Search results for: multi-objective evolutionary algorithms "MOEA"

Authors: Sergo Tsiramua, Sulkhan Sulkhanishvili, Elisabed Asabashvili, Lazare Kvirtia

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The paper presents logical-probabilistic methods, models, and algorithms for reliability assessment of complex systems, based on which a web application for structural analysis and reliability assessment of systems was created. It is important to design systems based on structural analysis, research, and evaluation of efficiency indicators. One of the important efficiency criteria is the reliability of the system, which depends on the components of the structure. Quantifying the reliability of large-scale systems is a computationally complex process, and it is advisable to perform it with the help of a computer. Logical-probabilistic modeling is one of the effective means of describing the structure of a complex system and quantitatively evaluating its reliability, which was the basis of our application. The reliability assessment process included the following stages, which were reflected in the application: 1) Construction of a graphical scheme of the structural reliability of the system; 2) Transformation of the graphic scheme into a logical representation and modeling of the shortest ways of successful functioning of the system; 3) Description of system operability condition with logical function in the form of disjunctive normal form (DNF); 4) Transformation of DNF into orthogonal disjunction normal form (ODNF) using the orthogonalization algorithm; 5) Replacing logical elements with probabilistic elements in ODNF, obtaining a reliability estimation polynomial and quantifying reliability; 6) Calculation of “weights” of elements of system. Using the logical-probabilistic methods, models and algorithms discussed in the paper, a special software was created, by means of which a quantitative assessment of the reliability of systems of a complex structure is produced. As a result, structural analysis of systems, research, and designing of optimal structure systems are carried out.

Keywords: complex systems, logical-probabilistic methods, orthogonalization algorithm, reliability of systems, “weights” of elements

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Authors: Feng-Sheng Wang, Chao-Ting Cheng

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Developing a drug from conception to launch is costly and time-consuming. Computer-aided methods can reduce research costs and accelerate the development process during the early drug discovery and development stages. This study developed a fuzzy multi-objective hierarchical optimization framework for identifying potential anticancer targets in a metabolic model. First, RNA-seq expression data of colorectal cancer samples and their healthy counterparts were used to reconstruct tissue-specific genome-scale metabolic models. The aim of the optimization framework was to identify anticancer targets that lead to cancer cell death and evaluate metabolic flux perturbations in normal cells that have been caused by cancer treatment. Four objectives were established in the optimization framework to evaluate the mortality of cancer cells for treatment and to minimize side effects causing toxicity-induced tumorigenesis on normal cells and smaller metabolic perturbations. Through fuzzy set theory, a multiobjective optimization problem was converted into a trilevel maximizing decision-making (MDM) problem. The applied nested hybrid differential evolution was applied to solve the trilevel MDM problem using two nutrient media to identify anticancer targets in the genome-scale metabolic model of colorectal cancer, respectively. Using Dulbecco’s Modified Eagle Medium (DMEM), the computational results reveal that the identified anticancer targets were mostly involved in cholesterol biosynthesis, pyrimidine and purine metabolisms, glycerophospholipid biosynthetic pathway and sphingolipid pathway. However, using Ham’s medium, the genes involved in cholesterol biosynthesis were unidentifiable. A comparison of the uptake reactions for the DMEM and Ham’s medium revealed that no cholesterol uptake reaction was included in DMEM. Two additional media, i.e., a cholesterol uptake reaction was included in DMEM and excluded in HAM, were respectively used to investigate the relationship of tumor cell growth with nutrient components and anticancer target genes. The genes involved in the cholesterol biosynthesis were also revealed to be determinable if a cholesterol uptake reaction was not induced when the cells were in the culture medium. However, the genes involved in cholesterol biosynthesis became unidentifiable if such a reaction was induced.

Keywords: Cancer metabolism, genome-scale metabolic model, constraint-based model, multilevel optimization, fuzzy optimization, hybrid differential evolution

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Authors: Okay Gunes

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Unsatisfactory experiences due to an information shortage regarding the future pay-offs of actual choices, yield satisficing decision-making. This research will examine, for the first time in the literature, the motivation behind suboptimal decisions due to uncertainty by subjecting Adam Smith’s and Jeremy Bentham’s assumptions about the nature of the actions that lead to satisficing behavior, in order to clarify the theoretical background of a “consumption-based satisfactory time” concept. The contribution of this paper with respect to the existing literature is threefold: Firstly, it is showed in this paper that Adam Smith’s uncertainty is related to the problem of the constancy of ideas and not related directly to beliefs. Secondly, possessions, as in Jeremy Bentham’s oeuvre, are assumed to be just as pleasing, as protecting and improving the actual or expected quality of life, so long as they reduce any displeasure due to the undesired outcomes of uncertainty. Finally, each consumption decision incurs its own satisfactory time period, owed to not feeling hungry, being healthy, not having transportation…etc. This reveals that the level of satisfaction is indeed a behavioral phenomenon where its value would depend on the simultaneous satisfaction derived from all activities.

Keywords: decision-making, idea and belief, satisficing, uncertainty

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Authors: Zidani Abla

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Lymphoma is a common type of cancer that affects the lymphatic system, including the lymph nodes, spleen and other associated organs. There are two main types of lymphoma: Hodgkin's lymphoma and non-Hodgkin's lymphoma. The epidemiological, clinical and biological features of lymphoma are poorly studied in Algeria. The main objective of our study is to investigate the epidemiological, clinical, paraclinical, etiological, evolutionary and biological characteristics of non-Hodgkin's lymphoma (NHL) in the hematology department of the University Hospital Center (HUC) of Batna. This is a study of 10 patients diagnosed at Batna University Hospital. 70% were male and 30% female (sex ratio M/F= 2.33). Median age was 51.7 years. Pain, especially abdominal pain, was the main reason for consultation. Stage IV predominated (40%), followed by stage III (20%). Abdominal adenopathies (34%) were the most abundant. Secondary hepatic localization was predominant. Large B-cell NHL predominated, accounting for 60% of cases, followed by small B-cell NHL (30%). Serology for hepatitis B and C, and human immunodeficiency virus (HIV) was negative. Biologically, a predominance of hyperleukocytosis, polynuclear neutrophilic leukocytosis, lymphopenia and hypoalbuminemia were present in the majority of cases. In summary, our results remain to be compared with other works for other periods and other regions in order to generalize lymphoma percentages for the entire Algerian population.

