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Advancing Smart Sensor Networks and Carbon-Based Biosensors Through Artificial Intelligence: A Deep Learning Approach to Optoelectronic Device Innovation

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IEEE Access 2025年 13卷 86083-86109页

作者： Luo, Keliang Universiti Putra Malaysia Faculty of Computer Science and Information Technology Software Engineering Department Kuala Lumpur43400 Malaysia

This research proposes a novel artificial decision-marking framework suitable for modern smart sensor networks and carbon-based biosensor systems which deals with uncertainty and the peculiarity of the data. To achieve the goals, the approach relies on the optoelectronic properties of carbon nanomaterials and already combines AI and deep learning. This raises the level of sensing, real-time data fusion, and adaptive decision-making within the environment to unprecedented levels. To enhance sensor evaluation in sensor manufacturing, we take a step further and implement Dombi Interval Valued Intuitionistic Fuzzy Sets (D-IVIFs) and work with Dombi Interval Valued Intuitionistic Fuzzy Dombi Bonferroni Mean (D-IVIFDBM) for hierarchical decision-making. Moreover, two Multi-Attribute Group Decision Making (MAGDM) methods IVIFWDBM and IVIFWDGBM are developed for expert evaluation aggregation in selection tasks of different criteria to enhance selection accuracy. These experiments did demonstrate improvements in decision accuracy as well as better overall performance than conventional models of comparison. The numerical experiments showed that these methods are more effective than traditional MAGDM models. Introducing such an advanced decision framework in deep learning systems enables improved adaptability, security, and resilience in next-generation sensor networks and biosensor devices. The new paradigm enables real-time signal interpretation and adaptive learning and provides effective solutions in harsh environments where severe fluctuations are common. This study assists in addressing the discrepancy between conceptual decision models and actual physical achievements in smart sensing, which will foster the development of more sophisticated and efficient optoelectronic devices. © 2013 IEEE.

关键词： Deep learning

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A Novel Framework for Gauging information Extracted from Smartphones Using Neutrosophic Logic

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Neutrosophic Sets and Systems 2025年 76卷 154-171页

作者： Abou Alzahab, R.M. Ismail, Amr Abd Elkhalik, S.H. Shams, M.Y. El-Bakry, H.M. Salama, A.A. Department of Mathematics and Computer Science Faculty of Science Port Said University Egypt Department of Cybersecurity College of Engineering and Information Technology Buraydah Private Colleges Buraydah51418 Saudi Arabia Department. of Communication and Computer Engineering Air Defense University Egypt Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh33516 Egypt Department of Information Systems. Faculty of Computer and Information Science Mansoura University Egypt

Smartphones contain a vast amount of information about their users, which can be used as evidence in criminal cases. However, the sheer volume of data can make it challenging for forensic investigators to identify and use the most relevant information. Neutrosophic logic is a generalization of fuzzy logic that allows for uncertainty and vagueness, making it a more potent tool for dealing with the ambiguity of real-world data. The proposed framework aims to identify and utilize the most relevant information for forensic investigators, making it easier to solve criminal cases using Neutrosophic logic. This novel approach provides a more powerful tool for dealing with the ambiguity of smartphone data, ultimately improving the accuracy and effectiveness of forensic investigations. Our research has utilized Neutrosophic logic to evaluate the degree of truth, falsity, or Indeterminacy of this information. Additionally, this study analyzes conversations between individuals using Excel’s fuzzy lookup add-in to determine the percentage of truth and false in each conversation. The results were compiled into a dataset and utilized a Neutrosophic Python code to evaluate the information. The results indicate the percentage of truth, false, and Indeterminacy in each conversation and which can be used to determine its admissibility as evidence and which not. © 2025, University of New Mexico. All rights reserved.

关键词： Smartphones

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Improved Deeplabv3+ Method for the Panax Notoginseng Disease Segmentation 11

Improved Deeplabv3+ Method for the Panax Notoginseng Disease...

