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Explore the Impact of Quantum Computing to Enhance Cryptographic Protocols and Network Security Measures 5

Explore the Impact of Quantum Computing to Enhance Cryptogra...

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5th IEEE international conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Ambika, S. Balaji, V. Rajasekaran, R.thalapathi Periyasamy, P.N. Kamal, N. E. G. S. Pillay Engineering College Department of Information Technology Tamil Nadu Nagapattinam India Chennai India Saveetha School of Engineering Simats Dept. of Computer Science and Engineering Chennai India Hindusthan Institute of Technology Department of Computer Science and Engineering India Grt Institute of Engineering and Technology Dept. of Computer Science and Engineering Tamil Nadu Tiruttani India

ISBN: (纸本)9798350383522

the revolutionary effects of quantum computing on security procedures for networks and cryptographic protocols are the focus of this study. Our research shows that conventional cryptographic algorithms are susceptible to quantum assaults;specifically, RSA and ECC encryption are very vulnerable. On the other hand, cryptographic algorithms that are immune to quantum attacks, such as multivariate polynomial cryptography and lattice-based algorithms, show promise in meeting these challenges. Key exchange rates between 200 kbps and 450 kbps and error rates as low as 0.5% are some of the significant achievements shown by Quantum Key Distribution (QKD) implementations. these applications show that QKD is a viable solution for protecting communication channels up to 200 km away. On top of that, there are remarkable outcomes when network security systems incorporate quantum advancements. the detection accuracy of Quantum-Enhanced Intrusion Detection Systems (QIDS) is 98.5% and the false positive rate is minimally 0.2%. the detection accuracy of quantum-secured communication systems is 99.2% and the false positive rate is an extremely low 0.1%. While quantum firewalls keep a detection accuracy of 97.8% and a false positive rate of 0.3%, quantum-safe cryptographic modules show a low latency of 3 ms. © 2024 IEEE.

关键词： cryptographic protocol data security quantum key distribution quantum-enhanced intrusion detection

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Risk Prediction for Power Grid Transmission Lines Based on Sensors Fusion 5

Risk Prediction for Power Grid Transmission Lines Based on S...

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5th international conference on Internet of things, Automation and Artificial Intelligence, IoTAAI 2023

作者： Xu, Shaoqing Zhang, Wenbao Chen, Zirui Zhang, Wenlong Xu, Bao Zeng, Fanyu State Grid Jilin Electric Power Co. Ltd. Information and Communication Company Jilin Changchun130021 China Zhuhai College of Science and Technology Guangdong Zhuhai519040 China

ISBN: (纸本)9798400716485

In order to improve the safety and stability of the transmission line of the power grid, the sensor factors such as meteorological data, satellite remote sensing data and ground equipment status data are fused to establish a state weight matrix, and the multi-factor hidden Markov prediction and learning model is used to realize the high-precision prediction of the power outage risk of the transmission line of the power grid. Firstly, according to the multi-factor analysis of the power outage of the line, the corresponding state observation set was established by using meteorological data, satellite remote sensing data and ground equipment monitoring methods. Secondly, the graph weight analysis method was used to establish the transmission line power outage risk assessment index model and establish the possible state set. thirdly, the multi-factor correction model was used to calculate the multi-factor combined power-off probability model through big data learning regression, and the evaluation and prediction of power grid transmission line outage under comprehensive change were realized. Finally, the simulation experiment is established, and the random sample data test proves that the consistency test results of the method in this paper are good, and the probability of normal prediction and power outage prediction is higher than 92%, which is feasible and stable. It can provide more adequate preparation time for subsequent efficient emergency response. © 2023 ACM.

关键词： Electric power transmission

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Assessment of rural built environment based on regional adaptability: An empirical study of the Huinong Center in Guangzhou Jingxia Village 5

Assessment of rural built environment based on regional adap...