Keywords: non Hodgkin's lymphoma, epidemiology, clinic, biology

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Authors: Lanchi Xie, Zhihui Li, Zhigang Li, Guiqiang Wang, Lei Xu, Yuwen Yan

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Image-based facial features can be classified into category recognition features and individual recognition features. Current automated face recognition systems extract a specific feature vector of different dimensions from a facial image according to their pre-trained neural network. However, to improve the efficiency of parameter calculation, an algorithm generally reduces the image details by pooling. The operation will overlook the details concerned much by forensic experts. In our experiment, we adopted a variety of face recognition algorithms based on deep learning, compared a large number of naturally collected face images with the known data of the same person's frontal ID photos. Downscaling and manual handling were performed on the testing images. The results supported that the facial recognition algorithms based on deep learning detected structural and morphological information and rarely focused on specific markers such as stains and moles. Overall performance, distribution of genuine scores and impostor scores, and likelihood ratios were tested to evaluate the accuracy of biometric systems and forensic experts. Experiments showed that the biometric systems were skilled in distinguishing category features, and forensic experts were better at discovering the individual features of human faces. In the proposed approach, a fusion was performed at the score level. At the specified false accept rate, the framework achieved a lower false reject rate. This paper contributes to improving the interpretability of the objective method of facial comparison and provides a novel method for human-machine collaboration in this field.

Keywords: likelihood ratio, automated facial recognition, facial comparison, biometrics

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Authors: L. Salomon, P. Sklenar

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The Andes hold the highest plant species diversity in the world. How this occurred is one of the most intriguing questions in studies addressing the origin and patterning of plant diversity worldwide. Recently, the explosive adaptive radiations found in high Andean groups have been pointed as triggers to this spectacular diversity. The Andes is the species-richest area for the biggest genus from the Asteraceae family: Senecio. There, the genus presents an incredible diversity of species, striking growth form variation, and large niche span. Even when some studies tried to disentangle the evolutionary story for some Andean species in Senecio, they obtained partially resolved and low supported phylogenies, as expected for recently radiated groups. The high-throughput sequencing (HTS) approaches have proved to be a powerful tool answering phylogenetic questions in those groups whose evolutionary stories are recent and traditional techniques like Sanger sequencing are not informative enough. Although these tools have been used to understand the evolution of an increasing number of Andean groups, nowadays, their scope has not been applied for Senecio. This project aims to contribute to a better knowledge of the mechanisms shaping the hyper diversity of Senecio in the Andean region, using HTS focusing on Senecio ser. Culcitium (Asteraceae), recently recircumscribed. Firstly, reconstructing a highly resolved and supported phylogeny, and after assessing the role of allopatric differentiation, hybridization, and genome duplication in the diversification of the group. Using the Hyb-Seq approach, combining target enrichment using Asteraceae COS loci baits and genome skimming, more than 100 new accessions were generated. HybPhyloMaker and HybPiper pipelines were used for the phylogenetic analyses, and another pipeline in development (Paralogue Wizard) was used to deal with paralogues. RAxML was used to generate gene trees and Astral for species tree reconstruction. Phyparts were used to explore as first step of gene tree discordance along the clades. Fully resolved with moderated supported trees were obtained, showing Senecio ser. Culcitium as monophyletic. Within the group, some species formed well-supported clades with morphologically related species, while some species would not have exclusive ancestry, in concordance with previous studies using amplified fragment length polymorphism (AFLP) showing geographical differentiation. Discordance between gene trees was detected. Paralogues were detected for many loci, indicating possible genome duplications; ploidy level estimation using flow cytometry will be carried out during the next months in order to identify the role of this process in the diversification of the group. Likewise, TreeSetViz package for Mesquite, hierarchical likelihood ratio congruence test using Concaterpillar, and Procrustean Approach to Cophylogeny (PACo), will be used to evaluate the congruence among different inheritance patterns. In order to evaluate the influence of hybridization and Incomplete Lineage Sorting (ILS) in each resultant clade from the phylogeny, Joly et al.'s 2009 method in a coalescent scenario and Paterson’s D-statistic will be performed. Even when the main discordance sources between gene trees were not explored in detail yet, the data show that at least to some degree, processes such as genome duplication, hybridization, and/or ILS could be involved in the evolution of the group.

Keywords: adaptive radiations, Andes, genome duplication, hybridization, Senecio

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Authors: Theodorus Holtzhausen

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With a growing number of professionals in healthcare moving freely between countries and also in general a more mobile global workforce, awareness of cultural differences have become more urgent for health care workers to apply proper care. There is a slowly emerging trend in health care due to globalisation that may create a more uniform cultural base for administering healthcare, but it is still very vulnerable to being hijacked and misdirected by major commercial interests. Veterinary clinics and medical clinics promoting alternative remedies lacking evidence based support and simultaneously practicing medicine as a science have become more common. Such ‘holistic’ clinics see these remedies more as a belief system causing no harm with minimal impact but with added financial benefit to the facility. With the inarguable acceptance and realisation of the interconnection between evolutionary aspects of cognition, knowledge and culture as a global but vulnerable cognition-gaining process affecting us all, we can see the enormous responsibility we carry. Such a responsibility for creating global well-being calling for an universally applicable ethic. Such an ethic with the potential of having significant impact on our cognition gaining process.

