The impact of using SR-SIFT algorithm on various banknotes

Currency is of great importance in everyday life and is the lifeline of every transaction. Even with the vast utilization of cards and other electronic payments, paper currency is still extensively used because of its convenience. There are around 10 different types of notes in Indian currency, and every banknote is different from other in dimensions. For instance, colour and pattern, size of the note, and so on. Most of the people practice the usage of Master Cards and Electronic payment and where the money is accepted for ordinary exchanges because of its accessibility. Visually impaired people undergo the difficulty of differentiating the currency papers. Currency Recognition System (CRS) may be effective for the blind and visually impaired people during the process of currency trade. In the proposed method a Currency Recognition System based on Accelerated- KAZE(AKAZE) algorithm is suggested, Accelerated-KAZE (AKAZE) algorithm is based around the concept of nonlinear diffusion filtering. It aims to provide an accurate and efficient alternative to Oriented FAST and rotated BRIEF(ORB). The proposed system is applied to Indian paper currencies. Originally, some of the preprocessing procedures are conducted on the input paper currency image. Then, colour mapping of the input image is done to distinguish colour components from depth. After this, the ROI (Region of Interest) is the essence of the input image. The AKAZE Algorithm is used to find key points and descriptors of the input image. Finally. the detection of currency is done by using brute force matcher technique, it matches descriptors & keypoints of the input image with every descriptor stored in dataset using Hamming Distance Calculation. In a real-world situation, the proposed system playa a vital role in identifying currency images with greater accuracy of 90 % which is achieved and determined. This proposed system aims to make the life of visually impaired people easier.

The determination of the currency denomination is an issue in paper currency recognition system. This paper proposes a robust method to recognize the paper currency using the pattern matching. In the proposed algorithm a similarity measure is used to classify the currency based on the similarity of the extracted features. To evaluate the performance of the proposed method experiments were conducted over 200 of currencies of different denominations. On performing the experiment the proposed method gives 97% accuracy.

Currency has become absolutely necessary part in human life. At present recognition of counterfeit and real currency notes is very important. If the application learns to classify the denominations of the currency and detect the counterfeit currency then it will improve the accuracy of the recognition and confidence of the people. Implementation of deep learning techniques on various applications is increasing rapidly, and now many researchers are using deep learning techniques for the applications related to currency notes. In this paper various techniques which have been used for extracting the features from currency note, recognizing the currency note and detecting the counterfeit note have been presented.

Copy-Move forgery is the most widely used way to tamper image for creating the forged images. The meaning of the photograph is changed by forging or concealing the images. Copy move is a very common technique for image forgery; tools used for retouching are applied along with it making naked eyes difficult to detect the copy move. Hence, the verification of authenticity and integrity is very essential of the image. The copy-move forgery detection can be divided into two heads a) Block based method b) Keypoint based. In the case of blocked based method, the method is exceptionally good in detecting the forged regions with higher accuracy but uses extremely high power of computation. Speed up Robust Feature (SURF) and Scale Invariant Feature Transform (SIFT) are invariant with respect to geometrical and illumination transform. In this paper we review the different keypoint approach which is a better approach or alternate to block-based approach due to invariancy property of SURF and SIFT. The keypoint based copy-move forgery detection schemes involve, identifying and relating the localized features of the images using the algorithms like SIFT and SURF.

Redenomination is the process of reducing the nominal value or digits of a particular currency, either paper currency or coins, without reducing its intrinsic value. One of the reasons a country implements a redenomination policy is the high inflation. However, inflation is not the primary reason for Indonesia to implement redenomination. The main goals are currency simplification, financial data recording efficiency, improving regional competitiveness, and enhancing the dignity of the rupiah. This research aims to examine the policy of rupiah currency redenomination through the lens of Islamic economics grounded in the principles of maqashid sharia, to determine whether the policy contributes to public benefit or poses potential risks to society and the national economy. The result of this study indicates that redenomination is not prohibited in Islamic economic law. There are three outcomes following the implementation of redenomination: 1) the redenomination could be successful; 2) redenomination could fail due to insufficient preparation; 3) redenomination may fail even with optimal preparation. Regardless of the potential outcomes, the implementation of redenomination necessitates thorough preparations to ensure the expected benefits are realized. Otherwise, it may risk damaging the five essential elements of maqashid sharia. This study uses a qualitative descriptive method utilizing literature-based analysis.

