Requirements prioritization based on multiple criteria using Artificial Intelligence techniques

Global software development (GSD) organizations encouraged to improve software quality of products. The GSD used highly skilled and advance technology at one place through the internet. The main challenges face in GSD with benefits are communication, coordination and control. These issues create ambiguities and incompleteness in requirements prioritization and traceability process due to lots of stakeholders and human efforts involved. Therefore, we proposed a framework to improve requirements prioritization and traceability process using artificial intelligent technique. The framework was evaluated using experiment and compared with existing solutions. Results described that proposed framework significantly improved requirements prioritization and traceability with less human interaction to reduce GSD challenges.

In a company producing off‐the‐shelf software for mass markets, the future development steps of the products cannot be negotiated with one or few customers. The decisions concerning the priorities of the requirements must be made within the company, the developer bearing all the financial risks included. This means that finding the right priorities for the requirements is important. However, requirements prioritization is recognized as a difficult activity in software product development. The literature offers methods for requirements prioritization, but many authors report that practices in companies are mostly informal. In this study, we evaluated two requirements prioritization methods in industrial product development projects. In the first case, the users of the system evaluated thepair‐wise comparison techniquefor prioritizing user needs. In the second case, practitioners evaluatedWiegers' methodfor change requests. In addition, we interviewed 11 practitioners from 6 companies about their current requirements prioritization practices and the models that they use as a basis of their prioritization decisions. Our findings indicate that prioritization methods may have limited ability to support decision‐making in a complex area like requirements prioritization in market‐driven product development. In addition, there are practical difficulties in the usage of methods, and therefore, prioritization results should be taken more as being indicative than as an ultimate truth. Copyright © 2006 John Wiley & Sons, Ltd.

A major environmental concern of today’s scientists is the inefficient exploitation of natural resources. The land is the ultimate source of wealth and the foundation on which civilization is constructed. Inappropriate land use, leads to destruction of the land resource, poverty and other social problems, and even to the destruction of civilization. To avoid such phenomena, land evaluation is employed, for rational land use planning and appropriate and sustainable use of natural and human resources (Rossiter, 1994). The management of land use is an interdisciplinary activity that relies on large amounts of information from different sources. Land evaluators need to collect information from soil surveyors, climatologists and census takers on land resource. They also need the expert knowledge of soil scientists, agronomists and economists on land use. In addition, land evaluators must select and apply the most appropriate analytical methods to evaluate land qualities and to combine these into overall physical and/or economic suitability. This evaluation is then calibrated against expert judgement and related experience. Finally they must present the results of the evaluation with reports and maps. This output has to be dynamic, considering the continuous refinement of the whole land evaluation process. The above characteristics of land evaluation denote that the management of such a process definitely requires the support of computer systems, especially expert systems, remote sensing and image processing systems, and geographical information systems (GIS). Such systems exist, but they are usually stand-alone units, hence human intervention (land evaluators) for the flow of information from one system towards the other is indispensable. Therefore, integrated systems are highly desirable. The latest research and development trends in this area progressively encompass Artificial Intelligence (AI) techniques to a greater extent, in order to achieve an optimal performance in the analysis of the vast geographical data. Expert systems were included early on, in an effort to model the domain knowledge of land evaluation from experts. Now, such systems introduce fuzzy logic to cope with uncertainty within the data sources and the inference procedure. Machine learning techniques are also included to model the land evaluation procedures when expert knowledge is insufficient or even absent. In general, there exists an amount of both symbolic and non-symbolic AI techniques, which scientists are keen on combining and integrating with traditional land information systems. This chapter is structured as follows. An overview of three of the most used AI techniques in land evaluation problems is given. Following that, the next section introduces ISLE (Tsoumakas and Vlahavas, 1999), an Intelligent System for Land Evaluation that is designed as a framework for the integration of AI techniques with a geographical information system. The final section discusses conclusions and future trends in this field.

