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A multi-fused convolutional neural network model for fruit image classification

International Journal of Cognitive computing in Engineering 2024年第1期5卷 416-424页

作者： Sinha, Bam Bahadur Dhanalakshmi, R. Computer Science and Engineering National Institute of Technology Sikkim 737139 India Computer Science and Engineering Indian Institute of Information Technology Tiruchirappalli Tamil Nadu 620012 India

Fruit categorization presents a significant challenge due to the diverse range of fruit types and their similarities in color, shape, size, and structure. This challenge is addressed in this research by proposing a multi-fused CNN approach that integrates three state-of-the-art deep learning models, namely EfficientNet-B0, MobileNetV2, and ResNet50V2, into a unified pipeline. The primary objective is to enhance prediction accuracy and mitigate overfitting issues commonly encountered with conventional techniques. Utilizing the Fruit-360 dataset (both small and full versions), the proposed multi-fused CNN model demonstrates superior performance, achieving an impressive accuracy rate of 99.32% and 97.15% in classifying fruit types across 24 and 131 different categories, respectively. This substantial improvement underscores the potential of the proposed approach in reliably detecting fruit types. In future work, efforts will be directed towards optimizing the model for enhanced processing speed, facilitating real-time performance in practical applications, particularly in agricultural automation. Additionally, rigorous experiments on diverse real-world fruit images will be conducted to further validate the robustness of the proposed model. © 2024 The Authors

关键词： Fruits

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Programmable directional color dynamics using plasmonics

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Microsystems & Nanoengineering 2024年第1期10卷 325-334页

作者： Gyurin Kim Doeun Kim Soeun Ko Jang-Hwan Han Juhwan Kim Joo Hwan Ko Young Min Song Hyeon-Ho Jeong School of Electrical Engineering and Computer Science Gwangju Institute of Science and TechnologyGwangju 61005Republic of Korea Department of Semiconductor Engineering Gwangju Institute of Science and TechnologyGwangju 61005Republic of Korea Artificial Intelligence(AI)Graduate School Gwangju Institute of Science and TechnologyGwangju 61005Republic of Korea

Adaptive multicolor filters have emerged as key components for ensuring color accuracy and resolution in outdoor visual ***,the current state of this technology is still in its infancy and largely reliant on liquid crystal devices that require high voltage and bulky structural ***,we present a multicolor nanofilter consisting of multilayered‘active’plasmonic nanocomposites,wherein metallic nanoparticles are embedded within a conductive polymer *** nanocomposites are fabricated with a total thickness below 100 nm using a‘lithography-free’method at the wafer level,and they inherently exhibit three prominent optical modes,accompanying scattering phenomena that produce distinct dichroic reflection and transmission ***,a pivotal achievement is that all these colors are electrically manipulated with an applied external voltage of less than 1 V with 3.5 s of switching speed,encompassing the entire visible ***,this electrically programmable multicolor function enables the effective and dynamic modulation of the color temperature of white light across the warm-to-cool spectrum(3250 K-6250 K).This transformative capability is exceptionally valuable for enhancing the performance of outdoor optical devices that are independent of factors such as the sun’s elevation and prevailing weather conditions.

关键词： directional prevailing exceptional

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MOHSQO: Multi objective harmony search algorithm based QoS optimization in delay tolerant networks

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Multimedia Tools and Applications 2025年 1-26页

作者： Sonkar, Nidhi Pandey, Sudhakar Kumar, Sanjay Department of Computer Science and Engineering National Institute of Technology Warangal Hanamkonda India Department of Information Technology National Institute of Technology Raipur Raipur India

