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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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A feature selection-aided deep learning based deepfake video detection method

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Multimedia Tools and Applications 2025年 1-24页

作者： Mohiuddin, Sk Roy, Ayush Pani, Saptarshi Malakar, Samir Sarkar, Ram Department of Computer Science Asutosh College Kolkata700026 India Department of Electrical Engineering Jadavpur University Kolkata700032 India Department of Computer Science UiT The Arctic University of Norway Tromsø9019 Norway Department of Computer Science and Engineering Jadavpur University Kolkata700032 India

The rapid advancements in deepfake technology are imposing significant challenges in detecting manipulated media contents. In this work, we have introduced a deepfake detection method that utilizes three pre-trained convolutional neural network models, namely DenseNet-121, EfficientNet-B0, and Xception to extract deep learning features from preprocessed extracted video frames. Next, these features are stacked to generate diverse feature representations, and thus end up with high-dimensional features. After that, a feature selection strategy is employed on the combined features to eliminate redundant and less effective features. The selected features are then fed into the K-nearest neighbors classifier to decide whether a questioned video is a fake video or not. Two widely used deepfake video datasets, namely FaceForensics++ and Celeb-DF (V2) are used to evaluate the performance of the proposed technique following two experimental setups: intra- and inter-datasets. The results show that the model achieves area under curve scores of 98.50% (85.70%) and 97.70% (79.00%) in the intra-dataset (inter-dataset) setups while trained on the FaceForensics++ and Celeb-DF (V2) datasets, respectively. The performance of the proposed model is comparable to that of state-of-the-art methods. Source code of the proposed method can be found at: here. © 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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Genetic-Based Keyword Matching DBSCAN in IoT for Discovering Adjacent Clusters

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computer Modeling in engineering & sciences 2023年第5期135卷 1275-1294页

作者： Byoungwook Kim Hong-Jun Jang Department of Computer Engineering Dongshin UniversityNaju58245Korea Department of Computer Science and Engineering Jeonju UniversityJeonju55069Korea

As location information of numerous Internet of Thing(IoT)devices can be recognized through IoT sensor technology,the need for technology to efficiently analyze spatial data is *** of the famous algorithms for classifying dense data into one cluster is Density-Based Spatial Clustering of Applications with Noise(DBSCAN).Existing DBSCAN research focuses on efficiently finding clusters in numeric data or categorical *** this paper,we propose the novel problem of discovering a set of adjacent clusters among the cluster results derived for each keyword in the keyword-based DBSCAN *** existing DBSCAN algorithm has a problem in that it is necessary to calculate the number of all cases in order to find adjacent clusters among clusters derived as a result of the *** solve this problem,we developed the Genetic algorithm-based Keyword Matching DBSCAN(GKM-DBSCAN)algorithm to which the genetic algorithm was applied to discover the set of adjacent clusters among the cluster results derived for each *** order to improve the performance of GKM-DBSCAN,we improved the general genetic algorithm by performing a genetic operation in *** conducted extensive experiments on both real and synthetic datasets to show the effectiveness of GKM-DBSCAN than the brute-force *** experimental results show that GKM-DBSCAN outperforms the brute-force method by up to 21 ***-DBSCAN with the index number binarization(INB)is 1.8 times faster than GKM-DBSCAN with the cluster number binarization(CNB).

关键词： Spatial clustering DBSCAN algorithm genetic algorithm textual information

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ViT-LSTM synergy: a multi-feature approach for speaker identification and mask detection

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Neural Computing and Applications 2024年第35期36卷 22569-22586页

作者： Nassif, Ali Bou Shahin, Ismail Bader, Mohamed Ahmed, Abdelfatah Werghi, Naoufel Department of Computer Engineering University of Sharjah Sharjah United Arab Emirates Department of Electrical Engineering University of Sharjah Sharjah United Arab Emirates Department of Electrical Engineering and Computer Sciences Khalifa University Abu Dhabi United Arab Emirates

