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arXiv 2024年

作者： Bollaert, Henri Palangetić, Marko Cornelis, Chris Greco, Salvatore Slowiński, Roman Department of Applied Mathematics Computer Science and Statistics Ghent University Krijgslaan 281 S9 Ghent9000 Belgium Faculty of Economics University of Catania Italy Institute of Computing Science Poznań University of Technology Poznań60-956 Poland Systems Research Institute Polish Academy of Sciences Warsaw01-447 Poland

Interpretability is the next frontier in machine learning research. In the search for white box models - as opposed to black box models, like random forests or neural networks - rule induction algorithms are a logical and promising option, since the rules can easily be understood by humans. Fuzzy and rough set theory have been successfully applied to this archetype, almost always separately. As both approaches offer different ways to deal with imprecise and uncertain information, often with the use of an indiscernibility relation, it is natural to combine them. The QuickRules [20] algorithm was a first attempt at using fuzzy rough set theory for rule induction. It is based on QuickReduct, a greedy algorithm for building decision superreducts. QuickRules already showed an improvement over other rule induction methods. However, to evaluate the full potential of a fuzzy rough rule induction algorithm, one needs to start from the foundations. Accordingly, the novel rule induction algorithm, Fuzzy Rough Rule Induction (FRRI), we introduce in this paper, uses an approach that has not yet been utilised in this setting. We provide background and explain the workings of our algorithm. Furthermore, we perform a computational experiment to evaluate the performance of our algorithm and compare it to other state-of-the-art rule induction approaches. We find that our algorithm is more accurate while creating small rulesets consisting of relatively short rules. © 2024, CC BY-NC-ND.

关键词： Decision trees

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An Ensemble Models for the Prediction of Sickle Cell Disease from Erythrocytes Smears

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EAI Endorsed Transactions on Pervasive Health and Technology 2023年 9卷

作者： Ayoade, Oluwafisayo Babatope Oladele, Tinuke Omolewa Imoize, Agbotiname Lucky Adeloye, Jerome Adetoye Awotunde, Joseph Bamidele Olorunyomi, Segun Omotayo Faboya, Olusola Theophilus Idowu, Ayorinde Oladele Department of Computing and Information Science School of Pure and Applied Sciences College of Science Bamidele Olumilua University of Education Science & Technology Ekiti State Ikere-Ekiti Nigeria Department of Computer Science Faculty of Communication and Information Sciences University of Ilorin Kwara State Nigeria Department of Electrical and Electronics Faculty of Engineering University of Lagos Lagos State Akoka 100213 Nigeria Ekiti State Data Center Office Old Governor’s Office Ekiti State Ado-Ekiti Nigeria

INTRODUCTION: The human blood as a collection of tissues containing Red Blood Cells (RBCs), circular in shape and acting as an oxygen carrier, are frequently deformed by multiple blood diseases inherited from parents. These hereditary diseases of blood involve abnormal haemoglobin (Hb) or anemia which are major public health issues. Sickle Cell Disease (SCD) is one of the common non-communicable disease and genetic disorder due to changes in hematological conditions of the RBCs which often causes the inheritance of mutant Hb genes by the patient.. OBJECTIVES: The process of manual valuation, predictions and diagnosis of SCD necessitate for a passionate time spending and if not done properly can lead to wrong predictions and diagnosis. Machine Learning (ML), a branch of AI which emphases on building systems that improve performance based on the data they consume is appropriate. Despite previous research efforts in predicting with single ML algorithm, the existing systems still suffer from high false and wrong predictions. METHODS: Thus, this paper aimed at performing comparative analysis of individual ML algorithms and their ensemble models for effective predictions of SCD (elongated shapes) in erythrocytes blood cells. Three ML algorithms were selected, and ensemble models were developed to perform the predictions and metrics were used to evaluate the performance of the model using accuracy, sensitivity, Receiver Operating Characteristics-Area under Curve (ROC-AUC) and F1 score metrics. The results were compared with existing literature for model(s) with the best prediction metrics performance.. RESULTS: The analysis was carried out using Python programming language. Individual ML algorithms reveals that their accuracies show MLR=87%, XGBoost=90%, and RF=93%, while hybridized RF-MLR=92% and RF-XGBoost=99%. The accuracy of RF-XGBoost of 99% outperformed other individual ML algorithms and Hybrid models. CONCLUSION: Thus, the study concluded that involving hybridized M

