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MeVs-deep CNN: optimized deep learning model for efficient lung cancer classification

Multimedia Tools and Applications 2024年 1-32页

作者： Sewatkar, Ranjana M. Y, Asnath Victy Phamila School of Computer Science and Engineering VIT Chennai Vandalur-Kelambakkam Road Chennai Tamil Nadu600127 India

Lung cancer is a dangerous condition that impacts many people. The type and location of cancer are critical factors in determining the appropriate medical treatment. Early identification of cancer cells can save numerous lives, making the development of automated detection techniques essential. Although many methods have been proposed by researchers over the years, achieving high prediction accuracy remains a persistent challenge. Addressing this issue, this research employs Memory-Enabled Vulture Search Optimization based on Deep Convolutional Neural Networks (MeVs-deep CNN) to develop an autonomous, accurate lung cancer categorization system. The data is initially gathered from the PET/CT dataset and preprocessed using the Non-Local Means (NL-Means) approach. The proposed MeVs optimization approach is then used to segment the data. The feature extraction process incorporates statistical, texture, and intensity-based features and Resnet-101-based features, resulting in the creation of the final feature vector for cancer classification and the multi-level standardized convolutional fusion model. Subsequently, the MeVs-deep CNN leverages the MeVs optimization technique to automatically classify lung cancer. The key contribution of the research is the MeVs optimization, which effectively adjusts the classifier's parameters using the fitness function. The output is evaluated using metrics such as accuracy, sensitivity, specificity, AUC, and loss function. The efficiency of the MeVs-deep CNN is demonstrated through these metrics, achieving values of 97.08%, 97.93%, 96.42%, 95.88%, and 2.92% for training phase;95.78%, 95.34%, 96.42%, 93.48%, and 4.22% for testing percentage;96.33%, 95.20%, 97.65%, 94.83%, and 3.67% for k-fold train data;and 94.16%, 95.20%, 93.30%, 91.66%, and 5.84% for k-fold test data. These results demonstrate the effectiveness of the research. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 202

关键词： Lung cancer

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STAL3D: Unsupervised Domain Adaptation for 3D Object Detection via Collaborating Self-Training and Adversarial Learning

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-12页

作者： Zhang, Yanan Zhou, Chao Huang, Di State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Beijing China

Existing 3D object detection suffers from expensive annotation costs and poor transferability to unknown data due to the domain gap, Unsupervised Domain Adaptation (UDA) aims to generalize detection models trained in labeled source domains to perform robustly on unexplored target domains, providing a promising solution for cross-domain 3D object detection. Although Self-Training (ST) based cross-domain 3D detection methods with the assistance of pseudo-labeling techniques have achieved remarkable progress, they still face the issue of low-quality pseudo-labels when there are significant domain disparities due to the absence of a process for feature distribution alignment. While Adversarial Learning (AL) based methods can effectively align the feature distributions of the source and target domains, the inability to obtain labels in the target domain forces the adoption of asymmetric optimization losses, resulting in a challenging issue of source domain bias. To overcome these limitations, we propose a novel unsupervised domain adaptation framework for 3D object detection via collaborating ST and AL, dubbed as STAL3D, unleashing the complementary advantages of pseudo labels and feature distribution alignment. Additionally, a Background Suppression Adversarial Learning (BS-AL) module and a Scale Filtering Module (SFM) are designed tailored for 3D cross-domain scenes, effectively alleviating the issues of the large proportion of background interference and source domain size bias. Our STAL3D achieves state-of-the-art performance on multiple cross-domain tasks and even surpasses the Oracle results on Waymo $\rightarrow$ KITTI and Waymo $\rightarrow$ KITTI-rain. IEEE

关键词： Feature extraction

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Autism spectrum disorder prediction system using machine learning and deep learning

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International Journal of Applied Systemic Studies 2024年第2期11卷 159-173页

作者： Sharma, Anshu Tanwar, Poonam School of Computer Science and Engineering Manav Rachna International Institute of Research and Studies Haryana Faridabad121002 India

Autism spectrum disease (ASD) is a neuro developmental illness that is both complicated and degenerative. A majority of known approaches use autism detection observation schedule (ADOS), pattern recognition, etc. to detect ASD with a small dataset, resulting in high accuracy but low generality. In this research, an ASD detection hybrid model is presented which is works on two different types of datasets. Firstly, behavioural datasets which works on logistic regression technique and secondly, facial dataset which works on based on convolutional neural network (CNN) Classifier in order to predict whether a person is suffering from autism or not. The suggested hybrid model which works on behavioural and facial dataset of a person beats state-of-the-art approaches in terms of accuracy, according to simulation findings. The proposed hybrid model had an average accuracy of 88% for the logistic regression model while it achieved an accuracy of 82.76% for the CNN model. © 2024 Inderscience Enterprises Ltd.

