Machine learning and Deep Learning Paradigms

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Received: 31 January 2025
Revised: 26 February 2025
Accepted: 3 March 2025
Published: 6 March 2025
Citation: Razzaq, K.; Shah, M.
Machine Learning and Deep Learning
Paradigms: From Techniques to
Practical Applications and Research
Frontiers. Computers 2025, 14, 93.
https://doi.org/10.3390/
computers14030093
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Review
Machine Learning and Deep Learning Paradigms: From
Techniques to Practical Applications and Research Frontiers
Kamran Razzaq * and Mahmood Shah *
Newcastle Business School, The University of Northumbria Newcastle, Newcastle upon Tyne NE1-4SE, UK
* Correspondence: kamran.razzaq@northumbria.ac.uk (K.R.); mahmood.shah@northumbria.ac.uk (M.S.)
Abstract: Machine learning (ML) and deep learning (DL), subsets of artificial intelligence
(AI), are the core technologies that lead significant transformation and innovation in various
industries by integrating AI-driven solutions. Understanding ML and DL is essential to
logically analyse the applicability of ML and DL and identify their effectiveness in different
areas like healthcare, finance, agriculture, manufacturing, and transportation. ML consists
of supervised, unsupervised, semi-supervised, and reinforcement learning techniques.
On the other hand, DL, a subfield of ML, comprising neural networks (NNs), can deal
with complicated datasets in health, autonomous systems, and finance industries. This
study presents a holistic view of ML and DL technologies, analysing algorithms and their
application’s capacity to address real-world problems. The study investigates the real-
world application areas in which ML and DL techniques are implemented. Moreover, the
study highlights the latest trends and possible future avenues for research and development
(R&D), which consist of developing hybrid models, generative AI, and incorporating ML
and DL with the latest technologies. The study aims to provide a comprehensive view on
ML and DL technologies, which can serve as a reference guide for researchers, industry
professionals, practitioners, and policy makers.
Keywords: machine learning; deep learning; artificial intelligence; data-driven decision-
making; intelligent solutions; data analysis
1. Introduction
The two technologies widely associated with the modern development of AI are ML
and DL. These fields are about designing systems that can find patterns in data samples,
make decisions, and even predict outcomes without direct human intervention. ML forms
a base for the whole process, and various algorithms are used for classification, regression,
clustering, etc. Again, going to subcategories of ML, DL builds upon these capabilities by
utilising artificial neural networks (ANNs) to process big, highly significant data. Com-
bined, both ML and DL have transformed industries, solving once unsolvable challenges
that have been posed.
ML has been used in various domains like cybersecurity, where ML models can help
detect frauds, and agriculture, where yields can be optimised using machine learning
tools [1,2]. Meanwhile, DL models perform well in image segmentation, natural language
processing (NLP), and other similar areas [3]. ML and DL not only contribute to optimi-
sation but also create opportunities for an automated society with self-driving cars [4],
intelligent cities [5], and prognostic maintenance for industries [6].
However, like every other technology, these are not without some drawbacks. The
‘black box’ character of many DL models is an important issue, as it is challenging to
Computers 2025, 14, 93 https://doi.org/10.3390/computers14030093
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https://creativecommons.org/licenses/by/4.0/
https://www.mdpi.com/journal/computers
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Computers 2025, 14, 93 2 of 27
understand how decisions are made in fields like healthcare or law [7]. Furthermore, ML
occasionally experiences data-related challenges, such as high-quality labelled datasets or
difficulties generalising across various domains [8]. The study highlights that solving these
issues is critical to achieving better utilisation and robustness of ML and DL systems.
Hence, to understand the workings of ML and DL technologies and their stages of
development, it is necessary to understand the principles on which these technologies are
built and analyse how they have become the advanced models practised today. Moreover,
implementing a more suitable ML or DL technique for a particular situation is challenging,
because the primary purpose of developing those techniques is different. Therefore, their
output might be different for different types of datasets. That is why it is important to
comprehensively analyse the ML and DL algorithms and their applications in various
disciplines, such as cybersecurity, business, finance, manufacturing, agriculture, market-
ing, healthcare, education, smart cities, entertainment, and much more discussed in the
preceding section of this paper.
Due to their significance and importance in data analysis, this study provides a holistic
overview of different ML and DL techniques that can be implemented to improve the appli-
cation’s overall performance. Therefore, the primary purpose of this study is to identify the
nature, potential, and learning capabilities of ML and DL techniques, underscoring their
set of procedures and applications in different real-world domains. Moreover, the study
targets primary research problems and future research directions, including professional
data interpretation, to generate the latest algorithms and techniques, the ML or DL model’s
operational excellence, and adopt economical devices. Therefore, the study aims to guide
academia and industry professionals aiming to study, investigate, and develop automatic
and smart systems in their respective domains using ML and DL techniques.
The study aims to achieve the following objectives:
- To identify a big picture of ML and DL technique application domains and define the
nature and characteristics of different types of real-world data they use.
- To provide an in-depth comparison of various ML and DL models, focusing on data
management, model size, exploitability, and computational demands.
- To comprehensively evaluate data handling and preprocessing by ML and DL tech-
niques, human intervention during the processing, and identify the effective use of
methods in future.
- To underline the future research directions and emerging trends based on our study’s
findings for efficient data analysis.
1.1. Machine Learning: The Beginning
Pre-supervised ML was initially recognised as an attempt to extend the existing and
computational-based approach, where machines learn from direct coding, to the concept
of ‘learning from the data’ [9]. In its early stages, ML mainly implied supervised learning
methods, in which the algorithm had to work with marked data to make a prediction or
classification [10]. The fundamental concept of ML was simple: an ideal learning algorithm
incorporates means by which its internal parameters adapt to the error of the predicted
and actual values and the best evolve with each subsequent iteration.
1.2. Early Machine Learning Algorithms and Evolution
While ML continued to evolve, scholars began investigating the possibility of develop-
ing techniques in which learning occurs in unstructured data. This was helpful for models
to find latent structures in the given data, for example, in cases of clustering whenit has been found that DL has performed better than ML, especially in
categories such as computer vision and natural language processing, where two broad
categories of deep models and large datasets produce superior performance.
Another interesting element mentioned was the interpretability of models. While
ML models are less complex and easily understandable, DL models are complex and
well-known for their ‘black box’ nature. The problem arises in some sectors like healthcare
and finance, where features necessary for decision-making must be understandable to the
human eye. However, today’s approaches like SHAP (Shapley Additive Explanations) and
LIME (Local Interpretable Model-agnostic Explanations) are trying to address this problem
of non-interpretable DL models.
Furthermore, the study highlights the need to select a proper method depending on
the context of the data and the research problem. For example, models based on the ML
approach are required for highly structured data with limited features. In contrast, models
based on the DL approach are needed when working with large volumes of unstructured
data such as images, videos, and texts.
6. Conclusions
The study provides a holistic view of machine learning and deep learning algorithms,
their techniques, data types, application domains, and their potential use in future. Accord-
ing to the research objectives, the study comprehensively discussed the different types of
machine learning and deep learning techniques implemented in various domains to solve
real-world problems. The study identified that the potential for machine learning or deep
learning can be assessed based on data and the problem-solving capability of algorithms.
Each technique has specific merits and demerits; using specific methods depends on the
amount or type of data available, the computing power available, and the type of issue
being faced.
Computers 2025, 14, 93 21 of 27
In the future, the expansion of machine learning and deep learning, as discussed in
the discussion section, will prompt radical changes in areas such as healthcare, finance,
transportation, agriculture, entertainment, and retail, where quick choices and forecasting
of information effects might make a huge difference.
It is also important to note that introducing energy-efficient algorithms will enhance
the reliability and efficiency of these models. Meanwhile, the drawbacks connected with
training deep learning models will also be mitigated, along with the growth of computa-
tional power and the availability of cloud services for AI usage.
6.1. Theoretical Contribution
The study presents a structured evaluation of machine learning and deep learning
techniques, comparing them based on their learning ability and data types, i.e., structured,
unstructured, semi-structured, and time series data. This provides a strong theoretical
framework for machine learning and deep learning research.
