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Disruptive Technologies (ICDT), International Conference on

作者： P. Iyappan Shikha Maheshwari A. Saranya M. Jayaprakash School of Computer Science and Engineering Vellore Institute of Technology Vellore Tamil Nadu India Directorate of Online Education Manipal University Jaipur Rajasthan India Department of Computer Science Vels Institute of Science Technology and Advanced Studies (VISTAS) Chennai Tamil Nadu India Department of IT RMK Engineering College Kavaraipettai Gummidipoondi Tamil Nadu India

The Internet of Things (IoT) is an open network model that aims to build and link the interactions between the devices and links. Conventional blockchain model aimed at increase the scalability but often it is limited by its capacity and performance. The deep learning algorithms aims to determine the parameters of the blockchain that finds the optimal value required to obtain an increased scalability without any limitations in its performance. In this paper, a deep learning model is integrated with the blockchain to improve the process of communication in a secured way. The deep learning model optimizes the necessary security parameters required to transfer the data in a secured way. The experimental validation shows an increased scalable task allocation than its predecessors.

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Enhanced Software Defect Prediction using Quantum Hamiltonian Generative Adversarial Network for Improved Software Performance Reliability

Enhanced Software Defect Prediction using Quantum Hamiltonia...

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International Conference on Inventive Computation Technologies (ICICT)

作者： Jyoti Chaudhary Vijaya Kumar A V Manikannan K Amol Sapatnekar Amit Barve Ramya Maranan Department of Computer Engineering The Technological Institute of Textile and Sciences Bhiwani Haryana India Department of Computer Science & Engineering Presidency University Bangalore Karnataka India Department of Computer Science and Engineering R.M.K Engineering College Thiruvalluvar Tamil Nadu India Faculty of Law Symbiosis Centre for Advanced Legal Studies and Research Symbiosis Law School Symbiosis International (Deemed University) Pune India Department of Computer Science and Engineering Parul Institute of Technology Faculty of Engineering and Technology Parul University Gujarat India Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai Tamil Nadu India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

The development of software systems with high quality and decreased maintenance costs depends on Software Defect Prediction (SDF). The current dataset qualities combined with feature redundancies make confident fault detection difficult through conventional methods. Current defect prediction techniques display multiple deficits through inaccurate results along with many false alarms and dataset bias. A high-performance quantum-inspired method must be researched in working to create an accurate precision model. The project will create the Enhanced Software Defect Prediction using Quantum Hamiltonian Generative Adversarial Network for Improved Software Performance Reliability (DBO-QH-GAN) model. Several software metrics need to be retrieved from the PROMISE datasets before this process can be initiated. The second step includes Fuzzy K- Top Matcher (FK- TM) functionality that removes undesirable noise factors such as redundant features and missing values as well as outliers. The Botox Optimization (BotO) algorithm selects five essential metrics that encompass cyclomatic complexity as well as Halstead measures and coupling and code churn and historical defect density. These preprocessed set of characteristics are fed to a Quantum Hamiltonian Generative Adversarial Network (QH-GAN) which identifies the defects from probabilistic thresholding and thereafter optimized using Duck Bevy Optimization (DBO) to achieve the optimal possible performance. Accuracy of the model was 99.2%, precision was 99.4%, and recall was 99.1 %. The DBO-QH-GAN model sets new standards for defect prediction by providing an improved enhancement of reliability along with decreased maintenance cost.

关键词： Quantum computing Costs Accuracy Software performance Predictive models Generative adversarial networks Software reliability Maintenance Optimization Standards

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m6A-TSHub:Unveiling the Context-specific m^(6)A Methylation and m^(6)A-affecting Mutations in 23 Human Tissues

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Genomics, Proteomics & Bioinformatics 2023年第4期21卷 678-694页

