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Enhancing Eye Health Diagnosis through Deep Transfer Learning: Unveiling Insights from Low Quality Fundus Images

Enhancing Eye Health Diagnosis through Deep Transfer Learnin...

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IEEE International Conference on System, Computation, Automation and Networking (ICSCAN)

作者： R. Sathishkumar T. Karthikeyan P. Praveen Kumar Chitra Rengarajan A. Tharagesh M. Govindarajan Dept. of CSE Manakula Vinayagar Institute of Technology Puducherry Dept. of Computer Science and Business Systems Sri Manakula Vinayagar Engineering College Puducherry Dept. of Information Technology Sri Manakula Vinayagar Engineering College Puducherry Centre for Agricultural Nanotechnology Tamil Nadu Agricultural University Tamil Nadu Dept. of CSE Annamalai University Chidambaram Tamil Nadu India

ISBN: (数字)9798331510022

ISBN: (纸本)9798331510039

Due to the frequency of eye illnesses, effective and precise diagnostic instruments are required. This work suggests an approach that uses low quality fundus images with deep transfer learning more precisely, the EfficientNetB0 architecture to improve eye health diagnosis. We tackle the problem caused by the quality of fundus photographs that are commonly found in clinical settings, which frequently display noise and abnormalities. Our methodology consists of pretraining the EfficientNetB0 model on a sizable dataset of excellent fundus photos, followed by fine-tuning it on a dataset of poor fundus photos. By employing this transfer learning technique, the model enhances its diagnostic capabilities by learning to identify significant features from the low-quality images. We ran tests on a variety of datasets that included fundus photos of varying degrees of deterioration in order to assess our approach. As compared to conventional techniques, the results reveal a significant improvement in diagnostic accuracy, demonstrating the effectiveness of deep transfer learning for improving eye health diagnosis from difficult fundus images. With fused features from MobileNet and DenseNet-121 models, the ANN specifically achieved accuracies of 98.5% for cataracts, 99.1% for diabetic retinopathy, 99% for glaucoma, and 99.5% for normal conditions.

关键词： Training Glaucoma Accuracy Image analysis Instruments Transfer learning Noise Imaging Artificial neural networks Retina

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BLOCK-SECURE: AI-Based Blockchain Enabled Secure Framework for IoMT Applications

BLOCK-SECURE: AI-Based Blockchain Enabled Secure Framework f...

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International Conference on e-health Networking, Applications and Services (HealthCom)

作者： Barkha Panchal Jitendra Bhatia Malaram Kumhar Sudeep Tanwar Ashit Kumar Dutta Joel J.P.C. Rodrigues Department of Computer Science and Engineering Institute of Technology Nirma University Ahmedabad India Dept. of Comp. Science and Information Systems College of Applied Sciences AllMaarefa University Kingdom of Saudi Arabia Amazonas State University Manaus - AM Brazil

ISBN: (数字)9798350350548

ISBN: (纸本)9798350350555

The Internet of Medical Things (IoMT) revolution-izes healthcare by integrating medical devices and systems.with the internet. However, the vast amounts of sensitive medical data in IoMT networks pose significant security and privacy concerns. Traditional security measures often fall short in identifying the malicious data attacks within the IoMT ecosystems. This paper introduces an AI-based non-malicious data classification scheme based on blockchain. We applied and evaluated the machine learning (ML) classifiers, such as support vector machine (SVM), random forest (RF), and K-Nearest neighbor (KNN). We evaluated the proposed framework based on various performance metrics that includes accuracy, precision, recall, and F1 score. The accuracy using SVM obtained 75.3%, RF is 75.9%, and K-NN is 84.3%. The results shows that KNN performs better than other models hy the factor of 9%.

关键词： Support vector machines Radio frequency Performance evaluation Accuracy Biological system modeling Nearest neighbor methods Blockchains Sensors Security Random forests

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Fuzzy-TOPSIS and the Urgency-Seriousness-Growth Scoring Technique to Determine Weights of Uncertainty Issues in School Reopening Factors During COVID-19

Fuzzy-TOPSIS and the Urgency-Seriousness-Growth Scoring Tech...

