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Journal of Intelligent Construction 2025年第4期2卷 1-12页

作者： Minghao Wen Dong Liang Haibo Ye Huawei Tu College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Department of Computer Science and Information Technology La Trobe University Melbourne Australia

With advancements in digital technology, the field of architectural design has increasingly embraced data and algorithms to enhance design efficiency and quality. Recent advancements in text-to-image (T2I) generation models have enabled the creation of images that correspond to textual descriptions. Howev-er, textual descriptions struggle to capture essential style characteristics in style images. In this study, we proposed a method for architectural facade design based on the stable diffusion model (SDM) that combined stylistic images or keywords as input with the structural conditions of content images to generate images with both stylistic and architectural features. By employing the con- strastive language-image pre-training (CLIP) image encoder to convert the style image into its initial image embedding and feature extraction from multilayer cross-attention and training optimization to obtain a pretrained image embed-ding, the proposed method extracts stylistic features from style images and converts them into corresponding embeddings. This process enables the generated images to embody stylistic features and artistic semantic information. Further-more, the T2I adapter model is employed to use the architectural structure of content images as conditional guidance, thereby ensuring that the generated images exhibit the corresponding structural features. By leveraging these two aspects, the proposed method can decorate architecture with stylistic features from stylistic images while preserving the architectural structure features of content images, resulting in images that reflect the content images after style transformation. Our method is mainly used in architectural design applications. It was capable of generating facade images from flat design drawings, three-di-mensional (3D) architectural models, and hand-drawn sketches and has achieved commendable results.

关键词： Training Adaptation models Visualization Translation Three-dimensional displays Diversity reception Text to image Feature extraction Nonhomogeneous media Diffusion models

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Twitter Media Sentiment Analysis to Convert Non-Informative to Informative Using QER

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Intelligent Automation & Soft Computing 2023年第3期35卷 3545-3555页

作者： C.P.Thamil Selvi P.Muneeshwari K.Selvasheela D.Prasanna Department of Computer Science and Engineering Sri Ranganathar Institute of Engineering and TechnologyCoimbatore641009TamilnaduIndia Department of Artificial Intelligence and Machine Learning Saveetha School of Engineering Chennai602105Tamil NaduIndia Department of Computer Science and Engineering Sreesakthi Engineering CollegeKaramadai641104Tamil NaduIndia Department of Computer Science and Engineering Mahendra Engineering CollegeNamakkal637503Tamil NaduIndia

The term sentiment analysis deals with sentiment classiﬁcation based on the review made by the user in a social *** sentiment classiﬁcation accuracy is evaluated using various selection methods,especially those that deal with algorithm *** this work,every sentiment received through user expressions is ranked in order to categorise sentiments as informative and *** order to do so,the work focus on Query Expansion Ranking(QER)algorithm that takes user text as input and process for sentiment analysis andﬁnally produces the results as informative or *** challenge is to convert non-informative into informative using the concepts of classiﬁers like Bayes multinomial,entropy modelling along with the traditional sentimental analysis algorithm like Support Vector Machine(SVM)and decision *** work also addresses simulated annealing along with QER to classify data based on sentiment *** the input volume is very fast,the work also addresses the concept of big data for information retrieval and *** result com-parison shows that the QER algorithm proved to be versatile when compared with the result of *** work uses Twitter user comments for evaluating senti-ment analysis.

关键词： Sentiments QER SVM bayes multinomial big data analysis simulated annealing

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Advancements in Skin Cancer Detection: A Comprehensive Review of Convolutional Neural Network Approaches 14

Advancements in Skin Cancer Detection: A Comprehensive Revie...

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14th International Conference on Software technology and Engineering, ICSTE 2024

作者： Thinakaran, Rajermani Somasekar, J. Neerugatti, Vikram Ganga, Krishna INTI International University Faculty of Data Science and Information Technology Negeri Sembilan Malaysia Department of Computer Science and Engineering Karnataka Banglore India

