• Main idea

The article illustrates how artificial intelligence plays a role in the sustainability of the energy industry. Artificial intelligence (AI) technology plays a critical role in technological developments and has changed consumption, trade, demand, and supply of energy in a great way. AI in the energy sector helps in decision-making and operationalization. For example, smart software is used to control the power grid autonomously. The study focused on AI’s recent advances in the management of demand and supply, hydrogen, and solar power generation.

• Research objectives

The research objective of the study was to explore how AI is used in the generation of power using hydrogen and solar, the recent development in AI technology, and the use of AI in the management control of demand and supply.

The study extensively reviewed published literature on the use of AI through selective assessment of information. The methodology applied for the paper is content analysis to illustrate how AI has contributed to the sustainability of the energy industry.

• Results and discussions

The study found that the use of AI in the energy sector has enhanced operational performance. It has also enabled new, and complex data related to the energy industry boosting efficiency in the sector which is highly competitive. AI has outperformed traditional models in computational efficiency, predictive maintenance control, energy efficiency optimization, robotics, IoT, smart grid, cyberattack protection, handling big data, and controllability.

The study is related to my research because it points out the application of AI technology in the real world and this case the energy sector. It highlights the need for players in the industry to focus on the development of AI technologies to remain competitive. The use of AI relates to matters of customer information security, privacy, and safety. These issues have been discussed in the paper and they are highly important in the application of AI.

Application of machine learning and artificial intelligence for covid-19 (SARS-CoV-2) pandemic

• Main idea

The study reviewed the application of AI and machine learning in the health sector and in particular how it was used in tackling the Covid-19 pandemic. During the pandemic, AI was a new technology used in supporting the fight against the disease. The study evaluated the role played by ML and AI in screening, prediction, forecasting, tracing contacts, and development of drugs for the virus.

• Research objectives

The research objective of the study was to evaluate the various applications of machine learning and AI in the management of COVID-19, the computational system of challenges in a real-world problem, as well as the use of AI in processes such as vaccination, contract tracing, prediction, and treatment.

• Methodology

The methodology used in collecting data and information involved selective assessment of research articles on databases that were related to ML and AI technology. A deep-learning algorithm was employed in the paper.

• Results and discussion

The paper mainly addressed current research that uses ML and AI technology and augmented researchers on many dimensions. It addressed a number of challenges and errors realized while using ML and AI technology. It further recommends and suggests various ways of tackling covid-19 pandemic now and in the future. A deep learning algorithm was used in the paper and it was realized to be better than other learning algorithms. ML and AI technology is highly helpful in screening, prediction, forecasting, tracing contacts, and development of medications during pandemics.

• Relate

The use of AI technology has advanced and can be applied in various fields. The medical and health sector is one of the areas in which machine learning and AI have been applied. During the COVID-19 pandemic, technology was helpful in the treatment and control of the disease.

 

The potential for artificial intelligence in healthcare

• Main idea

AI and related technologies have increasingly become instrumental in various businesses and society. The technologies are applicable in the healthcare industry. The paper examines the use of machine learning (ML) in medicine or the healthcare sector.

• Research objectives

The objective of the paper is to inform medical practitioners such as clinicians on the use of ML in diagnosing, classifying illnesses, predicting outcomes, and antimicrobial management in ID.

• Methodology

The study used a systematic search strategy to identify materials, articles, and journals for use. The methodology helped in identifying materials in various digital libraries. Multiple databases were identified as sources.  In the identified articles, data was extracted and analyzed using ML-CDSS.

• Results and discussion

The ML tools can be used to carry out a number of clinical trials and tests. The tools have multiple clinical results such as antibiotic regimen selection, SSI diagnosis, ICU sepsis prediction, and so on. Machine learning learns and improves from experience with no need to be programmed explicitly. On the other hand, an expert system is representative of the most recent Clinical Decision Support Systems (CDSS).

• Relate

Machine learning is helpful in overcoming challenges in expert systems. It was actually developed to overcome the constraints. The ML systems are able to interpret unknown situations after learning from data. This is because artificial findings of rules replace human hand-coded rules. Therefore, machine learning is highly applicable in various sectors including the health sector.