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Prakosa, Hendri Kurniawan, and Nur Rokhman. "Anomaly Detection in Hospital Claims Using K-Means and Linear Regression." IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 15, no. 4 (October 31, 2021): 391. http://dx.doi.org/10.22146/ijccs.68160.
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BPJS Kesehatan, which has been in existence for almost a decade, is still experiencing a deficit in the process of guaranteeing participants. One of the factors that causes this is a discrepancy in the claim process which tends to harm BPJS Kesehatan. For example, by increasing the diagnostic coding so that the claim becomes bigger, making double claims or even recording false claims. These actions are based on government regulations is including fraud. Fraud can be detected by looking at the anomalies that appear in the claim data.This research aims to determine the anomaly of hospital claim to BPJS Kesehatan. The data used is BPJS claim data for 2015-2016. While the algorithm used is a combination of K-Means algorithm and Linear Regression. For optimal clustering results, density canopy algorithm was used to determine the initial centroid.Evaluation using silhouete index resulted in value of 0.82 with number of clusters 5 and RMSE value from simple linear regression modeling of 0.49 for billing costs and 0.97 for length of stay. Based on that, there are 435 anomaly points out of 10,000 data or 4.35%. It is hoped that with the identification of these, more effective follow-up can be carried out.
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IKUOMOLA, A. J., and O. E. Ojo. "AN EFFECTIVE HEALTH CARE INSURANCE FRAUD AND ABUSE DETECTION SYSTEM." Journal of Natural Sciences Engineering and Technology 15, no. 2 (November 22, 2017): 1–12. http://dx.doi.org/10.51406/jnset.v15i2.1662.
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Due to the complexity of the processes within healthcare insurance systems and the large number of participants involved, it is very difficult to supervise the systems for fraud. The healthcare service providers’ fraud and abuse has become a serious problem. The practices such as billing for services that were never rendered, performing unnecessary medical services and misrepresenting non-covered treatment as covered treatments etc. not only contribute to the problem of rising health care expenditure but also affect the health of the patients. Traditional methods of detecting health care fraud and abuse are time-consuming and inefficient. In this paper, the health care insurance fraud and abuse detection system (HECIFADES) was proposed. The HECIFADES consist of six modules namely: claim, augment claim, claim database, profile database, profile updater and updated profiles. The system was implemented using Visual Studio 2010 and SQL. After testing, it was observed that HECIFADES was indeed an effective system for detecting fraudulent activities and yet very secured way for generating medical claims. It also improves the quality and mitigates potential payment risks and program vulnerabilities.
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Nortey, Ezekiel N. N., Reuben Pometsey, Louis Asiedu, Samuel Iddi, and Felix O. Mettle. "Anomaly Detection in Health Insurance Claims Using Bayesian Quantile Regression." International Journal of Mathematics and Mathematical Sciences 2021 (February 23, 2021): 1–11. http://dx.doi.org/10.1155/2021/6667671.
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Research has shown that current health expenditure in most countries, especially in sub-Saharan Africa, is inadequate and unsustainable. Yet, fraud, abuse, and waste in health insurance claims by service providers and subscribers threaten the delivery of quality healthcare. It is therefore imperative to analyze health insurance claim data to identify potentially suspicious claims. Typically, anomaly detection can be posited as a classification problem that requires the use of statistical methods such as mixture models and machine learning approaches to classify data points as either normal or anomalous. Additionally, health insurance claim data are mostly associated with problems of sparsity, heteroscedasticity, multicollinearity, and the presence of missing values. The analyses of such data are best addressed by adopting more robust statistical techniques. In this paper, we utilized the Bayesian quantile regression model to establish the relations between claim outcome of interest and subject-level features and further classify claims as either normal or anomalous. An estimated model component is assumed to inherently capture the behaviors of the response variable. A Bayesian mixture model, assuming a normal mixture of two components, is used to label claims as either normal or anomalous. The model was applied to health insurance data captured on 115 people suffering from various cardiovascular diseases across different states in the USA. Results show that 25 out of 115 claims (21.7%) were potentially suspicious. The overall accuracy of the fitted model was assessed to be 92%. Through the methodological approach and empirical application, we demonstrated that the Bayesian quantile regression is a viable model for anomaly detection.
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Bakeyalakshmi, P., and S. K. Mahendran. "Enhanced replica detection scheme for efficient analysis of intrusion detection in MANET." International Journal of Engineering & Technology 7, no. 1.1 (December 21, 2017): 565. http://dx.doi.org/10.14419/ijet.v7i1.1.10169.
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Nowadays, detection scheme of intrusion is placing a major role for efficient access and analysis in Mobile Ad-hoc network (MANET). In the past, the detection scheme of Intrusion was used to identify the efficiency of the network and in maximum systems it performs with huge rate of false alarm. In this paper, an Effective approach of the Enhanced Replica Detection scheme (ERDS) based on Sequential Probability Ratio Test (SPRT) is proposed to detect the malicious actions and to have a secure path without claim in an efficient manner. Also, provides strategies to avoid attacker and to provide secure communication. In order to have an efficient analysis of intrusion detection the proposed approach is implemented based on the anomaly. To achieve this, the detection scheme is established based on SPRT and demonstrated the performances of detection with less claim. The simulation results of control overhead, packet delivery ratio, efficient detection, energy consumption and average claims are carried out for the analysis of performance to show the improvement than the existing by using the network simulator tool. Also, the performance of the proposed system illustrated the detection of intrusion in the normal and attacker states of the network.
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Rahayu, Tiny, Mia Rahma Tika, and Sapta Lestariyowidodo. "Analysis Of Outside Claim Fragmentation On BPJS Claims In Hospital." KESANS : International Journal of Health and Science 1, no. 1 (October 30, 2021): 22–27. http://dx.doi.org/10.54543/kesans.v1i1.6.
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The Social Security Organizing Agency (BPJS) has provisions regardingfraud in which one form of fraud is the breakdown of service episodes that are not in accordance with medical indications (serviceunbundling or fragmentation)it is done by health care providers in Health Facilities Referral Follow-Up (FKRTL) the action is done intentionally, to get financial benefits from public relations. Health Insurance program in the National Social Security System through fraudulent acts that are not in accordance with the provisions of the laws and regulations. The purpose of this study is to analyze the occurrence of Fragmentation in Hospital X. This research method uses quantitative methods from the data obtained. The results of this study of the Hospital conducted fragmentation in february 33 files and march 24 files and the number of fragmentation in mountax services. The hospital argued not to experience losses because the claim package that has been arranged by the Health Insurance Organizing Agency (BPJS) Instead of the Health Insurance Organizing Agency (BPJS) prohibits fragmentation because it includesfraud. In this study, the hospital can fragment because it is not applied standard operating procedures properly and in accordance with PERMENKES Number 16 of 2020 the hospital must have a fraud prevention team in order to conduct early detection.
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Lomas, Dennis. "Representation of basic kinds: Not a case of evolutionary internalization of universal regularities." Behavioral and Brain Sciences 24, no. 4 (August 2001): 686–87. http://dx.doi.org/10.1017/s0140525x01500084.
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Shepard claims that “evolutionary internalization of universal regularities in the world” takes place. His position is interesting and seems plausible with regard to “default” motion detection and aspects of colour constancy which he addresses. However, his claim is not convincing with regard to object recognition. [Shepard]
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Ricchetti-Masterson, Kristen, Molly Aldridge, John Logie, Nittaya Suppapanya, and Suzanne F. Cook. "Exploring Methods to Measure the Prevalence of Ménière's Disease in the US Clinformatics™ Database, 2010-2012." Audiology and Neurotology 21, no. 3 (2016): 172–77. http://dx.doi.org/10.1159/000441963.
