A MULTI-EXPERT SYSTEM FOR SHOT CHANGE DETECTION IN MPEG MOVIES

2004 ◽  
Vol 18 (05) ◽  
pp. 933-956 ◽  
Author(s):  
MASSIMO DE SANTO ◽  
GENNARO PERCANNELLA ◽  
CARLO SANSONE ◽  
MARIO VENTO
Keyword(s):  
Expert System ◽  
Expert Systems ◽  
Change Detection ◽  
Digital Libraries ◽  
Research Problem ◽  
Experimental Results ◽  
Growing Up ◽  
Open Research ◽  
Set Up ◽  
Better Than

Shot Change Detection (SCD) in MPEG coded videos is a complex and still open research problem whose interest is growing up more and more due to the diffusion of Video Databases and Digital Libraries. Techniques providing fully satisfactory performances on complex video domains are not yet available even if a number of proposals exist; such proposals show very often to be complementary in their results. In this context, the Authors investigated the use of Multi-Expert Systems (MES) for approaching the SCD problem. In the present paper, we propose and discuss a strategy to select the SCD techniques to be combined and a method for choosing an effective combining rule. In order to assess the performance of the proposed MES, we set up a database that is significantly wider than the ones commonly used in the field. Experimental results demonstrate that the proposed system performs better than each of the single SCD technique considered.

Neurules and connectionist expert systems: Unexplored neuro-symbolic reasoning aspects

2021 ◽  
pp. 1-17
Author(s):  
Jim Prentzas ◽  
Ioannis Hatzilygeroudis
Keyword(s):  
Conflict Resolution ◽  
Expert Systems ◽  
Experimental Results ◽  
Symbolic Reasoning ◽  
Symbolic Methods ◽  
Reasoning Mechanism ◽  
Better Than

Neuro-symbolic approaches combine neural and symbolic methods. This paper explores aspects regarding the reasoning mechanisms of two neuro-symbolic approaches, that is, neurules and connectionist expert systems. Both provide reasoning and explanation facilities. Neurules are a type of neuro-symbolic rules tightly integrating the neural and symbolic components, giving pre-eminence to the symbolic component. Connectionist expert systems give pre-eminence to the connectionist component. This paper explores reasoning aspects about neurules and connectionist expert systems that have not been previously addressed. As far as neurules are concerned, an aspect playing a role in conflict resolution (i.e., order of neurules) is explored. Experimental results show an improvement in reasoning efficiency. As far as connectionist expert systems are concerned, variations of the reasoning mechanism are explored. Experimental results are presented for them as well showing that one of the variations generally performs better than the others.

Eliciting Expertise

1989 ◽  
Vol 2 (4) ◽  
pp. 12-13
Author(s):  
William deB. Mills
Keyword(s):  
Expert System ◽  
International Relations ◽  
Expert Systems ◽  
Human Behavior ◽  
College Freshman ◽  
Pedagogical Tool ◽  
Preliminary Step ◽  
Constant Change ◽  
Set Up ◽  
The Right

Constant change, unpredictable human behavior, and a complexity that defies mastery by any single student make international relations a field no one—least of all a beginner—can cope with by simply memorizing facts. One key to understanding international relations—whether for the college freshman or a professional researcher—is knowing how to ask the right questions. Constructing and using expert systems can be a highly instructional pedagogical tool in this regard.Expert systems are programs that reach conclusions based on a set of rules that examine evidence derived either from a database or from responses entered by the user to a series of canned questions. To design a truly “expert” system—one that reaches conclusions as reliable as those of a human expert—is an extremely time-consuming task requiring not only, obviously, the participation of an expert to formulate the rules but also a topic that can be reduced to rules. Such an endeavor is not the goal here.Instead, the goal here is the preliminary step of training students to ask the proper questions, an arena in which expert systems running on PCs can be set up to provide very pointed demonstrations. Indeed, this is easy enough so that students can easily be assigned the design of such a system themselves (see box).

SISTEM PAKAR MENDIAGNOSA VIRUS MAYORA DENGAN METODE VARIABLE CENTERED INTELLIGENT RULE SYSTEM (VCIRS)

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Siti Nurhena ◽  
Nelly Astuti Hasibuan ◽  
Kurnia Ulfa
Keyword(s):  
Artificial Intelligence ◽  
Immune System ◽  
Expert System ◽  
Expert Systems ◽  
Dengue Hemorrhagic Fever ◽  
Hemorrhagic Fever ◽  
Visual Basic ◽  
Artificial Intelligence Techniques ◽  
Individual Needs ◽  
Rule System

