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2023 IEEE International Conference on Network, Multimedia and information Technology, NMITCON 2023

作者： Sindhu Madhuri, G. Somashekhara Reddy, D. Mahesh, T.R. Rajan, T. Vanitha, K. Shashikala, H.K. Manipal Institute of Technology Manipal Academy of Higher Education Department of Information Technology Karnataka Bengaluru India Faculty of Engineering and Technology Department of Computer Science and Engineering Bangalore India School of Computer Science and Engineering Presidency University Bangalore India Karpagam Academy of Higher Education Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350300826

Diabetes Mellitus (DM) is primarily defined by hyperglycemia, polyuria, and polyphagia and as a result of a complex interaction of hereditary and environmental variables, it has developed into a severechronic metabolic condition. Numerous diabetes problems might arise from uncontrolled high blood sugar. Long-term diabetes causes severe complications, some of which are fatal. Everywhere in the world, the prevalence of diabetes in patients is increasing at epidemic rates. Every year, diabetes and related disorders consume a sizable percentage of the national health budget. Several risk factors influence the etiopathogenesis of the disease and the emergence of the epidemic. The untreatable condition of diabetes may be managed by maintaining self-care in daily life, providing appropriate education about diabetes, and making significant advancements in knowledge, attitudes, skills, and management. Diabetes should be diagnosed as soon as possible because it can lead to several illnesses, including kidney failure, stroke, blindness, heart attacks, and lower limb amputation. This study aims to use relevant variables, create a prediction algorithm using machine learning, and choose the best classifier to produce results that are as close to clinical outcomes as possible with reduced entropy. The proposed approach is on selecting the characteristics that ail in the early detection of Diabetes Miletus utilizing Predictive analysis. The computational techniques K-Nearest Neighbour (KNN) and Decision Tree (DT) have been employed to detect DM at an early stage. The KNN outperforms with the best performance when evaluated against different performance metrics. © 2023 IEEE.

关键词： Decision trees

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Programmer Performance Prediction with Cognitive Tests: A Granular Approach

Programmer Performance Prediction with Cognitive Tests: A Gr...

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2023 International Symposium on Networks, computers and Communications, ISNCC 2023

作者： Farghaly, Ayman Aboulmagd El-Kafrawy, Passent Mohammed School of Information Technology and Computer Science Nile University Cairo Egypt Effat College of Engineering Effat University Jeddah Saudi Arabia

ISBN: (纸本)9798350335590

Universities and students face the challenge of finding the career that best suits the students. Programming is currently one of the highly sought-after careers and is one of the highest paying jobs. However not all students succeed in this career. In fact failure rates in programming field is relatively high globally. In this research, we used a spatial rotation test and Non-Verbal reasoning test as well as gender, at the beginning of the semester to observe the relationship between those tests and students' grades at the end of the semester for 2 groups. Group 1 are computer science students studying programming course and group 2 are non-computer science students studying programming course. For each group, we created a predictive model using 2 methods. Method 1 uses the conventional aggregate score of cognitive tests and method 2 uses answers to selected questions in each test. In method 2, we applied a data-driven methodology which utilize individual answers to questions as input to the model. To the best of our knowledge we are the first in this field to apply this method. The modeling results show usefulness of those test to predict programming and non-programming course grades using both methods, however method 2 demonstrates much higher superiority over the aggregate method 1. © 2023 IEEE.

关键词： Students

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Reproducible reformation of a bilayer lipid membrane using microair bubbles

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Droplet 2023年第3期2卷 90-98页

作者： Izumi Hashimoto Toshihisa Osaki Hirotaka Sugiura Hisatoshi Mimura Sho Takamori Norihisa Miki Shoji Takeuchi Artificial Cell Membrane Systems Group Kanagawa Institute of Industrial Science and TechnologyKanagawaJapan Department of Mechanical Engineering Keio UniversityKanagawaJapan Institute of Industrial Science The University of TokyoTokyoJapan Graduate School of Information Science and Technology The University of TokyoTokyoJapan

