Artificial Intelligence Review最新文献

In breast cancer research, diverse data types and formats, such as radiological images, clinical records, histological data, and expression analysis, are employed. Given the intricate nature of natural phenomena, relying on the features of a single modality is seldom sufficient for comprehensive analysis. Therefore, it is possible to guarantee medical relevance and achieve improved clinical outcomes by combining several modalities. The presen study carefully maps and reviews 47 primary articles from six well-known digital libraries that were published between 2018 and 2023 for breast cancer classification based on multimodal deep learning fusion (MDLF) techniques. This systematic literature review encompasses various aspects, including the medical modalities combined, the datasets utilized in these studies, the techniques, models, and architectures used in MDLF and it also discusses the advantages and limitations of each approach. The analysis of selected papers has revealed a compelling trend: the emergence of new modalities and combinations that were previously unexplored in the context of breast cancer classification. This exploration has not only expanded the scope of predictive models but also introduced fresh perspectives for addressing diverse targets, ranging from screening to diagnosis and prognosis. The practical advantages of MDLF are evident in its ability to enhance the predictive capabilities of machine learning models, resulting in improved accuracy across diverse applications. The prevalence of deep learning models underscores their success in autonomously discerning complex patterns, offering a substantial departure from traditional machine learning approaches. Furthermore, the paper explores the challenges and future directions in this field, including the need for larger datasets, the use of ensemble learning methods, and the interpretation of multimodal models.

Computerized compliance of Personal Protective Equipment (PPE) is an emerging topic in academic literature that aims to enhance workplace safety through the automation of compliance and prevention of PPE misuse (which currently relies on manual employee supervision and reporting). Although trends in the scientific literature indicate a high potential for solving the compliance problem by employing computer vision (CV) techniques, the practice has revealed a series of barriers that limit their wider applications. This article aims to contribute to the advancement of CV-based PPE compliance by providing a comparative review of high-level approaches, algorithms, datasets, and technologies used in the literature. The systematic review highlights industry-specific challenges, environmental variations, and computational costs related to the real-time management of PPE compliance. The issues of employee identification and identity management are also discussed, along with ethical and cybersecurity concerns. Through the concept of CV-based PPE Compliance 4.0, which encapsulates PPE, human, and company spatio-temporal variabilities, this study provides guidelines for future research directions for addressing the identified barriers. The further advancements and adoption of CV-based solutions for PPE compliance will require simultaneously addressing human identification, pose estimation, object recognition and tracking, necessitating the development of corresponding public datasets.

Abdominal organs play a significant role in regulating various functional systems. Any impairment in its functioning can lead to cancerous diseases. Diagnosing these diseases mainly relies on radiologists’ subjective assessment, which varies according to professional abilities and clinical experience. Computer-Aided Diagnosis (CAD) system is designed to assist clinicians in identifying various pathological changes. Hence, automatic pancreas segmentation is a vital input to the CAD system in the diagnosis of cancer at its early stages. Automatic segmentation is achieved through traditional methods like atlas-based and statistical models, and nowadays, it is achieved through artificial intelligence approaches like machine learning and deep learning using various imaging modalities. This study investigates and analyses the various state-of-the-art multi-organ and pancreas segmentation approaches to identify the research gaps and future perspectives for the research community. The objective is achieved by framing the research questions using the PICOC framework and then selecting 140 research articles using a systematic process through the Covidence tool to conclude the answers to the respective questions. The literature search has been conducted on five databases of original studies published from 2003 to 2023. Initially, the literature analysis is presented in terms of publication, and the comparative analysis of the current study is presented with existing review studies. Then, existing studies are analyzed, focusing on semi-automatic and automatic multi-organ segmentation and pancreas segmentation, using various learning methods. Finally, the various critical issues, the research gaps and the future perspectives of segmentation methods based on published evidence are summarized.

