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Genetic algorithm framework for Bi-Objective Task scheduling in cloud computing systems 11

Genetic algorithm framework for Bi-Objective Task scheduling...

引用

11th International Conference on Distributed Computing and Internet Technology, ICDCIT 2015

作者： Ajeena Beegom, A.S. Rajasree, M.S. Dept. Of Computer Science and Engineering College of Engineering Trivandrum India IIITM-K Trivandrum India

ISBN: (纸本)9783319149769

Cloud computing gives an excellent opportunity for business enterprises as well as researchers to use the computing power, over Internet, without actually owning the infrastructure, there by reducing establishment and management cost. Task scheduling in cloud systems is challenging due to the conflicting objectives of end users and the cloud service providers. Running time and cost are two key factors that determine the optimal service from the cloud. In this paper, we focus on two objectives, makespan and cost, to be optimized simultaneously using genetic algorithm framework. Finding an optimal schedule, considering both of these conflicting objectives, is a search problem under NP-hard category. We have considered the scheduling of independent tasks and the proposed frame work can be used in public or hybrid cloud. © Springer International Publishing Switzerland 2015.

关键词： Genetic algorithms

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Applications and Approaches of Sentiment Analysis: A Current Review 6

Applications and Approaches of Sentiment Analysis: A Current...

引用

6th IEEE International Conference on Recent Advances in Intelligent Computational Systems, RAICS 2024

作者： Sandra, G. Silas, Salaja Rajsingh, Elijah Blessing Karunya Institute of Technology and Sciences Dept. of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350381689

Limitless amount of data is loaded in every moment in social media platforms in the forms of audio, videos, texts, and photos. They are rich sources of information, which has immense value. Hence extraction of information from them is inevitable. Many methods are employed for this and the popular among them to mine the information is sentiment analysis. To materialize the objectives of this study, a systematic review of articles published from 2013 to 2023 was done. Our study reviewed the relevance of sentiment analysis in social media by probing the different approaches followed, the platforms used and its applications, as its main objectives. Its results show that majority of studies applied machine learning approach or hybrid method for executing sentiment analysis. © 2024 IEEE.

关键词： applications approaches categories dataset sentiment analysis

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Automated Hydroponic Drip Irrigation Using Big Data 2

Automated Hydroponic Drip Irrigation Using Big Data

引用

2nd International Conference on Inventive Research in Computing Applications, ICIRCA 2020

作者： Ani, Akhil Gopalakirishnan, Prakash Amrita School of Engineering Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering Bangalore India

ISBN: (纸本)9781728153742

Modern Agricultural lands are renovated through Smart Farming using the IoT and Big Data technologies which help farmers to reduce the usage of natural resources to increase efficiency. This technological growth is driven by continuous improvements in digital tools and data. The traditional irrigation monitors and controls the involved variables for the cultivation of the crops which are the actions that farmers should implement in their crops to save water resources and assessing their growth. Hydroponic drip irrigation systems defined as growing plants and crops without the soil. Conversely, hydroponic irrigations systems have the potential of saving the water and other nutrients by dripping the water slowly to the roots of the plant. The present irrigation system provides only soil moisture and the surrounding temperatures with sensors. In this paper, the system is used to record the sensor values and can manipulate the nutrient values when required. This work is all about the drip control and applying it to hydroponic farming by developing an interface between human and software which allows a continuous monitor of pH and all sensors, including the position of the plant by capturing via camera and monitoring via mobile application. Using big data, the supply of nutrient values gets tracked and recorded. This recorded data gets useful to automate the irrigation system further. © 2020 IEEE.

关键词： Hydroponics

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Surface interpolation by adaptive neuro-fuzzy inference system based local ordinary kriging

引用

7th Asian Conference on computer Vision, ACCV 2006

作者： Ozkan, Coşkun Computer Engineering Dept. Institute of Science Erciyes University 38039 Kayseri Turkey

ISBN: (纸本)3540312196

A new approach to the Ordinary Kriging interpolation method based on the combination of local interpolation and variogram modelling with Adaptive Neuro-Fuzzy Inference System (ANFIS) is proposed for surface interpolation. In this method, the experimental variogram is modelled by ANFIS and this model is used to interpolate the unknown values of specific points in a new local manner. In this local way, all the unknown points are grouped based on each reference point. As the study data, two types of data sets coming from mathematical functions and a 3D scanning system are used. The tests show that the proposed method produces better performances for all data sets in comparison to the well known and highly approved interpolation methods;Ordinary Kriging, Triangle Based Cubic and Radial Basis Function-Multiquadric. Moreover, by the proposed method the computational complexity impressively decreases compared to the global Ordinary Kriging. © Springer-Verlag Berlin Heidelberg 2006.

