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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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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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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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Stairdepth: a novel staircase detection through depth maps generated by depth anything V2

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International Journal of Information technology (Singapore) 2025年第4期17卷 2075-2086页

作者： Shekar, Avire Laxmi Chandra Chandrika, Mukkolla Bhuvana Naidu, Vakkalagadda Hemanth Muppalaneni, Naresh Babu Department of Computer Science and Engineering Indian Institute of Information Technology Design and Manufacturing Kurnool Kurnool India

Detection of a staircase is an important task in the fields of both assistive technology and autonomous navigation with an aim to enable substantial improvement in safety and accessibility for both those with limited vision and those with mobility problems. Classic methods often perform poorly under challenging conditions, such as low lighting, irregular architectural arrangements, or occluded views. Whereas the integration of physical depth sensors has increased detection accuracy, this integration introduces increased complexity, cost and greater maintenance issues. Our work proposes a novel architecture capable of directly estimating depth maps from color images using a pre-trained model without relying on physical sensors for depth estimation. It detects the staircase efficiently, including the classification of stair lines into concave or convex, with the classification of surfaces as a tread or riser, hence localizing staircases accurately. Our proposed methodology demonstrated exceptional results, with 83.2% Precision, 90.8% Recall, and 72.2% Intersection over Union on stair line classification and 97.1% Pixel Accuracy and 82.9% Mean Pixel Accuracy on stair surfaces by outperforming the efficacy of current approaches pertaining to stair line and surface classification. © Bharati Vidyapeeth's institute of computer Applications and Management 2025.

关键词： Depth image Segmentation Stair surfaces Staircase detection StairDepth Stairline

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Enhanced EEG decoding with weight freezing in parallel deep neuro-fuzzy networks (PDNFN) for multi-neurological disorder diagnosis

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Iran Journal of computer science 2025年 1-16页

作者： Jain, Shraddha Srivastava, Rajeev Department of Computer Science and Engineering Indian Institute of Technology BHU Uttar Pradesh Varanasi 221011 India

This study introduces an improved EEG decoding model. It uses parameter freezing within a Parallel Deep Neuro-Fuzzy Network (PDNFN) to improve the accuracy and efficiency of diagnosing neurological disorders. The method outperforms other evaluated approaches, achieving an accuracy of 91.84%, precision of 92.15% and recall of 91.42%. Statistical validation-through p-values < 0.05 and confidence intervals-confirms the robustness of the results. Unlike traditional deep learning models that update all weights during training, this method freezes less important weights based on a mask matrix and input threshold. This technique encourages model sparsity, alleviates overfeeting, and reduces computational load without compromising key discriminative features. Compared to dropout, parameter freezing offers a more structures from of regularization, improving generalization in EEG analysis. The PDNFN architecture combines fuzzy logic with deep learning to handle the uncertainty in EEG signals and enhance feature learning. Experiments conducted across multiple publicly available EEG datasets demonstrate notable improvements in both classification performance and computational efficiency. Comparative evaluation further highlight its superiority over traditional fully connected networks and existing regularization methods. Overall, it provides a practical and efficient solution for real-time clinical diagnosis using EEG data. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： EEG decoding Electroencephalogram Fuzzy logic Neurological disorders Parallel deep euro-fuzzy networks (PDNFN) Sparse connections Weight freezing

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Spatio-Temporal NDVI Prediction for Rice Crop

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SN computer science 2025年第3期6卷 1-13页

作者： Dey, Anamika Sarkar, Somrita Mondal, Arijit Mitra, Pabitra Centre for Computational and Data Sciences Indian Institute of Technology Kharagpur West Bengal Kharagpur 721302 India Department of Computer Science and Engineering Indian Institute of Technology Patna Bihar Bihta 801106 India Department of Computer Science and Engineering Indian Institute of Technology Kharagpur West Bengal Kharagpur 721302 India

