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Momentous increase in the popularity of explainable machine learning models coupled with the dramatic increase in the use of synthetic data facilitates us to develop a cost-efficient machine learning model for fast intrusion detection and prevention at frontier areas using Wireless Sensor Networks (WSNs). The performance of any explainable machine learning model is driven by its hyperparameters. Several approaches have been developed and implemented successfully for optimising or tuning these hyperparameters for skillful predictions. However, the major drawback of these techniques, including the manual selection of the optimal hyperparameters, is that they depend highly on the problem and demand application-specific expertise. In this paper, we introduced Automated Machine Learning (AutoML) model to automatically select the machine learning model (among support vector regression, Gaussian process regression, binary decision tree, bagging ensemble learning, boosting ensemble learning, kernel regression, and linear regression model) and to automate the hyperparameters optimisation for accurate prediction of numbers of k -barriers for fast intrusion detection and prevention using Bayesian optimisation. To do so, we extracted four synthetic predictors, namely, area of the region, sensing range of the sensor, transmission range of the sensor, and the number of sensors using Monte Carlo simulation. We used 80% of the datasets to train the models and the remaining 20% for testing the performance of the trained model. We found that the Gaussian process regression performs prodigiously and outperforms all the other considered explainable machine learning models with correlation coefficient (R = 1), root mean square error (RMSE = 0.007), and bias = − 0.006. Further, we also tested the AutoML performance on a publicly available intrusion dataset, and we observed a similar performance. This study will help the researchers accurately predict the required number of k -barriers for fast intrusion detection and prevention.

The dramatic increase in the computational facilities integrated with the explainable machine learning algorithms allows us to do fast intrusion detection and prevention at border areas using Wireless Sensor Networks (WSNs). This study proposed a novel approach to accurately predict the number of barriers required for fast intrusion detection and prevention. To do so, we extracted four features through Monte Carlo simulation: area of the Region of Interest (RoI), sensing range of the sensors, transmission range of the sensor, and the number of sensors. We evaluated feature importance and feature sensitivity to measure the relevancy and riskiness of the selected features. We applied log transformation and feature scaling on the feature set and trained the tuned Support Vector Regression (SVR) model (i.e., LT-FS-SVR model). We found that the model accurately predicts the number of barriers with a correlation coefficient (R) = 0.98, Root Mean Square Error (RMSE) = 6.47, and bias = 12.35. For a fair evaluation, we compared the performance of the proposed approach with the benchmark algorithms, namely, Gaussian Process Regression (GPR), Generalised Regression Neural Network (GRNN), Artificial Neural Network (ANN), and Random Forest (RF). We found that the proposed model outperforms all the benchmark algorithms.

‘Diaspora' is a term that has undergone transformation throughout history. In its original sense, it referred to the Jewish population residing outside of their native land in Palestine. In its current usage, it encompasses any dispersion of people or linguistic and cultural phenomena originating from a localized source. The transnational narrative of Gun Island parallels the dispersion of both human and non-human animals caused by human-induced climate change. Humans migrate for various reasons, including environmental factors and economic opportunities, while non-human animals migrate solely due to pervasive climate change in the Anthropocene. This study argues that the novel invites readers to rethink the global perspective of diaspora from a more inclusive and ecological standpoint, recognizing that nonhuman animals also exhibit some features common to human diaspora groups. Examples include displacement from original habitats, encountering challenges in new environments, and bearing cultural or ecological relevance for source regions.

This work intends to give a method for the automatic superimposition of facial and cranium anatomical images coupled with integrating jaw movement. Using an automated alignment method will help to raise the accuracy and efficiency of the forensic face reconstruction procedure. Given their reliance on human participation, conventional approaches are prone to subjectivity and errors. Differential Genetic Algorithm (DGA) accounts for mandibular articulation and allows for exact alignment of skull and facial images, therefore reaching strong optimization. Forensic face reconstruction is a crucial field of research for the anthropological sciences and the criminal justice system. Although modern methods offer benefits, their dependability is not always guaranteed since they rely on human interaction. By using a DGA, the proposed approach overcomes this limit and boosts efficiency. Differential evolution and genetic algorithms, which can capture all the special features required for perfect face reconstruction, help to improve the alignment. This study aims to enhance the alignment parameters between image graphs of the skull and visage, and it also considers mandibular articulation using a DGA. Genetic operators and differential evolution support the program in efficiently investigating the domain of feasible solutions. Whether the superimposed images properly depict the intended face traits is found rather successfully by means of the fitness function. The proposed DGA has been proven to match images of the face and the cranium exactly by including the articulation of the jaw. The automatic overlay shows the possibilities of the forensic techniques since it generates results equal to or better than those acquired by hand.

