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2nd IEEE International Conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2023

作者： Sreedevi, B. Rosewelt, L. Antony Ramesh, Neela Sri Sai Ram Institute of Technology Computer Science and Engineering Chennai India Sri Sai Ram Institute of Technology Big Data Analytics Computer Science and Engineering Chennai India

ISBN: (纸本)9798350315905

Resembling man's evolution over time the life threatening diseases have also evolved with him a disease is basically a disorder in structure or functioning of an organism's body. There are a several kinds of diseases but the most dangerous ones are the infectious diseases. Infectious diseases occur when a foreign body such as bacteria, fungi, and virus enter the body and attack the immune system. That being the smaller problem of infectious diseases as the main problem it's a communicable disease that spreads when a uninfected person comes in contact with an infected person they are highly likely to contract the same. The prediction is performed using Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), Stochastic Gradient Descent SGD), Gradient Boosting (GD), Decision Trees (DT), Naive Bayes Classifier (NBC) and Voting Classifier (VC) ensemble technique. After the prediction a comparative analysis in terms of accuracy is performed. A Chatbot using Natural Learning Processing on a Neural Network is employed. The user is asked to upload a Chest X-ray if in possession as a second step confirmation for Tuberculosis, Pneumonia, Covid-19 and Healthy lung. In terms of highest accuracy for Covid-19 the GBC, VC, 56 tops the charts and for other diseases also GBC, VC, ANN top the charts. © 2023 IEEE.

关键词： Diseases

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Multi-level Knowledge Integration with Graph Convolutional Network for Cancer Molecular Subtype Classification

Multi-level Knowledge Integration with Graph Convolutional N...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Huang, Sujia Xiao, Shunxin Liu, Wenzhe Lu, Jielong Wu, Zhihao Wang, Shiping Rajapakse, Jagath C. Fuzhou University College of Computer and Data Science Fuzhou China Huzhou University School of Information Engineering Huzhou China Nanyang Technological University School of Computer Science and Engineering Singapore

ISBN: (纸本)9798350337488

Multi-omics data provides a wealth of information concerning disease mechanisms, which benefits the exploration of the intricate molecular phenomena underlying diseases. In recent years, considerable endeavors have been directed towards the combination of graph convolutional network, which has the powerful ability to gather information, with multi-omics learning methods to obtain more reliable results. For achieving this pursuit, an essential challenge is data integration. Against this backdrop, we propose a unified framework named multi-level knowledge integration with graph convolutional network, which effectively incorporates multiple prior knowledge and omics data to learn an intrinsic representation. In specific, the model consists of two subnetworks: an attribute-level module and a sample-level module. The former firstly aggregates the knowledge given by the prior biological graphs into low-dimensional embeddings, and then maximizes the consistency between these prior views via optimizing a contrastive loss for attaining the attribute-based representations. The latter leverages an encoder to dimensionalize the original multi-omics data to attain more dominant sample knowledge, and subsequently utilizes another contrastive loss to align these representations between multiple omics for learning the global sample-level information. Comprehensive experiments are performed to show that the proposed model surpasses other state-of-the-art methods. © 2023 IEEE.

关键词： cancer molecular subtype classification contrastive learning graph convolutional network Multi-omics data

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Video Frame Prediction Using Convolutional LSTM Encoder and Decoder 1

Video Frame Prediction Using Convolutional LSTM Encoder and ...

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1st International Conference on data, Computation and Communication, ICDCC 2024

作者： Revathy, G. Jagadeesan, Pavithra Vajubunnisa Begum, R. Vijay Anand, P. Jasmin, H. Gayathri, S. Department of Computer Science and Engineering Chennai India Karpaga vinayaga college of engineering and technology Department of Computer Science and Engineering Chennai India Justice Basheer Ahmed Sayeed College for Women Affiliated by University of Madras Department of Electronics and Communication Science Teynampet Chennai India CMR Institute of Technology Department of Artificial Intelligence and Data Science Bengaluru India K. Ramakrishnan College of Technology Department of Computer Science and Engineering Tamilnadu Trichy India

