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24th International Multidisciplinary Scientific Geoconference: Ecology, Economics, Education and Legislation, SGEM 2024

作者： Zyder, Václav Chlopecký, Jakub Hubáček, Jaroslav Rokosz, David Hrouda, Matěj Faculty of Materials Science and Technology Department of Industrial Systems Management VSB - Technical University of Ostrava Czech Republic Faculty of Economics Department of Systems Engineering VSB - Technical University of Ostrava Czech Republic Faculty of Economics Department of Applied Informatics VSB - Technical University of Ostrava Czech Republic

ISBN: (纸本)9786197603743

The contribution focuses on the optimization of the economic balance of the sorting line for the processing of municipal waste with the aim of increasing the efficiency and sustainability of waste management. This abstract provides an overview of the key economic aspects that affect sorting line operations, including cost, revenue and financial return analysis. Major cost items include investment in modern sorting line technology, installation and commissioning costs, and operating costs associated with maintenance, energy and labor. On the revenue side, the income from the sale of sorted materials such as plastics, metals and paper and the savings achieved by reducing the volume of waste sent to landfills are analysed. The study also takes into account possible subsidies and financial support available for environmental projects. The economic analysis of a purely municipal project (without the involvement of foreign capital, the involvement of public funds and subsidies) CEVYKO includes an assessment of the overall financial efficiency, the identification of potential risks and obstacles, and the proposal of measures to increase profitability and sustainability. Special emphasis is placed on long-term economic and environmental benefits, including improving the local economy, creating jobs and reducing the ecological footprint. The results of this analysis provide valuable information for decision-making processes and strategic planning in the field of municipal waste management. The recommendations arising from this study support the efficient allocation of resources and contribute to the achievement of the CEVYKO project goals. in the field of economic sustainability. © 2024 International Multidisciplinary Scientific Geoconference. All rights reserved.

关键词： Sustainable development

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Modeling, Control and Online Optimization of Microalgae-based Biomass Production in Raceway Reactors ⁎

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IFAC-PapersOnLine 2024年第14期58卷 235-240页

作者： Pablo Otalora Sigurd Skogestad José L. Guzmán Manuel Berenguel Department of Informatics University of Almería CIESOL ceiA3 04120 Almería Spain Department of Chemical Engineering Norwegian University of Science and Technology 7491 Trondheim Norway

Microalgae production in raceway photobioreactors is a very attractive process for biomass production because of its high sustainability. This paper presents a complete methodology for optimizing production in raceway photobioreactors. A first principles model has been developed that describes the dynamics and steady state of the system and has been used as the core of a real-time optimization to maximize the economic profit of the process, which handles the references of different variables of the system. For that, a steady-state real-time optimization approach complemented with PID controllers is presented in this work. The results obtained demonstrate the potential of steady-state real-time optimization for such systems, as well as the benefits of employing optimization techniques during process operation.

关键词： Real Time Optimization Process Control Microalgae Biomass Production

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Automated Detection of Poultry Farms from Aerial Images for Actionable AI System toward Biosecurity Applications

Automated Detection of Poultry Farms from Aerial Images for ...

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2023 IEEE Applied Imagery Pattern Recognition Workshop, AIPR 2023

作者： Li, Yu Das, Rana Duong, Cuong Lim, Teng-Teeh Haithcoat, Timothy Ersoy, Ilker Shyu, Chi-Ren University of Missouri Department of Electrical Engineering and Computer Science ColumbiaMO United States University of Missouri Biological Engineering ColumbiaMO United States University of Missouri Plant Science and Technology Extension ColumbiaMO United States University of Missouri Institute for Data Science and Informatics Columbia United States University of Missouri Institute for Data Science and Informatics Electrical Eng. and Computer Science Dept. ColumbiaMO United States

ISBN: (纸本)9798350359527

Poultry productions have shifted towards larger farms and often cluster in certain regions. However, many of the smaller farms with a considerable amount of production are not considered concentrated animal feeding operation under regulation in many regions, and therefore are not required to obtain a permit. As a result, information of these operations is not being recorded and monitored, contributing to an increased risk for biosecurity issues. The development in computer vision and availability of high-resolution remote sensing imagery allows the possibility for automatic localization and operation estimation of the unregistered farms, filling in the missing information needed for biosecurity risk management. The farm information extracted from images can be incorporated with other available geospatial data, such as road networks, watersheds, etc., to build a comprehensive knowledge base for actionable plans. In the current study, we present a framework for automated localization, analysis, and visualization of poultry farms from remote sensing images. A robust machine learning method was developed to computationally link the visual features from remote sensing imagery to Missouri department of Natural Resources Concentrated Animal Feeding Operation data, to identify the animal farm type, location, and potential capacity in the region. The developed process can be performed automatically on images of different timestamps to analyze temporal and spatial development of poultry production facilities of a region. This research contributes to the effective management and regulation of poultry and potentially other animal farms, providing insights into farm characteristics and capacities while facilitating informed decision-making. © 2023 IEEE.

