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An application of some machine learning methods for biological data modeling 15

An application of some machine learning methods for biologic...

15th Conference of the Euro-American Consortium for Promoting the Application of Mathematics in Technical and Natural Sciences, AMiTaNS 2023

作者： Sapundzhi, Fatima Lazarova, Meglena Dzimbova, Tatyana Georgiev, Slavi Department of Communication and Computer Engineering Faculty of Engineering South-West University Neofit Rilski 66 Ivan Myhailov Str. Blagoevgrad2700 Bulgaria Faculty of Applied Mathematics and Informatics Technical University of Sofia 8 St. Kliment Ohridski Blvd. Sofia1000 Bulgaria Institute of Molecular Biology Bulgarian Academy of Sciences Acad. G.Bonchev Str bl. 21 Sofia1113 Bulgaria Department of Information Modeling Institute of Mathematics and Informatics Bulgarian Academy of Sciences Acad. Georgi Bonchev Str. bl. 8 Sofia1113 Bulgaria Department of Applied Mathematics and Statistics Faculty of Natural Sciences and Education University of Ruse 8 Studentska Str. Ruse7004 Bulgaria

The development of fast and reliable methods for predicting the biological activity of the substances in computational biology is of a great importance. This improves the development of some new compounds while keeping costs low. Among many scientists, an attractive target for docking experiments is the Delta-opioid receptor (DOR) and delta-opioid ligands (DOL). Their biological efficacy can be measured by various techniques, which could facilitate the establishment of the relationship between the structure of the compounds and their biological effect. The relationship between the results of the computer experiments and the biological activity of these compounds is modelled by using machine learning regressors. The primary goal of this study is to determine the most appropriate neural network for modelling the relationship between in vitro and in silico results for DOR and delta-opioid ligands. © Published under licence by IOP Publishing Ltd.

关键词： Bioactivity

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Recent Advances of Chimp Optimization Algorithm:Variants and Applications

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Journal of Bionic engineering 2023年第6期20卷 2840-2862页

作者： Mohammad Sh.Daoud Mohammad Shehab Laith Abualigah Mohammad Alshinwan Mohamed Abd Elaziz Mohd Khaled Yousef Shambour Diego Oliva Mohammad AAlia Raed Abu Zitar College of Engineering Al Ain University112612Abu DhabiUnited Arab Emirates College of Computer Sciences and Informatics Amman Arab UniversityAmman11953Jordan Computer Science Department Prince Hussein Bin Abdullah Faculty for Information TechnologyAl Al-Bayt UniversityMafraq25113Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman19328Jordan MEU Research Unit Middle East UniversityAmman11831Jordan Applied Science Research Center Applied Science Private UniversityAmman11931Jordan School of Computer Sciences Universiti Sains Malaysia11800Pulau PinangMalaysia Faculty of Information Technology Applied Science Private UniversityAmman 11931Jordan Department of Mathematics Faculty of ScienceZagazig UniversityZagazig 7120001Egypt The Custodian of the Two Holy Mosques Institute for Haj and Umrah Research Umm Al-Qura UniversityMakkah 24382Saudi Arabia Depto.de Innovacio'n Basada en la Informacio'n y el Conocimiento Universidad de GuadalajaraCUCEI45129 GuadalajaraMexico Cyber Security Department Faculty of Science and Information TechnologyAl Zaytoonah University of JordanAmman 11733Jordan Sorbonne Center of Artificial Intelligence Sorbonne University-Abu Dhabi38044 Abu DhabiUnited Arab Emirates School of Engineering and Technology Sunway University Malaysia27500Petaling JayaMalaysia

Chimp Optimization Algorithm(ChOA)is one of the recent metaheuristics swarm intelligence *** has been widely tailored for a wide variety of optimization problems due to its impressive characteristics over other swarm intelligence methods:it has very few parameters,and no derivation information is required in the initial ***,it is simple,easy to use,flexible,scalable,and has a special capability to strike the right balance between exploration and exploitation during the search which leads to favorable ***,the ChOA has recently gained a very big research interest with tremendous audiences from several domains in a very short ***,in this review paper,several research publications using ChOA have been overviewed and ***,introductory information about ChOA is provided which illustrates the natural foundation context and its related optimization conceptual *** main operations of ChOA are procedurally discussed,and the theoretical foundation is ***,the recent versions of ChOA are discussed in detail which are categorized into modified,hybridized,and paralleled *** main applications of ChOA are also thoroughly *** applications belong to the domains of economics,image processing,engineering,neural network,power and energy,networks,*** of ChOA is also *** review paper will be helpful for the researchers and practitioners of ChOA belonging to a wide range of audiences from the domains of optimization,engineering,medical,data mining,and *** well,it is wealthy in research on health,environment,and public ***,it will aid those who are interested by providing them with potential future research.