Keywords: veterinary health care, ethics, wellbeing, veterinary clinics

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Authors: Supria Sarkar, Vasit Sagan, Sourav Bhadra, Meghnath Pokharel, Felix B.Fritschi

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Soybean and their derivatives are very important agricultural commodities around the world because of their wide applicability in human food, animal feed, biofuel, and industries. However, the significance of soybean production depends on the quality of the soybean seeds rather than the yield alone. Seed composition is widely dependent on plant physiological properties, aerobic and anaerobic environmental conditions, nutrient content, and plant phenological characteristics, which can be captured by high temporal resolution remote sensing datasets. Planet scope (PS) satellite images have high potential in sequential information of crop growth due to their frequent revisit throughout the world. In this study, we estimate soybean seed composition while the plants are in the field by utilizing PlanetScope (PS) satellite images and different machine learning algorithms. Several experimental fields were established with varying genotypes and different seed compositions were measured from the samples as ground truth data. The PS images were processed to extract 462 hand-crafted vegetative and textural features. Four machine learning algorithms, i.e., partial least squares (PLSR), random forest (RFR), gradient boosting machine (GBM), support vector machine (SVM), and two recurrent neural network architectures, i.e., long short-term memory (LSTM) and gated recurrent unit (GRU) were used in this study to predict oil, protein, sucrose, ash, starch, and fiber of soybean seed samples. The GRU and LSTM architectures had two separate branches, one for vegetative features and the other for textures features, which were later concatenated together to predict seed composition. The results show that sucrose, ash, protein, and oil yielded comparable prediction results. Machine learning algorithms that best predicted the six seed composition traits differed. GRU worked well for oil (R-Squared: of 0.53) and protein (R-Squared: 0.36), whereas SVR and PLSR showed the best result for sucrose (R-Squared: 0.74) and ash (R-Squared: 0.60), respectively. Although, the RFR and GBM provided comparable performance, the models tended to extremely overfit. Among the features, vegetative features were found as the most important variables compared to texture features. It is suggested to utilize many vegetation indices for machine learning training and select the best ones by using feature selection methods. Overall, the study reveals the feasibility and efficiency of PS images and machine learning for plot-level seed composition estimation. However, special care should be given while designing the plot size in the experiments to avoid mixed pixel issues.

Keywords: agriculture, computer vision, data science, geospatial technology

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Authors: Jun Zhou, Xingping Wang, Paul Milbourne

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Evidences in the world shows that the industry and population have being greatly concentrated in metropolitan regions which is getting to be the most important area for the economic power and people living standard in the future. In the meanwhile, the relevant innovation theories of Agglomeration, New Industrial Geography and Modern Evolutionary innovation prove that the reason why the agglomeration in world-class city and metropolitan areas and also verify innovation is the key point for the development of metropolis. The primary purpose of this paper is to analyze the geographical spatial characteristics of innovative subjects which contain firm, university, research institution, government and intermediary organ in metropolis throughout the amount data analysis in Yangtze River Metropolis in China. The results show three main conclusions. The first is different subjects in different regions have different spatial characteristics. The second one is different structure and pattern between the subjects also can produce different innovative effect. The last but not the least is agglomeration of innovative subjects’ is not only influenced by the innovative network or local policies but also affected by the localized industry characteristics and culture which are getting to be the most important crucial factors.

Keywords: metropolitan development, innovative subject, spatial, Yangtze River Metropolis, China

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Authors: Tolulope Aremu

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The key process steps to produce liquid detergent products will introduce potential defects, such as formulation, mixing, filling, and packaging, which might compromise product quality, consumer safety, and operational efficiency. Real-time identification and characterization of such defects are of prime importance for maintaining high standards and reducing waste and costs. Usually, defect detection is performed by human inspection or rule-based systems, which is very time-consuming, inconsistent, and error-prone. The present study overcomes these limitations in dealing with optimization in defect characterization within the process for making liquid detergents using Machine Learning algorithms. Performance testing of various machine learning models was carried out: Support Vector Machine, Decision Trees, Random Forest, and Convolutional Neural Network on defect detection and classification of those defects like wrong viscosity, color deviations, improper filling of a bottle, packaging anomalies. These algorithms have significantly benefited from a variety of optimization techniques, including hyperparameter tuning and ensemble learning, in order to greatly improve detection accuracy while minimizing false positives. Equipped with a rich dataset of defect types and production parameters consisting of more than 100,000 samples, our study further includes information from real-time sensor data, imaging technologies, and historic production records. The results are that optimized machine learning models significantly improve defect detection compared to traditional methods. Take, for instance, the CNNs, which run at 98% and 96% accuracy in detecting packaging anomaly detection and bottle filling inconsistency, respectively, by fine-tuning the model with real-time imaging data, through which there was a reduction in false positives of about 30%. The optimized SVM model on detecting formulation defects gave 94% in viscosity variation detection and color variation. These values of performance metrics correspond to a giant leap in defect detection accuracy compared to the usual 80% level achieved up to now by rule-based systems. Moreover, this optimization with models can hasten defect characterization, allowing for detection time to be below 15 seconds from an average of 3 minutes using manual inspections with real-time processing of data. With this, the reduction in time will be combined with a 25% reduction in production downtime because of proactive defect identification, which can save millions annually in recall and rework costs. Integrating real-time machine learning-driven monitoring drives predictive maintenance and corrective measures for a 20% improvement in overall production efficiency. Therefore, the optimization of machine learning algorithms in defect characterization optimum scalability and efficiency for liquid detergent companies gives improved operational performance to higher levels of product quality. In general, this method could be conducted in several industries within the Fast moving consumer Goods industry, which would lead to an improved quality control process.

