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LEViT- Leaf Disease identification and classification using an enhanced Vision transformers(ViT) model

Multimedia Tools and Applications 2024年 1-32页

作者： Prashanthi, Bh. Krishna, A. V. Praveen Rao, Ch. Mallikarjuna Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Guntur Vaddeswaram522502 India Department of Computer Science and Engineering Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad500090 India

The variety of crops, differences in climate, and the multiplicity of disease symptoms make early identification and evaluation of leaf diseases a challenging task. Although deep-learning methods have been created for this very reason, they often need adjustments to suit various regions and types of crops. This study presents a vision transformer-based deep learning model incorporating Explainable Artificial Intelligence (XAI) features to effectively detect and classify leaf diseases. The model is evaluated using the New Plant Disease Dataset, encompassing data from 14 plants with 38 leaf disease classifications. The primary objective is to enhance accuracy in leaf disease classification through the utilization of vision transformer learning. The proposed model, LEViT (Leaf Disease Identification and Classification using an Enhanced Vision Transformers Model), builds upon the ViT_B_32 model. It incorporates additional layers such as dense layers, dropout, and L2 regularization to improve performance and mitigate overfitting. Specialized preprocessing techniques tailored for transformers are employed to refine data quality. Incorporating Grad-CAM (Gradient-weighted Class Activation Mapping) enhances interpretability, enabling the identification of crucial regions influencing the model’s decisions within images. The model exhibits remarkable efficacy, boasting a training accuracy of 95.22%, validation accuracy of 96.19%, and test accuracy of 92.33%. These results underscore its robust ability to accurately identify leaf diseases across diverse crop types. Comparative analysis with state-of-the-art models in the field demonstrates LEViT’s superior performance, establishing it as a leading solution for leaf disease detection and classification. LEViT’s advanced architecture, coupled with fine-tuning, positions it as a highly promising tool for advancing crop management and yield improvement, thereby significantly contributing to the field of precision agriculture. © The

关键词： Crops

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A proposed deep learning model for multichannel ECG noise reduction

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Discover Artificial Intelligence 2025年第1期5卷 1-16页

作者： Maurya, Jay Prakash Manoria, Manish Joshi, Sunil Department of Computer Science Engineering Samrat Ashok Technological Institute Madhya Pradesh Vidisha India Director & Professor Department of Computer Science Engineering Rungta College of Engineering & Technology Bhilai Chhattisgarh Durg India

Heart disease is a critical concern of healthcare for everyone in today’s era. An effective and noninvasive indication of heart disease is an electrocardiogram (ECG). Understanding regular ECG signal patterns and comparisons with irregular, patterns may help to identify the serious nature of heart diseases such as arrhythmia. Comparison of ECG signal patterns is very difficult manually, and machine-based interpretation is a demand of society. Errors in ECG interpretation might result from noise contamination of ECG signals. ECG pretreatment from noise is therefore needed for precise analysis. This article proposed a novel deep learning-based solution for multichannel ECG noise reduction, through utilizing the capabilities of fully convolutional neural network along with the Jacobin regularization to ensure confining and preserving local information. Cascaded layered approach was framed in encoder and decoder sections of model where denoising and reconstruction process worked and compared on standard performance parameters with recent denoising deep autoencoders. This proposed work FCN-DAE with Jacobin regularization uses the noise stress test database (NSTDB) for the noise signal of ECG data sourced from the PhysioNet repository. The proposed model achieves 4.763 * 10–2 mv2 for signal space diversity (SSD), 0.288 mv for the median absolute deviation and 1.859 for the root mean square error (RMSE) in the conducted experiment. The experimental findings demonstrate that complex noise from the ECG signal may be removed up to 97.02%. © The Author(s) 2025.

