An artificial neural network is a computing model that consists of processing elements that perform based on settings activated through their system in functions. The neural networks work in hand with animal brains by biological connection depending on the functions trained on. The artificial neural system can be used for classification functions in establishing patterns and trends of intended search. The aspect of biological networks not mimicked by artificial networks is the ability to forget since human brains can forget, but artificial networks would adopt the repeated coding system. The similar character of artificial and biological networks is the ability to learn and adapt a function being trained. Most common artificial neural network architectures include convolutional, recurrent, and feed-forward neural networks in practical applications. The artificial neural networks’ main function is to trigger the nervous system to respond fast for pattern recognition and data prediction. Multiple functions perform different functions in the processing period. Artificial neural networks learn during supervised mode by extracting data in classified systems from noisy data and providing analytical data. Artificial neural network learning in unsupervised mode does not require human intervention while performing functions, and it includes responding to an input injected to establish a pattern.
The commonalities in comparison of multiple machine-learning methods in papers of the journal of hydrology and molecules were found to be predicting and classification functions under which predefined activation functions have been established. The comparison was conducted on three datasets in molecules paper where toxic and non-toxic molecules for living things were identified (Rácz et al., 2019). The datasets were classified according to the composition of the data, and most datasets’ results were based on data organization. The differences in machine-learning methods findings are based on the ability to interpret nonlinear and complex information, which requires physical supervision in giving instructions. The supervised machine-learning method performs according to instructions in predefined functions which perform the intended purpose (Parmezan et al., 2019). The two papers describe the certainty in the outcome of certain functions depending on the ability to interpret and categorize complex information.
Deep learning is a neural network with three or more layers that simulate the functions of the human brain and behavior as an element of machine learning methods. Deep learning networks can deal with large amounts of data by classifying according to systematic settings, which cannot be done by the machine-learning method since it handles moderate amounts of data. Learning paradigms in artificial intelligence include supervised, unsupervised, and reinforcement learning which is applied depending on the data composition. Supervised learning paradigm usually functions in models of prediction out of pairs of input and output. Unsupervised learning is instrumental in exploring data and performing less common tasks. Representation learning is training machine learning algorithms to learn important representations in performing functions.
Representation learning relates to deep and machine learning by classifying data according to representations coded in representation learning. Representations formulated perform functions in identifying data analyzed during the learning process, supplementing the machine’s functions and deep learning. The most commonly used function of artificial neural networks is the rectified linear activation function which is used for hidden layers. It is mostly used due to its ease of implementation in establishing the effectiveness of diverting previously used activation functions hence the fastest. The sigmoid activation function works for binary classification methods, with input and output only outranged between zero and nine. The activation function developed a functioning gradient problem and was widely used years back as a nonlinear activation function. Multilayer perception is a feed-forward neural network that produces outputs from inputs (Sandhya et al., 2021). The network is trained to identify outputs according to activation functions predefined and hence reliable in multiple learning systems. The function of summation in multilayer perception of machine learning is to combine functions into a single function by adding inputs for a certain output. The activation function determines the activation of neurons in processing according to its functions in input prediction of output using expressions from mathematical operations.
Cognitive computing refers to a technology involving scientific activation functions in the functions of artificial intelligence and signals detection processes. The computing system is aimed at modifying human thinking by applying artificial intelligence based on innovation limits. Vantage software in the finance industry is the first case in determining cognitive computing cases, which involves proving financial data and investment recommendations based on practical examination. The Vantage software provides reports and analytical data in decision-making processes under the IBM Watson computing system. Millions of data in markets are examined with the classification of data according to their departments which provide prediction patterns for decision-making. Welltok healthcare describes a case of medical attention advancements through cognitive computing, where consumers are given up-to-date health information. Capewell concierge is a powerful tool in Welltok that process large volumes of data and make instant decisions with intellectual recommendations. The relationship between healthcare givers and customers advances due to the connection created by the computing systems in the follow-up of patients with complications.
The system is instrumental in decongesting healthcare centers since many customers would be served outside the medical premises. The out-patient management would reduce infection transmission caused by contact and air exchange. WayBlazer is a company that facilitates travel and solves the problem of planning for trips in hotel and flight ticket booking. The case describes efficiency in using cognitive computing through websites that facilitate the fast booking and determining trip schedules (Buscema et al., 2018). The system provides customers platforms for engaging with the company in asking questions about the trips, services provided, and payment process. The feedback extracted from the customers is instrumental in decision-making because it exposes the exact feeling of the customers. The three cases above are artificial intelligence applications through technology that serves clients from the comfort of their locations and provides relevant data concerning the company operations.
Artificial intelligence would effectively determine business growth since technology and digital space marketing are evolving. Cognitive computing systems have established steps to ensure investment in technology results in profit.
References
Buscema, P. M., Massini, G., Breda, M., Lodwick, W. A., Newman, F., & Asadi-Zeydabadi, M. (2018). Artificial neural networks. In Artificial adaptive systems using auto contractive maps (pp. 11-35). Springer, Cham.
Parmezan, A. R. S., Souza, V. M., & Batista, G. E. (2019). Evaluation of statistical and machine learning models for time series prediction: Identifying the state-of-the-art and the best conditions for the use of each model. Information sciences, 484, 302-337.
Rácz, A., Bajusz, D., & Héberger, K. (2019). Multi-level comparison of machine learning classifiers and their performance metrics. Molecules, 24(15), 2811.
Sandhya, P., Mujumdar, A. M., & Biswas, A. (2021). Cognitive Computing and Its Applications. In Analyzing Future Applications of AI, Sensors, and Robotics in Society (pp. 47-68). IGI Global.
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