ISBN: (纸本)9798331532956

With rapid growth of audiovisual content now spreading through social networking sites, chances of exposure towards violent and mature content by the youth are growing. This project addresses the risk of detection of such content using advanced video frame prediction techniques. We present here an encoder-decoder model of Convolutional LSTMs in the film for violent and obscene sequence detection. The spatiotemporal nature of video data encourages our algorithm to exploit accurate predictions of future frames from past content, which can be used in real time for dangerous material detection. The study relies on recent literature that highlights the psychological implications arising from the viewing of violent content by teenagers, thus making the need for efficient automated detection mechanisms ever more pertinent. Furthermore, we present a complete definition of 'violence' for the standardized detection mechanisms and to cross-compare various studies. Using Violent Scene Detection, our results indicate high improvements in the detection accuracy and therefore become effective in this context for parental control and content moderation. Such results help safeguard children in the increasingly digital environment: traditional as well as currently ever-increasing. © 2024 IEEE.

关键词： Video signal processing

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PVDM-YOLOv8l: a solution for reliable pedestrian and vehicle detection in autonomous vehicles under adverse weather conditions

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Multimedia Tools and Applications 2024年 1-26页

作者： Tahir, Noor Ul Ain Zhang, Zuping Asim, Muhammad Iftikhar, Sundas A. Abd El-Latif, Ahmed School of Computer Science and Engineering Central South University Changsha410083 China EIAS Data Science Lab College of Computer and Information Sciences and Center of Excellence in Quantum and Intelligent Computing Prince Sultan University Riyadh11586 Saudi Arabia Gianforte School of Computing Montana State University Bozeman59717 United States Department of Mathematics and Computer Science Faculty of Science Menoufia University Shebin El-Koom32511 Egypt

Ensuring the safe navigation of autonomous vehicles in intelligent transportation system depends on their ability to detect pedestrians and vehicles. While transformer-based models for object detection have shown remarkable advancements, accurately identifying pedestrians and vehicles in adverse weather conditions remains a challenging task. Adverse weather introduces image quality degradation, leading to issues such as low contrast, reduced visibility, blurred edges, false detection, misdetection of tiny objects, and other impediments that further complicate the accuracy of detection. This paper introduces a novel Pedestrian and Vehicle Detection Model under adverse weather conditions, denoted as PVDM-YOLOv8l. In our proposed model, we first incorporate the Swin-Transformer method, which is designed for global extraction of feature of small objects to identify in poor visibility, into the YOLOv8l backbone structure. To enhance detection accuracy and address the impact of inaccurate features on recognition performance, CBAM is integrated between the neck and head networks of YOLOv8l, aiming to gather crucial information and obtain essential data. Finally, we adopted the loss function Wise-IOU v3. This function was implemented to mitigate the adverse effects of low-quality instances by minimizing negative gradients. Additionally, we enhanced and augmented the DAWN dataset and created a custom dataset, named DAWN2024, to cater to the specific requirements of our study. To verify the superiority of PVDM-YOLOV8l, its performance was compared against several commonly used object detectors, including YOLOv3, YOLOv3-tiny, YOLOv3-spp, YOLOv5, YOLOv6, and all the versions of YOLOv8 (n, m, s, l, and x) and some traditional models. The experimental results demonstrate that our proposed model achieved a 6.6%, 5.4%, 6%, and 5.1% improvement in precision, recall, F1-score and mean Average Precision (mAP) on the custom DAWN2024 dataset. This substantial improvement in accuracy ind

关键词： Vehicle detection

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An Optimized Steganographic Technique for 24-Bit Digital Images using Slab Partitioning

An Optimized Steganographic Technique for 24-Bit Digital Ima...

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2025 International Conference on Intelligent Computing and Control Systems, ICICCS 2025

作者： Acharjya, Pinaki Pratim Majumdar, Jayeeta Barman, Subhabrata Sinha, Jayashri Deb Ghorai, Ramsundar Ghosh, Arpan Haldia Institute of Technology Dept. of Computer Science and Engineering West Bengal Haldia India Haldia Institute of Technology Dept. of Computer Science and Engineering (Data Science) West Bengal Haldia India

ISBN: (纸本)9798331512088

Based on image slabs and sheets, a novel kind of image steganography method for digital images has been proposed in this research work. Using the binary representation of the pixel, this approach splits the cover image into 24 fixed-size sheets. The secret message's binary counterpart is then incorporated into the cover image. It looks through each sheet's rows and columns to locate the one that most closely matches the secret message's binary counterpart. The row or column's position and how it differs from the message it stores in the LSB slab. Compared to previous ways, the proposed technique somewhat modifies the cover image. © 2025 IEEE.

