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Degradation Mode recognition for Battery of Electric Vehicle Based on ART2 Network

Degradation Mode Recognition for Battery of Electric Vehicle...

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2013 3rd international conference on Mechanical Materials and Manufacturing Engineering(ICMMME 2013)

作者： Gang Yu Mimi Guo Shenzhen Key Laboratory of Digital Manufacturing Technology School of Mechanical Engineering and Automation Harbin Institute of Technology (HIT) Shenzhen Graduate School

In the process of describing the degradation process of battery of electric vehicle,degradation models under dynamic running conditions need to be analyzed in order to effectively forecast the remaining useful mileage and life of *** paper proposed a new method of pattern recognition based on ART2 network to distinguish the degradation mode of the battery of electric ***,the degradation mechanism is analyzed of the battery to extract the features of degradation *** the learning process of ART2 is designed for recognition of the degradation *** that,HMM is applied to research the dynamic changes of the degradation mode and predict future degradation mode ***,experimental analysis based on battery’s charge and discharge data proves the effectiveness of the proposed approach.

关键词： Battery Degradation mode ART2 pattern recognition

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ASystematic Mapping Study on Brain Tumors recognition Based on machine learning Algorithms

ASystematic Mapping Study on Brain Tumors Recognition Based ...

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international conference on Engineering Technology and their Applications (IICETA)

作者： Hayder Q. Flayyih Jumana Waleed Saad Albawi College of Science University of Diyala Iraq College of Engineering University of Diyala Baquba Iraq

ISBN: (数字)9781728182315

ISBN: (纸本)9781728182322

A brain tumor is a type of cancer of abnormal brain cells or mass. The skull is rather solid, and it encloses the brain. Any growth within such a confined space can cause problems. There could be cancerous (malignant) or noncancerous (benign) brain tumors. As benign or malignant tumors develop, they may increase the pressure within the skull. This can cause brain damage and could even be life-threatening. In the recent decade, lots of systems have been produced that provide automatic classification of brain tumors via utilizing means of magnetic resonance spectroscopy and pattern recognition. In spite of promising classification accuracies, none of these systems has established its direction into clinical practice, which is also concerning the missing transparency of the basis of their decision making. In this paper, a systematic mapping study on brain tumors recognition is presented. After four sorting methods, 857 relevant studies in total have been obtained. After applying four filters, 49 papers as primary studies related to the main topic are selected. The selected papers have classified respect to several facets. The results provide an overview of the existing relevant studies reported in the literature, highlight focus areas and research gaps.

关键词： Tumors Systematics Cancer Libraries Filtering Medical diagnostic imaging machine learning algorithms

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Classifying the Unstructured IT Service Desk Tickets Using Ensemble of Classifiers

Classifying the Unstructured IT Service Desk Tickets Using E...

引用

international conference on Computation System and Information Technology for Sustainable Solutions (CSITSS)

作者： S.P. Paramesh C. Ramya K.S. Shreedhara U.B.D.T College of Engineering Davanagere Karnataka India

Manual classification of IT service desk tickets may result in routing of the tickets to the wrong resolution group. Incorrect assignment of IT service desk tickets leads to reassignment of tickets, unnecessary resource utilization and delays the resolution time. Traditional machine learning algorithms can be used to automatically classify the IT service desk tickets. Service desk ticket classifier models can be trained by mining the historical unstructured ticket description and the corresponding label. The model can then be used to classify the new service desk ticket based on the ticket description. The performance of the traditional classifier systems can be further improved by using various ensemble of classification techniques. This paper brings out the three most popular ensemble methods ie, Bagging, Boosting and Voting ensemble for combining the predictions from different models to further improve the accuracy of the ticket classifier system. The performance of the ensemble classifier system is checked against the individual base classifiers using various performance metrics. Ensemble of classifiers performed well in comparison with the corresponding base classifiers. The advantages of building such an automated ticket classifier systems are simplified user interface, faster resolution time, improved productivity, customer satisfaction and growth in business. The real world service desk ticket data from a large enterprise IT infrastructure is used for our research purpose.

关键词： data models Support vector machines Bagging Feature extraction Boosting data mining

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Computer Aided Fracture Diagnosis Based on Integrated learning

Computer Aided Fracture Diagnosis Based on Integrated Learni...

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international conference on Information Systems and Computer Aided Education (ICISCAE)

作者： Feng Yang Bo Ding Network Information Center Shandong University of Traditional Chinese Medicine Jinan China

ISBN: (数字)9781728183046

ISBN: (纸本)9781728194608

Fracture refers to the complete or partial rupture of bone structure, which requires accurate diagnosis by orthopedic surgeons and treatment methods. Therefore, it is of great significance to study automatic fracture detection. In this paper, in order to solve the problem of low accuracy of fracture image determination caused by incomplete feature extraction of traditional features, a deep learning method is used to build a convolutional neural network model framework, and a fracture image detection method based on deep features and integrated learning is proposed. Using data enhancement to preprocess the MURA image data set, when extracting image features, Alexnet is used as a feature extractor to obtain sufficiently effective image features, and when training the classifier, the idea of integrated learning in machine learning is adopted. Train the classifiers after feature extraction, and give them different weight values according to the contribution of each classifier, to achieve better performance than a single classifier, and improve the accuracy of image classification. The experimental results of this method on the MURA data set show its good classification performance.

