International Journal of Intelligent Systems and Applications @ijisa
Статьи журнала - International Journal of Intelligent Systems and Applications
Все статьи: 1214
Genetic-based Summarization for Local Outlier Detection in Data Stream
Статья научная
Outlier detection is one of the important tasks in data mining. Detecting outliers over streaming data has become an important task in many applications, such as network analysis, fraud detections, and environment monitoring. One of the well-known outlier detection algorithms called Local Outlier Factor (LOF). However, the original LOF has many drawbacks that can’t be used with data streams: 1- it needs a lot of processing power (CPU) and large memory to detect the outliers. 2- it deals with static data which mean that in any change in data the LOF recalculates the outliers from the beginning on the whole data. These drawbacks make big challenges for existing outlier detection algorithms in terms of their accuracies when they are implemented in the streaming environment. In this paper, we propose a new algorithm called GSILOF that focuses on detecting outliers from data streams using genetics. GSILOF solve the problem of large memory needed as it has fixed memory bound. GSILOF has two phases. First, the summarization phase that tries to summarize the past data arrived. Second, the detection phase detects the outliers from the new arriving data. The summarization phase uses a genetic algorithm to try to find the subset of points that can represent the whole original set. our experiments have been done over real datasets. Our experiments confirming the effectiveness of the proposed approach and the high quality of approximate solutions in a set of real-world streaming data.
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Geno-Dwarf-ML: Structural Analysis of Machine Learning Techniques for Genetic Dwarfism Detection
Статья научная
Understanding the prevalence of genetic dwarfism and developing detection techniques are major difficulties. Genetic dwarfism is defined by below-average stature resulting from genetic alterations. In addition to advances in detection through machine learning algorithms, this abstract investigates the analytical interpretation and comparison of genetic dwarfism statistics. In the first section, we explore the epidemiological context of genetic dwarfism, including prevalence rates, frequencies of genetic mutations, and the range of clinical presentations in various groups. The figures emphasize the intricacy of genetic variants that lead to dwarfism and emphasize the necessity for rigorous analytical methods. Improving detection and diagnostic precision through the use of machine learning algorithms appears to be a potential approach. Machine learning algorithms are trained to identify minor patterns suggestive of genetic dwarfism by utilizing datasets that include genetic profiles, medical histories, and phenotypic features. Effective methods for determining genetic markers and forecasting clinical outcomes related to dwarfism include supervised learning algorithms (e.g., decision trees, support vector machines) and deep learning architectures e.g., Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs), Recurrent Neural Networks (RNNs), Autoencoders, Capsule Networks (CapsNets), Graph Convolutional Networks (GCNs), and Long Short-Term Memory (LSTM) networks). A side-by-side comparison highlights the benefits and drawbacks of machine learning techniques over conventional diagnostic techniques. Large-scale genetic data procshines but subtle pattern detection are areas where machine learning shines but deciphering intricate genetic connections and guaranteeing model interpretability in clinical settings continue to be difficult tasks. Moreover, the interdisciplinary aspect of tackling genetic dwarfism with modern computational tools is highlighted by ethical problems pertaining to data privacy, informed consent, and equitable access to genetic testing. Ultimately, this abstract summarizes the state of the art on genetic dwarfism statistics and machine learning applications, promoting ongoing multidisciplinary cooperation to maximize the effectiveness of therapeutic approaches and diagnosis for people with genetic dwarfism.
