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Explore the role and emphasis of K-Means, Decision Tree and Distance Based algorithms in data exception detection 2

Explore the role and emphasis of K-Means, Decision Tree and ...

引用

2nd international conference on computing Innovation and Applied Physics, CONF-CIAP 2023

作者： Chen, Xiaotian Beijing University of Posts and Telecommunications Beijing100876 China

K-Means, Decision Tree and Distance-Based algorithms are 3 important ways of classifying data. These three algorithms have different methods and focus on data classification. Therefore, they are always applied into different scenarios. K-Means algorithm is to divide three-dimensional data or two-dimensional data into several clusters to facilitate subsequent data processing and analysis. Decision Tree is based on the "tree"structure to make decisions. It is an important classification and regression method in data mining technology. It is a prediction analysis model expressed in the form of tree structure (including binary tree and multi tree). When it comes to the Distance-Based algorithm, it is a common anomaly detection method applicable to various data domains. It defines outliers based on the nearest neighbor distance. The purpose of this paper is to explore the kernel of three anomaly detection algorithms through an example of data anomaly detection. Therefore, in this paper, all three algorithms' commonalities and differences will be discussed and illustrated through a case of data exception handling. © Published under licence by IOP Publishing Ltd.

关键词： Decision trees

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data processing Optimization of Power Grid Dispatching Control Cloud Based on Edge-Cloud Collaborative computing 3

Data Processing Optimization of Power Grid Dispatching Contr...

引用

3rd IEEE international conference on Power, Electronics and Computer Applications, ICPECA 2023

作者： Sheng, Lei Wang, Chunying Liu, Yan Zhang, Ningning Wang, Zhaohe State Grid Henan Electric Power Company Information & Telecommunication Company Zhengzhou China

ISBN: (纸本)9781665472784

Cloud computing technology is widely used in power grid systems. In response to the data with strict delay requirements and a large amount of data generated by the operation of the power grid system. New challenges are raised to the delay of business processing. In order to achieve the goal of minimizing the system average latency time of task processing, based on the power grid dispatching cloud architecture, a delay optimization method based on SDN edge-cloud collaborative computing is proposed. The goal is to minimize the system average delay of task processing. Introduce continuously variable edge and cloud and task distribution coefficients. The delay optimization problem is transformed into the task allocation coefficient optimization problem. And finally get the task allocation optimization strategy based on the model calculation. The experimental results show that when the edge node tasks are reasonably loaded into the cloud center for processing, the system delay of the business can be effectively reduced. Compared with the previous methods, the edge-cloud collaborative computing method proposed in this paper reduces the system delay and utilizes the control mechanism of SDN that can coordinate cloud computing and edge computing. The algorithm can be used in the power system cloud platform system based on SDN. © 2023 IEEE.

关键词： Edge computing

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Skin Disease Classification Using Machine Learning 14

Skin Disease Classification Using Machine Learning

引用

14th international conference on computing Communication and Networking Technologies, ICCCNT 2023

作者： Reddy, B. Veerasekhar Nagaraju, T. Venkata Laya, K. Fatima, Shahzeen Charan, T. Vagdevi, Ch. Mlr Institute of Technology Department of Information Technology Hyderabad India

ISBN: (纸本)9798350335095

Since for all intents and purposes each sort of skin issue influences everybody consistently, dermatological sicknesses have been found to essentially affect the strength of millions of individuals. There is a requirement for further developed PC supported skill in the examination and determination of multi-type skin problems since human examination of such sicknesses calls for investment and exertion, and ebb and flow techniques are simply used to dissect specific sorts of skin diseases. PC helped ML procedures like Ensemble algorithms and data Mining algorithms are utilized in this work to anticipate skin disease continuously with more prominent exactness than past methodologies. © 2023 IEEE.

关键词： data mining

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Higher education recommendation system using data mining algorithm

Higher education recommendation system using data mining alg...

引用

international conference on Artificial Intelligence, Blockchain, computing and Security, ICABCS 2023

作者： Ponmaniraj, S. Kishore, S. Naga Kumar, G. Shashi Abhinay, C.H. Harish, B. Bharath Institute of Science and Technology Chennai India

ISBN: (纸本)9781032669663

In many organizations, machine learning and data mining techniques are used for analyzing large amount of available data’s, information’s for decision making process. In educational sector, Machine learning is used for wide variety of applications such as suggestion to the students based on 10th mark and interest. The process of self-analysis, critical thinking, and decision-making is the important milestones for a person’s life. Here, a survey on academic decisions and the variables that affect those decisions will be presented. A real case study using machine learning algorithms helped support decision-making by predicting courses and institutions. It is usually influenced by the views of your parents, friends, relatives, teachers, and the media that you choose a career. You need to plan your career wisely and as early as possible today, as there is choicer and greater competition. © 2024 The Author(s).

