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2023 International Conference on Next-Generation Computing, IoT and Machine Learning, NCIM 2023

作者： Mahmud, Md. Tareq Mahmud, Hasan Belali, Md Habibullah Rahman, Md. Obaidur Wang, Ke University of North Carolina at Charlotte Department of Electrical and Computer Engineering United States Institute of Information & Communication Technology Dhaka University of Engineering & Technology Gazipur Bangladesh

ISBN: (纸本)9798350316001

Rainfall forecasting has drawn the greatest interest in recent years due to its complexity and the importance of detecting the possibility of flooding, monitoring pollutant contamination, water supply and agricultural usage. Existing instrument-based and statistic models used in the meteorological department are old-fashioned and expensive. Due to the high volume of weather-related data captured by Rader and billions of sensors, inaccurate statistic models, and the hysteretic and reactive nature of instrument-based models, machine-learning (ML)-based forecasting has attracted huge attention in rainfall prediction. These ML models include different supervised ML models such as Linear Regression, Random Forest and Support Vector Machine (SVM). However, few existing works have examined the advantages and disadvantages of diverse ML models to study their efficiency in the domain of rainfall prediction. Therefore, this work proposes a comprehensive analysis of different ML models for accurate and fast rainfall prediction. In this work, we develop several ML models, namely linear regression, linear regression with parameter penalty, cross-validation with linear regression, linear regression with principal component analysis (PCA), support vector regression (SVR) without PCA, support vector regression (SVR) with PCA, and finally artificial neural networks (ANN). We train those models with climate data from Colorado, a significant American metropolis, for the years 2015 to 2018 and compare their performance (prediction accuracy) in terms of validation loss. The results demonstrate that the proposed artificial neural network achieves the best accuracy. © 2023 IEEE.

关键词： Linear regression

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Design of Chest Visual Based Image Reclamation Method Using Dual Tree Complex Wavelet Transform and Edge Preservation Smoothing Algorithm

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SN computer Science 2024年第4期5卷 352页

作者： Kumar, B. P. Pradeep Darshan, S. L. Shiva Naresh, E. Srinidhi, N.N. Shreyas, J. Department of ECE HKBKCE Karnataka Bangalore 560045 India Department of Computer Science and Engineering National Institute of Technology Warangal Warangal 506004 India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India

The proposed article put forward a new scheme for image reclamation using second phase discrete symlet transform for medical images. The current medical image reclamation approaches have limitations in providing accurate reclamation fallouts with high visual insight and low computational complexity. To address these issues, this model presents a methodology for creating a medical image database using Image Reclamation using DT-CWT and EPS filter suited for the Resolution Enhancement of query chest image sample well utilized to get better retrieval rate where DWT algorithm is utilized for feature extraction of query input images. Flat and perpendicular prognoses of summation of pixels are analyzed to extract BC quantities, which are then used to compute the matching score of similarity for the images present in the database. The system selects the samples that are most pertinent to the given query sample image based on the matching score. The system’s untrained database is used to obtain the photographs with the highest BC value. The projected method aims to improve the enhancement of sampled image by DT-CWT EPS algorithm to leads to increase the accuracy and efficiency of medical image reclamation for various research applications. © The Author(s) 2024.

关键词： B quantities DWT Healthcare systems Medical image Visual BIR

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Investigating the Benefits of Text Mining for information Analysis

Investigating the Benefits of Text Mining for Information An...

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2023 IEEE International Conference on Paradigm Shift in information Technologies with Innovative Applications in Global Scenario, ICPSITIAGS 2023

作者： Singh, Vaishali Venkadeshwaran, K. Jha, Gaurav Arunadevi, R. Sharma, Yogesh K. Maheswaran, S. Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Mechanical Engineering Karnataka 562112 India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Kongu Engineering College Department of Electronics and Communication Engineering Tamil Nadu Erode India

ISBN: (纸本)9798350329773

Textual content mining is the manner of extracting significant information and insights from large volumes of unstructured textual information. Many businesses are beginning to recognize the capacity of text mining in gaining effective insights and presenting greater accurate statistics. This paper will talk the ability blessings of using text mining strategies in data analysis. It's going to present quite number possible applications, such as herbal language processing, sentiment evaluation, subject matter modeling, and predictive analytics. It also evaluation the numerous technical techniques and algorithms used for textual content mining, together with lateens semantic indexing, and will look at how text mining can be used to guide a range of various commercial enterprise targets. Sooner or later, the paper will address how text mining can help pick out useful styles and traits in unstructured information, and the way it may be utilized in tandem with conventional data processing methods to achieve higher effects. This paper will finish by way of analyzing the capacity challenges that may be encountered when using text mining and how ability dangers can be mitigated. © 2023 IEEE.

