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检索条件"任意字段=Conference on Artificial Intelligence and Machine Learning in Defense Applications IV"

共 594 条记录，以下是71-80 订阅

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Tactical Agility for AI-Enabled Multi-Domain Operations 5

Tactical Agility for AI-Enabled Multi-Domain Operations

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conference on artificial intelligence and machine learning for Multi-Domain Operations applications V

作者： Sturzinger, Eric M. George, Shilpa A. Satyanarayanan, Mahadev Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (数字)9781510661936

ISBN: (纸本)9781510661929;9781510661936

Commanders must remain agile and adaptive in the future artificial intelligence (AI)-enabled multi-domain battlespace, where critical decisions are made at the tactical edge. Over-reliance on static, cloud-centric approaches to machine learning Operations (MLOps) compromises such agility and adaptability. These systems must operate in a dynamic threat environment, and learn to detect novel threats during operation. They must be able to perform this learning through the execution of tactical MLOps under austere and degraded conditions, especially limited wireless network bandwidth. In response to these requirements, this paper describes Hawk, a system that leverages edge proximity for rapid and iterative execution of the Observe stage of the Observe-Orient-Decide-Act loop. Central to this architecture is the use of tactical cloudlets. These mini data centers provide cloud-like computing resources without the communication latency to exascale data centers. Hawk enables a human to guide MLOps at low cognitive load, thus enabling an operational objective to be achieved at speed and scale while remaining usable and explainable.

关键词： Sensing machine learning Video Analytics Cloudlet Edge Computing Cloud Computing Low Bandwidth High Latency Austere Environments Hawk

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Influence of Video Dynamics on EEG-based Single-Trial Video Target Surveillance System 12

Influence of Video Dynamics on EEG-based Single-Trial Video ...

引用

12th International Winter conference on Brain-Computer Interface (BCI)

作者： Kwak, Heon-Gyu Kim, Sung-Jin Han, Hyeon-Taek Jeong, Ji-Hoon Korea Univ Dept Artificial Intelligence Seoul South Korea Chungbuk Natl Univ Dept Comp Sci Cheongju South Korea

ISBN: (纸本)9798350309430

Target detection models are one of the widely used deep learning-based applications for reducing human efforts on video surveillance and patrol. However, the application of conventional computer vision-based target detection models in military usage can result in limited performance, due to the lack of sample data of hostile targets. In this paper, we present the possibility of the electroencephalography-based video target detection model, which could be applied as a supportive module of the military video surveillance system. The proposed framework and detection model showed prospective performance achieving a mean macro F ss of 0.6522 with asynchronous real-time data from five subjects, in a certain video stimulus, but not on some video stimuli. By analyzing the results of experiments using each video stimulus, we present the factors that would affect the performance of electroencephalography-based video target detection models.

关键词： electroencephalography single-trial event-related potentials detection video target surveillance asynchronous brain-computer interface systems.

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Expediting the Targeting Process Responsibly using artificial intelligence 4

Expediting the Targeting Process Responsibly using Artificia...

引用

conference on artificial intelligence and machine learning for Multi-Domain Operations applications iv

作者： Lowrance, Christopher J. Harris, Elliott R. Pfaff, C. Anthony US Army War Coll 47 Ashburn Dr Carlisle PA 17013 USA

ISBN: (纸本)9781510651036;9781510651029

Absent the use of artificial intelligence (AI), each stage of the targeting process requires human input, which consumes time - a precious resource during high-intensity conflict. To counter that problem, the application of AI and related technologies could rapidly automate certain steps within the targeting process. The challenge, however, becomes how to responsibly speed up the process using AI, while also maintaining appropriate levels of human oversight based on level of risk acceptable to the commander. We address this challenge with the introduction of a fuzzy logic controller that is designed to automatically adapt and optimize the level of human-machine interaction based on the current targeting conditions. The logic controller uses the confidence of the AI algorithm at each stage of the targeting process, as well as the commander's risk tolerance at the time, to automatically determine what stages of the targeting process can be trusted for AI to accelerate and which ones require explicit human verification. The final stage of verifying and authorizing a fire mission is reserved for humans only to make.

