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MultiSCCHisto-Net-KD: A deep network for multi-organ explainable squamous cell carcinoma diagnosis with knowledge distillation

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computers in Biology and Medicine 2025年 185卷 109469-109469页

作者： Prabhu, Swathi Prasad, Keerthana Hoang, Thuong Lu, Xuequan Department of Information and Communication Technology Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India Manipal School of Information Sciences Manipal Academy of Higher Education Karnataka Manipal576104 India School of Information Technology Faculty of Science Engineering and Built Environment Deakin University GeelongVICVIC 3220 Australia Department of Computer Science and IT La Trobe University MelbourneVICVIC 3086 Australia

Squamous cell carcinoma is a prevalent cancer type that affects various organs in the human body. Manual analysis for detecting squamous cell carcinoma in histopathological images is time-consuming and may be subjective. Squamous cell carcinoma diagnosis is typically based on the differences in the architectural arrangement of squamous epithelial layers and the presence of keratinization. However, the existing literature has predominantly concentrated on identifying cellular irregularities with high magnification images and considering specific organs of squamous cell carcinoma origin. In contrast, relatively little attention has been given to recognizing structural abnormalities observable at low magnification images. In this paper, we consider squamous cell carcinoma histopathological images across different organs of origin captured at low magnification and these images are gathered from various centers to develop a robust model. We propose a novel deep neural network model (MultiSCCHisto-Net) that can detect squamous cell carcinoma of any organ irrespective of organ of origin. In addition, deep neural networks used for histopathological image analysis typically have many parameters, making them computationally expensive. To address this research gap, we incorporate knowledge distillation, which compresses knowledge from a complex teacher model (MultiSCCHisto-Net) into a smaller student model (MultiSCCHisto-Net-KD) while preserving performance and enhancing the generalization of the student model by learning from the teacher's intermediate layer features. Moreover, an explainable deep learning technique called gradient-weighted class activation mapping is incorporated to highlight the image areas that help to classify the sample into particular classes. This explainability significantly enhances our confidence in the proposed model outcomes. We evaluate the model's robustness using private multi-centric and publicly available datasets. Our results show that accur

关键词： Teaching

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COVID-19 detection on chest x-ray image using yolo based architecture

COVID-19 detection on chest x-ray image using yolo based arc...

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Artificial Intelligence and Smart Communication (AISC), International Conference on

作者： Bhupendra Patel Deep Kothadiya Ronak Patel U. & P U. Dept. Of Computer Engineering Chandubhai S. Patel Information & Technology(CSPIT) Faculty of Technology & Engineering (FTE) Charotar University of Science and Technology ( CHARUSAT) Anand Gujarat India

The whole world has been facing the problem of novel Coronavirus (COVID-19) since 2020. Over 88 million cases are confirmed and around 5 lacks deaths are accounted. Using the Lung-Computed Tomography (CT) Lesion Segmentation dataset, deep learning techniques may be used to quickly identify COVID-19 and the exact region that is infected. Based on CT, it is easy to identify the problem and the infected area, then assisting treatment of COVID-19. In the literature survey, research study has considered many research papers worked done work on identification of COVID-19 using chest/lungs X-ray image, and with that identified what are the deep learning-based models or methodology they have used for detecting COVID-19 result. To overcome their result, Authors have proposed a latest methodology of deep learning with the YOLO variant 7x to get optimum result of COVID -19 detection from lungs X-ray image. To identify COVID-19, Authors have applied proposed methodology on publically avail X-ray image-based dataset of COVID-19, proposed methodology has achieved good performance to detect COVID infection from lungs.

关键词： COVID-19 Deep learning Computed tomography Lung computer architecture Data models Lesions

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Analyzing Code Injection Attacks on Applications of Android Devices and Emulator

Analyzing Code Injection Attacks on Applications of Android ...

