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Emerging Trends in Information Technology and Engineering (ic-ETITE), Conference on

作者： Yash Bhojwani Rishab Singh Rachana Reddy Boominthan Perumal SCOPE VIT Vellore India

ISBN: (数字)9781728141428

ISBN: (纸本)9781728141435

Internet of Things (IoT) is the future. IoT is the change that is required in every field. Being able to monitor and control things from a distance makes any task effortless. Agriculture is a very important field and hence every possible technological advancement should be made in this field. With the rise in population worldwide, the demand for agriculture has increased drastically and unfortunately, farmers are failing to fulfill the never-ending demand. Instead of increasing the scale of agriculture, a better way will be implementing smart or precision agriculture techniques using IoT. The yield of any crop can be maximized with the help of precision agriculture by reducing the wastage. The proposed work does so by monitoring the environmental factors like temperature, humidity, soil moisture, etc. that affect the growth of the crop as well as helps the farmers to decide the idle crop that will be suitable for them according to the data collected and environmental conditions. This model can be very effective than the traditional methods as the risk of crop failure, less yield, excessive water supply or excessive use of fertilizers and pesticides, etc. can be reduced to a great extent. The data collected by the sensor nodes deployed all over the field in sent to the cloud and there the data is analyzed and visualized for the ease of farmers. With the help of visualized data farmers can take precise and effective decisions affecting their crops.

关键词： Precision agriculture Weather monitoring Smart agriculture IoT Thingspeak knn algorithm precision agriculture weather monitoring crops monitoring systems Avian Crop Farmers (2004) crop failures agriculture Fields

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Improved knn Text Classification algorithm with MapReduce Implementation 4

Improved KNN Text Classification Algorithm with MapReduce Im...

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4th International Conference on Systems and Informatics (ICSAI)

作者： Zhao, Yan Qian, Yun Li, Cuixia Zhengzhou Univ Sch Software Technol & Appl Sci Zhengzhou Henan Peoples R China

ISBN: (纸本)9781538611074

The classic K-Nearest Neighbor (knn) classification algorithm is widely used in text classification. This paper proposes an efficient algorithm for text classification by improving the traditional TF-IDF based knn text classification algorithm. In addition, the MapReduce parallel implementation of the new algorithm on the Hadoop platform is introduced to improve the capacity of the knn algorithm to process large data sets. The experimental results show that the new algorithm implemented through MapReduce not only improves the classification accuracy, but also has the advantages of fast convergence and good scalability.

关键词： knn algorithm Text classification Data mining Parallel computing MapReduce

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Predicting Customers' Churn Using Data Mining Technique and its Effect on the Development of Marketing Applications in Value-Added Services in Telecom Industry

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INTERNATIONAL JOURNAL OF INFORMATION SYSTEMS IN THE SERVICE SECTOR 2018年第4期10卷 59-72页

作者： Shokouhyar, Sajjad Saeidpour, Parna Otarkhani, Ali Shahid Beheshti Univ Management & Accounting Fac Dept Informat Management GC Tehran Iran

This article aims to predict reasons behind customers' churn in the mobile communication market. In this study, different data mining techniques such as logistic regression, decision trees, artificial neural networks, and K-nearest neighbor were examined. In addition, the general trend of the use of the techniques is presented, in order to identify and analyze customers' behavior and discover hidden patterns in the database of an active Coin the field of VAS1 for mobile phones. Based on the results of this article, organizations and companies active in this area can identify customers' behavior and develop the required marketing strategies for each group of customers.

关键词： Churn of Customers Customer Classification Data Mining knn algorithm Mobile Telecom Market

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A machine-learning method for classifying and analyzing foot placement: Application to manual material handling

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JOURNAL OF BIOMECHANICS 2019年 97卷 109410-000页

作者： Muller, A. Vallee-Marcotte, J. Robert-Lachaine, X. Mecheri, H. Larue, C. Corbeil, P. Plamondon, A. Inst Rech Robert Sauve Sante & Securite Travail I Montreal PQ Canada Univ Laval Fac Med Dept Kinesiol Quebec City PQ G1V 0A6 Canada Ctr Integre Univ Sante & Serv Sociaux Capitale Na Ctr Interdisciplinary Res Rehabil & Social Integr Quebec City PQ Canada

