International Journal of Intelligent Systems and Applications @ijisa
Статьи журнала - International Journal of Intelligent Systems and Applications
Все статьи: 1173
Multi-Objective Memetic Algorithm for FPGA Placement Using Parallel Genetic Annealing
Статья научная
Due to advancement in reconfigurable computing, Field Programmable Gate Array (FPGA) has gained significance due to its low cost and fast prototyping. Parallelism, specialization, and hardware level adaptation, are the key features of reconfigurable computing. FPGA is a programmable chip that can be configured or reconfigured by the designer, to implement any digital circuit. One major challenge in FPGA design is the Placement problem. In this placement phase, the logic functions are assigned to specific cells of the circuit. The quality of the placement of the logic blocks determines the overall performance of the logic implemented in the circuits. The Placement of FPGA is a Multi-Objective Optimization problem that primarily involves minimization of three or more objective functions. In this paper, we propose a novel strategy to solve the FPGA placement problem using Non-dominated Sorting Genetic Algorithm (NSGA-II) and Simulated Annealing technique. Experiments were conducted in Multicore Processors and metrics such as CPU time were measured to test the efficiency of the proposed algorithm. From the experimental results, it is evident that the proposed algorithm reduces the CPU consumption time to an average of 15% as compared to the Genetic Algorithm, 12% as compared to the Simulated Annealing, and approximately 6% as compared to the Genetic Annealing algorithm.
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Multi-agent-based Fuzzy Dispatching for Trucks at Container Terminal
Статья научная
At container terminals, containers are transported from the marshalling yard to the quay and vice versa by Container Trucks (CTs). This study discusses how to dispatch CTs by utilizing information about pickup and delivery locations and time in future delivery tasks based on dynamic dispatching strategy in which multiple tasks are matched with multiple CTs. n this paper, Multi-agent system (MAS) is used as the basis for an intelligent dispatch system. To aim at that the characteristic of management of container terminal is how to optimize resource of terminal, the trends of decision-making way for management of container terminal, research and application of Multi-Agent system is summarized. Relationship between transport tasks and service of CTS has been taken as a contract net using the fuzzy set theory and method. Considering the load of communication and consultation efficiency in system, the bidirectional negotiation mechanism is adopted. The dispatching model based on Contract Network Protocol (CNP) using bidirectional negotiation is provided for assigning optimal delivery tasks to CTs and fuzzy reasoning process of dispatching decisions is suggested. The method has both virtues of precision of static planning and flexibility of CNP and has been confirmed by cases.
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Multi-character fighting simulation
Статья научная
In the development of and research into multi-character fighting computer games, Non-Player Characters (NPCs) frequently seem less intelligent owing to them having a single focus. As such, multi-character fighting becomes one-on-one fighting; one character will encounter another character only once the previous opponent is defeated. This study develops a new model in multi-character fighting, in which each NPC can simultaneously fight against many characters. Following this model, each character becomes an agent that makes his own decisions. The first advantage of this model is the integration of multi-character behaviors in fights. Each character can seek out enemies/opponents, select one target opponent, avoid obstacles, approach the target opponent, change the target opponent, and then defeat the opponent or be defeated by the opponent; in other words, each character can thus fight against many opponents. All of the behaviors in the fight take place automatically. The second advantage of this model is that each character does not only focus on the opponent being targeted, but also on the other opponents surrounding him. Each character can move from one opponent to another, even when the target opponent is not yet defeated. The third advantage of this model is that each character can move to another fight cluster, thus ensuring that fights seem more dynamic. This research has experimented with the model using a 3D application that can run on personal computers or smart phones.
