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2nd International Conference on Cyber Resilience, ICCR 2024

作者： Fakhouri, Hussam N. Alsharaiah, Mohammad A. Al Hwaitat, Ahmad K. Alkalaileh, Mohannad Dweikat, Fawzi Fayez University Of Petra Faculty Of Information Technology Data Science And Artificial Intelligence Department Amman Jordan Al-Ahliyya Amman University Data Science And Artificial Intelligence Department Amman Jordan The University Of Jordan Amman Amman Jordan College Of Education Al Ain University Humanities And Social Sciences Al-Ain United Arab Emirates Hamdan Bin Mohammed Smart University Chancellor Office United Arab Emirates

ISBN: (纸本)9798350394962

Digital forensics, a field pivotal in the scientific examination and analysis of digital device data, has escalated in relevance as technology becomes ubiquitous. This research offers a detailed examination of the multifarious challenges confronting forensic practitioners, categorized into technical, procedural, organizational, and human aspects. Employing a multifaceted approach that includes a comprehensive literature review and case study analysis, this study elucidates the dynamic and evolving nature of these obstacles. Technical challenges, including the overwhelming data volumes and the complexities associated with IoT and mobile device forensics, are shown to significantly hinder effective data extraction and analysis. Procedural challenges are characterized by issues in standardization, inter-agency collaboration, and legal ambiguities, while organizational challenges encompass budgetary constraints, the need for reliable tool validation, and maintaining an unbroken chain of custody. Human factors also introduce significant variability and potential for error in forensic investigations. The study's findings indicate that these challenges are interrelated and require a cohesive approach to address. It concludes by proposing a series of integrated solutions and best practices aimed at mitigating these challenges. These include the adoption of advanced analytical tools, the development of universal procedural standards, enhanced training and resources for forensic practitioners, and a greater emphasis on ethical considerations. © 2024 IEEE.

关键词： Cybersecurity

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Advancing Alzheimer's Nodule Detection Through a Comprehensive Multi-Scene Deep Learning Framework 10

Advancing Alzheimer's Nodule Detection Through a Comprehensi...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Premkumar, C. Rajeshwari, R. Remya, R. Cholaathiraj, M.S. Reethika, A. Bharathidasan, S. Bannari Amman Institute of Technology Department of Artificial Intelligence and Data Science Sathyamangalam India Cmr Institute of Technology Department of Computer Science and Engineering Bengaluru India Vel Tech Rangarajan Dr Sagunthala R&d institute of science and Technology Department of Electronics and Communication Engineering Chennai India Bannari Amman Institute of Technology Department of Artificial Intelligence and Machine Learning Sathyamangalam India Sri Ramakrishna Engineering College Department of Electronics and Communication Engineering Coimbatore India Erode Sengunthar Engineering College Department of Electronics and Communication Engineering Erode India

ISBN: (纸本)9798350384369

Determining the precise location of Alzheimer's nodules is essential for estimating the risk of brain cancer. Conventional CAD modules, including MRI, PET, and CT, struggle with feature extraction and segmentation due to time limits and complexity. This study proposes an efficient brain nodule detection technique based on the Multi-Scene Deep Learning Framework (MSDLF) that makes use of the vesselness filter. A four-channel CNN model is developed using information from two image sequences to enhance radiologists' ability to recognise four-stage nodules. This adaptable model may be used in two different classes. The outcomes demonstrate how well the MSDLF performs while processing substantial volumes of image data for brain nodule recognition, improving with accuracy 98.86% and 98.45% of sensitivity by using decreasing false positives. © 2024 IEEE.

关键词： Magnetic resonance imaging

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MedFuseNet: Fusion of Multi-Modal data for Improved Cervical Cancer Diagnostic Accuracy 3

MedFuseNet: Fusion of Multi-Modal Data for Improved Cervical...

