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10th International Conference on Computing and Artificial Intelligence, ICCAI 2024

作者： Angelia, Randy Echavez Torres, John Louie B. Cagadas, Cristelle Ann G. Manigo, Annalee Computer Engineering Program College of Engineering Education University of Mindanao Philippines

ISBN: (纸本)9798400717055

An application was developed to address the time-consuming and subjective nature of physical examination and human judgment to classify the diverse families of Coleoptera more efficiently. Specifically focusing on the Coleoptera families in the Philippines, the application utilizes a Convolutional Neural Network (CNN)-based model as its classification architecture. To enhance the accuracy of the CNN model, researchers gathered images of Coleoptera from various families. The program achieves an impressive 94.44% accuracy rate and has demonstrated successful results in classifying Coleoptera families. This application offers a more streamlined and reliable approach to Coleoptera classification, benefiting researchers and enthusiasts. © 2024 ACM.

关键词： Convolutional neural networks

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Development of an RFID-based Children’s Disaster and Emergency PreparednessDevice 24

Development of an RFID-based Children’s Disaster and Emerge...

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8th International Conference on E-Society, E-education and E-Technology, ESET 2024

作者： Broas, Pauline Ingrid Derama, Lovely Mae Rosales, Rosban Bacus, John Computer Engineering Program College of Engineering Education University of Mindanao Davao City Philippines

ISBN: (纸本)9798400707094

RFID Technology has grown increasingly popular over recent years due to its efficiency. This study examines the development, implementation, and impact of an RFID-based cognitive learning system focusing on child preparedness in disasters and emergencies. This study aims to design a stand-alone interactive device, fabricate an RFID-based touchscreen interactive device for children, conduct functionality testing on the accuracy of the eLearnCards and responsiveness of the LCD Touchscreen, and evaluate system accuracy and performance on children using a paired T-test. A quantitative research approach is adopted, encompassing the collection and analysis of pre-test and post-test scores from a stratified random sample of 60 children from New Matina Elementary School with an age range of 7-12 years old. The results of this study signify that the RFID-based cognitive learning system substantially contributes to enhancing students’ disaster preparedness knowledge. This study concludes that it underscores the considerable potential of RFID-based educational technology in reinforcing disaster preparedness among children. © 2024 Copyright held by the owner/author(s).

关键词： Disasters

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Android-Based Application for Ripeness Classification and Shelf-Life Determination of Philippine Bananas 2

Android-Based Application for Ripeness Classification and Sh...

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2nd International Conference on computer Graphics and Image Processing, CGIP 2024

作者： Lumpay, Fernandrigo R. Canoy, Jon Waldson T. Caballero, Ryu Mhar A. Nepa, Kimberly M. College of Engineering Education University of Mindanao Computer Engineering Program Davao City Philippines

ISBN: (纸本)9798350374186

Lauded as the world's most popular fruit, bananas are one of the main exports of the Philippines. It contains essential nutrients that can have a protective impact on health. Commonly, farmers can distinguish the different types of bananas and potentially predict their shelf-life. Nevertheless, human judgment is not always reliable. It needs to have support and evidence to minimize the margin of error. Hence, this study focuses on developing an android application to determine the Philippine bananas' ripeness, variety, and shelf-life. This application identifies different bananas sold in the local market, specifically Cardava, Lakatan, and Latundan. Also, the application determines the shelf-life of bananas, such as the remaining days for unripe bananas to become ripe and ripe bananas to overripe. A Convolution Neural Network-based (CNN) model classifier is used as an architecture categorizing the Philippine local market bananas. The proponents used real-life image data to increase the accuracy of the CNN model. The application was proven effective and has reached 90% accuracy. © 2024 IEEE.

关键词： Machine learning

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Optimum classical beam-position sensing

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Physical Review Applied 2024年第4期22卷 L041004页

作者： Wenhua He Christos N. Gagatsos Dalziel J. Wilson Saikat Guha Wyant College of Optical Sciences Department of Electrical and Computer Engineering Program in Applied Mathematics

Beam-displacement measurements are widely used in optical sensing and communications; however, their performance is affected by numerous intrinsic and extrinsic factors, including beam profile, propagation loss, and receiver architecture. Here we present a framework for designing a classically optimal beam-displacement transceiver, using quantum estimation theory. We consider the canonical task of estimating the position of a diffraction-limited laser beam after passing through an apertured volume characterized by Fresnel-number product DF. As a rule of thumb, higher-order Gaussian modes provide more information about beam displacement, but are more sensitive to loss. Applying quantum Fisher information, we design mode combinations that optimally leverage this trade-off, and show that a greater than tenfold improvement in precision is possible, relative to the fundamental mode, for a practically relevant DF=100. We also show that this improvement is realizable with a variety of practical receiver architectures. Our findings extend previous works on lossless transceivers, may have immediate impact on applications, such as atomic force microscopy and near-field optical communication, and pave the way towards globally optimal transceivers using nonclassical laser fields.