ISBN: (纸本)9798350378955

Skin cancer is one of the most prevalent types of cancer globally, with its incidence steadily rising over the past decades. Early and accurate detection of skin cancer plays a pivotal role in improving patient outcomes and reducing mortality rates. In recent years, Convolutional Neural Networks (CNNs) have emerged as powerful tools for automated image analysis and classification, offering promising results in various medical imaging tasks, including skin cancer detection. This paper provides a comprehensive review and analysis of CNN-based approaches for skin cancer classification. We discuss the challenges associated with traditional methods of skin cancer diagnosis and highlight the potential of CNNs in addressing these challenges. Furthermore, we survey state-of-the-art CNN architectures, dataset collections, preprocessing techniques, and evaluation metrics commonly used in skin cancer classification tasks. We also delve into the transfer learning and data augmentation strategies employed to improve model generalization and robustness. Additionally, we review recent advancements in interpretability techniques for CNNs, which enhance the transparency and trustworthiness of automated skin cancer diagnosis systems. Finally, we discuss the limitations and future directions of CNN-based approaches in skin cancer classification, emphasizing the need for large-scale, diverse datasets, standardized evaluation protocols, and interdisciplinary collaborations to facilitate the clinical translation and deployment of CNN-based diagnostic tool. © 2024 IEEE.

关键词： Lung cancer

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IOT and Machine Learning Based Real-Time Protection Against Cyber-Physical Threat Mitigation 4

IOT and Machine Learning Based Real-Time Protection Against ...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024

作者： Saini, Harnit Mohammad, Faz Singh, Manoj Kumar Sharma, Kapil Kumar Shukla, Meenu Singh, Ramendra Ajay Kumar Garg Engineering College Department of Computer Science Ghaziabad India Gl Bajaj Institute of Technology and Management Department of Applied Computational Science and Engineering Greater Noida India Ims Engineering College Department of Computer Science & Engineering Ghaziabad India Ims Engineering College Department of Mca Ghaziabad India Maharishi University of Information Technology School of Engineering and Technology Noida India Ghaziabad India

ISBN: (纸本)9798350360165

An investigation is carried out into the dynamic relationship between machine learning algorithms used in IoT security, with detailed scrutiny of performance indicators to give a fuller view of what happens. The study pays particular attention to metrics like accuracy, precision, recall, and F1 score when ten varied samples are being examined. As a result, all the algorithms exhibit excellent performance with 95.5 %-98.2 % accuracy values, inspiring high reliability under diverse scenarios. Precision and recall metrics also depict the models' capabilities. Balancing precision and recall can be observed in F1 scores that are between 95.2% and 98.0%, and these numbers show that the approach based on this understanding of trade-offs is characterized by its operationalization. At the other end, results obtained from performance metrics go beyond numerical benchmarks to give a holistic appreciation of the algorithms' strength and areas for possible enhancement. The paper also does not provide only a theoretical approach to machine learning related to security in the IoT environment but also provides information about security considerations for the successful deployment of powerful, adaptive models into real-life scenarios. In this case, findings of the research help in understanding how to make sure that smart and effective measures are implemented during the process of developing security solutions considering all related issues in connection with the IoT technology landscape. © 2024 IEEE.

关键词： Adversarial machine learning

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Bioinspired activation of silent synapses in layered materials for extensible neuromorphic computing

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Journal of Materiomics 2023年第4期9卷 787-797页

作者： Yan Kang Yabo Chen Yinlong Tan Hao Hao Cheng Li Xiangnan Xie Weihong Hua Tian Jiang College of Advanced Interdisciplinary Studies National University of Defense TechnologyChangsha410073China Institute for Quantum Information&State Key Laboratory of High Performance Computing College of ComputerNational University of Defense TechnologyChangsha410073China Institute for Quantum Science and Technology College of ScienceNational University of Defense TechnologyChangsha410073China

Activation of silent synapses is of great significance for the extension of neural plasticity related to learning and *** by the activation of silent synapses via receptor insertion in neural synapses,we propose an efficient method for activating artificial synapses through the intercalation of Sn in layered a-MoO_(3).Sn intercalation is capable of switching on the response of layered a-MoO_(3)to the stimuli of visible and near infrared light by decreasing the *** mimics the receptor insertion process in silent neural *** Sn-intercalated MoO_(3)(Sn-MoO_(3))exhibits persistent photoconductivity due to the donor impurity induced by Sn *** enables the two-terminal Sn-MoO_(3)device promising optoelectronic synapse with an ultrahigh paired pulse facilitation(PPF)up to 199.5%.On-demand activation and tunable synaptic plasticity endow the device great potentials for extensible neuromorphic *** performance of the extensible artificial neural network(ANN)based on the Sn-MoO_(3)synapses are demonstrated in pattern ***,the recognition accuracy increases from 89.7%to 94.8%by activating more nodes into the *** is consistent with the recognition process of physical neural network during brain *** intercalation engineering of MoO_(3)may provide inspirations for the design of high-performance neuromorphic computing architectures.