作者： Bowen Song Daiyun Huang Yuxin Zhang Zhen Wei Jionglong Su João Pedro de Magalhães Daniel J.Rigden Jia Meng Kunqi Chen Key Laboratory of Gastrointestinal Cancer(Fujian Medical University) Ministry of EducationSchool of Basic Medical SciencesFujian Medical UniversityFuzhou 350004China Department of Mathematical Sciences Xi’an Jiaotong-Liverpool UniversitySuzhou 215123China Institute of Systems Molecular and Integrative BiologyUniversity of LiverpoolLiverpool L697ZBUnited Kingdom Department of Biological Sciences Xi’an Jiaotong-Liverpool UniversitySuzhou 215123China Department of Computer Science University of LiverpoolLiverpool L697ZBUnited Kingdom Institute of Ageing&Chronic Disease University of LiverpoolLiverpool L697ZBUnited Kingdom School of AI and Advanced Computing Xi’an Jiaotong-Liverpool UniversitySuzhou 215123China AI University Research Centre Xi’an Jiaotong-Liverpool UniversitySuzhou 215123China

As the most pervasive epigenetic marker present on mRNAs and long non-coding RNAs(lncRNAs),N6-methyladenosine(m^(6)A)RNA methylation has been shown to participate in essential biological *** studies have revealed the distinct patterns of m^(6)A methylome across human tissues,and a major challenge remains in elucidating the tissue-specific presence and circuitry of m^(6)A *** present here a comprehensive online platform,m^(6)A-TSHub,for unveiling the context-specific m^(6)A methylation and genetic mutations that potentially regulate m^(6)A epigenetic mark.m^(6)A-TSHub consists of four core components,including(1)m^(6)A-TSDB,a comprehensive database of 184,554 functionally annotated m^(6)A sites derived from 23 human tissues and 499,369 m^(6)A sites from 25 tumor conditions,respectively;(2)m^(6)A-TSFinder,a web server for high-accuracy prediction of m^(6)A methylation sites within a specific tissue from RNA sequences,which was constructed using multi-instance deep neural networks with gated attention;(3)m^(6)ATSVar,a web server for assessing the impact of genetic variants on tissue-specific m^(6)A RNA modifications;and(4)m^(6)A-CAVar,a database of 587,983 The Cancer Genome Atlas(TCGA)cancer mutations(derived from 27 cancer types)that were predicted to affect m^(6)A modifications in the primary tissue of *** database should make a useful resource for studying the m^(6)A methylome and the genetic factors of epitranscriptome disturbance in a specific tissue(or cancer type).m^(6)A-TSHub is accessible at ***/biologicalsciences/m^(6)ats.

关键词： N^(6)-methyladenosine Context-specific analysis Cancer mutation Genome analysis Functional annotation

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Synergizing Generative Adversarial Networks and Pseudo-Labeling for Improved Knee Osteoarthritis Detection

Synergizing Generative Adversarial Networks and Pseudo-Label...

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Sustainable computing and Smart Systems (ICSCSS), International Conference on

作者： Sajeev Ram Arumugam Sheela Gowr P Geetha Ponnaian Maharajan K Sankar Ganesh Karuppasamy Divya Muralitharan Department of AI&DS Sri Krishna Collefe of Engineering and Technology Coimbatore India Department of CSE Vels Institute of Science Technology and Advanced Studies Chennai India Department of CSE Saveetha Institute of Medical and Technical Sciences Chennai India Department of CSE School of computing Kalasalingam Academy of research and education India Department of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (数字)9798350379990

ISBN: (纸本)9798350391558

Progressive cartilage breakdown and joint inflammation are hallmarks of the common joint disease known as osteoarthritis (OA). The timely intervention and management of osteoarthritis (OA) depend on early detection. However, this can be difficult to achieve because labelled medical imaging data is scarce, and the disease's presentations are complex. A notable skew between the proportion of healthy and OA knee images can be seen in knee OA datasets, a phenomenon known as class imbalance. As a result, the model may underperform on the minority class (OA) and prioritize the majority class during training. The goal of the research is to further the development of more accurate and reliable techniques for knee OA detection. A novel method for knee osteoarthritis detection that first uses ResNet, DenseNet, VGG16, and VGG19 convolutional neural network (CNN) architectures for classification after generative adversarial networks (GANs) for data augmentation and pseudo-labeling is predicted. The aim of the work is to use labeled and unlabeled data to develop the robustness and accuracy of osteoarthritis detection. It is shown via thorough experimentation that the strategy is beneficial in enhancing classification performance, with ResNet obtaining the greatest accuracy and F1 score among the networks we studied. According to the research, pseudo-labeling and GAN-based data augmentation strategies can greatly improve osteoarthritis diagnosis accuracy and clinical significance. This work advances the field of medical image analysis and has potential benefits for bettering osteoarthritis patient treatment. The proposed approach performs well with an accuracy of 96.23%, precision of 0.963, recall of 0.959, and F1 score of 0.9246.

