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An optimized pair-wise comparison approach for automated feature weight assignment in content-based image retrieval system

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Multimedia Tools and applications 2024年 1-31页

作者： Rout, Narendra Kumar Ahirwal, Mitul Kumar Atulkar, Mithilesh Department of Computer Application NIT CG Raipur492010 India Department of Computer Science and Engineering MANIT M.P Bhopal462003 India

A content-based image retrieval system (CBIR) needs intensity/weighted importance for individual features of an image to find similar images in the database to achieve better results. Generally, these weights are assigned manually and are difficult to calculate as per the nature of the images. An experienced user may be able to retrieve images with higher accuracy by providing suitable weights for features in comparison to that of an inexperienced user. There is a need to develop a method that is independent of manual assignment of weights. To fulfil that need, an optimization-based pair-wise comparison approach has been developed for reducing the user intervention in the retrieval system and finding the optimal feature weight based on the nature of a benchmark COREL 10kimage database. The proposed work is implemented with different optimization techniques to optimize the pair-wise comparison method known as the Analytic hierarchy process (AHP). For optimization, algorithms such as Genetic algorithm (GA), Particle swarm optimization (PSO), Grey-wolf optimization (GWO), and Jaya algorithm (JAYA) have been used to get optimal weight for image feature. As per the results and observations, the AHP-PSO approach performs promising accuracy in terms of average precision and recall as compared to distinct optimized AHP approaches. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Genetic algorithms

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Review on Predicting the Landslide Susceptibility using Machine Learning Techniques 2

Review on Predicting the Landslide Susceptibility using Mach...

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2nd International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Sinha, Abhishek Giri, Shivam Kumar Yadav, Himanshu Prasad, Sanjeev Kumar Mishra, Priya School of Computer Science and Engineering Galgotias University Greater Noida India Department of Computer Application ABES Engineering College Ghaziabad India

ISBN: (纸本)9798331530389

This Landslide susceptibility prediction is a critical task for Alleviating the risks associated with landslide hazards, which have significant socio-economic and environmental impacts. Existing techniques face challenges related to data quality, model coherence, and generalizability across diverse geographical and climatic regions. Additionally, while advanced machine learning (ML) and deep learning (DL) models offer high accuracy, they often require significant computational resources, which limits their practical application in real-time scenarios. In this review, we aim to highlight the strengths, limitations, and potential improvements of machine learning techniques in predicting landslide susceptibility, providing a comprehensive reference for researchers and practitioners in this domain. Furthermore, the role of Explainable AI is examined to address the critical need for interpretable and transparent models in real-world applications. Hybrid ML-DL models outperform traditional methods in handling complex spatial-temporal data, with dynamic factors improving accuracy and reliability. Explainable AI enhances interpretability, while advancements and collaborations address data and generaliz ability challenges. © 2025 IEEE.

关键词： Adversarial machine learning

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Pruning Optimization of Deep Learning Model Based on Generalization Estimation

Pruning Optimization of Deep Learning Model Based on General...

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2024 International Conference on Artificial Intelligence, Deep Learning and Neural Networks, AIDLNN 2024

作者： Li, Xuanzhe Ke, Wenjun Wen, Wantao Liu, Shaotong Sheng, Xiaojuan School of Computer Science and Engineering Southeast University China Beijing Institute of Computer Technology and Application China

ISBN: (纸本)9798331520816

Existing research has shown that deep learning models contain considerable redundancy, requiring compression optimization to eliminate unnecessary parameters and improve computational efficiency. In model compression through pruning, many existing methods often use loss values to guide the identification of sub-network models. However, low training loss does not necessarily indicate good generalization ability. Consequently, reducing model parameters while preserving strong generalization performance has become a key area of research. Addressing the challenge of ensuring strong generalization performance of submodels after optimization through deep learning model pruning, we propose a channel-structured generalization estimation model. The model architecture incorporates Fully Connected Neural Networks (FCNN) and Gated Recurrent Units (GRU) as modules for channel feature extraction. Additionally, we introduce a differentiable pruning algorithm based on generalization estimation. Experimental results demonstrate the capability of the channel-structured generalization estimation model in predicting the generalization gap via regression. Furthermore, the proposed differentiable pruning algorithm effectively safeguards the generalization capability of sub-models. We conduct pruning experiments on NiN-structured convolutional neural network models on two datasets, CIFAR-10 and SVHN. Comparative experimental results demonstrate the effectiveness of this approach: achieving a 0.72% accuracy improvement over the original model on CIFAR-10;and a 0.12% accuracy improvement over the base model on SVHN while reducing FLOPs by 50.2%, validating the effectiveness of the proposed approach. © 2024 IEEE.

