Process of Machine Learning and Cognitive Science

Question: Discuss about the Process of Machine Learning and Cognitive Science. Answer: Introduction: This research proposal is considering about the process of understanding the utilization of machine learning and cognitive science in understanding autism. Autism is a critical disease that is combination of serious range of conditions characterized by challenges associated with social skills and behaviors (Crippa et al., 2015). Therefore, the main problem associated with the research topic is that the autism diseases are impacting on physical strengths and behaviors of human. Cognitive science and machine learning is considered as effective tools for understanding the disease. Autism is one psychological and behavioral disorder of human that causes abnormal behaviors among human. In addition to this, following are the causes of this disease: Impact of genes: This disease mainly comes through inheriting the properties of ancestral. The behavioral changes are incorporated among human that causes autism (Gabrieli, Ghosh Whitfield-Gabrieli, 2015). Environmental triggers: Environmental changes are also acts as triggers among autism patients. Pregnancy, addiction to alcohol etc causes environmental changes among human. Health conditions: Critical health conditions are also causes autism disease among human due to unexpected changes incorporated within habits of human (Maximo, Cadena Kana, 2014). Extent and Severity of Problem The extent and severity of the disease autism is nothing but the measure of the quantitative variation in autistic Symptomatology in affected families (Plitt et al., 2015). According to numerous studies and researches has highlighted the aggregation of autistic syndromes among victims or patients it is found that 20% of the patients face this disease for genetic purposes and rest of 10% patients face impacts due to their family members due to different consequences involved within their family due to difficulties faced by the patients. According to Mwangi, Tian Soares (2014), various environmental factors and facts also make the disease influenced and the patients face difficulties. Therefore these issues have great impact not only on the patients but also this provide impact on their family members as well as to the society. Justification for Research The diversified impacts and influences are considered as the main base of conducting this research process. In addition to this, this research process helps in solving various aspects as well as helping in understanding the causes of autism disease. In contrast with these facts, the study of cognitive science and machine learning helps in understanding the impacts of autism among people as well as on their family. This research is aiming at the facts and probabilities that are being highlighted by the quantitative and qualitative research on this topic. These quantitative and qualitative research findings will be helpful in clarifying the need for the research process with respect to various critical aspects and syndromes of the disease. This research proposal is considering about the use of cognitive science and machine learning process that helps in understanding the reason and impact of autism. In contrast with these facts, the disease is mainly transferred due among different generations through genes (Tager?Flusberg Kasari, 2013). The cognitive science helps in understanding the behavioral disorders and their impacts as well as causes. Besides this, the machine learning standards are helpful in understanding the spectrums that are highlighted among patients due to the identification of syndromes from autism (Plitt et al., 2015). There are various impactful situations such as behavioral disorders that not only impact on the patients but also impact on their family, these needs to be analyzed with respect to this research process. Research Aims This research is concerned about the process of understanding the cause of autism with respect to cognitive science and machine learning. Therefore, some objectives need to be identified for managing this research to complete it with success. Following are the concerned research objectives involved within this research process: To understand the accurate cause of autism To understand autism with respect to cognitive science and machine learning To understand the impact and importance of cognitive science and machine learning process Methodology Methodology is nothing but the process of managing the processes involved within any concerned research. There are various kinds of research methodology incorporated within any significant research process. In contrast with these facts, the impact of machine learning and cognitive science can be easily analyzed with the help of qualitative as well as quantitative study of responses from various resources. The quantitative study involved within this research process collects data from the surveys and response collections from individuals those are real life witnesses of autism and its impacts on human (Van de Cruys et al., 2014). The quantitative methodology provides the probability of victims those are facing consequences of this disease. These probabilities help the researcher in measuring the impact and extent of the concerned problem. In contrary with these facts, the qualitative study of responses is also very important for understanding the actual impact of autism upon human. The quantitative study of responses provides probability of impact of these diseases but the qualitative studies of responses provide the previous measures and real life case that can be helpful in finding the solutions of this disease (Vivanti, Dawson Rogers, 2017). Therefore, the methodology in this research of understanding the importance of cognitive science and machine learning on autism is mixed approach. Expected Outcomes The outcome of this research is concerned with the process of importance of machine learning and cognitive science. The machine learning has immense potential to enhance diagnostics and intervention research within the behavioral science. This research will provide effective identification of highly prevalent and heterogeneous syndrome of autism spectrum disorder. Cross disciplinary actions and methodologies used for managing the syndromes involved within this disease. Apart from the usages of machine learning, cognitive science is also helpful in understanding the causes and impact of autism. Neuropsychological theories are also known as cognitive theories and this is one traditional attempt of unifying as well as understanding the behavioral disorders among autism patients. Therefore, cognitive theories will provides support in understanding the diverse behavioral manifestations involved within human brain that causes various psychological disorders. References Crippa, A., Salvatore, C., Perego, P., Forti, S., Nobile, M., Molteni, M., Castiglioni, I. (2015). Use of machine learning to identify children with autism and their motor abnormalities.Journal of autism and developmental disorders,45(7), 2146-2156. Gabrieli, J. D., Ghosh, S. S., Whitfield-Gabrieli, S. (2015). Prediction as a humanitarian and pragmatic contribution from human cognitive neuroscience.Neuron,85(1), 11-26. Maximo, J. O., Cadena, E. J., Kana, R. K. (2014). The implications of brain connectivity in the neuropsychology of autism.Neuropsychology review,24(1), 16-31. Mwangi, B., Tian, T. S., Soares, J. C. (2014). A review of feature reduction techniques in neuroimaging.Neuroinformatics,12(2), 229-244. Plitt, M., Barnes, K. A., Wallace, G. L., Kenworthy, L., Martin, A. (2015). Resting-state functional connectivity predicts longitudinal change in autistic traits and adaptive functioning in autism.Proceedings of the National Academy of Sciences,112(48), E6699-E6706. Tager?Flusberg, H., Kasari, C. (2013). Minimally verbal school?aged children with autism spectrum disorder: the neglected end of the spectrum.Autism Research,6(6), 468-478. Van de Cruys, S., Evers, K., Van der Hallen, R., Van Eylen, L., Boets, B., de-Wit, L., Wagemans, J. (2014). Precise minds in uncertain worlds: predictive coding in autism.Psychological review,121(4), 649. Vivanti, G., Dawson, G., Rogers, S. J. (2017). Early Learning in Autism. InImplementing the Group-Based Early Start Denver Model for Preschoolers with Autism(pp. 1-12). Springer International Publishing.