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2023 IEEE International Conference on Communications, Computing, Cybersecurity and Informatics, CCCI 2023

作者： Kumar, Pankaj Obaidat, Mohammad S. Pandey, Piyush Wazid, Mohammad Das, Ashok Kumar Singh, Devesh Pratap Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India King Abdullah Ii School of Information Technology The University of Jordan Amman11942 Jordan School of Computing Srm University Jordan University of Science and Technology Beijing Beijing100083 China School of Computing Srm University TN Kattankulathur603203 India Algorithmic Research International Institute of Information Technology Center for Security Theory Hyderabad India

ISBN: (纸本)9798350302561

Today's world is rapidly growing towards the advanced concept of technology. Technology nowadays is at its peak of development and has great advantages in all fields of work, whether it is in industrial automation, farming, weather forecasting, or disease detection and control. Despite its great advantages, it does have its negative side because a major population is using technology and general-purpose computing devices (i.e., desktops, laptops, smartphones, tablets, etc.). Therefore, most of their personal data is available online. The data is constantly in danger of getting stolen by malicious actors (i.e., hackers), who can find a way to get their hands on this data and then use it for their personal gain (i.e., money-making). The best-known technique to get this information is by using malware exploitation on the machine of the target. Malware is an executable or a binary that is malicious in nature. It may have various devastating effects, and in order to detect and make the system defensive, we need to propose a scheme to detect the malware in the system, analyze the particular file, and then mitigate it. In this paper, we propose a secure machine learning-based malware detection and analysis scheme (in short, SML-MDA). The conducted security analysis and formal security verification of the proposed SMLMDA prove its security against various types of potential attacks. The proposed SML-MDA is then compared with the other similar competing schemes, and its performance has been found to be better in terms of various performance metrics like accuracy and F1-score. © 2023 IEEE.

关键词： Machine learning

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computer Vision and Its Algorithmic Evolution in Image Understanding

Computer Vision and Its Algorithmic Evolution in Image Under...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： B S Smitha Alok Jain D Sreenivasulu Saurabh Sharma B. Ravali Reddy Taqi Mohammed Khattab Al-Rubaye Department of Automobile Engineering New Horizon College of Engineering Bangalore India Lovely Professional University Phagwara India Computer Science and Engineering (Data Science) Institute of Aeronautical Engineering Dundigal Hyderabad India Lloyd Law College Plot No. 11 Knowledge Park II Greater Noida Uttar Pradesh Department of Computer Science and Engineering MLR Institute of Technology Hyderabad Telangana India Department of Medical Laboratory Technology College of Medical Technologies The Islamic University Najaf Iraq

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

As computer vision systems have improved, mostly through machine vision progress, people’s views on pictures have changed a lot. In response to this change, the Dynamic AdaptNet project is making an advanced system for analysing pictures that uses data from the surroundings. The manuscripts goals is to offer a full range of features, such as dynamic convolutional neural networks (CNNs), adaptable segmentation, and temporal attention. Standard, Baseline, Enhanced, Advanced, Optimised, and Specialised are some of the choices for the system. Standard is the easiest and most important to use. This study compares and contrasts different methods by checking how well they work based on several factors, such as success, adaptability, and resilience. Independent reviews and comparisons show that Dynamic AdaptNet is a complete tool for real-time picture analysis. This shows that it has the ability to make computer vision better.

关键词： computer vision Visualization Image segmentation Shape Reviews Heuristic algorithms Object segmentation Robustness Standards Resilience

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A review of Communication and Data Networks Algorithms Shaping Modem Connectivity 7

A review of Communication and Data Networks Algorithms Shapi...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Srinath, M.K. Gatla, Anitha Lakhanpal, Sorabh Sharma, Saurabh Reddy, B.Ravali Al-Rubaye, Taqi Mohammed Khattab New Horizon College of Engineering Department of Mechanical Engineering Bangalore India Institute of Aeronautical Engineering Department of Information Technology Dundigal Hyderabad500043 India Lovely Professional University Phagwara India Lloyd Law College Plot No. 11 Knowledge Park II Uttar Pradesh Greater Noida201314 India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India The Islamic University College of Medical Technologies Department of Medical Laboratory Technology Najaf Iraq

