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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Sharma, Sunil Menaka, C. Singh, Akhilendra Pratap Vivekananda Global University Department of Computer Science & Application Jaipur India Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350383348

Convolutional neural networks (CNNs) are a deep mastering method for the computerized pathology photograph category. With the improvement of superior imaging techniques, the quantity of virtual and representable pathology pix has increased notably. Automatic picture evaluation has become increasingly crucial to exploit those photographs for clinical selections. CNNs have attracted huge interest and were established to be very powerful for automated photograph classification and object detection. The improvement of deep getting-to-know based on Convolutional Neural Networks (CNNs) for computerized pathology photo classes has been a focal point of research in current years. This paper critiques the recent advances in CNNs in automatic pathology image type. The key architectures and algorithms for CNNs are summarized, and their capability applicability in pathology picture classification is discussed. This overview also introduces the maximum hit CNN methods that have been proposed and have proven promising consequences in pathology photograph category duties. The paper concludes with a dialogue of future research directions in this area. Convolutional neural networks (CNNs) are a deep mastering method for the computerized pathology photograph category. With the improvement of superior imaging techniques, the quantity of virtual and representable pathology pix has increased notably. To exploit those photographs for clinical selections, automatic picture evaluation has grown to be an increasingly number of crucial. CNNs have attracted huge interest and were established to be very powerful for automated photograph classification and object detection. The improvement of deep getting-to-know based on Convolutional Neural Networks (CNNs) for computerized pathology photo classes has been a focal point of research in current years. This paper critiques the recent advances in CNNs in automatic pathology image type. The key architectures and algorithms for CNNs are summarized,

关键词： classification Convolutional development discussion pathology

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Understanding the Dynamics of Sparse Coding in Auto-Encoders for Cancer Detection

Understanding the Dynamics of Sparse Coding in Auto-Encoders...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Thiruvenkadam, T. Acharjya, Kalyan Goyal, Manish Kumar Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Application Jaipur India

ISBN: (纸本)9798350383348

This summary outlines how sparse coding in vehicle encoders may be used to come across most cancers. It explains that by using learning sparse representations of raw records, you can benefit from a higher understanding of the underlying dynamics of cancer improvement. Furthermore, this summary outlines two of the most commonplace packages of vehicle encoders for cancer detection: function extraction and fitness tracking. This look gives insights into the capability of car encoders for efficient cancer analysis. For this reason, it should prove to be a vital element of most cancer detection and prevention. Sparse coding has been gaining interest inside the region of auto-encoder-based total methods for most cancer detection. With the increasing complexity of deep getting-to-know fashions being used for detection, the information and mastery of this research approach moderately encoded representations have turned out to be vital. Auto-encoders are used to encapsulate and encode inputs from high-dimensional facts into a lower-dimensional representation to keep the most crucial or crucial data from the facts. A selected type of vehicle encoder, a sparse automobile encoder, is designed to analyze sparsely encoded representations of records by introducing sparsity into the hidden layer of the automobile encoder via sure specialized regularizes. © 2024 IEEE.

关键词： automobile commonplace detection element interest

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Data-Driven Modeling and Working Condition Prediction in Process Industry Production 21

Data-Driven Modeling and Working Condition Prediction in Pro...

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21st International Conference on Networking, Sensing and Control, ICNSC 2024

作者： Zhang, Meifang Wang, Ziqi Bi, Jing Yuan, Haitao College of Computer Science Beijing University of Technology Beijing China School of Automation Science and Electrical Engineering Beihang University Beijing China

ISBN: (纸本)9798350365221

In the production process of the process industry, precise adjustment of working conditions presents a challenge due to the complexity of processes and unknown disturbances. Central control operators need to adjust setpoints based on deviations in process parameters and monitor target values to maintain system stability. However, many operating procedures excessively rely on human experience, increasing the uncertainty of the production process. In addition, the expert knowledge is not fully embedded in accumulated operations, limiting its potential in decision support. Therefore, data-driven modeling of production processes is essential for developing industrial expert systems to realize intelligent manufacturing. This work proposes a work condition prediction framework based on an Operation Mode Library (OML) to realize Working Condition Prediction (WCP), called for OML-WCP short. Taking the cement rotary kiln adjustment process as an example, a stable OML is constructed using Gaussian mixture clustering technology. Experimental results with real-life operation data of a cement plant reveal that the prediction accuracy of OML-WCP outperforms the existing methods. Moreover, the continuous accumulation of operating mode libraries can improve prediction accuracy in practical annlications. © 2024 IEEE.