关键词： Deep learning

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CLASSIFICATION OF ROYAL DELICIOUS APPLES USING HYBRID FEATURE SELECTION AND FEATURE WEIGHTING METHOD BASED ON SVM CLASSIFIER

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Scalable Computing 2025年第2期26卷 940-949页

作者： Kaur, Sandeep Sachan, Manoj Kumar Aggarwal, Ashwani Kumar Department of Computer Science and Engineering Sant Longowal Institute of Engineering and Technology Punjab Longowal148106 India Department of Electrical and Instrumentation Engineering Sant Longowal Institute of Engineering and Technology Punjab Longowal148106 India

Fruit safety is a critical component of the global economy, particularly within the agricultural sector. There has been a recent surge in the incidence of diseases affecting fruits, leading to economic setbacks in agriculture worldwide. Conventional manual assessment methods are laborious, prompting the exploration of automated computerized techniques for evaluating fruit quality. This research presents a novel method for assessing the quality of golden delicious apples. A dataset comprising 1256 apple images was gathered under controlled conditions. Afterward Feature extraction focuses on texture features like LBP, GLCM, GLDM, DTF, and Gabor features, color features, and shape and size features. A total of 18654 features are extracted and normalized using z-score. A hybrid method for feature selection and weighting involves the mRMR algorithm to eliminate redundant features and the Sine Cosine Optimization Algorithm for feature weighting, enhancing classification performance. The SVM machine learning technique, augmented with optimized features, yielded a 10.53% improvement in accuracy compared to SVM alone. Validation against state-of-the-art methods using Friedman’s mean rank test underscored the statistical significance of this approach across various metrics. © 2025 SCPE.

关键词： Fruits

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Enhanced MADM Strategy with Heptapartitioned Neutrosophic Distance Metrics

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Neutrosophic Sets and Systems 2025年 78卷 74-96页

作者： Eassa, Mohamed Myvizhi, M. Elkholy, Mohamed Abdelhafeez, Ahmed Elbehiery, Hussam Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt Applied Science Research Center Applied Science Private University Amman Jordan Department of Mathematics KPR Institute of Engineering and Technology Tamilnadu Coimbatore India Faculty of Computer Science& Engineering Alamein International University Egypt

In this work, a novel methodological approach to multi-attribute decision-making problems is developed and the notion of Heptapartitioned Neutrosophic Set Distance Measures (HNSDM) is introduced. By averaging the Penta-Partitioned Neutrosophic Distance Measures under particular conditional criteria, the suggested technique ranks the options. Additionally, we explore the fundamental characteristics of HNSDM and provide several illustrative theorems. The Heptapartitioned Neutrosophic Set Distance Measures are formulated based on the Hepta Neutrosophic Set. Lastly, a practical numerical example is provided to illustrate the efficacy of the suggested methodology. © 2025, University of New Mexico. All rights reserved.

关键词： Heptapartitioned Neutrosophic set Heptapartitioned Neutrosophic set and MADM strategy Heptapartitioned set Neutrosophic sine distance measure

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Plasmon-enhanced ultra-high photoresponse of single-wall carbon nanotube/copper/silicon near-infrared photodetectors

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Nano research 2024年第7期17卷 5930-5936页

作者： Yi-Ming Zhao Xian-Gang Hu Chao Chen Zuo-Hua Wang An-Ping Wu Hong-Wang Zhang Peng-Xiang Hou Chang Liu Hui-Ming Cheng Shenyang National Laboratory for Materials Science Institute of Metal ResearchChinese Academy of SciencesShenyang 110016China School of Materials Science and Engineering University of Science and Technology of ChinaShenyang 110016China National Engineering Research Center for Equipment and Technology of Cold Strip Rolling College of Mechanical EngineeringYanshan UniversityQinhuangdao 066004China Faculty of Materials Science and Engineering/Institute of Technology for Carbon Neutrality Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China

Single wall carbon nanotube(SWCNT)/Si heterojunction photodetectors have the advantages of high photoresponse ability and simple structure,however,their detection wavelength range are usually lower than 1100 nm,which limits their application in the infrared *** report a SWCNT/Cu/Si photodetector with both a high photoresponse and a detection range up to the infrared band by depositing a Cu nanoparticles(NPs)layer between a SWCNT film and a n-Si *** was found that the Cu NPs produce strong surface plasmon resonance(SPR)under laser irradiation,which breaks through the limitation of Si band gap and greatly improves the photoresponse of the SWCNT/Cu/Si photodetector in the near infrared *** responsivity(R)of the photodetector in the wavelength range of 1850–1200 nm reached 2.2–14.15 mA/W,which is the highest value in the reported plasmon enhanced n-Si based photodetectors,and about 20,000 times higher than that of a SWCNT/Si *** R value for 1550 nm wavelength used in optical communications reached~8.2 mA/W,which is 64%higher than the previously reported values of commonly used *** attribute the significant increase to the strong SPR and low Schottky barrier of Cu with n-Si,which facilitates the generation and transfer of the carriers.

