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Application of Machine Learning to Predict Diseases Based on Symptoms in Rural India

Application of Machine Learning to Predict Diseases Based on...

引用

International Conference on Biologically Inspired Techniques in Many-Criteria decision Making (BITMDM)

作者： Biswal, Suvasree S. Amarnath, T. Panigrahi, Prasanta K. Biswal, Nrusingh C. Indian Inst Informat Technol Dept Informat Technol Bhubaneswar Odisha India Indian Inst Sci Educ & Res Dept Phys Sci Kolkata W Bengal India Univ Maryland Dept Radiat Oncol Sch Med Baltimore MD 21201 USA

ISBN: (纸本)9783030390334;9783030390327

Living in a place away from hospitals and medical aid, it is often difficult for some people in rural India to diagnose a disease at an early state. Though hospitals in urban areas are using advanced technology for diagnosis and prognosis, disease prediction is one of the hypercritical tasks. Providing sophisticated and accurate algorithms and techniques to overcome this issue will be revolutionary. In this paper, we present a machine learning technique using decision tree algorithm (DTL) to interconnect the symptoms and rearrange them and retrieve the most probable diagnosis. This technique allows the system to self-learn without using programming. This paper presents a system for diagnosis of common diseases by entering the symptoms into the system.

关键词： Machine learning decision tree algorithm Information gain Entropy Root node Disease diagnosis

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Transport travel demand simulation model development for mixed-use building 5

Transport travel demand simulation model development for mix...

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5th IEEE Workshop on Advances in Information, Electronic and Electrical Engineering (AIEEE)

作者： Zenina, Nadezda Merkuryev, Yuri Romanovs, Andrejs Riga Tech Univ Dept Modeling & Simulat Kalku St 1 Riga Latvia

ISBN: (纸本)9781538641378

Transport travel demand models allow one to estimate future traffic changes after the changes in the functionality of a building object has been made and to identify the potential bottlenecks of transport system. The paper contains a use case on transport travel demand model development based on decision tree algorithms and simulation methods. decision tree algorithm that has flexible structures suitable for representing the relationships between the categories of transportation modes and do not require any knowledge about the initial specific model data structure was used in transportation mode choice. Transport travel demand simulation model was developed for mixed-use building in the area of Hanzas Street (Riga city, Latvia).

关键词： travel demand model simulation transport decision tree algorithm

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Quadrotor-Enabled Autonomous Parking Occupancy Detection

Quadrotor-Enabled Autonomous Parking Occupancy Detection

引用

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Wang, Yafeng Ren, Beibei Texas Tech Univ Dept Mech Engn Lubbock TX 79409 USA

ISBN: (纸本)9781728162126

Large special-events parking involves various parking scenarios, e.g., temporary parking and on-street parking. Their occupancy detection is challenging as it is unrealistic to construct gates/stations for temporary parking areas or build a sensor-based detection system to cover every single street. To address this issue, this study develops a quadrotor-enabled autonomous parking occupancy detection system. A camera-equipped quadrotor is flying over the parking lot first;then the images are captured by the on-board camera of the quadrotor and transferred to the ground station;finally, the ground station will process and release the occupancy information to the driver's mobile devices. The decision tree learning algorithm is adopted to determine the optimal flying speed for the quadrotor to balance the trade-off between the detection efficiency and accuracy. In order to tackle the complex environment in real-life parking, a convolutional neural network (CNN)-based vehicle detection model has been trained and implemented, where the realistic factors, e.g., passing pedestrians and tree blocking, are considered. Experiments are conducted to illustrate the effectiveness of the proposed system.

关键词： Parking occupancy detection quadrotor optimized flying speed decision tree algorithm convolutional neural network (CNN)

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Machine learning based cardiovascular disease prediction

Machine learning based cardiovascular disease prediction

引用

International Conference on Advanced Materials for Innovation and Sustainability (ICAMIS)

作者： Chinnasamy, P. Kumar, S. Arun Navya, V. Priya, K. Lakshmi Boddu, Siva Sruthi MLR Inst Technol Dept Comp Sci & Engn Hyderabad India Bethesda Inst Technol & Sci Dept Comp Sci & Engn Gwalior India East Point Coll Engn & Technol Dept Elect & Commun Engn Bangalore India PERI Inst Technol Dept Elect & Commun Engn Chennai India MLR Inst Technol Dept Comp Sci & Engn Hyderabad India

