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Correlation Between 3D Magnetic Resonance Imaging and Clinical Outcomes in Traumatic Brain Injury 15

Correlation Between 3D Magnetic Resonance Imaging and Clinic...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Srikantha, H. Joseph, Ginu Anie Yadav, Surendra School of Engineering and Technology Department of Civil Engineering Karnataka Bangalore India Arka Jain University Department of Nursing Jharkhand Jamshedpur India Vivekananda Global University Department of Computer Science & Application Jaipur India

ISBN: (纸本)9798350370249

This article identifies a correlation between the 3D Magnetic Resonance Imaging (MR) of patients with worrying brain harm (TBI) and the patient's medical results. The research team compares 3-D MR imaging outcomes with the patient's observe-up go-to facts, including neurologic assessments, Glasgow Coma Scale rating, and other parameters. The effects confirmed that patients who had a better degree of injury discovered from three-D MR scans had worse scientific effects in comparison to sufferers who had milder harm. This correlation became great for acute and subacute TBI patients. The authors endorse that 3-D MR scans can be used to predict the clinical results of TBI patients and can have the potential to inform treatment selections. © 2024 IEEE.

关键词： Article Correlation Glasgow Identifies Magnetic Resonance

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Point Process based time sensitive personalised recommendation

Point Process based time sensitive personalised recommendati...

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2022 International Conference on Machine Learning and Data Engineering, ICMLDE 2022

作者： Abbas, Khushnood Dong, Shi Khan, Asif School of Computer Science and Technology Zhoukou Normal University China Department of Computer Application Integral University UP Lucknow India

We frequently rely on suggestions in an online context, including those from search engine results, e-commerce product recommendations, movie recommendations, and so forth. These suggestions are made in response to our actions, such as when we search for products, songs, or movies, or at the very least, when we visit the website in order to receive a recommendation. These recommendations do not take into account time-sensitive factors, such as when we often utilise these services during the day or the week. We prefer to repeat some activities, like listening to music at a specific time of day. We presented a methodology in this paper that addresses two critical issues in personalised recommendation. 1) recommending the right items to the right people at the right time, and 2) estimating when a user will return to a service or product after performing repeated actions. The scholarly community has not yet examined this work in personalised recommendations. To do this, we offered the Hawkes process, in which we employed a customised initial intensity based on Hierarchical Poisson Factorization, and for a dynamic activity, we considered a sinusoidal function coupled with exponential effect decay. This is consistent with user activity cycles, such as music listening during a specific time of day. This is the first framework we've used, which is based on a probabilistic matrix factorization. © 2022 Elsevier B.V.. All rights reserved.

关键词： Machine learning

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Exploring Spectrum Sharing Algorithms for 6G Cellular Communication Networks 15

Exploring Spectrum Sharing Algorithms for 6G Cellular Commun...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Pattanaik, Ashutosh Shweta Ahmed, Zahid School of Engineering and Technology Department of Mechanical Engineering Karnataka Bangalore India Arka Jain University Department of Pharmacy Jharkhand Jamshedpur India Vivekananda Global University Department of Computer Science & Application Jaipur India

ISBN: (纸本)9798350370249

6G cell verbal exchange networks might be the successor to 5G networks, and their success relies heavily upon optimization measures that are enabled through the exploration of spectrum-sharing algorithms (SSAs). SSAs are hard and fast techniques implemented to maximize machine assets, performance metrics, and operational performance. That is performed by way of lowering interference, permitting spectrum contextualization, and improving the sharing of underutilized and congested spectrum. For 6G verbal exchange networks, a heightened attention on advanced database storage, distributed computing, and interference avoidance techniques must be taken a good way to ensure the maximum green use of be had spectrum. This paper explores a spread of SSAs for 6G mobile communication networks, consisting of dynamic spectrum get admission to, non-orthogonal more than one get right of entry to, and direct tool-enabled opportunistic spectrum usage. We evaluate current research that has been implemented to the improvement of SSAs and offer insight into future demanding situations and programs inside the subject. Furthermore, we talk about the significance of massive records and systems getting to know in the creation of SSA algorithms, providing capability enhancements to the ever-changing 6G communique networks. Standard, the usage of SSAs in 6G verbal exchange networks can make certain the most effective and green usage of be had spectrum, at the same time as additionally imparting extraordinary stages of aid flexibility © 2024 IEEE.