Aim/Purpose: This work intends to give a method for the automatic superimposition of facial and cranium anatomical images coupled with integrating jaw movement. Using an automated alignment method will help to raise the accuracy and efficiency of the forensic face reconstruction procedure. Given their reliance on human participation, conventional approaches are prone to subjectivity and errors. Differential Genetic Algorithm (DGA) accounts for mandibular articulation and allows for exact alignment of skull and facial images, therefore reaching strong optimization. Background: Forensic face reconstruction is a crucial field of research for the anthropological sciences and the criminal justice system. Although modern methods offer benefits, their dependability is not always guaranteed since they rely on human interaction. By using a DGA, the proposed approach overcomes this limit and boosts efficiency. Differential evolution and genetic algorithms, which can capture all the special features required for perfect face reconstruction, help to improve the alignment. Methodology: This study aims to enhance the alignment parameters between image graphs of the skull and visage, and it also considers mandibular articulation using a DGA. Genetic operators and differential evolution support the program in efficiently investigating the domain of feasible solutions. Whether the superimposed images properly depict the intended face traits is found rather successfully by means of the fitness function. Contribution: This work offers a suitable solution for progressive forensic facial reconstruction using a technique based on DGA for automated overlay. An improved level of accuracy and realism is shown by comparing the obtained result with other existing approaches and methods on mandibular articulation in the reconstructed facial images. Findings: The proposed DGA has been proven to match images of the face and the cranium exactly by including the articulation of the jaw. The automatic overlay shows the possibilities of the forensic techniques since it generates results equal to or better than those acquired by hand. Recommendation for Researchers: Scholars should improve the proposed method by means of more dataset integration and genetic algorithm configuration change. Future Research: In future research, this work can be enhanced using several deep learning algorithms to achieve better accuracy and performance.

Wireless Sensor Networks (WSNs) is a promising technology with enormous applications in almost every walk of life. One of the crucial applications of WSNs is intrusion detection and surveillance at the border areas and in the defense establishments. The border areas are stretched in hundreds to thousands of miles, hence, it is not possible to patrol the entire border region. As a result, an enemy may enter from any point absence of surveillance and cause the loss of lives or destroy the military establishments. WSNs can be a feasible solution for the problem of intrusion detection and surveillance at the border areas. Detection of an enemy at the border areas and nearby critical areas such as military cantonments is a time-sensitive task as a delay of few seconds may have disastrous consequences. Therefore, it becomes imperative to design systems that are able to identify and detect the enemy as soon as it comes in the range of the deployed system. In this paper, we have proposed a deep learning architecture based on a fully connected feed-forward Artificial Neural Network (ANN) for the accurate prediction of the number of k-barriers for fast intrusion detection and prevention. We have trained and evaluated the feed-forward ANN model using four potential features, namely area of the circular region, sensing range of sensors, the transmission range of sensors, and the number of sensor for Gaussian and uniform sensor distribution. These features are extracted through Monte Carlo simulation. In doing so, we found that the model accurately predicts the number of k-barriers for both Gaussian and uniform sensor distribution with correlation coefficient (R = 0.78) and Root Mean Square Error (RMSE = 41.15) for the former and R = 0.79 and RMSE = 48.36 for the latter. Further, the proposed approach outperforms the other benchmark algorithms in terms of accuracy and computational time complexity.

