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An Effective Lung Cancer Diagnosis Model Using Pre-Trained CNNs

computer Modeling in engineering & sciences 2025年第4期143卷 1129-1155页

作者： Majdi Rawashdeh Muath A.Obaidat Meryem Abouali Dhia Eddine Salhi Kutub Thakur Department of Business Information Technology Princess Sumaya University for TechnologyAmman11941Jordan Department of Computer Engineering Al Yamamah UniversityRiyadh11512Saudi Arabia Department of Computer Science John Jay College and the Graduate CenterThe City University of New YorkNew YorkNY 10019USA Department of Computer Science John Jay College at the City University of NewYorkNewYorkNY 10019USA LIMOSE Laboratory Department of Computer ScienceM’hamed Bougara UniversityBoumerdes35000Algeria Department of Professional Security Studies New Jersey City UniversityJersey CityNJ 07305USA

Cancer is a formidable andmultifaceted disease driven by genetic aberrations and metabolic *** 19% of cancer-related deaths worldwide are attributable to lung and colon cancer,which is also the top cause of death *** malignancy has a terrible 5-year survival rate of 19%.Early diagnosis is critical for improving treatment outcomes and survival *** study aims to create a computer-aided diagnosis(CAD)that accurately diagnoses lung disease by classifying histopathological *** uses a publicly accessible dataset that includes 15,000 images of benign,malignant,and squamous cell carcinomas in the *** addition,this research employs multiscale processing to extract relevant image features and conducts a comprehensive comparative analysis using four Convolutional Neural Network(CNN)based on pre-trained models such as AlexNet,VGG(Visual Geometry Group)16,ResNet-50,and VGG19,after hyper-tuning these models by optimizing factors such as batch size,learning rate,and *** proposed(CNN+VGG19)model achieves the highest accuracy of 99.04%.This outstanding performance demonstrates the potential of the CAD system in accurately classifying lung cancer histopathological *** study contributes significantly to the creation of a more precise CNN-based model for lung cancer identification,giving researchers and medical professionals in this vital sector a useful tool using advanced deep learning techniques and publicly available datasets.

关键词： Lung cancer machine learning computer aided diagnosis CNN medical imaging transfer learning

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InGaPχ^((2))integrated photonics platform for broadband,ultra-efficient nonlinear conversion and entangled photon generation

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Light(science & Applications) 2024年第12期13卷 3090-3098页

作者： Joshua Akin Yunlei Zhao Yuvraj Misra A.K.M.Naziul Haque Kejie Fang Holonyak Micro and Nanotechnology Laboratory and Department of Electrical and Computer Engineering University of Illinois at Urbana-ChampaignUrbanaIL 61801USA Illinois Quantum Information Science and Technology Center University of Illinois at Urbana-ChampaignUrbanaIL 61801USA

Nonlinear optics plays an important role in many areas of science and *** advance of nonlinear optics is empowered by the discovery and utilization of materials with growing optical *** we demonstrate an indium gallium phosphide(InGaP)integrated photonics platform for broadband,ultra-efficient second-order nonlinear *** InGaP nanophotonic waveguide enables second-harmonic generation with a normalized efficiency of 128,000%/W/cm^(2)at 1.55μm pump wavelength,nearly two orders of magnitude higher than the state of the art in the telecommunication C ***,we realize an ultra-bright,broadband time-energy entangled photon source with a pair generation rate of 97 GHz/mW and a bandwidth of 115 nm centered at the telecommunication C *** InGaP entangled photon source shows high coincidence-to-accidental counts ratio CAR>10^(4)and two-photon interference visibility>98%.The InGaP second-order nonlinear photonics platform will have wide-ranging implications for non-classical light generation,optical signal processing,and quantum networking.

