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检索条件"主题词=software algorithms"

共 12479 条记录，以下是431-440 订阅

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Optimizing Judicial Efficiency- A software Solution for Case Listing with Customized Case Flow Management

Optimizing Judicial Efficiency- A Software Solution for Case...

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International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS)

作者： Sangeetha V Ishwariya Pani Jay Jariwala Anirudha Dargar Rishu H Rai Srestha Agarwala Department of CSE Ramaiah Institute of Technology Bangalore India

ISBN: (数字)9798331505462

ISBN: (纸本)9798331505479

The most significant disadvantage of the administration of justice in India today is delay. The Indian judiciary has always struggled with delays and pending cases. Over the past five decades, India's judiciary proposed several solutions to address delays and pending cases. In the legal domain, a dynamic balance between automation and people is required. And also due to the increased complexity and volume of legal cases, there is a critical demand for improving efficiency, accuracy, and responsiveness in the judicial process. Artificial Intelligent(AI) is used to automate the process in the legal environment to make system operations more agile. The proposed framework will optimize the case organization and prioritize the development of user-friendly software integrated with Natural Language Processing(NLP) algorithms. The main objective is to develop a web application with differentiated case flow management. Implement AI algorithms for precise case categorization, fostering a seamless user experience, and ensuring adaptability across diverse legal environments. The result of the proposed system improves case management processes with enhanced user experience, and more effective decision-making through prioritized case listings. Also support a transparent judicial system, ultimately supporting societal well-being by fostering a more responsive legal environment.

关键词： Accuracy Law Systems operation software algorithms Transforms software User experience Delays Reliability Predictive analytics

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Design and Implementation of French Teaching Assistant software Based on Machine Translation Algorithm

Design and Implementation of French Teaching Assistant Softw...

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Telecommunications and Power Electronics (TELEPE), International Conference on

作者： Yuzi Cui Xi'an Fanyi University Xi'an Shaanxi China

ISBN: (数字)9798350369212

ISBN: (纸本)9798350380002

This article aims to design and implement a French teaching assistant software based on machine translation (MT) algorithm, so as to improve the learning efficiency and learning experience of French learners. In order to achieve this goal, this article deeply studies the theoretical basis of MT technology, and analyzes the functional requirements, performance requirements, user interface and interaction design of the software in detail. In the process of software design, hierarchical architecture and modular design are adopted to ensure the stability and expansibility of the software. The implementation of MT module is based on the deep learning framework, and it is trained through a large number of bilingual corpora to improve the accuracy and fluency of translation. The experimental results show that the proposed model achieves a high BLEU score of 0.95 on the test set, which shows that its translation output is close to that of human translation. Furthermore, the algorithm has a fast response speed and remains stable even when concurrent requests increase; Moreover, there is no obvious performance degradation when the software is used concurrently by multiple users. This study not only provides an efficient learning tool for French learners, but also provides a new idea for the application of MT technology in the teaching field.

关键词： Degradation Accuracy Education software algorithms User interfaces software Stability analysis Real-time systems Machine translation Thermal stability

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Security in IoT Networks. Simulation, capture, and traffic analysis using Contiki Cooja software and RPL protocols for anomaly detection

Security in IoT Networks. Simulation, capture, and traffic a...

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Inteligencia Ambiental, Ingeniería de software y Salud Electrónica y Móvil (AmITIC), V Congreso Internacional en

作者： Parra Calderón Sergio Samuel Gutiérrez Portela Fernando Diaz Triana Oscar Augusto Semillero de investigación INNOVASIC Universidad Cooperativa de Colombia Grupo de Investigación AQUA Universidad Cooperativa de Colombia

