Recognition of Arabic Air-Written Letters: Machine Learning, Convolutional Neural Networks, and Optical Character Recognition (OCR) Techniques.

Air writing is one of the essential fields that the world is turning to, which can benefit from the world of the metaverse, as well as the ease of communication between humans and machines. The research literature on air writing and its applications shows significant work in English and Chinese, while little research is conducted in other languages, such as Arabic. To fill this gap, we propose a hybrid model that combines feature extraction with deep learning models and then uses machine learning (ML) and optical character recognition (OCR) methods and applies grid and random search optimization algorithms to obtain the best model parameters and outcomes. Several machine learning methods (e.g., neural networks (NNs), random forest (RF), K-nearest neighbours (KNN), and support vector machine (SVM)) are applied to deep features extracted from deep convolutional neural networks (CNNs), such as VGG16, VGG19, and SqueezeNet. Our study uses the AHAWP dataset, which consists of diverse writing styles and hand sign variations, to train and evaluate the models. Prepossessing schemes are applied to improve data quality by reducing bias. Furthermore, OCR character (OCR) methods are integrated into our model to isolate individual letters from continuous air-written gestures and improve recognition results. The results of this study showed that the proposed model achieved the best accuracy of 88.8% using NN with VGG16.

Introducing new green machining technology to enhance process performance and reduce environmental pollution in the metal processing industry.

The pursuit of enhanced cooling and lubrication methods for machining processes that are energy-efficient, environmentally friendly, and cost-effective is receiving significant attention from both academia and industry. The reduction of CO2 emissions is closely tied to electrical and embodied energy consumption. This study introduces a novel LN2 oil-on-water (LNOoW) cooling/lubrication (lubricooling) approach for the machining of Ti-6Al-4V alloy. Machinability aspects, energy-related aspects, environmental-related aspects, and economic aspects are measured and compared. More specifically, surface quality, electrical energy, cutting forces, and tool wear were measured in machinability aspects. Similarly, specific total energy and specific cumulative Energy Demand (S_CED), specific carbon emission, and production costs were measured to investigate the energy and environmental and economic aspects, respectively. The LNOoW provided the best machinability results compared with other approaches. Result found that LNOoW produced 37.5% better surface quality, removed 159.17% more material, and reduced 50.56% specific cutting energy and 53.63% specific costs as compared to traditional dry cutting conditions. The 39% increment in specific carbon emissions observed in the LN2 oil-on-water (LNOoW) approach in comparison to the dry-cutting method can be mitigated through the implementation of sustainable practices in the production of liquid nitrogen (LN2). The information provided in this study serves as a valuable resource for the development of environmentally friendly machining processes. The study also helps get the sustainable development goals (SDGs) of the United Nations.

What people think about fast food: opinions analysis and LDA modeling on fast food restaurants using unstructured tweets.

With the rise of social media platforms, sharing reviews has become a social norm in today's modern society. People check customer views on social networking sites about different fast food restaurants and food items before visiting the restaurants and ordering food. Restaurants can compete to better the quality of their offered items or services by carefully analyzing the feedback provided by customers. People tend to visit restaurants with a higher number of positive reviews. Accordingly, manually collecting feedback from customers for every product is a labor-intensive process; the same is true for sentiment analysis. To overcome this, we use sentiment analysis, which automatically extracts meaningful information from the data. Existing studies predominantly focus on machine learning models. As a consequence, the performance analysis of deep learning models is neglected primarily and of the deep ensemble models especially. To this end, this study adopts several deep ensemble models including Bi long short-term memory and gated recurrent unit (BiLSTM+GRU), LSTM+GRU, GRU+recurrent neural network (GRU+RNN), and BiLSTM+RNN models using self-collected unstructured tweets. The performance of lexicon-based methods is compared with deep ensemble models for sentiment classification. In addition, the study makes use of Latent Dirichlet Allocation (LDA) modeling for topic analysis. For experiments, the tweets for the top five fast food serving companies are collected which include KFC, Pizza Hut, McDonald's, Burger King, and Subway. Experimental results reveal that deep ensemble models yield better results than the lexicon-based approach and BiLSTM+GRU obtains the highest accuracy of 95.31% for three class problems. Topic modeling indicates that the highest number of negative sentiments are represented for Subway restaurants with high-intensity negative words. The majority of the people (49%) remain neutral regarding the choice of fast food, 31% seem to like fast food while the rest (20%) dislike fast food.