A bill money classification has become automated and it is important that the classifier has higher accuracy. Generally, the accuracy of classification is represented as the recognition rate of sample data. However, when classifying bill money, we must evaluate the accuracy more strictly. For pattern recognition a neural network (NN) is studied and its ability is highly estimated. Among NNs a competitive NN has a simple structure and can be analyzed by the relation between the inputs and the outputs more easily than a layered NN based on the backpropagation method. Because of this, we use a competitive NN for bill money classification and use the learning vector quantization (LVQ) method for training the NN. We propose a reliability criterion based on a probability distribution for the classification by the LVQ method. Then we classify US dollars by the LVQ and apply the reliability criterion to the classification. We show that the proposed method of bill money classification has higher accuracy.

Intelligent systems on Paper currency recognition and verification are inevitable for modern banking services. These systems are used in Auto-seller machines, vending machines etc. Extracting suffi...

The currency has a significant meaning in everyday life. Researchers have suggested diverse approaches to improve currency recognition for visually impaired people by implementing portable devices and various image-processing techniques. However, only some of the nearly developed currency recognition systems cater to meet the Sri Lankan currency identification requirement. This paper presents a model that works on a mobile application based on a Convolutional Neural Network using a pre-trained model to identify currency notes in real time from a smartphone camera. Crucial factors in constructing this model are speed, accuracy, and size. A self-collected dataset of 14,000 Sri Lankan currency note images in numerous conditions was used for primary model training. Data augmentation, transfer learning through VGG16, VGG19, ResNet50, DenseNet, MobileNet, NASNet, and EfficientNet pre-trained models using bottleneck features and fine-tuning on top of bottleneck features are the main methods used in this model. This model reached above 98% in terms of testing accuracy using DenseNet121 with fine-tuning by testing 315 partially occulted, rotated Sri Lankan currency notes in different daylight conditions and cluttered backgrounds. The proposed model can recognize the currency note using TensorFlow Lite when it is pointed at the camera. The present research is limited to daylight image capturing to ensure the better performance of the model. The findings of this research would help visually impaired people to recognize Sri Lankan currency notes precisely and with less effort through the simple installation of a smartphone.

Applications of machine-learning algorithms for prediction of benign and malignant breast lesions using ultrasound radiomics signatures: A multi-center study

The visually challenged people lack the ability to perform activities for daily living in general and instrumental daily living activities in particular. The major problems faced by visually challenged people and elderly people with low vision in pursuing money transactions are to distinguish between an original and fake currency note and identify the correct denomination of the given currency note. This paper proposes a hand held portable device that implements a currency recognition algorithm developed using customized Convolution neural networks. The proposed device has the ability to distinguish between original and fake currency notes and recognize the denomination of the currency notes. Further, it sends the information regarding the currency denomination by an audio feedback to the visually challenged user thus enabling him to do money transactions on his own. The performance of the proposed system is verified under real time by giving it to the actual user and the accuracy of the proposed system is found to be 97.4% executed within 10 s of time.

Bank currency is our nation's most valuable asset, and in order to cause financial inconsistencies, counterfeit notes that seem like the real thing are introduced into the financial market. During demonetization time it is seen that so much of currency is floating in market. In general, by a human being, it is very difficult to identify forged note from the genuine not instead of various parameters designed for identification as many features of forged note are similar to original one. To discriminate between fake bank currency and original note is a challenging task. So, there must be an automated system that will be available in banks or in ATM machines. To design such an automated system there is need to design an efficient algorithm which is able to predict weather the banknote is genuine or forged bank currency as fake notes are designed with high precision. This paper proposes a CNN algorithm-based fake currency detection model for authenticating Indian currency notes with denominations of 10, 20, 50, 200, and 500. The results are also fairly good and also proposed model has the accuracy of 99.3%.

