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2024 International Conference on E-Mobility, Power Control and Smart Systems, ICEMPS 2024

作者： Jindal, Mayank Mimani, Sushant Pillai, Saniaikanth E Vadakkethil Somanathan Jiwani, Nasmin Kiruthiga, T. San Jose CA United States GoDaddy WA United States School of Electrical Engineering and Computer Science University of North Dakota Grand Forks United States University of The Cumberlands Dept. of Information Technology WilliamsburgKY United States Vetri Vinayaha College of Engineering and Technology Dept. of Ece Trichy India

ISBN: (纸本)9798350394399

Server less cloud computing has won vast recognition in current years because of its capability to provide green and price-effective solutions for numerous computing needs. On this model, cloud carriers manage the scaling and provisioning of sources, permitting users to consciousness totally on their application code. As an end result, server less computing gives an appealing opportunity to standard server-primarily based computing, in which customers needed to control the whole infrastructure themselves. However, with the rise of server less computing, there was a growing challenge about the effectiveness of algorithms on these surroundings. Traditional algorithms are designed to run on a set of resources, which may not translate properly to the dynamic and elastic nature of server less structures. This has caused a want for studies in developing algorithms mainly tailor-made for server less computing. One fundamental assignment on this place is the optimization of resources and price inside the server less environment. Since customers simplest pay for the resources they use, it's far critical to layout algorithms that minimize useful resource consumption and value. Previous research has already indicated that conventional algorithms that rely upon pre-determined aid allocation won't carry out nicely within the server less model. Moreover, server less systems additionally introduce new challenges in phrases of useful resource availability, network latency, and processing delays due to the pay-in keeping with-use model. © 2024 IEEE.

关键词： Cloud computing

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Inductive Program Synthesis via Iterative Forward-Backward Abstract Interpretation

arXiv

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arXiv 2023年

作者： Yoon, Yongho Lee, Woosuk Yi, Kwangkeun Seoul National University Dept. of Computer Science & Engineering Korea Republic of Hanyang University Dept. of Computer Science & Engineering Korea Republic of

A key challenge in example-based program synthesis is the gigantic search space of programs. To address this challenge, various work proposed to use abstract interpretation to prune the search space. However, most of existing approaches have focused only on forward abstract interpretation, and thus cannot fully exploit the power of abstract interpretation. In this paper, we propose a novel approach to inductive program synthesis via iterative forward-backward abstract interpretation. The forward abstract interpretation computes possible outputs of a program given inputs, while the backward abstract interpretation computes possible inputs of a program given outputs. By iteratively performing the two abstract interpretations in an alternating fashion, we can effectively determine if any completion of each partial program as a candidate can satisfy the input-output examples. We apply our approach to a standard formulation, syntax-guided synthesis (SyGuS), thereby supporting a wide range of inductive synthesis tasks. We have implemented our approach and evaluated it on a set of benchmarks from the prior work. The experimental results show that our approach significantly outperforms the state-of-the-art approaches thanks to the sophisticated abstract interpretation techniques. © 2023, CC BY.

关键词： Iterative methods

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Quantum transport simulation of a mid-wave infrared Ga-based type-II superlattice for curved focal-plane arrays 13

Quantum transport simulation of a mid-wave infrared Ga-based...

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Infrared Sensors, Devices, and applications XIII 2023

作者： Glennon, John Bellotti, Enrico Div. of Material Science and Engineering Boston University BostonMA02215 United States Dept. of Electrical and Computer Engineering Boston University BostonMA02215 United States

ISBN: (纸本)9781510665880

Type-II strained-layer superlattices have theoretically predicted advantages that make them an attractive material system for use in infrared technology. However, the influence of structural disorder has been demonstrated to negatively impact the performance of these materials. A recent development in infrared technology is the use of curved focal-plane arrays which promise improved uniformity of sensitivity and a reduction in the optical complexity of the device. Curving of the wafer introduces mechanical strain into the absorber layer which can also impact the vertical carrier mobility. This work reports on an analysis of the influence of external strain and disorder on the vertical hole mobility in curved focal-plane arrays utilizing a mid-wave InAs/GaSb absorber layers. A series of quantum transport calculations is performed over a range of strain configurations for two different structures: an ideal superlattice and a superlattice with graded interfaces and disorder. Finally, the impact these effects have on the quantum efficiency of a hypothetical curved-FPA is approximated, and a potential avenue for improved device design is suggested. © 2023 SPIE. All rights reserved.

关键词： Quantum efficiency

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Enhancing Brain Tumor Detection in MRI Images Through Fine-Tuning of VGG 16 Networks: A Precision Diagnostic Approach

Enhancing Brain Tumor Detection in MRI Images Through Fine-T...