关键词： Training Computational modeling Surgery Feature extraction Bones Classification algorithms data mining

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Requirements Prediction for Medical Products Based on Named Entity recognition

Requirements Prediction for Medical Products Based on Named ...

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Electronic Information Engineering and Computer Science (EIECS), 2021 international conference on

作者： Peiguang Ruan Ruanchong Liu Bo Guo Zhepeng Liu Chengying Zhang Alibaba Cloud Big Data Application College Zhuhai College of Science and Technology Zhuhai China The First Clinical Medicine School Guangdong Pharmaceutical University Guangzhou China School of Finance and Trade Zhuhai College of Science and Technology Zhuhai China College of Engineering Shantou University Shantou China School of Management Xihua University Chengdu China

Medical product demand forecasting is a necessary condition for building a medical intelligent system, and is an important means to alleviate the lack of medical resources, and the form of medical product demand is mainly unstructured text, including the number of medicines, medical record texts. These medical texts contain a large number of medical terms related to the patient health situation and medical terms are the most direct embodiment of medical knowledge. However, existing methods for requirements are concentrated on the neural network or machine learning algorithms, which ignores the tremendous information in the medical terms. In this work, we utilize the named entity recognition method to predict the demand of the medical products. Efficiently identifying these medical terms from medical texts is a key factor in ensuring the high performance of medical intelligence systems. Therefore, the goal of the medical named entity recognition task is to extract medically significant symptoms, medical history, diseases, etc. from medical texts and realize the downstream prediction task. From our extensive experiment simulation and comparison with existing prediction methods, we can conclude the proposed method can achieve the precise prediction accuracy with reasonable computation costs.

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A Novel machine learning Model for Sarcasm Detection on Facebook Comments

A Novel Machine Learning Model for Sarcasm Detection on Face...

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Applied Artificial Intelligence and Computing (ICAAIC), international conference on

作者： Soumya Puvvada Kusuma Mastan Valli Shaik Karthik Galla S Venkatrama Phani Kumar K Venkata Krishna Kishore Department of CSE VFSTR Deemed to be University Guntur Andhra Pradesh India

ISBN: (数字)9798350375190

ISBN: (纸本)9798350375206

This research focuses on identifying sarcasm in online comments using a combination of machine learning techniques, including feature extraction, pattern recognition, and analysis of emojis. Various methods were utilized to categorize comments into sarcastic and non-sarcastic categories, aiding computers in understanding the sentiment expressed. The experiments have yielded positive results, particularly with hierarchical clustering, which exhibited higher Silhouette Scores and improved cluster quality compared to other techniques. The study used a dataset containing 5,471 comments, labeled with sarcasm as (0 for sarcastic, 1 for non-sarcastic), with sentiment analysis facilitating comment categorization. By categorizing comments based on sentiment, computers can easily identify the underlying sentiment of each comment. The combination of features, including emojis and text, adds an extra layer of meaning, helping the system to identify the type of comment easily. The objective is to help computers understand human language better, enabling them to grasp the real meaning behind a joke and sarcastic sentence, there by making online communication more accurate and effective.

关键词： Computers Analytical models Sentiment analysis Accuracy Computational modeling Clustering algorithms machine learning

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A Sign Language recognition System Using Ionic Liquid Strain Sensor

A Sign Language Recognition System Using Ionic Liquid Strain...

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Material and Electrical Engineering conference (ISMEE), international Symposium on

作者： Chi Tran Nhu Phu Nguyen Dang Van Nguyen Thi Thanh Ha Tran Thi Thuy Van Dau Thanh Tung Bui Thanh Faculty of Electronics and Telecomunications VNU – University of Engineering and Technology Hanoi Vietnam School of Engineering and Built Environment Griffith University Queensland Australia

ISBN: (纸本)9781728177335

In this report, we develop a system converting sign language into voices and text. The system includes a glove attached self-developed ionic liquid-based strain sensors on each finger to detect the movement of them. When the hand move to express a letter lead to the resistance of the strain sensors on each finger change. Therefore, through the information from set of sensors, the system can recognize the letter that user want to convey. A measuring and data acquisition circuit also are developed to receive and analyze data from the sensors. Furthermore, a data digitizing method is proposed as a solution for decoding purpose. The system is tested with 10 distinguished basic letters in American Sign Language table (ASL). The results show the accuracy of system is 98%. The system operates stably with the low power of 0.81W at rest state and 1.71W when working. Besides, applications on smartphone and PC are also built to translate the data into text and speech. These apps make it easier to communicate with deaf people. With achieved results, the system is fully extensible to detect more letters in ALS table in the future.