Advances in artificial intelligence (AI) techniques that can be used for different purposes have enabled it to be used in many different industrial applications. These are mainly used for modeling, identification, optimization, prediction and control of complex systems under the influence of more than one parameter in industrial applications. With the increasing accuracy of AI techniques, it has also obtained a wide application area on friction stir welding (FSW), one of the production methods developed in recent years. In this study, commonly used AI techniques for FSW, results, accuracy and superiority of AI techniques are reviewed and evaluated. In addition, an overview of AI techniques for FSW in different material combinations is provided. Considering the articles examined; It is seen that welding speed, rotational speed, the plunge depth, spindle torque, shoulder design, base material, pin design/profile, tool type are used as input parameters and tensile strength, yield strength, elongation, hardness, wear rate, welding quality, residual stress, fatigue strength are used as output parameters. As can be seen from the studies, it made important contributions in deciding what input parameters should be in order to have the output parameter at the desired value. The most common used materials for FSW are Al, Ti, Mg, Brass, Cu and so on. When FSW studies using artificial intelligence techniques were examined, it was seen that 81% of the most used materials were AL alloys and 23% of them were made with dissimilar materials. The most commonly utilized AI techniques were said to be artificial neural networks (ANN), fuzzy logic, machine learning, meta-heuristic methods and hybrid systems. As a result of the examination, ANN was the most widely used method among these methods. However, in recent years, with the exploration of new hybrid methods it was seen that hybrid systems used with ANN have higher accuracy.

This paper presents a novel requirements prioritization (RP) method which complements ad-hoc ranking approaches and ordinal scale-based RP techniques with capabilities of handing the vague and subjective perceptions that stakeholders have when they rank candidate requirements. To consider the indeterminacy and the lack of knowledge of stakeholders, the suggested method represents requirements' rankings with Intuitionistic Fuzzy Sets. The final priority list of requirements with respect to the prioritization criteria is derived by applying a multi-criteria decision method that is based on objective criteria weights. The larger the hesitation associated with a requirements ranking according to a chosen criterion, the smaller would be the weight of this criterion for the calculation of the final priority list. Through a case study, we demonstrate that the method is suitable for prioritizing requirements in an agile development process.1

The past few years have witnessed a growing rate of attraction in adoption of Artificial Intelligence (AI) techniques to solve different engineering problems. Besides, Short Term Electrical Load Forecasting (STLF) is one of the important concerns of power systems and accurate load forecasting is vital for managing supply and demand of electricity. This study estimates short term electricity loads of Iran by means of Adaptive Neuro-Fuzzy Inference System (ANFIS), Artificial Neural Networks (ANN) and Genetic Algorithm (GA) which are the most successful AI techniques in this field. In order to improve forecasting accuracy, all AI techniques are equipped with preprocessing concept, and effects of this concept on performance of each AI technique are investigated. Finally, outcomes of the approaches are evaluated and compared by means of the mean absolute percentage error (MAPE). Results show that data preprocessing can significantly improve performance of the AI techniques. Meanwhile, ANFIS outcomes are more approximate to the actual loads than those of ANN and GA, so it can be considered as a suitable tool to deal with STLF problems.

