Inference Engine Module Research Articles

Hotel room personalization via ontology and rule-based reasoning

Purpose The increasingly competitive hotel industry and emerging customer trends where guests are more discerning and want a personalized experience has led to the need of innovative applications. Personalization is much more important for hotels, especially now in the post-COVID lockdown era, as it challenges their business model. However, personalization is difficult to design and realize due to the variety of factors and requirements to be considered. Differences are both in the offer (hotels and their rooms) and demand (customers’ profiles and needs) in the accommodation domain. As for the implementation, critical issues are in hardware-dependent and vendor-specific Internet of Things devices which are difficult to program. Additionally, there is complexity in realizing applications that consider varying customer needs and context via existing personalization options. This paper aims to propose an ontological framework to enhance the capabilities of hotels in offering their accommodation and personalization options based on a guest’s characteristics, activities and needs. Design/methodology/approach A research approach combining both quantitative and qualitative methods was used to develop a hotel room personalization framework. The core of the framework is a hotel room ontology (HoROnt) that supports well-defined machine-readable descriptions of hotel rooms and guest profiles. Hotel guest profiles are modeled via logical rules into an inference engine exploiting reasoning functionalities used to recommend hotel room services and features. Findings Both the ontology and the inference engine module have been validated with promising results which demonstrate high accuracy. The framework leverages user characteristics, and dynamic contextual data to satisfy guests’ needs for personalized service provision. The semantic rules provide recommendations to both new and returning guests, thereby also addressing the cold start issue. Originality/value This paper extends HoROnt in two ways, to be able to add: instances of the concepts (room characteristics and services; guest profiles), i.e. to create a knowledge base, and logical rules into an inference engine, to model guests’ profiles and to be used to offer personalized hotel rooms. Thanks to the standards adopted to implement personalization, this framework can be integrated into existing reservation systems. It can also be adapted for any type of accommodation since it is broad-based and personalizes varying features and amenities in the rooms.

Towards Secure Fitness Framework Based on IoT-Enabled Blockchain Network Integrated with Machine Learning Algorithms.

Blockchain technology has recently inspired remarkable attention due to its unique features, such as privacy, accountability, immutability, and anonymity, to name of the few. In contrast, core functionalities of most Internet of Things (IoT) resources make them vulnerable to security threats. The IoT devices, such as smartphones and tablets, have limited capacity in terms of network, computing, and storage, which make them easier for vulnerable threats. Furthermore, a massive amount of data produced by the IoT devices, which is still an open challenge for the existing platforms to process, analyze, and unearth underlying patterns to provide convenience environment. Therefore, a new solution is required to ensure data accountability, improve data privacy and accessibility, and extract hidden patterns and useful knowledge to provide adequate services. In this paper, we present a secure fitness framework that is based on an IoT-enabled blockchain network integrated with machine learning approaches. The proposed framework consists of two modules: a blockchain-based IoT network to provide security and integrity to sensing data as well as an enhanced smart contract enabled relationship and inference engine to discover hidden insights and useful knowledge from IoT and user device network data. The enhanced smart contract aims to support users with a practical application that provides real-time monitoring, control, easy access, and immutable logs of multiple devices that are deployed in several domains. The inference engine module aims to unearth underlying patterns and useful knowledge from IoT environment data, which helps in effective decision making to provide convenient services. For experimental analysis, we implement an intelligent fitness service that is based on an enhanced smart contract enabled relationship and inference engine as a case study where several IoT fitness devices are used to securely acquire user personalized fitness data. Furthermore, a real-time inference engine investigates user personalized data to discover useful knowledge and hidden insights. Based on inference engine knowledge, a recommendation model is developed to recommend a daily and monthly diet, as well as a workout plan for better and improved body shape. The recommendation model aims to facilitate a trainer formulating effective future decisions of trainee’s health in terms of a diet and workout plan. Lastly, for performance analysis, we have used Hyperledger Caliper to access the system performance in terms of latency, throughput, resource utilization, and varying orderer and peers nodes. The analysis results imply that the design architecture is applicable for resource-constrained IoT blockchain platform and it is extensible for different IoT scenarios.

