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AGENT-BASED SIMULATION FRAMEWORK FOR MULTI-VARIANT SURVEILLANCE

AGENT-BASED SIMULATION FRAMEWORK FOR MULTI-VARIANT SURVEILLA...

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2024 Winter Simulation Conference, WSC 2024

作者： Moon, Sifat Afroj Chen, Jiangzhuo Espinoza, Baltazar Lewis, Bryan Marathe, Madhav Outten, Joseph Venkatramanan, Srinivasan Vullikanti, Anil Warren, Andrew University of Virginia CharlottesvilleVA United States Dept. of Computer Science University of Virginia CharlottesvilleVA United States Computational Sciences and Engineering Division Oak Ridge National Lab. Oak RidgeTN United States

ISBN: (纸本)9798331534202

Early detection of an emerging VOC (Variant-Of-Concern) is essential for effective preparedness for a disease like COVID-19. The spreading of an emerging VOC not only depends on the disease dynamics of itself but also depends on the state of the circulating variants and the susceptibility of the population. Resources for testing are typically quite limited, and a number of strategies have been considered for deploying them. However, it has been difficult to evaluate the performance of such strategies, especially higher order effects, and inequities, while incorporating constraints on these resources. Here, we develop an agent-based surveillance framework, NETWORKDETECT, to understand the early warning system of an emerging VOC. Our framework allows us to incorporate various population heterogeneities and resource constraints. © 2024 IEEE.

关键词： Security systems

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Enhancing the Coexistence of 802.11 WLAN and 5G Networks using a Multi-Task Learning with Convolutional Neural Network Model

Enhancing the Coexistence of 802.11 WLAN and 5G Networks usi...

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Information Technology and Digital Innovation (ISITDI), International Symposium on

作者： Dody Ichwana Putra Shelvi Ekariani Dept. of Computer Engineering Universitas Andalas Padang Indonesia Dept. of Mathematics and Data Science Universitas Andalas Padang Indonesia

ISBN: (数字)9798350389241

ISBN: (纸本)9798350389258

The increasing need for high-speed internet for wireless devices has driven the development of 5G New Radio (NR) technology. The 5G NR operates in various spectrums such as sub-1 GHz, sub-6 GHz, and millimeter wave (mmWave) in the 26–28 GHz range. Conversely, the new standard Wi-Fi 6E operates on the unlicensed band and currently utilizes 2.4 GHz, 5 GHz, and 6 GHz. The coexistence of 5G NR sub-6 GHz and Wi-Fi 6E networks can result in interference and reduced performance for both technologies. To address this challenge, we introduce a Multi-Task Learning (MTL) approach using Convolutional Neural Networks (CNN) to improve the coexistence of 5G and Wi-Fi 6E networks. This proposes identifying and classifying packets transmitted and its modulation over both types of networks. The MTL-CNN model is trained to classify packets based on their type (e.g., 5G or Wi-Fi) and to identify the specific protocol used within each packet. We confirm the accuracy of this method with Wi-Fi and 5G signals produced by the MATLAB waveform simulator and subsequently deploy it on a hardware testbed using SDR. Our experimental results show that this method improves the accuracy of packet identification and classification, thus enhancing the coexistence between 5G and Wi-Fi 6E networks. MTL-CNN potentially leads to more efficient use of the wireless spectrum and improves the overall performance of wireless communication systems.

关键词： Wireless communication Accuracy 5G mobile communication Modulation Multitasking Mathematical models Convolutional neural networks Millimeter wave communication Wireless fidelity IEEE 802.11 Standard

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Utilizing Machine Learning Techniques for Crime Classification Under the Indian Penal Code (IPC) Based on Crime Reporting data

Utilizing Machine Learning Techniques for Crime Classificati...