关键词： Forecasting

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Optimizing Routing Paths in Mobile Wireless Sensor Networks: A Sub-Flow Adaptive Multipath Approach for Energy Efficiency and Delay Sensitivity

Optimizing Routing Paths in Mobile Wireless Sensor Networks:...

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International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： R. Vinston Raja Shavej Ali Siddiqui Avilasha BG L. Mohana Kannan Department of Computational Intelligence Faculty of Engineering and Technology School of Computing SRM Institute of Science and Technology Chennai Tamil Nadu India Deparment of Applied Science and Humanities Faculty of Engineering and Technology Khwaja Moinuddin Chishti Language University Lucknow India Deparment of Mechanical Engineering Dayanandasagar College of Engineering Bangalore India Department of Biomedical Engineering Erode Sengunthar Engineering College Perundurai Tamil Nadu India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

Wireless Sensor Networks (WSNs) have become crucial in various domains such as social applications, healthcare, and the military. However, their diversity makes them susceptible to a wide range of attacks, leading to challenges like poor throughput, high network latency, and excessive energy consumption. This study proposes a novel approach that combines residual Riemannian neural networks with the Sub-Flow Adaptive Multipath Routing algorithm for enhanced routing and attack detection in WSNs. By leveraging node position data, the Sub-Flow Adaptive Multipath Routing algorithm optimizes energy consumption and extends network lifespan. Simultaneously, residual Riemannian neural networks analyze residual patterns in network traffic data to accurately detect rogue nodes, thereby enhancing security. Blockchain technology is then employed to securely store classified data, ensuring data integrity and security. This integrated approach significantly improves network performance, maintains robust security against potential threats, and enhances the reliability of WSNs in demanding environments.

关键词： Wireless sensor networks Energy consumption Adaptive systems Neural networks Routing Throughput Delays Blockchains Security Reliability

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Learning Fine-Grained Information Alignment for Calibrated Cross-Modal Retrieval

Learning Fine-Grained Information Alignment for Calibrated C...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Jianhua Dong Shengrong Zhao Hu Liang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China

Masked Language Modeling (MLM) and Image-Text Matching (ITM) are always used in fusion encoder to learn the joint representation of images and text. In existing methods, the masking strategy of MLM leads to the neglect of image details during the modeling process. Meanwhile, the sampling strategy of ITM struggles to consistently select high-difficulty hard negative instances, reducing the effectiveness of constraints. This leads to challenges in aligning fine-grained information in cross-modal retrieval. In response to this challenge, a fine-grained information alignment-based visual language model (FAM) is proposed in this paper. On one hand, the attribute-based masking strategy is employed in MLM, helping the model focus on the details of objects in images during modeling. On the other hand, the robust hard negative sample generation strategy provides challenging negative samples for ITM by altering the relationships between objects. This enables the model to align relationships between objects in different modalities and thus calibrates cross-modal retrieval. Extensive experiments demonstrate the effectiveness of the model in cross-modal retrieval tasks.

