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Investigating the Effectiveness of algorithms in Serverless Cloud Computing

Investigating the Effectiveness of Algorithms in Serverless ...

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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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Flower Pollination Algorithm-Optimized Deep CNN Features for Almond(Prunus dulcis) Classification 4

Flower Pollination Algorithm-Optimized Deep CNN Features for...

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4th international conference on Emerging systems and Intelligent Computing, ESIC 2024

作者： Yurdakul, Mustafa Atabas, Irfan Tasdemir, Sakir Kirikkale University Engineering Faculty Computer Engineering Dept Kırıkkale Turkey

ISBN: (纸本)9798350349856

Almond is a nut rich in essential nutrients. In addition to being a food, it is also used in cosmetics and the pharmaceutical industry. The market value of almonds is determined according to the quality of the almonds. Manually determining the quality of almonds by humans is a prone to error, time-consuming, and tiring process. In this study, For this reasons, well-known twelve pre-trained CNNs were used to classify almonds as normal and damaged. Then, the most successful model was used as a feature extractor, and the features were classified with various machine learning algorithms. In addition to all these, features were selected by using the FPA algorithm, and the classification process was carried out. Experimental results showed that the use of CNNs as feature extractors and classification with machine learning algorithms can provide better results than the classical softmax structure. In addition, the proposed FPA-based feature extraction increases the classification performance. © 2024 IEEE.

关键词： Optimization

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Efficient Layouts of Complete t-Partite and Complete Networks into 3-Ary n-Cubes for Optimized Parallel Processing in Multiprocessing systems

Efficient Layouts of Complete t-Partite and Complete Network...

引用

2024 IEEE international conference on Signal Processing, Informatics, Communication and Energy systems, SPICES 2024

作者： Rajeshwari, S. Rajesh, M. Sowmiya, C. Easwari Engineering College Department of Mathematics Chennai600 089 India Vellore Institute of Technology School of Computer Science and Engineering Chennai600 127 India

ISBN: (纸本)9798350376135

Embedding problems has received a lot of attention in the last decades by various research dealt with many inter-connection networks. Through embedding the existing parallel algorithm of an interconnection network can be used for the embedded network, thus it plays a vital role in manipulating the parallel algorithms of interconnection network in multiprocessing systems. A variation of the k-Ary n-cube, notable for its adaptive network structure created to provide efficient parallel processing on multicomputers, is the 3-Ary n-cube. In this study, complete t-partite graphs and complete graphs are embedded into 3-Ary n-cubes with the least wirelengths. © 2024 IEEE.

关键词： Parallel processing systems

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Research on Efficient Energy Consumption Algorithm for Ocean Oil and Gas IoT Based on Deep Learning

Research on Efficient Energy Consumption Algorithm for Ocean...

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2024 international conference on Intelligent algorithms for Computational Intelligence systems, IACIS 2024

作者： Xiu, Zhiyuan Wang, Xueyi Zhang, Xuan Zhao, Yangjuan Liu, Lerui China University of Petroleum Safety and Ocean Engineering Beijing China Beijing University of Posts and Telecommunications Electrical and Electronic Engineering Beijing China

ISBN: (纸本)9798350360660

The management of offshore oil and gas is becoming more and more important. In this study, firstly, the data collected in the offshore oil and gas Internet of Things are prepared, including data cleaning and data standardization, to ensure the quality of input data. Next, a CNN model composed of convolution layer, aggregation layer and full connection layer is designed and constructed. These layers work together and are responsible for feature extraction and accurate prediction of future energy consumption trends. From the research results, the algorithm has obviously improved the energy saving rate and recall rate. The highest recall rate of CNN model can be as high as 95%, which is obviously better than the highest values of recurrent neural network (RNN) and long-term and short-term memory network (LSTM), which are 85% and 80% respectively. This proves the excellent ability of CNN model to identify abnormal energy consumption. © 2024 IEEE.

关键词： Offshore oil well production

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INTELLIGENT ENHANCEMENT SOLUTIONS FOR INDUSTRIAL ROBOTS USING AI TECHNOLOGY 2

INTELLIGENT ENHANCEMENT SOLUTIONS FOR INDUSTRIAL ROBOTS USIN...

