Evaluating the efficiency of the motorway network in Anbar Governorate

The subject of car transport is of special importance, therefore studies focused on analyzing car transport networks to detect problems that are reflected in the efficiency of traffic, since most of the current car road networks do not meet the requirements of population activities. Therefore, the researchers aim from this research to analyze the efficiency of motorways in Al-Anbar Governorate to reveal the problems that the network suffers from, whether it is related to winding road paths that are sleepy along the roads or the difference in accessibility and difficulty of communication between the nodes as well as the imbalance of the network density with the distribution of cities and administrative units In the province.Therefore, the research problem was identified by a scientific question about the efficiency of the road network of cars in Anbar Governorate, Anbar Governorate. The research hypothesis was formulated according to which the road network of cars in Anbar province is inefficient, as it suffers from a number of problems that made it not meet the requirements of economic and social activities and the requirements of developmen

Unmasking large language models by means of OpenAI GPT-4 and Google AI: A deep instruction-based analysis

Large Language Models (LLMs) have become a hot topic in AI due to their ability to mimic human conversation. This study compares the open artificial intelligence generative pretrained transformer-4 (GPT-4) model, based on the (GPT), and Google's artificial intelligence (AI), which is based on the Bidirectional Encoder Representations from Transformers (BERT) framework in terms of the defined capabilities and the built-in architecture. Both LLMs are prominent in AI applications. First, eight different capabilities were identified to evaluate these models, i.e. translation accuracy, text generation, factuality, creativity, intellect, deception avoidance, sentiment classification, and sarcasm detection. Next, each capability was assessed using instructions. Additionally, a categorized LLM evaluation system has been developed by means of using ten research questions per category based on this paper's main contributions from a prompt engineering perspective. It should be highlighted that GPT-4 and Google AI successfully answered 85 % and 68,7 % of the study prompts, respectively. It has been noted that GPT-4 better understands prompts than Google AI, even with verbal flaws, and tolerates grammatical errors. Moreover, the GPT-4 based approach was more precise, accurate, and succinct than Google AI, which was sometimes verbose and less realistic. While GPT-4 beats Google AI in terms of translation accuracy, text generation, factuality, intellectuality, creativity, and deception avoidance, Google AI outperforms the former when considering sarcasm detection. Both sentiment classification models did work properly. More importantly, a human panel of judges was used to assess and evaluate the model comparisons. Statistical analysis of the judges' ratings revealed more robust results based on examining the specific uses, limitations, and expectations of both GPT-4 and Google AI-based approaches. Finally, the two approaches' transformers, parameter sizes, and attention mechanisms have been examined.</p

Tensile and Morphology Properties of PLA/MMT-TiO&lt;sub&gt;2&lt;/sub&gt; Bionanocomposites

The aim of this study is to produce PLA nanocomposites by solvent casting incorporating Montmorillonite nanoclays (MMT) and titanium dioxide (TiO2) nanoparticles. The effects of difference loadings of MMT in PLA and different loadings of TiO2 on mechanical and morphology properties were studied. The nanocomposites were prepared by solvent casting at different loadings of MMT (0, 2, 4, 6 and 8 wt %) and different loadings of TiO2 (1 and 3 wt %) respectively. The properties such as tensile properties (tensile strength, elongation at break, and modulus of elasticity) and morphology were determined. The results indicate that 4 wt% of MMT loading produced the best tensile properties. However, the incorporation of TiO2 showed an improvement in the modulus of elasticity of PLA/MMT nanocomposites mainly at 1 wt % loading of TiO2.</jats:p

PLA/MMT-TiO&lt;sub&gt;2&lt;/sub&gt; Bionanocomposites: Chemical Structure and Surface Wettability

Polylactic Acid (PLA) has been used widely in packaging application because of its biodegradability. The aim of this study is to produce PLA nanocomposites by solvent casting incorporating montmorillonite nanoclays (MMT) and titanium dioxide (TiO2) nanoparticles. The effect of different loadings of MMT in PLA and different loadings of TiO2 on chemical structure and surface wettability were studied. The nanocomposites were prepared by solvent casting at different loadings of MMT (0, 2, 4, 6 wt %) and different loadings of TiO2 (1 and 3 wt %) respectively. The chemical structure and surface wettability were determined. The absorption peaks in the range of 3550-3200 cm-1 had increased after incorporating of TiO2 and it indicated that there is the presence of stretching vibration of O-H groups. Moreover, increasing the percentage of TiO2 mass in the nanocomposites decreased the contact angle with water which led to increasing the wettability of the nanocomposites.</jats:p

