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Performance analysis of blockchain-based secured distributed deep federated learning for wearable internet of things

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Cluster Computing 2025年第5期28卷 1-30页

作者： Singh, Surjit Arora, Aman Garg, Girik Goyal, Anushka Gandhi, Nipun Computer Science and Engineering Department Thapar Institute of Engineering & Technology Punjab Patiala147004 India

With the use of wearable technology, we can now gain useful data-driven insights into our daily routines and personal health. The combination of blockchain, machine learning, deep learning, and wearable Internet of Things (IoT) is creating a dynamic environment that presents both opportunities and difficulties with urgent worries about data security and privacy. Therefore, this work presents a hyperledger sawtooth-based secure distributed deep federated learning framework (SDDFL) for wearable IoT. A deep federated learning(FL) approach based on ANN, CNN, LSTM, and GRU is presented. The performance evaluation of the proposed approach has been done using Accuracy, RMSE, MAE, and MAPE. Locally, GRU performed consistently better as compared with others for different epochs with 99.28% for 50, 99.88% for 75, and 99.88% for 100 epochs. CNN-FL performed greatly with the highest test accuracy 97.78% for 4 and 98.22% for 6 nodes. GRU-FL has the highest train accuracy 99.91% for 4 nodes and for 6 nodes is also 99.91%. The results show that the proposed approach has high accuracy as compared with existing works. Further, the performance of the blockchain network has been evaluated using Throughput, Latency, and success rate. We get the maximum throughput of 48.8 and 50 for both write and read operations at 50TPS;and a success rate of 100 at 50 TPS and 10 TPS. The latency for open and query operations on the blockchain, showcasing a minimum of 0.21s and an average latency of 0.02s for read at 50TPS. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Federated learning

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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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Novel methodology for apple leaf disease classification with PCNN-IELM

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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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Seizure Prediction on EEG Signals using Feature Augmentation based Multi Model Ensemble

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Recent Patents on engineering 2025年第1期19卷 41-49页

作者： Anandaraj, A. Alphonse, P.J.A. Department of Computer Science and Engineering Sri Eshwar College of Engineering Coimbatore India Department of Computer Applications National Institute of Technology Tamilnadu Tiruchirappalli India

Background: Epilepsy is a neurological disorder that leads to seizures. This occurs due to excessive electrical discharge by the brain cells. An effective seizure prediction model can aid in improving the lifestyle of epilepsy patients. After analyzing various patents related to seizure prediction, it is observed that monitoring electroencephalography (EEG) signals of epileptic patients is an important task for the early diagnosis of seizures. Objective: The main objective of this paper is to assist epileptic patients to enhance their way of living by predicting the seizure in advance. Methods: This paper builds a feature augmentation-based multi-model ensemble-based architecture for seizure prediction. The proposed technique is divided into 2 broad categories;feature augmentation and ensemble modeling. The feature augmentation process builds temporal features while the multi-model ensemble has been designed to handle the high complexity levels of the EEG data. The first phase of the multi-model ensemble has been designed with heterogeneous classifier models. The second phase is based on the prediction results obtained from the first phase. Experiments were performed using the seizure prediction dataset from the University Hospital of Bonn. Results: Comparison indicates 98.7% accuracy, with improvement of 5% from the existing model. High prediction levels indicate that the model is highly capable of providing accurate seizure predictions, hence ensuring its applicability in real time. Conclusion: The result of this paper has been compared with existing methods of predicting seizures and it indicated that the proposed model has better enhancement in the accuracy levels. © 2025 Bentham Science Publishers.