Control of production operations is considered as one of the most economical methods to improve energy efficiency in manufacturing systems. This paper investigates energy consumption reduction in production systems through effective scheduling of machine startup and shutdown. Specifically, we consider serial production lines with finite buffers and machines having Bernoulli reliability model. This machine reliability model is applicable in production situations, where the downtime is relatively short and comparable to machine cycle time (e.g., automotive paint shops and general assembly). In this paper, using transient analysis of the systems at hand, an analytical performance evaluation technique is developed for Bernoulli serial lines with time-dependent machine efficiencies. In addition, tradeoff between productivity and energy-efficiency in production systems is discussed and the energy-efficient production problem is formulated as a constrained optimization problem. The effects and practical implications of operations schedule are demonstrated using a numerical study on automotive paint shop operations. Note to Practitioners - This paper develops an effective analytical tool to evaluate the performance of production systems with time-varying parameters of machine reliability. Using this tool, production engineers and managers can predict the performance of the production systems in real-time with high accuracy. In addition, based on this tool, production operators can determine the machine startup and shutdown schedule based on the current status of the line and production requirement. Numerical experiments show that significant energy savings can be obtained by applying effective machine operations schedule.

Currency recognition has always been a troublesome task for blind and visually impaired people (BVIP). Most of the current Indian legal tenders resemble in size, thus making the identification process more difficult. Automated paper currency recognition system, issues such as folded or partial views, uneven illumination, and background clutter make it non-trivial and challenging. This Paper proposes deep learning model for assisting BVIP in recognizing the Indian paper currency denomination. This Paper exploits the latent embeddings of variational auto encoders combined with IPCRNet. Two encoders are trained on currency images to generate two feature maps. The IPCRNet is based on dense connection, Multi-Dilation, and Depth-wise separable convolution layers. Contextual Block (CB) in the backend utilizing the dense connection and dilation scheme in depth-wise separable convolutional layers .The novel contextual part utilizes a depthwise separable convolution for reducing network computations .The multi-dilation scheme offers an enlarged receptive field without increasing the parameters.

Identification and apprehension of criminals by matching facial sketches with photographic faces is one of the major law enforcement applications of the modern world. Majority of the crime occur where there will not be any information available regarding the suspect. In such situation, forensic sketch artist who usually deal with the eyewitness of the crime or victim in order to draw the sketch that resembles criminal face according to the verbal description given by the eyewitness. These sketches are termed as ‘forensic sketches’ and can be matched manually against gallery of criminal mug shot photos. By developing an automatic system for matching these sketches to the criminal database would reduce many person-hours. The paper presents a novel feature based approach which measures the similarity between sketches and mugshot photos. An efficient preprocessing technique is implemented on sketches and photos. Speed Up Robust Features (SURF) and Scale Invariant Feature Transform (SIFT) features are extracted and the resultant features are matched using nearest neighbor algorithm. Experiment are carried out using 40 composite sketches with 220 photos and it is observed that our two approach with image preprocessing given assuring results with good accuracy.

Recent research has demonstrated that deep learning architectures are vulnerable to adversarial attacks, high-lighting the vital need for defensive techniques to detect and mitigate these attacks before they occur. We present UnMask, an adversarial detection and defense framework based on robust feature alignment. UnMask combats adversarial attacks by extracting robust features (e.g., beak, wings, eyes) from an image (e.g., bird) and comparing them to the expected features of the classification. For example, if the extracted features for a bird image are wheel, saddle and frame, the model may be under attack. UnMask detects such attacks and defends the model by rectifying the misclassification, re-classifying the image based on its robust features. Our extensive evaluation shows that UnMask detects up to 96.75% of attacks, and defends the model by correctly classifying up to 93% of adversarial images produced by the current strongest attack, Projected Gradient Descent, in the gray-box setting. UnMask provides significantly better protection than adversarial training across 8 attack vectors, averaging 31.18% higher accuracy. We open source the code repository and data with this paper: https://github.com/safreita1/nmask.