关键词： Cements

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A Novel Approach for Exploring Data-Driven Nutritional Insights Using Clustering and Dimensionality Reduction Techniques

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SN computer science 2024年第8期5卷 1-22页

作者： Garg, Nandini Dwivedi, Pulkit Apex Institute of Technology (CSE) Chandigarh University Mohali India School of Computer Science and Engineering IILM University Greater Noida India

The analysis of high-dimensional datasets poses significant challenges, particularly in big data analytics where extracting meaningful insights is crucial. Current techniques often struggle with maintaining a balance between preserving global data structures and capturing local relationships. In this study, we address these challenges by integrating Principal Component Analysis (PCA) and t-distributed Stochastic Neighbor Embedding (t-SNE) for dimensionality reduction, with a dedicated focus on the domain of nutrition. Our research specifically targets pressing health concerns among the youth, such as obesity and nutritional deficiencies, by simplifying the analysis of extensive nutrition datasets. We propose a methodology that enhances the selection of nutritious food alternatives through effective data simplification and analysis. The efficacy of our approach is demonstrated through comprehensive experimental results, which include detailed comparisons with state-of-the-art methods, and evaluations based on clustering accuracy, computational efficiency, and visualization quality. Additionally, we optimize the performance of clustering algorithms using hyperparameter tuning techniques, specifically the Elbow Method and the Silhouette Coefficient. Our findings highlight the significant role of dimensionality reduction in improving data analysis and machine learning processes. This study offers valuable insights for researchers and practitioners, contributing to a deeper understanding of how dimensionality reduction techniques can unlock latent knowledge within vast datasets. Ultimately, our research aims to facilitate more informed decision-making and drive innovation in the era of big data analytics, with practical applications extending across diverse domains, particularly in nutrition and health. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Clustering Dimensionality reduction Machine learning Nutrition PCA t-SNE

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Multi-RIS-Aided Secure MIMO Dual-Function Radar-Communication System Based on Signal-to-Interference-plus-Noise Ratio 7

Multi-RIS-Aided Secure MIMO Dual-Function Radar-Communicatio...

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7th IEEE Advanced Information technology, Electronic and Automation Control Conference, IAEAC 2024

作者： Elhag, Nihad A. A. Liu, Liang Wei, Ping Babikir, Sharief F. Gao, Lin University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu China University of Tennessee College of Engineering and Computer Science Chattanooga United States

ISBN: (纸本)9798350339161

In this paper, we introduce multiple Reconfigurable Intelligent Surfaces (RISs) aided secure MIMO Dual-Function Radar-Communication (DFRC) system for enhanced dual-functionality performance. We aim to optimize the Signal-to-Interference-plus-Noise Ratio (SINR) as a performance metric for radar, while simultaneously meeting the secrecy rate requirements for the communication link. Major effort is given to optimize multiple RISs configurations that counteract the presence of eavesdroppers and actively manage the propagation environment to benefit legitimate user. The optimization problem is solved iteratively by using CVX and the majorization-minimization (MM) algorithm. Numerical results demonstrate that the proposed method with multiple RISs can improve SINR for radar target detection and also secure communication link, which in turn enhances system performance. The proposed model is used to determine the optimum directions for RISs deployment. © 2024 IEEE.