Simple SummaryDistinguishing between benign vs. malignant bone lesions is often difficult on imaging. Many bone lesions are infrequent or rarely seen, and often only specialist radiologists have sufficient expertise to provide an accurate diagnosis. In addition, some benign bone tumours may exhibit potentially aggressive features that mimic malignant bone tumours, making the diagnosis even more difficult. The rapid development of artificial intelligence (AI) techniques has led to remarkable progress in image-recognition tasks, including the classification and characterization of various tumours. This study will review the most recent evidence for AI techniques in differentiating bone lesions on various imaging modalities using a systematic approach. Potential clinical applications of AI techniques for clinical diagnosis and management of bone lesions will also be discussed.An accurate diagnosis of bone tumours on imaging is crucial for appropriate and successful treatment. The advent of Artificial intelligence (AI) and machine learning methods to characterize and assess bone tumours on various imaging modalities may assist in the diagnostic workflow. The purpose of this review article is to summarise the most recent evidence for AI techniques using imaging for differentiating benign from malignant lesions, the characterization of various malignant bone lesions, and their potential clinical application. A systematic search through electronic databases (PubMed, MEDLINE, Web of Science, and clinicaltrials.gov) was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 34 articles were retrieved from the databases and the key findings were compiled and summarised. A total of 34 articles reported the use of AI techniques to distinguish between benign vs. malignant bone lesions, of which 12 (35.3%) focused on radiographs, 12 (35.3%) on MRI, 5 (14.7%) on CT and 5 (14.7%) on PET/CT. The overall reported accuracy, sensitivity, and specificity of AI in distinguishing between benign vs. malignant bone lesions ranges from 0.44–0.99, 0.63–1.00, and 0.73–0.96, respectively, with AUCs of 0.73–0.96. In conclusion, the use of AI to discriminate bone lesions on imaging has achieved a relatively good performance in various imaging modalities, with high sensitivity, specificity, and accuracy for distinguishing between benign vs. malignant lesions in several cohort studies. However, further research is necessary to test the clinical performance of these algorithms before they can be facilitated and integrated into routine clinical practice.

The intention stage of the human-computer interaction is a promising area for the application of artificial intelligence (AI) techniques. At this stage, the computer needs to have information on the intended tasks and actions of the user. At least three AI techniques – scripts, student models, and expert system dialogues – have the potential to help systems gather this information.

This paper presents a discussion of educational case studies used in security requirements assessment and requirements prioritization. Related to this, it introduces risk understanding as an added dimension to the requirements prioritization process. It should be self-evident that the final product should incorporate the requirements with the greatest value. Nevertheless, in a time when security is a preeminent concern it should also be clear that risk elements should also be considered. As such, activities to reconcile risk with value are always essential. However, since risk and value considerations are different, and sometimes opposed to each other, this paper presents a new process that will help decision makers reconcile these two factors within a single approach. This new process may also be incorporated into security requirements education and prioritization.

DDoS attacks have been the major threats for the Internet and can bring great loss to companies and governments. With the development of emerging technologies, such as cloud computing, Internet of things, artificial intelligence techniques, attackers can launch a huge volume of DDoS attacks with a lower cost, and it is much harder to detect and prevent DDoS attacks. Because DDoS traffic is similar to normal traffic. Some artificial intelligence techniques like machine learning algorithms have been used to classify DDoS attack traffic and detect DDoS attacks, such as Naive Bayes and Random forest tree. In the paper, we survey on the latest progress on the DDoS attack detection using artificial intelligence techniques and give recommendations on artificial intelligence techniques to be used in DDoS attack detection and prevention.

A comprehensive overview on signal processing and artificial intelligence techniques applications in classification of power quality disturbances

Underground mining activities cause ground deformations that threaten the stability of surface infrastructure and ecosystems, posing risks to both the environment and human populations. In response to these threats, this study focuses on developing a method for forecasting and modelling ground deformations, which are a consequence of underground mining activities, using advanced artificial intelligence (AI) techniques. The primary goal is to create a model utilising data from Differential Interferometric Synthetic Aperture Radar (DInSAR) and specialised mining data, enabling precise monitoring and forecasting of future changes which will support an adequate upgrade of the decision-making procedure in the mining industry.   The study employed two categories of neural networks: Convolutional Neural Networks (CNN) and Feedforward Neural Networks (FNN). In the application FNN, a detailed analysis was conducted on a per-pixel basis across the entire dataset. Each pixel, representing a specific point on the terrain, was analysed with its associated feature vector. This vector comprised multiple attributes derived from the mining data and DInSAR images, effectively capturing the local characteristics of each point, such as its relative position, historical deformation patterns, and proximity to mining activities. For the CNN method, the study focused on exploring the impact of different kernel sizes on model performance. Kernels in CNNs are small matrices used to process data across the image, essential for extracting and learning features crucial for understanding and predicting ground deformations. Varying kernel sizes allow the network to capture different aspects of the data. Considered features included the distance from the centre of the subsidence basin and the mining face at different time intervals. In the context of forecasting, the use of high-quality data is crucial. Unfortunately, some DInSAR images exhibited noise, due in part to a lack of stable coherence and adverse atmospheric effects. A key aspect of the study was therefore the creation and testing of a classifier for the suitability of DInSAR images for forecasting purposes. The analyses showed that the developed classifier achieved an accuracy of 83%. The training data for the prediction study came from the Budryk-Knothe method. The network was tasked with reproducing the operation of this method while simultaneously predicting six days ahead. The models were evaluated based on the mean squared error (MSE) in the areas of the subsidence basin. The test set consisted of specially prepared and trimmed DInSAR images. The FNN-based solution achieved the best results. For this network, satisfactory accuracy was achieved in determining the direction of settlement, with an MSE of 0.12, corresponding to a percentage error of approximately 10% (5 cm for a subsidence of 50 cm).   The  results from the study highlight the significant potential of integrating AI techniques with advanced geodetic methods, opening new possibilities in monitoring the impact of mining on the environment. Future work may focus on further optimization of AI algorithms to increase forecasting accuracy over longer periods and in various geological and operational conditions.