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Статья научная
This article introduces a novel variational approach for solving the inverse geodesic problem on a transcendental surface shaped as a cylindrical structure with a cycloidal generatrix, a type of geometry that has not been previously studied in this context. Unlike classical models that rely on symmetric surfaces such as spheres or spheroids, this method formulates the geodesic path as a functional minimization problem. By applying the Euler–Lagrange equation, an analytical integration of the corresponding second-order differential equation is achieved, resulting in a parametric expression that satisfies boundary conditions. The effectiveness of the proposed method for computing geodesic curves on transcendental surfaces has been rigorously evaluated through a series of numerical experiments. Analytical validation has been carried out using MathCad, while simulation and three-dimensional visualization have been implemented in Python. Numerical experiments are conducted and 3D visualizations of the geodesic lines are presented for multiple point pairs on the surface, demonstrating the accuracy and computational efficiency of the proposed solution. This enables a closed-form analytical representation of the geodesic curve, significantly reducing computational complexity compared to existing numerical-heuristic methods.
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Geometrical Framework Application Directions in Identification Systems. Review
Статья научная
The approaches review of the framework application in identification problems is fulfilled. It is showed that this concept can have different interpretations of identification problems. In particular, the framework is understood as a frame, structure, system, platform, concept, and basis. Two directions of this concept application are allocated: 1) the framework integrating the number of methods, approaches or procedures; b) the mapping describing in the generalized view processes and properties in a system. We give the review of approaches that are the basis of the second direction. They are based on the analysis of virtual geometric structures. These mappings (frameworks) differ in the theory of chaos, accidents, and the qualitative theory of dynamic systems. Introduced mappings (frameworks) are not set a priori, and they are determined based of the experimental data processing. The main directions analysis of geometrical frameworks application is fulfilled in structural identification problems of systems. The review includes following directions: i) structural identification of nonlinear systems; ii) an estimation of Lyapunov exponents; iii) structural identifiability of nonlinear systems; iv) the system structure choice with lag variables; v) system attractor reconstruction.
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Graph Coloring in University Timetable Scheduling
Статья научная
Addressing scheduling problems with the best graph coloring algorithm has always been very challenging. However, the university timetable scheduling problem can be formulated as a graph coloring problem where courses are represented as vertices and the presence of common students or teachers of the corresponding courses can be represented as edges. After that, the problem stands to color the vertices with lowest possible colors. In order to accomplish this task, the paper presents a comparative study of the use of graph coloring in university timetable scheduling, where five graph coloring algorithms were used: First Fit, Welsh Powell, Largest Degree Ordering, Incidence Degree Ordering, and DSATUR. We have taken the Military Institute of Science and Technology, Bangladesh as a test case. The results show that the Welsh-Powell algorithm and the DSATUR algorithm are the most effective in generating optimal schedules. The study also provides insights into the limitations and advantages of using graph coloring in timetable scheduling and suggests directions for future research with the use of these algorithms.
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Graphical Data Steganographic Protection Method Based on Bits Correspondence Scheme
Статья научная
The proposed method of graphical data protection is a combined crypto-steganographic method. It is based on a bit values transformation according to both a certain Boolean function and a specific scheme of correspondence between MSB and LSB. The scheme of correspondence is considered as a secret key. The proposed method should be used for protection of large amounts of secret graphical data.
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Grass Fibrous Root Optimization Algorithm
Статья научная
This paper proposes a novel meta-heuristic optimization algorithm inspired by general grass plants fibrous root system, asexual reproduction, and plant development. Grasses search for water and minerals randomly by developing its location, length, primary root, regenerated secondary roots, and small branches of roots called hair roots. The proposed algorithm explore the bounded solution domain globally and locally. Globally using the best grasses survived by the last iteration, and the root system of the best grass obtained so far by the iteration process and locally uses the primary roots, regenerated secondary roots and hair roots of the best global grass. Each grass represents a global candidate solution, while regenerated secondary roots stand for the locally obtained solution. Secondary generated hair roots are equal to the problem dimensions. The performance of the proposed algorithm is tested using seven standard benchmark test functions, comparing it with other meta-heuristic well-known and recently proposed algorithms.
