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Automatic Pavement Crack Detection Based on Octave Convolution Neural Network with Hierarchical Feature Learning

Journal of Beijing Institute of technology 2024年第5期33卷 422-435页

作者： Minggang Xu Chong Li Ying Chen Wu Wei Department of Civil Engineering of Nanjing Technical Vocational College Nanjing 210019China School of Artificial Intelligence and Advanced Computing(AIAC) Xi’an Jiaotong-Liverpool UniversitySuzhou 215400China Department of Electronic Engineering Shantou UniversityShantou 515063China School of Automation Science and Engineering South China University of TechnologyGuangzhou 510006China

Automatic pavement crack detection plays an important role in ensuring road *** images of cracks,information about the cracks can be conveyed through high-frequency and low-fre-quency signals that focus on fine details and global structures,*** output features obtained from different convolutional layers can be combined to represent information about both high-frequency and low-frequency *** this paper,we propose an encoder-decoder framework called octave hierarchical network(Octave-H),which is based on the U-Network(U-Net)architec-ture and utilizes an octave convolutional neural network and a hierarchical feature learning module for performing crack *** proposed octave convolution is capable of extracting multi-fre-quency feature maps,capturing both fine details and global *** propose a hierarchical feature learning module that merges multi-frequency-scale feature maps with different levels(high and low)of octave convolutional *** verify the superiority of the proposed Octave-H,we employed the CrackForest dataset(CFD)and AigleRN databases to evaluate this *** experimental results demonstrate that Octave-H outperforms other algorithms with satisfactory performance.

关键词： automated pavement crack detection octave convolutional network hierarchical feature multiscale multifrequency

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Intelligent personalised learning system based on emotions in e-learning

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Personal and Ubiquitous computing 2023年第6期27卷 2211-2223页

作者： Karthika, R. Jesi, V. Elizabeth Christo, Mary Subaja Deborah, L. Jegatha Sivaraman, Audithan Kumar, Sanjeev Department of Computer Science and Engineering University College of Engineering Tindivanam Tamil Nadu Melpakkam604001 India Department of Networking and Communications School of Computing College of Engineering SRM Institute of Science and Technology Chennai Kattankulathur603203 India Department of Information Technology Netaji Subhas University of Technology New Delhi110078 India Department of Electronics and Communication Engineering Netaji Subhas University of Technology New Delhi110078 India

One of the greatest challenges in the success of a personalised e-learning system lies in the behaviour identification of the learners during the learning and evaluation phase. The content delivery in an e-learning system must be modified and updated periodically according to the preference and behaviour of the learners. Usually, the behaviour of the learners drastically changes according to their affective states during the learning phase. The accurate identification of the learner’s negative emotions and addressing such emotions carefully in a positive sense can greatly provide success to the learners. In this paper, the learner’s emotions especially frustration emotion are automatically and accurately estimated using the information received from learning management systems (LMS) using the Takagi sugeno fuzzy inference engine. Based on this estimation, several motivational messages are distributed according to the identified emotions which had greatly helped them to succeed during e-learning. The motivational messages that are used for sending to the learners are based on regulatory fit theory. Several kinds of statistical tests are used in this paper for deep analysis. Two sets of learners, namely, control and experimental sets, are identified from undergraduate students. Experimental results are shown for these two sets to reveal the increase in their emotional strength after receiving the motivational messages. Statistical analysis t-test is also applied to the two sets, and the experimental results have shown that there is a significant difference between the two groups which shows the dominance of the proposed system. © 2023, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.

关键词： E-learning

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Optimized Learning Model for Brain-Computer Interface Using Electroencephalogram (EEG) for Neuroprosthetics Robotic Arm Design for Society 5.0

Optimized Learning Model for Brain-Computer Interface Using ...

