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Border Sensitive Knowledge Distillation for Rice Panicle Detection in UAV Images

computers, Materials & Continua 2024年第10期81卷 827-842页

作者： Anitha Ramachandran Sendhil Kumar K.S. School of Computer Science and Engineering VIT(Vellore Institute of Technology)VelloreVellore632014India

Research on panicle detection is one of the most important aspects of paddy phenotypic analysis.A phenotyping method that uses unmanned aerial vehicles can be an excellent alternative to field-based ***,it entails many other challenges,including different illuminations,panicle sizes,shape distortions,partial occlusions,and complex *** detection algorithms are directly affected by these *** work proposes a model for detecting panicles called Border Sensitive Knowledge Distillation(BSKD).It is designed to prioritize the preservation of knowledge in border areas through the use of feature *** feature-based knowledge distillation method allows us to compress the model without sacrificing its *** imitation mask is used to distinguish panicle-related foreground features from irrelevant background features.A significant improvement in Unmanned Aerial Vehicle(UAV)images is achieved when students imitate the teacher’s *** the UAV rice imagery dataset,the proposed BSKD model shows superior performance with 76.3%mAP,88.3%precision,90.1%recall and 92.6%F1 score.

关键词： Rice panicle detection UAV border sensitive method deep learning knowledge distillation

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A comprehensive survey of image compression methods: from prediction models to advanced techniques

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Multimedia Tools and Applications 2025年 1-39页

作者： Devadason, Joshua Rajah D, Aju School of Computer Science and Engineering Vellore Institute of Technology Vellore India

Images are used widely nowadays. Images are used in many fields such as medicine to terrain mapping. There is a need to compress the images and represent them in shorter form for effective transmission. Several techniques are reviewed in this paper. The first group of techniques is the data compression technique. The data compression methods are Huffman encoding, Lempel–Ziv Welch (LZW), arithmetic encoding, run length encoding, and Shannon-Fano encoding. The second group of methods is prediction, here the neighboring pixels are utilized in the prediction of the present pixel and the error is encoded using an entropy encoder. In wavelet-based techniques, the source image is transformed from the spatial domain to the wavelet domain, and the coefficients are encoded. In learning-based techniques, a neural network is used to model the image data for compression. Fractal encoding is a compression method that searches for similar blocks in an image and compresses the difference between the blocks. With an average bitrate of 4.87 bpp (bits per pixel) on the first 10 photos of the Digital Imaging and Communications in Medicine (DICOM) image collection, the results indicate that LZW is the top performing data compression technique among the data compression techniques. In prediction-based techniques, Context Adaptive Lossless Image Codec (CALIC) is the best performing with 3.5 bpp. The modern neural method, compressive autoencoder outperforms established methods such as Joint Photographic Experts Group (JPEG), by achieving a PSNR of 35 on the Kodak dataset compared with 32 PSNR on the same dataset at 1.0 bpp. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Pixels

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Skin cancer detection using optimized mask R-CNN and two-fold-deep-learning-classifier framework

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Multimedia Tools and Applications 2025年 1-28页

作者： Reddy, Akepati Sankar M.P, Gopinath School of Computer Science and Engineering Vellore Institute of Technology Vellore India

Skin cancer is a serious and potentially life-threatening condition caused by DNA damage in the skin cells, leading to genetic mutations and abnormal cell growth. These mutations can cause the cells to divide and grow uncontrollably, forming a tumor on the skin. To prevent skin cancer from spreading and potentially leading to serious complications, it's critical to identify and treat it as early as possible. An innovative two-fold deep learning based skin cancer detection model is presented in this research work. Five main stages make up the proposed model: Preprocessing, segmentation, feature extraction, feature selection, and skin cancer detection. Initially, the Min–max contrast stretching and median filtering used to pre-process the collected raw image. From the pre-processed image, the Region of Intertest (ROI) is identified via optimized mask Region-based Convolutional Neural Network (R-CNN). Then, from the identified ROI areas, the texture features like Illumination-invariant Binary Gabor Pattern (II-BGP), Local Binary Pattern (LBP), Gray-Level Co-occurrence Matrix (GLCM), Color feature such as Color Correlogram and Histogram Intersection, and Shape feature including Moments, Area, Perimeter, Eccentricity, Average bending energy are extracted. To choose the optimal features from the extracted ones, the Golden Eagle Mutated Leader Optimization (GEMLO) is used. The proposed Golden Eagle Mutated Leader Optimization (GEMLO) is the conceptual amalgamation of the standard Mutated Leader Algorithm (MLA) and Golden Eagle Optimizer are used to select best features (GEO). The skin cancer detection is accomplished via two-fold-deep-learning-classifiers, that includes the Fully Convolutional Neural Networks (FCNs) and Multi-Layer Perception (MLP). The final outcome is the combination of the outcomes acquired from Fully Convolutional Neural Networks (FCNs) and Multi-Layer Perception (MLP). The PYTHON platform is being used to implement the suggested model. Using the curre

