Journal articles: 'Object decision'

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Vernon, David, and Toby J. Lloyd-Jones. "The Role of Colour in Implicit and Explicit Memory Performance." Quarterly Journal of Experimental Psychology Section A 56, no. 5 (July 2003): 779–802. http://dx.doi.org/10.1080/02724980244000684.

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We present two experiments that examine the effects of colour transformation between study and test (from black and white to colour and vice versa, or from incorrectly coloured to correctly coloured and vice versa) on implicit and explicit measures of memory for diagnostically coloured natural objects (e.g., yellow banana). For naming and coloured-object decision (i.e., deciding whether an object is correctly coloured), there were shorter response times to correctly coloured-objects than to black-and-white and incorrectly coloured-objects. Repetition priming was equivalent for the different stimulus types. Colour transformation did not influence priming of picture naming, but for coloured-object decision priming was evident only for objects remaining the same from study to test. This was the case for both naming and coloured-object decision as study tasks. When participants were asked to consciously recognize objects that they had named or made coloured-object decisions to previously, whilst ignoring their colour, colour transformation reduced recognition efficiency. We discuss these results in terms of the flexibility of object representations that mediate priming and recognition.

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Carlson, Thomas A., J. Brendan Ritchie, Nikolaus Kriegeskorte, Samir Durvasula, and Junsheng Ma. "Reaction Time for Object Categorization Is Predicted by Representational Distance." Journal of Cognitive Neuroscience 26, no. 1 (January 2014): 132–42. http://dx.doi.org/10.1162/jocn_a_00476.

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How does the brain translate an internal representation of an object into a decision about the object's category? Recent studies have uncovered the structure of object representations in inferior temporal cortex (IT) using multivariate pattern analysis methods. These studies have shown that representations of individual object exemplars in IT occupy distinct locations in a high-dimensional activation space, with object exemplar representations clustering into distinguishable regions based on category (e.g., animate vs. inanimate objects). In this study, we hypothesized that a representational boundary between category representations in this activation space also constitutes a decision boundary for categorization. We show that behavioral RTs for categorizing objects are well described by our activation space hypothesis. Interpreted in terms of classical and contemporary models of decision-making, our results suggest that the process of settling on an internal representation of a stimulus is itself partially constitutive of decision-making for object categorization.

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Preston, Stephanie D., and Andrew D. MacMillan-Ladd. "Object attachment and decision-making." Current Opinion in Psychology 39 (June 2021): 31–37. http://dx.doi.org/10.1016/j.copsyc.2020.07.019.

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Cho, Yongkyun, and Seymour Ginsburg. "Decision problems of object histories." Information and Computation 83, no. 2 (November 1989): 245–63. http://dx.doi.org/10.1016/0890-5401(89)90060-6.

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Hyacenth, Chineke Amaechi. "Optimizing Multiple Object Tracking and Decision making using neuro–fuzzy." International Journal of Trend in Scientific Research and Development Volume-3, Issue-3 (April 30, 2019): 871–76. http://dx.doi.org/10.31142/ijtsrd23185.

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Biedrzycki, Jedrzej, and Robert Burduk. "Integration of Decision Trees Using Distance to Centroid and to Decision Boundary." JUCS - Journal of Universal Computer Science 26, no. 6 (June 28, 2020): 720–33. http://dx.doi.org/10.3897/jucs.2020.038.

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Plethora of ensemble techniques have been implemented and studied in order to achieve better classification results than base classifiers. In this paper an algorithm for integration of decision trees is proposed, which means that homogeneous base classifiers will be used. The novelty of the presented approach is the usage of the simultaneous distance of the object from the decision boundary and the center of mass of objects belonging to one class label in order to determine the score functions of base classifiers. This means that the score function assigned to the class label by each classifier depends on the distance of the classified object from the decision boundary and from the centroid. The algorithm was evaluated using an open-source benchmarking dataset. The results indicate an improvement in the classification quality in comparison to the referential method - majority voting method.

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Zhang, Hongjun, Chengxiang Yin, Xiuli Qi, Rui Zhang, and Xingdang Kang. "Cognitive Best Worst Method for Multiattribute Decision-Making." Mathematical Problems in Engineering 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/1092925.

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Pairwise comparison based multiattribute decision-making (MADM) methods are widely used and studied in recent years. However, the perception and cognition towards the semantic representation for the linguistic rating scale and the way in which the pairwise comparisons are executed are still open to discuss. The commonly used ratio scale is likely to produce misapplications and the matrix based comparison style needs too many comparisons and is not able to guarantee the consistency of the matrix when the number of objects involved is large. This research proposes a new MADM method CBWM (Cognitive Best Worst Method) which adopts interval scale to represent the pairwise difference and only compares each object to the best object and the worst object rather than all the other objects. CBWM is a vector based method which only needs 2n-3 pairwise comparisons and is more likely to generate consistent comparisons and reliable results. The theoretical analysis and a real world application demonstrate the effectiveness of CBWM.

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Huang, Zhili, Qiang He, Qinglan Chen, and Hongge Yue. "Hamming Similarity Programming Model for Multi-Attribute Decision-Making Objects with Attribute Values as Interval Numbers and Its Application." Symmetry 14, no. 10 (October 19, 2022): 2203. http://dx.doi.org/10.3390/sym14102203.

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With regard to the interval number-based uncertain multi-attribute decision making problem, in which the attribute weights are unknown and there is no preference on decision-making alternative objects, this paper presents a new decision-making approach. In this method, Hamming distance firstly is used to define the Hamming similarity degree of normative interval numbers, and the Hamming similarity degree of decision-making alternative objects, and then the Hamming similarity superiority relation theory to the comparison of interval numbers is proposed and some relevant results are obtained. Thus, by drawing on the idea of deviations maximization, an interval number-based decision-making object Hamming similarity programming model (IN-DMOHSPM) is established to calculate and solve the weight vector of attributes. Next, all of the selected alternative objects set is screened and sorted by using the overall Hamming similarity degree of each decision-making object compared with the ideal optimal object, and a new algorithm of Hamming similarity programming model for interval number-based multiple attribute decision-making objects is presented. Finally, the feasibility and utility of this model used in this paper are demonstrated by a numerical example.

