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Cultivating road safety: A comprehensive examination of intelligent ensemble-based road crack detection

Multimedia Tools and Applications 2025年第12期84卷 10747-10780页

作者： Navpreet Roul, Rajendra Kumar Rani, Rinkle Department of Computer Science and Engineering Thapar Institute of Engineering & Technology Punjab Patiala147004 India

Detecting and promptly identifying cracks on road surfaces is of paramount importance for preserving infrastructure integrity and ensuring the safety of road users, including both drivers and pedestrians. Presently, the predominant approach for crack detection relies on labor-intensive and costly manual inspections, exacerbated by the enduring challenge of concrete road cracks susceptible to environmental factors like temperature fluctuations, heavy traffic loads, and prolonged exposure to harsh weather conditions. This paper presents a comprehensive research initiative aimed at advancing road crack detection techniques. The proposed method leverages a classical Convolutional Neural Network (CNN) in conjunction with the Extreme Learning Machine (ELM) for efficient feature extraction and classification. A pivotal breakthrough is achieved by replacing the fully connected layer of the CNN with ELM, thus circumventing the time-consuming backpropagation process and significantly expediting the training process. This integration harnesses ELM’s swift learning capabilities and strong generalization performance, complemented by the CNN’s exceptional feature extraction abilities. The model’s effectiveness is rigorously assessed using the wide recognized CCIC, SDNET2018, and Synthesized datasets, and its proficiency in distinguishing between Crack and No-Crack regions is demonstrated through various performance metrics. Furthermore, the model’s performance is benchmarked against existing deep learning models using these performance metrics, showcasing its superior performance. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Roads and streets

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Leveraging machine learning for intelligent agriculture

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Discover Internet of Things 2025年第1期5卷 1-21页

作者： Sowmya, B.J. Meeradevi, A.K. Supreeth, S. Pradeep Kumar, D. Ravi Kumar, B.N. Rohith, S. Mishra, Divyansh Koushik, Abhishek Patil, Ankit U. Department of Artificial Intelligence and Data Science Ramaiah Institute of Technology Bengaluru560054 India Department of Artificial Intelligence and Machine Learning M S Ramaiah Institute of Technology Bengaluru560054 India School of Computer Science and Engineering REVA University Bengaluru560064 India Department of Computer Science Ramaiah Institute of Technology Bengaluru560054 India Department of Information Science and Engineering BMS Institute of Technology & Management Bengaluru560119 India Department of Electronics and Engineering Nagarjuna College of Engineering and Technology Bengaluru562164 India

Agriculture, the backbone of many economies, faces challenges like lack of information, outdated practices, and limited access to technology, hindering farmer productivity. This work proposes a user-friendly, multilingual platform leveraging Generative AI to address farmers' diverse needs. The platform encompasses various features to enhance agricultural practices. An LLM-powered Government Scheme Advisor functions as a multilingual chatbot offering intelligent guidance on government agricultural schemes and subsidies. The Disease Detection module utilizes AI technology for real-time identification and treatment recommendations, minimizing crop diseases and yield losses. The Soil Testing Centre feature locates nearby soil testing centers, providing essential information based on geographical data to assist farmers in optimizing soil quality. A Crop Recommendation feature employs Machine Learning algorithms to offer personalized crop recommendations, considering various factors and aiding informed decision-making. The Crop Planning Tool, with its intuitive user interface, simplifies planning planting schedules and managing resources. Additionally, the platform includes an MSP Center Locator to find nearby Minimum Support Price (MSP) centers based on location. By integrating these innovative solutions, this platform bridges the gap between conventional agricultural techniques and contemporary technology, equipping farmers with the resources and expertise essential for advancing productivity and sustainability. Multilingual support ensures accessibility for a wider audience, breaking down language barriers and promoting inclusivity in the agricultural sector. This work proposes an innovative, multilingual platform powered by Generative AI to address these issues. Key features include an LLM-driven chatbot for government scheme guidance, AI-based real-time disease detection, and location-based tools for soil testing and MSP center identification. Additionally, the platf

关键词： Smart agriculture

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Electrocardiogram-based heart disease prediction using hybrid deep feature engineering with sequential deep classifier