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3rd International Conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Vidhya, K. Nagarajan, B. Jenefa, A. Catherine Joy, R. Shirley, C.P. Joel, J. School of CST Karunya Institute of Tech. and Sci Coimbatore India Artificial Intelligence and Data Science KPR Institute of Engineering and Technology Coimbatore India Division of ECE Karunya Institute of Tech. and Sci Coimbatore India

ISBN: (纸本)9798331527549

Timely identification of cervical cancer is essential for effective therapy and enhanced patient outcomes. Conventional imaging methods, although fundamental, frequently inadequately represent the Detailed characteristics of cancer pathology because they depend on isolated data sources. MedFuseNet tackles these difficulties by presenting an advanced architecture that integrates multi-modal data to improve diagnostic accuracy and dependability significantly. This advanced model employs high-resolution medical imaging and incorporates patient-specific clinical data, establishing a comprehensive analytical foundation for diagnosis. Our dataset includes 4,049 annotated cervical cell images across a spectrum from normal to malignant, each enriched with detailed clinical parameters. MedFuseNet utilizes a hybrid architecture that integrates CNNs for image data and RNNs for sequential clinical data, facilitating a thorough analysis of various data sources. The methodological integration enables MedFuseNet to surpass conventional single-source models, with an accuracy of 98.5%, alongside significant enhancements in precision (97.9%) and recall (98.1%). These substantial diagnostic improvements highlight the promise of multi-modal data fusion in medical imaging, paving the way for creating more sophisticated, AI-driven diagnostic instruments that may revolutionize early cancer diagnosis and treatment approaches. © 2025 IEEE.

关键词： Lung cancer

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A New Model Based on Graph Neural Network Feedback for Crystal Property Prediction 5

A New Model Based on Graph Neural Network Feedback for Cryst...

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5th International Conference on Big data and artificial intelligence and Software engineering, ICBASE 2024

作者： Zhu, Jian Ren, Yongshen Zang, Huaijuan Zhan, Shu Hefei University of Technology School of Computer and Information Engineering Hefei China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China Kunming University of Science and Technology Faculty of Metallurgical and Energy Engineering Kunming China Kunming University of Science and Technology National Engineering Research Center of Vacuum Metallurgy Kunming China

ISBN: (纸本)9798331506612

The prediction of crystal properties is crucial in crystal design. Currently, most methods employ graph neural networks to model crystal structures and have achieved satisfactory prediction accuracy. However, accurately simulating the complex relationships between atoms and molecules in crystal structures remains challenging. The prediction performance of crystal graph networks also depends on the effective extraction of crystal features, typically requiring a close correlation between the properties to be predicted and the extracted features. In this study, a model retraining method is utilized, where the loss function in the retraining phase is constructed using feedback from a pre-trained crystal graph network model to adjust model parameters and enhance the accuracy of predicting crystal properties. This method enhances the predictive performance of multiple crystal graph neural networks and accelerates the development of high-performance crystals without the need to add various features or incorporate additional neural network layers. The research findings demonstrate the significant effectiveness of the proposed method in enhancing the prediction performance of four models: CGCNN, OGCNN, GATGNN, and ALIGNN. Optimization metrics support this claim. © 2024 IEEE.

关键词： Prediction models

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Parkinsonian Gait Severity in Older Adults with Dementia Using Natural Gait Video Analysis

Parkinsonian Gait Severity in Older Adults with Dementia Usi...

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2024 International Telecommunications Conference, ITC-Egypt 2024

作者： Islam, Eissa Medhat, Mariam Waleed, Farah Deif, Mohanad A. Alkhazaleh, Shawkat Hafez, Mohamed 6th of October City Department of Artificial intelligence 12566 Egypt Jadara University Faculty of Science and Information Technology Department of Mathematics Jordan FEQS INTI-IU-University Faculty of Engineering Nilai Malaysia

ISBN: (纸本)9798350351408

Parkinson's disease presents challenges in assessing severity, especially in older adults with dementia. This study introduces a novel approach using vision-based systems for gait analysis [1]. 2D human pose tracking in video data from a dementia unit extracted gait features. Correlation between these features and parkinsonism severity, assessed by UPDRS, was observed [6]. Five machine learning algorithms were evaluated, with KNN demonstrating superior performance [9]. Random Forest and Extra Trees also performed well [10], while Logistic Regression faced challenges [11], [12]. XGBoost showed exceptional predictive ability [8]. The study underscores the effectiveness of these algorithms in non-invasively identifying gait abnormalities, particularly highlighting KNN's adeptness with high-dimensional features [9]. This research advances healthcare diagnostics by showcasing machine learning's potential in early detection and ongoing monitoring of neurodegenerative disorders, promising improved medical outcomes. © 2024 IEEE.

