The 1st International Electronic Conference on Algorithms

This book presents 22 of the accepted presentations at the 1st International Electronic Conference on Algorithms which was held completely online from September 27 to October 10, 2021. It contains 16 proceeding papers as well as 6 extended abstracts. The works presented in the book cover a wide range of fields dealing with the development of algorithms. Many of contributions are related to machine learning, in particular deep learning. Another main focus among the contributions is on problems dealing with graphs and networks, e.g., in connection with evacuation planning problems

Applications of Artificial Intelligence in Battling Against Covid-19: A Literature Review

© 2020 Elsevier Ltd. All rights reserved.Colloquially known as coronavirus, the Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2), that causes CoronaVirus Disease 2019 (COVID-19), has become a matter of grave concern for every country around the world. The rapid growth of the pandemic has wreaked havoc and prompted the need for immediate reactions to curb the effects. To manage the problems, many research in a variety of area of science have started studying the issue. Artificial Intelligence is among the area of science that has found great applications in tackling the problem in many aspects. Here, we perform an overview on the applications of AI in a variety of fields including diagnosis of the disease via different types of tests and symptoms, monitoring patients, identifying severity of a patient, processing covid-19 related imaging tests, epidemiology, pharmaceutical studies, etc. The aim of this paper is to perform a comprehensive survey on the applications of AI in battling against the difficulties the outbreak has caused. Thus we cover every way that AI approaches have been employed and to cover all the research until the writing of this paper. We try organize the works in a way that overall picture is comprehensible. Such a picture, although full of details, is very helpful in understand where AI sits in current pandemonium. We also tried to conclude the paper with ideas on how the problems can be tackled in a better way and provide some suggestions for future works.Peer reviewe

A Survey on Evolutionary Computation for Computer Vision and Image Analysis: Past, Present, and Future Trends

Computer vision (CV) is a big and important field in artificial intelligence covering a wide range of applications. Image analysis is a major task in CV aiming to extract, analyse and understand the visual content of images. However, imagerelated tasks are very challenging due to many factors, e.g., high variations across images, high dimensionality, domain expertise requirement, and image distortions. Evolutionary computation (EC) approaches have been widely used for image analysis with significant achievement. However, there is no comprehensive survey of existing EC approaches to image analysis. To fill this gap, this paper provides a comprehensive survey covering all essential EC approaches to important image analysis tasks including edge detection, image segmentation, image feature analysis, image classification, object detection, and others. This survey aims to provide a better understanding of evolutionary computer vision (ECV) by discussing the contributions of different approaches and exploring how and why EC is used for CV and image analysis. The applications, challenges, issues, and trends associated to this research field are also discussed and summarised to provide further guidelines and opportunities for future research

Bioinformatics Applications Based On Machine Learning

The great advances in information technology (IT) have implications for many sectors, such as bioinformatics, and has considerably increased their possibilities. This book presents a collection of 11 original research papers, all of them related to the application of IT-related techniques within the bioinformatics sector: from new applications created from the adaptation and application of existing techniques to the creation of new methodologies to solve existing problems

Unmanned Aerial Vehicles (UAVs) in environmental biology: A Review

Acquiring information about the environment is a key step during each study in the field of environmental biology at different levels, from an individual species to community and biome. However, obtaining information about the environment is frequently difficult because of, for example, the phenological timing, spatial distribution of a species or limited accessibility of a particular area for the field survey. Moreover, remote sensing technology, which enables the observation of the Earth’s surface and is currently very common in environmental research, has many limitations such as insufficient spatial, spectral and temporal resolution and a high cost of data acquisition. Since the 1990s, researchers have been exploring the potential of different types of unmanned aerial vehicles (UAVs) for monitoring Earth’s surface. The present study reviews recent scientific literature dealing with the use of UAV in environmental biology. Amongst numerous papers, short communications and conference abstracts, we selected 110 original studies of how UAVs can be used in environmental biology and which organisms can be studied in this manner. Most of these studies concerned the use of UAV to measure the vegetation parameters such as crown height, volume, number of individuals (14 studies) and quantification of the spatio-temporal dynamics of vegetation changes (12 studies). UAVs were also frequently applied to count birds and mammals, especially those living in the water. Generally, the analytical part of the present study was divided into following sections: (1) detecting, assessing and predicting threats on vegetation, (2) measuring the biophysical parameters of vegetation, (3) quantifying the dynamics of changes in plants and habitats and (4) population and behaviour studies of animals. At the end, we also synthesised all the information showing, amongst others, the advances in environmental biology because of UAV application. Considering that 33% of studies found and included in this review were published in 2017 and 2018, it is expected that the number and variety of applications of UAVs in environmental biology will increase in the future