[...]Edward himself has fled an unhappy marriage and domineering parents. [...]her lover 's family reject her, citing Chinese traditional norms that forbid a pregnant woman from becoming a bride. [...]Lucia is fated to adopt the life of a courtesan in order to earn a living and she starts her own courtesan house where her daughter Violet is born.

Aims/hypothesisFew studies examine the association between age at diagnosis and subsequent complications from type 2 diabetes. This paper aims to summarise the risk of mortality, macrovascular complications and microvascular complications associated with age at diagnosis of type 2 diabetes.MethodsData were sourced from MEDLINE and All EBM (Evidence Based Medicine) databases from inception to July 2018. Observational studies, investigating the effect of age at diabetes diagnosis on macrovascular and microvascular diabetes complications in adults with type 2 diabetes were selected according to pre-specified criteria. Two investigators independently extracted data and evaluated all studies. If data were not reported in a comparable format, data were obtained from authors, presented as minimally adjusted ORs (and 95% CIs) per 1 year increase in age at diabetes diagnosis, adjusted for current age for each outcome of interest. The study protocol was recorded with PROSPERO International Prospective Register of Systematic Reviews (CRD42016043593).ResultsData from 26 observational studies comprising 1,325,493 individuals from 30 countries were included. Random-effects meta-analyses with inverse variance weighting were used to obtain the pooled ORs. Age at diabetes diagnosis was inversely associated with risk of all-cause mortality and macrovascular and microvascular disease (all p < 0.001). Each 1 year increase in age at diabetes diagnosis was associated with a 4%, 3% and 5% decreased risk of all-cause mortality, macrovascular disease and microvascular disease, respectively, adjusted for current age. The effects were consistent for the individual components of the composite outcomes (all p < 0.001).Conclusions/interpretationYounger, rather than older, age at diabetes diagnosis was associated with higher risk of mortality and vascular disease. Early and sustained interventions to delay type 2 diabetes onset and improve blood glucose levels and cardiovascular risk profiles of those already diagnosed are essential to reduce morbidity and mortality.

Summary Genome structural variation (SV) contributes strongly to trait variation in eukaryotic species and may have an even higher functional significance than single‐nucleotide polymorphism (SNP). In recent years, there have been a number of studies associating large chromosomal scale SV ranging from hundreds of kilobases all the way up to a few megabases to key agronomic traits in plant genomes. However, there have been little or no efforts towards cataloguing small‐ (30–10 000 bp) to mid‐scale (10 000–30 000 bp) SV and their impact on evolution and adaptation‐related traits in plants. This might be attributed to complex and highly duplicated nature of plant genomes, which makes them difficult to assess using high‐throughput genome screening methods. Here, we describe how long‐read sequencing technologies can overcome this problem, revealing a surprisingly high level of widespread, small‐ to mid‐scale SV in a major allopolyploid crop species, Brassica napus. We found that up to 10% of all genes were affected by small‐ to mid‐scale SV events. Nearly half of these SV events ranged between 100 bp and 1000 bp, which makes them challenging to detect using short‐read Illumina sequencing. Examples demonstrating the contribution of such SV towards eco‐geographical adaptation and disease resistance in oilseed rape suggest that revisiting complex plant genomes using medium‐coverage long‐read sequencing might reveal unexpected levels of functional gene variation, with major implications for trait regulation and crop improvement.

The most frequent cause of vehicle accidents (car, bike, truck, etc.) is the unexpected existence of barriers while driving. An automated braking system will assist and minimize such collisions and save the driver and other people’s lives and have a substantial influence on driver safety and comfort. An autonomous braking system is a complicated mechatronic system that incorporates a front-mounted ultrasonic wave emitter capable of creating and transmitting ultrasonic waves. In addition, a front-mounted ultrasonic receiver is attached to gather ultrasonic wave signals that are reflected. The distance between the impediment and the vehicle is determined by the reflected wave. Then, a microprocessor is utilized to control the vehicle’s speed depending on the detected pulse information, which pushes the brake pedal and applies the vehicle’s brakes extremely hard for safety. For work-energy at surprise condition for velocity 20 km/hr, the braking distance is 17.69 m, and for velocity 50 km/hr, the braking distance is 73.14.