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作者： SARA VAHID University of Georgia

学位级别：硕士

Usually datasets in the medical domain contain so many features while not all these features are useful for training the classifier. This research aims to find the best optimal subset of features for each medical datasets namely Heart Disease, Parkinson, Thoracic Surgery, Diabetic Retinopathy and Breast Cancer which are all publicly available in UCI repository. We use Filter and Wrapper as feature selection methods which are scheme independent and scheme specific, respectively. In Wrapper approach, we applied different classification algorithms such as Support Vector Machine (SVM), Naive Bayes, Decision Tree, Random Forest and K Nearest Neighbor (KNN) for both feature selection and classification. Afterwards, the same classifiers were applied on the dataset with selected features. The target of this study is comparative analysis of the effect of feature selection on 5 aforementioned datasets (these datasets are various in terms of data types and dimensionality) regarding the accuracy of the classifier.

关键词： Feature Selection Machine Learning classification algorithm Medical Domain

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A Literature Review on Remaining Useful Life Prediction of Lithium-Ion Batteries 7

A Literature Review on Remaining Useful Life Prediction of L...

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2023 7th International Conference On Computing, Communication, Control And Automation, ICCUBEA 2023

作者： Khandelwal, Kunal S. Shete, Vriendra V. MIT School of Engineering MIT ADT University Ece Deptt. Pune India

ISBN: (纸本)9798350304268

Lithium-ion batteries have evolved into a critical part of the power supply system, and predicting their remaining usable life is critical to ensuring their reliability. Intricate operational characteristics of lithium-ion batteries with the changes with their model parameter as they age, improving the accurateness in the online remaining usable life forecast is hard yet important for the reliable power supply application. We've analyzed research papers in different aspects like: dataset utilized for evaluations, different techniques utilized for Machine Learning, Analysis of SOH prediction, classification of RUL prediction, filtering based methods as well as different data-driven algorithms. The paper also summarizes, each of for accuracy, error, and cost. Moreover, the current challenges faced in the RUL prediction will be rendered, which will stand a milestone for the future investigators. © 2023 IEEE.

关键词： classification algorithm Research Gaps and Challenges Filtering based Methods Lithim-ion batteries Machine Learning (ML) Remaining Useful Life (RUL) State of Charge (SOC) State of Health (SOH) prediction

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Research on Machine Learning Method for Extracting Malicious Web Page Features 11

Research on Machine Learning Method for Extracting Malicious...

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11th Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2023

作者： Wang, Lin School of Marxism Hohai University Nanjing China

ISBN: (纸本)9798350333664

Based on the analysis of traditional webpage features of several malicious web pages: webpage URL, JavaScript code, and HTML code, this article defines several features for malicious web page recognition;Proposed several features based on HTTP request information;And a malicious web page recognition method based on feature fusion and machine learning was proposed, which combines HTTP request features with traditional web page features, and then uses machine learning classification algorithms to construct a web page classification model to distinguish between normal and malicious web pages. Select 500 normal web pages from Alexa, and select a total of 500 web pages from PhishTank and Malwaredomains as malicious web pages to construct a dataset and train a web page classification model. Through experiments, it was verified that the method based on URL features, webpage code features, and HTTP request features is superior to the method without HTTP request features, and it was found that the random forest classification algorithm is more suitable for malicious webpage recognition. © 2023 IEEE.

关键词： classification algorithm Feature selection Machine learning Malicious web pages

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Crop Advancement with Machine Learning 7

Crop Advancement with Machine Learning

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2023 7th International Conference On Computing, Communication, Control And Automation, ICCUBEA 2023

作者： Pawar, Rajendra Bisht, Vishnu Pratap Pandey, Ritika Lodhe, Urvik Pawar, Aryan Chouksey, Aaryan Jatale, Sarthak School of Computer Engineering & Technology Mit World Peace University Maharashtra Pune India

ISBN: (纸本)9798350304268

Crop prediction is a crucial task in agriculture that can help farmers and agricultural researchers decide which crops to grow based on environmental variables such as soil type, temperature, rainfall, and humidity. In this project, we propose a data-driven approach for crop prediction using K-means clustering and classification algorithms. We use K-means clustering to group the data into different clusters based on similarities in the environmental variables, and then we use a classification algorithm such as decision trees or support vector machines to predict the best crop to grow in a particular location. This approach can help farmers and agricultural researchers make more informed decisions about crop selection and improve crop yields. We validate our model by comparing historical data on crop yields to our predictions and conducting experiments by growing different crops in different locations. Overall, this project demonstrates the potential of machine learning algorithms for crop prediction in agriculture. © 2023 IEEE.