关键词： Classification (of information)

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Sentiment Analysis System for Image and Text Based Social Media Data 1

Sentiment Analysis System for Image and Text Based Social Me...

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1st IEEE Karachi Section Humanitarian Technology Conference, Khi-HTC 2024

作者： Baoyue, Hu Ashraf, Humaira Jhanjhi, Nz Verma, Sahil Taylor's University School of Computer Science and Engineering Malaysia Uttaranchal University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350373042

The paper provides an explicit outlook on the development of a comprehensive sentiment analysis systems for online social media by targeting user-generated text and images on platforms such as Twitter, Facebook, and Instagram. The system is developed to provide a complete sentiment analysis of users' emotions and expressions on social media. The project is composed of three interconnected sections, a Text-based sentiment analysis model, an Image-based sentiment analysis model and a web application prototype that integrates these models to provide real time results. The text-based model includes, data preprocessing, analysis using Word Cloud, analysis using the techniques of Word2Vec and implementation of multiple machine learning and deep learning models. Similarly, image-based model includes data preprocessing, data augmentation, deep learning methods for sentiment assessment. Final module is focused on representing application with real time demonstration, consisting with the practical implementation of these models. The prototype displays a real-time sentiment analysis and visualizes data. The paper is focused on a wide range of the field where the importance of the sentiment analysis is immense. For instance, companies can analyze how products are being analyzed in the market, politicians can determine how people are thinking of their speeches, teachers can know the interest of the lecture for the students. Therefore, the research signifies the importance of the Sentiment Analysis to extract the meaning or insight from the overwhelming unstructured, dynamic and always evolving nature of the social media content. © 2024 IEEE.

关键词： Image analysis

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A novel approach to multi-USV cooperative search in unknown dynamic marine environment using reinforcement learning

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Neural Computing and Applications 2024年 1-16页

作者： Song, Rui Gao, Senhui Li, Yao Shanghai University Shanghai200444 China School of Computer Engineering and Science Shanghai University Shanghai200444 China

Unmanned Surface Vehicles (USVs) are pivotal in diverse marine operations, including search and rescue, environmental monitoring, and maritime security. As their application grows, coordinating multiple USVs for collaborative searching in dynamic and uncharted marine environments presents significant challenges. These challenges stem from the dynamic characteristics of USVs and the interference issues in oceanic communications. Current algorithms often struggle with real-time decision-making and efficient coordination, which are vital for these operations. This paper introduces an innovative strategy for multi-USV collaborative search, leveraging particle swarm optimization (PSO) and reinforcement learning (RL). This approach allows USVs to adapt and respond in real-time, utilizing a dynamic target probability distribution map. Combining PSO with RL, the USVs proficiently maneuver through interference and circumvent obstacles, while ensuring effective teamwork. The proposed method offers a theoretical advancement in the field of multi-USV cooperative search, with potential implications for real-world maritime environments. The proposed method’s effectiveness and efficiency surpass those of conventional approaches, as evidenced by comprehensive simulations in various search scenarios. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Particle swarm optimization (PSO)

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Intelligent Web Spam Detection in the Consumer Internet of Things

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IEEE Transactions on Consumer Electronics 2024年第1期71卷 1-1页

作者： Wang, Rong Zhuang, Xu Zhu, Xiaogang Bashir, Ali Kashif Dabel, Maryam M. Al Yu, Keping College of Computer Science Sichuan Normal University Chengdu China OPPO Electronics Corporation Chengdu China School of Public Policy and Administration Nanchang University Nanchang Jiangxi China Department of Computing and Mathematics Manchester Metropolitan University UK Department of Computer Science and Engineering College of Computer Science and Engineering University of Hafr Al Batin Saudi Arabia Graduate School of Science and Engineering Hosei University Tokyo Japan