关键词： Median filters

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A Fusion Model for Personalized Adaptive Multi-Product Recommendation System Using Transfer Learning and Bi-GRU

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computers, Materials & Continua 2024年第12期81卷 4081-4107页

作者： Buchi Reddy Ramakantha Reddy Ramasamy Lokesh Kumar School of Computer Science and Engineering Vellore Institute of TechnologyVellore632014TamilnaduIndia

Traditional e-commerce recommendation systems often struggle with dynamic user preferences and a vast array of products,leading to suboptimal user *** address this,our study presents a Personalized Adaptive Multi-Product Recommendation System(PAMR)leveraging transfer learning and Bi-GRU(Bidirectional Gated Recurrent Units).Using a large dataset of user reviews from Amazon and Flipkart,we employ transfer learning with pre-trained models(AlexNet,GoogleNet,ResNet-50)to extract high-level attributes from product data,ensuring effective feature representation even with limited ***-GRU captures both spatial and sequential dependencies in user-item *** innovation of this study lies in the innovative feature fusion technique that combines the strengths of multiple transfer learning models,and the integration of an attention mechanism within the Bi-GRU framework to prioritize relevant *** approach addresses the classic recommendation systems that often face challenges such as cold start along with data sparsity difficulties,by utilizing robust user and item *** model demonstrated an accuracy of up to 96.9%,with precision and an F1-score of 96.2%and 96.97%,respectively,on the Amazon dataset,significantly outperforming the baselines and marking a considerable advancement over traditional *** study highlights the effectiveness of combining transfer learning with Bi-GRU for scalable and adaptive recommendation systems,providing a versatile solution for real-world applications.

关键词： Personalized recommendation systems transfer learning bidirectional gated recurrent units(Bi-GRU) performance metrics adaptive systems product reviews

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Intrusion detection system and fuzzy ant colony optimization based secured routing in wireless sensor networks

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Soft Computing 2024年第17-18期28卷 10345-10367页

作者： Subramani, Shalini Selvi, M. School of Computer Science and Engineering Vellore Institute of Technology Vellore India

Recent advances in wireless sensor networks (WSNs) have brought the sensor based monitoring developments to the surface in many applications. In such a scenario, the security of communication is a major challenge in the effective delivery of the collected data due to the presence of malicious nodes. Moreover, since security and minimization of energy consumption are critical factors in designing techniques for multi-hop secure routing in wireless sensor networks, it is necessary to address the issues of security in the routing process. Hence, this paper proposes a novel intrusion detection system for enhancing the security and Fuzzy based Ant Colony Optimization based Secured Quality of Service Routing Protocol (F-ACO-SQoSRP) for increasing the security of communication and network performance in WSNs. Using this proposed intrusion detection system (IDS), the proposed model identifies the distinct and malicious behaviours of nodes. Additionally, a clustering algorithm has been proposed in this work, wherein the cluster head selection (CHS) is based on quality of service (QoS) measures and the trust values of nodes are measured using the intrusion detection system results. The proposed ACO based routing framework also predicts the best optimum and secured path to allow for effective communication across each link. The simulation results obtained from this work proved that the proposed secured routing algorithm provides better performance in terms of security using robust trust values, increase in packet delivery ratio and network lifetime reduction in delay and energy consumption when this work is compared with the existing secured routing systems. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Quality of service