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Recent studies on the epidemiology of the inner-ear disorder Ménière's disease (MD) use disparate methods for sample selection, case identification and length of observation. Prevalence estimates vary geographically from 17 to 513 cases per 100,000 people. We explored the impact of case detection strategies and observation periods in estimating the prevalence of MD in the USA, using data from a large insurance claims database. Using case detection strategies of ≥1, ≥2 and ≥3 ICD-9 claim codes for MD within a 1-year period, the 2012 prevalence estimates were 66, 27 and 14 cases per 100,000 people, respectively. For ≥1, ≥2 and ≥3 insurance claims within a 3-year observation period, the prevalence estimates were 200, 104 and 66 cases per 100,000 people, respectively. Estimates based on a single claim are likely to overestimate prevalence; this conclusion is aligned with the American Academy of Otolaryngology-Head and Neck Foundation criteria requiring ≥2 definitive episodes for a definite diagnosis, and it has implications for future epidemiologic research. We believe estimates for ≥2 claims may be a more conservative estimate of the prevalence of MD, and multiyear estimates may be needed to allow for adequate follow-up time.
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Glanz, J. "Papers Face Off Over Claim Of Neutrino Mass Detection." Science 269, no. 5231 (September 22, 1995): 1671–72. http://dx.doi.org/10.1126/science.269.5231.1671.
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VIAENE, S., G. DEDENE, and R. DERRIG. "Auto claim fraud detection using Bayesian learning neural networks." Expert Systems with Applications 29, no. 3 (October 2005): 653–66. http://dx.doi.org/10.1016/j.eswa.2005.04.030.
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Harrag, Fouzi, and Mohamed Khalil Djahli. "Arabic Fake News Detection: A Fact Checking Based Deep Learning Approach." ACM Transactions on Asian and Low-Resource Language Information Processing 21, no. 4 (July 31, 2022): 1–34. http://dx.doi.org/10.1145/3501401.
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Fake news stories can polarize society, particularly during political events. They undermine confidence in the media in general. Current NLP systems are still lacking the ability to properly interpret and classify Arabic fake news. Given the high stakes involved, determining truth in social media has recently become an emerging research that is attracting tremendous attention. Our literature review indicates that applying the state-of-the-art approaches on news content address some challenges in detecting fake news’ characteristics, which needs auxiliary information to make a clear determination. Moreover, the ‘Social-context-based’ and ‘propagation-based’ approaches can be either an alternative or complementary strategy to content-based approaches. The main goal of our research is to develop a model capable of automatically detecting truth given an Arabic news or claim. In particular, we propose a deep neural network approach that can classify fake and real news claims by exploiting ‘Convolutional Neuron Networks’. Our approach attempts to solve the problem from the fact checking perspective, where the fact-checking task involves predicting whether a given news text claim is factually authentic or fake. We opt to use an Arabic balanced corpus to build our model because it unifies stance detection, stance rationale, relevant document retrieval and fact-checking. The model is trained on different well selected attributes. An extensive evaluation has been conducted to demonstrate the ability of the fact-checking task in detecting the Arabic fake news. Our model outperforms the performance of the state-of-the-art approaches when applied to the same Arabic dataset with the highest accuracy of 91%.
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Sowah, Robert A., Marcellinus Kuuboore, Abdul Ofoli, Samuel Kwofie, Louis Asiedu, Koudjo M. Koumadi, and Kwaku O. Apeadu. "Decision Support System (DSS) for Fraud Detection in Health Insurance Claims Using Genetic Support Vector Machines (GSVMs)." Journal of Engineering 2019 (September 2, 2019): 1–19. http://dx.doi.org/10.1155/2019/1432597.
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Fraud in health insurance claims has become a significant problem whose rampant growth has deeply affected the global delivery of health services. In addition to financial losses incurred, patients who genuinely need medical care suffer because service providers are not paid on time as a result of delays in the manual vetting of their claims and are therefore unwilling to continue offering their services. Health insurance claims fraud is committed through service providers, insurance subscribers, and insurance companies. The need for the development of a decision support system (DSS) for accurate, automated claim processing to offset the attendant challenges faced by the National Health Insurance Scheme cannot be overstated. This paper utilized the National Health Insurance Scheme claims dataset obtained from hospitals in Ghana for detecting health insurance fraud and other anomalies. Genetic support vector machines (GSVMs), a novel hybridized data mining and statistical machine learning tool, which provide a set of sophisticated algorithms for the automatic detection of fraudulent claims in these health insurance databases are used. The experimental results have proven that the GSVM possessed better detection and classification performance when applied using SVM kernel classifiers. Three GSVM classifiers were evaluated and their results compared. Experimental results show a significant reduction in computational time on claims processing while increasing classification accuracy via the various SVM classifiers (linear (80.67%), polynomial (81.22%), and radial basis function (RBF) kernel (87.91%).
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Sari, Panca Oktavia Candra, and Suharjito Suharjito. "Outlier Detection in Inpatient Claims Using DBSCAN and K-Means." JURNAL TEKNIK INFORMATIKA 15, no. 1 (June 24, 2022): 1–10. http://dx.doi.org/10.15408/jti.v15i1.25682.
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Health insurance helps people to obtain quality and affordable health services. The claim billing process is manually input code to the system, this can lack of errors and be suspected of being fraudulent. Claims suspected of fraud are traced manually to find incorrect inputs. The increasing volume of claims causes a decrease in the accuracy of tracing claims suspected of fraud and consumes time and energy. As an effort to prevent and reduce the occurrence of fraud, this study aims to determine the pattern of data on the occurrence of fraud based on the formation of data groupings. Data was prepared by combining claims for inpatient bills and patient bills from hospitals in 2020. Two methods were used in this study to form clusters, DBSCAN and KMeans. To find out the outliers in the cluster, Local Outlier Factor (LOF) was added. The results from experiments show that both methods can detect outlier data and distribute outlier data in the formed cluster. Variable that high effect becomes data outlier is the length of stay, claims code, and condition of patient when discharged from the hospital. Accuracy K-Means is 0.391, 0.003 higher than DBSCAN, which is 0.389.
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Berendt, Bettina, Peter Burger, Rafael Hautekiet, Jan Jagers, Alexander Pleijter, and Peter Van Aelst. "FactRank: Developing automated claim detection for Dutch-language fact-checkers." Online Social Networks and Media 22 (March 2021): 100113. http://dx.doi.org/10.1016/j.osnem.2020.100113.
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Mary Arockiam, Jenita, and Angelin Claret Seraphim Pushpanathan. "MapReduce-iterative support vector machine classifier: novel fraud detection systems in healthcare insurance industry." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 1 (February 1, 2023): 756. http://dx.doi.org/10.11591/ijece.v13i1.pp756-769.
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<span>Fraud in healthcare insurance claims is one of the significant research challenges that affect the growth of the healthcare services. The healthcare frauds are happening through subscribers, companies and the providers. The development of a decision support is to automate the claim data from service provider and to offset the patient’s challenges. In this paper, a novel hybridized big data and statistical machine learning technique, named MapReduce based iterative support vector machine (MR-ISVM) that provide a set of sophisticated steps for the automatic detection of fraudulent claims in the health insurance databases. The experimental results have proven that the MR-ISVM classifier outperforms better in classification and detection than other support vector machine (SVM) kernel classifiers. From the results, a positive impact seen in declining the computational time on processing the healthcare insurance claims without compromising the classification accuracy is achieved. The proposed MR-ISVM classifier achieves 87.73% accuracy than the linear (75.3%) and radial basis function (79.98%).</span>
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Le, Van Nhat Thang, Jae-Gon Kim, Yeon-Mi Yang, and Dae-Woo Lee. "Evaluating the Checklist for Artificial Intelligence in Medical Imaging (CLAIM)-Based Quality of Reports Using Convolutional Neural Network for Odontogenic Cyst and Tumor Detection." Applied Sciences 11, no. 20 (October 18, 2021): 9688. http://dx.doi.org/10.3390/app11209688.