Planar bilayer lipid membranes(BLMs)are widely used as models for cell membranes in various applications,including drug discovery and ***,the nanometer-thick bilayer structure,assembled through hydrophobic interactions of amphiphilic lipid molecules,makes such BLM systems mechanically and electrically *** this study,we developed a device to reform BLMs using a microair *** device consists of a double well divided by a separator with a microaperture,where a BLM was formed by infusing a lipiddispersed solvent and an aqueous droplet into each well in *** the BLM ruptured,a microair bubble was injected from the bottom of the well to split the merged aqueous droplet at the microaperture,which resulted in the reformation of two lipid monolayers on the split *** bringing the two droplets into contact,a new BLM was *** angled step design was introduced in the BLM device to guide the bubble and ensure the splitting of the merged *** also elucidated the optimal bubble inflow rate for the reproducible BLM *** a 4-channel parallel device,we demonstrated the individual and repeatable reformation of *** approach will aid the development of automated and arrayed BLM systems.

关键词： bubble bringing unstable

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Research on natural environment protection based on blockchain technology to facilitate ecological balance and regional revitalization 4

Research on natural environment protection based on blockcha...

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4th International Conference on Industrial IoT, Big Data and Supply Chain, IIoTBDSC 2023

作者： Wan, Chenye Fu, Taoran Wang, Haochen Wang, Xing Luo, Junyou Wang, Ziqian School of Information Science and Technology Nantong University Nantong China School of Computer and Communication Hunan Institute of Engineering Xiangtan China School of Computational Science and Electronics Hunan Institute of Engineering Xiangtan China School of Robotics Hunan University Changsha China

ISBN: (纸本)9798350341690

Now the Masai Mara population continues to expand, the wildlife in the reserve is facing serious threats to their survival. The purpose of this report is to establish a management strategy model for local protected areas that balances the mutual interests between local people and wildlife in ***, Masai Mara divides the protected area into three parts, planting area, protected area, and livestock area, and then conducts correlation analysis on different areas, and concludes that the reduction in the number of wild animals is closely related to the degree of population density, and the area of cultivated land and forest area is highly correlated. So a nonlinear programming model of multiple strategies is established to plan the planting area. Then establish a comprehensive evaluation model based on the entropy weight method, quantify various strategies, and use gray correlation prediction to obtain solutions. This article introduces blockchain related technologies and uses its decentralization, security and transparency to build an architecture that contributes to the actual government protection of wild animals. © 2023 IEEE.

关键词： Conservation

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Weighted Sampled Split Learning (WSSL): Balancing Privacy, Robustness, and Fairness in Distributed Learning Environments 29

Weighted Sampled Split Learning (WSSL): Balancing Privacy, R...

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29th IEEE International Conference on Parallel and Distributed systems, ICPADS 2023

作者： Osti, Manish Thakuri, Aashray Qolomany, Basheer Mulahuwaish, Aos Saginaw Valley State University University Center Department of Computer Science and Information Systems MI United States University of Michigan - Dearborn College of Engineering and Computer Science Dearborn United States University of Nebraska at Kearney Cyber Systems Department Kearney United States

ISBN: (纸本)9798350330717

This study presents Weighted Sampled Split Learning (WSSL), an innovative framework tailored to bolster privacy, robustness, and fairness in distributed machine learning systems. Unlike traditional approaches, WSSL disperses the learning process among multiple clients, thereby safeguarding data confidentiality. Central to WSSL's efficacy is its utilization of weighted sampling. This approach ensures equitable learning by tactically selecting influential clients based on their contributions. Our evaluation of WSSL spanned various client configurations and employed two distinct datasets: Human Gait Sensor and CIFAR-10. We observed three primary benefits: heightened model accuracy, enhanced robustness, and maintained fairness across diverse client compositions. Notably, our distributed frameworks consistently surpassed centralized counterparts, registering accuracy peaks of 82.63% and 75.51% for the Human Gait Sensor and CIFAR-10 datasets, respectively. These figures contrast with the top accuracies of 81.12% and 58.60% achieved by centralized systems. Collectively, our findings champion WSSL as a potent and scalable successor to conventional centralized learning, marking it as a pivotal stride forward in privacy-focused, resilient, and impartial distributed machine learning. © 2023 IEEE.