关键词： nonlinear optics coincidence

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A Provably Secure Lightweight Key Agreement Protocol for Wireless Body Area Networks in Healthcare System

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IEEE Transactions on Industrial Informatics 2023年第2期19卷 1683-1690页

作者： Zia, Maryam Obaidat, Mohammad S. Mahmood, Khalid Shamshad, Salman Saleem, Muhammad Asad Chaudhry, Shehzad Ashraf Comsats University Islamabad Department of Computer Science Sahiwal57000 Pakistan University of Jordan King Abdullah Ii School of Information Technology Amman11942 Jordan University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Amity University UP Noida201301 India National Yunlin University of Science and Technology Future Technology Research Center Yunlin64002 Taiwan The University of Lahore Department of Software Engineering Lahore54590 Pakistan Abu Dhabi University Department of Computer Science and Information Technology College of Engineering Abu Dhabi United Arab Emirates Nisantasi University Department of Computer Engineering Faculty of Engineering and Architecture Istanbul34398 Turkey

Wireless Body Area Network (WBAN) is a vital application of the Internet of Things (IoT) that plays a significant role in gathering a patient's healthcare information. This collected data helps special professionals like doctors or physicians analyze patients' health status to cure different diseases. However, collecting such information from an insecure channel can be threatening due to the potential security threats. Therefore, it is crucial to secure this sensitive information. This article proposes a secure and lightweight authentication protocol for WBAN. The devised protocol is scalable, secure, and lightweight compared to various relevant competing protocols. The informal security analysis shows that the designed protocol is lightweight, secure, and efficient in resisting various major attacks. The performance analysis demonstrates our protocol's supremacy over various competing protocols in terms of computation and communication costs, inducing efficiency of 20.3% and 12.3%, respectively. Moreover, the practical performance of the designed protocol from the network point of view is measured using the widely recognized NS3 simulation tool. © 2005-2012 IEEE.

关键词： Authentication

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Machine Unlearning: An Overview of the Paradigm Shift in the Evolution of AI 21

Machine Unlearning: An Overview of the Paradigm Shift in the...

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21st International Learning and technology Conference, L and T 2024

作者： Jaman, Layan Alsharabi, Reem Elkafrawy, Passent M. Effat University Effat College of Engineering Energy and Technology Research Center Computer Science Department Jeddah Saudi Arabia

ISBN: (纸本)9798350393569

The rapid advancements in artificial intelligence (AI) have primarily focused on the process of learning from data to acquire knowledge for smart systems. However, the concept of machine unlearning has emerged as a transformative paradigm shift in the field of AI, due to the amount of false information that have been learned over the past. Machine unlearning refers to the ability of AI systems to reverse or discard previously acquired knowledge or patterns, enabling them to adapt and refine their understanding in response to changing circumstances or new insights. This paper explores the concept of machine unlearning, its implications, methods, challenges, and potential applications. The paper begins by providing an overview of the traditional learning-based approaches in AI and the limitations they impose on system adaptability and agility. It then delves into the concept of machine unlearning, discussing various techniques and algorithms employed to remove or modify learned knowledge from AI models or datasets. © 2024 IEEE.

关键词： Adaptive algorithms

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A Two-stage Coarsening Method for a Streaming Graph with Preserving Key Features

A Two-stage Coarsening Method for a Streaming Graph with Pre...

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2024 International Conference on Generative Artificial Intelligence and Information Security, GAIIS 2024

作者： Sun, Zhipeng Zeng, Guosun Department of Computer Science and Technology Tongji University Shanghai200092 China Tongji Branch National Engineering & Technology Center of High Performance Computer Shanghai200092 China

ISBN: (纸本)9798400709562

With the rapid development of 5G and mobile internet, various application fields continue to generate diverse dynamic streaming graphs with large scales and complex structures. Analyzing, querying and mining such a streaming graph face great challenges. To address the challenge of low query efficiency and avoid data loss, this paper proposes a two-stage coarsening method that preserves key features of a streaming graph while improves query efficiency. In the initial stage, the size of the streaming graph is not that large yet. To preserve its structure and attributes as much as possible, a linear mapping method is used for graph coarsening. As the size of the streaming graph increases, a graph neural network model based on cluster pooling is constructed in the second stage. Using a large number of graph coarsening results obtained in the initial stage to train and optimize this model. Then, we use this optimized model to obtain the subsequent summaries, which achieves online efficient graph coarsening. Finally, extensive experiments indicate that our algorithm significantly improves the quality of streaming graph coarsening compared to traditional algorithms. Even compared to the state-of-the-art algorithm Auxo, our algorithm shows a better performance on preserving key features. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Coarsening

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Enhancing Wireless Sensor Network Data Collection through Aerial Unmanned Vehicles

Enhancing Wireless Sensor Network Data Collection through Ae...

