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On the Permittivity of XT-CF20

On the Permittivity of XT-CF20

引用

2023 IEEE Symposium on Electromagnetic Compatibility and Signal/Power Integrity, EMC+SIPI 2023

作者： Harmon, Aaron Zhang, Wei Khilkevich, Victor Missouri University of Science and Technology Dept. of Electrical and Computer Engineering Electromagnetic Compatibility Laboratory RollaMO United States

ISBN: (纸本)9798350309768

In this work, the permittivity of a 3D-printed carbon fiber-loaded anisotropic material, XT-CF20, is examined further. The microstructure of XT-CF20 is first examined via optical imaging and is shown to be composed of inclusions that are aligned with the print direction of the sample. The permittivity tensor for the aligned microstructure is then measured using a capacitive measurement technique and simulations are provided to demonstrate the validity of this measurement method. The simulated permittivity values for XT-CF20 samples with varying infill structures are then presented and compared to the measured permittivity values of said samples. An error of less than 12% between the simulated and measured permittivity values was observed validating the measured permittivity tensor and claims about the cause of the anisotropy presented in this work. The pronounced effect of a sample's infill on the permittivity tensor of the sample is then discussed along with the conclusions of this work and possible future topics of work for the authors. © 2023 IEEE.

关键词： Permittivity

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Efficient Fund Tracking system using Blockchain and GraphDB 7

Efficient Fund Tracking system using Blockchain and GraphDB

引用

7th International Conference on Image Information Processing, ICIIP 2023

作者： Kaushal, Arpit Nadda, Shiveen Modi, Praveen Jaypee University of Information Technology Dept. of Computer Science And Engineering And Information Technology H.P Solan India

ISBN: (纸本)9798350371406

Powerful Asset Tracking System is a cutting edge arrangement that utilizes the force of blockchain innovation and Graph database to give a vigorous and secure resource following stage. Conventional asset global positioning frameworks frequently face difficulties connected with straightforwardness, security and effectiveness. Notwithstanding, this framework conquers these limits by exploiting the blockchain and its decentralized and permanent nature, as well as the adaptability and versatility of diagram information bases. By incorporating blockchain innovation, the framework guarantees a straightforward and unapproved recording of asset exchanges. Every exchange is safely put away in blocks that structure a chain of information that can't be changed or erased. This increases responsiveness and disposes of the requirement for middle man, decreasing expenses and handling time. © 2023 IEEE.

关键词： Blockchain

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Leveraging data Mining for Cybersecurity Threat Detection 1

Leveraging Data Mining for Cybersecurity Threat Detection

引用

1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Kaushik, Priyanka Chouhan, Ganga Singh Mishra, Atul Kumar Bandil, Deepika Kumari, Meenakshi Chandra Sekar, P. Chandigarh University Dept. of CSE Chandigarh India Madhav University Dept. of Commerce & Management Pindwara India Institute of Busi. Mang. & Commerce Dept. of Management Aligarh India Usha Martin University Dept. of Busi. Mang. & Commerce Jharkhand India Dhanalakshmi Srinivasan College of Engineering Dept. of AI & Data Science Coimbatore India

ISBN: (纸本)9798350356816

In the face of increasingly sophisticated cyber threats, the application of data mining techniques has become essential for effective cybersecurity threat detection. This paper examines how data mining methodologies, including clustering, classification, and anomaly detection, can be leveraged to enhance cybersecurity defenses. By systematically analyzing large datasets from various sources such as network traffic, system logs, and user activities, data mining tools can identify patterns and anomalies that signify potential security threats. Will discuss the integration of these techniques with machine learning algorithms to improve the accuracy and efficiency of threat detection systems. The study explores several data mining approaches, evaluating their strengths and limitations in detecting both known and novel cyber threats. Additionally, it addresses the challenges associated with data privacy, the volume and complexity of data, and the need for real-time analysis. The results illustrate that data mining not only aids in early threat detection but also provides a foundation for developing adaptive and proactive cybersecurity strategies. The paper concludes by outlining future directions for research and advancements in data mining for cybersecurity, emphasizing the need for ongoing innovation to counter emerging threats. © 2024 IEEE.

关键词： Phishing

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An Effective Cartoonifying of an Image using Machine Learning 4

An Effective Cartoonifying of an Image using Machine Learnin...

引用

4th IEEE Annual Flagship India Council International Subsections Conference, INDISCON 2023

作者： Tomar, Suryansh Upadhyay, Nandini Guru Prasad, M.S. Divyashree, N. Mamgain, Vinayak Ram, Mangey Dept. of Computer Science and Engg. Dehradun India School of Computing and Information Technology Reva University Bengaluru India

ISBN: (纸本)9798350333558

Cartoonifying an image is the process of transforming a regular photograph into a cartoon-style image. This research paper proposes a method to cartoonify images using OpenCV, a popular open-source computer vision library using Python. The proposed method involves several steps, including edge detection, color quantization, and image smoothing. The edge detection step is used to extract edges from the input image. Then, in the color quantization step, the image palette is reduced to a fixed number of colors using the k-means clustering algorithm. Finally, the image is smoothed using a bilateral filter to create a cartoon-like effect. The proposed method is evaluated on several images, and the results show that the proposed method produces high-quality cartoon images with reduced noise and better visual appeal compared to existing methods. This method has potential applications in various fields, such as entertainment, advertising, and digital art, and can be easily integrated into existing applications. © 2023 IEEE.

