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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Mahalakshmi, R. Kumutha, K. Sakthivanitha, M. Lakshmi, R. Bagavathi Sirajudeen, Mohamed Padmanabhan, Sankar Vels Institute of Science Technology & Advanced Studies Department of Computer Applications-PG Tamil Nadu Chennai India Vels Institute of Science Technology Advanced Studies Department of Computer Applications-PG Tamil Nadu Chennai India Vels Institute of Science Technology & Advanced Studies Department of Computer Application Tamil Nadu Chennai India Vels Institute of Science Technology & Advanced Studies Department of Information Technology Tamil Nadu Chennai India Nilgiri College of Arts and Science Pg and Research Department of Computer Science Tami Nadu The Nilgiris India Hindustan Institute of Technology and Science Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331509675

Crop classification is essential for local and national governments to make informed agricultural decisions. Remote sensing technology has made it possible to employ high-resolution hyperspectral images (HSIs) for land cover classification for decades. Machine Learning (ML) and Deep Learning (DL) are becoming more popular for HSI categorization due to advances in processing capacity and technology. HSIs can accurately identify crop types due to their narrow and continuous spectral band reflection. This study compares traditional machine learning (ML) models like Support Vector Machines (SVM) and Random Forest (RF) to deep learning (DL) models like Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) for hyperspectral image classification (HSI). The HSI categorization was evaluated using performance criteria such as accuracy, precision, recall, and the F1-Score. CNN achieved the highest accuracy of 96%, demonstrating that it is the most reliable solution for complex HSI classification tasks because it successfully captures spatial and spectral data. © 2025 IEEE.

关键词： Classification Deep Learning(DL) Hyperspectral Images(HSI) Machine Learning(ML)

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Dimensionality Reduction Visualization Analysis of Financial Data based on Semantic Feature Group 3

Dimensionality Reduction Visualization Analysis of Financial...

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3rd International Conference on Machine Learning and computer application, ICMLCA 2022

作者： Wang, Ke Luo, Menghua Li, Xionglve Cai, Zhiping Long, Yang Computer College National University of Defense Technology Changsha China Information College GuiZhou University of Finance and Economics Guiyang China Big Data Application and Economics College Guizhou University of Finance and Economics Guiyang China

ISBN: (纸本)9781510664814

With the continuous development of data science and financial technology, financial data visualization methods have become an essential key technology in the field of financial data analysis today. From the technical point of view, the mainstream visualization analysis takes the fusion of large screen and multiple views, and the nature of its visualization effect is more focused on the enumeration display, without fully analyzing the data characteristics from the essence. The single view visualization analysis technology is difficult to get clear and effective visualization display through correlation analysis, dimensionality reduction algorithms and principal component analysis. From the application point of view, credit card customer data, as an important part of financial data, has positive practical significance in customer profiling, product recommendation and risk prediction, and the targeted improvement research of its visualization method has an important role. The semantic feature group method combines the domain knowledge and data distribution characteristics of credit card customer churn data, composes and analyzes the semantic feature groups, and obtains explicit visualization and analysis results by combining the understanding of the actual problem and the numerical characteristics of the data itself. The accuracy and efficiency of the data representation based on the semantic feature group method are verified by comparing the data dimensionality reduction visualization methods such as multi-view fusion method, T-distribution random neighborhood embedding and principal component analysis in the experiment. © 2023 SPIE.

关键词： Data visualization

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Apple Disease Recognition Based on Comprehensive application of Residual Neural Network 5

Apple Disease Recognition Based on Comprehensive Application...