关键词： Training Accuracy Neural networks Medical treatment Generative adversarial networks Data augmentation Robustness

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Instability of steady-state mixed-state symmetry-protected topological order to strong-to-weak spontaneous symmetry breaking

arXiv

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arXiv 2024年

作者： Shah, Jeet Fechisin, Christopher Wang, Yu-Xin Iosue, Joseph T. Watson, James D. Wang, Yan-Qi Ware, Brayden Gorshkov, Alexey V. Lin, Cheng-Ju Joint Quantum Institute NIST University of Maryland College ParkMD20742 United States Joint Center for Quantum Information and Computer Science NIST University of Maryland College ParkMD20742 United States Department of Computer Science Institute for Advanced Computer Studies University of Maryland College ParkMD20742 United States Google Quantum AI CA United States

Recent experimental progress in controlling open quantum systems enables the pursuit of mixed-state nonequilibrium quantum phases. We investigate whether open quantum systems hosting mixed-state symmetry-protected topological states as steady states retain this property under symmetric perturbations. Focusing on the decohered cluster state—a mixed-state symmetry-protected topological state protected by a combined strong and weak symmetry—we construct a parent Lindbladian that hosts it as a steady state. This Lindbladian can be mapped onto exactly solvable reaction-diffusion dynamics, even in the presence of certain perturbations, allowing us to solve the parent Lindbladian in detail and reveal previously-unknown steady states. Using both analytical and numerical methods, we find that typical symmetric perturbations cause strong-to-weak spontaneous symmetry breaking at arbitrarily small perturbations, destabilize the steady-state mixed-state symmetry-protected topological order. However, when perturbations introduce only weak symmetry defects, the steady-state mixed-state symmetry-protected topological order remains stable. Additionally, we construct a quantum channel which replicates the essential physics of the Lindbladian and can be efficiently simulated using only Clifford gates, Pauli measurements, and feedback. © 2024, CC BY.

关键词： Quantum channel

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Noninvasive Tracking of Every Individual in Unmarked Mouse Groups Using Multi-Camera Fusion and Deep Learning

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Neuroscience Bulletin 2023年第6期39卷 893-910页

作者： Feng Su Yangzhen Wang Mengping Wei Chong Wang Shaoli Wang Lei Yang Jianmin Li Peijiang Yuan Dong-Gen Luo Chen Zhang Department of Neurobiology School of Basic Medical SciencesBeijing Key Laboratory of Neural Regeneration and RepairCapital Medical UniversityBeijing100069China Chinese Institute for Brain Research Beijing102206China State Key Laboratory of Translational Medicine and Innovative Drug Development Nanjing210000China Peking-Tsinghua Center for Life Sciences Academy for Advanced Interdisciplinary StudiesPeking UniversityBeijing100871China School of Life Sciences Tsinghua UniversityBeijing100084China School of Biological Science and Medical Engineering Beihang UniversityBeijing100191China The Key Laboratory of Developmental Genes and Human Disease Institute of Life SciencesSoutheast UniversityNanjing210096JiangsuChina Institute for Artificial Intelligence the State Key Laboratory of Intelligence Technology and SystemsBeijing National Research Center for Information Science and TechnologyDepartment of Computer Science and TechnologyTsinghua UniversityBeijing100084China School of Mechanical Engineering and Automation Beihang UniversityBeijing100191China

Accurate and efficient methods for identifying and tracking each animal in a group are needed to study complex behaviors and social *** tracking methods(e.g.,marking each animal with dye or surgically implanting microchips)can be invasive and may have an impact on the social behavior being *** overcome these shortcomings,video-based methods for tracking unmarked animals,such as fruit flies and zebrafish,have been ***,tracking individual mice in a group remains a challenging problem because of their flexible body and complicated interaction *** this study,we report the development of a multi-object tracker for mice that uses the Faster region-based convolutional neural network(R-CNN)deep learning algorithm with geometric transformations in combination with multi-camera/multi-image fusion *** system successfully tracked every individual in groups of unmarked mice and was applied to investigate chasing *** proposed system constitutes a step forward in the noninvasive tracking of individual mice engaged in social behavior.