关键词： Cellular Contextualization Machine Learning Non-Orthogonal Spectrum-Sharing Successor

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Assessing the Robustness of Machine Learning Algorithms against Emerging Privacy and Security Threats in Medical applications 15

Assessing the Robustness of Machine Learning Algorithms agai...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Rao, Srikrishna Baskar Goswami, Sarbojeet Goyal, Manish Kumar School of Engineering and Technology Department of Aerospace Engineering Karnataka Bangalore India ARKA JAIN University Department of optometry Jharkhand Jamshedpur India Vivekananda Global University Department of Computer Science & Application Jaipur India

ISBN: (纸本)9798350370249

Using gadgets to realize algorithms in medical packages can provide new insights and talents, including new approaches to diagnose illnesses or reveal treatments. Rising privacy and security threats pose potential dangers to those structures that can restrict their efficacy and average performance. To make sure tool mastering algorithms are resilient to those threats, it is critical to assess their robustness. This summary offers an assessment of techniques to evaluate the robustness of tool-getting-to-know algorithms against emerging privacy and protection threats in medical programs. First, we check the kinds of threats and discuss how they'll affect the general performance of machine studying algorithms. Secondly, we compare a selection of strategies for measuring the robustness of gadgets and gaining knowledge of algorithms, which include static, dynamic, and adverse check strategies. Ultimately, we speak about techniques for mitigating threats in scientific packages, which provides for incorporating relaxed layout thoughts in tool studying structures and utilizing smoke assessments and strain tests. Through this assessment, we aim to provide more facts on the techniques for assessing the robustness of gadget-studying algorithms against rising privacy and security threats in scientific programs. © 2024 IEEE.

关键词： Adversarial machine learning

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Using Real-Time Integrated computer Vision and Deep Learning for Advanced Factory Safety 4

Using Real-Time Integrated Computer Vision and Deep Learning...

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4th IEEE International Conference on Electronic Communications, Internet of Things and Big Data, ICEIB 2024

作者： Tsai, Shang-Te Chang, Yu-Cheng Huang, Chao-Yi Yang, Fang-Yuan Chien, Wei Wu, Zong-Rong School Art and Design Sanming University Fujian China National Taipei University of Technology Department of Computer Science and Information Engineering Taipei Taiwan National Yunlin University of Science and Technology Department of Information Management Yunlin Taiwan Lunghwa University of Science and Technology Department of Computer Information and Network Engineering Taoyuan Taiwan National Yunlin University of Science and Technology Department of Technical and Vocational Program in Artificial Intelligence Yunlin Taiwan

ISBN: (纸本)9798350360721

Innovative safety in the workplace is vital as the high safety risks associated with electrical engineering construction can lead to injuries or even fatalities. Using computer vision technology, we experimented with scenarios such as 'normal operations' and 'unexpected incidents' to enhance safety measures. We integrated an Internet of Things system into the setup, enabling the system to quickly detect and alert to unexpected events in real-time, thereby improving workplace safety. © 2024 IEEE.

关键词： computer vision

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A Knowledge-Injected Curriculum Pretraining Framework for Question Answering 24

A Knowledge-Injected Curriculum Pretraining Framework for Qu...

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33rd ACM Web Conference, WWW 2024

作者： Lin, Xin Su, Tianhuang Huang, Zhenya Xue, Shangzi Liu, Haifeng Chen, Enhong School of Computer Science and Technology University of Science and Technology of China State Key Laboratory of Cognitive Intelligence Hefei China Guangdong OPPO Mobile Telecommunications Corp. Ltd Shenzhen China University of Science and Technology of China Hefei China Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China State Key Laboratory of Cognitive Intelligence Hefei China

