A comprehensive pharmaceutical recommendation system was designed based on the patients and drugs features extracted from Drugs.com and Druglib.com. First, data from these databases were combined, and a dataset of patients and drug information was built. Secondly, the patients and drugs were clustered, and then the recommendation was performed using different ratings provided by patients, and importantly by the knowledge obtained from patients and drug specifications, and considering drug interactions. To the best of our knowledge, we are the first group to consider patients conditions and history in the proposed approach for selecting a specific medicine appropriate for that particular user. Our approach applies artificial intelligence (AI) models for the implementation. Sentiment analysis using natural language processing approaches is employed in pre-processing along with neural network-based methods and recommender system algorithms for modeling the system. In our work, patients conditions and drugs features are used for making two models based on matrix factorization. Then we used drug interaction to filter drugs with severe or mild interactions with other drugs. We developed a deep learning model for recommending drugs by using data from 2304 patients as a training set, and then we used data from 660 patients as our validation set. After that, we used knowledge from critical information about drugs and combined the outcome of the model into a knowledge-based system with the rules obtained from constraints on taking medicine.

远程患者监测（RPM）系统的最新进展可以识别各种人类活动，以测量生命体征，包括浅表血管的细微运动。通过解决已知的局限性和挑战（例如预测和分类生命体征和身体运动），将人工智能（AI）应用于该领域的医疗保健领域越来越兴趣，这些局限性和挑战被认为是至关重要的任务。联合学习是一种相对较新的AI技术，旨在通过分散传统的机器学习建模来增强数据隐私。但是，传统的联合学习需要在本地客户和全球服务器上培训相同的建筑模型。由于缺乏本地模型异质性，这限制了全球模型体系结构。为了克服这一点，在本研究中提出了一个新颖的联邦学习体系结构Fedstack，该体系支持结合异构建筑客户端模型。这项工作提供了一个受保护的隐私系统，用于以分散的方法住院的住院患者，并确定最佳传感器位置。提出的体系结构被应用于从10个不同主题的移动健康传感器基准数据集中，以对12个常规活动进行分类。对单个主题数据培训了三个AI模型ANN，CNN和BISTM。联合学习体系结构应用于这些模型，以建立能够表演状态表演的本地和全球模型。本地CNN模型在每个主题数据上都优于ANN和BI-LSTM模型。与同质堆叠相比，我们提出的工作表明，当地模型的异质堆叠表现出更好的性能。这项工作为建立增强的RPM系统奠定了基础，该系统纳入了客户隐私，以帮助对急性心理健康设施中患者进行临床观察，并最终有助于防止意外死亡。

自闭症谱系障碍（ASD）是一种脑部疾病，其特征是幼儿时期出现的各种体征和症状。 ASD还与受影响个体的沟通缺陷和重复行为有关。已经开发了各种ASD检测方法，包括神经影像学和心理测试。在这些方法中，磁共振成像（MRI）成像方式对医生至关重要。临床医生依靠MRI方式准确诊断ASD。 MRI模态是非侵入性方法，包括功能（fMRI）和结构（SMRI）神经影像学方法。但是，用fMRI和SMRI诊断为专家的ASD的过程通常很费力且耗时。因此，已经开发了基于人工智能（AI）的几种计算机辅助设计系统（CAD）来协助专家医生。传统的机器学习（ML）和深度学习（DL）是用于诊断ASD的最受欢迎的AI方案。这项研究旨在使用AI审查对ASD的自动检测。我们回顾了使用ML技术开发的几个CAD，以使用MRI模式自动诊断ASD。在使用DL技术来开发ASD的自动诊断模型方面的工作非常有限。附录中提供了使用DL开发的研究摘要。然后，详细描述了使用MRI和AI技术在自动诊断ASD的自动诊断期间遇到的挑战。此外，讨论了使用ML和DL自动诊断ASD的研究的图形比较。最后，我们提出了使用AI技术和MRI神经影像学检测ASD的未来方法。