关键词： Signal to noise ratio

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Single-shot phase retrieval for randomly fluctuated and obstructed vortex beams

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science China(Physics,Mechanics & Astronomy) 2024年第4期67卷 72-80页

作者： Hao Zhang Jun’an Zhu Xingyuan Lu Zhiquan Hu Jianbo Gao Kun Liu Qiwen Zhan Yangjian Cai Chengliang Zhao School of Physical Science and Technology Soochow UniversitySuzhou 215006China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai 200093China Shandong Provincial Engineering and Technical Center of Light Manipulations Shandong Provincial Key Laboratory of Optics and Photonic DeviceSchool of Physics and ElectronicsShandong Normal UniversityJinan 250358China Joint Research Center of Light Manipulation Science and Photonic Integrated Chip of East China Normal University and Shandong Normal University East China Normal UniversityShanghai 200241China

Vortex beams with orbital angular momentum play a crucial role in increasing the information capacity in optical *** magnitude of orbital angular momentum determines the ability of information *** practice,a vortex beam can encounter random objects or turbulence during free-space propagation,resulting in information ***,accurately measuring the orbital angular momentum of a randomly fluctuated and obstructed vortex beam is a considerable ***,we propose a single-shot method for the phase retrieval of a randomly fluctuated and obstructed vortex beam by combining the phase-shift theorem and self-reference *** results reveal that the sign and magnitude of the initial orbital angular momentum can be simultaneously determined based on their quantitative relation with the number of coherence singularities on the observation plane,thus addressing the effects of random occlusion and atmospheric *** proposed method considerably improved the accurate decoding of orbital angular momentum information in nonideal freespace optical communications.

关键词： optical vortex phase retrieve orbital angular momentum measurement antiturbulence antidisturbance

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Glaucoma Stage Classification Using Image Empirical Mode Decomposition (IEMD) and Deep Learning from Fundus Images 4th

Glaucoma Stage Classification Using Image Empirical Mode Dec...

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4th EAI International Conference on Cognitive Computing and Cyber Physical Systems, IC4S 2023

作者： Shankar, D. Harsha, I. Sri Madhuri, P. Shyamala Naidu, J. N. S. S. Janardhana Madhuri, P. Krishna Cherukuvada, Srikanth Department of Computer Science and Engineering Vishnu Institute of Technology Kovvada India Department of Networking and Communications School of Computing SRM Institute of Science and Technology Chennai India

ISBN: (纸本)9783031488870

Glaucoma is an ocular pathology characterized by the gradual deterioration of neural cells in the eye, which is attributed to elevated intra ocular pressure within the retina. Glaucoma takes the second spot in terms of its prevalence as a neurodegenerative eye disease, failure to diagnose glaucoma at an early stage can lead to complete blindness. This underlying issue requires a streamlined system that uses experienced medical experts, little equipment, and less time. To categories the stages of glaucoma, a computer-Aided Diagnosis (CAD) method is used called Image Empirical Mode Decomposition (IEMD). Segmentation-based algorithms utilizing features like cup-to-disc ratio (CDR) and textural characteristics close to the optic disc region can distinguish glaucoma. To capture pixel variations for this investigation, the pre-processed fundus images are separated and transformed into a diverse range of intrinsic mode function (IMFs). The study employs deep learning-based framework for finding the optic nerve head from coloured fundus photographs, to transform the fundus images into a delimited region of interest (ROI) and utilizes multiple deep networks like RFCN and RCNN classifiers identified glaucoma stages separately. The CAD system serves as an automated tool for retinal image processing and demonstrates superior performance over the RFCN classifier, achieving an impressive accuracy of 96% with the RCNN classifier in classifying glaucoma stages. © 2024, ICST Institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： computer aided diagnosis

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Cyclone Intensity Estimation based on Deep Learning using INSAT-3D IR Image 8

Cyclone Intensity Estimation based on Deep Learning using IN...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Swetha, K. Gogineni, Neelima Srinivas, Gundeti Kumari, E.N. Vijaya Madgi, Manohar Benarji, T. Mlr Institute of Technology Department of Cse Hyderabad500043 India Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad Bachupally India Hyderabad501510 India Mlr Institute of Technology Dept of Computer Science Hyderabad India Kle Technological University School of Computer Science and Engineering Karnataka Hubballi580031 India Indur Institute of Engineering and Technology Department of Computer Science and Engineering TS Siddipet India

