AI-IoT based Smart Pill Expert System
هوش مصنوعی و اینترنت اشیا مبتنی بر سیستم های خبره هوشمند-2020
The paper discusses the implementation of a proposed Smart Pill Expert System (SPES) which is based on AI-IoT technology to automate pill dispensing with an effective user interface. The purpose of the proposed SPES is to provide expertise in the real-time diagnosis and thus support every individual and institution that is dependent on medication. Medical Non-Adherence (MNA) is one of the major factors of prolonged recovery, financial troubles, and premature deaths. This product is de veloped to be used in old age homes, hospices, and home healthcare centers and is capable of catering to the needs of single and multiple users simultaneously. With API and web services, new resources are provided for caregivers (family members, nurses, and doctors) to continuously track and monitor the users. Because of minimal human intervention, SPES has a failure rate of less than 5%.
Keywords: Smart Medication | Healthcare | Expert System | Artificial Intelligence | Internet of Things (IoT) | Cloud Computing
Smart frost measurement for anti-disaster intelligent control in greenhouses via embedding IoT and hybrid AI methods
اندازه گیری یخ زدگی هوشمند برای کنترل هوشمند ضد فاجعه در گلخانه ها از طریق تعبیه روش های اینترنت اشیا و هوش مصنوعی ترکیبی-2020
A novel Agro-industrial IoT (AIIoT) technology and architecture for intelligent frost forecasting in greenhouses via hybrid Artificial Intelligence (AI), is reported. The Internet of Things (IoT) allows the objects interconnection on the physical world using sensors and actuators via the Internet. The smart system was designed and implemented through a climatological station equipped with Artificial Neural Networks (ANN) and a fuzzy associative memory (FAM) for ecological control of the anti-frost disaster irrigation. The ANN forecasts the inside temperature of the greenhouses and the fuzzy control predicts the cropland temperatures for the activation of five output levels of the water pump. The results were compared to a Fourier-statistical analysis of hourly data, showing that the ANN models provide a temperature prediction with effectiveness higher than 90%, as compared to monthly data model. Moreover, results of this process were validated through the determination of the coefficient of variance analysis method (R2).
Keywords: Smart frost measurement in greenhouses | Anti-frost irrigation | Artificial Neural Network | Fuzzy expert system | Internet-of-things | Hybrid AI methods
Towards integrated dialogue policy learning for multiple domains and intents using Hierarchical Deep Reinforcement Learning
به سوی یادگیری سیاست گفتگوی یکپارچه برای چندین حوزه و اهداف با استفاده از یادگیری تقویتی عمیق سلسله مراتبی-2020
Creation of Expert and Intelligent Dialogue/Virtual Agent (VA) that can serve complicated and intricate tasks (need) of the user related to multiple domains and its various intents is indeed quite challenging as it necessitates the agent to concurrently handle multiple subtasks in different domains. This paper presents an expert, unified and a generic Deep Reinforcement Learning (DRL) framework that creates dialogue managers competent for managing task-oriented conversations embodying multiple domains along with their various intents and provide the user with an expert system which is a one stop for all queries. In order to address these multiple aspects, the dialogue exchange between the user and the VA is split into hierarchies, so as to isolate and identify subtasks belonging to different domains. The notion of Hierarchical Reinforcement Learning (HRL) specifically options is employed to learn optimal policies in these hierarchies that operate at varying time steps to accomplish the user goal. The dialogue manager encompasses a toplevel domain meta-policy, intermediate-level intent meta-policies in order to select amongst varied and multiple subtasks or options and low-level controller policies to select primitive actions to complete the subtask given by the higher-level meta-policies in varying intents and domains. Sharing of controller policies among overlapping subtasks enables the meta-policies to be generic. The proposed expert framework has been demonstrated in the domains of ‘‘Air Travel” and ‘‘Restaurant”. Experiments as compared to several strong baselines and a state of the art model establish the efficiency of the learned policies and the need for such expert models capable of handling complex and composite tasks.