Fuzzy Evaluation and Benchmarking Framework for Robust Machine Learning Model in Real-Time Autism Triage Applications

Abstract In the context of autism spectrum disorder (ASD) triage, the robustness of machine learning (ML) models is a paramount concern. Ensuring the robustness of ML models faces issues such as model selection, criterion importance, trade-offs, and conflicts in the evaluation and benchmarking of ML models. Furthermore, the development of ML models must contend with two real-time scenarios: normal tests and adversarial attack cases. This study addresses this challenge by integrating three key phases that bridge the domains of machine learning and fuzzy multicriteria decision-making (MCDM). First, the utilized dataset comprises authentic information, encompassing 19 medical and sociodemographic features from 1296 autistic patients who received autism diagnoses via the intelligent triage method. These patients were categorized into one of three triage labels: urgent, moderate, or minor. We employ principal component analysis (PCA) and two algorithms to fuse a large number of dataset features. Second, this fused dataset forms the basis for rigorously testing eight ML models, considering normal and adversarial attack scenarios, and evaluating classifier performance using nine metrics. The third phase developed a robust decision-making framework that encompasses the creation of a decision matrix (DM) and the development of the 2-tuple linguistic Fermatean fuzzy decision by opinion score method (2TLFFDOSM) for benchmarking multiple-ML models from normal and adversarial perspectives, accomplished through individual and external group aggregation of ranks. Our findings highlight the effectiveness of PCA algorithms, yielding 12 principal components with acceptable variance. In the external ranking, logistic regression (LR) emerged as the top-performing ML model in terms of the 2TLFFDOSM score (1.3370). A comparative analysis with five benchmark studies demonstrated the superior performance of our framework across all six checklist comparison points

Fuzzy Decision-Making Framework for Evaluating Hybrid Detection Models of Trauma Patients

This study introduces a new multi-criteria decision-making (MCDM) framework to evaluate trauma injury detection models in intensive care units (ICUs). This research addresses the challenges associated with diverse machine learning (ML) models, inconsistencies, conflicting priorities, and the importance of metrics. The developed methodology consists of three phases: dataset identification and pre-processing, hybrid model development, and an evaluation/benchmarking framework. Through meticulous pre-processing, the dataset is tailored to focus on adult trauma patients. Forty hybrid models were developed by combining eight ML algorithms with four filter-based feature-selection methods and principal component analysis (PCA) as a dimensionality reduction method, and these models were evaluated using seven metrics. The weight coefficients for these metrics are determined using the 2-tuple Linguistic Fermatean Fuzzy-Weighted Zero-Inconsistency (2TLF-FWZIC) method. The Vlsekriterijumska Optimizcija I Kompromisno Resenje (VIKOR) approach is applied to rank the developed models. According to 2TLF-FWZIC, classification accuracy (CA) and precision obtained the highest importance weights of 0.2439 and 0.1805, respectively, while F1, training time, and test time obtained the lowest weights of 0.1055, 0.0886, and 0.1111, respectively. The benchmarking results revealed the following top-performing models: the Gini index with logistic regression (GI-LR), the Gini index with a decision tree (GI_DT), and the information gain with a decision tree (IG_DT), with VIKOR Q score values of 0.016435, 0.023804, and 0.042077, respectively. The proposed MCDM framework is assessed and examined using systematic ranking, sensitivity analysis, validation of the best-selected model using two unseen trauma datasets, and mode explainability using the SHapley Additive exPlanations (SHAP) method. We benchmarked the proposed methodology against three other benchmark studies and achieved a score of 100% across six key areas. The proposed methodology provides several insights into the empirical synthesis of this study. It contributes to advancing medical informatics by enhancing the understanding and selection of trauma injury detection models for ICUs.</p