The industrial growth has escalated the use of induction motors as prime movers in modern industry. This is due to its low cost, simple construction and ruggedness. Although rugged, these may fail earlier than expected life due to, excessive mechanical, electrical and environmental stresses. Automatic Artificial Intelligence (AI)-based systems are nowadays widely employed in the domain of induction motor fault identification with high success rate. Artificial neural network are utilized extensively for the detection and diagnosis of various induction motor faults. These systems generally use supervised learning, where the models are pre-trained such that these are skilled enough to classify the absence or presence of faults in motor under investigation. In this paper, a highly effective approach for detection of different motor fault conditions, based on pattern recognition technique is presented. In the proposed method the statistical time domain features are computed from three phase motor current and used as inputs of ANN. Seven different classes of motor conditions: healthy, broken rotor bar, broken rotor bar with stator winding short circuit and inner and outer race bearing defects were considered. The results indicates that the proposed methodology is highly effective for diagnosis of various induction motor faults with high success rate.
Epilepsy is a disease of grave concern these days due to the negligence in its treatment in many parts of the world. Its detection and diagnose requires high skill, large amount of time and money. Thus, due to lack of treatment, epilepsy which can be diagnosed with simple epileptic drugs turn refractory. This can be avoided if it is detected at an early stage. Also, the data received after a patient undergo EEG is quite complex. Visualizing that data in an effective way and knowing important timestamps in a recorded EEG signal can help one save time and increase accuracy of detection. An automated system utilizing conventional machine learning is thus proposed in this study that uses features extracted from EEG signals. We have used a seizure detection model and visualized data and the result using various python libraries. Seizure detection is a model which is able to identify the presence of abnormal activities in the brain. Seizure prediction is a model which is able to predict in advance if he/she is going to face seizures in coming time by just studying the EEG signals of present state of that patient. Supervised Machine learning (random forest classifier) was employed to analyze recorded EEG signals for epilepsy detection. Data in the datasets was visualized using matplotlib. Classifier was visualized using Graphviz and pydot. Random forest model predicted epilepsy with a good accuracy of 96.87%, Sensitivity came out to be 98.4% and Specificity was 90.7%.
Background Screening colonoscopy is integral in the effort to identify and remove potentially cancerous lesions. Important quality indicators include the adenoma detection rate and more recently, the sessile/serrated adenoma detection rate. Natural language processing (NLP) is a computer-based linguistic technique that leverages artificial intelligence to abstract meaningful information from text. This tool carries the potential to automate the task of analyzing large volumes of colonoscopy and pathology reports to generate data on key performance metrics. Purpose The aim of this study is to systematically review the available literature on the performance of NLP in identifying the presence of an adenoma or a sessile/serrated adenoma in colonoscopy reports. Method We performed a systematic review and meta-analysis according to PRISMA recommendations. A comprehensive literature query was conducted on MEDLINE, EMBASE, CINAHL, and CDSR, through July 2022. Studies were included if they evaluated the performance of NLP in extracting data from colonoscopy reports. Our primary outcome was the performance of NLP models in correctly identifying an adenoma reported in a colonoscopy report. Two authors independently screened studies and abstracted data using an a priori designed data collection form. We pooled the sensitivity and specificity of our primary outcome using a univariate analysis first, followed by a bivariate analysis. Using the open-source package ‘mada’ which is written in R, we generated a summary estimate and a summary receiver operating characteristic curve. Result(s) From the 1030 unique studies obtained from our literature search, 13 studies met the inclusion criteria. Eligible studies were used for our meta-analysis. In the univariate analysis, the pooled sensitivity and specificity for detecting an adenoma by the NLP systems was 0.978 (95% CI 0.938-0.992) and 0.997 (95% CI 0.984-0.999), respectively. Similarly, in univariate analysis, the pooled sensitivity and specificity for detecting a sessile/serrated adenoma by the NLP systems was 0.984 (95% CI 0.929-0.996) and 1.0 (95% CI 0.998-1.000), respectively. In the bivariate analysis, the summary estimates for the sensitivity and specificity of the NLP system in detecting an adenoma were 0.973 (95% CI 0.929-0.990) and 0.992 (95%CI 0.978-0.997) respectively. For detecting a sessile/serrated adenoma, the summary estimates for sensitivity and specificity were 0.964 (95% CI 0.895-0.988) and 0.998 (95% CI 0.995-0.999) respectively. Conclusion(s) NLP models have excellent performance in extracting quality metric data from colonoscopy reports. Based on the available literature, we suggest integration of NLP in quality improvement efforts in colonoscopy. Please acknowledge all funding agencies by checking the applicable boxes below None Disclosure of Interest None Declared
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