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Nonparametric Statistical Feature Scaling Based Quadratic Regressive Convolution Deep Neural Network for Software Fault Prediction

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computers, Materials & Continua 2024年第3期78卷 3469-3487页

作者： Sureka Sivavelu Venkatesh Palanisamy School of Computer Science Engineering and Information Systems Vellore Institute of TechnologyVellore632014India

The development of defect prediction plays a significant role in improving software quality. Such predictions are used to identify defective modules before the testing and to minimize the time and cost. The software with defects negatively impacts operational costs and finally affects customer satisfaction. Numerous approaches exist to predict software defects. However, the timely and accurate software bugs are the major challenging issues. To improve the timely and accurate software defect prediction, a novel technique called Nonparametric Statistical feature scaled QuAdratic regressive convolution Deep nEural Network (SQADEN) is introduced. The proposed SQADEN technique mainly includes two major processes namely metric or feature selection and classification. First, the SQADEN uses the nonparametric statistical Torgerson–Gower scaling technique for identifying the relevant software metrics by measuring the similarity using the dice coefficient. The feature selection process is used to minimize the time complexity of software fault prediction. With the selected metrics, software fault perdition with the help of the Quadratic Censored regressive convolution deep neural network-based classification. The deep learning classifier analyzes the training and testing samples using the contingency correlation coefficient. The softstep activation function is used to provide the final fault prediction results. To minimize the error, the Nelder–Mead method is applied to solve non-linear least-squares problems. Finally, accurate classification results with a minimum error are obtained at the output layer. Experimental evaluation is carried out with different quantitative metrics such as accuracy, precision, recall, F-measure, and time complexity. The analyzed results demonstrate the superior performance of our proposed SQADEN technique with maximum accuracy, sensitivity and specificity by 3%, 3%, 2% and 3% and minimum time and space by 13% and 15% when compared with the two sta

关键词： Software defect prediction feature selection nonparametric statistical Torgerson-Gower scaling technique quadratic censored regressive convolution deep neural network softstep activation function nelder-mead method

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Analytical Formulation and Numerical Investigation of Electromechanical Dynamics in Circular Biconvex Diaphragm Capacitive Pressure Sensor

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IEEE Sensors Journal 2025年第11期25卷 18867-18875页

作者： Rukshana Bi, Gajula Motwani, Mayank Jindal, Sumit Kumar Vellore Institute of Technology School of Electronics Engineering Vellore632014 India Vellore Institute of Technology School of Computer Science and Engineering Vellore632014 India

Capacitive pressure sensors (CPSs) have attracted considerable interest due to their high sensitivity, low energy consumption, and potential for miniaturization, making them suitable for applications in automotive systems, consumer electronics, and industrial automation. The conventional designs of CPSs often face challenges such as limited sensitivity, mechanical instability, and difficulties in achieving increased sensitivity. This study presents a biconvex diaphragm using silicon, featuring a gradual thickness variation, with a maximum thickness at the center. This article offers a mathematical analysis of the sensor, featuring step-by-step derivations for parameters such as the maximum thickness of the diaphragm, working range, capacitance, and capacitive sensitivity. Finally, curves of capacitance and capacitive sensitivity with pressure are obtained in MATLAB, and the results are compared with those of prior works. The proposed sensor shows a more than threefold increase in capacitance and a nearly double improvement in capacitive sensitivity compared to the highest previously reported double touch mode capacitive pressure sensor (DTMCPS). The deflection of the diaphragm is obtained using COMSOL. © 2001-2012 IEEE.