关键词： and Distributed Learning Privacy-Preserving Machine Learning Robust Learning Unbiased Learning Weighted Sampled Split Learning (WSSL)

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An Adaptive Attitude Controller for A Quadrotor Transporting the Payload With Unknown Mass 38

An Adaptive Attitude Controller for A Quadrotor Transporting...

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38th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2023

作者： Qiu, Shihao Chen, Longlong Li, Shuang Lv, Zongyang Tang, Huayi Dalian University of Technology School of Computer Science and Technology Dalian China Dalian University of Technology School of Control Science and Engineering Dalian China Henan University of Technology College of Information Science and Engineering Zhengzhou China Dalian University of Technology School of Mechanical Engineering Dalian China

ISBN: (纸本)9798350303636

An adaptive control approach is presented in this article to deal with the impacts of carrying a payload with unknown mass by a quadrotor. This approach can effectively control the quadrotor's attitude stability. The paper commences by presenting the dynamic model of the quadrotor, followed by the development of a controller to manage its attitude control. Through Lyapunov analysis, the stability of the controller is validated. Ultimately, the proposed controller and a backstepping controller are used to controlling the attitude of the quadrotor, respectively, and experimental outcomes substantiate the efficiency and feasibility of the introduced controller. © 2023 IEEE.

关键词： Adaptive Control Quadrotor Unmanned Aerial Vehicle Unknown Mass Payload

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Decentralized MISO Cooperative Beamforming Design for Multi-Cell URLLC Network 7

Decentralized MISO Cooperative Beamforming Design for Multi-...

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7th International Conference on Intelligent Computing and Signal Processing, ICSP 2022

作者： Wang, Lihui Hefei University of Technology School of Computer Science and Information Engineering Hefei230601 China

ISBN: (纸本)9781665478571

Achieving low-latency for ultra-reliable and low- latency communications (URLLC) is one of the major challenge for the fifth-generation cellular networks. To address this challenge, the cooperation among base stations (BSs) has the potential to reduce latency and improve data speed. However, it requires a great quantity information interaction at cost of magnanimous utilization of the backhaul. In this paper, the author analyzes the duality between virtual uplink and downlink optimization problems. then, we propose an iterative decentralized algorithm to solve a max-min achievable rate problem based on the joint of the alternating direction method of multipliers(ADMMs) and successive convex approximation (SCA) method. Specifically, we can iteratively approximate the nonconvex problem into a convex problem by using the SCA method. In the second step, the ADMM method is used to transform the centralized optimization problem into a decentralized optimization problem that solves the problem locally. Each BS is regarded as a subproblem to work the problem out locally in parallel. By allowing BSs to exchange the required information until ADMM converges, the resulting formulation is optimized. Finally, simulation results show the effectiveness of our proposed algorithm. © 2022 IEEE.

关键词： Optimization

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Research on the Method of Color Compensation and Underwater Image Restoration Based on Polarization Characteristics 3

Research on the Method of Color Compensation and Underwater ...

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3rd International Conference on computer Vision, Image and Deep Learning and International Conference on computer engineering and Applications, CVIDL and ICCEA 2022

作者： Gong, Kaiqiang Hua, Dou Hefei University of Technology School of Computer Science and Information Engineering AnHui Heifei China

ISBN: (纸本)9781665459112

The attenuation degree of light in different bands is different when transmitting underwater, which leads to the problems of color distortion and blurred target details in the turbid underwater image. Combined with Monte Carlo simulation and measured experiments, this paper studied the polarization characteristics of light transmission in different wavelength bands underwater. The results show that the longer the wavelength of light, the stronger the ability of polarization retention underwater. Based on this feature, a new method of color compensation of underwater images is proposed. By fusing the polarization image with the intensity image, the exposure of the low-brightness areas in the image is improved, so as to compensate for the areas with serious color distortion in the underwater image. And then the image is deblurred and enhanced by using the dark channel prior principle. Comparative experimental results indicate that the method can effectively compensate for the color distortion of underwater images and significantly improves the visibility of underwater targets. © 2022 IEEE.

