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Distributed Systems, Computer Networks and Cybersecurity (ICDSCNC), International Conference on

作者： Archana Naik Kavitha Sooda Karunakara Rai b Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Bangalore India Department of Computer Science and Engineering B. M. S. College of Engineering Bangalore India Department of Electronics and Communications Nitte Meenakshi Institute of Technology Bangalore India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

Cloud environments offer extensive services, making them a preferred choice for businesses and startups to host and maintain databases. Efficient resource management, including automatic scaling, is crucial for sustaining virtual machine environments. This paper addresses the challenge of forecasting CPU workloads to optimize resource allocation. Terraform was utilized to create and launch instances in the cloud environment, specifically using Amazon Web Services (AWS). A CPU-intensive application was executed on the server, and CPU utilization data was collected at regular intervals to serve as the dataset for the experiment. Three models—Gated Recurrent Unit, Long Short-Term memory Autoencoder, and Seasonal Auto-Regressive Integrated moving Average—were compared for short-term workload prediction. The results demonstrate that SARImA outperforms the LSTm autoencoder by 2.91%, GRU by 1.78%, and LSTm autoencoder by 1.13% based on mAPE, mSE, mAE, and RmSE metrics.

关键词： measurement Cloud computing Web services Autoencoders Predictive models Virtual machines Performance analysis Resource management Servers Long short term memory

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Cloud-Driven Collaborative Filtering and Waiter Robots Transforming Customer Experiences in Restaurants

Cloud-Driven Collaborative Filtering and Waiter Robots Trans...

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2024 International Conference on Advances in modern Age Technologies for Health and engineering Science, AmATHE 2024

作者： Srinivasan, V. Prasanna mohankumar, N. Prabakaran, T. Sairam, A. Elangovan, K. Velmurugan, S. R.M.D. Engineering College Department Of Information Technology Tamil Nadu Chennai India Maharashtra Nagpur India Joginpally B.R. Engineering College Department Of Computer Science And Engineering Telangana Hyderabad India Saveetha School Of Engineering Saveetha Institute Of Medical And Technical Sciences Department Of Computer Science And Engineering Thandalam Tamil Nadu Chennai India Dhanalakshmi Srinivasan Engineering College Department Of Mechanical Engineering Tamil Nadu Chennai India T.J.S. Engineering College Department Of Electronics And Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350371567

To improve customer relations, this research investigates the possibility of using cloud-enabled collaborative filtering with robot waiters in restaurant environments. With the use of innovation, to revolutionize the way people eat out. It suggests using smart robots that can interact with customers and help the wait staff out. Customer preferences are analyzed, order suggestions are streamlined, and service is personalized using cloud-based collaborative filtering algorithms. Test this novel approach in a real-world restaurant setting to view how it changes things for the better in terms of customer happiness, service efficiency, and employee output. It seems that the combination of autonomous waiters and collaborative filtering on the cloud might completely change the way people dine out by making everything more streamlined and personalized to each individual's preferences. To the existing conversation about how robots, cloud computing, and customer service intersect in the restaurant industry, which is important since technology is still playing a major influence on how businesses engage with their customers. © 2024 IEEE.

关键词： Collaborative filtering

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Corrosion behaviour of Carbon Steel Fasteners in Neutral Chloride Solution

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Acta metallurgica Sinica(English Letters) 2022年第4期35卷 563-576页

作者： Enobong Felix Daniel Junhua Dong Xiaofang Li Ini-Ibehe Nabuk Etim Inime Ime Udoh Rongyao ma Lei Chen Changgang Wang Shenyang National Laboratory for Materials Science Institute of Metal ResearchChinese Academy of SciencesShenyang 110016China School of Materials Science and Engineering University of Science and Technology of ChinaShenyang 110016China School of Materials Science and Engineering Northeastern UniversityShenyang 110819China School of Environmental and Chemical Engineering Shenyang Ligong UniversityShenyang 110159China Department of Marine Biology Akwa Ibom State UniversityP.M.B 1167UyoNigeria

