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Mobile-based Application for COVID-19 Detection from Lung X-Ray Scans with Artificial Neural Networks (ANN) 7

Mobile-based Application for COVID-19 Detection from Lung X-...

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7th International Conference on Digital Arts, Media and technology, DAMT 2022 and 5th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications Engineering, NCON 2022

作者： Vong, Chanvichet Chanchotisatien, Passara Thammasat University School of ICT Sirindhorn International Institute of Technology Pathum Thani Thailand The University of Edinburgh School of Informatics Department of Computer Science Scotland United Kingdom

ISBN: (纸本)9781665495103

In early 2020, the World Health Organization (WHO) identified a novel coronavirus referred to as SARS-CoV-2, which is associated with the now commonly known COVID-19 disease. COVID-19 was shortly later characterized as a pandemic. All countries around the globe have been severely affected and the disease has accumulated a total of over 200 million cases and more than five million deaths in the past two years. Symptoms associated with COVID-19 vary greatly in severity. Some infected with COVID-19 are asymptomatic, while others experience critical disease with life-threatening complications. In this paper, a mobile-based application has been created to help classify Covid-19 and non-Covid-19 lung when given an image of a Chest X-Ray (CXR). A variety of different artificial neural networks (ANN) including our baseline model, InceptionV3, MobileNetV2, MobileNetV3, VGG16, and VGG19 were tested to see which would provide the optimal results. It is concluded that MobileNetV3 gives the best test accuracy of 95.49% and is considered a lightweight model suitable for a mobile-based application. © 2022 IEEE.

关键词： COVID-19

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Optimizing ASD Diagnosis: Enhanced Autism Spectrum Disorder Prediction Using Machine Learning Classifiers 2

Optimizing ASD Diagnosis: Enhanced Autism Spectrum Disorder ...

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2nd International Conference of Intelligent Methods, Systems and Applications, IMSA 2024

作者： Al Tawil, Arar Gomaa, Ibrahim A Zeid, Magdy Abd-Elghany Marzouk, AbdulRahman S. Hamad, Adam L. Zakaria, Khaled Y. Danial, Moshir A. Nassief, Mounir N. Abdelminaam, Diaa Salama Applied Science Private University Faculty of Information Technology Amman11931 Jordan Alobour high institute for computer and informatics Computer Science Department Cairo Egypt Misr International University Faculty of Computer Science Cairo Egypt Jadara University Jadara Research Center Irbid21110 Jordan Misr International University Faculty of Computers Egypt Middle East University MEU Research Unit Amman11831 Jordan

ISBN: (纸本)9798350362633

Autism Spectrum Disorder is a multidimensional neurological developmental disorder that includes a wide plethora of mental disorders targeting one's social and behavioral abilities. Gaining a perceptive understanding of a topic like ASD is essential in one's lifespan, whether the symptoms displayed are mild or severely debilitating. Due to the populous size of clinical terms under the disorder's spectrum and the drastic gap between each symptom's severity, the diagnosis of ASD became a long and tribulating task for experts and professionals alike. The purpose of this paper is to present and compare different Machine Learning Classifiers Categorical Boosting, K-Nearest Neighbors, Support Vector Machine, Logistic Regression, Decision Tree, Random Forest, Naïve Bayes and use their Training Algorithms Train-Test Splitting, K-Folds Cross-Validation to develop a quick and accurate Labeler ASD: 'YES' ? 'NO' for afflicted individuals based on multiple Datasets Generic, Adolescent, Children, Young, Adult, ASD aiming for an early diagnosis and an overall better understanding of the disorder. For each dataset, four Performance Metrics are extracted after applying the 7 ML models and the two training algorithms Accuracy, Precision, Recall, F1-Score. The resulting metrics are then displayed and compared to conclude the most accurate trained model for each dataset and with each of the training algorithms. © 2024 IEEE.

关键词： Diseases

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Conflict Between Artificial Intelligence and Traditional Designers: How Artificial Intelligence Transforms the Design Process 3rd

Conflict Between Artificial Intelligence and Traditional Des...

