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A Deep Learning Based Critical Analysis Of Skin Lesion Classification

A Deep Learning Based Critical Analysis Of Skin Lesion Class...

引用

2024 International Conference on Advances in Modern Age Technologies for Health and engineering science, AMATHE 2024

作者： Rahul Gupta, Meenu Kumar, Rakesh Obaid, Ahmed J. Chandigarh University Dept. Of Cse Punjab India University Of Kufa Faculty Of Computer Science And Mathematics Najaf Iraq College Of Technical Engineering National University Of Science And Technology Dhi Qar Iraq

ISBN: (纸本)9798350371567

In everyday life, various exposures at work or at home can lead to skin conditions such as allergies or infections. Skin lesions serve as essential indications, alerting to future issues and requiring immediate care and attention. To search for publications concerning skin lesions and diverse procedures, the most well-known scientific databases, such as web of science, scopus, and IEEE Xplore, were used. To find comparable documents, search engines use keywords such as 'skin lesion,' 'CNN,' 'skin diseases,' and so on. Following a comprehensive screening method, 658 abstracts from databases are collected, and 19 full-text articles are identified for consideration. This paper investigates the profound impact of machine learning in dermatology, with a focus on skin lesion classification. This study provides a critical examination of CNN-based models, emphasizing their importance and difficulties in increasing precision diagnosis and treatment in dermatology. Overall, this study provides helpful insights into various approaches for skin lesion classification. © 2024 IEEE.

关键词： Dermatology

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Lung Disease Classification Using Deep Learning Models from Chest X-ray Images 3

Lung Disease Classification Using Deep Learning Models from ...

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3rd International Conference on Intelligent Communication and Computational Techniques, ICCT 2023

作者： Sultana, Salma Pramanik, Anik Rahman, Md. Sadekur Jahangirnagar University Dept. of Statistics Dhaka Bangladesh Virtual Multidisciplinary Research Lab Dhaka Bangladesh Daffodil International University Dept. of Computer Science & Engineering Dhaka Bangladesh

ISBN: (纸本)9781665453578

In the very recent past, Infectious disease-related sickness has long posed a concern on a global scale. Each year, COVID-19, pneumonia, and tuberculosis cause a large number of deaths because they all affect the lungs. Early detection and diagnosis can increase the likelihood of receiving quality treatment in all circumstances. A low-cost, simple imaging approach called chest X-ray imaging enables to detection and screen lung abnormalities brought on by infectious diseases for example Covid-19, pneumonia, and tuberculosis. This paper provided a thorough analysis of current deep-learning methods for diagnosing Covid-19, pneumonia, and TB. According to the research papers reviewed, Deep Convolutional Neural Network is the most used deep learning method for identifying Covid-19, pneumonia, and TB from chest X-ray (CXR) images. We compared the proposed DNN to well-known DNNs like Efficient-NetB0, DenseNet169, and DenseNet201 in order to more accurately assess how well it performed. Our findings are equivalent to the state-of-the-art, and since the proposed CNN is lightweight, it may be employed for widespread screening in areas with limited resources. From three diverse publicly accessible datasets merged into one dataset, the suggested DNN generated the following precisions for that dataset: 99.15%, 98.89%, and 97.79% for EfficientNetB0, DenseNet169, and DenseNet201 respectively. The proposed network can help radiologists make quick and accurate diagnoses because it is effective at identifying COVID-19 and other lung contagious disorders utilizing chest X-ray images. This paper also gives young scientists a good insight into how to create CNN models that are highly efficient when used with medical images to identify diseases early. © 2023 IEEE.

关键词： COVID-19

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IFA in Named Data Networking (NDN) : Case Study on INHERENT Topology 9

IFA in Named Data Networking (NDN) : Case Study on INHERENT ...

引用

9th International Conference on Wireless and Telematics, ICWT 2023

作者： Tiarama, Ribka Gumelar, Agil Fuad Rayhan, Muhammad Nur Pirusita, Nauroha Shofiyyah Jupriyadi Ahdan, Syaiful Syambas, Nana Rachmana Bandung Institute of Technology Dept. of Telecommunication Engineering Bandung40132 Indonesia Universitas Teknokrat Indonesia Faculty of Engineering & Computer Science Lampung35132 Indonesia

ISBN: (纸本)9798350305029

Named Data Networking (NDN) is part of an NSF research project that started in 2010 and was created to be implemented as a future Internet architecture. NDN as a network service has evolved from an Internet host-based packet delivery model to an Internet content-based packet delivery model. Attackers design various attacks for NDN networks by exploiting the interaction between interest packets and data packets. IFA is one type of DDoS attack on NDN networks. IFA attackers would overload intermediate routing nodes by requesting a large number of fake Interest packets. In this paper, the interest-flooding attack model is tested by varying the attack time and location of the attacker. The results show that IFA attacks are more effective when the consumer is closer to the producer due to the increased latency resulting from the attack. © 2023 IEEE.