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Arseniev, Vladimir, and Andrei Yadrenkin. "Selection of a given space object from the multitude observed by a specialized spacecraft." Information and Control Systems, no. 2 (May 11, 2022): 11–19. http://dx.doi.org/10.31799/1684-8853-2022-2-11-19.

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Introduction: For the detection of most dangerous artificial space objects (space junk) in near-earth space, it is planned to use specialized spacecraft equipped with optoelectronic devices. In relation to this a problem arose when selecting the most dangerous object from the observed multitude, based on the measurements of inhomogeneous selective features that characterize these objects. Purpose: To form a composite non-dimensional indicator that depends on the quantity and quality of measurement information about the observed space objects, and that determines the decision rule for selecting the most dangerous object to maximize the probability of making the right decision. Results: A method is proposed for selecting the most dangerous space object under the condition of the limited amount of measurement information about physically inhomogeneous selective features of space objects located in the area which is observed by a specialized spacecraft. It should be noted that the measurement data on individual selective features of space objects may be absent. The proposed decision rule for the selection of the most dangerous space object takes into account not only inaccuracies and errors, but also the number of measurements of the selective characteristics of each object. The efficiency of the method has been demonstrated on a relevant example. Practical relevance: The simplicity of the determination of composite indicators characterizing space objects that are located in the area observed by a specialized spacecraft, and of the decision rule for selecting the most dangerous object makes it possible to solve this problem on board a specialized spacecraft in real time.

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Sari, Dola Permata, Marwan Usman, and Rose Rahmidani. "PENGARUH BAURAN PEMASARAN JASA TERHADAP KEPUTUSAN BERKUNJUNG WISATAWAN PADA OBJEK WISATA WATERBOOM DI KOTA SAWAHLUNTO." Jurnal Ecogen 1, no. 4 (December 21, 2018): 123. http://dx.doi.org/10.24036/jmpe.v1i1.4731.

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The purpose of this research is to know the influence of product, price, promotion, place, physical evidence, employee, process, productivity and quality to decision of tourist visiting at Waterboom tourist object. This type of research is causative. The population in this study is all tourists who visit the tourism object that is Waterboom in Sawahlunto city. The number of research samples was determined based on Cochran formula of 100 people and was chosen by using accidental sampling technique. The data that are used are the primary obtained through the distribution of questionnaires to selected tourists. The analytical method used is descriptive and quantitative analysis through path analysis. The results of this study indicate that: (1) The product has a significant influence on the tourist visiting decision on Waterboom tourism object, (2) Price has no significant effect on tourist visiting decision at Waterboom tourist object, (3) Promotion has no significant effect on tourist visiting decision on Waterboom tourism object, (4) Place has no significant effect on the tourist visiting decision on the Waterboom tourist object, (5) Physical evidence has a significant effect on the decision of tourists visiting the tourist object, that is Waterboom, (6) Employees have no significant effect on tourist visiting decisions on Waterboom tourist object, (7) The process has a significant influence on the decision of tourists visiting the tourist object that is Waterboom, (8) Productivity and quality significantly influence the decision of tourists visiting the Waterboom tourist object.Keywords: Visiting Decision, Product, Price, Promotion, Place, Physical Evidence, Employee, Process, and Productivity and Quality

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Chuandong, Gao, Wu Guoxiong, and Liu Guodong. "Highway Disaster Alignment Decision-making Model Under the Fragile Environment Condition in Mountain Area." Open Civil Engineering Journal 9, no. 1 (April 17, 2015): 115–19. http://dx.doi.org/10.2174/1874149501509010115.

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Highway disaster alignment is a complex multi-object decision-making problem especially under frail environment conditions such as in mountainous areas. In addition, these objects do not have public characteristics. This paper established the multi-object functional decision making model from the topographic, geologic environmental aspect that may grow and become the cause of all kinds of highway disasters. Using the multi-object decision making theory, this model considered the multi-object forthe highway alignment from the qualitative and quantitative aspect, which has an important academic significance and applied value. In the end of this paper, an example of the analysis is given which indicated the model and its effectivness.

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Xin, Xianwei, Jihua Song, and Weiming Peng. "Intuitionistic Fuzzy Three-Way Decision Model Based on the Three-Way Granular Computing Method." Symmetry 12, no. 7 (June 29, 2020): 1068. http://dx.doi.org/10.3390/sym12071068.

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Three-way decisions, as a general model for uncertain information processing and decisions, mainly utilize the threshold generated by the decision cost matrix to determine the decision category of the object. However, the determination of the threshold is usually accompanied by varying degrees of subjectivity. In addition, the potential symmetrical relationship between the advantages and disadvantages of the decision cost is also a problem worthy of attention. In this study, we propose a novel intuitionistic fuzzy three-way decision (IFTWD) model based on a three-way granular computing method. First, we present the calculation methods for the possibility of membership state and non-membership state, as well as prove the related properties. Furthermore, we investigate the object information granules, i.e., the fine-grained, medium-grained, and coarse-grained objects, by combining the state probability distribution and probability distribution. Then, for decision and evaluation issues, we define the superiority-compatibility relation and inferiority-compatibility relation for IFTWD model construction. In addition, we use the superiority degree and inferiority degree instead of the original thresholds and design a new method for evaluating decision cost. Finally, we focus on the algorithm research of the proposed model and present an empirical study of agricultural ecological investment in Hubei Province to demonstrate the effectiveness of our model.

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Soldan, Anja, H. John Hilton, and Yaakov Stern. "Bias effects in the possible/impossible object decision test with matching objects." Memory & Cognition 37, no. 2 (March 2009): 235–47. http://dx.doi.org/10.3758/mc.37.2.235.