关键词： Interpolation

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Co-clustering with augmented data matrix

Co-clustering with augmented data matrix

引用

Lecture Notes in computer science

作者： Wu, Meng-Lun Chang, Chia-Hui Liu, Rui-Zhe Dept. of Computer Science and Information Engineering National Central University Taoyuan Taiwan

ISBN: (纸本)9783642235436

Clustering plays an important role in data mining as many applications use it as a preprocessing step for data analysis. Traditional clustering focuses on the grouping of similar objects, while two-way co-clustering can group dyadic data (objects as well as their attributes) simultaneously. Most co-clustering research focuses on single correlation data, but there might be other possible descriptions of dyadic data that could improve co-clustering performance. In this research, we extend ITCC (Information Theoretic Co-Clustering) to the problem of co-clustering with augmented matrix. We proposed CCAM (Co-Clustering with Augmented Data Matrix) to include this augmented data for better co-clustering. We apply CCAM in the analysis of on-line advertising, where both ads and users must be clustered. The key data that connect ads and users are the user-ad link matrix, which identifies the ads that each user has linked;both ads and users also have their feature data, i.e. the augmented data matrix. To evaluate the proposed method, we use two measures: classification accuracy and K-L divergence. The experiment is done using the advertisements and user data from Morgenstern, a financial social website that focuses on the advertisement agency. The experiment results show that CCAM provides better performance than ITCC since it consider the use of augmented data during clustering. © 2011 Springer-Verlag.

关键词： Data mining

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CUPL: A compile-time uncoalesced memory access pattern locator for CUDA 13

CUPL: A compile-time uncoalesced memory access pattern locat...

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27th ACM International Conference on Supercomputing, ICS 2013

作者： Amilkanthwar, Madhur Balachandran, Shankar Dept. of Computer Science and Engineering Indian Institute of Technology Madras Chennai India

ISBN: (纸本)9781450321303

Coalesced memory access patterns in CUDA yields high performance but achieving such patterns in an application can be tedious. We propose a tool, CUPL, which locates uncoalesced access patterns (UCAP) in a given kernel at compile-time. CUPL does static analysis of a given kernel using polyhedral model and reports warnings if the input kernel exhibits UCAP. CUPL has two-fold use 1) It can help the programmer to locate regions of the code to optimize 2) It can help a compiler to perform efficient data layout transformations. Initial experiments show that CUPL reports warnings at appropriate places in kernels from Rodinia benchmark and NVIDIA SDK suites. © 2013 Authors.

关键词： Static analysis

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Stock Market Prediction and Portfolio Optimization 2

Stock Market Prediction and Portfolio Optimization

引用

2nd Global Conference for Advancement in Technology, GCAT 2021

作者： Gondkar, Atharva Thukrul, Jeevan Bang, Raghav Rakshe, Siddhika Sarode, Sambhaji Mit Adt University Dept. of Computer Science and Engineering MH Pune India

ISBN: (纸本)9780738132150

The highly volatile nature of the stock market has made stock price prediction as challenging as weather forecasting. Consequently, as a hint of this dread, people don't invest in the stock market. In this paper, we have discussed hybrid networks and a stacked LSTM network for stock price prediction. Additionally, it also focuses on portfolio optimization done using six different techniques, which focuses on creating best performing portfolios categorized on the basis of sectors. One hybrid neural network consists of 1D-Convolutional layers and LSTM layers, and the other is a combination of GRU and LSTM layers. The stock prices of SBI, Indian Bank, Bank of India are predicted using stacked LSTM and Hybrid Neural Networks and compared using the sliding window of time steps with variable width. The neural networks predict the following day's closing price using a variable sliding window. The RMSE, MSE, and MAE are used to evaluate the efficiency of these neural networks. The hybrid network is proving to be more competent in various situations. © 2021 IEEE.