关键词： Semantics

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Automated Detection of Harmful Insects in Agriculture: A Smart Framework Leveraging IoT, Machine Learning, and Blockchain

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第9期5卷 4787-4798页

作者： Rahman, Wahidur Hossain, Muhammad Minoar Hasan, Md. Mahedi Iqbal, Md. Sadiq Rahman, Mohammad Motiur Fida Hasan, Khondokar Moni, Mohammad Ali Mawlana Bhashani Science and Technology University Department of Computer Science and Engineering Tangail1902 Bangladesh Uttara University Department of Computer Science and Engineering Dhaka1230 Bangladesh Prime University Department of Management and Information System Mirpur Dhaka1216 Bangladesh Bangladesh University Department of Computer Science and Engineering Dhaka1207 Bangladesh School of Professional Studies CanberraNSW2052 Australia Artificial Intelligence and Cyber FuturesInstitute Charles Sturt University AI & Digital Health Technology BathurstNSW2795 Australia

Paddy cultivation is a significant global economic sector, with rice production playing a crucial role in influencing worldwide economies. However, insects in paddy farms predominantly impact the growth rate and ecological equilibrium of the agricultural field. Hence, the precise and timely identification of insects in agricultural settings presents a potential strategy for addressing this issue. This study aims to implement an automated system for paddy farming by employing a real-time framework that incorporates the Internet of Things (IoT), blockchain technology, and Deep Learning (DL) algorithms. The primary emphasis of the DL-based system is on the timely identification of pests. In contrast, integrating the IoT and blockchain technologies facilitates stablishing a fully automated system with security within the agricultural domain. The DL-based system includes a secondary dataset of paddy insects, and then preprocessing, feature extraction, and identification have been performed. Besides, an IoT-based system is embodied with a camera module and microprocessor, accompanied by some apparatus required to automate the whole system. In addition, the research also includes the blockchain to secure each individual data transmission among the several IoT components and the cloud server. While examining the proposed solution, various experimental data have been systematically documented and analyzed. The proposed framework attained a peak accuracy of 98.91% using the VGG19 model and ensemble classifiers to detect the pest with a specificity of 99.14% and a precision of 98.21%. The study additionally quantifies the mean duration of the cloud response when integrated with IoT, yielding an average time of 1.71 s after pest identification. Nevertheless, the system has exhibited a high level of efficacy in the context of real-time monitoring and automation of paddy farms. © 2020 IEEE.

关键词： Blockchain

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Autism Spectrum Disorder Detection Via Eye Gaze Analysis 2

Autism Spectrum Disorder Detection Via Eye Gaze Analysis

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Das, Sujay Chotia, Akshat Maheswari, K. M. Uma Sadiq, A.K. SRM Institute Of Science & Technology Department Of Computer Science & Engineering Chennai India University of Technology and Applied Sciences College of Computing and Information Science Suhar campus Oman

ISBN: (纸本)9798350365269

Autism is a complex neuro - developmental condition characterized by challenges in social interaction and communication. Early diagnosis and intervention are crucial for effective support. This paper presents a novel approach for autism detection leveraging Convolutional Neural Networks (CNNs) to analyze eye gaze patterns. The proposed method aims to provide an objective and efficient tool for the early screening of Autism. We begin by collecting eye gaze data from a diverse sample of individuals, including both Autism -diagnosed and non Autism individuals. The dataset is preprocessed to extract relevant features from the eye gaze sequences, capturing subtle but significant differences in gaze patterns between the two groups. A CNN architecture is designed and trained on this preprocessed dataset. The network is optimized to automatically learn discriminative features that distinguish between Autism and non Autism gaze patterns. Transfer learning techniques mobilenetv2 are employed to further enhance model performance. The trained CNN demonstrates promising results in accurately classifying individuals into Autism and neurotypical categories. Comprehensive evaluation metrics, including accuracy and loss, attest to the model's effectiveness. © 2024 IEEE.

