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

作者： Abushiba, Walid Johnson, Princy Benbakhti, Brahim Jamshid, Kashif Irfan, Muhammad Ihsan, Anaum College of Engineering Applied Science University Bahrain School of Engineering Liverpool John Moores University LiverpoolL3 3AF United Kingdom University of Mostaganem Department of Electronics and Electrical Engineering Mostaganem Algeria National College of Business Administration & Economics Department of Computer Science Multan Campus 60000 Pakistan The TIMES Institute Department of Computer Science Multan60000 Pakistan Lahore54890 Pakistan

ISBN: (纸本)9798350369106

Wireless communication networks are increasingly vulnerable to advanced malware attacks, posing significant risks to network security and user privacy. Traditional detection methods struggle with sophisticated malware, necessitating approaches that are more robust. This study introduces a deep learning-based detection system that leverages Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Graph Convolutional Networks (GCNs) to analyze network traffic and detect malicious activity. Results demonstrate that these models outperform traditional machine learning in accuracy, precision, and recall, offering improved resilience against emerging malware threats. The findings underscore deep learning's potential to enhance wireless network security and pave the way for future innovations in malware detection. Our proposed models achieved up to 96.3% accuracy and 98.2% precision, outperforming traditional machine-learning approaches by a significant margin. © 2024 IEEE.

关键词： CNN Deep Learning GCN Malware Detection Network Security RNN Wireless Communication Networks

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Optimizing Weighted Ensemble ML IDS Using Parallelization and Batch Learning in Healthcare Networks

Optimizing Weighted Ensemble ML IDS Using Parallelization an...

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2024 IEEE International Conference on E-Health Networking, Application and Services, HealthCom 2024

作者： Ward, Blake Gonzalez, Daniel Alt, Jake Fang, Dongfeng Xu, Shengjie The Department of Computer Science and Software Engineering College of Engineering California Polytechnic State University San Luis ObispoCA93407 United States The Department of Cyber Intelligence and Information Operations College of Applied Science and Technology University of Arizona TucsonAZ85721 United States

ISBN: (纸本)9798350350548

The increasing adoption of IoT devices in healthcare necessitates the development of lightweight and fast intrusion detection systems (IDS). This paper proposes a weighted ensemble ML IDS with parallelized batch learning to optimize runtime while maintaining ML performance. The proposed method is compared to other solutions, evaluating ML performance and runtime across three different datasets. Experimental results demonstrate that although batch learning introduces feedback delay that slightly degrades ML performance, parallel computing significantly reduces runtime. © 2024 IEEE.

关键词： Network intrusion

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DIS-Mine: Instance Segmentation for Disaster-Awareness in Poor-Light Condition in Underground Mines

DIS-Mine: Instance Segmentation for Disaster-Awareness in Po...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Jewel, Mizanur Rahman Elmahallawy, Mohamed Madria, Sanjay Frimpong, Samuel Missouri University of Science and Technology Computer Science Department RollaMO65401 United States Washington State University School of Engineering & Applied Sciences RichlandWA99354 United States Missouri University of Science and Technology Explosive & Mining Engineering Department RollaMO65401 United States

ISBN: (纸本)9798350362480

Detecting disasters in underground mining, such as explosions and structural damage, has been a persistent challenge over the years. This problem is compounded for first responders, who often have no clear information about the extent or nature of the damage within the mine. The poor-light or even total darkness inside the mines makes rescue efforts incredibly difficult, leading to a tragic loss of life. In this paper, we propose a novel instance segmentation method called DIS-Mine, specifically designed to identify disaster-affected areas within underground mines under low-light or poor visibility conditions, aiding first responders in rescue efforts. DIS-Mine is capable of detecting objects in images, even in complete darkness, by addressing challenges such as high noise, color distortions, and reduced contrast. The key innovations of DIS-Mine are built upon four core components: i) Image brightness improvement, ii) Instance segmentation with SAM Integration, iii) Mask R-CNN-Based Segmentation, and iv) Mask alignment with feature matching. On top of that, we have collected real-world images from an experimental underground mine, introducing a new dataset named ImageMine, specifically gathered in low-visibility conditions. This dataset serves to validate the performance of DIS-Mine in realistic, challenging environments. Our comprehensive experiments on the ImageMine dataset, as well as on various other datasets demonstrate that DIS-Mine achieves a superior F1 score of 86.00% and mIoU of 72.00%, outperforming state-of-the-art instance segmentation methods, with at least 15x improvement and up to 80% higher precision in object *** have made our dataset publicly accessible through ImageMine Dataset. © 2024 IEEE.