关键词： Agriculture classification algorithm Crop prediction Environmental variables K-means clustering

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Towards the Development of a Smart Parking System for Highly Populated Cities

Towards the Development of a Smart Parking System for Highly...

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2023 International Conference on Science, Engineering and Business for Sustainable Development Goals, SEB-SDG 2023

作者： Ogundokun, Roseline Oluwaseun Soladoye, Elijah Iseoluwa Asani, Emmanuel Oluwatobi Akande, Oladipupo Ishola, Oluwaseun Joshua Akinwumi, Oyinkansola Aremu, Charity Onye Babatunde, Akinbowale Nathaniel Landmark University Department of Computer Science Omu Aran Nigeria Kaunas University of Technology Department of Multimedia Engineering Lithuania Kwara State University Department of Computer Science Malete Nigeria

ISBN: (纸本)9798350324785

Thanks to innovative sensing systems for smart cities that integrate the future into the present, civilization is through one of its brief transitional phases. The congestion of urban areas necessitates the creation of an adaptable strategy formulation that integrates architecture, creativity, and technology to accommodate paradigm change. A unique sensor approach that is the foundation of a smart parking system (SPS) is provided to enhance accessibility in municipalities with an increasing vehicle pool. The SPS consists of a small single-board computer, a proximity sensor, a camera, a LED indication, a buzzer, and a battery and is designed to determine the availability of a parking spot. This smart transportation technology comprises three major components, specifically sensors, a mobile application, and a monitoring centre, all of which collaborate to monitor, regulate, analyze, and show real-time parking spot information to drivers. Also, the design and execution of the three components of the overall system are shown. © 2023 IEEE.

关键词： Churn prediction classification algorithm Dimensionality reduction Oversampling technique

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From Text to Visuals: Advancements in Depression Prediction Using AI and Machine Learning Techniques

From Text to Visuals: Advancements in Depression Prediction ...

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2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering, RMKMATE 2023

作者： Annapoorani, S. Saravanan, P. Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulatur India

ISBN: (纸本)9798350305708

Social lifestyle and COVID pandemic situation to produce a direct impact on mental health. Depression remains a primary cause of disability worldwide and contributes significantly to the global health burden. It can typically lead to suicidal ideation and have a negative impact on anyone's professional and personal life. Artificial intelligence, machine learning, and deep learning techniques can forecast depression in a timely manner utilizing a variety of resources. Different kinds of sources, such as surveys, social networking sites, video images, and speech, can be used to accurately identify depression without the assistance of a psychiatrist. People on social networking sites express their feelings through appropriate text, which may be examined using machine learning algorithms. Through visual and speech data, deep learning approaches properly provide remarkable accuracy in predicting depression. This survey study is useful in identifying depression accuracy in advanced ways utilizing diverse resources. © 2023 IEEE.

关键词： classification algorithm Deep Learning Depression Ensemble Method Machine Learning

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Predicting Preferred Topics of Authors based on Co-Authorship Network

Predicting Preferred Topics of Authors based on Co-Authorshi...

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IEEE RIVF International Conference on Computing and Communication Technologies - Research, Innovation, and Vision for the Future (RIVF)

作者： Nguyen Le Hoang Pham Vu Dang Khoa Do Phuc Univ Informat Technol VNU HCMC Ho Chi Minh City Vietnam

ISBN: (纸本)9781479913497;9781479913503

This paper focuses a common question in Social Network Analysis - evaluating how much a person prefers or non-prefers a specific issue. To realize this problem, we use the ILPnet2 database and model it as a co-authorship network in which the graph's nodes represent the authors and the links between two nodes means the two corresponding authors have some common papers. And what we have to do is predicting the preferred topics of authors in this network. Based on the original algorithm in [8], we propose a general algorithm with some basic assumptions and definitions and apply it to solve our problem. Finally, we use the ROC Analysis and Regression Estimation model to evaluate the Degree of Accuracy of the algorithm

关键词： social network ILPnet2 co-authorship classification algorithm relaxation labelling predicting preferred topic ROC analysis

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Design and Analysis of Electronic Head Protector for Taekwondo Sports

