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Evaluation of Robust classifier algorithm for Tissue Classification under Various Noise Levels

ETRI JOURNAL 2017年第1期39卷 87-96页

作者： Youn, Su Hyun Shin, Ki Young Choi, Ahnryul Mun, Joung Hwan Sungkyunkwan Univ Dept Biomechatron Engn Suwon South Korea Univ Sci & Technol Dept Energy & Power Convers Engn Daejeon South Korea

Ultrasonic surgical devices are routinely used for surgical procedures. The incision and coagulation of tissue generate a temperature of 40 degrees C-150 degrees C and depend on the controllable output power level of the surgical device. Recently, research on the classification of grasped tissues to automatically control the power level was published. However, this research did not consider the specific characteristics of the surgical device, tissue denaturalization, and so on. Therefore, this research proposes a robust algorithm that simulates noise to resemble real situations and classifies tissue using conventional classifier algorithms. In this research, the bioimpedance spectrum for six tissues (liver, large intestine, kidney, lung, muscle, and fat) is measured, and five classifier algorithms are used. A signal-to-noise ratio of additive white Gaussian noise diversifies the testing sets, and as a result, each classifier's performance exhibits a difference. The k-nearest neighbors algorithm shows the highest classification rate of 92.09% (p < 0.01) and a standard deviation of 1.92%, which confirms high reproducibility.

关键词： Ultrasonic surgical device Power level autocontrol Bioimpedance classifier algorithm Additive white Gaussian noise

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Decomposition of Intramuscular EMG Signals Using a Knowledge -based Certainty classifier algorithm

Decomposition of Intramuscular EMG Signals Using a Knowledge...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society

作者： H. Parsaei D. W. Stashuk T. M. Adel Dept. of Systems Design Eng. University of Waterloo Waterloo ON N2L 3G1 Canada

ISBN: (纸本)9781424441198

An automated system for resolving an intramuscular electromyographic (EMG) signal into its constituent motor unit potential trains (MUPTs) is presented. The system is intended mainly for clinical applications where several physiological parameters for each motor unit (MU), such as the motor unit potential (MUP) template and mean firing rate, are required. The system decomposes an EMG signal off-line by filtering the signal, detecting MUPs, and then grouping the detected MUPs using a clustering and a supervised classification algorithm. Both the clustering and supervised classification algorithms use MUP shape and MU firing pattern information to group MUPs into several MUPTs. Clustering is partially based on the K-means clustering algorithm. Supervised classification is implemented using a certainty-based classifier technique that employs a knowledge-based system to merge trains, detect and correct invalid trains, as well as adjust the assignment threshold for each train. The accuracy (93.2%±5.5%), assignment rate(93.9%±2.6%), and error in estimating the number of MUPTs(0.3±0.5) achieved for 10 simulated EMG signals comprised of 3- 11 MUPTs are encouraging for using the system for decomposing various EMG signals.

关键词： Intramuscular EMG Signals Using Knowledge -based Certainty classifier algorithm

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Employability Predictive Model Evaluator Using PART and JRip classifier 2018

Employability Predictive Model Evaluator Using PART and JRip...

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6th International Conference on Information Technology - IoT and Smart City (ICIT)

作者： Gatbonton, Twinkle Mae C. Aguinaldo, Betchie E. Isabela State Univ City Of Ilagan Isabela Philippines Isabela State Univ Cauayan Isabela Philippines

ISBN: (纸本)9781450366298

Employability is the most challenging outcome of higher education institution. It is evident that there are a number of information technology (IT) graduates that failed to address the need of information technology industry, hence, result to job mismatch and high unemployment rate. This presents the Employability Predictive Model Evaluator using PART and JRip classifier algorithm. The study also aims to determine the significant attributes in the development of dataset for predictive model and generate a predictive model for employability of IT graduates using PART and JRip classifier algorithm. The 4-year taken courses of graduates from school year 2013-2016 and employability status extracted from e-Graduate Tracer Survey (eGTS) were used as dataset of the study. The academic courses from the university registrar were clustered based on Commission on Higher Education of the Philippines information technology courses which includes General Education, IT Common Courses, IT Professional Courses, IT Elective Courses and On-the-Job Training Grade. JRip generated two (2) rules while PART generated seven (7) rules, which were used as the logical conditions in the development of Employability Predictive Model Evaluator for IT graduates.