In past few years, one of the really tough situations in medicine is already making predictions cardiac dysfunction. Almost any moment, roughly one patient dies through myocardial infarction with in present era. Due to the difficulty of forecasting cardiac sickness, it is vital to systematize the procedure in place to minimize the risk associated with and to educate the patient well enough in preparation. The risk of cardiovascular disease in the World is tremendous. The accurate and thorough assessment of a physician's cardiovascular risk is critical for reducing the incidence and severity attack and stroke and improving cardiovascular protection. In order to solve this issue, we are proposing method to discover these heart abnormalities as soon as reasonably practicable to avert fatal consequences. The next step is to determine whether or not the user is at risk of developing cardiovascular disease. It also answers difficult problems concerning heart detection of diseases, allowing clinicians to make better clinical decisions. Medical specialists have accumulated a large number of medical data that can be analysed and important fact retrieved. Machine learning approaches can improve in the prognosis and early identification of heart illness after hypothesis testing. Copyright (c) 2022 Elsevier Ltd. All rights *** and peer-review under responsibility of the scientific committee of the International Conference on Advanced Materials for Innovation and Sustainability.

关键词： Heart disease Machine learning K -nearest Classifier Ensemble classifier decision tree algorithm

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Clustering-Based Network Intrusion Detection System 5

Clustering-Based Network Intrusion Detection System

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5th IEEE Conference on Dependable and Secure Computing (IEEE DSC)

作者： Fan, Chun-I Lai, Yen-Lin Shie, Cheng-Han Natl Sun Yat Sen Univ Dept Comp Sci & Engn Kaohsiung Taiwan

ISBN: (数字)9781665421416

ISBN: (纸本)9781665421416

The increasing sophistication of network attacks and the inability of traditional defensive techniques such as firewalls or weak passwords against them have led researchers to propose network intrusion detection systems. Many network intrusion detection systems using machine learning techniques have been proposed, but the detection performance of some systems can be further improved. In addition, many systems adopted multiple machine learning classifiers to cooperate in generating detection results, but the individual classifiers in the system are often difficult to operate independently, limiting the flexibility of the system. This paper presents a Clustering-Based Network Intrusion Detection System, which applies the concept of clustering to detect network attacks by using the K-Nearest Neighbor algorithm for the initial detection of network attack types, and the decision tree algorithm specializes in detecting specific types of attacks. This improves the detection performance of the system and maintains the usability of an individual classifier.

关键词： Intrusion Detection System Machine Learning Clustering K-Nearest Neighbor algorithm decision tree algorithm

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Analytics on the Impact of Leadership Styles and Leadership Outcome 9th

Analytics on the Impact of Leadership Styles and Leadership ...

引用

9th Asian Conference on Intelligent Information and Database Systems (ACIIDS)

作者： Ahmad, Waseem Akhtaruzamman, Muhammad Waiariki Inst Technol Rotorua New Zealand

ISBN: (纸本)9783319544304

Data mining and machine learning approaches are helping organisations to streamline and optimise their processes and assisting management to devise more effective and efficient business strategies. In organisations, management or leadership style play an important role in employees motivation, work satisfaction, work commitment and productivity. Moreover, leadership style shapes up the organisational culture. The aim of this paper is to explore the impact of various leadership styles on employees using data mining techniques. A significant research has taken place in this area using simple statistical methods such as correlation and regression analysis. However, no research has been conducted in this area which used data mining algorithms to extract useful information from the data to examine how leadership styles influence/affect employees. In this research, rule and decision tree based algorithms are used to extract actionable information from the collected data.

关键词： Data mining Leadership styles Rule based algorithms decision tree algorithm Management

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Recognition of Different Emergency Situation through Body Gesture Using Microsoft Kinect Sensor 11

Recognition of Different Emergency Situation through Body Ge...

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11th IEEE International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management ( HNICEM )

作者： Tolentino, Roselito E. Guinto, Pinky Mae F. Maypa, Dorothy Ysabelle B. De La Salle Univ Dasmarinas Coll Engn Architecture & Technol Dasmarinas Philippines

ISBN: (纸本)9781728130446

This study aim to recognize gestures that may serve as an indication for an emergency situation. Different gesture that indicates an emergency condition has been observe and studied that will be used as comparison and recognition for recognition. In this study emergency situation are classified into three category namely medical, life threating condition and disaster. The acquisition of gestures was done using the latest camera technology of today which is the MS Kinect sensor. The researchers used the skeletal tracking capability of the sensor to acquire data that will represent the movement or gesture of the person. The recognition of this gesture with respect to an actual medical emergency will be done using decision tree algorithm.

关键词： Emergency situation Gesture recognition decision tree algorithm Microsoft Kinect

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Storage-Efficient tree Structure with Level-Ordered Unary Degree Sequence for Packet Classification 3

Storage-Efficient Tree Structure with Level-Ordered Unary De...