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Hauk, O., K. Patterson, A. Woollams, E. Cooper-Pye, F. Pulvermüller, and T. T. Rogers. "How the Camel Lost Its Hump: The Impact of Object Typicality on Event-related Potential Signals in Object Decision." Journal of Cognitive Neuroscience 19, no. 8 (August 2007): 1338–53. http://dx.doi.org/10.1162/jocn.2007.19.8.1338.

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Using an object decision task, event-related potentials (ERPs), and minimum norm current source estimates, we investigated early spatiotemporal aspects of cortical activation elicited by line drawings that were manipulated on two dimensions: authenticity and typicality. Authentic objects were those that match real-world experience, whereas nonauthentic objects were “doctored” by deletion or addition of features (e.g., a camel with its hump removed, a hammer with two handles). The main manipulation of interest for both authentic and nonauthentic objects was the degree of typicality in the object's structure: typical items are composed of parts that have tended to co-occur across many different objects in the perceiver's experience. The ERP pattern revealed a significant typicality effect at 116 msec after stimulus onset. Both atypical authentic objects (e.g., a camel with its hump) and atypical nonauthentic objects (e.g., a jackal with a hump) elicited stronger brain activation than did objects with typical structure. A significant effect of authenticity was observed at 480 msec, with stronger activation for the nonauthentic objects. The factors of typicality and authenticity interacted at 160 and 330 msec. The most prominent source of the typicality effect was the bilateral occipitotemporal cortex, whereas the interaction and the authenticity effects were mainly observed in the more anterior bilateral temporal cortex. These findings support the hypothesis that within the first few hundred milliseconds after stimulus presentation onset, visual-form-related perceptual and conceptual processes represent distinct but interacting stages in object recognition.

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Lee, Sujin, Sang-hyo Park, and Dongsan Jun. "Object-Cooperated Ternary Tree Partitioning Decision Method for Versatile Video Coding." Sensors 22, no. 17 (August 23, 2022): 6328. http://dx.doi.org/10.3390/s22176328.

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In this paper, we propose an object-cooperated decision method for efficient ternary tree (TT) partitioning that reduces the encoding complexity of versatile video coding (VVC). In most previous studies, the VVC complexity was reduced using decision schemes based on the encoding context, which do not apply object detecion models. We assume that high-level objects are important for deciding whether complex TT partitioning is required because they can provide hints on the characteristics of a video. Herein, we apply an object detection model that discovers and extracts the high-level object features—the number and ratio of objects from frames in a video sequence. Using the extracted features, we propose machine learning (ML)-based classifiers for each TT-split direction to efficiently reduce the encoding complexity of VVC and decide whether the TT-split process can be skipped in the vertical or horizontal direction. The TT-split decision of classifiers is formulated as a binary classification problem. Experimental results show that the proposed method more effectively decreases the encoding complexity of VVC than a state-of-the-art model based on ML.

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Grekov, R., and A. Borisov. "CHARACTERIZATION OF THE EFFICIENCY OF THE FEATURES AGGREGATE IN FUZZY PATTERN RECOGNITION TASK." Environment. Technology. Resources. Proceedings of the International Scientific and Practical Conference 1 (June 27, 1997): 78. http://dx.doi.org/10.17770/etr1997vol1.1858.

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Let a set of objects exist each of which is described by N features X1? ..., XN, where each feature X} is a real number. So each object is set by N-dimensional vector (Xl5 ..., XN) and represents a point in the space of object descriptions, RN.There are also set objects for which degrees of membership in either class are unknown. A decision rule should be determined that could enable estimation of the membership of either object with unknown degrees of membership in the given classes (Ozols and Borisov, 1996). To determine the decision rule, such features should be found which give a possibility to distinguish objects belonging to different classes, i.e. features that are specific for each class. That is why a subtask of estimation of the efficiency of features should be solved. A function 5 should be determined which could enable estimation of the efficiency of both separate features and of features groups.Thus, the task is reduced to the determination of a number of features from set N that will best describe groups of objects and will enable possibly correct recognition of the object's membership in a class.

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Peralta, Billy, and Luis Alberto Caro. "Improved Object Recognition with Decision Trees Using Subspace Clustering." Journal of Advanced Computational Intelligence and Intelligent Informatics 20, no. 1 (January 19, 2016): 41–48. http://dx.doi.org/10.20965/jaciii.2016.p0041.

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Generic object recognition algorithms usually require complex classificationmodels because of intrinsic difficulties arising from problems such as changes in pose, lighting conditions, or partial occlusions. Decision trees present an inexpensive alternative for classification tasks and offer the advantage of being simple to understand. On the other hand, a common scheme for object recognition is given by the appearances of visual words, also known as the bag-of-words method. Although multiple co-occurrences of visual words are more informative regarding visual classes, a comprehensive evaluation of such combinations is unfeasible because it would result in a combinatorial explosion. In this paper, we propose to obtain the multiple co-occurrences of visual words using a variant of the CLIQUE subspace-clustering algorithm for improving the object recognition performance of simple decision trees. Experiments on standard object datasets show that our method improves the accuracy of the classification of generic objects in comparison to traditional decision tree techniques that are similar, in terms of accuracy, to ensemble techniques. In future we plan to evaluate other variants of decision trees, and apply other subspace-clustering algorithms.

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Marcus, B. "Joyce: an object-oriented decision tree builder." ACM SIGPLAN Notices 24, no. 5 (May 1989): 121–23. http://dx.doi.org/10.1145/66068.66081.

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Randolph, Bonnie J., and Barbara Winstead. "Sexual decision making and object relations theory." Archives of Sexual Behavior 17, no. 5 (October 1988): 389–409. http://dx.doi.org/10.1007/bf01542480.

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Inuiguchi, Masahiro, and Keisuke Washimi. "Improving Rough Set Rule-Based Classification by Supplementary Rules." Journal of Advanced Computational Intelligence and Intelligent Informatics 19, no. 6 (November 20, 2015): 747–58. http://dx.doi.org/10.20965/jaciii.2015.p0747.