The global health crisis caused by the COVID-19 pandemic has brought new challenges to speaker identification systems, particularly due to the acoustic alterations caused by the widespread use of face masks. Aiming to mitigate these distortions and improve the accuracy of speaker identification, this study introduces a novel two-level classification system, leveraging a unique integration of Vision Transformers (ViT) and Long Short-Term Memory (LSTM). This ViT-LSTM model was trained and tested on an extensive dataset composed of diverse speakers, both masked and unmasked, allowing a comprehensive evaluation of its capabilities. Our experimental results demonstrate remarkable improvements in speaker identification, with an accuracy score of 95.67%, significantly surpassing traditional and other deep learning-based methods. Moreover, our framework also shows considerable strength in detecting the presence of a mask, achieving an accuracy of 91.15% and outperforming existing state-of-the-art models. This study provides the first-ever benchmark for mask detection in the context of speaker identification, opening new pathways for research in this emerging area and presenting a robust solution for speaker identification in the era of face masks. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Convolutional neural networks

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Data Augmentation and Random Multi-Model Deep Learning for Data Classification

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computers, Materials & Continua 2023年第3期74卷 5191-5207页

作者： Fatma Harby Adel Thaljaoui Durre Nayab Suliman Aladhadh Salim EL Khediri Rehan Ullah Khan Computer Science Department Future Academy-Higher Future Institute for Specialized Technological StudiesEgypt Department of Computer Systems Engineering Faculty of Electrical and Computer EngineeringUniversity of Engineering and TechnologyPeshawar25120Pakistan Department of Information Technology College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Computer Sciences Faculty of Sciences of GafsaUniversity of GafsaGafsaTunisia

In the machine learning(ML)paradigm,data augmentation serves as a regularization approach for creating ML *** increase in the diversification of training samples increases the generalization capabilities,which enhances the prediction performance of classifiers when tested on unseen *** learning(DL)models have a lot of parameters,and they frequently ***,to avoid overfitting,data plays a major role to augment the latest improvements in ***,reliable data collection is a major limiting ***,this problem is undertaken by combining augmentation of data,transfer learning,dropout,and methods of normalization in *** this paper,we introduce the application of data augmentation in the field of image classification using Random Multi-model Deep Learning(RMDL)which uses the association approaches of multi-DL to yield random models for *** present a methodology for using Generative Adversarial Networks(GANs)to generate images for data *** experiments,we discover that samples generated by GANs when fed into RMDL improve both accuracy and model *** across both MNIST and CIAFAR-10 datasets show that,error rate with proposed approach has been decreased with different random models.

关键词： Data augmentation generative adversarial networks classification machine learning random multi-model deep learning

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SwiftAgg+: Achieving Asymptotically Optimal Communication Loads in Secure Aggregation for Federated Learning

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IEEE Journal on Selected Areas in Communications 2023年第4期41卷 977-989页

作者： Jahani-Nezhad, Tayyebeh Maddah-Ali, Mohammad Ali Li, Songze Caire, Giuseppe Technische Universität Berlin Department of Electrical Engineering and Computer Science Berlin10587 Germany University of Minnesota Twin Cities Department of Electrical and Computer Engineering MinneapolisMN55455 United States The Hong Kong University of Science and Technology IoT Thrust Department of Computer Science and Engineering Sai Kung Hong Kong

We propose SwiftAgg+, a novel secure aggregation protocol for federated learning systems, where a central server aggregates local models of $N \in \mathbb {N}$ distributed users, each of size $L \in \mathbb {N}$ , trained on their local data, in a privacy-preserving manner. SwiftAgg+ can significantly reduce the communication overheads without any compromise on security, and achieve optimal communication loads within diminishing gaps. Specifically, in presence of at most $D=o(N)$ dropout users, SwiftAgg+ achieves a per-user communication load of $\left({1+\mathcal {O}\left({\frac {1}{N}}\right)}\right)L$ symbols and a server communication load of $\left({1+\mathcal {O}\left({\frac {1}{N}}\right)}\right)L$ symbols, with a worst-case information-theoretic security guarantee, against any subset of up to $T=o(N)$ semi-honest users who may also collude with the curious server. Moreover, the proposed SwiftAgg+ allows for a flexible trade-off between communication loads and the number of active communication links. In particular, for $T © 1983-2012 IEEE.