关键词： Activation of silent synapse Intercalation Layered materials Neuromorphic computing

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ELECTRE I-based Zone Head Selection in WSN-Enabled Internet of Things 3rd

ELECTRE I-based Zone Head Selection in WSN-Enabled Internet ...

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3rd International Conference on Machine Learning, Internet of Things and Big Data, ICMIB 2023

作者： Janakiraman, Sengathir Priya, M. Deva Malar, A. Christy Jeba Jacob, Suma Sira Department of Information Technology CVR College of Engineering Mangalpally Vastunagar Telangana Hyderabad India Department of Computer Science and Engineering Sri Eshwar College of Engineering Tamilnadu Coimbatore India Department of Information Technology Sri Krishna College of Technology Kovaipudur Tamilnadu Coimbatore India

ISBN: (纸本)9789819939312

In Wireless Sensor Network (WSN)-enabled Internet of Things (IoT) environment, potential resource utilization and efficient service delivery are of great interest. In specific, IoT-based networks completely depend on energy-efficient clustering architecture that is used for transferring data between heterogeneous devices, and optimal energy-aware deployment methods in WSN. Clustering-based energy-efficient routing and sensor node deployment are identified to extend network lifetime. Efficient selection of Zone Heads (ZHs) during the process of partitioning the network into different zones or clusters is essential for maximizing the reachability of nodes within clusters, and for achieving better communication with the Base Station (BS). In this paper, ELECTRE I-based Zone Head Selection (EZHS) scheme based on distinct factors such as number of times a node is selected as ZH, distance of the sensor node from the center, distance between adjacent nodes and level of energy is proposed, since they directly influence network lifetime and drain of node energy. It adopts the merits of ELECTRE I-based Multi-Criteria Decision Making (MCDM) model for determining the relative influence of impactful parameters during the process of ZH selection. The results of the proposed EZHS approach confirm an improvement in network lifetime by 21.98% and network stability by 22.94% in contrast to the benchmarked strategies. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Internet of things

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Lung and Colon Cancer Classification of Histopathology Images Using Convolutional Neural Network

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SN computer science 2024年第2期5卷 223页

作者： Singh, Onkar Kashyap, Kanchan Lata Singh, Koushlendra Kumar Machine Vision and Intelligence Lab Department of Computer Science and Engineering National Institute of Technology Jamshedpur Jharkhand India Computing Science Engineering Indian Institute of Information Technology Kottayam Kerala India

The greatest significant contributor to cancer-related morbidity and mortality worldwide is malignant lung tumors. Lung cancer frequency has been seen to be on the rise recently. Lung cancer histopathology diagnosis is a crucial part of the patient’s treatment. The current study aims to demonstrate the efficiency of convolutional neural networks for the identification of squamous cell carcinoma and adenocarcinoma of the lung and colon by investigating the diagnosis of histopathology images. Five state-of-the-art pre-trained (ImageNet) convolutional neural network architectures, VGG-19, InceptionResNetV2, DenseNet201, EfficientNetB6, and MobileNetV2, are employed in this investigation to tri-categorize lung cancer images (normal, adenocarcinoma, and squamous cell carcinoma), together with colon cancer images (normal and adenocarcinoma). Regularization strategies have been applied to fine-tune the learning rate for improving accuracy. The LC25000 dataset has been used to validate the proposed method. EfficientNetB6, VGG19, InceptionResNetV2, DenseNet201, and MobileNetV2 accuracy on test data is reported to be 93.12, 98.00, 97.92, 99.12, and 99.32 percent respectively. © 2024, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： DenseNet201 EfficientNetB6 Histopathology images InceptionResNetV2 Lung cancer MobileNetV2 VGG19

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Industrial Centric Node Localization and Pollution Prediction Using Hybrid Swarm Techniques

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computer Systems science & Engineering 2022年第8期42卷 545-560页

作者： R.Saravana Ram M.Vinoth Kumar N.Krishnamoorthy A.Baseera D.Mansoor Hussain N.Susila Department of Electronics and Communication Engineering Anna UniversityUniversity College of Engineering DindigulDindigul624622India Department of Computer Science and Engineering Anna UniversityUniversity College of Engineering DindigulDindigul624622India Department of Computer Science and Engineering Kongu Engineering CollegePerundurai638060India School of Computing Science and Engineering VIT Bhopal UniversityBhopal466114India Department of Computer Science and Engineering Sri Krishna College of Engineering and TechnologyCoimbatore641008India Department of Information Technology Sri Krishna College of Engineering and TechnologyCoimbatore641008India