关键词： Convolutional neural networks

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An Effective Cyber Security Threat Detection in Smart Cities Using Dueling Deep Q Networks 4

An Effective Cyber Security Threat Detection in Smart Cities...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Stanly Jayaprakash, J. Kodati, Sarangam Kanchana, A. Al-Farouni, Mohammed Ramachandra, A.C. Mahendra Institute of Technology Department of Computer Science and Engineering Namakkal India Cvr College of Engineering Department of Information Technology Hyderabad India Mahendra Engineering College for Women Department of Computer Science and Engineering Namakkal India College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350352931

In recent days, smart city technologies are developing rapidly with the incorporation of IoT devices and AI-driven systems has modernized and thus facilitated for enhancing productivity, sustainability, and connectivity. The motive is to mitigate the rising cyber threats which are affecting the interrelated organizations of smart cities. Despite advancements in threat detection technologies, challenges persist, including real-time anomaly detection in vast data streams, managing resource constraints on IoT devices, and ensuring adaptability against evolving attack vectors. Therefore, this research suggests a novel approach by using Dueling Deep Q Networks (DDQN) method. Initially, the data is collected from the dataset and then Inter Quartile Range (IQR) is imposed at the preprocessing stage for detection of outliers. Then, extraction of optimal features is done by using Term Frequency-Inverse Document Frequency (TF-IDF) and Recursive Feature Elimination (RFE) which transforms the data into statistical vectors and eliminates the least significance feature respectively. Finally, the obtained optimal features are detected using DDQN method as it has the ability to detect effectively with respect to the state of action. The proposed DDQN has resulted with accuracy of 99.32% when compared with Improved Bacterial Foraging Optimization using optimum deep learning for Anomaly detection (IBFO-ODLAD). © 2024 IEEE.

关键词： Cyber attacks

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Bayes-Q-Learning Algorithm in Edge Computing for Waste Tracking

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Intelligent Automation & Soft Computing 2023年第5期36卷 2425-2440页

作者： D.Palanikkumar R.Ramesh Kumar Mehedi Masud Mrim M.Alnfiai Mohamed Abouhawwash Department of Computer Science and Engineering Dr.NGP Institute of TechnologyCoimbatore641048India Department of Information Technology Sri Krishna College of TechnologyCoimbatore641042India Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif P.O.Box 11099Taif21944Saudi Arabia Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA

The major environmental hazard in this pandemic is the unhygienic dis-posal of medical *** wastage is not properly managed it will become a hazard to the environment and *** medical wastage is a major issue in the city,municipalities in the aspects of the environment,and *** efﬁcient supply chain with edge computing technology is used in managing medical *** supply chain operations include processing of waste collec-tion,transportation,and disposal of *** research works have been applied to improve the management of *** main issues in the existing techniques are ineffective and expensive and centralized edge computing which leads to failure in providing security,trustworthiness,and *** over-come these issues,in this paper we implement an efﬁcient Naive Bayes classiﬁer algorithm and Q-Learning algorithm in decentralized edge computing technology with a binary bat optimization algorithm(NBQ-BBOA).This proposed work is used to track,detect,and manage medical *** minimize the transferring cost of medical wastage from various nodes,the Q-Learning algorithm is *** accuracy obtained for the Naïve Bayes algorithm is 88%,the Q-Learning algo-rithm is 82%and NBQ-BBOA is 98%.The error rate of Root Mean Square Error(RMSE)and Mean Error(MAE)for the proposed work NBQ-BBOA are 0.012 and 0.045.

关键词： Binary bat algorithm naïve bayes supply chain edge medical wastage

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Design and Development of a Mobile-Based Data Acquisition System for Aerial Bridge Inspection 15

Design and Development of a Mobile-Based Data Acquisition Sy...

