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An efficient approach for cement strength prediction

International Journal of computers and Applications 2023年第1期45卷 8-18页

作者： Gaidhane, Vilas H. Kumar, Nand Mittal, Ravi Kant Rajevenceltha, J. Department of Electrical and Electronics Engineering Birla Institute of Technology and Science Dubai Campus Pilani United Arab Emirates Department of Computer Science Birla Institute of Technology and Science Pilani Dubai Campus United Arab Emirates Department of Mechanical Engineering Birla Institute of Technology and Science Pilani India

In this paper, a simple and computationally efficient approach is proposed to predict the cement strength. It is based on the mathematical concept of covariance matrix and polynomial coefficients. The polynomial coefficients are used to represent the features of cement strength data set. The efficiency and feasibility of the proposed approach is demonstrated on the cement strength data set collected from the cement industry for 2 days, 7 days and 28 days samples. Based on the number of dynamic input variables of the cement strength, the different prediction models such as SOM, linear and nonlinear regression and artificial neural network are designed and the various experimentations are carried to evaluate the proposed approach. Experimental results have shown the effectiveness of the proposed approach in the form of RMSE, R-square coefficients and computational time. It is observed that the proposed polynomial coefficient-artificial neural network approach performs better and predict the cement strength efficiently as compared to other existing methods. © 2019 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Forecasting

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Intelligent Decision-Making in Cybersecurity Using Machine Learning 12th

Intelligent Decision-Making in Cybersecurity Using Machine L...

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12th International Conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2024

作者： Sakhare, Nitin N. Maruthi Shankar, B. Kalnawat, Aarti Sharma, Abha Rege, P.R. Wagh, Kapil Adhar Department of Computer Engineering BRACT’s Vishwakarma Institute of Information Technology Maharashtra Pune India Department of Electronics and Communication Engineering Sri Krishna College of Engineering and Technology Tamil Nadu Coimbatore India Pune India Electronics and Communication Engineering Arya College of Engineering Rajasthan Jaipur India Department of Computer Engineering Vishwakarma Institute of Information Technology Maharashtra Pune India Nutan Maharashtra Institute of Engineering and Technology Maharashtra Pune India

ISBN: (纸本)9789819601462

In the constantly changing field of cybersecurity, the need for advanced decision-making tools is more crucial than ever. This study explores machine learning in order to introduce an innovative method for strengthening cybersecurity protocols. The methodology involves combining Random Forests (RF) and Support Vector Machines (SVM) using the AdaBoost algorithm in an ensemble framework. The ensemble model is intricately crafted to address the complexities of intrusion detection, utilizing the combined capabilities of Random Forest’s data handling and Support Vector Machine’s pattern discrimination. AdaBoost enhances the ensemble’s predictive abilities by iteratively adjusting weights to focus more on misclassified instances. This adaptive learning process enables the model to constantly develop and adjust to new cyber threats. Validation against benchmark datasets demonstrates an impressive accuracy rate of 97.78%, highlighting the strength of the ensemble methodology. The True Positive Rate (TPR) of 98.33% demonstrates the ensemble’s effectiveness in accurately detecting and responding to potential cyber threats. The results demonstrate the superior performance of the proposed ensemble method and highlight its ability to achieve a balance between accuracy and adaptability. This research significantly advances intelligent decision-making in cybersecurity by introducing a potent ensemble approach that smoothly integrates various machine learning algorithms. The method’s proven accuracy and sensitivity make it a powerful tool in combating the increasing complexity of cyber threats. The combination of RF, SVM, and AdaBoost sets a standard for future research in strengthening digital defenses and ensuring the resilience of critical information systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Masked Siamese Prompt Tuning for Few-Shot Natural Language Understanding

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第2期5卷 624-633页

作者： Ni, Shiwen Kao, Hung-Yu Chinese Academy of Sciences Shenzhen Institute of Advanced Technology Shenzhen518000 China National Cheng Kung University Department of Computer Science and Information Engineering Tainan701401 Taiwan