关键词： Masks

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IMAN: An Adaptive Network for Robust NPC Mortality Prediction with Missing Modalities

IMAN: An Adaptive Network for Robust NPC Mortality Predictio...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Huo, Yejing Huang, Guoheng Cheng, Lianglun He, Jianbin Chen, Xuhang Yuan, Xiaochen Zhong, Guo Pun, Chi-Man Guangdong University of Technology School of Computer Science and Technology China Huizhou University School of Computer Science and Engineering China University of Macau Department of Computer and Information Science China Macao Polytechnic University Faculty of Applied Sciences China Guangdong University of Foreign Studies School of Information Science and Technology China

ISBN: (纸本)9798350386226

Accurate prediction of mortality in nasopharyngeal carcinoma (NPC), a complex malignancy particularly challenging in advanced stages, is crucial for optimizing treatment strategies and improving patient outcomes. However, this predictive process is often compromised by the high-dimensional and heterogeneous nature of NPC-related data, coupled with the pervasive issue of incomplete multi-modal data, manifesting as missing radiological images or incomplete diagnostic reports. Traditional machine learning approaches suffer significant performance degradation when faced with such incomplete data, as they fail to effectively handle the high-dimensionality and intricate correlations across modalities. Even advanced multi-modal learning techniques like Transformers struggle to maintain robust performance in the presence of missing modalities, as they lack specialized mechanisms to adaptively integrate and align the diverse data types, while also capturing nuanced patterns and contextual relationships within the complex NPC data. To address these problem, we introduce IMAN: an adaptive network for robust NPC mortality prediction with missing modalities. IMAN features three integrated modules: the Dynamic Cross-Modal Calibration (DCMC) module employs adaptive, learnable parameters to scale and align medical images and field data;the Spatial-Contextual Attention Integration (SCAI) module enhances traditional Transformers by incorporating positional information within the self-attention mechanism, improving multi-modal feature integration;and the Context-Aware Feature Acquisition (CAFA) module adjusts convolution kernel positions through learnable offsets, allowing for adaptive feature capture across various scales and orientations in medical image modalities. Extensive experiments on our proprietary NPC dataset demonstrate IMAN's robustness and high predictive accuracy, even with missing data. Compared to existing methods, IMAN consistently outperforms in scenarios with incom

关键词： Patient treatment

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Image Reconstruction for Proton Therapy Range Verification via U-NETs 28th

Image Reconstruction for Proton Therapy Range Verification ...

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28th Annual Conference on Medical Image Understanding and Analysis, MIUA 2024

作者： Setterdahl, Lena M. Lionheart, William R. B. Holman, Sean Skjerdal, Kyrre Ratliff, Hunter N. Ytre-Hauge, Kristian Smeland Lathouwers, Danny Meric, Ilker Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen Norway Department of Mathematics University of Manchester Manchester United Kingdom Department of Physics and Technology University of Bergen Bergen Norway Department of Radiation Science and Technology Delft University of Technology Delft Netherlands

ISBN: (纸本)9783031669545

This study aims to investigate the capability of U-Nets in improving image reconstruction accuracy for proton range verification within the framework of the NOVO (Next generation imaging for real-time dose verification enabling adaptive proton therapy) project. NOVO aims to enhance the accuracy of proton range verification by imaging the distribution of prompt gamma-rays (PGs) and fast neutrons (FNs) produced by nuclear interactions within tissue. In this work, focus lies on FNs, leaving PGs as future work. A dataset consisting of Monte Carlo-based simple back-projection and ground truth images of FN production distributions in a homogeneous water phantom was utilized. Various U-Net models were trained to predict FN distributions, and a set of range landmark (RL) metrics were computed for evaluation. Linear regression models were established to correlate shifts in mean RL with true range shift magnitudes. Our findings demonstrate a strong linear correlation between the shifts in mean RL in U-Net predictions and the true range shift magnitudes. Multiple RL metrics, including weighted average, inflection point, edge, and peak, were explored. This study highlights the potential utility of U-Nets in enhancing image reconstruction accuracy for proton range verification. The observed correlations between RL shifts and true range shifts provide evidence of the ability of U-Nets to accurately predict images containing range information. Future research will focus on generating more realistic training data incorporating more clinically relevant phantoms, including tissue heterogeneities. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Tissue

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Metaheuristics approach to improve data analysis process for the healthcare sector 4th

Metaheuristics approach to improve data analysis process for...