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This review aimed to explore whether studies employing a convolutional neural network (CNN) for odontogenic cyst and tumor detection follow the methodological reporting recommendations, the checklist for artificial intelligence in medical imaging (CLAIM). We retrieved the CNN studies using panoramic and cone-beam-computed tomographic images from inception to April 2021 in PubMed, EMBASE, Scopus, and Web of Science. The included studies were assessed according to the CLAIM. Among the 55 studies yielded, 6 CNN studies for odontogenic cyst and tumor detection were included. Following the CLAIM items, abstract, methods, results, discussion across the included studies were insufficiently described. The problem areas included item 2 in the abstract; items 6–9, 11–18, 20, 21, 23, 24, 26–31 in the methods; items 33, 34, 36, 37 in the results; item 38 in the discussion; and items 40–41 in “other information.” The CNN reports for odontogenic cyst and tumor detection were evaluated as low quality. Inadequate reporting reduces the robustness, comparability, and generalizability of a CNN study for dental radiograph diagnostics. The CLAIM is accepted as a good guideline in the study design to improve the reporting quality on artificial intelligence studies in the dental field.
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Sun, Haixia, Jin Xiao, Wei Zhu, Yilong He, Sheng Zhang, Xiaowei Xu, Li Hou, Jiao Li, Yuan Ni, and Guotong Xie. "Medical Knowledge Graph to Enhance Fraud, Waste, and Abuse Detection on Claim Data: Model Development and Performance Evaluation." JMIR Medical Informatics 8, no. 7 (July 23, 2020): e17653. http://dx.doi.org/10.2196/17653.
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Background Fraud, Waste, and Abuse (FWA) detection is a significant yet challenging problem in the health insurance industry. An essential step in FWA detection is to check whether the medication is clinically reasonable with respect to the diagnosis. Currently, human experts with sufficient medical knowledge are required to perform this task. To reduce the cost, insurance inspectors tend to build an intelligent system to detect suspicious claims with inappropriate diagnoses/medications automatically. Objective The aim of this study was to develop an automated method for making use of a medical knowledge graph to identify clinically suspected claims for FWA detection. Methods First, we identified the medical knowledge that is required to assess the clinical rationality of the claims. We then searched for data sources that contain information to build such knowledge. In this study, we focused on Chinese medical knowledge. Second, we constructed a medical knowledge graph using unstructured knowledge. We used a deep learning–based method to extract the entities and relationships from the knowledge sources and developed a multilevel similarity matching approach to conduct the entity linking. To guarantee the quality of the medical knowledge graph, we involved human experts to review the entity and relationships with lower confidence. These reviewed results could be used to further improve the machine-learning models. Finally, we developed the rules to identify the suspected claims by reasoning according to the medical knowledge graph. Results We collected 185,796 drug labels from the China Food and Drug Administration, 3390 types of disease information from medical textbooks (eg, symptoms, diagnosis, treatment, and prognosis), and information from 5272 examinations as the knowledge sources. The final medical knowledge graph includes 1,616,549 nodes and 5,963,444 edges. We designed three knowledge graph reasoning rules to identify three kinds of inappropriate diagnosis/medications. The experimental results showed that the medical knowledge graph helps to detect 70% of the suspected claims. Conclusions The medical knowledge graph–based method successfully identified suspected cases of FWA (such as fraud diagnosis, excess prescription, and irrational prescription) from the claim documents, which helped to improve the efficiency of claim processing.
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Baggio, L., and G. A. Prodi. "False discovery rate: setting the probability of false claim of detection." Classical and Quantum Gravity 22, no. 18 (September 6, 2005): S1373—S1379. http://dx.doi.org/10.1088/0264-9381/22/18/s50.
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Gelmini, Graciela B. "DAMA detection claim is still compatible with all other DM searches." Journal of Physics: Conference Series 39 (May 1, 2006): 166–69. http://dx.doi.org/10.1088/1742-6596/39/1/040.
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Vrij, Aldert, Galit Nahari, Rebecca Isitt, and Sharon Leal. "Using the Verifiability lie Detection Approach in an Insurance Claim Setting." Journal of Investigative Psychology and Offender Profiling 13, no. 3 (March 28, 2016): 183–97. http://dx.doi.org/10.1002/jip.1458.
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Bach, Mirjana Pejić, Ksenija Dumičić, Berislav Žmuk, Tamara Ćurlin, and Jovana Zoroja. "Data mining approach to internal fraud in a project-based organization." International Journal of Information Systems and Project Management 8, no. 2 (October 6, 2021): 81–101. http://dx.doi.org/10.12821/ijispm080204.
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Data mining is an efficient methodology for uncovering and extracting information from large databases, which is widely used in different areas, e.g., customer relation management, financial fraud detection, healthcare management, and manufacturing. Data mining has been successfully used in various fraud detection and prevention areas, such as credit card fraud, taxation fraud, and fund transfer fraud. However, there are insufficient researches about the usage of data mining for fraud related to internal control. In order to increase awareness of data mining usefulness in internal control, we developed a case study in a project-based organization. We analyze the dataset about working-hour claims for projects, using two data mining techniques: chi-square automatic interaction detection (CHAID) decision tree and link analysis, in order to describe characteristics of fraudulent working-hour claims and to develop a model for automatic detection of potentially fraudulent ones. Results indicate that the following characteristics of the suspected working-hours claim were the most significant: sector of the customer, origin and level of expertise of the consultant, and cost of the consulting services. Our research contributes to the area of internal control supported by data mining, with the goal to prevent fraudulent working-hour claims in project-based organizations.
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Pannunzi, Mario, Alexis Pérez-Bellido, Alexandre Pereda-Baños, Joan López-Moliner, Gustavo Deco, and Salvador Soto-Faraco. "Deconstructing multisensory enhancement in detection." Journal of Neurophysiology 113, no. 6 (March 15, 2015): 1800–1818. http://dx.doi.org/10.1152/jn.00341.2014.
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The mechanisms responsible for the integration of sensory information from different modalities have become a topic of intense interest in psychophysics and neuroscience. Many authors now claim that early, sensory-based cross-modal convergence improves performance in detection tasks. An important strand of supporting evidence for this claim is based on statistical models such as the Pythagorean model or the probabilistic summation model. These models establish statistical benchmarks representing the best predicted performance under the assumption that there are no interactions between the two sensory paths. Following this logic, when observed detection performances surpass the predictions of these models, it is often inferred that such improvement indicates cross-modal convergence. We present a theoretical analyses scrutinizing some of these models and the statistical criteria most frequently used to infer early cross-modal interactions during detection tasks. Our current analysis shows how some common misinterpretations of these models lead to their inadequate use and, in turn, to contradictory results and misleading conclusions. To further illustrate the latter point, we introduce a model that accounts for detection performances in multimodal detection tasks but for which surpassing of the Pythagorean or probabilistic summation benchmark can be explained without resorting to early cross-modal interactions. Finally, we report three experiments that put our theoretical interpretation to the test and further propose how to adequately measure multimodal interactions in audiotactile detection tasks.
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Staib, James, Rashna Soonavala, Stacey Dacosta Byfield, Kimberly Badal, Kierstin Catlett, Liz Maffey, Mi-Ok Kim, Kenneth Wimmer, Yiwey Shieh, and Laura J. Esserman. "Abstract P5-04-08: Breast cancer screening using ultrasound increases recall, biopsy, and cancer detection rates." Cancer Research 83, no. 5_Supplement (March 1, 2023): P5–04–08—P5–04–08. http://dx.doi.org/10.1158/1538-7445.sabcs22-p5-04-08.