关键词： Color

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Reliable data Storage and Sharing using Block chain Technology and Two Fish Encryption 4

Reliable Data Storage and Sharing using Block chain Technolo...

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4th International Conference on Inventive Research in Computing Applications, ICIRCA 2022

作者： Sivanantham, S. Sakthivel, M. Krishnamoorthy, V. Balakrishna, N. Akshaya, V. Sree Vidyanikethan Engineering College Dept. of Computer Science and Systems Engineering Andhra Pradesh Tirupati India Sree Vidyanikethan Engineering College Dept. of Computer Science and Engineering Andhra Pradesh Tirupati India Bannari Amman Institute of Technology Dept. of Computer Science and Engineering Tamilnadu Sathyamangalam India

ISBN: (数字)9781665497077

ISBN: (纸本)9781665497077

Digital data that has been certified by a respected institution is valuable and can be saved or transmitted over the internet. However, the issues are ensuring the security and reliability of stored and shared data, as well as ensuring a secure, transparent, and fair data sharing process. As a result, The schemas for data production, storage, and sharing is suggested. A group signature scheme in the data production schema for a collection of respected organisations that provide similar services is used. A member of the group develops precious digital data from RD (raw data) provided by a DO (data owner) and then provides a certification based on the encipher of that data. The system saves the data's access address in the database after data owner uploading his or her data to the system in the data storage schema. Before submitting a data sharing proposal to the DO, every person on the system might validate the quality of shared data. The data sharing mechanism is carried out via a smart contract to incentivize honesty. The schemas assure the security properties of data storage and exchange, which include confidentiality, integrity, privacy, and non-repudiation. © 2022 IEEE.

关键词： Fish

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Deep Learning based Porosity Inversion from Seismic Attributes 5

Deep Learning based Porosity Inversion from Seismic Attribut...

引用

5th International Conference on data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Nagamani, K. Benarji, T. Nabi, Shaik Abdul Akana, Chandra Mouli Venkata Srinivas Polamuri, Subba Rao Indrasenareddy, M. Mlr Institute of Technology Department of Computer Science Engineering Hyderabad India Indus Institute of Engineering and Technology Dept. of Cse TS Siddipet India Jntuh Cse Dept Avniet India Department of Computer Science and Engineering Andhra Pradesh Odalarevu India Aditya University Department of Computer Science and Engineering Andhra Pradesh Surampalem India Bvrit Hyderabad College of Engineering for Women Department of Cse India

ISBN: (纸本)9798350389609

For many applications in earth sciences and engineering, such as hydrocarbon reservoir characterization and geothermal energy generation, subsurface porosity is crucial. Many seismic factors, including Rayleigh waves, P, SH, SV, Love, and so on, have an impact on porosity. Numerous other requirements, including incoming seismic amplitude, transmitted amplitude, reflected amplitude, and reflection coefficients, are also met by these signals. The goal of this study is to forecast porosity through the application of Bagging and Deep Neural Net classification techniques along with the previously described multicriteria dataset analysis. © 2024 IEEE.

关键词： Petroleum reservoir evaluation

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Machine Learning Techniques for Predicting Ovarian Cancer in its Early Stages Using Biomarkers

Machine Learning Techniques for Predicting Ovarian Cancer in...

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International Conference on Electrical Engineering and Information & Communication Technology (ICEEICT)

作者： Mir Jafikul Alam Jannat-E Anawar Khandaker Mohammad Mohi Uddin Md. Hasibur Rahman Md. Mahbubur Rahman Dept. of Computer Science and Engineering Dhaka International University Dhaka Bangladesh Dept. of Computer Science and Engineering Southeast University Dhaka Bangladesh Dept. of Computer Science and Engineering Bangladesh University of Business and Technology Dhaka Bangladesh

ISBN: (数字)9798350385779

ISBN: (纸本)9798350385786

The deadliest gynecological cancer affecting women is ovarian cancer, currently incurable with no effective medication treatments. The key focus of this research is to assess insights for early diagnosis using statistical analysis and machine learning techniques on data from clinical trials obtained from 349 patients. Several techniques, including Random Forest, Decision Tree, Gaussian NB, AdaBoost, and Logistic regression, were applied to find the most reliable factor for ovarian cancer prediction. A clinically evaluated raw dataset of benign samples and malignant ovarian tumor patient data set is used to develop early-stage ovarian cancer predictions, and the effectiveness of ML models was examined utilizing metrics including F1-score, Accuracy, Precision, and Recall. The proposed study shows better outcomes, with the Random Forest classifier exhibiting the highest accuracy for validation at 99% based on the test data of ovarian cancer predictions. Even though early-stage ovarian cancer detection is generally unavailable, cancer diagnosis may be greatly aided by machine learning.