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5th international conference on Green Energy, Environment, and Sustainable Development, GEESD 2024

作者： Chen, Jue Dai, Xuejuan Zhao, Fei Wang, Chen School of Arts and Design Guangdong University of Finance & Economics Guangdong Guangzhou510320 China School of Design South China University of Technology Guangzhou Higher Education Mega Centre Guangdong Guangzhou510006 China

ISBN: (纸本)9781510683006

this study attempts to introduce "regional adaptability" as an indicator for evaluating the Environmental sustainability in the context of New Rural Construction. the newly constructed Huinong Center in Jingxia Village, located on the outskirts of the Pearl River Delta region, was chosen as the research subject. through surveys of different groups of people regarding their evaluations of the built environment in the new rural areas and by using factor-regression analysis, nine independent variables were condensed from various influencing factors to generate a regional adaptability evaluation index model. the regression analysis further clarified these nine independent variables and identified four variables, namely, satisfaction with agricultural support, sensory satisfaction, usage satisfaction, and hygiene satisfaction, that had a positive impact on the new rural construction environment. these variables were determined to be the dominant factors influencing the changes in the rural built environment. © 2024 SPIE.

关键词： Natural environment

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Learning Self-Supervised Vision Transformers from Scratch for Aerial Person Re- Identification 13

Learning Self-Supervised Vision Transformers from Scratch fo...

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13th international conference on Information technology in Medicine and Education, ITME 2023

作者： Peng, Houfu Xu, Lili Lu, Xing Xia, Daoxun Guizhou Normal University Guizhou Key Laboratory of Information and Computing Science Guiyang China School of Big Data and Computer Science Guizhou Normal University Guiyang China Guizhou Normal University Engineering Laboratory for Applied Technology of Big Data in Education Guiyang China

ISBN: (纸本)9798350319156

In recent years, person re-identification (Re-ID) as a widely studied computer vision task, has reached a saturation state under closed-world setting, which encourages researchers to further explore more realistic scenarios. Among them, person Re- Idin aerial imagery is proposed and improved due to its unique practical importance in public security. However, since the aerial person images are taken by unmanned aerial vehicles (UAV s), influenced by camera height and angle of view, there are more serious problems such as weak appearance feature and occlusion than ground person images. Most of the current state-of-the-art person Re-ID methods on closed-world datasets are based on local convolution neural network, and hardly works well when applying them to aerial person Re- Idtasks directly. In this paper, we improve the emerging vision transformer (ViT) and apply it to the person Re- Idin aerial imagery. It should be noted that a large amount of data is required to be pretrained for ViTs to achieve competitive performance. Considering the limitations of data, computing power and flexibility in practical scenarios, we improve the pre-training process based on self-supervised learning, and achieve training ViTs from scratch with limited data. Specifically, in pre-training stage, the self-supervised paradigm based on parameter instance discrimination is applied to capture feature alignment and instance similarity, which alleviates the data-hungry of ViTs caused by the lack of inductive bias. Extensive comparative evaluation experiments are conducted on the aerial Re- Iddataset. Our method achieves a Rank-1 accuracy of 65.29% and a mean average precision (mAP) of 57.31%, which proves its effectiveness in aerial person Re-ID tasks. © 2023 IEEE.

关键词： Antennas

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An Empirical Brief Analysis of Novelistic Approaches for Detection Of Bone Marrow Cancer Health Monitoring through DL MODEL 5

An Empirical Brief Analysis of Novelistic Approaches for Det...

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5th IEEE international conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Reddy, Anuradha Ramaiah, V. Subba Ayyappa, R. Mohan Krishna Ghantasala, G. S. Pradeep Kurra, Mamatha Bathla, Priyanka Chitkara University Punjab India Mahatma Gandhi Institute of Technology Department of Computer Science and Engineering Hyderabad Gandipet India Alliance University Department of Computer Science and Engineering Bengaluru India Chandigarh University Mohali Punjab Gharuan India

ISBN: (纸本)9798350383522

Deep learning, with its ability to inevitably learn hierarchical depictions from multifaceted data, has shown promise in medical applications. However, tuning the hyperparameters of deep learning models is a vital task, and metaheuristic optimization techniques offer effective results. the objective is to assess their efficiency in enhancing the recital of deep learning models for bone marrow tumor prophecy. the study employs an assorted dataset, encompassing several patient attributes and medicinal gages. through a sequence of researches, we evaluate the convergence speed, resolution excellence, and sturdiness of each metaheuristic tactic when coupled with a deep learning model. the outcomes acme the fortes and paleness of each process, providing valuable intuitions for investigators and experts in the field of medical data investigation. this study contributes to the optimization of deep learning models for bone marrow tumor prediction, targeting to advance diagnostic accurateness and sustenance healthcare authorities in making informed verdicts for persistent care. © 2024 IEEE.

