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using algorithm » cosine algorithm (Expand Search)
data scheduling » task scheduling (Expand Search), ahead scheduling (Expand Search)
element » elements (Expand Search)
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81
Unsupervised Deep Learning for Classification Of Bats Calls Using Acoustic Data
Published 2021Get full text
doctoralThesis -
82
Comparative analysis of metaheuristic load balancing algorithms for efficient load balancing in cloud computing
Published 2023“…This paper provides a comparative analysis of various metaheuristic load balancing algorithms for cloud computing based on performance factors i.e., Makespan time, degree of imbalance, response time, data center processing time, flow time, and resource utilization. …”
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83
Customs Trade Facilitation and Compliance for Ecommerce using Blockchain and Data Mining
Published 2021“…Additionally, the Cross Industry Standard Process for Data Mining (CRISP-DM) methodology is employed for modelling the two proposed clustering algorithms to identify transactional risks. …”
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84
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85
An Effective Hash Based Assessment and Recovery Algorithm for Healthcare Systems
Published 2019Get full text
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masterThesis -
86
Forecasting the nearly unforecastable: why aren’t airline bookings adhering to the prediction algorithm?
Published 2021“…The resulting model achieves an 89% predictive accuracy using historical data. A unique aspect of the model is the incorporation of self-competence, where the model defers when it cannot reasonably make a recommendation. …”
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87
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88
Scalable parallel algorithms for dynamic programming on tree decomposition. (c2017)
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masterThesis -
89
Starvation Problem in CPU Scheduling for Multimedia Systems
Published 2002“…Multimedia applications have timing requirements that cannot generally be satisfied using the time-sharing algorithms of general-purpose operating systems. …”
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90
On end-to-end scheduling in wireless cellular networks
Published 2008“…Wireless cellular networks are required to meet the stringent QoS requirements of emerging data services. To efficiently utilize the available radio resources, we propose a new resource allocation algorithm for services that require end-to-end guarantees. …”
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conferenceObject -
91
Final exams scheduling for univeristies. (c2001)
Published 2001Get full text
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masterThesis -
92
Exploring Sentiment Analysis using Different Machine Learning Algorithms on Dialectal Arabic
Published 2021“…Therefore, big data analytics field emerged to tame the generated big data, and use it to provide useful insights to the world. …”
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93
Using machine learning algorithm for detection of cyber-attacks in cyber physical systems
Published 2022“…There are a variety of ways in which AI technology can aid in the growth and profitability of a variety of industries. Such data can be parsed using ML and AI approaches in designed to check attacks on CPSs. …”
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94
Predictive Model of Psychoactive Drugs Consumption using Classification Machine Learning Algorithms
Published 2023“…Our study aimed to use data mining classification techniques, in order to classify the individual into two categories: user or non-user. …”
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95
The Relation Between Respiratory & Acute Coronary Syndrome Using Data Mining Techniques
Published 2018“…The purpose of this study is to diagnose acute coronary syndrome using the widely available respiratory diagnosing tools and laboratory test results using data mining classification techniques (Decision tree, Gradient boosted tree, Neural Network, and Naïve Bias). …”
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96
Data-Driven Electricity Demand Modeling for Electric Vehicles Using Machine Learning
Published 2024Get full text
doctoralThesis -
97
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98
Deep Reinforcement Learning for Resource Constrained HLS Scheduling
Published 2022“…The two main steps in HLS are: operations scheduling and data-path allocation. In this work, we present a resource constrained scheduling approach that minimizes latency and subject to resource constraints using a deep Q learning algorithm. …”
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masterThesis -
99
Optimization of Interval Type-2 Fuzzy Logic System Using Grasshopper Optimization Algorithm
Published 2022“…The antecedent part parameters (Gaussian membership function parameters) are encoded as a population of artificial swarm of grasshoppers and optimized using its algorithm. Tuning of the consequent part parameters are accomplished using extreme learning machine. …”
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100