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Object Constraint Language (OCL) plays a key role in Unified Modeling Language (UML). In the UML standards, OCL is used for expressing constraints such as welldefinedness criteria. In addition OCL can be used for specifying constraints on the models and pre/post conditions on operations, improving the precision of the specification. As a result, OCL has received considerable attention from the research community. However, despite its key role, there is a common consensus that OCL is the least adopted among all languages in the UML. It is often argued that, software practitioners shy away from OCL due to its unfamiliar syntax. To ensure better adoption of OCL, the usability issues related to producing OCL statement must be addressed. To address this problem, this paper aims to preset a method involving using Natural Language expressions and Model Transformation technology. The aim of the method is to produce a framework so that the user of UML tool can write constraints and pre/post conditions in English and the framework converts such natural language expressions to the equivalent OCL statements. As a result, the approach aims at simplifying the process of generation of OCL statements, allowing the user to benefit form the advantages provided by UML tools that support OCL. The suggested approach relies on Semantic Business Vocabulary and Rules (SBVR) to support formulation of natural language expressions and their transformations to OCL. The paper also presents outline of a prototype tool that implements the method.

Efficient and cost effective ways of irrigation have emerged as the need of the hour due to limited sweet water resources, especially the countries that are seriously hit by a lack of sweet water reservoirs. The majority of the water is wasted due to inefficient ways of watering plants. In this paper, we propose an intelligent approach for efficient plant irrigation that has a database of daily water needs of a type of plant and decides the amount of water for a plant type on the basis of the current moisture in soil, humidity, and time of the day. This approach not only saves sweet water by efficient utilization, but also supports smart consumption of energy. Our approach employs IoT and a set of sensors to efficiently record plant data and their watering needs and the approach is implemented with a mobile phone application interface that is used to continuously monitor and control the efficient watering system. The results of this study are easy to reproduce as the sensors used are cheap and easy to access. The study discusses in this paper is experimented on small area (such as tunnel farm) but the results of the experiments show that the used approach can be generalized and can be used for large size fields for efficient irrigation. The results of the experiments also outperform the manual approach and the similar approaches for sensor based irrigation systems.

In the recent past, a few fire warning and alarm systems have been presented based on a combination of a smoke sensor and an alarm device to design a life-safety system. However, such fire alarm systems are sometimes error-prone and can react to non-actual indicators of fire presence classified as false warnings. There is a need for high-quality and intelligent fire alarm systems that use multiple sensor values (such as a signal from a flame detector, humidity, heat, and smoke sensors, etc.) to detect true incidents of fire. An Adaptive neuro-fuzzy Inference System (ANFIS) is used in this paper to calculate the maximum likelihood of the true presence of fire and generate fire alert. The novel idea proposed in this paper is to use ANFIS for the identification of a true fire incident by using change rate of smoke, the change rate of temperature, and humidity in the presence of fire. The model consists of sensors to collect vital data from sensor nodes where Fuzzy logic converts the raw data in a linguistic variable which is trained in ANFIS to get the probability of fire occurrence. The proposed idea also generates alerts with a message sent directly to the user’s smartphone. Our system uses small size, cost-effective sensors and ensures that this solution is reproducible. MATLAB-based simulation is used for the experiments and the results show a satisfactory output.

Typical fire monitoring and warning systems use a single smoke detector that is connected to a fire management system to give early warnings before the fire spreads out up to a damaging level. However, it is found that only smoke detector-based fire monitoring systems are not efficient and intelligent since they generate false warnings in case of a person is smoking, etc. There is need of a multi-sensor based intelligent and smart fire monitoring system that employs various parameters, such as presence of flame, temperature of the room, smoke, etc. To achieve such a smart solution, a multi-sensor solution is required that can intelligently use the data of sensors and generate true warnings for further fire control and management. This paper presents an intelligent Fire Monitoring and Warning System (FMWS) that is based on Fuzzy Logic to identify the true existence of dangerous fire and send alert to Fire Management System (FMS). This paper discusses design and application of a Fuzzy Logic Fire Monitoring and Warning System that also sends an alert message using Global System for Mobile Communication (GSM) technology. The system is based on tiny, low cost, and very small in size sensors to ensure that the solution is reproduceable. Simulation work is done in MATLAB ver. 7.1 (The MathWorks, Natick, MA, USA) and the results of the experiments are satisfactory.

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The Internet of Things (IoT) is the next generation of internet-connected information communication technologies (ICT). IoT typically integrates supply chain activities to enhance green supply chain performance (GSCP). Since every organization has different IoT capabilities in comparison with other organizations, GSCP can enable supply chain integration activities for enhanced performance. The implementation of an IoT system can reduce the consumption of organizational resources like energy, electricity, and time and can increase the operational speed to gain better logistics and, ultimately, improved supply chain performance. This study has developed and empirically tested the relationship between IoT capabilities, energy consumption behavior (ECB), supply chain integration, green training (GT), and supply chain practices. Such a multidisciplinary relationship has not previously been established in the literature. The proposed study can fulfill the literature gap and opens new horizons for interdisciplinary research. Data used in this study are collected through offline and online survey methods. A total number of 250 out of 400 respondents participated in the survey. Data has been analyzed through partial least square—structure equation modeling (PLS—SEM) technique. The results of this study empirically test the developed model. IoT has a positive effect on supplier integration (SI), and customer integration (CI). Furthermore, SI and CI have a mediating role between IoT and GSCP, and GT has a positive impact on GSCP. It is concluded that the implementation of IoT can integrate CI and SI to increase GSCP. GT and ECB can ultimately improve GSCP. Additionally, the use of technology and GT can motivate employees to save energy and protect the environment to increase GSCP.

In this paper, an intelligent approach is presented to measure customers’ loyalty to a specific product and assist new customers regarding a product’s key features. Our approach uses an aggregated sentiment score of a set of reviews in a dataset and then uses a fuzzy logic model to measure customer’s loyalty to a product. Our approach uses a novel idea of measuring customer’s loyalty to a product and can assist a new customer to take a decision about a particular product considering its various features and reviews of previous customers. In this study, we use a large sized data set of online reviews of customers from Amazon.com to test the performance of the customer’s reviews. The proposed approach pre-processes the input text via tokenization, Lemmatization and removal of stop words and then applies fuzzy logic approach to take decisions. To find similarity and relevance to a topic, various libraries and API are used in this work such as SentiWordNet, Stanford Core NLP, etc. The approach utilized focuses on identifying polarity of the reviews that may be positive, negative and neutral. To find customer’s loyalty and help in decision making, the fuzzy logic approach is applied using a set of membership functions and rule-based system of fuzzy sets that classify data in various types of loyalty. The implementation of the approach provides high accuracy of 94% of correct loyalty to the e-commerce products that outperforms the previous approaches.