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Towards Swarm Intelligence Ontology for Formal Description of Metaheuristics Algorithms 4

Towards Swarm Intelligence Ontology for Formal Description o...

4th International Conference on Computing, Networking, Telecommunications and engineering sciences Applications, CoNTESA 2023

作者： Tulicic, Domagoj Horvat, Marko Lovrencic, Sandra University of Applied Science in Information Technology Department of Computer System and Networks Zagreb Croatia University of Zagreb Faculty of Electrical Engineering and Computing Department of Applied Computing Zagreb Croatia University of Zagreb Faculty of Organization and Informatics Foundations of Information Sciences Department of Theoretical and Applied Varaždin Croatia

ISBN: (纸本)9798350382754

Swarm intelligence is a class of nature-inspired metaheuristic algorithms, that is specifically derived from biological systems in nature with an emphasis on their social interactions. These algorithms have been primarily developed to solve complex combinatorial optimization problems and are extremely popular among researchers in metaheuristic algorithms. This is evident from the numerous published scientific papers describing algorithms inspired by biological systems, ranging from insects such as ants and bees to the largest animals on the planet like whales. All these algorithms share some common concepts and relationships that can be explicitly identified, named, and formalized. These shared concepts and relationships can be organized and represented through the construction of an ontology. The goal of this paper is to present a formal ontology that standardizes vocabulary, as well as a set of rules enabling precise communication and reasoning about these algorithms. Ultimately, this will improve our understanding, analysis, research processes, and utilization of these computational approaches. © 2023 IEEE.

关键词： Swarm intelligence

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Towards Classifying File Segments in Memory Using Machine-Learning

Towards Classifying File Segments in Memory Using Machine-Le...

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HCT Information Technology Trends (ITT)

作者： Mohammed I. Al-Saleh Akram Alkouz Abdulsalam Alarabeyyat Majed Bouchahma Computer Science Department Jordan University of Science and Technology Computer Information System Higher Colleges of Technology Jordan-Salt Software Engineering Department Al-Balqa’ Applied University

Digital media triage is a main challenge that faces a digital investigator. Knowing what might be useful during crime investigation could greatly save the investigator’s time and enhance outcomes. Memory investigation can get potential benefit from the triage process because of its scattered and diverse contents. The Operating system’s paging scheme might map the contents of a file into non-consecutive page frames in the physical memory, making the file-type classification and identification process harder. This paper tackles the memory content triage problem at the page level using a machine-learning methodology.

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TCSloT: Text Guided 3D Context and Slope Aware Triple Network for Dental Implant Position Prediction

TCSloT: Text Guided 3D Context and Slope Aware Triple Networ...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Yang, Xinquan Xie, Jinheng Li, Xuechen Li, Xuguang Shen, Linlin Deng, Yongqiang Shenzhen University College of Computer Science and Software Engineering Shenzhen China National University of Singapore Show Lab Singapore Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen China Shenzhen University General Hospital Department of Stomatology Shenzhen China

ISBN: (纸本)9798350337488

In implant prosthesis treatment, the surgical guide of implant is used to ensure accurate implantation. However, such design heavily relies on the manual location of the implant position. When deep neural network has been proposed to assist the dentist in locating the implant position, most of them take a single slice as input, which do not fully explore 3D contextual information and ignores the influence of implant slope. In this paper, we design a Text Guided 3D Context and Slope Aware Triple Network (TCSloT) to integrate the perception of contextual information from multiple adjacent slices and awareness of variation of implant slopes. A Texture Variation Perception (TVP) module is correspondingly design to process the multiple slices and capture the texture variation among slices and a Slope-Aware Loss (SAL) is proposed to dynamically assign adaptive weights for the regression head. Additionally, we design a conditional text guidance (CTG) module to integrate the text condition (i.e., left, middle and right) from the CLIP to assist the implant position prediction. Extensive experiments on a dental implant dataset through five-fold cross-validation, demonstrated that the proposed TCSloT achieves superior performance than existing methods. © 2023 IEEE.

关键词： Dental Implant Multi-module Detection Multi-slice Learning Text Guided Detection Transformer

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Quantum Remote State Preparation Based on Quantum Network Coding

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computers, Materials & Continua 2022年第10期73卷 119-132页

作者： Zhen-Zhen Li Zi-Chen Li Yi-Ru Sun Haseeb Ahmad Gang Xu Xiu-Bo Chen School of Information Engineering Beijing Institute of Graphic CommunicationBeijing102600China School of Computer Science and Information Engineering Zhejiang Gongshang UniversityHangzhou310018China Information Security Center State key Laboratory of Networking and Switching TechnologyBeijing University of Posts and TelecommunicationsBeijing100876China Department of Computer Science National Textile UniversityFaisalabad37610Pakistan School of Information Science and Technology North China University of TechnologyBeijing100144China Advanced Cryptography and System Security Key Laboratory of Sichuan Province China