The study supports theoretical knowledge of machine learning and deep learning in
better decision-making and innovative solutions by providing information on the issues
arising from implementing these models in industries, including data quality, complexity
of model development, and model interpretability. In addition, the study presents the
author’s critical analysis of how the application of each technique contributes to meeting
the needs of the given industry and a proposal for the framework for selecting the right AI
model stated in terms of the task at hand and available resources.
6.2. Practical Implications
The study provides a comprehensive framework for machine learning and deep learn-
ing techniques, data types, and application areas, helping researchers, businesses, and
organisations seeking to evaluate the efficiencies and suitability of ML and DL in their
day-to-day operations. The study further provides an inclusive view of real-world applica-
tions of machine learning and deep learning in healthcare, agriculture, telecommunications,
retail, energy, entertainment, transportation, autonomous vehicles, computer vision, man-
ufacturing, marketing, and finance, offering valuable insights for the implementation of
these methods.
Moreover, the study highlighted the potential problems and future research direc-
tions, such as explainable AI, model transparency, federated learning, automated ma-
chine learning, Edge AI, self-supervised learning, and integrated hybrid models for better
decision-making and automation.
6.3. Future Directions
Although the current techniques present a solid base for machine learning and deep
learning methods and research, our study suggests some prospective future research trends.
- Interpretability and Explainability
According to the current literature, as discussed in Section 4.4, solving the ‘black box’ is-
sue involved in machine learning and deep learning models can produce more interpretable
and explainable approaches that can develop more confidence and comprehensibility.
- Model Efficiency
The computational complexity arises when the datasets are too large and vague.
Addressing the data complexity issues can be achieved by improving the model’s efficiency
and developing scalable architectures to solve real-world problems.
Computers 2025, 14, 93 22 of 27
- Incorporating Latest Technologies
Another potential research dimension could be the integration of the latest techniques
of ML and DL with blockchain technologies, quantum computing, and edge computing to
develop enhanced and more reliable systems. One of the significant advantages of quantum
computing and edge computing is that these algorithms can immensely boost the training
of machine learning and deep learning models and, therefore, ultimately, problem-solving.
- Hybrid Models
Advancements in generative adversarial networks and hybrid models could increase
automation and provide more robust solutions.
- Data Visualisation and Learning
As the data becomes more prominent, ML and DL algorithms deal efficiently with large
amounts of data in their raw format, such as text, images, videos, or sounds. By identifying
the latest techniques for data representation, augmentation, processing, and self-supervised
learning, the models can be enriched with generalisation among distinct databases.
In particular, the advances in practice show that there is more to be discovered in
creating new methods and algorithms based on machine learning and deep learning. It
is, therefore, crucial for academics and industrial practitioners to strive to develop more
innovative yet understandable and better explainable AI technologies for tackling future
complex issues.
Author Contributions: Conceptualization, K.R. and M.S.; methodology, K.R.; software, K.R.;
validation, K.R. and M.S.; formal analysis, K.R.; investigation, K.R.; resources, K.R.; data curation,
K.R.; writing—original draft preparation, K.R.; writing—review and editing, M.S.; visualisation, K.R.;
supervision, M.S. All authors have read and agreed to the published version of the manuscript.
Funding: This research received no external funding.
Conflicts of Interest: The authors declare no conflicts of interest.
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	Introduction 
	Machine Learning: The Beginning 
	Early Machine Learning Algorithms and Evolution 
	Critical Analysis: From Traditional ML to Modern Deep Learning 
	Linear Regression 
	Logistic Regression 
	Support Vector Machines (SVM) 
	K-Nearest Neighbours (KNN) 
	Decision Trees 
	Random Forests 
	Neural Networks 
	Principal Component Analysis (PCA) 
	Naive Bayes 
	K-Means 
	Advanced Deep Learning Models 
	Literature Review 
	Overview of Real-World Data and Machine Learning Techniques 
	Real-World Data and Its Relationship to Machine Learning 
	Structured Data 
	Unstructured Data 
	Semi-Structured Data 
	Time Series Data 
	Machine Learning Techniques and Their Applications 
	Supervised Learning 
	Unsupervised Learning 
	Semi-Supervised Learning 
	Reinforcement Learning 
	Overview of Real-World Data and Deep Learning Techniques 
	Real-World Data and Their Relationship to Deep Learning 
	Deep Learning Techniques and Their Applications 
	Convolutional Neural Networks (CNNs) 
	Recurrent Neural Networks (RNNs) 
	Long-Short-Term Memory (LSTM) 
	Generative Adversarial Networks (GANs) 
	Transformer Networks 
	Applications of Deep Learning in Real-World Scenarios 
	Machine Learning vs. Deep Learning 
	Applications Comparison of Machine Learning vs. Deep Learning 
	Discussion 
	Conclusions 
	Theoretical Contribution 
	Practical Implications 
	Future Directions 
	Referencesthe model
tries to group similar data instances even when their labels are not clearly defined [11].
Some of the most significant advancements, such as K-means clustering and the Principal
Computers 2025, 14, 93 3 of 27
Components Analysis (PCA), were unsupervised. Later, other methods of ML were intro-
duced, such as SVMs and Random Forest, with higher accuracy and elasticity [12]. These
techniques were more flexible and robust; thus, they were applicable to multidisciplinary
problems, including image, signal processing, speech, and other financial analysis.
However, the emergence of DL took the algorithms to another level while the field of
ML continued to advance. DL is usually a branch of ML that uses neural networks with
many layers; hence the term ‘deep’ in the name. Neural networks are based on the structure
of our brains, developed from a series of collections of nodes (neurons) used to process
information [13]. Convolutional Neural Networks (CNNs) emerged from feed-forward
neural networks and Recurrent Neural Networks from the previous structure.
One of the most essential reasons for developing DL was the vast amounts of data
and high-performance computing systems required to train deep neural networks. CNN,
for instance, has advanced to the status of the architecture of today’s DL in computer
vision applications such as facial recognition and object detection [14]. These models
employ convolution layers to obtain hierarchical features in images and do not require
special preprocessing for the seen and unseen data. Similarly, Recurrent Neural Networks
(RNNs) and their prominent derivative, Long-Short-Term Memory (LSTM) networks, find
significant application in NLP problems, including, but not limited to, machine translation,
sentiment analysis, and language generation [15–17].
Recent advancements in DL include the emergence of transformer models, which
have disrupted the NLP domain. The proposed architecture, developed by Vaswani [18],
replaced the self-attention mechanism to process sequential data rather than RNNs effi-
ciently. This has led to considerable advancements in machine translation, text generation,
and even conversational AI, as evidenced by models such as GPT-3.
Another potential area in DL is generative models, known as Generative Adversarial
Networks (GANs). GANs use two models, a generator and a discriminator, that are learned
simultaneously to produce realistic data, such as images and videos from noise [19]. This
has brought new opportunities to art reproduction, realistic imaging, and medical imaging.
Figure 1 describes the evaluation of ML and DL to this day.
Computers 2025, 14, x FOR PEER REVIEW 3 of 29 
 
[11]. Some of the most significant advancements, such as K-means clustering and the Prin-
cipal Components Analysis (PCA), were unsupervised. Later, other methods of ML were 
introduced, such as SVMs and Random Forest, with higher accuracy and elasticity [12]. 
These techniques were more flexible and robust; thus, they were applicable to multidisci-
plinary problems, including image, signal processing, speech, and other financial analysis. 
However, the emergence of DL took the algorithms to another level while the field 
of ML continued to advance. DL is usually a branch of ML that uses neural networks with 
many layers; hence the term ‘deep’ in the name. Neural networks are based on the struc-
ture of our brains, developed from a series of collections of nodes (neurons) used to pro-
cess information [13]. Convolutional Neural Networks (CNNs) emerged from feed-for-
ward neural networks and Recurrent Neural Networks from the previous structure. 
One of the most essential reasons for developing DL was the vast amounts of data 
and high-performance computing systems required to train deep neural networks. CNN, 
for instance, has advanced to the status of the architecture of today’s DL in computer vi-
sion applications such as facial recognition and object detection [14]. These models em-
ploy convolution layers to obtain hierarchical features in images and do not require spe-
cial preprocessing for the seen and unseen data. Similarly, Recurrent Neural Networks 
(RNNs) and their prominent derivative, Long-Short-Term Memory (LSTM) networks, 
find significant application in NLP problems, including, but not limited to, machine trans-
lation, sentiment analysis, and language generation [15–17]. 
