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International Conference on Smart Structures and Systems (ICSSS)

作者： M Sandhiya A S Aneetha Department of Computer Science and Engineering VELS Institute of Science Technology & Advanced Studies (VISTAS)

ISBN: (数字)9798350384208

ISBN: (纸本)9798350384215

An approach of early detection that makes use of an optimization technique that was brought about by learning techniques. Artificial intelligence technology breakthroughs that can scan a patient’s body and diagnose the conditions they have would be expected by certain doctors. To gain a deeper understanding of the human body, iridology is an applied health practice. Images of iris can be acquired and interpreted using a variety of innovative techniques. Information on the human body and organ health is contained in Iris. Iridology is an academic discipline that examines the strengths and limitations of iris tissue to gain insight into the health of the body’s various organs. Iridology has grown more well-liked and trustworthy as a result of the quick growth of picture processing. Many iridology-based methods have been created recently that use certain iris features to detect the human disorders. The information contained herein displays every iris recognition method that has been studied along with the precision of each region. Future iridology research’ design and methodological decisions can be influenced by the studies outcomes.

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A Survey of advanced Learning Techniques Used for Initial Detection of vital Human organs Disorders through Iris

A Survey of Advanced Learning Techniques Used for Initial De...

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International Conference on Smart Structures and Systems (ICSSS)

作者： M Sandhiya A S Aneetha Department of Computer Science and Engineering VELS Institute of Science Technology & Advanced Studies (VISTAS)

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Resource scheduling approach in cloud Testing as a Service using deep reinforcement learning algorithms

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CAAI Transactions on Intelligence Technology 2021年第2期6卷 147-154页

作者： Priyadarsini Karthik Karthik Sekhar Department of Computer Science and Engineering Vels Institute of Science Technology and Advanced Studies(VISTAS)ChennaiIndia Department of Electronics and Communication Engineering College of Engineering and TechnologySRM Institute of Science and TechnologyVadapalani CampusChennaiIndia

Many organizations around the world use cloud computing Testing as Service(Taas)for their *** computing is principally based on the idea of on-demand delivery of computation,storage,applications,and additional *** depends on delivering user services through Internet *** addition,it uses a pay-as-you-go business design to deliver user *** offers some essential characteristics including on-demand service,resource pooling,rapid elasticity,virtualization,and measured *** are various types of virtualization,such as full virtualization,para-virtualization,emulation,〇S virtualization,and application *** scheduling in Taas is among the most challenging jobs in resource allocation to mandatory tasks/jobs based on the required quality of applications and *** of the cloud environment,uncertainty,and perhaps heterogeneity,resource allocation cannot be addressed with prevailing *** situation remains a significant concern for the majority of cloud providers,as they face challenges in selecting the correct resource scheduling algorithm for a particular *** authors use the emergent artificial intelligence algorithms deep RM2,deep reinforcement learning,and deep reinforcement learning for Taas cloud scheduling to resolve the issue of resource scheduling in cloud Taas.

关键词： Service cloud prevailing

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Advancing Vascular Dementia Diagnosis Through Salp Swarm Optimized Convolutional Neural Network 8

Advancing Vascular Dementia Diagnosis Through Salp Swarm Opt...

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8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Mathkunti, Nivedita Manohara Ananthanagu, U. Abdulpur, Vidya N. Mangaiyarkarasi, D. Anitha, S. Alliance School of Advanced Computing Alliance Univerisity Department of Computer Science & Engineering Karnataka Bengaluru India Dr.D.Y Patil Pratishthan's College of Engineering Department of Electrical Engineering Maharastra Kolhapur India Institute of Science and Technology Avadi India Department of Artificial Intelligence & Machine Learning Karnataka Bengaluru562106 India

ISBN: (纸本)9798331505462

Vascular Dementia (VD) is a form of cognitive impairment seen in individuals, restricting from problems with blood vessels supplying the brain. These issues lead to strokes or reduced blood flow and gradually lead to the development of vascular dementia. Since it tends to progress slowly, diagnosis often comes after significant brain damage making prevention is crucial. Early identification of risk factors are vital to mitigate the risk of irreversible cognitive decline associated with vascular dementia. Accordingly, this research proposes an optimized neural network model for predicting vascular dementia at its earlier to improve the quality of life. The initial stage in this research involves preprocessing, where the input data is cleaned and normalized. These steps ensure that the vascular dementia dataset is cleaned of inconsistencies and prepared with normalized features for accurate analysis and modeling. Following preprocessing, relevant data features are selected using the chi-square approach, making the classification task easier. Finally, classification is performed utilizing a Convolutional Neural Network (CNN) and for fine-tuning of CNN parameters Salp Swarm Optimization (SSO) Algorithm is implemented. The execution of proposed work is processed using Python software and the outcome reveals superior performance in terms of accuracy and precision. Thereby the proposed method provides better performance with an accuracy of 94.5%, contrasted to existing methods in predicting VD. © 2024 IEEE.

