The complexity and variety of language included in policy and academic documents make the automatic classification of research papers based on the United Nations Sustainable Development Goals (SDGs) somewhat difficult. Using both pre-trained and contextual word embeddings to increase semantic understanding, this study presents a complete deep learning pipeline combining Bidirectional Long Short-Term Memory (BiLSTM) and Convolutional Neural Network (CNN) architectures which aims primarily to improve the comprehensibility and accuracy of SDG text classification, thereby enabling more effective policy monitoring and research evaluation. Successful document representation via Global Vector (GloVe), Bidirectional Encoder Representations from Transformers (BERT), and FastText embeddings follows our approach, which comprises exhaustive preprocessing operations including stemming, stopword deletion, and ways to address class imbalance. Training and evaluation of the hybrid BiLSTM-CNN model on several benchmark datasets, including SDG-labeled corpora and relevant external datasets like GoEmotion and Ohsumed, help provide a complete assessment of the model’s generalizability. Moreover, this study utilizes zero-shot prompt-based categorization using GPT-3.5/4 and Flan-T5, thereby providing a comprehensive benchmark against current approaches and doing comparative tests using leading models such as Robustly Optimized BERT Pretraining Approach (RoBERTa) and Decoding-enhanced BERT with Disentangled Attention (DeBERTa). Experimental results show that the proposed hybrid model achieves competitive performance due to contextual embeddings, which greatly improve classification accuracy. The study explains model decision processes and improves openness using interpretability techniques, including SHapley Additive exPlanations (SHAP) analysis and attention visualization. These results emphasize the need to incorporate rapid engineering techniques alongside deep learning architectures for effective and interpretable SDG text categorization. With possible effects on more general uses in policy analysis and scientific literature mining, this work offers a scalable and transparent solution for automating the evaluation of SDG research.
The first known use of the term conspiracy theory dated back to the nineteenth century. It is defined as a theory that explains an event or set of circumstances as the result of a secret plot by usually powerful conspirators. It is commonly used, but by no means limited to, extreme political groups. Since the emergence of COVID-19 as a global pandemic in December 2019, the conspiracy theory was present at all stages of the pandemic.
Different ANN architectures of MLP have been trained by BP and used to analyze Landsat TM images. Two different approaches have been applied for training: an ordinary approach (for one hidden layer M-H1-L & two hidden layers M-H1-H2-L) and one-against-all strategy (for one hidden layer (M-H1-1)xL, & two hidden layers (M-H1-H2-1)xL). Classification accuracy up to 90% has been achieved using one-against-all strategy with two hidden layers architecture. The performance of one-against-all approach is slightly better than the ordinary approach
Echocardiography is a widely used imaging technique to examine various cardiac functions, especially to detect the left ventricular wall motion abnormality. Unfortunately the quality of echocardiograph images and complexities of underlying motion captured, makes it difficult for an in-experienced physicians/ radiologist to describe the motion abnormalities in a crisp way, leading to possible errors in diagnosis. In this study, we present a method to analyze left ventricular wall motion, by using optical flow to estimate velocities of the left ventricular wall segments and find relation between these segments motion. The proposed method will be able to present real clinical help to verify the left ventricular wall motion diagnosis.
Sn effect on the phase transformation behavior, microstructure, and micro hardness of equiatomic Ni-Ti shape memory alloy was studied. NiTi and NiTiSn alloys were produced using vacuum induction melting process with alloys composition (50% at. Ni, 50% at.Ti) and (Ni 48% at., Ti 50% at., Sn 2% at.). The characteristics of both alloys were investigated by utilizing Differential Scanning Calorimetry, X- ray Diffraction Analysis, Scanning Electron Microscope, optical microscope and vicker's micro hardness test. The results showed that adding Sn element leads to decrease the phase transformation temperatures evidently. Both alloy samples contain NiTi matrix phase and Ti2Ni secondary phase, but the Ti2Ni phase content dec
... Show MoreThe current environment is witnessing several developments as a result of the changes taking place in all areas of economic, social , political and legal that led to the transformation of the industrial economy , which depends based on quantitative production to a knowledge economy which relies based on information and knowledge , as the central pillar of this economy during the trading of these information and knowledge between all individuals in general and decision makers , in particular, through information and communication technology of computers and the Internet to achieve sustainable human development in the social dimension. &
... Show MoreThe study aims to provide a Suggested model for the application of Virtual Private Network is a tool that used to protect the transmitted data through the Web-based information system, and the research included using case study methodology in order to collect the data about the research area ( Al-Rasheed Bank) by using Visio to design and draw the diagrams of the suggested models and adopting the data that have been collected by the interviews with the bank's employees, and the research used the modulation of data in order to find solutions for the research's problem.
The importance of the study Lies in dealing with one of the vital topics at the moment, namely, how to make the information transmitted via
... Show MoreThe issue of increasing the range covered by a wireless sensor network with restricted sensors is addressed utilizing improved CS employing the PSO algorithm and opposition-based learning (ICS-PSO-OBL). At first, the iteration is carried out by updating the old solution dimension by dimension to achieve independent updating across the dimensions in the high-dimensional optimization problem. The PSO operator is then incorporated to lessen the preference random walk stage's imbalance between exploration and exploitation ability. Exceptional individuals are selected from the population using OBL to boost the chance of finding the optimal solution based on the fitness value. The ICS-PSO-OBL is used to maximize coverage in WSN by converting r
... Show MoreAll modern critical approaches attempt to cover the meanings and overtones of the text, claiming that they are better than others in the analysis and attainment of the intended meanings of the text. The structural approach claims to be able to do so more than any other modern critical approach, as it claimed that it is possible to separate what is read from the reader, on the presumed belief that it is possible to read the text with a zero-memory. However, the studies in criticism of criticism state that each of these approaches is successful in dealing with the text in one or more aspects while failing in one or more aspects. Consequently, the criticism whether the approach possesses the text, or that the text rejects this possession, r
... Show MoreThe reciprocal relationship between the text and the mask in the printed product is one of the most important relationships that frame the level of communication between the appearance and the interior, although it is not cared for by some designers and publishing institutions. Therefore, the problem of research is determined by the following question (What is the dialectical relationship between the text and the mask in the literary books covers design?). The research aims to shed light on this problematic relationship at the level of reception and aesthetics at the same moment. The theoretical framework included two sections: the first (mask and text ... the concept and the mutual relationship), while the second section (trends in the
... Show MoreOne of the most important features of the Amazon Web Services (AWS) cloud is that the program can be run and accessed from any location. You can access and monitor the result of the program from any location, saving many images and allowing for faster computation. This work proposes a face detection classification model based on AWS cloud aiming to classify the faces into two classes: a non-permission class, and a permission class, by training the real data set collected from our cameras. The proposed Convolutional Neural Network (CNN) cloud-based system was used to share computational resources for Artificial Neural Networks (ANN) to reduce redundant computation. The test system uses Internet of Things (IoT) services through our ca
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