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3rd International Conference on Emerging Trends in Expert Applications and Security, ICE-TEAS 2024

作者： Gupta, Keshav Dev Pandey, Rishi Pathak, Devendra Nath Ranjan, Rakesh Gupta, Vijay Department of Computer Science Engineering Poornima College of Engineering Rajsthan Jaipur India Department of Animation & Multimedia Birla Institute of Technology Ranchi Mesra India Department of Computer Science & Engineering Poornima College of Engineering ISI-6 RIICO Institutional Area Sitapura Rajasthan Jaipur India Department of Computer Science & Engineering Babu Banarasi Das University Lucknow India Department of Computer Science International School of Informatics and Management Sector 12 Mahaveer Marg Jaipur Mansarovar India

ISBN: (纸本)9789819739905

The development of technology, especially the use of resources provided by artificial intelligence (AI), has advanced in areas where business is gaining importance and is an important fear of economic growth. The use of technical skills in the creation of products and services is not unique to design. In today's world, artificial intelligence (AI) has become an industry in many countries, and design is no exception. However, the lines of work performed by humans and machines are changing rapidly, and as a result, the global market is also changing a lot. This is the result of very rapidly developing technology. This raises the question of how this progress has and will continue to influence the future work of designers. What skills do designers need to enter or stay in this industry? The purpose of this article is to provide a summary of research findings on the impact of intelligence on the performance of designers, as well as an analysis that includes an evaluation of the potential and benefits of intelligent design solutions. Case studies and other scientific studies are used to analyze the applications and benefits of AI-based solutions. Studies can be theoretical or empirical. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： International trade

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Implementing a Robust and Scalable Detect-and-Avoid Algorithm for UAV Navigation in ROS

Implementing a Robust and Scalable Detect-and-Avoid Algorith...

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International Conference on Information, Intelligence, Systems and Applications (IISA)

作者： Dimitrios Meimetis Ioannis Daramouskas Vaios Lappas Isidoros Perikos Vaggelis Kapoulas Michael Paraskevas Department of Aerospace Science & Technology NKUA Athens Greece Computer Technology Institute and Press “Diophantus” Patras Greece Applied Mechanics Lab UoP Patras Greece Computer Engineering and Informatics Department UoP Patras Greece Electrical and Computer Engineering Dept. Computer Technology Institute and Press “Diophantus” University of Peloponnese Patras Greece

ISBN: (数字)9798350368833

ISBN: (纸本)9798350368840

Unmanned aerial vehicles (UAVs) have garnered significant attention from the research community during the last decade, due to their diverse capabilities and potential applications. One of the most critical functions that drones must execute efficiently is navigation in real-world environments. This paper presents a decentralized approach for enabling unmanned aerial vehicles (UAVs) to navigate safely in unknown environments and avoid obstacles. Leveraging the Optimal Reciprocal Collision Avoidance (ORCA) algorithm, implemented in the Robot Operating System (ROS), our method facilitates conflict detection and resolution in 2D environments. Through simulations using ROS, Gazebo, and Iris drones, we validate the effectiveness of our approach in scenarios with initial trajectory conflicts. Our work addresses the pressing need for UAVs to autonomously plan and execute safe flights, laying the groundwork for enhanced UAV capabilities in various real-world applications. The simulation results demonstrate the efficiency and robustness of our approach.

关键词： Simulation Autonomous aerial vehicles Aircraft navigation Sensor systems Robustness Real-time systems Trajectory Safety Sensors Drones

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Swa Bhasha 2.0: Addressing Ambiguities in Romanized Sinhala to Native Sinhala Transliteration Using Neural Machine Translation

Swa Bhasha 2.0: Addressing Ambiguities in Romanized Sinhala ...

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Advanced Research in Computing (ICARC), International Conference on

作者： Sachithya Dharmasiri T.G.D.K. Sumanathilaka School of Computing Informatics Institute of Technology Colombo Sri Lanka Department of Computer Science Swansea University Swansea Wales United Kingdom