关键词： Electroencephalography

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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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Informative task classification with concatenated embeddings using deep learning on crisisMMD

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International Journal of computers and Applications 2025年第2期47卷 123-140页

作者： Jain, Tarun Gopalani, Dinesh Kumar Meena, Yogesh Department of Computer Science and Engineering Manipal University Jaipur Jaipur India Department of Computer Science and Engineering Malaviya National Institute of Technology Jaipur India

Disastrous situations pose a formidable challenge, testing our resilience against nature's fury and the race against time to prevent the loss of human life. It is noted that in such situations that Microblogging platforms like Twitter(now X) have proven to be an invaluable resource for citizens to share the real-time updates in their locality. Building upon this premise, we are proposing a new hybrid robust approach that integrates transfer learning with selective features from Liwc and manually engineered features to achieve state-of-the-art results on the CrisisMMD dataset on the informative task. In our innovative approach, we harnessed the power of Deep learning combined with traditional ML practices to improve the model, we utilized OpenAI embeddings and minute details from the tweets like the sentiment, text polarity, Retweet address, links included etc. We employed a dense-dropout-based concatenate model for further processing. We established baselines of each technique individually as well as commonly used techniques like Tf-Idf, BoW, Glove embeddings. Notably, our proposed model exhibits a significant accuracy improvement, achieving an accuracy of 87.93%, surpassing the highest accuracy of traditional methods of 82.93% obtained with Tf-Idf and ensemble voting. Through this work, we conclude that there is a scope of improvement in disaster-related NLP work with the introduction of transformer-based architecture and Pretrained GPT which include a massive corpus of text improving the semantic coherency which in turn lead to higher evaluation metrics. This work in future can be extended to incorporate different embeddings as well as using techniques like Multi-headed attention as well as RNNs like GST, GRU, etc. © 2025 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Tweets

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Clustered Reinforcement Learning

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Frontiers of computer Science 2025年第4期19卷 43-57页

作者： Xiao MA Shen-Yi ZHAO Zhao-Heng YIN Wu-Jun LI National Key Laboratory for Novel Software Technology Department of Computer Science and TechnologyNanjing UniversityNanjing 210023China Department of Electrical Engineering and Computer Sciences University of CaliforniaBerkeleyCA 94720-1770USA

Exploration strategy design is a challenging problem in reinforcement learning(RL),especially when the environment contains a large state space or sparse *** exploration,the agent tries to discover unexplored(novel)areas or high reward(quality)*** existing methods perform exploration by only utilizing the novelty of *** novelty and quality in the neighboring area of the current state have not been well utilized to simultaneously guide the agent’s *** address this problem,this paper proposes a novel RL framework,called clustered reinforcement learning(CRL),for efficient exploration in *** adopts clustering to divide the collected states into several clusters,based on which a bonus reward reflecting both novelty and quality in the neighboring area(cluster)of the current state is given to the *** leverages these bonus rewards to guide the agent to perform efficient ***,CRL can be combined with existing exploration strategies to improve their performance,as the bonus rewards employed by these existing exploration strategies solely capture the novelty of *** on four continuous control tasks and six hard-exploration Atari-2600 games show that our method can outperform other state-of-the-art methods to achieve the best performance.

关键词： deep reinforcement learning exploration count-based method clustering K-means

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Test Case Optimization using Machine Learning based Hybrid Meta-Heuristic Approach

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Journal of Electronic Testing: Theory and Applications (JETTA) 2025年 1-20页

作者： Rao, Gajender Nandal, Deepak Research Scholar Department of Computer Science and Engineering Guru Jambheshwar University of Science and Technology Hisar India Department of Computer Science and Engineering Guru Jambheshwar University of Science and Technology Hisar India

