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29th International Conference on Information and Software Technologies (ICIST)

作者： Macionczyk, Rafal Moryc, Michal Buchtyar, Patryk Silesian Tech Univ Fac Appl Math Kaszubska 23 PL-44100 Gliwice Poland

ISBN: (纸本)9783031489808;9783031489815

Principal Component Analysis (PCA) is a well-known dimensionality reduction technique that has been widely used in various machine learning algorithms. This includes knn and Naive Bayes algorithms which can be time-consuming. The reduction of dimensions can have positive effects on those two algorithms by reducing the number of related types of data and decreasing the data they need to analyze. Here we present detailed findings about how the PCA algorithm affects them both in time efficiency and accuracy. All calculations regarding those values were carried out in Python programming language. The dataset used in research is the Titanic dataset, on which data cleaning and normalization were done. The data in this paper suggests that it is possible to maintain the same level of accuracy with great improvement in time efficiency. For the knn algorithm reducing the number of dimensions by one resulted in a 31.09% increase in accuracy and for the Naive Bayes algorithm an 18.18% increase while having an imperceptible effect on accuracy.

关键词： PCA algorithm knn algorithm Naive Bayes Classification algorithms analyze impact effect

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Farm Decision Support System(FDSS) with various environmental factors using Decision Trees 5

Farm Decision Support System(FDSS) with various environmenta...

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5th International Conference on Innovative Trends in Information Technology (ICITIIT)

作者： Keerthana, S. M. Bharathi, R. Elakkiya Sherlin, S. Ashna St Josephs Inst Technol Comp Sci & Engn Chennai India

ISBN: (纸本)9798350386813;9798350386820

The implementation of Crop Selection as a means to cultivate the most suitable crop at the optimal time is crucial for achieving high crop yield. Several factors play a significant role in influencing crop yield, such as temperature, humidity, soil potential of hydrogen, rainfall, and more. To address the challenges, the proposed system aims to provide an effective solution. The chosen approach utilizes the machine learning algorithm known as "Decision Trees". A variety of cash crops, like tea and coffee, as well as vegetable crops, like sugarcane and potatoes, are being considered for testing. In order to construct a reliable model that can anticipate genotype response under diverse conditions. By using this advanced technology and utilizing a robust dataset, the Crop Selection system aims to empower farmers, ultimately leading to enhanced crop yields and improved agricultural outcomes. The system uses the values of nitrogen, phosphorus, hydrogen content in soil and also temperature and rainfall. When given the values, the system implementing the algorithm, will select the proper crop.

关键词： Crop Selection Decision Tree knn algorithm Gaussian Bayes Random Forest

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Maximizing Router Efficiency in Named Data Networking with Machine Learning-Driven Caching Placement Strategy 6

Maximizing Router Efficiency in Named Data Networking with M...

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6th International Conference on Computer Communication and the Internet (ICCCI)

作者： Negara, Ridha Muldina Syambas, Nana Rachmana Mulyana, Eueung Wasesa, Novan Purba Inst Teknol Bandung Sch Elect Engn & Informat Bandung Indonesia Telkom Univ Sch Elect Engn Bandung Indonesia

ISBN: (纸本)9798350351774;9798350351767

The demand for multimedia content imposes considerable strain on IP technology, particularly in terms of latency. Named Data Networking (NDN) emerges as a future networking solution, characterized by a content-centric approach featuring storage capabilities on routers. However, challenges persist in effectively managing the limited storage capacity of routers, necessitating the development of efficient content replacement strategies. Traditional caching replacement algorithms often struggle to adapt to the dynamic nature and popularity of content across the network. Previous studies have proposed leveraging machine learning (ML) to enhance caching replacement strategies, ensuring eviction processes target only unpopular content. These endeavors have demonstrated promising results in enhancing router storage efficiency. Nonetheless, a critical inquiry arises regarding integrating ML models within the network: Should the ML model be deployed directly on routers or exclusively at the producer level? Bridging the gap between theoretical research and practical implementation is imperative. This study is dedicated to implementing ML algorithms on routers within the NDN. It shows how a simple K-Nearest Neighbors (knn) machine learning algorithm combined with the Least Recently Used (LRU) caching strategy could greatly improve the process of replacing NDN cache when embedded in all NDN routers. The proposed knn-LRU model works better than common algorithms like LRU, FIFO, and LFU in terms of cache hit ratio, latency, and link load when validated using the ICARUS caching simulator.