Keywords: liquid detergent manufacturing, defect detection, machine learning, support vector machines, convolutional neural networks, defect characterization, predictive maintenance, quality control, fast-moving consumer goods

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Authors: Vadim Vagin, Marina Fomina, Oleg Morosin

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This paper contains the description of argumentation approach for the problem of inductive concept formation. It is proposed to use argumentation, based on defeasible reasoning with justification degrees, to improve the quality of classification models, obtained by generalization algorithms. The experiment’s results on both clear and noisy data are also presented.

Keywords: argumentation, justification degrees, inductive concept formation, noise, generalization

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Authors: Sakshi Singh, Shampa Chakraverty

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One of the essential characteristics of Blockchain is the ability to validate the integrity of new transactions added to the Blockchain. Moreover, one of the essential consensus algorithms, Proof of Work, performs this job. In this work, we implemented the Proof of Work consensus method on the block formed by performing the transaction using Ganache. The primary goal of this implementation is to understand the process and record how Proof of Work works in reality on newly created blocks.

Keywords: proof of work, blockchain, ganache, smart contract

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Authors: Sergey Sorokin, Irina Sorokina, Alexander Yazenin

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We present a solution to the Maxmin u/E parameters estimation problem of possibility distributions in m-dimensional case. Our method is based on geometrical approach, where minimal area enclosing ellipsoid is constructed around the sample. Also we demonstrate that one can improve results of well-known algorithms in fuzzy model identification task using Maxmin u/E parameters estimation.

Keywords: possibility distribution, parameters estimation, Maxmin u\E estimator, fuzzy model identification

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Authors: Shivahari Revathi Venkateswaran

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Segmenting customers plays a significant role in churn prediction. It helps the marketing team with proactive and reactive customer retention. For the reactive retention, the retention team reaches out to customers who already showed intent to disconnect by giving some special offers. When coming to proactive retention, the marketing team uses churn prediction model, which ranks each customer from rank 1 to 100, where 1 being more risk to churn/disconnect (high ranks have high propensity to churn). The churn prediction model is built by using XGBoost model. However, with the churn rank, the marketing team can only reach out to the customers based on their individual ranks. To profile different groups of customers and to frame different marketing strategies for targeted groups of customers are not possible with the churn ranks. For this, the customers must be grouped in different segments based on their profiles, like demographics and other non-controllable attributes. This helps the marketing team to frame different offer groups for the targeted audience and prevent them from disconnecting (proactive retention). For segmentation, machine learning approaches like k-mean clustering will not form unique customer segments that have customers with same attributes. This paper finds an alternate approach to find all the combination of unique segments that can be formed from the user attributes and then finds the segments who have uplift (churn rate higher than the baseline churn rate). For this, search algorithms like fast search and recursive search are used. Further, for each segment, all customers can be targeted using individual churn ranks from the churn prediction model. Finally, a UI (User Interface) is developed for the marketing team to interactively search for the meaningful segments that are formed and target the right set of audience for future marketing campaigns and prevent them from disconnecting.

Keywords: churn prediction modeling, XGBoost model, uplift segments, proactive marketing, search algorithms, retention, k-mean clustering

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Authors: Zinhle Cindi, Collet Dandara, Mpiko Ntsekhe, Edson Makambwa, Miguel Larceda

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Background: Warfarin is the most commonly used drug in the management of thromboembolic disease. However, there is a huge variability in the time, number of doses or starting doses for patients to achieve the required international normalised ratio (INR) which is compounded by a narrow therapeutic index. Many genetic-association studies have reported on European and Asian populations which have led to the designing of specific algorithms that are now being used to assist in warfarin dosing. However, very few or no studies have looked at the pharmacogenetics of warfarin in African populations, yet, huge differences in dosage requirements to reach the same INR have been observed. Objective: We set out to investigate the distribution of 3 SNPs CYP4F2 c.1347C > T, VKORC1 g.-1639G > A and VKORC1 c.1173C > T among South African Mixed Ancestry (MA) and Black African patients. Methods: DNA was extracted from 383 participants and subsequently genotyped using PCR/RFLP for the CYP4F2 c.1347 (V433M) (rs2108622), VKORC1 g.-1639 (rs9923231) and VKORC1 c.1173 (rs9934438) SNPs. Results: Comparing the Black and MA groups, significant differences were observed in the distribution of the following genotypes; CYP4F2 c.1347C/T (23% vs. 39% p=0.03). All VKORC1 g.-1639G > A genotypes (p < 0.006) and all VKORC1 c.1173C > T genotypes (p < 0.007). Conclusion: CYP4F2 c.1347T (V433M) reduces CYP4F2 protein levels and therefore expected to affect the amount of warfarin needed to block vitamin k recycling. The VKORC1 g-1639A variant alters transcriptional regulation therefore affecting the function of vitamin k epoxide reductase in vitamin k production. The VKORC1 c.1173T variant reduces the enzyme activity of VKORC1 consequently enhancing the effectiveness of warfarin. These are preliminary results; more genetic characterization is required to understand all the genetic determinants affecting how patients respond to warfarin.