Accurately predicting the Normalized Difference Vegetation Index (NDVI) is crucial for effective agricultural planning and decision-making. Despite much literature on NDVI prediction, most of these methods do not consider spatial-temporal variations. Generating a dataset for predicting NDVI is another major bottleneck, as such data needs to be collected over an extensive period. In this work, we develop a semi-automated framework to generate a comprehensive time-series dataset after necessary preprocessing using the information from Sentinel-2, POWER Data Access Viewer, and the Google Earth Engine (GEE). We propose a deep learning-based model to predict NDVI based on its historical time series and meteorological and spatial information for the rice crop in India. Here, we use a Long Short-Term Memory (LSTM)-based model augmented with spatial information to predict the NDVI of the near-about region for which the data is unavailable. We compared the proposed approach with a method that uses a weighted average of nearest neighbors prediction. Also, we compare the proposed method with existing statistical techniques such as the Autoregressive (AR), AutoRegressive Integrated Moving Average (ARIMA), and Seasonal AutoRegressive Integrated Moving Average (SARIMA). Experimental results show that our methodology outperforms other techniques by a margin of 4% to 8%. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Autoregressive integrated moving average Long short-term memory Normalized difference vegetation index Seasonal autoregressive integrated moving average Sentinel-2 Weighted average of nearest neighbors prediction

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Isomorphic Classification of First Degree Cellular Automata

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Complex Systems 2025年第1期34卷 59-90页

作者： Vikrant, Vicky Bhattacharjee, Kamalika Mukherjee, Sukanya Department of Computer Science and Engineering National Institute of Technology Tamil Nadu Tiruchirappalli620015 India Department of Computer Science and Engineering Institute of Engineering and Management West Bengal Kolkata700091 India

This paper explores the concept of isomorphism in cellular automata (CAs), focusing on identifying and understanding isomorphic relationships between distinct CAs. A cellular automaton (CA) is said to be isomorphic to another CA if the configuration transition diagrams of the two CAs are identical. To illustrate how isomorphism entails the preservation of structural properties while allowing for variations in state representations, we exhaustively explore the isomorphic reversible rules in the first degree CAs (FDCAs), that is, the simple three-neighborhood CAs represented by eight parameters. The experimental study includes reversible FDCAs for states two to 10 with cell lengths of four to seven under the null boundary condition and states two to seven with cell lengths of four to seven under the periodic boundary condition. Given a CA, we also provide two synthesis methods for identifying the set of CAs that are isomorphic to it. The first method operates by permuting the states of a group of CA cell locations, whereas the second method involves exchanging cells at various points. We demonstrate that, depending on the range of exchanged cell positions, the isomorphic CAs produced in this way may have a bigger neighborhood than the input CA. Finally, we provide an exhaustive classification of the elementary CAs (ECAs) using the first synthesis method and compare it with existing results. © 2025, Complex Systems Publications, Inc. All rights reserved.

关键词： classification cycle structure FDCA first degree cellular automaton parameters reversibility synthesis schemes

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Composite spectral spatial pixel CNN for land-use hyperspectral image classification with hybrid activation function

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Multimedia Tools and Applications 2025年第12期84卷 10527-10550页

作者： Banerjee, Anasua Swain, Satyajit Rout, Minakhi Bandyopadhyay, Mainak School of Computer Engineering Kalinga Institute of Industrial Technology-Deemed-to-be-University Bhubaneswar India Department of Computer Science and Engineering National Institute of Technology Silchar India

Deep learning methods have played a prominent role in the development of computer visualization in recent years. Hyperspectral imaging (HSI) is a popular analytical technique based on spectroscopy and visible imaging which examines the pattern of light in a target and recognizes objects based on varying spectral properties. However, in remote sensing, detecting surface material via HSI analysis is a critical and difficult task. The performance of spectral-spatial data exploitation is well established to outperform typical spectral pixel-wise techniques. Because of its great feature extraction ability, convolutional neural networks (CNN) have emerged as a potent deep learning approach. CNN translates the input features of an image into an equivalent CNN feature map, in addition to naturally combining spectral and spatial information. However, spectral-spatial properties when combined with pixel-wise extraction can learn more minute details of objects present on the earth’s terrain. In this paper, a noble Composite Spectral Spatial Pixel CNN model for the classification of hyperspectral data is presented which is an amalgamation of 3D-2D-1D CNN. While the 3D and 2D CNN exploit the spectral-spatial features effectively, 1D CNN works on pixel-wise feature extraction. Further, to optimize the classification performance of the proposed model, a new hybrid activation function Flatten-T Swish is also used which is the combination of ReLU and Swish function. The proposed model is compared with other state-of-the-art models based on three popular HSI datasets, and it is found that the proposed model performs better among others in terms of classification and computation time, giving 98.87% accuracy for indian Pines, 99.92% accuracy for Pavia University, and 99.99% accuracy for Salinas Valley dataset. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Extraction

