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arXiv 2023年

作者： Ünlü, Atabey Çevrim, Elif Sarıgün, Ahmet Yiğit, Melih Gökay Çelikbilek, Hayriye Bayram, Osman Güvenilir, Heval Ataş Koyaş, Altay Kahraman, Deniz Cansen Olğaç, Abdurrahman Rifaioğlu, Ahmet Banoğlu, Erden Doğan, Tunca Biological Data Science Lab Dept. of Computer Engineering Hacettepe University Turkey Dept. of Bioinformatics Graduate School of Health Sciences Hacettepe University Turkey Dept. of Chemistry Middle East Technical University Turkey Dept. of Physics Middle East Technical University Turkey Dept. of Computer Engineering Middle East Technical University Turkey Dept. Of Artificial Intelligence Engineering Bahcesehir University Turkey Cancer Systems Biology Lab Graduate School of Informatics Middle East Technical University Turkey Dept. of Pharmaceutical Chemistry Faculty of Pharmacy Gazi University Turkey Laboratory of Molecular Modeling Evias Pharmaceutical R&D Ltd Turkey Dept. of Electrical and Electronics Engineering Iskenderun Technical University Turkey Institute for Computational Biomedicine Heidelberg University Germany

Discovering novel drug candidate molecules is one of the most fundamental and critical steps in drug development. Generative deep learning models, which create synthetic data given a probability distribution, offer a high potential for designing de novo molecules. However, for them to be useful in real-life drug development pipelines, these models should be able to design drug-like and target-centric molecules. In this study, we propose an end-to-end generative system, DrugGEN, for the de novo design of drug candidate molecules that interact with intended target proteins. The proposed method represents molecules as graphs and processes them via a generative adversarial network comprising graph transformer layers. The system is trained using a large dataset of drug-like compounds and target-specific bioactive molecules to design effective inhibitory molecules against the AKT1 protein, which is critically important in developing treatments for various types of cancer. We conducted molecular docking and dynamics to assess the target-centric generation performance of the model, as well as attention score visualisation to examine model interpretability. Results indicate that our de novo molecules have a high potential for interacting with the AKT1 protein at the level of its native ligands. Using the open-access DrugGEN codebase, it is possible to easily train models for other druggable proteins, given a dataset of experimentally known bioactive molecules. © 2023, CC BY-NC-SA.

关键词： Generative adversarial networks

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Detecting visual design principles in art and architecture through deep convolutional neural networks

arXiv

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arXiv 2021年

作者： Demir, Gözdenur Çekmiş, Aslı Yesilkaynak, Vahit Bugra Unal, Gozde Istanbul Technical University Faculty of Architecture Istanbul34437 Turkey Istanbul Technical University Faculty of Computer and Informatics Engineering Istanbul34469 Turkey

Visual design is associated with the use of some basic design elements and principles. Those are applied by the designers in the various disciplines for aesthetic purposes, relying on an intuitive and subjective process. Thus, numerical analysis of design visuals and disclosure of the aesthetic value embedded in them are considered as hard. However, it has become possible with emerging artificial intelligence technologies. This research aims at a neural network model, which recognizes and classifies the design principles over different domains. The domains include artwork produced since the late 20th century;professional photos;and facade pictures of contemporary buildings. The data collection and curation processes, including the production of computationally-based synthetic dataset, is genuine. The proposed model learns from the knowledge of myriads of original designs, by capturing the underlying shared patterns. It is expected to consolidate design processes by providing an aesthetic evaluation of the visual compositions with objectivity. © 2021, CC BY-NC-ND.

关键词： Deep neural networks

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Constructing a Cryptocurrency-Price Prediction Model Using Deep Learning 8

Constructing a Cryptocurrency-Price Prediction Model Using D...

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8th International Conference on engineering and Emerging Technologies, ICEET 2022

作者： Tran, Toai Kim Le, Thanh Thi Tuyet Bui, Thinh Tien Dang, Vang Quang Senkerik, Roman Ho Chi Minh City University of Technology and Education Faculty of Economics No 1 Vo Van Ngan Street Linh Chieu Ward Thu Duc City Viet Nam VSB-Technical University of Ostrava Faculty of Electrical Engineering and Computer Science Dept. of Computer Science 17. Listopadu 2172/15 Ostrava-Poruba708 00 Czech Republic Tomas Bata University in Zlin Faculty of Applied Informatics T. G. Masaryka 5555 Zlin76001 Czech Republic

ISBN: (纸本)9781665491068

The purpose of this study is to discover the optimal Deep Learning model for Bitcoin prediction among the Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM). Our empirical results indicate that LSTM is the optimal model for predicting Bitcoin price and trend with the prediction accuracy of 88.9%. Our study serves as a stepping stone for novice cryptocurrency investors and future studies of more advanced and sophisticated algorithms. Finally, given that the ideal model for predicting the price of cryptocurrencies is still a topic of controversy, the findings of this study will serve as a valuable empirical resource for future studies. © 2022 IEEE.

