Editorial

2 pages, 164 KiB

Open AccessEditorial

The 1st International Electronic Conference on Algorithms (IOCA 2021)

by Frank Werner

Comput. Sci. Math. Forum 2022, 2(1), 2023; https://doi.org/10.3390/csmf2022002023 - 01 Apr 2022

Cited by 1 | Viewed by 1492

This Special Issue of Computer Sciences and Mathematics Forum is dedicated to the 1st Electronic Conference on Algorithms (IOCA 2021), which was held completely online from 27 September to 10 October 2021 [...] Full article

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Research

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1 pages, 163 KiB

Open AccessAbstract

An Image-Based Algorithm for the Automatic Detection of Loosened Bolts

by Thanh-Canh Huynh, Nhat-Duc Hoang, Duc-Duy Ho and Xuan-Linh Tran

Comput. Sci. Math. Forum 2022, 2(1), 1; https://doi.org/10.3390/IOCA2021-10893 - 23 Sep 2021

Viewed by 839

Abstract

The bolted joint has been widely used to connect load-bearing elements in aerospace, civil, and mechanical engineering systems. During its service life, particularly under external dynamical loads, a bolted joint may undergo self-loosening. Bolt looseness causes a reduction in its load-bearing capacity and [...] Read more.

The bolted joint has been widely used to connect load-bearing elements in aerospace, civil, and mechanical engineering systems. During its service life, particularly under external dynamical loads, a bolted joint may undergo self-loosening. Bolt looseness causes a reduction in its load-bearing capacity and eventually leads to the failure of a bolted joint. This paper presents an automated image-based algorithm combining the Faster R-CNN model with image processing for the quick detection of loosened bolts in a structural connection. The algorithm is validated using a lab-scale bolted joint model for which various bolt-loosening events are simulated. The imagery data of the joint is captured and passed through the algorithm for bolt looseness detection. The obtained results show that the loosened bolts in the joint were well-detected and that their loosening degrees were precisely quantified; therefore, the image-based algorithm is promising for real-time structural health monitoring of realistic bolted joints. Full article

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2 pages, 179 KiB

Open AccessAbstract

Simple Methods for Traveling Salesman Problems

by Nodari Vakhania

Comput. Sci. Math. Forum 2022, 2(1), 6; https://doi.org/10.3390/IOCA2021-10914 - 13 Oct 2021

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Abstract

Here we will focus on an ongoing project on approximation algorithms for the Euclidean Traveling Salesman Problems (TSP) [...] Full article

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1 pages, 176 KiB

Open AccessAbstract

Vectorial Iterative Schemes with Memory for Solving Nonlinear Systems of Equations

by Ramandeep Behl, Alicia Cordero, Juan R. Torregrosa and Sonia Bhalla

Comput. Sci. Math. Forum 2022, 2(1), 17; https://doi.org/10.3390/IOCA2021-10892 - 22 Sep 2021

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Abstract

There exist in the literature many iterative methods for solving nonlinear problems. Some of these methods can be transferred directly to the context of nonlinear systems, keeping the order of convergence, but others cannot be directly extended to a multidimensional case. Sometimes, the [...] Read more.

There exist in the literature many iterative methods for solving nonlinear problems. Some of these methods can be transferred directly to the context of nonlinear systems, keeping the order of convergence, but others cannot be directly extended to a multidimensional case. Sometimes, the procedures are designed specifically for multidimensional problems by using different techniques, as composition and reduction or weight-function procedures, among others. Our main aim is not only to design an iterative scheme for solving nonlinear systems but also to assure its high order of convergence by means of the introduction of matrix accelerating parameters. This is a challenging area of numerical analysis wherein there are still few procedures defined. Once the iterative method has been designed, it is necessary to carry out a dynamical study in order to verify the wideness of the basins of attraction of the roots and compare its stability with other known methods. Full article

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1 pages, 152 KiB

Open AccessAbstract

Avoiding Temporal Confounding in Timeseries Forecasting Using Machine Learning

by Felix Wick and Ulrich Kerzel

Comput. Sci. Math. Forum 2022, 2(1), 19; https://doi.org/10.3390/IOCA2021-10881 - 19 Sep 2021

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Abstract

Timeseries forecasting plays an important role in many applications where knowledge of the future behaviour of a given quantity of interest is required. Traditionally, this task is approached using methods such as exponential smoothing, ARIMA and, more recently, recurrent neural networks such as [...] Read more.