关键词： Contrastive Learning

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A Comparative Study on Reinforcement Learning in Disease Prediction on Medical Data

A Comparative Study on Reinforcement Learning in Disease Pre...

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2024 International Conference on Advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Nijana, V. Rajendran, P. Selvi Hindustan Institute Of Technology And Science Computer Application Department India Hindustan Institute Of Technology And Science Department Of Computer Science And Engineering India

ISBN: (纸本)9798350364828

Medical imaging has been used extensively in healthcare in recent years for a variety of purposes, including disease diagnosis, treatment planning, and tracking the course of an illness. These applications entail taking pictures of the afflicted organ using a variety of modalities. Image segmentation and classification is two important process performed in disease diagnostic application. Deep reinforcement learning approach applied in many gaming application. DRL provides the accurate result in many real world applications, so researchers pay attention in DRL in medical data that helps the physicians in treating the patient. This study focus on surveying the various applications such as Image registration, lesion localization, image segmentation, image classification and Landmark detection using deep reinforcement learning in healthcare. In addition, this study investigates on the various DRL algorithms (Qlearning, deep deterministic policy gradient (DDPG), Twin Delayed Deep Deterministic Policy Gradient (TDDDPG), Deep Q Network (DQN) and Soft Actor-Critic (SAC) in Alzheimer's disease prediction. Among the DRL methods the DDPG achieved the highest accuracy in detecting the Alzheimer's disease with 97%. © 2024 IEEE.

关键词： Forecasting

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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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Predictive Analytics for Parkinson's Disease Detection Leveraging Deep Learning Algorithms

Predictive Analytics for Parkinson's Disease Detection Lever...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Kalpana, T. Thamilselvan, R. Chitra, K. Priyanka, S. Jahaan, R. Reshmi Thenmalar, T. Kongu Engineering College Department of Computer Application Erode India Kongu Engineering College Department of Computer Science and Engineering Erode India

ISBN: (纸本)9798331543617

Parkinson disorder is a neurological disease that progresses gradually which is typified by the brain's dopamine-producing neurons becoming exhausted. This neuronal loss results by symptoms including tremors, muscle stiffness, bradykinesia, and balance difficulties. Early detection and accurate prediction of PD progression are especially crucial for improving management and intervention strategies. This study builds a prediction model with the use of MRI datasets. To detect Parkinson's disease from MRI data, this study uses deep learning algorithms, such as Inception, Xception, and VGG19, and evaluates how well they perform in terms of accuracy. © 2024 IEEE.

关键词： Neurons

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An Energy Efficiency Enhanced through Duty Cycle based on Clusters in Wireless Body Area Network 3

An Energy Efficiency Enhanced through Duty Cycle based on Cl...

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3rd International Conference on Power Electronics and IoT applications in Renewable Energy and its Control, PARC 2024

作者： Rani, Anita Kumar, Rakesh Ram, Anant GLA University Computer Science of Engineering and Application Mathura India Computer Science Govt. Polytechnique College Jalaun India

ISBN: (纸本)9798350309409

Wireless body area network (WBANs) plays an important role in monitoring and helping the patient to maintain the health surveillance system. In this research paper, a variable duty cycle-based clustering algorithm is designed to increase the energy efficiency, by varying the awake and sleep period according to the criticality of the sense data that need to be transmitted. In the proposed approach, the network consists of two phases as LEACH Protocol. first steady phase and second setup phase. Firstly, applying the variable duty cycle and distance centroid has a maximum energy of alive node is considered as initial cluster heads (CHs). The successive CHs are recognized based on residual energy based on the threshold distance from the current cluster head (CH). After the formation of the clusters, the Cluster head aggregates the emergency data through alive nodes in Variable duty cycle values and sends it to the base station. In this algorithm no need to transmit the data in normal conditions of the patient (i.e.) they are dead nodes This technique provides more energy efficiency in the network. The performance evaluation of the proposed protocol Energy Efficient Duty Cycle-Based Cluster Approach (EEDCCA) is done using MATLAB tool and results are analyzed. The simulation result validates that the proposed technique increases the energy efficiency of the network and also Enhancement of Lifetime of the whole WBAN network. © 2024 IEEE.