关键词： digital image image slab secret message steganalysis steganography

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Deep learning at the service of metaheuristics for solving numerical optimization problems

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Neural Computing and Applications 2025年 1-36页

作者： Oyelade, Olaide N. Ezugwu, Absalom E. Saha, Apu K. Thieu, Nguyen V. Gandomi, Amir H. School of Electronics Electrical Engineering and Computer Science Queen’s University Belfast Bangladesh Unit for Data Science and Computing North-West University 11 Hoffman Street Potchefstroom2520 United States Department of Mathematics National Institute of Technology Agartala Tripura Agartala799046 India Faculty of Computer Science Phenikaa University Yen Nghia Hanoi Ha Dong12116 Viet Nam Department of Engineering and Information Technology University of Technology Sydney UltimoNSW2007 Australia

Integrating deep learning methods into metaheuristic algorithms has gained attention for addressing design-related issues and enhancing performance. The primary objective is to improve solution quality and convergence speed within solution search spaces. This study investigates the use of deep learning methods as a generative model to learn historical content, including global best and worst solutions, solution sequences, function evaluation patterns, solution space characteristics, population modification trajectories, and movement between local and global search processes. An LSTM-based architecture is trained on dynamic optimization data collected during the metaheuristic optimization process. The trained model generates an initial solution space and is integrated into the optimization algorithms to intelligently monitor the search process during exploration and exploitation phases. The proposed deep learning-based methods are evaluated on 55 benchmark functions of varying complexities, including CEC 2017 and compared with 13 biology-based, evolution-based, and swarm-based metaheuristic algorithms. Experimental results demonstrate that all the deep learning-based optimization algorithms achieve high-quality solutions, faster convergence rates, and significant performance improvements. These findings highlight the critical role of deep learning in addressing design issues, enhancing solution quality, trajectory, and performance speed in metaheuristic algorithms. © The Author(s) 2025.

关键词： Contrastive Learning

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Enhancing Residential Real Estate Search with Classification Strategies Using Diffusion and CLIP

Enhancing Residential Real Estate Search with Classification...

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2024 IEEE International Conference on Consumer Electronics, ICCE 2024

作者： Deng, Haojin Zhang, Wandong Yang, Yimin Nejad, Eman Western University Department of Electrical and Computer Engineering London Canada Wahi Realty Inc. Data Science Toronto Canada

ISBN: (纸本)9798350324136

The significance of the real estate search engine in the economy necessitates the development of a reliable room image luxury level annotation method that addresses current limitations, including the inability to assess room quality, underutilization of deep network capacities, and the need for more annotated house images. This paper proposes a novel real estate image annotation model, leveraging the diffusion model and contrastive language-image pre-Training (CLIP) network, through a multi-stage algorithm. First, the diffusion network is employed as a data augmentation technique to generate additional real estate images for network training. Then, a CLIP model is utilized to categorize images into the kitchen, bathroom, dining room, living room, and foyer. Finally, five CLIP models assess the condition of each room, categorizing it as contemporary and standard. Experimental results on a newly collected real estate image dataset demonstrate that the proposed approach surpasses existing house image classification algorithms. © 2024 IEEE.

关键词： contrastive language-image pre-Training image classification luxury level annotation Real-estate image

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Unsupervised Kernel-based Multi-view Feature Selection with Robust Self-representation and Binary Hashing 39

Unsupervised Kernel-based Multi-view Feature Selection with ...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Hu, Rongyao Gan, Jiangzhang Zhan, Mengmeng Li, Li Wei, Mengling School of Computer Science and Technology University of Electronic Science and Technology of China Chengdu611731 China Knowledge and Data Engineering Laboratory of Chinese Medicine School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu610054 China School of Computer Science and Technology Hainan University Haikou570228 China Beijing Information Science and Technology University Beijing100101 China