关键词： Classical optics Metrology Quantum Fisher information Quantum optics Quantum parameter estimation Devices Wireless communication networks Atomic force microscopy

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Cognitive Assessment of Students Using Testing Instruments on computer Networks Courses in Informatics and computer engineering education Study program at UNJ 8

Cognitive Assessment of Students Using Testing Instruments o...

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8th International Conference on education and Technology, ICET 2022

作者： Duskarnaen, Muhammad Ficky Sastrawijaya, Yuliatri Ajie, Hamidillah Arrazi, Muhammad Fakhruddin State University of Jakarta Informatics and Computer Engineering Education Study Program Jakarta Indonesia

ISBN: (数字)9781665492140

ISBN: (纸本)9781665492140

This study aims to design an assessment instrument to assess students' cognition in computer networking lessons. The design and design of the test instrument followed the ADDIE model, from the analysis stage to determine student targets and characteristics, then the design stage, determining learning objectives, learning material design, and question grids, at this stage the number of Subject Matter Experts (SME) was also determined as many as 6 people, for 6 Subject Matter Experts (SME), the minimum acceptable Content Validation Ratio (CVR) is 0.99, while the Content Validation Index (CVI) is 0.83. At the development stage, 81 items were designed as a draft assessment instrument. At the implementation stage, the Subject Matter Experts (SME) assessed each item on the draft assessment instrument. This study uses a quantitative content validity approach from the Lawshe model with a 3-point scale as follows: essential, useful but not necessary, and non-essential. Each Subject Matter Expert (SME) was given an instrument to assess each item in the test instrument. The Subject Matter Expert (SME) will determine whether an important question is given, normal or normal, or whether the question is not important given to students. At the evaluation stage, the Subject Matter Expert (SME) assessment of the draft test instrument will be compiled and the Content Validation Ratio (CVR) value and the Content Validation Index (CVI) value calculated to determine content validity. © 2022 IEEE.

关键词： Students

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From light sensing to adaptive learning:hafnium diselenide reconfigurable memcapacitive devices in neuromorphic computing

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Light(Science & Applications) 2025年第1期14卷 215-226页

作者： Bashayr Alqahtani Hanrui Li Abdul Momin Syed Nazek El-Atab Electrical and Computer Engineering Program Computer Electrical Mathematical Science and Engineering DivisionKing Abdullah University of Science and Technology(KAUST)ThuwalSaudi Arabia Electrical Engineering Department College of EngineeringPrincess Nourah Bint Abdulrahman University(PNU)RiyadhSaudi Arabia

Advancements in neuromorphic computing have given an impetus to the development of systems with adaptive behavior,dynamic responses,and energy efficiency *** charge-based or emerging memory technologies such as memristors have been developed to emulate synaptic plasticity,replicating the key functionality of neurons—integrating diverse presynaptic inputs to fire electrical impulses—has remained *** this study,we developed reconfigurable metal-oxide-semiconductor capacitors(MOSCaps)based on hafnium diselenide(HfSe2).The proposed devices exhibit(1)optoelectronic synaptic features and perform separate stimulus-associated learning,indicating considerable adaptive neuron emulation,(2)dual light-enabled charge-trapping and memcapacitive behavior within the same MOSCap device,whose threshold voltage and capacitance vary based on the light intensity across the visible spectrum,(3)memcapacitor volatility tuning based on the biasing conditions,enabling the transition from volatile light sensing to non-volatile optical data *** reconfigurability and multifunctionality of MOSCap were used to integrate the device into a leaky integrate-and-fire neuron model within a spiking neural network to dynamically adjust firing patterns based on light stimuli and detect exoplanets through variations in light intensity.

关键词： integrate volatile integrating

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Applications of information technology (IT) in Iraq's petroleum industry

Iraqi Journal for Computer Science and Mathematics

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Iraqi Journal for computer Science and Mathematics 2024年第2期5卷 112-121页

作者： Alkhaisi, Whalaa Almadany, Yasser Almashhadni, Mohammad Ibrahim Albawi, Saad Diyala university Department of chemical engineering Iraq Diyala university Department of computer engineering Iraq College of Education Department of Computer Iraq

The crucial role of information technology (IT) in operating and managing industrial systems is widely recognized, and Iraq's IT capabilities hold great promise for its industrial and other economic sectors. The demand for IT services has risen considerably, particularly for oil and gas companies, which face the challenge of boosting capital asset returns while ensuring safety and sustainability. They need to manage rising demands as well as competition for access to oil reserves while driving improved asset reliability, productivity, and performance. This application show how information technology is used in the petroleum industry. It specifically examines information technology applications in the upstream, midstream, and downstream important segments of the petroleum industry during the drilling process. The increased price volatility's noteworthy effect on the profitability of the energy industry is just one of the current supply chain management challenges. Better supply chain management aids in lowering operational costs, a challenge that many businesses are under demand to address right away. The study's theoretical and practical value is in exposing the characteristics and issues with the infrastructure supporting the Iraqi oil industry and in creating a control algorithm for the evolution of its Regular visual inspections of pipelines, offshore oil rigs, and other infrastructure may be carried out swiftly and safely thanks to technology. Light devices that enable temperature checks using infrared scanning are available in modern applications. Hazards like poisonous substances can be identified by other sensors from a safe distance. Enhancing oil and gas safety with artificial intelligence technologies protects the environment and ensures worker safety. © 2024 college of education, Al-Iraqia University. All rights reserved.