关键词： Floods

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Cross-Domain Adaptation Through Adversarial Teaching for Unmanned Ariel Vehicles in Smart Cities

Cross-Domain Adaptation Through Adversarial Teaching for Unm...

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Learning and Technology Conference (L&T)

作者： Qazi Mazhar Ul Haq Leethar Yao Bo-Yu Lin Dept. of IBPI and Computer Science and Engineering Yuan Ze University Zhongli Taiwan Dept. of Electrical Engineering National Taipei University of Technology Taipei Taiwan

ISBN: (数字)9798331532215

ISBN: (纸本)9798331532222

Person re-identification has been an important issue of surveillance systems in smart cities. However, this requires huge datasets to supervise deep learning models for accurately identifying and tracking people in smart cities. In this paper, we propose adversarial learning in integration with a mutual mean teaching model known as adversarial teaching to take the issue of domain adaptation. Initially, they learn a source dataset in a supervised way. This model information is then transferred to another model through unsupervised learning and pseudo-labels are generated by generative adversarial learning. The multi-class invariance is handled by adversarial learning. The performance of the proposed model is evaluated on two different datasets source and target datasets. Multiple experiments on Duke and Market datasets indicate higher accuracy of F-1 score on both datasets compared to previous methods, demonstrating the superior performance of the proposed method.

关键词： Adaptation models Smart cities Surveillance Education Generative adversarial networks Data augmentation Adversarial machine learning Robustness Unsupervised learning Identification of persons

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Hybrid CPU-GPU Acceleration of a Multithreaded Image Stitching Algorithm 7

Hybrid CPU-GPU Acceleration of a Multithreaded Image Stitchi...

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7th International Conference on computer science and engineering, UBMK 2022

作者： Tesfay, Shewit W. Demirdag, Zeynep G. Ugurdag, H. Fatih Ates, Hasan F. Özyǧein University Dept. of Electrical and Electronics Engineering Istanbul Turkey Istanbul Medipol University Dept. of Computer Engineering Istanbul Turkey

ISBN: (数字)9781665470100

ISBN: (纸本)9781665470100

Real-time image stitching is critical, especially in un-manned aerial vehicles, and its acceleration has received attention in recent years. This paper describes an image stitching acceleration scheme for heterogeneous (CPU+GPU) devices. Acceleration is attempted with both multithreading and multiprocessing. Most time-critical functions in the algorithm are offloaded on to the GPU. We crafted a 3-buffer ping-pong mechanism for synchro-nization and data transfer among threads/processes in order to maximize CPU utilization. We carried out our experiments on Nvidia Jetson AGX Xavier. Results show that more than 3x acceleration is achieved. © 2022 IEEE.

关键词： Graphics processing unit

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Exploring emerging spreaders through GCN-based link prediction and a novel centrality method

Exploring emerging spreaders through GCN-based link predicti...

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International Communication Systems and Networks and Workshops, COMSNETS

作者： Suman Nandi Giridhar Maji Animesh Dutta Dept. of Computer Science and Engineering National Institute of Technology Durgapur West Bengal India Dept. of Electrical Engineering Asansol Polytechnic West Bengal India

ISBN: (数字)9798331531195

ISBN: (纸本)9798331531201

Exploring influential spreaders and predicting missing links in complex networks is essential for understanding and effectively controlling network dynamics. This paper presents a Graph Convolutional Network (GCN)-based link prediction method to estimate the probability of future link formation. We incorporate node features that capture local and global topological connectivity structures and feed these into the GCN model, where convolutional layers aggregate neighboring information and transform node features. This approach enables the model to capture structural patterns by integrating local and global information from neighboring nodes. In the final layer, the GCN model computes a prediction score representing the likelihood of an edge’s existence, using insights gained during training. Finally, considering the predicted links, we update the network structure and introduce a novel centrality method called Emerging Spreader Centrality (ESC) to identify emerging spreaders within this augmented network. We conduct two separate experiments to evaluate the performance of the GCN-based link prediction and the ESC method, comparing their effectiveness with various state-of-the-art methods. Results demonstrate that our approach not only effectively predicts future links but also identifies emerging spreaders in the augmented networks.

关键词： Training Graph convolutional networks Communication systems Computational modeling Aggregates Transforms Complex networks Predictive models Feeds

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A Wearable RF Energy Harvester Based on Spoof Surface Plasmonic Antenna Array

A Wearable RF Energy Harvester Based on Spoof Surface Plasmo...

