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Remote Sensing data-Based Satellite Image Analysis in Water Quality Detection for Public Health data Modelling

Remote Sensing in Earth Systems sciences 2024年第4期7卷 532-541页

作者： Balakrishnan, S. Preetam Raj, P Michael Somasekar, J. Kumar, Kambala Vijaya Amutha, S. Sangeetha, A. Department of Computer Science and Engineering Aarupadai Veedu Institute of Technology (AVIT) Vinayaka Mission’s Research Foundation (Deemed to be University) Chennai India Department of Computer Science and Engineering MLR Institute of Technology Telangana Dundigal Hyderabad 500043 India Department of Computer Science and Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be-University) Karnataka Bengaluru 562112 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram AP India Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr. Sagunthala R & amp D Institute of Science and Technology Tamilnadu Chennai India Department of Artificial Intelligence and Data Science Kongunadu College of Engineering and Technology TamilNadu Tholurpatti India

One of the most important aspects of environmental sustainability is water quality monitoring. The ecology is impacted by poor water quality in addition to aquatic life. With the volume of data being collected from satellite systems, the issue of effectively and automatically extracting water bodies is growing more pressing. One of the most important things to do for managing and safeguarding water resources is to conduct extensive monitoring of water quality parameters, or WQPs. It is still difficult to monitor optically inactive WQPs in inland waters, such as total nitrogen (TN), ammoniacal nitrogen (AN), and total phosphorus (TP). Water quality (WQ) classification and prediction can be greatly enhanced with the use of artificial intelligence (AI). This research proposes novel method in healthcare detection by water quality analysis based on classification utilising ML model. Input is collected as healthcare dataset based on water quality as well as processed for noise removal and normalisation. The processed data features are extracted and classified utilising reinforcement Gaussian stacked layer network with variational fuzzy Markov basis neural network. In order to suggest best course of action for the water bodies, it is imperative that any study look into the spatiotemporal changes of the dominant water quality parameters (WQPs). WQP concentrations have typically been determined by extensive fieldwork. These findings could help identify and regulate non-point source pollution, offer a large-scale spatial picture of the water quality, and pinpoint vulnerable locations and times of water pollution. Experimental analysis is carried out for various water sample dataset in terms of detection accuracy, mean precision, recall, F-1 score and AUC. The proposed technique obtained 97% of detection accuracy, 95% of recall, 91% of F-1 score, 96% of AUC, and 94% of MAP. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Classification Fuzzy Markov basis Healthcare detection Machine learning model Water quality analysis

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PM2.5Forecasting Model based on Linear and Non-linear Hybrid Algorithm 15

PM2.5Forecasting Model based on Linear and Non-linear Hybrid...

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15th International Conference on Knowledge and Smart Technology, KST 2023

作者： Banjongkan, Anupong Kerdprasop, Nittaya Hirunyawanakul, Anusara Kerdprasop, Kittisak Suranaree University of Technology School of Computer Engineering Thailand King Mongkut's University of Technology North Faculty of Science Energy and Environment Data Science and Computation Rayong Campus Bangkok Thailand

ISBN: (纸本)9781665477123

Air pollution is one of the harmful problems that the world has focused on and needs to be solved urgently because air pollution has a direct impact on humans leading to premature death caused by various diseases such as asthma inflammatory respiratory disease, lung cancer, and so on. The air pollutants, especially tiny particulate matter (PM), are currently receiving attention because they are a major problem in many large and populated cities around the world. This paper proposed a time-series model for forecasting PM2.5 in advance through a machine learning process with a linear and non-linear hybrid algorithm. A hybrid algorithm that brings together the capabilities of autoregressive integrated moving average (ARIMA) and the adaptive-neuro fuzzy inference system (ANFIS) is used to find the linear and nonlinear correlation of the PM2.5 time-series data. The proposed model is called ARIMA-FIS which uses the gradient descent (GD) method in the learning process. The dataset used in this research is the daily recorded of PM2.5 values in Rayong province, which is the industrial city in Thailand. The results showed that the ARIMA-FIS model had the best performance in forecasting PM2.5 in advance with the least error at 3.46 of mean absolute error (MAE) and 5.11 of root mean square error (RMSE). The proposed model gave the percentage of RMSE almost 3% better than the other standard time-series models. © 2023 IEEE.