Recently, prompt-based learning has shown excellent performance on few-shot scenarios. Using frozen language models to tune trainable continuous prompt embeddings has become a popular and powerful methodology. For few-shot natural language understanding, even if we freeze the parameters of the pretrained language model, the learned pseudo-prompts might still be overfitted. In this article, we propose a novel masked Siamese prompt tuning (MSP-tuning) to improve few-shot natural language understanding. Concretely, MSP-tuning masks randomly out part of the prompt tokens to get a pair of masked Siamese prompts for each sample. Each training sample is then fed to the model twice with the masked Siamese prompts. Finally, MSP-tuning minimizes the Jensen-Shannon-divergence (JSD) between the two output probability distributions of the pretrained language model to regularize the model further. Experiment results on the few-shot GLUE benchmark and SuperGLUE benchmark show that MSP-tuning outperforms previous approaches. Numerically, our MSP-tuning achieves an average improvement of 1.79% (BERT-base) and 1.39% (BERT-large) of the GLUE benchmark and 1.90% (RoBERTa-base) and 1.71% (RoBERTa-large) of the SuperGLUE benchmark compared to state-of-the-art method P-tuning. Our method facilitates applying large pretrained language models in natural language understanding. © 2023 IEEE.

关键词： Bit error rate

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RGB-guided hyperspectral image super-resolution with deep progressive learning

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CAAI Transactions on Intelligence technology 2024年第3期9卷 679-694页

作者： Tao Zhang Ying Fu Liwei Huang Siyuan Li Shaodi You Chenggang Yan School of Computer Science and Technology Beijing Institute of TechnologyBeijingChina Satellite Information Intelligent Processing and Application Research Laboratory Beijing Institute of Remote SensingBeijingChina Key Laboratory of Spectral Imaging Technology Xi'an Institute of Optics and Precision Mechanics of Chinese Academy of SciencesXi'anChina Institute of Informatics University of AmsterdamAmsterdamNetherlands School of Communication Engineering Hangzhou Dianzi UniversityHangzhouChina

Due to hardware limitations,existing hyperspectral(HS)camera often suffer from low spatial/temporal ***,it has been prevalent to super-resolve a low reso-lution(LR)HS image into a high resolution(HR)HS image with a HR RGB(or mul-tispectral)image *** approaches for this guided super-resolution task often model the intrinsic characteristic of the desired HR HS image using hand-crafted ***,researchers pay more attention to deep learning methods with direct supervised or unsupervised learning,which exploit deep prior only from training dataset or testing *** this article,an efficient convolutional neural network-based method is presented to progressively super-resolve HS image with RGB image ***-ically,a progressive HS image super-resolution network is proposed,which progressively super-resolve the LR HS image with pixel shuffled HR RGB image ***,the super-resolution network is progressively trained with supervised pre-training and un-supervised adaption,where supervised pre-training learns the general prior on training data and unsupervised adaptation generalises the general prior to specific prior for variant testing *** proposed method can effectively exploit prior from training dataset and testing HS and RGB images with spectral-spatial *** has a good general-isation capability,especially for blind HS image *** experimental results show that the proposed deep progressive learning method out-performs the existing state-of-the-art methods for HS image super-resolution in non-blind and blind cases.

关键词： computer vision deep neural networks image processing image resolution unsupervised learning

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Investigation of symmetric and asymmetric eye patterns for bell's palsy diagnosis 21

Investigation of symmetric and asymmetric eye patterns for b...

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21st OITS International Conference on Information technology, OCIT 2023

作者： Prasad, Thati Ravi Naulegari, Janardhan Visvesvaraya National Institute of Technology Department of Computer Science and Engineering Nagpur India GITAM Deemed-To-be-University GITAM School of Technology Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350358230

The development of an objective tool for the diagnosis of Bell's palsy (BP) has garnered significant attention among researchers, as it can aid in early treatment. This study aimed to address three research questions in the context of classification of individuals with BP or non-BP conditions. First, whether symmetric and asymmetric parameters in eye features can be reliable indicators for classification of BP and non-BP individuals. The results showed that classifiers use these parameters for classification. Second, To investigate whether the integration of both symmetric and asymmetric patterns of eye features can improve the performance metrics of classification. The findings supported that combination gives better performance. Third, how the incorporation of machine learning classifiers utilizing symmetric and asymmetric eye patterns contributes to the development of an automated classification model for distinguishing BP and non-BP subjects. The findings indicate that the Support Vector Machine(SVM) classifier achieved the highest accuracy of 92% when using the combination of symmetric and asymmetric eye parameters. This supports that the SVM classifier in leveraging the complementary information offered by both patterns to accurately classifying the individuals with BP and non-BP conditions. Overall, this study highlights the potential of symmetric and asymmetric eye patterns as reliable indicators for distinguishing between BP patients and non-BP individuals. © 2023 IEEE.

