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IoT-Enabled Predictive Maintenance and Analytic Hierarchy Process Based Prioritization of Real-Time Parameters in a Diesel Generator: An Industry 4.0 Case Study

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SN computer Science 2024年第1期5卷 145页

作者： Mohapatra, Ambarish Gajendra Mohanty, Anita Tripathy, Pradyumna Kumar Department of Electronics and Instrumentation Engineering Silicon Institute of Technology Bhubaneswar India Department of Computer Science and Engineering Silicon Institute of Technology Bhubaneswar India

Diesel generator (DG) is a secondary power source that is becoming increasingly common for supplying continuous power to business and residential buildings. These hybrid machines provide the necessary electrical energy using both a diesel engine and an electric generator. Monitoring crucial machine characteristics on a regular basis is crucial for increasing their efficiency. condition monitoring systems (CMS) powered by the Internet of Things (IoT) have taken the role of Remote Monitoring (RM). Moreover, a key component of Industry 4.0 is predictive maintenance, which may be accomplished by implementing IoT-enabled RM. This paper portrays two aspects of the DG unit. Firstly, the performance analysis of the DG unit using a 4G-enabled IoT node installed on the motherboard. This research work develops and tests a framework for monitoring and analyzing key metrics, including engine speed, voltage and current generated, power factor, coolant, fuel, and battery health status. Secondly, the Analytic Hierarchy Process (AHP) can be used to rank the performance indicators that are most important for the efficient monitoring of the DG system in the context of IoT-enabled DG monitoring. The AHP technique enables decision-makers to objectively assess the relative weights of several criteria and to prioritize them. Using AHP, decision-makers can weigh each of these criteria based on how important they are in relation to one another. The performance of the DG system may then be assessed using these weights, and potential areas for improvement can be found. © 2024, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： AHP CMS Condition monitoring systems Diesel generator Industry 4.0 IoT Predictive maintenance Remote monitoring

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A novel approach for image retrieval in remote sensing using vision-language-based image caption generation

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Multimedia Tools and Applications 2025年第6期84卷 2985-3014页

作者： Yadav, Prem Shanker Tyagi, Dinesh Kumar Vipparthi, Santosh Kumar Department of Computer Science and Engineering Malaviya National Institute of Technology Rajasthan Jaipur302017 India School of Artificial Intelligence and Data Engineering Indian Institute of Technology Ropar Punjab Rupnagar140001 India

Recent advancements in satellite technologies have resulted in the emergence of Remote Sensing (RS) images. Hence, the primary imperative research domain is designing a precise retrieval model for retrieving the most pertinent images based on the query. Present Remote Sensing Image Retrieval (RSIR) systems use visual descriptors to characterize the primitives (such as various land-cover types) that are visible in the images. However, the visual descriptors are inadequate for defining the complicated content of RS images. To solve this problem, a new model is devised for image retrieval based on image captions. The goal is to generate textual illustrations with captions to define relations amongst objects precisely. Here, image captioning is attained based on the vision-language pre-training model. The image captions are utilized for generating features like term frequency-inverse document frequency (TF-IDF), length of text, and Bag of Words. Meanwhile, query text is utilized wherein features like TF-IDF, text length, and Bag of Words are obtained. The similarity between query text features and the image captions features has been computed on the basis of a hybrid similarity measure wherein weights are tuned with the proposed Honey Badger Political Optimizer (HBPO) to retrieve the image. The proposed HBPO provided enhanced efficiency with elevated precision of 93.3%, recall of 93.7%, F1-score of 93.5%, and Recall-Oriented Understudy for Gisting Evaluation (ROUGE) of 0.441. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Visual languages

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Deep Learning-Based Maize Crop Disease Classification Model in Telangana Region of South India

IEEE Transactions on AgriFood Electronics

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IEEE Transactions on AgriFood Electronics 2024年第2期2卷 627-637页

作者： Nagaraju, M. Chawla, Priyanka Hyderabad500043 India National Institute of Technology Department of Computer Science and Engineering Warangal506004 India