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5th International Conference on Machine Learning and Intelligent Systems, MLIS 2023

作者： Song, Xiedong Mariano, Vladimir Y. Shi, Xuemei School of Mathematics and Computer Application Technology Jining University Shandong Qufu273155 China Information Engineering Department Yantai Vocational College Shandong Yantai264003 China College of Computing and Information Technologies National University Manila0900 Philippines School of Computer Science Huainan Normal University Anhui Huainan232038 China

ISBN: (纸本)9781643684468

China is the world's largest apple growing country;Apples are affected by various diseases during the planting process, and timely detection of fruit tree diseases is of great significance for improving apple yield. With the development of artificial intelligence technology, The use of deep learning to detect the condition of fruit trees has become a research hotspot in forest science planting. ResNet50 is used to solve the bottleneck problem of recognition accuracy of traditional neural networks, and data enhancement is used to improve the quality of data samples. transfer learning is used to solve the application of small-scale data samples, and loss function is used to balance sample errors. Improve the ability to obtain important feature information by using a dual channel mixed attention mechanism. Finally, a comprehensive network model combining residual neural network, dual channel mixed attention mechanism, transfer learning and loss function is designed. The experimental results show that the effect of this comprehensive ResNet50 model is better than that of the original ResNet50 model and VGG19 model, and its classification accuracy rate can reach 96.87%. This method improves the performance in fruit disease detection, and has wider applicability in practical applications. © 2023 The authors and IOS Press.

关键词： Fruits

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A Modified Oppositional Chaotic Local Search Strategy Based Aquila Optimizer to Design an Effective Controller for Vehicle Cruise Control System

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Journal of Bionic Engineering 2023年第4期20卷 1828-1851页

作者： Serdar Ekinci Davut Izci Laith Abualigah Raed Abu Zitar Department of Computer Engineering Batman University72100BatmanTurkey Center for Engineering Application and Technology Solutions Ho Chi Minh City Open UniversityHo Chi MinhVietnam Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman19328Jordan Computer Science Department Prince Hussein Bin Abdullah Faculty for Information TechnologyAl Al-Bayt UniversityMafraq25113Jordan Faculty of Information Technology Middle East UniversityAmman11831Jordan Applied Science Research Center Applied Science Private UniversityAmman11931Jordan School of Computer Sciences Universiti Sains Malaysia11800PenangPulau PinangMalaysia Sorbonne Center of Artificial Intelligence Sorbonne University-Abu DhabiAbu DhabiUnited Arab Emirates

In this work,we propose a real proportional-integral-derivative plus second-order derivative(PIDD2)controller as an efficient controller for vehicle cruise control systems to address the challenging issues related to efficient *** this regard,this paper is the first report in the literature demonstrating the implementation of a real PIDD2 controller for controlling the respective *** construct a novel and efficient metaheuristic algorithm by improving the performance of the Aquila Optimizer via chaotic local search and modified opposition-based learning strategies and use it as an excellently performing tuning *** also propose a simple yet effective objective function to increase the performance of the proposed algorithm(CmOBL-AO)to adjust the real PIDD2 controller's parameters *** show the CmOBL-AO algorithm to perform better than the differential evolution algorithm,gravitational search algorithm,African vultures optimization,and the Aquila Optimizer using well-known unimodal,multimodal benchmark ***2019 test suite is also used to perform ablation experiments to reveal the separate contributions of chaotic local search and modified opposition-based learning strategies to the CmOBL-AO *** the vehicle cruise control system,we confirm the more excellent performance of the proposed method against particle swarm,gray wolf,salp swarm,and original Aquila optimizers using statistical,Wilcoxon signed-rank,time response,robustness,and disturbance rejection *** also use fourteen reported methods in the literature for the vehicle cruise control system to further verify the more promising performance of the CmOBL-AO-based real PIDD2 controller from a wider *** excellent performance of the proposed method is also illustrated through different quality indicators and different operating ***,we also demonstrate the good performing capability of the CmOBL-AO algorithm for real traffic *** show

关键词： Aquila optimizer Chaotic local search Modified opposition-based learning Real PIDD^(2)controller Vehicle cruise control system Bionic engineering

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Prediction of Epileptic Seizures based on EEG Signal using CNN Model

Prediction of Epileptic Seizures based on EEG Signal using C...

