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IoT enabled plant leaf disease segmentation and multi-classification using mayfly bald eagle optimization-enabled machine learning

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Multimedia Tools and Applications 2024年第21期83卷 59747-59781页

作者： Mishra, Monalisa Choudhury, Prasenjit Pati, Bibudhendu Department of Computer Science and Engineering National Institute of Technology Durgapur India Department of Computer Science Rama Devi Women’s University Bhubaneswar India

Agricultural production is critical to the economy. This is one of the reasons why disease detection in plants is so important in agricultural settings, as plant disease is rather common. Farmers are not engaged in increasing their agricultural productivity daily since there are no technologies in the previous system to detect diseases in various crops in an agricultural environment. With the exponential population growth, food scarcity is a huge concern globally. In addition to this, the productivity of agricultural products has been highly impacted by the rapid increase in phytopathological adversities. The main challenges in leaf segmentation and plant disease identification are prior knowledge is required for segmentation, the implementation still lacks the accuracy of results, and more tweaking is required. To reduce the devastating impacts of illnesses on the economy, early detection of illnesses in plants is therefore essential. This paper describes an approach for segmenting and detecting plant leaf diseases based on images acquired via the Internet of Things (IoT) network. Here, a plant leaf area is segmented with a UNet, whose trainable parameters are optimized using the Mayfly Bald Eagle Optimization (MBEO) algorithm. Further, plant type classification is carried out by the Deep batch normalized AlexNet (DbneAlexNet), optimized by the Sine Cosine Algorithm-based Rider Neural Network (SCA-based RideNN). Finally, the DbneAlexNet, with weights adapted by the MBEO algorithm, is used to identify plant disease. The Plant Village dataset is used to evaluate the proposed DbneAlexNet-MBEO for plant-type classification and disease detection. The efficiency of the UNet-MBEO for segmentation is examined based on the Dice coefficient and Intersectin over Union (IOU) and has achieved superior values of 0.927 and 0.907. Moreover, the DbneAlexNet-MBEO is examined considering accuracy, Test Negative Rate (TNR), and Test Positive Rate (TPR) and offered superior values of 0

关键词： Classification (of information)

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Semantic deep learning and adaptive clustering for handling multimodal multimedia information retrieval

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Multimedia Tools and Applications 2025年第13期84卷 11795-11831页

作者： Sattari, Saeid Yazici, Adnan Computer Engineering Department Middle East Technical University Ankara06531 Turkey Computer Science Department Nazarbayev University Astana010000 Kazakhstan

Multimedia data encompasses various modalities, including audio, visual, and text, necessitating the development of robust retrieval methods capable of harnessing these modalities to extract and retrieve semantic information from multimedia sources. This paper presents a highly scalable and versatile end-to-end framework for multimodal multimedia information retrieval. The core strength of this system lies in its capacity to learn semantic contexts within individual modalities and across different modalities, achieved through the utilization of deep neural models. These models are trained using combinations of queries and relevant shots obtained from query logs. One of the distinguishing features of this framework is its ability to create shot templates, representing videos that have not been encountered previously. To enhance retrieval performance, the system employs clustering techniques to retrieve shots similar to these templates. To address the inherent uncertainty in multimodal concepts, an improved variant of fuzzy clustering is applied. Additionally, a fusion method incorporating an OWA operator is introduced. This method employs various measures to aggregate ranked lists produced by multiple retrieval systems. The proposed approach leverages parallel processing and transfer learning to extract features from three distinct modalities, ensuring the adaptability and scalability of the framework. To assess its effectiveness and efficiency, the system is rigorously evaluated through experiments conducted on six widely recognized multimodal datasets. Remarkably, our approach outperforms previous studies in the literature on four of these datasets, achieving performance improvements ranging from 1.5% to 10.1% over the best reported results in those studies. The experimental findings, substantiated by statistical tests, conclusively establish the effectiveness of the proposed approach in the field of multimodal multimedia information retrieval. © The Author(s), und

关键词： Fuzzy clustering

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A new speaker-diarization technology with denoising spectral-LSTM for online automatic multi-dialogue recording

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Multimedia Tools and Applications 2024年第15期83卷 45407-45422页

作者： Chan, Din Yuen Wang, Jhing-Fa Chin, Hsu-Ting Department of Computer Science and Information Engineering National Chiayi University Chiayi City Taiwan Department of Electrical Engineering National Cheng Kung University Tainan Taiwan