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data science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： R. Ponnusamy Veeramalai Sankaradass HarshivKrishna M Sre Sandhya K Bharath Sampath Aashika B Dept. of Artificial Intelligence and Data Science Madha Engineering College Chennai Tamil Nadu India Dept. of Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

Crime classification using the Indian Penal Code (IPC) is a common task at each police station. It involves complex decision-making related to crimes and respective IPC sections for different cases. The IPC is the foundational criminal code in India, containing a list of all substantive penal laws with definitions and punishments for each offense. Automating the crime classification is helpful due to the complexity and sheer volume of crimes reported daily. This work uses machine learning techniques to automate crime classification based on IPC. To serve this purpose, this paper utilizes the K-Nearest Neighbors algorithm. The proposed system accepts an input crime report and textually corroborates the report to an IPC section. For this intent, the system relies on the bag-of-words technique for identifying sections under IPC by scrutinizing repetition and availability of key terms in the crime to 98% accuracy in classifying crimes. Hence, the automated method is quite effective for mapping crime reports into the corresponding sections of the IPC. Also, because the system can easily be deployed online, it will be easy to integrate with existing workflows at the police stations. So, case filing and management would become more effective. This reduces the amount of manual effort and also brings consistency in classification; for this reason, the tool is of immense value to law enforcement agencies nationwide. The innovation in the paper is the development of a machine learning-based system using the K-Nearest Neighbors (KNN) algorithm to automate the classification of crimes under the Indian Penal Code (IPC) based on textual crime reports.

关键词： Technological innovation Codes Accuracy Machine learning algorithms Automation Law enforcement Machine learning Nearest neighbor methods Classification algorithms System implementation

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Multi-modal Underwater Acoustic Target Recognition 5

Multi-modal Underwater Acoustic Target Recognition

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5th International Conference on Computer, Big data and Artificial Intelligence, ICCBD+AI 2024

作者： He, Tianxiang Xiang, Rui Zhou, Junlin Chen, Duanbing Feng, Sheng Yang, Jie Chengdu Union Big Data Technology Inc. Chengdu China Uestc School of Computer Science and Engineering Chengdu China Sichuan Jiuzhou Electric Co. Ltd. Dept. 804 Mianyang China

ISBN: (纸本)9798331533991

Underwater acoustic target recognition (UATR) has gained significant attention across various fields in recent years. However, traditional UATR methods, which primarily rely on data from a single sensor, have limitations in fully capturing target characteristics. This constraint ultimately impacts the accuracy and robustness of recognition. This paper presents a multi-modal UATR method that fuses data from two different sensors, thereby improving underwater target recognition accuracy. Additionally, a new training framework is proposed to facilitate the learning of consistent feature representations across diverse data sources, allowing for more effective capture of the intrinsic characteristics of acoustic data. Experimental results demonstrate that the method has positive effectiveness under various Signal-to-Noise Ratio (SNR) conditions and achieves an improvement in recognition accuracy ranging from 0.95% to 8.33%. © 2024 IEEE.

关键词： Self-supervised learning

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Estimating Surface Soil Moisture based on Remote Sensing data and Crop Yield Classification using Machine Learning

Estimating Surface Soil Moisture based on Remote Sensing Dat...

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International Conference on Communication and Electronics Systems (ICCES)

作者： G. Anuradha Maddi Susrith Kondreddy Gnana Siddhardha Dept. of computer science and engineering Velagapudi Ramakrishna Siddhartha Engineering College Vijayawada India Dept. of Artificial Intelligence and Data Science Velagapudi Ramakrishna Siddhartha Engineering College Vijayawada India

ISBN: (数字)9798350377972

ISBN: (纸本)9798350377989

Soil moisture plays a critical role in environmental monitoring and sustainable agricultural practices due to its significant impact on crop growth, water availability, and overall ecosystem health. Properly understanding and managing soil moisture is vital for efficient water usage, especially in agriculture. Traditional methods in measuring soil moisture have been confined to ground-based small-scale methods, which are high price due to high labor intensity and limited spatial coverage. This project addresses its limitation in the spatial view by integrating remote sensing data from the Sentinel-1 satellite. Sentinel-1 is particularly effective in monitoring the soil moisture as it captures data in all climate conditions. By combining the satellite data with Google Earth Engine (GEE) we create a soil moisture dataset. For analyzing the joint satellite and ground truth data, the machine learning technique using Random Forest (RF) is applied and classification of crop yield is done.

关键词： Earth Satellites Soil measurements Soil moisture Surface soil Sentinel-1 Moisture Sensors Remote sensing Crop yield

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Process Quality Assurance of Artificial Intelligence in Medical Diagnosis 6

Process Quality Assurance of Artificial Intelligence in Medi...