关键词： AI-enabled targeting process military artificial intelligence Department of defense modernization fires warfighting function fuzzy control and decision making

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Explainable artificial intelligence in Manufacturing Operations: A Bibliometric Analysis 43rd

Explainable Artificial Intelligence in Manufacturing Operati...

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43rd IFIP WG 5.7 International conference on Advances in Production Management Systems (APMS)

作者： Presciuttini, Anna Cantini, Alessandra Portioli-Staudacher, Alberto Politecn Milan Dept Management Econ & Ind Engn I-20156 Milan Italy

ISBN: (纸本)9783031716355;9783031716331;9783031716324

This paper presents a bibliometric analysis of explainable artificial intelligence (XAI) within the manufacturing sector, with a focus on the critical areas of quality, maintenance, and production. Despite the increasing integration of XAI in industrial applications, a bibliometric exploration of its impact across these specific dimensions remains uncharted. Our study fills this gap by employing bibliometric methods to map the landscape of XAI research in manufacturing, analyzing publication patterns and thematic evolutions. Understanding this landscape is crucial, as it not only highlights the current state and trajectory of XAI applications in manufacturing but also identifies key areas where further innovation and investigation can significantly enhance efficiency, transparency, and decision-making processes in the industry. Utilizing the Bibliometrix R-package and data from the Scopus database, we analyze 107 publications from 2019 to 2024. We chart the intellectual trajectory of XAI, delving into predominant themes and observing a research progression from foundational machine learning to its sophisticated applications, culminating in Industry 4.0 innovations. The analysis reveals an academic landscape where explainability is increasingly intertwined with the technological advances of smart manufacturing, spotlighting key topics and their evolution that reflect the field's dynamic nature. This investigation offers a novel lens on the bibliometric trends shaping the development of transparent, intelligent systems within the manufacturing sector.

关键词： Explainable artificial intelligence Manufacturing operations Literature Review Advanced Analytics machine learning

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Interpretable Remaining Useful Life Prediction Based on Causal Feature Selection and Deep learning 20th

Interpretable Remaining Useful Life Prediction Based on Caus...

引用

20th International conference on Intelligent Computing (ICIC)

作者： Li, Min Luo, Meiling Ke, Ting Tianjin Univ Sci & Technol Coll Artificial Intelligence Tianjin 300457 Peoples R China

ISBN: (纸本)9789819756711;9789819756728

Robust feature selection is crucial for enhancing the credibility and interpretability of machine learning models. Traditionally, deep learning networks directly learn features from raw data. However, the multidimensional data collected by sensors in dynamic systems may contain redundancy, noise, and high dimensionality, making it challenging to select the optimal feature set. To tackle this concern, we introduce a feature selection prediction framework based on causal discovery algorithms. It first identifies key features and learns their causal relationships, providing more interpretable and effective features. Subsequently, deep learning models are employed for prediction. This paper introduces a long short-term memory model that incorporates causal discovery and attention mechanisms. Our framework is applied to predict the remaining useful life (RUL) on the C-MAPSS dataset, demonstrating that causal feature selection contributes to the enhanced reliability, interpretability, and generalization of the RUL prediction model. Our approach outperforms traditional feature-unselected algorithms in terms of both generalization performance and interpretability.

关键词： Remaining Useful Life Prediction Causal discovery Feature election Attention Mechanism

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Advanced Mortality Prediction in Adult ICU: Introducing a Deep learning Approach in Healthcare 20th

Advanced Mortality Prediction in Adult ICU: Introducing a De...