关键词： Decision making

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Design Thinking –An Effective Method for Enhancing Intrusion Detection Systems through Hyper parameter Tuning in Deep Learning 2

Design Thinking –An Effective Method for Enhancing Intrusio...

引用

2nd international conference on Automation, computing and Renewable Systems, ICACRS 2023

作者： Dinesh, K. Kalaivani, D. Vijayaraghavan, T. Department of Computer Science Dr. SNS Rajalakshmi College of Arts and Science Coimbatore India

ISBN: (纸本)9798350340235

This research study explores the optimization of Intrusion Detection Systems (IDS) by fine-tuning hyperparameters for Deep Learning (DL) algorithms, incorporating data analytics. The research systematically analyzes the impact of hyperparameters on DL models, including CNNs, RNNs, and their variants. Diverse datasets are utilized to evaluate the robustness and versatility of the tuned models. The results demonstrate substantial improvements in IDS accuracy and efficiency, emphasizing the significance of hyperparameter tuning and data analytics. This research provides valuable insights for optimizing IDS performance, advancing network security in the face of evolving cyber threats. © 2023 IEEE.

关键词： Cyber Threats data Analytics Deep Learning algorithms Hyperparameter Tuning Intrusion Detection Systems (IDS)

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A Study on data Security and Privacy Issues in Cloud computing 3

A Study on Data Security and Privacy Issues in Cloud Computi...

引用

3rd international conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Gottipati, Kailash Nadh Peddisetty, Nikhila Pothireddy, Susmitha Botta, Gitanjani Yellamma, Pachipala Swain, Gandharba Koneru Lakshmaiah Education Foundation Department of Cse Andhra Pradesh Vaddeswaram522302 India

ISBN: (纸本)9781665462167

Cloud computing is an emerging field due to its high performance, high availability, and low cost. The cloud offers numerous services. Cloud services major use case is data storage. Cloud provides to the customer massive amounts of storage capacity. However, many cloud providers are still hesitant to implement cloud computing technology due to an inadequate security policies and protection measures, which leads to numerous challenges in cloud computing. The goal of this survey is to identify the different cryptographic algorithms eventually ensure the cloud highly secure. The main objective of this survey is to prevent unauthorized data access and to provide data security in the cloud by proposing a homomorphic encryption algorithm. © 2023 IEEE.

关键词： Cryptography

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Privacy-preserving Spatial dataset Search in Cloud 24

Privacy-preserving Spatial Dataset Search in Cloud

引用

33rd ACM international conference on Information and Knowledge Management (CIKM)

作者： Li, Pengyue Dai, Hua Wang, Sheng Yang, Wenzhe Yang, Geng Nanjing Univ Posts & Telecommun Sch Comp Sci Nanjing Peoples R China Wuhan Univ Sch Comp Sci Wuhan Peoples R China

ISBN: (纸本)9798400704369

The development of cloud computing has met the growing demand for dataset search in the era of massive data. In the field of spatial dataset search, the high prevalence of sensitive information in spatial datasets underscores the necessity of privacy-preserving search processing in the cloud. However, existing spatial dataset search schemes are designed on plaintext datasets and do not consider privacy protection in search processing. In this paper, we first propose a privacy-preserving spatial dataset search scheme. The density distribution-based similarity model is proposed to measure the similarity between spatial datasets, and then the order-preserving encrypted similarity is designed to achieve secure similarity calculation. With the above idea, the baseline search scheme (PriDAS) is proposed. To improve the search efficiency, a two-layer index is designed to filter candidate datasets and accelerate the similarity calculation between datasets. By using the index, the optimized search scheme (PriDAS+) is proposed. To analyze the security of the proposed schemes, the game simulation-based proof is presented. Experimental results on three real-world spatial data repositories with 100,000 spatial datasets show that PriDAS+ only needs less than 0.4 seconds to accomplish the search processing.

关键词： Cloud computing Spatial dataset dataset Search data Privacy

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A Classical Machine Learning Method for Locomotion and Transportation Recognition using both Motion and Location data

A Classical Machine Learning Method for Locomotion and Trans...

