This systematic review explores the need for explainability in computational intelligence methods for optimization, such as metaheuristic optimizers, including evolutionary algorithms and swarm intelligence. The work focuses on four aspects: (1) the contribution of Explainable AI (XAI) methods to interpreting metaheuristic performance; (2) the influence of problem features on search behavior and explainability;

Abstractive text summarization addresses information overload by generating paraphrased content that mimics human expression, yet it faces significant computational and linguistic challenges. This paper presents a detailed functional taxonomy of abstractive summarization, structured along four dimensions: techniques (including structure-based, semantic, and deep learning approaches, including large

Quantum image processing uses quantum hardware to revolutionize the storage, recovery, processing, and security of quantum images across diverse applications. Although researchers have explored various facets of quantum image processing, a comprehensive systematic literature review encompassing all domains is essential for theoretical and experimental progress. This article aims to bridge this gap

With the development of technology, drones, and satellites play an important role in human life. Related research problems receive great attention, especially in the computer vision community. Notably, the object detection models on aerial imagery take part in many applications in both civil and military domains. Although it has great potential and has achieved many achievements, it cannot be denied

Aspect-based sentiment analysis (ABSA) has gradually become an important technique for mining online reviews and is widely popular across various domains, such as producer–consumer, pharmaceutical reviews, political campaigns, and celebrity popularity. Aspect sentiment triplet extraction (ASTE) is a core technique within the ABSA, as it automatically extracts aspect terms, opinion terms, and sentiment

Process algebras (PAs) provide the mathematical foundation for several verification techniques and have profoundly influenced many areas of computer science. One of the main reasons for their success is their compact yet expressive and flexible syntax, which allows for the modeling of the relevant aspects of computation while abstracting away the irrelevant ones. Cybersecurity is no exception, and

Thanks to advancements in deep learning, speech generation systems now power a variety of real-world applications, such as text-to-speech for individuals with speech disorders, voice chatbots in call centers, cross-linguistic speech translation, etc. While these systems can autonomously generate human-like speech and replicate specific voices, they also pose risks when misused for malicious purposes

With the increasing need for efficient document retrieval in low-resource languages (LRLs), traditional retrieval methods struggle to overcome linguistic challenges such as data scarcity, morphological complexity, and orthographic variations. To address this, hybrid and neural ranking approaches have been explored, integrating statistical retrieval with transformer-based models to enhance search accuracy

With the rise of digital image sharing in fields like healthcare, defence, and multimedia, strong image encryption is needed to protect sensitive information. This study provides a detailed overview of the analysis of image encryption algorithms, focusing on their design principles and performance metrics. This study covers different encryption methods, from traditional symmetric and asymmetric to

Security infrastructure is vital in blockchain for its decentralized and distributed characteristics. Blockchain security infrastructure comprises several components: cryptographic algorithms, consensus protocols, key and identity management, network architecture, and smart contract deployment and execution. These components are vulnerable to the advancement of quantum computing and the realization

Large language models (LLMs) have showcased exceptional capabilities across various natural language processing (NLP) tasks in recent years, such as machine translation, text summarization, and question answering. Despite their impressive performance, the deployment of these models on edge devices, such as mobile phones, IoT devices, and edge computing nodes, is significantly hindered by their substantial

The security of cyber–physical systems is crucial due to their critical applications. The increasing success of machine learning (ML) has raised growing concerns about its impact on the cybersecurity of cyber–physical systems. Although several studies have assessed the cybersecurity of cyber–physical systems, there remains a lack of systematic understanding of how ML techniques can contribute to the

The COVID-19 pandemic has accelerated the use of AI and ML in healthcare, improving diagnosis, treatment, and resource allocation. This survey examines the AI applications in disease detection, differential diagnosis, and post-COVID complication analysis. Our findings show that 53% of the reviewed studies focus on COVID-19 detection, while only 14% address post-COVID complications. This reveals a gap

Multimodal data, which encompasses text, audio, image, and other modalities, is a popular research target in the field of visualization research. Existing visualization techniques for multimodal data are scattered and categorized by application domains, such as multimodal model analysis or online education. It lacks a comprehensive review from the perspective of data that summarizes the methodologies

In the recent past, the increasing use of online applications, from simple food orders to complex banking services, has highlighted the need for strong and reliable network security measures. Traditional password-based authentication schemes have proven vulnerable to various cyber threats, including password guessing, keylogging, phishing, and credential theft, etc. To address these challenges, the