Timeseries forecasting plays an important role in many applications where knowledge of the future behaviour of a given quantity of interest is required. Traditionally, this task is approached using methods such as exponential smoothing, ARIMA and, more recently, recurrent neural networks such as LSTM architectures or transformers. These approaches intrinsically rely on the autocorrelation or partial auto-correlation between subsequent events to forecast future values. Essentially, the past values of the timeseries are used to model its future behaviour. Implicitly, this assumes that the auto-correlation and partial auto-correlation is genuine and not spurious. In the latter case, the methods exploit the (partial) auto-correlation in the prediction even though they are not grounded in the causal data generation process of the timeseries. This can happen if some external event or intervention affects the value of the timeseries at multiple times. In terms of causal analysis, this is equivalent to introducing a confounder into the timeseries where the variable of interest at different times takes over the role of multiple variables in standard causal analysis. This effectively opens a backdoor path between different times that, in turn, leads to a spurious autocorrelation. If a forecasting model is built including such spurious correlations, the generalizability and forecasting power of the model is reduced and future predictions may consequently be wrong. Using a supervised learning approach, we show how machine learning can be used to avoid temporal confounding in timeseries forecasting, thereby limiting or avoiding the influence of spurious autocorrelations or partial autocorrelations. Full article

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1 pages, 150 KiB

Open AccessAbstract

Multi-Commodity Contraflow Problem on Lossy Network with Asymmetric Transit times

by Shiva Prakash Gupta, Urmila Pyakurel and Tanka Nath Dhamala

Comput. Sci. Math. Forum 2022, 2(1), 21; https://doi.org/10.3390/IOCA2021-10878 - 19 Sep 2021

Cited by 1 | Viewed by 585

Abstract

During the transmission of commodities from one place to another, there may be loss due to death, leakage, damage, or evaporation. To address this problem, each arc of the network contains a gain factor. The network is a lossy network with a gain [...] Read more.

During the transmission of commodities from one place to another, there may be loss due to death, leakage, damage, or evaporation. To address this problem, each arc of the network contains a gain factor. The network is a lossy network with a gain factor of at most one on each arc. The generalized multi-commodity flow problem deals with routing several distinct goods from specific supply points to the corresponding demand points on an underlying network with minimum loss. The sum of all commodities on each arc does not exceed its capacity. Motivated by the uneven road condition of transportation network topology, we incorporate a contraflow approach with orientation-dependent transit times on arcs and introduce the generalized multi-commodity contraflow problem on a lossy network with orientation-dependent transit times. In general, the generalized dynamic multi-commodity contraflow problem is NP-hard. For a lossy network with a symmetric transit time on anti-parallel arcs, the problem is solved in pseudo-polynomial time. We extend the analytical solution with a symmetric transit time on anti-parallel arcs to asymmetric transit times and present algorithms that solve it within the same time-complexity. Full article

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1 pages, 189 KiB

Open AccessAbstract

New Explicit Asymmetric Hopscotch Methods for the Heat Conduction Equation

by Mahmoud Saleh and Endre Kovács

Comput. Sci. Math. Forum 2022, 2(1), 22; https://doi.org/10.3390/IOCA2021-10902 - 26 Sep 2021

Cited by 1 | Viewed by 886

Abstract

This study aims at constructing new and effective fully explicit numerical schemes for solving the heat conduction equation. We use fractional time steps for the odd cells in the well-known odd–even hopscotch structure and fill it with several different formulas to obtain a [...] Read more.