关键词： Financial markets

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Intelligent self-management home multimedia service system

Intelligent self-management home multimedia service system

引用

2nd International Conference on Innovative Computing, Information and Control, ICICIC 2007

作者： Horng, Mong-Fong Liu, Tzer-Ming Hung, Yi-Hsiang Lin, Cheng-Yu Dept. of Computer Science and Information Engineering Shu-Te University Kaohsiung Taiwan

ISBN: (纸本)0769528821

In this paper, a zero-conf networking approach with open source code to multimedia service is developed and a self-managed multimedia service system is presented. The goal of zero-conf networking technique is to enable the automatic configuration of network devices, automatic service recovery, and automatic service conduct without professional operators involved. Zero-conf networks greatly reduce the inconvenience caused by the deployments of networks and services, ease the service access of users and improve the efficiency of multimedia service networks. Zero-conf networking technique not only benefits the promising digital home applications but also the applications on Ad hoc networks. In the implemented multimedia service system, users enjoy the simple and effective operational interface to retrieve the multimedia content from network without professional configuration. Experimental results demonstrate that the implemented system has the advantages of (1) easy user interface (2) low system resource requirement (3) high throughout of multimedia services (4) automatic service discovery. Besides, the performance of the Bonjour is also evaluated in various dimensions of server resource and network bandwidth to explore the critical factors of designing a multimedia service on zero-configuration networks. © 2007 IEEE.

关键词： Intelligent systems

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Epilepsy seizure detection by using bayesian optimize Bi-LSTM model 6

Epilepsy seizure detection by using bayesian optimize Bi-LST...

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6th International Conference on Parallel, Distributed and Grid Computing, PDGC 2020

作者： Sood, Vidhi Kumar, Deepak Athavale, Vijay Anant Gupta, Suresh Chand Panipat Institute of Engineering and Technology Kurukshetra University Dept. of Computer Science and Engineering Panipat India

ISBN: (纸本)9781728171326

In medical science field, the EEG signal classification is present with a plethora of applications. The health monitoring is depending on modern technology like EEG and ECG signal recording. The EEG signals are analyzed to identify the abnormal condition of the human brains. The unusual activity of the brain is known as the seizure The electrical signal generated in the braincauses epilepsy. In this proposed work, a deep learning model Bi-LSTM is projected for the epilepsy signal classification. The Bonn university EEG dataset is used for the testing purpose. The EEG signal classification has three significant steps data pre-processing, features extraction, and classification. Data pre-processing is done by DCT and filter conversion. The Hurst exponent and ARMA feature sets are extracted from the pre-process EEG signal. A Bayesian optimization tuned Bi-LSTM model is suggested for the EEG signal classification task. The epileptic EEG signals are recognized by the proposed method. The hyperparameters of the Bi- LSTM model is tuned by the Bayesian optimization rule. Three different class ictal, pre-ictal, and inter-ictal are classified from the EEG signal data. A comparative study is also provided for the epilepsy signal classification task. The classification accuracy of for ictal is 94%, pre-ictal is 92%, and inter-ictal is 91%, which more significant than the LSTM and SVM based classifier model. © 2020 IEEE.

关键词： Brain

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Emotion Based Music Recommendation System for Specially-Abled 6

Emotion Based Music Recommendation System for Specially-Able...

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6th International Conference on Recent Trends on Electronics, Information, Communication and Technology, RTEICT 2021

作者： Shreya, L. Nagarathna, N. B.M.S. College of Engineering Dept. of Computer Science and Engineering Bangalore India

ISBN: (纸本)9781665435598

Human emotion is a psycho physiological state of mind that manifests as good or negative responses to external and internal stimuli. Songs have long been a popular alternative for expressing human feelings as a medium of expression. Content-based recommendation engines are used by the majority of existing music recommendation systems. However, music selection is influenced not only by past tastes or musical substance, but also by the user's current mood. This research presents a framework for learning a user's emotion using data gathered from a wearable device that is combined with galvanic skin response (GSR), photo plethysmography (PPG), and electro encephalography (EEG) physiological sensor signals, as well as data taken via camera. This information is used as a supplement to the music recommendation engine. Sensor data and face expression data can thus improve the effectiveness and accuracy of the recommendation engine. © 2021 IEEE.

关键词： Emotion Recognition

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