Artificial Intelligence Review最新文献_第10页

Digital deception: generative artificial intelligence in social engineering and phishing 数字欺骗：社交工程和网络钓鱼中的生成人工智能

IF 10.7 2区计算机科学

Artificial Intelligence Review Pub Date : 2024-10-12 DOI: 10.1007/s10462-024-10973-2

Marc Schmitt, Ivan Flechais

引用次数: 0

IF 10.7 2区计算机科学

Artificial Intelligence Review Pub Date : 2024-10-12 DOI: 10.1007/s10462-024-10963-4

P. Alirezazadeh, F. Dornaika, J. Charafeddine

{"title":"Mises-Fisher similarity-based boosted additive angular margin loss for breast cancer classification","authors":"P. Alirezazadeh, F. Dornaika, J. Charafeddine","doi":"10.1007/s10462-024-10963-4","DOIUrl":"10.1007/s10462-024-10963-4","url":null,"abstract":"<div><p>To enhance the accuracy of breast cancer diagnosis, current practices rely on biopsies and microscopic examinations. However, this approach is known for being time-consuming, tedious, and costly. While convolutional neural networks (CNNs) have shown promise for their efficiency and high accuracy, training them effectively becomes challenging in real-world learning scenarios such as class imbalance, small-scale datasets, and label noises. Angular margin-based softmax losses, which concentrate on the angle between features and classifiers embedded in cosine similarity at the classification layer, aim to regulate feature representation learning. Nevertheless, the cosine similarity’s lack of a heavy tail impedes its ability to compactly regulate intra-class feature distribution, limiting generalization performance. Moreover, these losses are constrained to target classes when margin penalties are applied, which may not always optimize effectiveness. Addressing these hurdles, we introduce an innovative approach termed MF-BAM (Mises-Fisher Similarity-based Boosted Additive Angular Margin Loss), which extends beyond traditional cosine similarity and is anchored in the von Mises-Fisher distribution. MF-BAM not only penalizes the angle between deep features and their corresponding target class weights but also considers angles between deep features and weights associated with non-target classes. Through extensive experimentation on the BreaKHis dataset, MF-BAM achieves outstanding accuracies of 99.92%, 99.96%, 100.00%, and 98.05% for magnification levels of ×40, ×100, ×200, and ×400, respectively. Furthermore, additional experiments conducted on the BACH dataset for breast cancer classification, as well as on the LFW and YTF datasets for face recognition, affirm the generalization capability of our proposed loss function.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"57 12","pages":""},"PeriodicalIF":10.7,"publicationDate":"2024-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-024-10963-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142411474","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Advances in text-guided 3D editing: a survey 文本引导 3D 编辑的进展：调查

IF 10.7 2区计算机科学

Artificial Intelligence Review Pub Date : 2024-10-12 DOI: 10.1007/s10462-024-10937-6

Lihua Lu, Ruyang Li, Xiaohui Zhang, Hui Wei, Guoguang Du, Binqiang Wang

{"title":"Advances in text-guided 3D editing: a survey","authors":"Lihua Lu, Ruyang Li, Xiaohui Zhang, Hui Wei, Guoguang Du, Binqiang Wang","doi":"10.1007/s10462-024-10937-6","DOIUrl":"10.1007/s10462-024-10937-6","url":null,"abstract":"<div><p>In 3D Artificial Intelligence Generated Content (AIGC), compared with generating 3D assets from scratch, editing extant 3D assets satisfies user prompts, allowing the creation of diverse and high-quality 3D assets in a time and labor-saving manner. More recently, text-guided 3D editing that modifies 3D assets guided by text prompts is user-friendly and practical, which evokes a surge in research within this field. In this survey, we comprehensively investigate recent literature on text-guided 3D editing in an attempt to answer two questions: What are the methodologies of existing text-guided 3D editing? How has current progress in text-guided 3D editing gone so far? Specifically, we focus on text-guided 3D editing methods published in the past 4 years, delving deeply into their frameworks and principles. We then present a fundamental taxonomy in terms of the editing strategy, optimization scheme, and 3D representation. Based on the taxonomy, we review recent advances in this field, considering factors such as editing scale, type, granularity, and perspective. In addition, we highlight four applications of text-guided 3D editing, including texturing, style transfer, local editing of scenes, and insertion editing, to exploit further the 3D editing capacities with in-depth comparisons and discussions. Depending on the insights achieved by this survey, we discuss open challenges and future research directions. We hope this survey will help readers gain a deeper understanding of this exciting field and foster further advancements in text-guided 3D editing.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"57 12","pages":""},"PeriodicalIF":10.7,"publicationDate":"2024-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-024-10937-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142411528","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Federated learning-based natural language processing: a systematic literature review 基于联合学习的自然语言处理：系统文献综述