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IEEE International Conference on Fuzzy systems.(FUZZ-IEEE)

作者： Feby Artwodini Muqtadiroh Tsuyoshi Usagawa Diana Purwitasari Riris Diana Rachmayanti Eko Mulyanto Yuniarno Supeno Mardi Susiki Nugroho Mauridhi Hery Purnomo Dept. of Electrical Engineering Dept. of Information Systems Institut Teknologi Sepuluh Nopember Surabaya Indonesia Dept. of Computer Science and Electrical Engineering Kumamoto University Kumamoto Japan Dept. of Informatics Institut Teknologi Sepuluh Nopember Surabaya Indonesia Dept. of Health Promotion and Behavioral Sciences Faculty of Public Health Universitas Airlangga Surabaya Indonesia Dept. of Electrical Engineering Dept. of Computer Engineering Institut Teknologi Sepuluh Nopember Surabaya Indonesia

The decision to open schools during a pandemic is considerable controversy because it increases the risk of a spike in COVID-19 cases more quickly. However, the decision to open schools is currently based solely on national policies, which generalize opening schools in every region, potentially leading to unintended consequences during the COVID-19 crisis. This study aims to present new decision-making to rank regions determining which schools are safe to open. By focusing on problem prioritization, we integrate the Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) based on a fuzzy approach and the Urgency-Seriousness-Growth (USG) scoring technique, which we call FT-USG. First, we determine the weight of the expert's argument while putting special attention on the level of urgency, seriousness, and growth of COVID-19 to the school opening issue. The fuzzy number is then calculated based on the average weight generated from the USG result. Thus, the weighted normalization computations can be executed on each factor (criteria). Then, we calculate the rankings for each alternative using the Fuzzy TOPSIS method to determine which region and school can safely reopen. To provide a comprehensive ranking that serves as a basis for decision-making, we conduct a case study to show how the suggested approach is used to address the problems.

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Towards Identifying Social Bias in Dialog systems. Framework, Dataset, and Benchmark

Towards Identifying Social Bias in Dialog Systems: Framework...

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2022 Findings of the Association for Computational Linguistics: EMNLP 2022

作者： Zhou, Jingyan Deng, Jiawen Mi, Fei Li, Yitong Wang, Yasheng Huang, Minlie Jiang, Xin Liu, Qun Meng, Helen Dept. of Systems Engineering & Engineering Management The Chinese University of Hong Kong Hong Kong The CoAI Group DCST Institute for Artificial Intelligence State Key Lab of Intelligent Technology and Systems Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China Huawei Noah's Ark Lab Huawei Technologies Ltd

Warning: this paper contains content that may be offensive or upsetting. Among the safety concerns that hinder the deployment of open-domain dialog systems.(e.g., offensive languages, biases, and toxic behaviors), social bias presents an insidious challenge. Addressing this challenge requires rigorous analyses and normative reasoning. In this paper, we focus our investigation on social bias measurement to facilitate the development of unbiased dialog systems. We first propose a novel DIAL-BIAS FRAMEWORK for analyzing the social bias in conversations using a holistic method beyond bias lexicons or dichotomous annotations. Leveraging the proposed framework, we further introduce the CDIAL-BIAS DATASET which is, to the best of our knowledge, the first annotated Chinese social bias dialog dataset. We also establish a fine-grained dialog bias measurement benchmark, and conduct in-dept. analyses to shed light on the utility of detailed annotations in the proposed dataset. Lastly, we evaluate several representative Chinese generative models using our classifiers to unveil the presence of social bias in these systems. © 2022 Association for Computational Linguistics.

