GAIN AN UNDERSTANDING OF BIG DATA
Introduction
The significance of big data helps in managing innovation and controlling large datasets with the help of machine learning activities. It can help in making proper assumptions through which structured operations through machine learning can be developed without any issues. The purpose of this study is to acknowledge the usage of ANN algorithms to find the learning processes of a particular dataset. It can help in creating a reliable and flexible platform to arrange random functions through which accurate results can be incorporated successfully. Through the assistance of machine learning, learning patterns and accuracy of datasets can be acknowledged through which human errors can be maintained successfully.
Task 1: Analysing different machine learning techniques
Machine learning techniques help in identifying actual concepts of analysing problems in a particular dataset through which structure operations can be controlled adequately. Using machine learning techniques, the dataset can be arranged according to the characteristics and predict the result using supervised or unsupervised learning methods. As proposed by Baid et al. (2017), different techniques of machine learning including regression, clustering, classification, assemble methods, deep learning and neural nets and several others. Artificial neural network and k-mean neural network mainly belong to clustering and classification techniques. Using ANN algorithm, a range of a particular dataset can be defined through which accuracy and effectiveness can be controlled according to requirements.
Figure 1: Example of an ANN algorithm
(Source: Created by author)
From this algorithm, it has been identified that there are three constant values represented as a, b and c. In order to find these values, first initialise the whole dataset through its network according to biases and weights of that dataset. Therefore, finding the errors in iteration processes using appropriate functions in Matlab. Therefore, setting the maximum value of 1000 to perform network training using ANN method to get actual results. Therefore, setting the error message through goals in terms of criterion values can help in identifying errors and gathering specific results. Last, training the network with constant values to find the plot progression in gathering actual results.
Figure 2: Values of dataset
Figure 2: Performance wizard
Figure 3: Graph plot
Task 2: Understanding the concept of machine learning in terms of big data
ANN algorithm helps in storing the entire network through which adequate results can be incorporated according to requirements without any issues. Through the assistance of ANN algorithm, fault tolerance values of this algorithm are flexible and good to execute through which error patterns can be solved according to the requirements. ANN can also provide gradual corruption to maintain the relation between two variables due to which training and execution processes can be arranged adequately without any issues. As influenced by L’heureux et al. (2017), using specific requirements and structure, users can make different techniques of machine learning such as classification and clustering to execute this program. It would be helpful in managing all kinds of requirements due to which structure and effectiveness can be arranged successfully.
Apart from these, classification technique guides users to arrange similar characteristics dataset in a similar database to get rid of error compilation activities. Through the assistance of proper management and training, users can get less errors in their execution processes that can improve the progression process without any issues. In this way, quality processes and innovation through ANN algorithms can be executed according to requirements without any challenges. However, sometimes, due to lack of insufficient knowledge, users have failed to rearrange their task management processes through which the possibility of creating adequate knowledge and information cannot be arranged according to specifications. It would directly affect the training and execution processes and hinder the structure of ANN algorithms.
Task 3: Identifying the importance of machine learning algorithms
Machine learning algorithms can bring a structured format for handling datasets and training those data to get actual and structured results according to specification. Through the assistance of machine learning algorithms, users can easily train several datasets and improve the quality of data processing operations. As followed by Wäldchen and Mäder (2018), it can help users to get actual results with less errors due to which quality and efficiency in data training can be controlled without any issues. It can also bring structured changes that can arrange the sequence of data processing activities. In this study, ANN algorithm has been selected to perform training on a dataset through which structured operations can be executed properly. It would bring stability and control the process of handling all types of data managerial operations without any issues.
Apart from these, ANN algorithm performs brain processes to handle a large number of dataset and gather that data in a suitable dataset to perform training and execution operations. It can help in generating actual and required results due to which possibility of collecting adequate results can be performed without any issues. In this way, human errors can be maintained with the help of this algorithm that can create opportunities to incorporate structured processes depending on the characteristics of datasets. It would be helpful in arranging the procedure of managing all kinds of expectations and requirements of the training dataset without any issues.
Task 4 Exploring advantages and disadvantages of choosing machine learning algorithms
Advantages
- ANN algorithm can store all information within an entire network through which performance and structure can be developed according to needs within a large dataset.
- Users can easily use ANN algorithm as this algorithm is flexible and easy to use through which values of constants can be arranged adequately without any challenges.
- The value of fault tolerance is high due to which corruption in the dataset can be maintained and provide accurate and structured results according to characteristics of datasets.
- Distributed memory format can improve memory management processes in the context of primary and secondary memory.
- Training machines can also be performed with the help of ANN algorithm through which performance efficiency can be enhanced according to requirements.
