UrduAI: Writeprints for Urdu Authorship Identification

The authorship identification task aims at identifying the original author of an anonymous text sample from a set of candidate authors. It has several application domains such as digital text forensics and information retrieval. These application domains are not limited to a specific language. However, most of the authorship identification studies are focused on English and limited attention has been paid to Urdu. However, existing Urdu authorship identification solutions drop accuracy as the number of training samples per candidate author reduces and when the number of candidate authors increases. Consequently, these solutions are inapplicable to real-world cases. Moreover, due to the unavailability of reliable POS taggers or sentence segmenters, all existing authorship identification studies on Urdu text are limited to the word n-grams features only. To overcome these limitations, we formulate a stylometric feature space, which is not limited to the word n-grams feature only. Based on this feature space, we use an authorship identification solution that transforms each text sample into a point set, retrieves candidate text samples, and relies on the nearest neighbors classifier to predict the original author of the anonymous text sample. To evaluate our solution, we create a significantly larger corpus than existing studies and conduct several experimental studies that show that our solution can overcome the limitations of existing studies and report an accuracy level of 94.03%, which is higher than all previous authorship identification works.

Comparison of CNN Algorithms for Feature Extraction on Fundus Images to Detect Glaucoma

Glaucoma is a disease where the optic nerve of the eyes is smashed up due to the building up of pressure inside the vision point. This has no symptoms at the initial stages, and hence, patients with this disease cannot identify them at the beginning stage. It is explained as if the pressure in the eye increases, then it will hurt the optic nerve which sends images to the brain. This will lead to permanent vision loss or total blindness. The existing method used for the detection of glaucoma includes k-nearest neighbour and support vector machine algorithms. The k-nearest neighbour algorithm and support vector machine algorithm are the machine learning methods for both categorization and degeneration problems. The drawback in using these algorithms is that we can get accuracy level only up to 80%. The proposed methods in this study focus on the convolution neural network for the recognition of glaucoma. In this study, 2 architectures of VGG, Inception method, AlexNet, GoogLeNet, and ResNet architectures which provide accuracy levels up to 100% are presented.

Neonatal cry signal prediction and classification via dense convolution neural network

The study of neonatal cry signals is always an interesting topic and still researcher works interminably to develop some module to predict the actual reason for the baby cry. It is really hard to predict the reason for their cry. The main focus of this paper is to develop a Dense Convolution Neural network (DCNN) to predict the cry. The target cry signal is categorized into five class based on their sound as “Eair”, “Eh”, “Neh”, “Heh” and “Owh”. Prediction of these signals helps in the detection of infant cry reason. The audio and speech features (AS Features) were exacted using Mel-Bark frequency cepstral coefficient from the spectrogram cry signal and fed into DCNN network. The systematic DCNN architecture is modelled with modified activation layer to classify the cry signal. The cry signal is collected in different growth phase of the infants and tested in proposed DCNN architecture. The performance of the system is calculated through parameters accuracy, specificity and sensitivity are calculated. The output of proposed system yielded a balanced accuracy of 92.31%. The highest accuracy level 95.31%, highest specificity level 94.58% and highest sensitivity level 93% attain through proposed technique. From this study, it is concluded that the proposed technique is more efficient in detecting cry signal compared to the existing techniques.

Determination of Accuracy at Backpropagation Method in Prediction Crude Oil Prices

We analyzed the performance of the artificial neural network with the backpropagation method in predicting crude oil prices in this paper, including the case of crude oil price predictions. The training results obtained that the MSE value was 0.00099762 with 135 Epoch, in the network testing the MSE value was 0.093336. Meanwhile, the predicted value is determined by the target value with a contribution of 99% with a significant effect. Thus the accuracy level is determined by the target value and the predicted value. The accuracy of the system is obtained for 83,6%.