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11th International Conference on information Systems and Computing Technology, ISCTech 2023

作者： Lei, Lian Wang, Zilong Wang, Ruoxi Shen, Tao Yang, Qiliang Yang, Ling Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming China Kunming University of Science and Technology Faculty of Modern Agricultural Engineering Kunming China

ISBN: (纸本)9798350342406

Panax notoginseng diseases seriously affect Panax notoginseng yield and quality. The Panax notoginseng diseases precise identification and segmentation can provide the basis for disease control and treatment. Deep learning technologies gradually displace manual plant disease identification techniques due to their time-consuming, labor-intensive, inefficient, and subjective disadvantages. Consequently, it is essential to explore the potential application of deep learning-based semantic segmentation models in Panax notoginseng disease monitoring. However, due to the complexity of symptoms, ranging from small spots to large-scale diseases, achieving accurate disease segmentation is challenging. To address the above problem, we propose an improved Deeplabv3+ model for Panax notoginseng disease segmentation. Two modules are introduced to improve the performance of the algorithm. Specially, a Dual Attention Module (DAM) is added to improve multiscale feature extraction for achieving accurate segmentation of small disease regions. Moreover, we introduce a Squeeze and Excitation Module (SEM) into the backbone network, which can enhance the ability of feature extraction for large disease regions. Finally, a segmentation dataset of Panax notoginseng diseases is constructed to provide a benchmark for the segmentation of Panax notoginseng diseases. Experimental results show that our method can achieve 92.60% PA and 73.22% mIoU. This study can offer a valuable guide for future monitoring of Panax notoginseng diseases in cultivation trials. © 2023 IEEE.

关键词： Semantic Segmentation

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Transmission scheme based on collaborative AUV communication 16

Transmission scheme based on collaborative AUV communication

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16th International Conference on Signal Processing Systems, ICSPS 2024

作者： Sun, Jiahao Qi, Chao Liu, Zengli Zhao, Xuanzhi Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Computer Center Kunming University of Science and Technology Kunming China

ISBN: (纸本)9781510689251

Underwater wireless sensor networks can monitor ocean information, which provides a new approach to marine environmental monitoring, disaster warning and resource exploration. However, the development of underwater wireless sensing networks is limited by factors such as ocean currents and limited energy of the sensors themselves. Based on this situation, this paper proposes a transmission scheme based on collaborative communication with autonomous underwater vehicles (AUVs), in which autonomous underwater vehicles (AUVs) are used as a tool for distributed data collection and forwarding, which can solve the problem of imbalanced energy consumption of sensor nodes in the traditional multi-hop underwater communication network. At the same time, we consider the influence of ocean currents on the node positions to maximize the imitation of the real underwater environment, and the simulation results show that the proposed transmission scheme for collaborative communication with AUVs can significantly prolong the service life of underwater sensor networks. © 2025 SPIE.

关键词： Autonomous underwater vehicles

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Multiframe Blind Deconvolution based on Bright and Dark Channel Prior

Multiframe Blind Deconvolution based on Bright and Dark Chan...

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2023 International Conference on Algorithms, High Performance Computing, and Artificial Intelligence, AHPCAI 2023

作者： Ai, Haotian Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming Yunnan650000 China

ISBN: (纸本)9781510671881

Blind image restoration is a challenging and valuable subject in the field of computer vision. In this paper, under the framework of Total Variation (TV) regularization term, the bright and dark channel regularization term is introduced into the multi-frame image blind restoration algorithm, aiming at overcoming the shortcomings of low contrast and obvious ringing effect of the restored image on the basis of retaining the advantages of TV regularization. The bright channel regularization term can estimate the blur degree of the degraded image more accurately, while the dark channel regularization term can better resist noise interference. The split Bregman iterative algorithm is used to solve the objective function, and the details of the restored image are compensated in the iterative process, and further. The experimental results show that the algorithm in this paper has a certain deblurring effect, and the peak signal-to-noise ratio of the restored image is improved by about 0.53dB~1.5dB, and the structural similarity is improved by about 0.12~0.47, with better visual effects. © 2023 SPIE. All rights reserved.