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2nd international conference on Mechatronic Automation and Electrical engineering, ICMAEE 2024

作者： Wu, Di School of International Education Hebei University of Technology Tianjin300130 China

ISBN: (纸本)9781837242672

The integration of artificial intelligence (AI) into industrial robotics has revolutionized manufacturing by improving various aspects such as precision, speed, and energy efficiency. This paper delves into AI-driven solutions aimed at enhancing the performance of industrial robots, focusing on the application of advanced algorithms, machine learning models, and AI frameworks. Through a comprehensive performance comparison between AI-enhanced robotic systems and traditional methods, this study evaluates several key performance indicators (KPIs), including accuracy, flexibility, operational efficiency, and sustainability. The results underscore AI's transformative role in increasing overall system performance, achieving faster processing times, reducing energy consumption, and enhancing adaptability in complex manufacturing environments. Additionally, this paper explores the implications of these AI advancements for industrial automation, highlighting areas for future research, such as the optimization of AI algorithms, scalability across diverse industries, and improved integration with existing robotic systems. These findings emphasize the long-term potential of AI to reshape industrial automation, driving forward innovation in smart manufacturing and fostering sustainable development across the sector. © The Institution of engineering & Technology 2024.

关键词： Smart manufacturing

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Identification of Ayurvedic Plants Using Deep Learning

Identification of Ayurvedic Plants Using Deep Learning

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2024 IEEE international conference on Computing, Semiconductor, Mechatronics, Intelligent systems and Communications, COSMIC 2024

作者： Kambli, Prashanth Satrasala, Vikas Shivanna, K. Shreyash Sharma, Shaan Neralkar, Yash Nitin M S Ramaiah Institute of Technology Information Science & Engineering Bangalore India Computer Science & Engineering Sahyadri College of Engineering and Management Adyar Mangalore India

ISBN: (纸本)9798331517892

Deep learning algorithms have become pivotal in the accurate identification and classification of Ayurvedic plants, leveraging advanced techniques to distinguish between various species based on leaf images. Among these algorithms, the VGG16 model, a convolutional neural network (CNN), is particularly notable for its robust performance in image recognition tasks. The Ayurvedic plant detection database serves as a comprehensive resource for training and validating these models, facilitating high accuracy in plant detection. The integration of deep learning models presents significant potential for botany and health care applications. By offering reliable and precise identification of Ayurvedic plants, the proposed system supports botanists and healthcare professionals in making informed decisions. This leads to more accurate identification and utilization of traditional medicinal knowledge. These advancements not only improve the efficiency and reliability of plant detection systems but also build confidence in AI-driven solutions within the field of traditional medicine. The potential to revolutionize plant classification and enhance the understanding of medicinal plants underscores the importance of continued research and innovation in this domain. The model proposed ensures faster and easier way to recognize the medicinal plants and give its description to the reader using deep learning methods with the accuracy of 96% identification systems. © 2024 IEEE.

关键词： Medicinal chemistry

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Comparative Analysis of Machine Learning algorithms for Pulse Jammer Detection 18

Comparative Analysis of Machine Learning Algorithms for Puls...

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18th IEEE international conference on Advanced Networks and Telecommunications systems, ANTS 2024

作者： Srivastava, Srishti Chaudhary, Prerna Bhatnagar, Manav R. Netaji Subhas University of Technology Department of Electronics & Communication Engineering Delhi India Indian Institute of Technology Delhi Department of Electrical Engineering New Delhi India

ISBN: (纸本)9798350391725

This work compares various machine learning algorithms for predicting the presence of a random on/off jammer in wireless communication systems. We analyze four machine learning classification algorithms, Decision Trees, Support Vector Machines, K-nearest neighbors, and Random Forest. The study applies data pre-processing techniques to improve detection performance and assesses the algorithms using metrics such as accuracy, precision, recall, and F1 score. Among the tested methods, Random Forest and Decision Trees achieved the highest performance with an accuracy of 97.5%, precision of 1.00, recall of 0.95, and an F 1 score of 0.97. SVM demonstrated strong precision at 1.00 but slightly lower accuracy at {9 2. 5 %}, while KNN exhibited the lowest accuracy at 85%. These results highlight the effectiveness of Random Forest and Decision Trees for robust and efficient jammer detection. The findings have significant implications for enhancing the resilience of wireless communication systems against jamming attacks. In particular, we show the use-case of our analytical work in the classification-based application of wireless communication systems, specifically jammer detection. © 2024 IEEE.

关键词： Support vector machines

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A Scalable Machine Learning Model for Sales Forecasting using PySpark

A Scalable Machine Learning Model for Sales Forecasting usin...