关键词： Offshore oil well production

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Ant lion optimization based inertia weight optimized KGMO for mobility Management in Heterogeneous LTE cellular networks

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Multimedia Tools and Applications 2025年第17期84卷 17907-17928页

作者： Venkata Narasimha Reddy, G. Jayudu, T Venkata Naga Komarolu, Janardhan Rajesh, Nichenametla Reddy, B Lakshmi Narayana Department of Computer Science and Engineering St. Johns College of Engineering and Technology Yerrakota Yemmiganur India Anantapur India Nandyal India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur India Department of Computer Science and Engineering SV College of Engineering Kadapa India

This study proposes a hybrid optimization-based mobility management strategy employing Kinetic Gas Molecular Optimization (KGMO) and Ant Lion Optimization (ALO). Initially, KGMO calculates particle properties, such as gas molecule position and velocity, based on kinetic energy principles. While KGMO demonstrates expedited convergence in large-dimensional spaces, its limitations prompt the hybridization with ALO. ALO modifies the inertia weight of KGMO to mitigate these issues. The proposed KGMO-ALO approach is implemented and validated in a MATLAB environment, compared against other meta-heuristic techniques like Seagull and Monarch Butterfly Optimizations across various performance metrics including throughput, bit error rate, end-to-end delay, and HO. Existing techniques such as PSO, MBO, and SOA exhibit superior performance primarily in scenarios with high user counts. The underperformance of ALO is attributed to its reliance on the best antlion solution in each optimization round. By leveraging the hybrid KGMO-ALO approach, this study aims to overcome these limitations and advance mobility management strategies in heterogeneous LTE cellular networks. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Heterogeneous networks

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Papaya Leaf Disease Identification using ResNet with Transfer Learning 8

Papaya Leaf Disease Identification using ResNet with Transfe...

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8th International Conference on Robotics and Automation engineering, ICRAE 2023

作者： Madelo, Hari Krisna L. Prieto, Jeanette C. Bajao, John Richard D. Bacus, John A. Computer Engineering Program College of Engineering Education University of Mindanao Davao City Philippines

ISBN: (纸本)9798350327656

Papaya (Carica papaya) is an important tropical fruit crop known for its nutritional value and economic significance. It is widely cultivated in many tropical and subtropical regions due to its adaptability to various climatic conditions. Nonetheless, papaya plants can be affected by multiple leaf diseases like brown spot, black spot, powdery mildew, and mealybug, resulting in substantial reductions in yield and quality. Detecting these diseases early and accurately is vital to implement effective management strategies. This research introduces a mobile application that utilizes transfer learning and ResNet models, offering an accessible means to identify papaya leaf diseases. This enables timely interventions and enhances crop management practices. By leveraging pretrained ResNet model, which is a type of CNN, and fine-tuning them with a dataset of papaya leaf images, the system achieves an average accuracy of 88%. The automated system developed in this research could enhance productivity and sustainability in papaya cultivation, benefiting farmers and the agricultural industry. © 2023 IEEE.

关键词： Convolutional neural networks

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Efficient COVID-19 Prediction by Merging Various Deep Learning Architectures

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Informatica (Slovenia) 2024年第5期48卷 55-62页

作者： Oraibi, Zakariya A. Albasri, Safaa Department of Computer Science College of Education for Pure Sciences University of Basrah Basrah Iraq Electrical Engineering Department College of Engineering Mustansiriyah University Baghdad Iraq

In late 2019, COVID-19 virus emerged as a dangerous disease that led to millions of fatalities and changed how human beings interact with each other and forced people to wear masks with mandatory lockdown. The ability to diagnose and detect this novel disease can help in isolating the infected patients and curb the spread of the virus. Artificial intelligence techniques including machine learning and deep learning showed huge potential in accurately classifying COVID-19 chest X-ray images. In this paper, we propose to combine the feature maps of multiple powerful CNN models (Xception, VGG-16, VGG-19) using the rule of sum. Each of these models is trained from scratch and tested on the given test images. The dataset was collected from a large public repository of COVID images with three classes: COVID, Normal, and Pneumonia. During experiments, data augmentation is also applied to provide more training samples. Experimental results show that combining multiple models improve the classification accuracy and achieve better performance than standalone models. An accuracy of 97.91% was achieved using a combination of three models which outperforms state-of-the-art techniques. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Coronavirus

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