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Guide Me: A Research Work Area Recommender System
Статья научная
With the advent of Industrial Revolution, not only the choices in various fields increased but also the era of computer came into existence thereby revolutionizing the global market. People had numerous choices in front of them that often led to the confusion about what product might actually fulfill their requirements. So the need for having a system which could facilitate the selection criteria and eradicate the dilemma of masses, was realized and ultimately recommender systems of present day world were introduced. So we can refer recommender systems as software tools that narrow down our choices and provide us with the most suitable suggestions as per our requirements. In this paper, we propose a novel recommender system i.e. RWARS (Research Work Area Recommender System) that will recommend research work area to a user based on his/her characteristics similar to those of other users. The characteristics considered here are hobbies, subjects of interests, programming skills and future objectives. The proposed system will use Cosine Similarity approach of Collaborative Filtering.
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Handling Fuzzy Image Clustering with a Modified ABC Algorithm
Статья научная
Image segmentation can be cast as a clustering task where the image is partitioned into clusters. Pixels within the same cluster are as homogenous as possible whereas pixels belonging to different clusters are not similar in terms of an appropriate similarity measure. Several clustering methods have been proposed for image segmentation purpose among which the Fuzzy C-Means clustering algorithm. However this algorithm still suffers from some drawbacks, such as local optima and sensitivity to initialization. Artificial Bees Colony algorithm is a recent population-based optimization method which has been successfully used in many complex problems. In this paper, we propose a new fuzzy clustering algorithm based on a modified Artificial Bees Colony algorithm, in which a new mutation strategy inspired from the Differential Evolution is introduced in order to improve the exploitation process. Experimental results show that our proposed approach improves the performance of the basic fuzzy C-Means clustering algorithm and outperforms other population based optimization methods.
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Статья научная
The automatic recognition of handwriting is a particularly complex operation. Until now, there is no algorithm able to recognize handwriting without that; there are assumptions to take in advance to facilitate the task of the process. A handwritten text can contain letters lowercase, uppercase letters, characters sticks and digits. Therefore, it is capital to know extract and separate all these different units in order to process them with specific algorithms to their class handwriting. In this paper, we present a system for unconstrained handwritten text recognition, which allows to achieve this operation thanks to an intelligent segmentation based on an iterative cutting in a multi-script environment. The results obtained from the experimental protocol reach an "equal error rate" (EER) neighboring to 6%. These calculations were calculated with a relatively small base; however this same rate can be decreased with great bases. Our results are extremely encouraging for the simple reason that our approach is situated in a more general context unlike other approaches which define several non-rigid assumptions; this clearly makes the problem simpler and may make it trivial.
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Hardware-in-the-loop of Simulation for a Hydraulic Antilock Brake System
Статья научная
Hardware-In-the-Loop (HIL) of simulation policy is used as a rapid and economical tool for developing automotive systems effectively and for dangerous situations tests such as extreme road conditions or high travelling speeds. A method for building a HIL of simulation a hydraulic Antilock Braking System (ABS) based on MATLAB/Simulink is presented in this paper. The system is implemented for research purposes as well as for the application in educational process. It can help the user heightening the efficiency when developing the electronic device. Also, the system can be used as teaching demo software. Experiment tests of HIL scheme were carried to ensure the feasibility and effectiveness of the system.
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Статья научная
Healthcare is a fundamental human right. Vulnerable populations in healthcare refer to those who are at greater risk of suffering from health hazards due to various socio-economic factors, geographical barriers, and medical conditions. Mapping of this vulnerable population is a vital part of healthcare planning for any region. Very few such research regarding the distribution of healthcare service providers was carried out in the Nepali context previously. Thus, the results of vulnerability mapping can help with meaningful interventions for healthcare demands. This study focused on combining geo-analytics, unsupervised machine learning algorithms, and entropy methods for performing vulnerability mapping. K-means++ clustering algorithm was applied to household data of Ratnanagar municipality for the purpose of creating multiple clusters of households. An open-source routing machine was used to compute the distance to the nearest health service provider from each household in Ratnanagar municipality. The entropy method was used to evaluate the vulnerability measure of each cluster. Later, based on the population of different clusters in each ward and their respective vulnerability measures, each ward’s vulnerability measure was quantified. It can be observed that wards that are farther away from the east-west highway have higher vulnerability indices. This study found that machine learning algorithms can be effectively used in combination with the weighting method for vulnerability mapping. Using an unsupervised machine learning algorithm made sure that dimensions of vulnerability are visible.