ISBN: (数字)9798350364538

ISBN: (纸本)9798350364545

The study addresses the challenge of securing Internet of Things (IoT) networks by integrating simulation, capture, and analysis of network traffic. Currently, the security of these networks is compromised due to the lack of robust protocols and rigorous standards. The project utilizes Contiki Cooja and the RPL protocol, known for its security limitations, along with machine learning models for anomaly detection. A development framework is proposed that includes simulation scenarios with benign and malicious traffic, involving fifteen normal nodes and one vulnerable node. Attack modules such as Hello Flood, Rank Decrease, and Version Number Modification are implemented to recreate threat scenarios. Simulated traffic undergoes preprocessing to facilitate analysis and is evaluated using machine learning algorithms such as KNN, Random Forest, and Artificial Neural Networks to measure their effectiveness and performance in anomaly detection. The use of Cooja allows efficient simulation of complex network systems without requiring excessive computational resources, enabling the development of experiments in controlled and repeatable environments, thus significantly reducing costs associated with physical network deployment tests. Based on the results obtained, it was demonstrated that the algorithm with the highest accuracy in detecting attacks is Random Forest, achieving 97% detection for Hello Flood attack, 78% for Rank Decrease, and 95% for Version Number Modification. The attack with the lowest detection probability is Rank Decrease due to its behavior in the IoT network. It is important to conduct further simulations with a variety of attacks and IoT network protocols to optimally train the algorithms.

关键词： Protocols software algorithms Telecommunication traffic software Security Internet of Things Floods Anomaly detection Random forests Standards

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Systematic Literature Review of software Estimation in Global software Development

Systematic Literature Review of Software Estimation in Globa...

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Information Technology and Computing (ICITCOM), International Conference on

作者： Arifia Kasastra Ridi Ferdiana Syukron Abu Ishaq Alfarozi Department of Electrical and Information Engineering Universitas Gadjah Mada Yogyakarta Indonesia

ISBN: (数字)9798350379839

ISBN: (纸本)9798350379846

software development is always characterized by certain parameters. In the context of Global software Development (GSD), one of the key challenges for software developers is predicting the development effort of a software system based on developer details, size, complexity, and other measures. When development teams are spread apart, it presents a number of challenges. Communication and coordination become more complex, leading to hidden costs associated with managing software development across different locations. Hence, the software estimation models used for co-located software development are not suitable for estimation in GSD. software estimation in GSD has become a crucial area of research. This paper presents the findings of a systematic literature review on software estimation in the context of GSD, with the aim of providing researchers and practitioners with a comprehensive understanding of the current state of the art in this area. Over the last decade, only a handful of researchers have devoted their attention to estimating efforts in GSD. The results show that estimation methods in GSD have not been widely explored. The COCOMO-II method is the most widely adapted method for GSD, and Time Zone is the most agreed Cost Driver for GSD.

关键词： Costs Accuracy Computational modeling software algorithms Estimation software systems software software measurement Standards software development management

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Runtime Embedded software Malfunction Detection Based on Profile Generation of Current-Level Pattern Matcher

Runtime Embedded Software Malfunction Detection Based on Pro...

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International Conference on Electronics, Information and Communications (ICEIC)

作者： Sanghoon Lee Daejin Park Software Disaster Research Center Kyungpook National University Daegu Korea School of Electronics Engineering Kyungpook National University Daegu Korea

ISBN: (数字)9798331510756

ISBN: (纸本)9798331510763

As the demand for high-performance embedded systems grows, the complexity of embedded software increases, potentially leading to software errors and system malfunctions. In this paper, we propose a system to monitor malfunctions caused by software errors by extracting the normal operating current patterns of a target embedded system (TES) in actual operation and comparing them with the real time current TES data. By using a monitoring embedded system (MES), the normal operating current patterns can be automatically generated, making it easy to promptly detect malfunctions in the exposed TES. When the MES detects a malfunction, it applies algorithms to efficiently recover the malfunctioning TES. The proposed system employs two algorithms when a malfunction in the TES is detected. First, upon detecting a malfunction, the MES resets TES to restore it to normal operation. If the malfunction persists after the reset, the MES controls the TES to completely halt operation. Second, if a malfunction is detected, it stabilizes the abnormal current state to the normal current state through a proportional integral derivation (PID) control. The TES typically consumes about 75 to 95 mA of current during normal operation. The MES applies a specific error detection rate of 20 %, considering any consumption above 114 mA a malfunction and using the algorithms to control it. The system has been verified to reset TES upon detecting a malfunction and to stabilize the operational current through PID control during abnormal current states.