关键词： Time series

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Extremely Weak Feedback Method for Controlling Chaotic Resonance

Extremely Weak Feedback Method for Controlling Chaotic Reson...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Iinuma, Takahiro Ebato, Yudai Nobukawa, Sou Tran, Anh Tu Wagatsuma, Nobuhiko Inagaki, Keiichiro Doho, Hirotaka Yamanishi, Teruya Nishimura, Haruhiko Graduate School of Information and Computer Science Chiba Institute of Technology Chiba Japan Chiba Institute of Technology Department of Computer Science Japan Toho University Faculty of Science Department of Information Science Chiba Japan Chubu University Department of Artificial Intelligence and Robotics Aichi Japan Kouchi University Faculty of Education Teacher Training Division Kochi Japan Osaka Seikei University Faculty of Data Science Osaka Japan Yamato University Faculty of Informatics Osaka Japan

ISBN: (纸本)9798350337020

Chaotic resonance, resembling stochastic resonance, is induced by internal fluctuations (i.e., chaos). This phenomenon has been observed in numerous systems. The most representative form of chaotic resonance is the synchronization with respect to weak applied signals under chaos-chaos intermittency, where a chaotic orbit moves among multiple attractors. Chaotic resonance exhibits higher sensitivity than stochastic resonance, but its engineering applications are limited by concerns such as the requirement to adjust the state of chaos by internal system parameters to induce chaotic resonance rather than the external noise strength. However, achieving this adjustment is challenging, especially in biological systems. Therefore, to handle this limitation, we proposed a novel double-Gaussian-filtered reduced region of orbit (RRO) method (called the DG-RRO method) for obtaining perturbed feedback signals lower than the conventional RRO method. This DG-RRO feedback signal is determined by the inverse sign of the map function and double-Gaussian filters around the local maximum/minimum values of the map. Because of its fine local specification, the DG-RRO feedback signal induces a chaotic resonance by one-third the feedback strength of the conventional signal. This method may pave the way for using chaotic resonance in engineering applications. © 2023 IEEE.

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Box facets and cut facets of lifted multicut polytopes 24

Box facets and cut facets of lifted multicut polytopes

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Proceedings of the 41st International Conference on Machine Learning

作者： Lucas Fabian Naumann Jannik Irmai Shengxian Zhao Bjoern Andres Faculty of Computer Science TU Dresden Faculty of Computer Science TU Dresden and Center for Scalable Data Analytics and AI Dresden/Leipzig

The lifted multicut problem has diverse applications in the field of computer vision. Exact algorithms based on linear programming require an understanding of lifted multicut polytopes. Despite recent progress, two fundamental questions about these polytopes have remained open: Which lower box inequalities define facets, and which cut inequalities define facets? In this article, we answer the first question by establishing conditions that are necessary, sufficient and efficiently decidable. Toward the second question, we show that deciding facet-definingness of cut inequalities is NP-hard. This completes the analysis of canonical facets of lifted multicut polytopes.

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Nodule Detection Using Local Binary Pattern Features to Enhance Diagnostic Decisions

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computers, Materials & Continua 2024年第3期78卷 3377-3390页

作者： Umar Rashid Arfan Jaffar Muhammad Rashid Mohammed S.Alshuhri Sheeraz Akram Faculty of Computer Science and Information Technology The Superior UniversityLahore54600Pakistan Intelligent Data Visual Computing Research(IDVCR) Lahore54600Pakistan Department of Computer Science National University of TechnologyIslamabad45000Pakistan Department of Radiological Sciences and Medical Imaging College of AppliedMedical SciencesPrince Sattam Bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh12571Saudi Arabia