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2024 International Conference on Communication, Computing and Energy Efficient Technologies, I3CEET 2024

作者： Upadhyay, Deepak Sharma, Kunj Bihari Gupta, Mridul Upadhyay, Abhay Yadav, Satya Prakash Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era Deemed to be University Dept. of Computer Science and Engineering Dehradun India Graphic Era Deemed to be University Dept. of Electronics and Communication Engineering Dehradun India Bennett University School of Computer Science Engineering and Technology Greater Noida India

ISBN: (纸本)9798331541583

This paper explores the impact of fine-tuning on brain tumour diagnosis using MRI data and a pre-trained VGG 16 model on ImageNet. Our objectives in fine-tuning the model were to improve its generalisation ability and diagnostic accuracy. Our results demonstrate a considerable improvement in the model's performance, with decreased validation loss and increased validation accuracy. The findings show that, despite the possibility of overfitting, fine-tuning is a helpful technique for deep learning model optimisation in medical image analysis. By enhancing diagnostic skills in healthcare, particularly in the early detection of brain tumours, this tactic may enhance patient outcomes. © 2024 IEEE.

关键词： Deep learning

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Performance Analysis of Deep Learning Models in Driver Behavior Recognition

Performance Analysis of Deep Learning Models in Driver Behav...

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Cybernation and Computation (CYBERCOM), International Conference on

作者： Rameshwari Kapoor Rashmi Kuliyal Nilesh Bhanot Piyush Bagla Ruchira Rawat Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun Uttarakhand India Dept. of Computer Science and Engineering Graphic Era (Deemed to be University) University Dehradun Uttarakhand India

ISBN: (数字)9798331542344

ISBN: (纸本)9798331542351

The World Health Organization report states that nearly 1.25 million people lose their lives due to an increasing number of road accidents every year. The majority of accidents are reported due to driver errors, which include distractions like the use of mobile phones and fatigue, which causes drowsiness. This study aims to address these issues by performing a comparative analysis between different deep learning models representing two types of learning mechanisms: traditional deep learning and transfer learning. This study portrays the use of two datasets to accomplish this task. These two datasets are the Driver Drowsiness Dataset DDD and the State Farm Distracted Drivers Dataset. The purpose of this investigative research is to explore the execution and efficiency differences between three deep learning algorithms, the traditional convolutional neural network, ResNet50, and VGG16, in classifying driver behavior by training these models individually on both datasets. This study also targets developing an alarm system that sounds an alarm based on a threshold by using an ensemble of all the models trained for comparative study and averaging their predictions to classify driver behavior. VGG16 achieved an accuracy of 99.74% on the driver drowsiness dataset, whereas Custom CNN Architecture achieved an accuracy of 98.70 % on the driver distraction dataset.

关键词： Deep learning Training Analytical models Accuracy Road accidents Transfer learning Convolutional neural networks Driver behavior Vehicles Residual neural networks

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Deep Analysis of Machine Learning Techniques for the Effective Retrieval of Images Based on Content

Deep Analysis of Machine Learning Techniques for the Effecti...

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Disruptive Technologies (ICDT), International Conference on

作者： Ashish Jain Sudeep Varshney Dept. of Computer Science and Engineering SET Sharda University Greater Noida India Dept. of Computer Science and Engineering G L Bajaj Institute of Technology and Management Greater Noida India

Our research aims to investigate how machine learning techniques can be applied for efficient image retrieval based on content. It is a very difficult process to obtain accurate images from vast digital image collections. CBIR has been particularly interested in this area. It has been shown that content- or feature-based approaches are more effective than text-based query techniques. Research has shown that the performance of these methods varies depending on the type and size of the data set analyzed, with some algorithms performing well for specific small data sets and others for larger data sets. This article provides a comprehensive and systematic review of effective methods for obtaining the image. The paper's objective is to examine different ML classifiers that are applied via the researchers and compare their results based on accuracy and performance. These algorithms include SVM, KNN, random forest, BoVW, Naive Bayes, and LDA. This review paper can assist researchers in selecting the best classifier for their research, as well as in improving existing classifiers by combining them with other algorithms.

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Deep Neural Networks in Neuroimaging: A U-Net Model for Precise Brain Tumor Detection

Deep Neural Networks in Neuroimaging: A U-Net Model for Prec...

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Communication, Computing and Energy Efficient Technologies (I3CEET), International Conference on

作者： Deepak Upadhyay Kunj Bihari Sharma Mridul Gupta Abhay Upadhyay Satya Prakash Yadav Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Dept. of Electronics and Communication Engineering Graphic Era Deemed to be University Dehradun India School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

This work holds the promise of being a considerable breakthrough in medical imaging diagnosis by studying advanced U-Net topologies for accurate brain tumour segmentation from magnetic resonance imaging (MRI) data. In this research, we present a more sophisticated pipeline that covers data preprocessing, robust model validation, and architecture tuning of the deep learning techniques. Performing accurately at 94.7% accuracy, the model showed a decent precision and recall rates with extremely promising results against traditional segmentation approaches as well as current deep learning frameworks. These results show how U-Net models could improve the diagnoses accuracy and guide personalized treatment tactics. Our research demonstrates the transformative ability of deep learning in medical diagnostics and opens a pathway for additional strides in individualized therapy, albeit diffuse tumour border segmentation barrier and MRI image heterogeneity limitations.