This study aims at constructing new and effective fully explicit numerical schemes for solving the heat conduction equation. We use fractional time steps for the odd cells in the well-known odd–even hopscotch structure and fill it with several different formulas to obtain a large number of algorithm combinations. We generate random parameters in a highly inhomogeneous spatial distribution to set up discretized systems with various stiffness ratios, and systematically test these new methods by solving these systems. The best combinations are verified by comparing them to analytical solutions. We also show analytically that their rate of convergence is two and that they are unconditionally stable. Full article

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Other

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8 pages, 492 KiB

Open AccessProceeding Paper

A Bicriteria Model for Saving a Path Minimizing the Time Horizon of a Dynamic Contraflow

by Hari Nandan Nath, Tanka Nath Dhamala and Stephan Dempe

Comput. Sci. Math. Forum 2022, 2(1), 2; https://doi.org/10.3390/IOCA2021-10897 - 25 Sep 2021

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Abstract

The quickest contraflow in a single-source-single-sink network is a dynamic flow that minimizes the time horizon of a given flow value at the source to be sent to the sink allowing arc reversals. Because of the arc reversals, for a sufficiently large value [...] Read more.

The quickest contraflow in a single-source-single-sink network is a dynamic flow that minimizes the time horizon of a given flow value at the source to be sent to the sink allowing arc reversals. Because of the arc reversals, for a sufficiently large value of the flow, the residual capacity of all or most of the paths towards the source, from a given node, may be zero or reduced significantly. In some cases, e.g., for the movement of facilities to support an evacuation in an emergency, it is imperative to save a path from a given node towards the source. We formulate such a problem as a bicriteria optimization problem, in which one objective minimizes the length of the path to be saved from a specific node towards the source, and the other minimizes the quickest time of the flow from the source towards the sink, allowing arc reversals. We propose an algorithm based on the epsilon-constraint approach to find non-dominated solutions. Full article

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9 pages, 1309 KiB

Open AccessProceeding Paper

Unscented Kalman Filter Empowered by Bayesian Model Evidence for System Identification in Structural Dynamics

by Luca Rosafalco, Saeed Eftekhar Azam, Andrea Manzoni, Alberto Corigliano and Stefano Mariani

Comput. Sci. Math. Forum 2022, 2(1), 3; https://doi.org/10.3390/IOCA2021-10896 - 25 Sep 2021

Cited by 1 | Viewed by 1075

Abstract

System identification is often limited to parameter identification, while model uncertainties are disregarded or accounted for by a fictitious process noise. However, modelling assumptions may have a large impact on system identification. For this reason, we propose to use an unscented Kalman filter [...] Read more.

System identification is often limited to parameter identification, while model uncertainties are disregarded or accounted for by a fictitious process noise. However, modelling assumptions may have a large impact on system identification. For this reason, we propose to use an unscented Kalman filter (UKF) empowered by online Bayesian model evidence computation for the sake of system identification and model selection. This approach employs more than one model to track the state of the system and associates with each model a plausibility measure, updated whenever new measurements are available. The filter outcomes obtained for different models are then compared and a quantitative confidence value is associated with each of them. Only the system identification outcomes related to the model with the highest plausibility are considered. While the coupling of extended Kalman filters (EKFs) and Bayesian model evidence was already addressed, we modify the approach to exploit the most striking features of the UKF, namely, the ease of implementation and higher-order accuracy in the description of the evolution of the state mean and variance. A challenging identification problem related to structural dynamics is discussed to show the effectiveness of the proposed methodology. Full article

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10 pages, 2123 KiB

Open AccessProceeding Paper

A Novel Strategy for Tall Building Optimization via the Combination of AGA and Machine Learning Methods

by Mohammad Sadegh Es-haghi and Mohammad Sarcheshmehpour

Comput. Sci. Math. Forum 2022, 2(1), 4; https://doi.org/10.3390/IOCA2021-10882 - 20 Sep 2021

Cited by 2 | Viewed by 1145

Abstract

The optimum design of tall buildings, which have a proportionately huge quantity of structural elements and a variety of design code constraints, is a very computationally expensive process. In this paper, a novel strategy, with a combination of evolutionary algorithms and machine learning [...] Read more.