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International Conference on Computing, engineering, and Design (ICCED)

作者： Adhitia Erfina Rahmat Hidayat Rafli Radya Rizaldi Puput Riko Maulana Falentino Sembiring Departement of Infomation System Faculty of Engineering Computing and Design Nusa Putra University Sukabumi Indonesia Master of Computer Science Faculty of Information Technology Budi Luhur University Jakarta Indonesia

The Android operating system released by Google in 2007 has experienced very significant development. In 2019, the number of Android application users reached 2.5 billion. One of the contributing factors is the nature of the android application, which is very flexible and easy to install. Users can download APK files on the internet. Generally, APK files are stored in archive format, so they are pretty easy to manage. However, APK files may be used to spread malware behind this convenience. This study aims to provide an overview of the process of planting malware/backdoor on Android applications and how to prevent it. This study initiated the injection of malicious code on five android applications, namely WhatsApp, Telegram, DroidCam, Fifa Mobile, and Tokopedia. According to the results, four applications were successfully injected, while one application failed. The failure most likely occurred as a result of the application’s source code being obfuscated. Obfuscation is therefore strongly advised for Android applications to prevent code injection that could potentially lead to malware insertion.

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A Two-Chip-Cascaded 4D Millimeter-Wave Imaging Radar Aiming for Automotive SLAM

A Two-Chip-Cascaded 4D Millimeter-Wave Imaging Radar Aiming ...

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Global Symposium on Millimeter Waves, GSMM

作者： Wogong Zhang Nannan Li Congzhi Li Dongdong Dong Cheng Wang Erich Kasper CHUHANG Technology GmbH Leinfelden-Echterdingen Germany Nanjing Chuhang Technology Co. Ltd. Nanjing P.R. China Faculty 05: Computer Science Electrical Engineering & Information Technology University of Stuttgart Stuttgart Germany

ISBN: (数字)9798350384956

ISBN: (纸本)9798350384963

Since the concept of simultaneous localization and mapping (SLAM) was proposed and investigated in 1980s, it has been attracting continuous attentions not only from classic robotics regime but also from extended virtual reality (VR)/amplified reality (AR), unmanned aerial vehicle (UAV), and autonomous driving applications, respectively. This paper focuses on a SLAM oriented solution merely using millimeter-wave 4D imaging radar for autonomous parking function. The two-chip cascaded 4D radar offers totally 6 transmitting and 8 receiving physic channels in RF front-end. By applying sparse antenna array and multi-input multi-output (MIMO) technique, 48 virtual channels can be emulated, which achieve angle separation each of 1.5°on azimuth and 3°on elevation plane. With maximum detection range of 306 m, the implemented 4D radar can demonstrate reasonable imaging effect aiming for SLAM application by generating 10240 points/s in various on-vehicle low-speed outdoor test scenarios.

关键词： Simultaneous localization and mapping Azimuth Imaging Radar detection Radar imaging Millimeter wave radar Radar antennas

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Experimental research on the colour interpolation algorithm of the full colour image 3

Experimental research on the colour interpolation algorithm ...

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3rd International Conference on computers, information Processing and Advanced Education, CIPAE 2022

作者： Tang, Haoran Wang, Zhuoyang Yang, Jianhui Zhou, Sichen Leeds College Southwest Jiaotong University Chengdu611756 China International College Macao University of Science and Technology China College of Computer and Engineering Tianshi College Tianjin301723 China Xi'An Jiaotong University Faculty of Electronic and Information Engineering Xi'an710049 China

ISBN: (纸本)9781665468121

With the rapid development of multimedia technology, images have become the main data form of people's daily communication and information expression. In order to ensure fast data transmission, much effort has been made to compress the three-channel color image into the 2D matrix and then recover the image from the 2D matrix through interpolation algorithms. However, there usually exists different degrees of edge blurring or noise generation when reconstructing images, which significantly affects the accuracy of reconstruction. More importantly, we find that different interpolation algorithms may have drastic performance differences when dealing with different scene images. All of these aspects inspire us to explore the application boundary of interpolation algorithms. In this paper, we compare the representative bilinear interpolation, cok interpolation and Hibbard interpolation from the degree of edge blurring, the amount of noise generated and the processing speed. With extensive experiments, both visual and quantitative analysis shows an interesting but meaningful result. Three algorithms obtain a similar accuracy for the image with a single color or simple lines, while the Hibbard algorithm could restore the most realistic image when the image structure is complex. © 2022 IEEE.

关键词： Image reconstruction

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Investigating the Role of Blockchain in Enhancing Home Security: A Comprehensive Study

Investigating the Role of Blockchain in Enhancing Home Secur...