关键词： Neurodegenerative diseases

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Machine Learning in Healthcare: A Review of Applications, Opportunities and Challenges

Machine Learning in Healthcare: A Review of Applications, Op...

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： Trupti Thute Saniya Saratkar Aarti Chaudhari Rohini Raut Gayatri Thakare Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

This article explores the potential of machine learning (ML) in transforming healthcare. It provides an overview of ML and its various categories, highlighting how supervised and unsupervised learning algorithms can be utilized for tasks like disease diagnosis, treatment personalization, and resource allocation. The article delves into specific applications of ML in healthcare, including image analysis for early disease detection, visualization of complex medical data, and personalized medicine approaches. It further explores the exciting opportunities presented by ML, such as improved diagnostics and treatment, increased efficiency and cost reduction, and advancements in research and development. However, the article also acknowledges the challenges associated with implementing ML in healthcare, including data privacy concerns, real-time data requirements, and the complexity of certain models. Overall, the article emphasizes the multifaceted landscape of ML in healthcare, showcasing its potential to revolutionize the industry while acknowledging the hurdles that need to be addressed for successful integration.

关键词： data privacy Reviews Precision medicine Medical services Machine learning Real-time systems Resource management Unsupervised learning Research and development Diseases

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Spectroscopy and molecule opacity investigation on excited states of SiS

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Chinese Physics B 2024年第5期33卷 381-387页

作者：李瑞吕浩男桑纪群刘晓华梁桂颖吴勇 College of Teacher Education Qiqihar UniversityQiqihar 161006China National Key Laboratory of Computational Physics Institute of Applied Physics and Computational MathematicsBeijing 100088China Department of Physics College of ScienceQiqihar UniversityQiqihar 161006China School of Data Science and Artificial Intelligence Jilin Engineering Normal UniversityChangchun 130052China HEDPS Center for Applied Physics and TechnologyPeking UniversityBeijing 100084China

The SiS molecule,which plays a significant role in space,has attracted a great deal of attention for many *** to complex interactions among its low-lying electronic states,precise information regarding the molecular structure of SiS is *** obtain accurate information about the structure of its excited states,the high-precision multireference configuration interaction(MRCI)method has been *** method is used to calculate the potential energy curves(PECs)of the 18Λ–S states corresponding to the lowest dissociation limit of *** core–valence correlation effect,Davidson’s correction and the scalar relativistic effect are also included to guarantee the precision of the MRCI *** on the calculated PECs,the spectroscopic constants of quasi-bound and bound electronic states are calculated and they are in accordance with previous experimental *** transition dipole moments(TDMs)and dipole moments(DMs)are determined by the MRCI *** addition,the abrupt variations of the DMs for the 1^(5)Σ^(+)and 2^(5)Σ^(+)states at the avoided crossing point are attributed to the variation of the electronic *** opacity of SiS at a pressure of 100 atms is presented across a series of *** increasing temperature,the expanding population of excited states blurs the band boundaries.

关键词： SiS opacity excited state spectroscopic constant configuration interaction

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Subgroup-Adaptive Network for Robust Virtual Patient-Specific Quality Assurance 20

Subgroup-Adaptive Network for Robust Virtual Patient-Specifi...

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20th International Computer Conference on Wavelet Active Media technology and Information Processing, ICCWAMTIP 2023

作者： Zeng, Xiaoyang Ahmed, Awais Tunio, Muhammad Hanif Chengxiao, Yan Hou, Mengshu Xi, Rui School of Computer Science and Engineering University of Electronic Science and Technology of China - UESTC Sichuan611731 China School of Big Data and Artificial Intelligence Chengdu Technological University Sichuan611730 China

ISBN: (纸本)9798350318982

Deep learning-based automatic patient-specific quality assurance (PSQA) alleviates medical resource pressure and ensures the safety of patient treatment plans by predicting the actual dosage difference distribution or metrics. However, deep learning is often affected by extremely unbalanced data distributions, and in clinical practice, the number of normal plans is always much larger than that of abnormal plans. On the other hand, the distribution of patients' lesion sites could be more balanced. The performance of models is often seriously biased in these subgroups, posing a challenge to the reliability of deep learning in the clinical application of PSQA. In this work, we propose a subgroup-adaptive network to address the problem of subgroup drift in PSQA. Our experiments achieved a state-of-the-art (SOTA) MAE of 0.69. Our proposed work can significantly improve the reliability of deep learning in the practical clinical application of PSQA. © 2023 IEEE.

