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RNN-based Model for an Optimal COVID-19 Cases Detection using Clinical Reports

RNN-based Model for an Optimal COVID-19 Cases Detection usin...

引用

International Conference on Optimization and Applications (ICOA)

作者： Akib Khanday Salah Bouktif Ali Ouni dept. Computer Science and Software Engineering CIT UAE University Al Ain UAE ETS Montreal University of Quebec Montreal QC Canada

The global impact of COVID-19 has been significant due to its rapid spread and high transmission rate. A large number of individuals have been exposed to this dangerous virus, emphasizing the importance of early detection to potentially save numerous lives. This research paper introduces an enhanced method for COVID-19 detection, utilizing a Recurrent Neural Network (RNN) and leveraging early clinical reports. Specifically, a Long Short Term Memory (LSTM) model is trained using data obtained from the metadata of the available dataset published by the World Health organisation, specifically focusing on extracting clinical reports and labels. Several pre-processing techniques and word embedding are applied to train an LSTM-based classifier that accurately identifies positive COVID-19 cases. The implementation of this proposed approach yielded superior results compared to traditional machine learning algorithms. The model achieved a testing accuracy of 87%, and future works include expanding the dataset to enhance the efficiency of the model.

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ScoutSL: An Open-Source Simulink Search Engine

ScoutSL: An Open-Source Simulink Search Engine

引用

Conference on Model Driven engineering Languages and Systems Companion (MODELS-C)

作者： Sohil Lal Shrestha Alexander Boll Timo Kehrer Christoph Csallner Computer Science & Eng. Dept. University of Texas at Arlington Arlington TX USA Software Engineering Group University of Bern Bern Switzerland

Simulink is one of the most widely used modelling languages in safety-critical industries. Most models created in industrial settings are valuable intellectual property to their companies and are thus often not publicly available. But to study Simulink software engineering processes or to develop new Simulink tools, access to models with relevant properties is vital for researchers. We conducted a community survey to find out what kind of models and model metrics are of interest to researchers. With these results, we implemented ScoutSL (http://***), a tool that gives researchers easy online access to over 100k open-source Simulink models from which they can select a subset according to their needs. A short video demonstration is available online at https://***/HwsHL8LrVCM

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Application to Enhance the Programming Knowledge of Children

Application to Enhance the Programming Knowledge of Children

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Advancements in Computing (ICAC), International Conference on

作者： Anuga Dissanayake Vishwa Lakpriya Mahendran Sanjeevan Ahamed Fahmi Samanthi E.R. Siriwardana Mihiri Samaraweera Dept. of Computer Science and Software Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka Dept. of Information Technology Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (数字)9798331517878

ISBN: (纸本)9798331517885

This proposal outlines the development of a comprehensive educational platform aimed at bridging the Information Technology knowledge gap among Tamil-speaking students in Sri Lanka. The platform is designed to enhance programming skills through personalized quizzes, hands-on projects, and adaptive learning models. The application focuses on two primary components: the first component emphasizes project-based learning and assessments, leveraging advanced Artificial Intelligence models for generating educational content. This model is combined with Retrieval-Augmented Generation, which retrieves relevant external information to refine and tailor the educational content to each student's needs. This component keeps students engaged and challenged at appropriate levels, promoting deeper understanding and practical Information Technology skills. The second component involves a Level-based IT Fundamental Knowledge Evaluation system, which incorporates machine learning algorithms to predict student performance, recommend customized learning paths, and assess overall technical knowledge. The platform also integrates the OpenAI Application Programming Interface, which provides real-time Tamil translation, making the content more accessible to the target demographic. The platform empowers educators to better address individual student needs with real-time feedback and tailored experiences. Its innovative approach enhances Information Technology education, equipping Sri Lankan students with essential programming skills to succeed globally.

关键词： Adaptation models Translation Machine learning algorithms Computational modeling Retrieval augmented generation Learning (artificial intelligence) Real-time systems Proposals Information technology Programming profession

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Reliability Analysis of a Monitoring System for Extraterrestrial Habitats

Reliability Analysis of a Monitoring System for Extraterrest...

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IEEE International Conference on Wireless and Mobile Computing, Networking And Communications (WiMob)

作者： Saurabh Band Florian Stechmann Malavika Unnikrishnan Shadi Attarha Christiane Heinicke Andreas Willig Anna Foerster dept. Sustainable Communication Networks University of Bremen Germany Center of Applied Space Technology and Microgravity University of Bremen Germany Dept. of Computer Science and Software Engineering University of Canterbury New Zealand

ISBN: (数字)9798350387445

ISBN: (纸本)9798350387452

This paper presents a robust and dependable monitoring system designed for space missions where reliability is critical. Theoretically, we use the Markov chain model to evaluate the availability of the system, considering multiple instances of backup devices. The results indicate a substantial improvement in system availability with the inclusion of backup devices. The findings highlight the significance of redundancy in enhancing system dependability while considering practical constraints. Based on this insight, we design an algorithm to deploy backup devices in the system that would improve the system's availability without putting strain on the resources (e.g., channel congestion). It uses backup devices at the sensor node and gateway levels. The algorithm involves backup devices that actively monitor and take over in case of failures, enhancing the system's ability to recover lost or faulty data. We test the system in three failure scenarios: hard failures, sensor failures, and gateway failures. We perform the experiments on our hardware prototypes. Experimental results demonstrate that using backup devices significantly improves the Packet Reception Ratio (PRR) in failure scenarios. We observed an improvement of 30 - 34% in packet reception for different failure scenarios.