Recent advancements in DL include the emergence of transformer models, which 
have disrupted the NLP domain. The proposed architecture, developed by Vaswani [18], 
replaced the self-attention mechanism to process sequential data rather than RNNs effi-
ciently. This has led to considerable advancements in machine translation, text generation, 
and even conversational AI, as evidenced by models such as GPT-3. 
Another potential area in DL is generative models, known as Generative Adversarial 
Networks (GANs). GANs use two models, a generator and a discriminator, that are 
learned simultaneously to produce realistic data, such as images and videos from noise 
[19]. This has brought new opportunities to art reproduction, realistic imaging, and med-
ical imaging. Figure 1 describes the evaluation of ML and DL to this day. 
 
Figure 1. Evaluation of ML and DL until now. Figure 1. Evaluation of ML and DL until now.
Computers 2025, 14, 93 4 of 27
1.3. Critical Analysis: From Traditional ML to Modern Deep Learning
This evolution from conventional ML to current DL can be labelled as shifting from
one paradigm to another. Though traditional regression and classification models are
still in practice and adequate for some tasks, deep learning has surpassed them in many
others. DL works better than conventional models in handling complex structures like
images, speech, and text analysis [20]. However, this advantage is not without its costs. For
example, it requires large datasets and significant computation and is more of a ‘black box’
than traditional machine learning models.
The gap between ML and DL continues to narrow in some domains as ML techniques
become more sophisticated by incorporating advanced algorithms and feature engineering
methods. On the other hand, DL is still a rapidly developing field with much potential,
so we continue to see much work in areas like reinforcement learning, explainability of
models, and multimodal approaches for models that incorporate image and text.
ML comprises many models, most with specific mathematical representations and
utilisation. Here is an overview of different ML models, including key equations that
underpin their functionality.
1.3.1. Linear Regression
Equation:
Yi = f (Xi, , β) + ei
where
Yi: dependent variable;
f: function;
β: unknown parameters;
Xi, : independent variables;
ei: error terms.
Linear regression minimises the mean squared error (MSE) between predicted and
actual values:
MSE =
1
n ∑n
i=1
(
Yi − Ŷi
)
1.3.2. Logistic Regression
Before the definition of logistic regression, the introduction of the sigmoid function is
compulsory and is defined as
f (x) =
1
1 + e−x
where
e = base for natural logarithms.
Moreover, logistic regression is used to classify problems and output probabilities,
and its equation is
Y =
e(bo+b1X)
1 + e(bo+b1X)
Here,
x = input value;
y = output value;
bo = intercept term;
b1 = input coefficient.
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1.3.3. Support Vector Machines (SVM)
SVMs are used for classification to find the hyperplane that maximises the margin
between classes. The main formula for SVM is defined as
f (x) = sign(w ∗ x + b)
where
w: weight vector;
b: bias term;
x: input vector;
and classification is based on the class labels, positive or negative, illustrated below:
yi : Class labels (+1 or − 1)
Kernel functions (e.g., RBF, Polynomial) allow SVM to handle non-linear separable data by
projecting it into higher-dimensional spaces.
1.3.4. K-Nearest Neighbours (KNN)
KNN does not have a specific equation but relies on distance metrics, such as
d(x1, x2) =
√n
∑
i=1
(x1i − x2i)
This is the Euclidean distance. The majority class determines the predicted class among the
k-nearest neighbours.
1.3.5. Decision Trees
A decision tree splits data based on features to minimise impurity. Impurity Measures,
Gini Index (E):
G = 1 −
c
∑
i=1
Pi
2
Entropy:
H(x) = −
n
∑
i=1
p(xi) log2 p(xi)
Here pi is the proportion of samples in class i.
1.3.6. Random Forests
Random Forests are ensembles of decision trees. It joins different decision trees
to create predictions for classification and regression tasks. It consists of multiple sub-
equations. The two separate formulas for prediction are as follows:
For classification purposes, the prediction formula is
ŷ = mode (T1(x), T2(x), . . . ., Tn(x))
where
Ti(x): the number of trees Ti when x is input;
mode: most frequent class.
For regression purposes, the prediction is the average of the overall predictions
ŷ =
1
n ∑n
i=1 Ti(x)
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where
Ti(x): the number of trees Ti when x is input.
1.3.7. Neural Networks
As discussed earlier, neural networks consist of layers of neurons. For a multi-layer
perceptron (MLP): output of neuron,
yi = f
( n
∑
i=1
wijxi + bj
)
where
xi: i-th input to neuron;
wij: weight between input i-th input and neuron j;
bj: bias term for neuron j;
f: activation function (e.g., ReLU, Sigmoid, tanh).
This equation is commonly referred to as a linear combination of inputs, whereby the
inputs are combined in a weighted manner and then passed through an activation function
to produce the output. In deep learning, many such neurons are grouped in a stacked
structure to create a deep architecture capable of learning intricacies.
1.3.8. Principal Component Analysis (PCA)
PCA reduces dimensionality by finding orthogonal components that maximise vari-
ance. Covariance matrix,
Σ =
n
∑
i=1
(xi − x) (xi − x)T
Σ correspond to the covariance matrix.
1.3.9. Naive Bayes
Naive Bayes classifiers are based on Bayes’ Theorem. Its equation is represented as
follows:
P(yx1, x2, . . . , xn) =
P(x1, x2, . . . , xn)P(y)
∏n
i=1 P(xi|y)
It assumes features x1, x2,. . ., and xn are conditionally independent given class y.
1.3.10. K-Means
An unsupervised ML technique divides data into k groups by reducing the sum of
squared distance among inputs and their relevant group and centre points. The k-means
formula can be represented as follows:
J = ∑k
i=1 ∑xi∈Ci
∥ xj − µi ∥2
where
J: objective function;
Ci: a data point in i-th cluster;
∥ xj − µi ∥2: a squared Euclidean distance.
1.4. Advanced Deep Learning Models
Deep learning builds on these foundations with more complex structures.
CNNs for images
y = f (W ∗ X + b)
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where W is a kernel (filter), and ∗ denotes convolution.
RNNs for sequences
ht = f (Wxxt + Whht−1 + b)
where ht is the hidden state at time t.
Practitioners can effectively select and adapt techniques to solve diverse problems by
understanding these models and their equations.
Thus, knowing these models and their mathematical background makes it possible to
determine which model is more suitable for tasks, starting from simple linear regression to
DL models. A basic flow chart showing the evolution of ML from the less complex versions
to the new and more complicated DL versions is presented in Figure 2.
Computers 2025, 14, x FOR PEER REVIEW 7 of 29 
 
CNNs for images 𝑦 = 𝑓(𝑊 ∗ 𝑋 + 𝑏) 
where W is a kernel (filter), and ∗ denotes convolution. 
RNNs for sequences ℎ = 𝑓(𝑊 𝑥 + 𝑊 ℎ + 𝑏) 
where ℎ is the hidden state at time t. 
Practitioners can effectively select and adapt techniques to solve diverse problems by 
understanding these models and their equations. 
Thus, knowing these models and their mathematical background makes it possible 
to determine which model is more suitable for tasks, starting from simple linear regression 
to DL models. A basic flow chart showing the evolution of ML from the less complex 
versions to the new and more complicated DL versions is presented in Figure 2. 
 
Figure 2. ML models enhancements. 
This transition emphasises the change from low-complexity models that often need 
feature extraction/rescaling before learning, to models that can sample data directly and 
perform with competitive accuracy on challenging problems. 
Although these technologies are rapidly developing, scientists are paying significant 
attention to improving the understanding of their results and accelerating their perfor-
mance. For example, there are attempts to design new algorithms that are effective and 
comprehensible for users, with the need to specify why specific decisions were made. Ad-
ditionally, lightweight deep learning models are being created for resource-constrained 
devices, such as smartphones and IoT devices [21]. These advancements are crucial for 
expanding the accessibility of ML and DL to a broader range of applications, especially in 
low-resource settings. 