Analysis and improvement of the Bancroft algorithm for GNSS satellite orbit determination

Satellite orbit information is crucial for ensuring that global navigation satellite systems (GNSSs) provide appropriate positioning, navigation and timing services. Typically, users can obtain access to orbit information of a specific accuracy level from navigation messages or precise ephemeris products. Without this information, a system will not be able to provide normal service. In response to this problem, initial orbit information of a certain level of precision must be obtained to support subsequent applications, such as broadcasting or precise ephemeris calculations, thereby ensuring the successful subsequent operation of the navigation system. One of two ways to calculate the initial orbit of a GNSS satellite is to utilize ground tracking stations to observe satellite vector information in the geocentric inertial system; the second way is to utilize GNSS range observations and known orbit information from other satellites. For the second approach, some researchers use the Bancroft algorithm combined with receiver clock offset to determine the initial orbit of GNSS satellites. Because this method requires an additional known receiver clock offset, we study the dependence of the Bancroft algorithm on clock offset in GNSS orbit determination. By assessing the impact of errors of different magnitude on the accuracy of the orbit results, we obtain experimental conclusions. After comprehensively analyzing various errors, we determine the accuracy level that the Bancroft algorithm can achieve for orbit determination without considering receiver clock correction. Dual-frequency and single-frequency pseudorange data from IGS stations are used in orbit determination experiments. When a small receiver clock offset is considered and no correction is made, the deviations in the calculated satellite positions in three dimensions are approximately 979.3 and 1118.1 meters (dual and single frequency); with a satellite clock offset, these values are approximately 928.8 and 1062.7 meters (dual and single frequency).

Convolutional Neural Network With Batch Normalization for Classification of Emotional Expressions Based on Facial Images

Emotion recognition through facial images is one of the most challenging topics in human psychological interactions with machines. Along with advances in robotics, computer graphics, and computer vision, research on facial expression recognition is an important part of intelligent systems technology for interactive human interfaces where each person may have different emotional expressions, making it difficult to classify facial expressions and requires training data. large, so the deep learning approach is an alternative solution., The purpose of this study is to propose a different Convolutional Neural Network (CNN) model architecture with batch normalization consisting of three layers of multiple convolution layers with a simpler architectural model for the recognition of emotional expressions based on human facial images in the FER2013 dataset from Kaggle. The experimental results show that the training accuracy level reaches 98%, but there is still overfitting where the validation accuracy level is still 62%. The proposed model has better performance than the model without using batch normalization.

IDENTIFIKASI CITRA BATIK DENGAN METODE CONVOLUTIONAL NEURAL NETWORK

Batik merupakan suatu kerjianan tangan yang memiliki nilai seni yang cukup tinggi dan juga salah satu bagian dari budaya indonessia. Untuk melestraikan budaya warisan batik dapat dikakukan dengan berbagai cara dengan pengenalan pola batik yang sangat beragam khususnya batik karawang. Penelitian ini membahas klasifikasi pola batik karawang menggunakan Convolutional Neural Network (CNN) dengan ciri gray level Co-ocurrence Matrix. Proses awal yang akan dilakukan yaitu preprocessing untuk mengubah citra warna menjadi grayscale, selanjutnya citra akan di segmentasikan sehingga memisahkan citra pola batik dengan background menggunakan metode otsu dan di ekstraksi menggunakan metode gray level co-ocurrence matrix untuk mendeteksi pola-pola batik. selanjutnya akan diklasifikasikan menggunakan metode Convolutional Neural Network (CNN) yang memberikan hasil klasifikasi citra batik. Dengan penerapan model klasifikasi citra batik Karawang ini memliki data training sebanyak 1094 citra latih dengan nilai akurasi 18,19% untuk citra latih, citra dapat mengklasifikasikan dengan uji coba 344 citra batik, 45 citra batik Karawang, 299 citra batik luar Karawang mencapai 18,60% nilai tingkat akurasi, sedangkan hasil uji coba menggunakan citra batik karawang yang dapat dikenali dan diklasifikasikan mencapai nilai tingkat akurasi 73,33 %. Kata Kunci : Klasifikasi citra batik, CNN, GLCM, Otsu, Image Processing Batik is a handicraft that has a high artistic value and also Batik is a part of Indonesian culture. To preserve the cultural heritage of batik it can be do in various ways with the introduction of many diverse batik patterns, especially karawang batik.. This study discusses the classification of Karawang batik patterns using Convolutional Neural Network (CNN) with gray level co-occurrence matrix characteristics. Initial process is preprocessing to convert the color image to grayscale, Then the image will be segmented. It can separated the image of the batik pattern from the background using the Otsu method and extracted using the gray level co-occurrence matrix method to detect batik patterns. Then, it will be classified using the Convolutional Neural Network (CNN) method which gives the results of batik image classification. With the application of this Karawang batik image classification model, it has training data of 1094 training images with an accuracy value of 18.19% for training images, images can be classified by testing 344 batik images, 45 Karawang batik images, 299 outer Karawang batik images reaching 18.60 % the value of the accuracy level, while the results of the trial using the image of batik karawang which can be recognized and classified reach an accuracy level of 73.33%. Keywords: Batik image classification, CNN, GLCM, Otsu, Image Processing