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20th International conference on artificial intelligence applications and Innovations (AIAI)

作者： Simopoulos, Dimitrios Kosmidis, Dimitrios Koutsouki, Sotiria Bonnotte, Nicolas Anastassopoulos, George Democritus Univ Thrace Dept Med Med Informat Lab Alexandroupolis 68100 Greece Int Hellen Univ Dept Nursing Didymoteicho 68300 Greece Gen Hosp Kavala Intens Care Unit Kavala 65500 Greece Akkodis Germany Solut GmbH Res Div Flugfeld Allee 12 D-71063 Sindelfingen Germany

ISBN: (纸本)9783031632136;9783031632112;9783031632105

Accurate mortality prediction in Intensive Care Units (ICUs) is crucial for optimizing patient care and resource allocation. Traditional prediction models that are essential in guiding clinical decision-making and resource allocation in critical care settings, such as Acute Physiology and Chronic Health Evaluation (APACHE) and Simplified Acute Physiology Score (SAPS), while effective, have limitations that restrain their adaptability and prediction efficacy. Recent paces in machine learning (ML), especially in deep learning, present promising opportunities for enhancing prediction accuracy. This study provides a comprehensive evaluation of ML algorithms, encompassing deep learning, for predicting mortality in adult ICUs using certain clinical inputs. Various ML techniques underwent thorough examination, preprocessing, and hyperparameter optimization processes. An ensemble approach combining multiple models, such as CatBoost, LightGBM, Feedforward Neural Networks (FNNs) and Extra Trees, yielded higher performance, achieving an AUC of 0.873 and an accuracy of 81.82%, compared to the respective metrics delivered by the traditional APACHE iv model (AUC: 0.819). The current study bridges gaps in current research by exploring advanced ML methodologies and demonstrates the potential of deep learning in ICU mortality prediction. In doing so, it makes a significant stride towards advancing predictive analytics in healthcare.

关键词： Mortality prediction Intensive Care Units Healthcare machine learning Deep learning Ensemble learning

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Deep learning in Biometric Recognition: applications and Challenges 2

Deep Learning in Biometric Recognition: Applications and Cha...

引用

2nd IEEE International conference on Sensors, Electronics and Computer Engineering, ICSECE 2024

作者： Jiang, Zhengshuai Li, Haoyang Sui, Xinyi Cai, Yutian Yu, Guoxin Zhang, Wei School of Control Science and Engineering Shandong University Shandong Jinan250061 China The High School Affiliated to Renmin University of China Haidian Beijing100080 China Comprehensive Service Center of the Politics and Law Committee of the Shandong Provincial Committee Shandong Jinan250061 China

ISBN: (纸本)9798350373646

Biometric recognition serves as a crucial method for security authentication. With the continuous advancement of artificial intelligence technologies, the application of deep learning (DL) in constructing of biometric recognition systems has become increasingly prevalent. This manuscript delivers a comprehensive overview of the DL techniques used in biometric recognition systems. We systematically analyze the pros and cons of traditional machine learning compared to DL methods in tackling challenges within biometric recognition. Subsequently, the latest DL-based applications in biometric recognition filed and the SOTA DL techniques are summarized. Furthermore, a thorough introduction of adversarial attack technologies, their implications as well as the defense strategies are discussed. Finally, we offer insights into the current state and prospective trends of biometric recognition research. © 2024 IEEE.

关键词： Adversarial machine learning

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Analysis of machine learning-based Human Activity Prediction Model for Assistive Exoskeleton

Analysis of Machine Learning-based Human Activity Prediction...

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2024 International conference on artificial intelligence, Computer, Data Sciences, and applications, ACDSA 2024

作者： Sahoo, Shubhashisa Sharma, Mayank Yadish, Aniket Kumar, Sawan Centre for Artificial Intelligence and Robotics Bangalore India Renaissance Technology Private Limited New Delhi India Pes University Dept. of Mechanical Engineering Banglore India

ISBN: (纸本)9798350394528

Musculoskeletal disorders significantly affect the general population, especially those involving lower back discomfort. This is the most important reason for the decline in work efficiencies in industrial and defense applications. Under these conditions, an active exoskeleton will be quite helpful. Selecting an appropriate control strategy for an active exoskeleton requires the prediction of human motion profiles. However, the complex and dynamic nature of the exoskeleton model limits the model-based approach. Thus, data-driven parameter estimation models are preferred in this scenario. Human Activity Recognition (HAR) research has recently focused on deep neural networks. There is a noticeable delay between the observed motion profile and detected human activity due to the intrinsic latency in the exoskeleton control architecture. As a result, HAR is not enough to achieve the objective on its own. On the contrary, there is not much prior study on estimating and predicting human motion profiles. This study presented a brief comparison of the widely used predictive models. The authors emphasized human activity profile prediction by employing deep learning techniques to compensate for the lag and facilitate the real-time control strategy. According to their performance comparison, the Long Short-Term Memory (LSTM) model performed better than other neural networks and provided the highest prediction accuracy. © 2024 IEEE.

