Search results

Machine learning and artificial intelligence (AI) have arisen as the availability of larger data sources, statistical methods, and computing power have rapidly and simultaneously evolved. The transformation is leading to a revolution that will affect virtually every industry. Businesses that are slow to adopt modern data practices are likely to be left behind with little chance to catch up.

The purpose of this chapter is to provide a brief overview of machine learning and AI in the business setting. In addition to providing historical context, the chapter also provides justification for AI investment, even in industries in which data is not the core business function. The means by which computers learn is de-mystified and various algorithms and evaluation methods are presented. Lastly, the chapter considers various ethical and practical consequences of machine learning algorithms after implementation.

The complexity of atmospheric corrosion, further compounded by the effects of climate change, makes existing models inappropriate for corrosion prediction. The commonly used kinetic model and dose-response functions are restricted in their capacity to represent the non-linear behaviour of corrosion phenomena. The application of artificial intelligence (AI)-driven machine learning algorithms to corrosion data can better represent the corrosion mechanism by considering the dynamic behaviour due to changing climatic conditions. Effective use of materials, coating systems and maintenance strategies can then be made with such a corrosivity model. Accurate corrosion prediction will help to improve climate change resilience of the social, economic and energy infrastructure in line with the UN Sustainable Development Goals (SDGs) 7 (Affordable and Clean Energy), 9 (Industry, Innovation and Infrastructure) and 13 (Climate Action). This chapter discusses atmospheric corrosion prediction in relation to the SDGs and the influence of AI in overcoming the challenges.

In this chapter, drone and vision camera technology have been combined for monitoring the crop product quality. Three vegetable crops such as tomato, cauliflower, and eggplant are considered for quality monitoring; hence, image datasets are collected for those vegetables only. The proposed method classified the vegetables into two classes as rotten and nonrotten products so the images were collected for rotten and nonrotten products. Three different features information such as chromatic features, contour features, and texture features have been extracted from the dataset and further used to train a Gaussian kernel support vector machine algorithm for identifying the product quality. The system utilized multiple features such as chromatic, contour, and texture features in classifier training which enhances the accuracy and robustness of the system. Chromatic features were utilized for detecting the crop while other features such as contour and texture features were utilized for further classifier building to identify the crop product quality. The performance of the system is evaluated based on the true positive rate, false discovery rate, positive predictive value, and accuracy. The proposed system identified good and bad products with a 97.9% of true positive rate, 2.43 % of false discovery rate, 97.73% positive predictive value, and 95.4% of accuracy. The achieved results concluded that the results are lucrative and the proposed system is efficient in agriculture product quality monitoring.

From the perspective of any nation, rural areas generally present a comparable set of problems, such as a lack of proper healthcare, education, living conditions, wages and market opportunities. Some nations have created and developed the concept of smart villages during the previous few decades, which effectively addresses these issues. The landscape of traditional agriculture has been radically altered by digital agriculture, which has also had a positive economic impact on farmers and those who live in rural regions by ensuring an increase in agricultural production. We explored current issues in rural areas, and the consequences of smart village applications, and then illustrate our concept of smart village from recent examples of how emerging digital agriculture trends contribute to improving agricultural production in this chapter.

Key enabling technologies (KETs) are a set of six technological components that work together to address social challenges and build advanced for sustainable economies. Industry 5.0, the next industrial development, is designed to capitalize on specialists' unique creativity while also collaborating with powerful, intelligent, and precise technologies. Industry 5.0 outsourced repetitive and monotonous activities to robots/machines requiring employees to perform activities that involve critical thinking and are based on the 6R (Recognize, Reconsider, Realize, Reduce, Reuse, and Recycle), to improve production quality. With numerous supporting technical advancements, advanced and quick manufacturing concentrating on the interaction of machines and humans may be produced. Maintaining healthcare and nursing care, evaluating patients' health requirements using KETs, and giving care with manpower are all major advancements in Industry 5.0 today. Future studies may focus on providing healthcare using mainly technology and, therefore, no human workers. This chapter highlights healthcare advances in Industry 5.0, where KETs and people collaborate to create and innovate. In this framework, the purpose of this chapter is to present the deployment of KETs in the nursing patient care process.

Improved production with quality, safety, and security is the biggest challenge of the food industry. Modern technologies, including robotics and automation systems, can help to cope with such issues. This chapter gives a brief view of robotics and automation for the sustainable food industry along with packaging, warehousing, distribution, marketing, and consumer services. It describes the recently implemented solutions of robotic automation in different supply chain operations and various food commodities. The benefits of robotic and automation technology for perishable and semi-perishable items have also been covered. The present research may assist the food industry professionals, supply chain managers, and academicians in implementing automation and robotics in the food industry.

Heavy metals play a crucial role in the economic development of any nation. Industries utilizing heavy metals, consequently, emanate a large volume of metal-containing liquid effluents. Since metals are non-renewable and finite resources, their judicious and sustainable use is the key. Hazardous metal-laden water poses threat to human health and ecology. Apart from metals, these industrial effluents also consist of toxic chemicals. Conventional physical–chemical techniques are not efficient enough as it consumes energy and are, therefore, not cost effective.

It is known that biomaterials namely microorganisms, plants, and agricultural biomass have the competence to bind metals, in some cases, selectively, from aqueous medium. This phenomenon is termed as “metal biosorption.” Biosorption has immense potential of becoming an effective alternative over conventional methods. The authors in the present chapter have used secondary data from their previous research work and attempted to develop few strategic models through their feasibility studies for metal sustainability.

Purpose – This chapter outlines and describes a number of case studies detailing the experiences and activities of individual women scientists, innovators and entrepreneurs who have made substantial contributions to particular fields of science, engineering, technology and mathematics (STEM) activities.

Methodology/approach – The chapter employs a qualitative case approach to offer detailed insight into the experiences of successful women entrepreneurs.

Findings: These case study reports describe the tactics, strategies and achievements of successful female innovators within the STEM sector.

Research limitations/implications – Although offering rich descriptions of the achievements of women innovators, these cases cannot be generalised. However, they do illustrate that woman have a notable and meaningful presence within the field of entrepreneurial STEM innovation.

Practical implications – These cases can act as role model illustrations to encourage other women to act as STEM innovators and entrepreneurs.

Social implications – Indicative that despite gendered ascriptions which limit women's engagement with STEM subjects they can, and do, offer a critical contribution to innovation and entrepreneurial activity within the field.

Originality/value of chapter – A relatively rare celebration of women's achievement within the STEM sector.

Research to find new energy source is still an intensive work by researchers in this field. One of the energy sources with no negative impact to environment is solar energy. Solar cell is used to convert solar energy to electrical energy. The electrically powered solar cell in direct current (DC) power is not suitable for our daily office equipment since they need the alternating current (AC) power. This research has succeeded in realizing a solar cell automation tool based on Arduino Uno with input from solar energy, from which output AC voltage can be used for the needs of household appliances and office equipments. Output power of this tool is approximately 700 W, which can turn on the lights, charge the hand phones, laptops, and so forth.