关键词： Data mining predictive model classifier algorithm employability

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Comparative Analysis of Machine Learning algorithms for 5G Coverage Prediction: Identification of Dominant Feature Parameters and Prediction Accuracy

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IEEE ACCESS 2024年 12卷 18939-18956页

作者： Yuliana, Hajiar Iskandar Hendrawan Inst Teknol Bandung Sch Elect Engn & Informat Bandung 40132 Indonesia

5G technology is a key factor in delivering faster and more reliable wireless connectivity. One crucial aspect in 5G network planning is coverage prediction, which enables network providers to optimize infrastructure deployment and deliver high-quality services to customers. This study conducts a comprehensive analysis of machine learning algorithms for 5G coverage prediction, focusing on dominant feature parameters and accuracy. Notably, the Random Forest algorithm demonstrates superior performance with an RMSE of 1.14 dB, MAE of 0.12, and R2 of 0.97. The CNN model, the standout among deep learning algorithms, achieves an RMSE of 0.289, MAE of 0.289, and R2 of 0.78, showcasing high accuracy in 5G coverage prediction. Random Forest models exhibit near-perfect metrics with 98.4% accuracy, precision, recall, and F1-score. Although CNN outperforms other deep learning models, it slightly trails Random Forest in performance. The research highlights that the final Random Forest and CNN models outperform other models and surpass those developed in previous studies. Notably, 2D Distance Tx Rx emerges as the most dominant feature parameter across all algorithms, significantly influencing 5G coverage prediction. The inclusion of horizontal and vertical distances further improves prediction results, surpassing previous studies. The study underscores the relevance of machine learning and deep learning algorithms in predicting 5G coverage and recommends their use in network development and optimization. In conclusion, while the Random Forest algorithm stands out as the optimal choice for 5G coverage prediction, deep learning algorithms, particularly CNN, offer viable alternatives, especially for spatial data derived from satellite images. These accurate predictions facilitate efficient resource allocation by network providers, ensuring high-quality services in the rapidly evolving landscape of 5G technology. A profound understanding of coverage prediction remains pivotal for suc

关键词： Machine learning algorithms Prediction algorithms 5G mobile communication Deep learning Machine learning Predictive models Classification algorithms Performance evaluation 5G coverage prediction classifier algorithm deep learning performance evaluation feature importance

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CICIDS-2017 Dataset Feature Analysis With Information Gain for Anomaly Detection

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IEEE ACCESS 2020年 8卷 132911-132921页

作者： Kurniabudi Stiawan, Deris Darmawijoyo Idris, Mohd Yazid Bin Bin Bamhdi, Alwi M. Budiarto, Rahmat Univ Sriwijaya Fac Engn Palembang 30128 Indonesia Univ Dinamika Bangsa Fac Comp Sci Jambi 36138 Indonesia Univ Sriwijaya Fac Comp Sci Palembang 30128 Indonesia Univ Sriwijaya Fac Math & Nat Sci Palembang 30128 Indonesia Univ Teknol Malaysia Fac Engn Sch Comp Johor Baharu 81310 Malaysia Umm Al Qura Univ Coll Comp Dept Comp Sci Al Qunfudhah 21912 Saudi Arabia Albaha Univ Coll Comp Sci & IT Al Aqiq 657797738 Saudi Arabia

Feature selection (FS) is one of the important tasks of data preprocessing in data analytics. The data with a large number of features will affect the computational complexity, increase a huge amount of resource usage and time consumption for data analytics. The objective of this study is to analyze relevant and significant features of huge network traffic to be used to improve the accuracy of traffic anomaly detection and to decrease its execution time. Information Gain is the most feature selection technique used in Intrusion Detection System (IDS) research. This study uses Information Gain, ranking and grouping the features according to the minimum weight values to select relevant and significant features, and then implements Random Forest (RF), Bayes Net (BN), Random Tree (RT), Naive Bayes (NB) and J48 classifier algorithms in experiments on CICIDS-2017 dataset. The experiment results show that the number of relevant and significant features yielded by Information Gain affects significantly the improvement of detection accuracy and execution time. Specifically, the Random Forest algorithm has the highest accuracy of 99.86% using the relevant selected features of 22, whereas the J48 classifier algorithm provides an accuracy of 99.87% using 52 relevant selected features with longer execution time.

关键词： Feature extraction Classification algorithms Anomaly detection Information filters Support vector machines Filtering algorithms Feature selection anomaly detection information gain CICIDS-2017 dataset classifier algorithm

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A hybrid system for Parkinson's disease diagnosis using machine learning techniques

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INTERNATIONAL JOURNAL OF SPEECH TECHNOLOGY 2021年第3期25卷 583页

作者： Lamba, Rohit Gulati, Tarun Alharbi, Hadeel Fahad Jain, Anurag Maharishi Markandeshwar Maharishi Markandeshwar Engn Coll Dept Elect & Commun Engn Ambala Haryana India Univ Hail Coll Comp Sci & Engn Hail Saudi Arabia Univ Petr & Energy Studies Sch Comp Sci Virtualizat Dept Dehra Dun Uttarakhand India

Parkinson's disease is a neurodegenerative disorder that progresses slowly and its symptoms appear over time, so its early diagnosis is not easy. A neurologist can diagnose Parkinson's by reviewing the patient's medical history and repeated scans. Besides, body movement analysts can diagnose Parkinson's by analyzing body movement. Recent research work has shown that changes in speech can be used as a measurable indicator for early Parkinson's detection. In this work, the authors propose a speech signal-based hybrid Parkinson's disease diagnosis system for its early diagnosis. To achieve this, the authors have tested several combinations of feature selection approaches and classification algorithms and designed the model with the best combination. To formulate various combinations, three feature selection methods such as mutual information gain, extra tree, and genetic algorithm and three classifiers namely naive bayes, k-nearest-neighbors, and random forest have been used. To analyze the performance of different combinations, the speech dataset available at the UCI (University of California, Irvine) machine learning repository has been used. As the dataset is highly imbalanced so the class balancing problem is overcome by the synthetic minority oversampling technique (SMOTE). The combination of genetic algorithm and random forest classifier has shown the best performance with 95.58% accuracy. Moreover, this result is also better than the recent work found in the literature.