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2024 IEEE International Conference on computing, Semiconductor, Mechatronics, Intelligent Systems and Communications, COSMIC 2024

作者： Arun Kumar, S. Anand, L. Kannur, Anil School of Computing SRM Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu India School of Computing SRM Institute of Science and Technology Department of Networking and Communications Tamil Nadu Potheri SRM Nagar Kattankulathur India Dr. P.G. Halakatti College of Engineering and Technology Department of Computer Science and Engineering Karnataka Vijayapura India

ISBN: (纸本)9798331517892

The brain activity can be represented by EEG, which does it by measuring voltage fluctuations in the brain. in this paper, the work intends to develop a Brain-computer Interface-based system that can identify and forecast the class of motor action corresponding to the left or right direction. There are two datasets used in this study, relating to motor movements - left/right-hand movement and eye open/close. The collected data is pre-processed by applying a Convolution Neuron Network to obtain a frequency domain matrix. The result computed by applying CNN provides a condensed representation of the data. The second dataset captured brain impulses corresponding to two states of the eyes opened and eyes closed, over a period of two minutes. This system is designed to effectively separate and utilize signals, with the adoption of AI techniques in these devices emerging as the most viable option for Society. The overall efficiency of this system ranges from 80% to 87%. © 2024 IEEE.

关键词： Frequency domain analysis

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Intelligent traffic prediction system using hybrid convolutional neural networks for smart cities

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Multimedia Tools and Applications 2024年 1-19页

作者： J, Jeba Sonia G, Arun Kumar kumar, ERajesh Raju, K. Narasimha Sudha, V. Kshirsagar, Pravin R. Tirth, Vineet Rajaram, A. Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Chennai603203 India Department of Electronics and Communication Engineering JSS Academy of Technical Education Uttar Pradesh Noida201301 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522302 India Andhra Pradesh Visakhapatnam530048 India Department of Electronics and Communication Engineering Sona College of Technology Tamil Nadu Salem636005 India Department of Electronics & amp Telecommunication Engineering J D College of Engineering & ampManagement Maharashtra Nagpur India Mechanical Engineering Department Centre for Engineering and Technology Innovations College of Engineering King Khalid University Asir Abha61421 Saudi Arabia Department of Electronics and Communication Engineering EGS Pillay Engineering College Nagapattinam India

Intelligent Traffic Management Systems (ITMS) play a crucial role in advancing smart city applications by addressing the rapid increase in road traffic due to the growing number of vehicles. Effective real-time traffic prediction is essential to avoid congestion, yet existing systems often struggle with high delays and insufficient accuracy. This paper introduces an innovative ITMS based on a Hybrid Convolutional Neural Network (HCNN) to overcome these challenges. The proposed system integrates numerous smart devices, including smart vehicles, with an edge computing framework where the HCNN model is deployed. The HCNN combines Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to enhance prediction accuracy and reduce latency. Experimental results demonstrate that the HCNN model achieves up to 95% prediction accuracy while reducing delays by up to 50% compared to conventional methods. These improvements are significant for smart city applications, ensuring timely and precise traffic management. The study validates the effectiveness of the HCNN model in improving traffic prediction and proposes its potential for broader smart city implementation. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Convolutional neural networks

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Analyzing Spatiotemporal Congestion Value on Urban Road Networks based on Taxi GPS Data 15

Analyzing Spatiotemporal Congestion Value on Urban Road Netw...

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15th International Multi-Conference on Complexity, Informatics and Cybernetics, IMCIC 2024

作者： Alshikhe, Rania Jindal, Vinita Harland, James School of Computing Technologies College of Science Technology Engineering and Mathematics RMIT University MelbourneVIC Australia Keshav Mahavidyalaya University of Delhi Delhi India

ISBN: (纸本)9781950492787

Traffic congestion poses significant challenges, impacting travel times, decreasing fuel efficiency, and intensifying air pollution. Consequently, individuals face difficulties in punctually reaching work or school, while the increased fuel consumption contributes to elevated gas prices. Furthermore, air pollution arising from congestion leads to various health complications. This paper provides a spatiotemporal congestion analysis, the level of traffic congestion in a specific area is evaluated and understood for a specific time. Furthermore, this paper proposed the Road Congestion Matrix (RCM) which provides a novel definition of traffic congestion as that vehicle traffic flow on road networks involves the movement of vehicles on individual road segments. In the proposed method, we consider the congestion value for each edge. As a result, congested edges can be obtained within the given amount of time. Based on the congestion value of the road segment itself, we expect the answer to be able to identify the congested seed of traffic diffusion. In addition, since most exits are two-way roads, the proposed algorithm also takes into account the properties of the road segment facing in the opposite direction with considering the local and global thresholds on the proposed model. This study illustrates the effectiveness of our method by using real GPS taxi data and providing detailed analysis of the results. © IMCIC 2024. All rights reserved.

