Advances in computational intelligence最新文献

{"title":"Detection of cyber attacks on smart grids","authors":"Aditi Kar Gangopadhyay,&nbsp;Tanay Sheth,&nbsp;Tanmoy Kanti Das,&nbsp;Sneha Chauhan","doi":"10.1007/s43674-022-00042-y","DOIUrl":"10.1007/s43674-022-00042-y","url":null,"abstract":"<div><p>The paper analyzes observations using a logic-based numerical methodology in Python. The Logical Analysis of Data (LAD) specializes in selecting a minimal number of features and finding unique patterns within it to distinguish ‘positive’ from ‘negative’ observations. The Python implementation of the classification model is further improved by introducing adaptations to pattern generation techniques. Finally, a case study of the Power Attack Systems Dataset used to improvise Smart Grid technology is performed to explore real-life applications of the classification model and analyze its performance against commonly used techniques.</p></div>","PeriodicalId":72089,"journal":{"name":"Advances in computational intelligence","volume":"2 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50527969","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}

{"title":"New models of classifier learning curves","authors":"Vincent Berthiaume","doi":"10.1007/s43674-022-00040-0","DOIUrl":"10.1007/s43674-022-00040-0","url":null,"abstract":"<div><p>In machine learning, a classifier has a certain learning curve i.e. the curve of the error/success probability as a function of the training set size. Finding the learning curve for a large interval of sizes takes a lot of processing time. A better method is to estimate the error probabilities only for few minimal sizes and use the pairs size-estimate as data points to model the learning curve. Searchers have tested different models. These models have certain parameters and are conceived from curves that only have the general aspect of a real learning curve. In this paper, we propose two new models that have more parameters and are conceived from real learning curves of nearest neighbour classifiers. These two main differences increase the chance for these new models to fit better the learning curve. We test these new models on one-input and two-class nearest neighbour classifiers.</p></div>","PeriodicalId":72089,"journal":{"name":"Advances in computational intelligence","volume":"2 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43674-022-00040-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50487084","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}

{"title":"Resolvent and new activation functions for linear programming kernel sparse learning","authors":"Zhao Lu,&nbsp;Haoda Fu,&nbsp;William R. Prucka","doi":"10.1007/s43674-022-00038-8","DOIUrl":"10.1007/s43674-022-00038-8","url":null,"abstract":"<div><p>The resolvent operator and the corresponding Green’s function occupy a central position in the realms of differential and integral equations, operator theory, and in particular the modern physics. However, in the field of machine learning, when confronted with the complex and highly challenging learning tasks from the real world, the prowess of Green’s function of resolvent is rarely explored and exploited. This paper aims at innovating the conventional translation-invariant kernels and rotation-invariant kernels, through theoretical investigation into a new view of constructing kernel functions by means of the resolvent operator and its Green’s function. From the practical perspective, the newly developed kernel functions are applied for robust signal recovery from noise corrupted data in the scenario of linear programming support vector learning. In particular, the monotonic and non-monotonic activation functions are used for kernel design to improve the representation capability. In this manner, a new dimension is given for kernel-based robust sparse learning from the following two aspects: firstly, a new theoretical framework by bridging the gap between the mathematical subtleties of resolvent operator and Green’s function theory and kernel construction; secondly, a concretization for the fusion between activation functions design in neural networks and nonlinear kernels design. Finally, the experimental study demonstrates the potential and superiority of the newly developed kernel functions in robust signal recovery and multiscale sparse modeling, as one step towards removing the apparent boundaries between the realms of modern signal processing and computational intelligence.</p></div>","PeriodicalId":72089,"journal":{"name":"Advances in computational intelligence","volume":"2 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50523930","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}

{"title":"Multi-agent-based dynamic railway scheduling and optimization: a coloured petri-net model","authors":"Poulami Dalapati,&nbsp;Kaushik Paul","doi":"10.1007/s43674-022-00039-7","DOIUrl":"10.1007/s43674-022-00039-7","url":null,"abstract":"<div><p>This paper addresses the issues concerning the rescheduling of a static timetable in case of a disaster, encountered in a large and complex railway network system. The proposed approach tries to modify the existing schedule to minimise the overall delay of trains. This is achieved by representing the rescheduling problem in the form of a Petri-Net and the highly uncertain disaster recovery time in such a model is handled as Markov decision processes (MDP). For solving the rescheduling problem, a distributed constraint optimisation (DCOP)-based strategy involving the use of autonomous agents is used to generate the desired schedule. The proposed approach is evaluated on the real-time data set taken from the Eastern Railways, India by constructing various disaster scenarios using the Java Agent DEvelopment Framework (JADE). The proposed framework, when compared to the existing approaches, substantially reduces the delay of trains after rescheduling.</p></div>","PeriodicalId":72089,"journal":{"name":"Advances in computational intelligence","volume":"2 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43674-022-00039-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50486572","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}

