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Novel methodology for apple leaf disease classification with PCNN-IELM

Neural Computing and Applications 2025年第6期37卷 4895-4913页

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

Agriculture is crucial to the global economy, particularly in ensuring food security. Recent trends indicate that various plant diseases are causing substantial financial losses in the agricultural sector worldwide. Traditional manual inspection methods for detecting fruit and plant diseases are labor-intensive and inefficient. Adopting automated disease detection technologies could significantly enhance early diagnosis and reduce the economic impact of these diseases on agriculture. This study introduces an advanced model for classifying apple diseases by integrating a pre-trained convolutional neural network (PCNN), such as VGG16, VGG19, or ResNet50, with an incremental extreme learning machine (I-ELM) for efficient feature extraction and classification. A key innovation of this model is replacing the PCNN’s fully connected layer with the I-ELM, which eliminates the lengthy back-propagation process and significantly reduces training time. Integrating I-ELM with PCNN harnesses the rapid learning capabilities and robust generalization of I-ELM with the superior feature extraction abilities of CNNs. I-ELM simplifies the network architecture by avoiding the complex neural networks commonly used in other methods. The model’s effectiveness is rigorously evaluated on the well-known Plant Village dataset, demonstrating its ability to identify various apple diseases through performance metrics such as precision, sensitivity, specificity, accuracy, and the F1-score. Comparing existing deep learning models using these metrics highlights its superior performance. This innovation is up-and-coming for intelligent agricultural systems, offering an effective solution for classifying apple diseases and enabling timely and innovative farming practices. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Fruits

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Leveraging feature fusion ensemble of VGG16 and ResNet-50 for automated potato leaf abnormality detection in precision agriculture

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Soft Computing 2025年第4期29卷 2263-2277页

作者： Trivedi, Amit Kumar Mahajan, Tripti Maheshwari, Tanmay Mehta, Rajesh Tiwari, Shailendra Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Punjab Patiala147001 India

In the era of advancement in technology and modern agriculture, early disease detection of potato leaves will improve crop yield. Various researchers have focussed on disease due to different types of microbial infection in potato leaves using computer vision and machine learning approaches. In this paper, a data science approach for multiclass classification of potato normal and abnormal leaves due to fungal infection like early blight and late blight is performed using the ensembling of deep learning (DL) CNN models. Firstly, the performance of classification on potato disease is verified separately on VGG16 and ResNet-50 CNN models after pre-processing of the leaf dataset. The pre-processing includes noise removal and normalization. Further improvement in classification accuracy is achieved by the ensembling of VGG16 and ResNet-50 CNN models. The ensembling of CNN models is performed on the feature level by fusing features extracted using VGG16 and ResNet-50. From the experimental results, performed on publicly available datasets consisting of 2152 number of normal and abnormal images it is observed that the average classification accuracy of 98.22%, 96.16% and 95.68% is achieved using the proposed ensemble, VGG16 and ResNet-50 models respectively. The efficacy of the proposed approach (ensemble technique at feature level fusion) is verified in comparison with recently reported DL model-based approaches. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Classification (of information)

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

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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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SuspAct: novel suspicious activity prediction based on deep learning in the real-time environment

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Neural Computing and Applications 2024年第34期36卷 21307-21320页

作者： Kansal, Sachin Jain, Akshat Kumar Biswas, Moyukh Bansal, Shaurya Mahindru, Namay Kansal, Priya Computer Science Engineering Department Thapar Institute of Engineering and Technology Punjab Patiala India

In today’s evolving landscape of video surveillance, our study introduces SuspAct, an innovative ensemble model designed to detect suspicious activities in real time swiftly. Leveraging advanced Long-term Recurrent Convolutional Networks (LRCN), SuspAct represents a significant advancement in intelligent surveillance technology. By combining insights from various LRCN models through the Majority Voting ensemble technique, SuspAct enhances its overall robustness, outperforming traditional surveillance methods. Through rigorous experimentation on large-scale datasets, we demonstrate SuspAct’s superiority in proactive crime prevention, showcasing its potential to revolutionize security protocols and contribute substantially to public safety. Our work addresses the challenges posed by the escalating volume of video data and lays a strong foundation for future advancements in intelligent video surveillance technology. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Deep learning

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Heart sound classification method using gammatonegram and SVM

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Multimedia Tools and Applications 2024年 1-37页

作者： Taneja, Kriti Arora, Vinay Verma, Karun Computer Science and Engineering Department Thapar Institute of Engineering and Technology Punjab Patiala India

