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Fourth Industrial Revolution 4.0 in Artificial Intelligence

 

Fourth Industrial Revolution 4.0 in Artificial Intelligence

In the current age of the Fourth Industrial Revolution (4IR or Industry 4.0), the digital world has a wealth of data, such as Internet of Things (IoT) data, cybersecurity data, mobile data, business data, social media data, health data, etc. To intelligently analyze these data and develop the corresponding smart and automated applications, the knowledge of artificial intelligence (AI), particularly, machine learning (ML) is the key. Various types of machine learning algorithms such as supervised, unsupervised, semi-supervised, and reinforcement learning exist in the area.

Besides, the deep learning, which is part of a broader family of machine learning methods, can intelligently analyze the data on a large scale. In this paper, we present a comprehensive view on these machine learning algorithms that can be applied to enhance the intelligence and the capabilities of an application. Thus, this study’s key contribution is explaining the principles of different machine learning techniques and their applicability in various real-world application domains, such as cybersecurity systems, smart cities, healthcare, e-commerce, agriculture, and many more. We also highlight the challenges and potential research directions based on our study.

Overall, this paper aims to serve as a reference point for both academia and industry professionals as well as for decision-makers in various real-world situations and application areas, particularly from the technical point of view. We live in the age of data, where everything around us is connected to a data source, and everything in our lives is digitally recorded. For instance, the current electronic world has a wealth of various kinds of data, such as the Internet of Things (IoT) data, cybersecurity data, smart city data, business data, smartphone data, social media data, health data, COVID-19 data, and many more. The data can be structured, semi-structured, or unstructured, discussed brief in Sect.

“Types of Real-World Data and Machine Learning Techniques”, which is increasing day-by-day. Extracting insights from these data can be used to build various intelligent applications in the relevant domains. For instance, to build a data-driven automated and intelligent cybersecurity system, the relevant cybersecurity data can be used [105]; to build personalized context-aware smart mobile applications, the relevant mobile data can be used [103], and so on. Thus, the data management tools and techniques having the capability of extracting insights or useful knowledge from the data in a timely and intelligent way is urgently needed, on which the real-world applications are based.

Artificial intelligence (AI), particularly, machine learning (ML) have grown rapidly in recent years in the context of data analysis and computing that typically allows the applications to function in an intelligent manner. ML usually provides systems with the ability to learn and enhance from experience automatically without being specifically programmed and is generally referred to as the most popular latest technologies in the fourth industrial revolution (4IR or Industry 4.0).

“Industry 4.0” is typically the ongoing automation of conventional manufacturing and industrial practices, including exploratory data processing, using new smart technologies such as machine learning automation. Thus, to intelligently analyze these data and to develop the corresponding real-world applications, machine learning algorithms is the key. The learning algorithms can be categorized into four major types, such as supervised, unsupervised, semi-supervised, and reinforcement learning in the area, discussed brief in Sect. “Types of Real-World Data and Machine Learning Techniques”. The popularity of these approaches to learning is increasing day-by-day, which is  based on data collected from Google Trends over the last fee years. The x-axis of the figure indicates the specific dates and the corresponding popularity score within the range of 0 (minimum) to 100 (maximum) has been shown in y-axis.

According to the popularity indication values for these learning types are low in 2015 and are increasing day by day. These statistics motivate us to study on machine learning in this paper, which can play an important role in the real-world through Industry 4.0 automation. In general, the effectiveness and the efficiency of a machine learning solution depend on the nature and characteristics of data and the performance of the learning algorithms. In the area of machine learning algorithms, classification analysis, regression, data clustering, feature engineering and dimensionality reduction, association rule learning, or reinforcement learning techniques exist to effectively build data-driven systems. Besides, deep learning originated from the artificial neural network that can be used to intelligently analyze data, which is known as part of a wider family of machine learning approaches.

 Thus, selecting a proper learning algorithm that is suitable for the target application in a particular domain is challenging. The reason is that the purpose of different learning algorithms is different, even the outcome of different learning algorithms in a similar category may vary depending on the data characteristics.