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6th International Conference on Intelligent Systems and Computer Vision, ISCV 2024

作者： Moghadasi, Negin Piran, Misagh Valdez, Rupa S. Baek, Stephen Moghaddasi, Negar Polmateer, Thomas L. Lambert, James H. University of Virginia Dept. of Systems and Information Engineering CharlottesvilleVA United States Ruhr University of Bochum Dept. of Radiology Nuclear Medicine and Molecular Imaging Heart and Diabetes Center North-Rhine Westphalia Bad Oeynhausen Germany University of Virginia School of Data Science CharlottesvilleVA United States Western University of Health Sciences Dept. of Dentistry PomonaCA United States CCALS University of Virginia Dept. of Systems and Information Engineering CharlottesvilleVA United States

ISBN: (纸本)9798350350180

While artificial Intelligence (AI) has shown promising results in the healthcare field, it is undeniable that there are risks associated with AI in healthcare that society must acknowledge. This paper presents a comprehensive systems modeling framework aimed at evaluating trust and being responsive to reasons for mistrust and distrust in AI-assisted medical diagnosis, with a specific focus on the diagnosis of cardiac sarcoidosis, utilizing Explainable Artificial Intelligence (XAI) techniques. The design includes two primary sections: 1. Identifying the most and least disruptive scenarios to the system, as well as the most important initiatives for the system. 2. Utilizing XAI techniques such as SHapley Additive exPlanations (SHAP), Local Interpretable Model-agnostic Explanations (LIME), and Anchors models to provide explanations on how machine learning models justify their outcomes. The findings indicate the significance of employing explainable AI in critical domains like healthcare, where lives of patients are at risk. XAI can be employed to analyze the outcomes for AI users, determining the significance of features, improving comprehension of AI outputs, enhancing transparency, explainability, and interpretability of AI outputs, and facilitating data assessment. © 2024 IEEE.

关键词： Anchors Ethical AI Explainable AI LIME Multi-Criteria Decision Analysis Risks of AI SHAP Systems engineering

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Decision Making Algorithm for Blind Navigation Assistance using Deep Learning 1

Decision Making Algorithm for Blind Navigation Assistance us...

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1st International Conference on computational science and Technology, ICCST 2022

作者： Zulaikha Beevi, S. Harish Kumar, P. Harish, S. Lakshan, S.J. Vsb Engineering College Dept. of Computer Science and Engineering Karudayampalayam. Tamil Nadu Karur639 111 India Vsb Engineering College Dept. of Artificial Intelligence and Data Science Karudayampalayam Tamil Nadu Karur639 111 India

ISBN: (纸本)9781665476553

Blind people face several obstacles in their daily lives and technological interventions can help overcome these obstacles. In this research, we provide an AI-based autonomous assisting device that recognizes many objects and it will provide acoustic input to the user to help visually blind people to understand the surrounding better to understand their environment better. Multiple photos of objects relevant to visually impaired people were used to build a deep-learning model. Training photos are enhanced and manually annotated to improve the trained model's resilience. A distance-measuring sensor is included which recognise the objects using computer vision. The gadget is made more inclusive by recognizing the obstacles coming out of one place to another. After stage segmentation and obstacle detection, the aural information sent to the user is adj usted to get a lot of details in minimum time and speed up video processing. © 2022 IEEE.

关键词： Speech recognition

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The impact of entrepreneurship education on entrepreneurship intention in the engineering studies 27

The impact of entrepreneurship education on entrepreneurship...

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27th International Conference on Knowledge Based and Intelligent Information and engineering Sytems, KES 2023

作者： Kurata, Karin Kota, Kodama Morinaga, Takashi Ohnishi, Hiromasa SouTakahashi Sato, Jun Yajima, Kuniaki Dept. of Creative Engineering National Institute of Technology Tsuruoka College Tsuruoka997-8511 Japan Dept. of Creative Engineering National Institute of Technology Sendai College Sendai989-3128 Japan School of Data Science Nagoya City University Nagoya467-8501 Japan

Entrepreneurship education is characterized by interactive learning linked to business and community initiatives. In entrepreneurship course, activity sessions include (1) Causation, and effectuation (2) Devil's River, Valley of Death, and Darwin's Sea, (3) Case Studies, (4) Guest Speaker Sessions, (5) Lean Startup, Business Model Canvas (BMC), Value Proposition Canvas (VPC), etc. are often addressed. The goal of entrepreneurship education is to change students' mindsets in terms of how they view innovative and risk-taking activities in business. There are several ways to measure the change derived from entrepreneurship education, including affective, cognitive, and skill-based outcomes. A previous study examined the effect of entrepreneurship education on entrepreneurship in relation to three independent variables: perceived behavioral control (PBC), attitude (AT), and subjective norm (SN). Combined with the results of the Generic Skill measure, this study proposes an evaluation method for entrepreneurship education that focuses on entrepreneurship intention. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

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CA-CR: Context-Aware Congestion Resolution Method for Automated Guided Vehicles

CA-CR: Context-Aware Congestion Resolution Method for Automa...

