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FedLRes: enhancing lung cancer detection using federated learning with convolution neural network (ResNet50)

Neural Computing and Applications 2025年第14期37卷 8273-8284页

作者： Usharani, C. Selvapandian, A. Department of Artificial Intelligence and Data Science Ramco Institute of Technology Rajapalayam India Department of Electronics and Communication Engineering Gnanamani College of Technology Namakkal India

Lung cancer remains a leading global cause of mortality, necessitating efficient early detection. Lung cancer image analysis plays a pivotal role, yet current manual segmentation by oncologists is laborious. Our innovative (FedLRes) approach proposes a comprehensive system for automated diagnoses of lung cancer using federated learning with ResNet50. The lung cancer dataset from the Iraq-Oncology Teaching Hospital/National Centre for Cancer Diseases was gathered by the IQ-OTH/NCCD. The dataset consists of 1097 images, split into training (822 images) and validation (275 images) sets. The training set includes 312 normal, 420 malignant, and 90 benign cases. The training set's data are further enhanced by using data augmentation techniques, which are then applied to normalise images differences before being fed into the federated learning with ResNet50 architecture. This approach combines deep learning models trained on different datasets allowing for improvising accuracy and generalisation. The federated learning approach enables the use of distributed data while ensuring data privacy and security. The proposed approach compared with different state of art algorithms. Through rigorous experimentation, our system showcases remarkable advancements a classification accuracy of 99.40%. This innovative approach, utilising 3D input CT scan data, offers a potent and precise tool for early detection and effective treatment strategies against the scourge of lung cancer. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Lung cancer

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A Long Short-Term Memory Network-Based Approach for Predicting the Trends in the S&P 500 Index

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Journal of The Institution of Engineers (India): Series B 2024年第1期105卷 19-26页

作者： Siddesh, G.M. Sekhar, S. R. Mani Srinivasa, K.G. Department of Artificial Intelligence & Data Science M S Ramaiah Institute of Technology Bangalore India Department of Information Science and Engineering M S Ramaiah Institute of Technology Bangalore India Data Science and Artificial Intelligence Programme International Institute of Information Technology Naya Raipur India

The price level of a considered security in the stock market is largely determined by perceived consumer demand. Variations in price level in the stock market are essentially a manifestation of public psychology aiming to either cut losses or maximize profits. The assumption that market movements are controlled by a guiding trend is proved by establishing that the time-series variations of daily S&P 500 indices are a non-stationary process using the Dickey–Fuller test. This paper presents a long short-term memory (LSTM) network adaptation of an autoregressive predictive model for S&P 500 indices. Later, an LSTM network-based model is devised to learn the underlying patterns that are responsible for driving the trends in market dynamics. The proposed model accepts as input at each time-step a vector of prior S&P indices over the past two weeks and estimates the current market index. The network weights assume the correct settings by training the model using daily S&P indices over the past ten years. The proposed model decisively captures subtleties in market trends, which are in turn verified using the Granger causality approach. The result demonstrates that the proposed model achieved an accuracy of 82.6%, whereas random forest results in 79.12%. The results obtained are truly the latest in this line of predictive models and reaffirm that LSTM networks are powerful learning models capable of deciphering intricate underlying patterns. © 2023, The Institution of Engineers (India).

关键词： Long short-term memory

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Enhancing Network Intrusion Detection using Artificial Neural Networks: An Analysis of the UNSW-NB15 dataset 2

Enhancing Network Intrusion Detection using Artificial Neura...