关键词： Artificial intelligence Nature-inspired optimization algorithms Chimp optimization algorithm Optimization problems

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Input Image Pixel Interval method for Classification Using Transfer Learning 16

Input Image Pixel Interval method for Classification Using T...

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16th International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2022

作者： Anorboev, Abdulaziz Javokhir, Musaev Hong, Jeongkyu Nguyen, Ngoc Thanh Hwang, Dosam Yeungnam University Department of Computer Engineering Gyeongsan Korea Republic of Wroclaw University of Science and Technology Department of Applied Informatics Wroclaw50-570 Poland Nguyen Tat Thanh University Faculty of Information Technology Ho Chi Minh City70000 Viet Nam

ISBN: (数字)9781665498104

ISBN: (纸本)9781665498104

Deep learning has been used in many applications where patterns from past-trained data can be extracted to predict future outcomes. Deep learning is characterized by training and testing data with the identical input feature space and same data distribution. However, whereas the data distribution is same between the training and testing data, the results might be different. This study introduces input image preprocessing, an enhanced neural network optimization method, and prediction probability ensemble to minimize the number of trainable parameters but maintain the outcome accuracy. In the suggested methodology, input images are separated into pixel interval and the fully connected layer jointly used with saved weights. Outcome results of separated input images are ensembled to the corresponding class probabilities of the original image. The results of the proposed method were compared with those of other previous methods in the image classification task and achieved successful performance accuracies in several datasets. © 2022 IEEE.

关键词： Classification (of information)

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Automatic melanoma detection using discrete cosine transform features and metadata on dermoscopic images

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Journal of King Saud University - computer and Information Sciences 2024年第2期36卷

作者： Yousefi, Shamim Najjar-Ghabel, Samad Danehchin, Ramin Band, Shahab S. Hsu, Chung-Chian Mosavi, Amir Department of Computer Engineering University of Mohaghegh Ardabili Ardabil Iran Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Douliu Taiwan John von Neumann Faculty of Informatics Budapest Hungary Ludovika University of Public Service Budapest Hungary

Machine learning contributes in improving the accuracy of melanoma detection. There are extensive studies in classic and deep learning-based approaches for melanoma detection in the literature. Still, they are not accurate or require high learning data. This paper proposes a hybrid mechanism for automated melanoma detection on dermoscopic images based on Discrete Cosine Transform features and metadata. It is composed of three steps. First, extra information/artifacts are deleted;the remaining pixels are standardized for accurate processing. Second, the reliability of the mechanism is improved by the Radon transform, extra data is removed using the Top-hat filter, and the detection rate is increased by Discrete Wavelet Transform and Discrete Cosine Transform. Then, the number of features is reduced by Locality Sensitive Discriminant Analysis. The third step divides the images into learning and test ones to create image-based models using learning data. Finally, the best model is selected based on test data and improved by a metadata-based model. Simulation results show that the decision tree provides the most practical image-based model by improving accuracy and sensitivity. Besides, the comparison results demonstrate that our model improves the F-Value to detect melanoma superior to other approaches. © 2024 The Author(s)

关键词： Artificial intelligence Big data Data science Dermoscopic image Discrete cosine transform features Machine learning Melanoma detection Metadata

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Evaluating Asthma Symptoms in Relation to Indoor Air Quality: Insights from IoT-enabled Monitoring 4

Evaluating Asthma Symptoms in Relation to Indoor Air Quality...

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4th International Conference on Distributed Sensing and Intelligent Systems, ICDSIS 2023

作者： Mishra, Bhupesh Kumar Thakker, Dhavalkumar John, Reena Kureshi, Rameez Raja Ahmad, Baseer Jones, Will Li, Xiao School of Computer Science University of Hull Kingston upon HullHU6 7RX United Kingdom School of Computer Science University of Hull Kingston upon HullHU6 7RX United Kingdom Faculty of Engineering and Informatics University of Bradford Richmond Road BradfordBD7 1DP United Kingdom Beijing Capital Online Technology Ltd Beijing China