With the increasing growth of information through smart devices, increasing the quality level of human life requires various computational paradigms presentation including the Internet of Things, fog, and cloud. Between these three paradigms, the cloud computing paradigm as an emerging technology adds cloud layer services to the edge of the network so that resource allocation operations occur close to the end-user to reduce resource processing time and network traffic overhead. Hence, the resource allocation problem for its providers in terms of presenting a suitable platform, by using computational paradigms is considered a challenge. In general, resource allocation approaches are divided into two methods, including auction-based methods(goal, increase profits for service providers-increase user satisfaction and usability) and optimization-based methods(energy, cost, network exploitation, Runtime, reduction of time delay). In this paper, according to the latest scientific achievements, a comprehensive literature study (CLS) on artificial intelligence methods based on resource allocation optimization without considering auction-based methods in various computing environments are provided such as cloud computing, Vehicular Fog Computing, wireless, IoT, vehicular networks, 5G networks, vehicular cloud architecture,machine-to-machine communication(M2M),Train-to-Train(T2T) communication network, Peer-to-Peer(P2P) network. Since deep learning methods based on artificial intelligence are used as the most important methods in resource allocation problems; Therefore, in this paper, resource allocation approaches based on deep learning are also used in the mentioned computational environments such as deep reinforcement learning, Q-learning technique, reinforcement learning, online learning, and also Classical learning methods such as Bayesian learning, Cummins clustering, Markov decision process.

癫痫发作是最重要的神经障碍之一，其早期诊断将有助于临床医生为患者提供准确的治疗方法。脑电图（EEG）信号广泛用于癫痫癫痫发作检测，其提供了关于大脑功能的实质性信息的专家。本文介绍了采用模糊理论和深层学习技术的新型诊断程序。所提出的方法在Bonn大学数据集上进行了评估，具有六个分类组合以及弗赖堡数据集。可以使用可调谐Q小波变换（TQWT）来将EEG信号分解为不同的子带。在特征提取步骤中，从TQWT的不同子带计算了13个不同的模糊熵，并且计算它们的计算复杂性以帮助研究人员选择各种任务的最佳集合。在下文中，采用具有六层的AutoEncoder（AE）用于减少维数。最后，标准自适应神经模糊推理系统（ANFIS）以及其具有蚱蜢优化算法（ANFIS-GOA），粒子群优化（ANFIS-PSO）和育种群优化（ANFIS-BS）方法的变体分类。使用我们所提出的方法，ANFIS-BS方法在弗赖堡数据集上分为两类分为两类和准确度，在两类分类中获得99.46％的准确性，以及弗赖堡数据集的99.28％，达到最先进的两个人的表演。

由于癫痫发生是由于大脑的异常活性引起的，因此癫痫发作会影响您的大脑处理的任何过程。癫痫发作的一些体征和症状包括混乱，异常凝视以及快速，突然和无法控制的手动运动。癫痫发作检测方法涉及神经检查，血液检查，神经心理学检查和神经影像学方法。其中，神经影像学的方式受到了专业医生的极大关注。一种促进癫痫发作准确，快速诊断的方法是基于深度学习（DL）和神经成像方式采用计算机辅助诊断系统（CADS）。本文研究了利用神经影像学方式利用用于癫痫发作检测和预测的DL方法的全面概述。首先，讨论了用于使用神经影像模式的癫痫发作检测和预测的基于DL的CAD。此外，还包括了用于癫痫发作检测和预测的各种数据集的描述，预处理算法和DL模型。然后，已经介绍了有关康复工具的研究，其中包含脑部计算机接口（BCI），可植入，云计算，物联网（IoT），在现场可编程栅极阵列（FPGA）上的DL技术实现，等等。讨论部分是关于癫痫发作检测和预测研究之间的比较。使用神经影像模式和DL模型的癫痫发作检测和预测中最重要的挑战。此外，已经提出了数据集，DL，康复和硬件模型领域的未来工作建议。最后一部分致力于结论，并在该领域结合了最重要的发现。