The optimum design of tall buildings, which have a proportionately huge quantity of structural elements and a variety of design code constraints, is a very computationally expensive process. In this paper, a novel strategy, with a combination of evolutionary algorithms and machine learning methods, is developed for achieving the optimal design of tall buildings. The most time-consuming part is the analysis of tall buildings and the control of design code constraints requiring long and frequent analyses. The main idea is to use machine learning methods for this purpose. In this study, a practical methodology for obtaining the optimal design of tall building structures, regarding the constraints imposed by typical building codes, is introduced. The optimization process will be performed by a novel evolutionary algorithm, named asymmetric genetic algorithm (AGA), and in each iteration that requires checking the constraints for a large number of different structural states, machine learning methods, including MLP, GMDH and ANFIS-PSO are facilitators. More specifically, MLP (R² = 0.988) has performed better than GMDH (R² = 0.961) and ANFIS-PSO (R² = 0.953). By coupling ETABS and MATLAB software, various combinations of sections for structural elements are assigned and analyzed automatically, thus creating a database for training neural networks. The applicability of the suggested procedure is described through the determination of the optimal seismic design for a 40-story framed tube building. Results designate that the present method not only supports the precision of the methodology but also remarkably diminishes the computational time and memory needed in comparison with the existing classical methods. More importantly, the optimization process time is also significantly decreased. Full article

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7 pages, 514 KiB

Open AccessProceeding Paper

Maximum Multi-Commodity Flow with Proportional and Flow-Dependent Capacity Sharing

by Durga Prasad Khanal, Urmila Pyakurel, Tanka Nath Dhamala and Stephan Dempe

Comput. Sci. Math. Forum 2022, 2(1), 5; https://doi.org/10.3390/IOCA2021-10904 - 26 Sep 2021

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Abstract

Multi-commodity flow problems concerned with the transshipment of more than one commodity from respective sources to the corresponding sinks without violating the capacity constraints on the arcs. If the objective of the problem is to send the maximum amount of flow within a [...] Read more.

Multi-commodity flow problems concerned with the transshipment of more than one commodity from respective sources to the corresponding sinks without violating the capacity constraints on the arcs. If the objective of the problem is to send the maximum amount of flow within a given time horizon, then it becomes the maximum flow problem. In multi-commodity flow problems, the flow of different commodities departing from their sources arriving at the common intermediate node have to share the capacity through the arc. The sharing of the capacity in the common arc (bundle arc) is one of the major issues in the multi-commodity flow problems. In this paper, we introduce the maximum static and maximum dynamic multi-commodity flow problems with proportional capacity sharing and present polynomial time algorithms to solve the problems. Similarly, we investigate the maximum dynamic multi-commodity flow problems with flow-dependent capacity sharing and present a pseudo-polynomial time solution strategy. Full article

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8 pages, 2364 KiB

Open AccessProceeding Paper

Two Optimized IoT Device Architectures Based on Fast Fourier Transform to Monitor Patient’s Photoplethysmography and Body Temperature

by Janith Kodithuwakku, Dilki Dandeniya Arachchi, Saw Thiha and Jay Rajasekera

Comput. Sci. Math. Forum 2022, 2(1), 7; https://doi.org/10.3390/IOCA2021-10905 - 26 Sep 2021

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Abstract

The measurement of blood-oxygen saturation (SpO2), heart rate (HR), and body temperature are very critical in monitoring patients. Photoplethysmography (PPG) is an optical method that can be used to measure heart rate, blood-oxygen saturation, and many analytics about cardiovascular health of a patient [...] Read more.

The measurement of blood-oxygen saturation (SpO2), heart rate (HR), and body temperature are very critical in monitoring patients. Photoplethysmography (PPG) is an optical method that can be used to measure heart rate, blood-oxygen saturation, and many analytics about cardiovascular health of a patient by analyzing the waveform. With the COVID-19 pandemic, there is a high demand for a product that can remotely monitor such parameters of a COVID-19 patient. This paper proposes two major design architectures for the product with optimized system implementations by utilizing the ESP32 development environment and cloud computing. In one method, it discusses edge computing with the fast Fourier transform (FFT) and valley detection algorithms to extract features from the waveform before transferring data to the cloud, and the other method transfers raw sensor values to the cloud without any loss of information. This paper especially compares the performance of both system architectures with respect to bandwidth, sampling frequency, and loss of information. Full article

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7 pages, 471 KiB

Open AccessProceeding Paper

Quickest Transshipment in an Evacuation Network Topology

by Iswar Mani Adhikari and Tanka Nath Dhamala

Comput. Sci. Math. Forum 2022, 2(1), 8; https://doi.org/10.3390/IOCA2021-10879 - 19 Sep 2021

Viewed by 1067

Abstract

The quickest transshipment of the evacuees in an integrated evacuation network topology depends upon the evacuee arrival pattern in the collection network and their better assignment in the assignment network with appropriate traffic route guidance, destination optimization, and an optimal route. In this [...] Read more.