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Multi-objective Optimization of Subsonic Glider Wing Using Genetic Algorithm
Статья научная
The widespread adoption of Unmanned Aerial Vehicles (UAVs) can be traced to its flexibility and wide adaptability to various operating conditions and applications, comparably low cost of construction and maintenance and environmental friendliness as they can be easily configured for electric power. The use of electric power also favours its low noise applications such as surveillance. A major issue associated with surveillance, as addressed in this study is the compromise between Range and Endurance operation modes. The Range mode relates to being able to cover longer distances while the Endurance mode relates to spending longer times in the atmosphere for a fixed charge. Trying to balance the interplay of these parameters gave rise to a multi-objective optimization where the objectives are somewhat conflicting. This resulted in a set of Pareto solutions which are a set of design parameters (primarily angle of attack) that satisfy the joint requirements of the performance parameters of Range and Endurance. This study first considered a baseline aerodynamic design using traditional design methods. Design of Experiment techniques were then used to select the most favourable design points. This model was then used to build an input framework for Genetic Optimization algorithm deployed in the Global Optimization Toolbox of MATLAB. The result of this research shows that most of the region associated with medium angle of attack (AOA) setting (7 degrees) jointly satisfies good Range and Endurance performances with an average lift-to-drag ratio of 20 in the flight configuration considered. The implication of this result is that low velocity drag encountered in surveillance that requires a high AOA is largely reduced with the medium setting, albeit stabilized with other structural and aerodynamic settings, namely an aspect ratio of 13 and a taper ratio of 0.6.
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Multi-objective Structural Optimization Using Fuzzy and Intuitionistic Fuzzy Optimization Technique
Статья научная
In this paper, we develop an intuitionistic fuzzy optimization (IFO) approach for optimizing the design of plane truss structure with multiple objectives subject to a specified set of constraints. In this optimum design formulation, the objective functions are the weight of the truss and the deflection of loaded joint; the design variables are the cross-sections of the truss members; the constraints are the stresses in members. A classical truss optimization example is presented here in to demonstrate the efficiency of the Intuitionistic fuzzy optimization approach. The test problem includes a three-bar planar truss subjected to a single load condition. This multi-objective structural optimization model is solved by fuzzy optimization approach as well as intuitionistic fuzzy optimization approach. Numerical example is given to illustrate our approach. The result shows that the IFO approach is very efficient in finding the best discovered optimal solutions.
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Multi-objective monkey algorithm for drug design
Статья научная
Swarm intelligence algorithms are designed to mimic the natural behaviors of living organisms. The birds, animals and insects exhibit extraordinary problem solving behaviors and intelligence when living in colonies or groups. These unique behaviors form the basis for the design of the Metaheuristic which are helpful in solving several real-life combinatorial optimization problems. Monkey algorithm is developed based on the unique behaviors of monkeys such as mountain and tree climbing, jumping, watching and somersaulting. This paper reports for the first time the design and development of Multi-objective Monkey Algorithm (MoMA) and its use for the design of molecules with optimal drug-like properties. Finally, the performance of the proposed MoMA for Drug design (MoMADrug) is compared with the previously disclosed Multi-objective Genetic algorithm (MoGADdrug) for the design of drug-like molecules.
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Статья научная
Computational Intelligence (CI) is an as of emerging area in addressing complex real world problems. The WOA has taken its root from the collective intelligent foraging behavior of humpback whales (Megaptera Novaeangliae). The standard WOA is suffers from the selection of best agent while whales searching and encircling prey. This research paper deals with the multi-swarm cooperative strategies for finding the best agents which balances the two phase’s exploration and exploitation. The performance of invoked Multi-Swarm cooperative strategies into standard WOA i.e, MsWOA is first benchmarked on a set of 23 standard mathematical benchmark function problems which includes 7 Uni-Modal, 6 Multi-modal and 10 fixed dimension multi-modal functions. The obtained graphical and statistical results have been portrayed along with the previously established techniques. The obtained results depicts that the proposed cooperative strategies for WOA outperforms in solving optimization problems of standard benchmark functions. This paper also studies the numerical efficiency of proposed techniques on the problem of data clustering where 10 real data clustering problems have been taken from data repository https://archive.ics.uci.edu.data. Statistical results for the obtained cluster centroids, intra-cluster distances and inter-cluster distances confirms that the cooperative strategies for best whale agent selection improves the performance WOA for function optimization problems as well as data clustering problems.
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Статья научная
In this paper a multiband dielectric resonator with array of defect at the ground plane is proposed. Filter is constructed by placing high-quality factor 〖TE〗_01δ mode dielectric resonators on the microstripline. The focus is on the design process includes choosing optimum geometry of a dielectric resonator so that high Q can be achieved. This is designed without compromising miniaturization and efficiency. It is observed that the integration of dielectric resonator with DGS may be merged to achieve wide band.Two band with 6 GHz low pass filter and 2 GHz band pass filter has been achieved. The filter which is proposed for microwave communication is expected to have better quality factor compared to lumped elements-based BPF. The used MBDRF have bandwidth of 6GHz and 2 GHz with dielectric constant of 60±1.
