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6th International Conference on Applied Computational Intelligence in Information Systems, ACIIS 2023

作者： Lai, Jia Chi Lung Then, Yi Hwang, Siaw San Swinburne University of Technology Sarawak Campus Faculty of Engineering Computing & Science Sarawak Kuching93350 Malaysia Universiti Malaysia Sarawak Faculty of Engineering Kota Samarahan Sarawak 94300 Malaysia

ISBN: (纸本)9798350317428

The composting process exhibits dynamic changes in environmental variables due to underlying biological and physiochemical mechanisms, adding complexity to its modeling. This work focuses on developing a mathematical model for food waste composting in forced continuous aeration conditions. The model incorporates substrate degradation with microbial kinetics, heat balance, and mass balance to address the complexity. In this study, we compare the performance of nth order kinetics against first-order kinetics in predicting temperature variations during food waste composting. Additionally, a comparison is made between nth order kinetics and Monod kinetics for forecasting temperature variations in manure waste composting. The six-stage with fifth order ODE solver (ode45) in MATLAB is employed to numerically solve the differential equations and generate temperature profiles. The developed model successfully predicts peak temperature and the time taken to reach it, enabling better temperature control during the thermophilic phase. This control is vital for avoiding excessive temperatures that can inhibit microbial activities. Comparison of experimental and simulation data, using RRMSE and MAE validation, demonstrates good agreement in the performance of the nth order model when utilizing experimental heat of reaction and proposed kinetics. To further enhance the predictive model for composting processes, it is suggested to expand the nth order model to incorporate other factors such as oxygen concentration, pH, and moisture content. This expansion can contribute to improved prediction accuracy and performance. By employing intelligent and resilient digital innovations, this research strives to advance sustainable living practices in the domain of food waste composting. © 2023 IEEE.

关键词： Temperature control

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Bridging Theory and Practice in Outcome-Based Assessment: Developing a Web Application for Learning Outcome Alignment

Bridging Theory and Practice in Outcome-Based Assessment: De...

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International Conference on Educational and Information Technology (ICEIT)

作者： Abdulsalam K. Alhazmi Faculty of Engineering & Computing University of Science and Technology Aden Yemen

ISBN: (数字)9798331540883

ISBN: (纸本)9798331540890

Assessment remains a cornerstone of the educational process, with standardized testing often serving as a primary method for evaluating learning. However, as pedagogical approaches continue to evolve, Outcome-Based Assessment (OBA) has gained prominence as a more comprehensive model that aligns learning outcomes with teaching strategies. This paper explores the significance of OBA, grounded in frameworks of domain learning, and highlights its global adoption as a measure of institutional quality. By focusing on measurable learning outcomes across cognitive domains, OBA enhances student engagement, critical thinking, and skill development. Extending this theoretical foundation, the paper presents an Outcome Based Assessment System (OBAS) developed to facilitate the practical implementation of OBA. OBAS provides a structured framework that links program learning outcomes to course learning outcomes, integrating considerations of learning domains and difficulty levels. By supporting the creation of standardized and authentic OBA assessments, this application empowers educators and institutions to uphold educational quality, foster accountability, and align assessments with targeted learning objectives.

关键词： Industries Bridges Education Employment Focusing Learning (artificial intelligence) Benchmark testing Prediction algorithms Classification algorithms Information technology

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Optimizing Optical Attocells Positioning of Indoor Visible Light Communication System

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Computers, Materials & Continua 2023年第2期74卷 3607-3625页

作者： Mohammed S.M.Gismalla Asrul I.Azmi Mohd R.Salim Farabi Iqbal Mohammad F.L.Abdullah Mosab Hamdan Muzaffar Hamzah Abu Sahmah M.Supa’at School of Electrical Engineering Faculty of EngineeringUniversiti Teknologi MalaysiaSkudai81310Malaysia Lightwave Communications Research Group(LCRG) Faculty of Electrical EngineeringUniversiti Teknologi Malaysia(UTM)SkudaiJohorMalaysia Department of Electronics and Electrical Engineering Faculty of EngineeringInternational University of AfricaKhartoum12223Sudan Department of Communication Engineering Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia(UTHM)Parit RajaBatu Pahat86400JohorMalaysia Faculty of Computing and Informatics Universiti Malaysia SabahKota Kinabalu88400Malaysia Department of Computer Science University of São PauloSão PauloBrazil