The quickest transshipment of the evacuees in an integrated evacuation network topology depends upon the evacuee arrival pattern in the collection network and their better assignment in the assignment network with appropriate traffic route guidance, destination optimization, and an optimal route. In this work, the quickest transshipment aspect in an integrated evacuation network topology is revisited concerning a transit-based evacuation system. Appropriate collection approaches for the evacuees and their better assignment to transit vehicles for their quickest transshipment in an embedded evacuation network are presented with their solution strategies. Full article

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10 pages, 471 KiB

Open AccessProceeding Paper

A Generative Adversarial Network Based Autoencoder for Structural Health Monitoring

by Giorgia Colombera, Luca Rosafalco, Matteo Torzoni, Filippo Gatti, Stefano Mariani, Andrea Manzoni and Alberto Corigliano

Comput. Sci. Math. Forum 2022, 2(1), 9; https://doi.org/10.3390/IOCA2021-10887 - 22 Sep 2021

Viewed by 1433

Abstract

Civil structures, infrastructures and lifelines are constantly threatened by natural hazards and climate change. Structural Health Monitoring (SHM) has therefore become an active field of research in view of online structural damage detection and long term maintenance planning. In this work, we propose [...] Read more.

Civil structures, infrastructures and lifelines are constantly threatened by natural hazards and climate change. Structural Health Monitoring (SHM) has therefore become an active field of research in view of online structural damage detection and long term maintenance planning. In this work, we propose a new SHM approach leveraging a deep Generative Adversarial Network (GAN), trained on synthetic time histories representing the structural responses of both damaged and undamaged multistory building to earthquake ground motion. In the prediction phase, the GAN generates plausible signals for different damage states, based only on undamaged recorded or simulated structural responses, thus without the need to rely upon real recordings linked to damaged conditions. Full article

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7 pages, 983 KiB

Open AccessProceeding Paper

Parallel WSAR for Solving Permutation Flow Shop Scheduling Problem

by Adil Baykasoğlu and Mümin Emre Şenol

Comput. Sci. Math. Forum 2022, 2(1), 10; https://doi.org/10.3390/IOCA2021-10901 - 26 Sep 2021

Cited by 1 | Viewed by 1054

Abstract

This study presents a coalition-based parallel metaheuristic algorithm for solving the Permutation Flow Shop Scheduling Problem (PFSP). This novel approach incorporates five different single-solution-based metaheuristic algorithms (SSBMA) (Simulated Annealing Algorithm, Random Search Algorithm, Great Deluge Algorithm, Threshold Accepting Algorithm and Greedy Search Algorithm) [...] Read more.

This study presents a coalition-based parallel metaheuristic algorithm for solving the Permutation Flow Shop Scheduling Problem (PFSP). This novel approach incorporates five different single-solution-based metaheuristic algorithms (SSBMA) (Simulated Annealing Algorithm, Random Search Algorithm, Great Deluge Algorithm, Threshold Accepting Algorithm and Greedy Search Algorithm) and a population-based algorithm (Weighted Superposition Attraction–Repulsion Algorithm) (WSAR). While SSBMAs are responsible for exploring the search space, WSAR serves as a controller that handles the coalition process. SSBMAs perform their searches simultaneously through the MATLAB parallel programming tool. The proposed approach is tested on PFSP against the state-of-the-art algorithms in the literature. Moreover, the algorithm is also tested against its constituents (SSBMAS and WSAR) and its serial version. Non-parametric statistical tests are organized to compare the performances of the proposed approach statistically with the state-of-the-art algorithms, its constituents and its serial version. The statistical results prove the effectiveness of the proposed approach. Full article

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10 pages, 1464 KiB

Open AccessProceeding Paper

Advances in Crest Factor Minimization for Wide-Bandwidth Multi-Sine Signals with Non-Flat Amplitude Spectra

by Helena Althoff, Maximilian Eberhardt, Steffen Geinitz and Christian Linder

Comput. Sci. Math. Forum 2022, 2(1), 11; https://doi.org/10.3390/IOCA2021-10908 - 28 Sep 2021

Cited by 3 | Viewed by 1315

Abstract

Multi-sine excitation signals give spectroscopic insight into fast chemical processes over bandwidths from 10¹ Hz to 10⁷ Hz. The crest factor (CF) determines the information density of a multi-sine signal. Minimizing the CF yields higher information density and is the goal [...] Read more.