In AI pandemic applications, the online automatic AI recording apparatus for official councils such as court trials, business conferences and commercial meetings will become imperative because it could let the opinion identification and consensus of participants be synchronically available to implicitly diminish social costs such as follow-up disputes and controversies. Hence, in this study, an automatic on-line multi-dialogue recording system is completely constructed, where the unbounded interleaved-state recurrent neural networks (UIS-RNN) with proposed crux improvements is exploited to achieve confident speaker-diarization. For keeping the systematic robustness, a denoising spectral-LSTM, which is precisely modified from the dual-signal transformation LSTM (DTLN), can strengthen its subsequent crux-improved UIS-RNN and automatic speech recognition (ASR). Finally, the MacBERT model is set to rectify the possible wrong words in conversed sentences according to the learned rational context. For making our system being a practical software apparatus in the use of unmarked multi-person councils, we have also completed the convenient interfaces for the operations of ASR and speaker-diarization, which can exhibit on-line denoising efficacy and speaker-diarization results as well as offer real-time hand-crafted rectifications to common users. In extensive experiments, the proposed recording system can promise high accuracy rates of online speaker diarization and speech-separated ASR. Our proposed system had been examined by the cooperated law court staffs, who offered the noise-embedded speeches of practical court field to test our system. Since the tight recording burden had been indeed noticeably alleviated in their legal-action councils, the court staffs had endorsed that the proposed entire system could be a friendly labor-saving AI apparatus for on-line automatic multi-dialogue recording. © The Author(s), under exclusive licence to Springer science+Business Media,

关键词： Audio recordings

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A Network Model to Study the Underlying Characteristics of Phishing Sites

IEEE Networking Letters

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IEEE Networking Letters 2024年第1期6卷 46-49页

作者： Gayen, Avijit Santra, Sukriti Dey, Ayush Jana, Angshuman Indian Institute of Information Technology Guwahati Department of Computer Science and Engineering Guwahati781015 India Swami Vivekanda University Department of Computer Science and Engineering Kolkata700121 India Techno India University Department of Computer Science and Engineering Kolkata700091 India

In the rapidly evolving landscape of cyber threats, phishing continues to be a prominent vector for cyberattacks, posing significant risks to individuals, organizations and information systems. This letter delves into a comprehensive analysis of phishing sites, focusing on their distinctive characteristics through the lens of network perspectives. In this letter, we study the underlying characteristics of phishing sites from the hyperlinked connectivity perspectives. In this letter, we have used a phishing site dataset available at Mendeley data. As the dataset does not contain any hyperlinked information, We further collect the hyperlinks in each site and store that information in a JSON file. We further analyze the hyperlinks and the various features of those sites. The initial study reveals that though the Phishing sites mimic the original legitimate sites to trap people in their sites, it has distinct differences from the perspective of structural connectivity as well as the content of sites. © 2019 IEEE.

关键词： Data mining

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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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An enhanced AMBTC for color image compression using color palette

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Multimedia Tools and Applications 2024年第11期83卷 31783-31803页

作者： Xiong, Lizhi Zhang, Mengtao Yang, Ching-Nung Kim, Cheonshik School of Computer Science Nanjing University of Information Science and Technology Nanjing China Department of Computer Science and Information Engineering National Dong Hwa University Hualien Taiwan Department of Computer Engineering Sejong University Seoul Korea Republic of

Digital image has been used in various fields as an essential carrier. Many color images have been constantly produced since their more realistic description, which takes up much storage space and network bandwidth. Thus, color image compression has become an essential key technology. Absolute Moment Block Truncation Coding (AMBTC) has been widely studied as one of the classical image compression methods. However, in the existing methods, the visual quality of the reconstructed images and the compression rate are all relatively low. Therefore, this paper proposes an enhanced AMBTC for color image compression using a color palette. In the proposed method, the K-means clustering algorithm is utilized for training the image's palette pattern. The color palette obtained by K-mean will be more suitable for reconstructing this image than the standard color palette, and the visual quality will be higher. The six clustered central pixels are matched with the palette through a color difference formula, and the obtained index values are used as the quantization levels. Huffman coding is used to build a bitmap to achieve a higher compression rate, that is, a lower bit rate. At last, a block of a color image can be represented by six index values and a bitmap. Experimental results and theoretical analysis demonstrate that the proposed method has better visual quality and bit rate than similar schemes. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2023.

关键词： Color

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REGAC: Multi-Class Offensive Content Identification Using Graphical Approach

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2025年第4期52卷 931-952页

作者： Chinivar, Sneha Roopa, M.S. Arunalatha, J.S. Venugopal, K.R. Department of Computer Science and Engineering Bangalore University K R Circle Karnataka Bengaluru India Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Karnataka Bengaluru India Department of Computer Science and Engineering Bangalore University K R Circle Karnataka Bengaluru India Karnataka Bengaluru India

Online offensive behaviour continues to rise with the increasing popularity and use of social media. Various techniques have been used to address this issue. However, most existing studies consider offensive content identification as a binary or ternary problem, disregarding the potential for multi-class classification of derogatory content. In this work, we propose RoBERTa Embedding-based Graphical Approach Classifier (REGAC), which aims to designate the identified offensive content into varied classes,like age, gender, ethnicity, religion, and others to understand what exact qualities the bully usually targets in their victim so that pertinent measures can be taken to battle them. Additionally, the models’ efficiency is evaluated using an unbalanced dataset to identify types of offensive content. The word embeddings are generated for a balanced dataset using RoBERTa embedding model to bring out the best-fitting vectors. These vectors are then input into traditional Machine Learning algorithms (SVM, KNN, Logistic Regression, Random Forest, and XGBoost) and graph-based algorithms (Graph Convolution Network (GCN), GraphSAGE, and Graph Attention Network (GAT)) for fine-grained categorization of offensive textual content. The experimental results demonstrate the efficiency of combating social media’s offensive content of the proposed work with higher Precision, Recall, F1-Score, and Accuracy compared with most state-of-art approaches. © (2025.0), (International Association of Engineers). All Rights Reserved.