关键词： Object detection

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Automated User Experience (UX) Testing for Mobile Application: Strengths and Limitations

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2022年第4期16卷 30-45页

作者： Abuaddous, Hayfa. Y. Saleh, Ashraf Mousa Enaizan, Odai Ghabban, Fahad Al-Badareen, Anas Bassam Faculty of Computer Sciences and Informatics Amman Arab University Amman Jordan Department of Management Information System University of Tabuk Tabuk Saudi Arabia College of Computer Science and Engineering Taibah University Medina Saudi Arabia Department of Software Engineering Aqaba University of Technology Aqaba Jordan

User Experience (UX) evaluation has a significant importance for any interactive application. Mobile device applications have additional limitations to convey good user experiences (UX) due to the usage and features of mobile devices in ubiquitous computing contexts. There are a lot of automated tools available for evaluating, such applications particularly developed for Android and iOS. Automated UX evaluation provides a number of strengths which are able to overcome the limitations of non-automated (moderated) counterparts. Automated UX evaluation is regarded as unexplored field, especially when it is adopted for the UX evaluation of complex mobile application. Despite these, automated UX evaluation tools are unexplored and should only be used in coupling with traditional UX evaluation techniques. Hence, this study aims to discuss the literature in automated UX evaluation and to highlight the UX measurements that merit further investigation. This paper presents the most fre-quently used automated UX evaluation tools for mobile applications. Therefore, the study analyzes existing automated UX tools and frameworks and identifies which UX attributes and measurements of automated UX evaluation are likely to be of use in future research. Moreover, we discuss the strengths and limitations of automated UX evaluation methods for mobile applications. Finally, the paper provides an overview of automated UX evaluation techniques and tools. It also presents the strengths and limitations for comparing various evaluation methods. © 2022,International Journal of Interactive Mobile Technologies. All Rights Reserved.

关键词： Automation

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AlexNet Architecture Based Convolution Neural Network for Realtime Audio to Text Translator of Bisindo Hand Sign

AlexNet Architecture Based Convolution Neural Network for Re...

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2023 International Seminar on Application for technology of Information and Communication, iSemantic 2023

作者： Sujatmiko, Dhiki Sari, Christy Atika Rachmawanto, Eko Hari Krismawan, Andi Danang Altamer, Bilal R. Alkhafaji, Mohamed Ayad University of Dian Nuswantoro Study Program in Informatics Engineering Semarang Indonesia University of Dian Nuswantoro Study Program in Animation Semarang Indonesia Mosul University Presidency University of Mosul Department of Computer Science Mosul Iraq National University of Science and Technology Department of Computer Science DhiQar Iraq

ISBN: (纸本)9798350339215

Deafness is a condition that results in the loss of hearing function, hindering the reception of information such as oral communication that relies on auditory senses. Consequently, individuals with hearing impairment experience communication barriers and may have limited or no ability to respond. One solution is the use of sign language. In Indonesia, there are two known sign languages: Sibi and Bisindo. Both serve the same function but differ in their style of movement and expression. Bisindo is considered more flexible as it conveys meaning based on the Indonesian language. However, the universal understanding of this language solution is still limited among many people. Therefore, a program is needed to facilitate translation between deaf individuals who use sign language and their counterparts who do not communicate through sign language. CNN (Convolutional Neural Network) is a deep learning algorithm used for training visual input data recognition by computer systems. There are various CNN-based architectures, and one of them is AlexNet. Based on the author's testing, the AlexNet architecture proves to be suitable for real-time sign language translation. The evaluation of the system involved 7,800 datasets and 520 testing instances, with an average accuracy of 468 correct translations. When averaged, the system achieved a 90% accuracy rate, representing a 100% increase in accuracy compared to previous approaches. © 2023 IEEE.