关键词： Measurement Logistic regression Statistical analysis Forestry Predictive models Reliability Random forests

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COCOWEARS: A framework for COntinuum COmputing WEARable Systems 6

COCOWEARS: A framework for COntinuum COmputing WEARable Syst...

引用

6th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Aloi, Gianluca Frustaci, Fabio Gravina, Raffaele Guerrieri, Antonio Pio, Gianvito Savaglio, Claudio DIMES Dept. University of Calabria CS Rende87036 Italy ICAR-CNR CS Rende87036 Italy Dept. of Computer Science University of Bari Aldo Moro Via Orabona 4 BA Bari70125 Italy

The amount and diversity of data generated by the sensors available on current Wearable Computing Systems (WCS) impose the adoption of advanced, efficient (and often customized) data analysis methods, including Machine Learning (ML) / Artificial Intelligence (AI) and multi-sensor information fusion. With the diffusion of Cloud Computing and ARM/MCU architectures in the last decade, commercial WCS have gradually been introduced into the market, resulting in vertical solutions with poor pro-grammability, privacy, and smartness. To make WCS truly smart and Cloud/smartphone independent, we propose COCOWEARS: a framework for COntinuum Computing WEARable Systems. COCOWEARS relies on (i) sound theoretical foundations with a hardware/software system co-design methodology based on simulations, (ii) tailored AI/ML algorithms, and (iii) development tools to enable the engineering of next-generation WCS. A use case about the monitoring of operators' activities within the smart factory is provided to exemplify our approach, which is, to our knowledge, the first solution fully exploiting AI technologies at different levels of granularity, complexity, and user proximity, able to push towards massive adoption of WCS in the immediate future. © 2024 The Authors.

关键词： Wearable computers

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Convolutional Neural Network-based Stage Wise Classification of COVID Affected Lung CT Images 4

Convolutional Neural Network-based Stage Wise Classification...

引用

4th International Conference on Inventive Research in Computing Applications, ICIRCA 2022

作者： Prajwal, M.L. Monisha, B.L. Hemanth, N.A. Amrita School of Arts and Sciences Dept. Computer Science Mysore India

ISBN: (数字)9781665497077

ISBN: (纸本)9781665497077

Coronavirus disease is a contagious illness that mostly affects the lungs. Almost every continent has been infected with this virus. Countries are rushing to screen and treat people effectively in order to stop the spread of the condition. To identify patients with inflammation, the a test that uses reverse transcriptase chain reaction (RT PCR) employed. Due to PCR kits are in short deliver because of excessive demand, radiographic tactics including X rays and CT scans can be employed for pathological purposes. The early COVID 19 categorization will aid patients and physicians in scheduling appointments and making decisions. The proposed method intends to categorise COVID 19 sufferers lung CT images into normal, moderate, and final stages, using data from kaggle for stage-by-stage classification and prediction of COVID stage following classification. For lung CT scan pictures, this model is based on CNN, data augmentation, and resizing is done for better training, and the suggested model's maximum accuracy is up to 90%, F1 score is 96, and precision is up to 96. This suggested study can be used in the medical profession to assist doctors and COVID patients in the stage-by-stage categorization of pneumonia. © 2022 IEEE.

关键词： Coronavirus

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An Evolutionary Algorithm for the Travelling Sales Man problem

An Evolutionary Algorithm for the Travelling Sales Man probl...

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Electronics and Renewable Systems (ICEARS), International Conference on

作者： Shaik Mohammad Waseem Jeshwanth N L Lingesh A Matta Jayanth Bhargavi C H Batini Dhanwanth Dept. of Computer Science and Engineering Panimalar Institute of Technology (Anna University) Chennai India Dept. of Artificial Intelligence and Data Science Panimalar Institute of Technology (Anna University) Chennai India Dept. of Computer Science and Engineering Sai Spurthi Institute of Technology India Dept. of AI Engineering Jönköping University Jönköping Sweden

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

This study explores the use of Genetic Algorithms (GA) to solve the NP-hard combinatorial optimization problem known as the Travelling Salesman Problem (TSP). The suggested GA, called GA-P, performs better than conventional techniques because it uses a new randomized crossover threshold. According to experimental assessments, GA-P outperforms a normal Genetic Algorithm with fixed crossover (GA-N, 628.6) and approaches the best Branch and Bound solutions (B&B, 560.6) with an average cost of 617.4 for 16-node graphs. With average execution times of 0.1347 seconds for 16 nodes, GA-P also demonstrates constant computational efficiency, outperforming B&B's exponentially growing temporal complexity (169.37 seconds for 16 nodes). In order to balance solution quality and computational cost, hyperparameter tweaking revealed the ideal values of population percentage (pp = 1.1), crossover proportion (cp = 0.8), mutation threshold (mt = 0.3), and exploration probability (ep = 0.2). The results show that GA-P is a scalable and efficient substitute for TSP, with room for improvement via parallelization and exploratory techniques.

关键词： Renewable energy sources Costs Evolutionary computation Computational efficiency Complexity theory Optimization Tuning Genetic algorithms

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