The study also focuses on the intersection between ML and DL and other phenomena 
such as IoT and edge computing. Overlapping these fields makes it possible to develop 
systems capable of processing information inputs and making decisions on those inputs 
in real-time [5]. For example, smart cities can use such integrated systems to control traffic 
situations, minimise power consumption, and enhance safety. All these developments 
show how interconnected the future of technology is with the future of ML and DL. The 
study analyses algorithms, applications, and further development to present ML and DL’s 
status and future trends. 
This research aims to fill the identified gaps regarding implementing ML and DL in 
different sectors. The disparity between the recent popularity of ML and DL towards their 
applicability in fields such as healthcare, finance, and transportation leaves questions 
Figure 2. ML models enhancements.
This transition emphasises the change from low-complexity models that often need
feature extraction/rescaling before learning, to models that can sample data directly and
perform with competitive accuracy on challenging problems.
Although these technologies are rapidly developing, scientists are paying significant
attention to improving the understanding of their results and accelerating their perfor-
mance. For example, there are attempts to design new algorithms that are effective and
comprehensible for users, with the need to specify why specific decisions were made.
Additionally, lightweight deep learning models are being created for resource-constrained
devices, such as smartphones and IoT devices [21]. These advancements are crucial for
expanding the accessibility of ML and DL to a broader range of applications, especially in
low-resource settings.
The study also focuses on the intersection between ML and DL and other phenomena
such as IoT and edge computing. Overlapping these fields makes it possible to develop
systems capable of processing information inputs and making decisions on those inputs in
real-time [5]. For example, smart cities can use such integrated systems to control traffic
situations, minimise power consumption, and enhance safety. All these developments
show how interconnected the future of technology is with the future of ML and DL. The
study analyses algorithms, applications, and further development to present ML and DL’s
status and future trends.
This research aims to fill the identified gaps regarding implementing ML and DL
in different sectors. The disparity between the recent popularity of ML and DL towards
their applicability in fields such as healthcare, finance, and transportation leaves questions
unanswered, such as when the use of one over the other is appropriate given the type and
size of data available and the business needs. Second, the explainability of DL models
remains an issue in maintaining decision transparency, which is essential in applications
Computers 2025, 14, 93 8 of 27
such as healthcare and finance where models are used. Thestudy seeks to fill these gaps by
presenting a comprehensive comparison of what is known about the ML and DL academic
models and how those models should most appropriately be utilised based on a broad
examination of their possibilities and pitfalls. Moreover, this work aims to present an
overview of ML and DL using criteria like data handling, model complexity, time of
training, and interpretability. By explaining the identified differences and choices in this
work, researchers, practitioners, and organisations will be guided on which model suits
specific business issues. In addition, this research also aims to contribute to the proliferation
of knowledge of future trends in developing ML and DL models and applications, as
influenced by clouds, new chips, and extensive data. In conclusion, the goal is to increase
the knowledge of these technologies and how they are applied to make superior, more
innovative systems.
2. Literature Review
ML and DL have become crucial in identifying solutions to complex problems in
different domains. This literature review provides an overview of technological advance-
ments in ML and DL, outlining their advancement from initial neural net models to today’s
complex structures. These models stand out for creating summary-level representations
from raw data and can work in fields such as healthcare, cyber security, and imagery
recognition as displayed. However, there is still a significant problem based on the ‘black
box’ characteristic of DL, where the practitioners sometimes have no idea how these models
make their decisions.
In contrast, ML research focuses on its basic learning algorithms, categorised as
supervised, unsupervised, semi-supervised, and reinforcement learning, as shown in
Figure 3. From the above-mentioned tangible data and study, these algorithms solve
various real-life problems, including crop yield in the farming industry [22], cyber security
fraud [23], and management of resources in smart cities [5]. Despite this, it is essential to
note that ML algorithms provide great flexibility to the user throughout the analysis and
prediction. Most importantly, the models’ improvements highly depend on the quality and
quantity of data available for training.
Computers 2025, 14, x FOR PEER REVIEW 8 of 29 
 
unanswered, such as when the use of one over the other is appropriate given the type and 
size of data available and the business needs. Second, the explainability of DL models 
remains an issue in maintaining decision transparency, which is essential in applications 
such as healthcare and finance where models are used. The study seeks to fill these gaps 
by presenting a comprehensive comparison of what is known about the ML and DL aca-
demic models and how those models should most appropriately be utilised based on a 
broad examination of their possibilities and pitfalls. Moreover, this work aims to present 
an overview of ML and DL using criteria like data handling, model complexity, time of 
training, and interpretability. By explaining the identified differences and choices in this 
work, researchers, practitioners, and organisations will be guided on which model suits 
specific business issues. In addition, this research also aims to contribute to the prolifera-
tion of knowledge of future trends in developing ML and DL models and applications, as 
influenced by clouds, new chips, and extensive data. In conclusion, the goal is to increase 
the knowledge of these technologies and how they are applied to make superior, more 
innovative systems. 
2. Literature Review 
ML and DL have become crucial in identifying solutions to complex problems in dif-
ferent domains. This literature review provides an overview of technological advance-
ments in ML and DL, outlining their advancement from initial neural net models to to-
day’s complex structures. These models stand out for creating summary-level representa-
tions from raw data and can work in fields such as healthcare, cyber security, and imagery 
recognition as displayed. However, there is still a significant problem based on the ‘black 
box’ characteristic of DL, where the practitioners sometimes have no idea how these mod-
els make their decisions. 
In contrast, ML research focuses on its basic learning algorithms, categorised as su-
pervised, unsupervised, semi-supervised, and reinforcement learning, as shown in Figure 
3. From the above-mentioned tangible data and study, these algorithms solve various real-
life problems, including crop yield in the farming industry [22], cyber security fraud [23], 
and management of resources in smart cities [5]. Despite this, it is essential to note that 
ML algorithms provide great flexibility to the user throughout the analysis and prediction. 
Most importantly, the models’ improvements highly depend on the quality and quantity 
of data available for training. 
 
Figure 3. Machine learning algorithms. 
ML timeline starts with the creation of linear regression in the mid-19th century, one 
of the early forward prediction models [24]. The evolution process, such as decision trees, 
support vector machines, etc., extended its application range. ML progressed to a higher 
level to develop neural networks to become what we now call deep learning. These 
Figure 3. Machine learning algorithms.
ML timeline starts with the creation of linear regression in the mid-19th century,
one of the early forward prediction models [24]. The evolution process, such as decision
trees, support vector machines, etc., extended its application range. ML progressed to
a higher level to develop neural networks to become what we now call deep learning.
These advancements led to innovative uses like the conversational AI ChatGPT-4, which
applies DL techniques to provide human-like conversational responses. Explaining natural
language understanding, ChatGPT is a prime example of how neural structures make DL
one of the most essential applications today.
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Research in DL attempts to eliminate the need for large, labelled datasets by automat-
ing data preparation steps, including annotation [25]. Efficient models are also being
developed on limited computing platforms such as IoT sensors and smartphones, and
work well in low-memory environments [26]. Similarly, ML targets the enhancement of
algorithm stability through the incorporation of domain knowledge, especially in sensitive
sectors such as the health sector, where accuracy is highly valued [27].
In practice, the two have shown promise in healthcare and finance. For instance,
DL models are used for diagnosis and even screening of diseases such as cancer from
images of a patient’s organs or organs of other patients [28]. On the other hand, ML
algorithms improve financial portfolios and perform fraud checks on transactions in real-
time [29]. These applications demonstrate that ML and DL’s synergistic combination lets
each methodology successfully address domain-specific issues.
However, there are still intelligible problems to this day. Finally, the two works call for
further addressing the overreliance on annotated datasets and enhancing the approaches
to interpreting and explaining AI models, which will establish trust in the systems. For
example, transparency in decision-making is particularly crucial in sensitive domains like
healthcare, where the stakes are high [30].
Some promising areas for future engineering are technologies such as IoT and edge
computing combined with ML and DL [31]. Such developments imply that ML and DL
are ready to merge into daily life, like smart personal assistants and intelligent traffic
control systems. By highlighting research gaps and prospective lines of development,
these technologies will be able to expand innovative breakthroughs and optimise choosing
procedures across industries.