Multi-sine excitation signals give spectroscopic insight into fast chemical processes over bandwidths from 10¹ Hz to 10⁷ Hz. The crest factor (CF) determines the information density of a multi-sine signal. Minimizing the CF yields higher information density and is the goal of the presented work. Four algorithms and a combination of two of them are presented. The first two algorithms implement different iterative optimizations of the amplitude and phase angle values of the signal. The combined algorithm alternates between the first and second optimization algorithms. Additionally, a simulated annealing approach and a genetic algorithm optimizing the CF were implemented. Full article

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8 pages, 2175 KiB

Open AccessProceeding Paper

Two-Scale Deep Learning Model for Polysilicon MEMS Sensors

by José Pablo Quesada-Molina and Stefano Mariani

Comput. Sci. Math. Forum 2022, 2(1), 12; https://doi.org/10.3390/IOCA2021-10888 - 22 Sep 2021

Cited by 2 | Viewed by 934

Abstract

Microelectromechanical systems (MEMS) are often affected in their operational environment by different physical phenomena, each one possibly occurring at different length and time scales. Data-driven formulations can then be helpful to deal with such complexity in their modeling. By referring to a single-axis [...] Read more.

Microelectromechanical systems (MEMS) are often affected in their operational environment by different physical phenomena, each one possibly occurring at different length and time scales. Data-driven formulations can then be helpful to deal with such complexity in their modeling. By referring to a single-axis Lorentz force micro-magnetometer, characterized by a current flowing inside slender mechanical parts so that the system can be driven into resonance, it has been shown that the sensitivity to the magnetic field may become largely enhanced through proper (topology) optimization strategies. In our previous work, a reduced-order physical model for the movable structure was developed; such a model-based approach did not account for all the stochastic effects leading to the measured scattering in the experimental data. A new formulation is here proposed, resting on a two-scale deep learning model designed as follows: at the material level, a deep neural network is used a priori to learn the scattering in the mechanical properties of polysilicon induced by its morphology; at the device level, a further deep neural network is used to account for the effects on the response induced by etch defects, learning on-the-fly relevant geometric features of the movable parts. Some preliminary results are here reported, and the capabilities of the learning models at the two length scales are discussed. Full article

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8 pages, 636 KiB

Open AccessProceeding Paper

A Hybrid Deep Learning Approach for COVID-19 Diagnosis via CT and X-ray Medical Images

by Channabasava Chola, Pramodha Mallikarjuna, Abdullah Y. Muaad, J. V. Bibal Benifa, Jayappa Hanumanthappa and Mugahed A. Al-antari

Comput. Sci. Math. Forum 2022, 2(1), 13; https://doi.org/10.3390/IOCA2021-10909 - 29 Sep 2021

Cited by 6 | Viewed by 2230

Abstract

The COVID-19 pandemic has been a global health problem since December 2019. To date, the total number of confirmed cases, recoveries, and deaths has exponentially increased on a daily basis worldwide. In this paper, a hybrid deep learning approach is proposed to directly [...] Read more.

The COVID-19 pandemic has been a global health problem since December 2019. To date, the total number of confirmed cases, recoveries, and deaths has exponentially increased on a daily basis worldwide. In this paper, a hybrid deep learning approach is proposed to directly classify the COVID-19 disease from both chest X-ray (CXR) and CT images. Two AI-based deep learning models, namely ResNet50 and EfficientNetB0, are adopted and trained using both chest X-ray and CT images. The public datasets, consisting of 7863 and 2613 chest X-ray and CT images, are respectively used to deploy, train, and evaluate the proposed deep learning models. The deep learning model of EfficientNetB0 consistently performed a better classification result, achieving overall diagnosis accuracies of 99.36% and 99.23% using CXR and CT images, respectively. For the hybrid AI-based model, the overall classification accuracy of 99.58% is achieved. The proposed hybrid deep learning system seems to be trustworthy and reliable for assisting health care systems, patients, and physicians. Full article

