Maximum Clustering Research Articles

Microstructure-based modeling of the PMZ mechanical properties in 2219-T8 aluminum alloy TIG welding joint

Partially melted zone (PMZ) is generally the weak region in 2219-T8 aluminum alloy TIG welding joint. In this work, a microstructure-based numerical simulation was performed to analyze the tensile behavior of PMZ. The constitutive behavior and damage mechanism of eutectic structure and Al matrix were determined. Stress–strain distribution and fracture behavior in tensile process were analyzed. Quantitative relationships between eutectic structure and tensile properties of PMZ were proposed. The results showed that the maximum area and clustering of eutectic particles have significant effects on the tensile strength and elongation. Plastic strain mainly concentrated in matrix, and stress concentration occurred in larger eutectic. The tensile cracks initiated from eutectic structure and then propagated into matrix and resulted in the final fracture.

The complete mitochondrial genome of the sand dollar Astriclypeus mannii (Verrill, 1867) (Echinoidea: Astriclypeidae) in the subtidal sand flat in Jeju Island off the south coast of Korea

We sequenced the complete mitochondrial genome of sand dollar Astriclypeus mannii (Verrill 1867) (Echinoidea: Astriclypeidae) occurring in the subtidal sand flat in Jeju Island off the south coast of Korea. The mitochondrial genome was 15,744 bp in length and contained 13 protein-coding genes (PCGs), 22 transfer RNAs, two ribosomal RNAs, and 140 nucleotides representing the putative control region. We reconstructed the concatenated phylogenetic tree based on 13 PCGs of 18 echinoderms, including A. mannii. From the maximum likelihood clustering, A. mannii was grouped in the order Echinolampadacea. The complete mitochondrial sequence of A. mannii for the first time in this study provide valuable insight in understanding the evolution and phylogenetic analysis of echinoids (sea urchins).

Early-Warning Model of Financial Crisis: An Empirical Study Based on partnering of Biotech firms in china

The overfitting phenomenon in the financial crisis early warning model leads to low accuracy. A financial crisis early warning model is established based on the partnering of Biotech firms in china. Starting from the four sources of the crisis, use indicators to quantitatively reflect the main problems, and establish a financial crisis early warning index system. According to the characteristics of financial data, a rough set is used to discretize the data to make the data characteristics clearer. The financial crisis is classified based on a maximum entropy clustering algorithm to overcome the overfitting problem. Calculate the weight of the index, obtain the comprehensive score, judge the crisis category, and complete the establishment of the financial crisis early warning model. The experimental results show that the average accuracy of the financial crisis early warning model based on MEC is 0.9290, which is 0.1442 and 0.1697 higher than that based on the SVM and RBF neural network. Therefore, the judgment accuracy of this model is much higher than that of SVM-based and RBF-based early warning models, and the results are mostly in line with the actual situation.

Network space analysis–based identification of road traffic accident hotspots: a case study

This study proposed a method for identifying traffic accident (TA) hotspots. The method combines kernel density estimation (KDE) with network kernel density estimation (NKDE). The hotspots identified through NKDE can be overlaid on the high-risk areas (hot zones) identified using planar KDE, resulting in accurate hotspot identification. The research site was Zhenjiang City in Jiangsu Province, China; data on 410 fatal traffic accidents from 2017 to 2020 were collected. An average nearest neighbour (ANN) ratio (0.563) was obtained using the global auto nearest neighbour distance method; thus, the fatal TAs had a cluster-type distribution in the city. Subsequently, the maximum clustering distance (7812.842591 m) in the research site was analysed using Ripley’s K-function, which yielded a reasonable KDE bandwidth and enabled identifying the distribution of hot zones for fatal TAs. Precise hotspot identification was achieved by overlaying the results of NKDE-based analysis in these hot zones. The proposed method was verified using data on 131 fatal TAs during January to July 2021. The results revealed that, in Zhenjiang City, the identification rate for hot zones and hotspots in hot zones was 71.75% and 38.29%, respectively, and the overall hotspot identification rate was 27.48%, demonstrating the method’s ability to relatively accurately identify hotspot locations, information that can help traffic authorities implement preventive measures.

