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5th International Conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Anand, Piyush Ramesh, T. Agarwal, Trapty Preethi, D. Department of Computer Science and Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Luknow India Karnataka Bangalore India

ISBN: (纸本)9789819780426

Green Clustered mean Forecasting (ECMF) for Time series is a novel forecasting approach that is capable of efficaciously aggregating the underlying additives of a time collection through combining the top-primarily based Aggregation (HBA) technique alongside the mean Forecasting (MF) method. ECMF may be utilized to detect and become aware of companies of time collection intently associated with every other, therefore taking into account extra correct and reliable forecasts. By way of counting on HBA to discover any patterns in the information and then imposing MF to generate forecasts from the ones styles, ECMF can offer more accurate and reliable forecasts than the prevailing strategies. Moreover, this technique is computationally green, due to the hierarchical shape of the data, which reduces the quantity of forecasting experiments wanted. The ensuing forecasts are also easier to examine, as they may be generated from a standard head-based totally version. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Prediction models

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Investigating Time Series Clustering Algorithms for Hyper Spectral Image Analysis 5th

Investigating Time Series Clustering Algorithms for Hyper Sp...

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5th International Conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Mahalakshmi, R. Agarwal, Trapty Kudari, Jayashree M. Mehra, Ritika School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India Department of Computer Science & Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India

ISBN: (纸本)9789819780426

Time collection clustering algorithms (TSCAs) are of increasing significance within the evaluation and interpretation of hyper spectral image (HSI) facts. TSCAs are being used to become aware of spatiotemporal styles of spectral characteristics in HSI records by identifying areas of interest (ROIs) which can be associated with physical techniques and mapping the ones ROIs to clusters. One-of-a-kind clustering algorithms may be used to analyze HSI statistics and tease out diffused spatiotemporal modifications. This paper investigates the software of two exceptional TSCAs, particularly okay-method clustering and Self-Organizing Maps (SOMs) to HSI *** SOM set of rules changed into additionally capable of phase the hyperspectral facts into meaningful clusters primarily based on their spectral traits. The accuracy of the SOM results was also assessed. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Self organizing maps

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Automated Customer Feedback Generation Using Facial Expression Through OpenCV 1

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6th International Conference on Smart Computing and Informatics, SCI 2024

作者： Ayushi Arora, Deepak Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Uttar Pradesh Lucknow Campus Lucknow India

ISBN: (数字)9789819619818

ISBN: (纸本)9789819619801

A business needs to work on the right problems to be able to grow. These problems can only be provided by the people who are using the product or service made by the business. Accurate customer feedback is what allows a business to set its priorities straight. Thus, in this paper, we have attempted to make a simple and easy-to-use tool from already existing resources that will provide real-time customer reviews to business owners which will help them in improving their service. The result of the model execution includes the performance of various concepts that are face detection, face recognition, emotion detection, storage in the database and calculations to find the positive (happy and surprise), negative emotion (sad, fear, angry, and disgust) and neutral (neither positive nor negative). For understanding the functioning of the model, OpenCV and CNN fundamentals have been explained in detail in the paper. This paper concludes by discussing the future scopes of the tool created. In the past, CCTV cameras were used for security only, but now these can be used to get customers’ real-time reviews also which will be very beneficial to them to improve their services. This will allow them to grow and compete in the future, where survival is going to depend on how well a business uses AI in its day-to-day operations. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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NUMERICAL INVESTIGATION OF NONLINEAR INTERACTION OF GUIDED-WAVE MIXING AND MICRO-CRACKS IN PIPES 18

NUMERICAL INVESTIGATION OF NONLINEAR INTERACTION OF GUIDED-W...

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18th Symposium on Piezoelectricity, Acoustic Waves, and Device Applications, SPAWDA 2024

作者： Tan, Miaohong Sun, Maoxun Liu, Hongye Chen, Xiaorong School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China School of Mechanical Engineering University of Shanghai for Science and Technology Shanghai200093 China

ISBN: (纸本)9798331523732

Pipes are widely used in various industries but are subject to plastic deformation and corrosion, during which microcracks may appear. Ultrasonic guided wave mixing has gained attention due to its high sensitivity. This study employs phase inversion and two-dimensional Fourier transform to investigate the nonlinear interaction between counter-propagating guided waves and microcracks. The generated sum-frequency components and difference-frequency components contain multiple flexural modes and propagate toward both ends of the pipeline. The generated sum-frequency components and difference-frequency components are used to locate the microcracks. The circumferential location of the microcracks is determined by polar diagrams (i.e., the amplitudes of difference-frequency or sum-frequency components and angles), while the axial location is determined by the relationship between the amplitudes of difference-frequency components or sum-frequency components and distance. © 2024 IEEE.