关键词： cache replacement strategy content popularity machine learning named data networking knn algorithm LRU strategy future network

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Nonparametric monitoring of multivariate data via knn learning

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2021年第20期59卷 6311-6326页

作者： Li, Wendong Zhang, Chi Tsung, Fugee Mei, Yajun Shanghai Univ Finance & Econ Sch Stat & Management Shanghai Peoples R China Hong Kong Univ Sci & Technol Dept Ind Engn & Decis Analyt Kowloon Hong Kong Peoples R China Georgia Inst Technol H Milton Stewart Sch Ind & Syst Engn Atlanta GA 30332 USA

Process monitoring of multivariate quality attributes is important in many industrial applications, in which rich historical data are often available thanks to modern sensing technologies. While multivariate statistical process control (SPC) has been receiving increasing attention, existing methods are often inadequate as they are sensitive to the parametric model assumptions of multivariate data. In this paper, we propose a novel, nonparametrick-nearest neighbours empirical cumulative sum (knn-ECUSUM) control chart that is a machine-learning-based black-box control chart for monitoring multivariate data by utilising extensive historical data under both in-control and out-of-control scenarios. Our proposed method utilises thek-nearest neighbours (knn) algorithm for dimension reduction to transform multivariate data into univariate data and then applies the CUSUM procedure to monitor the change on the empirical distribution of the transformed univariate data. Extensive simulation studies and a real industrial example based on a disk monitoring system demonstrate the robustness and effectiveness of our proposed method.

关键词： Multivariate statistical process control knn algorithm machine learning categorical variable CUSUM empirical probability mass function

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Accuracy Improvement of Power Transformer Faults Diagnostic Using knn Classifier With Decision Tree Principle

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IEEE ACCESS 2021年 9卷 81693-81701页

作者： Kherif, Omar Benmahamed, Youcef Teguar, Madjid Boubakeur, Ahmed Ghoneim, Sherif S. M. Cardiff Univ Sch Engn Adv High Voltage Engn Res Ctr Cardiff CF24 3AA Wales Ecole Natl Polytech ENP Algiers Lab Rech Electrotech El Harrach 16200 Algeria Taif Univ Coll Engn Elect Engn Dept At Taif 21944 Saudi Arabia

Dissolved gas analysis (DGA) is the standard technique to diagnose the fault types of oil-immersed power transformers. Various traditional DGA methods have been employed to detect the transformer faults, but their accuracies were mostly poor. In this light, the current work aims to improve the diagnostic accuracy of power transformer faults using artificial intelligence. A knn algorithm is combined with the decision tree principle as an improved DGA diagnostic tool. A total of 501 dataset samples are used to train and test the proposed model. Based on the number of correct detections, the neighbor's number and distance type of the knn algorithm are optimized in order to improve the classifier's accuracy rate. For each fault, indeed, several input vectors are assessed to select the most appropriate one for the classifier's corresponding layer, increasing the overall diagnostic accuracy. On the basis of the accuracy rate obtained by knots and type of defect, two models are proposed where their results are compared and discussed. It is found that the global accuracy rate exceeds 93% for the power transformer diagnosis, demonstrating the effectiveness of the proposed technique. An independent database is employed as a complimentary validation phase of the proposed research.

关键词： Oil insulation Power transformer insulation Classification algorithms Discharges (electric) Oils Decision trees Databases DGA fault diagnosis power transformer oil knn algorithm decision tree principle

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A Comparative Analysis of Machine Learning Techniques for Breast Cancer Prediction 2

A Comparative Analysis of Machine Learning Techniques for Br...

