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BEFF-SIGS: blockchain-enhanced fog framework- securing IoT data integrity and green sustainability through scalable authentication-authorization

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International Journal of computers and Applications 2025年第3期47卷 293-311页

作者： Khan, Neelam Saleem Mir, Roohie Naaz Chishti, Mohammad Ahsan Department of Computer Science & Engineering NIT Srinagar J&K India

This paper introduces a comprehensive framework designed to address security concerns in the Internet of Things (IoT) environment while contributing to the global Green Environment Initiative. The proposed mechanism utilizes a secure and scalable Authentication-Authorization system, employing JSON Web Token (JWT) and Casbin to ensure that only authorized entities access critical data. Data processing is strategically offloaded to the fog layer to enhance scalability and reduce latency, optimizing IoT data handling. The framework actively supports environmental sustainability by promoting eco-friendly practices within industrial setups. Industries are incentivized to embrace environmentally responsible approaches through tax incentives, fostering a greener future. Our model is a robust mechanism to guarantee the consistency and authenticity of the data stored in the cloud environment, offering assurance against potential tampering attempts by securing data integrity. This holistic approach demonstrates how governments can leverage cutting-edge technology to incentivize industries and promote a sustainable and eco-friendly future. Utilizing blockchain technology ensures unparalleled transparency, robust security, and tamper-resistant record-keeping. We evaluate the impact on network consumption, latency, and energy consumption for analyzing the performance of the proposed system. The proposed framework offers better latency, network efficiency, and distributed computational power as major benefits over depending exclusively on cloud resources in IoT deployments. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Blockchain

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Blockchain enabled MediVault for healthcare system

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Multimedia Tools and Applications 2025年第15期84卷 14805-14825页

作者： Chaurasia, Brijesh Kumar Department of Computer Science and Engineering Pranveer Singh Institute of Technology Kanpur India

The management of healthcare data has significantly benefited from the use of cloud-assisted MediVault for healthcare systems, which can offer patients efficient and convenient digital storage services for storing their medical records. Nevertheless, there are security risks associated with the current digital healthcare data, as malicious parties may work with cloud storage service providers to alter patient records or to directly disclose health record content to other adversaries for monetary advantage. In this paper, a blockchain-enabled MediVault for healthcare systems is proposed not only to provide safe storage of healthcare data in digital form but also secure access for authenticated entities such as patients, doctors, and pharmacists. In this MediVault, we introduced NFT generation and storage over the cloud using Interplanetary File System (IPFS) and FireBase as per user adaptability, Ehtereum Blockchain for immutability, and encryption and decryption using asymmetric keys for confidentiality. The empirical results and the performance evaluation demonstrate that the proposed MediVault is secure, simple, and efficient with a limited computation overhead. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Blockchain

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Finding Friendly Neighborhood: Optimal D2D Relaying in mmWave IoT Networks

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IEEE Access 2025年 13卷 89193-89207页

作者： Mahendran, V. Badarla, Venkataramana Krishna, Vamshi Vijay Indian Institute of Technology Department of Computer Science and Engineering Tirupati517619 India

Device-to-Device (D2D) relaying helps improve the coverage range and throughput of the millimeter Wave (mmWave) networks. This work focuses on improving the uplink throughput in a single-cell mmWave-based Internet-of-Things (IoT) network. The key factors involve the placement of relays and neighbor selection (NS)(, i.e., the process of selecting source IoTs within the relay node's coverage range). The problem is formulated using model-based bandits, and the associated structural results are analyzed. Considering the resource-constrained nature of IoTs, we have developed a simple rule-based NS as an alternative to computationally intensive learning-based neighbor-selection processes from state of the art. By utilizing the rule-based NS, we show that there exists a conducive coverage range (called a friendly neighborhood) for the relay node to choose the neighbors. Accordingly, a geometry-based relay placement mechanism is proposed to maximize the uplink throughput. The optimality of the proposed solutions is shown analytically, and their performance concerning the state of the art is evaluated using extensive simulation. © 2013 IEEE.