An experimental model for simulating the corrosion of carbon steel fasteners(bolt and nut) composed of a contact carbon steel electrode(CCSE) and an exposed bare carbon steel plate electrode(bCSE) was designed. The effect of coupling on the corrosion process of the galvanically coupled carbon steel electrode was evaluated and compared with the self-corrosion process observed independently at the exposed and contact regions. Results obtained indicated that at an equal area ratio and uncoupled conditions, the corrosion rate is accelerated in the surface directly exposed to bulk solution compared to the bolt surface in contact with the nut. A coupling current was recorded when the exposed surface(bCSE) was electrically connected with the contact surface(CCSE);with the CCSE acting as the anode thereby suppressing the corrosion process in the exposed surface. by implication, the galvanic coupling between CCSE and bCSE increased the corrosion rate of CCSE. The diff erence in oxygen supply was responsible for the coupling effect observed in the system as there was no decrease in the solution pH. moreover, varying the cathode-to-anode area( S c/S a) ratio significantly influenced the corrosion current density as increased S c/S a ratio resulted in an accelerated galvanic corrosion process. The corroded surfaces and interfaces were analysed using stereomicroscopy and scanning electron microscopy. X-ray diff ractometry was adopted for corrosion product characterization. The results obtained showed supportive evidence of the corrosion behaviour in carbon steel fasteners.

关键词： bolt and nut Galvanic current density Zero resistance ammetry(ZRA) Coupling potential Differential aeration cell Carbon steel fasteners

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Optimal sizing and assessment of a hybrid energy based AC microgrid

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materials Today: Proceedings 2022年 49卷 326-332页

作者： S. madhura Venkatesh boddapati RV Institute of Technology and Management Bangalore India B.M.S College of Engineering Bangalore India

Renewable Energy Resources (RES) is growing with a wide popularisation due to its ability to mitigate global warming challenges and meet the increasing energy demand. Nevertheless, some of the challenges in implementing the RES system like intermittency and volatile profile give an enormous research and improvement scope. Since the grid expansion doesnt incur large investments after the RES system installation, they can significantly meet energy demands. In this paper, feasibility study and optimum solution for electricity needs for one of the blocks of RV college of engineering using RES is analysed for various systems. Four hybrid energy systems studies provide the technical details and the simulation results obtained from Hybrid Optimisation of multiple Energy Resources (HOmER) simulation tool. Hybrid energy resources like the combination of solar PV, biomass and diesel were analysed and compared. From the qualitative and quantitative results obtained for PV/biomass/Diesel/ battery system, it was observed that solar/biomass was the optimal solution for electrification among the renewable resources. This hybrid combination is the most feasible solution since there was no carbon emission and environmentally friendly.

关键词： Renewable Energy Hybrid microgrid Optimisation Energy feasibility

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Harmonics Analysis of multilevel Inverter using Low & High Switching Frequency Techniques for R and RL load

Harmonics Analysis of Multilevel Inverter using Low & High S...

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International Conference on Innovative mechanisms for Industry Applications (ICImIA)

作者： Pn Champa Abhay A Deshpandey Department of Electrical & Electronics Engineering B N M Institute of Technology Bengaluru India Department of Electronics & Communication Engineering RV College of Engineering Bengaluru India

In the field of Low & medium-voltage high-power energy conversion schemes, multilevel inverter (mLI) topologies have significantly aided the growth of modern power-electronic devices as an expressive alternative to conventional two-level inverters. This study aims to design a novel 5-level multilevel inverter administered by both low frequencies nearest level control (NLC) with symmetrical, asymmetrical (ANLC) topology and high frequency based advance phase disposition pulse width modulation scheme. The parameters preferred for analysis are % Output THD of both voltage and current, reactive power, true power with power factor, RmS Voltage and current for R & RL load. The critical evaluation of proposed low & high frequency multilevel inverter performance is verified and validated by mATLAb/SImULINK environment

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Customizing Tablets Using machine Learning Through Rapid Prototyping

Customizing Tablets Using Machine Learning Through Rapid Pro...