ISBN: (纸本)9798400701719

Knowledge-based question answering (KBQA) is a key task in natural language processing research, and also an approach to access the web data and knowledge, which requires exploiting knowledge graphs (KGs) for reasoning. In the literature, one promising solution for KBQA is to incorporate the pretrained language model (LM) with KGs by generating KG-centered pretraining corpus, which has shown its superiority. However, these methods often depend on specific techniques and resources to work, which may not always be available and restrict its application. Moreover, existing methods focus more on improving language understanding with KGs, while neglect the more important human-like complex reasoning. To this end, in this paper, we propose a general K nowledge-I njected C urriculum P retraining framework (KICP) to achieve comprehensive KG learning and exploitation for KBQA tasks, which is composed of knowledge injection (KI), knowledge adaptation (KA) and curriculum reasoning (CR). Specifically, the KI module first injects knowledge into the LM by generating KG-centered pretraining corpus, and generalizes the process into three key steps that could work with different implementations for flexible application. Next, the KA module learns knowledge from the generated corpus with LM equipped with an adapter as well as keeps its original natural language understanding ability to reduce the negative impacts of the difference between the generated and natural corpus. Last, to enable the LM with complex reasoning, the CR module follows human reasoning patterns to construct three corpora with increasing difficulties of reasoning, and further trains the LM from easy to hard in a curriculum manner to promote model learning. We provide an implementation of the general framework, and evaluate the proposed KICP on four real-word datasets. The results demonstrate that our framework can achieve higher performances, and have good generalization ability to other QA tasks. © 2024 ACM.

关键词： Curricula

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P4AI: E-application for Researching Student Interests based on Artificial Intelligence 10

P4AI: E-Application for Researching Student Interests based ...

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10th International Conference on Cyber and IT Service Management, CITSM 2022

作者： Adrian Sasmoko Manalu, Sonya Rapinta Indrianti, Yasinta Computer Science School of Computer Science Bina Nusantara University Jakarta Indonesia Bina Nusantara University Faculty of Humanities Education Department Jakarta Indonesia Mobile Application & Technology School of Computer Science Bina Nusantara University Jakarta Indonesia Universitas Agung Podomoro Entrepreneurship Department Jakarta Indonesia

ISBN: (数字)9781665460743

ISBN: (纸本)9781665460743

The disruptive era has led to various adaptation efforts so that it has an impact on a shift from the old profession being replaced with a variety of new professions. Therefore, it is necessary to have an instrument that is able to measure the interest and profession of students in accordance with current needs. This study aims to build an e-application based on artificial intelligence to measure Profiling for Aptitude Indicators (P4AI). The development of e-application is carried out using the waterfall method. The application development model that will be used is Agile with the Scrum model. Scrum is used to develop innovative products or services. The design of this application development will use UML and ERD based on the analyzed features. Algorithm, code, logic, and validation testing of application programs using integration testing This test method will test all modules if combined into one group to see if the program runs well or not. The results of the research are in the form of an e-application that users can use to conduct self-assessment about their interests and professions. Thus, this e-application can help the self-development needed related to the specialization and profession they have. © 2022 IEEE.

关键词： Integration testing

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Distributed Machine Learning System for Intrusion Detection 2

Distributed Machine Learning System for Intrusion Detection

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2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Patil, Harshal Lohumi, Yogesh Tejaswini, K.M. Sudheendramouli, H.C. Parikh, Swapnil Maranan, Ramya Sri Balaji University Pune India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Sri Siddhartha Institute of Technology Department of Mathematics Tumakuru India Gsss Simha SubbaMahalakshmi First Grade College Department of Computer Science and Application Mysuru India Parul University Department of Computer Science and Engineering Parul Institute of Engineering and Technology Post Limda India Saveetha School of Engineering Simats Department of Research and Innovation Chennai India

ISBN: (纸本)9798350373110

As network systems expand in complexity, the necessity for effective intrusion detection grows. This study compares two distributed machine learning systems utilizing different communication topologies: Parameter Server and Point-to-Point. The former employs centralized parameter management for efficient communication, while the latter facilitates direct node-to-node communication without a central server. Using extensive network traffic data, the models are assessed on performance metrics like accuracy, scalability, and false positive rates. Both systems show promising results, with median accuracy and F1 scores around 93%. However, limitations include potential data homogeneity issues, scalability challenges particularly with the Parameter Server becoming a bottleneck, concerns over model overfitting, and high computational overhead in maintaining models across numerous nodes. Addressing these drawbacks is crucial for improving the reliability of intrusion detection systems in complex network environments. © 2024 IEEE.

