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Research Landscapes, Hotspots and Emerging Frontiers of Combinatorial Optimization Algorithms on Graphs: A Scientometric Analysis (1987 - 2022)

Research Landscapes, Hotspots and Emerging Frontiers of Comb...

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international conference on Big data and Information Analytics (BigDIA)

作者： Boliang Sun Hai Chen Ziqi You Laboratory for Big Data and Decision National University of Defense Technology ChangSha People’s Republic of China College of System Engineering National University of Defense Technology ChangSha People’s Republic of China

This paper presents a scientometric analysis of the research landscapes, hotspots, and emerging frontiers of combinatorial optimization algorithms on graphs from 1987 to 2022. The study investigates technical maturity, country/region collaboration, institutional cooperation, journal co-citation, dual graph overlay, and hotspot frontier analysis based on co-citation and keyword co-occurrence using tools like CiteSpace, Bibliometrics R, and VOSviewer. The analysis reveals that research in this field has entered a mature stage, and countries such as the United States, China, and Japan have produced rich output in this field. The University of Tokyo, MIT, the Chinese Academy of sciences, Shanghai Jiao Tong University, Stanford University, and the University of California have formed 10 distinct collaborative clusters. The findings also suggest that research in the field of combination optimization mainly comes from two clusters, namely, computer science and mathematics, and that cross-disciplinary knowledge is also involved. In the field of graph-based combination optimization, deep learning, machine learning, graph neural networks, and reinforcement learning are now areas of intense investigation. The frontier direction of research in this field is represented by quantum computing technologies such as coherent ising machines, hybrid quantum-classical algorithms, and quantum algorithms. The research problems in this field are categorized into three main types from a keyword perspective: graph theory problems, classical optimization problems, and open problems. The corresponding methods for solving these problems are also categorized into combinatorial optimization techniques, stochastic optimization methods, and machine learning-based optimization methods. The study highlights the need for interdisciplinary collaboration, more research on quantum computing technologies, advanced optimization techniques, and the exploration of emerging research frontiers in combination o

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Rare Pattern Mining from data Stream Using Hash-Based Search and Vertical Mining

Rare Pattern Mining from Data Stream Using Hash-Based Search...

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international conference on Intelligent Systems and Sustainable computing, ICISSC 2021

作者： Vanamala, Sunitha Sree, L. Padma Bhavani, S. Durga Department of CS TSWRDCW Warangal East Telangana Warangal India Department of ECE VNR Vignan Jyothi Institute of Technology and Science Telangana Hyderabad India School of Information Technology JNTUH Telangana Hyderabad India

ISBN: (纸本)9789811900105

Rare itemset mining is the emerging research domain in data mining. Patterns with low support and high confidence are referred to as Rare Patterns, which are very interesting compared to frequent Patterns in certain application domains like analysis of network logs, online customer purchase behavior, online banking transaction analysis, sensor data analysis, stock market data analysis. Many applications generate large volumes of the continuous data streams. To analyze such data streams and to identify rare patterns, we need efficient algorithms that can process data streams. Many research articles on rare pattern mining are available for static databases. However, it is not possible to apply the algorithms designed for static databases to data streams. Hence, we need algorithms that are specifically designed for data stream processing, to mine important rare patterns. Rare pattern mining from the data stream is in the budding stage and only a few algorithms are available. To address this, we have proposed algorithm HEclat-RPStream, an Eclat based method to mine rare patterns from a data stream using a vertical mining with bitsets. The discovered patterns are maintained in a prefix-based rare pattern tree, it uses double hashing to maintain a rare pattern in the data stream. The algorithm also uses the Breadth First Search (BFS) and Depth First Search (DFS) to discover interesting large itemsets. To handle data streams, we have used a time sensitive sliding window approach which captures most recent patterns. The pruning technique based on two items is used to optimize the performance. The experimental results of the proposed method demonstrated good performance concerning execution time and the total number of rare patterns generated. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： data mining

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Research analysis on the application of big data and artificial intelligence in Chinese medicine diagnosis

Research analysis on the application of big data and artific...