关键词： Wireless communication Wireless sensor networks Space missions Redundancy Prototypes Logic gates Reliability theory Reliability engineering Monitoring Strain

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Dual-Modality Deep Feature-based Anomaly Detection for Video Surveillance

Dual-Modality Deep Feature-based Anomaly Detection for Video...

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Canadian Conference on Electrical and computer engineering (CCECE)

作者： Parth Lalitkumar Bhatt Dhruva Shah Christopher Silver Wandong Zhang Thangarajah Akilan Dept. of Computer Science Lakehead University Thunder Bay Canada Dept. of Electrical and Computer Engineering Lakehead University Thunder Bay Canada Dept. of Electrical and Computer Enggineering Western University London Canada Dept. of Software Engineering Lakehead University Thunder Bay Canada

Detecting anomalies in videos is not only crucial but also an intriguing task in surveillance systems. It is a sequential modeling problem in nature that requires careful selection of spatial and temporal dependent patterns from a sequence of frames. There are several research works from traditional approaches to modern deep learning-based techniques introduced to address this problem. However, there is a huge demand for research and development to ameliorate the performance of the existing solutions. In response to that, this study proposes an improved video anomaly detection model using deep features extracted from a dual-modality input representation. The proposed model demonstrates effectiveness in the benchmark–UCF crime dataset by achieving the best AUC of 87.52%, which is ≈ 12.3% improvement compared to a baseline. The application aspect of this work includes strengthening the security measures in common places, viz. airports, banks, public transits, schools, and shopping complexes by detecting aberrational or suspicious activities in surveillance videos.

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Mitigating Insecure Outputs in Large Language Models(LLMs): A Practical Educational Module

Mitigating Insecure Outputs in Large Language Models(LLMs): ...

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IEEE Annual International computer software and Applications Conference (COMPSAC)

作者： MD Abdul Barek Md Mostafizur Rahman Shapna Akter A.B.M Kamrul Islam Riad Md Abdur Rahman Hossain Shahriar Akond Rahman Fan Wu Department of Intelligent Systems and Robotics University of West Florida USA Dept. of Cybersecurity and Information Technology University of West Florida USA Center for Cybersecurity University of West Florida USA Computer Science and Software Engineering Auburn University USA Dept. of Computer Science Tuskegee University USA

ISBN: (数字)9798350376968

ISBN: (纸本)9798350376975

Large Language Models (LLMs) have extensive ability to produce promising output. Nowadays, people are increasingly relying on them due to easy accessibility, rapid and outstanding outcomes. However, the use of these results without appropriate scrutiny poses serious security risks, particularly when they are integrated with other software, APIs, or plugins. This is because the LLM outputs are highly dependent on the prompts they receive. Therefore, it is essential to carefully clean these outputs before using them in additional software environments. This paper is designed to teach students about the potential dangers of contaminated LLM output within the context of web development through prelab, hands-on, and postlab experiences. Hands-on lab provides practical guidance on how to handle LLM vulnerabilities to make applications safe with some real-world examples in Python. This approach aims to provide students with a deeper understanding of the precautions necessary to ensure software against the vulnerabilities introduced by LLM output.

关键词： Training Navigation Large language models Computational modeling software Security software development management

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Reinforcement Learning for Language Grounding: Mapping Words to Actions in Human-Robot Interaction

Reinforcement Learning for Language Grounding: Mapping Words...

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International Conference on Emerging Trends in Networks and computer Communications (ETNCC)

作者： Srinivasa Rao Adapa Himanshu Gupta Susmoy Mukherjee Praneeth Reddy Mallupalli Priyanka Avhad Kamal Upreti Software Engineer OpenLogix LLC Michigan USA Software Engineer Meta New Jersey USA Electrical and Electronics Engg. VIT Institute Tamil Nadu India Software Engineer Arcesium Hyderabad India Computer Engineering Veermata Jijabai Technological Inst. Mumbai India Dept. of Computer Science Christ University Delhi NCR Campus India

ISBN: (数字)9798350353266

ISBN: (纸本)9798350353273

Within the domain of human-robot communication, effective communication is paramount for seamless and smooth collaboration between humans and robots. A promising method for improving language grounding is reinforcement learning (RL), which enables robots to translate spoken commands into suitable behaviors. This paper presents a comprehensive review of recent advancements in RL techniques applied to the task of language grounding in human-robot interaction, focusing specifically on instruction following. Key challenges in this domain include the ambiguity of natural language, the complexity of action spaces, and the need for robust and interpretable models. Various RL algorithms and architectures tailored for language grounding tasks are discussed, highlighting their strengths and limitations. Furthermore, real-world applications and experimental results are examined, showcasing the effectiveness of RL-based approaches in enabling robots to understand and execute instructions from human users. Finally, promising directions for future research are identified, emphasizing the importance of addressing scalability, generalization, and adaptability in RL-based language grounding systems for human-robot interaction.