Software testing is a critical task that can be used to ensure the quality of the end product. Different types of applications process the input data with respect to a specific operation and its outcomes are generated accordingly. It is essential to test each application as per the supported functionality, and it is also necessary to optimize the test cases for applications to reduce the computational cost. To achieve this goal, there is a need to design different test cases as well as an intelligent solution is required to select and optimize the test cases according to the requirements. Particle swarm optimization (PSO) and Genetic Algorithm (GA) both are capable enough to produce the optimal solutions for software testing. However, there is no provision to perform the classification over their outcomes with respect to test case optimization using machine learning support. This paper presents a hybrid meta-heuristic method that selects the test cases on behalf of their attributes as well as it also performs classification over the test cases under the constraints of the test case’s attributes. For experiments, different classifiers are used (i.e. k-nearest neighbour (KNN), Decision Tree (DT), Logistic Regression (LOG), Support Vector Machine (SVM), Gaussian Naive Bayes (GNB), Stochastic gradient descent (SGD) and Random Forest (RDF) etc.) and analysis shows that GNB and SGD classifiers both have optimal accuracy, precision & recall values. As compared to existing algorithms, i.e. traditional PSO and Genetic algorithm, it outperforms in terms of optimal number of detected errors (1821), error rate (91.05) and coverage rate (90.86363636). The Proposed scheme can be extended to optimize the test cases for other software types, along with real-time interaction and dataset updation support for software testers. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Random forests

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Classifying Multi-Lingual Reviews Sentiment Analysis in Arabic and English Languages Using the Stochastic Gradient Descent Model

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computers, Materials & Continua 2025年第4期83卷 1275-1290页

作者： Yasser Alharbi Sarwar Shah Khan College of Computer Science and Engineering University of HailHail55436Saudi Arabia Department of Computer Science University of Engineering&TechnologyMardan23200Pakistan

Sentiment analysis plays an important role in distilling and clarifying content from movie reviews,aiding the audience in understanding universal views towards the ***,the abundance of reviews and the risk of encountering spoilers pose challenges for efcient sentiment analysis,particularly in Arabic *** study proposed a Stochastic Gradient Descent(SGD)machine learning(ML)model tailored for sentiment analysis in Arabic and English movie *** allows for fexible model complexity adjustments,which can adapt well to the Involvement of Arabic language *** adaptability ensures that the model can capture the nuances and specifc local patterns of Arabic text,leading to better *** distinct language datasets were utilized,and extensive pre-processing steps were employed to optimize the datasets for *** proposed SGD model,designed to accommodate the nuances of each language,aims to surpass existing models in terms of accuracy and *** SGD model achieves an accuracy of 84.89 on the Arabic dataset and 87.44 on the English dataset,making it the top-performing model in terms of accuracy on both *** indicates that the SGD model consistently demonstrates high accuracy levels across Arabic and English *** study helps deepen the understanding of sentiments across various linguistic *** many studies that focus solely on movie reviews,the Arabic dataset utilized here includes hotel reviews,ofering a broader perspective.

关键词： Sentiment analysis stochastic gradient descent reviews English IMDb dataset Arabic dataset

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M-Bi-GRU-CNN: a hybrid deep learning model with optimized feature selection for enhanced crop yield prediction

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Multimedia Tools and Applications 2025年 1-25页

作者： J, Madhuri M, Indiramma N, Nagarathna Department of Computer Science and Engineering Bangalore Institute of Technology Karnataka Bengaluru560004 India Department of Computer Science and Engineering BMS College of Engineering Karnataka Bengaluru560019 India

Weather variability significantly impacts crop yield, posing challenges for large-scale agricultural operations. This study introduces a deep learning-based approach to enhance crop yield prediction accuracy. A Multi-Head Attention-based Bi-Directional Gated Recurrent Unit (M-Bi-GRU) integrated with a Convolutional Neural Network (CNN) is proposed. The publicly available Crop Recommendation dataset is pre-processed using data cleaning and Min–Max normalization to eliminate inconsistencies. Then, the Adaptive Reptile Search Optimization (ARSO) algorithm is used to identify the most relevant features for the prediction task. The M-Bi-GRU-CNN model uses these selected features to predict crop yields. The implementation is carried out in Python, and the performance is measured using a variety of metrics. Experimental results demonstrate that the proposed model outperforms existing methods, achieving a Mean Absolute Error (MAE) of 0.45, Mean Absolute Percentage Error (MAPE) of 0.07, and Root Mean Square Error (RMSE) of 0.28. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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