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

作者： Girija Rani Karetla Tarigoppula V. S. Sriram Kapil Joshi Mehul Gor Shivaji Bothe Shruti Bhatia School of Computer Data and Mathematical Sciences Western Sydney University Australia Department of CSE NSRIT Visakhapatnam A.P. Uttaranchal Institute of Technology Uttaranchal University Department of PIET Parul University Department of IT& Business Analytics Sri Balaji University Pune (SBUP) Department of Computer Science and Engineering Graphic Era Hill University Dehradun Uttarakhand

A major obstacle to the transformation of health care is the ability to extract information and useful insights from complex, high-dimensional, and varied biological data. Modern biomedical research has seen the emergence of a number of new data sources, some of which are complicated, diverse, poorly documented, and typically unstructured, such as digital health records, neuroimaging, -omics, sensor data, and text. Prior to building predictive or classification models on top of the features, conventional data mining and computational science, complex data and insufficient domain expertise, there are several problems on both phases. New efficient paradigms for creating end-to-end learning approaches from complex information are provided by the most recent deep learning technology advancements.

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LG-Sleep: Local and Global Temporal Dependencies for Mice Sleep Scoring

arXiv

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arXiv 2024年

作者： Sartipi, Shadi Andersen, Mie Hauglund, Natalie Kjaerby, Celia Untiet, Verena Nedergaard, Maiken Cetin, Mujdat Department of Electrical and Computer Engineering University of Rochester RochesterNY United States Division of Glial Disease and Therapeutics Center for Translational Neuromedicine University of Copenhagen Copenhagen Denmark Goergen Institute for Data Science University of Rochester RochesterNY United States

Efficiently identifying sleep stages is crucial for unraveling the intricacies of sleep in both preclinical and clinical research. The labor-intensive nature of manual sleep scoring, demanding substantial expertise, has prompted a surge of interest in automated alternatives. Sleep studies in mice play a significant role in understanding sleep patterns and disorders and underscore the need for robust scoring methodologies. In response, this study introduces LG-Sleep, a novel subject-independent deep neural network architecture designed for mice sleep scoring through electroencephalogram (EEG) signals. LG-Sleep extracts local and global temporal transitions within EEG signals to categorize sleep data into three stages: wake, rapid eye movement (REM) sleep, and non-rapid eye movement (NREM) sleep. The model leverages local and global temporal information by employing time-distributed convolutional neural networks to discern local temporal transitions in EEG data. Subsequently, features derived from the convolutional filters traverse long short-term memory blocks, capturing global transitions over extended periods. Crucially, the model is optimized in an autoencoder-decoder fashion, facilitating generalization across distinct subjects and adapting to limited training samples. Experimental findings demonstrate superior performance of LG-Sleep compared to conventional deep neural networks. Moreover, the model exhibits good performance across different sleep stages even when tasked with scoring based on limited training samples. © 2024, CC BY-NC-ND.

关键词： Electroencephalography

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DP2-Pub: Differentially Private High-Dimensional data Publication with Invariant Post Randomization

arXiv

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arXiv 2022年

作者： Jiang, Honglu Yu, Haotian Cheng, Xiuzhen Pei, Jian Pless, Robert Yu, Jiguo The Department of Computer Science and Software Engineering Miami University OxfordOH45056 United States The Department of Computer Science The George Washington University WashingtonDC20052 United States The Department of Data Analytics The George Washington University WashingtonDC20052 United States The School of Computer Science and Technology Shandong University Qingdao266510 China The Departments of Computer Science Biostatistics and Bioinformatics Electrical and Computer Engineering Duke University DurhamNC27708 United States Big Data Institute Qilu University of Technology Jinan250353 China Shandong Fundamental Research Center for Computer Science Qilu University of Technology Shandong Jinan250353 China

A large amount of high-dimensional and heterogeneous data appear in practical applications, which are often published to third parties for data analysis, recommendations, targeted advertising, and reliable predictions. However, publishing these data may disclose personal sensitive information, resulting in an increasing concern on privacy violations. Privacy-preserving data publishing has received considerable attention in recent years. Unfortunately, the differentially private publication of high dimensional data remains a challenging problem. In this paper, we propose a differentially private high-dimensional data publication mechanism (DP2-Pub) that runs in two phases: a Markov-blanket-based attribute clustering phase and an invariant post randomization (PRAM) phase. Specifically, splitting attributes into several low-dimensional clusters with high intra-cluster cohesion and low inter-cluster coupling helps obtain a reasonable allocation of privacy budget, while a double-perturbation mechanism satisfying local differential privacy facilitates an invariant PRAM to ensure no loss of statistical information and thus significantly preserves data utility. We also extend our DP2-Pub mechanism to the scenario with a semi-honest server which satisfies local differential privacy. We conduct extensive experiments on four real-world datasets and the experimental results demonstrate that our mechanism can significantly improve the data utility of the published data while satisfying differential privacy. Copyright © 2022, The Authors. All rights reserved.