Today cardiovascular diseases have been posing a serious threat to human lives all over the world. Various automated decision-making systems have been proposed by the researchers to help cardiologists to diagnose heart health with more perfection, thus, reducing the frequency of fatalities. This research aims at classifying the Phonocardiogram (PCG) signals captured from the functioning of the cardiac system as normal or abnormal. The methodology proposed here has been validated using the benchmark dataset PhysioNet Computing in Cardiology (CinC) 2016. As of this writing, the studies carried out using contemporary techniques have so far been unable to generate pictorial representation from cardiac audio recordings that match the way the ear processes sound. The present research suggests a unique methodology for the detection of important events in an audio signal using a biologically-inspired depiction of the audio stream through a picture known as Gammatonegram which correlates to the processing of audio in the cochlea membrane of the inner human auditory system. In this study, texture-related features which include Linear Ternary Pattern (LTP), Local Directional Pattern (LDP), Geometric Local Textural Pattern (GLTP), and Local Phase Quantization (LPQ) have been extracted from a visual representation of PCG signal such as Spectrogram, Scalogram, Mel-spectrogram, and Gammatonegram. As compared to the case when researchers were employing other images, it has been noticed that the fusion of the features retrieved from the Gammatonegram has led to an increased overall classification performance metrics. The experiment yielded an overall accuracy of 94.00 % with precision and F1 scores of 91.77 and 93.61 respectively. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Heart

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Effect of cooling media on bead geometry,microstructure,and mechanical properties of wire arc additive manufactured IN718 alloy

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Advances in Manufacturing 2024年第1期12卷 124-149页

作者： Parveen Kumar Satish Kumar Sharma Ratnesh Kumar Raj Singh Department of Mechanical Engineering Thapar Institute of Engineering and TechnologyPatialaPunjab147004India

This work aims to present and explore thermal management techniques for the wire arc additive manufacturing(WAAM)of IN718 *** heat can be mitigated via air or water *** this study,the material was deposited under four different heat-input conditions with air or water *** air cooling,the layer is deposited in a normal atmospheric air environment,whereas with water cooling,the material is deposited inside a water tank by varying the water *** validate the air and water cooling thermal management techniques,IN718 single-pass and multilayer linear walls were deposited using the bidirectional gas metal arc welding based WAAM setup under four different heat input *** the deposition of single layers,the temperature profiles were recorded,and the geometric and microstructural features were *** multilayer wall structures,the mechanical properties(hardness,tensile strength,and elongation)were determined and assessed using the corresponding microstructural features explored through scanning electron microscopy(SEM),energy dispersive spectroscopy(EDS),and electron backscatter diffraction(EBSD)*** microstructure observed through SEM analysis in the building direction was found to be nonhomogenous compared with that in the deposition ***,water cooling was found to govern bead characteristics,such as wall width and *** grain size and anisotropy of the mechanical properties also decreased in the water-cooled ***,water cooling is an economical and efficient method to mitigate excessive heat accumulation in WAAM-deposited IN718.

关键词： Air cooling Water cooling IN718 Bead characteristics Microstructure Electron backscatter diffraction(EBSD)

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Modelling and analysis of randomly distributed optimised structure of mixed CNT bundle interconnects-impact on crosstalk induced delay

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Multimedia Tools and Applications 2025年第1期84卷 343-372页

作者： Mitra, Gaurav Kamboj, Sangeeta Rai, Mayank Kumar EIED Thapar Institute of Engineering and Technology Thapar University Punjab Patiala147004 India ECED Thapar Institute of Engineering and Technology Thapar University Punjab Patiala147004 India

Modern VLSI devices are manufactured using deep submicron technology. On-chip interconnects represent a major performance bottleneck for high-speed VLSI architectures. Nowadays, carbon nanotube (CNT) bundles have been projected as a possible nano interconnect material for increasing the operational speed and adaptable functionality of a system-on-chip. In this paper, a number of CNT bundles and their spatial orientations are considered, including single wall CNT (SWCNT), multi-wall CNT (MWCNT), and mixed CNT bundles (MCB). A good way to reduce the delay can be created by randomly distributing SWCNTs and MWCNTs of different diameters in an MCB. This can be a possible solution for a high speed VLSI connection. Although bundled SWCNTs and MWCNTs are easy, it is almost difficult to create an MCB with accurate SWCNT and MWCNT configurations. The main goal of this work is the optimized use of randomly distributed carbon nanotubes (RMCNTs) as nanocompounds. Furthermore, a new ATSO (Adaptive Transient Search Optimization) algorithm with Huang’s corner placement algorithm is studied to solve problems related to the placement of randomly distributed CNTs with different diameters in an MCB. This algorithm maximizes the tube density by distributing the CNTs randomly. Also, the temperature-dependent circuits are modelled for analyzing the crosstalk performance in the capacitively and inductively coupled MCB and RMCNT bundle (RMCNTB) interconnects at the far end of the victim line. The proposed optimized RMCNTB interconnect outperforms existing MCB interconnects in terms of propagation delay and power dissipation within a temperature range of 300 to 500 K. Furthermore, with an interconnect length of 1.5 mm, the proposed optimized RMCNTB structure exhibits a relative delay of 3.22% and a power dissipation of 10.51% less than the best MCB structure. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： System-on-chip