Delay Tolerant Networks (DTNs) have the ability to make communication possible without end-to-end connectivity using store-carry-forward technique. Efficient data dissemination in DTNs is very challenging problem due to compromising in Quality-of-Service (QoS) metrics such as delivery ratio, end-to-end delay and overhead ratio. That means, if an algorithm is design to find highest delivery ratio, then the algorithm does not bother about delay and overhead ratio. Researchers tried to improve the performance of network but, none of them had given satisfactory results to make a balance between all QoS metrics. In this paper, we have proposed a novel approach to optimize the performance of the network using Multi objective harmony search algorithm by creating a balance between all QoS metrics. Harmony search algorithm considers number of QoS parameters simultaneously. We have used characteristics of the node (Speed, residual energy, buffer capacity and encounters) to select the optimum forwarder node so that we can balance the performance metrics for network optimization. We have used ONE (Opportunistic Network Environment) simulator to simulate the proposed method and compared with exiting techniques. By analyzing the results, we can say that proposed method has the optimum performance. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Delay tolerant networks

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Utility-inspired generalizations of TOPSIS

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Neural computing and Applications 2025年 1-30页

作者： Szczęch, Izabela Susmaga, Robert Institute of Computing Science Poznan University of Technology Piotrowo 2 Poznan60-965 Poland

TOPSIS, a popular method for ranking alternatives, is based on aggregated distances to ideal and anti-ideal points. As such, it was considered to be essentially different from widely popular and acknowledged ’utility-based methods’, which build rankings from weight-averaged utility values. Nonetheless, TOPSIS has recently been shown to be a natural generalization of these ’utility-based methods’ on the grounds that the distances it uses can be decomposed into so-called weight-scaled means (WM) and weight-scaled standard deviations (WSD) of utilities. However, the influence that these two components exert on the final ranking cannot be in any way implemented in the standard TOPSIS. This is why, building on our previous results, in this paper we put forward modifications that make TOPSIS aggregations responsive to WM and WSD, achieving some amount of well interpretable control over how the rankings are influenced by WM and WSD. The modifications constitute a natural generalization of the standard TOPSIS method because, thanks to them, the generalized TOPSIS may turn into the original TOPSIS or, otherwise, following the decision-maker’s preferences, may trade off WM for WSD or WSD for WM. In the latter case, TOPSIS gradually reduces to a regular ’utility-based method’. All in all, we believe that the proposed generalizations constitute an interesting practical tool for influencing the ranking by controlled application of a new form of decision-maker’s preferences. © The Author(s) 2025.

关键词： Decision making

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Decomposition-based hybrid methods employing statistical, machine learning, and deep learning models for crude oil price forecasting

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Neural computing and Applications 2025年第18期37卷 12565-12610页

作者： Purohit, Sourav Kumar Panigrahi, Sibarama Department of Computer Science and Engineering Sambalpur University Institute of Information Technology Burla India Department of Computer Science and Engineering National Institute of Technology Rourkela Odisha Rourkela769008 India

Crude oil prices (COP) profoundly influence global economic stability, with fluctuations reverberating across various sectors. Accurate forecasting of COP is indispensable for governments, policymakers, and stakeholders to make well-informed decisions and effectively mitigate risks. The decomposition-based hybrid models have been showing promising COP forecasting accuracy than other time series forecasting methods. Despite this fact, no systematic study has been conducted to evaluate the true potential of different decomposition-based hybrid methods employing different forecasting models to forecast the COP. Therefore, a hybrid modeling framework is developed by combining efficient decomposition techniques, namely empirical mode decomposition (EMD), ensemble EMD (EEMD), complete EEMD with adaptive noise (CEEMDAN), and variational mode decomposition (VMD) with seven statistical models, fourteen machine learning (ML) models, and six deep learning (DL) models. Further, a systematic study is conducted on the resulting decomposition-based hybrid models to find the best hybrid model for COP forecasting. Three distinct train-test data splits are employed to ensure a reliable evaluation of the models using four performance metrics. Extensive statistical analysis is conducted to identify the optimal combination of the decomposition technique and forecasting model for precise COP prediction. The results demonstrate that the proposed decomposition-based hybrid model employing VMD and Huber Regression is statistically the best method among all alternatives to forecast monthly COP. The proposed hybrid method VMD-Huber Regression improves the root mean square error (RMSE) by 21% than CEEMDAN-ARIMA, 58.31% than EEMD-Theta, 13.18% than EMD-Random Walk, and 49.44% than VMD-TBATS hybrid methods in 60–40 Train-Test split ratio. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Mean square error