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Abstract Background: Ultrasound is often used as an adjunct to mammography for breast cancer (BC) screening. Usage of screening ultrasound (US) varies by state, likely due to differences in state-specific breast density notification laws and mandates requiring insurance coverage of supplemental screening for women at elevated risk of breast cancer. Screening US can increase cancer detection rates among women with dense breasts, but may increase recalls and benign biopsies. As more states adopt policies mandating insurance coverage for “medically necessary” breast cancer imaging, it is important to understand the impact to screening US utilization and subsequent service utilization. This analysis examines use of screening US by state as well as associated rates of recall, biopsy, and cancer detection. Methods: We analyzed deidentified administrative claims. We included women aged 18-74 years with ≥1 claim for screening mammography in 2018. First claim was index date. Continuous enrollment was required in a commercial (COM) or Medicare Advantage (MA) plan from 1/2016 to index date (baseline period) and from index date to 6 months after (follow-up period). Recall, biopsy, and cancer detection rates were calculated for the follow-up period. Recall was defined as ≥1 claim for mammography, diagnostic ultrasound, or MRI in the follow-up period. We used CPT/HCPCS codes to identify procedures. Screening US was identified by CPT 76641 (complete) with modifier 50 (bilateral) or LT/RT (left/right). Using ICD codes, cancer detection was defined as ≥1 claim for DCIS or invasive BC. We examined screening US rates by insurance type, state, and age. Proportions were compared with chi-squared tests. Results: 939,410 women met study criteria (70% COM, 30% MA; Tables 1-2). In the COM population, recall, biopsy, and cancer detection rates with screening US were approximately two-fold higher than without (recall: 26.1% vs. 11.8%; biopsy: 5.0% vs 1.6%; cancer detection: 1.0% vs. 0.4%). In the MA population, recall, biopsy, and cancer detection rates with screening US were roughly three-fold higher than without (recall: 23.6% vs 9.0%; biopsy: 5.2% vs 1.6%; cancer detection: 1.9% vs 0.7%). In NY, NJ, and CT, the rate of screening US usage was > 14 times higher than in all other states (29.1% vs 1.9%). These three states had higher recall and biopsy rates, but similar cancer detection rates compared to all other states (recall: 14.4% vs. 11.4%; biopsy: 2.5% vs 1.7%; cancer detection: 0.6% vs. 0.5%). All proportion differences reached statistical significance (p < 0.001). Conclusion: Screening US was associated with increases in recall and biopsy, but modest increases in absolute cancer detection rates. Observed state by state variation of screening US is likely driven by laws requiring zero patient payment insurance coverage of “medically necessary” imaging which, as is the case with NY, NJ, and CT, is interpreted to include screening US. Our results demonstrate that screening US may lead to a large increase in recall rates and biopsies without consequentially improving the cancer detection rate. Table 1: Recall, biopsy, and cancer detection rates by age with and without use of adjunctive breast screening ultrasound in a commercially insured U.S. population * values are suppressed to comply with requirements for data release Table 2: Recall, biopsy, and cancer detection rates by age with and without use of adjunctive breast screening ultrasound in a Medicare Advantage (MA) U.S. population * values are suppressed to comply with requirements for data release Citation Format: James Staib, Rashna Soonavala, Stacey Dacosta Byfield, Kimberly Badal, Kierstin Catlett, Liz Maffey, Mi-Ok Kim, Kenneth Wimmer, Yiwey Shieh, Laura J. Esserman. Breast cancer screening using ultrasound increases recall, biopsy, and cancer detection rates [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P5-04-08.
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Scherer, Klaus R. "Neuroscience findings are consistent with appraisal theories of emotion; but does the brain “respect” constructionism?" Behavioral and Brain Sciences 35, no. 3 (May 23, 2012): 163–64. http://dx.doi.org/10.1017/s0140525x11001750.
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AbstractI reject Lindquist et al.'s implicit claim that all emotion theories other than constructionist ones subscribe to a “brain locationist” approach. The neural mechanisms underlying relevance detection, reward, attention, conceptualization, or language use are consistent with many theories of emotion, in particular componential appraisal theories. I also question the authors' claim that the meta-analysis they report provides support for the specific assumptions of constructionist theories.
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Konstantinovskiy, Lev, Oliver Price, Mevan Babakar, and Arkaitz Zubiaga. "Toward Automated Factchecking." Digital Threats: Research and Practice 2, no. 2 (April 2021): 1–16. http://dx.doi.org/10.1145/3412869.
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In an effort to assist factcheckers in the process of factchecking, we tackle the claim detection task, one of the necessary stages prior to determining the veracity of a claim. It consists of identifying the set of sentences, out of a long text, deemed capable of being factchecked. This article is a collaborative work between Full Fact, an independent factchecking charity, and academic partners. Leveraging the expertise of professional factcheckers, we develop an annotation schema and a benchmark for automated claim detection that is more consistent across time, topics, and annotators than are previous approaches. Our annotation schema has been used to crowdsource the annotation of a dataset with sentences from UK political TV shows. We introduce an approach based on universal sentence representations to perform the classification, achieving an F1 score of 0.83, with over 5% relative improvement over the state-of-the-art methods ClaimBuster and ClaimRank. The system was deployed in production and received positive user feedback.
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Butler, Stephen A., Sarah A. Khanlian, and Laurence A. Cole. "Detection of Early Pregnancy Forms of Human Chorionic Gonadotropin by Home Pregnancy Test Devices." Clinical Chemistry 47, no. 12 (December 1, 2001): 2131–36. http://dx.doi.org/10.1093/clinchem/47.12.2131.
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Abstract Background: Home pregnancy testing devices claim >99% diagnostic accuracy for pregnancy and utility on the first day of the missed menses or earlier. We investigated the forms of human chorionic gonadotropin (hCG) in early pregnancy urines, the diagnostic accuracy claim, and the abilities of 15 devices to detect the different forms of hCG. Methods: We measured the concentrations of regular hCG and hyperglycosylated hCG (H-hCG, a large hCG variant) in 592 urines. Fifteen home devices were tested according to manufacturers’ instructions with regular hCG and H-hCG diluted in urine. Results: H-hCG was the principal hCG-related molecule in pregnancy urine in the 2 weeks following the missed menses (61% and 50% of total immunoreactivity in the 4th and 5th completed weeks of pregnancy, respectively). Of 15 home test devices, 2 had a detection limit of 6.3 IU/L for regular hCG, but poorer detection of H-hCG. Two devices detected 13 IU/L regular hCG, one with similar detection and one with poorer detection of H-hCG. Ten devices detected 25 IU/L regular hCG, 6 with poorer detection of H-hCG. One device detected 50 IU/L regular hCG, but better detected H-hCG. Overall, 9 of 15 devices did not detect H-hCG as well as regular hCG. Conclusions: H-hCG is the principal hCG immunoreactivity in early pregnancy urine. Home tests vary widely in detection limits for regular hCG (6.3–50 IU/L), and 9 of 15 devices (60%) had poorer detection limits for H-hCG than for hCG. The variation in analytical detection limits appears contradictory to the common claim for all devices of >99% detection of pregnancy on the first day of the missed menses or earlier. We suggest that manufacturers calibrate devices for both hCG and H-hCG and determine the detection rates for pregnancy rather than the proportion of positive results at arbitrary hCG concentrations.