关键词： Beamforming

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Electrocardiogram-based heart disease prediction using hybrid deep feature engineering with sequential deep classifier

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Multimedia Tools and Applications 2025年第10期84卷 7443-7475页

作者： Golande, Avinash L. Pavankumar, T. Department of Computer Science & Engineering KLEF A. P. Vijayawada India

Several approaches can detect early heart problems. Electrocardiograms (ECGs) are better and more affordable for early heart disease prediction. ECG data can better predict heart diseases and abnormalities. Standard methods are slow and limited to a narrow ECG signal pattern range. Newer deep learning-based algorithms require tedious and unimportant human feature extraction from raw ECG data. This study presents a new electrocardiogram-based cardiac disease detection system using an ensemble method for feature extraction and deep learning for classification. The approach includes pre-processing of input ECG signals, hybrid feature engineering, and disease categorization. Pre-processing the input signal preserved its integrity by reducing the artifact and noise using a robust filtering technique. ECG feature extraction should use generic automation and beat-specific features. We designed the hybrid feature engineering approach using a Convolutional Neural Network (CNN) and Stationary Wavelet Transform (SWT). The SWT method employs dynamic thresholding to segment ECG data into individual heartbeats, from which the beat-specific feature vector is derived. To automate the process of classifying cardiac diseases, a powerful 3-layer CNN has been trained to do so and now extracts those attributes automatically. Manifold learning of feature selection is then used to normalize the combined features from the two methods. Long Term Short Memory (LSTM), a deep learning classifier, has been given the ensemble feature vector. For LSTM, the sequential characteristics vector transmits information to the LSTM layer and then calculates the probabilities for each class to provide precise disease classification in the classification phase. The suggested model surpasses cutting-edge deep learning-driven techniques when used with a publicly available multi-disease ECG dataset. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Electrocardiograms

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Multi-word-representability of graphs

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Discrete Applied Mathematics 2025年 374卷 61-74页

作者： Kenkireth, Benny George Sajith, Gopalan Sasidharan, Sreyas IIT Guwahati Department of Computer Science and Engineering Assam Guwahati781039 India

A graph G=(V,E) is word-representable if there exists a word w over the alphabet V such that for any distinct pair of vertices x,y∈V, (x,y)∈E if and only if the occurrences of the letters x and y alternate in the word w. Word-representable graphs generalize many important graph classes such as 3-colorable graphs, comparability graphs, and graphs of vertex degree at most 3. The notion of multi-word representability of graphs is a generalization of the notion of word-representability. A graph G is k-multi-word-representable if it can be represented by a set of at most k words, each representing a word-representable subgraph of G, such that their union is G. 2-multi-word-representable graphs contain many graph classes, such as word-representable graphs, planar graphs, interval graphs, split graphs, and line graphs. We first investigate the problem of finding the largest word-representable induced subgraph of a graph. We solve this problem for graphs of size up to nine. The second problem that we study is determining whether it is possible to vertex partition graphs on n vertices into two sets, each inducing a word-representable graph. We solve this problem for graphs of size up to thirteen and for perfect graphs of size up to sixteen. These results help us to show that graphs with at most 20 vertices are 2-multi-word-representable. The third problem we address involves computing the multi-word-representation number for certain subclasses of perfect graphs. In particular, our investigation reveals that perfect graphs with up to 28 vertices, well-partitioned chordal graphs, and (6+y)-trees with at most (25+y) vertices, all subclasses of perfect graphs, are 2-multi-word-representable. Additionally, we establish that any chordal graph is O(loglogn)-multi-word-representable. © 2025 Elsevier B.V.

关键词： Trees (mathematics)

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Editor-in-Chief's foreword: understanding internet of energy (IoE)

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International Journal of computers and Applications 2025年第4期47卷 339-340页

作者： Pathan, Al-Sakib Khan Department of Computer Science and Engineering United International University Dhaka Bangladesh

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An ensembled-deep-learning paradigm trained with a self-improved coyote optimization algorithm (SI-COA) for crop disease detection

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Multimedia Tools and Applications 2025年第4期84卷 1697-1724页

作者： Shukla, Preeti Chandanan, Amit Kumar Department of Computer Science and Information Technology Guru Ghasidas Vishwavidyalaya CG Bilaspur India Department of Computer Science & Engineering Guru Ghasidas Vishwavidyalaya CG Bilaspur India

In recent times, drastic climate changes have caused a substantial increase in the growth of crop diseases. This causes large-scale demolition of crops, decreases cultivation, and eventually leads to the financial loss of farmers. Due to the rapid growth in a variety of diseases and adequate knowledge of farmers, identification, and treatment of the disease have become a major challenge. The leaves have texture and visual similarities which are attributes for the identification of disease type. Hence, computer vision employed with deep learning provides a way to solve this problem. In this research work, a novel voting-based ensembled-deep-learning paradigm is developed for crop disease detection. Initially, the collected raw images are pre-processed via Laplacian Filter and morphological operations. From, the pre-processed image, the ROI region is identified using K-means Clustering. Then, from the ROI regions, the features like texture feature (Chi-squared Local Binary Pattern (CLBP)), Scale Invariant Feature Transform (SIFT), Gray-Level Co-occurrence Matrix (GLCM)), color features, and shape features are extracted. Then, the optimal features are selected by using Self-Improved Coyote Optimization Algorithm (SI-COA) is an extended version of the standard Coyote Optimization Algorithm (COA) and is a metaheuristic for optimization that is inspired by the canis latrans species. For crop disease detection, the voting-based ensembled-deep-learning paradigm was used which includes the Attention-based Bi-LSTM, Recurrent Neural Networks (RNNs), and Optimized Deep Neural Networks (O-DNN). The weight of DNN is optimized via Self-Improved Coyote Optimization Algorithm (SI-COA). The overall outcome of this process is evaluated by using various performance metrics such as Precision, Recall, F1-Score, Accuracy, Sensitivity and Specificity, NPV, FNR, FPR, and MCC, as well. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2