关键词： Long short-term memory

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Study on the Helpfulness of Explainable artificial intelligence 2nd

Study on the Helpfulness of Explainable Artificial Intellige...

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2nd World conference on Explainable artificial intelligence (xAI)

作者： Labarta, Tobias Kulicheva, Elizaveta Froelian, Ronja Geissler, Christian Melman, Xenia von Klitzing, Julian Fraunhofer Heinrich Hertz Inst D-10587 Berlin Germany Tech Univ Berlin D-10587 Berlin Germany

ISBN: (纸本)9783031638022;9783031638039

Explainable artificial intelligence (XAI) is essential for building advanced machine learning-powered applications, especially in critical domains such as medical diagnostics or autonomous driving. Legal, business, and ethical requirements motivate using effective XAI, but the increasing number of different methods makes it challenging to pick the right ones. Further, as explanations are highly context-dependent, measuring the effectiveness of XAI methods without users can only reveal a limited amount of information, excluding human factors such as the ability to understand it. We propose to evaluate XAI methods via the user's ability to successfully perform a proxy task, designed such that a good performance is an indicator for the explanation to provide helpful information. In other words, we address the helpfulness of XAI for human decision-making. Further, a user study on state-of-the-art methods was conducted, showing differences in their ability to generate trust and skepticism and the ability to judge the rightfulness of an AI decision correctly. Based on the results, we highly recommend using and extending this approach for more objective-based human-centered user studies to measure XAI performance in an end-to-end fashion.

关键词： Explainable artificial intelligence (XAI) Goodness measures and evaluation Human-Centered XAI XAI User-Study XAI Evaluation

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Design & Development of an Automatic defense System for Warfare applications 15

Design & Development of an Automatic Defense System for Warf...

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15th International conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Tejas, R. Venkatesh, L. Shivamadu, D.N. Ravishankar, K. Ninawe, Swapnil S. Gunjal, Samyama G.H. Pavithra, G. Manjunath, T.C. Department of Electronics & Communication Engineering Dayananda Sagar College of Engineering Bangalore India Department of Computer Science & Engineering University Visvesvaraya College of Engineering Bangalore University Karnataka Bengaluru India

ISBN: (纸本)9798331300579

This paper presents the design and implementation of an automated defence system interface leveraging Arduino *** an era of evolving security threats, the development of automatic defense systems has become increasingly *** abstract provides a concise overview of automatic defense systems, focusing on their technologies and *** defense systems encompass a wide range of technologies, including sensors, artificial intelligence, robotics, and communication *** systems are designed to detect, analyze, and respond to potential threats in real-time, minimizing human intervention and response *** components of automatic defense systems include sensor arrays for threat detection, data processing algorithms for threat analysis, and autonomous or semi-autonomous response *** core typically involves sensors (radar, lidar), a command-and-control system for threat evaluation, and countermeasures (missiles, lasers, guns).applications of automatic defense systems span various domains, including military, homeland security, critical infrastructure protection, and public *** systems offer enhanced situational awareness, rapid threat response, and scalability to adapt to evolving security *** technology continues to advance, the integration of emerging technologies such as machine learning, drones, and IoT further enhances the capabilities of automatic defense *** project "Design & Development of an Automatic defense System for Warfare applications" focuses on creating a sophisticated, autonomous defense mechanism aimed at enhancing security and operational efficiency in military *** system integrates advanced sensor technologies, real-time data processing, and artificial intelligence to detect, identify, and neutralize potential threats with minimal human *** features include robust target recognition algorithms, adaptive threat response capabilities, and seamless in

关键词： Optical radar

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