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International Conference on Computation System and Information technology for Sustainable Solutions (CSITSS)

作者： Shashank T K Gururaja H S Seema Nagaraj Department of ME B.M.S. College of Engineering Bangalore India Department of ISE B.M.S. College of Engineering Bangalore India MCA Department Bangalore Institute of Technology Bangalore India

The use of cutting-edge technology like rapid prototyping and machine learning may change future tablet designs. The time it takes to make a tablet is drastically reduced thanks to these technologies, which could speed up the quality assurance process. Create novel and original dosage forms and hasten the production of new pharmaceuticals, integration of human learning, 3D printing, and machine learning can be used. This study analyzes and extracts data from a specific person using a natural language Processing methodology, then Processing Double Layer blowing Agent using a 3D printing technique called fused deposition of filaments. The result is that the release profile is influenced by the 3D printing process's Fill factor of the process. 3D printing fused, according to study, may present a fresh way to produce unit dosage forms or drugs with unique dosages and controlled-release characteristics.

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Velocity and thermal slip effects on mHD convective radiative two-phase flows in an asymmetric non-uniform channel

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Propulsion and Power Research 2021年第2期10卷 169-179页

作者： b.mallikarjuna S.H.C.V.Subba bhatta S.Ramprasad Department of Mathematics B.M.S.College of EngineeringBangaloreAffiliated to VTUBelgaumKarnatakaIndia Department of Mathematics M.S.Ramaiah Institute of TechnologyBangaloreAffiliated to VTUBelgaumKarnatakaIndia

This paper aims to examine the influence of thermal radiation on magnetohydrodynamics(mHD)convective two-phase flows through the asymmetric divergent channel under first-order velocity and thermal slip *** transformations are used to nondimensionalize the governed partial differential equations and then solved them numerically.A grid-independent test is done to check the consistency of the numerical method and then validated with existing published results in a limiting *** graphical illustrations are provided to explain the impacts of various emerging parameters on flow *** enhancement in particle velocity is observed with an increment in the velocity slip *** the temperature slip parameter improves a decrement in the fluid as well as particle temperature is *** increment in radiation parameter augments fluid *** type of flow has multifarious range applications like fluidized beds,gas cooling systems,polishing heart valves and internal cavities,etc.

关键词： Particulate suspension Two phase flows Heat transfer Thermal radiation Divergent channel

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A Fuzzy based CNN method for Covid 19 Classification in Noisy Chest X-ray Images

A Fuzzy based CNN Method for Covid 19 Classification in Nois...

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2023 International Conference on Integrated Intelligence and Communication Systems, ICIICS 2023

作者： mitra, Ankita Esakki madura, E. Kiruba, b. basker, N. Grace, N.Vini Antony Glory, E. Dr. B. C. Roy Engineering College Department of Ece Durgapur India Bannari Amman Institute of Technology Department of Artificial Intelligence and Data Science Erode India Sri Krishna College of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India Sona College of Technology Department of Cse Salem India R.M.D. Engineering College Department of Ece Kavaraipettai India Saveetha School of Engineering Department of Biosciences Chennai India

ISBN: (纸本)9798350315455

The COVID-19 pandemic has caused a global health crisis. Chest X-ray (CXR) imaging is most commonly utilized techniques for diagnosing COVID-19. However, accuracy of CXR-based diagnosis is limited by the presence of noisy labels. Noisy labels are labels that are incorrect or inaccurate. They can be caused by a variety of factors, such as human error, image artifacts, and variations in imaging protocols. In this paper, a novel method is introduced for COVID-19 CXR image classification in the presence of noisy labels. The primary objective of this research is to improve the accuracy of COVID-19 detection in X-ray images that are characterized by the presence of noise. To achieve this, proposed method incorporates direction-based fuzzy rules into our methodology, which allows to effectively identify and analyse edges and textures within the X-ray images. The labels are then subjected to undergone manifold learning and multidimensional scaling to eliminate noise. High confidence samples are further chosen as training set for label-recovered dataset in order to increase constancy and precision of a classification framework without compromising its intrinsic performance. The proposed method is evaluated on three publicly available COVID-19 CXR datasets. The results showed that our method can achieve an accuracy of 96.59% on the large dataset, 97.12% on the medium dataset, 98.79% on the small dataset. These results are comparable to the state-of-the-art methods for COVID-19 CXR image classification. This can help to improve the early diagnosis and treatment of COVID-19 patients. © 2023 IEEE.

关键词： chest X-ray COVID-19 Fuzzy membership noisy labels semisupervised learning

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machine learning based fingerprint pattern recognition with mach-Zehnder Interferometer

Machine learning based fingerprint pattern recognition with ...

