## 4 Results
### Literature Review
#### 4.2.4 Planning PD  
Some studies focus on ML-based project duration prediction in project planning: [PERSON] et al. [31] compared different ML techniques to forecast software development time, concluding that the Gaussian process algorithm has the highest accuracy. An effective ensemble averaging of three ML algorithms (SVM, NN, and Generalized Linear Models) was introduced in [32] for software project duration estimation. the paper [33] estimated the duration of diaphragm wall construction using a fusion of the Least Squares SVM and the firefly algorithm, which achieved very low deviation prediction. Moreover, the literature addresses AI-assisted scheduling issues in the construction sector: [PERSON] et al. [34] reviewed the research on automation in construction scheduling, where different AI approaches are employed: case-based reasoning, knowledge-based techniques, GAs, expert systems, and NNs. They concluded that GAs are dominant. Moreover, [PERSON] and [PERSON] [35] provided an automated project schedule planning framework that contains a knowledge-based expert system connected to Revid BIM and Primavera software. The developed AI-based scheduler retrieves construction design elements from BIM, and it provides the derived project activities and their sequencing on Primavera. The system validity was shown using a simple house building as a case study. Furthermore, the use of AI techniques for the IT project scheduling problem was manifested in a few investigations [36; 37; 38]. [PERSON] and [PERSON] [37] introduced an ML method for determining intelligent cooperation at IT project realization. The learning algorithm for a scheduling problem is presented with the results of computer experiments to prove its feasibility. A model for solving the software project scheduling problem using the firefly algorithm is provided in [36], which gives better results than GAs and ACO. In the work [38], ACO Extended and Max-Min Ant System heuristics were compared for the software project scheduling problem aimed at minimizing project duration. Findings revealed that ACO Extended is better than the Max-Min Ant System with respect to fitness value. Besides that, [PERSON] et al. [39] developed an NN estimation model to manipulate the timing problem for software projects. Their model predicts the estimation value of project time, which optimizes the scheduling process. There are many studies related to AI-based software project effort estimation:  
1. Fuzzy approaches: [PERSON] and [PERSON] [40] presented a model based on fuzzy logic. It was tested using the NASA93 dataset and concluded that the fuzzy model with triangular membership function outperforms the rest of the models. Furthermore, the authors in [41] provided a model by cascading fuzzy logic controllers, which improves the efficiency with clustering techniques. The NASA93 dataset was used as a case study, revealing that fuzzy models developed using subtractive clustering provide better results. [PERSON] et al. [42] presented an effective and accurate approach based on historical project data using the Gauss-Newton model to calibrate the parameters of the Constructive Cost Model and fuzzy logic to optimize it, thus Deming regression, expert judgment, and ML were also applied to enhance the model. [PERSON] et al. [43] consider fuzzy input values in NN;
2. Methods based on ML or/and NN: The work [44] suggested a k-nearest neighbour ML-algorithm, concluding that the combination of k-nearest neighbour and quadratic regression has the best response, accuracy improvement, and relative error reduction. [PERSON] et al. [45] presented a comparative study of different NN models (multilayerperceptron, general regression NN, radial basis function NN, and cascade correlation NN) and the International Software Benchmarking Standards Group dataset was used in the evaluation. The results showed that cascade correlation NN outperforms the other models.