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6 pages, 839 KiB

Open AccessProceeding Paper

A Novel Deep Learning ArCAR System for Arabic Text Recognition with Character-Level Representation

by Abdullah Y. Muaad, Mugahed A. Al-antari, Sungyoung Lee and Hanumanthappa Jayappa Davanagere

Comput. Sci. Math. Forum 2022, 2(1), 14; https://doi.org/10.3390/IOCA2021-10903 - 26 Sep 2021

Cited by 1 | Viewed by 1552

Abstract

AI-based text classification is a process to classify Arabic contents into their categories. With the increasing number of Arabic texts in our social life, traditional machine learning approaches are facing different challenges due to the complexity of the morphology and the delicate variation [...] Read more.

AI-based text classification is a process to classify Arabic contents into their categories. With the increasing number of Arabic texts in our social life, traditional machine learning approaches are facing different challenges due to the complexity of the morphology and the delicate variation of the Arabic language. This work proposes a model to represent and recognize Arabic text at the character level based on the capability of a deep convolutional neural network (CNN). This system was validated using five-fold cross-validation tests for Arabic text document classification. We have used our proposed system to evaluate Arabic text. The ArCAR system shows its capability to classify Arabic text in character-level. For document classification, the ArCAR system achieves the best performance using the AlKhaleej-balance dataset in terms of accuracy equal to 97.76%. The proposed ArCAR seems to provide a practical solution for accurate Arabic text representation, both for understanding and as a classifications system. Full article

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9 pages, 520 KiB

Open AccessProceeding Paper

AI-Based Misogyny Detection from Arabic Levantine Twitter Tweets

by Abdullah Y. Muaad, Hanumanthappa Jayappa Davanagere, Mugahed A. Al-antari, J. V. Bibal Benifa and Channabasava Chola

Comput. Sci. Math. Forum 2022, 2(1), 15; https://doi.org/10.3390/IOCA2021-10880 - 19 Sep 2021

Cited by 6 | Viewed by 1902

Abstract

Twitter is one of the social media platforms that is extensively used to share public opinions. Arabic text detection system (ATDS) is a challenging computational task in the field of Natural Language Processing (NLP) using Artificial Intelligence (AI)-based techniques. The detection of misogyny [...] Read more.

Twitter is one of the social media platforms that is extensively used to share public opinions. Arabic text detection system (ATDS) is a challenging computational task in the field of Natural Language Processing (NLP) using Artificial Intelligence (AI)-based techniques. The detection of misogyny in Arabic text has received a lot of attention in recent years due to the racial and verbal violence against women on social media platforms. In this paper, an Arabic text recognition approach is presented for detecting misogyny from Arabic tweets. The proposed approach is evaluated using the Arabic Levantine Twitter Dataset for Misogynistic, and it gained recognition accuracies of 90.0% and 89.0% for binary and multi-class tasks, respectively. The proposed approach seems to be useful in providing practical smart solutions for detecting Arabic misogyny on social media. Full article

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9 pages, 1131 KiB

Open AccessProceeding Paper

Health Monitoring of Civil Structures: A MCMC Approach Based on a Multi-Fidelity Deep Neural Network Surrogate

by Matteo Torzoni, Andrea Manzoni and Stefano Mariani

Comput. Sci. Math. Forum 2022, 2(1), 16; https://doi.org/10.3390/IOCA2021-10889 - 22 Sep 2021

Cited by 2 | Viewed by 968

Abstract

To meet the need for reliable real-time monitoring of civil structures, safety control and optimization of maintenance operations, this paper presents a computational method for the stochastic estimation of the degradation of the load bearing structural properties. Exploiting a Bayesian framework, the procedure [...] Read more.