3. Overview of Real-World Data and Machine Learning Techniques
ML has been widely adopted worldwide in recent years because it can solve many
problems using various real-worlddata. Choosing the proper ML techniques depends
on categorising the available and analysed data. Four data types exist, i.e., structured,
unstructured, semi-structured, and time series data, each with specific properties and
uses. As a result, several approaches to ML, including supervised, unsupervised, semi-
supervised, and reinforcement learning, correspond to the types of data. For example,
structured data, including financial records, applies supervised learning methods [32],
while unstructured data like images and videos work well with DL and unsupervised
learning [33]. These techniques have been tried and tested in healthcare, cybersecurity,
finance, and the management of smart cities to improve decision-making and operations.
Mostly, the effectiveness of building ML models is highly defined by the type and
quality of data used for training and modelling [34]. Different real-world data can be
grouped into different categories based on their characteristics and the kind of application
to be carried out.
3.1. Real-World Data and Its Relationship to Machine Learning
3.1.1. Structured Data
This data type is highly structured, conforms to a prescribed pattern, and is stored in
familiar structures such as tables like a relational database. Some examples include financial
operations, patient records, and inventory records [35]. Typical ML uses include supervised
learning to analyse quantitative data in fraud detection and predictive modelling.
3.1.2. Unstructured Data
Unlike structured data, unstructured data have no structural pattern, making it diffi-
cult to capture and analyse. This category includes text documents, images, audio in MP3
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format, and video. For example, it refers to customers’ feedback, posts, and shares on social
networks and multimedia materials. Techniques like NLP are broadly applied to derive
insights from unstructured data [35].
3.1.3. Semi-Structured Data
Semi-structured data are between the two categories since they provide some form
of organisation but are not rigidly defined in terms of structure [36]. ML includes XML,
JSON, and NoSQL databases, which are widely used in web and mobile apps. JSON further
helps in combining patients’ data to merge medical history with patient appointment
dates. Hence, it enhances data quality and improves data analysis efficiency for better
decisions. Recommender systems are commonly used in ML, and such applications use
semi-structured data.
3.1.4. Time Series Data
Time series information refers to values secured at different instances, usually equally
timed [37]. It helps reflect the time order of events and is valuable for studying dynamics,
tendencies and behaviours. Examples include stock prices, weather, power consumption,
and web hits. Possibly the most well-known application is the method for forecast methods,
where one aims to estimate future values from past ones [38]. For example, in finance,
one would use Autoregressive Integrated Moving Average (ARIMA) or Long-Short-Term
Memory (LSTM) networks to predict stocks or markets [39]. In meteorology, time series
data are paramount, so they are used to make weather predictions based on existing climate
data and sensors [40]. Similarly, Mariano-Hernández, Hernández-Callejo [41] stated that
energy management systems use time series data to forecast demand and manage the
available resources.
Standard ML techniques are used to develop significant patterns in processing the
mentioned data types. Depending on the data type and objectives, such techniques en-
compass supervised, unsupervised, semi-supervised, and reinforcement learning. The
following sections offer further descriptions of these techniques and the facets in which
they are utilised. Table 1 below summarises the data types, machine learning methods, and
real-world applications.
Table 1. Data types used by ML and DL.
Category Type/Technique Description/Examples
Types of Data
Structured Data Organised data, such as financial transactions and patient records, is stored in tables.
Unstructured Data Data in formats like text, images, or videos is used in sentiment analysis and visual
recognition applications.
Semi-structured Data Partially organised data, such as JSON or XML files, is standard in web applications.
Time Series Data Sequential data like stock prices or weather reports are used for forecasting.
3.2. Machine Learning Techniques and Their Applications
ML algorithms significantly develop intelligent systems that can learn from data and
make conclusions or decisions. These techniques are classified depending on the data they
operate on and the learning they use. Below is a detailed discussion of the four primary
ML methods with examples of how each is used.
3.2.1. Supervised Learning
Supervised learning is one of the prominent categories of ML [42]. Where the algo-
rithms work with a supervised dataset, which provides a dataset and the labelled data. The
aim is to teach the model how the inputs are related to the outputs to generalise when faced
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with new data. For example, one of the most familiar supervised learning applications is in
the email filtering systems [43]. Here, pre-labelled sets of emails are employed in model
training. Features such as the occurrence of some words, information about the sender, and
the structure of the email allow the model to determine whether the received messages
belong to spam or are genuine.
On another occasion, supervised learning is used to identify fraudulent events in
financial transactions. The model is trained on historical transaction data, each characterised
by the presence or absence of fraud. The model can further be used to categorise new
transactions based on patterns linked to fraud to negate or include them as fraudulent [1].
3.2.2. Unsupervised Learning
In unsupervised learning systems, the model deals with data with no labels, and the
system’s objective is to find patterns or structures or construct groups in the data [44].
Unlike supervised learning, no target function guides the learning process, and the data
structure must be learned autonomously. This technique is vital to understanding how
the data structures and relationships are intertwined. For example, in marketing, unsu-
pervised learning is applied to group customers based on their behaviours, preferences,
and previous purchases [45]. The model clusters the customers based on their similarities
and thus ensures that a separate marketing message is sent to each group. Both K-means
and hierarchical clustering are typical methods of customer segmentation. The unsuper-
vised learning technique can also identify when data display abnormality in patterns [46].
Therefore, the method requires no labelled datasets for training. For instance, it can be
used in network security to flag outliers as potential signs of a security incident, such as
unauthorised access.
3.2.3. Semi-Supervised Learning
Semi-supervised learning uses labelled and unlabelled data to exploit both supervised
and unsupervised learning. The technique is helpful when obtaining labelled data, which
is challenging and costly, while providing better access to a vast amount of unrelated but
valuable data. Semi-supervised learning uses the labelled set and the massive amount
of data in the unlabelled set to enhance accuracy and generalisation. For instance, semi-
supervised learning is heavily employed in different fields, such as computer vision,
especially in image annotation [47].
Meanwhile, semi-supervised learning has also been applied to speech recognition
systems where a large set of unlabelled speech data augments a small, labelled set of audio
samples [48].
3.2.4. Reinforcement Learning
In reinforcement learning (RL), the leading agent learns through environment interac-
tion. The purpose is to learn how to optimise a reward function using the trial-and-error
approach. The agent learns from the environment by acting upon it, and the performanceis
modified when the feedback is not favourable. RL is typically employed when the decision
maker faces a problem in which an agent must identify an optimal action sequence. RL
is applied to robotics, most of the time to autonomous robots [49], which requires the
completion of tasks or missions, such as moving around an area, picking up an object, or
joining parts together.
Moreover, RL has been studied and incorporated in the gaming industry with much
focus [50]. RL was employed in programmes and algorithms such as AlphaGo and half of
OpenAI’s Dota 2 agents that won over people in games such as Go and Dota 2 [51].
Another example of RL is the actor–critic model, which joins actors with critics to
enhance learning capability [52]. Proximal Policy Optimisation (PPO) and Deep Q-Network
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(DQN) are the most employed methods in this context. PPO is the latest actor–critic method,
which improves policies whilst sustaining constancy by controlling huge policy updates.
However, DQN is a value-based approach that merges deep NNs with Q-learning [53]. It
uses criticism to evaluate state-action values, thereby making better action selections.
In conclusion, each ML method has strengths and helps solve specific problems.
Supervised learning is well suited for cases where the data to be classified is already
labelled and accurate predictions are possible. At the same time, unsupervised learning
is prominently suited for finding latent structures within the data that are not labelled.
Semi-supervised learning is a beneficial solution when we have a small amount of labelled
data, and reinforcement learning is optimal when we regularly learn from interactions with
an environment, making sequential decisions. These are the basics by which real-world
issues in various organisations and corporations may be solved. Table 2 illustrates an
overview of ML methods and examples of practical usage.
Table 2. Types of machine learning algorithms.
Category Type/Technique Description/Examples
Machine Learning Techniques
Supervised Learning
Algorithms learn from labelled datasets to predict outcomes. Examples include
predicting housing prices based on features like location, size, and amenities or
email spam detection based on labelled email datasets.
Unsupervised Learning
Models identify patterns in unlabelled data to uncover hidden structures. For
example, customer segmentation in marketing can be used to group consumers
by behaviour or anomaly detection for fraud in financial transactions.
Semi-Supervised Learning
Combines both labelled and unlabelled data. This is useful when labelled data
are limited. For example, only a small subset of images are labelled in image
classification. The model can leverage unlabelled data for better generalisation.