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2nd IEEE International Conference on Integrated intelligence and Communication Systems, ICIICS 2024

作者： Arun, Chalana B. Anusha, M. Kodipalli, Ashwini Rao, Trupthi Rohini, B.R. Gargi, N. Department of Artificial Intelligence and Data Science Global Academy of Technology Bangalore India Department of Artificial Intelligence and Data Global Academy of Technology Bangalore India

ISBN: (纸本)9798331504960

Securing networks from illicit activities has remained the top priority in the constantly evolving world on information technology. Intrusion detection systems are struggling to keep up with the increasing complexity of network attacks which increased the demand for advanced methods such as Machine Learning and Deep Learning. This work emphasizes the usage of Artificial Neural Networks (ANN) for enhancing the skill to detect network incursion in IDS. Leveraging the data compilation UNSW-NB15, which evokes various internet traffic and threat instances, we created ANN-based IDS that takes benefit from neural network to detect specific patterns in data. To train and evaluate the model, multiple optimizers such as SGD, RMSprop, and Adam, in two configurations i.e. with and without dropout were compared. The reliability of newly built IDS is ensured by substantial data preparation, feature selection, along with the model tuning. The UNSW-NB15 dataset, well-known for its high data volume and accurate network inclusion, provides a significant standard to assess the impact of the suggested framework. The study shows that the ANN model using Adam optimizer and dropout outperforms with an accuracy of 94.81%, demonstrating the potential to enhance the safety of networks greatly. This paper highlights the productivity of ANNs in improving the practice of detection of network attacks, giving a solid method to combat the increasing cyber risk through extensive evaluation and testing. © 2024 IEEE.

关键词： Network intrusion

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Enabling secure modern web browsers against cache-based timing attacks

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International Journal of Security and Networks 2024年第1期19卷 43-54页

作者： Ganesan, Sangeetha Department of Artificial Intelligence and Data Science R.M.K College of Engineering and Technology Tamil Nadu Tiruvallur India

Web applications have grown to be the foundation of any kind of system, ranging from cloud services to the internet of things (IoT) systems. As a huge amount of sensitive data is processed in web applications, user privacy shows as the most important concern in web security. In the virtualisation system, cache side channel (CSC) attack techniques have become popular to retrieve the secret information of other users. This paper presents a run-time detection and prevention mechanism, called browser watcher (BW), for time-driven CSC attacks. The computation overhead of the proposed BW java script engine is monitored and tabulated for the different domains. The average cache miss rate is measured from 23% to 89%. Once the BW system identifies the attacker, then it prevents stealing the secret information of the victim. This makes it very hard for the attacker to find the memory access pattern of the victim. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Side channel attack

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Spatio-Temporal Location Recommendation for Urban Facility Placement via Graph Convolutional and Recurrent Networks

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Journal of Computer Science & technology 2024年第6期39卷 1419-1440页

作者： Pu Wang Jian-Jiang Lu Wei Chen Peng-Peng Zhao Lei Zhao Institute of Artificial Intelligence School of Computer Science and TechnologySoochow UniversitySuzhou 215006China Department of Data Resource and Information Development Soochow UniversitySuzhou 215006China

The ability to recommend candidate locations for service facility placement is crucial for the success of urban planning. Whether a location is suitable for establishing new facilities is largely determined by its potential popularity. However, it is a non-trivial task to predict popularity of candidate locations due to three significant challenges: 1) the spatio-temporal behavior correlations of urban dwellers, 2) the spatial correlations between candidate locations and existing facilities, and 3) the temporal auto-correlations of locations themselves. To this end, we propose a novel semi-supervised learning model, Spatio-Temporal Graph Convolutional and Recurrent Networks (STGCRN), aiming for popularity prediction and location recommendation. Specifically, we first partition the urban space into spatial neighborhood regions centered by locations, extract the corresponding features, and develop the location correlation graph. Next, a contextual graph convolution module based on the attention mechanism is introduced to incorporate local and global spatial correlations among locations. A recurrent neural network is proposed to capture temporal dependencies between locations. Furthermore, we adopt a location popularity approximation block to estimate the missing popularity from both the spatial and temporal domains. Finally, the overall implicit characteristics are concatenated and then fed into the recurrent neural network to obtain the ultimate popularity. The extensive experiments on two real-world datasets demonstrate the superiority of the proposed model compared with state-of-the-art baselines.