关键词： Pipeline corrosion

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A cancelable biometric authentication scheme based on geometric transformation

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Multimedia Tools and Applications 2025年第15期84卷 15407-15428页

作者： Champaneria, Vivek H. Patel, Sankita J. Zaveri, Mukesh A. Department of Computer Science And Engineering Sardar Vallabhbhai National Institute of Technology Icchanath Gujarat Surat395007 India

In digital identification technologies, fingerprint-based biometric systems are extensively utilized for authentication. Fingerprint authentication systems usually store the original minutiae points as user templates in the database which consist of minutiae bifurcation and ridge end points. Storing original minutiae points in the database can create a potential security risk to a person’s identity. An adversary might attack the database and compromise the user template. Various studies have demonstrated the feasibility of reconstructing the original fingerprint by utilizing the minutiae points. Existing research methods still need to be improved, and therefore, it is essential to provide security and robustness to fingerprint-based biometric systems. In this paper, a novel template protection technique using non-invertible transformation is proposed to create a secure and robust template. The transformation function uses the original minutiae positions, orientation details, and user key set values. The results demonstrated equal error rates of 1.5%, 0.85%, and 2.71% for the FVC2002 DB1, DB2, and DB3 fingerprint databases. The proposed method has demonstrated noteworthy performance improvement compared to other notable contributions. The outcome of the proposed method demonstrates practicality and effectiveness. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Database systems

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Mapping of City Development and Urban Flooding Using Deep Learning Model for Chennai City Study Area 5th

Mapping of City Development and Urban Flooding Using Deep Le...

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5th International Conference on Computing and Network Communications, CoCoNet 2023

作者： Senthilkumar, Pavithraa Arthur, Menaka Pushpa School of Computer Science and Engineering Vellore Institute of Technology Chennai Campus Chennai600127 India

ISBN: (纸本)9789819747108

People’s lives and property are put in jeopardy by extreme weather occurrences such as floods, but key infrastructures, which must work even under subpar conditions, are also severely damaged. There have been a number of attempts to use deep learning algorithms to map the urban flood regions. In order to determine how vulnerable an area is to catastrophic flooding, it is critical to evaluate the flood-prone zones. Using Sentinel satellite photos, along with deep learning model, can help to map flood-prone areas and assess their vulnerability. Flood hazard mapping was evaluated using a Convolutional Neural Network (CNN) model. The response variable was a flood inventory of Chennai Corporation’s flooded and non-flooded areas, while the predictor variables were flood-affecting factors. The flooded areas were then randomly divided into sections for the purpose of constructing flood models and testing them. According to the training and testing classification accuracy, the models were evaluated on their ability to predict outcomes. Flood protection and risk assessment might benefit from the use of the model. As a result, if a flood inventory is available, this method might be used to create a flood danger map for metropolitan areas. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Risk assessment

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Block Verification Mechanism Based on Zero-Knowledge Proof in Blockchain

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computer Systems science & engineering 2023年第5期45卷 1805-1819页

作者： Jin Wang Wei Ou Osama Alfarraj Amr Tolba Gwang-Jun Kim Yongjun Ren School of Computer&Communication Engineering Changsha University of Science&TechnologyChangsha410004China Department of Computer Science Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Department of Computer Engineering Chonnam National UniversityGwangju61186Korea School of Computer Science Engineering Research Center of Digital Forensics of Ministry of EducationNanjing University of Information Science&TechnologyNanjing210044China

Since transactions in blockchain are based on public ledger verification,this raises security concerns about privacy *** it will cause the accumulation of data on the chain and resulting in the low efficiency of block verification,when the whole transaction on the chain is *** order to improve the efficiency and privacy protection of block data verification,this paper proposes an efficient block verification mechanism with privacy protection based on zeroknowledge proof(ZKP),which not only protects the privacy of users but also improves the speed of data block *** is no need to put the whole transaction on the chain when verifying block *** just needs to generate the ZKP and root hash with the transaction information,then save them to the smart contract for ***,the ZKP verification in smart contract is carried out to realize the privacy protection of the transaction and efficient verification of the *** the data is validated,the buffer accepts the complete transaction,updates the transaction status in the cloud database,and packages up the ***,the ZKP strengthens the privacy protection ability of blockchain,and the smart contracts save the time cost of block verification.

关键词： Blockchain privacy protection zero-knowledge proof smart contract

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Prediction of strength properties of concrete under the influence of recycled aggregate using machine learning models

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Interactions 2024年第1期245卷 1-21页

作者： Ashwathi, R. Soundariya, R.S. Tharsanee, R.M. Yuvaraj, S. Ramya, R. Department of Civil Engineering Bannari Amman Institute of Technology Erode India Department of Computer Technology Bannari Amman Institute of Technology Erode India Department of Computer Science and Engineering Bannari Amman Institute of Technology Sathyamangalam India Department of Civil Engineering KPR Institute of Engineering and Technology Coimbatore India Department of Information Technology Dr NGP Institute of Technology Coimbatore India