关键词： Embedded systems PI control Runtime software algorithms Real-time systems PD control Protection Monitoring Pattern matching Embedded software

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Development and Application of an Intelligent Clash Check and Handling System Based on PDMS Plant Design software

Development and Application of an Intelligent Clash Check an...

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Artificial Intelligence and Digital Technology (ICAIDT), International Conference on

作者： Qian Hao Shaoyi Dong Shandong Electric Power Engineering Consulting Institute Corp. LTD Jinan China

ISBN: (数字)9798350386905

ISBN: (纸本)9798350386912

This paper proposes an intelligent clash check and handling system tailored for the 3D design software PDMS in the power industry, aiming to enhance its clash check capabilities. The system comprises multiple modules, including clash check configuration, scheduled and real-time clash checks, data storage, data retrieval, and processing modules. It allows users to set clash check times and objects based on design progress, perform clash checks automatically or manually, and store the results. Additionally, it utilizes algorithms to avoid redundant processing of clash data. Designers can retrieve and view clash data, formulate handling plans, and input the results, providing support for data traceability and design review. The application of this system will improve the functional integrity of the PDMS software in terms of clash check, providing an efficient tool for digital design in the power industry.

关键词： Three-dimensional displays Reviews software algorithms Memory software Power industry Real-time systems

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Experiences and Proposals of Use of Generative AI in Advanced software Courses

Experiences and Proposals of Use of Generative AI in Advance...

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IEEE Education Engineering (EDUCON)

作者： Daniel Palacios-Alonso Jaime Urquiza-Fuentes J. Ángel Velázquez-Iturbide Julio Guillén-García Department of Computing and Statistics Universidad Rey Juan Carlos Madrid Spain

ISBN: (数字)9798350394023

ISBN: (纸本)9798350394030

The last year, we have witnessed the popularization of generative artificial intelligence. Its output includes text, code, image, audio, speech, voice, music, and video. Therefore, it impacts education courses where students are required to elaborate on any of these artifacts. In particular, the generation of code affects informatics courses, where assignments usually ask students to develop and deliver programming code. The impact of generative artificial intelligence on informatics courses has been mainly studied for introductory programming courses. These studies have shown that generative artificial intelligence is able to produce highly sophisticated programs, but also that its results and rationale can be inaccurate. Moreover, the impact of generative artificial intelligence has not been studied for other informatics subjects. In this paper, we present our preliminary experience and proposals on three advanced software courses, namely video games, advanced algorithms and language processors. For the video games course, we present the opportunities of use of generative artificial intelligence and the results of a survey conducted with students on their use to obtain different media products. For the algorithms course, we present the result of a session driven by the instructor on different design techniques, showing the merits and demerits of the answers generated. For the language processors course, a proposal of use of generative artificial intelligence is presented, broken down into the parts of a typical language processor. The paper concludes with some suggestions for instructors.

关键词： Surveys Video games Program processors Codes Generative AI software algorithms software

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Hardware-software Co-Design for Path Planning by Drones

Hardware-Software Co-Design for Path Planning by Drones

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Ayushi Dube Omkar Patil Gian Singh Nakul Gopalan Sarma Vrudhula School of Computing and Augmented Intelligence Arizona State University Tempe United States

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

This work consists of two main components: designing a hardware-software co-design, MT + , for adapting the Mikami-Tabuchi algorithm for on-board path planning by drones in a 3D environment; and development of a specialized custom hardware accelerator CDU, as a part of MT + , for parallel collision detection. Collision detection is a performance bottleneck in path planning. MT + reduces the delay in path planning without using any heuristic. A comparative analysis between the state-of-the-art path planning algorithm A* and Mikami-Tabuchi is performed to show that Mikami-Tabuchi is faster than A* in typical real-world environments. In custom-generated environments, path planning using Mikami-Tabuchi shows a latency improvement of 1.7× across varying average sizes of obstacles and 2.7× across varying obstacle density over state-of-the-art path planning algorithm, A*. Further, the experiments show that the co-design achieves speedups over a full software implementation on CPU, averaging between 10% to 60% across different densities and sizes of obstacles. CDU area and power overheads are negligible against a conventional single-core processor.