Pulmonary nodules are small, round, or oval-shaped growths on the lungs. They can be benign (noncancerous) or malignant (cancerous). The size of a nodule can range from a few millimeters to a few centimeters in diameter. Nodules may be found during a chest X-ray or other imaging test for an unrelated health problem. In the proposed methodology pulmonary nodules can be classified into three stages. Firstly, a 2D histogram thresholding technique is used to identify volume segmentation. An ant colony optimization algorithm is used to determine the optimal threshold value. Secondly, geometrical features such as lines, arcs, extended arcs, and ellipses are used to detect oval shapes. Thirdly, Histogram Oriented Surface Normal Vector (HOSNV) feature descriptors can be used to identify nodules of different sizes and shapes by using a scaled and rotation-invariant texture description. Smart nodule classification was performed with the XGBoost classifier. The results are tested and validated using the Lung Image Consortium database (LICD). The proposed method has a sensitivity of 98.49% for nodules sized 3–30 mm.

关键词： Pulmonary nodules segmentation histogram thresholding

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Air Pollutant Prediction Using Shallow Architecture Machine Learning Algorithms 8th

Air Pollutant Prediction Using Shallow Architecture Machine ...

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8th World Conference on Smart Trends in Systems Security and Sustainability, WorldS4 2024

作者： Olaifa, Moses Malele, Vusumuzi Department of Computer Systems Engineering Faculty of ICT Tshwane University of Technology Pretoria South Africa Unit of Data Science and Computing Vaal Campus North-West University Vanderbijlpark South Africa

ISBN: (纸本)9789819786947

Over the past few decades, mortality rates associated with air quality pollution have risen in numerous countries around the globe. This pollution stems from different factors such as meteorological conditions, human activities from urban industrialization, vehicular emission, and so on. Air pollution constituents such as NO2, CO, SO, and coarse and fine particulate matter for different locations have been analyzed to learn both correlation and inter-dependency for long and short terms. However, many of these studies overlook datasets collected using low-cost tools and equipment. Most of the time, datasets of this nature are marred with inconsistencies and gaps at irregular intervals. This may render the air pollutant data unsuitable. In order to test the suitability of machine learning algorithms on such dataset, this study conducts a time-series forecast analysis using the prophet algorithm. Two additional models including linear regressor and random forest regressor are trained and tested on the same dataset and a comparative analysis of the three models is performed. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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A Function Fitting System Based on Genetic Algorithm 1

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18th International Conference on Green, Pervasive, and Cloud Computing, GPC 2023

作者： Sun, Qiuhong Wang, Jiaqi Zhou, Xiaokang School of Information Science and Engineering Hebei University of Science and Technology Shijiazhuang050018 China The Faculty of Data Science Shiga University Hikone522-8522 Japan Department of Computer Science North China Electric Power University Baoding071003 China The RIKEN Center for Advanced Intelligence Project RIKEN Tokyo103-0027 Japan

ISBN: (数字)9789819998937

ISBN: (纸本)9789819998920

With the development of science and technology, function fitting has penetrated into various fields of scientific research, scientific and technological innovation. For the function of fitting analysis of a given function image, there is no public software on the market that uses the idea of genetic algorithm to solve the problem of function fitting. In order to make up for the insufficiency of the existing software and seize the opportunity of function fitting in various industries, a function fitting system based on genetic algorithm was proposed. It is a low-threshold software with a wide range of applications. When designing the function of fitting and analyzing a given function image, the genetic algorithm is used. With the application of ray detection in Unity, the closest expression of function fitting is obtained. At the same time, the line graph of fitness reduction during genetic algorithm iteration is given. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Genetic algorithms

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A Resource-Efficient Federated Learning Framework for Intrusion Detection in IoMT Networks

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IEEE Transactions on Consumer Electronics 2025年

作者： Fahim-Ul-Islam, Md. Chakrabarty, Amitabha Alam, Md. Golam Rabiul Maidin, Siti Sarah Brac University Department of Computer Science and Engineering Bangladesh INTI International University Faculty of Data Science and Information Technology Nilai Malaysia