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2024 international conference on Knowledge engineering and Communication systems, ICKECS 2024

作者： Harshitha, Talluri Tanya, Simhadri Jaswanthi, Thadakaluru Venugopalan, Manju Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350359688

Sales forecasting is a useful tool used for estimating future demand and sales. It provides insightful information for strategic decision-making, resource allocation, and inventory control. This research aims to utilize various machine learning algorithms for constructing a robust sales forecasting model, with the help of the Machine Learning library in PySpark. PySpark is chosen for its scalability, parallel processing framework, and seamless integration with machine learning libraries, making it an ideal choice for handling large datasets. MLlib is a powerful machine learning library within PySpark, offering a comprehensive set of scalable and distributed algorithms that enable efficient processing and analysis of large datasets. The research also incorporates time-series analysis. The performance of the research is evaluated using evaluation metrics R-Square(R2) value Root Mean Squared Error (RMSE) and Mean Squared Error (MSE), Mean Absolute Error (MAE). Out of all experimented models, Gradient Boosting Regressor stands out as the best-performing model with R-squared value of 0.93, showcasing its effectiveness in accurate sales predictions. © 2024 IEEE.

关键词： Adversarial machine learning

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Impact of HVDC Technology on the Probabilistic Power Flow of the Italian Power System 24

Impact of HVDC Technology on the Probabilistic Power Flow of...

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24th EEEIC international conference on Environment and Electrical engineering and 8th I and CPS Industrial and Commercial Power systems Europe, EEEIC/I and CPS Europe 2024

作者： Benato, Roberto Gardan, Giovanni Rusalen, Luca University of Padova Department of Industrial Engineering Padova Italy

ISBN: (纸本)9798350355185

The rapid evolution of worldwide power systems needs accurate numerical models to forecast their correct operation. In this paper, two power flow algorithms developed at the University of Padova, named PFPD-PRB and PFPD-ACDC, are merged to analyze the static behavior of the Italian power system in a hypothesized scenario. These two algorithms allow evaluating the probabilistic (PFPD-PRB) and the AC/DC power flows (PFPD-ACDC) by exploiting the matrix approach. The presence of the planned Tyrrhenian and Adriatic HVDC links and of a given renewable energy scenario are considered. In fact, a 20% variable renewable energy generation on a low-load Italian scenario (22.5 GW of power absorption) is assessed. In particular, the solutions are given in terms of uncertainty of the power flow results, showing that these increase as the HVDC power import increase, and that such uncertainties are higher near the location of the vRES plants (i.e., Southern region of the Italian network). The methodology is general and can be applied to any transmission network including HVDC links and renewable energy sources. © 2024 IEEE.

关键词： AC/DC Power Flow HVDC links Italian Power systems Renewable Energy Sources Stochastic Power Flow

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Modular Multilevel Converter Topology with a Self-Balancing Sub Network for Grid Connected Converter Applications 3

Modular Multilevel Converter Topology with a Self-Balancing ...

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3rd IEEE international conference on Power Electronics, Intelligent Control and Energy systems, ICPEICES 2024

作者： Bhandare, Vikas S. Waware, Madhukar Mohale, Vijay Shivaji University Electrical Engineering Department India Sanjay Ghodawat University Electrical Engineering Department Kolhapur India University Grants Commission New Delhi India Walchand College of Engineering Sangli India

ISBN: (纸本)9798350387193

This The modular multilevel converter (MMC) has been extensively employed in HVDC transmission, motor drives, STATCOM, and grid connected converter applications due to its various benefits. Voltage balancing algorithms are commonly employed to reduce the voltage fluctuation of subnetworks, but they have drawbacks in terms of complexity of the algorithms and hardware expense. This paper proposes a novel clamp-based subnetwork with two capacitors and six transistors. The two capacitors' symmetrical functioning allows the subnetwork to produce three levels, and their voltages are self-balanced. Additionally, by adding two auxiliary balance branches between neighboring subnetworks, the capacitor voltages among various subnetworks of a MMC are also self-balanced. As a result, the new MMC can function correctly without the use of sophisticated balancing algorithms based on capacitor voltage balancing. The novel MMC features fewer conducting switches than conventional self-balanced sub module based MMCs, which is notably advantageous for high power applications. The proposed topology's operational theory and steady-state analysis are detailed. Finally, simulation results support the practicality of the proposed MMC based on a self-balancing subnetwork. © 2024 IEEE.

关键词： Capacitors

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