Machine Learning and Deep Learning: A Comparative Review

Alaskar, Hind; Saba, TanzilaSaba

doi:10.1007/978-981-33-6307-6_15

Cited by 17 publications

(13 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In the prediction of enzyme–substrate interactions, the most used algorithms are discriminative, since there exist limited training data [ 5 ]. Among the discriminative methods most used in the prediction of enzyme–substrate interactions, we find the algorithms based on support vector machines, neural networks, and decision trees [ 53 ]. Table 3 shows some enzyme-substrate interaction studies that have been conducted in recent years, specifying the algorithm used, the application, and the performance obtained.…”

Section: Artificial Intelligence Modelsmentioning

confidence: 99%

Machine Learning to Predict Enzyme–Substrate Interactions in Elucidation of Synthesis Pathways: A Review

Salas-Nuñez,

Barrera-Ocampo,

Caicedo

et al. 2024

Metabolites

View full text Add to dashboard Cite

Enzyme–substrate interactions play a fundamental role in elucidating synthesis pathways and synthetic biology, as they allow for the understanding of important aspects of a reaction. Establishing the interaction experimentally is a slow and costly process, which is why this problem has been addressed using computational methods such as molecular dynamics, molecular docking, and Monte Carlo simulations. Nevertheless, this type of method tends to be computationally slow when dealing with a large search space. Therefore, in recent years, methods based on artificial intelligence, such as support vector machines, neural networks, or decision trees, have been implemented, significantly reducing the computing time and covering vast search spaces. These methods significantly reduce the computation time and cover broad search spaces, rapidly reducing the number of interacting candidates, as they allow repetitive processes to be automated and patterns to be extracted, are adaptable, and have the capacity to handle large amounts of data. This article analyzes these artificial intelligence-based approaches, presenting their common structure, advantages, disadvantages, limitations, challenges, and future perspectives.

show abstract

Section: Artificial Intelligence Modelsmentioning

confidence: 99%

Machine Learning to Predict Enzyme–Substrate Interactions in Elucidation of Synthesis Pathways: A Review

Salas-Nuñez,

Barrera-Ocampo,

Caicedo

et al. 2024

Metabolites

View full text Add to dashboard Cite

show abstract

“…Ganatra and Patel (2018) discussed deep learning models and available development tools, while Cao et al (2020) reviewed geometric deep learning techniques, focusing on graph network algorithms and their applications. Alaskar and Saba (2021) conducted a comparative study of deep learning and machine learning, delineating their strengths and weaknesses. Furthermore, Sarker (2021) categorized deep learning into supervised, unsupervised, and hybrid models.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning: Historical Overview from Inception to Actualization, Models, Applications and Future Trends

Ekundayo,

Ezugwu

2024

Preprint

View full text Add to dashboard Cite

Deep learning stands at the forefront of contemporary machine learning techniques and is well-known for its outstanding predictive accuracy, adaptability to data variability, and remarkable ability to generalize across diverse domains. These attributes have spurred rapid progress and the emergence of novel iterations within the discipline. Yet, this swift evolution often obscures the foundational breakthroughs, with even trailblazing researchers at risk of fading into obscurity despite their seminal contributions. This study aims to provide a historical narrative of deep learning, tracing its origins from the cybernetic era to its current state-of-the-art status. We critically examine the contributions of individual pioneer scholars who have profoundly influenced the development of deep neural networks under the taxonomy of supervised, unsupervised, and reinforcement learning. Furthermore, the study also discusses the trending deep neural network architectures, explaining their operational principles, confronting associated challenges, exploring real-world applications, and outlining potential future trajectories that could offer a starting point for aspiring researchers in the field.

show abstract

“…Deep learning (DL) is a part of the broader family of Artificial Intelligence, which uses volumes of data to obtain a powerful representation of data. ML involves training computers to learn from data and make predictions, while DL uses artificial neural networks to automatically discover data representations (Alaskar 2021). Researchers have come a long way in developing powerful methodologies that can understand tabular data, audio, images, videos, and text.…”

Section: Introductionmentioning

confidence: 99%

Mapping the Geometry of Law Using Natural Language Processing

Bhupatiraju,

Chen,

Venkataramanan

2024

EJELS

View full text Add to dashboard Cite

Judicial documents and judgments are a rich source of information about legal cases, litigants, and judicial decision-makers. Natural language processing (NLP) based approaches have recently received much attention for their ability to decipher implicit information from text. NLP researchers have successfully developed data-driven representations of text using dense vectors that encode the relations between those objects. In this study, we explore the application of the Doc2Vec model to legal language to understand judicial reasoning and identify implicit patterns in judgments and judges. In an application to federal appellate courts, we show that these vectors encode information that distinguishes courts in time and legal topics. We use Doc2Vec document embeddings to study the patterns and train a classifier model to predict cases with a high chance of being appealed at the Supreme Court of the United States (SCOTUS). There are no existing benchmarks, and we present the first results at this task at scale. Furthermore, we analyze generic writing/judgment patterns of prominent judges using deep learning-based autoencoder models. Overall, we observe that Doc2Vec document embeddings capture important legal information and are helpful in downstream tasks.

show abstract

Machine Learning and Deep Learning: A Comparative Review

Cited by 17 publications

References 19 publications

Machine Learning to Predict Enzyme–Substrate Interactions in Elucidation of Synthesis Pathways: A Review

Machine Learning to Predict Enzyme–Substrate Interactions in Elucidation of Synthesis Pathways: A Review

Deep Learning: Historical Overview from Inception to Actualization, Models, Applications and Future Trends

Mapping the Geometry of Law Using Natural Language Processing

Contact Info

Product

Resources

About