It has been imperative to study and stabilize cohesive soils for use in the construction of pavement subgrade and compacted landfill liners considering their unconfined compressive strength (UCS). As long as natural cohesive soil falls below 200 kN/m2 in strength, there is a structural necessity to improve its mechanical property to be suitable for the intended structural purposes. Subgrades and landfills are important environmental geotechnics structures needing the attention of engineering services due to their role in protecting the environment from associated hazards. In this research project, a comparative study and suitability assessment of the best analysis has been conducted on the behavior of the unconfined compressive strength (UCS) of cohesive soil reconstituted with cement and lime and mechanically stabilized at optimal compaction using multiple ensemble-based machine learning classification and symbolic regression techniques. The ensemble-based ML classification techniques are the gradient boosting (GB), CN2, naïve bayes (NB), support vector machine (SVM), stochastic gradient descent (SGD), k-nearest neighbor (K-NN), decision tree (Tree) and random forest (RF) and the artificial neural network (ANN) and response surface methodology (RSM) to estimate the (UCS, MPa) of cohesive soil stabilized with cement and lime. The considered inputs were cement (C), lime (Li), liquid limit (LL), plasticity index (PI), optimum moisture content (OMC), and maximum dry density (MDD). A total of 190 mix entries were collected from experimental exercises and partitioned into 74-26% train-test dataset. At the end of the model exercises, it was found that both GB and K-NN models showed the same excellent accuracy of 95%, while CN2, SVM, and Tree models shared the same level of accuracy of about 90%. RF and SGD models showed fair accuracy level of about 65-80% and finally (NB) badly producing an unacceptable low accuracy of 13%. The ANN and the RSM also showed closely matched accuracy to the SVM and the Tree. Both of correlation matrix and sensitivity analysis indicated that UCS is greatly affected by MDD, then the consistency limits and cement content, and lime content comes in the third place while the impact of (OMC) is almost neglected. This outcome can be applied in the field to obtain optimal compacted for a lime reconstituted soil considering the almost negligible impact of compactive moisture.

Melanoma, one of the deadliest types of skin cancer, accounts for thousands of fatalities globally. The bluish, blue-whitish, or blue-white veil (BWV) is a critical feature for diagnosing melanoma, yet research into detecting BWV in dermatological images is limited. This study utilizes a non-annotated skin lesion dataset, which is converted into an annotated dataset using a proposed imaging algorithm (color threshold techniques) on lesion patches based on color palettes. A Deep Convolutional Neural Network (DCNN) is designed and trained separately on three individual and combined dermoscopic datasets, using custom layers instead of standard activation function layers. The model is developed to categorize skin lesions based on the presence of BWV. The proposed DCNN demonstrates superior performance compared to the conventional BWV detection models across different datasets. The model achieves a testing accuracy of 85.71 % on the augmented PH2 dataset, 95.00 % on the augmented ISIC archive dataset, 95.05 % on the combined augmented (PH2+ISIC archive) dataset, and 90.00 % on the Derm7pt dataset. An explainable artificial intelligence (XAI) algorithm is subsequently applied to interpret the DCNN\'s decision-making process about the BWV detection. The proposed approach, coupled with XAI, significantly improves the detection of BWV in skin lesions, outperforming existing models and providing a robust tool for early melanoma diagnosis.

The recent advancements in autonomous driving come with the associated cybersecurity issue of compromising networks of autonomous vehicles (AVs), motivating the use of AI models for detecting anomalies on these networks. In this context, the usage of explainable AI (XAI) for explaining the behavior of these anomaly detection AI models is crucial. This work introduces a comprehensive framework to assess black-box XAI techniques for anomaly detection within AVs, facilitating the examination of both global and local XAI methods to elucidate the decisions made by XAI techniques that explain the behavior of AI models classifying anomalous AV behavior. By considering six evaluation metrics (descriptive accuracy, sparsity, stability, efficiency, robustness, and completeness), the framework evaluates two well-known black-box XAI techniques, SHAP and LIME, involving applying XAI techniques to identify primary features crucial for anomaly classification, followed by extensive experiments assessing SHAP and LIME across the six metrics using two prevalent autonomous driving datasets, VeReMi and Sensor. This study advances the deployment of black-box XAI methods for real-world anomaly detection in autonomous driving systems, contributing valuable insights into the strengths and limitations of current black-box XAI methods within this critical domain.

