Browsing by Subject "CNN"
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Item Embargo A new CNN-LSTM architecture for activity recognition employing wearable motion sensor data: enabling diverse feature extraction(Elsevier, 2023-06-28) Koşar, Enes; Barshan, BillurExtracting representative features to recognize human activities through the use of wearables is an area of on-going research. While hand-crafted features and machine learning (ML) techniques have been sufficiently well investigated in the past, the use of deep learning (DL) techniques is the current trend. Specifically, Convolutional Neural Networks (CNNs), Long Short Term Memory Networks (LSTMs), and hybrid models have been investigated. We propose a novel hybrid network architecture to recognize human activities through the use of wearable motion sensors and DL techniques. The LSTM and the 2D CNN branches of the model that run in parallel receive the raw signals and their spectrograms, respectively. We concatenate the features extracted at each branch and use them for activity recognition. We compare the classification performance of the proposed network with three single and three hybrid commonly used network architectures: 1D CNN, 2D CNN, LSTM, standard 1D CNN-LSTM, 1D CNN-LSTM proposed by Ordóñez and Roggen, and an alternative 1D CNN-LSTM model. We tune the hyper-parameters of six of the models using Bayesian optimization and test the models on two publicly available datasets. The comparison between the seven networks is based on four performance metrics and complexity measures. Because of the stochastic nature of DL algorithms, we provide the average values and standard deviations of the performance metrics over ten repetitions of each experiment. The proposed 2D CNN-LSTM architecture achieves the highest average accuracies of 95.66% and 92.95% on the two datasets, which are, respectively, 2.45% and 3.18% above those of the 2D CNN model that ranks the second. This improvement is a consequence of the proposed model enabling the extraction of a broader range of complementary features that comprehensively represent human activities. We evaluate the complexities of the networks in terms of the total number of parameters, model size, training/testing time, and the number of floating point operations (FLOPs). We also compare the results of the proposed network with those of recent related work that use the same datasets.Item Open Access Deep receiver design for multi-carrier waveforms using CNNs(IEEE, 2020) Yıldırım, Y.; Özer, Sedat; Çırpan, H. A.In this paper, a deep learning based receiver is proposed for a collection of multi-carrier wave-forms including both current and next-generation wireless communication systems. In particular, we propose to use a convolutional neural network (CNN) for jointly detection and demodulation of the received signal at the receiver in wireless environments. We compare our proposed architecture to the classical methods and demonstrate that our proposed CNN-based architecture can perform better on different multi-carrier forms including OFDM and GFDM in various simulations. Furthermore, we compare the total number of required parameters for each network for memory requirements.Item Open Access Deepside: predicting drug side effects with deep learning(Bilkent University, 2019-09) Üner, Onur CanDrug failures due to unforeseen adverse effects at clinical trials pose health risks for the participants and cause substantial financial losses. Side effect prediction algorithms, on the other hand, have the potential to guide the drug design process. LINCS L1000 dataset provides a vast resource of gene expression profiles across different cell lines that are induced with different dosages taken at different time points. The state-of-the-art approach in the literature relies on high-quality experiments in LINCS L1000 and discard a large portion of the recorded experiments. In this study, we investigate whether more information can be extracted from this remaining set of experiments with a deep learning-based approach. We experiment with 6 different deep learning architectures that use (i) gene expression data from the LINCS L1000 project, (ii) chemical structure fingerprints of drugs, (iii) SMILES string representation of drug structure, and (iv) the atomic structure of the drug molecules. The multilayer perceptron (MLP) based model which uses chemical structures and gene expression features achieve 88% micro- AUC and 79% macro-AUC, thus offering better performance in comparison to the state-of-the-art studies on side effect prediction. We observe that the chemical structure is more predictive than the gene expression profiles despite the fact that the features are extracted with different deep learning models. Finally, the convolutional neural network-based model that uses only SMILES strings of the drugs provides 82% macro-AUC, and 88%micro-AUC improvements, better performing than the models that use gene expression and chemical structure features simultaneously.Item Open Access Exploiting lamina terminalis appearance and motion in prediction of hydrocephalus using convolutional LSTM network(Elsevier Masson s.r.l., 2022-08-24) Saygılı, Görkem; Özgöde Yigin, Büşra; Güney, Gökhan; Algın, OktayBackground Evaluation of the lamina terminalis (LT) is crucial for non-invasive evaluation of the CSF diversion for the treatment of hydrocephalus. Together with deep learning algorithms, morphological and physiological analyses of the LT may play an important role in the management of hydrocephalus. Aim We aim to show that exploiting the motion of LT can