In the early software development stages, the aim of estimation is to obtain a rough understanding of the timeline and resources required to implement a potential project. The current study is ...devoted to a method of preliminary estimation applicable at the beginning of the software development life cycle when the level of uncertainty is high. The authors’ concepts of the estimation life cycle, the estimable items breakdown structure, and a system of working-time balance equations in conjunction with an agile-fashioned sizing approach are used. To minimize the experts’ working time spent on preliminary estimation, the authors applied a decision support procedure based on integer programming and the analytic hierarchy process. The method’s outcomes are not definitive enough to make commitments; instead, they are supposed to be used for communication with project stakeholders or as inputs for the subsequent estimation stages. For practical usage of the preliminary estimation method, a semistructured business process is proposed.
For decision-making and governance, smart cities depend on tracking data collected via a substantial percentage of wireless sensing nodes. However, several limitations affect Wireless Sensor Network ...(WSN)-based Internet of Things (IoT) services, such as low battery life, recurrent connectivity problems due to multi-hop connections, and a limited channel capacity. Furthermore, in many systems, clustering and routing are handled independently, which prevents the adaptation of effective strategies for optimal energy usage and prolonged network lifespan. This research gathers data from heterogeneous IoT nodes linked via WSN and distributed across a smart infrastructure. There are two interrelated problems to be addressed with respect to energy efficiency computations: clustering and routing. We provide a new clustering strategy through which efficient routing of critical and regular data is handled. As a result, both clustering and routing have been significantly strengthened, which balances the communication load across different sectors of the smart infrastructure network. Minkowski distance and ranking strategy are used for routing and selecting cluster heads, respectively. Deterministic distributed–time division multiple access (DD-TDMA) scheduling is employed to balance the communication load across the network. The experimental results show that the proposed work outperforms some of the popular cluster-based routing strategies.
Estimation is an essential step of software development project planning that has a significant impact on project success—underestimation often leads to problems with the delivery or even causes ...project failure. An important aspect that the classical estimation methods are usually missing is the Agile nature of development processes in the implementation phase. The estimation method proposed in this article aims at software development projects implemented by Scrum teams with differentiated specializations. The method is based on the authors’ system of working-time balance equations and the approach to measuring project scope with time-based units—normalized development estimates. In order to reduce efforts spent on the estimation itself, an analysis of dependencies among project tasks is not mandatory. The outputs of the methods are not recommended to be treated as commitments; instead, they are supposed to be used to inform project stakeholders about the forecasted duration of a potential project. The method is simple enough to allow even an inexpensive spreadsheet-based implementation.
The Artificial Intelligence Recommender System has emerged as a significant research interest. It aims at helping users find things online by offering recommendations that closely fit their ...interests. Recommenders for research papers have appeared over the last decade to make it easier to find publications associated with the field of researchers’ interests. However, due to several issues, such as copyright constraints, these methodologies assume that the recommended articles’ contents are entirely openly accessible, which is not necessarily the case. This work demonstrates an efficient model, known as RPRSCA: Research Paper Recommendation System Using Effective Collaborative Approach, to address these uncertain systems for the recommendation of quality research papers. We make use of contextual metadata that are publicly available to gather hidden relationships between research papers in order to personalize recommendations by exploiting the advantages of collaborative filtering. The proposed system, RPRSCA, is unique and gives personalized recommendations irrespective of the research subject. Thus, a novel collaborative approach is proposed that provides better performance. Using a publicly available dataset, we found that our proposed method outperformed previous uncertain methods in terms of overall performance and the capacity to return relevant, valuable, and quality publications at the top of the recommendation list. Furthermore, our proposed strategy includes personalized suggestions and customer expertise, in addition to addressing multi-disciplinary concerns.
The paper deals with the issues concerning the modifications of the bithreshold neuron whose activation functions provide better ability of the solving the classification problems. The model of ...smoothed local bithreshold neuron is proposed, which is capable to recognize compact finite set of patterns in n-dimensional space. We design a binary classifier on the base of the feed-forward neural network whose hidden layer consists of such neurons with modified activations, propose the synthesis algorithm and estimate its time complexity alongside with the networks size. The simulation results demonstrate that the application of modified activations improves the accuracy of classification.
The model of the 3-layer feed-forward neural network is introduced whose first hidden layer consists of bithreshold neurons and the other layers-of single-threshold ones. The proposed network is ...capable to recognize compact finite set of patterns using a union of hyperrectangular decision regions in n-dimensional space. We design a multiclass classifier on the base of such network, propose the synthesis algorithm for it and estimate the networks size as well as the time of computations. The simulation results demonstrate that the application of the additional hidden layer improves the accuracy of classification.
Multithreshold Neural Units and Networks Kotsovsky, Vladyslav; Batyuk, Anatoliy
2023 IEEE 18th International Conference on Computer Science and Information Technologies (CSIT),
2023-Oct.-19
Conference Proceeding
We deal with theoretical issues concerning the application of the multithreshold architecture in the theory of neural computation. The way of representing a multithreshold function by 2-layer neural ...network consisting of single-threshold neural units with equal weights is established in the paper. We also study the complexity of the problem of the learning k-threshold neurons and prove that this problem is NP-hard if the number of thresholds is greater than one.
The paper deals with the issues concerning the application of methods of harmonic analysis in the theory of bithreshold neural units. We show that the values of spectral coefficients of first two ...orders of a dichotomy function of the finite set in n-dimensional space give answer to the question of the bithreshold separability of two subsets in which this dichotomy results. The established properties of bithreshold neurons can be useful in the design of off-line learning algorithms developed on the base of the spectral approach.
Decision List-Based Representation of Bithreshold Functions Kotsovsky, Vladyslav; Batyuk, Anatoliy
2021 IEEE 16th International Conference on Computer Sciences and Information Technologies (CSIT),
2021-Sept.-22, Volume:
1
Conference Proceeding
The paper deals with the issues concerning the computation of Boolean bithreshold functions by decision lists. We give and justify conditions ensuring the possibility of the realization of a decision ...list by a bithreshold neural unit. It is shown that a decision list comprising monomials and special 2-term-2-DNF formulas represents a bithreshold Boolean function. The simple and quick learning algorithm is proposed, which yields the weights and the thresholds of the sought bithreshold neuron. We also give the time complexity of this algorithm and estimate the upper bound on the size of the weight vector and thresholds.
Having precise information about how business processes are performed in real-life is an important competitive advantage for a modern company. The digital footprint left by processes in IT systems ...can be transformed to event data for further analysis by process mining techniques. One of the biggest challenge of process mining is to deal with streaming event data and provide operational support for the on-going processes. Current paper is devoted to a streaming process discovery algorithm implemented by the authors upon a near real-time process monitoring platform which is based on the lambda architecture.
