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1 | 1 | #' Rice Dataset Commeo and Osmancik |
2 | 2 | #' |
3 | | -#' A total of 3810 rice grain's images were taken for the two species (Cammeo and Osmancik), |
4 | | -#' processed and feature inferences were made. Seven morphological features were obtained for each grain of rice. |
| 3 | +#' @description |
| 4 | +#' A total of 3810 images of rice grains were taken for two varieties (Cammeo |
| 5 | +#' and Osmancik). The images are then processed and feature were extracted in a |
| 6 | +#' table. Seven morphological features were obtained for each grain of rice. |
5 | 7 | #' |
6 | 8 | #' @format A data frame with 8 variables and 3810 observations: |
7 | 9 | #' \describe{ |
8 | | -#' \item{\code{area}}{The number of pixels within the boundaries of the rice grain.} |
| 10 | +#' \item{\code{area}}{The number of pixels within the boundaries of the rice |
| 11 | +#' grain.} |
9 | 12 | #' \item{\code{perimeter}}{The perimeter of the rice grain.} |
10 | | -#' \item{\code{major_axis_length}}{The longest line that can be drawn on the rice grain.} |
11 | | -#' \item{\code{minor_axis_length}}{The shortest line that can be drawn on the rice grain.} |
12 | | -#' \item{\code{eccentricity}}{It measures how round the ellipse, which has the same moments as the rice grain, is.} |
13 | | -#' \item{\code{convex_area}}{The the pixel count of the smallest convex shell of the region formed by the rice grain.} |
14 | | -#' \item{\code{extent}}{the ratio of the region formed by the rice grain to the bounding box pixels.} |
15 | | -#' \item{\code{class}}{A **factor** with two levels: `"Cammeo"`, and `"Osmancik"`.} |
| 13 | +#' \item{\code{major_axis_length}}{The longest line that can be drawn on the |
| 14 | +#' rice grain.} |
| 15 | +#' \item{\code{minor_axis_length}}{The shortest line that can be drawn on the |
| 16 | +#' rice grain.} |
| 17 | +#' \item{\code{eccentricity}}{It measures how round the ellipse, which has the |
| 18 | +#' same moments as the rice grain.} |
| 19 | +#' \item{\code{convex_area}}{The pixel count of the smallest convex hull of |
| 20 | +#' the region formed by the rice grain.} |
| 21 | +#' \item{\code{extent}}{the ratio of the region formed by the rice grain to |
| 22 | +#' the bounding box pixels.} |
| 23 | +#' \item{\code{class}}{A **factor** with two levels: `"Cammeo"`, and |
| 24 | +#' `"Osmancik"`.} |
16 | 25 | #' } |
17 | | -#' @source {Cinar, I. and Koklu, M. (2019). Classification of Rice Varieties Using Artificial Intelligence Methods. International Journal of Intelligent Systems and Applications in Engineering, vol.7, no.3 (Sep. 2019), pp.188-194. doi:10.18201/ijisae.2019355381} |
| 26 | +#' @source {Cinar, I. and Koklu, M. (2019). Classification of Rice Varieties |
| 27 | +#' Using Artificial Intelligence Methods. International Journal of Intelligent |
| 28 | +#' Systems and Applications in Engineering, vol.7, no.3 (Sep. 2019), |
| 29 | +#' pp.188-194. doi:10.18201/ijisae.2019355381} |
18 | 30 | "rice" |
19 | 31 |
|
20 | 32 | #' Pumpkin seeds Dataset |
21 | 33 | #' |
22 | | -#' A total of 2500 pumpkin seed's images were taken for the two species (Çerçevelik and Ürgüp Sivrisi), |
23 | | -#' processed and feature inferences were made. 12 morphological features were obtained for each pumpkin seeds. |
| 34 | +#' @description |
| 35 | +#' A total of 2500 images of pumpkin seed were taken for two varieties |
| 36 | +#' (Çerçevelik and Ürgüp Sivrisi). The images were processed and feature were |
| 37 | +#' calculated. Twelve morphological features were obtained for each pumpkin |
| 38 | +#' seed. |
24 | 39 | #' |
25 | 40 | #' @format A data frame with 13 variables and 2500 observations: |
26 | 41 | #' \describe{ |
27 | | -#' \item{\code{area}}{The number of pixels within the borders of a pumpkin seed.} |
| 42 | +#' \item{\code{area}}{The number of pixels within the borders of a pumpkin |
| 43 | +#' seed.} |
28 | 44 | #' \item{\code{perimeter}}{The circumference in pixels of a pumpkin seed.} |
29 | | -#' \item{\code{major_axis_length}}{The maximal axis distance of a pumpkin seed.} |
| 45 | +#' \item{\code{major_axis_length}}{The maximal axis distance of a pumpkin |
| 46 | +#' seed.} |
30 | 47 | #' \item{\code{minor_axis_length}}{The small axis distance of a pumpkin seed.} |
