IU Indianapolis ScholarWorks :: Browsing by Subject "Classification"

Browsing by Subject "Classification"

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A Bi-Level Data-Driven Framework for Fault-Detection and Diagnosis of HVAC Systems feature explainability
(Elsevier, 2022-07) Movahed, Paria; Taheri, Saman; Razban, Ali; Mechanical Engineering, School of Engineering and Technology
Machine learning methods have lately received considerable interest for fault detection diagnostic (FDD) analysis of heating, ventilation, and air conditioning (HVAC) systems due to their high detection accuracy. Meanwhile, HVAC malfunctions are regarded as rare occurrences, hence normal operating data samples are much more accessible than data samples in faulty and malfunctioning conditions. The dominating frequency of normal operation in HVAC datasets have also led to heavily biased classification algorithms within the literature. Moreover, the focus of previous literature has been on increasing accuracy of the models while this leads to a high number of false positives (misleading alarms) in the system. To enhance the performance of diagnostic procedures and fill the mentioned gaps, this study proposes a novel data-driven framework. A bi-level machine learning framework is developed for diagnosing faults in air handling units and rooftop units based on principal component analysis (PCA), time series anomaly detection, and random forest (RF). It is shown that PCA can reduce the dataset dimension with one principal component accounting for 95% of data variance. Also, the random forest could classify the faults with 89% precision for single zone AHU, 85% precision for RTU, and 79% for multi-zone AHU.
Appendectomy or not in middle-aged male with non-inflamed appendix in Amyand’s hernia? Case report and literature review
(Elsevier, 2020) Millay, David S.; Ofoma, Chiedozie Max; Brounts, Lionel R.; Medicine, School of Medicine
Introduction: An Amyand's hernia is a rare disease where a vermiform appendix is found within an inguinal hernia sac. It is reported in the literature as having an incidence between 0.4%-1.0% of reported hernia cases. Typically, an incidental finding, Amyand's hernia is consequently found more frequently intra-operatively rather than preoperatively. Presentation of case: This case is a recount of a 56-year-old male, who presented in an outpatient setting for the evaluation of right inguinal pain and bulge. The patient was diagnosed with a vermiform appendix within the indirect hernia. The patient underwent elective repair of his inguinal hernia via Transabdominal Preperitoneal (TAPP) approach of the hernia with avoidance of appendectomy. Discussion: An Amyand's hernia presents a challenging diagnosis and the treatment algorithm is contingent on the condition of the appendix in individual cases. This case presents a Type 1 Amyand's hernia that was repaired through laparoscopic approach using prosthetic mesh. The aim of this case study highlights the approach to surgical decision making in the diagnosis and treatment of Amyand's hernia proposed in the current literature. Conclusion: This case presents a rare condition known as Amyand's hernia followed by a discussion on the epidemiology, diagnostic workup, and treatment options. Treatment is dependent on the state of the appendix found in the hernia sac and the clinical scenario. Comprehensive literature review shows that the true prevalence of this disease is lower than classically described and still has no clear standardized approaches.