The emergence of medical sensors in Smart Healthcare Systems (SHS) has enabled the development of sophisticated Internet of Medical Things (IoMT) networks which is crucial for tracking vital physiological parameters in consumer electronics. These networks are progressively incorporated into consumer devices allowing users to monitor their health parameters effortlessly. Nonetheless, they encounter considerable security and privacy issues stemming from weaknesses in data transfer. Despite the potential of Intrusion Detection Systems (IDS), there is a critical need for a real-time, highly precise attack detection system optimized for the edge-centric Internet of Medical Things (IoMT) environment in consumer sensor devices. Therefore, we introduce the federated kolmogorov-arnold network (FedIoMT), an advanced federated learning framework that utilizes meta-learning along with an advanced clustering method to achieve robust model aggregation. Our FedIoMT acts as both an adaptive personalized updating mechanism and a shared global classifier. In addition, we propose a novel intrusion detection model that uses the kolmogorov-arnold convolutional network (KANConvNet) as its local classifier, which improves scalability and interpretability in the FL framework updates. Our FedIoMT model demonstrates superior performance compared to other existing FL-based methods, achieving validation accuracies of 99.38%, 99.23%, 99.26%, and 99.2% across four benchmark IoMT datasets under the IID setting. FedIoMT utilizes significantly more floating point operations per second (FLOP) counts up to 2.8 times more than other models and also excels in memory efficiency, consuming up to 93% less peak memory across devices compared to the alternatives. We thoroughly evaluated FedIoMT on embedded consumer devices, demonstrating its resource-efficient characteristics. © 2025 IEEE.

关键词： Network intrusion

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Assessment of Heart-Attack Prediction using Fuzzy Rule Based System 8

Assessment of Heart-Attack Prediction using Fuzzy Rule Based...

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8th IEEE International Conference on Computing, Communication, Control and Automation, ICCUBEA 2024

作者： Saratkar, Saniya Chaudhari, Aarti Thute, Trupti Raut, Rohini Thakre, Gayatri Kumar, Hemant Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Design Maharashtra Wardha442107 India

ISBN: (纸本)9798350391770

This paper evaluates the prediction of heart attacks with a fuzzy rule-based system (FRBS). Since heart disease is the world's leading cause of death, precise prediction models are crucial for prevention and early intervention. With its reputation for managing imprecise and ambiguous data, fuzzy logic presents a viable method for raising prediction accuracy in medical diagnostics. In order to capture the complexity and unpredictability of cardiovascular risk variables, fuzzy rules are used in this work to investigate the creation and assessment of a fuzzy rule-based system (FRBS) for heart attack prediction. The efficacy and dependability of the FRBS in identifying those at high risk of heart attack are shown via testing and comparison with conventional prediction models. The results highlight fuzzy logic's potential to improve cardiovascular predictive analytics. © 2024 IEEE.

关键词： Prediction models

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A federated learning scheme meets dynamic differential privacy

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CAAI Transactions on Intelligence Technology 2023年第3期8卷 1087-1100页

作者： Shengnan Guo Xibin Wang Shigong Long Hai Liu Liu Hai Toong Hai Sam State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyangChina School of Big Data Key Laboratory of Electric Power Big Data of Guizhou ProvinceGuizhou Institute of TechnologyGuiyangChina School of Information Guizhou University of Finance and EconomicsGuiyangChina Faculty of Business and Communication INTI International UniversityNilaiMalaysia

Federated learning is a widely used distributed learning approach in recent years,however,despite model training from collecting data become to gathering parameters,privacy violations may occur when publishing and sharing models.A dynamic approach is pro-posed to add Gaussian noise more effectively and apply differential privacy to federal deep ***,it is abandoning the traditional way of equally distributing the privacy budget e and adjusting the privacy budget to accommodate gradient descent federation learning dynamically,where the parameters depend on computation derived to avoid the impact on the algorithm that hyperparameters are created *** also incorporates adaptive threshold cropping to control the sensitivity,and finally,moments accountant is used to counting the∈consumed on the privacy‐preserving,and learning is stopped only if the∈_(total)by clients setting is reached,this allows the privacy budget to be adequately explored for model *** experimental results on real datasets show that the method training has almost the same effect as the model learning of non‐privacy,which is significantly better than the differential privacy method used by TensorFlow.

关键词： data privacy machine learning security of data

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