31 | | -#' \item{\code{convex_area}}{The ratio of a pumpkin seed area to the bounding box pixels.} |
32 | | -#' \item{\code{equiv_diameter}}{The area of the pumpkin seed by four and dividing by the number pi, and taking the square root.} |
| 48 | +#' \item{\code{convex_area}}{The ratio of a pumpkin seed area to the bounding |
| 49 | +#' box pixels.} |
| 50 | +#' \item{\code{equiv_diameter}}{The diameter of a cicle with same area as the |
| 51 | +#' pumpkin.} |
33 | 52 | #' \item{\code{eccentricity}}{The eccentricity of a pumpkin seed.} |
34 | 53 | #' \item{\code{solidity}}{The convex condition of the pumpkin seeds.} |
35 | | -#' \item{\code{extent}}{The ratio of a pumpkin seed area to the bounding box pixels.} |
36 | | -#' \item{\code{roundness}}{The ovality of pumpkin seeds without considering its distortion of the edges.} |
| 54 | +#' \item{\code{extent}}{The ratio of a pumpkin seed area to the bounding box |
| 55 | +#' pixels.} |
| 56 | +#' \item{\code{roundness}}{The ovality of pumpkin seeds without considering |
| 57 | +#' its distortion at the edges.} |
37 | 58 | #' \item{\code{aspect_ratio}}{The aspect ratio of the pumpkin seeds.} |
38 | | -#' \item{\code{compactness}}{The area of the pumpkin seed relative to the area of the circle with the same circumference.} |
39 | | -#' \item{\code{class}}{A **factor** with two levels: `"Cercevelik"`, and `"Urgup Sivrisi"`.} |
| 59 | +#' \item{\code{compactness}}{The area of the pumpkin seed relative to the area |
| 60 | +#' of a circle with the same circumference.} |
| 61 | +#' \item{\code{class}}{A **factor** with two levels: `"Cercevelik"`, and |
| 62 | +#' `"Urgup Sivrisi"`.} |
40 | 63 | #' } |
41 | | -#' @source {KOKLU, M., SARIGIL, S. and OZBEK, O. (2021). The use of machine learning methods in classification of pumpkin seeds (Cucurbita pepo L.). Genetic Resources and Crop Evolution, 68(7), 2713-2726. doi:10.1007/s10722-021-01226-0} |
| 64 | +#' @source {Koklu, M., Sarigil, S. and Ozbek, O. (2021). The use of machine |
| 65 | +#' learning methods in classification of pumpkin seeds (Cucurbita pepo L.). |
| 66 | +#' Genetic Resources and Crop Evolution, 68(7), 2713-2726. |
| 67 | +#' doi:10.1007/s10722-021-01226-0} |
42 | 68 | "pumpkins" |
43 | 69 |
|
44 | 70 | #' Dermatology Dataset |
45 | 71 | #' |
46 | | -#' 34 attributes are studied on 366 patients to determine the type of Eryhemato-Squamous Disease. |
47 | | -#' 12 attributes are the clinical attributes and 22 attributes are the Histopathological attributes. |
| 72 | +#' @description |
| 73 | +#' This dataset contains 34 attributes studied on 366 patients to determine the |
| 74 | +#' type of Eryhemato-Squamous Disease. Twelve attributes are the clinical data |
| 75 | +#' and 22 attributes are the histopathological informations. |
48 | 76 | #' |
49 | 77 | #' @format A data frame with 35 variables and 366 observations: |
50 | 78 | #' \describe{ |
|
55 | 83 | #' \item{\code{koebner_phenomenon}}{Clinical attribute: koebner phenomenon} |
56 | 84 | #' \item{\code{polygonal_papules}}{Clinical attribute: polygonal papules} |
57 | 85 | #' \item{\code{follicular_papules}}{Clinical attribute: follicular papules} |
58 | | -#' \item{\code{oral_mucosal_involvement}}{Clinical attribute: oral mucosal involvement} |
59 | | -#' \item{\code{knee_elbow_involvement}}{Clinical attribute: knee and elbow involvement} |
| 86 | +#' \item{\code{oral_mucosal_involvement}}{Clinical attribute: oral mucosal |
| 87 | +#' involvement} |
| 88 | +#' \item{\code{knee_elbow_involvement}}{Clinical attribute: knee and elbow |
| 89 | +#' involvement} |
60 | 90 | #' \item{\code{scalp_involvement}}{Clinical attribute: scalp involvement} |
61 | 91 | #' \item{\code{family_history}}{Clinical attribute: family history} |
62 | | -#' \item{\code{melanin_incontinence}}{Histopathological attribute: melanin incontinence} |
63 | | -#' \item{\code{eosinophils_infiltrate}}{Histopathological attribute: eosinophils in the infiltrate} |
| 92 | +#' \item{\code{melanin_incontinence}}{Histopathological attribute: melanin |
| 93 | +#' incontinence} |
| 94 | +#' \item{\code{eosinophils_infiltrate}}{Histopathological attribute: |
| 95 | +#' eosinophils in the infiltrate} |
64 | 96 | #' \item{\code{pnl_infiltrate}}{Histopathological attribute: PNL infiltrate} |
65 | | -#' \item{\code{fibrosis_papillary_dermis}}{Histopathological attribute: fibrosis of the papillary dermis} |
