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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Elaheh Zendehrouh Mohammad S. E. Sendi Vince D. Calhoun Department of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA Tri-Institutional Center for Translational Research in Neuroimaging Data Science (TReNDS): Georgia State University Georgia Institute of Technology Emory University Atlanta Georgia Mclean Hospital and Harvard Medical School Boston MA

Alzheimer’s disease (AD) is the most prevalent age-related dementia and causes memory, reasoning, and social skills to deteriorate. In recent years many studies have explored the genetic risk of AD, but less work has been done to identify a brain imaging-based AD risk measure. The current study proposed a new neuroimaging-based measure of AD risk, called brain-wide risk score or BRS, based on multimodal brain features. Using the proposed AD BRS, we identified four AD biotypes from a large sample of subjects (N>37,000) from the UK Biobank dataset: one with high AD BRS, one with low AD BRS, and two with moderate AD BRS. Next, we further showed that the cognitive scores of the biotype with lower AD BRS are significantly better than those of other biotypes.

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Uncovering Android Zero-Day Threats: The Zero-Vuln Approach with Deep and Zero-Shot Learning

Uncovering Android Zero-Day Threats: The Zero-Vuln Approach ...

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Computing and data analytics (ICCDA), International Conference on

作者： Suresh Palarimath Pyingkodi Maran Wilfred Blessing N. R S. J Kavitha Cibi. A Subitha G. Sutherlin College of Computing and Information Sciences University of Technology and Applied Sciences Salalah Sultanate of Oman Department of Computer Applications Kongu Engineering College Erode India College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Sultanate of Oman Department of Computer Science and Engineering Vels Institute of Science Technology and Advanced Studies (VISTAS) Pallavaram Chennai India Data & Analytics / Visualization - Delivery Virtusa Consulting Services Private Limited Chennai India Department of Computer Science and Engineering Stella Mary's College of Engineering Kanyakumari India

ISBN: (数字)9798331542573

ISBN: (纸本)9798331542580

The growing occurrence of cybersecurity hazards, such as Android zero-day vulnerabilities, poses substantial difficulties because they are unattended and imperceptible. With the increasing popularity of Android smartphones, hackers are taking advantage of these weaknesses to quickly spread advanced malware. Conventional detection approaches frequently prove inadequate due to the absence of shown criteria for emergent threats. In order to address this crucial problem, this article presents the Zero-Vuln method, an advanced technique specifically developed to identify and categorize previously unidentified malware on Android platforms. Zero-Vuln combines sophisticated supervised deep learning methods with zero-shot learning algorithms to accurately detect zero-day threats. Through the utilization of comprehensive datasets, the method attains an average classification metric score of 84.22%, showcasing exceptional accuracy, precision, and recall. This advanced solution significantly improves the ability to identify and handle Android zero-day threats, representing a major progress in cybersecurity research and implementation. The Zero-Vuln strategy provides a strong and adaptable solution for the constantly changing mobile security risks, representing a notable advancement in the sector.

关键词： Measurement Instruments Zero shot learning Malware Mobile security Hazards Reliability Protection Smart phones Research and development

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Semantic Segmentation for Image Understanding

Semantic Segmentation for Image Understanding

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International Conference on Computing and Networking Technology (ICCNT)

作者： N. Thangarasu Jagmeet Sohal B Umamaheswari Jagtej Singh R Manjunatha Ganesh C. Shelke Department of Computer Science Karpagam Academy of Higher Education Coimbatore India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Computer Science and Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India Department of Data analytics and Mathematical Sciences School of Sciences JAIN (Deemed to be University) Bangalore Karnataka India Department of Information Technology Vishwakarma Institute of Information Technology Pune INDIA

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Semantic segmentation is a way of photo knowhow that entails labeling the objects in a picture on the pixel degree and assigning every pixel within the photograph a category label. This method is utilized in PC imaginative and prescient programs to construct a representation of a scene from digital photographs. It may be used to correctly phase discrete items consisting of vehicles, pedestrians, and timber, as well as more summary standards, which include shadows and reflections. On the way to achieve success, semantic segmentation algorithms require a large amount of designated labeling and accurate pixel-degree statistical analysis. Latest advances in deep mastering strategies have allowed for the improvement of more sophisticated algorithms that could achieve incredible effects on hard-picture segmentation datasets. Those strategies have progressed the accuracy and efficiency of the photograph know-how algorithms, allowing them to carry out duties that include scene understanding, item identification, scene type, and pixel-stage segmentation.