With the growing popularity of social media and instantaneous messaging, it is more important than ever to interact online in your native language. In Sinhala, both Romanized and native Sinhala are widely used. Due to the informal textual abbreviation known as “Singlish” however, attempts to translate Romanized Sinhala into native Sinhala via machine transliteration may result in errors. Rule-based transliteration systems may not be compatible with the ad hoc transliterations used in Singlish. To translate Romanized Sinhala back precisely and consistently into Native Sinhala, a novel NMT approach has been proposed. To address the complexities of casual Romanized Sinhala, a hybrid strategy combining rule-based and neural machine translation has been proposed. This strategy aims to eliminate word selection ambiguity by selecting the best word suggestions from a pool of predicted words using a suggestion algorithm. Combining the advantages of Suggestion algorithms and neural machine translation, the proposed transliterator has the potential to considerably enhance reverse transliteration and improve communication in native Sinhala by combining the strengths of both approaches. After completing the GRU model, the performance of the machine translation models on the BLEU test improved to 0.8, indicating high word-level translation accuracy. Significant potential exists for the proposed transliterator to enhance reverse transliteration and improve communication in Sinhala. While preliminary test results are promising, additional testing and refinement are required to improve the overall efficacy of machine translation models.

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On the Security Related Properties of Randomly Constructed Combinatorial Structures 8th

On the Security Related Properties of Randomly Constructed...

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8th International Symposium on Cyber Security, Cryptology, and Machine Learning, CSCML 2024

作者： Liagkou, V. Nastou, P.E. Spirakis, P. Stamatiou, Y.C. Computer Technology Institute and Press - "Diophantus" University of Patras Campus Patras26504 Greece Department of Computer Science University of Liverpool Liverpool United Kingdom Department of Business Administration University of Patras Patras26504 Greece Department of Informatics and Telecommunications University of Ioannina Koatakioi Arta47100 Greece Department of Mathematics Applied Mathematics and Mathematical Modeling Laboratory University of the Aegean Samos Greece

ISBN: (纸本)9783031769337

This paper explores the interplay between randomly generated structures, namely set families as well as second-order logic 0-1 laws, in security-related settings. The paper investigates how randomly generated structures can serve as powerful models for representing security properties of complex systems. By examining the 0-1 properties of these structures, conditions are stated which determine the presence or absence of specific security issues. The stated results provide a generic framework for studying insights into the probabilistic behavior of security attributes modeled by set families and mathematical logic with application potential in designing more robust systems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： computer circuits

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Contextuality of Code Representation Learning 23

Contextuality of Code Representation Learning

引用

Proceedings of the 38th IEEE/ACM International Conference on Automated Software Engineering

作者： Yi Li Shaohua Wang Tien N. Nguyen Department of Informatics New Jersey Institute of Technology New Jersey USA Computer Science Department The University of Texas at Dallas Texas USA

ISBN: (纸本)9798350329964

Advanced machine learning models (ML) have been successfully leveraged in several software engineering (SE) applications. The existing SE techniques have used the embedding models ranging from static to contextualized ones to build the vectors for program units. The contextualized vectors address a phenomenon in natural language texts called polysemy, which is the coexistence of different meanings of a word/phrase. However, due to different nature, program units exhibit the nature of mixed polysemy. Some code tokens and statements exhibit polysemy while other tokens (e.g., keywords, separators, and operators) and statements maintain the same meaning in different contexts. A natural question is whether static or contextualized embeddings fit better with the nature of mixed polysemy in source code. The answer to this question is helpful for the SE researchers in selecting the right embedding *** conducted experiments on 12 popular sequence-/tree-/graph-based embedding models and on the samples of a dataset of 10,222 Java projects with +14M methods. We present several contextuality evaluation metrics adapted from natural-language texts to code structures to evaluate the embeddings from those models. Among several findings, we found that the models with higher contextuality help a bug detection model perform better than the static ones. Neither static nor contextualized embedding models fit well with the mixed polysemy nature of source code. Thus, we develop HyCode, a hybrid embedding model that fits better with the nature of mixed polysemy in source code.