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Enhancing patient-independent detection of freezing of gait in Parkinson’s disease with deep adversarial network

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Neural Computing and Applications 2025年 1-21页

作者： Fahad, Md Shah Ranjan, Ashish Kumar, Gautam Department of Computer Science and Engineering Birla Institute of Technology Mesra Jharkhand Ranchi India Department of Computer Science and Engineering C. V. Raman Global University Odisha Bhubaneswar India Department of Computer Science and Engineering National Institute of Technology Delhi Delhi India

Freezing of gait (FoG) refers to sudden, relatively brief episodes of gait arrest in Parkinson’s disease, known to manifest in the advanced stages of the condition. Events of freezing are associated with tumbles, traumas, and psychological repercussions, significantly impacting the patient’s quality of life. The use of accelerometer data derived from sensors put on a patient’s body to detect FoG has previously been proposed using convolutional neural network (CNN)-based deep learning algorithms. Here, we combine a CNN + Long short-term Memory (LSTM)-attention model to detect FoG episodes—a first for the detection of FoG using the accelerometer data. CNN facilitates automatic feature extraction from the accelerometer data itself. The output from the CNN is fed to the LSTM network, which is known for capturing sequential information. Further, the attention mechanism introduces relative focus on individual sub-sequences during the training of the LSTM network. The proposed model is made patient independent using adversarial training. The proposed model achieved improvements of +7.60% (without adversarial training) and + 8.36% (with adversarial training) in the sensitivity values over state of the art. This is obtained with little or no compromise in specificity values. The corresponding improvements in accuracy values are +3.81 and +2.23%, respectively. These findings imply that the proposed method can detect FoG gaits satisfactorily and can be effective in achieving accurate monitoring and gait assistance for Parkinson’s disease patients during daily life and rehabilitation therapy. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Accelerometers

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Intrumer:A Multi Module Distributed Explainable IDS/IPS for Securing Cloud Environment

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computers, Materials & Continua 2025年第1期82卷 579-607页

作者： Nazreen Banu A S.K.B.Sangeetha Department of Computer Science and Engineering SRM Institute of Science and TechnologyVadapalani CampusChennai600026Tamil NaduIndia

The increasing use of cloud-based devices has reached the critical point of cybersecurity and unwanted network *** environments pose significant challenges in maintaining privacy and *** approaches,such as IDS,have been developed to tackle these ***,most conventional Intrusion Detection System(IDS)models struggle with unseen cyberattacks and complex high-dimensional *** fact,this paper introduces the idea of a novel distributed explainable and heterogeneous transformer-based intrusion detection system,named INTRUMER,which offers balanced accuracy,reliability,and security in cloud settings bymultiplemodulesworking together within *** traffic captured from cloud devices is first passed to the TC&TM module in which the Falcon Optimization Algorithm optimizes the feature selection process,and Naie Bayes algorithm performs the classification of *** selected features are classified further and are forwarded to the Heterogeneous Attention Transformer(HAT)*** this module,the contextual interactions of the network traffic are taken into account to classify them as normal or malicious *** classified results are further analyzed by the Explainable Prevention Module(XPM)to ensure trustworthiness by providing interpretable *** the explanations fromthe classifier,emergency alarms are transmitted to nearby IDSmodules,servers,and underlying cloud devices for the enhancement of preventive *** experiments on benchmark IDS datasets CICIDS 2017,Honeypots,and NSL-KDD were conducted to demonstrate the efficiency of the INTRUMER model in detecting network trafficwith high accuracy for different *** outperforms state-of-the-art approaches,obtaining better performance metrics:98.7%accuracy,97.5%precision,96.3%recall,and 97.8%*** results validate the robustness and effectiveness of INTRUMER in securing diverse cloud environments against sophisticated cyber threats.

关键词： Cloud computing intrusion detection system transformers and explainable artificial intelligence(XAI)

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