COMMERCIAL-OFF-THE SHELF VENDOR SELECTION: A MULTI-CRITERIA DECISION-MAKING APPROACH USING INTUITIONISTIC FUZZY SETS AND TOPSIS

Commercial-off-the-shelf (COTS) component selection is considered a critical task in effectively developing a component-based software system (CBSS). COTS vendor selection involves selecting the right vendors who can provide reliable COTS components at a suitable price and on time. However, COTS vendor selection is commonly a multi-criteria decision-making (MCDM) issue” associated with many paradoxical criteria for which the decision maker’s knowledge may be uncertain and ambiguous. This paper attempts to present “Intuitionistic Fuzzy Sets (IFS) combined with the technique for order preference by similarity to an ideal solution (TOPSIS) method” to appraise and choose the best COTS vendor under the environment of group decision-making while considering reliability, delivery time, compatibility, vendor support and functionality as benefit criteria. In contrast, price and maintenance are the cost criteria. The considered case study demonstrated the presented case effectively

Online collaborative modelling for the goal-oriented requirement language

Software modelling in the software development realm is the demand of the present and future. While developing a large complex software system or working in a team of modelers with distinct expertise, it becomes a necessity to work in a collaborative modelling environment. Textual notations have a long history in software engineering because of their swift editing style, simple usage, and high scalability. Graphical models, on the other hand, are often easier to understand. However, software modelling is usually carried out in a non-collaborative fashion to produce the textual and graphical models of a software system, due to which the challenges faced by modelers are exacerbated when the process of software modelling has to be performed in a distributed environment. The foundation of this thesis is the tColab framework, which enables online collaborative software modelling for the User Requirements Notation (URN). Furthermore, the thesis demonstrates the implementation process followed to produce a graphical representation of a URN model constructed using the Textual Goal-oriented Requirement Language (TGRL), a view of the User Requirement Notation (URN). The combined use of textual and graphical notations brings the benefits of both types of notations to the software modeler.The architecture of the tColab framework is based on the integration of Eclipse Che and the Theia IDE (Integrated Development Environment), which makes it resilient and highly customizable. tColab runs exclusively over the web, independent of any platform or operating system. The actual model implementation is administered by Theia, which supports LSP (Language Server Protocol), so that the textual GRL models can be built and their appropriate strategies can be evaluated. Their corresponding graphical models can be generated in a browser IDE with the help of the Sprotty framework. Eclipse Che adds support for web collaboration where multiple modelers can contribute to building and modifying the textual models and observing their graphical results in an online collaborative manner. This initiative aims to provide a multi-user web version of the jUCMNav tool, which is the most comprehensive URN modelling tool to date but only supports a single user in an Eclipse-based modelling environment. The visualization of the Textual GRL in a graphical GRL model is shown along with an illustration of tools and methodology. Moreover, a comprehensive set of test cases is generated and executed to validate the analysis of TGRL models supported by tColab.Pour developer un logiciel en utilisant une approche dirigée par les modèles en équipe, il est impératif d’avoir accès à un environment de modélisation coopératif. Depuis les débuts du génie logiciel, les languages textuels se sont établies parce qu’ils sont simples à utiliser et parce qu’ils s'appliquent aussi aux développement à grande échelle. Par contre les représentations graphiques de modèles sont souvent plus faciles à comprendre. De nos jours, la plupart des outils de modélisation ne gèrent pas la collaboration, ce qui complique le développement dirigée par les modèles considérablement surtout pour les équipes qui travaillent à distance.Cette these propose tColab, un cadre d’application qui permets la création collaborative de modèles qui utilisent le language URN (User Requirements Notation). C’est la première étape de développement d’outil collaboratif qui a comme but finale la construction d'une version multi- utilisateur collaborative de l’outil jUCMNav, qui est l’outil de modélisation URN le plus compréhensif de nos jours, mais qui n’est que mono-utilisateur pour le moment.tColab est construit en fusionnant Eclipse Che et Theia IDE, et ainsi est robuste et facilement extensible. tColab tourne entièrement dans un navigateur web, et ne repose donc pas sur une plate-forme ou système d’exploitation spécifique. L’implémentation des modèles est gerée par Theia, qui est accessible par le protocol de serveur de languages (LSP - Language Server Protocol). Ceci permet la construction de modèles GRL textuelles et l’évaluation des stratégies offerts par GRL. Nous démontrons comment générer une représentation graphique d’un modele URN exprimé dans le language TGRL (Textual Goal-oriented Requirements Language). La représentation graphique des modèles est construite dans le navigateur web en utilisant le cadre d’application Sprotty. La possibilité de travailler simultanément avec une représentation textuelle et graphique facilite donc la création et la modification de modèles ainsi que leur compréhension. Eclipse Che ajoute l’aspect collaboratif qui permet à plusieurs utilisateurs de construire et de modifier les modèles textuelles d’une manière collaborative ainsi que de visualiser la représentation graphique des modèles en temps réel. Cette thèse propose également une suite de tests compréhensive qui sert à valider l’analyse de modèle TGRL fourni par tColab