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Ahmed, Amjed Abbas Ali Alheeti, Khattab M. Najim, Ali Hamza Satar, Nurhizam Safie Mohd Aliesawi, Salah A. Hashim, Aisha Hassan Abdallah Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Bangi Malaysia Department of Computer Techniques Engineering Baghdad Iraq College of Computer and Information Technology Computer Networking Systems Department University of Anbar Anbar Iraq Department of Computer Technical Engineering Al-Diwaniyah Iraq Center for Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia College of Computer and Information Technology Department of Computer Networking Systems University of Anbar Anbar Iraq Faculty of Engineering Department of Electrical and Computer Engineering International Islamic University Malaysia Selangor Malaysia

ISBN: (纸本)9798350369106

This study addresses the critical aspect of data collection within Wireless Sensor Networks (WSNs), which consist of autonomous, compact sensor devices deployed to monitor environmental conditions. These networks have limited processing capacity, storage, short-range communication capabilities, and constrained battery power. A primary challenge in WSN s revolves around data gathering, particularly when employing static sinks, leading to premature energy depletion in nodes near these sinks. Integrating mobile collectors introduces the potential for energy conservation by shortening data routes. This article introduces three distinct algorithms for optimizing data collection in WSN s. Our approach involves an in-depth analysis of diverse strategies to mitigate packet collisions and facilitate the retransmission of lost packets. Our findings underscore the considerable impact that packet collisions can exert on the overall network performance during data gathering within wireless sensor networks. Through empirical exploration and analysis, we highlight the significance of these algorithms in enhancing the efficiency, energy conservation, and reliability of data collection processes within WSNs. This research sheds light on these algorithms' vital role in addressing the pressing challenges of data gathering and network performance optimization in wireless sensor networks. © 2024 IEEE.

关键词： Aircraft accidents

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Personality-based and trust-aware products recommendation in social networks

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Applied Intelligence 2023年第1期53卷 879-903页

作者： Vatani, Nasim Rahmani, Amir Masoud Javadi, Hamid Haj Seyyed Department of Computer Engineering Science and Research Branch Islamic Azad University Tehran Iran Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan Department of Mathematics and Computer Science Shahed University Tehran Iran

In recent years, with the development of technology, the shopping approach of people has moved towards pervasive online social shopping. As a result, how to create a recommendation algorithm that offers products based on the personal and different needs and tastes of people on social networks is a significant research issue. This article proposes a personality-based and trust-aware probabilistic product recommendation algorithm in social networks. We present a dynamic method for determining how similar people in social networks are. For this purpose, we consider the personality-based features of recommendation attributes of products in social networks. Then, the level of trust of products and types of correlations among the products is considered to create a probabilistic matrix of product recommendation. Moreover, for solving the cold start problem of products, we consider qualitative aspects of products while exploiting personality-based user behavior regarding their purchases. At last, the empirical experiments are conducted to analyze the impact of the algorithm’s different influence factors using the Amazon dataset. Moreover, the results of comprehensive experiments adopted to verify the proposed personalized recommendation algorithm’s effectiveness show that the proposed algorithm has the appropriate effectiveness and the higher accuracy. © 2022, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Behavioral research

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A Study on Student Performance Prediction and Intervention Mechanisms in MOOC 1

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14th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2022, and the 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2022

作者： Lakshmi, S. Maheswaran, C.P. Department of Computer Science and Engineering Noorul Islam Center for Higher Education Kumarakovil India Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (数字)9783031275241

ISBN: (纸本)9783031275234

The open online learning platform’s popularity has developed in the last few years as the Internet’s development has empowered. This has resulted in the creation of Massive Open Online Course (MOOCs), which has enrolled an enormous number of individuals worldwide. MOOCs are one of the major significant educational advancements, bringing new chances for higher along with vocational education. But one of the exigent threats faced by any MOOC is its low completion rate and student retention. To tackle these issues, it is inevitable to make early student success predictions and timely interventions for at-risk students. This paper inspects diverse methodologies associated with students’ performance prediction in MOOC. The intervention mechanism that aids in extending the Learning Process (LP) in MOOC is also carefully examined in this study. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Students