The Consumer Internet of Things (CIoT) integrates the advantage of Internet of Things (IoT) technologies to provide convenience in consumers’ daily lives. With the rapid development of the CIoT, data collected from consumer smart devices has increased exponentially. In the CIoT, web pages, as internet information carriers, offer spammers opportunities to conduct security attacks, which could harm the CIoT systems. Inspired by this challenge, this paper introduces an intelligent feature extraction method, page2vec, and a new classification algorithm, RFiRF, to detect web spam in the CIoT. page2vec is based on a score propagation model, which calculates each page’s goodness and badness scores through the links of a web graph. It is observed that different scoring functions can produce different web page features. Based on this observation, 20 to 30 scoring functions are designed and incorporated into page2vec. This way, page2vec can automatically extract web page features from a web graph. Unfortunately, page2vec also brings a high dimensionality problem when constructing binary classifiers for spam detection. To address this problem, a new classification algorithm, called Random Forest in Random Forest (RFiRF), is proposed. RFiRF replaces a random forest’s meta-classifier (decision tree) with a random forest. It divides the sparse data space into dense sub-spaces by randomly sampling training instances and features. Experiments on two benchmark datasets show that 1) page2vec features are much more predictive than the raw link-based and node2vec features, and 2) RFiRF outperforms some classification algorithms in most cases when facing the high dimensionality problem. We hope this paper can give peers valuable insights into the CIoT security. IEEE

关键词： Feature extraction

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Energy and Bandwidth Based Link Stability Routing Algorithm for IoT

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computers, Materials & Continua 2022年第2期70卷 3875-3890页

作者： D.Kothandaraman A.Balasundaram R.Dhanalakshmi Arun Kumar Sivaraman S.Ashokkumar Rajiv Vincent M.Rajesh School of Computer Science and Artificial Intelligence SR UniversityWarangalTSIndia School of Computer Science and Engineering Center for Cyber Physical SystemsVellore Institute of Technology(VIT)ChennaiIndia Department of Computer Science and Engineering KCG College of TechnologyChennaiIndia School of Computer Science and Engineering Vellore Institute of Technology(VIT)ChennaiIndia Department of Computer Science and Engineering Saveetha School of EngineeringSIMATSChennaiIndia

Internet of Things(IoT)is becoming popular nowadays for collecting and sharing the data from the nodes and among the nodes using internet ***,some of the nodes in IoT are mobile and dynamic in *** maintaining the link among the nodes,efficient bandwidth of the links among the mobile nodes with increased life time is a big challenge in IoT as it integrates mobile nodes with static nodes for data *** such networks,many routing-problems arise due to difficulties in energy and bandwidth based quality of *** to the mobility and finite nature of the nodes,transmission links between intermediary nodes may fail frequently,thus affecting the routing-performance of the network and the accessibility of the *** existing protocols do not focus on the transmission links and energy,bandwidth and link stability of the nodes,but node links are significant factors for enhancing the quality of the *** stability helps us to define whether the node is within or out of a coverage *** paper proposed an Optimal Energy and bandwidth based Link Stability Routing(OEBLS)algorithm,to improve the link stable route with minimized error rate and *** this paper,the optimal route from the source to the sink is determined based on the energy and bandwidth,link stability *** the existing routes,the sink node will choose the optimal route which is having less link stability *** stable link is determined by evaluating link stability value using distance and ***-energy of the node is estimated using the current energy and the consumed *** energy is estimated using transmitted power and the received *** bandwidth in the link is estimated using the idle time and channel capacity with the consideration of probability of collision.

关键词： Link stability internet of things optimal energy optimal bandwidth residual energy

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Unsupervised Learning for Insider Threat Prediction: A Behavioral Analysis Approach 17

Unsupervised Learning for Insider Threat Prediction: A Behav...

