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检索条件"主题词=software algorithms"

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Text Tone Analysis in Social Networks: Research, Evaluation and Comparison of Algorithm Performance

Text Tone Analysis in Social Networks: Research, Evaluation ...

引用

International Russian Smart Industry Conference (SmartIndustryCon)

作者： A. I. Martyshkin A. A. Zotkina V. V. Zuparova Penza state technological University PSTU Penza Russia

ISBN: (数字)9798331511241

ISBN: (纸本)9798331511258

The article is devoted to a comprehensive study of methods for analyzing the tonality of textual information in order to improve the effectiveness of information security monitoring and public opinion management in the digital environment. The authors conduct a comparative analysis of approaches based on lexicographic methods, machine learning with a teacher (naive Bayesian classifier and maximum entropy method) and systems using a set of rules. The study includes a detailed review of the algorithmic features, advantages and limitations of each method, as well as an evaluation of their adaptability when the topics of the analyzed texts change. The results of the comparative analysis show that machine learning methods demonstrate higher performance in terms of accuracy, completeness and F-measure compared to rule set based systems. The authors also draw attention to the dependence of the results on the specifics of the training sample and the peculiarities of the contextual application of algorithms. The practical significance of the work lies in the possibility of using the developed methods for the rapid detection of negative content and predicting changes in classification accuracy when adapting to new topics, which is especially relevant for information security and management decision support in a dynamically developing digital environment.

关键词： Machine learning algorithms Accuracy Social networking (online) Heuristic algorithms software algorithms Information security Machine learning Prediction algorithms Entropy Classification algorithms

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Unraveling the Potential of AI Towards Digital and Green Transformation in Kiwifruit Farming

Unraveling the Potential of AI Towards Digital and Green Tra...

引用

International Conference on software, Telecommunications and Computer Networks (SoftCOM)

作者： Theofanis Kalampokas Eleni Vrochidou Maria Tzampazaki George A. Papakostas Department of Informatics MLV Research Group Democritus University of Thrace Kavala Greece

ISBN: (数字)9789532901382

ISBN: (纸本)9798350354614

Growing awareness of the population of health benefits related to kiwifruit consumption, led to its increased market demand. As demands grow, producers need efficient and sustainable practices to provide enhanced outputs, in terms of yield quality and quantity, by optimally managing their inputs, in terms of resources. To achieve the latter goals, digitalization and precision operations of farming practices introduced Precision Agriculture (PA). PA have harnessed the potential of technological improvements to revolutionize kiwifruit industry. To this end, in this work, a narrative review is conducted to gather information and add to existing research on PA of kiwifruit. Research findings are analyzed and interpreted towards allowing readers to capture the broad range of current status and perspective of AI in kiwifruit farming. Results indicate a lack of public datasets, and limited multi-modal data fusion techniques; AI-based robotic automation for harvesting and pollination is reported, while automation for weeding, pruning and spraying has not yet been developed, leaving space for more innovations towards the overall digital and green transformation of the kiwifruit industry.

关键词： Technological innovation Automation Service robots Reviews software algorithms Spraying software Telecommunications Artificial intelligence Farming

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Development the Reinforcement Learning Model for Sources Identification of H2S Industrial Emissions

Development the Reinforcement Learning Model for Sources Ide...

引用

International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM)

作者： Aleksey Kychkin Igor Chernitsin Olga Vikentyeva Centre of Data and Artificial Intelligence Engineering National Research University Higher School of Economics Perm Russia

ISBN: (数字)9798331512194

ISBN: (纸本)9798331512200

Industry 4.0 concept focuses on sustainability problem that requires to control air emissions, especially for harmful substances like H2S, and reduction their impact on nature by using environmental monitoring and sources identification systems. This task requires solving inverse problem of dispersion models, which should establish complex mathematical dependences between the sensor data, the location and emission rate on a chimney in dynamics. In our research we propose the reinforcement learning (RL) model for H2S sources identification on given example of real-life data. The search algorithm is based on the Q-Learning that uses dispersion simulations on industrial emissions within different rates, meteorological data and landscape specifics for training. Python library and visualization tool as the industrial software application has been developed, with the help of which it is possible to analyze the possible H2S sources on large industries.

关键词： Training Q-learning software algorithms Systems architecture Companies Data models Mathematical models software Sustainable development Dispersion

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Research on Bitcoin-Compatible Off-Chain Multi-Channel Crowdfunding Method with Transaction Amount Privacy

Research on Bitcoin-Compatible Off-Chain Multi-Channel Crowd...