IF 10.7 2区计算机科学

Artificial Intelligence Review Pub Date : 2024-10-12 DOI: 10.1007/s10462-024-10970-5

Younas Khan, David Sánchez, Josep Domingo-Ferrer

{"title":"Federated learning-based natural language processing: a systematic literature review","authors":"Younas Khan, David Sánchez, Josep Domingo-Ferrer","doi":"10.1007/s10462-024-10970-5","DOIUrl":"10.1007/s10462-024-10970-5","url":null,"abstract":"<div><p>Federated learning (FL) is a decentralized machine learning (ML) framework that allows models to be trained without sharing the participants’ local data. FL thus preserves privacy better than centralized machine learning. Since textual data (such as clinical records, posts in social networks, or search queries) often contain personal information, many natural language processing (NLP) tasks dealing with such data have shifted from the centralized to the FL setting. However, FL is not free from issues, including convergence and security vulnerabilities (due to unreliable or poisoned data introduced into the model), communication and computation bottlenecks, and even privacy attacks orchestrated by honest-but-curious servers. In this paper, we present a systematic literature review (SLR) of NLP applications in FL with a special focus on FL issues and the solutions proposed so far. Our review surveys 36 recent papers published in relevant venues, which are systematically analyzed and compared from multiple perspectives. As a result of the survey, we also identify the most outstanding challenges in the area.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"57 12","pages":""},"PeriodicalIF":10.7,"publicationDate":"2024-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-024-10970-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142411523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Trustworthy human computation: a survey 值得信赖的人类计算：一项调查

IF 10.7 2区计算机科学

Artificial Intelligence Review Pub Date : 2024-10-12 DOI: 10.1007/s10462-024-10974-1

Hisashi Kashima, Satoshi Oyama, Hiromi Arai, Junichiro Mori

{"title":"Trustworthy human computation: a survey","authors":"Hisashi Kashima, Satoshi Oyama, Hiromi Arai, Junichiro Mori","doi":"10.1007/s10462-024-10974-1","DOIUrl":"10.1007/s10462-024-10974-1","url":null,"abstract":"<div><p>Human computation is an approach to solving problems that prove difficult using AI only, and involves the cooperation of many humans. Because human computation requires close engagement with both “human populations as users” and “human populations as driving forces,” establishing mutual trust between AI and humans is an important issue to further the development of human computation. This survey lays the groundwork for the realization of trustworthy human computation. First, the trustworthiness of human computation as computing systems, that is, trust offered by humans to AI, is examined using the RAS (reliability, availability, and serviceability) analogy, which define measures of trustworthiness in conventional computer systems. Next, the social trustworthiness provided by human computation systems to users or participants is discussed from the perspective of AI ethics, including fairness, privacy, and transparency. Then, we consider human–AI collaboration based on two-way trust, in which humans and AI build mutual trust and accomplish difficult tasks through reciprocal collaboration. Finally, future challenges and research directions for realizing trustworthy human computation are discussed.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"57 12","pages":""},"PeriodicalIF":10.7,"publicationDate":"2024-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-024-10974-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142411524","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A comprehensive investigation of multimodal deep learning fusion strategies for breast cancer classification 乳腺癌分类的多模态深度学习融合策略综合研究

IF 10.7 2区计算机科学

Artificial Intelligence Review Pub Date : 2024-10-12 DOI: 10.1007/s10462-024-10984-z