关键词： Convolutional neural networks

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Dynamically Reconfigurable Perception using Dataflow Parameterization of Channel Attention

Dynamically Reconfigurable Perception using Dataflow Paramet...

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Asilomar Conference on Signals, Systems & computers

作者： Yujunrong Mal Kshitij Nikha Jani Boutellier Benjamin Riggan Shuvra S. Bhattacharyya Department of Electrical and Computer Engi-neering University of Maryland USA Department of Electrical and Computer Engineering University of Nebraska-Lincoln USA School of Technology and Innovation University of Vaasa Finland Department of Electrical and Computer Engi-neering University of Nebraska-Lincoln USA Department of Electrical and Computer Engineering Institute for Advanced Computer Studies University of Maryland College Park USA

Despite the increasing performance of deep learning models for intelligent perception tasks, the size and complex-ity of state-of-the-art models have become increasingly large, which makes them difficult to deploy in resource-constrained edge computing environments. Moreover, the conditions and constraints under which these models operate may be im-possible to fully anticipate at design time, and may undergo significant changes during inference. In this paper, we address these challenges by developing a new parameterized design approach for image-based perception that enables efficient and dynamic reconfiguration of convolutions using channel attention. Compared to switching among sets of multiple complete neural network models, the proposed reconfiguration approach is much more streamlined in terms of resource requirements, while providing a high level of adaptability to handle unpredictable and dynamically-varying operational scenarios. The efficiency and adaptability of the proposed approach are demonstrated on ResNetSO integrated with the Convolutional Block Attention Module (CBAM) concept.

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Empirical Evaluation of Internet of Things Oriented Enriched Women Protection System with Smart Sensors Association

Empirical Evaluation of Internet of Things Oriented Enriched...

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2023 Annual International Conference on Emerging Research Areas: International Conference on Intelligent Systems, AICERA/ICIS 2023

作者： Rohini, S. Deepa, A. Kalaimani, A. Rajiv Gandhi, N. Sudha, L. Pandi, V.Samuthira R.M.K Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Jeppiaar Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Panimalar Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Civil Engineering Tamil Nadu Chennai India S.A Engineering College Department of Computer Sciece and Engineering Tamil Nadu Chennai India Chennai Institute of Technology Centre for Advanced Wireless Integrated Technology Tamil Nadu Chennai India

ISBN: (纸本)9798350303452

As a result of modernity and advances in technology, protecting women has emerged as an issue that has to be addressed immediately not just in India but also in a number of other countries. Due to the structural difficulties that are connected with handling cases of this nature, the police are unable to respond to valid calls for assistance. It is difficult to approach the murder scene in an individual manner, and it is hard to determine where the crime began and where the victims died. It is also difficult to determine who committed the crime. As a direct consequence of this, the proportion of crimes in India that are perpetrated against women and children continues to climb, sowing the seed of dread in the minds of the people who live in the nation. As a direct consequence of this, the government has enacted a multitude of regulations with the intention of safeguarding the well-being of women and children. If a female volunteer in this research feels uncomfortable or unsafe in any way, she is told to use the 'Panic Button'. All these communications are handled by using the latest technology called Internet of Things (IoT) and when the alarm is activated, the gadget makes use of technology known as the Global Positioning System (GPS) to determine the exact position of the user. It then sends a signal of distress through GSM to the user's phone as well as the closest police station. A module connected to the Internet of Things is used to perform continuous monitoring of the location, and the website is automatically updated to reflect any changes. The buzzer will go off as soon as the accelerometer detects that she has fallen, therefore alerting anybody in the vicinity to the fact that she requires assistance. The key advantage of this research is the development of a portable safety device that can be used to safeguard women no matter where they go. © 2023 IEEE.

关键词： Global positioning system

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An Efficient Approach of Brain Tumor Detection & Extraction using BWT with Auto Enhance Technique

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Procedia computer Science 2025年 258卷 4105-4116页

作者： Nilesh Bhaskarrao Bahadure Sudhanshu Gonge Jagdish Chandra Patni Bhoomi Shah Nagrajan Raju Prasenjeet D. Patil Department of Computer Science and Engineering School of Technology GSFC University Vadodara Gujarat India Department of Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune Maharashtra India Department of Computer Science and Engineering Alliance School of Advanced Computing Alliance University Bengaluru India Department of Electrical and Electronics Engineering SASTRA University Thanjavur Tamil Nadu India Department of Computer Science and Engineering MIT ADT University Pune India

Brain tumor detection and extraction of infected areas with utmost accuracy is essential in medical image analysis. Despite ex- cessive research, segmentation operation for brain tumor detection remains an exigent issue because medical images have diverse image contents, non-uniformity object texture, occlusion, image noise, and other factors that consistently influence and affect per- formance analysis. This paper introduces the most efficient brain tumor detection and extraction technique, using Berkely Wavelet Transformation-based segmentation with auto-enhancement techniques. If the input images are too dark or their intensity is not good enough, the auto-enhance technique will automatically adjust the image quality. The extensive experimental analysis and sim- ulation, with a comparison of the results with other state-of-the-art techniques available, prove the significance and effectiveness of the proposed algorithm. The primary intent of this paper is to develop a simple but performance-oriented algorithm for brain tumor detection and extraction, which is suitable for most imaging modalities and gives the best performance even if images are of poor quality.

