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Agile Time Quantum Round Robin Algorithm

Agile Time Quantum Round Robin Algorithm

引用

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

作者： Qais Mirkar, Sulalah Bobal, Amiksha Gattani, Sanjana Mukesh Patel School of Technology Management and Engineering Dept. of Information Technology Mumbai India Mukesh Patel School of Technology Management and Engineering CSE-Cyber Security Mumbai India

ISBN: (纸本)9798350376135

In multitasking operating systems, CPU scheduling algorithms are accountable for the queuing of tasks for execution on the CPU. The effectiveness of a CPU scheduling algorithm is measured through CPU utilization, throughput, and process waiting times. One such scheduling algorithm is Round Robin (RR), a preemptive methodology that allocates time in quanta (parts) to execute the processes in a loop. The problems with a static quantum, such as too many context switches and process starvation are addressed by the proposed algorithm named Agile Time Quantum Round Robin (ATQRR). The algorithm dynamically modifies the remaining time based on real-Time system factors. To ensure a thorough validation, a comprehensive analysis of the existing literature has been undertaken to compare ATQRR with other similar approaches. This review has focused on many criteria, like average turnaround time, waiting times, throughput, context switch overhead, and the fairness of process scheduling. © 2024 IEEE.

关键词： Scheduling algorithms

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Enhancing Cybersecurity through Genetic Algorithm-Based Phishing Website Detection

Enhancing Cybersecurity through Genetic Algorithm-Based Phis...

引用

2024 IEEE international conference on Signal Processing, Information, Communication and systems, SPICSCON 2024

作者： Biplob, Md Badiuzzaman Arefin, Mohammad Shamsul Reza, Ahmed Wasif Computer Science and Engineering Department Chittagong University of Engineering and Technology Chattogram Bangladesh Computer Science and Engineering Department East West University Dhaka Bangladesh

ISBN: (纸本)9798331510213

Cybercriminals design false webpages in an attempt to trick people into disclosing private information, such as passwords and credit card numbers. Strong cybersecurity measures are desperately needed, as phishing attacks are becoming more and more sophisticated. To improve cybersecurity, this study investigates the use of genetic algorithms for phishing website detection. We have used three real-world phishing website datasets to implement our work. Additionally, a thorough methodology that includes feature selection, data preprocessing, and the iterative process of the genetic algorithm is employed. This work proposes a hybrid-based method for phishing identification based on the website's URL that combines genetic and machine learning methods. The algorithm's effectiveness is demonstrated by the results, which also show its adaptability, relative advantages over conventional methods, and promising generalizability. By shedding light on the useful applications of genetic algorithms for phishing detection, the study advances the field of cybersecurity. The final model proposed in this work has a 99.99% accuracy rate on the test set. © 2024 IEEE.

关键词： Phishing

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A Comparative Study of Machine Learning Based Tank Pressure Prediction for Ships

A Comparative Study of Machine Learning Based Tank Pressure ...

引用

2024 international conference on Artificial Intelligence and Power systems, AIPS 2024

作者： Wu, Jianyang Tao, Yuanming Wang, Xiaochuan School of Naval Architecture and Maritime Zhejiang Ocean University Zhoushan316022 China

ISBN: (纸本)9798350392210

In order to predict the pressure trend in a ship's liquid tank, this paper presents a series of shaking tests using a six-degree-of-freedom shaking table, a pressure transducer and an acrylic transparent rectangular tank to obtain the inner wall pressure of the tank to produce the dataset through modelling experiments. Based on the collected data, four machine learning algorithms, namely Long Short-Term Memory Network (LSTM), Back Propagation Neural Network (BPNN), Support Vector Machine (SVM), and Random Forest (RF), were selected to construct a prediction model, and then the prediction model was utilised to predict the pressure on the inner wall of the tank over a period of time. The results show that compared with other algorithms, the LSTM algorithm is the best for predicting the pressure on the inner wall of the tank and has good generalisation ability. It has a RMSE of 0.60252 and an R2 of 0.84710, which has a good prospect for engineering applications. © 2024 IEEE.

关键词： Ocean engineering

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Mitigating Environmental Impacts on Optical Fiber Fault Detection Using Machine Learning 4

Mitigating Environmental Impacts on Optical Fiber Fault Dete...

引用

4th international conference on Power, Energy, Control and Transmission systems, ICPECTS 2024

作者： Lakshmi, G. Brindha, S. Sri Sai Ram Engineering College Department of Electronics and Communication Engineering Chennai India

ISBN: (数字)9798331508845

ISBN: (纸本)9798331508845

Fiber optic networks are integral to modern communication systems, offering high-speed data transmission over long distances. However, the performance of these networks can be significantly affected by environmental factors such as temperature, humidity, pressure, and mechanical stress. These factors can alter the physical properties of optical fibers, leading to signal degradation and potentially causing errors in fault detection. This paper investigates the impact of environmental factors on fiber fault detection systems, particularly those utilizing machine learning algorithms. We propose methods to mitigate these effects and enhance detection accuracy. The research is validated through simulations and case studies, providing practical guidelines for deploying fiber fault detection systems in various environmental conditions. © 2024 IEEE.

关键词： Optical fibers

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Investigating the Effectiveness of algorithms in Serverless Cloud Computing

Investigating the Effectiveness of Algorithms in Serverless ...

引用

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...

引用

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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INTELLIGENT ENHANCEMENT SOLUTIONS FOR INDUSTRIAL ROBOTS USING AI TECHNOLOGY 2

INTELLIGENT ENHANCEMENT SOLUTIONS FOR INDUSTRIAL ROBOTS USIN...

引用

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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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...

引用

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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Comparative Analysis of Machine Learning algorithms for Pulse Jammer Detection 18

Comparative Analysis of Machine Learning Algorithms for Puls...

引用

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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