The rapid growth in scale and complexity of data centers has established effective load balancing as a critical requirement for optimizing network performance, resource utilization, and service quality. This survey provides a comprehensive examination of load balancing strategies in data center networks, addressing challenges, established techniques, and emerging trends. The study begins with fundamental

The emerging field of quantum computing has shown it might change how we process information by using the unique principles of quantum mechanics. As researchers continue to push the boundaries of quantum technologies to unprecedented levels, distributed quantum computing raises as an obvious path to explore with the aim of boosting the computational power of current quantum systems. This paper presents

Over the past three decades, standardizing organizations (e.g., the National Institute of Standards and Technology and Internet Engineering Task Force) have investigated the efficiency of cryptographic algorithms and provided (technical) guidelines for practitioners. For example, the (Datagram) Transport Layer Security “(D)TLS” 1.2/1.3 was designed to help industries implement and integrate such methods

API use has become prevalent in current times and its purposeful management is of foremost importance to avoid undesired effects on client code. A plethora of studies focusing on the isolated investigation of different types of harmful API uses (e.g., API misuse and security vulnerabilities) have been conducted before. However, a comprehensive overview of possible harmful API uses is required to help

The emergence of food safety big data poses a huge challenge to data analysis and the application of technology. Intelligent visual analytics combines the advantages of artificial intelligence and visual analytics methods to process complex information more efficiently and accurately, providing technical support for intelligent food safety supervision. In this paper, we review the development and application

Optimization, as a fundamental pillar in engineering, computer science, economics, and many other fields, plays a decisive role in improving the performance of systems and achieving desired goals. Optimization problems involve many variables, various constraints, and nonlinear objective functions. Among the challenges of complex optimization problems is the extensive search space with local optima

The speed of response by search and rescue teams at sea is of vital importance, as survival may depend on it. Recent technological advancements have led to the development of more efficient systems for locating individuals involved in a maritime incident, such as the use of Unmanned Aerial Vehicles (UAVs) equipped with cameras and other integrated sensors. Over the past decade, several researchers

In digital era, human authentication and identification mostly relies on biometric traits of an individual. Amongst different biometrics, fingerprint has been playing a crucial role and employing as fundamental evidence due to some of its inherent properties. Moreover, it establishes itself as the strongest verification component in several applications like – court of law, criminal and forensic investigations

Data exploration is increasingly relevant to the average person in our data-driven world, as data is now often open source and available to the general public and other non-expert users via open data portals and other similar data sources. This has introduced the need for data exploration tools, methods and techniques to engage non-expert users in data exploration, and thus a proliferation of new research

In recent decades, there has been an increasing interest from the research community in various scientific and engineering fields, including robotic control, signal processing, image processing, feature selection, classification, clustering, and other issues. Many optimization problems are inherently complicated and complex. They cannot be solved by traditional optimization methods, such as mathematical

Aerial computing can facilitate the successful execution of tasks, ensuring low latency for Internet of things (IoT) devices. It gains greater significance and practicality by offering both edge and cloud computing services for IoT applications. However, in aerial computing, resources such as computing power, energy, and bandwidth are limited and constrained. Consequently, certain tasks must be offloaded

Natural Language Processing (NLP) techniques have gained significant traction within the healthcare domain for analyzing textual healthcare-related datasets, sourced primarily from Electronic Health Records (EHR) and increasingly from social networks. This study delves into applying NLP technologies within the healthcare sector, drawing insights from textual datasets from various sources. It reviews

This paper surveys heuristic methods for matrix bandwidth reduction, including low-cost methods and metaheuristics. This optimization min–max problem represents a demanding problem for heuristic methods. This paper poses the graph layout problem with its formal definition. The study also considers the application domains in which practitioners employ the linear graph layout problem on general matrices

Autism Spectrum Disorder (ASD) is a multifaceted neurodevelopmental condition characterized by social communication challenges, repetitive behaviors, and restricted interests. Early and accurate diagnosis is paramount for effective intervention and treatment, significantly improving the quality of life for individuals with ASD. This comprehensive review aims to elucidate the various methodologies employed

WebAssembly is revolutionizing the approach to developing modern applications. Although this technology was born to create portable and performant modules in web browsers, currently, its capabilities are extensively exploited in multiple and heterogeneous use-case scenarios. With the extensive effort of the community, new toolkits make the use of this technology more suitable for real-world applications