关键词： Computational linguistics

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Scalability in Autoencoder-based OFDM Communication System

Scalability in Autoencoder-based OFDM Communication System

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computer Vision and Machine Intelligence (CVMI), International Conference on

作者： Seizan Tsugawa Takao Toma Shiho Oshiro Tomohisa Wada Computer Science and Intelligent Systems Program University of the Ryukyus Okinawa Japan Magna Design Net Inc Okinawa Japan Information Technology Center University of the Ryukyus Okinawa Japan Dept. of Engineering Area of Computer Science and Intelligent Systems University of the Ryukyus Okinawa Japan

This paper proposed Scalability in Autoencoder-based Orthogonal Frequency Division Multiplexing(OFDM) communication system. In the previous research, only the comparison between IEEE802.11a and Autoencoder by the conventional OFDM communication system was performed, and it was proved that the communication system created by Autoencoder exceeded the performance of the conventional system. Therefore, in this paper, it uses IEEE802.11n and compare whether it can be improved by expanding the bandwidth and using ***802.11n standard has an FFT length of 128, a subcarrier number of 114 (108 for data), and modulation schemes of Quadrature Phase Shift Keying(QPSK), 16 Quadrature Amplitude Modulation (16QAM). The GI length is 800ns and the symbol length is 4000ns. In the simulation, a computer simulation was performed using a conventional OFDM communication system and a communication system generated by Autoencoder. Assuming that the simulation environment had an Signal-to-Noise Ratio (SNR) of 0 to 30 and an amplitude r of 0.0 to 1.0, the Symbol Error Rate (SER) status for each SNR was output. As a result of computer simulation, QPSK converged at SNR 27 ㏈ at IEEE 802.11a, but was able to reduce SER overwhelmingly to SNR 12 ㏈ at IEEE 802.11n. Also, in 16QAM the convergence at r=0.0 is the same as for SNR22 ㏈, but in IEEE 802.11a it does not converge after r=0.6, but in IEEE 802.11n it does not converge only at r=0.9 and r=1.0 . As a future task, it will use IEEE802.11ac, which enables communication speeds several times faster than IEEE802.11n, examine whether it is possible to further improve accuracy. And it will continue our research to correspond MIMO communication.

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Intelligent Constellation Generation based on Autoencoder Communication System

Intelligent Constellation Generation based on Autoencoder Co...

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computer Vision and Machine Intelligence (CVMI), International Conference on

作者： Kaisei Matsumoto Takao Toma Shiho Oshiro Tomohisa Wada Computer Science and Intelligent Systems Program University of the Ryukyus Okinawa Japan Magna Design Net Inc Okinawa Japan Information Technology Center University of the Ryukyus Okinawa Japan Dept. of Engineering Area of Computer Science and Intelligent Systems University of the Ryukyus Okinawa Japan

This paper discusses intelligent constellation generation based on autoencoder communication system. In previous studies, the amplitude was set to fluctuate between r=0.0 and 1.0. However, when checking the generated constellation, distortion was confirmed instead of the conventional symbol arrangement. Therefore, in this paper, it compares the case where the amplitude is constant, the case where the average amplitude within a Minibatch is 1, and the case where the average amplitude is 1 for Interval time. The communication standard used in this research is IEEE 802.11a, assuming wireless Local Area Network (LAN) specifications. The IEEE 802.11a standard has an Fast Fourier Transform (FFT) length of 64, a subcarrier number of 52, and Quadrature Phase Shift Keying (QPSK) and 16 Quadrature Amplitude Modulation (QAM), modulation methods. A guard interval of 800 ns is added and the symbol length is 4000 ns. First, a simulation was performed under the condition that the amplitude was kept constant. QPSK with 4 symbols, constant amplitude model is rounded more than previous research result. 16QAM with 16 symbols is arranged regularly like lined up on a line. Second, the simulation was performed under the condition that the average amplitude within the minibatch was set to 1. QPSK with 4 symbols, appears to rotate clockwise. 16QAM with 16 symbols has a more uniform symbol placement than previous research result. Third, a simulation was performed under the condition that the average amplitude within Interval time was set to 1. QPSK with 4 symbols, is the closest to square among QPSK output results so far. The direction is slightly tilted, but if it can be rotated a little more, it may be possible to reproduce the same symbol arrangement as before. 16QAM with 16 symbols, the symbol arrangement is biased as a whole. However, it can be seen that are arranged in line on the line, perhaps due to regularity. As future work, in addition to the conditions set this time, it will exa

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Establishing Wireless Intranet Network Using UAVs and Web Application for Emergency Communications

Establishing Wireless Intranet Network Using UAVs and Web Ap...