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Exploring image data association: A hybrid mining approach

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Multimedia Tools and Applications 2025年第9期84卷 5725-5740页

作者： Parashar, Nishtha Tiwari, Akhilesh Gupta, Rajendra Kumar Department of Computer Science and Engineering Madhav Institute of Technology & Science M.P. Gwalior India Department of Information Technology Madhav Institute of Technology & Science M.P. Gwalior India

In this paper, a new approach for mining image association rules is presented, which involves the fine-tuned CNN model, as well as the proposed FIAR and OFIAR algorithms. Initially, the image transactional database is generated using feature vectors obtained from the fine-tuned CNN architecture. The proposed FIAR algorithm is used to generate hash-indexed image association rules, which are further optimized using the proposed OFIAR algorithm. This methodology combines the strengths of the CNN model to extract histogram features from images, the FIAR algorithm to efficiently mine frequent image itemsets, and the OFIAR algorithm to optimize image association rules. The proposed methodology can be used to discover hidden relationships among images, leading to new insights in image processing and analysis. Efficient results were obtained with a minimum support of 0.50 and a minimum confidence of 0.50. Experiments were performed on the fruits image dataset consisting of 2618 images from six different classes, and the results show that image mining is feasible and can produce strong optimized image association rules that can be further used for classification purposes. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Association rules

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Multiuser Computation Offloading for Long-Term Sequential Tasks in Mobile Edge computing Environments

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Tsinghua science and technology 2023年第1期28卷 93-104页

作者： Huanhuan Xu Jingya Zhou Wenqi Wei Baolei Cheng School of Computer Science and Technology Soochow UniversitySuzhou 215006China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi 214125China Provincial Key Laboratory for Computer Information Processing Technology Soochow UniversitySuzhou 215006China School of Computer Science Georgia Institute of TechnologyAtlantaGA 30332USA

Mobile edge computing has shown its potential in serving emerging latency-sensitive mobile applications in ultra-dense 5G networks via offloading computation workloads from the remote cloud data center to the nearby network ***,current computation offloading studies in the heterogeneous edge environment face multifaceted challenges:Dependencies among computational tasks,resource competition among multiple users,and diverse long-term *** applications typically consist of several functionalities,and one huge category of the applications can be viewed as a series of sequential *** this study,we first proposed a novel multiuser computation offloading framework for long-term sequential ***,we presented a comprehensive analysis of the task offloading process in the framework and formally defined the multiuser sequential task offloading ***,we decoupled the long-term offloading problem into multiple single time slot offloading problems and proposed a novel adaptive method to solve *** further showed the substantial performance advantage of our proposed method on the basis of extensive experiments.

关键词： mobile edge computing sequential tasks computation offloading dependency

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Survey on age-related macular degeneration detection in OCT image

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International Journal of computers and Applications 2024年第2期46卷 101-112页

作者： Deepti, S. Renu Karthik, P.C. Department of Data Science and Business Systems School of computing SRM Institute of Science and Technology Kattankulathur India

Age-related Macular Degeneration (AMD) is the most common eye disease that causes visual impairment in elder people. Prevalently, AMD is detected by Spectral Domain Optical Coherence Tomography (SD-OCT) for diagnosis and monitoring purposes. In this work, 50 papers are reviewed to examine various approaches and their performance metrics. This paper reviewed various existing works related to AMD detection using different imaging modalities for quality images with high resolution. The investigation of case studies is analyzed on the basis of data collected from various patients undergoing several treatments and scans obtained from various imaging modalities. Furthermore, the analysis extends to the works that used different AI techniques, such as Deep Learning (DL), Machine Learning (ML), and Unsupervised Framework, for the detection of AMD-related diseases. Also, the analysis of different performance measures is done that shows the variations in terms of performance metrics such as accuracy, sensitivity, specificity, and so on. The outcome of this deep analysis shows that the system with the DL model reaches its utmost performance in AMD detection. Finally, gaps and challenges that are still lagging with this research area are summarized, which motivates the researchers to take away the work in an effective way. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Diagnosis