Simulation Methodology-Based Context-Aware Architecture Design for Behavior Monitoring of Systems

Generally, simulation models are constructed to replicate and predict the behavior of real systems that currently exist or are expected to exist in the future. Once a simulation model is implemented, the model can be connected to a real system for which the model has been built through sensors or networks so that important activities in the real system can be monitored indirectly through the model. This article proposes a modeling formalism BM-DEVS (Behavior Monitor-DEVS) that defines simulation models capable of monitoring the desired behavior patterns within the models so that the target system’s behavior can be monitored indirectly. In BM-DEVS, an extension of classic Discrete Event System Specification (DEVS), the behavior to be monitored is expressed as a set of temporal logic (TL) production rules within a multi-component model that consists of multiple component models to be monitored. An inference engine module for reasoning with the TL rules is designed based on the abstract simulator that carries out instructions in the BM-DEVS models to perform the simulation process. The major application of BM-DEVS is in the design and implementation of the context-aware architecture needed for various intelligent systems as a core constituent. Essentially all systems where some form of behavior monitoring is required are candidate applications of BM-DEVS. This research is motivated by the view that there exists symmetry between the real-world and the cyber world, in that the problems in both environments should be expressed with the same basic constituents of time and space; this naturally leads to adopting spatiotemporal variables composed of simulation models and developing a problem solver that exploits these variables.

A decision support system on the obesity management and consultation during childhood and adolescence using ontology and semantic rules

Obesity is defined as abnormal or excessive fat accumulation that presents a risk to health according to the World Health Organization (WHO). Pediatric or childhood obesity is the most prevalent nutritional disorder among children and adolescents worldwide. In pediatric or childhood obesity, constant monitoring of the pediatric patients by health experts is required to provide efficient obesity management and treatment. Therefore, the patients are examined on a regular basis, the measurements are compared against predefined percentile values and the development of the pediatric patient is examined. This study discusses the design, implementation, and potential use of an ontology-based obesity management and consultation system which is a decision support system for health experts during treatments of the children and adolescent patients. The system does not only share instant gathered medical data to health experts but also examines the data as a smart medical assistant. The system includes an ontology-based inference engine module, which is a decision support module, and used to infer certain personalized suggestions for patients. Suggestions in four categories emerged as a result: (1) Development Feedback Suggestions, (2) Calorie Intake Suggestions and Physical Activities, (3) Mom Suggestions, and (4) Obesity Treatment Stage Suggestions. The methodologies applied and main technical contributions are discussed in three aspects: (1) Obesity Tracking Ontology, (2) Semantic Web Rule Knowledge base, and (3) Inference Engine Module. In this study, unlike other similar studies, ontology and rule based smart medical assistant which have different functionalities from adults' obesity management is considered especially for obesity management of children and adolescents. The system also includes intensive pediatric health care expert involvement. Eighty case studies from real anonymous pediatric patients are analyzed and discussed in this experimental study. The results retrieved from 80 case studies are promising in demonstrating the applicability, effectiveness and efficiency of the proposed approach. The inference engine module of the proposed system can be integrated semantically into intelligent and distributed decision support systems, and the system ontology can be used as a knowledge base in similar systems.

A Review of Emotion Recognition Using EEG Data and Machine Learning Techniques

Using AI to help humans with handling their emotions and identifying their stress levels in the current stressful lifestyle will greatly help them manage their lifestyle. Using the deep learning techniques, it can be made possible by creating a virtual bot to observe and understand human emotions. In this paper, the researcher try to review the comments from Reddit that are used, preprocessed and trained using Deep Neural Network to learn the emotions of the user. The inference engine module, which is a hybrid network consisting of convolutional neural network and recurrent neural network, is also interfaced. The model provides a high accuracy of response. The selection of frequency bands plays an important role in discerning patterns of brain-related emotions. This document explores a new method for selecting appropriate thematic bands instead of using fixed bands to detect emotions. A common spatial technique and machine machines were used to classify the emotional states. This document describes a number of possible technologies aimed at communication and other applications; however, they represent only a small sample of the extensive future potential of these technologies. We have also focused on relatively anticipated breakthroughs in the discussion of applications in sensory, BCI technologies; but breakthroughs like the new portable sensor technology, which offers ultra-high-resolution spatial and time-based activity in the brain, opens the door to a much broader range of applications. Keywords: Emotions, EEG, Machine Learning, Deep Learning, Systems and Signals DOI: 10.7176/ISDE/11-4-04 Publication date: August 31 st 2020