关键词： Noninvasive tracking Deep learning Multi-camera Mouse group Social interaction

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Cervical Cancer Classification Using Combined Machine Learning and Deep Learning Approach

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computers, Materials & Continua 2022年第9期72卷 5117-5134页

作者： Hiam Alquran Wan Azani Mustafa Isam Abu Qasmieh Yasmeen Mohd Yacob Mohammed Alsalatie Yazan Al-Issa Ali Mohammad Alqudah Department of Biomedical Engineering Jordan University of Science and TechnologyIrbid22110Jordan Department of Biomedical Systems and Informatics Engineering Yarmouk University 556Irbid21163Jordan Faculty of Electrical Engineering Technology Campus Pauh PutraUniversiti Malaysia Perlis02000ArauPerlisMalaysia Advanced Computing Centre of Excellence(CoE)Universiti Malaysia Perlis(UniMAP)02000ArauPerlisMalaysia The Institute of Biomedical Technology King Hussein Medical CenterRoyal Jordanian Medical ServiceAmman11855Jordan Department of Computer Engineering Yarmouk UniversityIrbid21163Jordan

Cervical cancer is screened by pap smear methodology for detection and classification *** smear images of the cervical region are employed to detect and classify the abnormality of cervical *** this paper,we proposed the first system that it ables to classify the pap smear images into a seven classes *** smear images are exploited to design a computer-aided diagnoses system to classify the abnormality in cervical images *** features that have been extracted using ResNet101 are employed to discriminate seven classes of images in Support Vector Machine(SVM)*** success of this proposed system in distinguishing between the levels of normal cases with 100%accuracy and 100%*** top of that,it can distinguish between normal and abnormal cases with an accuracy of 100%.The high level of abnormality is then studied and classified with a high *** the other hand,the low level of abnormality is studied separately and classified into two classes,mild and moderate dysplasia,with∼92%*** proposed system is a built-in cascading manner with five models of polynomial(SVM)*** overall accuracy in training for all cases is 100%,while the overall test for all seven classes is around 92%in the test phase and overall accuracy reaches 97.3%.The proposed system facilitates the process of detection and classification of cervical cells in pap smear images and leads to early diagnosis of cervical cancer,which may lead to an increase in the survival rate in women.

关键词： Classification deep learning machine learning pap smear images resnet101 support vector machines

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#EpiTwitter: Public Health Messaging During the COVID-19 Pandemic

arXiv

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arXiv 2024年

作者： Rao, Ashwin Sabri, Nazanin Guo, Siyi Raschid, Louiqa Lerman, Kristina Thomas Lord Department of Computer Science University of Southern California Los AngelesCA90007 United States Information Sciences Institute University of Southern California Marina del ReyCA90292 United States Department of Computer Science University of California - San Diego La Jolla CA92093 United States Institute for Advanced Computer Studies University of Maryland College ParkMD20740 United States

Background: Effective communication is crucial during health crises, and social media has become a prominent platform for public health experts to inform and to engage with the public. At the same time, social media also platforms pseudo-experts who may promote contrarian views. Despite the significance of social media, key elements of communication such as the use of moral or emotional language and messaging strategy, particularly during the COVID-19 pandemic, has not been explored. Objective: This study aims to analyze how notable public health experts (PHEs) and pseudo-experts communicated with the public during the COVID-19 pandemic. Our focus is the emotional and moral language they used in their messages across a range of pandemic issues. We also study their engagement with political elites and how the public engaged with PHEs to better understand the impact of these health experts on the public discourse. Methods: We gathered a dataset of original tweets from 489 PHEs and 356 pseudo-experts on Twitter (now X) from January 2020 to January 2021, as well as replies to the original tweets from the PHEs. We identified the key issues that PHEs and pseudo-experts prioritized. We also determined the emotional and moral language in both the original tweets and the replies. This approach enabled us to characterize key priorities for PHEs and pseudo-experts, as well as differences in messaging strategy between these two groups. We also evaluated the influence of PHE language and strategy on the public response. Results: Our analyses revealed that PHEs focus on masking, healthcare, education, and vaccines, whereas pseudo-experts discuss therapeutics and lockdowns more frequently. PHEs typically used positive emotional language across all issues, expressing optimism and joy. Pseudo-experts often utilized negative emotions of pessimism and disgust, while limiting positive emotional language to origins and therapeutics. Along the dimensions of moral language, PHEs and pseud