关键词： Accuracy Pedestrians Statistical analysis Semantic segmentation Reflection Labeling Standards

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Leveraging Digital Twins and AI for Enhanced Clinical Decision Support in Endometrial Cancer Treatment

Leveraging Digital Twins and AI for Enhanced Clinical Decisi...

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IoT Based Control Networks and Intelligent Systems (ICICNIS), International Conference on

作者： Suresh Palarimath Pyingkodi Maran Wilfred Blessing N. R Cibi. A S. J. Kavitha Steffi S College of Computing and Information Sciences University of Technology and Applied Sciences Salalah Sultanate of Oman Department of Computer Application Kongu Engineering College Erode India College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Sultanate of Oman Data & Analytics Visualizaation – Delivery Virtusa Consulting Service Private Limited Chennai India Department of Computer Science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai India Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

Digital twins are a relatively new concept that have lately garnered interest in the industrial sector. Digital twins are virtual models that mirror actual products. In this research, we suggest using the technology of digital twins to the field of healthcare in order to enhance clinical decision support and to provide more individualized treatment for patients. When artificial intelligence (AI) is combined with digital twins, healthcare professionals are able to more effectively analyze large volumes of varied data and increase their ability to make diagnostic and therapeutic decisions. In this research, we provide a conceptual framework for leveraging digital twins and AI to solve existing limits in cancer care, notably endometrial cancer therapy. This research study mainly focuses on cancer care, and more specifically on endometrial cancer treatment. In addition, we analyze the possible challenges and opportunities that may arise throughout the process of integrating this technology in healthcare settings. Our overarching objective is to improve the standard of treatment as well as the clinical results for people who have cancer.

关键词： Accuracy Cancer treatment Medical services Machine learning Digital twins Mirrors Artificial intelligence Wearable sensors Standards Cancer

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An Empirical Approach of Developing Domain Based Ontology in the Field of Bioinformatics

An Empirical Approach of Developing Domain Based Ontology in...

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Advance Computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： Devanolla Suresh E.Afreen Banu R. Karthikeyan Pramod Kumar Naik Safaa Hallol Mohammed Tareq Hafdhi Abdtawfeeq Department of Computer Science & Technology Madanapalle Institute of Technology & Science Madanapalle CSE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai Department of CSE Artificial Intelligence and Data Analytics Sri Ramachandra Institute of Higher Education and Research Department of CSE Dayananda Sagar University Bengaluru Collage of Pharmacy National University of Science and Technology Dhi Qar Iraq Al-Farahidi university Baghdad Iraq

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

From a macro viewpoint, it is essential to comprehend the similarities across human illnesses in order to identify underlying mechanisms at the microscopic level. Previous studies have shown that employing machine learning methods or merging many data sources can improve performance in sickness similarity measurement. However, there is currently a gap in the development of a useful framework for information extraction and integration from a range of biological data using deep learning models. We introduce CoGO, a Contrastive learning system that predicts commonalities in illnesses based on gene networks and ontology structure. Gene ontology (GO) and gene interaction network domain information is integrated into CoGO using graph deep learning models. Graph deep learning models are first used to encode gene and GO word features from independent graph structure data. Gene and GO characteristics are then mapped via a nonlinear projection to a shared embedding space. Next, by using cross-view contrastive loss to increase the agreement of related gene-GO links, meaningful gene representations are generated. Finally, to calculate disease similarity, CoGO computes the cosine similarity of sickness representation vectors derived from related gene embeddings.