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Multilevel Thresholding for Image Segmentation using the Galaxy-based Search Algorithm
Статья научная
In this paper, image segmentation of gray-level images is performed by multilevel thresholding. The optimal thresholds for this purpose are found by maximizing the between-class variance (the Otsu’s criterion). The optimization (maximization) is conducted by a novel nature-inspired search algorithm, which is called Galaxy-based Search Algorithm or GbSA. The proposed GbSA is a metaheuristic for continuous optimization. It resembles the spiral arms of some galaxies to search for the optimal thresholds. The GbSA also uses a modified Hill Climbing algorithm as a local search. The GbSA also utilizes chaos for improving its performance, which is implemented by the logistic map. Experimental results show that the GbSA finds the optimal or very near optimal thresholds in all runs of the algorithm.
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Multiobjective Multipath Adaptive Tabu Search for Optimal PID Controller Design
Статья научная
The multipath adaptive tabu search (MATS) has been proposed as one of the most powerful metaheuristic optimization search techniques for solving the combinatorial and continuous optimization problems. The MATS employing the adaptive tabu search (ATS) as the search core has been proved and applied to various real-world engineering problems in single objective optimization manner. However, many design problems in engineering are typically multiobjective under complex nonlinear constraints. In this paper, the multiobjective multipath adaptive tabu search (mMATS) is proposed. The mMATS is validated against a set of multiobjective test functions, and then applied to design an optimal PID controller of the automatic voltage regulator (AVR) system. As results, the mMATS can provide very satisfactory solutions for all test functions as well as the control application.
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Статья научная
The purpose of Biomedical Natural Language Processing (BioNLP) is to capture biomedical phenomena from textual data by extracting relevant entities, information and relations between biomedical entities (i.e. proteins and genes). In general, in most of the published papers, only binary relations were extracted. In a recent past, the focus is shifted towards extracting more complex relations in the form of bio-molecular events that may include several entities or other relations. In this paper we propose an approach that enables event trigger extraction of relatively complex bio-molecular events. We approach this problem as a detection of bio-molecular event trigger using the well-known algorithm, namely Conditional Random Field (CRF). We apply our experiments on development set. It shows the overall average recall, precision and F-measure values of 64.27504%, 69.97559% and 67.00429%, respectively for the event detection.
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Статья научная
When we acquiring the Electrocardiogram (ECG) signal from the person, the signal amplitude (PQRST) and timing values are changes due to various artefacts. The different artefacts are Baseline wander, power line interference, muscle artefact, motion artefact and the channel noise also added sometimes during the transmission of the signal for diagnosis purpose. The adaptive filters play vital role for reduction of noise in the desired signals. In this paper we proposed, block based error normalized Recursive Least Square (RLS) adaptive algorithm and sign based RLS adaptive algorithm, which are used for reduction of muscle artifact noise and base line wander noise in the ECG signal. From the simulation result we analyzed that, comparing to Least Mean Square algorithm, the proposed RLS algorithm gives fast convergence rate with high signal to noise ratio and less mean square error.
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Nanotechnology Method Comparison for Early Detection of Cancer
Статья научная
Since 1999, cancer has been the leading cause of death under the age of 85 years and the eradication of this disease has been the long sought-after goal of scientists and physicians. Cancer is a disease in which abnormal cells divide uncontrollably. These abnormal cells have the ability to invade and destroy normal body cells, which is life threatening. One of the most important factors in effective cancer treatment is the detection of cancerous tumour cells in an early stage. Nanotechnology brings new hope to the arena of cancer detection research, owing to nanoparticles’ unique physical and chemical properties, giving them the potential to be used in the detection and monitoring of cancer. One such approach is quantum dots based detection which is rapid, easy and economical enabling quick point-of-care screening of cancer markers. QDs have got unique properties which make them ideal for detecting tumours. On the other hand, Gold nanoparticles have been in the bio-imaging spotlight due to their special optical properties. Au-NPs with strong surface-plasmon-enhanced absorption and scattering have allowed them to emerge as powerful imaging labels and contrast agents. This paper includes the comparative study of both the methods. Compared with quantum dots, the gold-nanoparticles are more than 200 times brighter on a particle-to-particle basis, although they are about 60 times larger by volume. Thus, Gold nanoparticles in suspension, offers advantages compared with quantum dots in that the gold appears to be non-toxic and the particles produce a brighter, sharper signal.
