ACM Computing Surveys

In the past years, the automotive industry has experienced a technological revolution driven by the increasing demand of connectivity and data to develop driver-assistance systems and autonomous vehicles, and improve the mobility experience. To provide ...

A comprehensive review is presented on the latest approaches to solutions focusing on multi-hop and mesh LoRa networks through the evaluation of simulations and real-world experiments, based on papers published between 2015 and 2023. The approaches are ...

Generative models such as generative adversarial networks and autoencoders have gained a great deal of attention in the medical field due to their excellent data generation capability. This article provides a comprehensive survey of generative models for ...

Benefiting from the rapid development of deep learning, 2D and 3D computer vision applications are deployed in many safe-critical systems, such as autopilot and identity authentication. However, deep learning models are not trustworthy enough because of ...

A fundamental issue in blockchain systems is their scalability in terms of data storage, computation, communication, and security. To resolve this issue, a promising research direction is coding theory, which is widely used for distributed storage, ...

Fashion is the manner in which we introduce ourselves to the world and has become perhaps the biggest industry on the planet. In recent years, fashion-related research has received a lot of attention from computer vision researchers as a result of the ...

Artificial Intelligence (AI) and, in particular, Machine Learning (ML), have emerged to be utilized in various applications due to their capability to learn how to solve complex problems. Over the past decade, rapid advances in ML have presented Deep ...

The introduction of smart contracts has expanded the applicability of blockchains to many domains beyond finance and cryptocurrencies. Moreover, different blockchain technologies have evolved that target special requirements. As a result, in practice, ...

This survey paper examines emerging network approaches for military Command and Control (C2) systems. An extensive literature review is provided along the text on network-centric C2 systems. Also, it provides a comprehensive analysis of the paradigm based ...

We use sensor technologies in many areas of everyday life. We use sensors to check and study various phenomena and to improve our lives. Hence, the sensors are used in medicine, industry, sports, and many other aspects of everyday life. Interconnected ...

Malware is one of the most common and severe cyber threats today. Malware infects millions of devices and can perform several malicious activities including compromising sensitive data, encrypting data, crippling system performance, and many more. Hence, ...

Deep learning (DL) has demonstrated its remarkable success in a wide variety of fields. The development of a DL model is a time-consuming and resource-intensive procedure. Hence, dedicated GPU accelerators have been collectively constructed into a GPU ...

Electronic Health Records (EHRs) are a valuable asset to facilitate clinical research and point of care applications; however, many challenges such as data privacy concerns impede its optimal utilization. Deep generative models, particularly Generative ...

In this survey, we review the key developments in the field of malware detection using AI and analyze core challenges. We systematically survey state-of-the-art methods across five critical aspects of building an accurate and robust AI-powered malware-...

The generation of biomedical data is of such magnitude that its retrieval and analysis have posed several challenges. A survey of recommender system (RS) approaches in biomedical fields is provided in this analysis, along with a discussion of existing ...

General-purpose knowledge bases (KBs) are a cornerstone of knowledge-centric AI. Many of them are constructed pragmatically from web sources and are thus far from complete. This poses challenges for the consumption as well as the curation of their ...

The rapid development of Machine Learning (ML) has demonstrated superior performance in many areas, such as computer vision and video and speech recognition. It has now been increasingly leveraged in software systems to automate the core tasks. However, ...

Deep Reinforcement Learning (DRL) is an essential subfield of Artificial Intelligence (AI), where agents interact with environments to learn policies for solving complex tasks. In recent years, DRL has achieved remarkable breakthroughs in various tasks, ...

Deep learning (DL) plays an important role in modern agriculture, especially in plant pathology using leaf images where convolutional neural networks (CNN) are attracting a lot of attention. While numerous reviews have explored the applications of DL ...

Utilization of the Internet in our everyday lives has made us vulnerable in terms of privacy and security of our data and systems. Therefore, there is a pressing need to protect our data and systems by improving authentication mechanisms, which are ...

In the last decade, cybercrime has risen considerably. One key factor is the proliferation of online cybercrime communities, where actors trade products and services, and also learn from each other. Accordingly, understanding the operation and behavior of ...

Knowledge graph (KG) refinement refers to the process of filling in missing information, removing redundancies, and resolving inconsistencies in KGs. With the growing popularity of KG in various domains, many techniques involving machine learning have ...

The fourth industrial revolution, i.e., Industry 4.0, is associated with Cyber-Physical Systems (CPS), which are entities integrating hardware (e.g., smart sensors and actuators connected through the Industrial Internet of Things) together with control ...

Drug–drug interaction (DDI) is an important part of drug development and pharmacovigilance. At the same time, DDI is an important factor in treatment planning, monitoring effects of medicine and patient safety, and has a significant impact on public ...

Knowledge graph embedding (KGE) is an increasingly popular technique that aims to represent entities and relations of knowledge graphs into low-dimensional semantic spaces for a wide spectrum of applications such as link prediction, knowledge reasoning ...

The novel paradigm Social Internet of Things (SIoT) improves the network navigability, identifies suitable service providers, and addresses scalability concerns. Ensuring trustworthy collaborations among devices is a key aspect in SIoT and can be realized ...

In this article, we focus on the opacity issue of sub-symbolic machine learning predictors by promoting two complementary activities—symbolic knowledge extraction (SKE) and symbolic knowledge injection (SKI)—from and into sub-symbolic predictors. We ...