Manufacturing high-tech complex products which contain multi-scale complex features without outsourcing considering company's capabilities is quite difficult. Outsourcing some processes to other independent companies is a crucial step toward fabricating a product. To find the most suitable partner company several critical parameters should be considered including company machine-park, skilled personnel, infrastructure, etc. Having comprehensive information about necessary machine tool(s) to outsourcing related manufacturing process is essential. Focusing on Wire Electro Discharge Machining (WEDM) process, the objective of this paper is to introduce a platform to store and analyze information and data about part(s) and machine tools and show out coming results as a list of capable machine tools to produce the desired parts with multi-scale features.

<span style="font-size: 9pt; font-family: 'Times New Roman', serif;">Convolution neural networks (CNN or ConvNet), a deep neural network class inspired by biological processes, are immensely used for image classification or visual imagery. These networks need various parameters or attributes like number of filters, filter size, number of input channels, padding stride and dilation, for doing the required task. In this paper, we focused on the hyperparameter, i.e., filter size. Filter sizes come in various sizes like 3×3, 5×5, and 7×7. We varied the filter sizes and recorded their effects on the models' accuracy. The models' architecture is kept intact and only the filter sizes are varied. This gives a better understanding of the effect of filter sizes on image classification. CIFAR10 and FashionMNIST datasets are used for this study. Experimental results showed the accuracy is inversely proportional to the filter size. The accuracy using 3×3 filters on CIFAR10 and Fashion-MNIST is 73.04% and 93.68%, respectively.</span>

This paper aims at using imperialist competitive algorithm based fuzzy logic (FICA), to control an automatic voltage regulator (A VR) in order to increase the stability and obtain more controllability of the system. For the stabilization of the automatic voltage regulator a proportional-integral-derivative controller (PlD) was used. We applied the FICA, which is the combination of the imperialist competitive algorithm (ICA) and fuzzy logic to determine the optimal coefficients of the proportional integral-derivative controller (PID). The new algorithm solves the main problems of the imperialist competitive algorithm (ICA), which are entrapped in local optimum points and low-speed convergence. This way, the control of the effective parameters of the (ICA) likes as the assimilation coefficient and the cost of colonies is performed with high accuracy and speed. We will show that the results obtained with the proposed intelligent algorithm (FICA), have higher convergence rate and more accuracy in comparison with the other algorithms.

Deep Neural Networks (DNN) in the past few years have revolutionized the computer vision by providing the best results on a large number of problems such as image classification, pattern recognition, and speech recognition. One of the essential models in deep learning used for image classification is convolutional neural networks. These networks can integrate a different number of features or so-called filters in a multi-layer fashion called convolutional layers. These models use convolutional, and pooling layers for feature abstraction and have neurons arranged in three dimensions: Height, Width, and Depth. Filters of 3 different sizes were used like 3×3, 5×5 and 7×7. It has been seen that the accuracy on the training data has been decreased from 100% to 97.8% as we increase the filter size and also the accuracy on the test data set decreases for 3×3 it is 98.7%, for 5×5 it is 98.5%, and for 7×7 it is 97.8%. The loss on the training data and test data per 10 epochs could be seen drastically increasing from 3.4% to 27.6% and 12.5% to 23.02%, respectively. Thus it is clear that using the filters having lesser dimensions is giving less loss than those having more dimensions. However, using the smaller filter size comes with the cost of computational complexity, which is very crucial in the case of larger data sets.

Identification of online hate is the prime concern for natural language processing researchers; social media has augmented this menace by providing a virtual platform for online harassment. This study identifies online harassment using the trolling aggression and cyber-bullying dataset from shared tasks workshop. This work concentrates on extreme pre-processing and ensemble approach for model building; this study also considers the existing algorithms like the random forest, logistic regression, multinomial Naïve Bayes. Logistic regression proves to be more efficient with the highest accuracy of 57.91%. Ensemble bidirectional encoder representation from transformers showed promising results with 62% precision, which is better than most existing models.