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SENSORS 2022年第4期22卷 1415-1415页

作者： Qureshi, Fayez Krishnan, Sri Ryerson Univ Dept Elect Comp & Biomed Engn Toronto ON M5B 2K3 Canada

Electronic point scoring systems (PSS) for vests are heavily relied upon in taekwondo. However, no classification and assessment of legal and illegal taekwondo techniques exist. This is also referred to as hit-validation and the objective of this research is to create an electronic helmet (eHelmet) for hit-validation. Three main studies were performed to achieve this objective: Robustness Testing, Sensor Placement and classification of Impacts to the head. The first two studies are preliminary to the main classification of Impacts study. This is needed as no data sets using an IMU are currently available for taekwondo. Robustness Testing: proved that IMU can in-fact be used in the inherently harsh environments of taekwondo with a linear response. The calculated response for the IMU is: f(x) = mx + b, where m is 0.2947 and b is 1.499 (accelerometer) and f(x) = mx + b, where m is 28.33 and b is 84.8 (gyroscope). Sensor Placement: Qualitatively and quantitatively concluded the ideal location for the sensor and electronics is indeed the back of the head, based on durability, cost, human factors, and signal quality. classification of Impacts: IMU classified real-world impacts with 90% accuracy. The two classes were roundhouse kick (legal) and punch (illegal). An eHelmet using an IMU is capable of classifying impacts with high accuracy. The benefit of our system includes low cost, lightweight algorithm for on-device computing (edge computing), and real-time classification. Furthermore, it possesses all the safety requirements of current protective headgear.

关键词： signal analysis classification algorithm IMU point scoring system taekwondo

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Irrigator Responses to Changes in Water Availability in Idaho’s Snake River Plain

Irrigator Responses to Changes in Water Availability in Idah...

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作者： Chance, Eric Wilson Virginia Tech | University

Understanding irrigator responses to previous changes in water availability is critical to building effective institutions that allow for efficient and resilient management of water resources in the face of potentially increasing scarcity due to climate change. Using remote sensing data, I examined irrigator responses to seasonal changes in water availability in Idaho’s Snake River Plain over the past 33 years. Google Earth Engine’s high performance cloud computing and big data processing capabilities were used to compare the performance of three spectral indices, three compositing algorithms and two sensors for 2002 and 2007 for distinguishing between irrigated and non-irrigated parcels. We demonstrate that, on average, the seasonal-maximum algorithm yields a 60% reduction in county scale root mean square error (RMSE) over the accepted single-date approach. We use the best performing classification method, a binary threshold of the seasonal maximum of the Normalized Difference Moisture Index (NDMI), to identify irrigated and non-irrigated lands in Idaho’s Snake River Basin for 1984-2016 using Landsat 5-8 data. NDMI of irrigated lands was found to generally increase over time, likely as a result of changes in agricultural practices increasing crop productivity. Furthermore, we find that irrigators with rights to small areas, and those with only surface water rights are more likely to have a major reduction (>25%) in irrigated area and conversely those with a large, groundwater rights are more likely to have major increases (>25%) in the extent of their irrigation.

关键词： Agriculture classification algorithm irrigation Snake River Plain time series Thesis

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An approach to human motor skill training for uniform group performance

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INTERNATIONAL JOURNAL OF INDUSTRIAL ERGONOMICS 2020年 75卷 102894-102894页

作者： Ma, Wenqi Kaber, David Zahabi, Maryam Yanfeng Automot Interiors Holland MI USA Univ Florida Ind & Syst Engn Dept Gainesville FL USA Texas A&M Univ Ind & Syst Engn Dept College Stn TX USA

Prior research has focused on designing training approaches for novice operators to support maximum motor skill development. However, in production operations, workers must be trained to uniform performance levels to prevent 'bottlenecks' or work-in-process inventory accumulation. This study introduces a new approach to support assignment of training protocols to operators to achieve comparable levels of motor performance. Thirty-six participants performed a computer-based motor test. Based on performance classification results, each participant was assigned to a specific haptic virtual reality training condition. Results revealed participants identified as 'medium' or 'low' performers achieved levels of motor performance comparable to 'high' performers through 1-h training. Relevance to Industry: Findings can be applied to operator training in manual assembly operations, promoting a group of novice workers to achieve uniform performance levels and mitigating production bottlenecks.

关键词： Motor skill performance Training classification algorithm Assembly operations Virtual reality

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