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Markov chain quasi-Monte Carlo method for forecasting fire hotspots in Sarawak, Malaysia

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Environmental science and Pollution Research 2024年第35期31卷 48608-48619页

作者： Zakaria, Nurul Nnadiah Daud, Hanita Sokkalingam, Rajalingam Othman, Mahmod Abdul Kadir, Evizal Mohd Aris, Muhammad Naeim Muhammad, Noryanti Maharani, Warih Fundamental and Applied Sciences Department Universiti Teknologi PETRONAS Perak Darul Ridzuan Bandar Seri Iskandar32610 Malaysia Information System Department Universitas Islam Indragiri Tembilahan29212 Indonesia Informatics Engineering Department Universitas Islam Riau Pekanbaru28284 Indonesia Department of Mathematical Sciences Faculty of Science and Technology Universiti Kebangsaan Malaysia Selangor Darul Ehsan Bangi43600 Malaysia Centre of Excellence in Artificial Intelligence & amp Data Science Universiti Malaysia Pahang Al-Sultan Abdullah Lebuh Persiaran Tun Khalil Yaakob Pahang Darul Makmur Kuantan26300 Malaysia Centre for Mathematical Sciences Universiti Malaysia Pahang Al-Sultan Abdullah Lebuh Persiaran Tun Khalil Yaakob Pahang Darul Makmur Kuantan26300 Malaysia School of Computing Telkom University Bandung40257 Indonesia

Stochastic modeling approaches have attracted many researchers to the field. However, fire hotspot detection suffers from not using a Markov chain quasi-Monte Carlo (MCQMC) as a forecasting methodology. This paper proposes improvements to the computational time by combining the strengths of the Markov chain Monte Carlo (MCMC) and quasi-Monte Carlo (QMC) methods. The proposed method can lead to more precise and stable results, particularly in problems with high-dimensional integration or complex probability distributions. The proposed method is applied to a case study of fire hotspot detection in Sarawak, Malaysia. The outcome of this study reveals that the MCQMC method is more computationally efficient, taking only 0.0746 seconds compared to MCMC’s 0.0914 seconds and QMC’s 0.0994 seconds. It is shown that the best option derived by the proposed method is effective in predicting fire hotspots and providing quick solutions to protect the environment and communities from forest fires. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Fires

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MPTA-Net: Multi-Scale Perception Network with Triple-View Attention for Polyp Segmentation 4

MPTA-Net: Multi-Scale Perception Network with Triple-View At...

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4th International Conference on Neural Networks, Information and Communication Engineering, NNICE 2024

作者： Chen, Zhihong Yao, Lisha Liu, Yue Qiu, Bingjiang Han, Chu Tang, Haoyang Du, Yujia Liu, Zaiyi Fang, Gang Guangzhou University Institute of Computing Science and Technology Guangzhou China Guangdong Prov. Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangzhou China South China University of Technology School of Medicine Guangzhou China Department of Radiology Guangzhou China Jiangxi College of Applied Technology School of Information Engineering Ganzhou China Guangdong Provincial People's Hospital Guangdong Academy of Sciences Guangdong Cardiovascular Institute Guangzhou China Southern Medical University Medical Research Institute Guangzhou China Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming China

ISBN: (纸本)9798350394375

Accurate segmentation of polyps in endoscopic images is pivotal for the early detection and diagnosis of colorectal cancer (CRC). Nonetheless, the varied appearance of polyp foregrounds and the intricate background interference significantly diminish the efficacy of pixel-level predictions. To address this issue, we propose a multi-scale perception network with triple-view attention (MPTA-Net) for polyp segmentation. The model primarily revolves around two core principles. The first integrates multiscale spatial information into the network's hierarchical encoding process to better preserve detailed polyp semantic information. The second introduces a triple-view attention mechanism that analyzes feature maps from three distinct perspectives to focus on and localize semantically relevant areas, thereby enhancing segmentation accuracy. Experimental results reveal that the proposed model outperforms existing mainstream segmentation models, effectively addressing under-segmentation, over-segmentation, and edge blurriness issues in polyp segmentation, thereby achieving better identification of challenging areas. © 2024 IEEE.