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In rough set approaches, decision rules are induced from a given data set consisting of attribute values and a decision value. Induced rules are used to classify new objects, but this classification is not perfect, perhaps because the given data set does not include all possible patterns. No induced decision rules are matched totally for objects having missing patterns, and partially matched decision rules are used to estimate their classes. The classification accuracy of such an object is usually lower than that of an object totally matching decision rules. To improve the classification accuracy, we propose adding supplementary rules to the induced rules, defining the supplementary rules to improve the classification accuracy of objects only partially matching decision rules. We propose an algorithm for inducing supplementary rules, considering four classifiers consisting of supplementary rules together with originally induced rules.We investigate their performance. We also compare their classification accuracies to that of conventional classifier with originally induced rules.

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Mitchell, Jennifer A., and David W. Biers. "Decision Statistic Mapping and Number of Information Dimensions on Decision Making with Graphical Displays." Proceedings of the Human Factors Society Annual Meeting 36, no. 18 (October 1992): 1503–7. http://dx.doi.org/10.1177/154193129203601830.

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This study sought to: (1) analytically separate the components of a graphical display which contributed to performance on integrated and separable tasks; and (2) determine the effect of the number of dimensions of information which had to be integrated. To that end, the study employed a 7 × 3 mixed design with seven displays manipulated between-subjects and the number of information dimensions (three, six, and nine) manipulated within-subjects. The seven displays examined included two bar graphs (non-object and object formats), two midline displays (non-object and object formats), a direct graphical display, and two numerical displays (numerical separable and numerical integrative). Based upon propositions generated from emergent feature theory, the ability to integrate information in these displays should be a function of the faithfulness, saliency, and directness of mapping the decision statistic onto the display. Results indicated that the displays which directly represented the integrated decision, the numerical integrative and the direct graphical displays, resulted in the best performance. Intermediate performance was obtained on those displays (i.e. the object bar graph, the non-object midline, and the object midline) which incorporated faithfulness, saliency, or both, respectively. The worst performance on the integrated task was exhibited for those displays (i.e. the numerical separable and the non-object bar) which did not represent directness, faithfulness, or saliency. For both the integrated and separable tasks, accuracy increased as the number of information dimensions increased. The unexpected direction of this effect was attributed to subjects” investing more resources in performing the task at the six or nine cue levels due to the perceived increase in difficulty of the task.

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Daniielian, Anait. "Eco-design: the methodological approach in designing." USEFUL online journal 2, no. 3 (October 5, 2018): 15–28. http://dx.doi.org/10.32557/useful-2-3-2018-0004.

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A system of concepts and definitions was generalized, the basic principles of creating eco- objects are defined and formulated in the article. A model of the system representation of the eco-object is proposed to determine the concepts of environmental sustainability and structure ecologization. The concept of ecologization of the life cycle of the object is formulated, the appropriative scheme is proposed. The concept of ecologization of the object's functioning is presented. As a result of the structural analysis of the concept, three concepts of the functioning of the eco-object during the exploitation were formulated and highlighted. The factors that directly or indirectly influence decision-making in the process of shaping were systematized, formulated, presented in the schematic form. As a result of the dissertation research on the basis of analysis of examples from the design practice, systematization of data, development of the typology of objects of eco-architecture in terms of geometric schemes, constructive solutions and their interrelations, elaboration of a proposal for a new method of rational, multivariate use of ecological principles in the objects of different structure and purpose, the algorithm of modeling objects of eco-design as the basis of the decision-making system was developed.

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Syambas, Nana Rachmana. "An Approach for Predicting the Shape and Size of a Buried Basic Object on Surface Ground Penetrating Radar System." International Journal of Antennas and Propagation 2012 (2012): 1–13. http://dx.doi.org/10.1155/2012/919741.

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Surface ground-penetrating radar (GPR) is one of the radar technology that is widely used in many applications. It is nondestructive remote sensing method to detect underground buried objects. However, the output target is only hyperbolic representation. This research develops a system to identify a buried object on surface GPR based on decision tree method. GPR data of many basic objects (with circular, triangular, and rectangular cross-section) are classified and extracted to generate data training model as a unique template for each type of basic object. The pattern of object under test will be known by comparing its data with the training data using a decision tree method. A simple powerful algorithm to extract feature parameters of object which is based on linear extrapolation is proposed. The result showed that tested buried basic objects can be correctly predicted and the developed system works properly.

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Wang, Hongkai, Yanyong Guan, Jilin Huang, and Jianting Shen. "Decision Rules Acquisition for Inconsistent Disjunctive Set-Valued Ordered Decision Information Systems." Mathematical Problems in Engineering 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/936340.

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Set-valued information system is an important formal framework for the development of decision support systems. We focus on the decision rules acquisition for the inconsistent disjunctive set-valued ordered decision information system in this paper. In order to derive optimal decision rules for an inconsistent disjunctive set-valued ordered decision information system, we define the concept of reduct of an object. By constructing the dominance discernibility function for an object, we compute reducts of the object via utilizing Boolean reasoning techniques, and then the corresponding optimal decision rules are induced. Finally, we discuss the certain reduct of the inconsistent disjunctive set-valued ordered decision information system, which can be used to simplify all certain decision rules as much as possible.

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Mezei, Ady-Daniel, Levente Tamás, and Lucian Buşoniu. "Sorting Objects from a Conveyor Belt Using POMDPs with Multiple-Object Observations and Information-Gain Rewards." Sensors 20, no. 9 (April 27, 2020): 2481. http://dx.doi.org/10.3390/s20092481.

