Xavier & Sandeep-Discussion

Xavier & Sandeep-Discussion

Task 1

Chapter 17 Summary and Evaluation

In every society, there must exist problems and issues that challenge prevent growth and progress. The problems could be as a result of politics, education, health, transportation and communication, commerce, or agriculture. However, to solve the problems, federal governments have always formulated policies and regulations that are in line with the growth and wellbeing of the citizens. The government’s attempt is significant. If the problems are not addressed immediately, they can develop into uncontrollable stages that will endanger the society socio-economic development and growth

The public policy offers guidance and determines the present and future decisions that society makes towards solving societal problems. In policy formulation, entities first identify the policy problem to be addressed, then formulate policy proposals through political channels. Before any policy is implemented, it had first to be legitimized. Legitimization of policy involves executive orders, interpretations, rules, and decisions that influence the setting policy directions.

Moreover, the worlds in which policies are developed have become more complex and unpredictable. Citizens are raising expectations, and an increase in demand for services that create individuals need satisfaction (Mann & Voß, 2017). The stagnation in education, infrastructure, and social need have intertwined, making it challenging for governments and agencies to effectively articulate policies. However, significant challenges in developing countries are institutional rigidity, high dependence on donors, budgetary constraints, and politics.

For successful policy formulation and implementation, the government needs not just to conceive policies bur rather design policies (Martinuzzi & Sedlacko, 2017). Policy design facilitates in ensuring that the action planned id realistic and viable.

Task 2

Neural Networks

A neural network represents an emerging technology that has been rooted in several disciplines. They have specific features like the ability to adapt and learn from the environment and to approximate complex pictures and mappings. The first neural network for implementing the Boolean non-threshold function has traditionally been developed (Van Gerven & Bohte, 2017). The research of neural networks was guided by advanced technologies in a digital computer by an analysis of human brains ‘ function in an entirely different way. The human brain can now be termed as a parallel, high complex nonlinear computer that can organize neutrons on the computation of motor control, pattern recognition, and perceptions.

The interconnection between neurons has made the human brain to work faster than the first digital computer (Van Gerven & Bohte, 2017). Ideally, the neutral network gains computing power by either massive parallel distribution on the system or by its learning and generalization capability. Neural networks reflect modern technology that is rooted in many fields. For a recent time, this common and significant field of science and technology has grown extensively. Neural networks have several interesting characteristics, such as the capacity to learn and acclimatize to the environment and the ability to handle very complex mappings.

Contributions of CNN and ANN

The transition of the neural network to deep learning algorithms from machine learning algorithms has profoundly impacted how human interaction with the world. Convolutional Neural Network (CNN), Artificial Neutral Network (ANN), and Recurrent Neutral Network (RNN) are the cores of a deep learning revolution that now offer powering applications (Chen et al., 2019). CNN models are currently used in various forms and domains as they are prevalent in video and image processing projects. It captures spatial features from images, learns filters automation to extract relevant and right elements from the input data, and follows the parameter sharing concept.

On the other hand, CNN constitutes three layers (input, hidden, and output), where each layer learns certain weights (Chen et al., 2019). They can learn any nonlinear function through the interpretation of any weights that map inputs to outputs.

References

Chapter 17 – Challenges to Policy-Making in Developing Countries and the Roles of Emerging Tools, Methods and Instruments

Chen, M., Challita, U., Saad, W., Yin, C., & Debbah, M. (2019). Artificial neural networks-based machine learning for wireless networks: A tutorial. IEEE Communications Surveys & Tutorials, 21(4), 3039-3071.

Mann, C., & Voß, J. P. (2017). Challenging futures–concepts for engaging with dynamics of policy instrument design. In Transdisciplinary Research and Sustainability (pp. 267-289). Routledge.

Martinuzzi, A., & Sedlacko, M. (2017). Knowledge brokerage for sustainable development: Innovative tools for increasing research impact and evidence-based policy-making. Routledge.

Van Gerven, M., & Bohte, S. (2017). Artificial neural networks as models of neural information processing. Frontiers in Computational Neuroscience, 11, 114.