Research on hair removal algorithm of dermatoscopic images based on maximum variance fuzzy clustering and optimization Criminisi algorithm

Hair removal is one of the significant challenges before applying the automatic segmentation and classification process for skin lesions in a computer-aided diagnosis system for melanoma. Under this background, an efficient hair removal algorithm for dermatoscopic images, is offered in this paper. First, grayscale and wave valley detection were performed on the image, and the hair regions determined by maximum variance fuzzy clustering based on the color and shape characteristics of the image, were corrected by region growth, and were repaired by criminisi algorithm with improved priority and matching criteria. Second, the improved priority is combination of the traditional one and characteristics of structural strength and data coherence. The color weight is introduced into the sample block matching criterion, and the ant colony algorithm is used to optimize the search path of the matching block. Third, we also proposed a qualitative and quantitative method evaluating hair extraction-repair, and five typical hair repair experiments are shown in details with validated verification algorithm by using collected and ISIC 2019 data sets. The last, Experimental results show that the accuracy of our hair detection experiments in the ISIC2019 dataset, can be improved by 2–7% with the accuracy of hair repair experiments improved by 2–5% on average.

Effect of amplitude and wavelength in the cooperative motion of Taylor lines

In the present study, we have modeled the clustering of binary Taylor line swimmers in a Newtonian fluid. The fluid is modeled using the particle-based simulation method multiparticle collision dynamics, while for the Taylor line, we use a bead spring arrangement with a sine wave passing through it using a bending wave potential. The binary swimmers have the same propulsion speed, but they differ from each other in the shape of the Taylor line ensured by different amplitudes and lengths. We observe that an optimal amplitude exists for the Taylor line swimmer leading to large clusters formation. The size distribution of the clusters was observed to follow a power-law behavior followed by an exponential cutoff. We also calculated the probability of finding a bead of the swimmer as a function of the fractional distance from the center of the bounding circle and observed that when the amplitude is close to the optimal value, the swimmer forms clusters closer to the walls; otherwise, the swimmer stays almost uniformly distributed around the bounding area. When the ratio of the amplitude is high, the clusters formed at the center of the bounding area become stable and have a wedge-shape. By calculating the speed of the swimmer as a function of the fractional distance from the center of the bounding area, we show that the swimmers speed increases with the maximum at the walls. The simulation with a smaller system has revealed that the maximum clustering happens for an amplitude parameter of ∼0.17.

Forecast Method of Distributed Photovoltaic Power Generation Based on EM-WS-CNN Neural Networks

In order to cope with the challenges of dispatching of power grids brought by large-scale distributed photovoltaic power generation related to production and consumers, a maximum expected sample weighted convolutional neural network (EM-WS-CNN) is proposed to forecast the distributed photovoltaic output. First, the distance correlation coefficient and the principal component analysis method are used to extract the comprehensive meteorological factors from the original meteorological data, and then the 6 statistical indexes of the comprehensive meteorological factors and historical power data are used as the clustering characteristics. The historical data are divided into different weather types by using the maximum expectation clustering, and the training samples are weighted based on the membership matrix. Finally, the weighted training data are used to construct the EM-WS-CNN model. In the experimental analysis, the above-mentioned method is compared with the CNN model, and the results show that the proposed method has higher accuracy and robustness.

Mind-set of Recent Medical Graduates: A Channel for Teaching Medical Ethics and Professionalism in Bangladesh

Background: The medical ethics and professionalism teaching have an influence on knowledge, attitude and skills of physicians. The scope of learning medical ethics and professionalism is limited in Curriculum for Medical Education in Bangladesh. There is an unhealthy situation in physician-patient relationship as a consequence of this limitation. Objectives: This study was aimed to assess attitude of recent medical graduates towards medical ethics and professionalism. The findings of this study might be helpful to strengthen medical ethics and professionalism in the medical education. Methods: This descriptive study was conducted on recent medical graduates of seven medical colleges under four universities in Bangladesh from January 2018 to June 2019. Data were collected using a pretested self administered semi-structured questionnaire, based on attitude measuring scale regarding medical ethics and professionalism. Results: Attitude scores of 308 recent medical graduates were in ethical conduct, autonomy, information of wrong doing during treatment, confidentiality, informed consent, sharing the condition of patient to close relatives, paternalism, treating of violent patient, abortion, euthanasia, religious beliefs influencing the choice of treatment, physician-patient relationship, informed decision making, continued medical education and role of pharmaceutical representative were assessed. The average scores were ranging from 2.1 to 4.2; with maximum clustering around score ‘3’ or “not sure”. Majority of the study population thought that the current standard of ethics practice was lower than expectation (59.1%) and should give more attention (76.6%) than the present. Furthermore, most of them (85.4%) felt a need for a separate module on medical ethics and professionalism in undergraduate medical curriculum. Conclusion: The recent medical graduates showed a positive attitude towards medical ethics and professionalism. So, a favourable environment was observed for learning medical ethics and professionalism. Thus, to strengthening of medical ethics and professionalism in medical education curricula is suggested. Bangladesh Med Res Counc Bull 2021; 47(1): 57-61

