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Data Mining with Comprehensive Oppositional Based Learning for Rainfall Prediction

computers, Materials & Continua 2023年第2期74卷 2725-2738页

作者： Mohammad Alamgeer Amal Al-Rasheed Ahmad Alhindi Manar Ahmed Hamza Abdelwahed Motwakel Mohamed I.Eldesouki Department of Information Systems College of Science&Art at MahayilKing Khalid UniversityAbha62529Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Science College of Computing and Information SystemMecca24382Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharj16278Saudi Arabia Department of Information System College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAlKharj16278Saudi Arabia

Data mining process involves a number of steps fromdata collection to visualization to identify useful data from massive data *** same time,the recent advances of machine learning(ML)and deep learning(DL)models can be utilized for effectual rainfall *** this motivation,this article develops a novel comprehensive oppositionalmoth flame optimization with deep learning for rainfall prediction(COMFO-DLRP)*** proposed CMFO-DLRP model mainly intends to predict the rainfall and thereby determine the environmental ***,data pre-processing and correlation matrix(CM)based feature selection processes are carried *** addition,deep belief network(DBN)model is applied for the effective prediction of rainfall ***,COMFO algorithm was derived by integrating the concepts of comprehensive oppositional based learning(COBL)with traditional MFO ***,the COMFO algorithm is employed for the optimal hyperparameter selection of the DBN *** demonstrating the improved outcomes of the COMFO-DLRP approach,a sequence of simulations were carried out and the outcomes are assessed under distinct *** simulation outcome highlighted the enhanced outcomes of the COMFO-DLRP method on the other techniques.

关键词： Data mining rainfall prediction deep learning correlation matrix hyperparameter tuning metaheuristics

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Combination of CRITIC and PROMETHEE II Methods as An Early Detection of Potential Dropout Students 6

Combination of CRITIC and PROMETHEE II Methods as An Early D...

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6th International Conference on Information and Communications Technology, ICOIACT 2023

作者： Yusa, Mochammad Coastera, Funny Farady Rahmadani, Deni Sari, Julia Purnama Kusrini, Kusrini Hartanto, Anggit Dwi University of Bengkulu Faculty of Engineering Department of Informatics Bengkulu Indonesia University of Bengkulu Faculty of Engineering Department of Information System Bengkulu Indonesia Universitas Amikom Yogyakarta Magister of Informatics Engineering Faculty of Computer Science Yogyakarta Indonesia Universitas Amikom Yogyakarta Faculty of Computer Science Department of Information System Yogyakarta Indonesia

ISBN: (纸本)9798350315639

Dropout (DO) has negative implications for individuals and educational institutions. The field of education data mining (EDM) offers valuable contributions to prevent dropout cases and improve student retention. This study focuses on implementing EDM to identify students at risk of dropping out by integrating academic activity criteria with real-time data sources. Previous studies have encountered limitations in classification and clustering techniques, with classification relying on labeled training data (supervised learning) and clustering constrained by attribute or criteria weight (unsupervised learning). This study aims to address these limitations by combining the PROMETHEE II ranking approach and the CRITIC weighting method, producing a list of students with dropout potential. The results can aid stakeholders in implementing preventive measures. The study involves several steps: 1) Collecting data through integration with academic portals;2) Filtering and selecting criteria for student academic activities;3) Transforming and normalizing the data;4) Applying automatic weighting using CRITIC;5) Calculating criteria rankings using the PROMETHEE II technique;and 6) Validating the ranking outcomes. © 2023 IEEE.

关键词： Decision support systems

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Cluster-preserving sampling algorithm for large-scale graphs

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science China(Information sciences) 2023年第1期66卷 60-76页

作者： Jianpeng ZHANG Hongchang CHEN Dingjiu YU Yulong PEI Yingjun DENG National Digital Switching System E&T Research Center Information Engineering University Network Systems Department of the Strategic Support Force School of Computer Science and Technology Eindhoven University of Technology Center for Applied Mathematics Tianjin University

Graph sampling is a very effective method to deal with scalability issues when analyzing largescale graphs. Lots of sampling algorithms have been proposed, and sampling qualities have been quantified using explicit properties(e.g., degree distribution) of the sample. However, the existing sampling techniques are inadequate for the current sampling task: sampling the clustering structure, which is a crucial property of the current networks. In this paper, using different expansion strategies, two novel top-leader sampling methods(i.e., TLS-e and TLS-i) are proposed to obtain representative samples, and they are capable of effectively preserving the clustering structure. The rationale behind them is to select top-leader nodes of most clusters into the sample and then heuristically incorporate peripheral nodes into the sample using specific expansion strategies. Extensive experiments are conducted to investigate how well sampling techniques preserve the clustering structure of graphs. Our empirical results show that the proposed sampling algorithms can preserve the population's clustering structure well and provide feasible solutions to sample the clustering structure from large-scale graphs.