关键词： Image enhancement

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UltraSegNet:A Hybrid Deep Learning Framework for Enhanced Breast Cancer Segmentation and Classification on Ultrasound Images

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Computers, Materials & Continua 2025年第5期83卷 3303-3333页

作者： Suhaila Abuowaida Hamza Abu Owida Deema Mohammed Alsekait Nawaf Alshdaifat Diaa Salama Abd Elminaam Mohammad Alshinwan Department of Computer Science Faculty of Prince Al-Hussein Bin Abdallah II for Information TechnologyAl Al-Bayt UniversityMafraq25113Jordan Medical Engineering Department Faculty of EngineeringAl-Ahliyya Amman UniversityAmman19328Jordan Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Faculty of Information Technology Applied Science Private UniversityAmman11931Jordan Faculty of Computers Science Misr International UniversityCairo11800Egypt Jadara Research Center Jadara UniversityIrbid21110Jordan

Segmenting a breast ultrasound image is still challenging due to the presence of speckle noise,dependency on the operator,and the variation of image *** paper presents the UltraSegNet architecture that addresses these challenges through three key technical innovations:This work adds three things:(1)a changed ResNet-50 backbone with sequential 3×3 convolutions to keep fine anatomical details that are needed for finding lesion boundaries;(2)a computationally efficient regional attention mechanism that works on high-resolution features without using a transformer’s extra memory;and(3)an adaptive feature fusion strategy that changes local and global featuresbasedonhowthe image isbeing *** evaluation on two distinct datasets demonstrates UltraSegNet’s superior performance:On the BUSI dataset,it obtains a precision of 0.915,a recall of 0.908,and an F1 score of *** the UDAIT dataset,it achieves robust performance across the board,with a precision of 0.901 and recall of ***,these improvements are achieved at clinically feasible computation times,taking 235 ms per image on standard GPU ***,UltraSegNet does amazingly well on difficult small lesions(less than 10 mm),achieving a detection accuracy of *** is a huge improvement over traditional methods that have a hard time with small-scale features,as standard models can only achieve 0.63–0.71 *** improvement in small lesion detection is particularly crucial for early-stage breast cancer *** from this work demonstrate that UltraSegNet can be practically deployable in clinical workflows to improve breast cancer screening accuracy.

关键词： Breast cancer ultrasound image segmentation classification deep learning

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BAMGAN: A KBQA method based GAN and bi-directional attention 6

BAMGAN: A KBQA method based GAN and bi-directional attention

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6th International Conference on Advanced Electronic Materials, Computers, and Software engineering, AEMCSE 2023

作者： Guo, Jiazhi Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming Yunnan650504 China

ISBN: (纸本)9781510668249

Knowledge-based question answering(KBQA) involves using knowledge base technology to generate answers to natural language processing(NLP) questions. KBQA is one of the most challenging tasks in the field of NLP. Answer selection plays a vital role in KBQA, as it requires selecting the correct answer from a pool of candidate answers. However, knowledge bases still struggle to identify the correct answer from multiple candidate answers. To address this issue, In this paper, we propose BAMGAN, a KBQA method based on generative adversarial networks. This method uses generative adversarial networks (GANs) to improve answer selection accuracy in deep neural networks. Experimental results show the effectiveness of the BAMGAN. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Generative adversarial networks

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Representation-Enhanced Semi-Supervised Learning for Flotation Working Condition Recognition 43

Representation-Enhanced Semi-Supervised Learning for Flotati...

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43rd Chinese Control Conference, CCC 2024

作者： Ai, Mingxi Li, Peng Zhang, Jin Xie, Yongfang Tang, Zhaohui Yunnan University School of Information Science and Engineering Kunming650500 China Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming650504 China Central South University School of Automation Changsha410083 China

ISBN: (纸本)9789887581581

Timely and accurate recognition of flotation working conditions plays a crucial role in stabilizing process indicators and formulating production operation strategies. Soft sensing methods based on deep learning allow for automatic learning of features from input froth images directly, and is considered a promising flotation monitoring approach. However, the high demand for a large number of labeled samples severely hinders the application of such methods because acquiring froth videos is easily in flotation plants, but manually adding labels to video frames is often expensive. Therefore, a representation-enhanced semi-supervised learning method for flotation working condition recognition is proposed in this paper. Firstly, based on the consistency assumption, a student-teacher network is developed. It leverages massive unlabeled froth image data by ensuring the consistency of model outputs under variations in the input space. Secondly, to guide the student-teacher network learn class-aware cluster representation from unlabeled data, a novel prototypical contrastive learning method with hard negative mixing is proposed. Comparative and ablation experiments on industrial flotation dataset show the effectiveness of the proposed method. © 2024 Technical Committee on Control Theory, Chinese Association of automation.