关键词： location recommendation popularity prediction spatio-temporal correlation deep learning

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A Tutorial on Federated Learning from Theory to Practice:Foundations,Software Frameworks,Exemplary Use Cases,and Selected Trends

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 824-850页

作者： M.Victoria Luzón Nuria Rodríguez-Barroso Alberto Argente-Garrido Daniel Jiménez-López Jose M.Moyano Javier Del Ser Weiping Ding Francisco Herrera Department of Software Engineering Andalusian Research Institute in Data Science and Computational Intelligence(DaSCI)University of GranadaGranada 18071Spain Department of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence(DaSCI)University of GranadaGranada 18071Spain Department of Communications Engineering University of the Basque Country(UPV/EHU)and also with TECNALIABasque Research&Technology Alliance(BRTA)Spain School of Information Science and Technology Nantong UniversityNantong 226019China

When data privacy is imposed as a necessity,Federated learning(FL)emerges as a relevant artificial intelligence field for developing machine learning(ML)models in a distributed and decentralized *** allows ML models to be trained on local devices without any need for centralized data transfer,thereby reducing both the exposure of sensitive data and the possibility of data interception by malicious third *** paradigm has gained momentum in the last few years,spurred by the plethora of real-world applications that have leveraged its ability to improve the efficiency of distributed learning and to accommodate numerous participants with their data *** virtue of FL,models can be learned from all such distributed data sources while preserving data *** aim of this paper is to provide a practical tutorial on FL,including a short methodology and a systematic analysis of existing software ***,our tutorial provides exemplary cases of study from three complementary perspectives:i)Foundations of FL,describing the main components of FL,from key elements to FL categories;ii)Implementation guidelines and exemplary cases of study,by systematically examining the functionalities provided by existing software frameworks for FL deployment,devising a methodology to design a FL scenario,and providing exemplary cases of study with source code for different ML approaches;and iii)Trends,shortly reviewing a non-exhaustive list of research directions that are under active investigation in the current FL *** ultimate purpose of this work is to establish itself as a referential work for researchers,developers,and data scientists willing to explore the capabilities of FL in practical applications.

关键词： data privacy distributed machine learning federated learning software frameworks

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Network Diffusion Framework to Simulate Spreading Processes in Complex Networks

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Big data Mining and Analytics 2024年第3期7卷 637-654页

作者： MichałCzuba Mateusz Nurek Damian Serwata Yu-Xuan Qiu Mingshan Jia Katarzyna Musial Radosław Michalski Piotr Bródka Department of Artificial Intelligence Wrocław University of Science and TechnologyWrocław 50-370Poland Data Science Institute University of Technology SydneyUltimoNSW 2007Australia

With the advancement of computational network science,its research scope has significantly expanded beyond static graphs to encompass more complex *** introduction of streaming,temporal,multilayer,and hypernetwork approaches has brought new possibilities and imposed additional *** instance,by utilising these advancements,one can model structures such as social networks in a much more refined manner,which is particularly relevant in simulations of the spreading ***,the pace of advancement is often too rapid for existing computational packages to keep up with the functionality *** results in a significant proliferation of tools used by researchers and,consequently,a lack of a universally accepted technological stack that would standardise experimental methods(as seen,e.g.,in machine learning).This article addresses that issue by presenting an extended version of the Network Diffusion ***,a survey of the existing approaches and toolkits for simulating spreading phenomena is shown,and then,an overview of the framework ***,we report four case studies conducted with the package to demonstrate its usefulness:the impact of sanitary measures on the spread of COVID-19,the comparison of information diffusion on two temporal network models,and the effectiveness of seed selection methods in the task of influence maximisation in multilayer *** conclude the paper with a critical assessment of the library and the outline of still awaiting challenges to standardise research environments in computational network science.

关键词： computational framework seed selection influence maximisation spreading models temporal networks multilayer networks network science network control

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SourcePlag - Source Code Plagiarism Detection Based on Abstract Syntax Trees 3

SourcePlag - Source Code Plagiarism Detection Based on Abstr...

