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5th International Conference on Inventive Computation Technologies (ICICT)

作者： Patel, Bhavinkumar A. Parikh, Ajay Gujarat Vidyapith Dept Comp Sci Ahmadabad Gujarat India

ISBN: (纸本)9781728146850

Complete Blood Count (CBC) test is considered as a very important test to evaluate the overall health of a person. It plays a significant role in finding an infection, leukaemia, cancer, anaemia, blood count, deficiency of vitamins and minerals, etc. Gujarat Vidyapith is a deemed to be university in which newly admitted students are medically checked up. Here, the health condition of students, proper diagnosis and appropriate treatments are taken care of. CBC test is considered as one of the most important sources for health analysis. However, there are many other variability and confidentiality in the CBC test, which is difficult to be evaluated directly using Machine Learning algorithm. Therefore, a model is developed in this paper using the naive bayes Classifier which can predict Anemia by analyzing the probabilities of effects of various parameters of the CBC test. It will help to detect anemia at an early stage so that the probability of serious complications can be reduced. Students can take precautions to improve their health standards. Ultimately, it will affect the level of concentration, regularity and mental health of students. Therefore, the level of education can be improved.

关键词： Anemia CBC Parameters naive bayes algorithm Prediction Machine Learning Healthcare

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Effective Asthma Disease Prediction Using naive bayes - Neural Network fusion technique 3

Effective Asthma Disease Prediction Using Naive Bayes - Neur...

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International Conference Parallel Distributed Grid Computing

作者： Aneja, Saloni Lal, Sangeeta JIIT Noida Noida Uttar Pradesh India

ISBN: (纸本)9781479976836

Asthma is a lung disease caused by the inflammation and narrowing of the airways that causes recurrent attacks of breathlessness and wheezing, and often can be life-threatening. Around 15-20 million people are suffering from asthma in India[1]. This paper aims at analyzing various data mining techniques for the prediction of asthma. The observations show that the fusion approach of naive bayes and neural network proved to be the best among classification algorithms in the diagnosis of asthma. This methodology is evaluated using 1024 raw data obtained from a city hospital. The proposed approach helps patients in their diagnosis of asthma.

关键词： asthma naive bayes algorithm Neural Network Fusion Of naive bayes and Neural Network Classifier

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The Mechanism of Spam Comment Detection using Countvectorizer and naive bayes Machine Learning algorithms in Python 1

The Mechanism of Spam Comment Detection using Countvectorize...

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1st International Conference on Technologies for Smart Green Connected Society 2021, ICTSGS 2021

作者： Puneeth, M.B. Ramakrishnan, V. Department of CSE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai602105 India

ISBN: (纸本)9781607685395

Aim: The aim of this study is to predict Novel spam comments using naive bayes and SVM algorithms to improve the *** is the process of posting unwanted and awkward comments on specific posts in any type of social sharing medium or video sharing medium. Materials and Methods: The Dataset used for training and testing of proposed text precision models is created using the comments of some popular videos from Youtube with 5 attributes and Sample size = 150. The framework that retrieves suitable data adapts SVM algorithm and compares it with naive bayes algorithm. Results and Discussion: The retrieval accuracy of SVM classifier is (96.72%) and naive bayes algorithm is (90.89%). There exists a statistical significant difference between SVM and naive bayes with (p © The Electrochemical Society

关键词： Countvectorizer Machine Learning naive bayes algorithm Novel Spam detection Support vector machine

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Anomaly detection system based on K-nearest neighbors and naive bayes

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JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2024年第6期24卷 3545-3556页

作者： Liu, Xiaomei Beijing Informat Sci & Technol Univ Sch Management Sci & Engn 55 Hao Beijing 100192 TaiHangLu Peoples R China

This paper conducts a study and analysis of the SEA dataset, grouping the data and digitizing features to obtain corresponding labels. Subsequently, the K-Nearest Neighbors (KNN) algorithm is applied to the dataset to investigate its performance, revealing that the Manhattan distance is the optimal distance formula for researching this problem using the KNN algorithm. The study explicitly selects frequent words chosen by individual users as features for calculation. During the computation process, grid search is employed to find the optimal parameters, and a model is created using these optimal parameters. The paper then applies the naive bayes algorithm to the dataset, comparing the strengths and weaknesses of different types of naive bayes methods. An analysis is conducted on the differences and advantages/disadvantages of various feature extraction methods, highlighting that the accuracy of Bernoulli naive bayes is higher than that of Multinomial naive bayes.