关键词： Contrastive Learning

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Inpainting of cloud-occlusion sea surface temperature image from a novel generative network using multi-scale physical constraints

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Multimedia Tools and Applications 2024年 1-12页

作者： Diao, Yaning Zuo, Zijie Li, Xiu Wei, Qichen Zhang, Ze Chen, Xin Liang, Xinyue Faculty of Information Science and Engineering Ocean University of China BinHai Road QingDao266100 China

Sea surface temperature (SST) is considered as an important environmental indicator, which has a wide impact on climate systems, marine ecosystems, and human activities. Due to the limitations of observation conditions, occlusion caused by clouds, and so on, SST data are often missing. Compared with traditional interpolation-based methods which reconstruct SST data only relying on current data, inpainting-based methods recently have successfully carried out the advantages of utilizing historical images to train a deep neural network (DNN), such as denoising diffusion model (DDM). However, although historical SST data can be used to better refill the missing data by considering SST data reconstruction as an image inpainting task that is constrained by semantics, SST images are mainly constrained by physical laws but semantics. Thus, this paper proposes a DDM-based inpainting network with multi-scale physical constraints. The proposed framework mainly consists of three modules including Global Average Estimation Module (GAEM) to generate an initial estimation of corrupted SST images by considering historical SST images as a global physical constraint, Local Deviation Repair Module (LDRM) to obtain the bias between the initial estimation and current SST image as local physical constraint, and Multi-Scale Fusion Module (MSFM) apply a multi-scale decoupling scheme to guarantee the efficient fusion of the "global" and "local" constraints and back to a complete SST image. Sufficient experiments have been conducted to verify the effectiveness and physical consistency compared with prior SOTA methods on the public AVHRR Pathfinder SST dataset and the results indicated the superiority of the proposal. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep neural networks

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Enhanced Decision Making in Smart Grid Management by Optimizing Adaptive Multi-Agent Reinforcement Learning with Vehicle-to-Grid Systems

Decision Making: Applications in Management and Engineering

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Decision Making: Applications in Management and engineering 2024年第1期7卷 494-530页

作者： Suanpang, Pannee Jamjuntr, Pitchaya Department of Information Technology Faculty of Science & Technology Suan Dusit University Bangkok Thailand Department of Electrical Engineering Faculty of Engineering King Mongkut's University of Technology Thonburi Bangkok Thailand

This research proposes a decision-making framework in which the Adaptive Multi-Agent Reinforcement Learning (MARL) model and the concept of Vehicle-to-Grid (V2G) interactivity are employed to improve the effective management of smart grids. The research hypothesis introduces innovations for improving the efficiency and security of power systems in the global south, primarily by controlling the net energy transmission between the defined electric vehicles (EVs) and the grid. Other issues that require attention to ensure the proper functioning of smart grids include demand response, load management, and energy storage optimization. In this instance, these gaps are filled by the system’s proposed framework. With the help of MARL, the system dynamics' autonomous learning aspects allow the system to adapt to the capacity of renewable energy sources and electricity demand, which is also time-dependent. Because of the MARL, the autonomous coordination of decision-making has resulted in very positive changes in the system's effectiveness. In particular, this framework permitted an increase of 13.6% in the total energy exchange between EVs and the grid, and the grid stability index improved from 0.84 to 0.87 compared to what would have been achieved with the conventional methods. Enhanced energy management and pricing rehabs added another 22% to net savings. Further, it is stated that deploying MARL-based V2G systems in developing areas has many benefits, including more robust grid reliability and energy security and better integration of renewable energy resources. Such changes aid in reducing fossil fuel use and greenhouse gas emissions. © 2024 Regional Association for Security and crisis management. All rights reserved.

关键词： Vehicle-to-grid

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