Major fields such as military applications,medical fields,weather forecasting,and environmental applications use wireless sensor networks for major computing *** play a vital role in emerging technologies of the 20th *** of sensors in needed locations is a very serious *** environment is home to every living being in the *** growth of industries after the industrial revolution increased pollution across the *** to recent uncontrolled growth and development,sensors to measure pollution levels across industries and surroundings are *** interesting and challenging task is choosing the place to fit the *** meta-heuristic techniques have been introduced in node *** intelligent algorithms have proven their efficiency in many studies on localization *** this article,we introduce an industrial-centric approach to solve the problem of node localization in the sensor ***,our work aims at selecting industrial areas in the sensed *** use random forest regression methodology to select the polluted ***,the elephant herding algorithm is used in sensor node *** two algorithms are combined to produce the best standard result in localizing the sensor *** check the proposed performance,experiments are conducted with data from the KDD Cup 2018,which contain the name of 35 stations with concentrations of air pollutants such as PM,SO_(2),CO,NO_(2),and O_(3).These data are normalized and tested with *** results are comparatively analyzed with other swarm intelligence algorithms such as the elephant herding algorithm,particle swarm optimization,and machine learning algorithms such as decision tree regression and multi-layer *** can indicate our proposed algorithm can suggest more meaningful locations for localizing the sensors in the *** proposed method achieves a lower root mean square value with 0.06 to 0.08 for lo

关键词： Wireless sensor networks node localization industrial-centric approach random forest regression elephant herding optimization swarm intelligence pollution

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Polyp-DDPM: Diffusion-Based Semantic Polyp Synthesis for Enhanced Segmentation 46

Polyp-DDPM: Diffusion-Based Semantic Polyp Synthesis for Enh...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Dorjsembe, Zolnamar Pao, Hsing-Kuo Xiao, Furen National Taiwan University of Science and Technology Department of Computer Science and Information Engineering Taipei Taiwan National Taiwan University College of Medicine Institute of Medical Device and Imaging Taipei Taiwan

ISBN: (纸本)9798350371499

This study introduces Polyp-DDPM, a diffusion-based method for generating realistic images of polyps conditioned on masks, aimed at enhancing the segmentation of gastrointestinal (GI) tract polyps. Our approach addresses the challenges of data limitations, high annotation costs, and privacy concerns associated with medical images. By conditioning the diffusion model on segmentation masks - binary masks that represent abnormal areas - Polyp-DDPM outperforms state-of-the-art methods in terms of image quality (achieving a Fréchet Inception Distance (FID) score of 78.47, compared to scores above 95.82) and segmentation performance (achieving an Intersection over Union (IoU) of 0.7156, versus less than 0.6828 for synthetic images from baseline models and 0.7067 for real data). Our method generates a high-quality, diverse synthetic dataset for training, thereby enhancing polyp segmentation models to be comparable with real images and offering greater data augmentation capabilities to improve segmentation models. The source code and pretrained weights for Polyp-DDPM are made publicly available at https://***/mobaidoctor/polyp-ddpm. © 2024 IEEE.

关键词： Semantics

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A circuit area optimization of MK-3 S-box

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Cybersecurity 2025年第1期8卷 61-71页

作者： Yanjun Li Weiguo Zhang Yiping Lin Jian Zou Jian Liu Information Industry Information Security Evaluation Center The 15th Research Institute of China Electronic Technology Group CorporationBeijing 100083China Henan Key Laboratory of Network Cryptography Technology Zhengzhou 450001China Beijing Electronic Science and Technology Institute No.7 Fufeng RoadFengtai DistricBeijing 100083China College of Computer and Data Science Fuzhou UniversityFuzhou 350108China

In MILCOM 2015,Kelly et *** the authentication encryption algorithm MK-3,which applied the 16-bit *** paper aims to implement the 16-bit S-box with less circuit ***,we classifed the irreducible polynomials over F_(2n)into three *** we compared the logic gates required for multiplication over the fnite feld constructed by the three types of irreducible *** to the comparison result,we constructed the composite felds, F_((2^(4))^(2))and F_((2^(8))^(2)).Based on the isomorphism of fnite felds,the operations over F_(2)^(16)can be conducted over F_((2^(8))^(2)).Similarly,elements over F28 can be mapped to the corresponding elements over F_((2^(4)))^(2).Next,the SAT solver was used to optimize the operations over smaller feld F_(2)^(4).At last,the architecture of the optimized MK-3 S-box was worked *** with the implementation proposed by the original designer,the circuit area of the MK-3 S-box in this paper is reduced by at least 55.9%.

关键词： irreducible operations optimization

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