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International Conference on Communication and Electronics Systems (ICCES)

作者： Suraj Kumar Shiva Prakash Department of Information Technology and Computer Application Madan Mohan Malaviya University of Technology Gorakhpur Uttar Pradesh India

Millions of patients suffer from epilepsy each year, a chronic nervous illness with a growing global prevalence. In a lot of situations, it might cause critical injuries or patient deaths. So, the automatic prediction of the epileptic seizure saves the patients from injury as well as from death. In this age of technological advancements, the internet of things (IoT) has started to offer a variety of solutions in the healthcare sector through the use of machine learning, deep learning, and cloud-based services. Internet of thing, Edge computing, and Cloud computing helps in the detection of such nervous disorders using deep learning and machine learning. Most of the models were developed preciously but there are many issues related to the prediction and security of the data of patients. This research study proposed an autonomous cloud-edge integrated seizure prediction model based on a convolutional neural network. The proposed model detects and classifying the epileptic seizure using an EEG dataset. The proposed model shows average training accuracy and average validation accuracy are 99.27% and 96.37%, respectively and the average losses for training and validation are 2.10 % and 14.23%, respectively. The result shows that overall accuracies and losses are better than the wellknown existing works. All implementation for seizure prediction is done in Python language.

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Understanding the Impact AI has on Data Access Performance and Efficiency 3

Understanding the Impact AI has on Data Access Performance a...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Rege, Pallavi R. Ahluwalia, Gunveen Kumar, Ritesh Narasimhayya, B.E. Yadav, Surendra Karthikeyan, Kavitha Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Computer Science and Engineering Karnataka Bangalore India Vivekananda Global University Department of Computer Science & Application Jaipur India Prince Shri Venkateshwara Padmavathy Engineering College Department of Civil Engineering Chennai127 India

ISBN: (纸本)9798350319125

AI has come to be more and more outstanding in recent years in records get entry to overall performance and efficiency. As a result, there were big modifications to how facts are processed and accessed. AI has enabled organizations to extra effectively and efficaciously technique facts, ensuing in accelerated operational efficiency and improved customer satisfaction. This paper presents a comprehensive exploration of the effect of AI on records get entry to performance and performance. It examines the usage of AI to streamline statistics processing and get admission to, and discusses the impact it has on consumer revel in and patron satisfaction. additionally, the paper considers the capacity for future innovation and the way facts get admission to overall performance and performance may be further more desirable via AI. The paper concludes with implications for similarly research and improvement. © 2023 IEEE.

关键词： Capacity Implications Satisfaction Similarly

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Unsharp Mask Sharpening Detection via Global Analysis 35

Unsharp Mask Sharpening Detection via Global Analysis

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35th Irish Systems and Signals Conference, ISSC 2024

作者： Lu, Rong-Hui Chen, Tzong-Jer Pan, Junhong Wuyi University Information Technology and Laboratory Management Center Fujian Wuyishan354300 China School of Mathematics and Computer Science Wuyi University Fujian Wuyishan354300 China Wuyi University Fujian Key Laboratory of Big Data Application and Intellectualization for Tea Industry China

ISBN: (纸本)9798350352986

Image sharpening prediction plays a significant role in image restoration, forensics, and computer vision. This work proposes a new method for detecting sharpening and indexing in digital images. Images initially undergo manipulation using Gaussian filters and Unsharp Masking (UM) with various degrees of intensity. This is followed by transformation into the frequency domain using Fourier transform. The resulting amplitude values are calculated, and their absolute values are summed along the frequency axis to produce a profile curve. These data curves are subsequently smoothed. The UM curves exhibit concavity and symmetry relative to the curve of the original image. The values of these curves at the concave positions, corresponding to various UM intensities, can be utilized as a sharpness index by comparing them to the curve values of the original image at the same positions. These sharpness indices are dependent on the UM amplification factors. This methodology finds utility in medical image processing and computer vision systems for sharpness detection and indexing. The features identified in this study, based on Fourier power spectrum analysis, can be input into a machine learning classifier for classification. © 2024 IEEE.

关键词： Fourier transforms

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News-Driven Stock Price Forecasting in Indian Markets: A Comparative Study of Advanced Deep Learning Models 3

News-Driven Stock Price Forecasting in Indian Markets: A Com...