ISBN: (纸本)9798350350067

The world of today is highly interconnected and undergoes rapid changes. Algorithms are essential for data networks and communication systems. This study looks at how the complexity of algorithms affects network design. It focusses on the GROA (Global Routing Optimisation Algorithm), LBEA (Load Balancing and Efficient Allocation), and QKD-NS systems in particular. We aim to educate individuals on the impact of various methods on security, route completion time, and resource constraints. Based on the results, GROA creates variable paths, QKD-NS boosts safety with quantum-based innovations, and LBEA efficiently divides up resources based on what the network requires. According to the study, putting these many technologies together could lead to strong partnerships that can handle security breaches, traffic jams, and changes in the network. This study focusses on how advances in technology have led to better computer design, which in turn has made the internet safer and easier to use. The paper elucidates the application of these novel mathematical discoveries to enhance the performance and durability of data networks. These new details help communication networks improve by ensuring that they are safe and can meet today's link standards. © 2024 IEEE.

关键词： Traffic congestion

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Blockchain and Machine Learning for Optimized Resource Allocation in Cloud Computing Services 7

Blockchain and Machine Learning for Optimized Resource Alloc...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Rangaiah, Yagateela Pandu Yaragudri, Hanamant Rani, K.Pushpa Singh, Navdeep Albawi, Ali Kalyani, Susmita Institute of Aeronautical Engineering Department of Electronics and Communication Enfineering Dundigal Hyderabad500043 India New Horizon College of Engineering Department of Mechanical Engineering Bangalore India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India Lovely Professional University Phagwara India The Islamic University College of Medical Technology Radiology Techniques Department Najaf Iraq Lloyd Law College Plot No. 11 Knowledge Park II Uttar Pradesh Greater Noida201315 India

ISBN: (纸本)9798350350067

Resource efficiency becomes increasingly critical as cloud computing services alter. This article uses bitcoin and machine learning to optimize resource utilization. We study PRDA, BSCA, and DRAA algorithms and propose 'Blockchain and Machine Learning for Optimized Resource Allocation in Cloud Computing Services.' We employ blockchain, machine learning, and smart contracts to make resource sharing safer, clearer, and adaptable to cloud contexts. A comparative test demonstrates our resource allocation strategy works. PRDA reliably predicts resource usage using real-time data and prior patterns. This greatly reduces expenses and waste. The BSCA ensures safe and transparent delivery via guidelines. In multi-tenant cloud systems, DRAA optimizes resource consumption via reinforcement learning. This fixes underuse, exorbitant pricing, and speed loss. With this strategy, cloud service providers can remain open, keep rates low, and serve customers promptly. © 2024 IEEE.

关键词： Adversarial machine learning

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1D Beam Steering by a Waveguide-Integrated MEMS Cantilever in the Visible Spectrum

1D Beam Steering by a Waveguide-Integrated MEMS Cantilever i...

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CLEO: Applications and Technology, A and T 2022

作者： Azadeh, Saeed Sharif Mak, Jason C.C. Chen, Hong Luo, Xianshu Chen, Fu-Der Chua, Hongyao Lo, Guo Qiang Sacher, Wesley D. Poon, Joyce K.S. Max Planck Institute of Microstructure Physics Weinberg 2 Halle06120 Germany Department of Electrical and Computer Engineering University of Toronto 10 King's College Road TorontoON Canada Advanced Micro Foundry Pte. Ltd. 11 Science Park Road Singapore Science Park II 117685 Singapore

We demonstrate one-dimensional beam steering in the visible spectrum using an electrothermally actuated waveguide-integrated MEMS cantilever. A 24° scan range, 29 mW power consumption, and resonant scan rate of 4... 详细信息

ISBN: (纸本)9781957171050

关键词： Waveguides

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Revolutionizing Internet of Things with Blockchain for Enhanced Security and Data Management