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We consider a robot that must sort objects transported by a conveyor belt into different classes. Multiple observations must be performed before taking a decision on the class of each object, because the imperfect sensing sometimes detects the incorrect object class. The objective is to sort the sequence of objects in a minimal number of observation and decision steps. We describe this task in the framework of partially observable Markov decision processes, and we propose a reward function that explicitly takes into account the information gain of the viewpoint selection actions applied. The DESPOT algorithm is applied to solve the problem, automatically obtaining a sequence of observation viewpoints and class decision actions. Observations are made either only for the object on the first position of the conveyor belt or for multiple adjacent positions at once. The performance of the single- and multiple-position variants is compared, and the impact of including the information gain is analyzed. Real-life experiments with a Baxter robot and an industrial conveyor belt are provided.

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Glukhikh, Igor N., and Dmitry V. Nikiforov. "Decision making for modeling of oil and gas fields by using case based reasoning." Tyumen State University Herald. Physical and Mathematical Modeling. Oil, Gas, Energy 5, no. 3 (October 14, 2019): 147–63. http://dx.doi.org/10.21684/2411-7978-2019-5-3-147-163.

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This article describes a case-based reasoning (CBR) method as a decision-making tool in modeling oil and gas fields, its existing and potential application in the industry. The main direction for the application of the CBR method is the search for analogous objects for the design of oil and gas field development. The current engineering practice involves a vaguely formalized method of analogies, which does not allow defining the object as much as possible authentically the analogue that does not cause errors. The analogue object serves not only as a source of ready-made near optimal design solutions, but also as additional information about the object of development and key decisions in modeling hydrocarbon fields. This paper considers the CBR method as the main tool for finding analogue objects, the main methods of extracting precedents from the database, and gives an idea of the object of development as a precedent. Proceeding from the peculiarities of presenting the object of development as a precedent and the peculiarities of applying the methods of extracting precedents, the authors have developed the concept of searching for analogue objects. In its implementation, it will allow for a different degree of information content of precedents stored in the database and will accelerate the procedure of extracting precedents from the database. The principal novelty is that the presented conceptual scheme allows using the methods of extracting precedents in the conditions of insufficient input data, which is important for the design of oil and gas fields.

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Kohut, Yurii, and Iryna Yurchak. "Recommendation System for Purchasing Goods Based on the Decision Tree Algorithm." Advances in Cyber-Physical Systems 6, no. 2 (December 17, 2021): 121–27. http://dx.doi.org/10.23939/acps2021.02.121.

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Over the past few years, interest in applications related to recommendation systems has increased significantly. Many modern services create recommendation systems that, based on user profile information and his behavior. This services determine which objects or products may be interesting to users. Recommendation systems are a modern tool for understanding customer needs. The main methods of constructing recommendation systems are the content-based filtering method and the collaborative filtering method. This article presents the implementation of these methods based on decision trees. The content-based filtering method is based on the description of the object and the customer’s preference profile. An object description is a finite set of its descriptors, such as keywords, binary descriptors, etc., and a preference profile is a weighted vector of object descriptors in which scales reflect the importance of each descriptor to the client and its contribution to the final decision. This model selects items that are similar to the customer’s favorite items before. The second model, which implements the method of collaborative filtering, is based on information about the history of behavior of all customers on the resource: data on their purchases, assessments of product quality, reviews, marked product. The model finds clients that are similar in behavior and the recommendation is based on their assessments of this element. Voting was used to combine the results issued by individual models — the best result is chosen from the results of two models of the ensemble. This approach minimizes the impact of randomness and averages the errors of each model. The aim: The purpose of work is to create real competitive recommendation system for short period of time and minimum costs.

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Ravinder M., Arunima Jaiswal, and Shivani Gulati. "Deep Learning-Based Object Detection in Diverse Weather Conditions." International Journal of Intelligent Information Technologies 18, no. 1 (January 2022): 1–14. http://dx.doi.org/10.4018/ijiit.296236.

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The number of different types of composite images has grown very rapidly in current years, making Object Detection, an extremely critical task that requires a deeper understanding of various deep learning strategies that help to detect objects with higher accuracy in less amount of time. A brief description of object detection strategies under various weather conditions is discussed in this paper with their advantages and disadvantages. So, to overcome this transfer learning has been used and implementation has been done with two Pretrained Models i.e., YOLO and Resnet50 with denoising which detects the object under different weather conditions like sunny, snowy, rainy, and hazy weather. And comparison has been made between the two models. The objects are detected from the images under different conditions where Resnet50 is identified to be the best Model.

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Sun, Jun. "Study on Mobile Object Tracking and Intercept Technology Research." Applied Mechanics and Materials 686 (October 2014): 241–45. http://dx.doi.org/10.4028/www.scientific.net/amm.686.241.

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Background difference method [1] is one of the effective paths of improving robot’s vision reaction ability, and robots use background difference method to find the moving object in vision range and conduct tracking monitoring of moving objects. Then it uses support vector to conduct learning fitting of moving object, which can effectively predict the moving trend of moving object, and then it fabricates corresponding decision programs to conduct intercept capture of moving objects.

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Tran, ThanhHuyen, and Le Ba Dung. "A method for incremental updating three-way decision with incomplete information system." Journal of Military Science and Technology, no. 83 (November 18, 2022): 59–71. http://dx.doi.org/10.54939/1859-1043.j.mst.83.2022.59-71.

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In recent years, the three-way decisions theory has been developed in both theoretical and practical applications. In fact, data are often incomplete and often change over time. To solve this problem, a method of updating the three-way decisions in the dynamic incomplete information system is proposed. First, we consider the relationship between the change of conditional probabilities for the change of the three regions. Then, we consider the change of conditional probability when objects and attribute values of the object change. From that change, we propose a method for updating three-way decisions for two situations, namely, the objects vary, and the attribute values of an object vary. Finally, we give an example to illustrate this method.

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Chen, Xiaozhi, and Ligeng Zou. "Three-Way Decision Models Based on Ideal Relations in Multi-Attribute Decision-Making." Entropy 24, no. 7 (July 17, 2022): 986. http://dx.doi.org/10.3390/e24070986.

