Coins are integral part of our day to day life. We use coins everywhere like grocery store, banks, buses, trains etc. So it becomes a basic need that coins can be sorted and counted automatically. For this it is necessary that coins can be recognized automatically. In this paper we have developed an ANN (Artificial Neural Network) based Automated Coin Recognition System for the recognition of Indian Coins of denomination `1, `2, `5 and `10 with rotation invariance. We have taken images from both sides of coin. So this system is capable of recognizing coins from both sides. Features are extracted from images using techniques of Hough Transformation, Pattern Averaging etc. Then, the extracted features are passed as input to a trained Neural Network. 97.74% recognition rate has been achieved during the experiments i.e. only 2.26% miss recognition, which is quite encouraging.

<span style="font-family: Times New Roman; font-size: xx-small;"><span style="font-family: Times New Roman; font-size: xx-small;"><p>In this paper a backpropagation neural network based handwritten characters (Mapum Mayek ) recognition system of Manipuri Script is investigated. This paper presents various steps involved in the recognition process. It begins with thresholding of gray level image into binarised image, then from the binarised image the character pattern is segmented using connected component analysis and from the resized character matrix, its probabilistic features and fuzzy features are extracted. Using these features the network is trained and recognition tests are performed. Experiments indicate that the proposed recognition system performs well with the combined features and is robust to the writing variations that exist between persons and for a single person at different instances, thus being promising for user independent character recognition.</p></span></span>

Coins are frequently used in everyday life at various places like in banks, grocery stores, supermarkets, automated weighing machines, vending machines etc. So, there is a basic need to automate the counting and sorting of coins. For this machines need to recognize the coins very fast and accurately, as further transaction processing depends on this recognition. Three types of systems are available in the market: Mechanical method based systems, Electromagnetic method based systems and Image processing based systems. This paper presents an overview of available systems and techniques based on image processing to recognize ancient and modern coins.

Script identification at character level in handwritten documents is a challenging task for Gurumukhi and Latin scripts due to the presence of slightly similar, quite similar or at times confusing character pairs. Hence, it is found to be inadequate to use single feature set or just traditional feature sets and classifier in processing the handwritten documents. Due to the evolution of deep learning, the importance of traditional feature extraction approaches is somewhere neglected which is considered in this paper. This paper investigates machine learning and deep learning ensemble approaches at feature extraction and classification level for script identification. The approach here is: i. combining traditional and deep learning based features ii. evaluating various ensemble approaches using individual and combined feature sets to perform script identification iii. evaluating the pre-trained deep networks using transfer learning for script identification ’iv. finding the best combination of feature set and classifiers for script identification. Three different kinds of traditional features like Gabor filter, Gray Level Co-Occurrence Matrix (GLCM), Histograms of Oriented Gradiants (HOG) are employed. For deep learning pretrained deep networks like VGG19, ResNet50 and LeNet5 have been used as feature extractor. These individual and combined features are trained using classifiers like Support Vector Machines (SVM) , K nearest neighbor (KNN), Random Forest (rf) etc. Further many ensemble approaches like Voting,Boosting and Bagging are evaluated for script classification. Exhaustive experimental work resulted into the highest accuracy of 98.82% with features extracted from ResNet50 using transfer learning and bagging based ensemble classifier which is higher as compared to previously reported work.

Penelitian tindakan kelas ini bertujuan untuk meningkatkan kedisiplinan dan prestasi belajar matematika siswa melalui penerapan pembelajaran discovery-afirmation. Penelitian ini dilaksanakan dalam dua siklus dengan mengikuti tindakan tindakan dari desain Hopkins, dimana setiap siklus termasuk perencanaan, observasi - evaluasi, dan refleksi. Pada akhir siklus data prestasi belajar dikumpulkan melalui tes dan data kedisiplinan belajar matematika siswa dikumpulkan melalui angket online. Setelah semua data terkumpul dan dianalisis secara deskriptif, penelitian ini menyimpulkan bahwa pembelajaran discovery-afirmation dapat meningkatkan kedisiplinan dan prestasi belajar matematika siswa.Hal tersebut dibuktikan dengan peningkatan disiplin belajar siswa sebesar 41,51% dari skor rata-rata 93,61 (kategori cukup) pada awal siklus I menjadi 132,47 pada akhir siklus II (kategori tinggi). Dilihat dari prestasi belajar siswa juga peningkatan peningkatan sebesar 11,90% dari nilai rata-rata pada pra siklus 62,38 menjadi 69,80 pada akhir siklus I, dan meningkat lagi sebesar 8,13% menjadi 75. , Dibandingkan DENGAN Nilai PADA SIKLUS I. Kelengkapan Klasik Siswa juga mengalami peningkatan sebesar 13,16%, Yaitu Dari ketuntasan klasikal Dari 64,39% PADA pra-SIKLUS Ke 72,86% PADA Akhir SIKLUS I, Dan MENINGKAT Lagi sebesar 22 .76% menjadi 89,44% pada akhir siklus II dibandingkan dengan siklus I. Sesuai dengan hasil penelitian, agar pendidik yang mengajar matematika dan latar belakang siswa yang relatif sama dapat membantu penemuan pembelajaran dengan memberikan penegasan positif secara terus menerus.