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An Improved Convolutional Neural Networks: Quantum Pseudo-Transposed Convolutional Neural Networks

IEEE Access 2025年 13卷 37108-37117页

作者： Hai, Li Liang, Chen Yaming, Hao Wenli, Yu Fengquan, Shi Shandong Technology and Business University School of Information and Electronic Engineering Yantai264005 China Shandong Technology and Business University School of Computer Science and Technology Yantai264005 China

Recent advancements in quantum machine learning have spurred the development of hybrid quantum-classical convolutional neural networks (HQCCNNs), which have demonstrated promising potential for image classification tasks. Building on the operational principles of classical transposed convolutional neural networks (CNNs), we introduce a novel quantum variant: the Quantum Pseudo-Transposed Convolutional Neural Network (QPTCNN). The QPTCNN adapts the concept of classical transposed CNNs to the quantum domain, leveraging a hybrid quantum-classical framework that combines a quantum convolutional layer with a classical fully connected layer. In the QPTCNN, the quantum convolutional layer emulates a transposed convolution operation, ensuring that the output feature map retains the same dimensions as the input image. This is accomplished using rotational angle encoding and a ring-structured quantum circuit, interconnected by two-qubit control gates such as CNOT and CRY gates, facilitating efficient quantum convolution. We evaluated the performance of the QPTCNN on the MNIST and Fashion-MNIST datasets, with two distinct versions of the model: Model A, which utilizes a CNOT-gate entanglement circuit, and Model B, which employs a CRY-gate entanglement circuit. The results demonstrate that both Model A and Model B achieve strong performance across the datasets. However, Model A outperforms Model B, achieving higher classification accuracy and lower loss compared to earlier models. These findings suggest that the QPTCNN is highly capable of learning and extracting relevant feature information from input images, making it well-suited for high-performance image classification tasks. This work represents a significant advancement in quantum-enhanced image classification. © 2013 IEEE.

关键词： Convolutional neural networks

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MSA: Mamba Semantic Alignment Networks for Remote Sensing Change Detection

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 10625-10639页

作者： Huang, Zhenyang Duan, Peng Yuan, Genji Li, Jinjiang Shandong Technology and Business University School of Information and Electronic Engineering Yantai264005 China Shandong Technology and Business University School of Computer Science and Technology Yantai264005 China

With the rapid advancement of Earth observation technologies, remote sensing change detection (CD) has become a crucial method for monitoring surface changes. It is widely used in areas, such as urban expansion, disaster assessment, and resource detection. Current deep learning-based CD methods typically extract feature information from remote sensing images through downsampling and then aggregate early features with deeper ones during upsampling. However, directly aggregating these features without addressing spatial misalignment due to resolution changes can compromise the accuracy of change detection. In addition, there is a need to address the challenge of inadequate long-range dependency modeling in image processing. To tackle these challenges, this article proposes Mamba semantic alignment networks (MSA) for remote sensing CD. MSA introduces the semantic offset correction block, which corrects spatial misalignment during feature aggregation by incorporating a learnable semantic offset map, thereby reducing classification errors caused by feature mismatches. Furthermore, MSA incorporates the global dependency enhancement block, leveraging the Mamba architecture and the lossless downsampling and reversibility of wavelet transforms to significantly enhance global feature modeling. We evaluated MSA on three datasets, and the experimental results demonstrate that MSA outperforms mainstream methods across all three datasets. © 2008-2012 IEEE.

关键词： Change detection

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Single-Valued Neutrosophic Graph with Heptapartitioend Structure

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Neutrosophic Sets and Systems 2025年 80卷 728-748页

作者： Fathi, Hanaa Myvizhi, M. Abdelhafeez, Ahmed Abdellah, Mohamed Refaat Eassa, Mohamed Sawah, Mohamed S. Elbehiery, Hussam College of Information Technology Amman Arab University Amman Jordan KPR Institute of Engineering and Technology Tamilnadu Coimbatore India Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt Applied Science Research Center Applied Science Private University Amman Jordan The Department of Computer Science College of Information Technology Misr University for Science and Technology Cairo Egypt Department of Computer Science Faculty of Information Technology Ajloun National University P.O.43 Ajloun26810 Jordan

This study applies single-valued neutrosophic sets, which extend the frameworks of fuzzy and intuitionistic fuzzy sets, to graph theory. We introduce a new category of graphs called Single-Valued Heptapartitioned Neutrosophic Graphs (SVHNG) and investigate their characteristics through comprehensive proofs and illustrative examples. © 2025, University of New Mexico. All rights reserved.