关键词： machine learning K-nearest neighbors algorithm naive bayes algorithm feature extraction anomaly detection

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Classification Technique of Interviewer-Bot Result using naive bayes and Phrase Reinforcement algorithms

INTERNATIONAL JOURNAL OF EMERGING TECHNOLOGIES IN LEARNING

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INTERNATIONAL JOURNAL OF EMERGING TECHNOLOGIES IN LEARNING 2018年第2期13卷 33-47页

作者： Sarosa, Moechammad Junus, Mochammad Hoesny, Mariana Ulfah Sari, Zamah Fatnuriyah, Martin State Polytech Malang Dept Elect Engn Malang East Java Indonesia Univ Muhammadiyah Malang Malang East Java Indonesia Myria Technol Malang East Java Indonesia Brawijaya Univ Dept Elect Engn Malang Indonesia

In recent years, both foreign and national companies tend to conduct English-based interviews when recruiting new employees. Consequently, college graduate must be ready for English-based interviews during the process of seeking employment. To meet these requirements potential candidates tend to practice conversing in English with someone who is proficient in the language. Nevertheless, it is not easy to have someone who is not only proficient in English, but also have a good understanding of common interview questions. This paper presents the development of a machine which is able to provide practice on English-based interviews, specifically on job interviews. Interviewer machine (interviewer bot) is expected to help students practice speaking English appropriately for job interview. The interviewer machine design uses words from a chat bot database named ALICE to mimic human intelligence that can be applied to a search engine using AIML. naive bayes algorithm is used to classify the interview results into three categories: POTENTIAL, TALENT and INTEREST students. Furthermore, based on the classification result, the summary is made at the end of the interview session by using phrase reinforcement algorithms. By using this bot, students are expected to practice their listening and speaking skills, also to be familiar with the questions often asked in job interviews so that they can prepare the proper answers. In addition, the bot users could know their potential, talent and prospects in finding a job. Hence, they could apply to the appropriate companies. Based on the validation results of 50 respondents, the accuracy degree of interviewer chat-bot (interviewer engine) response obtained 86.93%.

关键词： Job interview Words classification Natural language processing Phrase reinforcement algorithm naive bayes algorithm

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Estimation and simulation for route choice proportion in multi-period urban rail transit control systems

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MEASUREMENT & CONTROL 2023年第1-2期56卷 60-70页

作者： Yang, Yiyuan Sun, Ranran Wang, Peiyue Yi, Yang Zhu, Guangyu Beijing Jiaotong Univ Beijing Res Ctr Urban Traff Informat Sensing & Se 3 Shangyuancun Beijing 100044 Peoples R China Beijing Jiaotong Univ Key Lab Transport Ind Big Data Applicat Technol C Beijing Peoples R China Yangzhou Univ Coll Informat Engn Yangzhou Jiangsu Peoples R China

As we all known, estimating the proportion of passenger route choice is of great significance in almost every aspect of urban rail transit control system, including passenger allocation, fare clearing and flow control strategies. Existing researches only pay attention to the route choice through travel time, but usually ignore the influence in different periods of the day. Therefore, this paper proposes a novel estimation method for the proportion of passenger route choice in different periods. Firstly, by introducing the normalized value of passenger flow and the standard coefficient of peak passenger flow, the train operation time is divided into peak and flat periods. Secondly, the travel time distribution of each route can be obtained by estimating the expected value and standard deviation of passenger travel time in each different period. The naive bayes algorithm is further employed to realize the identification of the proportion of passenger route choices. Finally, this proposed algorithm is applied to Hangzhou Metro. The result shows that by using the segmented estimation, the error can be reduced by more than 60% compared with the whole-day experiment, which indicates the superiority of the method.

关键词： Urban rail transit system naive bayes algorithm proportion of route choice traffic control parameter estimation

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Plastic debris detection along coastal waters using Sentinel-2 satellite data and machine learning techniques

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MARINE POLLUTION BULLETIN 2024年第Pt A期209卷 117106页

作者： Nivedita, V. Begum, S. Sabarunisha Aldehim, Ghadah Alashjaee, Abdullah M. Arasi, Munya A. Sikkandar, Mohamed Yacin Jayasankar, T. Vivek, S. SRMIST Ramapurm Dept Comp Sci & Engn Chennai 600089 Tamil Nadu India PSR Engn Coll Dept Biotechnol Sivakasi 626140 India Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Comp Sci Dept POB 84428 Riyadh 11671 Saudi Arabia Northern Border Univ Coll Sci Dept Comp Sci Ar Ar Saudi Arabia King Khalid Univ Appl Coll RijalAlmaa Dept Comp Sci Abha Saudi Arabia Majmaah Univ Coll Appl Med Sci Dept Med Equipment Technol Al Majmaah 11952 Saudi Arabia Anna Univ Univ Coll Engn Dept Elect & Commun Engn BIT Campus Tiruchirappalli 620024 India GMR Inst Technol Dept Civil Engn Razam 532127 Andhra Pradesh India

Few studies have effectively shown how to use satellites that gather optical data to monitor plastic debris in the marine environment. For the first time, floating macro-plastics distinguishable from seaweed are identified in optical data from the European Space Agency's Sentinel-2 satellites. Case studies from three Brazilian areas, selected for suspected macro-plastics in Sentinel-2 data, utilized a unique Floating Debris Index (FDI) for the Sentinel-2 Multi-Spectral Instrument (MSI) to detect surface material patches. Sub-pixel-scale detection revealed macro-plastics mixed with seaweed and sea foam. Using a Machine Learning-based naive bayes algorithm, we classified materials and identified macro-plastics, achieving an 87.25 % accuracy in identifying suspected plastics. Temporal analysis tracked plastic debris movement and accumulation. This methodology is scalable and transferable, with potential applications for monitoring marine plastic pollution in other coastal regions globally.