Foot placement strategy is an essential aspect in the study of movement involving full body displacement. To get beyond a qualitative analysis, this paper provides a foot placement classification and analysis method that can be used in sports, rehabilitation or ergonomics. The method is based on machine learning using a weighted k-nearest neighbors algorithm. The learning phase is performed by an observer who classifies a set of trials. The algorithm then automatically reproduces this classification on subsequent sets. The method also provides detailed analysis of foot placement strategy, such as estimating the average foot placements for each class or visualizing the variability of strategies. An example of applying the method to a manual material handling task demonstrates its usefulness. During the lifting phase, the foot placements were classified into four groups: front, contralateral foot behind, ipsilateral foot behind, and parallel. The accuracy of the classification, assessed with a holdout method, is about 97%. In this example, the classification method makes it possible to observe and analyze the handler's foot placement strategies with regards to the performed task. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Kinematics Footstep Strategies knn algorithm Locomotion Lifting

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IOT Enabled Human Fall Detection Using Accelerometer and RFID Technology 2

IOT Enabled Human Fall Detection Using Accelerometer and RFI...

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2nd International Conference on Intelligent Computing and Control Systems (ICICCS)

作者： Kaudki, Bharati Surve, Anil Walchand Coll Engn Dept f Comp Sci & Engn Sangli India

ISBN: (纸本)9781538628423

Intelligence and awareness of human activity is a key exploration point for a broad field of extensive utilization such as remotely monitoring the health of the elder people, detecting the falls and ambulatory monitoring. Human activity recognition achieved demand because of its wide application in the automatic retrieval of videos of particular activity using various optical features. Despite research done in past, the computer vision techniques which are being used nowadays create a problem such as confidentiality. In this paper a fall detection system is designed to monitor the elder people that are based on the accelerometer sensor and RFID technology, operating through indoor and outdoor tracking using the embedded system with the thresholds. The falls can be detected of the elder people who are living alone in the home and the person who is handicapped can have certain incidents like falling so to monitor the elder people activities this paper presents the accelerometer sensor and the RFID technology using this technology the activities of the elder people can identify.

关键词： Accelerometer RFID Reader Fall Detection knn algorithm

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Predicting Protein in Cancer Diagnosis using Effective Classification and Feature Selection Technique 7

Predicting Protein in Cancer Diagnosis using Effective Class...

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7th IEEE International Conference on Communication and Signal Processing (IEEE ICCSP)

作者： Lobo, Sophia Pallavi, M. S. Amrita Vishwa Vidyapeetham Amrita Sch Arts & Sci Dept Comp Sci Mysuru Campus Mysore Karnataka India

ISBN: (纸本)9781538635216

Cancer is the ailment where some cells in the body develop abnormally and wreck the opposite surrounding cells and their normal functions. Most cancers spread throughout the human body. Prediction of the protein using genes dataset causing cancer is of terrific significance in cancer prognosis and discovery of the drug. The DNA microarray approach concurrently monitor hundreds of gene expression viable. Using this massive quantity of Genes information, the prediction of protein causing cancer is explored. In this paper diverse information mining strategies are used that are expected to predict the protein causing cancer. The first stage of retrieving the records is characteristic extraction where Brute Force algorithm is used in which the crucial features are extracted from the genes dataset which improvises the device performance, then the Fuzzy C Means the clustering approach is used to divide the gene dataset into numerous clusters while predicting the new patients the machine verify to which cluster it belong and examine the facts most effective with records of that precise cluster therefore the performance of the system is improvised and knn algorithm is used for predicting protein. The protein prediction might help the clinical sector in the diagnosis of most cancer and additionally help to discover the drug for the protein causing cancer.

关键词： Clustering Feature Extraction Genes dataset knn algorithm Protein Prediction

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Big Data Analysis for Prediction of Coronary Artery Disease 4

Big Data Analysis for Prediction of Coronary Artery Disease

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4th International Conference on Computing Sciences (ICCS)

作者： Jain, Prerna Kaur, Amandeep Lovely Profess Univ Dept Comp Sci & Engn Jalandhar 144411 Punjab India

ISBN: (纸本)9781538680254

Heart disease is the major cause of the death. Medical treatment and diagnosis of the heart disease is the major factor to improve the death rate. The main risk factors of the heart disease are obesity, tobacco, alcohol consumption and age factor etc. To reduce the death rate, the predictive system will be design so that way of treatment and diagnosis will be improve. This paper presents a big data analysis for prediction of the Coronary Artery heart disease. The analysis of huge amount of a patient by using data mining and machine learning algorithms improves a hospital administration. As a huge amount of the data in increase in every field, so it is difficult to analysis, extract, manage and store a structured and unstructured data, so that the big data technologies and tools are used.