关键词： Parkinson&apos s disease Decision support system Feature selection classifier algorithm Machine learning

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An algorithm Based on the Construction of Braun's Cathode Ray Tube as a Novel Technique for Data Classification

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16th International Conference on Neural Information Processing (ICONIP 2009)

作者： Swiecicki, Mariusz Cracow Univ Technol Inst Comp Modelling Krakow Poland

ISBN: (纸本)9783642106828

In this article we have presented a model used for a classification of multidimensional data in a broader sense, called Braun's cathode machine. The internal structure of the machine presented on this paper has been based on the architecture of a cathode-ray tube Braun's tube. For a machine model described this way a machine training algorithm has been proposed as well as response computing algorithms. In the final chapter we have presented the results of the machine tests for the notions connected with the classification and self-organization of multidimensional data.

关键词： Braun's machine classifier algorithm Self-organization of data algorithm Data mining Computational intelligence

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21st Century Learning Skills Predictive Model Using PART algorithm 2019

21st Century Learning Skills Predictive Model Using PART Alg...

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3rd International Conference on Machine Learning and Soft Computing (ICMLSC)

作者： Aguinaldo, Betchie E. Isabela State Univ Cauayan Isabela Philippines

ISBN: (纸本)9781450366120

The skillset of a graduate is the key to success to perform successfully in their chosen career path, thus, academic institution continuously develops strategic ways to develop and prepare the 21st century Learning skills of the student before they completed the higher education studies. This paper presents the 21st Century Learning Predictive Model in Programming Logic Formulation using PART classifier algorithm. It also aims to determine the significant attributes in the development of dataset for predictive model and generate a predictive model for 21st Century Learning Skills using PART classifier algorithm. Six (6) standardized questionnaire of 21st Century Learning Skills of one hundred eight (180) students were coded and used as a dataset of the study. The result of the customized assessment exam in programming logic formulation was used as response attributes of the datasets. As a result of the study, five (5) rules were generated using PART classifier algorithm. Among the 21st Century Learning skills, Communication is the strongest predicting attributes of successful performance of students in programming logic formulation.

关键词： Data Mining Predictive Model classifier algorithm Employability

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Role of Artificial Intelligence and Machine Learning in Resolving the Issues and Challenges with Prosthetic Knees 4th

Role of Artificial Intelligence and Machine Learning in Reso...

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4th International Conference on Information, Communication and Computing Technology (ICICCT)

作者： Salwan, Deepali Kant, Shri Pandian, G. Pt Deen Dayal Upadhyaya Natl Inst Persons Phys Di New Delhi India Sharda Univ Res & Technol Dev Ctr Sch Engn & Technol Dept Comp Sci Greater Noida India

ISBN: (纸本)9789811513848;9789811513831

From the ancient times until today, in the age of Artificial Intelligence, field of prosthetics is where human is continuously striving to do better. Upper limb amputation deprives the person from routine activities, which the amputee never thought was important, on the other hand lower limb amputation restricts the person's physical movements to a great extent. In this article we will highlight the issues and challenges with the active lower limb prosthetic from the socket to the knee, the material, the sensors and the algorithms used for controlling the movement. How each of them plays a pivotal role in providing a comfortable gait which resembles the natural human gait, this article throws light upon where are we in terms of advancement in lower limb prosthetic and the issues and challenges which are still there even with the finest active artificial knee available.

关键词： Prosthetic-knee Artificial-knee Lower limb amputation Microprocessor prosthetic knee Sensors classifier algorithm Sockets and liners

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A PREDICTIVE MODEL FOR KIDNEY FAILURE E-HEALTH 3

A PREDICTIVE MODEL FOR KIDNEY FAILURE E-HEALTH

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3rd IEEE International Conference on Sensing, Signal Processing and Security (ICSSS)

作者： Ancy, S. Cornelius, K. Jeppiaar Inst Technol Dept IT Madras Tamil Nadu India St Peters Engn Coll Dept IT Madras Tamil Nadu India

ISBN: (纸本)9781509049295

Big data is the large set of dataset. It involves extraction, selection, analyzing and interpolation of data. Big data is used wide assortment in medical fields for analyzing the patient's medical history, prediction of future effects and clinical decision making. It can also be used as a tool to store large number of data. It helps us to understand the diseases and also paves way to predict the disease and its future effects caused by the disease. In this paper we use RBFNN (Radial Basis Function Neural Network) with classifier algorithm with the use of parameters to determine the condition of a patient as a normal or a kidney failure patient. The proposed method reveals the stages of the kidney failure patient and treatment and clinical decision.

关键词： big data Radial basis function neural network kidney failure classifier algorithm clinical decision

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