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4th International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2022

作者： Rahul, Kumar Banyal, Rohitash Kumar Department of Basic and Applied Science NIFTEM Sonipat India Department of Computer Science and Engineering Rajasthan Technical University Kota India

The metaheuristics approach is important for solving many engineering applications, including healthcare. In the healthcare sector, many problems related to patients' treatments are not covered, and it is one of the issues for healthcare services across the globe. Healthcare support functions like clinical decision support, patient profiling, customized care, and health tracking could be the area where data analysis effectively provides end-user support. This paper focused on the metaheuristics algorithm concept with data mining technologies to provide a healthcare sector solution mechanism. This paper identifies the data analysis problem of the healthcare sector. It reviews various recent technologies used in healthcare and related sectors. It focused on the importance of metaheuristics algorithms, artificial intelligence, and implementation methodologies. © 2022 Elsevier B.V.. All rights reserved.

关键词： Data mining

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Hybrid Deep Learning Enabled Air Pollution Monitoring in ITS Environment

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computers, Materials & Continua 2022年第7期72卷 1157-1172页

作者： Ashit Kumar Dutta Jenyfal Sampson Sultan Ahmad T.Avudaiappan Kanagaraj Narayanasamy Irina V.Pustokhina Denis A.Pustokhin Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityAd DiriyahRiyadh13713Kingdom of Saudi Arabia Department of Electronics and Communication Engineering Kalasalingam Academy of Research and EducationKrishnankoil626128India Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAl-Kharj 11942Saudi Arabia Department of Computer Science and Engineering K.Ramakrishnan College of TechnologyTiruchirappalli621112India Department of Computer Applications J.J College of Arts and Science(Autonomous)Affiliated to Bharathidasan UniversityPudukkottai622422India Department of Entrepreneurship and Logistics Plekhanov Russian University of Economics117997MoscowRussia Department of Logistics State University of Management109542MoscowRussia

Intelligent Transportation Systems(ITS)have become a vital part in improving human lives and modern *** aims at enhancing road safety and environmental *** is a tremendous increase observed in the number of vehicles in recent years,owing to increasing *** vehicle has its own individual emission rate;however,the issue arises when the emission rate crosses a standard *** to the technological advances made in Artificial Intelligence(AI)techniques,it is easy to leverage it to develop prediction approaches so as to monitor and control air *** current research paper presents Oppositional Shark Shell Optimization with Hybrid Deep Learning Model for Air Pollution Monitoring(OSSOHDLAPM)in ITS *** proposed OSSO-HDLAPM technique includes a set of sensors embedded in vehicles to measure the level of *** addition,hybridized Convolution Neural Network with Long Short-Term Memory(HCNN-LSTM)model is used to predict pollutant level based on the data attained earlier by the *** HCNN-LSTM model,the hyperparameters are selected and optimized using OSSO *** order to validate the performance of the proposed OSSO-HDLAPM technique,a series of experiments was conducted and the obtained results showcase the superior performance of OSSO-HDLAPM technique under different evaluation parameters.

关键词： Deep learning air pollution environment monitoring internet of things intelligent transportation systems oppositional learning LSTM model

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Enhancing Stability in Autonomous Control Systems Through Fuzzy Gain Scheduling (FGS) and Lyapunov Function Analysis

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International Journal of applied and Computational mathematics 2024年第4期10卷 1-15页

作者： Venkatesh, R. Rao, Deepak Dasaratha Sangeetha, V. Subbalakshmi, Ch. Bala Dhandayuthapani, V. Mekala, R. Department of Mathematics Hindusthan Institute of Technology Coimbatore 641032 India Indian Institute of Technology Patna India Department of Computer Science and Engineering Ramaiah Institute of Technology Bangalore India Department of Computer Science and Engineering Guru Nanak Instructions Technical Campus (Autonomous) Ibrahimpatnam Ranga Reddy District Telagana State Hyderabad 501506 India Department of IT College of Computing and Information Sciences University of Technology and Applied Sciences Shinas Campus Shinas Oman Department of Information Technology M.Kumarasamy College of Engineering Tamilnadu Karur India