关键词： Traffic congestion

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Optimization of Traffic and Time Control with Sensor-Driven Transmission Control System using MANET and Machine Learning 7

Optimization of Traffic and Time Control with Sensor-Driven ...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Kathirvel, N. Vidyalakshmi, R. Raihana, A. Mohanraj, A. Uma, S. Saranya, N. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science And Technology School of Computing Department of Information Technology India Karpagam College of Engineerng Department of Eee Coimbatore India Sri Krishna College of Engineering And Technology Department of Information Technology Coimbatore India Sri Eshwar College of Engineering Department of Computer Science And Engineering India Hindustan College of Engineering And Technology Department of Computer Science And Engineering India

ISBN: (纸本)9798350359299

In the dynamic landscape of modern urban environments, the optimization of transportation and traffic management systems is paramount for ensuring efficiency, sustainability, and the overall well-being of inhabitants. This study introduces an innovative solution through the deployment of a sophisticated network of strategically positioned sensors at crucial locations, encompassing roadway sections, entry and exit points, and parking spaces within a smart city framework. These sensors continuously capture real-time data on traffic flow, vehicle counts, and parking spot occupancy, forming the foundational elements of an intelligent system. Robust machine learning models, notably Convolutional Neural Networks (CNN), VGG19, k-Nearest Neighbors (KNN), and Support Vector Machines (SVM), are employed to process and analyze the acquired sensor data. CNN, renowned for its prowess in image recognition, emerges as the leading performer, achieving an impressive accuracy of 97.6%. This model excels in deciphering complex spatial patterns, significantly enhancing the precision of traffic forecasts. Additionally, performance metrics, encompassing precision, recall, F1 score, and accuracy, underscore the efficacy of each machine learning model. The exceptional precision and recall values across all models showcase their robustness in identifying and predicting traffic situations, while accuracy scores affirm their overall efficiency in optimizing transportation and traffic management within smart city environments. © 2024 IEEE.

关键词： Sensor networks

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Application of hybrid capsule network model for malaria parasite detection on microscopic blood smear images

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Multimedia Tools and Applications 2025年第7期84卷 3459-3485页

作者： Kumar, S. Aanjan Muchahari, Monoj Kumar Poonkuntran, S. Kumar, L. Sathish Dhanaraj, Rajesh Kumar Karthikeyan, P. Department of Computer Application and Science Institute of Engineering and Management West Bengal Kolkata India School of Computing Science and Engineering VIT Bhopal University Bhopal-Indore Highway Kothrikalan Madhya Pradesh Sehore466114 India Pune India Department of Electronics & Communication Engineering Velammal College of Engineering & Technology Tamil Nadu Madurai625009 India

Today, malaria is a dangerous disease caused by Plasmodium parasites and transmitted by the bite of Anopheles mosquitoes. It is estimated that more than 200,000 cases of malaria will occur every day worldwide, and more than 400,000 people will die from malaria. Early diagnosis of malaria is important to mitigate the morbidity and mortality of the disease. In this paper, a fully trained capsule neural network combined with a convolutional neural network model is proposed for malaria detection from blood smear microscopic images. In malaria diagnosis, the hybrid CapsNet model can detect malaria in blood samples. The hybrid model is optimized based on malaria data in microscopic images of infected and uninfected blood. Images are processed and enhanced through rotation before being fed into the hybrid CapsNet model. The CapsNet hybrid model is examined for malaria data with a learning rate of 0.07 and a batch size of 20. Hybrid CapsNet model shows improved results in detecting malaria from microscopic images measured against the currently available standard malaria test. Constraints like malaria parasite detection rate, accuracy, F1 index and blood smear classification is used to gauge the attainment of the hybrid CapsNet model. Detection rate of 99%, accuracy of 99.08% and FAR of 0.97% is attained by the hybrid CapsNet model as contrary to the traditional state-of-the-art model, the DSCN-Net, where the detection rate was 98%, the accuracy was 97.2%, and the FAR was 0.99%. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Diseases

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Unlocking the Power of Long Short-Term Memory Networks: A Text Classification Approach 14

Unlocking the Power of Long Short-Term Memory Networks: A Te...