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IEEE Sensors Applications Symposium, SAS

作者： Jihoon Yang Juhyun Kim Sion Kang Haeeun Ok Yijun Yoo Hyewon Koh Pilkyo Kim Alexander L. Head Anthony Smith Dept. of Computer Education Jeju National University Jeju Republic of Korea Dept. of Management Information Systems Jeju National University Jeju Republic of Korea Dept. of Computer Science and Engineering Chungnam National University Daejeon Republic of Korea Dept. of Computer Engineering Jeju National University Jeju Republic of Korea Major of Housing & Family Welfare(Faculty of Human Ecology & Welfare) Jeju National University Jeju Republic of Korea Dept. of Computer Science & Statistics Jeju National University Jeju Republic of Korea Dept. of Computer and Information Technology Purdue University West Lafayette Indiana USA

Every year, natural disasters such as earthquakes, hurricanes, volcanic eruptions, and tsunamis occur around the world. Natural disasters not only cause economic damage and casualties, but also destroy entire infrastructures. When communication infrastructure is damaged, information exchange between rescuers and victims or among rescuers themselves is not smooth, which inevitably reduces the effectiveness of relief activities. The purpose of this study is to establish a communication network using unmanned aerial vehicles (UAVs) known as drones, and to provide smooth communication services to areas where communication facilities have been destroyed through web application development. A low-altitude aerial base station (ABS) is built using a drone and provides a wireless intranet communication network. The intranet-only web application is developed to provide a service that allows people to send and receive messages and location information. In future research, separating the communication network between rescuers and non-rescuers and using multiple ABSs to build the network could serve more people in a wider disaster area.

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USDOT Tier-1 University Transportation Center for Advancing Cybersecurity Research and Education

USDOT Tier-1 University Transportation Center for Advancing ...

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Mobility, Operations, Services and technologies (MOST), IEEE International Conference on

作者： Yunpeng Jack Zhang Kailai Wang Lingguang Song Amaury Lendasse Houbing Herbert Song Zhu Han Rakesh Verma Arlei Silva Carlos Rubio-Medrano Zhixia Richard Li Guohui Zhang Dept. of Information Science Technology University of Houston Houston TX USA Dept. of Construction Management University of Houston Houston TX USA Dept. of Information Systems University of Maryland Baltimore County Baltimore MD USA Dept. of Electrical & Computer Engineering University of Houston Houston TX USA Dept. of Computer Science University of Houston Houston TX USA Dept. of Computer Science Rice University Houston TX USA Dept. of Computer Science Texas A&M University-Corpus Christi Corpus Christi TX USA Dept. of Civil and Architectural Engineering and Construction Management University of Cincinnati Cincinnati OH USA Dept. Civil and Environmental Engineering University of Hawai’i at Mānoa Honolulu HI USA

The Transportation Cybersecurity Center for Advanced Research and Education (CYBER-CARE) is a US Department of Transportation (USDOT) Tier-1 University Transportation Center (UTC) funded in 2023. CYBER-CARE primarily focuses on the USDOT statutory research priority area of “Reducing Transportation Cybersecurity Risks.” CYBER-CARE aims to establish a fundamental knowledge basis and explore advanced theory to mitigate the impacts of large-scale cyberattacks on transportation infrastructure and connected and automated vehicle (CAV) systems. The research projects at CYBER-CARE will develop conceptual frameworks, construct comprehensive datasets, explore novel analytical approaches, support the implementation of public policies and infrastructure investments, and build a high-quality industry workforce through education. All CYBER-CARE research projects can be organized into four thrusts: CAV cybersecurity, transportation data security, advanced traffic management system (ATMS) cybersecurity, and next-generation transportation cybersecurity systems. In addition, CYBER-CARE will accelerate industry collaborations, foster new technologies, and provide professionals with the skills and opportunities needed to become successful leaders in their fields. Notably, as CYBER-CARE will prioritize engagement with underrepresented minorities, these communities stand to benefit from professional development training in transportation cybersecurity.