关键词： Image reconstruction

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A Three-Model Deep Learning Framework for ASL Recognition: Integrating CNN, Skeletal Features, and Multi-Modal Fusion

A Three-Model Deep Learning Framework for ASL Recognition: I...

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International Conference on Trends in Electronics and Informatics (ICEI)

作者： Karthikeyan J Vedant Passi Akshit Mahajan Md Zaid Zeya Divyam Jain Manas Gautam School of Computer Science Engineering and Information System VIT University Vellore India School of Computer Science and Engineering VIT University Vellore India

ISBN: (数字)9798331544607

ISBN: (纸本)9798331544614

American Sign Language (ASL) recognition is an important way of enhancing deaf and hard-of-hearing individu-als' access to communication. In this present paper, we propose a multi-modal deep learning model with the incorporation of image-based, skeletal-based, and hybrid recognition methods in the attempt to foster enhanced ASL classification accuracy. Our method takes real-time video input and utilizes Convolutional Neural Networks (CNNs) for image-based recognition as well as Multi-Layer Perceptrons (MLPs) for skeletal feature extraction. MediaPipe Hands is used to identify 21 hand landmarks, which are further used as input for skeletal-based classification. The dataset, which is gathered from Kaggle, comprises 78,000 images of 26 ASL alphabets (A-Z), and each model is trained individually to achieve the best performance. An ensemble final model uses the most confident classification out of the three approaches to achieve maximum accuracy and reliability. The system is real-time with confidence-based filtering and TTS support for better usability. The experimental results show that the multi-modal fusion approach outperforms single-modality models with high accuracy even in the case of diverse lighting conditions and hand orientations. Future directions involve using the model for sentence-level ASL recognition with LSTMs and deployment with optimizations on mobile and edge devices using TensorFlow Lite.

关键词： Hands Deep learning Adaptation models Sign language Accuracy Translation Computational modeling Real-time systems Mobile handsets Convolutional neural networks

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Xabcd: A Better Model for Named Entity Recognition in Cyber Threat Intelligence 9

Xabcd: A Better Model for Named Entity Recognition in Cyber ...

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9th International Conference on Intelligent Computing and Signal Processing, ICSP 2024

作者： Wang, Ruiqi Zheng, Zhiqiang Ao, Shengqin Fu, Yuxia Wang, Xuren Jiang, Zhengwei Xie, Gang School of Big Data and Computer Science Guizhou Normal University Guiyang China Information Engineering College Capital Normal University Beijing China Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798350376548

Due to the ambiguity between naming entity nouns and daily usage nouns in cyber threat intelligence, such as hacker organizations, attack tools, and attack techniques, the understanding of their content heavily relies on cyber security knowledge. How to quickly and accurately extract named entities related to network security from multi-source texts has become a hot and difficult research topic in the current threat intelligence field. In this paper, a named entity recognition framework from cyber threat intelligence unstructured text is designed. For the business requirements in the field of cyber threat intelligence, this framework completely covers data acquisition, text classification, entity and relationship definition, entity and relationship analysis and modeling and ability application. The framework is available for experimental exploration of entity recognition technology for threat intelligence. In addition, this paper proposes an entity recognition model for cyber threat intelligence, Xabcd, based on data and feature knowledge. Based on open network security text corpus, Xabcd model obtained the key indicators such as precision, recall, and F1-score as 97.35%, 97.15% and 97.23% respectively, which are all higher than the baseline model levels, so the experimental results demonstrate the feasibility and effectiveness of the model. © 2024 IEEE.

关键词： Cyber attacks

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