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Student Conference on Research and Development (SCOReD)

作者： Seyedmostafa Safavi Sadaf Safavi Zarina Shukur School of Technology Asia Pacific University of Technology & Innovation Kuala Lumpur Malaysia Faculty of Computer Engineering Mashhad Branch Islamic Azad University Mashhad Iran Faculty of Information Science & Technology Universiti Kebangsaan Malaysia Selangor Malaysia

ISBN: (数字)9798350318821

ISBN: (纸本)9798350318838

Blockchain technology demonstrates exceptional potential as a self-sufficient and robust data structure, earning recognition in various fields such as finance, supply chain management, and healthcare. Yet, its application within the realm of residential security remains largely untapped. This study probes into the potential of blockchain technology to fortify home security systems, conducting a thorough review of blockchain literature and basing theoretical evaluation on its inherent security features, with a particular emphasis on its implementation in home security. The paper critically examines various blockchain models, assessing their suitability for application in home security systems. It also presents a thoughtful discussion of the challenges and constraints that may arise when applying blockchain technology in this specific context. The findings of this investigation underscore blockchain’s ability to revolutionize home security systems, proposing innovative methods with a focus on data privacy, access management, and fraud prevention. The study also brings attention to technical and regulatory hurdles that need to be surmounted to fully unleash the potential of blockchain in home security systems. Thus, the paper not only highlights the vast possibilities of employing blockchain technology in home security but also underscores the necessity for careful consideration and resolution of related challenges.

关键词： Access control Technological innovation Supply chain management Reviews Scalability Blockchains Fraud

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A Method of Deep Reinforcement Learning for Simulation of Autonomous Vehicle Control 16

A Method of Deep Reinforcement Learning for Simulation of Au...

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16th International Conference on Evaluation of Novel Approaches to Software engineering, ENASE 2021

作者： Huynh, Anh T. Nguyen, Ba-Tung Nguyen, Hoai-Thu Vu, Sang Nguyen, Hien D. Faculty of Software Engineering University of Information Technology Ho Chi Minh City Viet Nam Faculty of Information Systems University of Information Technology Ho Chi Minh City Viet Nam Faculty of Computer Science University of Information Technology Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh City Viet Nam

ISBN: (纸本)9789897585081

Nowadays autonomous driving is expected to revolutionize the transportation sector. Carmakers, researchers, and administrators have been working on this field for years and significant progress has been made. However, the doubts and challenges to overcome are still huge, regarding not only complex technologies but also human awareness, culture, current traffic infrastructure. In terms of technical perspective, the accurate detection of obstacles, avoiding adjacent obstacles, and automatic navigation through the environment are some of the difficult problems. In this paper, an approach for solving those problems is proposed by using of Policy Gradient to control a simulated car via reinforcement learning. The proposed method is worked effectively to train an agent to control the simulated car in Unity ML-agents Highway, which is a simulating environment. This environment is chosen from some criteria of an environment simulating autonomous vehicle. The testing of the proposed method got positive results. Beside the average speed was well, the agent successfully learned the turning operation, progressively gaining the ability to navigate larger sections of the simulated raceway without crashing. Copyright © 2021 by SCITEPRESS - Science and technology Publications, Lda. All rights reserved

关键词： Reinforcement learning

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Performance Evaluation of D2D Communications in of LTE-A Network

Performance Evaluation of D2D Communications in of LTE-A Net...

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Sciences and Techniques of Automatic Control and computer engineering MI-STA, IEEE International Maghreb Meeting of the Conference on

作者： Mona Bakri Hassan Rashid A. Saeed Othman O. Khalifa Elmustafa Sayed Ali Rania A. Mokhtar Department of Electronics Engineering Sudan University of Science and Technology Khartoum Sudan Department of Computer Engineering College of Computers and Information Technology Taif University Taif Saudi Arabia Department of Electrical and Computer Engineering International Islamic University Malaysia Gombak Malaysia Libyan Center for Engineering Resarch and Information Technology Bani Waleed Libya Department of Electrical and Electronics Engineering Faculty of Engineering Red Sea University Portsudan Sudan

In LTE-Advanced, device-to-device (D2D) communication is a technology enables to increase the coverage and capacity coverage in metropolitan areas. Improvements in energy efficiency, throughput, latency, spectrum efficiency, and interference reduction have all been proposed. It is used in many domains, including applications, network traffic offloading, and public safety, and is recognized as one of the viable ways for the 5G wireless communications system. LTE advanced might perform worse in D2D communications because of multipath impact and delay dispersion. It is an essential and important element for the design of receivers in mobile communication systems. In this study, the spectrum efficiency, throughput, data rate, signal-to-interference-pulse-noise ratio, and latency of LTE-based D2D communications will be examined (SINR).