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2025 2nd International Conference on algorithms, software Engineering and Network Security (ASENS)

作者： Ruijia Zang Zhengyuan Deng Shaofen Fan Yumeng Xie Jing Liang Guo Zhao Boying Chi Xuming Han Network & Educational Technology Center Jinan University Guangzhou China

ISBN: (数字)9798331520298

ISBN: (纸本)9798331520304

The emergence of blockchain technology has solved the drawbacks of the traditional Internet crowdfunding model. Its decentralized feature effectively avoids single-point failures brought about by the centralized architecture. Moreover, on-chain transactions are publicly transparent, traceable and immutable, providing security guarantees for transactions. However, the issues of blockchain scalability and scripting languages have always been the focus of attention in both the industrial and academic *** scalability issue refers to the overly long confirmation time for on-chain transactions, and the transaction throughput is difficult to meet the requirements of actual application scenarios, with users facing high transaction fee expenditures. To address these two issues, the payment channel scheme implemented with the Bitcoin-Compatible scripting language is currently one of the most effective *** the scheme proposed in this paper, the investors and the crowdfunding initiators conduct transactions through the payment channels constructed by both parties using the Bitcoin-Compatible scripting language and meet the application functions of crowdfunding. That is, only when the investment amounts participating in the crowdfunding meet the required amounts of the crowdfunding can the transaction be regarded as successful, and the funds will be transferred to the initiator. If the amounts do not meet the initiator's requirements, all the funds will be returned to the investors.

关键词： Privacy Crowdfunding Scalability software algorithms Receivers Throughput Blockchains Proposals Protection software engineering

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Adaptive Security Framework Integrated Anomaly Detection for Automated Seaports

Adaptive Security Framework Integrated Anomaly Detection for...

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2025 2nd International Conference on algorithms, software Engineering and Network Security (ASENS)

作者： Lei Guo Jun Xu Cloud Data Center/Network Security Center COSCO Shipping Technology Co.Ltd. China

ISBN: (数字)9798331520298

ISBN: (纸本)9798331520304

The increasing automation of maritime ports introduces complex cybersecurity challenges that conventional industrial control system protections fail to address. This paper proposes a cybersecurity framework for automated ports integrating real-time anomaly detection, adaptive risk scoring, and safety-constrained response optimization. The system demonstrates 92.7% detection rates and 2.3% false positive rate, enabled by continuous Bayesian updates and quantile regression. Results validate effective protection for port automation systems against evolving cyber-physical threats without disrupting critical operations.

关键词： Automation Remotely guided vehicles software algorithms Seaports Real-time systems Protection Computer security Anomaly detection Optimization software engineering

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A Stripped Binary Code Vulnerability Detection Method Based on Sensitive Function Identification

A Stripped Binary Code Vulnerability Detection Method Based ...

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2025 2nd International Conference on algorithms, software Engineering and Network Security (ASENS)

作者： Binghong Liu Wei Wang Yiming Zhang Xingyu Huang Jianhua Zhou Wei Huo Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9798331520298

ISBN: (纸本)9798331520304

With the increasing number of embedded devices, the demand for these devices is continually growing. Even in today's fast-paced technological era, many vulnerabilities still exist in embedded devices. Firmware vendors often release code packages in binary form due to reasons such as copyright and security concerns, and they typically remove symbol information upon release, complicating reverse analysis. Symbol information is fundamental to analyzing binary code in firmware, which is crucial for the accuracy, precision, and recall of vulnerability detection in binary code. The absence of symbol information, coupled with optimizations during compilation and the mixed arrangement of data and instructions, further complicates vulnerability detection in binary code. To address these issues, we propose a method for stripped binary code vulnerability detection based on sensitive function identification, which extracts attributes from the stripped binary code in the target firmware and uses the Euclidean distance metric algorithm to identify and label sensitive functions in the binary code, detecting five types of potential vulnerabilities. Experimental results show that vulnerability detection based on sensitive function identification is feasible and significantly improves accuracy, precision, and recall.

关键词： Measurement Accuracy Scalability software algorithms Symbols Binary codes Euclidean distance Robustness Microprogramming software engineering

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Research on Traffic Scheduling Algorithm Based on Link Criticality in SDN Environment

Research on Traffic Scheduling Algorithm Based on Link Criti...

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Advanced algorithms and Control Engineering (ICAACE), International Conference on

作者： Lingjiao Wang Meiling Song Hua Guo School of Automation and Electronic Information Xiangtan University Xiangtan China

ISBN: (数字)9798331535087

ISBN: (纸本)9798331535094

software-Defined Networks (SDN) traffic scheduling algorithms are critical for improving network performance and resource utilization, achieving energy efficiency, and load balancing. In high-traffic environments, the traffic of some links in the network approaches or exceeds its capacity limit, triggering link congestion and possible link failures. For this reason, this paper proposes a link criticality-based traffic scheduling algorithm, TSA-LC, which implements a rerouting strategy mainly for large flows on congested links in the network. First, the KSP algorithm is used to filter the feasible shortest path set for large flows on congested links. Then, link criticality metrics related to parameters such as link utilization and median centrality are designed to calculate the criticality of all paths in the path set. Finally, these paths are evaluated comprehensively by the fitness function, and the path with the highest fitness is selected as the rerouting path. The algorithm effectively reduces the risk of link congestion and failure and improves the overall performance of the network.