contribute to the evaluation of hydrocephalus using deep learning algorithms. Methods The dataset contains 61 True-fisp data with routine sequences 37 of which are labeled as ‘hydrocephalus’ and the others as ‘normal condition’. A fifteen-year experienced neuroradiologist divided data into two groups. The first group, ‘hydrocephalus’, consists of patients with typical MRI findings (ventriculomegaly, enlargement of the third ventricular recesses and lateral ventricular horns, decreased mamillo-pontine distance, reduced frontal horn angle, thinning/elevation of the corpus callosum, and non-dilated convexity sulci), and the second group contains samples that did not show any symptoms or neurologic abnormality and labeled as ‘normal condition’. The region of interest was determined by the radiologist supervisor to cover the LT. To achieve our purpose, we used both spatial and spatio-temporal analysis with two different deep learning architectures. We utilized Convolutional Neural Networks (CNN) for spatial and Convolutional Long Short-Term Memory (ConvLSTM) models for spatio-temporal analysis using an ROI around LT on sagittal True-fisp images. Results Our results show that 80.7% classification accuracy was achieved with the ConvLSTM model exploiting LT motion, whereas 76.5% and 71.6% accuracies were obtained by the 2D CNN model using all frames, and only the first frame from only spatial information, respectively. Conclusion We suggest that the motion of the LT can be used as an additional attribute to the spatial information to evaluate the hydrocephalus.Item Open Access General reuse-centric CNN accelerator(IEEE, 2021-03-09) Çiçek, Nihat Mert; Ning, L.; Öztürk, Özcan; Shen, X.This paper introduces the first general reuse-centric accelerator for CNN inferences. Unlike prior work that exploits similarities only across consecutive video frames, general reuse-centric accelerator is able to discover similarities among arbitrary patches within an image or across independent images, and translate them into computation time and energy savings. Experiments show that the accelerator complements both prior software-based CNN and various CNN hardware accelerators, producing up to 14.96X speedups for similarity discovery, up to 3.33X speedups for a convolutional layer.Item Open Access General reuse-centric CNN accelerator(Bilkent University, 2021-02) Çiçek, Nihat MertReuse-centric CNN acceleration speeds up CNN inference by reusing computa-tions for similar neuron vectors in CNN’s input layer or activation maps. This new paradigm of optimizations is however largely limited by the overheads in neuron vector similarity detection, an important step in reuse-centric CNN. This thesis presents the first in-depth exploration of architectural support for reuse-centric CNN. It proposes a hardware accelerator, which improves neuron vector similar-ity detection and reduces the energy consumption of reuse-centric CNN inference. The accelerator is implemented to support a wide variety of network settings with a banked memory subsystem. Design exploration is performed through RTL sim-ulation and synthesis on an FPGA platform. When integrated into Eyeriss, the accelerator can potentially provide improvements up to 7.75X in performance. Furthermore, it can make the similarity detection up to 95.46% more energy-eÿcient, and it can accelerate the convolutional layer up to 3.63X compared to the software-based implementation running on the CPU.Item Open Access In Press, Corrected Proof: Exploiting lamina terminalis appearance and motion in prediction of hydrocephalus using convolutional LSTM network(Elsevier, 2021-02-12) Saygılı, G.; Yigin, B. Ö.; Güney, G.; Algın, OktayBackground Evaluation of the lamina terminalis (LT) is crucial for non-invasive evaluation of the CSF diversion for the treatment of hydrocephalus. Together with deep learning algorithms, morphological and physiological analyses of the LT may play an important role in the management of hydrocephalus. Aim We aim to show that exploiting the motion of LT can contribute to the evaluation of hydrocephalus using deep learning algorithms. Methods The dataset contains 61 True-fisp data with routine sequences 37 of which are labeled as ‘hydrocephalus’ and the others as ‘normal condition’. A fifteen-year experienced neuroradiologist divided data into two groups. The first group, ‘hydrocephalus’, consists of patients with typical MRI findings (ventriculomegaly, enlargement of the third ventricular recesses and lateral ventricular horns, decreased mamillo-pontine distance, reduced frontal horn angle, thinning/elevation of the corpus callosum, and non-dilated convexity sulci), and the second group contains samples that did not show any symptoms or neurologic abnormality and labeled as ‘normal condition’. The region of interest was determined by the radiologist supervisor to cover the LT. To achieve our purpose, we used both spatial and spatio-temporal analysis with two different deep learning architectures. We utilized Convolutional Neural Networks (CNN) for spatial and Convolutional Long Short-Term Memory (ConvLSTM) models for spatio-temporal analysis using an ROI around LT on sagittal True-fisp images. Results Our results show that 80.7% classification accuracy was achieved with the ConvLSTM model exploiting LT motion, whereas 76.5% and 71.6% accuracies were obtained by the 2D CNN model using all frames, and only the first frame from only spatial information, respectively. Conclusion We suggest that the motion of the LT can be used as an additional attribute to the spatial information to evaluate the hydrocephalus.