关键词： single wall carbon nanotube photodetector silicon surface plasmon resonance copper

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Classification of Ultrasound Breast Cancer Image Using Tuning Up the Hyper-Parameter of Convolutional Neural Network

SSRN

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SSRN 2023年第7期17卷 264-269页

作者： Bansal, Satish Jadon, Rakesh S. Gupta, Sanjay Kumar Prestige Institute of Management & Research Gwalior India Department of Computer Engineering Madhav Institute of Technology and Science Gwalior India SOS in Computer Science & Applications Jiwaji University Gwalior India

Breast cancer in women is a significant public health concern worldwide, with many cases going undiagnosed until the advanced stages. Early detection is crucial for proper treatment and improved outcomes. There are some pre-trained models used by authors for the detection of breast tumor, but these models require extensive computation power due to their many layers and parameters. To address this issue, it is required to proposed convolutional neural network (CNN) model with fewer training parameters for classification of ultrasound images dataset to determine that a particular image is either benign or malignant. In this paper, CNN model is proposed with changes in some hyperparameters like the number of filters, filter size, batch normalization, learning rate, epoch, and batch size, to achieve better accuracy with less computational power. The proposed model was compared to other pre-trained models, including ResNet50, Efficient Net, and VGG16, using two databases (database A for training and validation data, and database B for testing data). Our proposed classifier outperformed than pre-trained classifiers in terms of accuracy. © 2023, The Authors. All rights reserved.

关键词： Neural network models

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Quadratic and Lagrange interpolation-based butterfly optimization algorithm for numerical optimization and engineering design problem

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Soft Computing 2025年第1期29卷 157-194页

作者： Sharma, Sushmita Saha, Apu Kumar Chakraborty, Sanjoy Deb, Suman Sahoo, Saroj Kumar Department of Computer Science and Engineering Indian Institute of Information Technology Tripura Agartala India Department of Computer Science and Engineering National Institute of Technology Tripura Agartala India Department of Mathematics National Institute of Technology Agartala Tripura Agartala India Department of Information Technology Bir Bikram Memorial College Tripura Agartala India

This work proposes a novel and improved Butterfly Optimization Algorithm (BOA), known as LQBOA, to solve BOA’s inherent limitations. The LQBOA uses Lagrange interpolation and simple quadratic interpolation techniques with adapted parameter settings to enhance the search strategy of BOA and to achieve a better balance of diversification and intensification. LQBOA’s performance was examined on 45 traditional benchmark issues as well as the IEEE CEC 2017 benchmark suites with 10, 30 and 50 dimensions, and the results were compared against popular state-of-the-art and modified algorithms. It was discovered that in more than 85% of cases, the proposed LQBOA outperformed the compared algorithms. The Friedman rank test and Wilcoxon rank test were used to validate the suggested LQBOA’s rank and significance. Additionally, convergence and diversity analyses were performed to investigate its convergence speed and searching behavior, respectively. Furthermore, LQBOA has been successfully deployed to solve twelve real-world issues including several engineering design problems and two multiple gravity assist spacecraft trajectory problems. The results of these problems were compared to those of a wide range of algorithms, demonstrating the superior performance of the proposed LQBOA. In conclusion, LQBOA makes a significant addition to the optimization area, and its application could greatly improve the performance of numerous optimization jobs. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Lagrange multipliers

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Electrocardiogram Classification Using Convolutional Neural Network 5th

Electrocardiogram Classification Using Convolutional Neural ...