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Multimedia Tools and Applications 2025年第10期84卷 7443-7475页

作者： Golande, Avinash L. Pavankumar, T. Department of Computer Science & Engineering KLEF A. P. Vijayawada India

Several approaches can detect early heart problems. Electrocardiograms (ECGs) are better and more affordable for early heart disease prediction. ECG data can better predict heart diseases and abnormalities. Standard methods are slow and limited to a narrow ECG signal pattern range. Newer deep learning-based algorithms require tedious and unimportant human feature extraction from raw ECG data. This study presents a new electrocardiogram-based cardiac disease detection system using an ensemble method for feature extraction and deep learning for classification. The approach includes pre-processing of input ECG signals, hybrid feature engineering, and disease categorization. Pre-processing the input signal preserved its integrity by reducing the artifact and noise using a robust filtering technique. ECG feature extraction should use generic automation and beat-specific features. We designed the hybrid feature engineering approach using a Convolutional Neural Network (CNN) and Stationary Wavelet Transform (SWT). The SWT method employs dynamic thresholding to segment ECG data into individual heartbeats, from which the beat-specific feature vector is derived. To automate the process of classifying cardiac diseases, a powerful 3-layer CNN has been trained to do so and now extracts those attributes automatically. Manifold learning of feature selection is then used to normalize the combined features from the two methods. Long Term Short Memory (LSTM), a deep learning classifier, has been given the ensemble feature vector. For LSTM, the sequential characteristics vector transmits information to the LSTM layer and then calculates the probabilities for each class to provide precise disease classification in the classification phase. The suggested model surpasses cutting-edge deep learning-driven techniques when used with a publicly available multi-disease ECG dataset. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Electrocardiograms

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A novel cluster based reliable security enhancement in FANET directed by game theory

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Soft Computing 2025年第2期29卷 1091-1106页

作者： Gupta, Shikha Sharma, Neetu Department of Computer Science Engineering College Ajmer Rajasthan Ajmer305001 India

The emergence of interconnected UAVs has given rise to the creation of flying ad hoc networks (FANETs) aimed at efficiently facilitating network-dependent services. However, FANET encountered considerable challenges in achieving reliability due to security issues influenced by the existence of malicious nodes. These nodes continuously engage in communication and present a significant threat. These issues are precisely addressed in this article by introducing a novel methodology enhancing network security through a game theory-driven decision tree approach. The proposed strategy involves the implementation of a cluster mechanism based on path similarity techniques. Additionally, an optimized strategy put forth to minimize cluster overhead. Comparing with state-of-the-art protocols in identifying malicious nodes within the network, the results indicate a notable enhancement averaging at 52.84%. Moreover, in aspects such as delay reduction, precision rate increase, and message drop minimization, the experimental outcomes exhibit average improvements of 42.17%, 65.24%, and 46.63%, respectively. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Decision trees

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Flu-Net: two-stream deep heterogeneous network to detect flu like symptoms from videos using grey wolf optimization algorithm

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Journal of Ambient Intelligence and Humanized Computing 2023年第6期14卷 7733-7745页

作者： Gupta, Himanshu Imran, Javed Sharma, Chandani Department of Computer Science and Engineering Quantum University Roorkee India Computer Science and Engineering Department Thapar Institute of Engineering and Technology Patiala India

The outbreak of COVID-19 (also known as Coronavirus) has put the entire world at risk. The disease first appears in Wuhan, China, and later spread to other countries, taking a form of a pandemic. In this paper, we try to build an artificial intelligence (AI) powered framework called Flu-Net to identify flu-like symptoms (which is also an important symptom of Covid-19) in people, and limit the spread of infection. Our approach is based on the application of human action recognition in surveillance systems, where videos captured by closed-circuit television (CCTV) cameras are processed through state-of-the-art deep learning techniques to recognize different activities like coughing, sneezing, etc. The proposed framework has three major steps. First, to suppress irrelevant background details in an input video, a frame difference operation is performed to extract foreground motion information. Second, a two-stream heterogeneous network based on 2D and 3D Convolutional Neural Networks (ConvNets) is trained using the RGB frame differences. And third, the features extracted from both the streams are combined using Grey Wolf Optimization (GWO) based feature selection technique. The experiments conducted on BII Sneeze-Cough (BIISC) video dataset show that our framework can 70% accuracy, outperforming the baseline results by more than 8%. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： COVID-19