新的冠状病毒造成了超过一百万的死亡，并继续迅速传播。这种病毒靶向肺部，导致呼吸窘迫，这可以轻度或严重。肺的X射线或计算机断层扫描（CT）图像可以揭示患者是否感染Covid-19。许多研究人员正在尝试使用人工智能改善Covid-19检测。我们的动机是开发一种可以应对的自动方法，该方法可以应对标记数据的方案是耗时或昂贵的。在本文中，我们提出了使用依赖于Sobel边缘检测和生成对冲网络（GANS）的有限标记数据（SCLLD）的半监督分类来自动化Covid-19诊断。 GaN鉴别器输出是一种概率值，用于在这项工作中进行分类。建议的系统使用从Omid Hosparing收集的10,000 CT扫描培训，而公共数据集也用于验证我们的系统。将该方法与其他最先进的监督方法进行比较，例如高斯过程。据我们所知，这是第一次提出了对Covid-19检测的半监督方法。我们的系统能够从有限标记和未标记数据的混合学习，该数据由于缺乏足够量的标记数据而导致的监督学习者失败。因此，我们的半监督训练方法显着优于卷积神经网络（CNN）的监督培训，当标记的训练数据稀缺时。在精度，敏感性和特异性方面，我们的方法的95％置信区间分别为99.56±0.20％，99.88±0.24％和99.40±0.1.18％，而CNN的间隔（训练有素的监督）为68.34 + - 4.11％，91.2 + - 6.15％，46.40 + - 5.21％。

Uncertainty quantification (UQ) plays a pivotal role in the reduction of uncertainties during both optimization and decision making, applied to solve a variety of real-world applications in science and engineering. Bayesian approximation and ensemble learning techniques are two of the most widely-used UQ methods in the literature. In this regard, researchers have proposed different UQ methods and examined their performance in a variety of applications such as computer vision (e.g., self-driving cars and object detection), image processing (e.g., image restoration), medical image analysis (e.g., medical image classification and segmentation), natural language processing (e.g., text classification, social media texts and recidivism risk-scoring), bioinformatics, etc. This study reviews recent advances in UQ methods used in deep learning, investigates the application of these methods in reinforcement learning, and highlight the fundamental research challenges and directions associated with the UQ field.

准确诊断自闭症谱系障碍（ASD），随后有效康复对该疾病的管理至关重要。人工智能（AI）技术可以帮助医生应用自动诊断和康复程序。 AI技术包括传统机器学习（ML）方法和深度学习（DL）技术。常规ML方法采用各种特征提取和分类技术，但在DL中，特征提取和分类过程是智能的，一体地完成的。诊断ASD的DL方法已经专注于基于神经影像动物的方法。神经成像技术是无侵入性疾病标志物，可能对ASD诊断有用。结构和功能神经影像技术提供了关于大脑的结构（解剖结构和结构连接）和功能（活性和功能连接）的实质性信息。由于大脑的复杂结构和功能，提出了在不利用像DL这样的强大AI技术的情况下使用神经影像数据进行ASD诊断的最佳程序可能是具有挑战性的。本文研究了借助DL网络进行以区分ASD进行的研究。还评估了用于支持ASD患者的康复工具，用于利用DL网络的支持患者。最后，我们将在ASD的自动检测和康复中提出重要挑战，并提出了一些未来的作品。

The rise in data has led to the need for dimension reduction techniques, especially in the area of non-scalar variables, including time series, natural language processing, and computer vision. In this paper, we specifically investigate dimension reduction for time series through functional data analysis. Current methods for dimension reduction in functional data are functional principal component analysis and functional autoencoders, which are limited to linear mappings or scalar representations for the time series, which is inefficient. In real data applications, the nature of the data is much more complex. We propose a non-linear function-on-function approach, which consists of a functional encoder and a functional decoder, that uses continuous hidden layers consisting of continuous neurons to learn the structure inherent in functional data, which addresses the aforementioned concerns in the existing approaches. Our approach gives a low dimension latent representation by reducing the number of functional features as well as the timepoints at which the functions are observed. The effectiveness of the proposed model is demonstrated through multiple simulations and real data examples.