Keywords: Dialogue management | Multi-domain | Multi-intent | Hierarchical Reinforcement Learning | Options
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
هوش مصنوعی قابل توضیح (XAI): مفاهیم ، طبقه بندی ها ، فرصت ها و چالش ها در برابر هوش مصنوعی مسئول-2020
In the last few years, Artificial Intelligence (AI) has achieved a notable momentum that, if harnessed appropriately, may deliver the best of expectations over many application sectors across the field. For this to occur shortly in Machine Learning, the entire community stands in front of the barrier of explainability, an inherent problem of the latest techniques brought by sub-symbolism (e.g. ensembles or Deep Neural Networks) that were not present in the last hype of AI (namely, expert systems and rule based models). Paradigms underlying this problem fall within the so-called eXplainable AI (XAI) field, which is widely acknowledged as a crucial feature for the practical deployment of AI models. The overview presented in this article examines the existing literature and contributions already done in the field of XAI, including a prospect toward what is yet to be reached. For this purpose we summarize previous efforts made to define explainability in Machine Learning, establishing a novel definition of explainable Machine Learning that covers such prior conceptual propositions with a major focus on the audience for which the explainability is sought. Departing from this definition, we propose and discuss about a taxonomy of recent contributions related to the explainability of different Machine Learning models, including those aimed at explaining Deep Learning methods for which a second dedicated taxonomy is built and examined in detail. This critical literature analysis serves as the motivating background for a series of challenges faced by XAI, such as the interesting crossroads of data fusion and explainability. Our prospects lead toward the concept of Responsible Artificial Intelligence , namely, a methodology for the large-scale implementation of AI methods in real organizations with fairness, model explainability and accountability at its core. Our ultimate goal is to provide newcomers to the field of XAI with a thorough taxonomy that can serve as reference material in order to stimulate future research advances, but also to encourage experts and professionals from other disciplines to embrace the benefits of AI in their activity sectors, without any prior bias for its lack of interpretability.
Keywords: Explainable Artificial Intelligence | Machine Learning | Deep Learning | Data Fusion | Interpretability | Comprehensibility | Transparency | Privacy | Fairness | Accountability | Responsible Artificial Intelligence
Developing an Artificial Intelligence (AI) Management System to Improve Product Quality and Production Efficiency in Furniture Manufacture
ایجاد سیستم مدیریت هوش مصنوعی (AI) برای بهبود کیفیت محصول و کارایی تولید در ساخت مبلمان-2020
At present, there are some problems in Chinese furniture production industry, such as low production efficiency, low accuracy, and lack of innovation for products. To resolve those problems, an AI management system is developed to improve the product quality and production efficiency in furniture enterprises in this paper. The AI management system is an organic body consisted of a data management system and an expert system. The model of information transmission and control for furniture manufacture by AI management is developed. It provides technical solutions for the AI application in furniture manufacture.
Key words: artificial intelligence (AI) | management | Furniture
Characterizing Linux-based malware: Findings and recent trends
مشخص کردن بدافزار مبتنی بر لینوکس: یافته ها و روندهای اخیر-2020
Malware targeting interconnected infrastructures has surged in recent years. A major factor driving this phenomenon is the proliferation of large networks of poorly secured IoT devices. This is exacerbated by the commoditization of the malware development industry, in which tools can be readily obtained in specialized hacking forums or underground markets. However, despite the great interest in targeting this infrastructure, there is little understanding of what the main features of this type of malware are, or the motives of the criminals behind it, apart from the classic denial of service attacks. This is vital to modern malware forensics, where analyses are required to measure the trustworthiness of files collected at large during an investigation, but also to confront challenges posed by tech-savvy criminals (e.g., Trojan Horse Defense). In this paper, we present a comprehensive characterization of Linux-based malware. Our study is tailored to IoT malware and it leverages automated techniques using both static and dynamic analysis to classify malware into related threats. By looking at the most representative dataset of Linux-based malware collected by the community to date, we are able to show that our system can accurately characterize known threats. As a key novelty, we use our system to investigate a number of threats unknown to the community. We do this in two steps. First, we identify known patterns within an unlabeled dataset using a classifier trained with the labeled dataset. Second, we combine our features with a custom distance function to discover new threats by clustering together similar samples. We further study each of the unknown clusters by using state-of-the-art reverse engineering and forensic techniques and our expertise as malware analysts. We provide an in-depth analysis of what the most recent unknown trends are through a number of case studies. Among other findings, we observe that: i) crypto-mining malware is permeating the IoT infrastructure, ii) the level of sophistication is increasing, and iii) there is a rapid proliferation of new variants with minimal investment in infrastructure.
Keywords: Malware forensics | IoT | Embedded systems | Data analytics | Machine learning | Expert systems
A knowledge-based expert system to assess power plant project cost overrun risks
یک سیستم خبره مبتنی بر دانش برای ارزیابی هزینه ریسک بیش ازحد پروژه نیروگاهی-2019
Preventing cost overruns of such infrastructure projects as power plants is a global project management problem. The existing risk assessment methods/models have limitations to address the complicated na- ture of these projects, incorporate the probabilistic causal relationships of the risks and probabilistic data for risk assessment, by taking into account the domain experts’ judgments, subjectivity, and un- certainty involved in their judgments in the decision making process. A knowledge-based expert system is presented to address this issue, using a fuzzy canonical model (FCM) that integrates the fuzzy group decision-making approach (FGDMA) and the Canonical model ( i.e. a modified Bayesian belief network model) . The FCM overcomes: (a) the subjectivity and uncertainty involved in domain experts’ judgment, (b) sig- nificantly reduces the time and effort needed for the domain experts in eliciting conditional probabilities of the risks involved in complex risk networks, and (c) reduces the model development tasks, which also reduces the computational load on the model. This approach advances the applications of fuzzy-Bayesian models for cost overrun risks assessment in a complex and uncertain project environment by addressing the major constraints associated with such models. A case study demonstrates and tests the application of the model for cost overrun risk assessment in the construction and commissioning phase of a power plant project, confirming its ability to pinpoint the most critical risks involved ̶ in this case, the complex- ity of the lifting and rigging heavy equipment, inadequate work inspection and testing plan, inadequate site/soil investigation, unavailability of the resources in the local market, and the contractor’s poor plan- ning and scheduling.