Reinforcement Learning
Systems learn by interacting with an environment and adjusting actions to
maximise rewards. Examples include autonomous driving, where an AI learns to
drive a car by trial and error, or robotics for tasks like sorting or assembling parts.
AI, specifically ML, is now widespread and is used to make better decisions and
enhance productivity in various industries. For example, it is used in healthcare to predict
possible patient outcomes, find new drugs, and diagnose illnesses at early stages [54–56].
For instance, in the IBM Watson Health project, algorithms assist doctors in determining
how patients will react to a specific treatment projected from prior health records [57]. ML is
heavily used in cybersecurity since it helps compare changes in the frequency and intensity
of network traffic and determine what a threat is, such as a phishing attack [58,59]. Security
solutions like Darktrace use ML to create systems that isolate real-time threats depending
on the network’s behaviour [60]. In finance, machine learning is used in credit-scoring
models and trading applications [61].
Firms such as FICO use statistical models to evaluate the risks of giving credit, while
Robo-advisors like Betterment incorporate ML techniques to invest [62,63]. In smart cities,
ML is used in traffic management, energy consumption, and security [5]. Some case
studies include the Barcelona Smart City project, which incorporates ML to run the urban
system [64]. Some general fields where ML is applied are described in Table 3.
Meanwhile, Table 4 presents examples of using ML in practice across different fields,
demonstrating these approaches’ applicability. In healthcare, ML models are used for
patient prognosis, drug development and early-stage disease screening; well-known exam-
ples of such systems are IBM Watson Health [57] and Google Health AI [65], for detecting
diabetic retinopathy. In e-commerce and marketing, ML is used in recommendation sys-
tems to provide individual customer offers, for example, those used by Amazon or Netflix,
as well as customer segmentation for advertising purposes like in Facebook Ads or Google
AdWords [66]. Tesla and Waymo’s self-driving cars use ML to guide systems and come to
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decisions [67]. Today, finance is one of the most significant sectors that use ML, as it can
detect fraud; it is used in PayPal and American Express [68].
Table 3. Applications of ML in various domains.
Category Type/Technique Description/Examples Authors
Applications of Machine Learning
Healthcare
Predicting patient outcomes, drug discovery, and early disease
diagnosis. For example, IBM Watson Health uses AI to predict
patient responses to treatments.
[54–57]
Cybersecurity
Detecting anomalies in network traffic and identifying phishing
attacks. Real-time detection systems like Darktrace use ML to
spot threats.
[58,59]
Finance
Optimising credit scoring models and automated trading
systems. Companies like FICO and Betterment use machine
learning to enhance financial decisions.
[61,63]
Smart Cities
The Barcelona Smart City project uses AI for urban management,
managing traffic flow, optimising energy consumption, and
improving public safety.
[64]
Table 4. Techniques and applications of ML in various fields.
Category Type/Technique Description/Examples References
Healthcare Predictive Modelling Predicting disease outbreaks and patient risk analysis. Examples: IBM
Watson Health and Google Health’s AI for diabetic retinopathy detection. [57,65]
E-commerce Recommendation Systems Suggesting products based on customer preferences and past behaviour.
Examples: Amazon and Netflix. [69–71]
Marketing Customer Segmentation Segmenting customers for targeted advertising. Examples: Facebook Ads,
Google AdWords. [72–74]
Transportation Autonomous Vehicles Enabling self-driving cars to navigate and make decisions. Examples:
Tesla, Waymo. [67]
Finance Fraud Detection Identifying fraudulent transactions. Examples: PayPal and American
Express. Forecasting is another application in finance. [1,38,43,68,75]
Manufacturing Predictive Maintenance and
Anomaly Detection
Predicting equipment failures to reduce downtime and detect anomalies
to avoid quality issues or inefficiencies. Examples: GE Predix, Siemens
MindSphere, Philips, Tesla, and Honeywell.
[76,77]
Retail Inventory Management Optimising stock levels and restocking. Examples: Walmart, Target. [78,79]
Education Adaptive Learning Systems Personalising learning experiences for students. Examples: Duolingo
and Coursera. [80–82]
Agriculture Crop Prediction Predicting crop yields based on environmental factors. Examples: John
Deere, the Climate Corporation. [83,84]
Entertainment Content Personalization Tailoring movie/show recommendations. Examples: Spotify and YouTube. [85]
ML is also critically important in manufacturing; predictive maintenance models help
decrease the time that equipment is out of service due to failure, exemplified by products
like GE Predix and Siemens MindSphere [76]. Organisations like Walmart and Target
use ML to track their stocks and enhance the restocking processes [78,79]. In education,
personal collectable learning systemslike Duolingo and Coursera are flexible depending
on the learner [80–82]. Sample use of agriculture involves using environmental data
to predict crop yields using ML, which John Deere and the Climate Corporation have
implemented [83,84]. Finally, entertainment hiring ML, with music or video streaming
services, like Spotify or YouTube, provides recommendations to make the utilisation more
engaging [85].
4. Overview of Real-World Data and Deep Learning Techniques
DL has become widely popular for processing vast amounts of data precisely where
traditional ML approaches cannot adequately capture the essence of vast and unstructured
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data [86]. Techniques, such as artificial neural networks (ANNs), have improved fields
such as image recognition, the analysis of natural language, and speech recognition [87,88].
These techniques allow models to learn a hierarchical representation of the data and were
popular because they could process raw data from the real world.
4.1. Real-World Data and Their Relationship to Deep Learning
Big data gives us real-world data of different types, which can be analysed with certain
advantages and odds. Regarding data characteristics, the most frequent kinds of data in
real-life applications are similar to ML, i.e., structured, unstructured, semi-structured, and
time series data.
Tabular data, like numerical records in databases concerning financial transactions,
medical records, and inventory, are analysed using traditional ML approaches, including
regression models, decision trees, and Random Forests [35]. However, Recurrent Neural
Networks (RNNs) and deep feed-forward networks can be used successfully in time series
forecasting and classification for structured data tasks where feature extraction and complex
patterns are significant [89].
Unstructured data, such as images, audio, text, and video, are most applicable to deep
learning approaches. Grid structures such as CNNs and NLP, including transform models
like BERT and GPT, are developed to handle unstructured data [90,91]. These techniques
disrupt specific domains, such as computer vision, speech recognition, and translation.
Complex, semi-structured data like XML or JSON is common in web apps, IoT devices,
and social media. Autoencoder and deep reinforcement learning can extract features and
perform tasks such as clustering, classification, and anomaly detection in semi-structured
data [92].
Time series data, the sequence of records at an interval, are used in applications such
as weather prediction, financial prediction, and sensor data analysis. RNNs, LSTMs, and
GRUs are special categories of deep learning models used to capture temporal patterns in
time series data [93,94]. They use previous observations to make subsequent predictions.
4.2. Deep Learning Techniques and Their Applications
The deep learning method aims to work with large sets of data and recognise various
features that may remain unnoticed by most conventional algorithms. In the subsequent
section, the best-known DL models are discussed, alongside the domains in which they
are used.
4.2.1. Convolutional Neural Networks (CNNs)
CNNs are used for image and video analysis to a large extent [14]. Such networks
employ convolutional layers that recognise a given image’s patterns, edges, and textures.
CNN is extensively used in image classification, object detection, and facial recognition [95].
CNNs have been used in healthcare to analyse medical images and diagnose diseases
such as cancer from CT scans or X-ray images [96]. Nowadays, CNNs play a crucial role
in developing autonomous driving systems because they allow cars to identify objects,
pedestrians, traffic signs, and others in real-time [14].
Abdou [96] further stated that CNNs are used to diagnose images such as X-rays,
MRIs, and CT scans to discover tumours or fractures. Another example is using CNNs
to detect objects and classify them to identify potential obstacles and make navigation
decisions [97].
4.2.2. Recurrent Neural Networks (RNNs)
RNNs are preferable for processing sequential data in which the current input func-
tion depends on previous inputs [98]. They are used in time series forecasting, analysis
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of natural languages, and speech recognition techniques. Due to their ability to detect
dependencies in sequences, RNNs are helpful in activities such as machine translation,
language modelling, and forecasting [20]. For instance, RNNs and their kinds—LSTMs and
GRUs—are employed in machine translation, speech-to-text conversion, and sentiment
analysis tasks. Meanwhile, Ahmed, Alam [20] addresses the implications of RNNs in
predicting stock prices or a specific cryptocurrency trend based on past data.