关键词： Long short-term memory

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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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Impact of machine learning-based imputation techniques on medical datasets- a comparative analysis

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Multimedia Tools and Applications 2025年第9期84卷 5905-5925页

作者： Tiwaskar, Shweta Rashid, Mamoon Gokhale, Prasad Department of Computer Engineering Faculty of Science and Technology Vishwakarma University Pune India

In the realm of medical datasets, particularly when considering diabetes, the occurrence of data incompleteness is a prevalent issue. Unveiling valuable patterns through medical data analysis is crucial for early and precise medical predictions. However, the quality of data and the proper handling of missing data hold significant significance. To address this challenge, imputation stands as a robust approach. The main goal of this paper aims to provide a comprehensive investigation into the effects brought about by Machine Learning (ML) based imputation techniques, specifically K Nearest Neighbor Imputation (KNNI), Multiple Imputation by Chained Equations (MICE), and MissForest. Results of all three techniques are compared with the complete dataset for five missing rates (10% to 50%), and evaluated using four categories of evaluation criteria i.e. (1) model performance, (2) imputation error rate (Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Coefficient of Determination (R^2) values), (3) Pearson correlation analysis and, (4) model selection basis (Bayesian information criterion (BIC), Akaike information criterion (AIC), values). Model performance includes accuracy, precision, recall, F1 score, and Matthews Correlation Coefficient (Mcoff) score of four ML classifiers viz. (a) Random Forest (RF), (b) Support vector machine (SVM), (c) AdaBoost, (d) XGBoost (XGB). For all missing rate cases, the MissForest technique is better than the KNNI and MICE in accuracy and Mcoff in 80% of cases, precision in 40% of cases, recall in 60% of cases, F1 score, MAE, RMSE, R^2 in 100% of cases, AIC in 80% of cases, and BIC values in 100% of cases. Also, the correlation analysis confirms that the MissForest imputation preserves association between the variables, like the complete dataset. Overall, our findings suggest that MissForest is a better machine learning-based imputation technique for handling missing data in diabetes research. © The Author(s), under exclusive lice

关键词： Adaptive boosting

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Multimodal emotion recognition model via hybrid model with improved feature level fusion on facial and EEG feature set

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Multimedia Tools and Applications 2025年第1期84卷 1-36页

作者： Singh, Pratima Tripathi, Mukesh Kumar Patil, Mithun B. Shivendra Neelakantappa, Madugundu Department of Computer Science and Engineering Galgotias University Gautam Buddh Nagar Uttar Pradesh India Department of Computer Science and Engineering Vardhaman College of Engineering Telangana Hyderabad India Department of Computer Science and Engineering N K Orchid College of Engineering & Technology Maharastra Solapur India Department of Computer Application D. K. College Dumraon Bihar Buxar India Department of Information Technology Vasavi College of Engineering Telangana Hyderabad India

In recent years, academics have placed a high value on multi-modal emotion identification, as well as extensive research has been conducted in the areas of video, text, voice, and physical signal emotion detection. This paper proposes a novel multimodal emotion recognition model that employs a hybrid model with AMIG-based feature fusion on facial and EEG feature sets. The EEG signal and facial image are subjected to preprocessing to remove unwanted background noises with the Butterworth filter and Viola Jones Algorithm, respectively. While considering the pre-processed EEG signal, features such as EWFS-transform, wavelet features, and CSP-based features are extracted. In the proposed EWFS-transform, the window function is modified by using the frequency function and updated STFT. Conversely, the features including SE-AMM-EST-based features;LGXP and GLCM are extracted while considering the preprocessed face image. In the proposed SE-AMM-EST-based features, the mean of shape is updated and covariance in PCA for extracting texture-based features. In order to extract redundant free essential features, AMIG-based feature fusion is proposed. Further, the fused features are fed into a proposed hybrid model that includes LSTM and AML-CNN models. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Textures

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