One of India's main crops, maize, accounts for 2-3% of global production. Disease detection in maize fields has become increasingly difficult due to a lack of knowledge about disease symptoms. Furthermore, manual disease detection methods take a lot of time and are not effective. Recent developments in convolutional neural networks (CNNs) have exhibited remarkable performance in disease recognition and classification. A CNN is a deep learning technique that extracts the features from an image and performs the disease classification effectively. The optimization of hyperparameters is a tedious problem that impacts the performance of a model. The main purpose of the present research is to support future research to configure suitable hyperparameters to a model. In the present work, a deep CNN is proposed for the classification of seven different diseases of maize crop. Several hyperparameters, such as image size, batch size, number of epochs, optimizers, learning rate, kernel size, and number of hidden layers, were tested with various values in the experimental approach. The obtained results show that running the model for 200 epochs improved the classification accuracy with 87.44%. It also states that choosing input image sizes of 168 × 168 and 224 × 224 resulted in a good classification accuracy of 84.66% and 85.23%, respectively. The proposed deep CNN model has attained 85.83% classification accuracy with the Adam optimizer and a learning rate of 0.001. However, the results achieved by other optimizers, such as root-mean-square propagation (81.95%) and stochastic gradient descent (79.66%), are not better when compared with the Adam optimizer. Finally, the results have provided a better knowledge in selecting appropriate hyperparameters to the application of plant disease classification. © 2024 IEEE.

关键词： Convolutional neural networks

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An Anchor-Aware Graph Autoencoder Fused with Gini Index Model for Link Prediction

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SN computer Science 2024年第6期5卷 745页

作者： Kumar, Shambhu Bisht, Dinesh Jain, Arti Computer Science & amp Engineering and IT Jaypee Institute of Information Technology Department of Mathematics Jaypee Institute of Information Technology

Link prediction stands as a crucial network challenge, garnering attention over the past decade, with its significance heightened by the escalating volume of network data. In response to the pressing need for swift research focus, this study introduces an innovative approach—the Anchor-aware Graph Autoencoder integrated with the Gini Index (AGA-GI)—aimed at gathering data on the global placements of link nodes within the link prediction framework. The proposed methodology encompasses three key components: anchor points, node-to-anchor paths, and node embedding. Anchor points within the network are identified by leveraging the graph structure as an input. The determination of anchor positions involves computing the Gini indexes (GI) of nodes, leading to the generation of a candidate set of anchors. Typically, these anchor points are distributed across the network structure, facilitating substantial informational exchanges with other nodes. The location-based similarity approach computes the paths between anchor points and nodes. It identifies the shortest path, creating a node path information function that incorporates feature details and location similarity. The ultimate embedding representation of the node is then formed by amalgamating attributes, global location data, and neighbourhood structure through an auto-encoder learning methodology. The Residual Capsule Network (RCN) model acquires these node embeddings as input to learn the feature representation of nodes and transforms the link prediction problem into a classification task. The suggested (AGA-GI) model undergoes comparison with various existing models in the realm of link prediction. These models include Attributes for Link Prediction (SEAL), Embeddings, Subgraphs, Dual-Encoder graph embedding with Alignment (DEAL), Embeddings and Spectral Clustering (SC), Deep Walk (DW), Graph Auto-encoder (GAE), Variational Graph Autoencoders (VGAE), Graph Attention Network (GAT), and Graph Conversion Capsule Link (G

关键词： Capsule network Ideal Anchor Point Link prediction Path Estimation Portion Path information function Residual module Shortest path distance

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MindCare: Machine Learning based Personalized Mental Health Monitor 4

MindCare: Machine Learning based Personalized Mental Health ...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Ushashree, P. Naik, Archana Geethanjali College of Engineering and Technology Department of Computer Science and Engineering Hyderabad India Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798331540364

Mental health challenges are growing in recent years, emphasizing the need for effective monitoring and inter- vention systems. This paper presents a comprehensive approach to detect mental unstabilities by analyzing the text entered by the user. Utilizing a combination of sentiment analysis and machine learning techniques, the proposed system preprocesses text data to extract sentiment scores and other key features, which are then used to train models for accurate mental health assessments. The system also offers personalized recommendations based on the user's mental state. Experimental results demonstrate the effectiveness of the approach, with the model achieving accuracy of 82.31% for negative text classification. These results underscore the potential of the system to serve as a valuable tool in the early detection and management of mental health issues. © 2024 IEEE.