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Heart Beat Classification Using Particle Swarm Optimization
Статья научная
This paper proposes a novel system to classify three types of electrocardiogram beats, namely normal beats and two manifestations of heart arrhythmia. This system includes three main modules: a feature extraction module, a classifier module, and an optimization module. In the feature extraction module, a proper set combining the shape features and timing features is proposed as the efficient characteristic of the patterns. In the classifier module, a multi-class support vector machine (SVM)-based classifier is proposed. For the optimization module, a particle swarm optimization algorithm is proposed to search for the best value of the SVM parameters and upstream by looking for the best subset of features that feed the classifier. Simulation results show that the proposed algorithm has very high recognition accuracy. This high efficiency is achieved with only little features, which have been selected using particle swarm optimizer.
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Heart Disease Prediction Using Modified Version of LeNet-5 Model
Статья научная
Particularly compared to other diseases, heart disease (HD) claims the lives of the greatest number of people worldwide. Many priceless lives can be saved with the help of early and effective disease identification. Medical tests, an electrocardiogram (ECG) signal, heart sounds, computed tomography (CT) images, etc. can all be used to identify HD. Of all sorts, HD signal recognition from ECG signals is crucial. The ECG samples from the participants were taken into consideration as the necessary inputs for the HD detection model in this study. Many researchers analyzed the risk factors of heart disease and used machine learning or deep learning techniques for the early detection of heart patients. In this paper, we propose a modified version of the LeNet-5 model to be used as a transfer model for cardiovascular disease patients. The modified version is compared to the standard version using four evaluation metrics: accuracy, precision, recall, and F1-score. The achieved results indicated that when the LeNet-5 model was modified by increasing the number of used filters, this increased the model's ability to handle the ECGs dataset and extract the most important features from it. The results also showed that the modified version of the LeNet-5 model based on the ECGs image dataset improved accuracy by 9.14 percentage points compared to the standard LeNet-5 model.
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Heart Diseases Diagnosis Using Neural Networks Arbitration
Статья научная
There is an increase in death rate yearly as a result of heart diseases. One of the major factors that cause this increase is misdiagnoses on the part of medical doctors or ignorance on the part of the patient. Heart diseases can be described as any kind of disorder that affects the heart. In this research work, causes of heart diseases, the complications and the remedies for the diseases have been considered. An intelligent system which can diagnose heart diseases has been implemented. This system will prevent misdiagnosis which is the major error that may occur by medical doctors. The dataset of statlog heart disease has been used to carry out this experiment. The dataset comprises attributes of patients diagnosed for heart diseases. The diagnosis was used to confirm whether heart disease is present or absent in the patient. The datasets were obtained from the UCI Machine Learning. This dataset was divided into training, validation set and testing set, to be fed into the network. The intelligent system was modeled on feed forward multilayer perceptron, and support vector machine. The recognition rate obtained from these models were later compared to ascertain the best model for the intelligent system due to its significance in medical field. The results obtained are 85%, 87.5% for feedforward multilayer perceptron, and support vector machine respectively. From this experiment we discovered that support vector machine is the best network for the diagnosis of heart disease.