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Subudhi, Sharmila, and Suvasini Panigrahi. "Two-Stage Automobile Insurance Fraud Detection by Using Optimized Fuzzy C-Means Clustering and Supervised Learning." International Journal of Information Security and Privacy 14, no. 3 (July 2020): 18–37. http://dx.doi.org/10.4018/ijisp.2020070102.
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A novel two-stage automobile insurance fraud detection system is proposed that initially extracts a test set from the original imbalanced insurance dataset. A genetic algorithm based optimized fuzzy c-means clustering is then applied on the remaining data set for undersampling the majority samples by eliminating the outliers among them. Thereafter, the detection of the fraudulent claims occurs in two stages. In the first stage, each insurance record is passed to the clustering module that identifies the claim as genuine, malicious, or suspicious. The genuine and malicious samples are removed and only the suspicious instances are further scrutinized in the second stage by four trained supervised classifiers − Decision Tree, Support Vector Machine, Group Method for Data Handling and Multi-Layer Perceptron individually for final decision making. Extensive experiments and comparative analysis with another recent approach using a real-world automobile insurance dataset justifies the effectiveness of the proposed system.
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Munavalli, Sahana, and Sanjeevakumar M. Hatture. "Fraud Detection in Healthcare System using Symbolic Data Analysis." International Journal of Innovative Technology and Exploring Engineering 10, no. 9 (July 30, 2021): 1–7. http://dx.doi.org/10.35940/ijitee.h9269.0710921.
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In the era of digitization the frauds are found in all categories of health insurance. It is finished next to deliberate trickiness or distortion for acquiring some pitiful advantage in the form of health expenditures. Bigdata analysis can be utilized to recognize fraud in large sets of insurance claim data. In light of a couple of cases that are known or suspected to be false, the anomaly detection technique computes the closeness of each record to be fake by investigating the previous insurance claims. The investigators would then be able to have a nearer examination for the cases that have been set apart by data mining programming. One of the issues is the abuse of the medical insurance systems. Manual detection of frauds in the healthcare industry is strenuous work. Fraud and Abuse in the Health care system have become a significant concern and that too inside health insurance organizations, from the most recent couple of years because of the expanding misfortunes in incomes, handling medical claims have become a debilitating manual assignment, which is done by a couple of clinical specialists who have the duty of endorsing, adjusting, or dismissing the appropriations mentioned inside a restricted period from their gathering. Standard data mining techniques at this point do not sufficiently address the intricacy of the world. In this way, utilizing Symbolic Data Analysis is another sort of data analysis that permits us to address the intricacy of the real world and to recognize misrepresentation in the dataset.
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Breiding, Peter, Sarah Burke-Spolaor, Tao An, Karishma Bansal, Prashanth Mohan, Gregory B. Taylor, and Yingkang Zhang. "Deep Very Long Baseline Interferometry Observations Challenge Previous Evidence of a Binary Supermassive Black Hole Residing in Seyfert Galaxy NGC 7674." Astrophysical Journal 933, no. 2 (July 1, 2022): 143. http://dx.doi.org/10.3847/1538-4357/ac7466.
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Abstract Previous Ku-band (15 GHz) imaging with data obtained from the Very Long Baseline Array (VLBA) had shown two compact, subparsec components at the location of a presumed kiloparsec-scale radio core in Seyfert galaxy NGC 7674. It was then presumed that these two unresolved and compact components were dual radio cores corresponding to two supermassive black holes (SMBHs) accreting surrounding gas and launching radio-bright relativistic jets. However, utilizing the original VLBA data set used to claim the detection of a binary SMBH, in addition to later multiepoch/multifrequency data sets obtained from both the VLBA and the European very long baseline interferometry (VLBI) network, we find no evidence to support the presence of a binary SMBH. We place stringent upper limits to the flux densities of any subparsec-scale radio cores that are at least an order of magnitude lower than the original VLBI radio-core detections, directly challenging the original binary SMBH detection claim. With this in mind, we discuss the possible reasons for the nondetection of any VLBI radio cores in our imaging, the possibility of a binary SMBH still residing in NGC 7674, and the prospect of future observations shedding further light on the true nature of this active galactic nucleus.
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Abdel-Kader, Hafez, Sylvia V. Fagan, Govtnd K. Menon, Larry S. Wigman, and Frederic Chapin. "Determination of Ardacin in Premix, Supplement, and Animal Feed by a Rapid and Sensitive Two-Peak Liquid Chromatographic Method with Correlation to a Gradient Multipeak Liquid Chromatographic Method." Journal of AOAC INTERNATIONAL 77, no. 6 (November 1, 1994): 1341–46. http://dx.doi.org/10.1093/jaoac/77.6.1341.
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Abstract A rapid and sensitive 2-peak liquid chromatographic (LC) method is described for extracting and quantitating ardacin in premix, supplement, and animal feed formulations. Ardacin is extracted from the formulations and analyzed after dilution or cleanup by reversed-phase LC with UV detection at 220 nm. The method correlates well with a more information-rich gradient multipeak LC method. Recoveries for premix formulations ranged from 96.8% (relative standard deviation [RSD], 0.8%) to 103.7% (RSD, 1.3%) for laboratory samples spiked at levels ranging from 1.6 to 39.6% ardacin. Recoveries for protein supplement mash formulations ranged from 98.7% of claim (RSD, 4.1%) to 106.0% of claim (RSD, 7.7%) at ardacin levels ranging from 37.5 to 600 mg/lb. Recoveries for cattle feed ranged from 90.0% of claim (RSD, 11.9%) to 105.6% of claim (RSD, 2.7%) at ardacin levels ranging from 4 to 30 g/ton.
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Mall, Sunita, Prasun Ghosh, and Parita Shah. "Management of Fraud: Case of an Indian Insurance Company." Accounting and Finance Research 7, no. 3 (April 29, 2018): 18. http://dx.doi.org/10.5430/afr.v7n3p18.
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Frauds in insurance are typically where a fraudster tries to gain undue benefit from the insurance contract by ignorance or wilful manipulation. Using the claims data in motor insurance obtained from a Mumbai based insurance company for the time period of 2010-2016, this study focuses on studying the pattern exhibited by those claims which have been rejected and accepted as well. The prime objective of the study is to identify the important or the significant triggers of fraud and predicting the fraudulent behaviour of the customers using the identified triggers in an existing algorithm. This study makes use of statistical techniques like logistic regression & CHAID (Chi Square Automatic Interaction Detection) technique to identify the significant fraud triggers and to determine the probability of rejection & acceptance of each claim coming in future respectively. Data mining techniques like decision tree and confusion matrix are used on the important parameters to find all possible combinations of these significant variables and the bucket for each combination.This study finds that variables like Seats/Tonnage, No Claim Bonus, Type of Vehicle, Gross Written Premium, Sum Insured, Discounts, State Similarity and Previous Insurance details are found to be significant at 1% level of significance. The variables like Branch Code and Risk Types are found to be significant at 5% level of signify cance. The Gain chart depicts that our model is a fairly good model. This research would help the insurance company in settling the legitimate claims within less time and less cost and would also help in identifying the fraudulent claims.
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Palevičius, Paulius, Mayur Pal, Mantas Landauskas, Ugnė Orinaitė, Inga Timofejeva, and Minvydas Ragulskis. "Automatic Detection of Cracks on Concrete Surfaces in the Presence of Shadows." Sensors 22, no. 10 (May 11, 2022): 3662. http://dx.doi.org/10.3390/s22103662.