The emergence of neuromorphic computing, inspired by the structure and function of the human brain, presents a transformative framework for modelling neurological disorders in drug development. This article investigates the implications of applying neuromorphic computing to simulate and comprehend complex neural systems affected by conditions like Alzheimer’s, Parkinson’s, and epilepsy, drawing from extensive literature. It explores the intersection of neuromorphic computing with neurology and pharmaceutical development, emphasizing the significance of understanding neural processes and integrating deep learning techniques. Technical considerations, such as integrating neural circuits into CMOS technology and employing memristive devices for synaptic emulation, are discussed. The review evaluates how neuromorphic computing optimizes drug discovery and improves clinical trials by precisely simulating biological systems. It also examines the role of neuromorphic models in comprehending and simulating neurological disorders, facilitating targeted treatment development. Recent progress in neuromorphic drug discovery is highlighted, indicating the potential for transformative therapeutic interventions. As technology advances, the synergy between neuromorphic computing and neuroscience holds promise for revolutionizing the study of the human brain’s complexities and addressing neurological challenges.

Bengali is the official language of Bangladesh and is widely used in Bangladesh and West Bengal in India. Due to the growing accessibility of the internet and smart devices, the use of digital text material and documents in Bengali is growing with time. An automated Bengali Sentence Validation System is proposed in this study to effectively determine the correctness of sentences in such extensively available Bengali content. As far as we know, no substantial work has been done in the field of Bengali Sentence Validation utilizing deep learning approaches. Due to the lack of linguistic resources, sophisticated Natural Language Processing tools, and benchmark datasets, developing an automated Sentence Validation System for a limited-resource language like Bengali is challenging. Additionally, Bengali Sentences come in two morphological varieties (Sadhu-bhasha and Cholito-bhasha), making the validation process more challenging. The proposed automated Bengali Sentence Validation system contains the CNN-BiLSTM hybrid classifier model. As of now, there is no standard dataset for Bengali sentence validation. Due to the lack of a standard dataset, we collected Bengali sentences from different sources in Bangladesh and developed a Bengali Sentence Validation (BSV) Dataset with around 5000 labelled sentences arranged into two categories such as correct and incorrect. Experimental results demonstrate that the proposed system outperformed other classifier models and existing approaches for Bengali Sentence Validation and is able to categorize a wide range of Bengali sentences based on their correctness. The system’s F1 score for the Bengali Sentence Validation is 98%.

Mechanics-related tasks often present unique challenges in achieving accurate geometric and physical representations, particularly for non-uniform structures. Graph neural networks (GNNs) have emerged as a promising tool to tackle these challenges by adeptly learning from graph data with irregular underlying structures. Consequently, recent years have witnessed a surge in complex mechanics-related applications inspired by the advancements of GNNs. Despite this process, there is a notable absence of a systematic review addressing the recent advancement of GNNs in solving mechanics-related tasks. To bridge this gap, this review article aims to provide an in-depth overview of the GNN applications in mechanics-related domains while identifying key challenges and outlining potential future research directions. In this review article, we begin by introducing the fundamental algorithms of GNNs that are widely employed in mechanics-related applications. We provide a concise explanation of their underlying principles to establish a solid understanding that will serve as a basis for exploring the applications of GNNs in mechanics-related domains. The scope of this paper is intended to cover the categorisation of literature into solid mechanics, fluid mechanics, and interdisciplinary mechanics-related domains, providing a comprehensive summary of graph representation methodologies, GNN architectures, and further discussions in their respective subdomains. Additionally, open data and source codes relevant to these applications are summarised for the convenience of future researchers. This article promotes an interdisciplinary integration of GNNs and mechanics and provides a guide for researchers interested in applying GNNs to solve complex mechanics-related tasks.