关键词： Forecasting

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Unveiling the Wireless Network Limitations in Federated Learning

Unveiling the Wireless Network Limitations in Federated Lear...

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IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, DySPAN

作者： Mümtaz Cem Eriş Burak Kantarci Sema Oktug Faculty of Computer and Informatics Engineering Istanbul Technical University Istanbul Turkey University of Ottawa Ottawa ON Canada

ISBN: (纸本)9781665413404

With the advent of 5G and beyond (5GB) communications, decentralized Machine Learning models in various 5GB use cases have become critical. However, wireless network settings are often overlooked in federated learning although they are the crucial factors for edge devices to send their gradient updates. In this paper, the consequences of the background traffic on the performance of the federated learning are evaluated profoundly. Simulation results point out that the convergence is delayed by 60 communication rounds to reach 0.9 accuracy when the background network traffic is dense. Moreover, it is observed that the background traffic with packet interarrival time below 400 ms reduce the accuracy by half in the first 30 communication rounds.

关键词： Performance evaluation Deep learning 5G mobile communication Wireless networks Simulation Neural networks Dynamic spectrum access

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Revealing Key Amino Acids on HMGB1 Box A Domain which can be used for Peptide Targeting

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Neuroscience Applied 2024年 3卷

作者： O.M. Apaydin O. Sensoy S. Baday Istanbul Technical University Institute of Computer Science Department Informatics Istanbul Türkiye Istanbul Technical University Department of Artificial Intelligence and Data Engineering- Faculty of Computer Informatics and Engineering Istanbul Türkiye

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The role of slicing in test-driven development

arXiv

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arXiv 2024年

作者： Dieste, Oscar Tosun, Ayse Vegas, Sira Santos, Adrian Uyaguari, Fernando Kyykkä, Jarno Juristo, Natalia Escuela Técnica Superior de Ingenieros Informáticos Universidad Politécnica de Madrid Boadilla del Monte28660 Spain Faculty of Computer and Informatics Istanbul Technical University Istanbul34469 Turkey Faculty of Information Technology and Electrical Engineering University of Oulu Oulu90014 Finland Cuenca10204 Ecuador Ericsson Jorvas 02420 Finland

Test-driven development (TDD) is a widely used agile practice. However, very little is known with certainty about TDD’s underlying foundations, i.e., the way TDD works. In this paper, we propose a theoretical framework for TDD, with the following characteristics: a) Each TDD cycle represents a vertical slice of a (probably also small) user story, b) vertical slices are captured using contracts, implicit in the developers’ minds, and c) the code created during a TDD cycle is a sliced-based specification of a code oracle, using the contracts as slicing pre/post-conditions. We have checked the connections among TDD, contracts and slices by means of a controlled experiment conducted in industry. Copyright © 2024, The Authors. All rights reserved.

关键词： Specifications

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Training Hacks and a Frugal Man's Net with Application to Glioblastoma Segmentation 6

Training Hacks and a Frugal Man's Net with Application to Gl...

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6th International Conference on Advanced Technologies for Signal and Image Processing, ATSIP 2022

作者： Abdallah, Jawher Ben Marrakchi-Kacem, Linda Rekik, Islem Université de la Manouba Institut Supérieur de Biotechnologie de Sidi Thabet Tunis Tunisia Université Tunis El Manar L3S Ecole Nationale d'Ingénieurs de Tunis Tunis Tunisia Istanbul Technical University Basira Lab Faculty of Computer and Informatics Istanbul Turkey University of Dundee School of Science and Engineering Computing United Kingdom

ISBN: (纸本)9781665451161

In this paper, we investigate the effectiveness of training a sparse Neural Network on a limited number of samples in the context of brain tumor segmentation. Nowadays, Deep Learning architectures are getting deeper, more sophisticated and environmentally unfriendly in an effort to improve their segmentation performance. We use a brain tumor segmentation dataset and apply simple practices to reduce the needed computational resources to allow cheap and fast training. We also present a lighter, cheaper version of the U-Net dubbed Frugal U-Net stemming from our investigation on how far we can push the original U-Net by decreasing its parameter count using dept.-Wise Separable Convolutions instead of regular ones, all the while preserving the minimum levels of accuracy required in Medical Imaging. Our methodology is useful in clinical facilities where high-computation resources are limited. © 2022 IEEE.