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Comprehensive overview of Alzheimer's disease utilizing Machine Learning approaches

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Multimedia Tools and Applications 2024年第37期83卷 85277-85329页

作者： Kumar, Rahul Azad, Chandrashekhar Department of Computer Science and Engineering National Institute of Technology Jamshedpur India

Alzheimer's disease is a common and complex brain disorder that primarily affects the elderly. Because it is progressing and has few effective therapies, it requires a thorough understanding of the condition;our study offers a comprehensive analysis of AD with a dual approach that combines both bibliometric and experimental analyses. The bibliometric analysis applies statistical and mathematical techniques to figure out the states of AD research, including publishing trends, prestigious journals, and collaborative networks. Concurrently, the experimental examination explores current advancements, focusing on Machine Learning, Deep Learning, and Metaheuristic approaches, tackling complex issues resulting from varied datasets. The experimental work is fascinating because it uses twenty classifiers and two datasets, initially without feature selection and then with seven feature selection techniques. This thorough investigation focuses on developments in disease processes, therapeutic approaches, and diagnostic tool development. This research offers a multidimensional overview of Alzheimer's disease by combining bibliometric and experimental methods, addressing problems and highlighting the shortcomings of earlier studies. By helping academics, policymakers, and healthcare professionals navigate the complexities of AD, this novel methodology advances a more thorough understanding of the Alzheimer's disease domain. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Speech processing

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Deep Learning Based Signal Detection for Quadrature Spatial Modulation System

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China Communications 2024年第10期21卷 78-85页

作者： Shu Dingyun Peng Yuyang Yue Ming Fawaz AL-Hazemi Mohammad Meraj Mirza School of Computer Science and Engineering Macao University of Science and TechnologyMacaoChina Department of Computer and Network Engineering University of JeddahSaudi Arabia Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia

With the development of communication systems, modulation methods are becoming more and more diverse. Among them, quadrature spatial modulation(QSM) is considered as one method with less capacity and high efficiency. In QSM, the traditional signal detection methods sometimes are unable to meet the actual requirement of low complexity of the system. Therefore, this paper proposes a signal detection scheme for QSM systems using deep learning to solve the complexity problem. Results from the simulations show that the bit error rate performance of the proposed deep learning-based detector is better than that of the zero-forcing(ZF) and minimum mean square error(MMSE) detectors, and similar to the maximum likelihood(ML) detector. Moreover, the proposed method requires less processing time than ZF, MMSE,and ML.

关键词： bit error rate complexity deep learning quadrature spatial modulation

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IoT enabled plant leaf disease segmentation and multi-classification using mayfly bald eagle optimization-enabled machine learning

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Multimedia Tools and Applications 2024年第21期83卷 59747-59781页

作者： Mishra, Monalisa Choudhury, Prasenjit Pati, Bibudhendu Department of Computer Science and Engineering National Institute of Technology Durgapur India Department of Computer Science Rama Devi Women’s University Bhubaneswar India

Agricultural production is critical to the economy. This is one of the reasons why disease detection in plants is so important in agricultural settings, as plant disease is rather common. Farmers are not engaged in increasing their agricultural productivity daily since there are no technologies in the previous system to detect diseases in various crops in an agricultural environment. With the exponential population growth, food scarcity is a huge concern globally. In addition to this, the productivity of agricultural products has been highly impacted by the rapid increase in phytopathological adversities. The main challenges in leaf segmentation and plant disease identification are prior knowledge is required for segmentation, the implementation still lacks the accuracy of results, and more tweaking is required. To reduce the devastating impacts of illnesses on the economy, early detection of illnesses in plants is therefore essential. This paper describes an approach for segmenting and detecting plant leaf diseases based on images acquired via the Internet of Things (IoT) network. Here, a plant leaf area is segmented with a UNet, whose trainable parameters are optimized using the Mayfly Bald Eagle Optimization (MBEO) algorithm. Further, plant type classification is carried out by the Deep batch normalized AlexNet (DbneAlexNet), optimized by the Sine Cosine Algorithm-based Rider Neural Network (SCA-based RideNN). Finally, the DbneAlexNet, with weights adapted by the MBEO algorithm, is used to identify plant disease. The Plant Village dataset is used to evaluate the proposed DbneAlexNet-MBEO for plant-type classification and disease detection. The efficiency of the UNet-MBEO for segmentation is examined based on the Dice coefficient and Intersectin over Union (IOU) and has achieved superior values of 0.927 and 0.907. Moreover, the DbneAlexNet-MBEO is examined considering accuracy, Test Negative Rate (TNR), and Test Positive Rate (TPR) and offered superior values of 0