This study presents a comprehensive analysis of artificial intelligence (AI) techniques to predict healthcare expenditure per capita (pcHCE) in Turkey. Well‐known AI techniques such as random forest (RF), artificial neural network (ANN), multiple linear regression (MLR), support vector regression (SVR), and relevance vector machine (RVM) were used to forecast pcHCE. Twenty‐nine years of historical data from 1990 to 2018 were used in the training and testing phases of the models. Gross domestic product per capita, life expectancy at birth, unemployment rate, crude birth rate, and the number of physicians and hospitals were used as input variables for the analysis. A genetic algorithm‐based feature selection (GAFS) method was applied to all models to select the relevant and optimal feature subset in the prediction of pcHCE. The comparative results showed that the GAFS method improved the overall performance of all base AI models. The hybrid GAFS‐RF model performed best among all AI‐based prediction methods, with a 99.86% correlation of determination (R2) value at the testing stage.

Piles are used in substructures of different infrastructural constructions. Due to the complex nature of soil, there are different empirical models to predict the bearing capacity of piles. The objective of the present study is to develop prediction models for vertical loaded driven piles in cohesionless soil using a novel artificial intelligence (AI) technique multi-objective genetic programming (MOGP). Two other recent AI techniques, multivariate adaptive regression spline (MARS) and functional network (FN), are also used to compare the efficacy of different AI techniques. The results MOGP, MARS and FN models are compared in terms of different statistical parameters such as correlation coefficient (R), absolute average error, root-mean–square-error, overfitting ratio and P50. A ranking criteria approach has been implemented to assess the performance of the prediction models developed in this study along with other AI and empirical models available in the literature. The predictive model equations based on the MOGP, MARS and FN are also presented.

Shear connectors play a prominent role in the design of steel-concrete composite systems. The behavior of shear connectors is generally determined through conducting push-out tests. However, these tests are costly and require plenty of time. As an alternative approach, soft computing (SC) can be used to eliminate the need for conducting push-out tests. This study aims to investigate the application of artificial intelligence (AI) techniques, as sub-branches of SC methods, in the behavior prediction of an innovative type of C-shaped shear connectors, called Tilted Angle Connectors. For this purpose, several push-out tests are conducted on these connectors and the required data for the AI models are collected. Then, an adaptive neuro-fuzzy inference system (ANFIS) is developed to identify the most influencing parameters on the shear strength of the tilted angle connectors. Totally, six different models are created based on the ANFIS results. Finally, AI techniques such as an artificial neural network (ANN), an extreme learning machine (ELM), and another ANFIS are employed to predict the shear strength of the connectors in each of the six models. The results of the paper show that slip is the most influential factor in the shear strength of tilted connectors and after that, the inclination angle is the most effective one. Moreover, it is deducted that considering only four parameters in the predictive models is enough to have a very accurate prediction. It is also demonstrated that ELM needs less time and it can reach slightly better performance indices than those of ANN and ANFIS.