Intelligent Problem Solving Research Articles

A Swarm Intelligence inspired algorithm for contour detection in images

A Swarm Intelligence inspired algorithm for contour detection in images

Application of artificial neural network for prediction of retention time for some pesticides in liquid chromatography

The quantitative structure-retention relationships (QSRR) method is employed to predict the retention times (RTs) of pesticides by molecular descriptors which were calculated by Dragon software. After the calculation molecular descriptors for all molecules, a suitable set of molecular descriptors were selected by using genetic algorithm (GA) and then the data set was randomly divided into training and prediction set. The selected five descriptors were used to build QSRR models with multi-linear regression (MLR) and generalized regression neural network (GRNN) which were built and optimized with intelligent problem solver (IPS) in Statistica 7.1software. Both linear and nonlinear models show good predictive ability, of which GRNN model demonstrated a better performance than that of the MLR model. The root mean square error of cross validation (RMSECV) of the training and the prediction set for the GRNN model was 1.345 and 2.810, and the correlation coefficients (R) were 0.955 and 0.927 respectively, while the square correlation coefficient of the cross validation (Q 2 loo) Q 2 loo on the GRNN model was 0.951, revealing the reliability of this model. The resulting data indicated that GRNN could be used as a powerful modeling tool for the QSRR studies.

Application of Artificial Neural Network for Prediction of Retention Time for some Pesticides in Liquid Chromatography

The quantitative structure-retention relationships (QSRR) method is employed to predict the retention times (RTs) of pesticides by molecular descriptors which were calculated by Dragon software. After the calculation molecular descriptors for all molecules, a suitable set of molecular descriptors were selected by using genetic algorithm (GA) and then the data set was randomly divided into training and prediction set. The selected five descriptors were used to build QSRR models with multi-linear regression (MLR) and generalized regression neural network (GRNN) which were built and optimized with intelligent problem solver (IPS) in Statistica 7.1software. Both linear and nonlinear models show good predictive ability, of which GRNN model demonstrated a better performance than that of the MLR model. The root mean square error of cross validation (RMSECV) of the training and the prediction set for the GRNN model was 1.345 and 2.810, and the correlation coefficients (R) were 0.955 and 0.927 respectively, while the square correlation coefficient of the cross validation (Q 2 loo) Q 2 loo on the GRNN model was 0.951, revealing the reliability of this model. The resulting data indicated that GRNN could be used as a powerful modeling tool for the QSRR studies.

Intelligence in the internet age: The emergence and evolution of Open Source Intelligence (OSINT)

Intelligence in the internet age: The emergence and evolution of Open Source Intelligence (OSINT)

Local policing and transnational organized crime

This paper discusses the problems associated with a crime management model that is underpinned by the geographical reach of the investigating agencies, and the merits of different models of policing that work to counter transnational organized crime. It analyzes the different policing approaches to this level of criminality from three organizing frameworks; intelligence led policing, problem-solving policing, and goal-oriented policing. It suggests that there is a puzzle at the heart of any policing solution, based on the principle of subsidiarity – how to link local policing activity with the efforts to tackle national and transnational organized criminality whilst maintaining a democratic mandate that privileges a mutual structure over the conglomerate model. It concludes with a suggestion to look towards an Intelligent Problem Solving solution that prioritizes the grassroots by focusing on threat, harm, and risk within a crime continuum.

Intelligent problem solving in process control of an event filter cluster for a particle physics experiment

Intelligent problem solving in process control of an event filter cluster for a particle physics experiment

Artificial Immune System for Protein Folding Model

To improve protein folding simulations, artificial immune system (AIS) was investigated on a 2D lattice model. AIS is a novel intelligent problem solving technique inspired from vertebrate immune system. Compared with stand genetic algorithm (SGA), AIS contains four new operators such as cloning selection, inverse mutation, pairwise interchange mutation, and receptor editing. In the experiments, we compared AIS with the immune genetic algorithm (IGA) for 4 chains of different lengths of 20, 36, 48, and 64. The results show that AIS has a higher success rate but costs more time. Keyword: Artificial Immune System, Protein Folding Model, Immune Genetic Algorithm

A Chinese view on rebuilding the integrity of management research

Purpose – The purpose of this paper is to introduce the indigenous He‐Xie management theory (HXMT) through the lens of a holistic perspective from Chinese philosophical wisdom.Design/methodology/approach – The paper revisited the challenges faced by orthodox management theories, proposing management scholars should borrow Eastern philosophical intelligence for problem solving when considering the tradition of Chinese culture on human orientation and its intelligence on tolerating and coping with ambiguity, uncertainty, and complexity. By tracing back the core concept of harmony in Confucianism, the paper identified the mechanisms for achieving harmony by differentiating further the general understanding of harmony and its original meaning in Chinese characters. The theoretical framework of HXMT was introduced then by integrating both evolutionism and constructivism perspectives.Findings – The theoretical explaining power of HXMT is rooted in the time‐honored philosophies of Asia. HXMT got its gene from tr...