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Cloud computing, Big data and Internet of Things (3CBIT), international conference on

作者： Yanping Zhuang Xuan Zhang Simin Luo Guiling Lin Xiaoyu Tian Yi Chen Mingyin Lai Aimin Gong Department of Traditional Chinese Medicine Hainan Medical University Haikou China

ISBN: (纸本)9781665492263

The application of modern technologies such as big data and artificial intelligence in life and work has inspired scholars to apply them to Chinese medicine diagnosis. And the application of big data is just the right way to digitise and organise and classify millennia of knowledge, experience and literature on Chinese medicine diagnosis. Based on this, the robot will be made intelligent through artificial intelligence-related technology, thus promoting the transmission, development and innovation of Chinese medicine diagnosis. In this paper, the literature is obtained by searching the title "Big data TCM" or "Artificial Intelligence TCM" on CNKI. The obtained literature was read, analyzed and summarized in order to provide theoretical help for further realizing the objectification of TCM diagnosis.

关键词： Technological innovation Cloud computing Big data Internet of Things Artificial intelligence Robots

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Insights into today’s cyber trends and threats : Meta and Base Classifier Models for Network Traffic Classification

Insights into today’s cyber trends and threats : Meta and B...

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advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： Rajeev Kumar Kavita Verma Department of Computer Science Chandigarh University Gharuan (Mohali)

The interconnectivity between the computers that provide a single network with lots of advantages is termed as the network. A network is a scenario that allow to collect and connect a number of computers for exchanging the information and offering facilities to other resources. To detect malicious activities from the network various classification techniques are designed. The existing technique is SVM which is applied to classify data into malicious and normal classes. This research work designed a stacking technique in order to classify the network traffic. The base classifier which is used, is known as SVM and meta-classifier is KNN classifier. The proposed model is the hybrid for the network traffic classification. Python is employed to execute the designed technique and the metrics namely accuracy, precision and recall are considered to analyze the designed technique.

关键词： Support vector machines Computers Measurement Computational modeling Stacking Telecommunication traffic Market research

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TELL ME: Design of an Intelligence-Empowered Recommendation System 11th

TELL ME: Design of an Intelligence-Empowered Recommendation ...

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11th international conference on Computer science and its Applications, CSA 2019 and 14th KIPS international conference on Ubiquitous Information technologies and Applications, CUTE 2019

作者： Lai, Kuan-Hua Yen, Neil Y. Hung, Jason C. School of Computer Science and Engineering University of Aizu Aizu-Wakamatsu Japan Department of Computer Science and Information Engineering National Taichung University of Science and Technology Taichung Taiwan

ISBN: (纸本)9789811593420

With the advances in science and technology, the life becomes smarter and more convenient. In recent years, the Artificial intelligence (AI) is a popular topic. AI can be used in many areas such as healthcare, Automotive, Finance, etc. For instance, the smart phone could be a multi-function entity such as voice connecting, data storing, information displaying, etc. These are expected to provide more significant and precise function for each user based on personal requirement. This study, with the concern on high-quality service provision, seeks for the possibility in developing location-based service(s) based on the concept of anticipatory computing. We attempt to achieve the concept of request-free and realize the scenario of ‘act before you act.’ Hence, three main portions will be discussed. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Location based services

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Vision Based Vehicle-Pedestrian Detection and Warning System

Vision Based Vehicle-Pedestrian Detection and Warning System

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advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： Bhumika Loungani Janvi Agrawal Lija Jacob Department of Data Science Christ University Pune India

Road Sense must be respected and obeyed by both the pedestrian and the driver. Moreover, urbanization has led to a steadfast rise in the fleet of vehicles, their speed, as well as non-compliance with road safety measures, and other such factors have provoked an inescapable increase of accidents in road traffic involving pedestrians. Pedestrian collisions can be predicted and prevented. At the very basic, there has to be vehicle and pedestrian detection along with speed estimation, which can be further applied to Vehicle-Pedestrian Collisions and various emerging fields like Industrial Automation, Transportation, Automotive, Security/Surveillance, or in Dangerous environments. This paper reviews the literature on vehicle and pedestrian detection based on two significant categories: pre-processing phase and detection phase, with a detailed comparative analysis. The papers reviewed cover video-based surveillance systems.

关键词： Automation Machine vision Semantics Estimation Video surveillance Road safety Vehicle dynamics

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Research Fronts on Plant Protection Unmanned Aerial Vehicle (UAV) from the Joint of Papers and Patents

Research Fronts on Plant Protection Unmanned Aerial Vehicle ...