关键词： Bone Marrow Cancer Prediction Deep learning model Diagnostic Accuracy Healthcare Decision Support Hyperparameter Tuning Medical data Analysis Metaheuristic Optimization

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Enhancing Lung Segmentation through Preprocessing of Medical data Using Convolutional Neural Networks 5

Enhancing Lung Segmentation Through Preprocessing of Medical...

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5th IEEE international conference on Cybernetics, Cognition and Machine Learning Applications, ICCCMLA 2023

作者： Swathi, R. Liyaz, P. Swarnalatha, S. Akuthota, Kumar Reddy A, Basi Depuru, SivaKumar Sri Venkateswara College of Engineering Dept. of CSE A.P India Wipro Karnataka Banglore India Sri Venkateswara College of Engineering Dept. of IT A.P Tirupati India School of Computing Madanapalle Institute of Technology & Science Dept. of CSE A.P India School of Computing Mohan Babu University Dept. of CSE A.P India

ISBN: (纸本)9798350338287

Lung cancer is the leading cause of cancer-related death for women and the second leading cause of cancer-related death for men, according to a report by the World Health Organization in 2020. Detecting lung cancer through a three-dimensional X-ray of a patient's lungs, or a chest X-ray, is a common practice, but it can lead to errors caused by the human factor. To address this issue, advanced algorithms, and CAD technologies can be used to detect lung cancer. A network model can be trained using deep structural learning to detect details and deviations in the X-ray image. However, creating an accurate model using this technique is both time-consuming and advanced, and the model must be trained correctly. Despite the extensive research on various network architectures, the preprocessing technique of lung segmentation and its influence on such models remains largely unexplored. thus, in this study, we aim to investigate the effect of applying lung segmentation to the training and testing data used by the CNN model on accuracy and loss. To achieve this, we developed two CNN models, one with and one without lung segmentation, respectively. the models were evaluated and compared, and our results demonstrate that lung segmentation can mitigate overtraining, resulting in a significant improvement in accuracy. We anticipate that this study will pave the way for further research in similar problem areas. © 2023 IEEE.

关键词： Convolution

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Leveraging Machine Learning to Optimize Real-Time data Analysis Performance 5th

Leveraging Machine Learning to Optimize Real-Time Data Analy...

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5th international conference on data Science, Machine Learning and Applications, ICDSMLA 2023

作者： Singh, Vaishali Kaushik, Harshita Verma, Archana Raghavendra, R. Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Department of Computer Science and Application Vivekananda Global University Jaipur India Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Karnataka Bangalore India

ISBN: (纸本)9789819780426

Leveraging system mastering (ML) algorithms has become an increasingly exciting technique for optimizing actual-time facts analysis performance. In the ever-growing age of massive facts, ML permits us to effectively make the experience of this information extra quickly and correctly. Traditionally, real-time statistics analysis requires complex guide evaluation or is constrained by the insufficient skills of conventional rule-based total strategies. ML can automate this method and identify patterns in the records that may not be apparent at once, even optimizing performance significantly. In addition, ML algorithms, including supervised and unsupervised methods, can be applied to label facts because it comes in, making it simpler to identify styles inside the statistics before a manual evaluation. this technique is called predictive analytics, and it dramatically reduces the time spent manually exploring the statistics. Usually, leveraging system learning to optimize the performance of actual-time records evaluation can improve the process of creating a sense of these facts. through that specialization in predictive analytics and utilizing supervised and unsupervised techniques, we will quickly and appropriately analyze the statistics and make powerful selections. It can be pretty helpful in a selection of industries and conditions. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Predictive analytics

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Abnormal Trajectory Recognition Algorithm of Networked Vehicles Based on Improved Particle Swarm Optimization 5

Abnormal Trajectory Recognition Algorithm of Networked Vehic...

