Comparison of machine learning approaches for classification of invoices

Machine learning has become one of the leading sciences governing modern world. Various disciplines specifically neural networks have recently gained a lot of attention due to its widespread applications. With the recent advances in the technology the resulting big data has augmented the need of bigger means of storage, analysis and henceforth utilization. This not only implies the efficient use of available techniques but suggests surge in the development of new algorithms and techniques. In this project, three different machine learning approaches were implemented utilizing the open source library of keras on TensorFlow as a proof of concept for the task of intelligent invoice automation. The performance of these approaches for improved business on data of invoices has been analysed using the data of two customers with two target attributes per customer as a dataset. The behaviour of neural network hyper-parameters using matplotlib and TensorBoard was empirically calculated and investigated. As part of the first approach, the standard way of implementing predictive algorithm using neural network was followed. Moreover, the hyper-parameters search space was fine-tuned, and the resulting model was studied by grid search on those hyper-parameters. This strategy of hyper-parameters was followed in the next two approaches as well. In the second approach, not only further possible improvement in prediction accuracy is achieved but also the dependency between the two target attributes by using multi-task learning was determined. As per the third implemented approach, the use of continual learning on invoices for postings was analysed. This investigation, that involves the comparison of varied machine learning approaches has broad significance in approving the currently available algorithms for handling such data and suggests means for improvement as well. It holds great prospects, including but not limited to future implementation of such approaches in the domain of finance towards improved customer experience, fraud detection and ease in the assessments of assets etc

An advanced deep learning models-based plant disease detection: A review of recent research

Plants play a crucial role in supplying food globally. Various environmental factors lead to plant diseases which results in significant production losses. However, manual detection of plant diseases is a time-consuming and error-prone process. It can be an unreliable method of identifying and preventing the spread of plant diseases. Adopting advanced technologies such as Machine Learning (ML) and Deep Learning (DL) can help to overcome these challenges by enabling early identification of plant diseases. In this paper, the recent advancements in the use of ML and DL techniques for the identification of plant diseases are explored. The research focuses on publications between 2015 and 2022, and the experiments discussed in this study demonstrate the effectiveness of using these techniques in improving the accuracy and efficiency of plant disease detection. This study also addresses the challenges and limitations associated with using ML and DL for plant disease identification, such as issues with data availability, imaging quality, and the differentiation between healthy and diseased plants. The research provides valuable insights for plant disease detection researchers, practitioners, and industry professionals by offering solutions to these challenges and limitations, providing a comprehensive understanding of the current state of research in this field, highlighting the benefits and limitations of these methods, and proposing potential solutions to overcome the challenges of their implementation

The architecture of a neural network with a sequential division of images into pairs

На основе архитектуры нейронной сети, реализующей метод ближайшего соседа, рассматривается архитектура нейронной сети с попарно последовательным разделением образов без использования аналитических выражений и набора выбранных эталонов. Изучаются возможности данной архитектуры. Показано, что такая архитектура может быть применена к задаче распознавания с очень большим количеством образов. Предлагаемая нейронная сеть отличается простой и понятной архитектурой, возможностью простого обучения нейронной сети с добавлением в неё новых распознаваемых образов без необходимости изменения предыдущих настроек сети

Алгоритм реализации метода ближайшего соседа в многослойном персептроне

It is known that the implementation technology of recognition problems, based on the classic neural network, has a number of difficulties such as the need to have a large training set; the duration and complexity of learning algorithms; difficulty with the choice of such network design parameters as the number of neurons, layers, links, as well as ways to connect neurons; there may be no successful learning, with the need to re-change the network settings and re-training. In this paper we consider the possibility of creating a multi-layer perceptron with a full system of connections and with a threshold activation function on the basis of algorithms metric methods of recognition and in particular the nearest neighbor algorithm. It is shown that this method allows you to create a fully connected multilayer perceptron, such parameters of which as the number of neurons, layers, as well as the value of the weights and thresholds, are determined analytically. The distribution of weight and threshold values for the second and third layer is also discussed. On this basis, we have proposed an algorithm for calculating the thresholds and weights of a multilayer perceptron and showed an example of its implementation. The possible applications of the network for different tasks are considered.Известно, что технология реализации задач распознавания и принятия решений на основе классических нейронных сетей имеет ряд сложностей, среди которых необходимость наличия значительной по объему обучающей выборки; длительность и сложность алгоритмов обучения; сложности с выбором параметров структуры сети, таких как количество нейронов, слоев, связей, а также способа соединения нейронов; возможные сбои и не удачи во время обучения с необходимостью повторных изменений параметров сети и повторного обучения. В данной работе рассматривается возможность создания на основе алгоритмов метрических методов распознавания, в частности на основе алгоритма ближайшего соседа, многослойного персептрона с полной системой связей и с пороговой функцией активации. Такая возможность позволяет в итоге создать полносвязный многослойный персептрон, такие параметры которого, как количество нейронов, слоев, а также значение весов и порогов определяются аналитически. Также рассмотрена закономерность, определяющая распределение весовых и пороговых значений для второго и третьего слоя полученного многослойного персептрона, на основе которого предложен алгоритм вычисления пороговых и весовых значений многослойного персептрона, а также приведен пример, реализующий данный алгоритм. Также рассмотрены возможные применения полученных сетей для разных задач