The diagnosis process is the first step to knowing a type of disease. This type of disease caused by mosquitoes is one of the major viruses (MAVY), dengue hemorrhagic fever (DHF) and malaria. Sometimes not everyone can find the virus that is carried by this mosquito, usually children who are susceptible to this virus because the immune system that has not been built perfectly is perfect. To know for sure which virus is infected by mosquitoes, it can diagnose by seeing symptoms perceived symptoms. Expert systems are one of the most used artificial intelligence techniques today because expert systems can act as consultations. In this case the authors make a system to start a diagnosis process with variable centered intelligent rule system (VCIRS) methods through perceived symptoms. With the facilities provided for users and administrators, allowing both users and administrators to use this system according to their individual needs. This expert system is made with the Microsoft Visual Basic 2008 programming language.Keywords: Expert System, Mayora Virus, Variable Centered Intelligent Rule System (VCIRS)The diagnosis process is the first step to knowing a type of disease. This type of disease caused by mosquitoes is one of the major viruses (MAVY), dengue hemorrhagic fever (DHF) and malaria. Sometimes not everyone can find the virus that is carried by this mosquito, usually children who are susceptible to this virus because the immune system that has not been built perfectly is perfect. To know for sure which virus is infected by mosquitoes, it can diagnose by seeing symptoms perceived symptoms.Expert systems are one of the most used artificial intelligence techniques today because expert systems can act as consultations. In this case the authors make a system to start a diagnosis process with variable centered intelligent rule system (VCIRS) methods through perceived symptoms.With the facilities provided for users and administrators, allowing both users and administrators to use this system according to their individual needs. This expert system is made with the Microsoft Visual Basic 2008 programming language.Keywords: Expert System, Mayora Virus, Variable Centered Intelligent Rule System (VCIRS)

THE SOURCES OF AUXIN HORMONE TO GROWTH OF SHOOT PINEAPPLE STEM CUTTING (Ananascomosus.L. Merr.)

2016 ◽  
Vol 1 (1) ◽  
pp. 45-52
Author(s):  
Palupi Puspitorini
Keyword(s):  
Water Concentration ◽  
Urine Concentration ◽  
Experimental Results ◽  
Stem Cuttings ◽  
Randomized Design ◽  
Natural Hormone ◽  
Cow Urine ◽  
Urine Concentrations ◽  
Completely Randomized Design ◽  
Better Than

The aim of this study was to select the best sources of auxin of which it can stimulate the growth of shoots Pineapple plant cuttings. This research is compiled in a completely randomized design (CRD) with 4 treatments and 6 replications. The Data were statistically Analyzed by the DMRT. Level of treatment given proves that no treatment 0%, cow urine concentration of 25%, young coconut water concentration of 25% and Rootone F 100 mg / cuttings. The results showed that cow urine concentrations of 25% and Rootone F 100 mg give the best results in stimulating the growth of shoots pineapple stem cuttings. Experimental results concluded that the effect of this natural hormone were better than the shoots without given hormone.           

A Computational Method for the Identification of Endolysins and Autolysins

2020 ◽  
Vol 27 (4) ◽  
pp. 329-336 ◽  
Author(s):  
Lei Xu ◽  
Guangmin Liang ◽  
Baowen Chen ◽  
Xu Tan ◽  
Huaikun Xiang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Cell Wall ◽  
Experimental Results ◽  
Computational Method ◽  
Lytic Enzyme ◽  
Support Vector ◽  
Lytic Enzymes ◽  
Data Set ◽  
Optimal Feature ◽  
Better Than

Background: Cell lytic enzyme is a kind of highly evolved protein, which can destroy the cell structure and kill the bacteria. Compared with antibiotics, cell lytic enzyme will not cause serious problem of drug resistance of pathogenic bacteria. Thus, the study of cell wall lytic enzymes aims at finding an efficient way for curing bacteria infectious. Compared with using antibiotics, the problem of drug resistance becomes more serious. Therefore, it is a good choice for curing bacterial infections by using cell lytic enzymes. Cell lytic enzyme includes endolysin and autolysin and the difference between them is the purpose of the break of cell wall. The identification of the type of cell lytic enzymes is meaningful for the study of cell wall enzymes. Objective: In this article, our motivation is to predict the type of cell lytic enzyme. Cell lytic enzyme is helpful for killing bacteria, so it is meaningful for study the type of cell lytic enzyme. However, it is time consuming to detect the type of cell lytic enzyme by experimental methods. Thus, an efficient computational method for the type of cell lytic enzyme prediction is proposed in our work. Method: We propose a computational method for the prediction of endolysin and autolysin. First, a data set containing 27 endolysins and 41 autolysins is built. Then the protein is represented by tripeptides composition. The features are selected with larger confidence degree. At last, the classifier is trained by the labeled vectors based on support vector machine. The learned classifier is used to predict the type of cell lytic enzyme. Results: Following the proposed method, the experimental results show that the overall accuracy can attain 97.06%, when 44 features are selected. Compared with Ding's method, our method improves the overall accuracy by nearly 4.5% ((97.06-92.9)/92.9%). The performance of our proposed method is stable, when the selected feature number is from 40 to 70. The overall accuracy of tripeptides optimal feature set is 94.12%, and the overall accuracy of Chou's amphiphilic PseAAC method is 76.2%. The experimental results also demonstrate that the overall accuracy is improved by nearly 18% when using the tripeptides optimal feature set. Conclusion: The paper proposed an efficient method for identifying endolysin and autolysin. In this paper, support vector machine is used to predict the type of cell lytic enzyme. The experimental results show that the overall accuracy of the proposed method is 94.12%, which is better than some existing methods. In conclusion, the selected 44 features can improve the overall accuracy for identification of the type of cell lytic enzyme. Support vector machine performs better than other classifiers when using the selected feature set on the benchmark data set.