Road infrastructure networks are crucial in facilitating smart mobility, as indicated by the emergence of innovative transportation concepts that offer improved efficiency and environmental sustainability. This study seeks to review the literature regarding road pavement condition assessment performance improvement tools which utilize various computer vision and photogrammetry tools aided by machine

The Artificial Hummingbird Algorithm (AHA) is a metaheuristic optimization technique inspired by the behaviours and foraging strategies of hummingbirds. Known for their extraordinary agility and accuracy in collecting nectar, hummingbirds provide an exemplary framework for tackling complex optimization problems. Developed by Zhao et al. in 2022, AHA has swiftly attracted interest within the research

Cover selection is the process of selecting a suitable cover for steganography. Cover selection is crucial to maintain the steganographic characteristics performances and further avoid detection of hidden messages by eavesdroppers. Numerous existing reviews have focused mainly on the implementation and performance of steganography methods. Existing reviews have demonstrated inadequate depth of analysis

Medical image segmentation plays an important role in computer-aided diagnosis. Attention mechanisms that distinguish important parts from irrelevant parts have been widely used in medical image segmentation tasks. This paper systematically reviews the basic principles of attention mechanisms and their applications in medical image segmentation. First, we review the basic concepts of attention mechanism

Graph Neural Networks (GNNs) have become a prominent technique for the analysis of graph-based data and knowledge extraction. This data can be either structured or unstructured. GNN approaches are particularly beneficial when it comes to examining non-euclidean data. Graph data formats are well-known for their capability to represent intricate systems and understand their relationships. GNNs have significantly

Waste management has grown to become one of the leading global challenges due to the massive generation of thousands of tons of waste that is produced daily, leading to severe environmental degradation, the risk of public health, and resource depletion. Despite efforts directed towards solving these problems, traditional methods of sorting and categorizing waste are inefficient and unsustainable, thus

The rapid advancement in healthcare data collection technologies and the importance of using multimodal data for accurate diagnosis leads to a surge in multimodal data characterized by different types, structures, and missing values. Machine learning algorithms for predicting or analyzing usually demand the completeness of data. As a result, handling missing data has become a critical concern in the

The quest to find reliable biomarkers in autism spectrum disorders (ASD) is an ongoing endeavour to identify both underlying causes and measurable indicators of this neurodevelopmental condition. Machine learning (ML) and advanced deep learning (DL) techniques have enhanced biomarker identification in neuroimaging and behavioral studies, aiding in diagnostic accuracy and early detection. This review

Cardiovascular diseases (CVDs) are a global health concern, demanding advanced healthcare solutions. Accurate identification of CVDs via electrocardiogram (ECG) analysis is complex. Artificial Intelligence (AI) offers potential in improving diagnostic accuracy and uncovering new associations between ECG patterns and heart health risks. This paper reviews AI's historical evolution in CVD diagnosis,

Cyberattacks have increased radically over the last years, while the exploitation of Artificial Intelligence (AI) leads to the implementation of even smarter attacks which subsequently require solutions that will efficiently confront them. This need is indulged by incorporating Federated Intrusion Detection Systems (FIDS), which have been widely employed in multiple scenarios involving communication

Knowledge graph embedding (KGE) is a hot topic in the field of Knowledge graphs (KG). It aims to transform KG entities and relations into vector representations, facilitating their manipulation in various application tasks and real-world scenarios. So far, numerous models have been developed in KGE to perform KG embedding. However, several challenges must be addressed when designing effective KGE models

The sharing of data and digital assets in a decentralized settling is entangled with various legislative challenges, including, but not limited to, the need to adhere to legal requirements with respect to privacy and copyright. In order to provide more control to data and digital asset owners, usage control could be used to make sure that consumers handle data according to privacy, licenses, regulatory

The emergence of the Internet of Things (IoT) signifies a transformative wave of innovation, establishing a network of devices designed to enrich everyday experiences. Developing intelligent and secure IoT applications without compromising user privacy and the transparency of model decisions causes a significant challenge. Federated Learning (FL) serves as a innovative solution, encouraging collaborative

As society’s dependence on Information and Communication Technologies (ICTs) grows, providing efficient and resourceful services entails many complexities that require, among others, scalable systems that are provided with intelligent and automated management. In parallel, the different components of cloud computing are continuously evolving to enhance their capabilities towards leveraging the next