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Applied Artificial Intelligence (ICAPAI), International Conference on

作者： Yigit Can Dundar Bernt Arild Bremdal Anne Håkansson Randi Karlsen Dept. of Computer Science UiT - The Arctic University of Norway Tromsø Norway Dept. of Computer Science and Computational Engineering UiT - The Arctic University of Norway Narvik Norway

ISBN: (数字)9798350349764

ISBN: (纸本)9798350349771

Modern logistics solutions use Automated Guided Vehicles (AGVs) in order to streamline logistics tasks in various work environments. While handling logistics tasks, AGVs can encounter congestions in the form of traffic or blockages which can cause slow-downs and deadlocks. Congestions can occur randomly and unexpectedly in work environments that are unknown to the AGVs. In unknown environments, access to environmental information is limited. Therefore, avoiding or resolving a congestion is a difficult task for AGVs. In order to compensate for the lack of environmental information, the contextual information of the congestion can be used. This paper presents the Context-Aware Congestion Resolution (CACR) method for AGVs in unknown work environments. The method uses contextual information in the form of number of agents, the vehicles’ current battery levels, task priorities and agent positioning to resolve congestions through a multi-agent negotiation. The negotiation uses the contextual information to find various solutions to random and unpredictable congestion scenarios. The negotiations are aided with reinforcement learning to train AGVs to move towards a collective utility maximizing behavior. To test the AGVs performance, the trained behavior is compared to a baseline, non-trained behavior for evaluation of reinforcement learning’s impact on optimizing the resolution of congestions. Findings indicate that the trained behavior shows better scaling with number of agents, better battery utilization and collective utility compared to baseline.

关键词： Training Remotely guided vehicles Reinforcement learning System recovery Batteries Resource management Proposals

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Enhancing the Security of Industrial Cyber-Physical Systems using AI Algorithms

Enhancing the Security of Industrial Cyber-Physical Systems ...

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2024 IEEE International Conference on Intelligent Systems and Advanced Applications, ICISAA 2024

作者： Ghosh, Amarjeet Kumar Bhupathi, Hari Prasad Bhagyalakshmi, L. Poddar, Hitha Gandhi, T. Jain, Juhi IES College Of Technology Department of Electronics & Communication Engineering M. P. Bhopal462044 India Stellantis NV FCA Technology Centre 1000 Chrysler Dr Auburn HillsMI United States Rajalakshmi Engineering College Dept. of ECE TN Chennai India Easwari Engineering College Dept. of CSE TN Chennai India MLR Institute of Technology Dept. of CSE Telangana Hyderabad India GGITS Dept of Data Science M.P Jabalpur India

ISBN: (纸本)9798331539948

Industrial cyber-physical systems (CPS) must be secure at a time when automation and linkages are crucial to business. This study uses powerful AI to make industrial CPS safer than previous approaches. The proposed response improves speed metrics, making it a good alternative for protecting these vital systems. RNNs detect unusual things, Kmeans grouping finds patterns, and Q-learning changes our responses. These systems are adept.at identifying odd behavior, grouping tendencies, and altering threat responses. Our solution outperforms traditional security approaches in many key performance areas. The new method's threat detection accuracy is 98.2%, up from 94.5%. The false positive rate drops by 40.0%, reducing needless warnings and issues. This reduces the false negative rate by 43.8%, making threat identification more sensitive. Our method's response time has fallen by 33.3%, making it responsive to new threats. We utilize system resources more efficiently by 21.4%. Scalability is 'high,' meaning it adapts to business changes. We proposed a cutting-edge way to make commercial CPS safer. Its excellent AI algorithms defend critical industrial equipment from cyber-physical attacks. Threat detection outperforms previous techniques in accuracy, false alarms, reaction time, resource use, and growth. As businesses employ connected systems, our technique protects industrial operations effectively and preventatively. © 2024 IEEE.

关键词： Quantum computers

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