关键词： Network intrusion

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Towards a Self-contained Data-driven Global Weather Forecasting Framework 41

Towards a Self-contained Data-driven Global Weather Forecast...

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41st International Conference on Machine Learning, ICML 2024

作者： Xiao, Yi Bai, Lei Xue, Wei Chen, Hao Chen, Kun Chen, Kang Han, Tao Ouyang, Wanli Department of Computer Science and Technology Tsinghua University Beijing China Shanghai Artificial Intelligence Laboratory Shanghai China Qinghai University and Intelligent Computing Application Laboratory of Qinghai Province China School of Information Science and Technology Fudan University Shanghai China

Data-driven weather forecasting models are advancing rapidly, yet they rely on initial states (i.e., analysis states) typically produced by traditional data assimilation algorithms. Four-dimensional variational assimilation (4DVar) is one of the most widely adopted data assimilation algorithms in numerical weather prediction centers;it is accurate but computationally expensive. In this paper, we aim to couple the AI forecasting model, FengWu, with 4DVar to build a self-contained data-driven global weather forecasting framework, FengWu-4DVar. To achieve this, we propose an AI-embedded 4DVar algorithm that includes three components: (1) a 4DVar objective function embedded with the FengWu forecasting model and its error representation to enhance efficiency and accuracy;(2) a spherical-harmonic-transform-based (SHT-based) approximation strategy for capturing the horizontal correlation of background error;and (3) an auto-differentiation (AD) scheme for determining the optimal analysis fields. Experimental results show that under the ERA5 simulated observational data with varying proportions and noise levels, FengWu-4DVar can generate accurate analysis fields;remarkably, it has achieved stable self-contained global weather forecasts for an entire year for the first time, demonstrating its potential for real-world applications. Additionally, our framework is approximately 100 times faster than the traditional 4DVar algorithm under similar experimental conditions, highlighting its significant computational efficiency. Copyright 2024 by the author(s)

关键词： Data assimilation

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Empirical Evaluation of Stock Market Prediction System using Intelligent Learning Scheme with Data Processing Logic 5

Empirical Evaluation of Stock Market Prediction System using...

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5th International Conference on Intelligent Communication Technologies and Virtual mobile Networks, ICICV 2024

作者： Prabakar, S. Kumar, Ankit Jayakarthik, R. Venkatesh, D. Pratheeba, R.S. Khan, Bhola Isbr Business School Finance and Accounting Bangalore India Faculty of Science Swami Vivekanand Subharti University Department of Computer Application Meerut India Saveetha College of Liberal Arts and Sciences Simats Dept of Computer Science India Commerce School of Commerce and Management Mohan Babu University Tirupati India Velammal Institute of Technology Department of Computer Science and Engineering Panchetti601204 India Mjp Rohilkhand University Department of Regional Economics Bareilly India

ISBN: (纸本)9798350385649

The Stock Market Prediction System using Intelligent Learning Scheme (SMS-ILS) presented in this study integrates advanced methodologies to enhance the accuracy and reliability of stock market predictions. The system encompasses a preprocessing phase, addressing missing data and outliers, followed by feature extraction utilizing Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNN) to capture intricate temporal dependencies. A Deep Belief Network (DBN) is employed for classification, categorizing stocks based on learned representations from historical and technical indicators. Grey Wolf Optimization (GWO) optimizes the system's parameters, ensuring adaptability to dynamic market conditions. Notably, the system achieves an impressive accuracy of 97%, emphasizing its efficacy in making correct predictions. This multi-model approach, integrating LSTM, RNN, DBN, and GWO, provides a comprehensive and adaptive framework for navigating the complexities of stock market prediction. The study emphasizes the importance of choosing metrics aligned with specific goals and conducting thorough analysis to ensure the robustness of the evaluation results in the challenging and dynamic context of financial markets. © 2024 IEEE.

关键词： Long short-term memory

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