关键词： graph sampling clustering structure top-leader nodes expansion strategies large-scale graphs

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LBA: Matching Theory Based Latency-Sensitive Binary Offloading in IoT-Fog Networks 16

LBA: Matching Theory Based Latency-Sensitive Binary Offloadi...

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16th International Conference on COMmunication systems and NETworkS, COMSNETS 2024

作者： Soni, Priyanka Deshlahre, Omkar Chand Satpathy, Anurag Addya, Sourav Kanti National Institute of Technology Karnataka Cloud and Smart System Services Lab Department of Computer Science and Engineering India Missouri University of Science and Technology RollaMO United States

ISBN: (纸本)9798350383119

The Internet of Things (IoT) is growing more popular with applications like healthcare services, traffic monitoring, video streaming, smart homes, etc. These applications produce an enormous amount of data, so a realistic option in this instance is to offload computational tasks to their proximity fog nodes (FNs) instead of the remote cloud. However, a negligent offloading strategy may cause anomalous computational traffic load at the FNs, causing congestion that may adversely affect the latency. However, the latency of task flows from IoT devices comprises communications latency at BS and computational latency at FNs. Therefore, designing offloading algorithms to distribute the computational load at FN evenly and efficiently utilize the FN resources is crucial. To solve this problem, we proposed LBA in a fog network with a binary offloading strategy using the matching theory-based approach. We utilize the Analytic Hierarchy Process (AHP) to generate the preference list. Furthermore, the binary offloading technique follows the deferred acceptance algorithm (DAA) to produce a stable assignment, and the complete offloading problem is modeled as a one-to-many matching game. Comprehensive simulations ensure that LBA can accomplish a better-balanced assignment for homogeneous and heterogeneous input concerning all the baseline algorithms. © 2024 IEEE.

关键词： Analytic Hierarchy Process (AHP) Binary offloading Fog computing IoT Load balancing Matching Theory

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Prediction of Geopolymer Concrete Compressive Strength Using Convolutional Neural Networks

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computer Modeling in engineering & sciences 2024年第5期139卷 1455-1486页

作者： Kolli Ramujee Pooja Sadula Golla Madhu Sandeep Kautish Abdulaziz S.Almazyad Guojiang Xiong Ali Wagdy Mohamed Department of Civil Engineering VNR Vignana Jyothi Institute of Engineering and TechnologyHyderabadTelangana500090India Department of Information Technology VNR Vignana Jyothi Institute of Engineering and TechnologyHyderabadTelangana500090India Department of Computer Science LBEF Campus(Asia Pacific University of Technology&InnovationMalaysia)Kathmandu44600Nepal Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Guizhou Key Laboratory of Intelligent Technology in Power System College of Electrical EngineeringGuizhou UniversityGuiyang550025China Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmmanJordan

Geopolymer concrete emerges as a promising avenue for sustainable development and offers an effective solution to environmental *** attributes as a non-toxic,low-carbon,and economical substitute for conventional cement concrete,coupled with its elevated compressive strength and reduced shrinkage properties,position it as a pivotal material for diverse applications spanning from architectural structures to transportation *** this context,this study sets out the task of using machine learning(ML)algorithms to increase the accuracy and interpretability of predicting the compressive strength of geopolymer concrete in the civil engineering *** achieve this goal,a new approach using convolutional neural networks(CNNs)has been *** study focuses on creating a comprehensive dataset consisting of compositional and strength parameters of 162 geopolymer concrete mixes,all containing Class F fly *** selection of optimal input parameters is guided by two distinct *** first criterion leverages insights garnered from previous research on the influence of individual features on compressive *** second criterion scrutinizes the impact of these features within the model’s predictive *** to enhancing the CNN model’s performance is the meticulous determination of the optimal *** a systematic trial-and-error process,the study ascertains the ideal number of epochs for data division and the optimal value of k for k-fold cross-validation—a technique vital to the model’s *** model’s predictive prowess is rigorously assessed via a suite of performance metrics and comprehensive score ***,the model’s adaptability is gauged by integrating a secondary dataset into its predictive framework,facilitating a comparative evaluation against conventional prediction *** unravel the intricacies of the CNN model’s learning trajectory,a loss plot is deployed to elucidate its learning rat

