With the growth of residential rooftop PV adoption in recent decades, the problem of 1 effective layout design has become increasingly important in recent years. Although a number 2 of automated methods have been introduced, these tend to rely on simplifying assumptions and 3 heuristics to improve computational tractability. We demonstrate a fully automated layout design 4 pipeline that attempts to solve a more general formulation with greater geometric flexibility that 5 accounts for shading losses. Our approach generates rooftop areas from satellite imagery and uses 6 MINLP optimization to select panel positions, azimuth angles and tilt angles on an individual basis 7 rather than imposing any predefined layouts. Our results demonstrate that although several common 8 heuristics are often effective, they may not be universally suitable due to complications resulting 9 from geometric restrictions and shading losses. Finally, we evaluate a few specific heuristics from the 10 literature and propose a potential new rule of thumb that may help improve rooftop solar energy 11 potential when shading effects are considered.

Patient triage at emergency departments (EDs) is necessary to prioritize care for patients with critical and time-sensitive conditions. Different tools are used for patient triage and one of the most common ones is the emergency severity index (ESI), which has a scale of five levels, where level 1 is the most urgent and level 5 is the least urgent. This paper proposes a framework for utilizing machine learning to develop an e-triage tool that can be used at EDs. A large retrospective dataset of ED patient visits is obtained from the electronic health record of a healthcare provider in the Midwest of the US for three years. However, the main challenge of using machine learning algorithms is that most of them have many parameters and without optimizing these parameters, developing a high-performance model is not possible. This paper proposes an approach to optimize the hyperparameters of machine learning. The metaheuristic optimization algorithms simulated annealing (SA) and adaptive simulated annealing (ASA) are proposed to optimize the parameters of extreme gradient boosting (XGB) and categorical boosting (CaB). The newly proposed algorithms are SA-XGB, ASA-XGB, SA-CaB, ASA-CaB. Grid search (GS), which is a traditional approach used for machine learning fine-tunning is also used to fine-tune the parameters of XGB and CaB, which are named GS-XGB and GS-CaB. The six algorithms are trained and tested using eight data groups obtained from the feature selection phase. The results show ASA-CaB outperformed all the proposed algorithms with accuracy, precision, recall, and f1 of 83.3%, 83.2%, 83.3%, 83.2%, respectively.

The issue of left before treatment complete (LBTC) patients is common in emergency departments (EDs). This issue represents a medico-legal risk and may cause a revenue loss. Thus, understanding the factors that cause patients to leave before treatment is complete is vital to mitigate and potentially eliminate these adverse effects. This paper proposes a framework for studying the factors that affect LBTC outcomes in EDs. The framework integrates machine learning, metaheuristic optimization, and model interpretation techniques. Metaheuristic optimization is used for hyperparameter optimization--one of the main challenges of machine learning model development. Three metaheuristic optimization algorithms are employed for optimizing the parameters of extreme gradient boosting (XGB), which are simulated annealing (SA), adaptive simulated annealing (ASA), and adaptive tabu simulated annealing (ATSA). The optimized XGB models are used to predict the LBTC outcomes for the patients under treatment in ED. The designed algorithms are trained and tested using four data groups resulting from the feature selection phase. The model with the best predictive performance is interpreted using SHaply Additive exPlanations (SHAP) method. The findings show that ATSA-XGB outperformed other mode configurations with an accuracy, area under the curve (AUC), sensitivity, specificity, and F1-score of 86.61%, 87.50%, 85.71%, 87.51%, and 86.60%, respectively. The degree and the direction of effects of each feature were determined and explained using the SHAP method.

Network intrusion detection systems (NIDSs) play an important role in computer network security. There are several detection mechanisms where anomaly-based automated detection outperforms others significantly. Amid the sophistication and growing number of attacks, dealing with large amounts of data is a recognized issue in the development of anomaly-based NIDS. However, do current models meet the needs of today's networks in terms of required accuracy and dependability? In this research, we propose a new hybrid model that combines machine learning and deep learning to increase detection rates while securing dependability. Our proposed method ensures efficient pre-processing by combining SMOTE for data balancing and XGBoost for feature selection. We compared our developed method to various machine learning and deep learning algorithms to find a more efficient algorithm to implement in the pipeline. Furthermore, we chose the most effective model for network intrusion based on a set of benchmarked performance analysis criteria. Our method produces excellent results when tested on two datasets, KDDCUP'99 and CIC-MalMem-2022, with an accuracy of 99.99% and 100% for KDDCUP'99 and CIC-MalMem-2022, respectively, and no overfitting or Type-1 and Type-2 issues.