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Deep learning-based methods, especially convolutional neural networks, have been developed to automatically process the images of concrete surfaces for crack identification tasks. Although deep learning-based methods claim very high accuracy, they often ignore the complexity of the image collection process. Real-world images are often impacted by complex illumination conditions, shadows, the randomness of crack shapes and sizes, blemishes, and concrete spall. Published literature and available shadow databases are oriented towards images taken in laboratory conditions. In this paper, we explore the complexity of image classification for concrete crack detection in the presence of demanding illumination conditions. Challenges associated with the application of deep learning-based methods for detecting concrete cracks in the presence of shadows are elaborated on in this paper. Novel shadow augmentation techniques are developed to increase the accuracy of automatic detection of concrete cracks.
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Derry, J. F. "The Role of Expertise in Discovery. Comment on Sutton and Griffiths (2018). Using Date Specific Searches on Google Books to Disconfirm Prior Origination Knowledge Claims for Particular Terms, Words, and Names. Social Sciences 7: 66." Social Sciences 11, no. 7 (July 4, 2022): 289. http://dx.doi.org/10.3390/socsci11070289.
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In claiming that expertise are unnecessary for making discoveries that contribute to knowledge, Sutton and Griffiths in their 2018 paper made analogous comparisons with metal detection, then proceeded to provide six examples in support of their argument. This response demonstrates the fallacy of that analogy, and reveals how each of those six examples were undermined by a lack of expertise in the relevant disciplines. The mistakes contained in that paper by Sutton and Griffiths make it evident that expertise are required to identify the validity of a discovery, and ensure that a claim is not false. This assurance is particularly needed for the bold claims made by the Sutton and Griffiths paper.
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Thapa, Aakash, Teerayut Horanont, and Bipul Neupane. "Parcel-Level Flood and Drought Detection for Insurance Using Sentinel-2A, Sentinel-1 SAR GRD and Mobile Images." Remote Sensing 14, no. 23 (December 1, 2022): 6095. http://dx.doi.org/10.3390/rs14236095.
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Floods and droughts cause catastrophic damage in paddy fields, and farmers need to be compensated for their loss. Mobile applications have allowed farmers to claim losses by providing mobile photos and polygons of their land plots drawn on satellite base maps. This paper studies diverse methods to verify those claims at a parcel level by employing (i) Normalized Difference Vegetation Index (NDVI) and (ii) Normalized Difference Water Index (NDWI) on Sentinel-2A images, (iii) Classification and Regression Tree (CART) on Sentinel-1 SAR GRD images, and (iv) a convolutional neural network (CNN) on mobile photos. To address the disturbance from clouds, we study the combination of multi-modal methods—NDVI+CNN and NDWI+CNN—that allow 86.21% and 83.79% accuracy in flood detection and 73.40% and 81.91% in drought detection, respectively. The SAR-based method outperforms the other methods in terms of accuracy in flood (98.77%) and drought (99.44%) detection, data acquisition, parcel coverage, cloud disturbance, and observing the area proportion of disasters in the field. The experiments conclude that the method of CART on SAR images is the most reliable to verify farmers’ claims for compensation. In addition, the CNN-based method’s performance on mobile photos is adequate, providing an alternative for the CART method in the case of data unavailability while using SAR images.
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Simard, Daphnée, and Wynne Wong. "ALERTNESS, ORIENTATION, AND DETECTION." Studies in Second Language Acquisition 23, no. 1 (March 2001): 103–24. http://dx.doi.org/10.1017/s0272263101001048.
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This paper critically examines Tomlin and Villa's (1994) fine-grained analysis of attention and Leow's (1998) attempt to operationalize their model. Our position is that whereas Tomlin and Villa have moved the attention research forward by describing the nature of attentional processes and by pointing out that detection is a critical function of SLA, their claim that alertness and orientation are not necessary for detection to occur is currently unsupportable and does not reflect the complex nature of SLA. We argue that Leow's efforts to provide empirical support for this model fall short of that goal. Additionally, we cast doubt on Tomlin and Villa's position that awareness is not required for the detection of L2 data by arguing that the issue of awareness as well as the role of attentional functions must be viewed from a more interactive perspective in terms of the nature of the task, the nature of the linguistic item, and individual learner differences. We conclude by proposing research orientations that may help advance the discussion on this topic.
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Kim, Hoseong, Jaeguk Hyun, Hyunjung Yoo, Chunho Kim, and Hyunho Jeon. "Adversarial Attacks for Deep Learning-Based Infrared Object Detection." Journal of the Korea Institute of Military Science and Technology 24, no. 6 (December 5, 2021): 591–601. http://dx.doi.org/10.9766/kimst.2021.24.6.591.
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Recently, infrared object detection(IOD) has been extensively studied due to the rapid growth of deep neural networks(DNN). Adversarial attacks using imperceptible perturbation can dramatically deteriorate the performance of DNN. However, most adversarial attack works are focused on visible image recognition(VIR), and there are few methods for IOD. We propose deep learning-based adversarial attacks for IOD by expanding several state-of-the-art adversarial attacks for VIR. We effectively validate our claim through comprehensive experiments on two challenging IOD datasets, including FLIR and MSOD.
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Danziger, Shai, Alan Kingstone, and Robert D. Rafal. "Orienting to Extinguished Signals in Hemispatial Neglect." Psychological Science 9, no. 2 (March 1998): 119–23. http://dx.doi.org/10.1111/1467-9280.00021.
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This study tested for spatial orienting effects, without awareness, to signals presented in the neglected hemifield of 2 hemispatialneglect patients. The experiment adapted a spatial precuing paradigm for measuring the effects of visual attention. Contralesional orienting hastened subsequent target detection at the location of an extinguished precue. These findings validate a claim that orienting can occur independently of overt detection and indicate that location information is registered in the neglected field.
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Lambert, Bruce L., William Galanter, King Lup Liu, Suzanne Falck, Gordon Schiff, Christine Rash-Foanio, Kelly Schmidt, Neeha Shrestha, Allen J. Vaida, and Michael J. Gaunt. "Automated detection of wrong-drug prescribing errors." BMJ Quality & Safety 28, no. 11 (August 7, 2019): 908–15. http://dx.doi.org/10.1136/bmjqs-2019-009420.
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BackgroundTo assess the specificity of an algorithm designed to detect look-alike/sound-alike (LASA) medication prescribing errors in electronic health record (EHR) data.SettingUrban, academic medical centre, comprising a 495-bed hospital and outpatient clinic running on the Cerner EHR. We extracted 8 years of medication orders and diagnostic claims. We licensed a database of medication indications, refined it and merged it with the medication data. We developed an algorithm that triggered for LASA errors based on name similarity, the frequency with which a patient received a medication and whether the medication was justified by a diagnostic claim. We stratified triggers by similarity. Two clinicians reviewed a sample of charts for the presence of a true error, with disagreements resolved by a third reviewer. We computed specificity, positive predictive value (PPV) and yield.ResultsThe algorithm analysed 488 481 orders and generated 2404 triggers (0.5% rate). Clinicians reviewed 506 cases and confirmed the presence of 61 errors, for an overall PPV of 12.1% (95% CI 10.7% to 13.5%). It was not possible to measure sensitivity or the false-negative rate. The specificity of the algorithm varied as a function of name similarity and whether the intended and dispensed drugs shared the same route of administration.ConclusionAutomated detection of LASA medication errors is feasible and can reveal errors not currently detected by other means. Real-time error detection is not possible with the current system, the main barrier being the real-time availability of accurate diagnostic information. Further development should replicate this analysis in other health systems and on a larger set of medications and should decrease clinician time spent reviewing false-positive triggers by increasing specificity.
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Darney, P. Ebby. "Automatic Car Damage detection by Hybrid Deep Learning Multi Label Classification." December 2021 3, no. 4 (December 10, 2021): 341–52. http://dx.doi.org/10.36548/jaicn.2021.4.006.