关键词： MATLAB

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2P3FL:A Novel Approach for Privacy Preserving in Financial Sectors Using Flower Federated Learning

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computer Modeling in engineering & sciences 2024年第8期140卷 2035-2051页

作者： Sandeep Dasari Rajesh Kaluri School of Computer Science Engineering and Information Systems Vellore Institute of TechnologyVellore632014India

The increasing data pool in finance sectors forces machine learning(ML)to step into new *** data has significant financial implications and is *** users data from several organizations for various banking services may result in various intrusions and privacy *** a result,this study employs federated learning(FL)using a flower paradigm to preserve each organization’s privacy while collaborating to build a robust shared global ***,diverse data distributions in the collaborative training process might result in inadequate model learning and a lack of *** address this issue,the present paper proposes the imple-mentation of Federated Averaging(FedAvg)and Federated Proximal(FedProx)methods in the flower framework,which take advantage of the data locality while training and guaranteeing global *** improves the privacy of the local *** analysis used the credit card and Canadian Institute for Cybersecurity Intrusion Detection Evaluation(CICIDS)***,recall,and accuracy as performance indicators to show the efficacy of the proposed strategy using FedAvg and *** experimental findings suggest that the proposed approach helps to safely use banking data from diverse sources to enhance customer banking services by obtaining accuracy of 99.55%and 83.72%for FedAvg and 99.57%,and 84.63%for FedProx.

关键词： Federated learning FedAvg FedProx flower framework privacy preservation financial sectors

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Comprehensive analysis of various imputation and forecasting models for predicting PM2.5 pollutant in Delhi

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Neural Computing and Applications 2025年第17期37卷 11441-11458页

作者： Karnati, Hemanth Soma, Anuraag Alam, Adnan Kalaavathi, B. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of IoT School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India

Understanding and predicting air quality is pivotal for public health and environmental management, especially in urban areas like Delhi. This study utilizes a comprehensive dataset from the Central Pollution Control Board, detailing air pollutant concentrations at the ITO (Income Tax Office), Delhi station, from 2017 to 2023. Our focus on pollutants such as PM2.5, PM10, NO2, NH3, SO2, ozone, and CO highlights the challenges posed by missing data in environmental studies. Through the application of several imputation models, including KNN, linear regression, forward fill + backward fill, Fourier + KNN, linear interpolation, and statistical methods, we identified combination of forward fill and backward fill as the most effective method for addressing data gaps, specifically for ozone measurements. Building on this imputed dataset, we employ various forecasting models to predict PM2.5 levels, a critical pollutant. Our exploration includes time series and deep learning approaches, such as LSTM, Bi-LSTM, CNN-LSTM, GRU, Deep AR, WaveNet, TCN, ARIMA, SARIMA, and Prophet. The performance of these models is evaluated using daily data, with Bi-LSTM and LSTM with attention emerging as the top performers due to their accuracy in predicting PM2.5 concentrations. The implications of our findings extend beyond academic interest, offering practical insights for environmental policy and health advisories in Delhi. By demonstrating the efficacy of specific imputation and forecasting techniques, our research contributes to the broader effort of improving air quality monitoring and prediction. The success of Bi-LSTM and LSTM with attention models, in particular, suggests a promising avenue for future studies aiming to enhance the precision of environmental forecasts. © The Author(s) 2025.

关键词： Time series

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Analysis of IoT Applications in Highly Precise Agriculture Farming

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Recent Patents on engineering 2025年第2期19卷 71-81页

作者： Reddy N, Latha Masila P, Gopinath School of Computer Science and Engineering Vellore Institute of Technology Tamilnadu Vellore632014 India

Background: The IoT (Internet of Things) assigns to the capacity of Device-to-Machine (D2M) connections, which is a vital component in the development of the digital economy. IoT integration with a human being enables real-time decision-making in communication, collaboration, and technology analytics. Furthermore, environmental factors impacting plants, such as air humidity, temperature, air quality index, and soil wetness, are not frequently documented, emphasizing the development of a data monitoring system for future agricultural research and development. Methods: An IoT-based Intelligent Farming System is proposed. An innovative IoT-based intelligent farming system is developed that integrates real-time data monitoring, machine learning algorithms, and IoT technology to address the identified gaps and challenges. Results: In the face of climate change, extreme weather, and environmental constraints, increased food demand must be satisfied. Intelligent agriculture enabled by IoT technology can reduce waste and increase productivity for producers and farmers, from fertilizer use to tractor trips. Conclusion: In conclusion, this patent paper provides insightful and informative commentary on the progress made in technology within the agriculture industry and the challenges that still need to be overcome to achieve optimal outcomes. © 2025 Bentham science Publishers.

关键词： IoT agriculture soil quality weather conditions machine learning environmental constraints

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