Classification and prediction of cognitive trajectories of cognitively unimpaired individuals
(Frontiers Media, 2023-03-13) Kim, Young Ju; Kim, Si Eun; Hahn, Alice; Jang, Hyemin; Kim, Jun Pyo; Kim, Hee Jin; Na, Duk L.; Chin, Juhee; Seo, Sang Won; Alzheimer’s Disease Neuroimaging Initiative; Radiology and Imaging Sciences, School of Medicine
Objectives: Efforts to prevent Alzheimer's disease (AD) would benefit from identifying cognitively unimpaired (CU) individuals who are liable to progress to cognitive impairment. Therefore, we aimed to develop a model to predict cognitive decline among CU individuals in two independent cohorts. Methods: A total of 407 CU individuals from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and 285 CU individuals from the Samsung Medical Center (SMC) were recruited in this study. We assessed cognitive outcomes by using neuropsychological composite scores in the ADNI and SMC cohorts. We performed latent growth mixture modeling and developed the predictive model. Results: Growth mixture modeling identified 13.8 and 13.0% of CU individuals in the ADNI and SMC cohorts, respectively, as the "declining group." In the ADNI cohort, multivariable logistic regression modeling showed that increased amyloid-β (Aβ) uptake (β [SE]: 4.852 [0.862], p < 0.001), low baseline cognitive composite scores (β [SE]: -0.274 [0.070], p < 0.001), and reduced hippocampal volume (β [SE]: -0.952 [0.302], p = 0.002) were predictive of cognitive decline. In the SMC cohort, increased Aβ uptake (β [SE]: 2.007 [0.549], p < 0.001) and low baseline cognitive composite scores (β [SE]: -4.464 [0.758], p < 0.001) predicted cognitive decline. Finally, predictive models of cognitive decline showed good to excellent discrimination and calibration capabilities (C-statistic = 0.85 for the ADNI model and 0.94 for the SMC model). Conclusion: Our study provides novel insights into the cognitive trajectories of CU individuals. Furthermore, the predictive model can facilitate the classification of CU individuals in future primary prevention trials.
Classification of Breast Cancer Cell Lines into Subtypes Based on Genetic Profiles
(2015-03-16) Pawar, Aniruddha Vikram; Li, Lang
Today we know that there are several different types of breast cancer. Accurate identification breast cancer subtype is extremely important in treating this disease effectively. Consequently the process of invtro development of drugs to treat this disease should be naturally subtype specific. Until now several studies have identified multiple breast cancer cell lines and these cell lines have served as invaluable invitro tumor models. However very few of these cell lines are classified as per their subtypes. In this thesis an effort is made to classify 59 of such breast cancer cell lines using genetic profile comparison approach. This approach is based on comparing characteristic features such as copy number and gene expression of a given cell line to those observed from the tissue samples of different breast subtypes. The tissue data for this comparison comes from The Cancer Genome Atlas (TCGA) while cell line data is taken from Cancer Cell Line Encyclopedia (CCLE).
Classification of Diseases with Accumulation of Tau
(Wiley, 2022) Kovacs, Gabor G.; Ghetti, Bernardino; Goedert, Michel; Pathology and Laboratory Medicine, School of Medicine
Classification of road side material using convolutional neural network and a proposed implementation of the network through Zedboard Zynq 7000 FPGA
(2017-12) Rahman, Tanvir; Christopher, Lauren
In recent years, Convolutional Neural Networks (CNNs) have become the state-of- the-art method for object detection and classi cation in the eld of machine learning and arti cial intelligence. In contrast to a fully connected network, each neuron of a convolutional layer of a CNN is connected to fewer selected neurons from the previous layers and kernels of a CNN share same weights and biases across the same input layer dimension. These features allow CNN architectures to have fewer parameters which in turn reduces calculation complexity and allows the network to be implemented in low power hardware. The accuracy of a CNN depends mostly on the number of images used to train the network, which requires a hundred thousand to a million images. Therefore, a reduced training alternative called transfer learning is used, which takes advantage of features from a pre-trained network and applies these features to the new problem of interest. This research has successfully developed a new CNN based on the pre-trained CIFAR-10 network and has used transfer learning on a new problem to classify road edges. Two network sizes were tested: 32 and 16 Neuron inputs with 239 labeled Google street view images on a single CPU. The result of the training gives 52.8% and 35.2% accuracy respectively for 250 test images. In the second part of the research, High Level Synthesis (HLS) hardware model of the network with 16 Neuron inputs is created for the Zynq 7000 FPGA. The resulting circuit has 34% average FPGA utilization and 2.47 Watt power consumption. Recommendations to improve the classi cation accuracy with deeper network and ways to t the improved network on the FPGA are also mentioned at the end of the work.