Keywords: algorithms, pharmacogenetics, thromboembolic disease, warfarin

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Authors: Valentin Père, Mathieu Milhé, Fabien Baillon, Jean-Louis Dirion

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Microgrids offer potential solutions to meet the need for local grid stability and increase isolated networks autonomy with the integration of intermittent renewable energy production and storage facilities. In such a context, sizing production and storage for a given network is a complex task, highly depending on input data such as power load profile and renewable resource availability. This work aims at developing an operating cost computation methodology for different microgrid designs based on the use of deep reinforcement learning (RL) algorithms to tackle the optimal operation problem in stochastic environments. RL is a data-based sequential decision control method based on Markov decision processes that enable the consideration of random variables for control at a chosen time scale. Agents trained via RL constitute a promising class of Energy Management Systems (EMS) for the operation of microgrids with energy storage. Microgrid sizing (or design) is generally performed by minimizing investment costs and operational costs arising from the EMS behavior. The latter might include economic aspects (power purchase, facilities aging), social aspects (load curtailment), and ecological aspects (carbon emissions). Sizing variables are related to major constraints on the optimal operation of the network by the EMS. In this work, an islanded mode microgrid is considered. Renewable generation is done with photovoltaic panels; an electrochemical battery ensures short-term electricity storage. The controllable unit is a hydrogen tank that is used as a long-term storage unit. The proposed approach focus on the transfer of agent learning for the near-optimal operating cost approximation with deep RL for each microgrid size. Like most data-based algorithms, the training step in RL leads to important computer time. The objective of this work is thus to study the potential of Batch-Constrained Q-learning (BCQ) for the optimal sizing of microgrids and especially to reduce the computation time of operating cost estimation in several microgrid configurations. BCQ is an off-line RL algorithm that is known to be data efficient and can learn better policies than on-line RL algorithms on the same buffer. The general idea is to use the learned policy of agents trained in similar environments to constitute a buffer. The latter is used to train BCQ, and thus the agent learning can be performed without update during interaction sampling. A comparison between online RL and the presented method is performed based on the score by environment and on the computation time.

Keywords: batch-constrained reinforcement learning, control, design, optimal

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Authors: Christian H. Sanabria-Montaña, Rodrigo Huerta-Quintanilla

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A lattice network is a special type of network in which all nodes have the same number of links, and its boundary conditions are periodic. The most basic lattice network is the ring, a one-dimensional network with periodic border conditions. In contrast, the Cartesian product of d rings forms a d-dimensional lattice network. An analytical expression currently exists for the clustering coefficient in this type of network, but the theoretical value is valid only up to certain connectivity value; in other words, the analytical expression is incomplete. Here we obtain analytically the clustering coefficient expression in d-dimensional lattice networks for any link density. Our analytical results show that the clustering coefficient for a lattice network with density of links that tend to 1, leads to the value of the clustering coefficient of a fully connected network. We developed a model on criminology in which the generalized clustering coefficient expression is applied. The model states that delinquents learn the know-how of crime business by sharing knowledge, directly or indirectly, with their friends of the gang. This generalization shed light on the network properties, which is important to develop new models in different fields where network structure plays an important role in the system dynamic, such as criminology, evolutionary game theory, econophysics, among others.

Keywords: clustering coefficient, criminology, generalized, regular network d-dimensional

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Authors: Mehmet Hakan Karaata

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In this paper, we first coin a new graph theocratic problem with numerous applications. Second, we provide two algorithms for the problem. The first solution is using a brute-force techniques, whereas the second solution is based on an initial identification of the cycles in the given graph. We then provide a correctness proof of the algorithm. The applications of the problem include graph analysis, graph drawing and network structuring.

Keywords: algorithm, cycle, graph algorithm, graph theory, network structuring

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Authors: C. A. Barros, Ana P. Barroso

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Any industrial company needs to determine the amount of variation that exists within its measurement process and guarantee the reliability of their data, studying the performance of their measurement system, in terms of linearity, bias, repeatability and reproducibility and stability. This issue is critical for automotive industry suppliers, who are required to be certified by the 16949:2016 standard (replaces the ISO/TS 16949) of International Automotive Task Force, defining the requirements of a quality management system for companies in the automotive industry. Measurement System Analysis (MSA) is one of the mandatory tools. Frequently, the measurement system in companies is not connected to the equipment and do not incorporate the methods proposed by the Automotive Industry Action Group (AIAG). To address these constraints, an R&D project is in progress, whose objective is to develop a web and cloud-based MSA tool. This MSA tool incorporates Industry 4.0 concepts, such as, Internet of Things (IoT) protocols to assure the connection with the measuring equipment, cloud computing, artificial intelligence, statistical tools, and advanced mathematical algorithms. This paper presents the preliminary findings of the project. The web and cloud-based MSA tool is innovative because it implements all statistical tests proposed in the MSA-4 reference manual from AIAG as well as other emerging methods and techniques. As it is integrated with the measuring devices, it reduces the manual input of data and therefore the errors. The tool ensures traceability of all performed tests and can be used in quality laboratories and in the production lines. Besides, it monitors MSAs over time, allowing both the analysis of deviations from the variation of the measurements performed and the management of measurement equipment and calibrations. To develop the MSA tool a ten-step approach was implemented. Firstly, it was performed a benchmarking analysis of the current competitors and commercial solutions linked to MSA, concerning Industry 4.0 paradigm. Next, an analysis of the size of the target market for the MSA tool was done. Afterwards, data flow and traceability requirements were analysed in order to implement an IoT data network that interconnects with the equipment, preferably via wireless. The MSA web solution was designed under UI/UX principles and an API in python language was developed to perform the algorithms and the statistical analysis. Continuous validation of the tool by companies is being performed to assure real time management of the ‘big data’. The main results of this R&D project are: MSA Tool, web and cloud-based; Python API; New Algorithms to the market; and Style Guide of UI/UX of the tool. The MSA tool proposed adds value to the state of the art as it ensures an effective response to the new challenges of measurement systems, which are increasingly critical in production processes. Although the automotive industry has triggered the development of this innovative MSA tool, other industries would also benefit from it. Currently, companies from molds and plastics, chemical and food industry are already validating it.