关键词： Grounding Reviews Scalability Human-robot interaction Collaboration Reinforcement learning Oral communication Robustness Noise measurement Robots

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An In-depth Study on Handwritten Signature Authentication Using Unique Writer Traits

An In-Depth Study on Handwritten Signature Authentication Us...

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Advances in Computing, Communication and Networking (ICAC2N), International Conference on

作者： M. Vamsikrishna Haritha Murari M. Manikandan Nilamadhab Mishra Raghavendra Gupta T Rohit Patel Dept. of Computer Applications Aditya University Kakinada India Sr Software Engineer Spark Infotech Inc Phoenix Arizona USA Dept. of Computer Science and Engineering VeL Tech. R Dr.S Tamilnadu India Dept. of Computer Science and Engineering VIT Bhopal University Sehore India HITM Hyderabad Telenagana India Dept of CSE Calorx Teachers’ University Gujrat India

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

Handwriting verification, a subset of signature verification, involves using handwritten text to verify or authorize someone’s identification. Although simple handwriting can serve as a signature, it is often modified to suit the signer’s preferences. The uniqueness of each person’s handwritten signature makes it essential for accurate distinction from others. This study presents a handwritten signature verification system using a writer-dependent approach. While signature verification is widely used for identity verification and security, it is susceptible to forgeries. The writer-dependent approach considers the unique characteristics of an individual’s signature, training the system to recognize them and making forgery replication more challenging. An extensive review of the literature on signature verification systems, focusing on the writer-dependent approach, was conducted. A system was then developed and implemented based on this approach, utilizing a dataset of signatures from 12 writers. Results indicate that the writer-dependent approach outperforms other methods in terms of accuracy and robustness. The system demonstrated high precision and recall rates, underscoring its effectiveness in detecting both genuine and forged signatures. This study highlights the writer-dependent approach’s efficacy in enhancing the accuracy and security of signature verification systems, with implications for developing more reliable and efficient identity verification systems in today’s digital age.

关键词： Training Accuracy Reviews Focusing Authentication Information age Forgery Robustness Security Character recognition

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Distress Study Through Machine-Learning Techniques

Distress Study Through Machine-Learning Techniques

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2021 International Conference on Biomedical Innovations and Applications, BIA 2021

作者： Marinova, Ginka Kalcheva, Neli Technical University of Varna Dept. of Computer Science and Engineering Varna Bulgaria Technical University of Varna Dept. of Software and Internet Technologies Varna Bulgaria

ISBN: (纸本)9781665445818

Machine learning as a contemporary approach in research provides an opportunity to cover and process a large amount of data. This is a condition for the synthesis of reliable characteristic descriptors of distress. The process of model selection is a factor in achieving the necessary adequacy and improving the quality of results. Age is a risky reason to take into account the development of cancer. This paper analyzed classification models for predicting distress in patients with the oncological disease, segmented into three age groups. Experimental results of a study conducted on the age of the patient and assessment of distress, using three methods of machine-learning are presented. © 2022 IEEE.

关键词： Support vector machines

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Leveraging artificial intelligence of things for anomaly detection in advanced metering infrastructures 2

Leveraging artificial intelligence of things for anomaly det...

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2nd IEEE International Conference on Cyberspace, CYBER NIGERIA 2020

作者： Ogu, Reginald E. Ikerionwu, Charles I. Ayogu, Ikechukwu I. Dept. of Software Engineering Federal University of Technology Owerri Owerri Nigeria Dept. of Computer Science Federal University of Technology Owerri Owerri Nigeria

ISBN: (纸本)9781665444095

The integration of more sensory and actuation components to the Smart Grid produces high volume of data. Consequently, this big data stretches the transmission, processing, and storage capabilities of the Smart Grid infrastructures. The vulnerability of advanced metering infrastructures (AMIs) is on the rise, as more devices are connected to the Internet these days. The aforementioned realities have continued to necessitate a debate on the future of cloud-centered artificial intelligence (AI) services for latency-sensitive user-centric IoT applications. It is rapidly becoming necessary to leverage on the applicability of EdgeAI directly on IoT sensory nodes involved in energy metering. This paper proposes the applicability of Artificial Intelligence situated on smart meter, to perform micro analytics at the edge of AMI networks: Artificial Intelligence of Things. Therefore, a functional AMI model based on IoT and EdgeAI is presented herein. Additionally, an integration architecture for the anticipated Smart Grid based on IoT and EdgeAI is presented. On implementation, the proposed model would provide high performance analytics and Edge computing capabilities to enable AMIs initiate instant data check at the source and relay relevant real-time data to the Utility through the Internet. © 2021 IEEE.

关键词： Energy utilization

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