Traditional control methods may struggle to adapt to the nonlinear and uncertain characteristics in Autonomous Vehicle Control. In recent years, fuzzy control techniques, such as the Takagi–Sugeno fuzzy controller, have emerged as promising approaches for handling such complexities. Fuzzy controllers utilize linguistic variables and fuzzy logic to model system behavior, offering flexibility and robustness in dealing with uncertainties. Furthermore, Lyapunov function analysis provides a powerful tool for assessing the stability of dynamical systems. By employing Lyapunov functions, researchers can mathematically prove the stability of a system and derive stability criteria, contributing to a deeper understanding of system behavior. This paper investigates the enhancement of stability in control systems by employing Fuzzy Gain Scheduling combined with Lyapunov function analysis. Stability is a crucial aspect of control systems, ensuring their reliable and efficient operation in various dynamic environments. Traditional control techniques often struggle to handle the nonlinear and uncertain nature of modern systems. FGS offers a flexible and adaptive approach to control by adjusting controller gains based on system operating conditions. Additionally, Lyapunov function analysis provides a rigorous mathematical framework for stability assessment, enabling the verification of system stability properties. By integrating FGS and Lyapunov function analysis, this research aims to develop a robust control strategy capable of ensuring stability across a range of operating conditions. Simulation and experimental results are presented to demonstrate the effectiveness of the proposed approach in enhancing stability and performance in control systems. Specifically, the settling time was reduced by 20%, and overshoot was minimized to 5% of the steady-state value. Furthermore, in experimental tests conducted on a real-world control system setup, the proposed approach demonstrated rob

关键词： Control systems Fuzzy gain scheduling (FGS) Lyapunov function analysis Performance Simulation Stability

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Image cyberbullying detection and recognition using transfer deep machine learning

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International Journal of Cognitive Computing in engineering 2024年第1期5卷 14-26页

作者： Almomani, Ammar Nahar, Khalid Alauthman, Mohammad Al-Betar, Mohammed Azmi Yaseen, Qussai Gupta, Brij B. School of Computing Skyline University College University City of Sharjah P.O. Box 1797 Sharjah United Arab Emirates I.T. department Al-Huson University College AlBalqa Applied University Irbid Jordan Computer Science Department Faculty of I.T. Yarmouk University Irbid Jordan Department of Information Security Faculty of Information Technology University of Petra Amman Jordan Ajman University Ajman United Arab Emirates Department of Computer Science and Information Engineering Asia University Taichung413 Taiwan Symbiosis International University Pune India Department of Electrical and Computer Engineering Lebanese American University Beirut1102 Lebanon Dehradun India

Cyberbullying detection on social media platforms is increasingly important, necessitating robust computational methods. Current approaches, while promising, have not fully leveraged the combined strengths of deep learning and traditional machine learning for enhanced performance. Moreover, online content complexity requires models that can capture nuanced contexts beyond text, which many current methods lack. This research proposes a novel hybrid approach using deep learning models as feature extractors and machine learning classifiers to improve cyberbullying detection. Extracting features using pre-trained deep learning models like InceptionV3, ResNet50, and VGG16, then feeding them into classifiers like Logistic Regression and Support Vector Machines, enhances understanding of the complex contexts where cyberbullying occurs. Experiments on an image dataset showed that combining deep learning and machine learning achieved higher accuracy than using either approach alone. This novel framework bridges the gap in existing literature and contributes to broader efforts to combat cyberbullying through more nuanced, context-aware detection methods. The hybrid technique demonstrates the potential of blending deep learning's representation learning strengths with machine learning's sample efficiency and interpretability. © 2023

关键词： Learning systems

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Enhance Image-to-Image Generation with LLaVA-generated Prompts 5

Enhance Image-to-Image Generation with LLaVA-generated Promp...

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5th International Conference on Information science, Parallel and Distributed Systems, ISPDS 2024

作者： Ding, Zhicheng Li, Panfeng Yang, Qikai Li, Siyang Columbia University Fu Foundation School of Engineering and Applied Science New York United States University of Michigan Department of Electrical and Computer Engineering Ann Arbor United States University of Illinois Urbana-Champaign Department of Computer Science Urbana United States Pace University Lubin School of Business New York United States

ISBN: (纸本)9798350352719

This paper presents a novel approach to enhance image-to-image generation by leveraging the multimodal capabilities of the Large Language and Vision Assistant (LLaVA). We propose a framework where LLaVA analyzes input images and generates textual descriptions, hereinafter LLaVA-generated prompts. These prompts, along with the original image, are fed into the image-to-image generation pipeline. This enriched representation guides the generation process towards outputs that exhibit a stronger resemblance to the input image. Extensive experiments demonstrate the effectiveness of LLaVA-generated prompts in promoting image similarity. We observe a significant improvement in the visual coherence between the generated and input images compared to traditional methods. Future work will explore fine-tuning LLaVA prompts for increased control over the creative process. By providing more specific details within the prompts, we aim to achieve a delicate balance between faithfulness to the original image and artistic expression in the generated outputs. © 2024 IEEE.

关键词： Visual languages

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