Advances in computational intelligence最新文献_第3页

The impact of digital twins on the evolution of intelligent manufacturing and Industry 4.0 数字孪生对智能制造和工业4.0演进的影响。

Advances in computational intelligence Pub Date : 2023-06-07 DOI: 10.1007/s43674-023-00058-y

Mohsen Attaran, Sharmin Attaran, Bilge Gokhan Celik

引用次数: 4

A survey on cyber threat intelligence sharing based on Blockchain 基于区块链的网络威胁情报共享研究

Advances in computational intelligence Pub Date : 2023-05-23 DOI: 10.1007/s43674-023-00057-z

Ahmed El-Kosairy, Nashwa Abdelbaki, Heba Aslan

{"title":"A survey on cyber threat intelligence sharing based on Blockchain","authors":"Ahmed El-Kosairy, Nashwa Abdelbaki, Heba Aslan","doi":"10.1007/s43674-023-00057-z","DOIUrl":"10.1007/s43674-023-00057-z","url":null,"abstract":"<div><p>In recent years, cyber security attacks have increased massively. This introduces the need to defend against such attacks. Cyber security threat intelligence has recently been introduced to secure systems against security attacks. Cyber security threat intelligence (CTI) should be fast, trustful, and protect the sender's identity to stop these attacks at the right time. Threat intelligence sharing is vitally important since it is considered an effective way to improve threat understanding. This leads to protecting the assets and preventing the attack vectors. However, there is a paradox between the privacy safeguard needs of threat intelligence sharing; the need to produce complete proper threat intelligence feeds to be shared with the community, and other challenges and needs that are not covered in the traditional CTI. This paper aims to study how Blockchain technology can be incorporated with the CTI to solve the current issues and challenges in the traditional CTI. We collected the latest contributions that use Blockchain to overcome the conventional CTI problems and compared them to raise the reader’s awareness about the different methods used. Also, we mentioned the uncovered areas for each paper to offer a wide range of details and information about different areas that need to be investigated. Furthermore, the prospect challenges of integrating the Blockchain and CTI are discussed.</p></div>","PeriodicalId":72089,"journal":{"name":"Advances in computational intelligence","volume":"3 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50507816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deception as a service: Intrusion and Ransomware Detection System for Cloud Computing (IRDS4C) 欺骗即服务：用于云计算的入侵和勒索软件检测系统（IRDS4C）

Advances in computational intelligence Pub Date : 2023-05-20 DOI: 10.1007/s43674-023-00056-0

Ahmed El-Kosairy, Nashwa Abdelbaki

引用次数: 1

Corn cash-futures basis forecasting via neural networks 基于神经网络的玉米现金期货基差预测

Advances in computational intelligence Pub Date : 2023-04-12 DOI: 10.1007/s43674-023-00054-2

Xiaojie Xu, Yun Zhang

{"title":"Corn cash-futures basis forecasting via neural networks","authors":"Xiaojie Xu, Yun Zhang","doi":"10.1007/s43674-023-00054-2","DOIUrl":"10.1007/s43674-023-00054-2","url":null,"abstract":"<div><p>Cash-futures basis forecasting represents a vital concern for various market participants in the agricultural sector, which has been rarely explored due to limitations on data and traditional econometric methods. The current study explores usefulness of the nonlinear autoregressive neural network technique for the forecasting problem in a unique and proprietary data set of daily corn cash-futures basis across nearly five-hundred cash markets from sixteen most important harvest states in the United States over a 5-year period. Through investigations of various model settings across the hidden neuron, delay, data splitting ratio, and algorithm, a chosen model with five delays and twenty hidden neurons is reached, trained using the Levenberg–Marquardt algorithm and data splitting ratio of 70% vs. 15% vs. 15% for training, validation, and testing. This model results in accurate and stable performance across the cash markets explored, which illustrates usefulness of the machine learning technique for corn cash-futures basis forecasting. Particularly, the model leads to average relative root mean square errors (RRMSEs) of 9.97%, 8.51%, and 9.64% for the training, validation, and testing phases, respectively, and the average RRMSE of 9.83% for the overall sample across all cash markets. Results here might be used as standalone technical forecasts or combined with fundamental forecasts for forming perspectives of cash-futures basis trends and carrying out policy analysis. The empirical framework here is easy to implement, which is an essential consideration to many decision makers, and has potential to be generalized for forecasting cash-futures basis of other commodities.</p></div>","PeriodicalId":72089,"journal":{"name":"Advances in computational intelligence","volume":"3 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43674-023-00054-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50473535","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 11

Optimization of deep learning models: benchmark and analysis 深度学习模型的优化：基准和分析

Advances in computational intelligence Pub Date : 2023-03-30 DOI: 10.1007/s43674-023-00055-1