关键词： Heptapartitioend Neutrosophic Set Neutrosophic Graph Neutrosophic Sets SVHN-Graph

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Design and Optimization of a Novel Multiheater Microhotplate With Thermal Crosstalk

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IEEE Sensors Journal 2025年第9期25卷 15893-15905页

作者： Yang, Youpeng Wei, Guangfen He, Aixiang Jiao, Shasha Zhang, Wei Gao, Cong Shandong Technology and Business University School of Information and Electronic Engineering Yantai264005 China Shandong Technology and Business University School of Computer Science and Technology Yantai264005 China

As demand grows for high-sensitivity and high-selectivity gas sensors in the Internet of Things (IoT) andenvironmental monitoring fields, metal oxide semiconductor(MOX) gas sensors based on microhotplates have attractedsignificant research interest due to their low-power consump-tion and high integration. While array designs can addressthe issue of low sensor selectivity, integrating multiple sen-sor elements may result in increased power *** overcome this limitation, this article proposes a novel mul-tiheater integrated microhotplate design based on thermalcrosstalk, integrating multiple heaters on a single micro-hotplate to generate encodable multiple temperature *** COMSOL finite element simulation, the effects ofheater sizes, geometry, and thickness on the performance of the microhotplate were systematically studied. The resultsindicate that with a heater radius of 100 μm, the microhotplate exhibits excellent temperature uniformity at a workingtemperature of 300 ◦C, with more than 70% of the heating area having a temperature gradient of less than 10 ◦***, the optimized heater geometry significantly reduces the mechanical deformation of the microhotplate,ensuring structural stability. Most notably, the thermal crosstalk effect effectively reduces the power consumption of themicrohotplate. At a working temperature of 300 ◦C, the dual-heater design consumes approximately 4.68 mW less powerthan the single-heater configuration. By introducing an etched window design, the thermal efficiency of the microhotplateis further enhanced, with a peak temperature increase of approximately 40 ◦C. The innovative multiheater microhotplatedesign based on thermal crosstalk achieves low-power temperature control across all heaters, laying a solid foundationfor the development and widespread adoption of high-performance portable gas sensors. © 2001-2012 IEEE.

关键词： Oxide semiconductors

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DCL-depth: monocular depth estimation network based on iam and depth consistency loss

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Multimedia Tools and Applications 2025年第8期84卷 4773-4787页

作者： Han, Chenggong Lv, Chen Kou, Qiqi Jiang, He Cheng, Deqiang The School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China The School of Computer Science and Technology China University of Mining and Technology Xuzhou221116 China

The self-supervised monocular depth estimation algorithm obtains excellent results in outdoor environments. However, traditional self-supervised depth estimation methods often suffer from edge blurring in complex textured regions and the loss of depth information in pixels within weakly-textured areas. To enhance the perception ability of the deep network for complex textured areas and the accuracy of depth estimation in weakly-textured regions, the following methods are proposed in this paper. First of all, the image activity measure (IAM) is used to segment the image features. Based on the multi-directional distribution of image contours, the network's perception ability has been improved, resulting in effective enhancement of depth estimation in complex regions. Furthermore, a new loss function called depth consistency loss (DCL) is proposed, which is based on recursive recurrent networks. The DCL aims to measure the similarity between the output images of the first-order network and the second-order network, and the network's constraint on weak-texture regions has been strengthened. By employing this approach, the accuracy of estimating depth information in weakly-textured regions can be enhanced. Through adequate experimentation on the public indoor datasets, the results show that our network outperforms the compared algorithms in terms of accuracy and visualization of predicted depth. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Complex networks

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Cumulative histogram as a feature selection technique for anomaly detection

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Multimedia Tools and Applications 2025年第12期84卷 10095-10107页

作者： Nassar, Mostafa Salama, Rania A. Saleeb, Adel A. El-bahnasawy, Nirmeen A. Ahmed, Hossam Eldin H. Abd El-Samie, Fathi E. Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Information Technology Faculty of Computers and Information Suez University Suez Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh11671 Saudi Arabia

The enhancement of Intrusion Detection Systems (IDS) is required to ensure protection of network resources and services. This is a hot research topic, especially in the presence of advanced intrusions and attacks. This paper provides a comparison between Distributed Cumulative Histogram (DCH) as a Feature Selection (FS) technique, information Gain Ratio (IGR) FS and wrapper-based FS in terms of accuracy and Root Mean Square Error (RMSE). The utilization of DCH of the traffic instances in normal and attack cases allows us to compare the traffic charts. We can observe the difference between effective features and less effective ones. We verify the feasibility of using DCH as an FS technique in the field of anomaly detection with just six selected features giving more accurate results with most classifiers compared to the IGR and wrapper-based FS. We applied our experiments on the modern UNSW dataset with the WEKA simulation platform that contains a group of classification, feature reduction and selection techniques. © The Author(s) 2024.