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5th International Conference on Computing, Communications, and Cyber Security, IC4S 2023

作者： Kachhadiya, Kashyap Hasmani, Abdullah Gorasiya, Drashti Patel, Bansari Patel, Krishna Vadhavana, Vaishali Department of Computer Science and Engineering Devang Patel Institute of Advance Technology and Research Charotar University of Science and Technology Changa India

ISBN: (纸本)9789819773701

Circulation of Blood relies significantly precise electrical initiation. Arrhythmias can arise from any disturbance in the excitatory wave’s regular pattern in the heart. Electrocardiograms (ECG) are a popular diagnostic tool due to their speed, affordability, and non-invasive nature. This article presents a method for classifying ECGs that makes use of Convolutional Neural Network (CNNs) and the Continuous Wavelet Transform. Using Continuous Wavelet Transform (CWT), ECG signals are broken down into different time–frequency parts, while CNN extracts feature from the resulting 2D-scalogram. To further enhance the model considering the RR interval for arrhythmia diagnosis, 4 RR interval parameters are taken out and connected to CNN parameters to provide an ECG that is completely integrated classifier layer. This proposed model examines the impact of CNN and CWT on ECG classification alongside RR intervals. After training Accuracy of 97.27% was achieved. Based on the proposed method further models can designed for efficient clinical applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Exploring diversity and time-aware recommendations: an LSTM-DNN model with novel bidirectional dynamic time warping algorithm

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Soft Computing 2025年第4期29卷 2003-2013页

作者： Li, Te Chen, Liqiong Sun, Huaiying Hou, Mengxia Lei, Yunjie Zhi, Kaiwen School of Computer and Engineering East China University of Science and Technology Meilong road Shanghai200237 China School of Computer Science and Information Engineering Shanghai Institute of Technology Haiquan road Shanghai201400 China School of Chemistry and Environmental Engineering Shanghai Institute of Technology Haiquan road Shanghai201400 China

With the advent of the Web 3.0 era, the amount and types of data in the network have sharply increased, and the application scenarios of recommendation algorithms are continuously expanding. Location recommendation has gradually become one of the popular application scenarios in recommendation algorithms. Traditional recommendation algorithms not only ignore the temporal attribute of data when recommending information to users, but also blindly pursue the recommendation accuracy, which will cause certain "information cocoon room" problems. Therefore, this article treats user historical data as a time series and proposes an LSTM-DNN model based on the novel bidirectional Dynamic Time Warping (DTW) algorithm. Firstly, in response to the issue of different users consuming different amounts of information, this article proposes a novel bidirectional DTW algorithm to calculate the similarity between different users. Secondly, this article supplements the user dataset from three perspectives: "utilization" and "exploration" of information, and spatiotemporal attributes of data, which alleviates the problem of data sparsity and cold start in the dataset to a certain extent. Moreover, it effectively enhances the diversity of recommendation results. Finally, this paper constructs an Long Short-Term Memory-Deep Neural Networks (LSTM-DNN) to dynamically obtain user interests and preferences, and proposes a new metric Cumulative Self-System Diversity (CSSD) to measure the diversity of algorithm recommendation results. Experiments have shown that the model effectively enhances the diversity of recommendation results while ensuring recommendation accuracy. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Time series

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Malaria Prediction Using Fusion Learning with Enhanced Trust and Interpretability 5th

Malaria Prediction Using Fusion Learning with Enhanced Trust...

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5th International Conference on Recent Trends in Machine Learning, IoT, Smart Cities, and Applications, ICMISC 2024

作者： Gangone, Anjali Swapna, Mandu Bharat Kumar, G.J. Department of Computer Science and Engineering CMR Institute of Technology Hyderabad India Department of Computer Science and Engineering Sreyas Institute of Engineering and Technology Telangana Hyderabad India

ISBN: (纸本)9789819788606

Malaria, a significant global health threat, is traditionally diagnosed through manual examination of blood smears for parasite-infected cells, a method limited by its reliance on the examiner’s expertise. To overcome these limitations, this study employs deep learning for malaria prediction, leveraging models like XCEPTION, AlexNet, DenseNet, and MobileNet, enhanced with transfer learning, to classify blood smears as parasitized or uninfected using Anaconda software. The models were trained on a dataset comprising 80% of the online medical records, with 10% used for validation and the remaining 10% for testing on 224 × 224 pixel images. DenseNet emerged as the top performer with a 97% accuracy rate, followed by MobileNet, AlexNet, and XCEPTION. The study further incorporates the LIME (Local Interpretable Model-agnostic Explanations) technique for interpretability, providing understandable explanations for predictions to bolster trust and transparency in the deep learning model’s diagnostic capabilities. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electronic health record

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