关键词： Class F fly ash compressive strength geopolymer concrete prediction deep learning convolutional neural network

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Improving Arabic Text Classification Using P-Stemmer

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Recent Advances in computer science and Communications 2022年第3期15卷 404-411页

作者： Kanan, Tarek Hawashin, Bilal Alzubi, Shadi Almaita, Eyad Alkhatib, Ahmad Maria, Khulood Abu Elbes, Mohammed Department of Computer Science AlZaytoonah University Amman Jordan Department of Computer Information System AlZaytoonah University Amman Jordan Department of Power and Mechatronics Engineering Tafila Technical University Tafilah Jordan

Introduction: Stemming is an important preprocessing step in text classification, and could contribute to increasing text classification accuracy. Although many works have proposed stemmers for the English language, few stemmers have been proposed for Arabic text. Arabic language has gained increasing attention in the previous decades and the need to further improve Arabic text classification. Methods: This work combined the use of the recently proposed P-stemmer with various classifiers to find the optimal classifier for the P-stemmer in terms of Arabic text classification. As part of this work, a synthesized dataset was collected. Results: The previous experiments show that the use of P-stemmer has a positive effect on classification. The degree of improvement is classifier-dependent, which is reasonable as classifiers vary in their methodologies. Moreover, the experiments show that the best classifier with the P-Stemmer is NB. This is an interesting result as this classifier is well-known for its fast learning and classification time. Discussion: First, the continuous improvement of the P-stemmer by more optimization steps is necessary to further improve the Arabic text categorization. This can be made by combining more classifiers with the stemmer, by optimizing the other natural language processing steps, and by improving the set of stemming rules. Second, the lack of sufficient Arabic datasets, especially large ones, is still an issue. Conclusion: In this work, an improved P-stemmer was proposed by combining its use with various classifiers. In order to evaluate its performance, and due to the lack of Arabic datasets, a novel Arabic dataset was synthesized from various online news pages. Next, the P-stemmer was combined with Naïve Bayes, Random Forest, Support Vector Machines, K-Nearest Neighbor, and K-Star. © 2022 Bentham science Publishers.

关键词： Decision trees

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Towards Large-Scale Benchmark Dataset for Remote Sensing Object Detection on Battlefield

Towards Large-Scale Benchmark Dataset for Remote Sensing Obj...

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2024 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2024

作者： Kim, Yechan Park, JongHyun Kim, Sihyun Kim, Sungheon Kim, SooYeon Ko, YeongMin Oh, Junggyun Jeon, Moongu School of Electrical Engineering and Computer Science GIST Korea Republic of Kyungpook National University Department of Robot and Smart System Engineering Korea Republic of HDC Labs Korea Republic of

ISBN: (纸本)9798331530839

Precise object detection allows military personnel to clearly understand their surroundings, leading to planning effective military strategies. Particularly, satellites and drones allow real-time surveillance over large areas, which is crucial for military operations. Collaboration among military, academic, and industrial institutions is required to promote innovation in military target detection. However, potential security concerns usually restrict access to real data from the military. To mitigate this issue, this paper proposes a novel approach to generate synthetic data for remote sensing object detection on the battlefield with ARMA3, one of the renowned military tactic games. With our method, the data for model training can be easily generated without any manpower. To demonstrate the efficacy of our approach, we provide a detailed analysis of the examples from our method. As ARMA3 is well-known for its realistic military combat simulation, we believe our method can effectively contribute to military object detection in remote sensing. © 2024 IEEE.

关键词： Military satellites

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An Improved Semi-Supervised Learning-Based U-Net for Underwater Image Enhancement 5

An Improved Semi-Supervised Learning-Based U-Net for Underwa...

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5th International Seminar on Artificial Intelligence, Networking and Information Technology, AINIT 2024

作者： Han, Jiqun Liu, Xinyu Tan, Dongxu Zhu, Jiehui School of Computer Science and Engineering Northeastern University Shenyang China Guangdong Institute of Intelligent Unmanned System Technology Research and Development Department Guangzhou China

ISBN: (纸本)9798350385557

The attenuation and scattering of light in underwater environments often lead to degradation issues such as image blurring, color cast, and low contrast. Due to the lack of high-quality paired datasets, the performance of deep learning-based methods is limited. To address these challenges, we propose an improved U-Net architecture based on semi-supervised learning for underwater image enhancement. First, a multi-scale input-level fusion module is proposed to allow our network to fully exploit global spatial information from input images, natural light fields, and depth maps. Second, a multi-branch hybrid convolutional attention residual block is designed to improve the network's feature extraction capability and focus on important spatial and channel information in underwater images. Finally, a semi-supervised learning approach is used to fully exploit unpaired datasets, improving the generalization ability of the network model. Through qualitative and quantitative analysis, the proposed network achieves satisfactory image enhancement results in various underwater scenarios. © 2024 IEEE.