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Model-Heterogeneous Federated Graph Learning With Prototype Propagation

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第3期6卷 676-689页

作者： Liu, Zhi Zhou, Hanlin He, Xiaohua Yuan, Haopeng Du, Jiaxin Wang, Mengmeng Shen, Guojiang Kong, Xiangjie Xia, Feng Zhejiang University of Technology College of Computer Science & Technology Hangzhou310023 China RMIT University School of Computing Technologies MelbourneVIC3000 Australia

Federated graph learning (FGL) enables clients to collaboratively train a robust graph neural network (GNN) while ensuring their private graph data never leaves the local. However, existing FGL frameworks require all clients to train the identical GNN model, which limits their real-world applicability. Although many model-heterogenous frameworks have been proposed for traditional nongraph federated learning settings, directly transferring them to the FGL setting typically results in suboptimal performance. To fill the gap, this article presents federated prototype propagation network (FedPPN), a lightweight FGL framework that supports clients to train fully customized models. FedPPN only transmits prototypes between clients and the server for knowledge sharing. The core idea is propagating global prototypes on each client's local graph, generating prototype-based node representations and predictions. The prototype-based prediction can then be ensembled with the prediction of local GNN, allowing clients to achieve accurate prediction. We evaluate our FedPPN on six benchmark datasets with different heterogeneous model setups. Experimental results show that our FedPPN outperforms advanced baselines in model accuracy without adding any trainable parameters on clients or the server. Besides, FedPPN's communication cost is significantly lower than methods that rely on model parameter exchange. © 2024 IEEE.

关键词： Federated learning

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Anomaly graph: leveraging dynamic graph convolutional networks for enhanced video anomaly detection in surveillance and security applications

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Neural computing and Applications 2024年第20期36卷 12011-12028页

作者： Chiranjeevi, V. Rahul Malathi, D. Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur Chennai603203 India

Video abnormality behavior identification plays a pivotal role in improving the safety and security of surveillance systems by identifying unusual events within video streams. However, existing methods face challenges in capturing complex spatiotemporal anomalies effectively. To address this, we propose an anomaly graph approach that leverages dynamic graph representations and dynamic graph convolutional networks (GCN) for video anomaly detection. Anomaly graph constructs dynamic graphs where nodes represent objects or regions of interest within video frames while the edges encode spatial and temporal relationships. The GCN architecture extracts spatiotemporal embeddings from the dynamic graphs and allows the model to identify anomalies involving both spatial and temporal cues. Anomaly graph introduces uniqueness scores to quantify frame distinctiveness for precise anomaly detection while it employs adaptive reconstruction errors to pinpoint spatially localized anomalies. Real-time alerts are generated for detected anomalies to ensure timely responses to security incidents, and online fine-tuning (OLT) is incorporated as a dynamic learning mechanism to adapt to evolving anomaly patterns existing in dynamic environments. The extensive experimentation on benchmark datasets including UCSD Ped2 and CUHK Avenue is carried out for diverse performance measures, and the anomaly graph exhibits enhanced performance in the detection of anomalous events by achieving a detection accuracy of 98.72% and precision of 97.18%. Moreover, it is found to have achieved better values compared to other related methods for video anomaly detection. Overall, the anomaly graph introduces a robust video anomaly detection framework that excels in identifying complex spatiotemporal anomalies and empowers surveillance and security systems to detect anomalies more effectively. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Anomaly detection

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