关键词： Audition

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FPGA design and implementation for montgomery multiplication algorithm using MATLAB HDL coder

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Bulletin of the National Research Centre 2024年第1期48卷 1-16页

作者： Elsayed, Ghada Abass, Eman S. Computer Science and Informatics Faculty The British University in Egypt Cairo Egypt Electrical Engineering Department Faculty of Engineering Modern University for Technology and information (MTI University) Cairo Egypt

Modular multiplication for large numbers is especially important in cryptography algorithms such as RSA and elliptic curves. The Montgomery algorithm is the most famous and efficient one for calculating it. Hardware implementation for cryptography co-processors is better than software implementation in terms of speed and security. Many FPGA designs for the Montgomery multiplication algorithm was published based on hardware description languages like VERILOG and VHDL. This paper proposes the FPGA design and implementation using MATLAB HDL Coder. The algorithm is modified such that it can fit any small/large FPGA by introducing scaling factor. The design is configurable in both modulus length and the scaling factor. This paper performs a comparison between the synthesizing results for different scales and for different modulus lengths. The synthesizing is performed up to 8K bit modulus length, and it can be increased easily. In this paper, implementation of different modulus lengths with different frequencies and with different area utilization can be easily achieved. The design utilizes different area resources for each configuration. The target is xc7vx330t-2ffg1157 Virtex-7 Xilinx FPGA. The maximum frequency is 80.81 MHz for 4096-bit modulus length with 8-bit data width and 2 for serialization factor. The minimum area utilization is achieved for minimum configurations, i.e., 1024-bit modulus length with 8-bit data width and for unity serialization factor. This paper proposes a scalable and configurable FPGA design for Montgomery multiplication co-processor-based HDL coder design.

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Secure Image Communication Using Galois Field,Hyper 3D Logistic Map,and B92 Quantum Protocol

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Computers, Materials & Continua 2024年第12期81卷 4435-4463页

作者： De Rosal Ignatius Moses Setiadi Nova Rijati Ahmad Rofiqul Muslikh Bonifacius Vicky Indriyono Aceng Sambas Faculty of Computer Science Universitas Dian NuswantoroSemarang50131Central JavaIndonesia Research Center for Quantum Computing and Materials Informatics Faculty of Computer ScienceUniversitas Dian NuswantoroSemarang50131Central JavaIndonesia Faculty of Information Technology Universitas MerdekaMalang65146East JavaIndonesia Faculty of Economics and Business Universitas STRADA IndonesiaKediri64133East JavaIndonesia Faculty of Informatics and Computing Universiti Sultan Zainal AbidinCampus BesutBesut22200TerengganuMalaysia Department of Mechanical Engineering Universitas Muhammadiyah TasikmalayaTamansari GobrasTasikmalaya46196Indonesia

In this paper,we propose a novel secure image communication system that integrates quantum key distribution and hyperchaotic encryption techniques to ensure enhanced security for both key distribution and plaintext ***,we leverage the B92 Quantum Key Distribution(QKD)protocol to secure the distribution of encryption keys,which are further processed through Galois Field(GF(28))operations for increased *** encrypted plaintext is secured using a newly developed Hyper 3D Logistic Map(H3LM),a chaotic system that generates complex and unpredictable sequences,thereby ensuring strong confusion and diffusion in the encryption *** hybrid approach offers a robust defense against quantum and classical cryptographic attacks,combining the advantages of quantum-level key distribution with the unpredictability of hyperchaos-based *** proposed method demonstrates high sensitivity to key changes and resilience to noise,compression,and cropping attacks,ensuring both secure key transmission and robust image encryption.

关键词： Galois field hyper 3D logistic map hyperchaotic map image security improved logistic map secure communication quantum security

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PR-FCNN: a data-driven hybrid approach for predicting PM2.5 concentration

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Discover Artificial Intelligence 2024年第1期4卷 75页

作者： Inam, Syed Azeem Khan, Abdullah Ayub Mazhar, Tehseen Ahmed, Noor Shahzad, Tariq Khan, Muhammad Amir Saeed, Mamoon M. Hamam, Habib Department of Artificial Intelligence and Mathematical Sciences Sindh Madressatul Islam University Karachi74000 Pakistan Department of Computer Science Bahria University Karachi Campus Sindh Karachi75000 Pakistan Department of Computer Science and information Technology School Education Department Government of Punjab Layyah 31200 Pakistan Department of Computer Science Shaheed Zulfiqar Ali Bhutto Institute of Science and Technology Karachi Pakistan Department of Computer Science COMSATS University Islamabad Sahiwal Campus Sahiwal57000 Pakistan School of Computing Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Selangor Shah Alam40450 Malaysia Sana’a Yemen Faculty of Engineering Uni de Moncton MonctonNBE1A3E9 Canada School of Electrical Engineering University of Johannesburg Johannesburg 2006 South Africa Av. Grandes Ecoles Libreville BP 1989 Gabon Bridges for Academic Excellence Spectrum Tunis Tunisia