Visible light communication(VLC),which is a prominent emerging solution that complements the radio frequency(RF)technology,exhibits the potential to meet the demands of fifth-generation(5G)and beyond *** random movement of mobile terminals in the indoor environment is a challenge in the VLC *** model of optical attocells has a critical role in the uniform distribution and the quality of communication links in terms of received power and signal-to-noise ratio(SNR).As such,the optical attocells positions were optimized in this study with a developed try and error(TE)*** optimized optical attocells were examined and compared with previous *** novel approach had successfully increased minimum received power from−1.29 to−0.225 dBm,along with enhanced SNR performance by 2.06 *** bit error rate(BER)was reduced to 4.42×10−8 and 6.63×10−14 by utilizing OOK-NRZ and BPSK modulation techniques,*** optimized attocells positions displayed better uniform distribution,as both received power and SNR performances improved by 0.45 and 0.026,*** the results of the proposed model are optimal,it is suitable for standard office and room model applications.

关键词： Visible light communication(VLC) optical attocell received power signal-to-noise ratio(SNR) bit error rate(BER) coefficient of variation(CV)

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Intelligent Predictive Model for Hepatitis C 3

Intelligent Predictive Model for Hepatitis C

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3rd IEEE International Conference on Artificial Intelligence, ICAI 2023

作者： Shahzadi, Mehreen Bukhari, Faisal Shafi, Numan University of the Punjab Faculty of Computing & Information Technology Department of Data Science Lahore Pakistan University of Engineering and Technology Department of Computer Science Lahore Pakistan

ISBN: (纸本)9798350322125

Hepatitis C is the liver's festering that can lead to severe liver damage, usually caused by the hepatitis C virus. Hepatitis C has different stages. It is tough to cure in it's last stages;at the same time, it is expensive and painful process. The current research, however, is an alternative precaution to this issue. Hepatitis C can be predicted early by using multiple factors. The dataset related to hepatitis C was not publicly available. To overcome this challenge, the healthy and HCV effected samples were collected from different hospitals in Punjab. A questionnaire based survey was taken including different HCV related factor i.e. gender, weight loss, hives/ rashes, swelling, jaundice, drug addiction history, hepatic encephalopa-thy (drowsiness, slurred speech), Ascites (fluid buildup in belly/ abdomen), spider angiomas (Spiderlike blood vessels), shared syringe usage, medical history, and severeness. Different cleaning, scaling, and feature selection techniques were applied to collect the best feature data. After selection, various machine learning algorithms were applied. Random forest, KNN, Decision Tree, SVC, and MLP were used, but MLP yielded optimal results in all classification algorithms. We have gained 95.9 % accuracy when tested on unknown data based on the MLP model. As the predictions' results were satisfactory, it would be helpful for the people and act as a critical awareness. © 2023 IEEE.

关键词： Decision trees

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Adaptive Trans-Bidirectional Long Short Term Memory for Detecting COVID19 Epidemic Fake News on Social Media

Adaptive Trans-Bidirectional Long Short Term Memory for Dete...

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2023 International Conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Rathinapriya, V. Kalaivani, J. SRM Institute of Science and Technology College of Engineering and Technology Department of Computing Technologies Tamil Nadu Kattankulathur603203 India SRM Institute of Science and Technology College of Engineering and Technology Faculty of Engineering and Technology Department of Computing Technologies Tamil Nadu Kattankulathur603203 India

ISBN: (纸本)9798350319200

Arguing in a disinformation era in the wake of the COVID19 pandemic is a challenging endeavour for many superpower nations. On social media, disinformation and rumours move such as any actual news, and most of the moment individuals are misguided with that data. Believing in rumours can have severe repercussions in both the society and individual. This makes it more severe in the event of a global epidemic at a point that it has caused chaos among nations and people. Fake news can not only produce cognitive uncertainty during periods like the COVID19 pandemic, but it can also endanger the lives of individuals. To address these difficulties, during pandemics, a deep learning-based detection of fake news is created to determine whether the news is fake or real. Initially, for this identification of fake news, the text data from twitter and other social networks is gathered. The collected data is applied to the text preprocessing strategies for improving text information quality. Further, the fake news from the social media is detected through "Adaptive Transformer Bidirectional Long Short Term Memory (ATrans-Bi-LSTM)"to identify the fake or real news. The hyperparameters from the Bi-LSTM network are tuned via the Komodo Mlipir Algorithm (KMA) to improve the effectiveness over the fake news detection on social networks. The classified outcome is compared with the traditional detection of fake news models in terms of various performance measures to ensure the effectiveness. © 2023 IEEE.