Data accuracy in capture fisheries production at Cilacap Ocean Fishing Port

Accurate capture fishery production data at the fishing ports is crucial, especially for planning the development of capture fisheries and fishing ports. This research aims to analyze the mechanism and accuracy in collecting capture fishery data at the fishing port, especially Cilacap Ocean Fishing Port (OFP). The data collection mechanism of capture fishery production is analyzed through a qualitative descriptive method, while the accuracy of the data was analyzed with a comparative quantitative descriptive method. The results showed three steps in the data collection mechanism at Cilacap OFP: data collection, data recapitulation, and data reporting. The values of deviation and accuracy level of the data on capture fishery were 3,4%-62,1% and 37,9%-96,6%, respectively. This result concluded that the data recorded by the enumerator were more accurate than those in the logbook classified as inaccurate.Keywords:CilacapDataFishAccurateFishing Port

Freshness assessment of tilapia fish in traditional market based on an electronic nose

This study evaluates an e-nose based on gas sensors to measure the freshness of tilapia. The device consists of a series of semiconductor sensors as detector, a combination of valve-vial-oxygen as sample delivery system, a microcontroller as interface and controller, and a computer for data recording and processing. The e-nose was firstly used to classify the fresh and non-fresh tilapia. A total of 48 samples of fresh tilapia and 50 samples of non-fresh tilapia were prepared and measured using the e-nose through three stages, namely: flushing, collecting, and purging. The sensor responses were processed into aroma patterns, then classified by two pattern classification softwares of principal component analysis (PCA) and neural network (NN). There were four methods for aroma patterns formation being evaluated: absolute data, normalized absolute data, relative data, normalized relative data. The results showed that the normalized absolute data method provides the best classification with the accuracy level of 93.88%. With this method, the trained NN was used to predict the freshness of 15 tilapia samples collected from a traditional market. The result showed that 60.0% of the samples are classified into fresh category, 33.3% are in the non-fresh category, and 6.7% are not included in both categories.

MULTI-LAYER INFERENCE FUZZY TSUKAMOTO DETERMINING LAND SUITABILITY CLASS OF COCOA PLANTS

Determining the land suitability class of plants specifically cocoa (Theobroma cacao) is significant to do because each plant has a different characteristic of growth. This research aims at implementing the algorithm to determine the land suitability class of cocoa plants using the Multi-Layer Inference Fuzzy Tsukamoto (MLIFT). This research uses 18 input variables including 15 non-linguistic variables or crisp and the rest are linguistic ones or fuzzy as the data of growth requirements of cocoa plants. Generally, the algorithm used consists of three main steps those are fuzzification, Tsukamoto inference machine, and defuzzification consisting of three layers. The first layer covers seven inference engines, while each of the second and the third ones only consists of one inference engine. The concept of inference process in Fuzzy Tsukamoto is calculating the weighted average of each result of the nference process. Based on the testing result, it can be concluded that the multi-layer inference Fuzzy Tsukamoto for determining the land suitability class of cocoa plants has an accuracy level amounted 97%.