Deep Learning in Medical Image Analysis

The accelerating power of deep learning in diagnosing diseases will empower physicians and speed up decision making in clinical environments. Applications of modern medical instruments and digitalization of medical care have generated enormous amounts of medical images in recent years. In this big data arena, new deep learning methods and computational models for efficient data processing, analysis, and modeling of the generated data are crucially important for clinical applications and understanding the underlying biological process. This book presents and highlights novel algorithms, architectures, techniques, and applications of deep learning for medical image analysis

Bayesian gravitation based classification for hyperspectral images.

Integration of spectral and spatial information is extremely important for the classification of high-resolution hyperspectral images (HSIs). Gravitation describes interaction among celestial bodies which can be applied to measure similarity between data for image classification. However, gravitation is hard to combine with spatial information and rarely been applied in HSI classification. This paper proposes a Bayesian Gravitation based Classification (BGC) to integrate the spectral and spatial information of local neighbors and training samples. In the BGC method, each testing pixel is first assumed as a massive object with unit volume and a particular density, where the density is taken as the data mass in BGC. Specifically, the data mass is formulated as an exponential function of the spectral distribution of its neighbors and the spatial prior distribution of its surrounding training samples based on the Bayesian theorem. Then, a joint data gravitation model is developed as the classification measure, in which the data mass is taken to weigh the contribution of different neighbors in a local region. Four benchmark HSI datasets, i.e. the Indian Pines, Pavia University, Salinas, and Grss_dfc_2014, are tested to verify the BGC method. The experimental results are compared with that of several well-known HSI classification methods, including the support vector machines, sparse representation, and other eight state-of-the-art HSI classification methods. The BGC shows apparent superiority in the classification of high-resolution HSIs and also flexibility for HSIs with limited samples

Method validation of High Performance Liquid Chromatography for determination of mycolic acid profile of Mycobacterium tuberculosis

This study developed the High Performance Liquid Chromatography (HPLC) for INH resistant M. tuberculosis (MTB) isolate identification based on a chromatogram profile of mycolic acids (MAs). The aim of this study was to validate the HPLC for determining the characteristic profile of MAs chromatogram of the INH resistant MTB. The optimum derivatization process obtained was as follows: the minimum biomass weight was 25 mg. The experimental temperature was performed in (80-90)o C using a water bath for minimum 30 minutes in order to complete the MAs derivatization. Reagent volume used in the range of (200-500 μL) were not influenced the MAs chromatogram profile. The optimum condition of HPLC was as follows: mobile phase was methanol:isopropanol (60:40) for 3 minutes, followed by gradient elution (4:96) in 50 minutes. Thereafter, the mobile phase composition change gradually for 40 minutes to a final composition of (60:40). The sample volume was 20 μL and the mobile phase flow rate was 1 mL/minute. The result of this study showed that the MAs chromatogram profile of INH resistant MTB looked like H37Rv MTB strain. The chromatogramp profile was a cluster with 6 characteristic peaks at the end of the analysis. The other short chain carbon fatty acids were eluted in the first 15 minutes

Antibacterial Activity of Butanol Extract from Cell Free Fermentation Broth of Streptomyces spp.Isolated from Vegetable Plantation Soil

Thirty five Streptomyces spp. have been isolated from vegetable plantation soil collected from Sidoarjo, East Java, Indonesia. The isolation and fermentation process to obtain antibacterial substances were performed in the International Streptomyces Projects (ISP)-4 media on rotary shaker at 150 rpm, 28oC for seven days. In vitro antibacterial properties testing of one day to five days free cell fermentation broth (CFFB) of the Streptomyces isolates have been carried out by diffusion agar method using Gram positive and Gram negative bacteria as test microorganisms. The aims of this research are to extract the CFFB of the potential isolates by n-butanol and screen in vitro anti bacterial activities of the extract. It was found that the n-butanol extract of the CFFB of Streptomyces B2 and K2 strain showed their activities against Methicillin-resistant Staphylococcus aureus (MRSA) ATCC 12323701 with MIC of 0,1 and 0,5 ppm respectively. Streptomyces B10 and K6 strain showed their potential activities against Bacilus subtilis ATCC 6633 and Escherichia coli ATCC 25922 with MIC 0,273 and 2,93 respectively. Both isolates are also active against Mycobacterium tuberculosis H37Rv and patients isolate strain