ISBN: (纸本)9781839539954

Air pollution appears in the form of outdoor air quality and indoor air quality (IAQ). Particulate Matters (PM2.5 and PM10) and CO2, among many air pollutants, are responsible for worsening IAQ. IAQ has been linked to lung illnesses such as asthma, chronic obstructive pulmonary disease (COPD), and lung cancer. This linkage is related to how much any individual gets exposed to poor IAQ, individuals’ daily indoor activities, and related health risks. The study presented in this paper focused on the analysis and identification of the relationship between IAQ, daily indoor activities and health concerns and their effect on breathing issues in five asthma patients in Bradford, UK. IAQ data were collected using an IoT-enabled, low-cost device capable of monitoring CO2, PM2.5, PM10, relative humidity, and temperature. In addition, indoor daily activities and breathing issues that relate to asthma severity are also recorded using a digital platform. These recorded data are statistically analysed using correlation and measure of central tendency. Results indicate a strong positive correlation between breathing issues and indoor measured pollutants (0.72, 0.62 and 0.63 correlation coefficient with CO2, PM10 and PM2.5 respectively). IAQ is strongly correlated with indoor activity in terms of window opening hours and breathing issues as asthma symptoms. It was observed that when the mean readings of PM2.5, PM10 and CO2 are higher (22μg/m3, 24μg/m3 and 700 ppm respectively - in this study), the asthma patients reported experiencing worsening symptoms. This study highlights the importance of managing IAQ and window-opening habits to potentially mitigate asthma symptoms and improve patient care. © The Institution of engineering & Technology 2023.

关键词： Indoor air pollution

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Design and Implementation of Regional Food Distribution Platform Based on Big Data

Design and Implementation of Regional Food Distribution Plat...

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2021 IEEE International Conference on Artificial Intelligence and Industrial Design, AIID 2021

作者： Wei, Wei Liang, Huiqing Zhang, Beibei Damasevicius, Robertas Scherer, Rafal School of Computer Science and Engineering Xi'an University of Technology Xi'an710048 China Kaunas University of Technology Faculty of Informatics Multimedia Engineering Department Kaunas Lithuania Czestochowa University of Technology Czestochowa Poland

ISBN: (纸本)9780738110837

In recent years, the rapid development of big data has made people's daily life very convenient, and at the same time, tasting all kinds of food has become an important activity for people to travel. Due to the vast territory of China, there are many types of cuisines with great differences, it is very important for travelers to understand the special regional cuisines in the area. This project aggregates regional food data based on big data, and provides tourists with efficient, stable and professional data retrieval and analysis services through a visual data interface, and provides intuitive, accurate, and real-time data support for the decision-making of finding characteristic regional food. This thesis first conducted a relevant understanding of big data and the overall situation of regional cuisine, analyzed the distribution of food in the sub-provincial city of Xi'an, explored the research methods and implementation methods of related projects at home and abroad, based on this, summarized the research of this project the goal. At the same time, the focus of this project is to analyze the price, score and popularity of regional food data analysis in my country, and to summarize, proofread and organize the data obtained before into standard and standardized data. Through the classification of basic information, the visual analysis and display of data is realized, and the key data urgently needed by decision makers are extracted from it. To analyze the needs of decision makers, establish corresponding strategies and measures to improve the quality of data services. The establishment of this system provides a useful supplement and improvement to the existing industry data analysis system. The system is mainly divided into six modules, which are data collection, data review, data summary, and visual data display modules. Among them, data collection includes crawling relevant data from the Internet and retrieving key data. The data audit function includes classifyi

关键词： Big data

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An accurate detection of micro-collapse during the lyophilisation of a 5% w/v lactose solution using a combination of novel techniques: Intelligent laser speckle imaging (ILSI) and through-vial impedance spectroscopy (TVIS)

arXiv

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arXiv 2024年

作者： Orun, Ahmet Vadesa, Anand Smith, Geoff School of Computer Science and Informatics Faculty of Computing Engineering and Media De Montfort University LeicesterLE1 9BH United Kingdom DMU LyoGroup School of Pharmacy Faculty of Health and life Sciences De Montfort University Leicester United Kingdom

Context: In a freeze drying (FD) process, an accurate observation and control of the process parameters at critical stages are at high importance. Particularly accurate and timely identification of the critical temperature (Tc) at the end of primary drying phase would lead to heating energy cost reduction and product waste elimination by ending the process before the product’s micro-collapse stage. Aim: Within this work, a combination of novel techniques optical technique called Intelligent Laser Speckle Imaging (ILSI) in association with product image texture analysis, coupled to an electrical impedance technique called through vial impedance spectroscopy (TVIS) has been applied onto the real-time product states observation process to identify the specific structural product surface/subsurface characteristics and hence the micro-collapse stage. Method: 2 cycles – one providing a profile for standard approach (non-collapsed) and another with a temperature ramp through Tc to micro-collapse, TVIS provides an assessment of the onset of micro-collapse through the assessment of the acceleration in drying rate whereas the ILSI with pattern recognition detects the change in microstructure © 2024, CC BY-NC-ND.