关键词： Graph embeddings

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Emergency Vehicle Direction Detection Using Mel-Frequency Cepstral Coefficients and Deep Learning 18

Emergency Vehicle Direction Detection Using Mel-Frequency Ce...

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18th IEEE International Conference on Vehicular Electronics and Safety, ICVES 2024

作者： Bhushan, Shourya Chaturvedi, Manish Iitram Department of Electrical and Computer Science Engineering Ahmedabad India

ISBN: (纸本)9798350387278

Emergency vehicles face difficulty in navigating through traffic and locating the shortest route to the incident site. In the developing countries or under disaster conditions when limited communication and Intelligent Transportation System (ITS) infrastructure is available, the situation becomes more challenging. This paper addresses these challenges by leveraging the unique sound generated by emergency vehicles to detect the direction of the emergency vehicles. We use the Mel-frequency cepstral coefficients (MFCC) and a Convolutional Neural Network (CNN) stacked with Long Short-Term Memory (LSTM) layers to accurately detect the movement direction of outgoing emergency vehicles at a junction. MFCC facilitates effective feature extraction from audio signals, while the CNN-LSTM architecture enables robust temporal and spatial pattern recognition. The information can be used to adapt the traffic lights at the downstream junction to enable the uninterrupted movement of the emergency vehicle through the junction. The experiment results demonstrate 98.56% accuracy in detecting movement direction of an emergency vehicle. The solution can improve emergency response under resource constrained environment and in turn improve public safety and well-being. © 2024 IEEE.

关键词： Convolutional neural networks

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REAL-TIME GLUCOSE MONITORING SYSTEM AND DIETARY RECOMMENDATIONS FOR DIABETES MELLITUS USING FOG COMPUTING

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Telecommunications and Radio engineering (English translation of Elektrosvyaz and Radiotekhnika) 2025年第5期84卷 55-67页

作者： Sathish, N. Elangovan, D. Nagalakshmi, R. Suresh, G. Chitra, Devi D. Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Information Technology Saveetha Engineering College Chennai India Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India Department of Artificial Intelligence and Data Science Kings Engineering College Sriperumbudur India Department of Computer Science And Engineering S.A. Engineering College Chennai India

This systematic review gave special attention to diabetes and the advancements in food and nutrition needed to prevent or manage diabetes in all its forms. There are two main forms of diabetes mellitus: Type 1 (T1D) and Type 2 (T2D). The loss of beta cells brought on by an autoimmune reaction causes the pancreas to be unable to produce enough insulin, which leads to Type 1 diabetes. A deficiency of insulin could arise as the illness worsens. The most frequent cause is a combination of excessive body weight and inadequate exercise. Currently, 90–95% of patients have Type 2 diabetes mellitus (T2DM). Age-specific normal blood sugar ranges exist and knowing them is crucial for determining a target blood sugar level that is both healthy and appropriate. The main takeaway is that diet regimens ought to be tailored to the patient’s requirements and should account for their capacity for change. Carbohydrates are the nutrients that have the biggest impact on the rise in blood glucose. In this paper, we have proposed a novel method for glucose monitoring and diet recommendations for diabetic patients using fog computing. A paradigm known as fog computing makes use of the benefits that come from using both cloud and edge devices to support modern computing systems by offering high-quality services, cutting latency, facilitating mobility, supporting multi-tenancy, and many other features. © 2025 by Begell House, Inc.

关键词： Fog computing

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ImageNet Classification Using WordNet Hierarchy

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第4期5卷 1718-1727页

作者： Chatterjee, Ankita Mukherjee, Jayanta Das, Partha Pratim Indian Institute of Technology Kharagpur Department of Computer Science and Engineering Kharagpur721302 India Ashoka University Department of Computer Science Sonipat131029 India

Convolutional neural networks (ConvNets) have become increasingly popular for image classification tasks. All contemporary computer vision problems are being dominated by ConvNets. Conventional training methods using cross-entropy loss for training have constantly outperformed the state-of-the-art technique to set a new standard in the ImageNet classification challenge. However, growing accuracy come at the cost of enormous number of parameters and computations. Further, classical learning algorithms do not utilize the semantic relationship between the classes present in the dataset. Thus, interpreting the behavior of the model become difficult even though the results may be desirable. Hence, we demonstrate a classification method by leveraging the WordNet hierarchy on the ImageNet dataset to establish class relationships and label embedding. The model is trained using cross entropy with soft labels based on the semantic similarity between the generated output and the ground truth. Unlike categorical cross entropy, it does not treat every predicted label as equally erroneous. The method generates meaningful neighboring classes in the feature space of the true label. © 2020 IEEE.

关键词： Neural networks

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