关键词： Deep learning Biological system modeling Soft sensors Microscopy Merging Ontologies Information retrieval

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Emotion correlation mining through deep learning models on natural language text

arXiv

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arXiv 2020年

作者： Wang, Xinzhi Kou, Luyao Sugumaran, Vijayan Luo, Xiangfeng Zhang, Hui School of Computer Engineering and Science Shanghai University Shanghai200444 China Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai200444 China Institute of Public Safety Research Department of Engineering Physics Tsinghua University Beijing Beijing100084 China Key Laboratory of City Integrated EmergencyResponse Science Tsinghua University Beijing100084 China Department of Decision and Information Sciences Oakland University RochesterMI48309 United States Center for Data Science and Big Data Analytics Oakland University RochesterMI48309 United States

Emotion analysis has been attracting researchers’ attention. Most previous works in the artificial-intelligence field focus on recognizing emotion rather than mining the reason why emotions are not or wrongly recognized. The correlation among emotions contributes to the failure of emotion recognition. In this article, we try to fill the gap between emotion recognition and emotion correlation mining through natural language text from Web news. The correlation among emotions, expressed as the confusion and evolution of emotion, is primarily caused by human emotion cognitive bias. To mine emotion correlation from emotion recognition through text, three kinds of features and two deep neural-network models are presented. The emotion confusion law is extracted through an orthogonal basis. The emotion evolution law is evaluated from three perspectives: one-step shift, limited-step shifts, and shortest path transfer. The method is validated using three datasets: 1) the titles;2) the bodies;and 3) the comments of news articles, covering both objective and subjective texts in varying lengths (long and short). The experimental results show that in subjective comments, emotions are easily mistaken as anger. Comments tend to arouse emotion circulations of love–anger and sadness–anger. In objective news, it is easy to recognize text emotion as love and cause fear–joy circulation. These findings could provide insights for applications regarding affective interaction, such as network public sentiment, social media communication, and human–computer interaction. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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A Swarm Artificial Intelligence Approach for Effective Treatment of Chronic Conditions

A Swarm Artificial Intelligence Approach for Effective Treat...

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International Workshop on Design of Reliable Communication Networks (DRCN)

作者： Kitty Kioskli Spyridon Papastergiou School of Computer Science and Electronic Engineering University of EssexInstitute of Analytics and Data Science (IADS) Essex United Kingdom Gruppo Maggioli Research and Development Lab Athens Greece trustilio B.V. Amsterdam Netherlands Department of Informatics Piraeus University of Piraeus Greece Focal Point Brussels Belgium

Long-term conditions or chronic diseases are multifaceted and challenging. Current treatment options, for patients with long-term conditions, are mainly pharmacological, causing numerous adverse drug events and pressing for alternative management strategies such as personalized interventions. Areas of machine learning, such as deep learning, would enable researchers to develop predictive modelling algorithms, using continuous monitoring and allowing assessing the medical risk for long-term conditions and their related complications. In this paper, we claim that harmonization of data, novel machine learning algorithms, swarm-based technologies, and the involvement of the entire healthcare community will lead to acceptable and effective personalized healthcare. Our proposed approach aims to amplify the intelligence of the healthcare community. Based upon the patients’ characteristics empowers better decisions, personalised medical risk prediction and recommendations of acceptable and effective interventions. Our future work includes the validation of the SwarmAI framework by actively engaging relevant stakeholders.

关键词： Drugs Machine learning algorithms Medical services Pressing Predictive models Reliability engineering Prediction algorithms

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A sparse semismooth Newton based proximal majorization-minimization algorithm for nonconvex square-root-loss regression problem

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2020年第1期21卷 9253-9290页

作者： Peipei Tang Chengjing Wang Defeng Sun Kim-Chuan Toh School of Computer and Computing Science Zhejiang University City College Hangzhou China School of Mathematics and National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu China Department of Applied Mathematics The Hong Kong Polytechnic University Hung Hom Hong Kong Department of Mathematics and Institute of Operations Research and Analytics National University of Singapore Singapore

In this paper, we consider high-dimensional nonconvex square-root-loss regression problems and introduce a proximal majorization-minimization (PMM) algorithm for solving these problems. Our key idea for making the proposed PMM to be efficient is to develop a sparse semismooth Newton method to solve the corresponding subproblems. By using the Kurdyka-Łojasiewicz property exhibited in the underlining problems, we prove that the PMM algorithm converges to a d-stationary point. We also analyze the oracle property of the initial subproblem used in our algorithm. Extensive numerical experiments are presented to demonstrate the high efficiency of the proposed PMM algorithm.