 Thus, it is important to understand the principles of various machine learning algorithms and their applicability to apply in various real-world application areas, such as IoT systems, cybersecurity services, business and recommendation systems, smart cities, healthcare and COVID-19, context-aware systems, sustainable agriculture, and many more that are explained brief in Sect. “Applications of Machine Learning”. Based on the importance and potentiality of “Machine Learning” to analyze the data mentioned above, in this paper, we provide a comprehensive view on various types of machine learning algorithms that can be applied to enhance the intelligence and the capabilities of an application.

Thus, the key contribution of this study is explaining the principles and potentiality of different machine learning techniques, and their applicability in various real-world application areas mentioned earlier. The purpose of this paper is, therefore, to provide a basic guide for those academia and industry people who want to study, research, and develop data-driven automated and intelligent systems in the relevant areas based on machine learning techniques.

Keywords Machine learning · Deep learning · Artificial intelligence · Data science · Data-driven decision-making · Predictive analytics · Intelligent applications

'Acharya Ramchandra Shukla' was born in 1884 in a village named Agona in Basti district, Uttar Pradesh, India. His father Pt. Chandrawali Shukla was a Sarayuparin Brahmin. He was a supervisor Kanungo and biased of Urdu. Shuklji had studied till the Intermediate. After this, he did the job. Then he left the job and became a teacher. He started writing in Hindi from his student life. Impressed by Shuklaji's ability, Nagari Pracharini Sabha, Kashi called him to work in the Hindi literature. Shuklaji was appointed Hindi teacher in Hindu University and later became the Head of Hindi Department. He died in 1941 AD. Following are the major compositions of Acharya Ramchandra Shukla- 'Charan Vinod', 'Radhakrishna Das', 'Chintamani Triveni', 'Surdas', 'Ras Mimamsa', 'History of Hindi literature' etc. He edited 'Bhramar Geetasar', 'Bharatendu Sahitya', 'Tulsi Granthavali' and 'Jayasi Granthavali'. The talent of Acharya Ramchandra Shukla Ji was multi-faceted. He was a great essayist, critic and thinker. He is considered the first basic critic of Hindi. His history of Hindi literature is considered to be superior in history. Acharya Ramchandra Shukla was the pride of Hindi. Full name of 'Dr. A.P.J. Abdul Kalam' was 'Dr. Avul Pakir Jainulabdeen Abdul Kalam'. He was born on October 15, 1931 at Dhanushkothi in the temple town Rameshwaram in Tamil Nadu. He was born in a poor family, but he was an exceptionally brilliant child. Kalam passed the B.Sc. examination from Saint Joseph College, Thiruchirapalli. He joined Madras Institute of Technology (MIT). His further knowledge in the field got upgraded when he joined Defense Research and Development Organization (DRDO) in 1958 and Indian Space Research Organization (ISRO) in 1963. He is known as the Missile Man of India. The various Indian Missiles of world order like Prithvi, Trishul, Akash, Agni, etc. are mainly the result of his efforts and caliber. Dr. A.P.J. Abdul Kalam became the 11th President of India. He served the country from 2002 to 2007. For his excellence and brilliance, he was awarded the prestigious Bharat Ratna in 1998; Padma Vibhushan in 1990; and Padma Bhushan in 1981. Dr Kalam expired on Monday 27 July 2015. He suddenly fell unconscious when he was delivering a lecture at the Indian Institute of Management at Shillong. On 30 July 2015, the former President was laid to rest at Rameswaram's Pei Karumbu Ground with full state honours. Over 350,000 people attended the last rites, including the Prime Minister, the governor of Tamil Nadu and the chief ministers of Karnataka, Kerala and Andhra Pradesh. Dr. A.P.J. Abdul Kalam was mainly interested in work. He was a bachelor. He was not interested in going abroad. He wanted to serve his motherland first. He said that he thinks his first and foremost duty is to serve his motherland. He was fond of music and the Koran and the Gita. Ever since becoming the head of the Indian State, he had been having interaction with children all over the country. He was by no means a miracle man. His advice to the youngster of the nation was to "dream dream and convert these into thoughts and later into actions".
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