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17th International Conference on Security of Information and Networks, SIN 2024

作者： Mehmood, Rahat Singh, Priyanka Jeffery, Zoe School of Physics Engineering and Computer Science University of Hertfordshire Hatfield United Kingdom School of Electrical Engineering and Computer Science The University of Queensland Brisbane Australia

ISBN: (纸本)9798331509736

Most of the devastating cyber-attacks are caused by insiders with access privileges inside an organization. The main reason of insider attacks being more effective is that they don't have many security barriers before they get into the critical resources of the system. Different machine learning techniques have been previously utilized to identify insider threats within cy-bersecurity domain whereas research done in predicting insider attacks is not significant. Moreover, machine learning models used for prediction and detection face a critical limitation as they require training on labeled datasets, rendering them less effective for real-time data streams which lack threat presence indicators. This work presents an unsupervised machine learning approach that predicts insider threat using behavior analysis for real-time threat data. Patterns are identified in user behavior, to make predictions about benign and malicious insiders. Features are selected by analyzing activities performed. Selected features are utilized to feed machine learning model which extracts anomalous behavior among users, using anomalies in their activity patterns followed by learning methods for threat detection. A dataset that contains selected features from CERT r4.2 is used to make predictions. The performance of Isolation Forest (iForest) is compared with other algorithms of the same category including One-class SVM, Local Outlier Factor (LOF) and DBSCAN to evaluate the new approach. The iForest shows the best performance accuracy 80 percent and recall 84.2 percent. © 2024 IEEE.

关键词： Adversarial machine learning

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A Wavelet-based Diffusion Model for Low-light Image Enhancement in Raw Space 16

A Wavelet-based Diffusion Model for Low-light Image Enhancem...

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2024 16th International Conference on Graphics and Image Processing, ICGIP 2024

作者： Zhang, Xin-Yu Xu, Yang Wei, Zhi-Hui Wu, Ze-Bin School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (数字)9781510688780

ISBN: (纸本)9781510688773

Low-light image enhancement (LLIE) in Raw space has posed a challenge in the field of image processing and computational photography. Different from image processing in sRGB space, Raw images store more image information and thus have more space for image processing. Existing methods often struggle to approximate real images in terms of image detail enhancement and color restoration. Drawing inspiration from the latest advancements in denoising diffusion models, we introduce an innovative framework that leverages wavelet transform and diffusion models to tackle the low light image enhancement challenge in Raw space. In our method, the input low-light image is first decomposed into different frequency sub-bands by discrete wavelet transform (DWT) from both image and feature levels. Then a redesigned diffusion model is trained to adaptively denoise and enhance the different frequency sub-bands. In order to achieve better results in human eye perception, we also improved the loss function during training process. Finally, the enhanced result of the low-light image is finally reconstructed through inverse wavelet transform (IWT) and subsequent processing, transitioning from Raw space to sRGB space. Extensive experiments on SID and ELD datasets show that our refined method outperforms several existing methods, delivering superior performance both quantitatively and qualitatively. © 2025 SPIE.

关键词： Discrete wavelet transforms

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QRALadder: QoE and Resource Consumption-Aware Encoding Ladder Optimization for Live Video Streaming 31st

QRALadder: QoE and Resource Consumption-Aware Encoding Ladd...

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31st International Conference on Multimedia Modeling, MMM 2025

作者： Zhu, Yingqian Gao, Guanyu School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9789819620630

Live video streaming demands high user Quality of Experience (QoE) and requires significant computing power and bandwidth for video encoding and transmission. The standard adaptive live streaming approach encodes the source video content at multiple predefined bitrates, allowing users to select the most appropriate bitrate for optimal rate adaptation. The current methods for encoding ladder design primarily focus on optimizing user QoE. However, the relationship between the encoding ladder and user QoE is complex due to the dynamic nature of live streaming, where the number of users fluctuates, and each user experiences varying and evolving bandwidth conditions. On the other hand, resource consumption in live streaming significantly impacts operational costs, as it requires substantial resources for both video encoding and delivery. To address this challenge, we propose QRALadder, a QoE and resource consumption-aware approach to encoding ladder design. Our method simultaneously optimizes user QoE while considering the impact of the encoding ladder on resource consumption in live streaming, and it can dynamically select the most suitable encoding ladder based on the current system state. We use the Dueling Double Deep Q Networks (Dueling DDQN) algorithm to learn the optimal encoding ladder, maximizing overall performance by analyzing user QoE feedback and resource consumption about system states. We conduct comparative experiments using various types of video content and real-world bandwidth traces to validate our approach. The results show that QRALadder can achieve higher overall performance compared to baseline methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Bandwidth

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