Ontology learning, particularly axiom learning, is a challenging task that focuses on building expressive and decidable ontologies. The literature proposes several research efforts aimed to resolve the complexities inherent in axiom and rule learning, which seeks to automatically infer logical constructs from diverse data sources. The goal of this paper is to conduct a comprehensive review of existing

The raster format of images and data is commonly intended as a synonymous of a square grid. Indeed, the square is not the only shape that can tessellate the plane. Other grids are well-known, and recently they have moved out of the fields of art and mathematics, and have started being of interest for technological applications. After introducing the main types of non-square grids, this paper presents

The Internet of Things (IoT) is the basis of smart agriculture technology since it connects all aspects of intelligent systems in other industries and agricultural applications. The current farming methods are sufficient to supply adequate food in the future due to the fast-expanding global population. Smart farming aims to increase farm output and efficiency by leveraging state-of-the-art information

Software Defined network (SDN) represents a sophisticated networking approach that separates the control logic from the data plane. This separation results in a loosely coupled architecture between the control and data planes, enhancing flexibility in managing and transforming network configurations. Additionally, SDN provides a centralized management model through the SDN controller, simplifying network

Due to the increasing popularity of application scenarios such as cloud computing, and the growing concern of users about the security and privacy of their data, information security and privacy protection technologies are facing new challenges. Consequently, Homomorphic Encryption (HE) technology has been developed. HE technology has evolved from Partially Homomorphic Encryption (PHE) to fully homomorphic

This review provides an overview of apple detection and localization using image analysis and artificial intelligence techniques for enabling robotic fruit harvesting in orchard environments. Classic methods for detecting and localizing infield apples are discussed along with more advanced approaches using deep learning algorithms that have emerged in the past few years. Challenges faced in apple detection

The dynamic and decentralized architecture of fog computing, which extends cloud computing closer to the edge of the network, offers benefits such as reduced latency and enhanced bandwidth. However, the existing fog architecture introduces unique security challenges due to the large number of distributed fog nodes, often deployed in diverse and resource-constrained environments. Further, the proximity

Object detection has been used extensively in daily life, and in computer vision, this sub-field is highly significant and challenging. The field of object detection has been transformed by deep learning. Deep learning-based methods have shown to be remarkably effective at identifying and localizing objects in images and video streams when it comes to object detection. Deep learning algorithms can

Demographic change in the world is a reality, and as a result, the number of elderly people is growing in both developed and developing countries, posing several social and economic issues. Most elderly people choose to stay alone at home rather than living with their families who can take care of them. Robots have the potential to revolutionize elderly care by providing aid, companionship, and monitoring

Machine Learning (ML) is currently being exploited in numerous applications, being one of the most effective Artificial Intelligence (AI) technologies used in diverse fields, such as vision, autonomous systems, and the like. The trend motivated a significant amount of contributions to the analysis and design of ML applications against faults affecting the underlying hardware. The authors investigate

Cluster-based routing techniques have become a key solution for managing data flow in Wireless Sensor Networks (WSNs), which often struggle with limited resources and dynamic network conditions. With the growing need for efficient data management in these networks, it is more important than ever to understand and enhance these techniques. This survey evaluates recent cluster-based routing protocols

Internet-of-things (IoT) is a revolutionary paragon that brings automation and easiness to human lives and improves their experience. Smart Homes, Healthcare, and Agriculture are some of their amazing use cases. These IoT applications often employ Machine Learning (ML) techniques to strengthen their functionality. ML can be used to analyze sensor data for various, including optimizing energy usage

The burgeoning field of automated negotiation systems represents a transformative approach to resolving conflicts and allocating resources with enhanced efficiency. This paper presents a thorough survey of this discipline, emphasising the implications of human factors, the application of machine learning techniques, and the real-world deployments of these systems. In traditional manual negotiation

Artificial Intelligence (AI) and Machine Learning (ML) are being used more and more to handle complex tasks in many different areas. As a result, interconnected information systems are growing, which means that autonomous systems are needed to help them adapt, find complex patterns, and make better decisions in areas like cybersecurity, finance, healthcare, authentication, marketing, and supply chain

Artificial Intelligence (AI) plays a crucial role in the digital transformation of organizations, with the influence of AI applications expanding daily. Given this context, the development of these AI systems to guarantee their effective operation and usage is becoming more essential. To this end, numerous international standards have been introduced in recent years. This paper offers a broad review