Revolutionizing Internet of Things with Blockchain for Enhan...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： G Anitha K Gopal Alok Jain Rafal Emad Nagarjuna Thandra Preeti Tewari Department of Computer Science and Engineering Institute of Aeronautical Engineering Hyderabad Telangana India Department of Mechanical Engineeieng New Horizon College of Engineering Bangalore India Lovely Professional University Phagwara India Hilla University College Babylon Iraq Department of Computer Science and Engineering MLR Institute of Technology Hyderabad Telangana India Lloyd Law College Plot No. 11 Knowledge Park II Greater Noida Uttar Pradesh

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

This paper introduces a paradigm that might transform the Internet of Things. Blockchain technology makes this system workable. Five approaches comprise the framework. Each is crucial for a powerful, automated, and scalable Internet of Things ecosystem. The framework has five techniques. Making a permanent record is the first step. This record is unchangeable. The second program handles steps. The third way secures messages. The fourth program prioritizes customer privacy. The fifth technique addresses expansion and interoperability. Comparison testing and graphic presentations reveal that the technique outperforms all key qualities. The reason is: Since reviews are detailed, the technology is supposed to transform data management and security in the fast-growing Internet of Things. This modification will likely be major.

关键词： Data privacy Ecosystems Smart contracts Transforms Software Blockchains Software reliability Internet of Things Interoperability Testing

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A New Approach Towards Identification of Individuals Belonging to Different Subpopulations Using Machine Learning Models and CLC Data

A New Approach Towards Identification of Individuals Belongi...

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IEEE International Conference on Data Mining Workshops (ICDM Workshops)

作者： Małgorzata Charytanowicz Kajetan Perzanowski Maciej Januszczak Aleksandra Wołoszyn-Gałęza Maria Sobczuk Piotr Kulczycki Department of Computer Science Lublin University of Technology Lublin Poland Systems Research Institute Polish Academy of Sciences Warsaw Poland Institute of Biological Sciences John Paul II Catholic University of Lublin Lublin Poland Museum and Institute of Zoology Polish Academy of Sciences Ustrzyki Dolne Poland Chair of Animal Genetics and Conservation Warsaw University of Life Sciences Warsaw Poland Faculty of Physics and Applied Computer Science AGH University of Krakow Krakow Poland

ISBN: (数字)9798331530631

ISBN: (纸本)9798331530648

Spatial distributions of wisents (European bison) Bison bonasus (Linnaeus 1758), were studied in the Bieszczady Mountains (south-eastern Poland) on the basis of telemetric data obtained from two subpopulations: the western – inhabiting the forest districts of Baligród, Komańcza, Cisna and Lesko, and the eastern – dwelling within the districts of Lutowiska, Stuposiany, and in Bieszczadzki National park. Data was collected between 2002-2021. In our study, we propose a novel approach for classifying wisent subpopulations that utilizes machine learning methods and CLC data. For this purpose, the performances of eight algorithms: Naive Bayes, Logistic Regression, Support Vector Machine, Multilayer Perceptron, Random Forest, Extreme Gradient Boosting, k-Nearest Neighbors and Decision Tree were investigated. The algorithms were compared according to the following indicators: accuracy, Cohen’s-Kappa, precision, recall and F1 score. Their assessment was enhanced through the application of statistical inference (the Friedman test with post-hoc analysis) and SHAP values. The lowest results were achieved by Naive Bayes and Logistic Regression methods (accuracy of 73.69% and 74.43%, respectively), whilst significantly higher results were achieved by eXtreme Gradient Boosting, k-Nearest Neighbors and Decision Tree classifiers, with the classification accuracy exceeding 90% (91.81%, 92.8%, 93.47%, respectively). Based on the results, we conclude that CLC data represents a valuable source of information regarding the affinity of wisents towards different habitat conditions.