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3rd International Conference for Advancement in technology, ICONAT 2024

作者： Attaluri, Kaushal Tripathi, Mukesh Kumar Reddy, Srinithi Shivendra Software Engineer-Machine Learning and AI Pegasystems India Worldwide Pvt Ltd Hyderabad India Vardhaman College of Engineering Department of Computer Science and Engineering Hyderabad India Vasavi College of Engineering Department of Information Technology Hyderabad India D. K. College Department of Computer Application Bihar Dumraon India

ISBN: (纸本)9798350354171

Forecasting stock market prices is a challenging task for traders, analysts, and engineers due to the myriad of variables influencing stock prices. However, the advent of artificial intelligence (AI) and natural language processing (NLP) has significantly advanced stock market forecasting. AI's ability to analyze complex data sets allows for more informed predictions. Despite these advancements, stock price forecasting remains an area where AI has not yet achieved optimal results. In this paper, we forecast stock prices using 30 years of historical data from various national banks in India sourced from the National Stock Exchange. We employ advanced deep learning models, including multivariate multi-step Long Short-Term Memory (LSTM), Facebook Prophet with LightGBM and Optuna, and Seasonal Auto-Regressive Integrated Moving Average (SARIMA). Additionally, we analyze news data from tweets and reliable sources like Business Standard and Reuters, recognizing their significant impact on stock price movements. © 2024 IEEE.

关键词： Contrastive Learning

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Internet of Everything: A Review on Smart Healthcare Taxonomy, Research Challenges and Methodologies

Internet of Everything: A Review on Smart Healthcare Taxonom...

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International Conference on Computing and Networking technology (ICCNT)

作者： Anu Raj Shiva Prakash Jyoti Srivastava Information Technology & Computer Application Department Madan Mohan Malaviya University of Technolgy Gorakhpur India

The Internet of Everything (IoE) has the potential to revolutionize many aspects of our daily lives, including healthcare, business and industrial world etc. Smart healthcare systems that utilize IoE technologies could greatly improve patient outcomes, increase efficiency, and reduce costs. However, there are several challenges that must be overcome in order to develop effective and reliable power supply systems for IoE-enabled healthcare devices. IoE healthcare devices may be deployed in a variety of settings, and the power supply system must be able to monitor and manage these devices remotely. The development of high-performance smart healthcare systems for the IoE requires the development of reliable and scalable power supply systems that can overcome the challenges of distribution, connection, charging technologies, and management. This paper mainly emphasizes on the IoE and their components that are used in the smart healthcare system and rising of the research challenges. In this paper, by addressing the taxonomy of healthcare and research challenges, researchers and businesses can develop innovative solutions that will transform healthcare delivery and improve patient outcomes.

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Tensor Graph Convolutional Network for Dynamic Graph Representation Learning 7

Tensor Graph Convolutional Network for Dynamic Graph Represe...

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7th International Symposium on Autonomous Systems, ISAS 2024

作者： Wang, Ling Yuan, Ye The School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China The Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Engineering Research Center of Big Data Application For Smart Cities Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing400714 China College of Computer and Information Science Southwest University Chongqing400715 China

ISBN: (纸本)9798350363173

Dynamic graphs (DG) represent evolving interactions between entities in various real-world scenarios. Many existing DG representation learning models employ a combination of graph convolutional networks and sequence neural networks to capture spatial-temporal dependencies. However, this hybrid approach often fails to effectively capture the spatial-temporal continuity inherent in DGs. In this paper, we propose a novel approach called the Tensor Graph Convolutional Network (TGCN) to learn DG representations within a unified convolution framework based on tensor products. Our approach is founded on two key principles: (a) representing DG information in tensor form, and (b) leveraging tensor product operations to design a graph convolutional network that simultaneously models spatial and temporal features. Experimental results on three real-world DG datasets demonstrate that our proposed model achieves state-of-the-art performance. © 2024 IEEE.

关键词： Tensors

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