Geometry Tutoring Supported by an Intelligent Drawing Interface and Automatic Problem Solving

Geometry Tutoring Supported by an Intelligent Drawing Interface and Automatic Problem Solving

Geometry Tutoring Supported by an Intelligent Drawing Interface and Automatic Problem Solving

In a scientific domain, learning comprises studying a finite set of principles of the domain and applying them to solve a wide variety of problems. Therefore an intelligent tutoring system in a scientific domain is required to possess an adequate methodology to deal with this principle. We suggest a tutoring architecture for geometry where the domain principles are automatically converted to inference operators for use by the domain-independent, inferential portion of the tutoring system. An important part of this architecture is an intelligent drawing interface that facilitates automatic conversion of the figures in geometry into an internal form that is suitable for problemsolving tutoring. During student problem solving, the system monitors the student’s steps, tracks a step that has multiple inferences, and gives hints and explanations. We discuss the advantages of our approach in enhancing the performance and interactivity of the tutoring system.

New antituberculotics originated from salicylanilides with promising in vitro activity against atypical mycobacterial strains

New antituberculotics originated from salicylanilides with promising in vitro activity against atypical mycobacterial strains

New Einsteins need positive environment, independent spirit

Lee Smolin’s piece “Why No ‘New Einstein’?” certainly raised a variety of reactions. I agree with Smolin that scientific progress in physics is hindered by current US hiring and funding practices. A number of the letters published in the January 2006 issue rallied around this idea. However, I was surprised that no one mentioned how those practices become a cycle that squelches creative scientific ideas not only of faculty but also of graduate students.Many promising physics graduate students I knew were shut out of following their dreams of PhDs in physics, even though they were creative and intelligent problem solvers. Most are no longer working in physics. Could one of those students have been another Einstein? We’ll never know.I have read numerous articles in Physics Today about the woes of low undergraduate and graduate enrollment in physics. As Smolin says, the situation is created by physics faculty and by the culture that has developed in physics departments.The letter from Peter Thejll nearly hit the problem on the head, I believe. He wrote that physics PhD students in Denmark “are generally treated like employees.” If you read the obituaries in Physics Today, you will notice that many of the older physicists earned their PhDs less than 5 years after their bachelor’s degrees. But today’s PhD candidates aren’t nearly that lucky. They are looking at a sentence of 7 to 10 years as physics graduate students. They are a source of cheap labor, and they need to remain in their advisers’ and departments’ good graces so they can complete their long-sought-after PhDs. Even if these students are retained, how much creativity has been crushed out of them as they have learned how to play the game of survival in academia? And if they become faculty members, do they perpetuate the cycle for their own survival?© 2006 American Institute of Physics.

UbiES: Applying ubiquitous computing technologies to an expert system for context-aware proactive services

ubiES: Applying ubiquitous computing technologies to an expert system for context-aware proactive services

Component value selection for active filters using parallel tabu search algorithm

Component value selection for active filters using parallel tabu search algorithm

Failure Reasoning in Multiple-Strategy Proof Planning

Failure Reasoning in Multiple-Strategy Proof Planning

A new method for adaptive IIR filter design based on tabu search algorithm

A new method for adaptive IIR filter design based on tabu search algorithm

Hybrid artificial intelligence methods in oceanographic forecast models

An approach to hybrid artificial intelligence problem solving is presented in which the aim is to forecast, in real time, the physical parameter values of a complex and dynamic environment: the ocean. In situations in which the rules that determine a system are unknown or fuzzy, the prediction of the parameter values that determine the characteristic behavior of the system can be a problematic task. In such a situation, it has been found that a hybrid artificial intelligence model can provide a more effective means of performing such predictions than either connectionist or symbolic techniques used separately. The hybrid forecasting system that has been developed consists of a case-based reasoning system integrated with a radial basis function artificial neural network. The results obtained from experiments in which the system operated in real time in the oceanographic environment, are presented.

Applying successive improvement transformations for intelligent problem solving

An improvement transformation is a transformation which has the following two properties, namely, (1) after applying it, all the already achieved subgoals are still being accomplished and (2) additional new subgoals or partial new subgoals may be accomplished. By using improvement transformations, an intelligent problem‐solving method is presented with illustrative examples. This method consists of three parts: (1) goal decomposition and subgoal ordering; (2) successive improvement transformations; and (3) backtrack algorithms. The intelligent problem‐solving method using successive improvement transformations may have useful applications in goal decomposition, goal regression, genetic algorithms, robotics, and future expert system design.

Genetic production systems for intelligent problem solving

The paper discusses an evolutionary knowledge approach to intelligent problem solving. A rule-based production system is used to model the problem and the means by which the problem space should be searched. Search heuristics are modelled as production rules. These rules are redundant as there may be more than one view on the best method for building solutions. Some rules may have complex reasoning for their actions, others have none. Deciding which rule is most appropriate is solved by a genetic algorithm and ultimately only the ‘fitter’ rules will survive. The approach eliminates the necessity of designing problem specific search or variation operators, leaving the genetic algorithm to process patterns independent of the problem at hand. Learning methods and how they aid evolution is also discussed: they are Lamarckian learning and the Baldwin effect. The approach is tested on a scheduling problem.

Accolades and brickbats

Applied Intelligence: The International Journal of Artificial Intelligence, Neural Networks, and Complex Problem-Solving Technologies. Editor-in-chief Moonis Ali. Kluwer. 4/yr. DFL384. The International Journal on Artificial Intelligence Tools. Editor N. G. Bourbakis. World Scientific. 4/yr. $195 (institutional); $100 (personal, and institutions from developing countries).