关键词： Berkeley wavelet transformation enhancement magnetic resonance imaging (MRI) segmentation morphology

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Scalable and Efficient Real-Time Data Processing in Cloud-Based Manufacturing Systems 15

Scalable and Efficient Real-Time Data Processing in Cloud-Ba...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Kale, Meenal R. Labhane, Suchitra Manu, S.E. Mehta, Pradnya Amudha, A. Gupta, Anchal College of Engineering Nagpur Department of Mathematics and Humanities Yeshwantrao Chavan Maharashtra India Datta Meghe Institute of Management Studies Department of MBA India Deemed to be University School of Engineering and Technology Civil Engineering JAIN Karnataka Bangalore India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Karpagam Academy of Higher Education Department of Electrical and Electronics Engineering Coimbatore641021 India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350370249

Cloud-based technologies have redefined the manufacturing industry, but there are hurdles to overcome when trying to reign in all that real-time data created throughout your production process. This research discusses the Scalable and Efficient Real-Time Data Processing Solution in Cloud-based Manufacturing Systems. It uses a hybrid architecture incorporating edge and cloud computing technology for real-time data processing and analysis. On one end, the edge devices collect data from the factory floor in real time and process them to an extent similar to what is known as preprocessing but performing it at local sites. At the same time, cloud servers target enhanced computational tasks and longerterm analytics, which are measurements of hours or days. The proposed solution needs to be highly scalable and can process vast volumes of data from multiple sources simultaneously. It also incorporates techniques for efficient data processing, like adjusting the frequency and filtering of read points to reduce calculation time and storage needs. © 2024 IEEE.

关键词： Smart manufacturing

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Polarization-based underwater geolocalization with deep learning

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eLight 2023年第1期3卷 122-135页

作者： Xiaoyang Bai Zuodong Liang Zhongmin Zhu Alexander Schwing David Forsyth Viktor Gruev Department of Electrical and Computer Engineering University of Illinois at Urbana-ChampaignUrbanaIL 61801USA Department of Computer Science University of Illinois at Urbana-ChampaignUrbanaIL 61801USA Department of Bioengineering University of Illinois at Urbana-ChampaignUrbanaIL 61801USA Beckman Institute for Advanced Science and Technology University of Illinois at Urbana-ChampaignUrbanaIL 61801USA Carle Illinois College of Medicine University of Illinois at Urbana-ChampaignUrbanaIL 61820USA

Water is an essential component of the Earth’s climate,but monitoring its properties using autonomous underwater sampling robots remains a significant challenge due to lack of underwater geolocalization *** methods for underwater geolocalization rely on tethered systems with limited coverage or daytime imagery data in clear waters,leaving much of the underwater environment *** in turbid waters or at night has been considered unfeasible due to absence of identifiable *** this paper,we present a novel method for underwater geolocalization using deep neural networks trained on-10 million polarization-sensitive images acquired globally,along with camera position sensor *** approach achieves longitudinal accuracy of-55 km(-1000 km)during daytime(nighttime)at depths up to-8 m,regardless of water *** clear waters,the transfer learning longitudinal accuracy is-255 km at 50 m *** leveraging optical data in conjunction with camera position information,our novel method facilitates underwater geolocalization and offers a valuable tool for untethered underwater navigation.

关键词： Underwater geolocalization Ocean exploration Celestial-based navigation Polarization Deep learning

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Reinforcement Learning Technique Based Automated Feature Analysis of Gene Expression Data

Reinforcement Learning Technique Based Automated Feature Ana...

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International Conference on Circuit, Power and Computing Technologies (ICCPCT)

作者： Jayakrishnan R S. Meera Computer Science & Engineering Vels Institute of Science Technology and Advanced Studies Chennai Tamil Nadu Department of Computer Science & Engineering Vels Institute of Science Technology and Advanced Studies Chennai Tamil Nadu

Immunohistochemistry and reverse transcription polymerase chain reaction, it is feasible to find molecular markers that are specific to cancer; nevertheless, there are no pathognomonic molecular markers available for the vast majority of solid tumors at this time. In this paper, we develop a Reinforcement learning Technique based automated feature analysis of gene expression data. This provides support for the idea that, on average, all the validation findings and training data from each epoch were utilized. This proposed method helps to acquiring knowledge at a more leisurely pace is, in the end, what makes episodic deep RL possible as a potential choice for students to pursue. To put it another way, the slow but steady accumulation of knowledge paves the path for the rapid extension of one existing body of information. The proposed methods provide 98 percent of the accuracy result than existing ANN and DNN. The proposed framework outperformed existing methods in terms of increase accuracy rate and reduced execution time than other methods

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