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Sentiment analysis, which aims to identify the positive or negative tone of a given text, has seen a surge in interest over the past two decades, making it one of the most studied areas of study in the fields of Natural Language Processing and Information Extraction. Due to the ambiguous nature of sarcasm, however, sarcasm detection is an essential part of sentiment analysis. The task becomes exceedingly challenging when applied to a language with a more intricate morphology and a lack of available resources, such as Telugu.
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A recent study [1] alerts on the limitations of evaluating anomaly detection algorithms on popular time-series datasets such as Yahoo, Numenta, or NASA, among others. In particular, these datasets are noted to suffer from known flaws suchas trivial anomalies, unrealistic anomaly density, mislabeled ground truth, and run-to-failure bias. The TELCO dataset corresponds to twelve different time-series, with a temporal granularity of five minutes per sample, collected and manually labeled for a period of seven months between January 1 and July 31, 2021.
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Wireless underground sensor networks (WUSNs) promise to deliver substantial social and economic benefits across different verticals. However, many of the relevant application scenarios are located in remote areas with no supporting infrastructure available. To address this challenge, we conceptualize in this study the underground direct-to-satellite (U-DtS) connectivity approach, implying the reception of the signals sent by the underground massive machine-type communication (mMTC) sensors by the gateways operating on the low Earth orbit satellites.
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We conducted a randomized controlled clinical trial to evaluate the efficacy of a brain-computer interface ( BCI ) -based visual and motor feedback motor imagery therapy system on cognitive, psychological and limb movement in hemiplegic stroke patients. We recruited more than 100 patients and randomly divided them into three groups : conventional treatment group, MI group and MI group based on brain-computer interface. The data set contains the evaluation data of these three groups of patients before and after treatment.
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This dataset includes data from 43 hospitalized patients with type 2 diabetes mellitus. Dexcom G5 and Dexcom G6 mobile continuous glucose measurement (CGM) devices were used to measure continuous blood glucose levels. The data collection period is from April 2019 to January 2022. We selected 43 patients whose records with a more extended recording period of more than seven days. Data was collected for 7 to 10 days at 5-minute intervals. This data can be used for glucose level prediction or hypoglycemia occurrence prediction for patient with type 2 diabetes mellitus.
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The focus has been on investors’ hopes for the stock market for a considerable amount of time.Becauseoftheanticipatedhighreturns,thisisthepreferredinvestmentoption.However,dueto the significance of accurate forecasting, such investments are high-risk. In order to analyze stock market forecasts, investors utilize a technical analyst and AI technologies.
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The accurate placement of a needle into the spinal column is critical for spinal anesthesia, spinal taps, and other spinal procedures. Currently, the insertion of the needle is guided by visual and palpation feedback, which can be limited in accuracy and reliability. This study presents a novel approach to providing tactile feedback during needle insertion into the spinal column. This study aims to investigate the effectiveness of providing feedback during the insertion of a needle into the epidural column.
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As the world has entered the ‘era of limits’, it is quintessential to prioritize and emphasize the importance of limits to growth. Principles of system thinking along with the system dynamics methodology help delve into the roots of complex system behavior to better predict them. A system is an interconnection of independent but inter-related parts where a positive change in one part of the system may prove to be calamitous for some other part and thereby for the system as a whole.
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Abstract—This study proposes a multiclass model to classify the severity of knee osteoarthritis (KOA) using bioimpedance measurements. The experimental setup considered three types of measurements using eight electrodes: global impedance with adjacent pattern, global impedance with opposite pattern, and direct impedance measurement, which were taken using an electronic device proposed by authors and based on the Analog Devices AD5933 impedance converter. The study comprised 37 participants, 25 with healthy knees and 13 with three different degrees of KOA.
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This dataset is a subset from the Oxford University Our World in Data Covid 19 Dataset. This dataset contains data points collected on an ongoing basis from Johns Hopkins University, Center for Systems Science and Engineering COVID-19 data, OXFORD COVID-19 Government Response Tracker, and European Centre for Disease Control, from January 2020 to present.
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