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Automating image-based automobile insurance claims processing is a significant opportunity. In this research work, car damage categorization that is aided by the hybrid convolutional neural network approach is addressed and hence the deep learning-based strategies are applied. Insurance firms may leverage this paper's design and implementation of an automobile damage classification/detection pipeline to streamline car insurance claim policy. Using deep convolutional networks to detect car damage is now possible because of recent improvements in the artificial intelligence sector, mainly due to less computation time and higher accuracy with a hybrid transformation deep learning algorithm. In this paper, multiclass classification proposed to categorize the car damage parts such as broken headlight/taillight, glass fragments, damaged bonnet etc. are compiled into the proposed dataset. This model has been pre-trained on a wide-ranging and benchmark dataset due to the dataset's limited size to minimize overfitting and to understand more common properties of the dataset. To increase the overall proposed model’s performance, the CNN feature extraction model is trained with Resnet architecture with the coco car damage detection datasets and reaches a higher accuracy of 90.82%, which is much better than the previous findings on the comparable test sets.
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Robson, Gregory J. "Two Psychological Defenses of Hobbes’s Claim Against the “Fool”." Hobbes Studies 28, no. 2 (October 27, 2015): 132–48. http://dx.doi.org/10.1163/18750257-02802003.
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A striking feature of Thomas Hobbes’s account of political obligation is his discussion of the Fool, who thinks it reasonable to adopt a policy of selective, self-interested covenant breaking. Surprisingly, scholars have paid little attention to the potential of a psychological defense of Hobbes’s controversial claim that the Fool behaves irrationally. In this paper, I first describe Hobbes’s account of the Fool and argue that the kind of Fool most worth considering is the covert, long-term Fool. Then I advance and critically assess two psychological arguments according to which the Fool’s policy of self-interested covenant breaking is prudentially irrational. The first argument holds that, taken together, the deep guilt from early-stage covenant breaking, the cumulative guilt from continued covenant breaking, and the high statistical risk of detection during high-volume covenant breaking (which increases greatly when one is desensitized to guilt) render the Fool’s policy irrational. The second argument holds that the Fool’s policy is irrational because it puts him at risk of adopting a psychologically intolerable view of his fellow covenanters and, specifically, the extent to which they can be trusted.
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-Amigo, Carmen Diaz, and Bert Popping. "Labeling Regulations, Detection Methods, and Assay Validation." Journal of AOAC INTERNATIONAL 95, no. 2 (March 1, 2012): 337–48. http://dx.doi.org/10.5740/jaoacint.sge_diaz-amigo.
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Abstract Gluten is a commonly used cereal derivative found in bakery products, among other items. In some susceptible individuals, however, it triggers immune responses of different kinds; there is, to a lesser extent, the wheat allergy that is immunoglobulin E (IgE)-mediated and leads to histamine release and typical allergic symptoms. In this case, other water-soluble proteins, like albumins, are also involved. On the other hand, there is, more frequently, celiac disease (CD), where the gluten causes immune reactions in the intestines of certain individuals, leading to degeneration of villi, which typically leads to malabsorption of nutrients and, consequently, malnutrition. The only currently effective health strategy for affected consumers is avoidance of gluten-containing products, based on clear labeling rules. However, despite unanimously accepted Codex definitions by all member jurisdictions, the national implementation of equivalent laws shows significant differences. In the context of CD and in support of the gluten-free statement, regulatory enforcement, as well as manufacturers' quality controls are mostly based on analytical results. However, numerous methods are available, some of which have been validated better than others, and many provide different results on identical samples. Reasons include detection of different gluten components and variability in extraction efficiency due to different buffer compositions, especially from processed foods. Last but not least, the lack of reference materials is hindering the process of generating comparable data across different ELISA kits, as well as other methods. How can such data still be used to support a gluten-free claim? New methodologies, in particular mass spectrometric analysis of gluten derived peptides, are being introduced in numerous laboratories. This methodology is not only capable of detecting gluten derived peptides but can also differentiate between and quantitate wheat, barley, rye, and oat. This paper presents analytical limitations, as well as promising new approaches in support of industry and enforcement activities to ensure compliance with the gluten-free claim under the current regulatory framework.
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Daneman, Nick, Xiaomu Ma, Melanie Eng-Chong, Sandra Callery, and Astrid Guttmann. "Validation of Administrative Population-Based Data Sets for the Detection of Cesarean Delivery Surgical Site Infection." Infection Control & Hospital Epidemiology 32, no. 12 (December 2011): 1213–15. http://dx.doi.org/10.1086/662623.
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We validated population-based hospital, emergency room, and physician claim databases for the detection of surgical site infections against the reference standard of clinical surveillance. Although these data sets are highly specific and could be used to define research cohorts, their low sensitivity and positive predictive value make them inadequate for use as quality indicators.
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Shi, Wenzhong, Min Zhang, Rui Zhang, Shanxiong Chen, and Zhao Zhan. "Change Detection Based on Artificial Intelligence: State-of-the-Art and Challenges." Remote Sensing 12, no. 10 (May 25, 2020): 1688. http://dx.doi.org/10.3390/rs12101688.
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Change detection based on remote sensing (RS) data is an important method of detecting changes on the Earth’s surface and has a wide range of applications in urban planning, environmental monitoring, agriculture investigation, disaster assessment, and map revision. In recent years, integrated artificial intelligence (AI) technology has become a research focus in developing new change detection methods. Although some researchers claim that AI-based change detection approaches outperform traditional change detection approaches, it is not immediately obvious how and to what extent AI can improve the performance of change detection. This review focuses on the state-of-the-art methods, applications, and challenges of AI for change detection. Specifically, the implementation process of AI-based change detection is first introduced. Then, the data from different sensors used for change detection, including optical RS data, synthetic aperture radar (SAR) data, street view images, and combined heterogeneous data, are presented, and the available open datasets are also listed. The general frameworks of AI-based change detection methods are reviewed and analyzed systematically, and the unsupervised schemes used in AI-based change detection are further analyzed. Subsequently, the commonly used networks in AI for change detection are described. From a practical point of view, the application domains of AI-based change detection methods are classified based on their applicability. Finally, the major challenges and prospects of AI for change detection are discussed and delineated, including (a) heterogeneous big data processing, (b) unsupervised AI, and (c) the reliability of AI. This review will be beneficial for researchers in understanding this field.
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Hurlbut, Jeffrey A., Justin R. Carr, Emma R. Singleton, Kent C. Faul, Mark R. Madson, Joseph M. Storey, and Terri L. Thomas. "Solid-Phase Extraction Cleanup and Liquid Chromatography with Ultraviolet Detection of Ephedrine Alkaloids in Herbal Products." Journal of AOAC INTERNATIONAL 81, no. 6 (November 1, 1998): 1121–27. http://dx.doi.org/10.1093/jaoac/81.6.1121.
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Abstract A solid-phase extraction (SPE) cleanup and a liquid chromatographic (LC) method with UV detection is presented for analysis of up to 7 ephedrine alkaloids in herbal products. Alkaloids from herbal products are extracted with acidified buffer, isolated on a propylsulfonic acid SPE column, eluted with a high-ionic-strength buffer, and separated by LC with detection at 255 nm. LC separation is performed by isocratic elution on a YMC phenyl column with 0.1 M sodium acetate-acetic acid (pH = 4.8) containing triethyl-amine and 2% acetonitrile. Ephedrine alkaloids are completely separated in 15 min. Average recovery of 5 common alkaloids from 3 spiked matrixes is 90%, with an average relative standard deviation (RSD) of 4.4% for alkaloid spikes between 0.5 and 16 mg/g. Average quantitation of ephedrine and pseudoephedrine from 6 herbal products is 97% of declared label claims, and average quantitation of synephrine from an herbal dietary product is 85% of label claim (RSD, 3.2%). Recoveries of synephrine, norephedrine, ephedrine, pseudoephedrine, N-methylephedrine, and N-methylpseudoephedrine spiked in 4 herbal products averaged 95%. Results of ruggedness testing and of a second laboratory validation of the procedure are also presented.