在过去的十年中，基于深度学习的算法在遥感图像分析的不同领域中广泛流行。最近，最初在自然语言处理中引入的基于变形金刚的体系结构遍布计算机视觉领域，在该字段中，自我发挥的机制已被用作替代流行的卷积操作员来捕获长期依赖性。受到计算机视觉的最新进展的启发，遥感社区还见证了对各种任务的视觉变压器的探索。尽管许多调查都集中在计算机视觉中的变压器上，但据我们所知，我们是第一个对基于遥感中变压器的最新进展进行系统评价的人。我们的调查涵盖了60多种基于变形金刚的60多种方法，用于遥感子方面的不同遥感问题：非常高分辨率（VHR），高光谱（HSI）和合成孔径雷达（SAR）图像。我们通过讨论遥感中变压器的不同挑战和开放问题来结束调查。此外，我们打算在遥感论文中频繁更新和维护最新的变压器，及其各自的代码：https：//github.com/virobo-15/transformer-in-in-remote-sensing

Masader（Alyafeai等，2021）创建了一种元数据结构，用于分类阿拉伯NLP数据集。但是，开发一种简单的方法来探索这种目录是一项艰巨的任务。为了为探索目录的用户和研究人员提供最佳体验，必须解决一些设计和用户体验的挑战。此外，用户与网站的交互可能提供了一种简单的方法来改善目录。在本文中，我们介绍了Masader Plus，该网络接口供用户浏览masader。我们演示了数据探索，过滤和简单的API，该API允许用户从后端检查数据集。可以使用此链接https://arbml.github.io/masader探索masader plus。可以在此处找到的视频录制说明界面的录制https://www.youtube.com/watch?v=setDlseqchk。

残疾人在医疗保健，就业和政府政策等各个领域的各种复杂的决策过程中受到各种复杂的决策。这些环境通常已经不透明他们影响的人并缺乏充分的残疾观点代表，它迅速采用人工智能（AI）技术来用于数据分析以告知决策，从而增加因不当或不公平的算法而造成的伤害风险增加。本文介绍了一个通过残疾镜头进行严格检查AI数据分析技术的框架，并研究了AI技术设计师选择的残疾定义如何影响其对残疾分析对象的影响。我们考虑了三种残疾的概念模型：医学模型，社会模型和关系模型；并展示在每个模型下设计的AI技术如何差异很大，以至于与彼此不相容和矛盾。通过讨论有关医疗保健和政府残疾福利中AI分析的常见用例，我们说明了技术设计过程中的特定考虑因素和决策点，这些因素和决策点影响了这些环境中的电力动态和包容性，并有助于确定其对边缘化或支持的方向。我们提出的框架可以作为对AI技术的深入批判性检查的基础，并开发用于残疾相关的AI分析的设计实践。

静态机器学习模型的理想化，经过训练并永远部署，这是不切实际的。随着输入分布的变化，该模型不仅会失去准确性，因此减少对受保护类别的偏见的任何约束都可能无法按预期工作。因此，研究人员已经开始探索随着时间的推移保持算法公平性的方法。一项工作重点是动态学习：每批次后重新训练，而另一个工作则介绍了强大的学习，该学习试图使算法与未来所有可能的变化进行鲁棒性。动态学习试图在发生后不久减少偏见，而健壮的学习通常会产生（过于）保守的模型。我们提出了一种预期的动态学习方法，用于纠正算法在发生偏见之前减轻算法。具体而言，我们利用有关下一个周期中人口亚组（例如，男性和女性申请人的相对比率）的相对分布的预期，以确定正确的参数，以实现重要性权衡方法。对多个现实世界数据集的实验的结果表明，这种方法有望预期偏差校正。

近年来，知识蒸馏（KD）被认为是模型压缩和加速度的关键解决方案。在KD中，通过最大限度地减少两者的概率输出之间的分歧，一项小学生模型通常从大师模型中培训。然而，如我们实验中所示，现有的KD方法可能不会将老师的批判性解释知识转移给学生，即两种模型所做的预测的解释并不一致。在本文中，我们提出了一种新颖的可解释的知识蒸馏模型，称为XDistillation，通过该模型，解释信息都从教师模型转移到学生模型。 Xdistillation模型利用卷积的自动统计学器的想法来近似教师解释。我们的实验表明，由Xdistillation培训的模型优于传统KD方法的那些不仅在预测准确性的术语，而且对教师模型的忠诚度。

基于全面的生物识别是一个广泛的研究区域。然而，仅使用部分可见的面，例如在遮盖的人的情况下，是一个具有挑战性的任务。在这项工作中使用深卷积神经网络（CNN）来提取来自遮盖者面部图像的特征。我们发现，第六和第七完全连接的层，FC6和FC7分别在VGG19网络的结构中提供了鲁棒特征，其中这两层包含4096个功能。这项工作的主要目标是测试基于深度学习的自动化计算机系统的能力，不仅要识别人，还要对眼睛微笑等性别，年龄和面部表达的认可。我们的实验结果表明，我们为所有任务获得了高精度。最佳记录的准确度值高达99.95％，用于识别人员，99.9％，年龄识别的99.9％，面部表情（眼睛微笑）认可为80.9％。