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3rd International Symposium on Computing and Networking (CANDAR)

作者： Kitamura, Yuta Iwata, Akira Mohri, Masami Shiraishi, Yoshiaki Nagoya Inst Technol Dept Sci & Engn Simulat Nagoya Aichi Japan Gifu Univ Informat & Multimedia Ctr Gifu Japan Kobe Univ Dept Elect & Elect Engn Kobe Hyogo Japan

ISBN: (纸本)9781467397971

Packet classification is an essential function in Internet routers that provides advanced network services such as virtual private networks (VPNs) and quality of service (QoS). decision tree-based packet classification algorithms such as HiCuts and HyperCuts allow the tradeoff between storage and throughput in the experimental result with the real life rulesets. However, the memory consumption of these algorithms remains high when high throughput is required. In this paper, we propose the tree data structure whose storage is small maintaining high throughput. It is based on the two ideas: The first is to use Level Ordered Unary Degree Sequence (LOUDS) which is a succinct representation of a tree structure. A general tree of n nodes is usually represented in pointer form, requiring 64n bits in 64-bit architecture, whereas LOUDS requires 2n+o(n) bits. Because LOUDS can access any child node from its parent one in constant time, the tree traversal is fast. The second is reducing the information contained in the tree node. This also reduces storage requirements without affecting search speed. The proposed data structure requires up to approximately one ninth less memory than pointer-formed tree, and the time complexity of a packet classification is equivalent to the pointer-formed tree.

关键词： packet classification decision tree algorithm succinct representation level-ordered unary degree sequence

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Data Mining algorithms(KNN & DT) Based Predictive Analysis on Selected Candidates in Academic Performance 11

Data Mining Algorithms(KNN & DT) Based Predictive Analysis o...

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11th International Conference on Cloud Computing, Data Science and Engineering (Confluence)

作者： Ramalingam, M. Ilakkiya, R. Kongu Engn Coll Dept Informat Technol Erode Tamil Nadu India

ISBN: (纸本)9781665414517

we are presenting a predictive analysis of the academic performance of the students in Kongu Engineering College based on the academic performance of the passed out students. The performance of the students both in academic and in the placement is degrading every year which needs to be analyzed in order to improve the performance of the students for the upcoming batches. The placement performance of the students purely depends on academic performance and also on co-curricular performance. So, both will be taken for consideration. The involvement in the co-curricular activities and extra-curricular activities also affects the placement performance of the students. Thus, the data relating in the academic results, co-curricular activities, extracurricular involvement details and other personal details has to be considered for the prediction. The datasets are collected from various resources and the primary thirty six parameters were considered as parameters that have high impact on placement performance. The missing details were filled manually with the help of the faculties. Chi-square method is used for selecting the features that matches both the placed and non-placed students. Classification models were created to predict the placement performance of the students at the end of the last semester for each dataset.

关键词： Data Mining KNN decision tree algorithm Data Analytics

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A flow redirection decision mechanism using data mining on NEMO environments

A flow redirection decision mechanism using data mining on N...

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10th International Conference on Advanced Communication Technology

作者： Jung, Yukyong Kim, Mihui Chae, KiJoon Yoon, Jooyoung Jin, Jongsam Ewha Womans Univ Dept Comp Sci & Engn Seoul South Korea KT Infra Lab Mobile Internet Dev Dept Seoul South Korea

ISBN: (纸本)9788955191356

As constructing ubiquitous environment, the various researches for the integration of heterogeneous networks are now under way. Multihoming is one of important issues In these researches to provide effective mobility. In order to support high quality service and seamless connectivity, it is necessary to provide an efficient FR (Flow Redirection) for the integrated network. Especially, mobile router with multiple interfaces should decide FR based on network condition for the efficient use of resources. In this paper, we propose a FR decision mechanism on mobile network (NEMO) via combined data mining technologies with multiple attributes presenting network condition. We assume that the mobile router has WLAN and WiBro interface toward Internet. At first, In order to heuristically choose candidate attributes, we analyze the WLAN/WiBro specification documents [5][6] and the considered QoS parameters in these networks. Then, we apply various data obtaining from QualNet [12] In various cases to decision tree algorithm, in order to theoretically select the influential attributes in FIR decision. Finally, me acquire a FR decision model through the neural network with data for chosen attributes. Our simulation results show that our FR can provide the improved performances in comparison with current handover using only signal strength.

关键词： multihoming flow redirection decision algorithm decision tree algorithm neural network mobile network

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