BACKGROUND: Machine learning techniques have been shown to be efficient in identifying health misinformation, but the results may not be trusted unless they can be justified in a way that is understandable.
OBJECTIVE: This study aimed to provide a new criteria-based system to assess and justify health news quality. Using a subset of an existing set of criteria, this study compared the feasibility of 2 alternative methods for adding interpretability. Both methods used classification and highlighting to visualize sentence-level evidence.
METHODS: A total of 3 out of 10 well-established criteria were chosen for experimentation, namely whether the health news discussed the costs of the intervention (the cost criterion), explained or quantified the harms of the intervention (the harm criterion), and identified the conflicts of interest (the conflict criterion). The first step of the experiment was to automate the evaluation of the 3 criteria by developing a sentence-level classifier. We tested Logistic Regression, Naive Bayes, Support Vector Machine, and Random Forest algorithms. Next, we compared the 2 visualization approaches. For the first approach, we calculated word feature weights, which explained how classification models distill keywords that contribute to the prediction; then, using the local interpretable model-agnostic explanation framework, we selected keywords associated with the classified criterion at the document level; and finally, the system selected and highlighted sentences with keywords. For the second approach, we extracted sentences that provided evidence to support the evaluation result from 100 health news articles; based on these results, we trained a typology classification model at the sentence level; and then, the system highlighted a positive sentence instance for the result justification. The number of sentences to highlight was determined by a preset threshold empirically determined using the average accuracy.
RESULTS: The automatic evaluation of health news on the cost, harm, and conflict criteria achieved average area under the curve scores of 0.88, 0.76, and 0.73, respectively, after 50 repetitions of 10-fold cross-validation. We found that both approaches could successfully visualize the interpretation of the system but that the performance of the 2 approaches varied by criterion and highlighting the accuracy decreased as the number of highlighted sentences increased. When the threshold accuracy was ≥75%, this resulted in a visualization with a variable length ranging from 1 to 6 sentences.
CONCLUSIONS: We provided 2 approaches to interpret criteria-based health news evaluation models tested on 3 criteria. This method incorporated rule-based and statistical machine learning approaches. The results suggested that one might visually interpret an automatic criterion-based health news quality evaluation successfully using either approach; however, larger differences may arise when multiple quality-related criteria are considered. This study can increase public trust in computerized health information evaluation.

Alkali Activated Slag Concrete (AASC) has been a sustained research activity over the past two decades. Its promising characteristics and being environmentally friendly compared to Ordinary Portland Cement made AASC of exceptional interest. However, there is still no firm mix design, for the AASC, that can provide desirable fresh and hardened properties based on the composition of the binder and activator. This research specifically aims to investigate the affecting parameters on the slump and compressive strength of alkali-activated slag/lime-based concrete and provide a better understanding of the potential reasons for these characteristics. The experimental program consisted of two stages; the first stage studied the effect of different binder and activator compositions, and the second stage studied the water-to-binder ratio and binder content effects on the slump and compressive strength of alkali-activated slag/lime-based concrete. The binder and activator compositions were defined through two main parameters, the hybrid factor (HF = CaO/Si2O + Al2O3) and the solution modulus (Ms = SiO2/Na2O). The compressive strength, initial slump, and slump loss were measured to evaluate the different mixes and specify the optimum range of compositions. Based on the studied parameters, the effective range to achieve desirable slump and concrete compressive strength is from HF 0.6 up to 0.8 at Ms 1.5, this would achieve a compressive strength of more than 30 MPa and a slump of 100 mm after 90 min.