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Cloud computing, Big data and Internet of Things (3CBIT), international conference on

作者： Hongjun Li Xiaona Liu Xiongkui He Library China Agricultural University Beijing China CAAS Institute of Food Science and Technology Beijing China College of Science China Agricultural University Beijing China

ISBN: (纸本)9781665492263

Plant protection unmanned aerial vehicle (UAV) has been greatly developed since the new millennium, and therefore many new technologies and products have emerged. It is difficult for researchers to seek key technologies and fronts from millions of publications. This paper established a process of publication search strategy and took plant protection UAV as an example to test the strategy. The search strategy of combination of paper and patent is a feasible method for screen research fronts. Agronomy and crop science is the most widely used field of plant protection UAV, followed by forestry and horticulture. China is a leading country on plant protection UAV and contributes most of the publications. South China Agriculture University, Shenzhen Dajiang Technology Co. Ltd. and Spanish National Research Council are among the top organizations. The fronts of plant protection UAV include detection and recognition of target and variable spraying, droplet deposition and drift, flight path design and anti-crash function, releasing of nature enemy.

关键词： Patents Target recognition Horticulture Spraying Crops Organizations Forestry

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Effectiveness Analysis Of Blockchain Mechanisms Using Consensus Algorithms

Effectiveness Analysis Of Blockchain Mechanisms Using Consen...

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advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： Munish Sabharwal Fayzullo Makhmadiyarovich Nazarov Bunyod Eshtemirov School of Computing Science & Engineering Galgotias University Samarkand State University

Recently, along with the development of information technology, blockchain technology has been developing all over the world. On the basis of blockchain technology, it will be possible to develop the digital economy, ensure information security, and increase reliability in data exchange. Due to the high potential of blockchain technology, it has led to the study of its wide application in various fields. These processes have led to the creation of blockchain-based systems for various purposes. Some blockchain mechanisms can also be used to increase data reliability in information systems that provide services in computer networks. However, at the same time, most of the blockchain-based systems have several disadvantages related to their performance and security. To solve these shortcomings, it is necessary to improve the security of blockchain-based systems. The main component of any blockchain-based system is its important consensus algorithm, which in many ways makes it possible to ensure the authenticity and security of information. Consensus algorithms serve to prove the authenticity of executed transactions. The application of consensus algorithms to the blockchain technology, depending on the functional task, allows to increase the reliability of the data. In this article, the effectiveness of using consensus algorithms based on blockchain mechanisms is studied.

关键词： Computer network reliability Information security Distributed information systems Consensus algorithm Computer networks Blockchains Reliability

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Hybrid Classification Approach with Textural Feature Analysis for Plant Disease Detection

Hybrid Classification Approach with Textural Feature Analysi...

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advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： Ravindar Kumar Vikas Jindal Department of Computer Science Chandigarh University Mohali

The significant task of image processing in the domain of plant disease is of diagnosing the diseases occurred on plants because much complicated data is employed as input. Different stages are executed to detect the plant diseases using several techniques. The earlier work suggests SVM technique for diagnosing plant infections. This work projects a voting mechanism to improve various parameters like accuracy, precision and recall in comparison with the traditional methods. The projected mechanism was simulated using MATLAB. An analysis is conducted on the results with regard to some metrics. The outcomes exhibited that the projected mechanism is more effective as compared to other method. The proposed model achieves accuracy of 96 percent which approx. 3 percent higher as compared to existing SVM classifier.

关键词： Support vector machines Measurement Plant diseases Image segmentation Prediction algorithms Feature extraction Mathematical models

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Novel Approach for Group Communications using various Cybersecurity Techniques

Novel Approach for Group Communications using various Cybers...

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advances in computing, Communication & Materials (ICACCM), 2020 international conference on

作者： Jayanth Anala Dhanush Reddy Pothala Ruthvik Reddy. T Manikandan K School of Computer Science and Engineering Vellore Institute of Technology Vellore

ISBN: (纸本)9781665474405

Internet surfers and smartphone owners frequently utilize chatting software, although these software applications are built with security encryption, cyber-attacks are created to break this encryption. This paper is concerned with the finding of the best cryptography algorithm, which is better safe for assaults, simple to encrypt and has more security features. When a message is being transmitted, message security is a crucial factor to take into account. Many cryptographic methods are used to do this. We will be comparing different types of cryptographic algorithms and coming up with a better and most secure application. We opted to compare Ron Rivest, Adi Shamir and Leonard Adleman algorithm (RSA), the Triple data Encryption Standard (3DES) algorithm and the Elliptic curve cryptography(ECC) since these three algorithms are the ones that messaging platforms utilize the most. As previously said, we'll use a few metrics to compare different cryptographic methods.

关键词： Measurement Three-dimensional displays Protocols Software algorithms NIST Elliptic curve cryptography Software

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