关键词： Cost reduction

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Model of Visualization and Analytics for Open Data (Case: Election Voters & Kids Disability Category)

Model of Visualization and Analytics for Open Data (Case: El...

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Advanced Innovations in Smart Cities (ICAISC), International Conference on

作者： Ripto Mukti Wibowo Bahjat Fakieh Muhammad Sher Ramzan Ahmed Saeed Alzahrani Muazzam Siddiqui Bander Alzahrani Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Department of Computer Science Faculty of Computing and Information Technology King Abdulaziz University Luddy School of Informatics Computing and Engineering Indiana University Bloomington

Several regional head elections had to be postponed due to the pandemic, including in Indonesia because of the COVID-19 pandemic. Several big cities in Indonesia are of concern because of their large population and GDP. This study conducts analysis and testing of datasets taken from Open Data in a city in Indonesia. In addition to conducting research on regional head elections, we also present information on voters from the category of kids with disabilities. The steps used in this research are using regional mapping data of the city of Surabaya in the Election of the Regional Head. Download the data or dataset for the Regional Head Election ampersand Categories of kids with disabilities. Based on the dataset voters from the category of children with disabilities are more than 5 *** this research, we use Python to process our datasets & Big Data technology. Data cleaning or cleansing, Exploratory Data Analysis, and Empirical Cumulative Distribution Functions (ECDF) in python are also needed. Result from ECDF chart with steady increase (increment of 0.1). The highest variance value is in Electoral District 5 = 6.090 and the lowest value is in Electoral District 4 = 0.90. The result of Open Data is graphical data visualization and candidate scores to help as an alternative for the 2024 Regional Head Election and the Category of kids with disabilities.

关键词： Technological innovation Data analysis Pandemics Voting Data visualization Big Data Data models

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Application of Photovoltaic Systems for Power Supply of Heat Pumps

Application of Photovoltaic Systems for Power Supply of Heat...

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International Conference on Smart Systems and Technologies (SST)

作者： Alen Mazalica Dominik Buzov Danijel Topić KONČAR - Electronics and Informatics Ltd. for Production and Service Zagreb Croatia IN ELEKTRO D.O.O. Slavonski Brod Croatia Faculty of Electrical Engineering Computer Science and Information Technlogy Osijek Osijek Croatia

ISBN: (数字)9798350386394

ISBN: (纸本)9798350386400

Electricity consumption worldwide is increasing day by day, and as we all aim to reduce the harmful impact on the environment due to increased electricity production, humanity is turning to electricity generation from renewable energy sources. In 2021, a total of 315 GW of new renewable energy sources were installed. The Republic of Croatia recognized the importance of renewable energy sources in due time, and the share of renewable energy sources in the final electricity consumption is above the European Union average. This paper aims to demonstrate the profitability of photovoltaic systems for powering heat pumps, specifically replacing gas heating with an air-water heat pump powered by a photovoltaic system.

关键词： Photovoltaic systems Renewable energy sources Heat pumps Electricity Europe Boilers Turning Smart systems Resistance heating Investment

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Using Spark Distributed Deep Learning to Analyze NetFlow in Data Lake System 10th

Using Spark Distributed Deep Learning to Analyze NetFlow in ...

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10th International Conference on Frontier Computing, FC 2020

作者： Jiang, Cheng-Tian Yang, Chao-Tung Chan, Yu-Wei Kristiani, Endah Liu, Jung-Chun Department of Computer Science Tunghai University Taichung City407302 Taiwan College of Computing and Informatics Providence University Taichung City Taiwan Department of Industrial Engineering and Enterprise Information Tunghai University Taichung City407302 Taiwan Department of Informatics Faculty of Engineering and Computer Science Krida Wacana Christian University Jakarta11470 Indonesia

ISBN: (纸本)9789811601149

The usage of data to improve information security has become the focus of research in the development of big data and artificial intelligence. However, the traditional single database technology is unable to handle the current complex and massive data processing while network attacks have also occurred frequently and produce massive data. This paper proposes a framework for storing and analyzing Web log data based on the concept of data lakes. The need for processing, accessing, querying, analyzing, and visualizing large amounts of data in a short time is also critical. Therefore, the Grafana tool was used to provide the network administrator a real-time monitoring and visualization system management. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Lakes

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