Figure 1.5 Correlation between AI, ML, and DL.

1.4 Correlation Between AI, ML, and DL

Figure 1.5 [9] depicts the correlation among ML, DL, and AI. Here, as we can see that DL is the subset of ML, and ML is the subset of AI. Hence, initially, AI came into the existence first, and later, ML erupted from it. To be more specific and denser, DL is derived from ML further.

1.5 Machine Learning–Based Smart Applications

      1.5.1 Supervised Learning–Based Applications

       1.5.1.1 Email Spam Filtering

      It helps in filtering junk e-mail or unwanted commercial e-mail and bulk e-mail from the true e-mails. With the usage of these learning algorithms, spam filter helps the user not to be flooded with the bulk or junk e-mails. The spam filter learns by watching the pattern of genuine e-mails and junk e-mails [11].

       1.5.1.2 Face Recognition

      Human face is not unique. Various factors cause to vary the face. With the help of these learning algorithms, face recognition has become easier. Face recognition is used in various situations such as security measure at an ATM, criminal justice system, image tagging in social networking sites like Facebook, an image database investigation, and areas of surveillance [11].

       1.5.1.3 Speech Recognition

To recognize the speech, the ML methods can be used. It involves two different learning phases: The first phase is speaker dependent where, after purchasing, the software user has to train the model by his/her voice to achieve accuracy, and in the second phase, before the software is shipped, the model is trained by default. It is speaker independent fashion [12].

       1.5.1.4 Handwriting Recognition

      Automated handwriting recognition through supervised ML really solves a complex problem of humans and cut down a large amount of time. Therefore, it is being utilized in various applications [12].

       1.5.1.5 Intrusion Detection

      Intrusion is the biggest problem of today’s era. When a person or a process wants to enter unauthorizedly into another network, it is known as Intrusion. Therefore, this intrusion detection is important to scrutinize and to identify the threats or violations to the computer security. Learning algorithms helps in finding the intrusion.

       1.5.1.6 Data Center Optimization

      Huge energy requirement and environmental responsibility are rising a pressure day by day to Data Center (DC) companies to keep a DC operating efficiently. The ML algorithms help the DC to monitor the energy consumptions and pollution levels relentlessly to improve the operating efficiency [13].

      1.5.2 Unsupervised Learning–Based Applications

       1.5.2.1 Social Network Analysis

      Identification of a person with in a large or small circle on social media platforms such as Facebook and Instagram has become easier with the help of unsupervised learning. It assists in maintain the similar posts in the proper way [14].

       1.5.2.2 Medical Records

      Automation helped the medical industry to manage the records in better way. Now, e-medical records have turn out to be ubiquitous [15]. Therefore, medical data is getting shape of medical facts and surprisingly helping to understand the disease in better way.

1.5.2.3 Speech Activity Detection

      Speech activity detection (SAD) helps to detect the presence or absence of human speech for speech processing. ML assists to reduce the unwanted noisy and long non-speech intervals from the speech. SAD helps in making human-computer interfaces. It helps the hearing-impaired people to use the machine or computer using the voice commands [16]. It is language independent program. SAD is having two types: supervised SAD and unsupervised SAD. Supervised SAD uses the available training data and models a system accordingly, while unsupervised SAD is a feature-based technique.

       1.5.2.4 Analysis of Cancer Diagnosis

      Nowadays, human life is being saved with the help of medical science and technology. Therefore, the contribution of technology to fight against the cancer is not surprising anymore. It is first step to find the type of cancer in order to cure it. Now, it is possible with the help of classification process by collecting patient samples. Some ML techniques like radial basis function (RBF), Bayesian networks, and neural networks trees are used to detect the cancer and its type [17].

      1.5.3 Semi-Supervised Learning–Based Applications

       1.5.3.1 Mobile Learning Environments

      Mobile learning means with the help of mobile device and internet facility, we can learn anywhere any time. To learn from mobile, various mobile apps are available which are based on various ML algorithms. Such type of learning is similar to where network bandwidth is consumed to operate [17].

       1.5.3.2 Computational Advertisement

Online computational advertisement is the new concept in this scientific era. It is different from classical or traditional advertisement process. Computational advertisement is based on best match of the users. It is reached to the relevant user in digital format or online mode by using various ML techniques, and those are based on recommendation system, text analysis, information retrieval, classification, modeling, and optimization techniques. Within short span and in cost-effective way, it targets the number of relevant person [18].

       1.5.3.3 Sentiment Analysis

      Sentiment analysis is different from the text analysis. Text analysis is focused to retrieve the facts and information but not be able to find the customer’s sentiments which lead to misunderstand the customers need. This misunderstanding may be loss of the valuable information. Hence, sentiment analysis is important to find the product’s review either a positive or negative. Sentiment categorization used in movie reviews, recommendation systems, and business intelligence applications [18].

      1.5.4 Reinforcement Learning–Based Applications

       1.5.4.1 Traffic Forecasting Service

      Traffic forecasting system is the real-time prediction of the traffic on the road. Day by day, numbers of vehicles are increasing on the road, which leads to increase the road accident. So, it is very necessary for traffic management. Using ML method, we can predict the real-time traffic and easily solve this problem. Such types of the systems find the digital traffic flow using satellite map and routing-based information [19].