关键词： Logistic regression Accuracy Machine learning algorithms Nearest neighbor methods Boosting Inference algorithms Classification algorithms Bayes methods Telemetry Decision trees

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FRSM: A Novel Fault-Tolerant Approach for Redundant-Path-Enabled Service Migration in Mobile Edge Computing 1

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29th International Conference on Web Services, ICWS 2022 Held as Part of the Services Conference Federation, SCF 2022

作者： Zhao, Jiale Dai, Mengxuan Xia, Yunni Ma, Yong He, Meibin Peng, Kai Li, Jianqi Li, Fan Fu, Xiaodong School of Computer Chongqing University Chongqing400030 China School of Computer and Information Engineering Jiangxi Normal University Nanchang330022 China Department of Big Data and AI of Jiangxi Telecom Nanchang330029 China Engineering College Huaqiao University Quanzhou362021 China Global Energy Interconnection Research Institute Co. Ltd. Beijing102209 China Key Laboratory of Fundamental Synthetic Vision Graphics and Image Science for National Defense Sichuan University Chengdu610065 China Data Science Research Center Faculty of Science Kunming University of Science and Technology Kunming650031 China

ISBN: (数字)9783031235795

ISBN: (纸本)9783031235788

Mobile Edge Computing (MEC) provides users with low-latency, highly responsive services by deploying Edge Servers (ESs) near applications. MEC allows any edge-hosted application or service to be migrated between different edge resource providers without being locked into a single provider. Nevertheless, due to its complexity and dynamics, the real edge computing environment is prone to errors and failures, reducing the reliability of edge service migration. This paper proposes a novel fault-tolerant method for redundant path service migration. The method utilizes sliding-window-based model and identifies a set of service migration paths, enabling the evaluation of the time-varying failure rate of ESs. The method combines resubmission and replication mechanisms and decides the edge service migration scheme by selecting multiple redundant migration paths. We also conduct extensive simulations and show that our proposed method outperforms traditional solutions in several metrics. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Mobile edge computing

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PIEMAP: Personalized Inverse Eikonal Model from Cardiac Electro-Anatomical Maps 11th

PIEMAP: Personalized Inverse Eikonal Model from Cardiac Elec...

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11th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2020 held in Conjunction with MICCAI 2020

作者： Grandits, Thomas Pezzuto, Simone Lubrecht, Jolijn M. Pock, Thomas Plank, Gernot Krause, Rolf Institute of Computer Graphics and Vision Graz University of Technology Graz Austria Center for Computational Medicine in Cardiology Institute of Computational Science Università della Svizzera italiana Lugano Switzerland Institute of Biophysics Medical University of Graz Graz Austria BioTechMed-Graz Graz Austria

ISBN: (纸本)9783030681067

Electroanatomical mapping, a keystone diagnostic tool in cardiac electrophysiology studies, can provide high-density maps of the local electric properties of the tissue. It is therefore tempting to use such data to better individualize current patient-specific models of the heart through a data assimilation procedure and to extract potentially insightful information such as conduction properties. Parameter identification for state-of-the-art cardiac models is however a challenging task. In this work, we introduce a novel inverse problem for inferring the anisotropic structure of the conductivity tensor, that is fiber orientation and conduction velocity along and across fibers, of an eikonal model for cardiac activation. The proposed method, named PIEMAP, performed robustly with synthetic data and showed promising results with clinical data. These results suggest that PIEMAP could be a useful supplement in future clinical workflowss of personalized therapies. © 2021, Springer Nature Switzerland AG.

关键词： Heart

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Towards narrative medical visualization

arXiv

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arXiv 2021年

作者： Meuschke, Monique Garrison, Laura Smit, Noeska Bruckner, Stefan Lawonn, Kai Preim, Bernhard Department of Simulation and Graphics University of Magdeburg Germany Institute of Computer Science University of Jena Germany

Narrative visualization aims to communicate scientific results to a general audience and garners significant attention in various applications. Merging exploratory and explanatory visualization could effectively support a non-expert understanding of scientific processes. Medical research results, e.g., mechanisms of the healthy human body, explanations of pathological processes, or avoidable risk factors for diseases, are also interesting to a general audience that includes patients and their relatives. This paper discusses how narrative techniques can be applied to medical visualization to tell data-driven stories about diseases. We address the general public comprising people interested in medicine without specific medical background knowledge. We derived a general template for the narrative medical visualization of diseases. Applying this template to three diseases selected to span bone, vascular, and organ systems, we discuss how narrative techniques can support visual communication and facilitate understanding of medical data. Other scientists can adapt our proposed template to inform an audience on other diseases. With our work, we show the potential of narrative medical visualization and conclude with a comprehensive research agenda. © 2021, CC BY-NC-ND.

关键词： Visualization

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