关键词： code representation learning

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Atomic Lifecycle for Cyber Threat Intelligence

Atomic Lifecycle for Cyber Threat Intelligence

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Information Security and Cryptology (ISCTURKEY), International Conference on

作者： Süleyman Muhammed Arikan Aynur Koçak Mustafa Alkan Computer Forensics Institute of Informatics Gazi University Ankara Turkiye Electrical and Electronics Engineering Faculty of Technology Gazi University Ankara Turkiye

ISBN: (数字)9798331533649

ISBN: (纸本)9798331533656

The utilisation of cyber threat intelligence provides a multitude of advantages in the assurance of security. A growing body of research is being published on the subject of cyber threat intelligence. However, the precise sequence of steps involved in the cyber threat intelligence life cycle remains unclear. The life cycle presented in each study is not consistent. Moreover, the transition from data to intelligence throughout the life cycle is not sufficiently delineated. The intelligence produced in many studies can be of different types. In our study, all existing lifecycles were examined and their naming approaches were analysed. The objective was to present a cyber threat intelligence life cycle that is as atomic as possible, with each step clearly expressed. This should facilitate a more rigorous comparison of studies in the existing literature on cyber threat intelligence. Furthermore, the life cycle can be utilised in new cyber threat intelligence studies. Researchers who aspire to implement best practices in their study are able to run more effective processes by following the life cycle.

关键词： Accuracy Cyber threat intelligence Security Best practices Standards

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PIT Performance Measurement using NFD Pipeline Parameters in Named Data Networking (NDN)

PIT Performance Measurement using NFD Pipeline Parameters in...

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International Conference on Wireless and Telematics (ICWT)

作者： Adi Sucipto Syaiful Ahdan Iskandar Nana Rachmana Syambas School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia Faculty of Engineering and Computer Science Universitas Teknokrat Indonesia Indonesia

ISBN: (数字)9798350377057

ISBN: (纸本)9798350377064

Although NDN is an entirely new architecture, its design principles are based on the success of the current Internet, they represent our knowledge of the advantages and disadvantages of the current Internet architecture, and they can be implemented gradually using the operations of the current Internet. The Pending Interest Table (PIT), the Forwarding Information Base (FIB), and the Content Store (CS) are the three data structures found inside the NDN router. Interest sent upstream to the content source is tracked by the Pending Interest Table (PIT), for data to be transmitted to requesters downstream. PIT is also used for measurement, loop detection, and the containment of recently satisfied interest. In this study, we did the simulation to measure and analyze the performance of PIT in NDN using NFD Pipeline Measurements. The NDNSim simulator was used in the research to observe PIT performance. The findings of the simulation and analysis indicated that in an NDN network, Satisfied Interest was influenced by the granularity of a prefix. At the number of interests 1000, the results showed a decrease in packets, including a decrease of around 21% in the Ininterest packet, and around 30% inSatisfiedInterest and InData. The effect of Prefix length also decreased in Ininterest ranging from 0.014-0.015%, while in inSatisfiedInterest and InData it ranged from 4.2-8.5%. The PIT’s performance was influenced by the length of the prefix and the number of interest messages the customer sent.

关键词： Wireless communication Simulation Pipelines Memory management Machine learning Telematics Data structures

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Joint Packet Scheduling and UAV Trajectory Design in Air-Ground Integrated Network

Joint Packet Scheduling and UAV Trajectory Design in Air-Gro...

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2023 IEEE Globecom Workshops, GC Wkshps 2023

作者： Li, Shichao Yu, Zhiqiang Chen, Hongbin Zhang, Ning Dong, Mianxiong School of Information and Communication Guilin University of Electronic Technology Guilin541004 China University of Windsor Department of Electrical and Computer Engineering WindsorONN9B3P4 Canada Muroran Institute of Technology Department of Sciences and Informatics Muroran050-8585 Japan

ISBN: (纸本)9798350370218

Compared with the terrestrial network, the air-ground integrated network consisting of unmanned aerial ve-hicles (UAV s) and high altitude platforms (HAPs) offers the advantages of large coverage, high capacity, and seamless connection, which can provide effective communication services for the Internet of remote things (IoRT). In this paper, we investigate the joint packet scheduling and UAV trajectory design problem, with the objective of minimizing the average packet queue delay of IoRT in the air-ground integrated network. Since the problem is a non-convex in nature, we reformulate it into a Markov decision process (MDP) firstly. Then, based on the multi-agent deep deterministic policy gradient (MADDPG) method, we propose a joint packet scheduling and UAV trajectory design (JPSTD) algorithm. Simulation results show that the proposed JPSTD algorithm outperforms the benchmark algorithms in terms of reducing the average packet queue delay. © 2023 IEEE.

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