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In recent years, research on applications of three-way decision (e.g., TWD) has attracted the attention of many scholars. In this paper, we combine TWD with multi-attribute decision-making (MADM). First, we utilize the essential idea of TOPSIS in MADM theory to propose a pair of new ideal relation models based on TWD, namely, the three-way ideal superiority model and the three-way ideal inferiority model. Second, in order to reduce errors caused by the subjectivity of decision-makers, we develop two new methods to calculate the state sets for the two proposed ideal relation models. Third, we employ aggregate relative loss functions to calculate the thresholds of each object, divide all objects into three different territories and sort all objects. Then, we use a concrete example of building appearance selection to verify the rationality and feasibility of our proposed models. Furthermore, we apply comparative analysis, Spearman’s rank correlation analysis and experiment analysis to illustrate the consistency and superiority of our methods.

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Rosburg, Timm, Axel Mecklinger, and Christian Frings. "When the Brain Decides." Psychological Science 22, no. 12 (November 3, 2011): 1527–34. http://dx.doi.org/10.1177/0956797611417454.

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Humans can make fast and highly efficient decisions by using simple heuristics that are assumed to exploit basic cognitive functions. In the study reported here, we used event-related potentials (ERPs) to disclose the psychological mechanisms underlying one of the most frugal decision rules, namely, the recognition heuristic. According to this heuristic, whenever two objects have to be ranked by a specific criterion and only one object is recognized, the recognized object is ranked higher than the unrecognized object. Using a standard recognition-heuristic paradigm, we predicted participants’ decisions by analyzing an ERP correlate of familiarity-based recognition occurring 300 to 450 ms after stimulus onset. The measure remained a significant predictor even when later ERP correlates were taken into account. These findings are evidence for the thesis that simple heuristics exploit basic cognitive processes. Specifically, the findings show that familiarity—that is, recognition in the absence of recollection—contributes to decisions made on the basis of such heuristics.

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Marques, J. Frederico, and Ana Raposo. "Structural dimensions of object pictures: Organization and relation to object decision and naming." Visual Cognition 19, no. 6 (June 2011): 705–29. http://dx.doi.org/10.1080/13506285.2011.575414.

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R., Ronald R., Zhishuai Yin, Yingzi Lin, and Sagar Kamarthi. "Communications and Decisions of Autonomous Vehicles in Virtual Environments." International Journal of Virtual Reality 9, no. 2 (January 1, 2010): 53–61. http://dx.doi.org/10.20870/ijvr.2010.9.2.2770.

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In many virtual environments, autonomous objects, such as people and vehicles, are essential to increase the feeling of presence. A goal is to have autonomous objects behave as humans, or in the case of vehicles, as if humans controlled them. Such objects are known as intelligent autonomous objects. We present a combination of a communication model and a decisionmaking model to achieve the goal of modeling autonomous objects that behave intelligently. Both models are attached to autonomous objects that represent people and vehicles in a virtual environment. This enables such an autonomous object to be an independent entity that is self-motivated and self-controlled. These intelligent autonomous objects are able to communicate with other autonomous objects via their communication model according to decisions reached by their decision-making model. The decision-making model relies on the communication model to investigate possible outcomes before making decisions. The communication model defines senders, receivers, contents, and channels (media through which content is transferred) in realtime to gather desired information from specified objects. The decision-making model is divided into two levels, the global level and the local level. These work, respectively, with global information perceived by a perception model and the local information received by the communications model. A group of logic rules are formulated as decision trees to model the process of making decisions on the basis of real-time activities. We used traffic and people, in a virtual environments based driving simulator, as examples of intelligent communicating autonomous objects

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Zhu, Hao Dong, and Hong Chan Li. "Attribute Reduction Algorithm for Fuzzy Decision Table with Membership Degrees and Weights." Applied Mechanics and Materials 198-199 (September 2012): 1367–71. http://dx.doi.org/10.4028/www.scientific.net/amm.198-199.1367.

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The classical rough set can not show the fuzziness and the importance of objects in decision procedure because it uses definite form to express each object. In order to solve this problem, this paper firstly introduces a special decision table in which each object has a membership degree to show its fuzziness and has been assigned a weight to show its importance in decision procedure. And then, the special decision table is studied and the relevant rough set model is provided. In the meantime, relevant definitions and theorems are proposed. On the above basis, an attribute reduction algorithm is presented. Finally, feasibility of the relevant rough set model and the presented attribute reduction algorithm are verified by an example.

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Hoseini, Pourya, Janelle Blankenburg, Mircea Nicolescu, Monica Nicolescu, and David Feil-Seifer. "Active Eye-in-Hand Data Management to Improve the Robotic Object Detection Performance." Computers 8, no. 4 (September 23, 2019): 71. http://dx.doi.org/10.3390/computers8040071.

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Adding to the number of sources of sensory information can be efficacious in enhancing the object detection capability of robots. In the realm of vision-based object detection, in addition to improving the general detection performance, observing objects of interest from different points of view can be central to handling occlusions. In this paper, a robotic vision system is proposed that constantly uses a 3D camera, while actively switching to make use of a second RGB camera in cases where it is necessary. The proposed system detects objects in the view seen by the 3D camera, which is mounted on a humanoid robot’s head, and computes a confidence measure for its recognitions. In the event of low confidence regarding the correctness of the detection, the secondary camera, which is installed on the robot’s arm, is moved toward the object to obtain another perspective of the object. With the objects detected in the scene viewed by the hand camera, they are matched to the detections of the head camera, and subsequently, their recognition decisions are fused together. The decision fusion method is a novel approach based on the Dempster–Shafer evidence theory. Significant improvements in object detection performance are observed after employing the proposed active vision system.

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Fendley, Mary, and S. Narayanan. "Decision Aiding to Overcome Biases in Object Identification." Advances in Human-Computer Interaction 2012 (2012): 1–7. http://dx.doi.org/10.1155/2012/790304.