关键词： Medical imaging

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An optimization-based algorithm for simultaneous shaping of poles and zeros for non-collocated vibration suppression

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IFAC-PapersOnLine 2022年第16期55卷 394-399页

作者： Adrian Saldanha Haik Silm Wim Michiels Tomáš Vyhlídal Department of Computer Science KU Leuven Celestijnenlaan 200A 3001 Leuven Belgium Dept. Instrumentation and Control Engineering Faculty of Mechanical Engineering and Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Prague 6 Czechia

This article presents a control design method for simultaneous shaping of the poles and zeros of linear time-invariant systems, motivated by the application of non-collocated vibration suppression to flexible multi-body systems. An entire suppression of vibrations at a target mass for a given excitation frequencies can be recast into the problem of assigning zeros of the transfer function from the excitation force to the target mass' position. The design requirement of achieving sufficient damping in the closed loop system combined with suppressing vibrations at the target, leads to the minimization of the spectral abscissa function of the closed loop system as a function of the controller parameters, subject to zero location constraints. These constraints exhibit polynomial dependence on the controller parameters. We present two approaches to solve the optimization problem which are both based on constraint elimination followed by application of an algorithm for non-smooth unconstrained optimization. The design approach is applicable to delay-free models as well as time-delay models of retarded and neutral type. Simulations results illustrate its applicability to a spring-mass-damper system.

关键词： vibration suppression zero placement time-delay systems spectral optimization

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Optimization of a circular slotted patch with a defected grounded monopole patch antenna using artificial neural networks

Optimization of a circular slotted patch with a defected gro...

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Antennas and Propagation & USNC/URSI National Radio Science Meeting

作者： Rania Ibtissam Benmelouka Djamel Sayad Rami Zegadi Mohamed Lamine Bouknia Yamina Tighilt Kamil Karaçuha Issa Elferghani Jonathan Rodriguez Chemseddine Zebiri Dept. of Electronics Laboratoire d'Electronique de puissance et commande industrielle (LEPCI) University of Ferhat Abbas Setif Algeria Dept. of Electrical Engineering Laboratoire d'Electrotechnique de Skikda (LES) University of 20 Aout 1955 Skikda Algeria Electrical Engineering Department Istanbul Technical University Istanbul Turkey Instituto de Telecomunicãõcs Campus Universitário de Santiago Aveiro Portugal Faculty of Engineering and Informatics University of Bradford Bradford UK

ISBN: (数字)9798350369908

ISBN: (纸本)9798350369915

This article describes the use of artificial neural networks for the estimate of reflection coefficients. In this work, synthesis and analysis are studied where parametric analysis presents the simulated values necessary for training and testing neural networks with HFSS simulation software. The simulation and results produced by ANNs are compared with HFSS simulation. The proposed antenna measures 60 by 70 mm 2 .

关键词： Antenna measurements Training Patch antennas Conferences Artificial neural networks Antennas and propagation Software

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Target Capturing in an Ellipsoidal Region with a Swarm of Quadrotor UAVs

Target Capturing in an Ellipsoidal Region with a Swarm of Qu...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： M. Alp Merzi Veysel Gazi Giuseppe Fedele Luigi D’ Alfonso Antonio Bono Dept. Electrical and Electronics Eng. Faculty of Engineering Marmara University Istanbul Turkey Dept. Control and Automation Eng. Faculty of Electrical and Electronics Engineering Yildiz Technical University Istanbul Turkey Dept. of Informatics Modeling Electronics and Systems Eng. University of Calabria Rende Italy

ISBN: (数字)9781665405935

ISBN: (纸本)9781665405942

In a recent paper [1], we developed a target capturing strategy for swarms composed of double integrator agents. In this paper, we build on the results in [1] and focus on the implementation of the methodology for a swarm composed of quadrotor UAVs. We utilize both MATLAB and ROS environments under realistic conditions for that purpose. In ROS, we utilize Ultra-Violet LED blinkers based Ultra-Violet Direction And Ranging (UVDAR) system for relative localization. The sensing and localization errors serve as realistic errors for the assumptions in [1] for the uncertainties of the velocity and acceleration of the target. We verify in a realistic scenario that the agents converge to a user-defined ellipsoidal ring around the target despite the uncertainties and the more realistic agent dynamics.

关键词： Location awareness Uncertainty Ultraviolet sources Light emitting diodes Distance measurement Sensors Aircraft

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