FSOA‐DNFNet: Incremental indexing and image classification using hybrid optimization‐based deep neuro Fuzzy network

AbstractThe rapid evolution and tremendous growth of internet has provided massive growth of unstructured data that leads to a complexity while retrieving dynamic data effectively. The rapid growth in data volume has imposed many challenging constraints, such as necessity to retrieve data completely even if newly arrived samples are occurred and storing of huge volume of data. This has paved a way for concentrating more on incremental learning that functions on information streams. To speed up retrieval, clustering methods and indexes are utilized and periodic updating of clusters is very substantial because of dynamic nature of databases. Moreover, the standard of clustering techniques purely based on data representation techniques, in which traditional methods faced problems like dimensionality explosion and sparsity. To address such limitations, an effectual strategy is developed for incremental indexing and image classification using proposed Feedback Social Optimization Algorithm (FSOA). The image classification is effectively carried out using Deep neuro fuzzy optimizer and it is trained by employing the proposed FSOA and newly FSOA is derived by the integration of Feedback Artificial Tree (FAT) Algorithm and Social Optimization Algorithm (SOA). Moreover, the proposed FSOA has achieved the maximum clustering accuracy of 93.382, the maximum testing accuracy of 94.4, the maximum sensitivity of 91.892, and the maximum specificity of 96.058.

Real-time dynamic prediction model of carbon efficiency with working condition identification in sintering process

Accurate prediction of carbon efficiency is a prerequisite for achieving energy saving and consumption reduction in an iron ore sintering process, and is the key to guaranteeing the quality and yield of sintered ore. This paper proposes an original real-time dynamic prediction model for carbon efficiency prediction in the process. A Savitzky–Golay filter is used to eliminate noise of the actual production data collected from a cooperative sintering plant, and the correlation between carbon efficiency and process parameters is determined by mutual information. A modified version of maximum entropy clustering algorithm is presented for identifying working conditions to accurately discriminate between anomalies and normal working conditions. Then, the real-time dynamic prediction model of carbon efficiency based on broad learning is established by taking into account the process characteristics and using the prediction error information under normal working conditions. The proposed model is demonstrated to be valid by carrying out some experiments with actual production data. The experimental comparative analysis show that this model has good generalization capabilities and high real-time prediction accuracy, and is superior to other advanced methods in dynamic prediction of carbon efficiency.

Population Genetic Structure and Biodiversity Conservation of a Relict and Medicinal Subshrub Capparis spinosa in Arid Central Asia

As a Tertiary Tethyan relict, Capparis spinosa is a typical wind-preventing and sand-fixing deciduous subshrub in arid central Asia. Due to its medicinal and energy value, this species is at risk of potential threat from human overexploitation, habitat destruction and resource depletion. In this study, our purpose was to evaluate the conservation strategies of C. spinosa according to its genetic structure characteristics and genetic diversity pattern among 37 natural distributional populations. Based on genomic SNP data generated from dd-RAD sequencing, genetic diversity analysis, principal component analysis, maximum likelihood phylogenetic trees and ADMIXTURE clustering, the significant population structure and differentiation were explored. The results showed the following: (1) Six distinct lineages were identified corresponding to geographic locations, and various levels of genetic diversity existed among the lineages for the natural habitat heterogeneity or human interferences; (2) The lineage divergences were influenced by isolation by distances, vicariance and restricted gene flow under complex topographic and climatic conditions. Finally, for the preservation of the genetic integrity of C. spinosa, we suggest that conservation units should be established corresponding to different geographic groups, and that attention should be paid to isolated and peripheral populations that are experiencing biodiversity loss. Simultaneously, monitoring and reducing anthropogenic disturbances in addition to rationally and sustainably utilizing wild resources would be beneficial to guarantee population resilience and evolutionary potential of this xerophyte in response to future environmental changes.