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Dutta, Umesh Kaushik, Simran Iyer, Srinidhi Singh, Ina Manav Rachna International Institute of Research and Studies Department of Electronics and Communication Engineering Haryana Faridabad India Manav Rachna International Institute of Research and Studies Manav Rachna Innovation & Incubation Centre Haryana Faridabad India

ISBN: (纸本)9798350327533

One of the major diseases found in women worldwide is Breast Cancer. More than 2.2M cases have been reported with about a 30% mortality rate in accordance with WHO. The rapid advancement of technology has led to breakthroughs in different fields such as medicine, automotive, etc. Despite advancements in screening technologies, achieving high sensitivity and specificity for early detection remains a challenge. Machine Learning is one of the front runners and is currently playing a vital role in solving real-world problems, particularly in the healthcare domain. Using different Machine Learning algorithms prediction of Breast Cancer has been tallied in this work. Various algorithms such as SVM (Support Vector Machine), knn (K-Nearest Neighbours), DT (Decision Tree), and RF (Random Forest) have vast application areas. In this study, these algorithms have been tested and evaluated on the basis of precision, recall, and accuracy which are the key performance metrics for the evaluation of the model. The dataset for breast Cancer has been pulled from Kaggle, which is the most prominently used dataset by researchers. The simulation results revealed that Random Forest outperformed the others with the precision of 94.5%, recall of 94%, and accuracy of 95%, clearly showing its potential to be used for early prediction of Breast Cancer. © 2024 IEEE.

关键词： Breast Cancer Prediction DT algorithm knn algorithm Modelling using ML RF algorithm SVM algorithm

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Attack prediction using machine learning in Wireless Sensor networks

Attack prediction using machine learning in Wireless Sensor ...

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

作者： Mohana Priya, N. Bennila Thangammal, C. Leelabvathi, U. Radley, Sheryl ECE Meenakshi College of Engineering Chennai India ECE R.M.D. Engineering College Tiruvallur India ECE Gojan School of Business and Technology Redhills Chennai India

ISBN: (纸本)9798350305708

The Wireless Sensor Network has garnered significant interest in both research and development circles, owing to its exceptional utility in a variety of domains such as medical, military and defense, environmental, industrial, facility protection infrastructure, and commercial fields. This network enables remote interaction between sensors, thereby enhancing its applicability. Analysis of ecology and target node tracking for communication are some of the uses for wireless sensor networks (WSNs). The communication between the network and other nodes can be enabled using wireless interface such as sensor nodes. However, the complexity of WSNs often makes it challenging to secure the devices. When a sensor node is placed in an insecure environment, it can be vulnerable to attacks. A supervised machine learning (SML) technique can be used to analyze a dataset by capturing various information, such as variable identification, univariate and bivariate analysis. A comparative analysis was undertaken to ascertain the most precise algorithm for predicting the type of data. The proposed machine learning algorithm can be assessed and compared based on parameters such as sensitivity, specificity, precision, and accuracy. © 2023 IEEE.

关键词： knn algorithm supervised machine learning (SML) Voting Classifier WSN attacks

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Diet Plan and Home Exercise Recommendation system using Smart Watch 1

Diet Plan and Home Exercise Recommendation system using Smar...

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1st International Conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Gaikwad, Shreeraj Awatade, Pratik Sirdeshmukh, Yadnesh Prasad, Chandan JSPM's Rajarshi Shahu College of Engineering Department of Information Technology Pune India

ISBN: (纸本)9798350330915

Obesity and sedentary lifestyles have become significant health concerns globally. To solve these issues, this system presents a Diet Plan and Home Exercise Recommendation System (SYP) that utilizes users' smartwatch data and pathological information to generate personalized diet plans and exercise recommendations. The proposed SYP system leverages the capabilities of modern smartwatches, which are equipped with various sensors to collect comprehensive health data. Additionally, it incorporates users' pathological data obtained through medical tests. By integrating these diverse datasets, the system can provide tailored recommendations based on an individual's specific health profile. The SYP system operates in two main phases: data collection and recommendation generation. During the data collection phase, the system collects and analyzes data from users' smartwatches and pathological records, such as heart rate, sleep patterns, blood pressure, glucose levels, and body mass index. The collected data is then processed to extract relevant features and construct a comprehensive health profile for each user. In the recommendation generation phase, the SYP system employs machine learning algorithms to analyze the user's health profile and generate personalized diet plans and home exercise recommendations. The generated recommendations are designed to be practical, feasible, and aligned with the user's health objectives. © 2023 IEEE.