关键词： "Adaptive Transformer Bidirectional Long Short Term Memory" Detecting COVID19 Epidemic Fake News Komodo Mlipir Algorithm Text Data

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Feature selection using modified chaotic satin bowerbird algorithm with deep transfer learning for Multispectral Image Classification

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International Journal of Information Technology (Singapore) 2024年 1-13页

作者： Rajakani, M. Kavitha, R.J. Rajesh, S. Department of Data Science and Business Systems School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology SRM Nagar Kattankulathur Chennai India Department of Electronics and Communication Engineering University College of Engineering Panruti Panruti 607106 India Department of Information Technology Mepco Schlenk Engineering College Sivakasi India

Multispectral image classification has received significant attention among research communities and academicians. Recently, several artificial intelligence (AI) models can be used for the extraction of prominent features. Besides, deep learning (DL) methods become more familiar and gained interest in the remote sensing community for the classification of multispectral and hyperspectral images. With this motivation, this paper presents an optimized feature learning with deep transfer learning enabled multispectral image classification (OFLDTL-MSIC) technique. The proposed OFLDTL-MSIC technique aims to categorize the different class labels of the multispectral images. Besides, the OFLDTL-MSIC technique applies multi-level discrete wavelet transform (DWT) based image decomposition technique. Moreover, the EfficientNet technique is applied as a feature extractor to generate a collection of features. Furthermore, selecting optimal features extracted by a EfficientNet involves identifying and retaining the most relevant and informative features The improved chaotic satin bowerbird optimization (CSBO) algorithm which uses dynamic population size is proposed for feature selection and kernel extreme learning machine (KELM) model is applied for the classification process. In order to ensure the enhanced performance of the OFLTDL-MSIC technique, a wide range of simulations take place using the Madurai LISS IV multispectral images and the results are examined under several aspects. The extensive comparative study highlighted the better performance of the OFLTDL-MSIC technique over the recent methods. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2024.

关键词： Deep learning Image classification Multispectral images Parameter tuning Remote sensing

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Enhancing handwritten text feature extraction through key point detection and graph representation

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Soft computing 2024年第21期28卷 12977-12990页

作者： Rahman, Atta Ur Alahmadi, Tahani Jaser Alsenani, Yousef S. Ali, Sania Faculty of Computer Science and Engineering Ghulam Ishaq Khan Institute of Engineering Sciences and Technologies P.O. Box 23460 Topi Pakistan P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia Department of Computer Science University of Science and Technology Bannu P.O. Box 28100 Khyber Pakhtunkhwa Bannu Pakistan

Handwriting typically consists of a wide range of writing forms with substantial differences in the placements and size of those writing shapes. The arrangement, organization, and spatial association of individual letters, words, and phrases on a written page are all examples of handwritten text structure. This study proposed a novel approach in which a handwritten text structure is primarily transformed into a Cartesian XY-coordinate system while maintaining its shape characteristics. The points on the contour of handwritten text with local maximum bending values are considered hypothetical breakdown points called Key Points (KPs). From the skeletal representation of handwriting, this approach first identifies KPs and their coordinate positions. After that, KPs are transformed into a graph-based representation with vertices and edges. With the use of geometric graphs, this representation attempts to capture the spatial and temporal organization of handwriting. Previous techniques focused on statistical methods, which offer fixed-size descriptions;however, graph-based representations are flexible in size and reveal the relationship between text structure. Graph representations enable the incorporation of contextual information by integrating extra features which improve text accuracy and understanding. The proposed approach is tested for writer identification and verification tasks on four benchmark datasets (CERUG-EN, CVL, Firemaker, and IAM) and one custom-built dataset. The findings demonstrated that the proposed approach obtained state-of-the-art accuracies across all the mentioned datasets. Particularly, it achieved the highest accuracy of 99.75% for identification and 99.81% for verification on the Firemaker dataset. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Feature extraction

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Recent Advances and Benchmarking of NewSQL for OLTP and OLAP in the Big Data Age 13

Recent Advances and Benchmarking of NewSQL for OLTP and OLAP...