关键词： Information theory

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A novel deep dual self-attention and Bi-LSTM fusion framework for Parkinson’s disease prediction using freezing of gait: a biometric application

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Multimedia Tools and Applications 2024年第33期83卷 80179-80200页

作者： Habib, Zeeshan Mughal, Muhammad Ali Khan, Muhammad Attique Hamza, Ameer Alturki, Nazik Jamel, Leila Department of Electrical Engineering HITEC University Taxila Pakistan Department of Computer Science HITEC University Taxila Pakistan Department of Computer Science and Mathematics Lebanese American University Beirut Lebanon Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh Saudi Arabia

Parkinson’s disease (PD) disorder is caused by the imbalance of inhibitory dopamine and excitatory acetylcholine neurotransmitters, which causes hindrance in locomotion. Freezing of gait (FOG), tremors, and bradykinesia are the most debilitating and disconcerting symptoms of Parkinson’s Disease (PD). FOG episodes occur due to neurological control disorder and motorized impairments, which sternly hinder voluntary movement and forward locomotion. The computational intelligence techniques can effectively predict PD from the sensor data. To detect the FOG episodes often experienced by PD patients, this paper utilizes a 5G spectrum that operates at 4.8 GHz. The main idea of the proposed system is to extract the wireless channel characteristics information (containing amplitude variances) that can be integrated into a 5G communication system. This system collects data through wireless devices such as dipole antennas, network interface cards, and RF signal generators. Five human activities were performed: fast walking, slow walking, sitting on a chair, FOG episodes, and voluntary stopping. We proposed a novel deep dual self-attention and Bi-LSTM fusion architecture for PD prediction to train the collected dataset. Two architectures have been proposed, namely Deep Dual Attention Neural Network (D2ANN) and BiLSTM Neural Network (BNN), for the classification of PD using Chanel State Information (CSI) data. After that, the most prominent information is extracted using self-attention activation. A newly informed weighted fusion approach is proposed, and tree growth optimization is employed for the best feature selection. Finally, the selected features are classified using a multilayer full SoftMax neural network. The experimental process was conducted on FOG wireless sensor network data and obtained the highest accuracy of 98.66%. Compared to the results with state-of-the-art techniques, the proposed framework shows improved accuracy, recall, and precision rates. © The Author(s

关键词： Network architecture

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A systematic literature review of visual feature learning: deep learning techniques, applications, challenges and future directions

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Multimedia Tools and Applications 2024年 1-58页

作者： Abdullahi, Mohammed Oyelade, Olaide Nathaniel Kana, Armand Florentin Donfack Bagiwa, Mustapha Aminu Abdullahi, Fatimah Binta Junaidu, Sahalu Balarabe Iliyasu, Ibrahim Ore-ofe, Ajayi Chiroma, Haruna Department of Computer Science Ahmadu Bello University Zaria Zaria Nigeria Department of Mechanical Engineering Ahmadu Bello University Zaria Nigeria Department of Computer Engineering Ahmadu Bello University Zaria Nigeria College of Computer Science and Engineering University of Hafr Al Batin Hafar Al-Batin31991 Saudi Arabia School of Electronics Electrical Engineering and Computer Science Queen’s University Belfast Belfast United Kingdom