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Статья научная
The increasing trends in intelligent control systems design has provide means for engineers to evolve robust and flexible means of adapting them to diverse applications. This tendency would reduce the challenges and complexity in bringing about the appropriate controllers to effect stability and efficient operations of industrial systems. This paper investigates the effect of two nature inspired algorithms, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), on PID controller for optimum tuning of a Fuzzy Logic Controller for Poultry Feed Dispensing Systems (PFDS). The Fuzzy Logic Controller was used to obtain a desired control speed for the conceptualized intelligent PFDS model. Both GA and PSO were compared to investigate which of the two algorithms could permit dynamic PFDS model to minimize feed wastage and reduce the alarming human involvement in dispensing poultry feeds majorly in the tropics. The modelling and simulation results obtained from the study using discrete event simulator and computational programming environment showed that PSO gave a much desired results for the optimally tuned FLC-PID, for stable intelligent PFDS with fast system response, rise time, and settling time compared to GA.
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Nearest Neighbor Classifier Method for Making Loan Decision in Commercial Bank
Статья научная
Bank plays the central role for the economic development world-wide. The failure and success of the banking sector depends upon the ability to proper evaluation of credit risk. Credit risk evaluation of any potential credit application has remained a challenge for banks all over the world till today. Artificial neural network plays a tremendous role in the field of finance for making critical, enigmatic and sensitive decisions those are sometimes impossible for human being. Like other critical decision in the finance, the decision of sanctioning loan to the customer is also an enigmatic problem. The objective of this paper is to design such a Neural Network that can facilitate loan officers to make correct decision for providing loan to the proper client. This paper checks the applicability of one of the new integrated model with nearest neighbor classifier on a sample data taken from a Bangladeshi Bank named Brac Bank. The Neural network will consider several factors of the client of the bank and make the loan officer informed about client’s eligibility of getting a loan. Several effective methods of neural network can be used for making this bank decision such as back propagation learning, regression model, gradient descent algorithm, nearest neighbor classifier etc.
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NegMiner: An Automated Tool for Mining Negations from Electronic Narrative Medical Documents
Статья научная
Mining negations from electronic narrative medical documents is one of the prominent data mining applications. Since medical documents are freely written, it is impossible to consider all possible sentence structures in advance and so frequent update of mining algorithms is inevitable. Unfortunately most of the proposed algorithms in the literature are too complex to be easily updated. Besides, most of them cannot be easily ported to other natural languages. The simple NegEx algorithm utilizes only two regular expressions and sets of terms to mine negations from narrative medical documents and so does not suffer from these shortcomings. Meanwhile, it has shown impressive mining results and so it is the most widely adopted algorithm. This paper proposes the Negation Mining (NegMiner) tool to address some of the shortcomings of the NegEx algorithm. The NegMiner exploits some basic syntactic and semantic information to deal with contiguous and multiple negations. It is a user-friendly tool that facilitates the task of knowledge base update and the task of document analysis through the use of PDF files. This also makes it able to deal with the existence of a medical finding several times in a single sentence. Experimental results have shown the superiority of the mining results of the NegMiner in comparison to the simulated NegEx algorithm.
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Neural Network based Modeling and Simulation of Transformer Inrush Current
Статья научная
Inrush current is a very important phenomenon which occurs during energization of transformer at no load due to temporary over fluxing. It depends on several factors like magnetization curve, resistant and inductance of primary winding, supply frequency, switching angle of circuit breaker etc. Magnetizing characteristics of core represents nonlinearity which requires improved nonlinearity solving technique to know the practical behavior of inrush current. Since several techniques still working on modeling of transformer inrush current but neural network ensures exact modeling with experimental data. Therefore, the objective of this study was to develop an Artificial Neural Network (ANN) model based on data of switching angle and remanent flux for predicting peak of inrush current. Back Propagation with Levenberg-Marquardt (LM) algorithm was used to train the ANN architecture and same was tested for the various data sets. This research work demonstrates that the developed ANN model exhibits good performance in prediction of inrush current’s peak with an average of percentage error of -0.00168 and for modeling of inrush current with an average of percentage error of -0.52913.