Species‐specific responses to landscape features shaped genomic structure within Alaska galliformes

AbstractAimConnectivity is vital to the resiliency of populations to environmental change and stochastic events, especially for cold‐adapted species as Arctic and alpine tundra habitats retract as the climate warms. We examined the influence of past and current landscapes on genomic connectivity in cold‐adapted galliformes as a critical first step to assess the vulnerability of Alaska ptarmigan and grouse to environmental change. We hypothesize that the mosaic of physical features and habitat within Alaska promoted the formation of genetic structure across species.LocationAlaska, United States of America.TaxaPtarmigan and Grouse (Galliformes: Tetraoninae).MethodsWe collected double digest restriction‐site‐associated DNA sequence data from six ptarmigan and grouse species (N = 13–145/species) sampled across multiple ecosystems up to ~10 degrees of latitude. Spatial genomic structure was analysed using methods that reflect different temporal scales: (1) principal components analysis to identify major trends in the distribution of genomic variation; (2) maximum likelihood clustering analyses to test for the presence of multiple genomic groupings; (3) shared co‐ancestry analyses to assess contemporary relationships and (4) effective migration surfaces to identify regions that deviate from a null model of isolation by distance.ResultsLevels of genomic structure varied across species (ΦST =0.009–0.042). Three general patterns of structure emerged: (1) east‐west partition located near the Yukon‐Tanana uplands; (2) north‐south split coinciding with the Alaska Range and (3) northern group near the Brooks Range. Species‐specific patterns were observed; not all landscape features were barriers to gene flow for all ptarmigan and grouse and temporal contrasts were detected at the Brooks Range.Main conclusionsWithin Alaska galliformes, patterns of genomic structure coincide with physiographic features and highlight the importance of physical and ecological barriers in shaping how genomic diversity is arrayed across the landscape. Lack of concordance in spatial patterns indicates that species behaviour and habitat affinities play key roles in driving the contrasting patterns of genomic structure.

Unsupervised Clustering of Individuals Sharing Selective Attentional Focus Using Physiological Synchrony.

Research on brain signals as indicators of a certain attentional state is moving from laboratory environments to everyday settings. Uncovering the attentional focus of individuals in such settings is challenging because there is usually limited information about real-world events, as well as a lack of data from the real-world context at hand that is correctly labeled with respect to individuals' attentional state. In most approaches, such data is needed to train attention monitoring models. We here investigate whether unsupervised clustering can be combined with physiological synchrony in the electroencephalogram (EEG), electrodermal activity (EDA), and heart rate to automatically identify groups of individuals sharing attentional focus without using knowledge of the sensory stimuli or attentional focus of any of the individuals. We used data from an experiment in which 26 participants listened to an audiobook interspersed with emotional sounds and beeps. Thirteen participants were instructed to focus on the narrative of the audiobook and 13 participants were instructed to focus on the interspersed emotional sounds and beeps. We used a broad range of commonly applied dimensionality reduction ordination techniques-further referred to as mappings-in combination with unsupervised clustering algorithms to identify the two groups of individuals sharing attentional focus based on physiological synchrony. Analyses were performed using the three modalities EEG, EDA, and heart rate separately, and using all possible combinations of these modalities. The best unimodal results were obtained when applying clustering algorithms on physiological synchrony data in EEG, yielding a maximum clustering accuracy of 85%. Even though the use of EDA or heart rate by itself did not lead to accuracies significantly higher than chance level, combining EEG with these measures in a multimodal approach generally resulted in higher classification accuracies than when using only EEG. Additionally, classification results of multimodal data were found to be more consistent across algorithms than unimodal data, making algorithm choice less important. Our finding that unsupervised classification into attentional groups is possible is important to support studies on attentional engagement in everyday settings.

Urban Hotspot Area Detection Using Nearest-Neighborhood-Related Quality Clustering on Taxi Trajectory Data

Urban hotspot area detection is an important issue that needs to be explored for urban planning and traffic management. It is of great significance to mine hotspots from taxi trajectory data, which reflect residents’ travel characteristics and the operational status of urban traffic. The existing clustering methods mainly concentrate on the number of objects contained in an area within a specified size, neglecting the impact of the local density and the tightness between objects. Hence, a novel algorithm is proposed for detecting urban hotspots from taxi trajectory data based on nearest neighborhood-related quality clustering techniques. The proposed spatial clustering algorithm not only considers the maximum clustering in a limited range but also considers the relationship between each cluster center and its nearest neighborhood, effectively addressing the clustering issue of unevenly distributed datasets. As a result, the proposed algorithm obtains high-quality clustering results. The visual representation and simulated experimental results on a real-life cab trajectory dataset show that the proposed algorithm is suitable for inferring urban hotspot areas, and that it obtains better accuracy than traditional density-based methods.