Keywords: Cost overruns | Risk assessment | Power plant projects | Fuzzy logic | Canonical model
Data-based structure selection for unified discrete grey prediction model
Data-based structure selection for unified discrete grey prediction model-2019
Grey models have been reported to be promising for time series prediction with small samples, but the diversity kinds of model structures and modelling assumptions restrains their further applications and developments. In this paper, a novel grey prediction model, named discrete grey polynomial model, is proposed to unify a family of univariate discrete grey models. The proposed model has the capacity to represent most popular homogeneous and non-homogeneous discrete grey models and furthermore, it can induce some other novel models, thereby highlighting the relationship between the models and their structures and assumptions. Based on the proposed model, a data-based algorithm is put forward to se- lect the model structure adaptively. It reduces the requirement for modeler’s knowledge from an expert system perspective. Two numerical experiments with large-scale simulations are conducted and the re- sults show its effectiveness. In the end, two real case tests show that the proposed model benefits from its adaptive structure and produces reliable multi-step ahead predictions.
Keywords: Grey system theory | Discrete grey model | Structure selection | Matrix decomposition
In this paper, a novel problem in transshipment networks has been proposed. The main aims of this pa- per are to introduce the problem and to give useful tools for solving it both in exact and approximate ways. In a transshipment network it is important to decide which are the best paths between each pair of nodes. Representing the network by a graph, the union of thesepaths is a delivery subgraph of the original graph which has all the nodes and some edges. Nodes in this subgraph which are adjacent to more than two nodes are called switches because when sending the flow between any pair of nodes, switches on the path must adequately direct it. Switches are facilities which direct flows among users. The installation of a switch involves the installation of adequate equipment and thus an allocation cost. Furthermore, traversing a switch also implies a service cost or allocation cost. The Switch Location Prob- lem is defined as the problem of determining which is the delivery subgraph with the total lowest cost. Two of the three solutions approaches that we propose are decomposition algorithms based on articula- tion vertices, the exact and the math-heuristic ones. These two approaches could be embedded in expert systems for locating switches in transshipment networks. The results should help a decision maker to select the adequate approach depending on the shape and size of the network and also on the exter- nal time-limit. Our results show that the exact approach is a valuable tool if the network has less than 10 0 0 nodes. Two upsides of our heuristics are that they do not require special networks and give good solutions, gap-wise. The impact of this paper is twofold: it highlights the difficulty of adequately locating switches and it emphasizes the benefit of decomposing algorithms.
Keywords: Discrete location | Math-heuristic | Articulation vertex | Block-Cutpoint graph
TAPSTROKE: A novel intelligent authentication system using tap frequencies
TAPSTROKE: رویکرد سیستم احراز هویت هوشمند با استفاده از فرکانسهای آهسته-2019
Emerging security requirements lead to new validation protocols to be implemented to recent authen- tication systems by employing biometric traits instead of regular passwords. If an additional security is required in authentication phase, keystroke recognition and classification systems and related interfaces are very promising for collecting and classifying biometric traits. These systems generally operate in time- domain; however, the conventional time-domain solutions could be inadequate if a touchscreen is so small to enter any kind of alphanumeric passwords or a password consists of one single character like a tap to the screen. Therefore, we propose a novel frequency-based authentication system, TAPSTROKE, as a prospective protocol for small touchscreens and an alternative authentication methodology for existing devices. We firstly analyzed the binary train signals formed by tap passwords consisting of taps instead of alphanumeric digits by the regular (STFT) and modified short time Fourier transformations (mSTFT). The unique biometric feature extracted from a tap signal is the frequency-time localization achieved by the spectrograms which are generated by these transformations. The touch signals, generated from the same tap-password, create significantly different spectrograms for predetermined window sizes. Finally, we conducted several experiments to distinguish future attempts by one-class support vector machines (SVM) with a simple linear kernel for Hamming and Blackman window functions. The experiments are greatly encouraging that we achieved 1.40%–2.12% and 2.01%–3.21% equal error rates (EER) with mSTFT; while with regular STFT the classifiers produced quite higher EER, 7.49%–11.95% and 6.93%–10.12%, with Hamming and Blackman window functions, separately. The whole methodology, as an expert system for protecting the users from fraud attacks sheds light on new era of authentication systems for future smart gears and watches.
Keywords: Tapstroke | Keystroke | Authentication | Biometrics | Frequency | Short time Fourier transformation | Support vector machines