Fatima-Zahrae Nakach, Ali Idri, Evgin Goceri

{"title":"A comprehensive investigation of multimodal deep learning fusion strategies for breast cancer classification","authors":"Fatima-Zahrae Nakach, Ali Idri, Evgin Goceri","doi":"10.1007/s10462-024-10984-z","DOIUrl":"10.1007/s10462-024-10984-z","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"57 12","pages":""},"PeriodicalIF":10.7,"publicationDate":"2024-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-024-10984-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142411525","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A systematic review of computer vision-based personal protective equipment compliance in industry practice: advancements, challenges and future directions 系统回顾基于计算机视觉的个人防护设备在工业实践中的合规性：进步、挑战和未来方向

IF 10.7 2区计算机科学

Artificial Intelligence Review Pub Date : 2024-10-10 DOI: 10.1007/s10462-024-10978-x

Arso M. Vukicevic, Milos Petrovic, Pavle Milosevic, Aleksandar Peulic, Kosta Jovanovic, Aleksandar Novakovic

{"title":"A systematic review of computer vision-based personal protective equipment compliance in industry practice: advancements, challenges and future directions","authors":"Arso M. Vukicevic, Milos Petrovic, Pavle Milosevic, Aleksandar Peulic, Kosta Jovanovic, Aleksandar Novakovic","doi":"10.1007/s10462-024-10978-x","DOIUrl":"10.1007/s10462-024-10978-x","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"57 12","pages":""},"PeriodicalIF":10.7,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-024-10978-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142411190","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A systematic literature review on pancreas segmentation from traditional to non-supervised techniques in abdominal medical images 关于腹部医学图像中胰腺分割（从传统技术到非监督技术）的系统性文献综述

IF 10.7 2区计算机科学

Artificial Intelligence Review Pub Date : 2024-10-10 DOI: 10.1007/s10462-024-10966-1

Suchi Jain, Geeta Sikka, Renu Dhir

{"title":"A systematic literature review on pancreas segmentation from traditional to non-supervised techniques in abdominal medical images","authors":"Suchi Jain, Geeta Sikka, Renu Dhir","doi":"10.1007/s10462-024-10966-1","DOIUrl":"10.1007/s10462-024-10966-1","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"57 12","pages":""},"PeriodicalIF":10.7,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-024-10966-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142411200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Neuromorphic computing for modeling neurological and psychiatric disorders: implications for drug development 用于神经和精神疾病建模的神经形态计算：对药物开发的影响

IF 10.7 2区计算机科学

Artificial Intelligence Review Pub Date : 2024-10-10 DOI: 10.1007/s10462-024-10948-3

Amisha S. Raikar, J Andrew, Pranjali Prabhu Dessai, Sweta M. Prabhu, Shounak Jathar, Aishwarya Prabhu, Mayuri B. Naik, Gokuldas Vedant S. Raikar

{"title":"Neuromorphic computing for modeling neurological and psychiatric disorders: implications for drug development","authors":"Amisha S. Raikar, J Andrew, Pranjali Prabhu Dessai, Sweta M. Prabhu, Shounak Jathar, Aishwarya Prabhu, Mayuri B. Naik, Gokuldas Vedant S. Raikar","doi":"10.1007/s10462-024-10948-3","DOIUrl":"10.1007/s10462-024-10948-3","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"57 12","pages":""},"PeriodicalIF":10.7,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-024-10948-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142411182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A hybrid approach for Bengali sentence validation 孟加拉语句子验证的混合方法

IF 10.7 2区计算机科学

Artificial Intelligence Review Pub Date : 2024-10-07 DOI: 10.1007/s10462-024-10795-2

Juel Sikder, Prosenjit Chakraborty, Utpol Kanti Das, Krity Dhar

{"title":"A hybrid approach for Bengali sentence validation","authors":"Juel Sikder, Prosenjit Chakraborty, Utpol Kanti Das, Krity Dhar","doi":"10.1007/s10462-024-10795-2","DOIUrl":"10.1007/s10462-024-10795-2","url":null,"abstract":"<div><p>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%. </p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"57 11","pages":""},"PeriodicalIF":10.7,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-024-10795-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142410443","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0