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2022 International Applied Computational Electromagnetics Society Symposium, ACES-China 2022

作者： Liu, Zhu Li, Jinxin Xiao, Pei Hunan Normal University Dept. Physics and Electronics Changsha China Hunan University Dept. Computer Science and Electronic Engineering Changsha China Hunan University Dept. Electrical and Information Engineering Changsha China

ISBN: (纸本)9781665452366

This paper presents a wearable RF energy harvester based on spoof surface plasmonic (SSP) antenna array. Four separate antennas are connected by the four bending SSP waveguides, and the SSP antenna array is formed. The loop is applied to excite the SSP structure to generate surface waves and converted to the radiative wave into the space. The rectify circuit is integrated with loop to output DC power and fabricated by the polyimide so that it can conform with different sensing modules. The proposed harvester is fabricated by the conductive and nylon fabrics in a compact and low-profile structure to be easily integrated with clothing. The maximum harvested RF-DC power is 126.24 μ W under a power density of 2.75 μ W/cm2 when the proposed structure is worn on phantom. The simulation and measurement results demonstrate that the wearable RF energy harvester has the potential to power body sensor network. © 2022 IEEE.

关键词： Antenna arrays

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Threshold Sensitivity in Two-Channel Modulo ADCs: Analysis and Robust Reconstruction

Threshold Sensitivity in Two-Channel Modulo ADCs: Analysis a...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Wenyi Yan Lu Gan Yimin D. Zhang Dept. of Electrical and Electronic Engineering Brunel University London UK Dept. of Electrical and Computer Engineering Temple University Philadelphia PA USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

This paper presents a comprehensive analysis of two-channel modulo analog-to-digital converters (ADCs) systems, focusing on the sensitivity of ADC thresholds. By exploiting analytic number theory, we first investigate the relationship among ADC threshold precision, maximum signal dynamic range, and error tolerance. Our analysis reveals that even slight deviations in ADC thresholds can substantially impact the maximum reconstructed signal dynamic range and error tolerance. To address these sensitivity issues, we propose a novel approach that strategically sacrifices signal dynamic range to stabilise error tolerance in the presence of slight ADC threshold variations. We also introduce a low-complexity reconstruction algorithm that exploits this trade-off, thereby enhancing system robustness. Simulation results validate the theoretical framework and confirm the efficiency of our proposed algorithm.

关键词： Sensitivity Quantization (signal) Heuristic algorithms Simulation Signal processing algorithms Dynamic range Reconstruction algorithms Robustness Speech processing Analog-digital conversion

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On-Demand Current Pulse Activation of RF Monopole Antenna Biosensor Arrays with Nitrocellulose Membranes

On-Demand Current Pulse Activation of RF Monopole Antenna Bi...

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2022 United States National Committee of URSI National Radio science Meeting, USNC-URSI NRSM 2022

作者： Shanmugadas, Sindu Lundsquit, Jonathan Topsakal, Erdem Ozgur, Umit Avrutin, Vitaliy Virginia Commonwealth University Dept. of Electrical and Computer Engineering RichmondVA23284 United States

ISBN: (数字)9781946815156

ISBN: (纸本)9781946815156

Continuous glucose monitors currently on the market are expensive and uncomfortable due to their short operational lifespans of 14 days or less limited by biofouling. To address this problem, we propose a biosensor array skin patch that would increase the device functional lifetime significantly. Each sensor, based on a quarter wave monopole antenna, is housed in a PDMS well. The sensors read changes in voltage reflection coefficient (dB). Combustible nitrocellulose membranes prevent early contact of sensors with the interstitial fluid. A current pulse passed through a metal filament sufficiently disintegrated the membrane on top to allow interstitial fluid to enter thereby activating a new sensor. © 2022 USNC-URSI.

关键词： Monopole antennas

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Accelerating supersingular isogeny Diffie-Hellman (SIDH) cryptosystem for the security of resource-constrained IoT devices with FPGA 3

Accelerating supersingular isogeny Diffie-Hellman (SIDH) cry...

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3rd IEEE International Conference on Electronic engineering, ICEEM 2023

作者： Khadra, Shaimaa Abo Ismail, Nabil A. Attiya, Gamal M. Abdulrahman, Salah Eldin S. E. High Institute of Engineering and Technology Dept. Mechatronics Engineerng Al-Mahalla-El-Kobra Egypt Menoufia University Faculty of Electronic En. Dept. Computer Science & Engineering Menouf Egypt

ISBN: (纸本)9798350323511

It is imperative to note that post-quantum cryptography, such as supersingular isogeny Diffie-Hellman (SIDH), is essential for ensuring that Internet of Things (IoT) devices have a restricted amount of resources. The primary challenges in assuring the security of IoT devices are addressed by this work's analysis of SIDH implementations designed for field programmable gate array (FPGA) architecture. Extensive efforts towards implementing the architecture for a rapid and constant-time FPGA implementation of SIDH. This quantum-resistant cryptographic primitive is a crucial component for adhering to NIST's PQC standardization. Our goal with this paper is to demonstrate how FPGA architectures can enhance the parallelism of SIDH, making it a more practical option for securing resource-limited IoT devices. Our focus is on providing a reliable speed record for SIDH, which is crucial for ensuring the security of IoT devices. The design for isogeny computation was built over p434 in Xilinx Virtex 6 and produced 45ms for public key generation in addition to 35ms for secret key generation. © 2023 IEEE.

关键词： Internet of things

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