ISBN: (纸本)157735897X

Unsupervised multi-view feature selection involves selecting a subset of crucial features across diverse views to diminish feature dimensionality without leveraging label information. While numerous studies may risk losing semantic information when applied to real-world multi-view datasets. In this study, we introduce a novel model, Unsupervised Kernel-based Multi-view Feature selection with Robust self-representation and Binary hashing (UKMFS), which aims to identify robust consistent graph representation across views and leverage binary hashing codes to guide feature selection. Specifically, we first explore the underlying geometry by unifying the dimension of multi-view data with non-linear kernel mapping. Then, we search for consistent graphs across views by fusing unique graph representations of each view in a self-representation manner. Additionally, we impose low-rank constraints on the graph of each view to mitigate noise and unimportant parts to preserve the main structures and patterns. Furthermore, we design an unsupervised hashing feature selection model to exploit reliable binary labels across views and weighted matrices from each view. Finally, an effective optimization method is customized to solve the formulated problem. Comprehensive experiments on public multiview datasets indicate that our proposed method achieves state-of-the-art performance compared with the representative comparison methods regarding the clustering and the feature selection task. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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An Overview of Different Types of Recommendations Systems-A Survey 4

An Overview of Different Types of Recommendations Systems-A ...

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4th International Conference on Innovative Trends in Information Technology, ICITIIT 2023

作者： Duraisamy, Premkumar Yuvaraj, S. Natarajan, Yuvaraj Niranjani, V. KPR Institute of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India Sri Eshwar College of Engineering Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9781665494144

In recent years the boom of internet and social media usage everyone spend their invaluable time in social media app and looking for the solution for all kind of their problems. This work analysis deeply on how recommendation system works and its types in different platforms. Most of the modern recommendation system use machine learning algorithms like linear regression, random forest regression and support vector model with collaborative filtering method. Recommendation is nothing but an choice making system. It is vary from person to person based on their interest, culture, locality, education background, interpersonal skills etc., The huge item can be filtered from one by one based on each parameter and finally it will reach the right recommendation item. The research community has worked tremendous way in the field of recommendation system and produced huge variety of result. This survey enlightening the ideas about variety of recommendation system and techniques used by the research community. © 2023 IEEE.

关键词： Recommender systems

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Federated Causally Invariant Feature Learning 39

Federated Causally Invariant Feature Learning

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Guo, Xianjie Yu, Kui Cui, Lizhen Yu, Han Li, Xiaoxiao School of Computer Science and Information Engineering Hefei University of Technology China Key Laboratory of Knowledge Engineering with Big Data of Ministry of Education China School of Software Shandong University China College of Computing and Data Science Nanyang Technological University Singapore Department of Electrical and Computer Engineering The University of British Columbia Canada Vector Institute Canada

ISBN: (纸本)157735897X

Federated feature selection (FFS) is a promising field for selecting informative features while preserving data privacy in federated learning (FL) settings. Existing FFS methods focus on capturing the correlations between features and labels. They struggle to achieve satisfactory performance in the face of data distribution heterogeneity among FL clients, and cannot address the out-of-distribution (OOD) problem that arises when a significant portion of clients do not actively participate in FL training. To address these limitations, we propose Federated Causally Invariant Feature Learning (FedCIFL), a novel approach for learning causally invariant features in a privacy-preserving manner. We design a sample reweighting strategy to eliminate spurious correlations introduced by selection bias and iteratively estimate the federated causal effect between each feature and the labels (with the remaining features initially treated as confounders). By iteratively refining the confounding feature set to identify the true confounders, FedCIFL mitigates the impact of limited local data on the accuracy of federated causal effect estimation. Theoretical analysis proves the correctness of FedCIFL under reasonable assumptions. Extensive experiments on synthetic and real-world datasets demonstrate the superiority of FedCIFL against eight state-of-the-art baselines, beating the best-performing approach by 3.19%, 9.07% and 2.65% in terms of average test Accuracy, RMSE and F1 score, respectively. It is a first-of-its-kind FFS approach capable of handling Non-IID and OOD data simultaneously. The source code is available at https://***/Xianjie-Guo/FedCIFL. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Federated learning

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