关键词： Image enhancement

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MPI Allgather Utilizing CXL Shared Memory Pool in Multi-Node Computing systems

MPI Allgather Utilizing CXL Shared Memory Pool in Multi-Node...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Ahn, Hooyoung Kim, Seonyoung Park, Yoomi Han, Woojong Ahn, Shinyoung Tran, Tu Ramesh, Bharath Subramoni, Hari Panda, Dhabaleswar K. Supercomputing System Research Section Electronics and Telecommunications Research Institute Korea Republic of The Ohio State University Department of Computer Science and Engineering United States

ISBN: (纸本)9798350362480

In Artificial Intelligence (AI) and high-performance computing (HPC), growing data and model sizes require distributed processing across multiple nodes due to single-node limitations, increasing inter-node communication. To address these challenges, we propose a novel MPI allgather method leveraging CXL technology, which supports composable architectures and dynamic resource allocation in data centers and HPC systems. Notably, CXL 3.1 facilitates cache coherence among nodes. The proposed allgather method uses the CXL shared memory pool as a communication buffer, outperforming existing algorithms for two reasons: First, CXL provides lower latency than Ethernet and IB, and second, by using the CXL shared memory pool as a shared communication buffer across multiple nodes, it significantly reduces the number of communications. To the best of our knowledge, this work is the first to explore combining MPI collective communication with CXL technology to optimize MPI allgather. Our proposed allgather method significantly reduces communication latency compared to traditional allgather methods by up to 42.14x, with a minimum improvement of 2.91x, as measured using the OSU Micro-Benchmark (OMB), a standard MPI benchmarking suite. © 2024 IEEE.

关键词： Resource allocation

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Attention-CoviNet: A Deep-Learning Approach to Classify Covid-19 Using Chest X-Rays 1

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1st International Conference on Advanced Computing, Machine Learning, Robotics and Internet Technologies, AMRIT 2023

作者： Karkera, Thejas Singh, Chandra Bekal, Anush Shetty, Shailesh Prajwal, P. Department of Electronics Communication Engineering Sahyadri College of Engineering and Management Mangalore India Department of Computer Science and Business System Srinivas Institute of Technology Karnataka Mangaluru India Sahyadri College of Engineering and Management Mangalore India

ISBN: (数字)9783031472213

ISBN: (纸本)9783031472206

Covid-19 pandemic is spreading across the world at a breakneck pace. As of September 2022, JHU CSSE COVID-19 Data and Our World in Data have recorded up to 5.2 M deaths due to Covid-19. One of the essential steps of fighting this pandemic is to detect the disease early. Studies show some abnormalities in the chest X-rays of the Covid-19 patients, and these features help classify the covid-19 positive patients from the negative patients. In recent years in the field of medical x-rays, Deep Learning models have proven to have the ability to learn and identify features that a trained person can only identify. Due to the exponential increase in the covid-19 cases, the waiting time for covid results has increased, resulting in the late diagnosis of patients for Covid-19. Classifying a patient as Covid-19 positive for a genuinely covid positive patient is as essential as classifying a patient Covid-19 negative who is genuinely covid-negative. Here we present a detailed quantitative analysis on the performance of state-of-the-art models like ResNet-50, Dense-Net, Mobile-Net-V2, and MNAS-Net on classifying patients for Covid positive, Pneumonia positive, and Normal by evaluating Accuracy, Precision, Recall, and Jaccard Index. We propose a new deep neural network classification model for low-end devices that uses two Attention mechanisms. Figure 1. shows the attention map with confidence percentage for each classes. Our model uses fewer parameters and FLOPS than other state-of-the-art models and recorded a 2% increase in Accuracy and other evaluation parameters. The dataset used for implementation is a public dataset COVID-DATASET and NIH Chest X-ray from kaggle. It has over 1000 images of Covid Chest X-ray, Pneumothrax, Mass, Pneumonia, Cardiomegaly, Nodule, Effusion, Atelectasis and inflitration. All the model implementations are implemented on PyTorch. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： COVID-19

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