SISTEM PAKAR UNTUK DIAGNOSA PENYAKIT ANAK YANG DIDUKUNG OLEH BAHASA PEMOGRAMAN VISUAL

2017 ◽  
Vol 3 (2) ◽  
pp. 108
Author(s):  
Dian Permata Sari
Keyword(s):  
Expert System ◽  
Expert Systems ◽  
Human Knowledge ◽  
Inference Method ◽  
Medical Field ◽  
Forward Chaining ◽  
At Home

<p>Sistem pakar merupakan sistem yang mengadopsi pengetahuan manusia ke komputer yang dirancang untuk memodelkan kemampuan menyelesaikan masalah seperti layaknya seorang pakar. Dengan sistem pakar ini, orang awam pun dapat menyelesaikan masalahnya atau hanya sekedar mencari suatu informasi berkualitas yang sebenarnya hanya dapat diperoleh dengan bantuan para ahli di bidangnya. Salah satunya yaitu dibidang medis untuk mendiagnosapenyakit anak. Mengetahui gejala dari suatu penyakit secara dini dapat menjadi bantuan pertama yang dapat dilakukan para orang tua di rumah jika anak mereka terserang penyakit.Basis pengetahuan disusun sedemikian rupa kedalam database dengan beberapa tabel. Penarikan kesimpulan dalam sistem pakar ini menggunakan metode inferensi <em>forward chaining</em>. Sistem pakar akan memberikan pertanyaan-pertanyaan kepada user berupa gejala dari beberapa penyakit dan user akan menjawab pertanyaan tersebut. Hingga <em>user</em> akan mendapatkan solusi dari hasil pertanyaan tadi. </p><p><em><br /></em></p><p><em>Expert systems are systems that adopt human knowledge into computers designed to model the ability to resolve problems like an expert. Through thisexpert systems,commoner cansolvetheproblem orjustlookingfor a qualityinformationthat can onlybeobtainedwiththehelpofexperts in thefield. One ofthemis in the medical field to diagnosethe children's illness.Knowingthesymptomsofanillnessearly can bethefirstaidto parents if their children stricken withthedisease at home.</em><em>Knowledgebase is arranged into a highlystructureddatabasewithmultipletables. Inferences in this expert system uses forward chaining inference method. Expert systems will provide questions to the user in the form of the symptoms of some diseases and the user will answer that question. Until the user will get the solution of the question.</em></p>

Determination of trace concentrations of copper by FIA-FAAS after preconcentration on chelating sorbents

1989 ◽  
Vol 54 (7) ◽  
pp. 1785-1794 ◽  
Author(s):  
Vlastimil Kubáň ◽  
Josef Komárek ◽  
Zbyněk Zdráhal
Keyword(s):  
Sampling Frequency ◽  
Copper Determination ◽  
Flow Rates ◽  
Sample Injection ◽  
Injection Flow ◽  
Chelating Sorbents ◽  
Set Up ◽  
Trace Concentrations ◽  
Better Than

A FIA-FAAS apparatus containing a six-channel sorption equipment with five 3 x 26 mm microcolumns packed with Spheron Oxin 1 000, Ostsorb Oxin and Ostsorb DTTA was set up. Combined with sorption from 0.002M acetate buffer at pH 4.2 and desorption with 2M-HCl, copper can be determined at concentrations up to 100, 150 and 200 μg l-1, respectively. For sample and eluent flow rates of 5.0 and 4.0 ml min-1, respectively, and a sample injection time of 5 min, the limit of copper determination is LQ = 0.3 μg l-1, repeatability sr is better than 2% and recovery is R = 100 ± 2%. The enrichment factor is on the order of 102 and is a linear function of time (volume) of sample injection up to 5 min and of the sample injection flow rate up to 11 ml min-1 for Spheron Oxin 1 000 and Ostsorb DTTA. For times of sorption of 60 and 300 s, the sampling frequency is 70 and 35 samples/h, respectively. The parameters of the FIA-FAAS determination (acetylene-air flame) are comparable to or better than those achieved by ETA AAS. The method was applied to the determination of traces of copper in high-purity water.

