Forecasting Stock Exchange Data using Group Method of Data Handling Neural Network Approach

The increasing uncertainty of the natural world has motivated computer scientists to seek out the best approach to technological problems. Nature-inspired problem-solving approaches include meta-heuristic methods that are focused on evolutionary computation and swarm intelligence. One of these problems significantly impacting information is forecasting exchange index, which is a serious concern with the growth and decline of stock as there are many reports on loss of financial resources or profitability. When the exchange includes an extensive set of diverse stock, particular concepts and mechanisms for physical security, network security, encryption, and permissions should guarantee and predict its future needs. This study aimed to show it is efficient to use the group method of data handling (GMDH)-type neural networks and their application for the classification of numerical results. Such modeling serves to display the precision of GMDH-type neural networks. Following the US withdrawal from the Joint Comprehensive Plan of Action in April 2018, the behavior of the stock exchange data stream and commend algorithms has not been able to predict correctly and fit in the network satisfactorily. This paper demonstrated that Group Method Data Handling is most likely to improve inductive self-organizing approaches for addressing realistic severe problems such as the Iranian financial market crisis. A new trajectory would be used to verify the consistency of the obtained equations hence the models' validity

Effects of a proper feature selection on prediction and optimization of drilling rate using intelligent techniques

One of the important factors during drilling times is the rate of penetration (ROP), which is controlled based on different variables. Factors affecting different drillings are of paramount importance. In the current research, an attempt was made to better recognize drilling parameters and optimize them based on an optimization algorithm. For this purpose, 618 data sets, including RPM, flushing media, and compressive strength parameters, were measured and collected. After an initial investigation, the compressive strength feature of samples, which is an important parameter from the rocks, was used as a proper criterion for classification. Then using intelligent systems, three different levels of the rock strength and all data were modeled. The results showed that systems which were classified based on compressive strength showed a better performance for ROP assessment due to the proximity of features. Therefore, these three levels were used for classification. A new artificial bee colony algorithm was used to solve this problem. Optimizations were applied to the selected models under different optimization conditions, and optimal states were determined. As determining drilling machine parameters is important, these parameters were determined based on optimal conditions. The obtained results showed that this intelligent system can well improve drilling conditions and increase the ROP value for three strength levels of the rocks. This modeling system can be used in different drilling operations

Multiobjective strategies for New Product Development in the pharmaceutical industry

New Product Development (NPD) constitutes a challenging problem in the pharmaceutical industry, due to the characteristics of the development pipeline. Formally, the NPD problem can be stated as follows: select a set of R&D projects from a pool of candidate projects in order to satisfy several criteria (economic profitability, time to market) while coping with the uncertain nature of the projects. More precisely, the recurrent key issues are to determine the projects to develop once target molecules have been identified, their order and the level of resources to assign. In this context, the proposed approach combines discrete event stochastic simulation (Monte Carlo approach) with multiobjective genetic algorithms (NSGAII type, Non-Sorted Genetic Algorithm II) to optimize the highly combinatorial portfolio management problem. In that context, Genetic Algorithms (GAs) are particularly attractive for treating this kind of problem, due to their ability to directly lead to the so-called Pareto front and to account for the combinatorial aspect. This work is illustrated with a study case involving nine interdependent new product candidates targeting three diseases. An analysis is performed for this test bench on the different pairs of criteria both for the bi- and tricriteria optimization: large portfolios cause resource queues and delays time to launch and are eliminated by the bi- and tricriteria optimization strategy. The optimization strategy is thus interesting to detect the sequence candidates. Time is an important criterion to consider simultaneously with NPV and risk criteria. The order in which drugs are released in the pipeline is of great importance as with scheduling problems

Multiobjective strategies for New Product Development in the pharmaceutical industry

New Product Development (NPD) constitutes a challenging problem in the pharmaceutical industry, due to the characteristics of the development pipeline. Formally, the NPD problem can be stated as follows: select a set of R&D projects from a pool of candidate projects in order to satisfy several criteria (economic profitability, time to market) while coping with the uncertain nature of the projects. More precisely, the recurrent key issues are to determine the projects to develop once target molecules have been identified, their order and the level of resources to assign. In this context, the proposed approach combines discrete event stochastic simulation (Monte Carlo approach) with multiobjective genetic algorithms (NSGAII type, Non-Sorted Genetic Algorithm II) to optimize the highly combinatorial portfolio management problem. In that context, Genetic Algorithms (GAs) are particularly attractive for treating this kind of problem, due to their ability to directly lead to the so-called Pareto front and to account for the combinatorial aspect. This work is illustrated with a study case involving nine interdependent new product candidates targeting three diseases. An analysis is performed for this test bench on the different pairs of criteria both for the bi- and tricriteria optimization: large portfolios cause resource queues and delays time to launch and are eliminated by the bi- and tricriteria optimization strategy. The optimization strategy is thus interesting to detect the sequence candidates. Time is an important criterion to consider simultaneously with NPV and risk criteria. The order in which drugs are released in the pipeline is of great importance as with scheduling problems