关键词： Classification (of information)

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AtheroRisk: An Integrated computer Software System for Stroke Risk Stratification Utilizing Carotid Plaque Analysis in Ultrasound Videos

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SN computer Science 2025年第2期6卷 1-16页

作者： Gemenaris, Michalis Liapi, Georgia D. Markides, Christos Constantinou, Kyriacos Loizou, Christos P. Neophytou, Michalis Kardoulas, Dimitrios Pattichis, Marios S. Griffin, Maura Nicolaides, Andrew Pattichis, Constantinos S. Kyriacou, Efthyvoulos Department of Electrical Engineering Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus Department of Computer Science and Engineering Frederick University Nicosia Cyprus Department of Computer Science University of Cyprus Nicosia Cyprus American Medical Center Nicosia Cyprus Vascular Surgery Clinic Attikon General University Hospital Athens Greece Department of Electrical and Comp. Engineering Univ. of New Mexico Albuquerque NM United States Vascular Screening and Diagnostic Center Nicosia Cyprus

In this study we propose and evaluate AtheroRisk, a standalone integrated computer software system for the analysis of carotid B-mode ultrasound (U/S) images and videos. Our goal was to provide a tool to help physicians stratify stroke risk. The presented system is based on outcomes from different research studies, as well as European guidelines for the evaluation and treatment of carotid artery disease. The goal is to enable reproducible analysis of atherosclerotic plaques in real time. AtheroRisk brings together analysis of U/S image and/or video of the carotid arteries facilitating anonymization, standardization, noise filtering and segmentation of plaques;followed by image and motion feature extraction to derive plaque-composition and stability. All the above steps are combined to determine the annual stroke risk rate and the 5-year stroke-free survival rate. The system is powered by an SQLite local database, in which the end user can save and manage data extracted from processing and analysis in an anonymized way. The first version of AtheroRisk was developed following an incremental software development process. The developed system was verified using 54 U/S videos (27 Asymptomatic, AS;27 Symptomatic, SY cases) while the clinician’s satisfaction and comments were collected through a questionnaire-based validation process. The results show that the integrated system proposed in this study can be successfully used for the automated image and video analysis of the CCA plaque in ultrasound videos. © The Author(s) 2025.

关键词： Carotid plaque analysis Motion analysis Plaque features Stroke risk stratification Ultrasound video

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Tomato Leaves Disease Detection Using Gray Level Co-Occurrence Matrix (GLCM) and Image Processing Techniques

Tomato Leaves Disease Detection Using Gray Level Co-Occurren...

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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Arikaran, N. Prabu, G. Ejoumalai, Arya Bhuvanesh, A. Kamalesh, S. Sathishkumar, R. Manakula Vinayagar Institute of Technology Department of Computer Science and Engineering Puducherry India Sri Manakula Vinayagar Engineering College Department of Information Technology Puducherry India

ISBN: (纸本)9798331510022

Crop diseases pose a serious danger to agricultural productivity and food security, making accurate detection techniques urgently needed. Convolutional Neural Networks (CNN) are used in this project's automated approach to better detect damaged tomato leaves using image processing. First, a sizable dataset with tagged photos of both healthy and diseased leaves is gathered from sources such as the Plant Village collection. To increase the diversity and quality of data, preprocessing techniques like data augmentation, scaling, and standardization are used. To guarantee accuracy and robustness, the CNN model is trained on well-labeled datasets after extracting complex features from the images. Techniques like batch normalization and dropout help reduce overfitting. Metrics including accuracy, precision, recall, and F1-score are used to evaluate the model's performance, showing significant improvements over conventional methods. With potential for wider uses in precision farming, the findings show that CNNs are a viable tool for the early and precise diagnosis of tomato leaf diseases. In order to enhance performance across a range of crops and diseases, future research will concentrate on transfer learning and growing datasets. The proposed model achieved 97.2% ACC, 95.7% Spec, 94.5% Recall, 98% Prec, and 97.5% F1-Score. © 2024 IEEE.