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Advanced Helical Antenna Design for X-Band Applications Using AI

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Progress in Electromagnetics Research C 2025年 153卷 201-211页

作者： Zeain, Mohammed Yousif Abu, Maisarah Toding, Apriana Zakaria, Zahriladha Alsariera, Hussein Ullah, Ihsan Abdulbari, Ali Abdulateef Yon, Hamizan Taha, Bilal Salman Inam Abbasi, Muhammad Hang Tuah Jaya Melaka Durian Tunggal76100 Malaysia Makassar Indonesia Department of Computer Science and Engineering Yuan Ze University Taoyuan Taiwan Information Technology Center University of Technology Baghdad Iraq Antenna Research Center College of Engineering School of Electrical Engineering Universiti Teknologi MARA Malaysia Department of Electrical and Electronics University Tenaga National Malaysia Malaysia

This paper presents the design, fabrication, and characterization of a novel 3D-printed helical antenna operating within the 9.4–10.8 GHz frequency band. The antenna, employing a lightweight paper substrate and a strip-based helical structure, exhibits robust circular polarization characteristics and wideband operation. Rigorous simulations predict a peak CP gain of 11.7 dBi at 9.8 GHz and a high simulated radiation efficiency of 95%. Experimental measurements validate these predictions, achieving a peak CP gain of 11.6 dBi at 9.8 GHz. This research demonstrates the potential of 3D-printed helical antennas for diverse applications in modern wireless communication systems, including 5G, satellite communication, and radar. Furthermore, this study leverages the power of Artificial Intelligence (AI) by employing the Grey Wolf Optimizer (GWO), a sophisticated metaheuristic algorithm, to optimize the antenna’s design. The GWO algorithm is utilized to efficiently search the design space and identify optimal values for key parameters, such as the number of turns, helix pitch, and helix diameter, with the objective of maximizing antenna gain to achieve a target of 15 dBi. This research highlights the potential of AI-driven optimization techniques in advancing the design of high-performance antennas for emerging wireless communication systems. © 2025, Electromagnetics Academy. All rights reserved.

关键词： Helical antennas

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Ground-Glass Lung Nodules Recognition Based on CatBoost Feature Selection and Stacking Ensemble Learning

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Journal of Shanghai Jiaotong University (science) 2024年 1-10页

作者： Miao, Jun Chang, Yiru Chen, Chen Zhang, Maoxuan Liu, Yan Qi, Honggang Guo, Zhijun Xu, Qian School of Computer Science Beijing Information Science and Technology University Beijing100101 China Pathological Information Engineering Technology Center Jinan Supercomputing Technology Research Institute Jinan250100 China School of Computer Science and Technology University of the Chinese Academy of Sciences Beijing100049 China Department of Radiology Tianjin Kanghui Hospital Tianjin300385 China Department of Radiology Huabei Petroleum General Hospital Hebei Renqiu062550 China

Aimed at the issue of high feature dimensionality, excessive data redundancy, and low recognition accuracy of using single classifiers on ground-glass lung nodule recognition, a recognition method based on CatBoost feature selection and Stacking ensemble learning was proposed. First, the method uses a feature selection algorithm to filter important features and remove features with less impact, achieving the effect of data dimensionality reduction. Second, random forests classifier, decision trees, K-nearest neighbor classifier, and light gradient boosting machine were used as base classifiers, and support vector machine was used as meta classifier to fuse and construct the ensemble learning model. This measure increases the accuracy of the classification model while maintaining the diversity of the base classifiers. The experimental results show that the recognition accuracy of the proposed method reaches 94.375%. Compared to the random forest algorithm with the best performance among single classifiers, the accuracy of the proposed method is increased by 1.875%. Compared to the recent deep learning methods (ResNet+GBM+Attention and MVCSNet) on ground-glass pulmonary nodule recognition, the proposed method’s performance is also better or comparative. Experiments show that the proposed model can effectively select features and make recognition on ground-glass pulmonary nodules. © Shanghai Jiao Tong University 2024.

关键词： Feature Selection

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