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13th IEEE Symposium on Computer Applications and Industrial Electronics, ISCAIE 2023

作者： Chung, John Zu An Chang, Miko May Lee Phang, Swee King Swinburne University of Technology Faculty of Engineering Computing and Science Sarawak Campus Kuching Malaysia Taylor's University School of Engineering Kuala Lumpur Malaysia

ISBN: (纸本)9798350347319

While the never-ending debate between relational and NoSQL database is ongoing, a new competitor, NewSQL, has quietly entered the field. Introduced in 2011, NewSQL is not as popular as relational or NoSQL database. The main selling point of NewSQL is its ability to scale horizontally while preserving ACID properties and thus preserving the support to handle OLTP workloads. Given the active research and evolving developments of NewSQL in the last decade, this research paper aims to identify how far has NewSQL advanced to and how comparable is it to existing database systems in terms of On-Line Transaction Processing (OLTP) and On-Line Analytics Processing (OLAP) to accommodate big data. Three research questions have been formulated as part of the systemic literature review (SLR) followed by an experimental benchmarking to validate the results from the SLR. The results show that while NewSQL still has room to improve, it is definitely ready to be used in productions, albeit having certain obstacles which may need to be addressed such as expertise in deployment and maintenance, as well as performance. One limitation of this research is that the testing was conducted on a single node and future research could include performance testing on multiple nodes. © 2023 IEEE.

关键词： Information management

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Efficient 3D Hilbert Curve Encoding and Decoding Algorithms

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Chinese Journal of Electronics 2022年第2期31卷 277-284页

作者： JIA Lianyin LIANG Binbin LI Mengjuan LIU Yong CHEN Yinong DING Jiaman Faculty of Information Engineering and Automation Kunming University of Science and Technology Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and Technology Library Yunnan Normal University School of Information Science and Engineering Guangxi University for Nationalities School of Computing Informatics and Decision Systems Arizona State University

Hilbert curve describes a one-to-one mapping between multidimensional space and 1 D *** traditional 3D Hilbert encoding and decoding algorithms work on order-wise manner and are not aware of the difference between different input data and spend equivalent computing costs on them, thus resulting in a low efficiency. To solve this problem, in this paper we design efficient 3D state views for fast encoding and decoding. Based on the state views designed, a new encoding algorithm(JFK-3HE) and a new decoding algorithm(JFK-3HD) are proposed. JFK-3HE and JFK-3HD can avoid executing iteratively encoding or decoding each order by skipping the first 0 s in input data, thus decreasing the complexity and improving the efficiency. Experimental results show that JFK-3HE and JFK-3HD outperform the state-of-the-arts algorithms for both uniform and skew-distributed data.

关键词： order-wise manner multidimensional space encoding state-of-the-arts algorithms encoding algorithm parallel algorithms JFK-3HE statistical distributions decoding solid modelling design efficient 3D JFK-3HD traditional 3D Hilbert encoding equivalent computing costs different input data decoding algorithm Hilbert curve state views iterative methods

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LF-ViT: Reducing Spatial Redundancy in Vision Transformer for Efficient Image Recognition 38

LF-ViT: Reducing Spatial Redundancy in Vision Transformer fo...

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38th AAAI Conference on Artificial Intelligence, AAAI 2024

作者： Hu, Youbing Cheng, Yun Lu, Anqi Cao, Zhiqiang Wei, Dawei Liu, Jie Li, Zhijun AIoT Lab Faculty of Computing Harbin Institute of Technology China Swiss Data Science Center Zurich Switzerland School of Cyber Engineering Xidian University China

ISBN: (纸本)1577358872

The Vision Transformer (ViT) excels in accuracy when handling high-resolution images, yet it confronts the challenge of significant spatial redundancy, leading to increased computational and memory requirements. To address this, we present the Localization and Focus Vision Transformer (LF-ViT). This model operates by strategically curtailing computational demands without impinging on performance. In the Localization phase, a reduced-resolution image is processed;if a definitive prediction remains elusive, our pioneering Neighborhood Global Class Attention (NGCA) mechanism is triggered, effectively identifying and spotlighting class-discriminative regions based on initial findings. Subsequently, in the Focus phase, this designated region is used from the original image to enhance recognition. Uniquely, LF-ViT employs consistent parameters across both phases, ensuring seamless end-to-end optimization. Our empirical tests affirm LF-ViT's prowess: it remarkably decreases Deit-S's FLOPs by 63% and concurrently amplifies throughput twofold. Code of this project is at https://***/edgeai1/***. Copyright © 2024, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Image recognition

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