关键词： Fruits

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Personal Safety and Health Monitoring System for Emergency Assistance using IOT 3

Personal Safety and Health Monitoring System for Emergency A...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Poovanna, K.B. Rai, Arjun Roy, Sritama Vellore Institute of Technology Department of Computer Science Chennai600127 India Vellore Institute of Technology School of Electronics Engineering Chennai600127 India

ISBN: (纸本)9798350372120

Internet of Things (IoT) has the potential to transform how we live and work. One of the most promising applications of IoT is in the area of emergency response. In this research work, we have presented a smart medical application through IoT environment that combines a pulse rate sensor, a geographic sensor, and an SMS sender using SIM800L and Arduino microcontroller. The work has designed to help people in distress by sending their location and pulse rate to the police or a concerned person. The work has the potential to be used to help people in a variety of situations, such as when they are lost, injured, or being attacked. It is significant because it demonstrates the potential of IoT to be used to improve emergency responses. It also highlights the importance of using open source hardware and software in IoT domain. © 2024 IEEE.

关键词： Internet of things

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Real-Time Monitoring Mosul Water Supply

Real-Time Monitoring Mosul Water Supply

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2023 International Conference on engineering, Science and Advanced technology, ICESAT 2023

作者： Kaiys, Mohammed Khidhir, Abdulsattar Mohammed Northern Technical University Department of Computer Technology Engineering Mosul Iraq College of Mosul Technical Institute Computer Systems Technology Technical Engineering Mosul Iraq

ISBN: (纸本)9798350344400

UN studies show Iraq is the fifth country most prone to desertification. The Nineveh Water Directorate, which distributes water to the populace, faces several challenges. However, the drying up of the Tigris River, which flows through the governorate of Nineveh, is the biggest. All of this harmed the pumping and processing of water in the clean water distribution networks that supply the residents of Mosul city. The technology under consideration keeps track of how much pure water is pumped by the water booster stations directly linked to the water supply system for residential areas. Connecting the measuring instruments to the Arduino UNO, GSM SIM900, and current-to-volt converter will allow the reading of measuring values from the devices. A Data SIM card was used, and a private network was devoted to the Nineveh Water Directorate using an access point name (APN) for data transfer. The communication network uses SIM cards fitted with Asiacell company for Cellular Communications. Data is transferred and stored on a computer using the Softwareserial Arduino library. All stations in the city will eventually be connected to this system since the results of the planned system's monitoring of the quantity of water pushed are regarded as positive and encouraging. So that monitoring in these stations will be automated, contemporary, and free from the outdated regular practices that interfere with the equitable water distribution to the populace. © 2023 IEEE.

关键词： Internet of things

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A Review of Implementing Exploring Mobile Edge Computing within the Framework of 5G and 6G Networks

A Review of Implementing Exploring Mobile Edge Computing wit...

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2023 International Conference on Advances in Computation, Communication and Information technology, ICAICCIT 2023

作者： Batra, Raman Kumar, Sanjeev Bargavi, Manju Noida Institute of Engineering and Technology Department of Mechanical Engineering Uttar Pradesh Greater Noida India Tula's Institute Department of Computer Science and Engineering Dehradun India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350344387

The integration of edge computing is critical to the development of Beyond 5G (B5G) and 6G networks, as the volume of data and processing demands continue to rise. Using the radio access network (RAN), mobile edge computing (MEC) is essential for putting advanced cloud computing features in close proximity to end users. Even though MEC and its possible applications have been thoroughly studied, there hasn't been much conversation about the crucial elements required for MEC to be deployed successfully in order to satisfy various application demands. The important considerations that must be made while establishing edge infrastructure in B5G and 6G networks are outlined in this article. These factors include things like user density, server location, capacity, edge architecture, and security protocols. Moreover, it includes concrete assessments of edge computing-dependent applications, such forecasting and video conversion using deep learning. Additionally, the study provides insightful viewpoints that clarify open research questions related to edge computing within the framework of 6G networks. © 2023 IEEE.

关键词： and Cloud computing Distributed computing Edge processing Next-generation 6G communication networks

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