关键词： Semantics

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Automated Spam Review Detection Using Hybrid Deep Learning on Arabic Opinions

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Computer Systems science & Engineering 2023年第9期46卷 2947-2961页

作者： IbrahimM.Alwayle Badriyya B.Al-onazi Mohamed K.Nour Khaled M.Alalayah Khadija M.Alaidarous Ibrahim Abdulrab Ahmed Amal S.Mehanna Abdelwahed Motwakel Department of Computer Science College of Science and ArtsSharurahNajran UniversitySaudi Arabia Department of Language Preparation Arabic Language Teaching InstitutePrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Computer Department Applied CollegeNajran UniversityNajran66462Saudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Online reviews regarding purchasing services or products offered are the main source of users’*** gain fame or profit,generally,spam reviews are written to demote or promote certain targeted products or *** practice is called review *** the last few years,various techniques have been recommended to solve the problem of spam *** spam detection study focuses on English reviews,with a lesser interest in other *** review detection in Arabic online sources is an innovative topic despite the vast amount of data ***,this study develops an Automated Spam Review Detection using optimal Stacked Gated Recurrent Unit(SRD-OSGRU)on Arabic Opinion *** presented SRD-OSGRU model mainly intends to classify Arabic reviews into two classes:spam and ***,the presented SRD-OSGRU model follows different levels of data preprocessing to convert the actual review data into a compatible ***,unigram and bigram feature extractors are *** SGRU model is employed in this study to identify and classify Arabic spam *** the trial-and-error adjustment of hyperparameters is a tedious process,a white shark optimizer(WSO)is utilized,boosting the detection efficiency of the SGRU *** experimental validation of the SRD-OSGRU model is assessed under two datasets,namely DOSC *** extensive comparison study pointed out the enhanced performance of the SRD-OSGRU model over other recent approaches.

关键词： Arabic text spam reviews machine learning deep learning white shark optimizer

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Optimization of enhanced weathering networks with alternative transportation modes

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Carbon Resources Conversion 2022年第2期5卷 167-176页

作者： Raymond R.Tan Beatriz A.Belmonte Michael Francis D.Benjamin Viknesh Andiappan Kathleen B.Aviso Department of Chemical Engineering De La Salle University2401 Taft Avenue0922 ManilaPhilippines Research Center for the Natural and Applied Sciences/Department of Chemical Engineering University of Santo TomasEspana Blvd.1015 ManilaPhilippines School of Engineering and Physical Sciences Heriot-Watt University Malaysia62200PutrajayaWilayah Persekutuan PutrajayaMalaysia Faculty of Engineering Computing and ScienceSwinburne University of TechnologyJalan Simpang Tiga93350KuchingSarawakMalaysia

Enhanced weathering of alkaline rocks and minerals is a negative emissions technology (NET) that is potentiallyscalable to deliver gigaton-level carbon dioxide removal (CDR) for climate change mitigation. This techniquerelies on the acceleration of naturally occurring weathering reactions with water and carbon dioxide by reducingthese substances into a fine powder with high specific surface area. The ex situ enhanced weathering processchain consists of the acquisition of suitable natural or synthetic materials, grinding to a fine particle size,transportation, and application on suitable sites. Future enhanced weathering systems can be envisioned assupply chain-like networks which have to be optimized to deliver maximum CDR given physical *** is a notable research gap in the literature on decision support for such systems. To address this gap, amixed integer linear programming (MILP) model is developed in this work to optimize enhanced weatheringnetworks for CDR. The model also incorporates the availability of multiple transportation options and constraintson network topology. Two test cases are used to demonstrate the model capability to determine optimal andnear-optimal networks. The top ten solutions in these two scenarios yield total CDR levels of 3.4316–3.4363 Mtand 15.27017–15.27960 Mt.

关键词： Process integration Optimization Mathematical programming Carbon drawdown Carbon management network Decarbonization

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Dengue Dashboard for Forecasting the Future Trend of Dengue Cases in Pahang

Dengue Dashboard for Forecasting the Future Trend of Dengue ...