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15th IEEE International Conference on Humanoid, Nanotechnology, information technology, Communication and Control, Environment, and Management, HNICEM 2023

作者： Himongala, Ryan P. Aleluya, Earl Ryan M. Clar, Steve E. Pao, Jeanette C. Alagon, Francis Jann A. Salaan, Carl John O. Lavilles, Rabby Q. College of Computer Studies Department of Computer Applications Philippines College of Engineering Department of Computer Engineering and Mechatronics Philippines College of Engineering Department of Electrical Engineering Philippines Graduate School of Engineering Philippines College of Computer Studies Mindanao State University - Iligan Institute of Technology Department of Information Technology Iligan City9200 Philippines

ISBN: (纸本)9798350381177

Infrastructures such as bridges are ubiquitous in today's world, but over time, corrosion, continuous vibrations, heavy loads, natural disasters, and aging have actively weakened their structural integrity. Hence, it is crucial to periodically assess the bridge's condition to maintain its structural soundness. Nevertheless, conventional bridge inspection techniques involve inspectors to visually assess a bridge's condition and manually capture images of its defects. UAVs for bridge inspections typically improve the conventional approach. This method provides a visual system by mounting a camera on the UAV equipped with a software application dedicated to data acquisition. However, the application operates primarily on desktop computers or laptops while incompatible with smartphones and tablets, limiting its portability and making it inconvenient for inspectors. In this study, the researchers developed an application for mobile devices to further improve data acquisition. The application is designed to display a real-time video feed from the shelled UAV, capture visual data of bridge defects, and allow users to review the data after the inspection. The researchers tested and evaluated the application's performance regarding its functionality. Results show that the application has successfully improved the portability and efficiency of the inspection process, achieving its intended features and functionalities while adhering to the requirements. © 2023 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Optimal scheduling of graph states via path decompositions

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Physical Review A 2025年第1期111卷 012627-012627页

作者： Samuel J. Elman Jason Gavriel Ryan L. Mann Centre for Quantum Software and Information School of Computer Science Faculty of Engineering and Information Technology University of Technology Sydney NSW 2007 Australia Centre for Quantum Computation and Communication Technology Faculty of Engineering and Information Technology University of Technology Sydney NSW 2007 Australia

We study the optimal scheduling of graph states in measurement-based quantum computation, establishing an equivalence between measurement schedules and path decompositions of graphs. We define the spatial cost of a measurement schedule based on the number of simultaneously active qubits and prove that an optimal measurement schedule corresponds to a path decomposition of minimal width. Our analysis shows that approximating the spatial cost of a graph is NP-hard, while for graphs with bounded spatial cost, we establish an efficient algorithm for computing an optimal measurement schedule.

关键词： Quantum computers

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MSEs Credit Risk Assessment Model Based on Federated Learning and Feature Selection

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computers, Materials & Continua 2023年第6期75卷 5573-5595页

作者： Zhanyang Xu Jianchun Cheng Luofei Cheng Xiaolong Xu Muhammad Bilal School of Computer and Software Nanjing University of Information Science&TechnologyNanjing210044China State Key Laboratory Novel Software Technology Nanjing UniversityNanjing210023China Department of Computer and Electronics Systems Engineering Hankuk University of Foreign StudiesYongin-siGyeonggi-do17035Korea

Federated learning has been used extensively in business inno-vation scenarios in various *** research adopts the federated learning approach for the first time to address the issue of bank-enterprise information asymmetry in the credit assessment ***,this research designs a credit risk assessment model based on federated learning and feature selection for micro and small enterprises(MSEs)using multi-dimensional enterprise data and multi-perspective enterprise *** proposed model includes four main processes:namely encrypted entity alignment,hybrid feature selection,secure multi-party computation,and global model ***,a two-step feature selection algorithm based on wrapper and filter is designed to construct the optimal feature set in multi-source heterogeneous data,which can provide excellent accuracy and *** addition,a local update screening strategy is proposed to select trustworthy model parameters for aggregation each time to ensure the quality of the global *** results of the study show that the model error rate is reduced by 6.22%and the recall rate is improved by 11.03%compared to the algorithms commonly used in credit risk research,significantly improving the ability to identify ***,the business operations of commercial banks are used to confirm the potential of the proposed model for real-world implementation.

关键词： Federated learning feature selection credit risk assessment MSEs

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