CBNAAT Based Identification and Demographic Pattern of Drug Resistant Mycobacterium Tuberculosis in South Kashmir, India- a One Year Retrospective Study

India is the highest TB burden country in the world having an estimated incidence of 26.9 lakh cases in 2019. With a population of 1.32 billion, India has the highest burden of drug resistant TB (DR-TB) in the world. North zone of India is the second highest MDR-TB prevalent zone after the West zone of India. MDR TB treatment involves prolonged treatment with injectable second-line drugs, associated with more adverse effects, suboptimal treatment outcomes and higher risks of mortality compared to patients with drug-sensitive TB and those with lesser resistant forms of TB.
 Materials methods: This retrospective study was conducted in the department of Microbiology Government Medical College Anantnag, data was analyzed from March 2017 to February 2018. Non-sterile specimens were processed by Modified Petroff Method. Sterile specimens were concentrated by centrifugation and smear and cultures was inoculated from the sediment. CBNAAT assay was performed by Gene Xpert (Cepheid) 4 system according to the manufacturers’ recommendations.
 Results: Of the total 1497 clinically suspected tuberculosis specimens collected, 1370 (91.5%) were pulmonary and 127 (8.5%) were presumptive extra pulmonary tuberculosis received from different anatomical sites. Maximum clustering of cases was seen in 10-20 years age group. Out of the total 1497 samples 200 were CBNAAT confirmed Mycobacterium Tuberculosis positive samples. In which 155 were pulmonary and 45 were extra pulmonary. The average percentage positivity rate (i.e. percentage of MTB positive samples out of total samples tested) was 13.3% (200/1497). Rifampicin resistance (RR-TB) was seen in 5.5% (11/200) samples. Out of the samples detected positive (200): 155 were pulmonary samples and out of these 155 pulmonary samples 8 were found to be RR MTB 5.1% (8/155). Also out of the 200 positive samples 45 were extra pulmonary and out of these 45 extra pulmonary samples 3 were found to be RR MTB 6.6% (3/45).
 Conclusion: In this study we found that in our region 5.5% cases of TB were RR-TB, 3.2% were new cases and 13% RR-TB was seen in previously treated cases of MTB. The screening of drug resistance has to be expanded to offer universal DST including expanded DST .The second and most important activity is to strengthen drug resistance surveillance under the various national programs with inclusion of laboratories in the private sector as well. The state level regional studies also give us the opportunity to plan and execute intervention prioritization, based on the drug resistance trends observed.

Genetic diversity and effective population sizes of thirteen Indian cattle breeds

BackgroundThe genetic structure of a diverse set of 15 Indian indigenous breeds and non-descript indigenous cattle sampled from eight states was examined, based on 777 k single nucleotide polymorphism (SNP) genotypes obtained on 699 animals, with sample sizes ranging from 17 to 140 animals per breed. To date, this is the largest and most detailed assessment of the genetic diversity of Indian cattle breeds.ResultsAdmixture analyses revealed that 109 of the indigenous animals analyzed had more than 1% Bos taurus admixture of relatively recent origin. Pure indigenous animals were defined as having more than 99% Bos indicus ancestry. Assessment of the genetic diversity within and between breeds using principal component analyses, F statistics, runs of homozygosity, the genomic relationship matrix, and maximum likelihood clustering based on allele frequencies revealed a low level of genetic diversity among the indigenous breeds compared to that of Bos taurus breeds. Correlations of SNP allele frequencies between breeds indicated that the genetic variation among the Bos indicus breeds was remarkably low. In addition, the variance in allele frequencies represented less than 1.5% between the Indian indigenous breeds compared to about 40% between Bos taurus dairy breeds. Effective population sizes (Ne) increased during a period post-domestication, notably for Ongole cattle, and then declined during the last 100 generations. Although we found that most of the identified runs of homozygosity are short in the Indian indigenous breeds, indicating no recent inbreeding, the high FROH coefficients and low FIS values point towards small population sizes. Nonetheless, the Ne of the Indian indigenous breeds is currently still larger than that of Bos taurus dairy breeds.ConclusionsThe changes in the estimates of effective population size are consistent with domestication from a large native population followed by consolidation into breeds with a more limited population size. The surprisingly low genetic diversity among Indian indigenous cattle breeds might be due to their large Ne since their domestication, which started to decline only 100 generations ago, compared to approximately 250 to 500 generations for Bos taurus dairy cattle.