关键词： Electronic health record

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Analysis of multimodal fusion strategies in deep learning for ischemic stroke lesion segmentation on computed tomography perfusion data

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Multimedia Tools and Applications 2025年第10期84卷 7493-7518页

作者： Raju, Chintha Sri Pothu Neelapu, Bala Chakravarthy Laskar, Rabul Hussain Muhammad, Ghulam Speech and Image Processing Lab Department of ECE National Institute of Technology Silchar Assam Silchar788010 India Department of Biotechnology and Medical Engineering National Institute of Technology Rourkela Odisha Rourkela769008 India Department of Computer Engineering College of Computer and Information Sciences King Saud University Riyadh11543 Saudi Arabia

Stroke poses a significant risk to human life. Segmenting and immediately treating the stroke core stops its further development, therefore, enhancing the likelihood of survival. Convolutional neural networks (CNN) have been very successful in medical image segmentation, namely in the field of deep learning, and have produced the most advanced outcomes. Multi-modal images provide superior outcomes in the segmentation of stroke lesions compared to single-modal images. The integration of input from several modalities at various levels is crucial in determining performance and producing diverse outcomes in deep learning models that use multimodalities. Further investigation is required to explore the optimal methods for processing multimodal data in CNNs, the influence of fusion on CNN learning, and the effect of fusion strategies on lesions of varying sizes. To examine the impact of a multi-modal fusion method on lesion segmentation, we assessed four models using distinct fusion techniques, including early, late, bottleneck, and hierarchical fusions. This study discusses the various fusion procedures used in segmenting the lesion using computed tomography perfusion data. In addition, both quantitative and qualitative assessments, including deep feature analysis and feature similarity, were conducted to assess the impact of the fusion technique on the model’s performance. Furthermore, we examined the influence of fusion techniques on the size of the lesion. In addition, we analyzed the advantages and disadvantages of several multi-modal fusion systems. Our findings demonstrate that the bottleneck fusion technique got the highest dice score, 0.582, on the Ischemic Stroke Lesion Segmentation 2018 validation data as a result of its capacity to construct complex relationships across several modalities. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Convolutional neural networks

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Indian Sign Language Recognition Using Video Vision Transformer 3

Indian Sign Language Recognition Using Video Vision Transfor...

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3rd International Conference for Advancement in technology, ICONAT 2024

作者： Ojaswee Sreemathy, R. Turuk, Mousami Jagdale, Jayashree Anish, Mohammad Pune Institute of Computer Technology Department of E&TC Engineering Pune India Pune Institute of Computer Technology Department of Information Technology Pune India

ISBN: (纸本)9798350354171

Sign language is a way of communication that uses hand shapes, orientation, movements, and facial expressions to express instead of spoken words like normal language. Different regions have developed their own versions of sign language. In India, Indian sign language (ISL) is the primary mode of communication for more than 5 million speech or hearing impaired people. However, the knowledge of signing and understanding the ISL is only limited to the deaf community, which can make it hard to communicate with the people who don't understand the language. Thus, effective recognition of ISL gestures is vital for seamless interaction between these impaired people and the broader society. The rise of artificial intelligence and computer vision models has provided us with many effective tools to tackle this challenge. While there has been some progress in this field, most research has focused on static signs or American Sign Language, leaving video-based recognition for ISL largely unexplored. To bridge this gap, our paper proposes an approach that utilizes Video Vision Transformer (ViViT) architecture for ISL recognition. We have experimented with different layers and different numbers of attention heads to get a model that works best for the assigned task. Our architecture gives an overall accuracy of 96.69% and a top-5 accuracy of 99.55% on the sign recognition task using our own created VISL-PICT dataset. © 2024 IEEE.

关键词： Audition

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An Efficient Approach for EEG Seizure Detection using CNN with Feature Extraction 7

An Efficient Approach for EEG Seizure Detection using CNN wi...