4.2.3. Long-Short-Term Memory (LSTM)
LSTMs are a particular kind of RNN developed to solve the vanishing gradients issue
that can arise when training on long sequences. They are most helpful in learning temporal
dependencies in the sequences and are applied in language modelling and time series
prediction [99,100]. For example, speech-to-text is one application that uses LSTMs in
systems such as Google Voice Assistant [101]. Additionally, LSTMs help predict future
frames in a video based on the sequential relationship between past frames [102,103].
4.2.4. Generative Adversarial Networks (GANs)
GANs consist of two neural networks, a generator and a discriminator, which are in
an adversarial setup with each other [104]. GANs have been applied to generate images,
videos, and even audio that seem real. GANs also improve data quality by creating
fake data for developing models, such as developing counterfeit photos and videos. For
example, GANs are used in the generation of new images for artistic, fashion, and gaming
purposes or in any other context that requires realistic synthetic images [105]. A GAN is
the perfect tool for creating additional data, especially for areas such as medical imaging,
where data labelling is rarely easy to come by [106].
4.2.5. Transformer Networks
BERT, GPT, T5, and other transformer models have been presented as a revolutionary
step in NLP [107]. Unlike conventional recurrent neural networks, transformers deploy
self-encodings to capture relationships between words in a sentence regardless of the space
between them. Li, Tang [108] describes how generative models, like GPT-3, produce text
that looks and feels like input and can be applied in content creation, chatbots, virtual
assistants, etc. Figure 4 comprehensively represents the application areas of transformers.
CNN, along with transformers, design a hybrid robust architecture, which outperforms
in enhancing performance and effectiveness in computer vision and multi-modal learn-
ing [109]. These models are trained on large volumes of text data and then on specific
application tasks, like text classification, question answering, and translation, e.g., language
learning models (LLMs), as illustrated in Figure 5. Some examples of transformer models,
such as BERT, are used in MANY NLP tasks, including sentiment analysis, document
classification and question-answering [110].
4.3. Applications of Deep Learning in Real-World Scenarios
Various sectors have adopted deep learning to address issues, understand patterns,
and make multiple processes more efficient by designing original, intelligent approaches.
At first, DL applications extended to medical imaging to help diagnose diseases, from
imaging (like tumours from X-ray or MRI scans) to genomics for drug discovery or creating
personalised medicines [111]. Moreover, DL is extensively utilised in computer vision for
object detection and image segmentation tasks. Saood and Hatem [112] applied U-Net and
SegNet to segment COVID-19 CT images. Meanwhile, Singh, Ahuja [113] used R-CNN,
YoloV3, and Mask RCNN to simultaneously detect swimming crabs and face masks during
the COVID-19 era.
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Computers 2025, 14, x FOR PEER REVIEW 16 of 29 
 
[109]. Thesemodels are trained on large volumes of text data and then on specific appli-
cation tasks, like text classification, question answering, and translation, e.g., language 
learning models (LLMs), as illustrated in Figure 5. Some examples of transformer models, 
such as BERT, are used in MANY NLP tasks, including sentiment analysis, document 
classification and question-answering [110]. 
 
Figure 4. Application areas of transformers. 
4.3. Applications of Deep Learning in Real-World Scenarios 
Various sectors have adopted deep learning to address issues, understand patterns, 
and make multiple processes more efficient by designing original, intelligent approaches. 
At first, DL applications extended to medical imaging to help diagnose diseases, from 
imaging (like tumours from X-ray or MRI scans) to genomics for drug discovery or creat-
ing personalised medicines [111]. Moreover, DL is extensively utilised in computer vision 
for object detection and image segmentation tasks. Saood and Hatem [112] applied U-Net 
and SegNet to segment COVID-19 CT images. Meanwhile, Singh, Ahuja [113] used R-
CNN, YoloV3, and Mask RCNN to simultaneously detect swimming crabs and face masks 
during the COVID-19 era. 
When combined with reinforcement learning, multi-agent reinforcement learning 
helps achieve consistent convergence and excel performance [114]. It further supports dis-
tributed decision-making between interrelating agents. 
Figure 4. Application areas of transformers.
Computers 2025, 14, x FOR PEER REVIEW 17 of 29 
 
 
Figure 5. Examples of LLMs with Free and Paid Versions. 
Another application of DL is self-driving cars that use DL algorithms to recognise 
objects and signs on the road and make decisions instantly [115]. In the financial domain, 
DL models work in credit scoring, credit risk, fraud detection, algorithmic trading, and 
stock market prediction [116]. ML improves traffic conditions, controls pollution levels, 
and increases security with cameras sensing odd behaviours [117]. Finally, the applica-
tions of DL include recommendations of content (Netflix, YouTube) and the generation of 
realistic 3D models for gaming with virtual reality [118]. Table 5 further discusses the 
applications of DL in various domains. 
 
Figure 5. Examples of LLMs with Free and Paid Versions.
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When combined with reinforcement learning, multi-agent reinforcement learning
helps achieve consistent convergence and excel performance [114]. It further supports
distributed decision-making between interrelating agents.
Another application of DL is self-driving cars that use DL algorithms to recognise
objects and signs on the road and make decisions instantly [115]. In the financial domain,
DL models work in credit scoring, credit risk, fraud detection, algorithmic trading, and
stock market prediction [116]. ML improves traffic conditions, controls pollution levels, and
increases security with cameras sensing odd behaviours [117]. Finally, the applications of
DL include recommendations of content (Netflix, YouTube) and the generation of realistic
3D models for gaming with virtual reality [118]. Table 5 further discusses the applications
of DL in various domains.
Table 5. Applications of DL in various domains.
Category Type/Technique Description/Examples References
Healthcare
Medical Imaging
DL is used to diagnose diseases by analysing X-rays,
MRI scans, and other medical images. Examples
include IBM Watson Health and Google DeepMind
for eye disease detection.
[57,111,119]
Genomics
DL helps in drug discovery, identifying genetic
patterns, and creating personalised medicines. An
example is deep genomics.
[120,121]
Autonomous Vehicles Object Detection and Decision-Making
Self-driving cars use DL to identify objects and signs
and make real-time decisions on the road. Examples
include Tesla and Waymo.
[67]
Finance
Credit Scoring and Fraud Detection DL models assess credit risk and detect fraudulent
activities. Examples: Kabbage and PayPal. [122]
Algorithmic Trading
DL aids in the development of trading algorithms
and stock market predictions. Examples: Quant
Connect, AlphaGo.
[123,124]
Smart Cities
Traffic Management
DL helps optimise traffic flow and reduce congestion
through real-time analysis. Examples: IBM Smart
Cities, Siemens.
[125]
Pollution Control and Security
DL can control pollution and enhance security with
surveillance cameras. Cisco and Hitachi’s smart city
solutions are examples.
[5,126,127]
Entertainment
Content Recommendation DL algorithms suggest content to users based on
their preferences. Examples: Netflix and YouTube. [128,129]
Virtual Reality and 3D Modelling
DL creates realistic 3D models and environments for
gaming and VR. Examples are Unity and Epic
Games’ Unreal Engine.
[130–132]
Deepfake Technology DL models generate realistic fake videos and images.
Examples: DeepFaceLab, Zao. [133–136]
4.4. Machine Learning vs. Deep Learning
To summarise, ML and DL, on the other hand, are subsets of AI that work in different
ways and utilise various approaches, means, and methods [137]. Although the ML models
are easier to train and much faster, the more complex DL models need a much larger
dataset and take more time to train because of higher computational intensity. Moreover,
the models in ML are usually more explicable, and it is easier to know how the models
make certain decisions, while DL models are labelled as ‘black boxes’ due to the complexity
of the models. In conclusion, ML is better suited for small datasets and quicker outcomes,
while DL is more suitable for large datasets and complicated problems [138]. This section
details the differences between ML and DL decisions by analysing the techniques, data
management, model calibrations, explanatory models, data intensity, and computational
power recorded in Table 6.
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Table 6. Machine learning vs. deep learning.