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2024 International Conference on Photonics Solutions, ICPS 2024

作者： Usman, Abdullahi Khun-In, Ravivudh bhatranand, Apichai Chayawan, Chirasil muhammad, Khalid Sabo buranasiri, Prathan Department of Physics with Electronics Federal University P.M.B 1157Kebbi State Birnin Kebbi Nigeria Department of Physics Faculty of Science Chulalongkorn University Bangkok10330 Thailand Department of Electronics and Telecommunication Engineering Faculty of Engineering King Mongkut's University of Technology Thonburi Bangkok10140 Thailand Artificial Intelligence Photonics Advanced Research Laboratory Department of Physics School of Science King Mongkut's Institute of Technology Ladkrabang Bangkok10520 Thailand Electronic and Optoelectronic Device Research Unit School of Science King Mongkut's Institute of Technology Ladkrabang Bangkok10520 Thailand

ISBN: (数字)9781510688308

ISBN: (纸本)9781510688292

Fingerprints serve as an excellent means of individual identification. This study employed transmission-mode image detection using a mach-Zehnder interferometer to produce two-dimensional fingerprint images. The research aims to develop a fingerprint pattern recognition system by integrating novel algorithms for ridge orientation analysis. The fingerprint patterns considered in this study include loops, arches, and whorls. The objective is to achieve high-resolution fingerprint patterns to analyze fingerprint variability, distinguish foreground from background regions, and trace ridge line orientations. The recognition algorithm is implemented using image processing techniques, with a preprocessing workflow that includes binarization, resizing, and histogram equalization for image enhancement. After preprocessing, the image undergoes ridge orientation analysis using the gradient method, and both similarity scores and pattern categorization were successfully achieved. The algorithm's performance was evaluated through mATLAb simulations, with results demonstrating the proposed system's accuracy and reliability across various fingerprint samples. The interferometric setup was also adapted to recognize microorganisms within a microfluidic channel, highlighting its potential applications in biometrics, forensics, and medical diagnostics. © 2025 SPIE.

关键词： mach-Zehnder interferometers

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Comparison of Recurrent Neural Network model for Future Electric Power Prediction 6

Comparison of Recurrent Neural Network Model for Future Elec...

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6th International Conference on I-SmAC (IoT in Social, mobile, Analytics and Cloud), I-SmAC 2022

作者： balasubramaniyan, S. Duraipandy, P. Kalyani, N. Shanmugaraja, P. Shobana, D. Vimala, m. Mailam Engineering College Department of Electrical and Electronics Engineering Mailam Tamil Nadu Villupuram604 304 India J B Institute of Engineering and Technology Department of Electrical and Electronics Engineering Telangana Hyderabad500075 India R. M. K College of Engineering and Technology Department of Computer Science Engineering Tamil Nadu Thiruvallur601206 India Sona College of Technology Department of Information Technology Tamil Nadu Salem636005 India Panimalar Institute of Technology Department of Electrical and Electronics Engineering Tamil Nadu Chennai600125 India R. M. K. Engineering College Department of Electrical and Electronics Engineering Chennai601206 India

ISBN: (数字)9781665469418

ISBN: (纸本)9781665469418

The increasing demand for electricity can be directly attributed to the exponential rise in both the global population and the sophistication of modern technologies. because electric power is utilized simultaneously with its generation at the power station, it is crucial to precisely anticipate electric power consumption in advance to ensure a reliable power supply. Estimating future electricity needs is critical to the success of any electricity provider. Having accurate predictions of electricity use has several practical and financial benefits, including ensuring the security and stability of the power grid. This research employs a Recurrent Neural Network (RNN) model based on a Long Short-Term memory (LSTm) network to anticipate electricity usage, aiming to resolve the discrepancy between the need for precise prediction and the limitations of conventional methods. The model incorporates conventional LSTm and hybrid Convolutional Neural Networks and Long Short-Term memory (CNN-LSTm). First, we'll examine the Kaggle data we collected on electricity consumption. Second, we utilize an RNN model to forecast future power consumption as a whole. Finally, we will utilize loss and mean Square Error (mSE) to qualitatively and statistically assess the performance of the model. According to the research conducted in the experiments, the CNN-LSTm prediction model outperforms traditional LSTm models in terms of reliable prediction. © 2022 IEEE.

关键词： Long short-term memory

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