关键词： Diet Recommendation Home Exercise knn algorithm Smartwatch data

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U-Net Architecture for Detecting Glaucoma with Retinal Fundus Images 1

U-Net Architecture for Detecting Glaucoma with Retinal Fundu...

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1st International Conference on Advances in Electrical, Electronics and Computational Intelligence, ICAEECI 2023

作者： Nandhini, S. Jagadeesan, J. Anitha, E. Vinayaka Mission's Research Foundation Aarupadai Veedu Institute of Technology Department of Computer Science and Engineering Tamil Nadu India Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu India Loyola - ICAM College of Engineering and Technology Department of Information Technology Chennai India

ISBN: (纸本)9798350342796

Ophthalmologists use retinal images to determine eye disease, for example glaucoma disease. This diagnosis is made by measuring changes in the cup-to-disc ratio. Eye Cup Segmentation Helps Fossil Ophthalmologists Calculate CDRs of Retinal Background Images. In this article, we propose a machine learning approach to perform segmentation tasks using the U-Net architecture. This proposed technique was evaluated using 650 colour retinal fundus images. Next, U-Net has 160 opportunities. Additionally, we compared the segmentation results with ground reality images. According to the test results, the eyecup's segmentation performance reaches 98.42% of the membrane, with a loss of 0.15. Fundus images help detect glaucoma. In the past, several experiments have been conducted to detect this disease. This paper proposes an ideal framework aimed at building intelligent solutions that help identify glaucoma using MATLAB software tools. The diagnosis of glaucoma is made by measuring changes in the bowl-to-disc ratio. Eye Cup Segmentation Helps Fossil Ophthalmologists Calculate CDRs of Retinal Background Images. As we propose a deep learning method using U-Net architecture to perform segmentation tasks, we collected many fundus images as data. Subsequent pre-processing of the occlusion figure is accompanied by classification and segmentation. The pre-processing module uses several filters such as Gaussian filter and guided image filter to remove noise. The division into healthy and sick eyes helps with further administration. © 2023 IEEE.

关键词： Deep Learning Glaucoma detection knn algorithm Machine Learning U-Net Architecture

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Exploring the relationship between user's characteristics and movie recommendations using a knn-based recommender system

Exploring the relationship between user's characteristics an...

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作者： Muhao Hu Wufan Xiao Yuzhang Li College of Sci and Engineering University of Minnesota College of Arts and Sciences University of Washington College of Electronic Information Tianjin University of Science and Technology

The central aim of this research is to elucidate the degree to which demographic variables,including but not limited to age and gender,bear on the performance of movie recommendation algorithms within film recommendation ***,we endeavor to uncover any existent correlations between these user characteristics and the resultant outputs of such *** the expansive dataset available via MovieLens,we employ a linear regression model to ascertain the four critical variables(age,gender,occupation,and the average user rating for films previously watched) that have the most profound influence on movie recommendation *** these salient factors have been determined,we assign their respective weights and incorporate these into a knn *** then subject the resultant model to rigorous testing to verify the accuracy of our results and to ascertain whether the integration of these weighted elements enhances the overall precision of the movie recommendation *** extant literature predominantly focuses on the amalgamation of knn with other algorithms,our study charts a novel course by using linear *** methodology allows us to intuitively illustrate the relationship between user demographics and the movie recommendation system and enables us to evaluate whether emphasizing certain characteristics can augment the system's *** findings suggest that of all the user characteristics examined,the mean of users' ratings for movies previously watched exerts the greatest influence on the outputs of the movie recommendation ***,incorporating weights reflective of the average user ratings across all movie features within the knn algorithm can significantly bolster the accuracy of the resultant movie recommendations.

关键词： Movie Recommendation System knn algorithm linear regression Weight Based knn Recommender System

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