This study aims to conduct an exhaustive comparison between the performance of human translators and artificial intelligence-powered machine translation systems, specifically examining the top three systems: Spider-AI, Metacate, and DeepL. A variety of texts from distinct categories were evaluated to gain a profound understanding of the qualitative differences, as well as the strengths and weaknesses, between human and machine translations. The results demonstrated that human translation significantly outperforms machine translation, with larger gaps in literary texts and texts characterized by high linguistic complexity. However, the performance of machine translation systems, particularly DeepL, has improved and in some contexts approached that of human performance. The distinct performance differences across various text categories suggest the potential for developing systems tailored to specific fields. These findings indicate that machine translation has the capacity to bridge the gap in translation productivity inefficiencies inherent in human translation, yet it still falls short of fully replicating human capabilities. In the future, a combination of human translation and machine translation systems is likely to be the most effective approach for leveraging the strengths of each and ensuring optimal performance. This study contributes empirical support and findings that can aid in the development and future research in the field of machine translation and translation studies. Despite some limitations associated with the corpus used and the systems analysed, where the focus was on English and texts within the field of machine translation, future studies could explore more extensive linguistic sampling and evaluation of human effort. The collaborative efforts of specialists in artificial intelligence, translation studies, linguistics, and related fields can help achieve a world where linguistic diversity no longer poses a barrier.
There is various human biometrics used nowadays, one of the most important of these biometrics is the face. Many techniques have been suggested for face recognition, but they still face a variety of challenges for recognizing faces in images captured in the uncontrolled environment, and for real-life applications. Some of these challenges are pose variation, occlusion, facial expression, illumination, bad lighting, and image quality. New techniques are updating continuously. In this paper, the singular value decomposition is used to extract the features matrix for face recognition and classification. The input color image is converted into a grayscale image and then transformed into a local ternary pattern before splitting the image into
... Show MoreGod, may He be glorified and exalted be He, has given every human being the right to life and a dignified life, and has warned against transgression against any of its sanctities without a legitimate right. No one, regardless of his status or authority, can deprive a person of his rights that the Sharia came to preserve, and whoever does that has declared all people to war, as all humanity is in solidarity. In raising the hand that is simplified to harm a person and oppress him unjustly and exalted in the land.
If this is the case, the Sharia came to establish the right of people, groups and individuals, to defend their sanctities, preserve their security, recover their usurped rights, repel the aggression of the aggressors, and oppre
This study explores the role of nanomaterials in the performance of asphalt binders and mixtures. Two commonly available nanomaterials, i.e., nanosilica (NS) and nanoalumina (NA), were used at contents of 0%, 2%, 4%, 6%, and 8% by weight of asphalt binder. A set of experiments was carried out at the binder level to investigate properties such as penetration, softening point, aging-related mass loss, nanomaterial dispersion (storage stability), and workability (rotational viscosity). In addition, the suitability of NS and NS was also assessed through the testing of nanomodified asphalt mixtures, which focused on Marshall properties, the resilient modulus, moisture susceptibility, permanent deformation, and fatigue resistance. The findings in
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The research aims to determine the impact of the strategy performance evaluation and of the Standards (leadership, people, knowledge, processes, financial) in the achievement of organizational effectiveness in accordance with the dimensions (planning and setting goals, Exploitation of the Environment, achieve the goals, the ability to adapt, information management and communications) and the relationship between them, the problem of the research in the growing interest in the process of performance evaluation for organizations, the erroneous belief that the performance evaluation activity is useful, and the fact that performance evaluation process is one of the main tasks of the work of the Office of the Inspecto
... Show MoreThis paper details the process of designing, analysing, manufacturing, and testing an integrated solid-state hydrogen storage system. Analysis is performed to optimise flow distribution and pressure drop through the channels, and experimental investigations compare the effects of profile shape on the overall power output from the fuel cell. The storing of hydrogen is given much attention in the selection of a storage medium, and the effect of a cooling system to reduce the recharging time of the hydrogen storage vessel. The PTFE seal performed excellently, holding pressure over 60 bar, despite requiring changing each time the cell is opened. The assembly of the vessel was simple and straightforward, and there was no indication of pressure
... Show MoreDeep learning (DL) plays a significant role in several tasks, especially classification and prediction. Classification tasks can be efficiently achieved via convolutional neural networks (CNN) with a huge dataset, while recurrent neural networks (RNN) can perform prediction tasks due to their ability to remember time series data. In this paper, three models have been proposed to certify the evaluation track for classification and prediction tasks associated with four datasets (two for each task). These models are CNN and RNN, which include two models (Long Short Term Memory (LSTM)) and GRU (Gated Recurrent Unit). Each model is employed to work consequently over the two mentioned tasks to draw a road map of deep learning mod
... Show MoreCerium (III), Neodymium (III) and Samarium (III) Complexes existent a wide range of implementation that stretch from their play in the medicinal and pharmaceutical area because of their major significant pharmacological characteristic such as antifungal, anti-cancer, anti-bacterial ,anti-human immunodeficiency virus ,antineoplastic, anti-inflammation,inhibition corrosion,in some industrial (polymers, Azo dye).It is likely to open avenuesto research among various disciplines such as physics, electronics, chemistry and materials science by these complexes that contain exquisitely designed organic molecules.This paper reviews the definition, importance and various applications of Cerium (III), Neodymium (III) and Samarium (III) Complexes anddi
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