A neural network decision expert system for alpine meadow degradation in the Sanjiangyuan region

This paper introduced detailed the design of intelligent system for decision-making. Firstly, the overall design of grassland degradation decision-making system of Sanjiangyuan based on neural network is carried out, including man–machine interface module, knowledge base module, neural network module and inference engine module. Secondly, the specific functions of each module are introduced. Finally, the design of neural network was introduced detailed, the entire BP neural network consists of three layers of structure. There are five nodes in the input layer, five nodes in the output layer, and six nodes in the hidden layer.

A nifty collaborative intrusion detection and prevention architecture for Smart Grid ecosystems

Smart Grid (SG) systems are critical, intelligent infrastructure utility services connected through open networks that are potentially susceptible to cyber-attacks with very acute security risks of shutdown, loss of life, and loss of revenue. Traditional intrusion detection systems based on signature and anomaly techniques are no longer sufficient to protect SGs due to their new connectivity and management challenges, the ever-rapidly-evolving masquerades, and cyber criminality levied against them. SGs require cyber-security systems to render them resilient and protected through advanced Intrusion Detection and Prevention System (IDPS) techniques and mechanisms. This paper proposes a smart collaborative advanced IDPS to provide the best possible protection of SGs with a fully distributed management structure that supports the network and host based detections and the prevention of attacks. By facilitating a reliable, scalable, and flexible design, the specific requirements of IDPS for SGs can be more easily met via a fuzzy risk analyzer, an independent and ontology knowledge-based inference engine module. These can work collaboratively by managing functions across multiple IDPS domains. A set of extensive and intensive simulated experiments shows that with its smart advanced components incorporating soft computing machine-learning techniques and a rich ontology knowledge base with fuzzy logic analysis, it detects and prevents intrusions more efficiently. The multi-faceted results of the simulation also show that the proposed Collaborative Smart IDPS (CSIDPS) system increases the intrusion detection accuracy and decreases the false positive alarms when compared to traditional IDPSs. This is epitomized by the skillful use of the confusion matrix technique for organizing classifiers, visualizing their performance, and assessing their overall behavior. In the final analysis, the CSIDPS architecture is designed toward contributing to de facto norms for SG ecosystems.

A Type-2 Fuzzy Image Processing Expert System for Diagnosing Brain Tumors.

The focus of this paper is diagnosing and differentiating Astrocytomas in MRI scans by developing an interval Type-2 fuzzy automated tumor detection system. This system consists of three modules: working memory, knowledge base, and inference engine. An image processing method with three steps of preprocessing, segmentation and feature extraction, and approximate reasoning is used in inference engine module to enhance the quality of MRI scans, segment them into desired regions, extract the required features, and finally diagnose and differentiate Astrocytomas. However, brain tumors have different characteristics in different planes, so considering one plane of patient's MRI scan may cause inaccurate results. Therefore, in the developed system, several consecutive planes are processed. The performance of this system is evaluated using 95 MRI scans and the results show good improvement in diagnosing and differentiating Astrocytomas.

A Knowledge-Based Industrial Robot Selection System for Manufacturing Industries

Nowadays, robots can be used for different applications and environment. The capabilities and specification of the robot will determine its usage. The large number of the robot in the market has made the selection of the suitable robot becoming difficult. This paper presents the development of knowledge-based system for robot selection using expert system shell. The aim of the work is to select the suitable industry robot for manufacturing industry. The selection criteria are based upon application, payload, maximum reach, number of axes, repeatability and mounting position. The system consists of several modules such as knowledge acquisition module, inference engine module and user interface module. The development system helps the industrial engineers to select the most appropriate robot for an application.

