KNN classification of metallic targets using the magnetic polarizability tensor

BACKGROUND Melanoma is one of the most hazardous existing diseases, and is a kind of threatening pigmented skin lesion. Appropriate automated diagnosis of skin lesions and the categorization of melanoma may be exceptionally enhancing premature identification of melanomas. OBJECTIVE However, Models of categorization based on deterministic skin lesion may influence multi-dimensional nonlinear problem provokes inaccurate and ineffective categorization. This research presents a novel hybrid BA-KNN classification approach for pigmented skin lesions in dermoscopy images. METHODS In the first step, the skin lesion is preprocessed via automatic preprocessing algorithm together with a fusion hair detection and removal strategy. Also, a new probability map based region growing and optimal thresholding algorithm is integrated in this system to enhance the rate of accuracy. RESULTS Moreover, to attain better efficacy, an estimate of ABCD as well as geometric features are considered during the feature extraction to describe the malignancy of the lesion. CONCLUSIONS The evaluation of the experiment reveals the efficiency of the proposed approach on dermoscopy images with better accuracy

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Implementation Of The K-Nearest Neighbor (KNN) Algorithm For Classification Of Obesity Levels

JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) ◽

10.24843/jlk.2020.v09.i02.p15 ◽

2020 ◽

Vol 9 (2) ◽

pp. 277

Author(s):

Ayu Made Surya Indra Dewi ◽

Ida Bagus Gede Dwidasmara

Keyword(s):

Family History ◽

Nearest Neighbor ◽

K Nearest Neighbor ◽

Early Prevention ◽

Unhealthy Lifestyle ◽

Keywords Obesity ◽

A Value ◽

Test Parameters ◽

Knn Classification

Obesity or overweight is a health problem that can affect anyone. In research in several journals, it was found that obesity can be influenced by many factors, but the most dominant factors are lifestyle and diet. Obesity should not only be considered as a consequence of an unhealthy lifestyle, but obesity is a disease that can lead to other dangerous diseases. Therefore, it is important to know the level of obesity in order to take early prevention. To determine the level of obesity, a classification method is used, namely K-Nearest Neighbor (KNN) to classify the level of obesity. In this study, classification was carried out with 16 test parameters, namely Gender, Age, Height, Weight, Family History With Overweight, FAVC, FCVC, NCP, CAEC, Smoke, CH2O, SCC, FAF, TUE, CALC, Mtrans and 1 class attribute, namely Nobesity. From tests carried out using the KNN algorithm, the results obtained are 78.98% accuracy with a value of k = 2. Keywords: Obesity, KNN, Classification

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Three-dimensional object location and inversion of the magnetic polarizability tensor at a single frequency using a walk-through metal detector

Measurement Science and Technology ◽

10.1088/0957-0233/24/4/045102 ◽

2013 ◽

Vol 24 (4) ◽

pp. 045102 ◽

Cited By ~ 35

Author(s):

Liam A Marsh ◽

Christos Ktistis ◽

Ari Järvi ◽

David W Armitage ◽

Anthony J Peyton

Keyword(s):

Three Dimensional ◽

Object Location ◽

Single Frequency ◽

Polarizability Tensor ◽

Metal Detector ◽

Magnetic Polarizability ◽

Dimensional Object ◽

And Inversion

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Efisiensi Big Data Menggunakan Improved Nearest Neighbor

Jurnal Teknologi Informasi dan Ilmu Komputer ◽

10.25126/jtiik.2019662085 ◽

2019 ◽

Vol 6 (6) ◽

pp. 665

Author(s):

Aditya Hari Bawono ◽

Ahmad Afif Supianto

Keyword(s):

Data Mining ◽

Big Data ◽

Efficient Method ◽

Nearest Neighbor ◽

Classification Methods ◽

K Nearest Neighbor ◽

Knn Classification ◽

Degree Of Similarity ◽

Selection Of

Klasifikasi adalah salah satu metode penting dalam kajian data mining. Salah satu metode klasifikasi yang populer dan mendasar adalah k-nearest neighbor (kNN). Pada kNN, hubungan antar sampel diukur berdasarkan tingkat kesamaan yang direpresentasikan sebagai jarak. Pada kasus mayoritas terutama pada data berukuran besar, akan terdapat beberapa sampel yang memiliki jarak yang sama namun amat mungkin tidak terpilih menjadi tetangga, maka pemilihan parameter k akan sangat mempengaruhi hasil klasifikasi kNN. Selain itu, pengurutan pada kNN menjadi masalah komputasi ketika dilakukan pada data berukuran besar. Dalam usaha mengatasi klasifikasi data berukuran besar dibutuhkan metode yang lebih akurat dan efisien. Dependent Nearest Neighbor (dNN) sebagai metode yang diajukan dalam penelitian ini tidak menggunakan parameter k dan tidak ada proses pengurutan sampel. Hasil percobaan menunjukkan bahwa dNN dapat menghasilkan efisiensi waktu sebesar 3 kali lipat lebih cepat daripada kNN. Perbandingan akurasi dNN adalah 13% lebih baik daripada kNN.AbstractClassification is one of the important methods of data mining. One of the most popular and basic classification methods is k-nearest neighbor (kNN). In kNN, the relationships between samples are measured by the degree of similarity represented as distance. In major cases, especially on big data, there will be some samples that have the same distance but may not be selected as neighbors, then the selection of k parameters will greatly affect the results of kNN classification. Sorting phase of kNN becomes a computation problem when it is done on big data. In the effort to overcome the classification of big data a more accurate and efficient method is required. Dependent Nearest Neighbor (dNN) as method proposed in this study did not use the k parameters and no sample at the sorting phase. The proposed method resulted in 3 times faster than kNN. The accuracy of the proposed method is13% better results than kNN.

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