The upsurge in urbanization is depleting natural resources due to the wide usage of aggregates in construction, posing a menace to further progress. If the contemporary state persists, it becomes a threat for further progress. As an initiative to preserve the natural resources, recycled aggregate is employed as a partial additive of fine aggregate, this practice endorses sustainability but demands a cautious investigation of its effect on concrete strength. Predicting the strength characteristics of concrete becomes crucial in this situation. Machine Learning (ML) algorithms estimate these properties, enabling engineers to evaluate and enhance the concrete mix with recycled materials, confirming durability and performance without solely depending on outdated testing methods The proposed work attempts how ML algorithms have been utilized to forecast the strength properties of concrete. Input samples were collected from the experimental setup for three grades of concrete (M15, M20, M25) with four various proportions (5%, 10%, 15% and 20%) at 7, 14, 21 and 28 ages of curing excluding the conventional mix. The ML models are developed by training the collected laboratory samples with respect to four parameters– cement, fine aggregate (FA), coarse aggregate (CA) and recycled aggregate (RA). The trained models predict the three properties of concrete– compressive strength, split tensile strength and flexural strength. The test outcomes are further validated using statistical measures such as correlation coefficient– R2, Mean Square Error (MSE) and Mean Absolute Error (MAE) to assess the performance of the model. The performance evaluation of the proposed approach shows that The Support Vector Regressor outperforms the other ML models, achieving an R² value of about 90% and showing a reduced error rate in terms of MSE and MAE. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Tensile testing

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Fault diagnosis of wind turbine gearbox based on wavelet packet denoising and CNN-Swin Transformer-LSTM 3

Fault diagnosis of wind turbine gearbox based on wavelet pac...

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2024 3rd International Conference on Image Processing, Object Detection, and Tracking, IPODT 2024

作者： Zhang, Tao Wang, Yi School of Computer Science and Engineering Sichuan University of Science & Engineering Yibin644000 China

ISBN: (数字)9781510685529

ISBN: (纸本)9781510685512

The working environment of wind turbine gearboxes is complex and variable, with strong noise, which makes traditional fault diagnosis methods inadequate for accurate fault identification. To address this issue, this paper proposes a fault diagnosis method based on Wavelet Packet Denoising combined with CNN-Swin Transformer-LSTM. Firstly, the original signal is decomposed, denoised, and reconstructed using wavelet packets to highlight the effective periodic impact components within the signal, and the reconstructed signal is converted into two-dimensional wavelet time-frequency images. Then, Convolutional Neural Networks (CNN) are used to extract basic feature information from the images. The feature maps are then input into a Swin Transformer model to automatically extract multi-scale feature information based on the self-attention mechanism. Following this, Long Short-Term Memory (LSTM) networks are employed to capture temporal features of the data. Additionally, the Convolutional Block Attention Module (CBAM) is introduced to enhance feature representation capability. Finally, the method classifies different fault types. Experimental verification shows that the proposed method achieves an accuracy of 99.62% and 99.46% on two working condition datasets, respectively. Under conditions of strong noise and variable working conditions, the fault diagnosis accuracy reaches 92.24% and 96.16%. The experimental results demonstrate that this model possesses strong feature learning capabilities, robust anti-interference ability, and good generalization performance. Compared to other existing diagnosis techniques, it exhibits superior diagnostic performance and reliability. © 2024 SPIE.

关键词： Wind turbines

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On Differentiation Among Pilot Signals of Multiple Mobile Targets in Retro-Reflective Beamforming for Wireless Power Transmission

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engineering 2023年第11期30卷 55-62页

作者： Xin Wang Long Li Tie Jun Cui Mingyu Lu School of Electrical Engineering Chongqing UniversityChongqing 400044China School of Electronic Engineering Xidian UniversityXi’an 710071China State Key Laboratory of Millimeter Waves School of Information Science and EngineeringSoutheast UniversityNanjing 210096China Department of Electrical and Computer Engineering West Virginia University Institute of TechnologyBeckleyWV 25801USA

An experimental study is conducted on several retro-reflective beamforming schemes for wireless power transmission to multiple wireless power receivers(referred to herein as“targets”).The experimental results demonstrate that,when multiple targets broadcast continuous-wave pilot signals at respective frequencies,a retro-reflective wireless power transmitter is capable of generating multiple wireless power beams aiming at the respective targets as long as the multiple pilot signals are explicitly separated from one another by the wireless power ***,various practical complications are identified when the pilot signals of multiple targets are not appropriately differentiated from each other by the wireless power ***,when multiple pilot signals are considered to be carried by the same frequency,the wireless power transmission performance becomes heavily dependent on the interaction among the pilot signals,which is highly undesirable in *** conclusion,it is essential for a retro-reflective wireless power transmitter to explicitly discriminate multiple targets’pilot signals among each other.

关键词： Wireless power transmission Microwave antenna array Retro-reflective beamforming Pilot signal Multiple access

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