As an innovative theory and technology,quantum network coding has become the research hotspot in quantum network *** this paper,a quantum remote state preparation scheme based on quantum network coding is *** with the general quantum remote state preparation schemes,our proposed scheme brings an arbitrary unknown quantum state finally prepared remotely through the quantum network,by designing the appropriate encoding and decoding steps for quantum network *** is worth mentioning,from the network model,this scheme is built on the quantum k-pair network which is the expansion of the typical bottleneck network—butterfly ***,it can be treated as an efficient quantum network preparation scheme due to the characteristics of network coding,and it also makes the proposed scheme more applicable to the large-scale quantum *** addition,the fact of an arbitrary unknown quantum state remotely prepared means that the senders do not need to know the desired quantum ***,the security of the proposed scheme is ***,this scheme can always achieve the success probability of 1 and 1-max flow of value ***,the communication efficiency of the proposed scheme is ***,the proposed scheme turns out to be practicable,secure and efficient,which helps to effectively enrich the theory of quantum remote state preparation.

关键词： Quantum remote state preparation quantum network coding quantum k-pair network

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Enhancing Pulmonary Nodule Classification performance with TriCaps-RL: A Capsule Network Reinforcement Learning Approach

Journal of Network Intelligence

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Journal of Network Intelligence 2024年第3期9卷 1441-1459页

作者： Zheng, Guang-Yuan Zhang, Fu-Quan Cheng, Chen Shan, Hong-Tao Soomro, Nouman Qadeer Liu, Yi College of Information Technology Shanghai Jian Qiao University Shanghai201306 China School of Mathematics and Computer Science Yanan University Shaanxi 716000 China School of Computer and Data Science Minjiang University No.200 Xiyuangong Road Fuzhou University Town Fuzhou350108 China Digital Media Art Key Laboratory of Sichuan Province Sichuan Conservatory of Music Chengdu610021 China Fuzhou Technology Innovation Center of intelligent Manufacturing information System Minjiang University Fuzhou350108 China Fujian Province University Fuzhou350300 China College of Business Shanghai Jian Qiao University Shanghai201306 China College of Electronic and Electrical Engineering Shanghai University of Engineering Science Shanghai201620 China Software Department Mehran University of Engineering & Technology Sindh 76062 Pakistan

Distinguishing the manifestations of pulmonary nodules poses a significant challenge in the medical field, demanding the expertise of experienced radiologists. This complexity results in the high cost and inadequacy of sample labeling. In response, this study introduces a reinforcement learning approach, denoted as TriCaps-RL, for the classification of pulmonary nodules based on CT signs. This approach initially employs the Q-value loss to train a single CapsNet. When the performance of the CapsNet plateaus and further enhancement becomes challenging, the loss function is progressively replaced by a triplet metric to augment performance. Consequently, a more nuanced differentiation among various types of pulmonary nodules is achieved. The proposed method can improve its performance through interactions with radiologists during their CT image reading process, thereby mitigating the shortage of radiologists and addressing the time-consuming issue of training sample labeling in medical image research. This paper delves into the fine-grained classification of pulmonary nodules using deep reinforcement learning based on CT imaging signs. In comparison to previous studies that primarily concentrate on the benign/malignant classification of lung nodules, this fine-grained classification of nodules founded on CT imaging signs proves to be more valuable for medical practitioners in making accurate diagnostic decisions. © 2024, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Lung cancer

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Automatic Early Detection of Tomato Blight Disease Using Deep Neural Networks

Automatic Early Detection of Tomato Blight Disease Using Dee...

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Technological Advancements in Computational sciences (ICTACS), International Conference on

作者： Anangsha Halder Subhrendu Guha Neogi Shibdas Dutta Soumallya Chandra Chandra Pritha Guha Majumdar Akash Sarkar Department of Computer Science and Engineering The Neotia University Sarisha West Bengal India Department of Computer Science and Engineering Brainware University Kolkata West Bengal India DCG Data-Core System India Pvt. Ltd Kolkata West Bengal India

ISBN: (数字)9798350387490

ISBN: (纸本)9798350387506

Crop disease control is crucial for food security since plant illnesses diminish agricultural production. Crop productivity and quality depend on leaf diseases like tomatoes. Early plant disease identification utilizing computer vision and artificial intelligence can lessen disease severity and overcome the limitations of continuous human monitoring. Automatic Early detection and alert systems reduce disease and boost agricultural productivity by classifying tomato diseases from tomato leaf images using Deep Neural Network Models. Our study examined leaf photos to classify tomato diseases and healthy plants using AlexNet, ResNet-9, VGG-19, and a modified CNN model for tomato plant disease diagnosis using deep neural network architectures. The proposed CNN analyses images to identify edges, colours, and textures. The suggested model can recognize healthy and diseased tomato leaves with 98% accuracy, making it a valuable crop management tool that promotes sustainable agriculture and global food security.