{"title":"An unsupervised autonomous learning framework for goal-directed behaviours in dynamic contexts","authors":"Chinedu Pascal Ezenkwu,&nbsp;Andrew Starkey","doi":"10.1007/s43674-022-00037-9","DOIUrl":"10.1007/s43674-022-00037-9","url":null,"abstract":"<div><p>Due to their dependence on a task-specific reward function, reinforcement learning agents are ineffective at responding to a dynamic goal or environment. This paper seeks to overcome this limitation of traditional reinforcement learning through a task-agnostic, self-organising autonomous agent framework. The proposed algorithm is a hybrid of TMGWR for self-adaptive learning of sensorimotor maps and value iteration for goal-directed planning. TMGWR has been previously demonstrated to overcome the problems associated with competing sensorimotor techniques such SOM, GNG, and GWR; these problems include: difficulty in setting a suitable number of neurons for a task, inflexibility, the inability to cope with non-markovian environments, challenges with noise, and inappropriate representation of sensory observations and actions together. However, the binary sensorimotor-link implementation in the original TMGWR enables catastrophic forgetting when the agent experiences changes in the task and it is therefore not suitable for self-adaptive learning. A new sensorimotor-link update rule is presented in this paper to enable the adaptation of the sensorimotor map to new experiences. This paper has demonstrated that the TMGWR-based algorithm has better sample efficiency than model-free reinforcement learning and better self-adaptivity than both the model-free and the traditional model-based reinforcement learning algorithms. Moreover, the algorithm has been demonstrated to give the lowest overall computational cost when compared to traditional reinforcement learning algorithms.</p></div>","PeriodicalId":72089,"journal":{"name":"Advances in computational intelligence","volume":"2 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43674-022-00037-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50442527","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}

{"title":"Machine learning cutting forces in milling processes of functionally graded materials","authors":"Xiaojie Xu,&nbsp;Yun Zhang,&nbsp;Yunlu Li,&nbsp;Yunyao Li","doi":"10.1007/s43674-022-00036-w","DOIUrl":"10.1007/s43674-022-00036-w","url":null,"abstract":"<div><p>Machine learning approaches can serve as powerful tools in the machining optimization process. Criteria, such as accuracy and stability, are important to consider when choosing among different models. For the industrial application, it also is essential to balance cost, applicabilities, and ease of implementations. Here, we develop Gaussian process regression models for predicting the main cutting force (<i>R</i>) and its components in three directions of the coordinate system (<span>(F_{x})</span>, <span>(F_{y})</span>, and <span>(F_{z})</span>) based on two predictors: the depth of cut (<span>(a_{p})</span>) and the feed rate (<i>f</i>) in milling processes of functionally graded materials. The model performance shows high accuracy and stability, and the models are thus promising for estimating the cutting force and its component in a fast, cost effective, and robust fashion.</p></div>","PeriodicalId":72089,"journal":{"name":"Advances in computational intelligence","volume":"2 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50518489","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}

{"title":"Comparative analysis of super-resolution reconstructed images for micro-expression recognition","authors":"Pratikshya Sharma,&nbsp;Sonya Coleman,&nbsp;Pratheepan Yogarajah,&nbsp;Laurence Taggart,&nbsp;Pradeepa Samarasinghe","doi":"10.1007/s43674-022-00035-x","DOIUrl":"10.1007/s43674-022-00035-x","url":null,"abstract":"<div><p>It is an established fact that the genuineness of facial micro-expression is an effective means for estimating concealed emotions (Li et al. in Micro-expression recognition under low-resolution cases. SciTePress, Science and Technology Publications, Setúbal, 2019). Conventionally, analysis of these expressions has been performed using high resolution images which are ideal cases. However, in a real-world scenario, capturing expressions with high resolution images may not always be possible particularly using low-cost surveillance cameras. Faces captured using such cameras are often very tiny and of poor resolution. Due to the loss of discriminative features these images may not be of much use particularly for identifying certain minute facial details. To make these images useful, enhancing the textural information becomes essential and super-resolution algorithms can be ideal to achieve this. In this work, we utilize algorithms based on deep learning and generative adversarial network for transforming low-resolution micro-expression images into super-resolution images and examine their fitness particularly for micro-expression recognition. The proposed approach is tested on simulated dataset obtained from two popular spontaneous micro-expression datasets namely CASME II and SMIC-VIS; the experimental results demonstrate that the method achieved favourable results with the best recognition performance recorded as 61.63%. The significance of this work is: first, it thoroughly investigates reconstruction performance of several deep learning super-resolution algorithms on simulated low-quality micro-expression images; second, it provides a comprehensive analysis of the results obtained employing these reconstructed images to determine their contribution in addressing image quality issues specifically for micro-expression recognition.\u0000</p></div>","PeriodicalId":72089,"journal":{"name":"Advances in computational intelligence","volume":"2 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43674-022-00035-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50482157","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}