关键词： Resistance Electrical engineering machine learning algorithms Liquids Gesture recognition Assistive technologies Capacitive sensors

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Lead Forecasting using LSTM based Deep learning Architecture for Sentiment Analysis

Lead Forecasting using LSTM based Deep Learning Architecture...

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international conference on Information and Communications Technology (ICOIACT)

作者： Rajesh Puravankara C Narendra Babu MS Ramaiah University of Applied Sciences India

ISBN: (数字)9781728173566

ISBN: (纸本)9781728173573

Marketers adopt various strategies to improve the efficiency of lead conversion by understanding more about their prospective customers. Advancements in data mining and machine leaning techniques helped industry to adopt these techniques to the business benefit. Use of modern deep learning technology for marketing industry is limited. This paper proposes a methodology for identifying the probability of conversion of a lead by employing a structured and unstructured data analysis and modelling. A binary classification model is built using feed forward neural network for structured data classification. The unstructured user comments data is represented using a long short term memory (LSTM) network which has the ability to capture context information effectively. This method provide a comprehensive approach to consider both structured and unstructured data so as to utilize the data effectively to know the customer more and there by improve the probability of predicting the lead conversion. This methodology provide a strong basis for using LSTM based sentiment analysis forecasting of unstructured data along with the classification performed using structured data.

关键词： Deep learning Sentiment analysis Neural networks Lead Predictive models data models Long short term memory

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Blood donor's safety using data mining

Blood donor's safety using data mining

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international conference on Green Computing and Internet of Things (ICGCIoT)

作者： Rohini Patil Madhu Poi Pooja Pawar Tejashree Patil Namrata Ghuse Dept. of Computer Science SITRC Nasik India

ISBN: (纸本)9781467379113

Blood is most essential constituent in human body. Blood centers collect, process and transport blood to hospitals and other health care centers. Sometimes blood is collected directly from blood donors rather than taking it from blood bank. Nowadays donor safety is major issue as blood is collected from different donors. Small amount of donors face reactions after donating blood and if not treated on time then it may lead to serious injury. The reactions that occur in the donor during blood donation at different hospitals and blood banks is collected, organized and analyzed by the DonorHART tool [1, 3]. The donor reactions information are captured and analyzed by DonorHART system[1]. The tool also monitors and researches the risks involved for donors at the time of blood donation or after the donation process. data mining makes an effort to reveal the patterns in data that are difficult to detect and recognize with automatic pattern recognition. It is analyzing of data and then summarizing it into different form which is called as information and is taken from different databases. The proposed system is a web based application which helps to reduce the human mistakes or errors and different techniques can be applied for blood type classification, diagnosing diabetic symptoms, classifying blood type and identifying different donors reaction and apply preventive measures against them.

关键词： Blood data mining Classification algorithms Safety Cloud computing pattern matching machine learning algorithms

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Personalized Emotion Detection from Text using machine learning

Personalized Emotion Detection from Text using Machine Learn...

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Computing, Analytics and Networks (Indo-Taiwan ICAN), Indo – Taiwan 2nd international conference on

作者： Reema Goyal Navneet Chaudhry Mandeep Singh Department of Computer Science and Engineering IMS Engineering College Ghaziabad UP Department of Computer Science and Engineering Chandigarh University Gharuan Punjab India University Institue of Computing Chandigarh University Gharuan Punjab India

ISBN: (纸本)9781665499453

Social media platforms have evolved into essential tools for sharing emotions with the rest of the globe. People utilize text, photos, music, and video to express their emotions or their points of view. On the Internet, these social media platforms generate a vast volume of unstructured data. All the existing emotion detection models based on the supervised learning methodology require to collect labelled data which is time- consuming task. Next, for languages that are underutilized in terms of NLP, such as Polish vocabulary. To tackle these issues, this paper presents a model that functions as an emotional awareness system. It is based on open forums with automated audio information and text analysis for user-based emotion detection. The model is a key component of many natural computer programmers for processing natural language. The proposed model is implemented using PocketSphinx as an ASR. In addition to this, the model also uses the Word2Vec model, K-means clustering, and the Term frequency-inverse document Vectorizer for text analysis and segmentation. Further, the data from the international Survey on Emotion Antecedents and Reactions (ISEAR) was used to test and train the model. Finally, the concept evolves into a user-based system that will act as a personal assistant for segregating people's emotions as they experiment with new apps. The ISEAR database will be used for performance testing. When utilized in the database, the suggested model greatly outperforms previous models, with an accuracy of 80.25 percent and an F1 score accuracy of 88.86 percent. As the future work, this algorithm can be integrated into an application where emotion is used for recommendation.

关键词： Emotion recognition Analytical models Vocabulary Text analysis Social networking (online) databases Text recognition

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