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Human decision makers typically use heuristics under time-pressured situations. These heuristics can potentially degrade task performance through the impact of their associated biases. Using object identification in image analysis as the context, this paper identifies cognitive biases that play a role in decision making. We propose a decision support system to help overcome these biases in this context. Results show that the decision support system improved human decision making in object identification, including metrics such as time taken to identify targets in an image set, accuracy of target identification, accuracy of target classification, and quantity of false positive identification.

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Caputi, Nicoletta. "Object decision and multiple sclerosis: a preliminary study." Functional Neurology 32, no. 2 (2017): 69. http://dx.doi.org/10.11138/fneur/2017.32.2.069.

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Kim, Song-Ju, Masashi Aono, and Etsushi Nameda. "Efficient decision-making by volume-conserving physical object." New Journal of Physics 17, no. 8 (August 11, 2015): 083023. http://dx.doi.org/10.1088/1367-2630/17/8/083023.

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Vitkovitch, Melanie, and Geoffrey Underwood. "Visual field differences in an object decision task." Brain and Cognition 19, no. 2 (July 1992): 195–207. http://dx.doi.org/10.1016/0278-2626(92)90045-n.

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Paraschiv, S., S. Onoprienko, and I. Spasenko. "As for decision making rules based on research results while forensic science activities." Theory and Practice of Forensic Science and Criminalistics 23, no. 1 (July 27, 2021): 99–106. http://dx.doi.org/10.32353/khrife.1.2021.07.

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While forensic research, obtained results should be evaluated in order to ensure adoption of correct conclusion regarding the research object. For this purpose forensic experts should use established decision rules to substantiate a statement about conformity of an object or certain requirements, or parameters, or characteristics. Currently, the need to ensure that a decision is made based on results of forensic examination using the concept of decision rules is constantly increasing. Therefore, consideration of the issue of establishing rules for making decisions while forensic science activity at international and national levels is relevant. Relevance of this issue is justified not only by the requirements of ІSO/ IEC 17025:2017 for testing laboratories but by the requirements for the objectivity of expert opinion. ISO/IEC 17025:2017 recognizes that there is no single rule for deciding on conformity. In other words, in a specific situation there will be its own decision making rule. This also applies to forensic science activity. Therefore, this article purpose is necessary for forensic science activity to determine: general guidelines for choosing appropriate decision-making rule; directives for determining the components of decision making rules depending on the object and the task of forensic examination; rules for applying certain requirements to the research results of carried out in accordance with requirements of research methods, research object and the type of quantitative or qualitative research. Thus, this article defines and considers the process of establishing the rules for making decisions, criteria which the rules for m

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Li, Jingzheng, Xiangjian Chen, Pingxin Wang, and Xibei Yang. "Local view based cost-sensitive attribute reduction." Filomat 32, no. 5 (2018): 1817–22. http://dx.doi.org/10.2298/fil1805817l.

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In traditional cost-sensitive attribute reduction, the variation of decision cost is referred to as a global difference of costs because the considered decision cost is the variation of sum of decision costs over all objects. However, such reduction does not take the variation of decision costs of each object into account. To solve this problem, a local view based cost-sensitive attribute reduction is introduced. Firstly, through considering the variation of decision costs of single object if the used attributes change, a local difference of costs is presented. Secondly, on the basis of the fuzzy decision-theoretic rough set model, a new significance function is given to measure the importance of attribute. Finally, the experimental results illustrate that by comparing the traditional reduction, the proposed local view can decreases both global and local differences of costs effectively on several UCI data sets.

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Kim, Hayea. "What makes consumers take risks in self–other purchase decision making? The roles of impression management and self-construal." Social Behavior and Personality: an international journal 50, no. 2 (February 9, 2022): 1–11. http://dx.doi.org/10.2224/sbp.11170.

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I investigated how impression management (IM) affected consumers' purchase decision making for themselves and for others, and analyzed how IM, self-construal, and self–other decision making affected consumers' risk taking. Participants were 224 students from a university in Korea. Results show that participants with lower IM took greater risks when making decisions for others (vs. themselves). However, those with higher IM showed no significant difference in risk taking between decision objects. Further, participants with independent self-construal who engaged in lower IM tended to take greater risks when making decisions for others (vs. themselves). However, for participants with higher IM or interdependent selfconstrual, there were no significant differences in risk-taking levels regardless of decision object. I sought to understand IM in consumer behavior by treating it as an individual difference variable and assessing it using a standardized scale. The results present strategic guidelines for application of IM to advertising, especially risk-sensitive product categories and purchase situations.

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Kunkevich, D. P., I. L. Kovaleva, and A. A. Baryshau. "Decision synthesis in engineering design of technological attachment." «System analysis and applied information science», no. 2 (August 19, 2021): 47–53. http://dx.doi.org/10.21122/2309-4923-2021-2-47-53.

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Synthesis of the engineering design decisions is considered. The fixtures for locating and clamping parts to be tooled at the manufacturing operations are the design objects. An approach to topology and parametric design are proposed. Most efficiently these tasks are solved at the function level. Finally, geometry models and design drawings are needed for manufacturing. Fixture action is to mate bases of fixture object with function faces of fixture locators. This is constraining the degrees of freedom of object. Every base constrains certain degree of freedom. In accordance with quantity and quality of that degrees, certain locators are appointed to the base and them must be positioned resulting in the a locating chart. The quality of the locating chart may be evaluated by the several criteria, for example, by it dimensions. Unsatisfactory solution may be followed by redistributing degrees of freedom among the bases or editing positions of locators within the bases. Thus the design is decomposed onto two independent tasks of different levels.

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Jiang, Ming-xin, Chao Deng, Zhi-geng Pan, Lan-fang Wang, and Xing Sun. "Multiobject Tracking in Videos Based on LSTM and Deep Reinforcement Learning." Complexity 2018 (November 19, 2018): 1–12. http://dx.doi.org/10.1155/2018/4695890.