To meet the need for reliable real-time monitoring of civil structures, safety control and optimization of maintenance operations, this paper presents a computational method for the stochastic estimation of the degradation of the load bearing structural properties. Exploiting a Bayesian framework, the procedure sequentially updates the posterior probability of the damage parameters used to describe the aforementioned degradation, conditioned on noisy sensors observations, by means of Markov chain Monte Carlo (MCMC) sampling algorithms. To enable the analysis to run in real-time or quasi real-time, the numerical model of the structure is replaced with a data-driven surrogate used to evaluate the (conditional) likelihood function. The proposed surrogate model relies on a multi-fidelity (MF) deep neural network (DNN), mapping the damage and operational parameters onto sensor recordings. The MF-DNN is shown to effectively leverage information between multiple datasets, by learning the correlations across models with different fidelities without any prior assumption, ultimately alleviating the computational burden of the supervised training stage. The low fidelity (LF) responses are approximated by relying on proper orthogonal decomposition for the sake of dimensionality reduction, and a fully connected DNN. The high fidelity signals, that feed the MCMC within the outer-loop optimization, are instead generated by enriching the LF approximations through a deep long short-term memory network. Results relevant to a specific case study demonstrate the capability of the proposed procedure to estimate the distribution of damage parameters, and prove the effectiveness of the MF scheme in outperforming a single-fidelity based method. Full article

(This article belongs to the Proceedings of The 1st International Electronic Conference on Algorithms)

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8 pages, 1797 KiB

Open AccessProceeding Paper

Learning the Link between Architectural Form and Structural Efficiency: A Supervised Machine Learning Approach

by Pooyan Kazemi, Aldo Ghisi and Stefano Mariani

Comput. Sci. Math. Forum 2022, 2(1), 18; https://doi.org/10.3390/IOCA2021-10891 - 22 Sep 2021

Viewed by 1215

Abstract

In this work, we exploit supervised machine learning (ML) to investigate the relationship between architectural form and structural efficiency under seismic excitations. We inspect a small dataset of simulated responses of tall buildings, differing in terms of base and top plans within which [...] Read more.

In this work, we exploit supervised machine learning (ML) to investigate the relationship between architectural form and structural efficiency under seismic excitations. We inspect a small dataset of simulated responses of tall buildings, differing in terms of base and top plans within which a vertical transformation method is adopted (tapered forms). A diagrid structure with members having a tubular cross-section is mapped on the architectural forms, and static loads equivalent to the seismic excitation are applied. Different ML algorithms, such as kNN, SVM, Decision Tree, Ensemble methods, discriminant analysis, Naïve Bayes are trained, to classify the seismic response of each form on the basis of a specific label. Presented results rely upon the drift of the building at its top floor, though the same procedure can be generalized and adopt any performance characteristic of the considered structure, like e.g., the drift ratio, the total mass, or the expected design weight. The classification algorithms are all tested within a Bayesian optimization approach; it is then found that the Decision Tree classifier provides the highest accuracy, linked to the lowest computing time. This research activity puts forward a promising perspective for the use of ML algorithms to help architectural and structural designers during the early stages of conception and control of tall buildings. Full article

(This article belongs to the Proceedings of The 1st International Electronic Conference on Algorithms)

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8 pages, 853 KiB

Open AccessProceeding Paper

Deep Learning Methodologies for Diagnosis of Respiratory Disorders from Chest X-ray Images: A Comparative Study

by Akhil Appu Shetty, Navya Thirumaleshwar Hegde, Aldrin Claytus Vaz and Chrompet Ramesh Srinivasan

Comput. Sci. Math. Forum 2022, 2(1), 20; https://doi.org/10.3390/IOCA2021-10900 - 26 Sep 2021

Viewed by 1694

Abstract

Chest radiography needs timely diseases diagnosis and reporting of potential findings in the images, as it is an important diagnostic imaging test in medical practice. A crucial step in radiology workflow is the fast, automated, and reliable detection of diseases created on chest [...] Read more.

Chest radiography needs timely diseases diagnosis and reporting of potential findings in the images, as it is an important diagnostic imaging test in medical practice. A crucial step in radiology workflow is the fast, automated, and reliable detection of diseases created on chest radiography. To overcome this issue, an artificial intelligence-based algorithm such as deep learning (DL) are promising methods for automatic and fast diagnosis due to their excellent performance analysis of a wide range of medical images and visual information. This paper surveys the DL methods for lung disease detection from chest X-ray images. The common five attributes surveyed in the articles are data augmentation, transfer learning, types of DL algorithms, types of lung diseases and features used for detection of abnormalities, and types of lung diseases. The presented methods may prove extremely useful for people to ideate their research contributions in this area. Full article

(This article belongs to the Proceedings of The 1st International Electronic Conference on Algorithms)

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