Keywords: automotive Industry, industry 4.0, Internet of Things, IATF 16949:2016, measurement system analysis

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Authors: Atitallah Sofien, Bouyahia Olfa, Hadj Salah Ibrahim, Ben Saleh Foued, Missaoui Nada, Ben Rabeh Rania, Yahyaoui Salem, Mazigh Sonia, Boukthir Samir

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Introduction: Carbon monoxide (CO) poisoning is a common pathology responsible for high morbidity and mortality worldwide. Aim: The purpose of this study was to determine the epidemiological profile of CO poisoning as well as its clinical, paraclinical, therapeutic, and evolutionary aspects. Methods: Our study included observations of CO poisoning in children hospitalized in the pediatric department C of the Children's Hospital in Tunis over a period of 3 years. Results: We have collected 199 cases of CO poisoning in children. The average age was 5.43 years, with a sex ratio of 0.98. The source of CO was inside the home in 73.2% of cases, and it was the gas bath heater in 68.8% of cases. The intoxication was collective in 93.5% of the cases, and it occurred during the month of January in 35.8% of the cases. The clinical manifestations were headaches in 69.5% of cases. The rate of carboxyhemoglobin was pathological in 73.9% of cases. All patients received normobaric oxygen therapy, and only 3.6% of patients had a hyperbaric oxygen therapy session. We did not deplore any case of death in our study. Conclusion: CO poisoning remains a public health problem in Tunisia with high morbidity. The risk of secondary complications, particularly neuropsychiatric, requires clinical and possibly neuroradiological monitoring of these victims.

Keywords: poisoning, carbon monoxide, children, hyperbaric oxygenation

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Authors: Anna Ouskova Leonteva, Michel Risser, Anne Jeannin-Girardon, Pierre Parrend, Pierre Collet

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This paper proposes an efficient optimization tool for an active magnetic regenerator (AMR) model, operating in two modes: magnetic refrigeration system (MRS) and thermo-magnetic generator (TMG). The aim of this optimizer is to improve the design of the AMR by applying a multi-physics multi-scales numerical model as a core of evaluation functions to achieve industrial requirements for refrigeration and energy conservation systems. Based on the multi-objective non-dominated sorting genetic algorithm 3 (NSGA3), it maximizes four different objectives: efficiency and power density for MRS and TMG. The main contribution of this work is in the simultaneously application of a CPU-parallel NSGA3 version to the AMR model in both modes for studying impact of control and design parameters on the performance. The parametric study of the optimization results are presented. The main conclusion is that the common (for TMG and MRS modes) optimal parameters can be found by the proposed tool.

Keywords: ecological refrigeration systems, active magnetic regenerator, thermo-magnetic generator, multi-objective evolutionary optimization, industrial optimization problem, real-world application

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Authors: Alime Cengiz, Talip Kahyaoglu

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Roasting process has the major importance to obtain desired aromatic taste of nuts. In this study, two kinds of roasting process were applied to hulled sesame seeds - vacuum oven and hot air roasting. Efficiency of Gene Expression Programming (GEP), a new soft computing technique of evolutionary algorithm that describes the cause and effect relationships in the data modelling system, and response surface methodology (RSM) were examined in the modelling of roasting processes over a range of temperature (120-180°C) for various times (30-60 min). Color attributes (L*, a*, b*, Browning Index (BI)), textural properties (hardness and fracturability) and moisture content were evaluated and modelled by RSM and GEP. The GEP-based formulations and RSM approach were compared with experimental results and evaluated according to correlation coefficients. The results showed that both GEP and RSM were found to be able to adequately learn the relation between roasting conditions and physical and textural parameters of roasted seeds. However, GEP had better prediction performance than the RSM with the high correlation coefficients (R2 >0.92) for the all quality parameters. This result indicates that the soft computing techniques have better capability for describing the physical changes occuring in sesame seeds during roasting process.

Keywords: genetic expression programming, response surface methodology, roasting, sesame seed

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Authors: Amit Kumar Happy

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This paper is motivated by the importance of multi-sensor image fusion with a specific focus on infrared (IR) and visual image (VI) fusion for various applications, including military reconnaissance. Image fusion can be defined as the process of combining two or more source images into a single composite image with extended information content that improves visual perception or feature extraction. These images can be from different modalities like visible camera & IR thermal imager. While visible images are captured by reflected radiations in the visible spectrum, the thermal images are formed from thermal radiation (infrared) that may be reflected or self-emitted. A digital color camera captures the visible source image, and a thermal infrared camera acquires the thermal source image. In this paper, some image fusion algorithms based upon multi-scale transform (MST) and region-based selection rule with consistency verification have been proposed and presented. This research includes the implementation of the proposed image fusion algorithm in MATLAB along with a comparative analysis to decide the optimum number of levels for MST and the coefficient fusion rule. The results are presented, and several commonly used evaluation metrics are used to assess the suggested method's validity. Experiments show that the proposed approach is capable of producing good fusion results. While deploying our image fusion algorithm approaches, we observe several challenges from the popular image fusion methods. While high computational cost and complex processing steps of image fusion algorithms provide accurate fused results, they also make it hard to become deployed in systems and applications that require a real-time operation, high flexibility, and low computation ability. So, the methods presented in this paper offer good results with minimum time complexity.