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ACM international Joint conference on Pervasive and Ubiquitous computing / ACM international Symposium on Wearable computing (UbiComp/ISWC)

作者： Habault, Guillaume Ung, Huy Quang Wada, Shinya KDDI Res Inc Fujimino Saitama Japan

ISBN: (纸本)9798400702006

The fifth edition of Sussex-Huawei Locomotion-Transportation (SHL) recognition challenge aims at determining the mode of locomotion and transportation of users using sensor-equipped devices. This recognition task relies on measurements captured from Inertial Measurement Unit and radio sensors placed on the user's hand, bag, hips and torso. The provided dataset presents several challenges, including its size, asynchronicity between the data types, lack of time continuity, and imbalanced distribution of the locomotion and transportation modes. To address these issues and enhance the recognition performance, our team, KDDI Research, performed data pre-processing and hand-crafted additional features. We explored different classifiers based on both Machine Learning or Deep Learning methods. Finally, the XGBoost Classifier model achieved the highest accuracy and f1-score across different validation datasets (bag, hands, hips, and torso). This model, used for our final submission on the testing dataset, achieved an average accuracy of 0.75 on these datasets.

关键词： recognition challenge gradient boosting tree locomotion mode transportation mode imu gps satellite xgboost classifier

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Uncertain data processing Algorithm for Base Station Energy Consumption 7

Uncertain Data Processing Algorithm for Base Station Energy ...

引用

7th international conference on Electronic Information Technology and Computer Engineering (EITCE)

作者： Zuo, Xinyu Nie, Zhangzhen Liu, Wenwen Wang, Nan Tianjin Normal Univ Tianjin Peoples R China

ISBN: (纸本)9798400708305

The current base station management faces challenges such as imprecise information perception, a lack of precise prediction techniques for load and energy consumption, and the absence of refined optimization methods for multi-source comprehensive scheduling. It can only achieve a quantitative complementarity of energy on the supply side, the grid side, and the load side, without considering the differences in energy quality of various forms of energy and their optimal scheduling in conversion, transmission, storage, and utilization. Simultaneously, there is a severe deficiency in crowss-temporal and spatial allocation and utilization of energy, as well as the use of edge computing and big data analytics for precise prediction and optimization scheduling. This has resulted in low overall energy utilization efficiency, high carbon emissions, and other issues. There is an urgent need to break through the key technologies of accurate perception, precise prediction, precise scheduling, and fine control in the energy Internet of Things system for base stations. The project team has put forth a scientifically sound solution, addressing issues related to precise perception of base station status, accurate load prediction, fine optimization of energy management, and precise control of comprehensive energy systems. They have proposed the "Uncertain data processing Algorithm for Base Station Energy Consumption" to tackle and solve the challenge of precise load prediction in energy IoT based on high-noise, low-quality data.

关键词： Nearest neighbor algorithms Base station Energy consumption reduction Base station status awareness

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Reversible data Hiding Scheme Based on Prediction Error Bit-plane Compression 21

Reversible Data Hiding Scheme Based on Prediction Error Bit-...

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21st IEEE international Symposium on Parallel and Distributed processing with Applications, 13th IEEE international conference on Big data and Cloud computing, 16th IEEE international conference on Social computing and Networking and 13th international conference on Sustainable computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Yuan, Guo Heng, Chao Ping, Zhai Ziqi, Liu Haibo, Li Wenpeng, Li Heilongjiang University College of Computer Science and Technology Harbin China Qiqihar University College of Computer and Control Engineering Qiqihar China

ISBN: (纸本)9798350329223

How to maximize embedding capacity is one of the current challenges in the field of reversible data hiding. A reversible data hiding scheme is proposed based on the rearrangement and compression of prediction error bit-planes in this paper. The image holder first predicts the pixels to obtain the error map, then decomposes them into bit-planes, rearranges and compresses them to maximize the embedding space, and embeds the auxiliary information and secret information into the freed space according to certain rules to generate a cryptographic image. After acquiring the cryptographic image, the image receiver extracts the embedded secret information and recovers the original image using the auxiliary information. In this paper, a rearrangement strategy based on full pixel correlation was built to maximize compression efficiency and further compress the auxiliary information to free up more embeddable space and improve embedding capacity. A large number of test has revealed that the embedding rate of this method is 30% higher on average than the current state-of-the-art algorithm. © 2023 IEEE.

关键词： Pixels

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