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5th international conference on Computer Information Science and Artificial Intelligence, CISAI 2022

作者： Gao, Wei Zhang, Haibo Li, Nan Dalian University of Science and Technology Dalian China

ISBN: (纸本)9781510662520

To improve the recognition hit rate of the abnormal trajectory of networked vehicles, reduce the actual recognition difference, and expand the corresponding trajectory coverage, this paper studies the abnormal trajectory recognition algorithm of networked vehicles based on improved particle swarm optimization. In this paper, the abnormal trajectory conversion is preprocessed, and the calculation matrix of unit recognition is designed, and then the abnormal trajectory recognition algorithm model under the improved particle swarm optimization is constructed. Finally, the design of recognition algorithm is realized by using the dimension reduction processing of abnormal data. the test results show that compared with the traditional tracking anomaly recognition algorithm test group and the traditional multi-dimensional feature anomaly recognition test group, the improved particle swarm recognition algorithm test group designed in this paper has a relatively high recognition hit rate, which indicates that the algorithm has a small error and high accuracy in the process of practical application, and has practical application value. © 2023 SPIE.

关键词： Trajectories

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Analysis of development efficiency and spatio-temporal evolution of logistics industry in East China under carbon constraints 5

Analysis of development efficiency and spatio-temporal evolu...

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5th international conference on Green Energy, Environment, and Sustainable Development, GEESD 2024

作者： Chen, Ningning Song, Shiyuan Qiu, Liwei School of Management Science and Engineering University of Jinan Shandong Jinan250022 China Jinan Techfin Information Technology Co. Ltd. Shandong Jinan250100 China

ISBN: (纸本)9781510683006

this study employs a non-expected output super-efficiency slacks-based measure (SBM) model to construct an input-output index system under the condition of carbon constraints. the objective of this study is to provide a comprehensive analysis of the static efficiency of the development of the logistics industry in East China between the years 2012 and 2021. the decomposition of the Global-Malmquist-Luenberger (GML) index permitted a comprehensive analysis of the evolution of the logistics industry in the region over time. In addition, the efficiency data for specific years were employed to investigate the alterations in the spatial distribution of the development of the logistics industry. the findings of the study indicate that the efficiency of logistics industry development under carbon constraints in East China is generally low. In terms of temporality, the efficiency value of the logistics industry in the region has undergone a process of change, initially exhibiting a decline and subsequently an increase. In the spatial dimension, the region exhibiting the highest efficiency in logistics, centered on Shanghai, gradually shows a trend of moving northward. this results in the formation of a phenomenon of polarization between the north and the south. the findings of the research have led to the formulation of a series of recommendations for the environmentally conscious and coordinated development of the logistics industry in the East China region. these recommendations are designed to facilitate the advancement of a more environmentally conscious, efficient, and coordinated logistics industry in this region. © 2024 SPIE.

关键词： Carbon sequestration

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Machine Learning Based Cardiovascular Disease Prediction 3

Machine Learning Based Cardiovascular Disease Prediction

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3rd international conference on Advancement in Electronics and Communication engineering, AECE 2023

作者： Saraswathi, P. Prathiksaa, J.G. Prabha, M. Jhoshitha, N.A. It Velammal College of Engineering and Technology Madurai India

ISBN: (纸本)9798350330724

Heart disease is an illness that affects the heart and manifests itself in a variety of ways, including blood vessel issues, irregular heartbeats, and diseases of the heart muscle or valves. In recent years, researchers have found that Machine Learning can help to detect heart problems by analyzing digital electrocardiogram (ECG) data. By using this technology, doctors can detect heart issues earlier and save lives. the process involves analyzing large sets of medical data, which includes information about patients' medical history. Logistic Regression, Support Vector Machine (SVM), and Random Forest are most widely utilized machine learning techniques to perform classification. Also, it predicts whether a patient has heart ailments or not. In this research, Decision Tree Classifiers are modeled along with the mentioned techniques which predict the heart related issues. A performance measure like accuracy of each technique is analyzed and the best one is identified to guess the heart disease. © 2023 IEEE.

关键词： Electrocardiograms

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