关键词： Three-dimensional displays software algorithms Parallel processing Path planning software Delays Collision avoidance Hardware acceleration Intelligent robots Drones

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Enhanced DDoS Detection in software Defined Networking Using Ensemble-Based Machine Learning

Enhanced DDoS Detection in Software Defined Networking Using...

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International Conference on Electrical Engineering and Information & Communication Technology (ICEEICT)

作者： Ms. Bithi Md. Alamgir Hossain Md. Kawsar Ahmed Rabeya Sultana Ishtiaq Ahammad Md Shihabul Islam Department of Computer Science and Engineering Prime University Dhaka Bangladesh Department of Computer Science and Engineering Northern University Bangladesh Dhaka Bangladesh

ISBN: (数字)9798350385779

ISBN: (纸本)9798350385786

The burgeoning adoption of software Defined Networking (SDN) has revolutionized network management, yet it introduces unprecedented challenges, notably the susceptibility to Distributed Denial-of-Service (DDoS) attacks. Recognizing this imperative, our research delves into fortifying SDN security, proposing a novel approach that marries machine learning prowess with the intricacies of SDN architecture. This study endeavors to bolster DDoS detection within SDN environments, strategically leveraging an ensemble-based Random Forest (RF) algorithm and Recursive Feature Elimination. The overarching goal is to enhance the efficacy of SDN security measures, providing a dynamic defense against evolving DDoS threats. An implementation process unfolds through comprehensive data preprocessing, featuring the strategic selection of key features via Recursive Feature Elimination. Central to our approach is the application of an ensemble-based Random Forest algorithm, which has been rigorously trained using a dedicated dataset tailored for software Defined Networking. A comprehensive assessment follows, where critical performance indicators such as Recall, Accuracy, Precision, F-1 Score, and Area Under the Curve (AUC) substantiate the reliability of our method. The outcome is a paradigm shift in DDoS detection within SDN. Our ensemble-based RF algorithm not only exhibits commendable accuracy but also outperforms traditional methods across key metrics. The strategic feature selection contributes not only to heightened efficiency but also bolsters the overall resilience of SDN networks against DDoS incursions. Beyond the confines of conventional methodologies, this model, attaining almost 100% accuracy, heralds a milestone in SDN security.

关键词： Radio frequency Heuristic algorithms software algorithms Feature extraction Denial-of-service attack Security software defined networking

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Examining the Accuracy of AI Detection software Tools in Education

Examining the Accuracy of AI Detection Software Tools in Edu...

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Intelligent Data Science Technologies and Applications (IDSTA), International Conference on

作者： Mohanad Halaweh Ghaleb El Refae College of Business Al Ain University Al Ain UAE

ISBN: (数字)9798350354751

ISBN: (纸本)9798350354768

Educators have raised concerns about the utilization of ChatGPT in generating unoriginal text and the possibility of plagiarism. To address these concerns, various AI text detection software tools have been developed to evaluate whether a text is AI generated or human generated. The aim of this research is to empirically examine the accuracy of AI detection tools in identifying AI-generated texts. An experiment was conducted using textual data generated by ChatGPT, which was assessed using Turnitin and four other AI detection tools. Through multiple iterations and interventions, the text was paraphrased by ChatGPT until it appeared original and could not be detected as AI-generated by Turnitin’s AI detection tool. The findings revealed that all the AI detection software tools that were examined failed to detect the AI-generated text by ChatGPT in the final iteration. The findings provide valuable insights that have implications for various stakeholders, including educators, researchers, and AI text detection software developers. Based on the tools examined, educators and researchers do not need to set a specific threshold or percentage, including 0%, to determine what qualifies as acceptable AI-generated text. This is because establishing such a threshold can be misleading, considering the current limitations in the algorithms of these tools. Furthermore, the data generated in this paper can provide a solid basis for replicated research or software testing and assessment. It can be utilized to evaluate the accuracy of alternative AI detection tools and any future advancements in the tools mentioned in this investigation.

关键词： software testing Accuracy Plagiarism software algorithms Text detection Chatbots Solids Stakeholders Artificial intelligence software tools

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