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Multiple-object tracking is a challenging issue in the computer vision community. In this paper, we propose a multiobject tracking algorithm in videos based on long short-term memory (LSTM) and deep reinforcement learning. Firstly, the multiple objects are detected by the object detector YOLO V2. Secondly, the problem of single-object tracking is considered as a Markov decision process (MDP) since this setting provides a formal strategy to model an agent that makes sequence decisions. The single-object tracker is composed of a network that includes a CNN followed by an LSTM unit. Each tracker, regarded as an agent, is trained by utilizing deep reinforcement learning. Finally, we conduct a data association using LSTM for each frame between the results of the object detector and the results of single-object trackers. From the experimental results, we can see that our tracker achieves better performance than the other state-of-the-art methods. Multiple targets can be steadily tracked even when frequent occlusions, similar appearances, and scale changes happened.

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Xu, Xiao Lin, Guang Lin Xu, and Jia Li Feng. "Research on Ranking Model Based on Multi-User Attribute Comprehensive Evaluation Method." Applied Mechanics and Materials 644-650 (September 2014): 2399–402. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.2399.

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In evaluation and decision-making, for an object system with multiple attributes and indicators, it is a typical pattern to calculate an assessment value on each object using some methods to get the ranking of objects, so as to choose the best according to sorting result of the values.The paper establishes the evaluation ranking model on the basis of multi-user attribute comprehensive evaluation method which focuses on how to give a comprehensive evaluation on objects taking the users’ preferences on different attributes in an evaluation group into account,and thereby produce evaluation values and sorting results of objects.Simulation verifies the ranking model in evaluation and decision-making.

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ASHIKHMIN, ОLEG V., and ALENA P. SHESTAKOVA. "DIGITALIZATION OF TECHNOLOGICAL AND DESIGN DECISION-MAKING PROCESSES IN MODERN CONSTRUCTION." Architecture, Construction, Transport, no. 2(100) (2022): 95–103. http://dx.doi.org/10.31660/2782-232x-2022-2-95-103.

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The results of the research allow us to extend the scope of the "decision tree" method introduction into the processes of making design decisions and exploring of variants of technological equipment when choosing construction equipment for excavation works by the example of the construction object "Architectural ensemble "Voznesensky" in Tyumen.To achieve the research purpose the following problems were solved: variants of the traditional approach to determination of building equipment requirements were considered; models and methods of management decisions were analyzed; the "decision tree" method with its advantages and disadvantages was studied; the "decision tree" method was successfully used in the digital experiment and recommended for the implementation in developing an algorithm for decision-making and choosing variants of mechanization of excavation works using Deductor Academic Studio program to build an actual object. Nowadays we've never known any cases of direct application of the "decision tree" method in project management in the Russian construction industry. There are no published results of researches about its introduction in processes of making design and industrial decisions.

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Firdausi, Kamalia. "STUDY OF CONTRACT OBJECT IN MULTI-SERVICE FINANCING USING IJARAH IN ISLAMIC BANK." JCH (Jurnal Cendekia Hukum) 6, no. 2 (March 29, 2021): 225. http://dx.doi.org/10.33760/jch.v6i2.316.

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In a case of a dispute between an islamic bank and a customer related to multi-service financing using an ijarah contract at an islamic bank, the customer is suing for the cancellation of the contract on the multi-service financing on the grounds that the object of the contract is not the object of the contract, so the contract should be null and void. However, the court decision stated that he rejected the customer's claim. This research was conducted to examine the application of sharia principles in legal considerations in court decisions regarding contract objects in multi-service financing using the ijarah contract. This research is a normative legal research using the statutory approach method. The results of this study indicate that the legal considerations in court decisions regarding the object of the contract in multi-service financing using the ijarah contract are formally correct, but materially there is still a possibility of gharar that is not in accordance with sharia principles.

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Wu, Chuxiong, Xiaopeng Li, Fei Zuo, Lannan Luo, Xiaojiang Du, Jia Di, and Qiang Zeng. "Use It-No Need to Shake It!" Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 6, no. 3 (September 6, 2022): 1–25. http://dx.doi.org/10.1145/3550322.

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Implicit authentication for traditional objects, such as doors and dumbbells, has rich applications but is rarely studied. An ongoing trend is that traditional objects are retrofitted to smart environments; for instance, a contact sensor is attached to a door to detect door opening (but cannot tell "who is opening the door"). We present the first accurate implicit-authentication system for retrofitted everyday objects, named MoMatch. It makes an authentication decision based on a single natural object use, unlike prior work that requires shaking objects. MoMatch is built on the observation that an object has a motion typically because a human hand moves it; thus, the object's motion and the legitimate user's hand movement should correlate. The main challenge is, given the small amount of data collected during one object use, how to measure the correlation accurately. We convert the correlation measurement problem into an image comparison problem and resolve it using neural networks successfully. MoMatch does not need to profile the user's biometric information and is resilient to mimicry attacks.

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Inayah, Nurul. "ANALISIS PENGARUH FAKTOR SITUASIONAL TERHADAP KEPUTUSAN MASYARAKAT MENABUNG DI BANK SYARIAH KOTA MEDAN." Studia Economica : Jurnal Ekonomi Islam 7, no. 1 (July 1, 2021): 1. http://dx.doi.org/10.30821/se.v7i1.9743.

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Saving is an activity to set aside some of the money they have to keep. People's decision to save occurs based on internal factors, external factors and situational factors. The research was aimed to analyze the customer’s saving decision at sharia bank based on situational factors. This study uses a quantitative approach with Multiple Linear Regression analysis using Eviews version 8 software. The results show that there is a relationship between bank services on saving decisions and bank physical objects on saving decisions if partially reviewed. Likewise, it is also examined simultaneously the influence of bank services and bank physical objects on saving decisions. Therefore, improving services and the availability of convenient physical object facilities by banks is highly recommended to increase the interest and motivation to save the public. This can be a step to accelerate marketing for Islamic banks in increasing the number of customers.

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Journal articles on the topic 'Object decision'

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