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Joseph, Liya Izudheen, Sminu Narayanana, Uma Nair, Abhinav R Ashok Gopal, K.A. Rajagiri School of Engineering & Technology Department of Computer Science and Engineering Ernakulam India CVV Institute of Science and Technology Department of Computer Science and Engineering Ernakulam India

ISBN: (纸本)9798350372816

Epileptic seizures are a significant agony for those who suffer from them. Epileptic studies primarily focus on understanding the abnormal behavior of brain signals. Detecting seizures in EEG signals manually is a very chaotic and challenging task. Therefore, we propose an automatic seizure detection system that drastically reduces the manual effort required for seizure detection while providing accurate results. The proposed method involves extracting twelve statistical features from segmented data and adjusting labels to facilitate binary classification using a Convolutional Neural Network (CNN). The dataset is normalized and balanced using SMOTE to address class imbalance. Evaluation metrics such as accuracy, precision and recall are computed on the test set with results visualized through accuracy and loss curves. User input allows for predictions on specific test data, determining the presence of seizures based on model predictions. This structured approach ensures reliable seizure detection through the use of advanced deep learning techniques and comprehensive model evaluation. Furthermore, a comparative study was conducted to assess the effectiveness of the proposed model against conventional machine learning algorithms such as Logistic Regression, Support Vector Machine (SVM), Gradient Boosting and Decision Tree. Our analysis reveals that CNN consistently demonstrates superior performance compared to these methods. © 2024 IEEE.

关键词： Convolutional neural networks

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Designing a skilled soccer team for RoboCup: exploring skill-set-primitives through reinforcement learning

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Neural Computing and Applications 2025年 1-36页

作者： Abreu, Miguel Reis, Luís Paulo Lau, Nuno LIACC/LASI/FEUP Artificial Intelligence and Computer Science Laboratory Faculty of Engineering University of Porto Porto Portugal IEETA/LASI/DETI Institute of Electronics and Informatics Engineering of Aveiro Department of Electronics Telecommunications and Informatics University of Aveiro Aveiro Portugal

The RoboCup 3D soccer simulation league serves as a competitive platform for showcasing innovation in autonomous humanoid robot agents through simulated soccer matches. Our team, FC Portugal, developed a new codebase from scratch in Python after RoboCup 2021. The team’s performance relies on a set of skills centered around novel unifying primitives and a custom, symmetry-extended version of the proximal policy optimization algorithm. Our methods have been thoroughly tested in official RoboCup matches, where FC Portugal has won the last two main competitions, in 2022 and 2023. This paper presents our training framework, as well as a timeline of skills developed using our skill-set-primitives, which considerably improve the sample efficiency and stability of skills, and motivate seamless transitions. We start with a significantly fast Sprint-Kick developed in 2021 and progress to the most recent skill set, including a multi-purpose omnidirectional walk, a dribble with unprecedented ball control, a solid kick, and a push skill. The push addresses low-level collision scenarios and high-level strategies to increase ball possession. We address the resource-intensive nature of this task through an innovative multi-agent learning approach. Finally, we release the team’s codebase to the RoboCup community, providing other teams with a robust and modern foundation upon which they can build new features. © The Author(s) 2025.

关键词： Reinforcement learning

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Indian Sign Language Recognition in Real Time using YOLO NAS 3

Indian Sign Language Recognition in Real Time using YOLO NAS

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3rd International Conference for Advancement in technology, ICONAT 2024

作者： Raj, Rohit Sreemathy, R. Turuk, Mousami Jagdale, Jayashree Anish, Mohammad Pune Institute of Computer Technology Department of E&TC Engineering Pune India Pune Institute of Computer Technology Department of Information Technology Pune India

ISBN: (纸本)9798350354171

Normal people typically communicate using voice, while deaf individuals use sign language to communicate with each other. However, a challenge arises when deaf people need to interact with those who do not understand sign language. To address this issue, we propose developing a model that can recognize Indian Sign Language and translate it into text. Deep learning approaches have been used into computer vision in recent years, greatly advancing the area of sign language identification and improving accessibility for the deaf. To achieve real-time recognition of Indian Sign Language, we have used YOLO NAS (You Only Look Once Neural Architecture Search) object detection *** YOLO NAS architecture achieves 0.9568 mean Average Precision (mAP) with intersection over union (IoU) threshold of 50% across 100 classes, using our selfcreated Sign Language dataset. © 2024 IEEE.

关键词： Object detection

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