关键词： Measurement Scheduling algorithms Heuristic algorithms software algorithms Filtering algorithms Load management Energy efficiency Classification algorithms Resource management software defined networking

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Research on Intelligent Personalized Exercise Path Recommendation Algorithm Based on Traffic and Health Big Data

Research on Intelligent Personalized Exercise Path Recommend...

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2025 2nd International Conference on algorithms, software Engineering and Network Security (ASENS)

作者： Yinghan Hong Sirui Liang Guizhen Mai Hanghao Luo Siyuan Luo Hui Cao Feijiang Huang School of Computer Science (School of Artificial Intelligence) Guangzhou Maritime University Guangzhou China School of Computer and Information Engineering Hanshan Normal University Chaozhou China

ISBN: (数字)9798331520298

ISBN: (纸本)9798331520304

The personalized exercise path recommendation problem based on traffic and health big data is a key challenge in intelligent health management, requiring multi-dimensional optimization of factors like traffic, environment, and user health. To address high-dimensional challenges, this paper proposes the Intelligent Personalized Exercise Path Recommendation Algorithm (IPEPR Algorithm), integrating traffic flow and health data for optimized path recommendations. IPEPR algorithm dynamically adjusts routes in real-time based on user needs and changing conditions. Comparative experiments across different Body Mass Index (BMI) groups, using the traditional A-star algorithm as a control, show that IPEPR algorithm improves recommendation accuracy and real-time performance. Particularly in overweight and obese groups, it significantly increases calorie consumption and optimizes exercise intensity and efficiency, demonstrating its advantages in personalized exercise planning.

关键词： Adaptation models Accuracy Heuristic algorithms software algorithms Big Data Real-time systems Data models User experience Planning Artificial intelligence

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A Novel Routing Equipment Fault Diagnosis Scheme Based On Machine Learning In Short Distance Communication Scenario

A Novel Routing Equipment Fault Diagnosis Scheme Based On Ma...

引用

IEEE Information Technology and Mechatronics Engineering Conference (ITOEC)

作者： Zhaoxiong Zhou Wugong Tan Chao Pan Jinyu Li China Mobile Internet of Things Co. Ltd. Chongqing China School of Tropical Agriculture and Forestry Hainan University Danzhou China

ISBN: (数字)9798331529482

ISBN: (纸本)9798331529499

In the short distance communication scenario, the stable and efficient operation of routing equipment is very important, but there is no method to achieve high fault identification accuracy. In this paper, a fault diagnosis scheme for routing equipment based on machine learning in short distance communication scenarios is proposed. A generative adversarial network based on quantum state optimization is adopted for data augmentation, a neural network based on locally sensitive honeypot optimization algorithm is adopted to effectively identify and extract key features, and an improved autoencoder is adopted for feature dimension reduction. The classification performance is optimized by support vector machine based on covariate matrix. The experimental results show that this scheme can achieve a high accuracy of fault identification of routing equipment, and has greater deployment and promotion value.

关键词： Fault diagnosis Accuracy software algorithms Neural networks Quantum state Routing Feature extraction software Classification algorithms Optimization

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Optimization of Low-Latency Multi-Service Coordination in Virtual Power Plants Based on Dynamic Spectrum Sharing

Optimization of Low-Latency Multi-Service Coordination in Vi...

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Advanced algorithms and Control Engineering (ICAACE), International Conference on

作者： Chen Wu Lei Zhang Wei Huang Yingbin Dai Dongbo Qu Lirui Xie Yixuan Chen Na Pu Run Huang Deyong Liu Junming Wang Junwei Yang Grid Planning Research Center Yunnan Power Grid Co. Ltd Kunming China Qujing Power Supply Bureau Yunnan Power Grid Co. Ltd. Qujing China LongShine Technology Group Co. Ltd. Wuxi China

ISBN: (数字)9798331535087

ISBN: (纸本)9798331535094

Taking distributed energy in virtual power plants as the research object, the influencing factors of generalized data transmission time and the delay requirements of various businesses in virtual power plants were explored. A priority queue model for spectrum switching was constructed, and the improved MRFO-GBO algorithm was adopted to achieve flexible switching between different spectra. The relationship simulation experiment between channel occupancy rate and generalized data transmission time was designed using Matlab software. The results show that as the channel occupancy rate of cognitive users increases, the generalized data transmission time shows an increasing trend. By comparing MRFO-GBO with other algorithms, it can be found that this algorithm not only has a fast iteration speed, but also has a better optimal solution than other algorithms. Finally, taking the distributed energy regulation business as an example, when the busy probability of PU is in the range of (0, 0.9), CU can meet the delay requirements of distributed energy regulation business.

关键词： Heuristic algorithms software algorithms Switches Virtual power plants software Regulation Delays Data communication MATLAB Business

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