Disadvantages
- Unexplained behaviours of neural networks can violate the overall structure of performance effectiveness in training a dataset.
- This option can decrease trust among networks due to which performance efficiency and structured management cannot be synchronised properly.
- Probing solutions cannot be helpful for users while using ANN algorithms due to which structured efficiency and operational changes cannot be handled adequately.
- ANN algorithm takes more time than other neural networks due to which users can face delays while getting their actual results.
- These issues can directly affect the training methods and also slow the process of getting accurate and structured results.
Task 5: Performing deeper analysis and understanding
ANN algorithm uses several functions including logsig, tansig, purelin and trainlm. These functions help in plotting the graph according to the dataset through which structured results can be gathered according to users’ expectations. It can help in managing all requirements through which performance structure can be arranged successfully that can control the sequential processes. According to Pouyanfar et al. (2018), it would bring adequate results due to which the possibility of getting accurate results from big data analysis can be arranged successfully. It can also develop the way of managing all kinds of requirements due to which structured results through big data analysis can be arranged properly.
On the other hand, ANN algorithms can also help in creating opportunities in handling all tasks and requirements related to big data by training and executing datasets. It would be helpful in synchronising all expectations to develop the graph management operations according to requirements. It would also control the validation and help in gathering actual results in terms of training a particular dataset. Through the assistance of big data management, ANN algorithms can improve the structure of a dataset by training and processing structure. It can help in delivering accurate results according to expectations of users from a large dataset.
Task 6: Explaining the way of improving the quality of results
Improvement in results can be helpful in synchronising all types of datasets through which structured performance and quality results can be maintained adequately. Through the assistance of training functions using constant values can help in getting actual and required results. In the words of Rudin (2019), it would bring structured changes in ANN algorithm to get actual and required results that can improve the quality assurance in getting results. It would help in developing the quality of results and also improve the sequential outcomes through the possibility of executing all tasks and specifications that can manage the structure of training a dataset.
Through the assistance of initialisation in ANN algorithm, training a proper dataset can help in providing accurate results through which operational tasks and changes can be arranged properly. Clustering technique plays a crucial and effective role in arranging a dataset with the same characteristics through which training similar data types can help in getting actual results. It would help in executing all types of tasks and also improve quality in data processing that can lead to get actual and accurate results without any challenges. It would also improve the quality management processes and also make proper assumptions according to requirements without any issues.
Task 7: Requirement for using ANN in image processing
Image processing is one of the crucial and effective methods for plotting images or graphs from a particular dataset using several functions. ANN algorithm plays an important role in arranging image recognition, reduction and segmentation by classifying all values according to the requirements. As followed by Saggi and Jain (2018), it can help in creating a structure for all values through which training of all data can help in getting actual and structured results. It would bring structured changes and help in creating opportunities that can help in getting matrix values which contain pixels of an image. Through the pixel density and segmentation, the actual values of a dataset can be analysed through which users can gather effective and required results without any challenges. These processes can improve the procedure of image processing that can help in managing result processes with the help of specific values.
Conclusion
From the above study, it has been identified that ANN or Artificial Neural Network algorithm provides a significant way of controlling training and execution of a particular dataset without any issues. It can help in managing all types of operations that can lead to getting actual results with less errors from a dataset. Using machine learning algorithms, a large dataset can be performed through which operational and required results can be gathered according to requirements. It would be helpful in synchronising all functional and non-functional operations according to specifications according to the result and structure of a dataset. Through the assistance of clustering and classification, arrangement of a dataset can be controlled that can improve the performance structure of a dataset. It can help users to incorporate that structured dataset by performing training and other operational tasks within ANN algorithms.
Reference List
Baid, P., Gupta, A. and Chaplot, N., (2017). Sentiment analysis of movie reviews using machine learning techniques. International Journal of Computer Applications, 179(7), pp.45-49.
L’heureux, A., Grolinger, K., Elyamany, H.F. and Capretz, M.A., (2017). Machine learning with big data: Challenges and approaches. Ieee Access, 5, pp.7776-7797.
Pouyanfar, S., Sadiq, S., Yan, Y., Tian, H., Tao, Y., Reyes, M.P., Shyu, M.L., Chen, S.C. and Iyengar, S.S., (2018). A survey on deep learning: Algorithms, techniques, and applications. ACM Computing Surveys (CSUR), 51(5), pp.1-36.
Rudin, C., (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1(5), pp.206-215.
Saggi, M.K. and Jain, S., (2018). A survey towards an integration of big data analytics to big insights for value-creation. Information Processing & Management, 54(5), pp.758-790.
Wäldchen, J. and Mäder, P., (2018). Machine learning for image based species identification. Methods in Ecology and Evolution, 9(11), pp.2216-2225.
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