Optimization of Intrusion Detection Systems Determined by Ameliorated HNADAM-SGD Algorithm

Information security is of pivotal concern for consistently streaming information over the widespread internetwork. The bottleneck flow of incoming and outgoing data traffic introduces the issues of malicious activities taken place by intruders, hackers and attackers in the form of authenticity obstruction, gridlocking data traffic, vandalizing data and crashing the established network. The issue of emerging suspicious activities is managed by the domain of Intrusion Detection Systems (IDS). The IDS consistently monitors the network for the identification of suspicious activities, and generates alarm and indication in the presence of malicious threats and worms. The performance of IDS is improved by using different machine learning algorithms. In this paper, the Nesterov-Accelerated Adaptive Moment Estimation–Stochastic Gradient Descent (HNADAM-SDG) algorithm is proposed to determine the performance of Intrusion Detection Systems IDS. The algorithm is used to optimize IDS systems by hybridization and tuning of hyperparameters. The performance of algorithm is compared with other classification algorithms such as logistic regression, ridge classifier and ensemble algorithms where the experimental analysis and computations show the improved accuracy with 99.8%, sensitivity with 99.7%, and specificity with 99.5%

A Machine-Learning Approach for Prediction of Water Contamination Using Latitude, Longitude, and Elevation

One of the significant issues that the world has faced in recent decades has been the estimation of water quality and location where safe drinking water is available. Due to the unexpected nature of the mode of water contamination, it is not easy to analyze the quality and maintain it. Some machine-learning techniques are used for predicting contaminating factors but there is no technique that can predict the contamination using latitude, longitude, and elevation. The main aim of this paper is to put factors such as water body location and elevation, which are used as inputs, into the different machine-learning techniques that predict the contamination. The results are reviewed and analyzed according to groundwater contamination and the chemical composition of the groundwater location. Non-changeable factors such as latitude, longitude, and elevation are used to predict pH, temperature, turbidity, dissolved oxygen hardness, chlorides, alkalinity, and chemical oxygen demand. Such a study has not been conducted in the past where location-based factors are used to predict the water contamination of any area. This research focuses on creating a relationship between the location base factors affecting the water contamination in a given area

A Machine-Learning Approach for Prediction of Water Contamination Using Latitude, Longitude, and Elevation

One of the significant issues that the world has faced in recent decades has been the estimation of water quality and location where safe drinking water is available. Due to the unexpected nature of the mode of water contamination, it is not easy to analyze the quality and maintain it. Some machine-learning techniques are used for predicting contaminating factors but there is no technique that can predict the contamination using latitude, longitude, and elevation. The main aim of this paper is to put factors such as water body location and elevation, which are used as inputs, into the different machine-learning techniques that predict the contamination. The results are reviewed and analyzed according to groundwater contamination and the chemical composition of the groundwater location. Non-changeable factors such as latitude, longitude, and elevation are used to predict pH, temperature, turbidity, dissolved oxygen hardness, chlorides, alkalinity, and chemical oxygen demand. Such a study has not been conducted in the past where location-based factors are used to predict the water contamination of any area. This research focuses on creating a relationship between the location base factors affecting the water contamination in a given area

The Use of Linaclotide in Children with Functional Constipation or Irritable Bowel Syndrome: A Retrospective Chart Review

Background: Linaclotide is a well-tolerated and effective agent for adults with functional constipation (FC) or irritable bowel syndrome with constipation (IBS-C). However, data in children are lacking. The aim of this study is to examine the efficacy and safety of linaclotide in children. Methods: We performed a retrospective review of children < 18 years old who started linaclotide at our institution (Nationwide Children's Hospital, Columbus, Ohio). We excluded children already using linaclotide or whom had an organic cause of constipation or abdominal pain. We recorded information on patient characteristics, medical and surgical history, symptoms, clinical response, course of treatment, and adverse events at baseline, first follow-up, and after 1 year of linaclotide use. A positive clinical response was based on the physician’s global assessment of symptoms at the time of the visit as documented. Results: We included 93 children treated with linaclotide for FC (n = 60) or IBS-C (n = 33); 60% were female; median age was 14.7 years (IQR 13.2–16.6). Forty-five percent of patients with FC and 42% with IBS-C had a positive clinical response at first follow-up a median of 2.5 and 2.4 months after starting linaclotide, respectively. Approximately a third of patients experienced adverse events and eventually 27% stopped using linaclotide due to adverse events. The most common adverse events were diarrhea, abdominal pain, nausea, and bloating. Conclusion: Nearly half of children with FC or IBS-C benefited from linaclotide, but adverse events were relatively common. Further prospective, controlled studies are needed to confirm these findings and to identify which patients are most likely to benefit from linaclotide