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Bitcoin Price Prediction Using Cuckoo Search Algorithm for Feature Selection with LSTM Model

Bitcoin Price Prediction Using Cuckoo Search Algorithm for F...

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Electrical, Energy, and Artificial Intelligence (EICEEAI), International engineering Conference on

作者： Elnaz Radmand Jamshid Pirgazi Mahmoud Odeh Ali Ghanbari Sorki Faculty of Electrical and Computer Engineering University of Science and Technology of Mazandaran Behshahr Iran Cyber security Department School of information technology Zarqa University Zarqa Jordan College of Engineering University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798350373363

ISBN: (纸本)9798350373370

Deep learning is now a top method for predicting the price of Bitcoin, providing an excellent and well thought out strategy. This technique makes use of a wealth of historical data from reliable sources covering the first ten years of Bitcoin. Technical data and complex patterns are deeply integrated into the dataset to enhance the model's comprehension of complex Bitcoin price patterns. These additions greatly deepen our understanding of the various factors affecting the price of Bitcoin. This method is based on a strategic procedure that involves the careful selection of certain high-performing characteristics. These traits go through a stringent screening process that uses advanced algorithms such as the Cuckoo algorithm. The chosen features are then smoothly incorporated into an encoder-decoder model of LSTM (Long Short-Term Memory), improving the model's ability to predict Bitcoin prices. In this case, it is critical to apply deep learning methods, especially the LSTM encoder-decoder model. Through the integration of technical indicators and sophisticated selection techniques with historical Bitcoin data, this approach leverages deep learning capabilities to identify intricate patterns in Bitcoin pricing movements. In the end, this gives the model the ability to forecast future changes in the price of Bitcoin with accuracy.

关键词： Deep learning Bitcoin Pricing Predictive models Prediction algorithms Feature extraction Data models

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A Fast and Effective Multiple Kernel Clustering Method on Incomplete Data

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computers, Materials & Continua 2021年第4期67卷 267-284页

作者： Lingyun Xiang Guohan Zhao Qian Li Gwang-Jun Kim Osama Alfarraj Amr Tolba Changsha Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation School of Computer and Communication EngineeringChangsha University of Science and TechnologyChangsha410114China Hunan Key Laboratory of Smart Roadway and Cooperative Vehicle-Infrastructure Systems Changsha University of Science and TechnologyChangsha410114China Faculty of Engineering and Information Technology Global Big Data Technologies CentreUniversity of Technology SydneyUltimoNSW2007Australia Department of Computer Engineering Chonnam National UniversityGwangju61186Korea Computer Science Department Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Department of Mathematics and Computer Science Faculty of ScienceMenoua UniversityShebin-El-kom32511Egyp

Multiple kernel clustering is an unsupervised data analysis method that has been used in various scenarios where data is easy to be collected but hard to be ***,multiple kernel clustering for incomplete data is a critical yet challenging *** the existing absent multiple kernel clustering methods have achieved remarkable performance on this task,they may fail when data has a high value-missing rate,and they may easily fall into a local *** address these problems,in this paper,we propose an absent multiple kernel clustering(AMKC)method on incomplete *** AMKC method rst clusters the initialized incomplete ***,it constructs a new multiple-kernel-based data space,referred to as K-space,from multiple sources to learn kernel combination ***,it seamlessly integrates an incomplete-kernel-imputation objective,a multiple-kernel-learning objective,and a kernel-clustering objective in order to achieve absent multiple kernel *** three stages in this process are carried out simultaneously until the convergence condition is *** on six datasets with various characteristics demonstrate that the kernel imputation and clustering performance of the proposed method is signicantly better than state-of-the-art ***,the proposed method gains fast convergence speed.

关键词： Multiple kernel clustering absent-kernel imputation incomplete data kernel k-means clustering

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