The atmosphere’s fine articulate Matter (PM2.5) poses various health-related risks. Even though multiple efforts have been made to lower the emissions of these substances, the mortality rate is continuously increasing, requiring immediate inclination of the scientific community towards the design and development of advanced predictive models. Conventional statistical approaches have become dormant due to their limitations in capturing the innate relationships between the pollutants, particularly for predicting PM2.5 concentrations. In contrast, machine and deep learning techniques have shown great potential for forecasting air quality, providing more accuracy than its predecessor techniques. The present study investigates the utilization of hybrid approaches by integrating machine learning models with deep learning models to improve the prediction capabilities of PM2.5 concentration. It uses datasets from the World Air Quality Index (WAQI) and the State of Global Air (SOGA) to analyze the performance of the models on both the daily and annual data, respectively. This ensures the model’s effectiveness on a diversified dataset. The present study implements Random Forest (RF), Polynomial Regression (PR), XGBoost, and Extra Tree Regressor (ETR) coupled with Fully Connected Neural Network (FCNN), Long Short-Term Memory (LSTM), and Bi-directional LSTM (Bi-LSTM) for obtaining optimized results. Finally, after a thorough investigation, the hybrid PR model coupled with FCNN (PR-FCNN) is found to be the best model with improved R-squared (R2) values, portraying its potential for predicting PM2.5 concentration accurately. Based on the experimentation, the preset study recommends implementing hybrid approaches, offering better predictive accuracy in forecasting air pollutants, especially PM2.5. © The Author(s) 2024.

关键词： Health risks

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Oil Palm Leaf Disease Detection on Natural Background Using Convolutional Neural Networks 11

Oil Palm Leaf Disease Detection on Natural Background Using ...

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11th IEEE International Conference on Communication, Networks and Satellite, COMNETSAT 2022

作者： Septiarini, Anindita Hamdani, Hamdani Junirianto, Eko Thayf, Mohammad Sofyan S. Triyono, Gandung Henderi Mulawarman University Faculty of Engineering Department of Informatics Samarinda Indonesia Polytechnic of Agriculture Department of Information Technology Samarinda Indonesia Stmik Kharisma Makassar Department of Information System Makassar Indonesia Universitas Budi Luhur Faculty of Information Technology Jakarta Indonesia University of Raharja Informatics Engineering Faculty of Science and Technology Tangerang Indonesia

ISBN: (纸本)9781665460309

Oil palm plant diseases typically manifest themselves on the leaves, resulting in reduced crop quality. It is necessary to solve this issue as the need for premium-quality palm oil keeps growing. Despite the fact that various automatic detection models for oil palm leaf disease have been developed, their performance was frequently inadequate due to the similarity of class characteristics. This work proposes a method that automatically detects the oil palm leaf disease on a natural background to distinguish between infected and healthy leaf classes. The method was developed using deep learning based on Convolution Neural Network (CNN) model. The private dataset consists of 600 oil palm leaf images (300 healthy and 300 infected) on a natural background. In order to decrease the computation time, pre-processing was carried out, which consists of resizing and normalizing the image, followed by augmentation. Augmentation was applied by rotation, flip, shear, and zooming techniques. Furthermore, the CNN model was employed to detect oil palm leaf disease using Tensorflow 2.5.0 framework with 224 × 224 input data. The proposed method successfully achieved the highest performance, revealed by the accuracy value of 1. © 2022 IEEE.

关键词： Neural network models

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A Dynamic Forecast Demand Scenario Analysis to Design an Automated Parcel Lockers Network in Pamplona (Spain) Using a Simulation-Optimization Model

A Dynamic Forecast Demand Scenario Analysis to Design an Aut...

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2023 Winter Simulation Conference, WSC 2023

作者： Izco, Irene Serrano-Hernandez, Adrian Faulin, Javier Sawik, Bartosz Public University of Navarra Institute of Smart Cities Campus Arrosadia s/n Pamplona31006 Spain Agh University of Science and Technology Department of Business Informatics and Engineering Management Krakow30059 Poland

ISBN: (纸本)9798350369663

The disruptions experienced by the last mile delivery processes during the SARS-CoV-2 pandemic have inevitably raised the dilemma of alternative last mile approaches in Urban Logistics (UL). Self-Collection Delivery Systems (SCDS) suppose an improvement for both courier companies and customers, providing flexibility of time-windows and reducing overall mileage, delivery time and, gas emissions. Drawing a distinction from previous works involving hybrid modeling for automated parcel lockers (APL) network design, this study integrates a System Dynamics Simulation Model (SDSM) to forecast e-commerce demand in Pamplona (Spain), and considers the scalability of the model for other cities. A bi-criteria Facility Location Problem (FLP) is proposed and solved with an ϵ-constraint method, where ϵ is defined as the level of coverage of the total demand, and four different cases of demand coverage are run. The simulation and demand forecast was carried out using Anylogic software, being CPLEX the optimization solver. © 2023 IEEE.

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