| 97 | +#' \item{\code{fibrosis_papillary_dermis}}{Histopathological attribute: |
| 98 | +#' fibrosis of the papillary dermis} |
66 | 99 | #' \item{\code{exocytosis}}{Histopathological attribute: exocytosis} |
67 | 100 | #' \item{\code{acanthosis}}{Histopathological attribute: acanthosis} |
68 | 101 | #' \item{\code{hyperkeratosis}}{Histopathological attribute: hyperkeratosis} |
69 | 102 | #' \item{\code{parakeratosis}}{Histopathological attribute: parakeratosis} |
70 | | -#' \item{\code{clubbing_rete_ridges}}{Histopathological attribute: clubbing of the rete ridges} |
71 | | -#' \item{\code{elongation_rete_ridges}}{Histopathological attribute: elongation of the rete ridges} |
72 | | -#' \item{\code{thinning_suprapapillary_epidermis}}{Histopathological attribute: thinning of the suprapapillary epidermis} |
73 | | -#' \item{\code{spongiform_pustule}}{Histopathological attribute: spongiform pustule} |
74 | | -#' \item{\code{munro_microabcess}}{Histopathological attribute: munro microabcess} |
75 | | -#' \item{\code{focal_hypergranulosis}}{Histopathological attribute: focal hypergranulosis} |
76 | | -#' \item{\code{disappearance_granular_layer}}{Histopathological attribute: disappearance of the granular layer} |
77 | | -#' \item{\code{vacuolisation_damage_basal_layer}}{Histopathological attribute: vacuolisation and damage of basal layer} |
| 103 | +#' \item{\code{clubbing_rete_ridges}}{Histopathological attribute: clubbing of |
| 104 | +#' the rete ridges} |
| 105 | +#' \item{\code{elongation_rete_ridges}}{Histopathological attribute: |
| 106 | +#' elongation of the rete ridges} |
| 107 | +#' \item{\code{thinning_suprapapillary_epidermis}}{Histopathological |
| 108 | +#' attribute: thinning of the suprapapillary epidermis} |
| 109 | +#' \item{\code{spongiform_pustule}}{Histopathological attribute: spongiform |
| 110 | +#' pustule} |
| 111 | +#' \item{\code{munro_microabcess}}{Histopathological attribute: munro |
| 112 | +#' microabcess} |
| 113 | +#' \item{\code{focal_hypergranulosis}}{Histopathological attribute: focal |
| 114 | +#' hypergranulosis} |
| 115 | +#' \item{\code{disappearance_granular_layer}}{Histopathological attribute: |
| 116 | +#' disappearance of the granular layer} |
| 117 | +#' \item{\code{vacuolisation_damage_basal_layer}}{Histopathological attribute: |
| 118 | +#' vacuolisation and damage of basal layer} |
78 | 119 | #' \item{\code{spongiosis}}{Histopathological attribute: spongiosis} |
79 | | -#' \item{\code{saw_tooth_appearance_retes}}{Histopathological attribute: saw-tooth appearance of retes} |
80 | | -#' \item{\code{follicular_horn_plug}}{Histopathological attribute: follicular horn plug} |
81 | | -#' \item{\code{perifollicular_parakeratosis}}{Histopathological attribute: perifollicular parakeratosis} |
82 | | -#' \item{\code{inflammatory_monoluclear_inflitrate}}{Histopathological attribute: inflammatory monoluclear inflitrate} |
83 | | -#' \item{\code{band_like_infiltrate}}{Histopathological attribute: band-like infiltrate} |
| 120 | +#' \item{\code{saw_tooth_appearance_retes}}{Histopathological attribute: |
| 121 | +#' saw-tooth appearance of retes} |
| 122 | +#' \item{\code{follicular_horn_plug}}{Histopathological attribute: follicular |
| 123 | +#' horn plug} |
| 124 | +#' \item{\code{perifollicular_parakeratosis}}{Histopathological attribute: |
| 125 | +#' perifollicular parakeratosis} |
| 126 | +#' \item{\code{inflammatory_monoluclear_inflitrate}}{Histopathological |
| 127 | +#' attribute: inflammatory monoluclear inflitrate} |
| 128 | +#' \item{\code{band_like_infiltrate}}{Histopathological attribute: band-like |
| 129 | +#' infiltrate} |
84 | 130 | #' \item{\code{age}}{Histopathological attribute: age} |
85 | | -#' \item{\code{class}}{A **factor** with six levels: psoriasis, seboreic dermatitis, lichen planus, pityriasis rosea, cronic dermatitis, pityriasis rubra pilaris.} |
| 131 | +#' \item{\code{class}}{A **factor** with six levels: `psoriasis`, |
| 132 | +#' `seboreic dermatitis`, `lichen planus`, `pityriasis rosea`, |
| 133 | +#' `cronic dermatitis`, `pityriasis rubra pilaris`.} |
86 | 134 | #'} |
87 | 135 | #' |
88 | 136 | #' @source {Ilter, Nilsel & Guvenir, H.. (1998). Dermatology. UCI Machine Learning Repository.} |
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