关键词： Anomaly detection

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PBCounter:weighted model counting on pseudo-boolean formulas

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Frontiers of computer Science 2025年第3期19卷 55-63页

作者： Yong LAI Zhenghang XU Minghao YIN College of Computer Science and Technology Jilin UniversityChangchun 130012China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin UniversityChangchun 130012China School of Computer Science and Information Technology Northeast Normal UniversityChangchun 130017China

In Weighted Model Counting(WMC),we assign weights to literals and compute the sum of the weights of the models of a given propositional formula where the weight of an assignment is the product of the weights of its *** current WMC solvers work on Conjunctive Normal Form(CNF)***,CNF is not a natural representation for human-being in many *** by the stronger expressive power of Pseudo-Boolean(PB)formulas than CNF,we propose to perform WMC on PB *** on a recent dynamic programming algorithm framework called ADDMC for WMC,we implement a weighted PB counting tool *** compare PBCounter with the state-of-the-art weighted model counters SharpSAT-TD,ExactMC,D4,and ADDMC,where the latter tools work on CNF with encoding methods that convert PB constraints into a CNF *** experiments on three domains of benchmarks show that PBCounter is superior to the model counters on CNF formulas.

关键词： weighted model counting pseudo-boolean constraint algebraic decision diagrams preprocessing techniques

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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 Comprehensive Review of Sensor Fusion Techniques in SLAM: Trends, Challenges and Future Directions 1

A Comprehensive Review of Sensor Fusion Techniques in SLAM: ...

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1st International Conference on AIML-Applications for engineering and technology, ICAET 2025

作者： Dhaigude, Veerendra Kulkarni, Anagha Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Cummins College of Engineering for Women Department of Information Technology Pune India

ISBN: (纸本)9798350355611

Simultaneous Localization and Mapping (SLAM) equips machines with the capability to map and navigate unfamiliar environments autonomously. By employing sensor fusion, SLAM's precision and dependability are significantly improved as data from a variety of sensors are combined. This review delves into the core principles of SLAM, explores diverse methods of sensor fusion, and examines the incorporation of artificial intelligence in SLAM systems. Additionally, it addresses the technical hurdles and environmental factors that affect SLAM's performance. Future research directions are also identified, emphasizing the promising role of interdisciplinary approaches and emerging technologies in enhancing SLAM's development. © 2025 IEEE.

关键词： Kalman filters

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UAV-Assisted Zero Knowledge Model Proof for Generative AI: A Multi-Agent Deep Reinforcement Learning Approach

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IEEE Internet of Things Journal 2025年第10期12卷 13441-13454页

作者： Hao, Min Shang, Chen Wang, Siming Jiang, Wenchao Nie, Jiangtian Singapore University of Technology and Design Pillar of Information Systems Technology and Design 487372 Singapore Nanyang Technological University School of Computer Science and Engineering 638788 Singapore

As more users seek generative AI models to enhance work efficiency, generative AI and Model-as-a-Service will drive transformative changes and upgrades across all industries. However, when users utilize generative AI models provided by the service provider, they cannot be certain that the model's quality matches the provider's claims. Considering the need to protect intellectual property, the service provider will not disclose model details for user verification. To this end, we take the Internet of Vehicles as research background, proposing a zero knowledge model proof architecture based on UAVs. We also introduce a multi-agent reinforcement learning algorithm to optimize the verification process. In specific, we first propose a verification scheme for the key operations of generative adversarial networks based on non-interactive zero knowledge proof. The zero knowledge proof architecture ensures that model parameters cannot be stolen during the verification process. After that, we propose an age of verification (AoV) metric to ensure the timeliness and freshness of zero knowledge proof. We also construct a trade-off optimization problem between the energy consumption of UAV as a verifier and the AoV of edge servers as service providers, and transform the problem based on Lyapunov optimization theory. Following that, we propose an enhanced multi-agent proximal policy optimization algorithm to enable the collaborative verification of edge servers by multiple UAVs. The algorithm simulation results demonstrate that the reward value of our proposed algorithm is over 10% higher than that of the standard algorithm, with a faster and more stable overall convergence speed. Additionally, the zero knowledge proof performance test results indicate that the verification delay in our proposed architecture is less than 500ms during the verification phase, meeting practical requirements. © 2025 IEEE.

关键词： Generative adversarial networks

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