global context. The decoder module
uses an attentive temporal pooling mechanism to get fixed length
time-independent feature representation. The total number of parameters in the
model is around 22.1 M, which is relatively light compared to using some
large-scale pre-trained speech models. We achieved an EER of 15.6% in the
closed track and 11.1% in the open track (baseline system 22.1%). We also
curated additional LangId data from YouTube videos (having Singaporean
speakers), which will be released for public use.Comment: Accepted by Interspeech 2023, 5 pages, 1 figure, 4 table