UNASSIGNED: Cancer is positioned as a major disease, particularly for middle-aged people, which remains a global concern that can develop in the form of abnormal growth of body cells at any place in the human body. Cervical cancer, often known as cervix cancer, is cancer present in the female cervix. In the area where the endocervix (upper two-thirds of the cervix) and ectocervix (lower third of the cervix) meet, the majority of cervical cancers begin. Despite an influx of people entering the healthcare industry, the demand for machine learning (ML) specialists has recently outpaced the supply. To close the gap, user-friendly applications, such as H2O, have made significant progress these days. However, traditional ML techniques handle each stage of the process separately; whereas H2O AutoML can automate a major portion of the ML workflow, such as automatic training and tuning of multiple models within a user-defined timeframe.
UNASSIGNED: Thus, novel H2O AutoML with local interpretable model-agnostic explanations (LIME) techniques have been proposed in this research work that enhance the predictability of an ML model in a user-defined timeframe. We herein collected the cervical cancer dataset from the freely available Kaggle repository for our research work. The Stacked Ensembles approach, on the other hand, will automatically train H2O models to create a highly predictive ensemble model that will outperform the AutoML Leaderboard in most instances. The novelty of this research is aimed at training the best model using the AutoML technique that helps in reducing the human effort over traditional ML techniques in less amount of time. Additionally, LIME has been implemented over the H2O AutoML model, to uncover black boxes and to explain every individual prediction in our model. We have evaluated our model performance using the findprediction() function on three different idx values (i.e., 100, 120, and 150) to find the prediction probabilities of two classes for each feature. These experiments have been done in Lenovo core i7 NVidia GeForce 860M GPU laptop in Windows 10 operating system using Python 3.8.3 software on Jupyter 6.4.3 platform.
UNASSIGNED: The proposed model resulted in the prediction probabilities depending on the features as 87%, 95%, and 87% for class \'0\' and 13%, 5%, and 13% for class \'1\' when idx_value=100, 120, and 150 for the first case; 100% for class \'0\' and 0% for class \'1\', when idx_value= 10, 12, and 15 respectively. Additionally, a comparative analysis has been drawn where our proposed model outperforms previous results found in cervical cancer research.

Background: Tick-borne diseases are an emerging threat to public health throughout the temperate world, leading to a growing field of research aimed at developing and testing intervention strategies for reducing human-tick encounters or prevalence of infection in ticks. Various wide-spectrum chemical acaricides have proven effective for controlling tick populations, but many of these have potential deleterious side-effects on health and the environment. In addition to chemical acaricides, certain compounds such as diatomaceous earth have been shown to have physical acaricidal properties. We hypothesized that dolomitic lime (CaMg(CO3)2, a corrosive, desiccant mineral that is already used extensively in agricultural and forestry contexts to balance the pH of soils, may affect ticks\' locomotory activity, habitat position, or survival and that this should manifest as a reduction in the number of questing ticks collected by dragging. Objective: This study aimed to formally assess this hypothesis in a controlled laboratory setting. Methods: We carried out a microcosm experiment, with one control and three treated microcosm trays, each replicating the natural substrate characterizing I. scapularis habitat in northeastern North America. Each tray was infested with 200 living larvae and 50 nymphs, and then treated with 0 (control), 50, 100, or 500 g/m2 of lime powder. Ticks were collected by microdragging 24 and 72 h postliming. Results: Efficacy of liming at reducing the number of collected questing ticks ranged from 87% to 100% for larvae and 0% to 69% for nymphs 24 h postliming and from 91% to 93% for larvae and -47% to 65% for nymphs 72 postliming. Conclusion: This study provides the first experimental evidence of the potential efficacy of liming for impairing activity of questing immature ticks. Given that lime is a low-cost material, that methods for widespread application in deciduous woodlands already exist, and that it has been documented as having a limited negative impact on the environment, further assessment of lime application as a public health risk reduction intervention for tick-borne diseases is warranted.