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Classifying chest x-rays for COVID-19 through transfer learning: a systematic review

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Multimedia Tools and Applications 2025年第2期84卷 689-748页

作者： Mallick, Devanshi Singh, Arshdeep Ng, Eddie Yin-Kwee Arora, Vinay Thapar Institute of Engineering & Technology Punjab Patiala India Wipro Limited Gurugram India School of Mechanical and Aerospace Engineering Nanyang Technological University Singapore Singapore Computer Science Engineering Department Thapar Institute of Engineering & Technology Punjab Patiala India

This study makes a comprehensive assessment of the predominant Transfer Learning (TL) techniques employed for the classification of COVID-19 cases in Chest X-rays (CXR) images. The methodologies have been selected on the basis of their merits and demerits, suitability, and possible impact on the development of the region being studied. The study examines the various methods of TL employed in the classification of COVID-19 cases with the objective to gain a deeper understanding about all the aspects of these methodologies. It can be of great significance for the researchers and medical professionals in making well-informed decisions about the implementation of these techniques to improve the precision and effectiveness of COVID-19 diagnosis. The practical consequences of these techniques help in early identification of such cases for having a suitable intervention. As many as 48 studies conducted during the period 2020–2023 have been included in the current research work for having an assessment about the problem under investigation. The study has specifically focused on transfer learning-based models utilized for the identification of COVID-19 through CXR pictures. It highlights the challenges posed by dataset dynamics, methodological variations, and performance metrics of different models. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： COVID-19

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Robust and high capacity image steganography technique using spiral-walk inter-block DCT coefficient differencing

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Multimedia Tools and Applications 2024年第39期83卷 86405-86424页

作者： Kapoor, Samridhi Shivani, Shivendra Thapar Institute of Engineering and Technology Patiala India

Steganography conceals secret information within the cover image so that its presence cannot be detected. Embedding capacity, imperceptibility, and security are three important aspects of a steganographic technique. The paper proposes a new algorithm for concealing binary data in grayscale images by dividing the host image into 8×8 DCT block, and in each block, four embedding locations are selected to embed 4 bits of secret message, increasing the cover image’s embedding capacity. The algorithm computes the difference between the DCT coefficients at the same position of two adjacent blocks to embed the secret bits into the cover image. Furthermore, various steganalysis techniques such as median filtering, gaussian noise, salt& pepper, chi-square, and histogram analysis were performed to check the robustness of the proposed method. The result of the proposed algorithm constructs a stego image with a PSNR of 51.3678 dB, which is higher than that of the existing steganography techniques. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Steganography

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DeepBio:A Deep CNN and Bi-LSTM Learning for Person Identification Using Ear Biometrics

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computer Modeling in engineering & Sciences 2024年第11期141卷 1623-1649页

作者： Anshul Mahajan Sunil K.Singla Department of Electrical and Instrumentation Engineering Thapar Institute of Engineering and TechnologyPatiala147004India

The identification of individuals through ear images is a prominent area of study in the biometric *** recognition systems have faced challenges during the COVID-19 pandemic due to mask-wearing,prompting the exploration of supplementary biometric measures such as ear *** research proposes a Deep Learning(DL)framework,termed DeepBio,using ear biometrics for human *** employs two DL models and five datasets,including IIT Delhi(IITD-I and IITD-II),annotated web images(AWI),mathematical analysis of images(AMI),and *** augmentation techniques such as flipping,translation,and Gaussian noise are applied to enhance model performance and mitigate *** extraction and human identification are conducted using a hybrid approach combining Convolutional Neural Networks(CNN)and Bidirectional Long Short-Term Memory(Bi-LSTM).The DeepBio framework achieves high recognition rates of 97.97%,99.37%,98.57%,94.5%,and 96.87%on the respective *** analysis with existing techniques demonstrates improvements of 0.41%,0.47%,12%,and 9.75%on IITD-II,AMI,AWE,and EARVN1 datasets,respectively.

关键词： Data augmentation convolutional neural network bidirectional long short-term memory deep learning ear biometrics

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