ISBN: (纸本)9798350376425

It is the tropical cyclone that is one of the natural disasters that occurs in India the most frequently. The east coast of the Bay of Bengal and the west coast coastal areas of the Arabian Sea are the locations where approximately five to six tropical cyclones form each year. The ratio of these storms is approximately four to one. For a very long time, specialists have been working on the development of a method that can analyze and evaluate the power or severity of a Tropical Cyclone (TC) in real time. The method that is utilized the majority of the time is a manual process that is accomplished through the utilization of cloud photographs collected from satellites. Due to the fact that there is no method that is universally acknowledged for estimating intensity results, this leads in the estimation process being subject to bias. Tropical cyclones have the potential to generate a wide range of extremely hazardous meteorological conditions, such as storms, strong gusts, and lightning strikes. © 2024 IEEE.

关键词： Tropical cyclone

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FAB-D2PIM: A Full Anonymous Blockchain-based Distributed Privacy-Preserving Incentive Mechanism for Crowdsensing 5

FAB-D2PIM: A Full Anonymous Blockchain-based Distributed Pri...

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5th International Conference on Industrial Artificial Intelligence, IAI 2023

作者： Cheng, Jiameng Zhang, Hongwei Tianjin University of Technology School of Computer Science and Engineering Tianjin China National Engineering Laboratory for Computer Virus Prevention and Control Technology Tianjin China

ISBN: (纸本)9798350325294

Privacy-preserving incentive mechanism in crowd-sensing systems is an ongoing research challenge, partly due to the untrusted centralized servers and sensitive data leakage. In this paper, we focus on a secure incentive way with blockchain. First, we analyze the limitations in traditional crowdsensing systems with a trusted third party (TTP) and identify three ways privacy in the incentive mechanism (IM) of crowdsensing: identity, data, and revenues. Then, we propose a novel decentralized system for IM based on a full anonymous blockchain called FAB-D2PIM. In the proposed system, we use a one-way aggregate signature scheme (Boneh et al. EUROCRYPT 2003) for identity privacy and homomorphic Public Key Encryption (Boneh et al. TCC 2005) for sensing data and reward privacy. The proposed scheme not only protects the privacy of the workers but also decreases the operation rate of the task publishing center. © 2023 IEEE.

关键词： Blockchain

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Ensemble Techniques for the Prediction of Heart Disease with Reduced Entropy 2

Ensemble Techniques for the Prediction of Heart Disease with...

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2nd IEEE International Conference on Interdisciplinary Approaches in technology and Management for Social Innovation, IATMSI 2024

作者： Mahesh, T.R. Mohamed Musthafa, M. Rahman, A.M.J.Md. Zubair Viswanath, J. Vanitha, K. Vinoth Kumar, V. Department of Computer Science and Engineering Bangalore India Tamil Nadu Erode India Al- Ameen Engineering College Department of Computer Science and Engineering Tamil Nadu Erode India Madanapalle Institute of Technology & Science Department of Ai Andhra Pradesh India Department of Computer Science and Engineering Coimbatore India Vit University School of Information Technology and Engineering Tamil Nadu Vellore India

ISBN: (纸本)9798350360523

Healthcare industry generates a vast amount of data, the majority of which is sophisticated and massive in size. This information, however, is not "extracted"in order to uncover hidden facts for effective decision-making. Diagnosing heart disease, a noncommunicable disease, is one of the more difficult problems in medicine because it entails the patient's past health history. An accurate and effective automated system can be quite beneficial in detecting cardiac problems. Modern data mining techniques may be able to solve this issue. In the healthcare industry, various information-extracting technologies such as association rule mining, classification, and clustering are used to forecast cardiac disease. In order to address this issue, the research investigates the use of machine learning methods for cardiac disease prediction, including Naive Bayes (NB), logarithmic regression (LR), Support Vector Machine (SVM), the Decision Tree (DT), Random Forests (RF), and the k-nearest-neighbor algorithm (KNN). The Random Forest ensemble technique outperforms with 99% accuracy. © 2024 IEEE.

关键词： Forecasting

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