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Iakubovskyi, D. "New emission line at ~3.5 keV - observational status, connection with radiatively decaying dark matter and directions for future studies." Advances in Astronomy and Space Physics 4, no. 1-2 (2014): 9–14. http://dx.doi.org/10.17721/2227-1481.4.9-14.
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Recent works of Bulbul et al. (2014) and Boyarsky et al. (2014), claiming the detection of the extra emission line with energy ∼3.5 keV in X-ray spectra of certain clusters of galaxies and nearby Andromeda galaxy, have raised a considerable interest in astrophysics and particle physics communities. A number of new observational studies claim detection or non-detection of the extra line in X-ray spectra of various cosmic objects. In this review I summarise existing results of these studies, overview possible interpretations of the extra line, including intriguing connection with radiatively decaying dark matter, and show future directions achievable with existing and planned X-ray cosmic missions.
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Hernández-Pajares, Manuel, and Alberto García-Rigo. "Comments on: Confirming geomagnetic Sfe by means of a solar flare detector based on GNSS." Journal of Space Weather and Space Climate 10 (2020): 15. http://dx.doi.org/10.1051/swsc/2020015.
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We report two comments affecting the paper “Curto JJ, Juan JM & Timoté CC, 2019. Confirming geomagnetic Sfe by means of a solar flare detector based on GNSS. J Space Weather Space Clim 9: A42. https://doi.org/10.1051/swsc/2019040”: The first comment is the reporting of two mistakes which distorts the central model used for the measurement and detection of solar flares with GNSS, that might affect as well the most part of results and discussions contained in the paper. And the second comment is the clarification about the authors’ claim of presenting the first work of using the electron content enhancement estimation at the subsolar point for characterizing solar flares with GNSS data, which is not accurate due to the existence of such previous definition and usage.
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Pick, Ron Korenblum, Vladyslav Kozhukhov, Dan Vilenchik, and Oren Tsur. "STEM: Unsupervised STructural EMbedding for Stance Detection." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 10 (June 28, 2022): 11174–82. http://dx.doi.org/10.1609/aaai.v36i10.21367.
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Stance detection is an important task, supporting many downstream tasks such as discourse parsing and modeling the propagation of fake news, rumors, and science denial. In this paper, we propose a novel framework for stance detection. Our framework is unsupervised and domain-independent. Given a claim and a multi-participant discussion -- we construct the interaction network from which we derive topological embedding for each speaker. These speaker embedding enjoy the following property: speakers with the same stance tend to be represented by similar vectors, while antipodal vectors represent speakers with opposing stances. These embedding are then used to divide the speakers into stance-partitions. We evaluate our method on three different datasets from different platforms. Our method outperforms or is comparable with supervised models while providing confidence levels for its output. Furthermore, we demonstrate how the structural embedding relate to the valence expressed by the speakers. Finally, we discuss some limitations inherent to the framework.
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Awajan, Albara. "A Novel Deep Learning-Based Intrusion Detection System for IoT Networks." Computers 12, no. 2 (February 5, 2023): 34. http://dx.doi.org/10.3390/computers12020034.
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The impressive growth rate of the Internet of Things (IoT) has drawn the attention of cybercriminals more than ever. The growing number of cyber-attacks on IoT devices and intermediate communication media backs the claim. Attacks on IoT, if they remain undetected for an extended period, cause severe service interruption resulting in financial loss. It also imposes the threat of identity protection. Detecting intrusion on IoT devices in real-time is essential to make IoT-enabled services reliable, secure, and profitable. This paper presents a novel Deep Learning (DL)-based intrusion detection system for IoT devices. This intelligent system uses a four-layer deep Fully Connected (FC) network architecture to detect malicious traffic that may initiate attacks on connected IoT devices. The proposed system has been developed as a communication protocol-independent system to reduce deployment complexities. The proposed system demonstrates reliable performance for simulated and real intrusions during the experimental performance analysis. It detects the Blackhole, Distributed Denial of Service, Opportunistic Service, Sinkhole, and Workhole attacks with an average accuracy of 93.74%. The proposed intrusion detection system’s precision, recall, and F1-score are 93.71%, 93.82%, and 93.47%, respectively, on average. This innovative deep learning-based IDS maintains a 93.21% average detection rate which is satisfactory for improving the security of IoT networks.
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Oron-Gilad, Tal, Joachim Meyer, and Daniel Gopher. "Detecting Changes in Dynamic Functions with Tables and Graphs." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 44, no. 21 (July 2000): 3–435. http://dx.doi.org/10.1177/154193120004402115.
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The relative efficiency of graphic and tabular displays for detecting changes in the amplitude of periodic sine functions, simulating a dynamic process, was assessed. Line graphs had an advantage over tables for the detection of changes and for the correct identification of the changed function. However, the advantage depended on the type of change that could occur. A large difference between the displays was evident when both increases and decreases in amplitude were possible, and differences were much smaller when amplitudes could only increase. These results indicate that participants adapted their detection methods to the types of possible changes. The findings demonstrate the utility of graphic displays for process control and substantiate the claim that graphic displays have an advantage when the displayed information has inherent structure and when the task requires the use of this structure. In addition, task performance was shown to be the subject of adaptive changes, which depend on the context in which the task is performed.
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Thompson, WG. "New Results from a Three-Year Annual Modulation Search with COSINE-100." Journal of Physics: Conference Series 2156, no. 1 (December 1, 2021): 012048. http://dx.doi.org/10.1088/1742-6596/2156/1/012048.
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Abstract COSINE-100 is a direct detection dark matter experiment that is testing DAMA/LIBRA’s claim of dark matter discovery. Located in South Korea’s Yangyang underground laboratory, C0SINE-100 comprises 106 kg of sodium iodide detectors surrounded by a ∼2000 L liquid scintillator veto. In this talk, I will present new results from an annual modulation search using three years of data and the impact on DAMA/LIBRA’s discovery claim. I will also discuss improvements over our previous modulation analysis, including lowering the analysis threshold to 1 keV and the development of a more robust time-dependent background model. In addition, I will review ongoing R&D projects for, and the physics reach of future phases of the experiment.
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Goh, Hui Hwang, Sy yi Sim, Asad Shaykh, Md Humayun Kabir, Chin Wan Ling, Qing Shi Chua, and Kai Chen Goh. "Transmission Line Fault Detection: A Review." Indonesian Journal of Electrical Engineering and Computer Science 8, no. 1 (October 1, 2017): 199. http://dx.doi.org/10.11591/ijeecs.v8.i1.pp199-205.
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<p>Transmission line is the most important part of the power system. Transmission lines a principal amount of power. The requirement of power and its allegiance has grown up exponentially over the modern era, and the major role of a transmission line is to transmit electric power from the source area to the distribution network. The exploded between limited production, and a tremendous claim has grown the focus on minimizing power losses. Losses like transmission loss and also conjecture factors as like as physical losses to various technical losses, Another thing is the primary factor it has a reactive power and voltage deviation are momentous in the long-range transmission power line. In essentially, fault analysis is a very focusing issue in power system engineering to clear fault in short time and re-establish power system as quickly as possible on very minimum interruption. However, the fault detection that interrupts the transmission line is itself challenging task to investigate fault as well as improving the reliability of the system. The transmission line is susceptible given all parameters that connect the whole power system. This paper presents a review of transmission line fault detection.</p>