Rasheed Ahmad, Izzat Alsmadi, Mohammad Al-Ramahi

{"title":"Optimization of deep learning models: benchmark and analysis","authors":"Rasheed Ahmad, Izzat Alsmadi, Mohammad Al-Ramahi","doi":"10.1007/s43674-023-00055-1","DOIUrl":"10.1007/s43674-023-00055-1","url":null,"abstract":"<div><p>Model optimization in deep learning (DL) and neural networks is concerned about how and why the model can be successfully trained towards one or more objective functions. The evolutionary learning or training process continuously considers the dynamic parameters of the model. Many researchers propose a deep learning-based solution by randomly selecting a single classifier model architecture. Such approaches generally overlook the hidden and complex nature of the model’s internal working, producing biased results. Larger and deeper NN models bring many complexities and logistic challenges while building and deploying them. To obtain high-quality performance results, an optimal model generally depends on the appropriate architectural settings, such as the number of hidden layers and the number of neurons at each layer. A challenging and time-consuming task is to select and test various combinations of these settings manually. This paper presents an extensive empirical analysis of various deep learning algorithms trained recursively using permutated settings to establish benchmarks and find an optimal model. The paper analyzed the Stack Overflow dataset to predict the quality of posted questions. The extensive empirical analysis revealed that some famous deep learning algorithms such as CNN are the least effective algorithm in solving this problem compared to multilayer perceptron (MLP), which provides efficient computing and the best results in terms of prediction accuracy. The analysis also shows that manipulating the number of neurons alone at each layer in a network does not influence model optimization. This paper’s findings will help to recognize the fact that future models should be built by considering a vast range of model architectural settings for an optimal solution.</p></div>","PeriodicalId":72089,"journal":{"name":"Advances in computational intelligence","volume":"3 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50526712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Gorge: graph convolutional networks on heterogeneous multi-relational graphs for polypharmacy side effect prediction Gorge:用于多药副作用预测的异构多关系图上的图卷积网络

Advances in computational intelligence Pub Date : 2023-03-03 DOI: 10.1007/s43674-023-00053-3

Yike Wang, Huifang Ma, Ruoyi Zhang, Zihao Gao

{"title":"Gorge: graph convolutional networks on heterogeneous multi-relational graphs for polypharmacy side effect prediction","authors":"Yike Wang, Huifang Ma, Ruoyi Zhang, Zihao Gao","doi":"10.1007/s43674-023-00053-3","DOIUrl":"10.1007/s43674-023-00053-3","url":null,"abstract":"<div><p>Determining the side effects of multidrug combinations is a very important issue in drug risk studies. However, designing clinical trials to determine frequencies is often time-consuming and expensive, and previous work has often been limited to using the target protein of a drug without screening. Although this alleviates the sparsity of the raw data to some extent, blindly introducing proteins as auxiliary information can lead to a large amount of noisy information being added, which in turn leads to less efficient models. For this reason, we propose a new method called Gorge (graph convolutional networks on heterogeneous multi-relational graphs for polypharmacy side effect prediction). Specifically, we designed two protein auxiliary pathways directly related to drugs and combined these two auxiliary pathways with a multi-relational graph of drug side effects, which both alleviates data sparsity and filters noisy data. Then, we introduce a query-aware attention mechanism that generates different attention pathways for drug entities based on different drug pairs, fine-grained to determine the extent of information delivery. Finally, we output the exact frequency of drug side effects occurring through a tensor factorization decoder, in contrast to most existing methods that can only predict the presence or association of side effects, but not their frequency. We found that Gorge achieves excellent performance on real-world datasets (average AUROC of 0.822 and average AUPR of 0.775), outperforming existing methods. Further examination provides literature evidence for highly ranked predictions.</p></div>","PeriodicalId":72089,"journal":{"name":"Advances in computational intelligence","volume":"3 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50446531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A transparent machine learning algorithm to manage diabetes: TDMSML 管理糖尿病的透明机器学习算法：TDMSML

Advances in computational intelligence Pub Date : 2023-02-10 DOI: 10.1007/s43674-022-00051-x