Opportunities for the digital transformation of the banana sector supply chain based on software with artificial intelligence

Artificial intelligence offers great opportunities for the supply chain, being this a competitive advantage for today’s changing market. This article aims to identify the impacts and opportunities that artificial intelligence software can offer to facilitate the operation and improve the performance of the supply chain in the banana sector in Colombia. The work methodology consists of six steps in which a total of 72 investigations were obtained. The sources of information were four databases. As a main conclusion, the supply chain of the banana sector has everything necessary for intelligent software based solutions to be implemented in order to achieve adaptation, flexibility and sensitivity to the context and domain of execution

Decision support continuum paradigm for cardiovascular disease: Towards personalized predictive models

Clinical decision making is a ubiquitous and frequent task physicians make in their daily clinical practice. Conventionally, physicians adopt a cognitive predictive modelling process (i.e. knowledge and experience learnt from past lecture, research, literature, patients, etc.) for anticipating or ascertaining clinical problems based on clinical risk factors that they deemed to be most salient. However, with the inundation of health data and the confounding characteristics of diseases, more effective clinical prediction approaches are required to address these challenges. Approximately a few century ago, the first major transformation of medical practice took place as science-based approaches emerged with compelling results. Now, in the 21st century, new advances in science will once again transform healthcare. Data science has been postulated as an important component in this healthcare reform and has received escalating interests for its potential for ‘personalizing’ medicine. The key advantages of having personalized medicine include, but not limited to, (1) more effective methods for disease prevention, management and treatment, (2) improved accuracy for clinical diagnosis and prognosis, (3) provide patient-oriented personal health plan, and (4) cost containment. In view of the paramount importance of personalized predictive models, this thesis proposes 2 novel learning algorithms (i.e. an immune-inspired algorithm called the Evolutionary Data-Conscious Artificial Immune Recognition System, and a neural-inspired algorithm called the Artificial Neural Cell System for classification) and 3 continuum-based paradigms (i.e. biological, time and age continuum) for enhancing clinical prediction. Cardiovascular disease has been selected as the disease under investigation as it is an epidemic and major health concern in today’s world. We believe that our work has a meaningful and significant impact to the development of future healthcare system and we look forward to the wide adoption of advanced medical technologies by all care centres in the near future.Open Acces

Advanced analytical methods for fraud detection: a systematic literature review

The developments of the digital era demand new ways of producing goods and rendering services. This fast-paced evolution in the companies implies a new approach from the auditors, who must keep up with the constant transformation. With the dynamic dimensions of data, it is important to seize the opportunity to add value to the companies. The need to apply more robust methods to detect fraud is evident. In this thesis the use of advanced analytical methods for fraud detection will be investigated, through the analysis of the existent literature on this topic. Both a systematic review of the literature and a bibliometric approach will be applied to the most appropriate database to measure the scientific production and current trends. This study intends to contribute to the academic research that have been conducted, in order to centralize the existing information on this topic

Development of soft computing and applications in agricultural and biological engineering

Soft computing is a set of “inexact” computing techniques, which are able to model and analyze very complex problems. For these complex problems, more conventional methods have not been able to produce cost-effective, analytical, or complete solutions. Soft computing has been extensively studied and applied in the last three decades for scientific research and engineering computing. In agricultural and biological engineering, researchers and engineers have developed methods of fuzzy logic, artificial neural networks, genetic algorithms, decision trees, and support vector machines to study soil and water regimes related to crop growth, analyze the operation of food processing, and support decision-making in precision farming. This paper reviews the development of soft computing techniques. With the concepts and methods, applications of soft computing in the field of agricultural and biological engineering are presented, especially in the soil and water context for crop management and decision support in precision agriculture. The future of development and application of soft computing in agricultural and biological engineering is discussed