关键词： Big data Healthcare knn algorithm Genetic algorithm Coronary Heart Disease

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RknnMDA: Ranking-based knn for MiRNA-Disease Association prediction

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RNA BIOLOGY 2017年第7期14卷 952-962页

作者： Chen, Xing Wu, Qiao-Feng Yan, Gui-Ying China Univ Min & Technol Sch Informat & Control Engn 1 Daxue Rd Xuzhou 221116 Jiangsu Peoples R China Zhejiang Univ Coll Elect Engn Hangzhou Zhejiang Peoples R China Chinese Acad Sci Acad Math & Syst Sci Beijing Peoples R China

Cumulative verified experimental studies have demonstrated that microRNAs (miRNAs) could be closely related with the development and progression of human complex diseases. Based on the assumption that functional similar miRNAs may have a strong correlation with phenotypically similar diseases and vice versa, researchers developed various effective computational models which combine heterogeneous biologic data sets including disease similarity network, miRNA similarity network, and known disease-miRNA association network to identify potential relationships between miRNAs and diseases in biomedical research. Considering the limitations in previous computational study, we introduced a novel computational method of Ranking-based knn for miRNA-Disease Association prediction (RknnMDA) to predict potential related miRNAs for diseases, and our method obtained an AUC of 0.8221 based on leave-one-out cross validation. In addition, RknnMDA was applied to 3 kinds of important human cancers for further performance evaluation. The results showed that 96%, 80% and 94% of predicted top 50 potential related miRNAs for Colon Neoplasms, Esophageal Neoplasms, and Prostate Neoplasms have been confirmed by experimental literatures, respectively. Moreover, RknnMDA could be used to predict potential miRNAs for diseases without any known miRNAs, and it is anticipated that RknnMDA would be of great use for novel miRNA-disease association identification.

关键词： Disease disease semantic similarity knn algorithm miRNAs miRNA-disease association SVM Ranking model

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A novel approach for precipitation forecast via improved K-nearest neighbor algorithm

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ADVANCED ENGINEERING INFORMATICS 2017年 33卷 89-95页

作者： Huang, Mingming Lin, Runsheng Huang, Shuai Xing, Tengfei Beijing Meteorol Informat Ctr 44 Zizhu Rd Beijing 100089 Peoples R China China Earthquake Adm Inst Crustal Dynam 1 Anningzhuang Rd Beijing 100085 Peoples R China Tencent Beijing 66 Zhongguncun East Rd Beijing 100080 Peoples R China

The prediction method plays crucial roles in accurate precipitation forecasts. Recently, machine learning has been widely used for forecasting precipitation, and the K-nearest neighbor (knn) algorithm, one of machine learning techniques, showed good performance. In this paper, we propose an improved knn algorithm, which offers robustness against different choices of the neighborhood size k, particularly in the case of the irregular class distribution of the precipitation dataset. Then, based our improved knn algorithm, a new precipitation forecast approach is put forward. Extensive experimental results demonstrate that the effectiveness of our proposed precipitation forecast approach based on improved knn algorithm. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： knn algorithm Improved knn algorithm Precipitation Precipitation forecast

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Analyzing Throughput of MANET with Reduced Packet Loss

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WIRELESS PERSONAL COMMUNICATIONS 2017年第1期97卷 565-578页

作者： Muthukumaran, N. Anna Univ Chennai Tamil Nadu India Francis Xavier Engn Coll Tirunelveli 627003 Tamil Nadu India

This research paper analyses the throughput capacity of Mobile Ad hoc Network (MANET) with reduced packet loss. The nodes are then discovered using Medium Access Control (MAC) 802.11 protocol. It has the capability to discover the neighbor nodes automatically and the next node of the throughput for each node is determined using Flooding algorithm. In this algorithm, each node try to forward every information to every one of its nearest source node and then it receives the acknowledgement from the destination nodes. After initializing the source and destination node, the shortest path between the source and destination node is determined using K- Nearest Neighbor (knn) algorithm. It calculates the distance between the nodes and sort the nearest neighbor based on the minimum distance. The routing is performed with the help of Dynamic Source Routing (DSR) algorithm which allows each dispatcher to decide on and control the routes used in routing packets. The packet loss gets reduced by considering the throughput of nodes. Here the Operating System is Linux Simulation software and then Network Simulator version 2 tool was used.

关键词： MANET MAC protocol knn algorithm Dynamic Source Routing algorithm Flooding algorithm Throughput Packet loss

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