关键词： COVID-19

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Optimization of complex system reliability using hybrid grey wolf optimizer

Decision Making: Applications in Management and Engineering

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Decision Making: Applications in Management and Engineering 2021年第2期4卷 241-256页

作者： Negi, Ganga Kumar, Anuj Pant, Sangeeta Ram, Mangey Department of Mathematics Graphic Era Deemed to be University Dehradun India Department of Mathematics University of Petroleum and Energy Studies Dehradun India Department of Mathematics Computer Science and Engineering Graphic Era Dehradun India Institute of Advanced Manufacturing Technologies Peter the Great St. Petersburg Polytechnic University Saint Petersburg Russia

Reliability allocation to increase the total reliability has become a successful way to increase the efficiency of the complex industrial system designs. A lot of research in the past have tackled this problem to a great extent. This is evident from the different techniques developed so far to achieve the target. Metaheuristics like simulated annealing, Tabu search (TS), Particle Swarm Optimization (PSO), Cuckoo Search Optimization (CS), Genetic Algorithm (GA), Grey wolf optimization technique (GWO) etc. have been used in the recent years. This paper proposes a framework for implementing hybrid PSO-GWO algorithm (HPSOGWO) for solving reliability allocation and optimization problems of Complex bridge system and Life support system in space capsule. The supremacy/competitiveness of the proposed framework are demonstrated from the numerical experiments. Comparison of the results obtained by HPSOGWO with previously used algorithms named PSO and GWO shows that in one problem named the Complex bridge system, the HPSOGWO uses lesser number of function evaluations as compared to PSO and GWO. Hence, the overall solutions obtained by HPSOGWO are not only comparable to the previously obtained results by some of the other well-known optimization methods, but also better than that. © 2021 by the authors.

关键词： Cost functions

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CancerAI: A Deep Learning Framework for Ovarian Cancer Prediction

CancerAI: A Deep Learning Framework for Ovarian Cancer Predi...

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Electronics and Sustainable Communication Systems (ICESC), 2020 International Conference on

作者： G. Revathy P. Uma Maheswari S. Madhavi Vijay Anand P R. Vajubunnisa Begum H. Jasmin Department of Computer Science and Engineering Vels Institute of Science Technology & Advanced Studies (VISTAS) Chennai India Department of Computer science and Business systems K. Ramakrishnan college of Engineering Trichy India Department of Information Technology Vel tech Multi tech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai India Department of Artificial Intelligence and Data Science CMR Institute of Technology Bengaluru India Department of Electronics and Communication Science Justice Basheer Ahmed Sayeed College for Women Affiliated by University of Madras Chennai India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

Ovarian cancer is the fifth most common cause of death from cancer in women. This is mainly because it is often diagnosed at a late stage, as the earliest symptoms are unclear and inconsistent. Existing diagnostic techniques, such as biomarkers, biopsies, and imaging tests, have notable drawbacks such as reliance on subjective interpretation, inconsistency across different observers, and time-consuming testing procedures. This research study introduces an innovative deep learning framework that employs a convolutional neural network (CNN) algorithm to accurately predict and diagnose ovarian cancer, thereby overcoming the existing constraints. The CNN was trained using a dataset of histopathology images. The dataset was partitioned into test and training subsets to enhance the model's performance. The algorithm demonstrated an impressive accuracy rate of 96%, successfully detecting 97.12% of malignant instances and precisely categorizing 95.02% of healthy cells. This method effectively mitigates the challenges related to human expert evaluation, including elevated rates of misclassification and variability across different observers, while also reducing the time required for analysis. The results underscore the capability of this CNN-based technique to offer a more precise, effective, and dependable strategy for forecasting and detecting ovarian cancer. Subsequent investigations will prioritize the integration of new breakthroughs in deep learning to further amplify the efficacy of the suggested approach.

关键词： Deep learning Training Accuracy Histopathology Transfer learning Observers Prediction algorithms Partitioning algorithms Convolutional neural networks Ovarian cancer

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