关键词： Quality assurance

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PROVABLE SIM-TO-REAL TRANSFER IN CONTINUOUS DOMAIN WITH PARTIAL OBSERVATIONS 11

PROVABLE SIM-TO-REAL TRANSFER IN CONTINUOUS DOMAIN WITH PART...

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11th International Conference on Learning Representations, ICLR 2023

作者： Hu, Jiachen Zhong, Han Jin, Chi Wang, Liwei School of Computer Science Peking University China Center for Data Science Peking University China Department of Electrical and Computer Engineering Princeton University United States National Key Laboratory of General Artificial Intelligence School of Intelligence Science and Technology Peking University Center for Data Science Peking University Beijing Institute of Big Data Research China

Sim-to-real transfer, which trains RL agents in the simulated environments and then deploys them in the real world, has been widely used to overcome the limitations of gathering samples in the real world. Despite the empirical success of the sim-to-real transfer, its theoretical foundation is much less understood. In this paper, we study the sim-to-real transfer in continuous domain with partial observations, where the simulated environments and real-world environments are modeled by linear quadratic Gaussian (LQG) systems. We show that a popular robust adversarial training algorithm is capable of learning a policy from the simulated environment that is competitive to the optimal policy in the real-world environment. To achieve our results, we design a new algorithm for infinite-horizon average-cost LQGs and establish a regret bound that depends on the intrinsic complexity of the model class. Our algorithm crucially relies on a novel history clipping scheme, which might be of independent interest. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

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Hybrid Color Texture Features Classification Through ANN for Melanoma

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Intelligent Automation & Soft Computing 2023年第2期35卷 2205-2218页

作者： Saleem Mustafa Arfan Jaffar Muhammad Waseem Iqbal Asma Abubakar Abdullah S.Alshahrani Ahmed Alghamdi Faculty of Computer Science&Information Technology Superior UniversityLahore54600Pakistan Department of Software Engineering Superior UniversityLahore54600Pakistan Department of Computer Science&Artificial Intelligence College of Computer Science&EngineeringUniversity of Jeddah21493Saudi Arabia Department of Software Engineering College of Computer Science and EngineeringUniversity of Jeddah21493Saudi Arabia

Melanoma is of the lethal and rare types of skin *** is curable at an initial stage and the patient can survive *** is very difﬁcult to screen all skin lesion patients due to costly *** are requiring a correct method for the right treatment for dermoscopic clinical features such as lesion borders,pigment networks,and the color of *** challenges are required an automated system to classify the clinical features of melanoma and non-melanoma *** trained clinicians can overcome the issues such as low contrast,lesions varying in size,color,and the existence of several objects like hair,reﬂections,air bubbles,and oils on almost all *** contour is one of the suitable methods with some drawbacks for the segmentation of irre-gular *** entropy and morphology-based automated mask selection is pro-posed for the active contour *** proposed method can improve the overall segmentation along with the boundary of melanoma *** this study,features have been extracted to perform the classiﬁcation on different texture scales like Gray level co-occurrence matrix(GLCM)and Local binary pattern(LBP).When four different moments pull out in six different color spaces like HSV,Lin RGB,YIQ,YCbCr,XYZ,and CIE L*a*b then global information from different colors channels have been ***,hybrid fused texture features;such as local,color feature as global,shape features,and Artiﬁcial neural network(ANN)as classiﬁers have been proposed for the categorization of the malignant and *** had been carried out on datasets Dermis,DermQuest,and *** results of our advanced method showed super-iority and contrast with the existing state-of-the-art techniques.

关键词： Gray level co-occurrence matrix local binary pattern artiﬁcial neural networks support vector machines color skin cancer dermoscopic

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