The Kepler Optimisation Algorithm (KOA) is a recently proposed algorithm that is inspired by Kepler’s laws to predict the positions and velocities of planets at a given time. However, although promising, KOA can encounter challenges such as convergence to sub-optimal solutions or slow convergence speed. This paper proposes an improvement to KOA by integrating chaotic maps to solve complex engineering problems. The improved algorithm, named Chaotic Kepler Optimization Algorithm (CKOA), is characterized by a better ability to avoid local minima and to reach globally optimal solutions thanks to a dynamic diversification strategy based on chaotic maps. To confirm the effectiveness of the suggested approach, in-depth statistical analyses were carried out using the CEC2020 and CEC2022 benchmarks. These analyses included mean and standard deviation of fitness, convergence curves, Wilcoxon tests, as well as population diversity assessments. The experimental results, which compare CKOA not only to the original KOA but also to eight other recent optimizers, show that the proposed algorithm performs better in terms of convergence speed and solution quality. In addition, CKOA has been successfully tested on three complex engineering problems, confirming its robustness and practical effectiveness. These results make CKOA a powerful optimisation tool in a variety of complex real-world contexts. After final acceptance, the source code will be uploaded to the Github account: nawal.elghouate@usmba.ac.ma.

The continuous improvement in the connectivity, automation and autonomy levels of Intelligent Connected Vehicles (ICVs) significantly increases the probability of potential security threats. Over-the-Air (OTA) is a promising technique for upgrading features of ICVs and enhancing their reliability and security against environmental disturbances as well as malicious attacks. To better understand the potential security risks and possible countermeasures, we survey research works in ICV security during OTA from cloud upgrade, terminal upgrade, and object upgrade. We also summarize existing methods in OTA upgrading techniques and systematically investigate the overall framework of OTA upgrading methods from the perspectives of Software-Over-the-Air (SOTA) and Firmware-Over-the-Air (FOTA).We further discuss possible mitigation strategies and open issues yet to be resolved in this research direction. This survey shows that OTA provides a powerful technique for upgrading the ICV features and improving ICV security.

The edge computing paradigm is becoming increasingly commercialized due to the widespread adoption of wireless communication technologies and the growing demand for compute-intensive mobile applications. Edge computing complements the cloud computing model by deploying computation, storage, and network resources to the edge locations of wireless access networks, empowering end devices to run resource-intensive applications. In order to promote the commercialization of edge computing, it is important to explore effective ways to reduce the cost of edge computing networks. This paper provides a comprehensive review of the research findings in recent years, offering a clear perspective on the research dynamics. This paper first recalls the architectural framework of edge computing. Then, the main optimization objectives and optimization methods are comprehensively described. Mainstream mathematical models for cost reduction are then shown in depth. The paper also discusses the methods used to evaluate the effectiveness. Then, typical examples of typical application scenarios for edge computing networks are examined in depth. Finally, the paper identifies some unresolved issues. We expect future research to make more attempts in these directions.

In the realm of electrical distribution, managing power quality is critical due to its significant impact on infrastructure and customer satisfaction. Addressing issues such as voltage sags and swells, along with current and voltage harmonics, is imperative. The innovative approach proposed in this paper centers on a dual-phase control strategy using a Universal Power Quality Conditioner that integrates series and parallel compensations to rectify these disturbances simultaneously. Our methodology introduces a hybrid control scheme that employs adaptive dynamic neural networks (ADNN), a sinusoidal tracking filter (STF), and a proportional-integral (PI) controller optimized via an improved krill herd (IKH) algorithm. The first phase utilizes the ADNN-based adaptive integrated estimator for quick and accurate disturbance detection and estimation. Subsequently, the second phase employs the STF, omitting the Low Pass Filter and employing a Phase Locking Loop to generate precise reference voltages and currents for the series and parallel active filters based on dynamic load and source conditions. This advanced control mechanism not only enhances system efficacy but also reduces the need for extensive computational resources. Furthermore, the performance of both series and parallel inverters is finely tuned through a PI controller optimized with the IKH algorithm, improving the DC link voltage regulation. Our extensive testing under various conditions, including voltage imbalances and harmonic disturbances, demonstrates the robustness of the proposed solution in both transient and steady-state scenarios.