关键词： Bayesian networks

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An Efficient Algorithm to Classify the Mitotic Cell using Ant Colony Algorithm

An Efficient Algorithm to Classify the Mitotic Cell using An...

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computer, Power and Communications (ICCPC), International Conference on

作者： R.G. Vidhya J. Seetha Sudhir Ramadass S. Dilipkumar Ajith Sundaram G. Saritha Department of ECE HKBK College of Engineering Bangalore India Department of Computer science and Business systems Panimalar Engineering College Chennai India Data analyst Sterck systems Chennai Department of Analytics Scope VIT Vellore Selection grade School of Business Kochi Department of ECE Sri Sai Ram institute of technology Chennai Tamilnadu India

Cancer that originates in the breast tissue then spreads to the chest wall is called breast cancer. Doctors routinely examine mammograms for signs of cancer; however, aberrant macrocalcifications and microcalcifications might appear on mammograms when the picture quality is subpar. Always get checked out if you see anything out of the ordinary, especially if it involves your breasts, such abnormal calcium deposits. For this mammographic deposit to be properly interpreted, top-notch picture quality is necessary. Many different breast cancer screening methods and the many breast cancer phases are still the subject of active study. In order to construct effective medical image processing systems, experts use methods including the Ant Colony Algorithm (ACA), the Improved Adaptive Fuzzy C-Means (IAFCM), and TNM (the size of the breast tumor (T), the lymph nodes around the tumor, and metastasized). Classes were determined using an MPIG, or a modified Poisson inverse gradient classifier. More than five hundred picture modalities are used across all methods. Medical professionals that rely on images to establish diagnoses or treatments might find the results of this research useful.

关键词： Image segmentation Sensitivity Breast tissue Medical services Feature extraction Breast cancer Mammography

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Transforming Shopping with IoT: Exploring the Intelligent Way to Design the Smart Trolley using IoT Principles

Transforming Shopping with IoT: Exploring the Intelligent Wa...

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Big data & Machine Learning (ICBDML), IEEE International Conference on

作者： M. Jayaprakash A. Hency Juliet A. Jaya Mabel Rani N.T. Velusudha S. Lakshmisridevi V. Samuthira Pandi Department of Information Technology R.M.K. Engineering College Kavaraipettai Tamil Nadu India Department of Data Analytics Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Computer Science and Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Computer Science and Engineering BS Abdur Rahman Crescent Institute of Science and Technology Chennai Tamil Nadu Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Chennai Tamil Nadu India

ISBN: (数字)9798350374100

ISBN: (纸本)9798350374117

The interconnection of commonplace items is revolutionizing people's daily lives, thanks to the Internet of Things (IoT). A smart shopping system, consisting of interconnected products, might one day be a reality in supermarkets. In this Internet of Things system, products may be individually identified using low-cost RFID tags, which, when added to an RFID-enabled smart shopping cart, can be read automatically. This means shoppers won't have to endure the checkout line at all—billing can be handled right in the cart. The integration of RFID-equipped smart shelving into this system allows for the monitoring of inventory and, perhaps, the update of a central server. The suggested shopping system, which is dubbed IoT Assisted Smart Trolley (IoTAST), is cross-validated with the standard Smart Trolley (ST) model to clearly evaluate its performance. Since an RFID reader can automatically read all objects instead of a labourer physically scanning them, inventory management becomes considerably easier with this sort of technology. We construct a working system to test its functioning, determine the design criteria of a sophisticated purchasing system, and then create a reliable communication mechanism that renders the system feasible. This study validates the viability of the system. This is the first smart shopping system that takes security into account that we are aware of.

关键词： Protocols Prototypes Machine learning Reliability engineering Security Internet of Things Servers

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Towards a multimodal neuroimaging-based risk score for Alzheimer’s disease by combining clinical and large N>37000 population data

Towards a multimodal neuroimaging-based risk score for Alzhe...

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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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