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Software Engineering & Computer Systems and International Conference on Computational science and Information Management (ICSECS-ICOCSIM), International Conference on

作者： Muhammad Khairul Syafiq Mustafa Junaida Sulaiman Faculty of Computing College of Computing and Applied Sciences Universiti Malaysia Pahang Pekan Pahang Malaysia Faculty of Computing College of Computing and Applied Sciences Centre For Data Science & Artificial Intelligence Soft Computing & Intelligent Systems (SPINT) Universiti Malaysia Pahang Pekan Pahang Malaysia

ISBN: (数字)9781665414074

ISBN: (纸本)9781665414081

The study is concerned about the rising number of dengue cases in Pahang. As a preventive solution, an analytical dashboard is proposed to forecast the trend of dengue cases in Pahang for the next two years using Auto Regressive Integrated Moving Average (ARIMA) model. Due to its efficiency to solve linear problems, ARIMA model is a popular model used by the researchers and economists to forecast a time-series data. The dashboard contains various information of dengue cases for instances the total number of dengue cases in Pahang, total number of dengue cases by district, a map that shows the green zone and the red zone which indicates the risk of the dengue infection in that area and a graph that displays the forecasted trend of dengue cases in Pahang from 2021 to 2023 using the best- fitted ARIMA model. The dashboard and the forecasted data trend of dengue is generated to assist JKN Pahang to plan a preventive measure regarding the rising number of dengue cases in thefuture.

关键词： Temperature Rain Scientific computing Meteorological factors Weather forecasting Predictive models Market research

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Dynamic Analysis and Circuit Design of a New 3D Highly Chaotic System and its Application to Pseudo Random Number Generator (PRNG) and Image Encryption

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SN Computer science 2024年第4期5卷 420页

作者： Sambas, Aceng Benkouider, Khaled Kaçar, Sezgin Ceylan, Nurdoğan Vaidyanathan, Sundarapandian Sulaiman, Ibrahim Mohammed Mohamed, Mohamad Afendee Ayob, Ahmad Faisal Mohamad Muni, Sishu Shankar Faculty of Informatics and Computing Universiti Sultan Zainal Abidin Campus Besut Terengganu Kuala 22200 Malaysia Department of Mechanical Engineering Universitas Muhamadiyah Tasikmalaya Tamansari Gobras Tasikmalaya 46196 Indonesia Department of Electronics Faculty of Technology Badji-Mokhtar University B.P. 12 Sidi Ammar Annaba 23000 Algeria Department of Electrical and Electronics Engineering Sakarya University of Applied Sciences Serdivan Sakarya 54050 Turkey Department of Mechanical Engineering Adıyaman University Merkez Adıyaman 02040 Turkey Centre for Control Systems Vel Tech University Tamil Nadu Chennai 600062 India School of Quantitative Science University Utara Malaysia Sintok Kedah Bukit Kayu Hitam 06010 Malaysia Faculty of Education and Arts Sohar University Sohar 311 Oman Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia Terengganu Gong Badak Terengganu Kuala Terengganu 21300 Malaysia Department of Physical Sciences Department of Physical Sciences Indian Institute of Science Education and Research Mohanpur Kolkata 741246 India

In this paper, a new 3D dynamical system with four quadratic nonlinear terms is presented. It is shown that the proposed chaotic system has two saddle-foci, unstable, equilibrium points. Thus, the proposed chaotic system exhibits self-excited chaotic attractors. Dynamical analysis methods such as Lyapunov exponents spectrum, bifurcation diagrams, and phase portraits are used to explore the complex dynamical behaviors of the proposed chaotic system and analyze its basic qualitative properties. It is shown that the maximal Lyapunov exponent (MLE) of the new chaotic system is 7.196, which is a high value. The proposed highly chaotic system exhibits high complexity and it will be very useful for applications in cryptography, encryption and secure communications. The physical feasibility of the proposed system is verified by implementing its electronic circuit schematic using Multisim software. Additionally, a PRNG has been developed using the derived state variables and analyze with NIST-800–22 test. Lastly, we developed a PRNG-based image encryption and employ it for an encryption. The experimental outcomes obtained prove that the 3D chaotic system-based encryption application presented in this section has a very good performance. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Chaos Image encryption Pseudo random number generator

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