This investigation examines the effect of clamshell ash (CSA) and lime additives on the physico-mechanical characteristics of kaolinite clay soil stabilized at the optimum silica fume content. Laboratory tests were performed to assess plasticity, shrink-swell characteristics, compaction characteristics, unconfined compressive strength (UCS), shear strength characteristics, mineralogical and morphological microstructure characteristics of stabilized soil specimens. The kaolinite clay soil was stabilized at its optimum silica fume content (6%) to produce the highest strength and was altered with three non-identical proportions of clamshell ash and lime (3%-9%). Cylindrical soil specimens, 76 mm in height and 38 mm in diameter, were moulded and treated for curing periods of 1, 7, 14, and 30 days to examine the strength of the altered soil. The findings revealed that, adding clamshell ash and lime significantly alters the plasticity, shrink-swell, maximum dry unit weights, and optimum moisture contents of the silica fume-stabilized soil. In terms of strength, the beneficial effects of CSA and lime additives were found to be more significant with more extended curing periods. Incremental increases in curing periods resulted in further enhancements in UCS, cohesion, and internal friction angle, indicating continued strength development over time. Microstructural analysis using field emission scanning electron microscopy and X-ray diffraction provided insights into the interparticle bonding mechanisms and microstructural changes induced by the addition of CSA and lime. The emergence of cementitious phases and pozzolanic responses between soil particles and stabilizers contributed to the densification and strengthening of the stabilized soil matrix. The findings of this study provide valuable insights into the potential of clamshell ash and lime additives to enhance the engineering properties of kaolinite clay soil stabilized with silica fume. These results have implications for sustainable soil stabilization practices, offering a promising approach to improve the performance of soils for various engineering applications, including construction and geotechnical projects.

Local Interpretability Model-agnostic Explanations (LIME) is a well-known post-hoc technique for explaining black-box models. While very useful, recent research highlights challenges around the explanations generated. In particular, there is a potential lack of stability, where the explanations provided vary over repeated runs of the algorithm, casting doubt on their reliability. This paper investigates the stability of LIME when applied to multivariate time series classification. We demonstrate that the traditional methods for generating neighbours used in LIME carry a high risk of creating \'fake\' neighbours, which are out-of-distribution in respect to the trained model and far away from the input to be explained. This risk is particularly pronounced for time series data because of their substantial temporal dependencies. We discuss how these out-of-distribution neighbours contribute to unstable explanations. Furthermore, LIME weights neighbours based on user-defined hyperparameters which are problem-dependent and hard to tune. We show how unsuitable hyperparameters can impact the stability of explanations. We propose a two-fold approach to address these issues. First, a generative model is employed to approximate the distribution of the training data set, from which within-distribution samples and thus meaningful neighbours can be created for LIME. Second, an adaptive weighting method is designed in which the hyperparameters are easier to tune than those of the traditional method. Experiments on real-world data sets demonstrate the effectiveness of the proposed method in providing more stable explanations using the LIME framework. In addition, in-depth discussions are provided on the reasons behind these results.

The local interpretable model-agnostic explanation (LIME) method was used to interpret two machine learning models of compounds penetrating the blood-brain barrier. The classification models, Random Forest, ExtraTrees, and Deep Residual Network, were trained and validated using the blood-brain barrier penetration dataset, which shows the penetrability of compounds in the blood-brain barrier. LIME was able to create explanations for such penetrability, highlighting the most important substructures of molecules that affect drug penetration in the barrier. The simple and intuitive outputs prove the applicability of this explainable model to interpreting the permeability of compounds across the blood-brain barrier in terms of molecular features. LIME explanations were filtered with a weight equal to or greater than 0.1 to obtain only the most relevant explanations. The results showed several structures that are important for blood-brain barrier penetration. In general, it was found that some compounds with nitrogenous substructures are more likely to permeate the blood-brain barrier. The application of these structural explanations may help the pharmaceutical industry and potential drug synthesis research groups to synthesize active molecules more rationally.