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Automatic Interaction and Activity Recognition from Videos of Human Manual Demonstrations with Application to Anomaly Detection

Automatic Interaction and Activity Recognition from Videos o...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Elena Merlo Marta Lagomarsino Edoardo Lamon Arash Ajoudani Human-Robot Interfaces and Interaction Laboratory Istituto Italiano di Tecnologia Genoa Italy Dept. of Informatics Bioengineering Robotics and Systems Engineering University of Genoa Genoa Italy Dept. of Electronics Information and Bioengineering Politecnico di Milano Milan Italy Dept. of Information Engineering and Computer Science University of Trento Trento Italy

This paper presents a new method to describe spatio-temporal relations between objects and hands, to recognize both interactions and activities within video demonstrations of manual tasks. The approach exploits Scene Graphs to extract key interaction features from image sequences while simultaneously encoding motion patterns and context. Additionally, the method introduces event-based automatic video segmentation and clustering, which allow for the grouping of similar events and detect if a monitored activity is executed correctly. The effectiveness of the approach was demonstrated in two multi-subject experiments, showing the ability to recognize and cluster hand-object and object-object interactions without prior knowledge of the activity, as well as matching the same activity performed by different subjects.

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Self-Optimizing Distributed Cloud Computing with Dynamic Neural Resource Allocation and Fault-Tolerant Multi-Agent systems

Self-Optimizing Distributed Cloud Computing with Dynamic Neu...

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Ubiquitous Computing and Intelligent information systems.(ICUIS), International Conference on

作者： N. Tripura P. Divya Koushik Reddy Chaganti Kota Venkateswara Rao P. Rajyalakshmi P. Naresh Dept of MBA Vignan Institute of Technology And Science Hyderabad Dept. of Computer and Information Systems University of the Cumberlands KY USA Dept of CSE Andhra Loyola Institute of Engineering and Technology Vijayawada Dept of CSE TKR College of Engineering and Technology Hyderabad Dept of CSE Dayananda Sagar University Bangalore

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529642

The exponential growth of distributed cloud systems.necessitates an intelligent form of workload management that ensures optimum performance, scalability, and reliability. Traditional approaches based on static or heuristic resource allocation and fault-tolerance mechanisms cannot capture the dynamic nature of modern workloads and heterogeneous infrastructures. Drawing from this source of inspiration, this work develops a holistic framework integrating machine learning models that facilitate self-optimizing distributed cloud computing for dynamic workload management. Three components are significant in the proposed framework: (1) DNRA; this uses a hybrid GNN and RL architecture for real-time resource allocation, offering up to 25% improvement in resource utilization and a 30% reduction in task latency. (2) Workload-Aware Transfer Learning Scheduler (WATLS), leveraging transfer learning to predict workload dynamics across heterogeneous nodes, achieving 20% higher task throughput and 15% energy savings through proactive scheduling. (3) Multi-Agent Predictive Fault-Tolerant System (MAP-FTS), combining multi-agent systems.with predictive fault detection to preemptively redistribute tasks and schedule maintenance, reducing system downtime by 50% and enhancing fault detection accuracy to 90%. The framework integrates novel methodologies towards answering the main challenges of resource allocation, workload prediction, and fault tolerance. The amount of progress achieved through cumulative contributions includes improvement in system reliability and scalability up to 5x their nodes and respective scalable energy efficiency without any degradation of performance. This work paves the pathway for an adaptive and intelligent next generation of distributed cloud systems.

关键词： Fault tolerance Cloud computing Event detection Scalability Fault tolerant systems Transfer learning Dynamic scheduling Ubiquitous computing Resource management Multi-agent systems

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