关键词： Productivity Plant diseases Image color analysis Computational modeling Plants (biology) Image edge detection Crops Food security Artificial neural networks Monitoring

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Sal-Guide Diffusion: Saliency Maps Guide Emotional Image Generation through Adapter

Sal-Guide Diffusion: Saliency Maps Guide Emotional Image Gen...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Xiangru Lin Shenghua Zhong Yan Liu Gong Chen College of Computer Science Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen China College of Computer Science Shenzhen University Shenzhen China Department of Computer Science The Hong Kong Polytechnic University HK China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

The existing text-to-image generation methods based on stable diffusion yield better results in low-semantic prompt but often neglect the generation quality of high-semantic prompt such as emotional vocabulary, resulting in poor emotional image generation. In order to address this issue, we propose a novel approach called Sal-Guide Diffusion, which leverages saliency maps to guide emotional image generation with the goal of producing superior emotionally expressive images. In order to let the saliency maps guide the diffusion process, we introduce a lightweight adapter to extract emotional information from saliency maps and incorporate it into the diffusion process. Experimental results demonstrate that our proposed method generates higher-quality images across eight emotional dimensions, excelling in both generalization, emotional congruence, and subjective preference compared to stable diffusion or similar methods.

关键词： Vocabulary Image synthesis Buildings Diffusion processes Text to image Data mining

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A comparative analysis of MQTT and IoT application protocols 2

A comparative analysis of MQTT and IoT application protocols

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2nd IEEE International Conference on Electronic engineering, ICEEM 2021

作者： Al Enany, Marwa O Harb, Hany M. Attiya, Gamal Ministry of Higher Education Higher Institute of Computer Science and Information System Computer Science Department Egypt Al-Azhar University Faculty of Engineering Systems and Computers Department Cairo Egypt Menoufia University Faculty of Electronic Engineering Computer Science and Engineering Department Egypt

ISBN: (纸本)9781665418423

Choosing the appropriate IoT protocol for a certain application was and still a critical issue for IoT applications, as protocols vary in behavior in different applications and under different network status. IoT application protocols have a recent focus of attention especially those protocols for the low-powered IoT applications and constrained devices. A brief survey of IoT layers and applied protocols for each layer and their applications were presented, besides, a detailed survey of IoT protocols with a presentation of the main researches that have been done to evaluate their performance. As a use case for this comparative study, the MQTT protocol was chosen because of its simplicity and its publish /subscribe model to be evaluated and compared against other application protocols. © 2021 IEEE.

关键词： Surveys

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Prediction of piRNA-mRNA interactions based on an interactive inference network

Prediction of piRNA-mRNA interactions based on an interactiv...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Liu, Yajun Li, Ru Li, Aimin Fei, Rong Guo, Xie Wu, Fang-Xiang Xi'an University of Technology Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an China Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an China University of Saskatchewan Division of Biomedical Engineering Department of Computer Science Department of Mechanical Engineering Saskatoon Canada

ISBN: (纸本)9798350337488

As the largest class of small non-coding RNAs, piRNAs primarily present in the reproductive cells of mammals, which influence post-transcriptional processes of mRNAs in multiple ways. Effective methods for predicting piRNA and mRNA target relationships can help identify piRNA functions, investigate the possibility of piRNAs as biomarkers and therapeutic targets. In this study, we propose a computational approach for classifying the relationships of piRNA-mRNA pairs based on an interactive inference network (IIN). First, we gather piRNA-mRNA target data, collect sequence data by position alignment, and construct a benchmark dataset. Furthermore, a reliable negative set is constructed by positive-unlabeled learning. Finally, we view a piRNA and a mRNA sequence as a premise and hypothesis sentence, respectively, and IIN model is used to predict the relationship between them. The experiments demonstrate that our method effectively characterizes piRNA-mRNA interaction and could be beneficial for researchers to investigate piRNA functions. © 2023 IEEE.

关键词： interactive inference network noncoding RNA piRNA-mRNA interactions positive-unlabeled learning

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Ultrahigh-coherence microcomb generation and low-noise amplification using integrated photonics

Ultrahigh-coherence microcomb generation and low-noise ampli...

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2024 IEEE Photonics Conference, IPC 2024

作者： Zhang, Xuguang Zhou, Zixuan Guo, Yijun Zhuang, Minxue Jin, Warren Shen, Bitao Chen, Yujun Huang, Jiahui Hu, Weiwei Shu, Haowen Bowers, John E. Wang, Xingjun Chang, Lin University of California Santa Barbara Department of Electrical and Computer Engineering Santa BarbaraCA United States Peking University State Key Laboratory of Advanced Optical Communications System and Networks School of Electronics Beijing China Peking University Frontiers Science Center for Nano-Optoelectronics Beijing China

ISBN: (纸本)9798350361957

We demonstrate a high-coherence parallel integrated light source utilizing a self-injection locked microcomb and injection locking amplification. Our strategy exhibits a record high on-chip gain of 60 dB, nearly 0 dB noise factor, which enable more than 30 highly coherent channels in integrated photonics whose linewidths down to 10 Hz, power exceeding 20 dBm per line and a wall plug efficiency of 19%. © 2024 IEEE.

关键词： Photonic integrated circuits

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