关键词： Energy efficiency

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Smart cities and the IoT: an in-depth analysis of global research trends and future directions

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Discover Internet of Things 2024年第1期4卷 19页

作者： Bhardwaj, Vivek Anooja, A. Vermani, Lovkesh Singh Sunita Dhaliwal, Balwinder Kaur School of Computer Science and Engineering Manipal University Jaipur Rajasthan Jaipur India School of Computer Application JECRC University Rajasthan Jaipur India Multani Mal Modi College Punjab Patiala India Department of Computer Science and Engineering Lovely Professional University Punjab Phagwara India

Extensive scientific investigation is necessary because every government wants to construct smart cities. This is why examining how researchers approach this area of study is critical. This study investigates global research trends in smart cities and the Internet of Things (IoT) by analyzing 14,309 articles from the Scopus database (2010–2024). Using text mining and latent semantic analysis (LSA) via the KNIME tool, the research identifies key areas and trends in smart city development. The k-means clustering algorithm predicts future research directions, highlighting the most active countries, influential authors, and significant sources in the field. The results underscore the need for additional research in sectors where IoT applications for smart cities are still in their early stages. This analysis also offers insights into fostering international collaborations among institutions and researchers. The findings suggest that future research should focus on developing secure, scalable solutions to address challenges across various industries. Overall, this study provides a comprehensive overview of smart city research, offering valuable guidance for researchers and policymakers aiming to advance the integration of IoT technologies into urban environments. © The Author(s) 2024.

关键词： Latent semantic analysis

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Sarcasm Detection in X Data Using Node Embedding and Graph Convolutional Networks 4

Sarcasm Detection in X Data Using Node Embedding and Graph C...

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4th International Conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Tyagi, Aditya Dayal Garg, Setu Sharma, Swati Tomar, Vivek Verma, Kimmi Computer Science & Application Noida India Its Engineering College Electronics & Communication Engineering Noida India Kiet Group of Institutions Computer Science & Engineering Ghaziabad India Computer Science & Engineering Dehradun India Abes Engineering College Computer Science & Engineering Ghaziabad India

ISBN: (纸本)9798350364729

An extensive amount of attention has been paid to the implications that the detection of sarcasm on online social networks such as Facebook, X etc. could have for the reason of sarcasm detection, sentiment analysis, content regulation, and public opinion. In light of the fact that traditional methods frequently struggle to comprehend the nuances of sarcasm, there is a growing interest in more advanced methodology that make uses of network architectures and deep learning. For the purpose of identifying instances of sarcasm in X data, this study presents a novel method that combines Graph Convolutional Networks (GCNs) and node embeddings. Our solution makes use of a generalized convolutional neural network (GCN), constructs a user-word interaction network, and incorporates word2vec for initial text embedding in order to capture both textual and contextual characteristics. Using our methodology, we demonstrate that it is superior to traditional methods in terms of both accuracy and robustness. © 2024 IEEE.

关键词： Graph embeddings

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Crime Analysis Using Graph-Based Feature Selection 11th

Crime Analysis Using Graph-Based Feature Selection

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11th International Conference on Intelligent Computing and applications, ICRTC 2023

作者： Das, Priyanka Dutta, Arindam Das, Bikash Kar, Madhuja Chakraborty, Sudipta Department of Computer Application and Science Institute of Engineering and Management West Bengal Kolkata India

ISBN: (纸本)9789819717231

The current study extracts all potential crime attributes from reported offenses and selects just those that are needed for crime pattern assessment. To accomplish this, before using a crime report, it is processed, and criminal features are gathered to build feature vectors for each report. Based on a cosine relationship score, a weighted and undirected graph evolved, with the distinguishing qualities as vertices and the inverse of the similarity rating as the weight of the accompanying edge. The threshold number is determined by averaging all edge weights. Two separate subgraphs have been formed based on the threshold. The resulting two subgraphs could be a collection of several components that were used individually as input to the next iteration of the clustering operation. Each component’s threshold has been changed independently, and they have been further partitioned into more compact subgraphs. The Silhouette Index, a cluster validation metric, is computed at each level of iteration, and the technique occurs over and over just in case the cluster quality improves. Eventually, the graph clustering method categorizes features. Thus, the primary improvement of this paper is feature selection, which not only improves the efficiency but also the veracity of the following mining process. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Crime

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