Aspect Machine Learning Deep Learning
Data Type Structured data Unstructured data (images, text, audio)
Algorithms Supervised, unsupervised, reinforcement CNNs, RNNs, GANs, Transformers
Feature Engineering Requires manual feature extraction Automatic feature extraction
Data Volume Smaller datasets Large datasets
Model Complexity Lower complexity, simpler models Higher complexity, multiple layers
Training Time Faster, quicker iterations Slower, requires more computational power
Interpretability Higher (e.g., decision trees) Low (‘black box’ models)
ComputationalRequirements Lower computational power High computational power (GPUs, TPUs)
Application Predictive modelling, classification, regression Image recognition, NLP, speech recognition,
autonomous vehicles
4.5. Applications Comparison of Machine Learning vs. Deep Learning
The study attempts to illustrate some of the primary differences between ML and a
subfield of ML known as DL, in terms of the approaches, data management, models that
they apply, the interpretability of machines, the data that feeds them, and computational
strength. While ML incorporates basic data models like regression and classification, DL
comprises more sophisticated models like neural networks capable of handling structured
data such as images, videos, and text. Hence, DL may need more data resources and com-
puting power but earns a high reputation for feature extraction and standard applications
such as image or voice recognition, translation, etc. Table 7 makes it easy to compare and
see which approach is more suitable for which type of problem.
Table 7. ML and DL applications.
Industry/Field Machine Learning Applications Deep Learning Applications
Healthcare
- Predicting patient outcomes and treatment responses
- Disease classification (e.g., cancer detection)
- Diagnostic support using electronic health records
(EHR) data
- Drug discovery through predictive models
- Medical image analysis using CNNs for tumour
detection, organ segmentation, and disease detection
(e.g., MRI, CT scans)
- Genomicsand drug discovery (e.g., deep genomics,
Insilico Medicine)
- Personalised medicine, creating treatment plans
based on genetic data
Finance
- Fraud detection using transaction data
- Credit scoring models for loan approval
- Risk analysis for investment portfolios
- Algorithmic trading and stock price prediction
- Credit risk analysis using deep neural networks
- Fraud detection with deep learning-based anomaly
detection systems
- Predicting financial trends with time series models
and neural networks (e.g., LSTMs)
E-commerce
- Product recommendation systems based on past
customer behaviour (e.g., collaborative filtering)
- Customer segmentation for targeted marketing
- Personalised product suggestions based on
browsing patterns
- Personalised recommendations using deep
collaborative filtering (e.g., Netflix, Amazon)
- Image-based product search using CNNs for visual
similarity matching
- Chatbots and customer service automation using
RNNs and LSTMs
Manufacturing
- Predictive maintenance to reduce equipment failure
using sensor data
- Supply chain optimisation through demand forecasting
- Quality control by analysing defects in products
using ML models
- Predictive maintenance using time series forecasting
and deep learning models (e.g., LSTMs)
- Quality control using computer vision and CNNs for
defect detection in production lines
Computer Vision
- Image recognition, object detection, and classification
- Facial recognition using traditional ML algorithms
- Handwriting recognition (e.g., OCR)
- Object and facial recognition using deep
convolutional neural networks (CNNs)
- Real-time video processing for object detection (e.g.,
YOLO, Faster R-CNN)
- Emotion detection using facial expressions and CNNs
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Table 7. Cont.
Industry/Field Machine Learning Applications Deep Learning Applications
Natural Language
Processing (NLP)
- Sentiment analysis for customer feedback
- Text classification (e.g., spam detection,
topic categorisation)
- Named Entity Recognition (NER)
- Machine translation (e.g., Google Translate)
- Sentiment analysis with transformers like BERT
- Text generation using models like GPT-3, RNNs,
and LSTMs
- Machine translation using sequence-to-sequence
models and attention mechanisms
- Speech-to-text systems (e.g., Google Speech, Siri)
Autonomous Vehicles
- Object detection (e.g., pedestrians, vehicles) using
traditional image recognition models
- Path planning and navigation using decision trees
and reinforcement learning
- Object, pedestrian, and road sign recognition
using CNNs
- Autonomous driving decision-making with deep
reinforcement learning
- End-to-end self-driving car systems that combine
perception, planning, and control using deep learning
Entertainment
- Content recommendations based on user preferences
(e.g., Netflix, Spotify)
- User behaviour analysis to suggest relevant content
- Predicting trends in media consumption
- Content generation for movies, video games, and
music using GANs (Generative Adversarial Networks)
- Creating realistic 3D models and environments for
virtual reality using deep learning
- Personalised recommendations using deep
collaborative filtering (e.g., YouTube, Netflix)
Retail
- Customer segmentation for targeted promotions
- Inventory management and demand forecasting
using time series analysis
- Dynamic pricing based on market conditions and
consumer behaviour
- Visual search and recommendation systems
using CNNs
- Personalised shopping experiences based on
customer data using deep learning models
- Chatbots for customer service powered by RNNs
and LSTMs
Energy
- Predicting energy consumption patterns and
optimising grid management
- Forecasting renewable energy generation (e.g., wind
and solar)
- Energy price prediction using historical data
- Smart grid optimisation using deep learning for
real-time data processing
- Predicting energy demand and supply with
LSTM networks
- Fault detection and predictive maintenance for
energy infrastructure
Telecommunications
- Churn prediction models to identify customers likely
to leave
- Network traffic analysis and optimisation
- Customer service automation with chatbots
- Speech recognition for customer support services
using deep learning
- Real-time network traffic prediction using deep
learning models
- Anomaly detection in network data
using autoencoders
Agriculture
- Crop yield prediction using weather and soil data
- Precision agriculture using sensor data for
irrigation management
- Pest and disease detection through satellite imaging
- Crop classification and disease detection using
CNNs for satellite and drone imagery
- Predicting crop growth and yields with
deep-learning models
- Real-time pest detection using computer vision and
deep learning
5. Discussion
The study aimed to discuss and identify ML and DL’s current and potential devel-
opments, emphasising their utilisation across different sectors. AI, particularly ML and
DL, are gaining immense popularity and actively changing various industries, including
business, healthcare, finance, and many others. In this research, we demonstrated how
these techniques are used, how they perform in practice, and their merits and demerits.
According to the first objective, the study identified four significant data types that
ML and DL techniques use: structured, unstructured, semi-structured, and time series data.
The data were collected from different sources, such as healthcare, e-commerce, marketing,
transportation, finance, manufacturing, retail, agriculture, education, and entertainment
industries, and processed using several ML and DL techniques.
For the study’s second objective, a comparison table followed by a comprehensive
discussion was designed. The literature holistically evaluates the differences between ML
and DL regarding data types, algorithms they use, feature engineering, data volume, model
complexity, training times, interpretability, computational requirements, and application areas.
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This study further identified the specific tasks that ML and DL can handle in various
application domains. Table 7 discusses the specific techniques for the tasks in detail. For
example, in healthcare, ML is used to predict patient outcomes, disease classification,
diagnostic processes, and drug discovery, while DL is used to analyse medical images,
genomics, and personalised medicine. Similarly, in computer vision, ML is used for image
recognition, object detection and classification, and handwriting recognition, and DL is
used for facial recognition, real-time video processing, and emotion detection. In other
words, ML and DL have vast application domains with huge, specialised tasks.
Finally, for the fourth objective, the study identified the latest trends and future
directions for ML and DL research. The study explores the applicability of ML and DL
for explainable AI, federated learning, and advanced models to improve efficiency and
scalability. Moreover, automated ML, privacy-preserving AI, and adversarial AI can be
valuable for enhancing overall security, privacy, and decision-making ability. Some other
techniques, like Edge AI and quantum ML, can be used for low latency processing of
data, and another technique, self-supervised ML, supports reducing the extra dependency
on labelled data. At the same time, the hybrid models, i.e., integrating AI with DL, can
enhance adaptability.
Furthermore, as far as the central insights of the study are concerned, the most pro-
found understanding is the distinction between ML and DL. DL works with more complex
models, which need numerous data and powerful computations. On the other hand, ML
models are generally less complex, less time-consuming, and less demanding regarding the
computing resources required to train them. Still, they may be less effective at capturing
the finer details of pattern data, for example, when applied to image or voice recognition.
In many cases,