关键词： Marine plastic debris Sentinel-2 satellites Floating Debris Index (FDI) naive bayes algorithm Temporal analysis

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Green building energy consumption data detection method based on naive bayesian algorithm

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INTERNATIONAL JOURNAL OF GLOBAL ENERGY ISSUES 2022年第5-6期44卷 379-395页

作者： Shi, Jingchen Xinxiang Vocat & Tech Coll Dept Architectural Engn Xinxiang Henan Peoples R China

In order to overcome the accuracy of energy consumption data acquisition and the low timeliness of the detection process existing in the traditional detection methods, a green building energy consumption data detection method based on naive bayes algorithm is designed in this paper. After collecting energy consumption data, cluster processing is carried out. Then, on the basis of the analysis of bayesian basic principle, the naive bayesian classification model was designed based on the fast clustering calculation results, and the green building energy usage was analysed, and the energy consumption data quota index was designed, and the energy consumption data verification was completed by combining the naive bayesian classification model. The experimental results show that the building energy consumption load collected by this method is closer to the actual energy consumption load, and the detection process takes less than 3.5 min, which fully demonstrates the effectiveness of this method.

关键词： naive bayes algorithm building energy consumption data data detection energy consumption quota data clustering

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A novel approach to market segmentation selection using artificial intelligence techniques

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JOURNAL OF SUPERCOMPUTING 2023年第2期79卷 1235-1262页

作者： Chang, Yu-Teng Fan, Neng-Hsun Yu Da Univ Sci & Technol Dept Informat Management Miaoli Taiwan

In the era of globalization and the Internet, due to the unprecedented business pressure from competition, in order to make long-term benefits and achieve sustainable development, enterprises should select competitive markets for their products to maximize the benefits of limited resources. The key to corporate survival and development is to find the most profitable customer bases all over the world and develop products to meet their demands. Traditionally, market targeting relies on the decisions of a small number of senior managers in enterprises;however, due to novel and changing customer demands, the business environment has become increasingly complex. If previous traditional methods are adopted, enterprises may select the wrong markets, which can lead to complete destruction. Therefore, this study proposes a new market targeting method and replaces human decisions with artificial intelligence (AI) algorithms, in order to render market targeting more scientific and systematic, improve the quality of marketing decisions, maximize corporate profits, occupy the optimal market with limited resources, and achieve the goal of sustainable business. This study applied three AI algorithms, the naive bayes algorithm, J48 algorithm, and OneR algorithm, for model training and analytical prediction of the testing datasets. According to the results, the model accuracies of the naive bayes algorithm, J48 algorithm, and OneR algorithm are 100, 91.7, and 83.3%, respectively;the F-measures of the naive bayes algorithm, J48 algorithm, and OneR algorithm are 1, 0.909, and 0.8, respectively, which indicates that the three algorithms have reliable predictions. The results show that AI algorithms can help enterprises in market targeting.

关键词： Market segmentation Artificial intelligence naive bayes algorithm J48 algorithm OneR algorithm

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A Review on Disease Detection from Medical Images using Machine Learning 6

A Review on Disease Detection from Medical Images using Mach...

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6th International Conference on Trends in Electronics and Informatics (ICOEI)

作者： Mim, Tanjima Akhanda Rimi, Tanzina Afroz Daffodil Int Univ Dept Comp Sci & Engn Dhaka Bangladesh

ISBN: (纸本)9781665483285;9781665483292

As a result of developments in imaging and processing, artificial intelligence may be used to perform a variety of radiological imaging tasks, including risk assessment, detection, diagnosis, prognosis, and therapy response, as well as multi-omics illness discovery. After reading this succinct overview of the topic, the reader will be able to recognize the nomenclature, numerous subfields, and components of machine learning, as well as its therapeutic potential. Radiomics is described as the process of transforming pictures into usable data. It is a subset of computer-aided diagnostics. Quantitative radiomics' ultimate purpose is to either give projected image-based illness phenotypes for precision medicine or to provide quantitative image-based phenotypes for data mining in concert with other-omics for discovery (i.e., imaging genomics). Due to the complexity of deep networks, the constant improvement of computer software and hardware, and the difficulty of detecting minute changes in disease states versus variations in common things, well-annotated enormous data sets are required for deep learning to succeed in radiology. With imaging exams performed often in clinical practice, machine learning in radiology is predicted to have a significant clinical influence in the future, offering improved decision support during medical image interpretation. The term "decision support" is crucial since it implies that computers will assist people in making better decisions. Radiologists may be able to use computers in their everyday work to help them work more effectively with colleagues from other medical fields, which could lead to better precision medicine.

关键词： Artificial Neural Networks (ANN) Artificial Intelligence Machine Learning and a variety of machine learning algorithms Including K-Nearest Neighbors Support Vector Machines(SVM) Decision Trees naive bayes algorithm Deep Learning Convolutional Neural Networks (CNN)

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