Keywords: image fusion, IR thermal imager, multi-sensor, multi-scale transform

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Authors: Srushti Bhatt, M. B. Kiran

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Two-sided assembly line balancing problem is yet to be addressed simply to compete for the global market for manufacturers. The task assigned in an ordered sequence to get optimum performance of the system is known as assembly line balancing problem mainly classified as single and two sided. It is very challenging in manufacturing industries to balance two-sided assembly line, wherein the set of sequential workstations the task operations are performed in two sides of the line. The conflicting major objective in two-sided assembly line balancing problem is either to maximize /minimize the performance parameters. The present study emphases on combining different evolutionary algorithm; ant colony, Tabu search and petri net method; and compares their results of an algorithm for solving two-sided assembly line balancing problem. The concept of multi objective optimization of performance parameters is now a day adopted to make a decision involving more than one objective function to be simultaneously optimized. The optimum result can be expected among the selected methods using multi-objective optimization. The performance parameters considered in the present study are a number of workstation, slickness and smoothness index. The simulation of the assembly line balancing problem provides optimal results of classical and practical problems.

Keywords: Ant colony, petri net, tabu search, two sided ALBP

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Authors: Seyed-Ali Sadegh-Zadeh, Mahboobe Bahrami, Sahar Ahmadi, Seyed-Yaser Mousavi, Hamed Atashbar, Amir M. Hajiyavand

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This paper presents an investigation into the concept of neural reshaping, which is crucial for achieving strong artificial intelligence through the development of AI algorithms with very high plasticity. By examining the plasticity of both human and artificial neural networks, the study uncovers groundbreaking insights into how these systems adapt to new experiences and situations, ultimately highlighting the potential for creating advanced AI systems that closely mimic human intelligence. The uniqueness of this paper lies in its comprehensive analysis of the neural reshaping process in both human and artificial intelligence systems. This comparative approach enables a deeper understanding of the fundamental principles of neural plasticity, thus shedding light on the limitations and untapped potential of both human and AI learning capabilities. By emphasizing the importance of neural reshaping in the quest for strong AI, the study underscores the need for developing AI algorithms with exceptional adaptability and plasticity. The paper's findings have significant implications for the future of AI research and development. By identifying the core principles of neural reshaping, this research can guide the design of next-generation AI technologies that can enhance human and artificial intelligence alike. These advancements will be instrumental in creating a new era of AI systems with unparalleled capabilities, paving the way for improved decision-making, problem-solving, and overall cognitive performance. In conclusion, this paper makes a substantial contribution by investigating the concept of neural reshaping and its importance for achieving strong AI. Through its in-depth exploration of neural plasticity in both human and artificial neural networks, the study unveils vital insights that can inform the development of innovative AI technologies with high adaptability and potential for enhancing human and AI capabilities alike.

Keywords: neural plasticity, brain adaptation, artificial intelligence, learning, cognitive reshaping

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Authors: Rui Wu

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In the volatile modern manufacturing environment, new orders randomly occur at any time, while the pre-emptive methods are infeasible. This leads to a real-time scheduling method that can produce a reasonably good schedule quickly. The dynamic Flexible Job Shop problem is an NP-hard scheduling problem that hybrid the dynamic Job Shop problem with the Parallel Machine problem. A Flexible Job Shop contains different work centres. Each work centre contains parallel machines that can process certain operations. Many algorithms, such as genetic algorithms or simulated annealing, have been proposed to solve the static Flexible Job Shop problems. However, the time efficiency of these methods is low, and these methods are not feasible in a dynamic scheduling problem. Therefore, a dynamic rule selection scheduling system based on the reinforcement learning method is proposed in this research, in which the dynamic Flexible Job Shop problem is divided into several parallel machine problems to decrease the complexity of the dynamic Flexible Job Shop problem. Firstly, the features of jobs, machines, work centres, and flexible job shops are selected to describe the status of the dynamic Flexible Job Shop problem at each decision point in each work centre. Secondly, a framework of reinforcement learning algorithm using a double-layer deep Q-learning network is applied to select proper composite dispatching rules based on the status of each work centre. Then, based on the selected composite dispatching rule, an available operation is selected from the waiting buffer and assigned to an available machine in each work centre. Finally, the proposed algorithm will be compared with well-known dispatching rules on objectives of mean tardiness, mean flow time, mean waiting time, or mean percentage of waiting time in the real-time Flexible Job Shop problem. The result of the simulations proved that the proposed framework has reasonable performance and time efficiency.

Keywords: dynamic scheduling problem, flexible job shop, dispatching rules, deep reinforcement learning

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Authors: My Hachem Aouragh, Hind Ragragui, Abdellah El-Hmaidi, Ali Essahlaoui, Abdelhadi El Ouali

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This study investigates soil erosion susceptibility in the Oued Mikkes watershed, located in the Meknes-Fez region of northern Morocco, utilizing advanced techniques such as deep learning algorithms and remote sensing integrated within Geographic Information Systems (GIS). Spanning approximately 1,920 km², the watershed is characterized by a semi-arid Mediterranean climate with irregular rainfall and limited water resources. The waterways within the watershed, especially the Oued Mikkes, are vital for agricultural irrigation and potable water supply. The research assesses the extent of erosion risk upstream of the Sidi Chahed dam while developing a spatial model of soil loss. Several important factors, including topography, land use/land cover, and climate, were analyzed, with data on slope, NDVI, and rainfall erosivity processed using deep learning models (DLNN, CNN, RNN). The results demonstrated excellent predictive performance, with AUC values of 0.92, 0.90, and 0.88 for DLNN, CNN, and RNN, respectively. The resulting susceptibility maps provide critical insights for soil management and conservation strategies, identifying regions at high risk for erosion across 24% of the study area. The most high-risk areas are concentrated on steep slopes, particularly near the Ifrane district and the surrounding mountains, while low-risk areas are located in flatter regions with less rugged topography. The combined use of remote sensing and deep learning offers a powerful tool for accurate erosion risk assessment and resource management in the Mikkes watershed, highlighting the implications of soil erosion on dam siltation and operational efficiency.