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3rd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Sharma, Nakul Shinde, Siddharth Bhosale, Swarup Patil, Suyog Vishwakarma Institute of Information Technology Faculty of Engineering Department of Artificial Intelligence and Data Science Pune India Vishwakarma Institute of Information Technology Department of Artificial Intelligence and Data Science Pune India

ISBN: (纸本)9798350354348

Code plagiarism poses a significant challenge in programming communities, necessitating effective detection mechanisms. This paper introduces a novel system that employs Abstract Syntax Trees (ASTs) for code representation and comparison. The system utilizes ASTs to capture the structural essence of code, facilitating a comprehensive analysis of code similarity, it offers flexibility for application across multiple programming languages. In addition to leveraging the Levenshtein Distance Algorithm for Python code comparison, the system incorporates node counting for other languages such as Java and C/C++. By integrating AST-based representation and a combination of comparison techniques, the system offers a robust solution for identifying plagiarized code instances accurately across various programming environments. Through a detailed exploration of the system's methodology, this paper underscores its potential to address the pervasive issue of code plagiarism in programming communities. © 2024 IEEE.

关键词： C++ (programming language)

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ControlVideo: conditional control for one-shot text-driven video editing and beyond

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Science China(Information Sciences) 2025年第3期68卷 150-162页

作者： Min ZHAO Rongzhen WANG Fan BAO Chongxuan LI Jun ZHU Department of Computer Science and Technology Institute for AI Tsinghua-Bosch Joint ML CenterTsinghua Laboratory of Brain and Intelligence Lab Tsinghua University ShengShu Technology Gaoling School of Artificial Intelligence Renmin University of China Beijing Key Laboratory of Big Data Management and Analysis Methods Pazhou Laboratory (Huangpu)

This paper presents ControlVideo for text-driven video editing — generating a video that aligns with a given text while preserving the structure of the source video. Building on a pre-trained text-to-image diffusion model, ControlVideo enhances the fidelity and temporal consistency by incorporating additional conditions(such as edge maps), and fine-tuning the key-frame and temporal attention on the source video-text pair via an in-depth exploration of the design space. Extensive experimental results demonstrate that ControlVideo outperforms various competitive baselines by delivering videos that exhibit high fidelity w.r.t. the source content, and temporal consistency, all while aligning with the text. By incorporating low-rank adaptation layers into the model before training, ControlVideo is further empowered to generate videos that align seamlessly with reference images. More importantly, ControlVideo can be readily extended to the more challenging task of long video editing(e.g., with hundreds of frames), where maintaining long-range temporal consistency is crucial. To achieve this, we propose to construct a fused ControlVideo by applying basic ControlVideo to overlapping short video segments and key frame videos and then merging them by pre-defined weight functions. Empirical results validate its capability to create videos across 140 frames, which is approximately 5.83 to 17.5 times more than what previous studies achieved. The code is available at https://***/thu-ml/controlvideo.

关键词： diffusion models controllable generation text-driven editing video editing long video editing

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Evaluating the Performance of Pre-Trained CNNs for Multi-Class Skin Disease Detection 4

Evaluating the Performance of Pre-Trained CNNs for Multi-Cla...

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4th International Conference on Multimedia Processing, Communication and Information technology, MPCIT 2024

作者： Ubale, Ganesh Kashyap, Aryan Kalal, Sahil Kamble, Omkar Pande, Soham Mhamane, Saniya Vishwakarma Institute of Technology Department of Artificial Intelligence and Data Science Pune India

ISBN: (纸本)9798350375466

This study evaluates the performance of pre-trained convolutional neural networks (CNNs) for multi-class skin disease detection. We investigate the efficacy of four models: Xception, InceptionResNetV2, ResNet50, and VGG16. The models are trained and tested on two publicly available datasets: DermNet (6 diseases) and a skin disease classification dataset. We compare the performance of each model based on metrics like accuracy, precision, recall, and F1-score. This research aims to identify the most suitable pre-trained CNN architecture for accurate skin disease classification using readily available datasets. The findings can contribute to the development of automated diagnostic tools to assist dermatologists in early and precise skin disease detection. © 2024 IEEE.

关键词： Convolutional neural networks

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