scholarly journals Knowledge Acquisition and Representation for High-Performance Building Design: A Review for Defining Requirements for Developing a Design Expert System

2021 ◽  
Vol 13 (9) ◽  
pp. 4640
Author(s):  
Seung-Yeoun Choi ◽  
Sean-Hay Kim
Keyword(s):  
Decision Support ◽  
Expert System ◽  
Expert Systems ◽  
Knowledge Acquisition ◽  
Data Analytics ◽  
High Performance ◽  
Problem Definition ◽  
Design Expert ◽  
Decision Support Process ◽  
High Performance Building

New functions and requirements of high performance building (HPB) being added and several regulations and certification conditions being reinforced steadily make it harder for designers to decide HPB designs alone. Although many designers wish to rely on HPB consultants for advice, not all projects can afford consultants. We expect that, in the near future, computer aids such as design expert systems can help designers by providing the role of HPB consultants. The effectiveness and success or failure of the solution offered by the expert system must be affected by the quality, systemic structure, resilience, and applicability of expert knowledge. This study aims to set the problem definition and category required for existing HPB designs, and to find the knowledge acquisition and representation methods that are the most suitable to the design expert system based on the literature review. The HPB design literature from the past 10 years revealed that the greatest features of knowledge acquisition and representation are the increasing proportion of computer-based data analytics using machine learning algorithms, whereas rules, frames, and cognitive maps that are derived from heuristics are conventional representation formalisms of traditional expert systems. Moreover, data analytics are applied to not only literally raw data from observations and measurement, but also discrete processed data as the results of simulations or composite rules in order to derive latent rule, hidden pattern, and trends. Furthermore, there is a clear trend that designers prefer the method that decision support tools propose a solution directly as optimizer does. This is due to the lack of resources and time for designers to execute performance evaluation and analysis of alternatives by themselves, even if they have sufficient experience on the HPB. However, because the risk and responsibility for the final design should be taken by designers solely, they are afraid of convenient black box decision making provided by machines. If the process of using the primary knowledge in which frame to reach the solution and how the solution is derived are transparently open to the designers, the solution made by the design expert system will be able to obtain more trust from designers. This transparent decision support process would comply with the requirement specified in a recent design study that designers prefer flexible design environments that give more creative control and freedom over design options, when compared to an automated optimization approach.

Standards, Codes of Practice and Expert Systems

1988 ◽  
Vol 21 (1) ◽  
pp. 5-9 ◽  
Author(s):  
E G McCluskey ◽  
S Thompson ◽  
D M G McSherry
Keyword(s):  
Expert System ◽  
Expert Systems ◽  
Engineering Design ◽  
Performance Criteria ◽  
Design Problems ◽  
Codes Of Practice ◽  
Engineering Design Problems ◽  
And Performance

Many engineering design problems require reference to standards or codes of practice to ensure that acceptable safety and performance criteria are met. Extracting relevant data from such documents can, however, be a problem for the unfamiliar user. The use of expert systems to guide the retrieval of information from standards and codes of practice is proposed as a means of alleviating this problem. Following a brief introduction to expert system techniques, a tool developed by the authors for building expert system guides to standards and codes of practice is described. The steps involved in encoding the knowledge contained in an arbitrarily chosen standard are illustrated. Finally, a typical consultation illustrates the use of the expert system guide to the standard.

Devanagari Text Detection From Natural Scene Images

2020 ◽  
Vol 10 (3) ◽  
pp. 44-59
Author(s):  
Sankirti Sandeep Shiravale ◽  
R. Jayadevan ◽  
Sanjeev S. Sannakki
Keyword(s):  
Edge Detection ◽  
Image Understanding ◽  
Text Detection ◽  
Experimental Results ◽  
Combined Approach ◽  
Natural Scene ◽  
Light Conditions ◽  
The Individual ◽  
Natural Scene Images ◽  
Better Than

Text present in a camera captured scene images is semantically rich and can be used for image understanding. Automatic detection, extraction, and recognition of text are crucial in image understanding applications. Text detection from natural scene images is a tedious task due to complex background, uneven light conditions, multi-coloured and multi-sized font. Two techniques, namely ‘edge detection' and ‘colour-based clustering', are combined in this paper to detect text in scene images. Region properties are used for elimination of falsely generated annotations. A dataset of 1250 images is created and used for experimentation. Experimental results show that the combined approach performs better than the individual approaches.

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