Keywords: soil erosion, GIS, remote sensing, deep learning, Mikkes Watershed, Morocco

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Authors: Ermias A. Tegegn, Million Meshesha

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Human Immunodeficiency Virus and Acquired Immunodeficiency Syndrome (HIV/AIDS) is a major cause of death for most African countries. Ethiopia is one of the seriously affected countries in sub Saharan Africa. Previously in Ethiopia, having HIV/AIDS was almost equivalent to a death sentence. With the introduction of Antiretroviral Therapy (ART), HIV/AIDS has become chronic, but manageable disease. The study focused on a data mining technique to predict future living status of HIV/AIDS patients at the time of drug regimen change when the patients become toxic to the currently taking ART drug combination. The data is taken from University of Gondar Hospital ART program database. Hybrid methodology is followed to explore the application of data mining on ART program dataset. Data cleaning, handling missing values and data transformation were used for preprocessing the data. WEKA 3.7.9 data mining tools, classification algorithms, and expertise are utilized as means to address the research problem. By using four different classification algorithms, (i.e., J48 Classifier, PART rule induction, Naïve Bayes and Neural network) and by adjusting their parameters thirty-two models were built on the pre-processed University of Gondar ART program dataset. The performances of the models were evaluated using the standard metrics of accuracy, precision, recall, and F-measure. The most effective model to predict the status of HIV patients with drug regimen substitution is pruned J48 decision tree with a classification accuracy of 98.01%. This study extracts interesting attributes such as Ever taking Cotrim, Ever taking TbRx, CD4 count, Age, Weight, and Gender so as to predict the status of drug regimen substitution. The outcome of this study can be used as an assistant tool for the clinician to help them make more appropriate drug regimen substitution. Future research directions are forwarded to come up with an applicable system in the area of the study.

Keywords: HIV drug regimen, data mining, hybrid methodology, predictive model

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Authors: Yu-Ching Cheng, Yi-Kai Juan, Daniel Castro

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Energy consumption of buildings has been an increasing concern for researchers and practitioners in the last decade. Sustainable building renovation can reduce energy consumption and carbon dioxide emissions; meanwhile, it also can extend existing buildings useful life and facilitate environmental sustainability while providing social and economic benefits to the society. School buildings are different from other designed spaces as they are more crowded and host the largest portion of daily activities and occupants. Strategies that focus on reducing energy use but also improve the students’ learning environment becomes a significant subject in sustainable school buildings development. A decision model is developed in this study to solve complicated and large-scale combinational, discrete and determinate problems such as school renovation projects. The task of this model is to automatically search for the most cost-effective (lower cost and higher quality) renovation strategies. In this study, the search process of optimal school building renovation solutions is by nature a large-scale zero-one programming determinate problem. A* is suitable for solving deterministic problems due to its stable and effective search process, and genetic algorithms (GA) provides opportunities to acquire global optimal solutions in a short time via its indeterminate search process based on probability. These two algorithms are combined in this study to consider trade-offs between renovation cost and improved quality, this decision model is able to evaluate current school environmental conditions and suggest an optimal scheme of sustainable school buildings renovation strategies. Through adoption of this decision model, school managers can overcome existing limitations and transform school buildings into spaces more beneficial to students and friendly to the environment.

Keywords: decision model, school buildings, sustainable renovation, genetic algorithm, A* search algorithm

Procedia PDF Downloads 117

Authors: Sogand Barghi

Abstract:

The integration of artificial intelligence (AI) in accounting practices is reshaping the landscape of financial management. This paper explores the innovative applications of AI in the realm of accounting, emphasizing its transformative impact on efficiency, accuracy, decision-making, and financial insights. By harnessing AI's capabilities in data analysis, pattern recognition, and automation, accounting professionals can redefine their roles, elevate strategic decision-making, and unlock unparalleled value for businesses. This paper delves into AI-driven solutions such as automated data entry, fraud detection, predictive analytics, and intelligent financial reporting, highlighting their potential to revolutionize the accounting profession. Artificial intelligence has swiftly emerged as a game-changer across industries, and accounting is no exception. This paper seeks to illuminate the profound ways in which AI is reshaping accounting practices, transcending conventional boundaries, and propelling the profession toward a new era of efficiency and insight-driven decision-making. One of the most impactful applications of AI in accounting is automation. Tasks that were once labor-intensive and time-consuming, such as data entry and reconciliation, can now be streamlined through AI-driven algorithms. This not only reduces the risk of errors but also allows accountants to allocate their valuable time to more strategic and analytical tasks. AI's ability to analyze vast amounts of data in real time enables it to detect irregularities and anomalies that might go unnoticed by traditional methods. Fraud detection algorithms can continuously monitor financial transactions, flagging any suspicious patterns and thereby bolstering financial security. AI-driven predictive analytics can forecast future financial trends based on historical data and market variables. This empowers organizations to make informed decisions, optimize resource allocation, and develop proactive strategies that enhance profitability and sustainability. Traditional financial reporting often involves extensive manual effort and data manipulation. With AI, reporting becomes more intelligent and intuitive. Automated report generation not only saves time but also ensures accuracy and consistency in financial statements. While the potential benefits of AI in accounting are undeniable, there are challenges to address. Data privacy and security concerns, the need for continuous learning to keep up with evolving AI technologies, and potential biases within algorithms demand careful attention. The convergence of AI and accounting marks a pivotal juncture in the evolution of financial management. By harnessing the capabilities of AI, accounting professionals can transcend routine tasks, becoming strategic advisors and data-driven decision-makers. The applications discussed in this paper underline the transformative power of AI, setting the stage for an accounting landscape that is smarter, more efficient, and more insightful than ever before. The future of accounting is here, and it's driven by artificial intelligence.

Keywords: artificial intelligence, accounting, automation, predictive analytics, financial reporting

Procedia PDF Downloads 69