Fault Detection Systems via a Novel hybrid Methodology for Fuzzy Logic Systems based on Individual base inference and Statistical Process Control

This paper presents a novel methodology to model fuzzy logic systems used as fault detection systems. This method is based in a hybrid model assembled via Individual Base Inference (IBI), Statistical Process Control (SPC) tool in the form of Control Charts (CC) and MacVicar-Whelan Methodologies. The novel method provides a simplification of the necessary calculus of inference engine module with the approaches SPC tool (CC: X-S and X-R). The inference engine is assembled via a novel methodology based in the concepts used to create CC and can be made with the specification for R charts or X-S charts. This methodology consists in the hybridization of Macvicar-Whelan method for the assignation of fuzzy labels to provide rules. The IBI is created with the wide spread of real historical data. This data forms the Universe of Discourse (UOD) and the SPC tool is used to provide the fuzzy rules and rule reduction. The aim of this paper is to test the novel method and to compare it with a classic method to prove that this method is an efficient form to rule deduction without lost reliability in the output approximation. The proposed method was tested in an electrical power system.

Feature Recognition System for Rotational Features

This paper presents the development of a knowledge-based features recognition system for mechanical parts. The main aim of the research is to concentrate on recognition of rotational features, namely, blind hole, through hole and slanting hole to be used in automatic process planning system. The rule-based technique applied to recognise rotational features. The recognition criteria are based upon geometrical data from STandard for Exchange of Product data file (STEP) of computer-aided design (CAD) system. The system consists of several modules, namely, pre-processing module, inference engine module and user interface module. The developed system succeed the design activities in manufacturing industries.

Developing a group decision support system based on fuzzy information axiom

Information axiom, one of two axioms of axiomatic design methodology which is proposed to improve a design, is used to select the best design among proposed designs. In the literature, there are a lot of studies related to using of information axiom for the solution of decision making problems. Moreover, applications of information axiom have been increasing day by day. However, calculation procedure of information axiom is not only incommodious but also difficult for decision makers. In this paper, a decision support system (DSS) based on fuzzy information axiom (FIA) is developed in order to make this decision procedure easy. The developed system consists of a knowledge base module including facts and rules, inference engine module including FIA and aggregation method, and a user interface module including entrance windows. The main aim of this study is to present a DSS tool to help the decision makers to solve their decision problems by modifying data-base of the program. In this paper, an application procedure will be presented based on the optimal selection of location for emergency service to illustrate the implementation procedure of the proposed model.

Expert System for Diagnosing Silicone Oil-Filled Transformers

In this paper, expert system for diagnosing silicone oil-filled transformer was developed using dissolved gas analysis (DGA). DGA was used to the proposed expert system since it has been verified that it is very efficient diagnostic method for oil transformer. In addition, it is reasonable that fuzzy rule, degree of inclusion and fuzzy measure must be considered to handle the uncertainty of gas boundary and rules. The proposed expert system consists of knowledge based module, inference engine module and human-machine interface (HMI) module. The knowledge base consists of the knowledge using the rule. The inference engine module is used to the fuzzy rule. The history of the transformer gas data is managed by the database. The effect of the proposed expert system is verified by case studies.

Development of a machine troubleshooting expert system via fuzzy multiattribute decision-making approach

Most current machine fault diagnosis systems emphasize on the correctness of the hypothesized result; however, in time-constrained situations, the efficiency of the diagnostic process becomes more important and should not be overlooked. This paper presents an Integrated Machine Troubleshooting Expert System (IMTES) that enhances the efficiency of the diagnostic process, improves the completeness and consistency of the knowledge base, and assists users in developing and maintaining their diagnostic systems. IMTES consists of five modules: a diagnostic tree module establishes the hierarchical structure regarding the function or connectivity of the diagnostic system, a fuzzy multiattribute decision-making module determines the most efficient diagnostic process and creates a “meta knowledge base” to control the diagnosis process, a knowledge base module captures the human expertise and deep knowledge to diagnose the possible machine fault, an inference engine module controls the diagnosis process and deals with the uncertainty from the user input and knowledge base itself, and a learning module uses the failure-driven learning method to train the knowledge base from the past actual cases. The system has been successfully implemented on MS-Windows environment, and it is written in MS Visual BASIC. To validate the system performance, IMTES is compared to EXACT, an expert system for automobile air-compressor troubleshooting, using 50 sample cases. The result shows that IMTES can reduce the number of queries by 20.7%.