关键词： nonconvex square-root regression problems proximal majorization-minimization semismooth Newton method

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IoT-Enhanced Innovations in Drug Delivery Systems for Targeted Therapy in Autoimmune Disorders

IoT-Enhanced Innovations in Drug Delivery Systems for Target...

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International Conference on Advanced Computing and Communication Systems (ICACCS)

作者： V Bhuvaneshwari R Sadaieswaran P Malathi N Legapriyadharshini T Thirumalaikumari S R Raja Department of Computer Science and Engineering Acharya College of Engineering Achariyapuram Puducherry India Department of Computer Applications Tagore College of arts and science Chennai Tamil Nadu India Department of Data Analytics Saveetha College of Liberal Arts and Sciences SIMATS Deemed to be University Chennai Tamil Nadu India Department of Computer Application Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Computer Science Saveetha College of Liberal Arts and Sciences SIMATS Deemed to be University Chennai Tamil Nadu India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384376

The growing number of autoimmune diseases necessitates the needs novel drug delivery system alternatives to improve targeted therapy. The study deals with the immediate requirement for improvements by investigating the limitations of existing systems, highlighting their limitations in precision, patient adherence, and real-time monitoring. The proposed system combines Internet of Things (IoT) technology into medicine distribution, providing an extensive structure that makes use of smart devices, sensors, and networking solutions. The integration attempts to improve patient participation and treatment efficacy by ensuring tailored regimens. The study demonstrates promising discoveries in optimizing therapeutic interventions, cost-effectiveness, and data-driven insights through an in-depth evaluation of case studies and successful implementations. The proposed IoT-enhanced drug delivery system not only addresses the limitations of existing systems but also represents an evolution for healthcare professionals and pharmaceutical businesses. Patient privacy and data security are essential for ethical and regulatory reasons. The study's results and analysis highlight the proposed system's potential for overcoming limitations and enhancing targeted therapy for autoimmune disorders, setting the groundwork for a future in which IoT seamlessly interacts with drug delivery systems to transform patient care.

关键词： data privacy data security Transforms Drug delivery Sensor systems Real-time systems Autoimmune diseases Internet of Things Smart devices Intelligent sensors

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GPR-SCSANet: Unequal-Length Time Series Normalization with Split-Channel Residual Convolution and Self-Attention for Brain Age Prediction

GPR-SCSANet: Unequal-Length Time Series Normalization with S...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Fangling Sun Chuang Liang Tülay Adali Daoqiang Zhang Rongtao Jiang Vince D. Calhoun Shile Qi Department of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Department of Computer Science and Electrical Engineering University of Maryland Baltimore MD USA Department of Radiology and Biomedical Imaging Yale School of Medicine New Haven USA Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University Georgia Institute of Technology Emory University Atlanta GA USA

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Functional magnetic resonance imaging (fMRI), as a non-invasive method to reveal brain function alterations, frequently yields time series with unequal lengths in real-world scenarios, which may arise from factors such as motion artifacts, participant state, and differing scan protocols. This variability conflicts with the traditional methods relying on isometric inputs, which poses a significant challenge for the downstream applications such as brain age prediction. To address this challenge, we introduced Gaussian Process Regression (GPR) to normalize the length of time series and proposed split-channel residual convolution (SC) and self-attention mechanisms (SA) to perform brain age estimation, called GPR-SCSANet. Results showed that the proposed framework, GPR-SCSANet, is able to fully utilize the inherent information and learn richer feature representations from unequal-length fMRI time courses, which significantly improved the prediction accuracy across 3 brain atlases and 5 prediction models. The results demonstrated the effectiveness and robustness of the proposed GPR-SCSANet, showcasing the potential for broader applications in brain age prediction task.

关键词： Accuracy Protocols Convolution Time series analysis Predictive models Functional magnetic resonance imaging Brain modeling Robustness data models Stability analysis

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