Visual Feature Learning (VFL) is a critical area of research in computer vision that involves the automatic extraction of features and patterns from images and videos. The applications of VFL are vast, including object detection and recognition, facial recognition, scene understanding, medical image analysis, and autonomous vehicles. In this paper, we propose to conduct extensive systematic literature review (SLR) on VFL based on deep learning algorithms. The paper conducted an SLR covering deep learning algorithms such as Convolutional Neural Networks (CNNs), Autoencoders, and Generative Adversarial Networks (GANs) including their variants. The review highlights the importance of VFL in computer vision and the limitations of traditional feature extraction techniques. Furthermore, it provides an in-depth analysis of the strengths and weaknesses of various deep learning algorithms for solving problems in VFL. The discussion of the applications of VFL provides an insight into the impact of VFL on various industries and domains. The review also analyzed the challenges faced by VFL, such as data scarcity and quality, overfitting, generalization, interpretability, and explainability. The discussion of future directions for VFL includes hybrid techniques, unsupervised feature learning, continual learning, attention-based models, and explainable AI. These techniques aim to address the challenges faced by VFL and improve the performance of the models. The systematic literature review concludes that VFL is a rapidly evolving field with the potential to transform many industries and domains. The review highlights the need for further research in VFL and emphasizes the importance of responsible use of VFL models in various applications. The review provides valuable insights for researchers and practitioners in the field of computer vision, who can use these insights to enhance their work and ensure the responsible use of VFL models. © The Author(s), under exclusive licence to

关键词： computer vision

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Multistage Parameter Optimization for Rule Generation for Multistage Manufacturing Processes

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IEEE Transactions on Industrial Informatics 2024年第3期20卷 3857-3867页

作者： Wahyuni, Ida Chang, Chin-Chun Yang, Hua-Sheng Wang, Wei-Jen Liang, Deron National Central University Department of Computer Science and Information Engineering Taoyuan32001 Taiwan National Taiwan Ocean University Department of Computer Science and Engineering Keelung20224 Taiwan

Defects in multistage manufacturing processes (MMPs) decrease profitability and product quality. Therefore, MMP parameter optimization within a range is essential to prevent defects, achieve dynamic accuracy, and accommodate manufacturing tolerances. However, existing studies only focused on optimization in a single manufacturing stage of MMP, such as the weaving stage in fabric manufacturing. Furthermore, existing methods optimize for a single value rather than a range. Thus, we propose a novel approach called multistage parameter optimization for rule generation (MPORG) to prevent the occurrence of defects in MMPs. In the proposed approach, key parameters are identified and optimized to a range for each defect type. Subsequently, the optimized parameters for each defect type are merged. Our approach is novel because it optimizes parameters to a range rather than a single value, allowing engineers to select a value in this range according to their experience. It also provides results that are specific to a product type. Our approach outperformed the classification and regression tree (CART) algorithm and multiresponse CART method in experiments on an empirical fabric manufacturing dataset that we gathered. The experimental results demonstrated that the MPORG approach can prevent the occurrence of single-type or multiple-type defects by approximately 89%. © 2005-2012 IEEE.

关键词： Defects

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A user-adaptive privacy-preserving authentication of IoMT using zero knowledge proofs with ECC

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Multimedia Tools and Applications 2025年 1-32页

作者： Misra, Garima Hazela, Bramah Chaurasia, Brijesh Kumar Department of Computer Science and Engineering Amity School of Engineering and Technology Lucknow Amity University Uttar Pradesh India Department of Computer Science & Engineering Pranveer Singh Institute of Technology Utter Pradesh Kanpur India

The Internet of Things (IoT) has developed into a crucial component for meeting the connection needs of the current smart healthcare systems. The Internet of Medical Things (IoMT) consists of medical devices that are connected to patients in order to sense their medical parameters and exchange that information with healthcare professionals so they can deliver remote healthcare services. The need to protect the patient’s private information from being misused makes IoMT security an urgent issue that merits further study. Elliptic Curve cryptography (ECC), a popular substitute for traditional public key encryption like RSA, has several advantages. ECC is a prime contender for implementation on constrained devices with confined memory and speed, as well as wireless communication protocols that use low power like IoMT environment. The economic complexity of the current security protocols makes them inappropriate, hence new security protocols must be developed for resource-constrained and diverse IoT networks. This work suggests an authenticated key agreement protocol for the IoMT that is based on elliptic curve encryption based zero-knowledge proof (ZKP) protocols. The proposed mechanism completes in order of milliseconds up to 300 ms for 160 bits. The research will include the comparison between computational delays in traditional protocols and ZKP-ECC protocol. This paper provides a protocol to obtain privacy of data in IoMT environment on the basis of zero knowledge phenomenon and anonymous communication by ZKP-ECC. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Authentication Protocol

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