{"title":"How to generate data for acronym detection and expansion","authors":"Sing Choi,&nbsp;Piyush Puranik,&nbsp;Binay Dahal,&nbsp;Kazem Taghva","doi":"10.1007/s43674-021-00024-6","DOIUrl":"10.1007/s43674-021-00024-6","url":null,"abstract":"<div><p>Finding the definitions of acronyms in any given text has been an on going problem with multiple proposed solutions. In this paper, we use the bidirectional encoder representations from transformers question answer model provided by Google to find acronym definitions in a given text. Given an acronym and a passage containing the acronym, our model is expected to find the expansion of the acronym in the passage. Through our experiments, we show that this model can correctly predict 94% of acronym expansions assuming a Jaro–Winkler threshold distance of greater than 0.8. One of the main contributions of this paper is a systematic method to create datasets and use them to build a corpus for acronym expansion. Our approach for data generation can be used in many applications where there are no standard datasets.</p></div>","PeriodicalId":72089,"journal":{"name":"Advances in computational intelligence","volume":"2 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50475933","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}

{"title":"Machine learning for diabetes clinical decision support: a review","authors":"Ashwini Tuppad,&nbsp;Shantala Devi Patil","doi":"10.1007/s43674-022-00034-y","DOIUrl":"10.1007/s43674-022-00034-y","url":null,"abstract":"<div><p>Type 2 diabetes has recently acquired the status of an epidemic silent killer, though it is non-communicable. There are two main reasons behind this perception of the disease. First, a gradual but exponential growth in the disease prevalence has been witnessed irrespective of age groups, geography or gender. Second, the disease dynamics are very complex in terms of multifactorial risks involved, initial asymptomatic period, different short-term and long-term complications posing serious health threat and related co-morbidities. Majority of its risk factors are lifestyle habits like physical inactivity, lack of exercise, high body mass index (BMI), poor diet, smoking except some inevitable ones like family history of diabetes, ethnic predisposition, ageing etc. Nowadays, machine learning (ML) is increasingly being applied for alleviation of diabetes health burden and many research works have been proposed in the literature to offer clinical decision support in different application areas as well. In this paper, we present a review of such efforts for the prevention and management of type 2 diabetes. Firstly, we present the medical gaps in diabetes knowledge base, guidelines and medical practice identified from relevant articles and highlight those that can be addressed by ML. Further, we review the ML research works in three different application areas namely—(1) risk assessment (statistical risk scores and ML-based risk models), (2) diagnosis (using non-invasive and invasive features), (3) prognosis (from normoglycemia/prior morbidity to incident diabetes and prognosis of incident diabetes to related complications). We discuss and summarize the shortcomings or gaps in the existing ML methodologies for diabetes to be addressed in future. This review provides the breadth of ML predictive modeling applications for diabetes while highlighting the medical and technological gaps as well as various aspects involved in ML-based diabetes clinical decision support.</p></div>","PeriodicalId":72089,"journal":{"name":"Advances in computational intelligence","volume":"2 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50475932","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}

{"title":"Design of adaptive hybrid classification model using genetic-based linear adaptive skipping training (GLAST) algorithm for health-care dataset","authors":"Manjula Devi Ramasamy,&nbsp;Keerthika Periasamy,&nbsp;Suresh Periasamy,&nbsp;Suresh Muthusamy,&nbsp;Hitesh Panchal,&nbsp;Pratik Arvindbhai Solanki,&nbsp;Kirti Panchal","doi":"10.1007/s43674-021-00030-8","DOIUrl":"10.1007/s43674-021-00030-8","url":null,"abstract":"<div><p>Machine-learning techniques are being used in the health-care industry to improve care delivery at a lower cost and in less time. Artificial Neural Network (ANN) is well-known machine-learning techniques for its diagnostic applications, but it is also increasingly being utilized to guide health-care management decisions. At the same time, in the healthcare industry, ANN has made significant progress in solving a variety of real-world classification problems that range from linear to non-linear and also from simple to complex. In this research work, an Adaptive Hybrid Classification Model named as Genetic-based Linear Adaptive Skipping Training (GLAST) Algorithm has been proposed for the health-care dataset. It has been designed as two-stage process. In first stage, Genetic Algorithm (GA) is adapted to optimize the Learning rate. After optimizing the Learning rate, the optimal Learning rate has been set to the ANN model is <i>ŋ</i> = 1<i>e</i>−4. In the second stage, The training process is carried out using the Linear Adaptive Skipping Training (LAST) algorithm, which reduces the total training time and thus increases the training speed. As a result, the highlighted characteristics of LAST have been integrated with GA to accomplish rapid classification and enhance computational efficiency. On 8 different health-care datasets extracted from the UCI Repository, the proposed GLAST algorithm outperforms both the BPN and LAST algorithms in terms of accuracy and training time, according to simulation results. The result analyses have proved that the efficiency of this proposed GLAST Algorithm outperforms over the existing techniques such as BPN and LAST in terms of accuracy and training time. On various datasets, experimental results show that GLAST improves accuracy from 4 to 17% over BPN training algorithm and reduces overall training time from 10 to 57% over BPN training algorithm.</p></div>","PeriodicalId":72089,"journal":{"name":"Advances in computational intelligence","volume":"2 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50507453","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}