Classifying the unknown: Insect identification with deep hierarchical Bayesian learning
(Wiley, 2023) Badirli, Sarkhan; Picard, Christine Johanna; Mohler, George; Richert, Frannie; Akata, Zeynep; Dundar, Murat
1. Classifying insect species involves a tedious process of identifying distinctive morphological insect characters by taxonomic experts. Machine learning can harness the power of computers to potentially create an accurate and efficient method for performing this task at scale, given that its analytical processing can be more sensitive to subtle physical differences in insects, which experts may not perceive. However, existing machine learning methods are designed to only classify insect samples into described species, thus failing to identify samples from undescribed species. 2. We propose a novel deep hierarchical Bayesian model for insect classification, given the taxonomic hierarchy inherent in insects. This model can classify samples of both described and undescribed species; described samples are assigned a species while undescribed samples are assigned a genus, which is a pivotal advancement over just identifying them as outliers. We demonstrated this proof of concept on a new database containing paired insect image and DNA barcode data from four insect orders, including 1040 species, which far exceeds the number of species used in existing work. A quarter of the species were excluded from the training set to simulate undescribed species. 3. With the proposed classification framework using combined image and DNA data in the model, species classification accuracy for described species was 96.66% and genus classification accuracy for undescribed species was 81.39%. Including both data sources in the model resulted in significant improvement over including image data only (39.11% accuracy for described species and 35.88% genus accuracy for undescribed species), and modest improvement over including DNA data only (73.39% genus accuracy for undescribed species). 4. Unlike current machine learning methods, the proposed deep hierarchical Bayesian learning approach can simultaneously classify samples of both described and undescribed species, a functionality that could become instrumental in biodiversity monitoring across the globe. This framework can be customized for any taxonomic classification problem for which image and DNA data can be obtained, thus making it relevant for use across all biological kingdoms.
Comparative Analysis of AML Classification Systems: Evaluating the WHO, ICC, and ELN Frameworks and Their Distinctions
(MDPI, 2024-08-22) Salman, Huda; Medicine, School of Medicine
Comprehensive analyses of the molecular heterogeneity of acute myelogenous leukemia, AML, particularly when malignant cells retain normal karyotype, has significantly evolved. In 2022, significant revisions were introduced in the World Health Organization (WHO) classification and the European LeukemiaNet (ELN) 2022 guidelines of acute myeloid leukemia (AML). These revisions coincided with the inception of the first version of the International Consensus Classification (ICC) for AML. These modifications aim to improve diagnosis and treatment outcomes via a comprehensive incorporation of sophisticated genetic and clinical parameters as well as facilitate accruals to innovative clinical trials. Key updates include modifications to the blast count criteria for AML diagnosis, with WHO 2022 eliminating the ≥20% blast requirement in the presence of AML-defining abnormalities and ICC 2022 setting a 10% cutoff for recurrent genetic abnormalities. Additionally, new categories, such as AML with mutated TP53 and MDS/AML, were introduced. ELN 2022 guidelines retained risk stratification approach and emphasized the critical role of measurable residual disease (MRD) that increased the use of next-generation sequencing (NGS) and flow cytometry testing. These revisions underscore the importance of precise classification for targeted treatment strategies and improved patient outcomes. How much difference versus concordance these classifications present and the impact of those on clinical practice is a continuing discussion.
