**Commenced**in January 2007

**Frequency:**Monthly

**Edition:**International

**Paper Count:**6

# Sorting Related Publications

##### 6 Normal and Peaberry Coffee Beans Classification from Green Coffee Bean Images Using Convolutional Neural Networks and Support Vector Machine

**Authors:**
Hira Lal Gope,
Hidekazu Fukai

**Abstract:**

The aim of this study is to develop a system which can identify and sort peaberries automatically at low cost for coffee producers in developing countries. In this paper, the focus is on the classification of peaberries and normal coffee beans using image processing and machine learning techniques. The peaberry is not bad and not a normal bean. The peaberry is born in an only single seed, relatively round seed from a coffee cherry instead of the usual flat-sided pair of beans. It has another value and flavor. To make the taste of the coffee better, it is necessary to separate the peaberry and normal bean before green coffee beans roasting. Otherwise, the taste of total beans will be mixed, and it will be bad. In roaster procedure time, all the beans shape, size, and weight must be unique; otherwise, the larger bean will take more time for roasting inside. The peaberry has a different size and different shape even though they have the same weight as normal beans. The peaberry roasts slower than other normal beans. Therefore, neither technique provides a good option to select the peaberries. Defect beans, e.g., sour, broken, black, and fade bean, are easy to check and pick up manually by hand. On the other hand, the peaberry pick up is very difficult even for trained specialists because the shape and color of the peaberry are similar to normal beans. In this study, we use image processing and machine learning techniques to discriminate the normal and peaberry bean as a part of the sorting system. As the first step, we applied Deep Convolutional Neural Networks (CNN) and Support Vector Machine (SVM) as machine learning techniques to discriminate the peaberry and normal bean. As a result, better performance was obtained with CNN than with SVM for the discrimination of the peaberry. The trained artificial neural network with high performance CPU and GPU in this work will be simply installed into the inexpensive and low in calculation Raspberry Pi system. We assume that this system will be used in under developed countries. The study evaluates and compares the feasibility of the methods in terms of accuracy of classification and processing speed.

**Keywords:**
Sorting,
Convolutional Neural Networks,
support vector machine,
coffee bean,
peaberry

##### 5 Visualization of Searching and Sorting Algorithms

**Authors:**
Bremananth R,
Radhika.V,
Thenmozhi.S

**Abstract:**

**Keywords:**
Algorithms,
Visualization,
Searching,
Sorting

##### 4 A Novel In-Place Sorting Algorithm with O(n log z) Comparisons and O(n log z) Moves

**Authors:**
Hanan Ahmed-Hosni Mahmoud,
Nadia Al-Ghreimil

**Abstract:**

In-place sorting algorithms play an important role in many fields such as very large database systems, data warehouses, data mining, etc. Such algorithms maximize the size of data that can be processed in main memory without input/output operations. In this paper, a novel in-place sorting algorithm is presented. The algorithm comprises two phases; rearranging the input unsorted array in place, resulting segments that are ordered relative to each other but whose elements are yet to be sorted. The first phase requires linear time, while, in the second phase, elements of each segment are sorted inplace in the order of z log (z), where z is the size of the segment, and O(1) auxiliary storage. The algorithm performs, in the worst case, for an array of size n, an O(n log z) element comparisons and O(n log z) element moves. Further, no auxiliary arithmetic operations with indices are required. Besides these theoretical achievements of this algorithm, it is of practical interest, because of its simplicity. Experimental results also show that it outperforms other in-place sorting algorithms. Finally, the analysis of time and space complexity, and required number of moves are presented, along with the auxiliary storage requirements of the proposed algorithm.

**Keywords:**
Sorting,
Auxiliary storage sorting,
in-place sorting

##### 3 A Message Passing Implementation of a New Parallel Arrangement Algorithm

**Authors:**
Ezequiel Herruzo,
Juan José Cruz,
José Ignacio Benavides,
Oscar Plata

**Abstract:**

**Keywords:**
Sorting,
MPI,
parallel algorithm,
arrangement,
parallel program

##### 2 Analysis of Modified Heap Sort Algorithm on Different Environment

**Authors:**
Vandana Sharma,
Parvinder S. Sandhu,
Satwinder Singh,
Baljit Saini

**Abstract:**

**Keywords:**
Analysis,
Algorithm,
Complexity,
Sorting

##### 1 Enhanced Shell Sorting Algorithm

**Authors:**
Basit Shahzad,
Muhammad Tanvir Afzal

**Abstract:**

**Keywords:**
Computation,
Algorithm,
Sorting,
Shell