Amrit Kumar Verma, Saroj Kr. Biswas, Manomita Chakraborty, Arpita Nath Boruah

{"title":"A transparent machine learning algorithm to manage diabetes: TDMSML","authors":"Amrit Kumar Verma, Saroj Kr. Biswas, Manomita Chakraborty, Arpita Nath Boruah","doi":"10.1007/s43674-022-00051-x","DOIUrl":"10.1007/s43674-022-00051-x","url":null,"abstract":"<div><p>Diabetes is nowadays a very common medical problem among the people worldwide. The disease is becoming more prevalent with the modern and hectic lifestyle followed by people. As a result, designing an adequate medical expert system to assist physicians in treating the disease on time is critical. Expert systems are required to identify the major cause(s) of the disease, so that precautionary measures can be taken ahead of time. Several medical expert systems have already been proposed, but each has its own set of shortcomings, such as the use of trial and error methods, trivial decision-making procedures, and so on. As a result, this paper proposes a Transparent Diabetes Management System Using Machine Learning (TDMSML) expert system that uses decision tree rules to identify the major factor(s) of diabetes. The TDMSML model comprises of three phases: rule generation, transparent rule selection, and major factor identification. The rule generation phase generates rules using decision tree. Transparent rule selection stage selects the transparent rules followed by pruning the redundant rules to get the minimized rule-set. The major factor identification stage extracts the major factor(s) with range(s) from the minimized rule-set. These factor(s) with certain range(s) are characterized as major cause(s) of diabetes disease. The model is validated with the Pima Indian diabetes data set collected from Kaggle.</p></div>","PeriodicalId":72089,"journal":{"name":"Advances in computational intelligence","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50468175","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

LAD in finance: accounting analytics and fraud detection 金融领域的LAD：会计分析和欺诈检测

Advances in computational intelligence Pub Date : 2023-01-30 DOI: 10.1007/s43674-023-00052-4

Aditi Kar Gangopadhyay, Tanay Sheth, Sneha Chauhan

{"title":"LAD in finance: accounting analytics and fraud detection","authors":"Aditi Kar Gangopadhyay, Tanay Sheth, Sneha Chauhan","doi":"10.1007/s43674-023-00052-4","DOIUrl":"10.1007/s43674-023-00052-4","url":null,"abstract":"<div><p>The paper explores advancements in accounting analytics using the logical analysis of data (LAD) approach by identifying fraudulent firms and transactions. The straightforward approach to fraud detection with an analytic model is to identify possible predictors of fraud associated with known fraudsters and their historical actions. LAD is a machine learning methodology that combines Boolean functions, optimization, and logic ideas in alignment with the traditional approach. The key characteristic of the LAD is discovering minimal sets of features necessary for explaining all observations and detecting hidden patterns in the data capable of distinguishing observations describing “positive” outcome events from “negative” outcome events. The combinatorial optimization model described in the paper represents a variation on the general theme of set covering and concludes with an outline of LAD applications to detect fraudulent firms and financial frauds. The dataset consists of Annual data of 777 firms from 14 different sectors. The results demonstrate 97.4% accuracy with an F1 score of 0.97. Another dataset on credit card transactions and finance is also used to test the effectiveness of LAD in finance. With the appearance of the immense growth of financial fraud cases, these promising results lead to future advancements in analytical audit fieldwork.</p></div>","PeriodicalId":72089,"journal":{"name":"Advances in computational intelligence","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50526002","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

User structural information in priority-based ranking for top-N recommendation 基于优先级的前N推荐排序中的用户结构信息

Advances in computational intelligence Pub Date : 2023-01-06 DOI: 10.1007/s43674-022-00050-y

Mohammad Majid Fayezi, Alireza Hashemi Golpayegani

{"title":"User structural information in priority-based ranking for top-N recommendation","authors":"Mohammad Majid Fayezi, Alireza Hashemi Golpayegani","doi":"10.1007/s43674-022-00050-y","DOIUrl":"10.1007/s43674-022-00050-y","url":null,"abstract":"<div><p>The recommender system is a set of data recovery tools and techniques used to recommend items to users based on their selection. To improve the accuracy of the recommendation, the use of additional information (e.g., social information, trust, item tags, etc.) in addition to user-item ranking data has been an active area of research for the past decade.</p><p>In this paper, we present a new method for recommending top-N items, which uses structural information and trust among users within the social network and extracts the implicit connections between users and uses them in the item recommendation process. The proposed method has seven main steps: (1) extract items liked by neighbors, (ii) constructing item features for neighbors, (iii) extract embedding trust features for neighbors, (iv) create user-feature matrix, (v) calculate user’s priority, (vi) calculate item’s priority and finally, (vii) recommend top-N items. We implement the proposed method with three datasets for recommendations. We compare our results with some advanced ranking methods and observe that the accuracy of our method for all users and cold-start users improves. Our method can also create more items for cold-start users in the list of recommended items.</p></div>","PeriodicalId":72089,"journal":{"name":"Advances in computational intelligence","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43674-022-00050-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50455557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fish recognition model for fraud prevention using convolutional neural networks 基于卷积神经网络的防欺诈鱼类识别模型

Advances in computational intelligence Pub Date : 2022-12-19 DOI: 10.1007/s43674-022-00048-6

Rhayane S. Monteiro, Morgana C. O. Ribeiro, Calebi A. S. Viana, Mário W. L. Moreira, Glácio S. Araúo, Joel J. P. C. Rodrigues

引用次数: 4