关键词： Deep learning Image segmentation Accuracy Magnetic resonance imaging Brain modeling Robustness Topology Telecommunication computing Planning Tuning

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Towards Automated Nanoenergetic Reaction Characterization with Computational Vision

Towards Automated Nanoenergetic Reaction Characterization wi...

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2023 IEEE Applied Imagery Pattern Recognition Workshop, AIPR 2023

作者： Gaines, Timothy B. Hurt, J. Alex Keller, James M. Scott, Grant J. Boyle, Camden I. Maschmann, Matthew R. Price, Stanton R. University of Missouri Dept. of Electrical Engineering and Computer Science Columbia United States University of Missouri Dept. of Mechanical and Aerospace Engineering Columbia United States U.S. Army Engineer Research and Development Center VicksburgMS United States

ISBN: (纸本)9798350359527

In order to utilize aluminum-based nanoenergetic materials effectively, the mechanisms by which reactive aluminum fuel escapes a passivating aluminum shell must be better understood. These reactions can be classified based upon pre-and post-reaction imagery taken of the material. To aide in quantitatively understanding these reactions we investigate creating an improved reaction classifier through leveraging larger, state-of-The-Art deep learning models. Additionally, we experiment with various pre-Training methods and image analogs to enable effective training of large models even with a small dataset. Image features used from the past experimentation may be used in conjunction with reaction imagery to aid in reaction classification. © 2023 IEEE.

关键词： Aluminum

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High Heels Are No More an Accessory of Fashion for Women- A Study Unrevealing the Health Effects of Wearing High Heels 10

High Heels Are No More an Accessory of Fashion for Women- A ...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Banu, Shaik Balkhis Akhtar, Saman Waseem Arshad, Sana Banu, Shaik Rehana Chandini, Shaik Ghantasala, G.S. Pradeep Fatima College of Health Sciences Dept of Physiotherapy Al Ain United Arab Emirates University of Bolton Dept. Management Studies Ras al-Khaima United Arab Emirates De Mont Fort University Dept. Business Management Dubai United Arab Emirates Lincoln University College Dept. Business Management Selangor Malaysia Gulf American University Dept. Civil Engineering Sharjah United Arab Emirates Alliance University Dept of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798350353068

The high heels are one of the most loved accessories of women. High heels have marked their entry not only into the fashion industry but also into the corporate sector as a symbol of must-wear accessories with corporate dressing. High heels are surely the most loved accessories of women but it is also a fact that high heels are the cause of foot pain and many other health conditions of foot health. The study is conducted with the intent to know whether wearing high heels is affecting the foot health of women or not. The study is conducted between the two controlled groups. each group consists of 125 women in each. One group represents women wearing high heels on a regular basis and the other group represents women not wearing high heels on a regular basis. The study reveals that the women who are not wearing high heels foot health is much better compared to the women who are wearing high heels. © 2024 IEEE.

关键词： Wear of materials

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Design and Development of an IoT Based Smart Vaccine Carrier and Real-Time Monitoring System

Design and Development of an IoT Based Smart Vaccine Carrier...

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Sustainable Technologies for Industry 4.0 (STI), International Conference on

作者： Raisa T. Hasanat Nafees Mansoor Nabeel Mohammed Mohammad S. Rahman Sifat Momen Mirza Rasheduzzaman EA Technology Capenhurst United Kingdom Dept. of Computer Science and Engineering University of Liberal Arts Bangladesh Dhaka Bangladesh Dept. of Electrical and Computer Engineering North South University Dhaka Bangladesh Dept. of Computer Science and Engineering United International University Dhaka Bangladesh Dept. of Electrical and Electronic Engineering BRAC University Dhaka Bangladesh

ISBN: (数字)9798331531973

ISBN: (纸本)9798331531980

Vaccination stands as the most reliable defense against a spectrum of life-threatening diseases. However, the eq-uitable distribution of vaccines in developing and least-developed nations face formidable challenges due to the absence of efficient and transparent cold chain management systems. This article introduces a real-time data-centric monitoring system designed to track the distribution and transportation of vaccines. Additionally, this research presents an energy-efficient vaccine carrier based on thermoelectric cooling, boasting minimal power consumption requirements. This innovative carrier ensures the safe transportation of vaccines even to the most remote regions. Moreover, the proposed system regulates the temperature and humidity of individual carriers by continuously monitoring sensor data. Furthermore, the system includes real-time tracking and reporting features. Anticipated outcomes include a significant increase in vaccination coverage in remote areas and enhanced transparency in the distribution process.

关键词： Temperature sensors Temperature distribution Power demand Cold chain Transportation Humidity Real-time systems Vaccines Reliability Monitoring

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