关键词： Classification (of information)

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Explainable AI for Pancreatic Cancer Prediction and Survival Prognosis: An Interpretable Deep Learning and Machine Learning Approach

Informatica (Slovenia)

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Informatica (Slovenia) 2024年第4期48卷 623-640页

作者： Srinidhi, B. Bhargavi, M.S. Department of Computer Science and Engineering Bangalore Institute of Technology Bengaluru India

Pancreatic cancer's devastating impact and low survival rates call for improved detection methods. While Artificial Intelligence has shown remarkable progress, its increasing complexity has led to "black box" models, hindering their acceptance in critical fields like healthcare. To address this, Explainable Artificial Intelligence (XAI) has gained traction, aiming to create transparent AI systems. In this study, we propose a comprehensive approach that combines the power of Deep Learning for pancreatic cancer detection using Computed Tomography (CT) images and Machine Learning (ML) for survival prognosis based on clinical data. By leveraging CT images with Deep learning models such as Convolutional Neural Networks, VGG-16 and DenseNet-201, effective diagnosis of Pancreatic Cancer is achieved and comprehensive insights into the tumor's spatial characteristics are obtained. DenseNet-201 outperformed the other models in terms of accuracy and interpretability with a predictive accuracy of 95%. The integration of ML techniques such as Stochastic Gradient Descent, Naïve Bayes and Extra Tree classifiers with clinical data predicts the chances of survival, providing vital information for treatment planning and personalized care. To validate the model's accuracy and interpretability, a comprehensive XAI validation is conducted using state-of-the-art techniques like Local Interpretable Model-agnostic Explanations and Shapley Additive Explanations. These methods provide localized explanations for predictions, allowing clinicians to understand risk and survival chances. This study holds immense potential to aid healthcare professionals in diagnosis, prognosis, and personalized treatment strategies, contributing to enhanced patient outcomes in the fight against pancreatic cancer. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Stochastic systems

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mLBOA-DML: modified butterfly optimized deep metric learning for enhancing accuracy in intrusion detection system

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Journal of Reliable Intelligent Environments 2023年第3期9卷 333-347页

作者： Prabhakaran, Varun Kulandasamy, Ashokkumar Department of Computer Science and Engineering St.Joseph’s College of Engineering Chennai India Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India

Intrusion detection is a prominent factor in the cybersecurity domain that prevents the network from malicious attacks. Cloud security is not satisfactory for securing the user’s information because it is based on standard protocols. Hence, cloud users cannot fully trust the security offered by cloud service providers. The state-of-the-art techniques create clusters for classes and manually label the unknown classes to detect novel attacks. This notion binds the network traffic associated with each attack together and drifts the similarity between the same attacks. These techniques are often prone to errors and degrade performance. To overcome this drawback, various researchers have developed different intrusion detection system which relies on specific attack patterns to distinguish between normal and abnormal behavior. This paper presents a modified Lagrange interpolated Butterfly optimization algorithm-based deep metric learning (mLBOA-DML) architecture for intrusion detection to detect both host-based and network attacks. DML architecture parameters are optimized utilizing mLBOA algorithm via its global optimization capability for increasing the accuracy of attack prediction. DML algorithm does both feature extraction and classification processes. When evaluated using the UNSW-NB15 and NSL-KDD datasets, the proposed model offers improved accuracy near 99% for both datasets. © 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

关键词： Intrusion detection

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