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14th International Conference on computing Communication and Networking Technologies, ICCCNT 2023

作者： Pandiaraj, A. Ramshankar, N. Venkatesan, R. SRM Institute of Science and Technology Computing Technologies School of Computing Chennai India RMD.Engineering College Computer Science and Engineering Tamilnadu Chennai India Bannari Amman Institute of Technology Information Technology Tamilnadu Erode India

ISBN: (纸本)9798350335095

Text classification typically suffers from the fundamental issue of latter-day data with a very high dimension, which hinders the model developed because training time grows exponentially with the number of features used and model risk of overfitting increases with increasing number of features. Since the recurrent structure is ideal for processing long varied texts, recurrent neural networks (RNNs) are among the most often used designs in natural language processing (NLP). RNN using the Long Short-Term Memory (LSTM) architecture is one of the deep learning techniques suggested in this study. In natural language processing for English, one element stores the subject's gender, while another element records whether the subject is singular or plural. Long Short-Term Memory (LSTM) supplies elements that are intended to be able to record an input characteristic. LSTM will discover these properties throughout the training procedure. The results of this study are most accurate when variable word sequence characteristics are taken into account. © 2023 IEEE.

关键词： Long short-term memory

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A gearbox fault diagnosis algorithm based on convolutional neural network and fusion coordinate attention mechanism

A gearbox fault diagnosis algorithm based on convolutional n...

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2023 International Conference on Computer Application and Information Security, ICCAIS 2023

作者： Ni, Hongtao Zhang, Leilei Wu, Chuanjin Suzhou City University Computing Science and Artificial Intelligence College Suzhou215104 China Yancheng Institute of Technology School of Mechanical Engineering Yancheng224051 China Shanghai Ocean University College of Engineering Science and Technology Shanghai201306 China

ISBN: (纸本)9781510675032

In response to the challenges of low accuracy, difficult feature extraction, and poor real-time performance in traditional gearbox fault diagnosis algorithms, this paper proposes a gearbox fault diagnosis algorithm based on convolutional neural network (CNN) and fused coordinate attention mechanism (FCAM) stay, a dataset of various gearbox fault images is collected and divided into training, validation, and test sets. Then, a lightweight MobileNetV2-based neural network model with the FCAM is constructed using Pytorch Framework. Subsequently, the training and validation data are input into the CNN with the fused attention mechanism for fault diagnosis model training, and the trained recognition model is saved. Finally, this model is used for the diagnosis and recognition of gearbox fault images in the test set. This algorithm is capable of automatically extracting gearbox fault features and classification, offering advantages in terms of reliability and real-time performance. © 2024 SPIE.

关键词： Failure analysis

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NLP-Based SMS Spam Detection Using Ensemble Learning 4

NLP-Based SMS Spam Detection Using Ensemble Learning

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4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Rajalakshmi, M. Rengaraj, R. Karthikeyan, M. Aruna, S. Srm Institute of Science and Technology School of Computing Dept of Computing Technologies KTR Campus Chennai India Sri Sivasubramaniya Nadar College of Engineering Dept of Electrical and Electronics Engineering Kalavakkam Chennai India Srm Insitute of Science & Technology Dept of Electrical and Electronics Engineering Ramapuram Chennai India

ISBN: (纸本)9798331508845

In today's environment, spam text messages are a common issue and a source of frustration for users. This study suggests a novel method for identifying SMS spam that makes use of natural language processing (NLP), multiple model creation, and ensemble learning. The suggested approach entails pre-processing the data using natural language processing (NLP) techniques including lemmatizing, stemming, and stopword removal in order to extract pertinent features from the text data and convert it into numerical representations. The suggested approach's effectiveness was assessed using an actual dataset and contrasted with conventional machine learning algorithms like SVM and Naïve *** altered data is used to train the numerous classifiers, and an ensemble learning technique is then used to combine their predictions and produce a more accurate outcome. More precision was provided by the final model than by conventional models. In order to lessen the quantity of spam messages that users get, the research's conclusions may be helpful in the development of an efficient spam SMS detector. Furthermore, the model was hosted using the streamlit web app framework on the cloud-based platform Railway, making it instantly accessible to users without the need to install any software. © 2024 IEEE.

关键词： Contrastive Learning

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