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Neural network modeling and correlation analysis of brain plasticity mechanisms in stroke patients
Статья научная
The aim of this research is the study of pathogenic signs, prognostically significant for the outcome of the disease and restoration of impaired functions at various stages of recovery after a stroke. This work describes a new method of applying a group of artificial neural network algorithms for each of the criteria and for each period of rehabilitation, and it is aimed at analyzing the structural and functional support of motor and higher cognitive functions, including speech and language as well as brain plasticity after ischemic stroke. The functional magnetic resonance imaging (fMRI, DTI) and clinical data machine learning algorithms were used. Self-organizing Kohonen and probabilistic neural network-based models with different structures and parameters were developed and applied for each criterion for periods of 3, 6, and 12 months of rehabilitation. For correlation analyses and modeling additional classifiers, we used: Decision Tree (DT), Support Vector Machine (SUM), k-Nearest Neighbor (KNN) clustering, and Logistic Regression (LR). In the performance evaluation, sensitivity, specificity, accuracy, error rate, and f-measure were used. The using of clinical parameters and mathematical modeling for analysis of brain plasticity mechanisms in stroke patients allowed in some cases to predict cognitive functions within the accuracy of 85-97%. Moreover, it is shown that the functional systems is represented by various brain structures, its synchronous activity and structural connectivity ensures the rapid and most complete restoration of motor and higher cognitive functions, including speech and language (effective post-stroke plasticity of the brain) after a course of neurorehabilitation.
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Neural-Based Cuckoo Search of Employee Health and Safety (HS)
Статья научная
A study using the cuckoo search algorithm to evaluate the effects of using computer-aided workstations on employee health and safety (HS) is conducted. We collected data for HS risk on employees at their workplaces, analyzed the data and proposed corrective measures applying our methodology. It includes a checklist with nine HS dimensions: work organization, displays, input devices, furniture, work space, environment, software, health hazards and satisfaction. By the checklist, data on HS risk factors are collected. For the calculation of an HS risk index a neural-swarm cuckoo search (NSCS) algorithm has been employed. Based on the HS risk index, IHS four groups of HS risk severity are determined: low, moderate, high and extreme HS risk. By this index HS problems are allocated and corrective measures can be applied. This approach is illustrated and validated by a case study. An important advantage of the approach is its easy use and HS index methodology speedily pointing out individual employee specific HS risk.
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Neurolingua Stress Senolytics: Innovative AI-driven Approaches for Comprehensive Stress Intervention
Статья научная
Introducing an innovative approach to stress detection through multimodal data fusion, this study addresses the critical need for accurate stress level monitoring, essential for mental health assessments. Leveraging diverse data sources—including audio, biological sensors, social media, and facial expressions—the methodology integrates advanced algorithms such as XG-Boost, GBM, Naïve Bayes, and BERT. Through separate preprocessing of each dataset and subsequent feature fusion, the model achieves a comprehensive understanding of stress levels. The novelty of this study lies in its comprehensive fusion of multiple data modalities and the specific preprocessing techniques used, which improves the accuracy and depth of stress detection compared to traditional single-modal methods. The results demonstrate the efficacy of this approach, providing a nuanced perspective on stress that can significantly benefit healthcare, wellness, and HR sectors. The model's strong performance in accuracy and robustness positions it as a valuable asset for early stress detection and intervention. XG-Boost achieved an accuracy rate of 95%, GBM reached 97%, Naive Bayes achieved 90%, and BERT attained 93% accuracy, demonstrating the effectiveness of each algorithm in stress detection. This innovative approach not only improves stress detection accuracy but also offers potential for use in other fields requiring analysis of multimodal data, such as affective computing and human-computer interaction. The model's scalability and adaptability make it well-suited for incorporation into existing systems, opening up opportunities for widespread adoption and impact across various industries.
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