Novel Prediction Model for Steel Mechanical Properties with MSVR Based on MIC and Complex Network Clustering

Traditional mechanical properties prediction models are mostly based on experience and mechanism, which neglect the linear and nonlinear relationships between process parameters. Aiming at the high-dimensional data collected in the complex industrial process of steel production, a new prediction model is proposed. The multidimensional support vector regression (MSVR)-based model is combined with the feature selection method, which involves maximum information coefficient (MIC) correlation characterization and complex network clustering. Firstly, MIC is used to measure the correlation between process parameters and mechanical properties, based on which a complex network is constructed and hierarchical clustering is performed. Secondly, we evaluate all parameters and select a representative one for each partition as the input of the subsequent model based on the centrality and influence indicators. Finally, an actual steel production case is used to train the MSVR prediction model. The prediction results show that our proposed framework can capture effective features from the full parameters in terms of higher prediction accuracy and is less time-consuming compared with the Pearson-based subset, full-parameter subset, and empirical subset input. The feature selection method based on MIC can dig out some nonlinear relationships which cannot be found by Pearson coefficient.

A new species of cone snail, Conus laccadivensis sp. nov.: (Gastropoda: Conidae) from the Islands of Lakshadweep Archipelago

The present paper describes the cone snail, Conus laccadivensis sp. nov., based on the morphological and molecular characters of the materials collected from Kalpeni and Androth Islands of Lakshadweep Archipelago. The species closely resembles the tropical species Conus distans (Hwass in Bruguière (1792) which is widely distributed throughout the Indo-Pacific regions. Morphologically C laccadivensis sp. nov., differs from C. distans in shell colour and spire characteristics. Conus laccadivensis sp. nov., possess distinct colour patterns with large and closely spaced coronations. The maximum likelihood clustering approaches using the mitochondrial genes such as COI, 16S rRNA and 12S rRNA revealed a separate genetic cluster confirming the newly identified species.

Fine-Scale Space-Time Cluster Detection of COVID-19 in Mainland China Using Retrospective Analysis.

Exploring spatio-temporal patterns of disease incidence can help to identify areas of significantly elevated or decreased risk, providing potential etiologic clues. The study uses the retrospective analysis of space-time scan statistic to detect the clusters of COVID-19 in mainland China with a different maximum clustering radius at the family-level based on case dates of onset. The results show that the detected clusters vary with the clustering radius. Forty-three space-time clusters were detected with a maximum clustering radius of 100 km and 88 clusters with a maximum clustering radius of 10 km from 2 December 2019 to 20 June 2020. Using a smaller clustering radius may identify finer clusters. Hubei has the most clusters regardless of scale. In addition, most of the clusters were generated in February. That indicates China’s COVID-19 epidemic prevention and control strategy is effective, and they have successfully prevented the virus from spreading from Hubei to other provinces over time. Well-developed provinces or cities, which have larger populations and developed transportation networks, are more likely to generate space-time clusters. The analysis based on the data of cases from onset may detect the start times of clusters seven days earlier than similar research based on diagnosis dates. Our analysis of space-time clustering based on the data of cases on the family-level can be reproduced in other countries that are still seriously affected by the epidemic such as the USA, India, and Brazil, thus providing them with more precise signals of clustering.

A novel fault diagnosis method for analog circuits with noise immunity and generalization ability

To enhance the reliability of analog circuits in complex electrical systems, a novel fault diagnosis method is presented in this paper. A denoising autoencoder and a sparse autoencoder are combined, producing a feature extraction model named the denoising sparse deep autoencoder (DSDAE) that can obtain effective information from signals contaminated by noise. Compared with traditional feature extraction methods, the DSDAE model can be used to implement adaptive feature learning. Then, linear discriminant analysis is adopted to perform linear dimensionality reduction, thereby obtaining the maximum clustering features of the signals. Finally, a fault diagnosis model based on a support vector machine (SVM) with high versatility and accuracy is developed to identify the fault classes of analog circuits. In addition, the salp swarm algorithm, which is capable of convergence and strong global optimization, is employed to intelligently optimize the SVM classifier. The method is comprehensively evaluated with three typical analog circuits from the ISCAS’97 circuit set. The experimental results illustrate that the proposed fault diagnosis method can achieve excellent fault identification accuracy and generalization performance even under noise interference conditions.