Advances in Distributed Computing and Artificial Intelligence Journal

The process of finding the correct sense of a word in context is known as word sense disambiguation (WSD). In the field of natural language processing, WSD has become a growing research area. Over the decades, so many researchers have proposed the many approaches to WSD. A development of this field has created the significant impact on several Web-based applications such as information retrieval and information extraction. This paper contains the description of various approaches such as knowledge-based, supervised, unsupervised and semi-supervised. This paper also describes the various applications of WSD, such as information retrieval, machine translation, speech recognition, computational advertising, text processing, classification of documents and biometrics.

Chandra Ganesh

Sanjay K. Dwivedi

Satya Bhushan Verma

Manish Dixit

ADCAIJ ADVANCES IN DISTRIBUTED COMPUTING AND ARTIFICIAL INTELLIGENCE JOURNAL

A Systematic Analysis of Various Word Sense Disambiguation Approaches

各种词义消歧方法的系统分析

In today’s cashless society, the increasing threat of credit card fraud demands our attention. To protect our financial security, it is crucial to develop robust and accurate fraud detection systems that stay one step ahead of the fraudsters. This study dives into the realm of machine learning, evaluating the performance of various algorithms - logistic regression (LR), decision tree (DT), and random forest (RF) - in detecting credit card fraud. Taking innovation, a step further, the study introduces the integration of a genetic algorithm (GA) for feature selection and optimization alongside LR, DT, and RF models. LR achieved an accuracy of 99.89 %, DT outperformed with an accuracy of 99.936 %, and RF yielded a high accuracy of 99.932 %, whereas GA-RF (a5) achieved an accuracy of 99.98 %. Ultimately, the findings of this study fuel the development of more potent fraud detection systems within the realm of financial institutions, safeguarding the integrity of transactions and ensuring peace of mind for cardholders.

Sunil Kumar Patel

Devina Panday

Optimizing Credit Card Fraud Detection: A Genetic Algorithm Approach with Multiple Feature Selection Methods

优化信用卡欺诈检测：一种结合多种特征选择方法的遗传算法方法

As a paradigm shift in network security, the idea of Zero Trust Architecture has attracted a lot of attention recently. This study intends to investigate the assessment and application of Zero Trust Architecture in business networks. Network segmentation, continuous authentication, least privilege access, and micro-segmentation are some of the basic ideas and elements of Zero Trust Architecture that are covered in this research. By taking a comprehensive approach to network security, the study evaluates how well Zero Trust Architecture mitigates security risks and shrinks the attack surface. It looks into the difficulties and factors to be taken into account when adopting Zero Trust Architecture, including scalability, user experience, and operational complexity. To shed light on the real-world application of Zero Trust Architecture, the paper also investigates empirical data and case studies from real-world scenarios. The influence of Zero Trust Architecture on operational processes and network performance are also be covered, along with recommended practices and various deployment strategies. Additionally, the research assesses how well Zero Trust Architecture conforms to regulatory standards, compliance needs, and existing security frameworks. The results of this study help us comprehend Zero Trust Architecture and its possible advantages and disadvantages. By offering a thorough evaluation framework and useful suggestions for effective implementation, it is helpful to organizations looking to adopt Zero Trust Architecture. The study's findings add to the corpus of information on Zero Trust Architecture and its role in strengthening network security in the face of evolving cyber threats.

Pushpendra Kumar Verma

Bharat Singh

Preety Shubham

Kumar Sharma

Rakesh Prasad Joshi

Evaluating the Effectiveness of Zero Trust Architecture in Protecting Against Advanced Persistent Threats

评估零信任架构在防御高级持续性威胁中的有效性

This paper studies five classic multi-target detection methods in different noisy environments, including Akaike information criterion, ration criterion, Rissanen's minimum description length, Gerschgorin disk estimator and Eigen-increment threshold methods. Theoretical and statistical analyses of these methods have been done through simulations and a real-world water tank experiment. It is known that these detection approaches suffer from array errors and environmental noises. A new diagonal correction algorithm has been proposed to address the issue of degraded detection performance in practical systems due to array errors and environmental noises. This algorithm not only improves the detection performance of these multi-target detection methods in low signal-to-noise ratios (SNR), but also enhances the robust property in high SNR scenarios.

Yuhong Yin

Qian Jia

Huiqi Xu

Guanglei Fu

Performance Research on Multi-Target Detection in Different Noisy Environments

不同噪声环境下多目标检测的性能研究

The outbreak of COVID-19, a contagious respiratory disease, has had a significant impact on people worldwide. To prevent its spread, there is an urgent need for an easily accessible, fast, and cost-effective diagnostic solution. According to studies, COVID-19 is frequently accompanied by coughing. Therefore, the identification and classification of cough sounds can be a promising method for rapidly and efficiently diagnosing the disease. The COVID-19 epidemic has resulted in a worldwide health crisis, and stopping the disease's spread depends on a quick and precise disease diagnosis. COVID-19 has been detected using medical imaging modalities such as chest X-rays and computed tomography (CT) scans due to their non-invasive nature and accessibility. This research provides an in-depth examination of deep learning-based strategies for recognising COVID-19 in medical images. The benefits and drawbacks of various deep learning approaches and their applications in COVID-19 detection are discussed. The study also examines publicly available datasets and benchmarks for evaluating deep learning model performance. Furthermore, the limitations and future research prospects for using deep learning in COVID-19 detection are discussed. This survey's goal is to offer a comprehensive overview of the current state of advancement in deep learning-based COVID-19 detection using medical images. This can aid researchers and healthcare professionals in selecting appropriate approaches for an effective diagnosis of the disease.

Dhanshri M. Mali

S. A. Patil

A Review on Covid-19 Detection Using Artificial Intelligence from Chest CT Scan Slices

基于胸部CT扫描切片使用人工智能进行新冠检测的综述

Abbreviation
Journal Impact	1.80
Quartiles(Global)	COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE(Q3)

Publication Information	Publisher: University of Salamanca，Publishing cycle: ，Journal Type: journal，Open Access Journals: Yes
Basic data	Year of publication: 2019，Proportion of original research papers: 90.48%，Self Citation Rate:0.00%， Gold OA Rate: 89.33%
Average review cycle	网友分享经验：4 Weeks

Advances in Distributed Computing and Artificial Intelligence Journal

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A Systematic Analysis of Various Word Sense Disambiguation Approaches

Optimizing Credit Card Fraud Detection: A Genetic Algorithm Approach with Multiple Feature Selection Methods

Evaluating the Effectiveness of Zero Trust Architecture in Protecting Against Advanced Persistent Threats

Performance Research on Multi-Target Detection in Different Noisy Environments

A Review on Covid-19 Detection Using Artificial Intelligence from Chest CT Scan Slices

A Systematic Analysis of Various Word Sense Disambiguation Approaches

Optimizing Credit Card Fraud Detection: A Genetic Algorithm Approach with Multiple Feature Selection Methods

Evaluating the Effectiveness of Zero Trust Architecture in Protecting Against Advanced Persistent Threats

Performance Research on Multi-Target Detection in Different Noisy Environments

A Review on Covid-19 Detection Using Artificial Intelligence from Chest CT Scan Slices

ISSN	2255-2863
h-index	6