Concordance between DSM-IV and DSM-5 criteria for delirium diagnosis in a pooled database of 768 prospectively evaluated patients using the delirium rating scale-revised-98
(BioMed Central, 2014-09-30) Meagher, David J.; Morandi, Alessandro; Inouye, Sharon K.; Ely, Wes; Adamis, Dimitrios; Maclullich, Alasdair J.; Rudolph, James L.; Neufeld, Karin; Leonard, Maeve; Bellelli, Giuseppe; Davis, Daniel; Teodorczuk, Andrew; Kreisel, Stefan; Thomas, Christine; Hasemann, Wolfgang; Timmons, Suzanne; O’Regan, Niamh; Grover, Sandeep; Jabbar, Faiza; Cullen, Walter; Dunne, Colum; Kamholz, Barbara; Van Munster, Barbara C.; De Rooij, Sophia E.; De Jonghe, Jos; Trzepacz, Paula T.; Department of Psychiatry, School of Medicine
Background The Diagnostic and Statistical Manual fifth edition (DSM-5) provides new criteria for delirium diagnosis. We examined delirium diagnosis using these new criteria compared with the Diagnostic and Statistical Manual fourth edition (DSM-IV) in a large dataset of patients assessed for delirium and related presentations. Methods Patient data (n = 768) from six prospectively collected cohorts, clinically assessed using DSM-IV and the Delirium Rating Scale-Revised-98 (DRS-R98), were pooled. Post hoc application of DRS-R98 item scores were used to rate DSM-5 criteria. ‘Strict’ and ‘relaxed’ DSM-5 criteria to ascertain delirium were compared to rates determined by DSM-IV. Results Using DSM-IV by clinical assessment, delirium was found in 510/768 patients (66%). Strict DSM-5 criteria categorized 158 as delirious including 155 (30%) with DSM-IV delirium, whereas relaxed DSM-5 criteria identified 466 as delirious, including 455 (89%) diagnosed by DSM-IV (P <0.001). The concordance between the different diagnostic methods was: 53% (ĸ = 0.22) between DSM-IV and the strict DSM-5, 91% (ĸ = 0.82) between the DSM-IV and relaxed DSM-5 criteria and 60% (ĸ = 0.29) between the strict versus relaxed DSM-5 criteria. Only 155 cases were identified as delirium by all three approaches. The 55 (11%) patients with DSM-IV delirium who were not rated as delirious by relaxed criteria had lower mean DRS-R98 total scores than those rated as delirious (13.7 ± 3.9 versus 23.7 ± 6.0; P <0.001). Conversely, mean DRS-R98 score (21.1 ± 6.4) for the 70% not rated as delirious by strict DSM-5 criteria was consistent with suggested cutoff scores for full syndromal delirium. Only 11 cases met DSM-5 criteria that were not deemed to have DSM-IV delirium. Conclusions The concordance between DSM-IV and the new DSM-5 delirium criteria varies considerably depending on the interpretation of criteria. Overly-strict adherence for some new text details in DSM-5 criteria would reduce the number of delirium cases diagnosed; however, a more ‘relaxed’ approach renders DSM-5 criteria comparable to DSM-IV with minimal impact on their actual application and is thus recommended.
Cross-sectional imaging-based severity scoring of chronic pancreatitis: why it is necessary and how it can be done
(SpringerLink, 2020-05) Dasyam, Anil K.; Shah, Zarine K.; Tirkes, Temel; Dasyam, Navya; Borhani, Amir A.; Radiology and Imaging Sciences, School of Medicine
Chronic pancreatitis (CP) remains a diagnostic challenge as clinical symptoms are non-specific, histopathological appearances are varied and pathogenesis remains incompletely understood. Multiple classifications and grading systems have been proposed for CP, but none leverage the full capabilities of cross-sectional imaging modalities and are not widely accepted or validated. CT and MRI/MRCP are useful in identifying a wide spectrum of histopathological changes in CP and can also assess exocrine reserve of pancreas. Advanced MRI techniques such as T1 mapping and extracellular volume fraction can potentially identify early CP. Cross-sectional imaging-based severity scoring can quantify CP disease burden and may have positive implications for clinicians and researchers. In this review, we discuss the need for cross-sectional imaging-based severity scoring for CP, role of CT, and MRI/MRCP in assessment of CP and how these modalities can be used to obtain severity scoring for CP. We summarize relevant information from recently published CT and MRI/MRCP reporting standards for CP, and from international guidelines for cross-sectional imaging and severity scoring for CP.

Browsing by Subject "Classification"

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