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Helicopter Control Using Fuzzy Logic and Narma-L2 Techniques
Статья научная
Helicopter instability is one of the most limitations that should be addressed in a nonlinear application. Accordingly, researchers are invited to design a robust and reliable controller to obtain a stable system and enhance its overall performance. The present study focuses on the use of the intelligent system in controlling the pitch and yaw angles. This lead to controlling the elevation and the direction of the helicopter. Further to the application of the Linear Quadratic Regulator (LQR) controller, this research implemented the Proportional Integral Derivative (PID), Fuzzy Logic Control (FLC), and Artificial Neural Network (ANN). The results show that FLC achieved a good controllability for both angles, particularly for the pitch angle in comparison to the nonlinear auto regressive moving average (NARMA-L2). Moreover, NARMA-L2 requires further improvement by using, for example, the swarm optimization method to provide better controllability. The PID controller, on the other hand, had a greater capability in controlling the yaw angle in comparison to the other controllers implemented. Accordingly, it is suggested that the integration of PID and FLC may lead to more optimal outcomes.
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Heuristic-based Approach for Dynamic Consolidation of Software Licenses in Cloud Data Centers
Статья научная
Since its emergence, cloud computing has continued to evolve thanks to its ability to present computing as consumable services paid by use, and the possibilities of resource scaling that it offers according to client’s needs. Models and appropriate schemes for resource scaling through consolidation service have been considerably investigated, mainly, at the infrastructure level to optimize costs and energy consumption. Consolidation efforts at the SaaS level remain very restrained mostly when proprietary software are in hand. In order to fill this gap and provide software licenses elastically regarding the economic and energy-aware considerations in the context of distributed cloud computing systems, this work deals with dynamic software consolidation in commercial cloud data centers 〖DS〗^3 C. Our solution is based on heuristic algorithms and allows reallocating software licenses at runtime by determining the optimal amount of resources required for their execution and freed unused machines. Simulation results showed the efficiency of our solution in terms of energy by 68.85% savings and costs by 80.01% savings. It allowed to free up to 75% physical machines and 76.5% virtual machines and proved its scalability in terms of average execution time while varying the number of software and the number of licenses alternately.
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Статья научная
Attention Deficit Hyperactive Disorder (ADHD) is a disruptive neurobehavioral disorder characterized by abnormal behavioral patterns in attention, perusing activity, acting impulsively and combined types. It is predominant among school going children and it is tricky to differentiate between an active and an ADHD child. Misdiagnosis and undiagnosed cases are very common. Behavior patterns are identified by the mentors in the academic environment who lack skills in screening those kids. Hence an unsupervised learning algorithm can cluster the behavioral patterns of children at school for diagnosis of ADHD. In this paper, we propose a hierarchical clustering algorithm to partition the dataset based on attribute dependency (HCAD). HCAD forms clusters of data based on the high dependent attributes and their equivalence relation. It is capable of handling large volumes of data with reasonably faster clustering than most of the existing algorithms. It can work on both labeled and unlabelled data sets. Experimental results reveal that this algorithm has higher accuracy in comparison to other algorithms. HCAD achieves 97% of cluster purity in diagnosing ADHD. Empirical analysis of application of HCAD on different data sets from UCI repository is provided.
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Статья научная
The Huge amount of Big Data is constantly arriving with the rapid development of business organizations and they are interested in extracting knowledgeable information from collected data. Frequent item mining of Big Data helps with business decision and to provide high quality service. The result of traditional frequent item set mining algorithm on Big Data is not an effective way which leads to high computation time. An Apache Hadoop MapReduce is the most popular data intensive distributed computing framework for large scale data applications such as data mining. In this paper, the author identifies the factors affecting on the performance of frequent item mining algorithm based on Hadoop MapReduce technology and proposed an approach for optimizing the performance of large scale frequent item set mining. The Experiments result shows the potential of the proposed approach. Performance is significantly optimized for large scale data mining in MapReduce technique. The author believes that it has a valuable contribution in the high performance computing of Big Data.
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Статья научная
Scheduling is the process of improving the performance of a parallel and distributed system. Parallel systems are part of distributed systems. Parallel systems refers to the concept of run parallel jobs that can be run simultaneously on several processors. Load balancing and scheduling are very important and complex problems in multiprocessor systems. So that problems are an NP-Complete problems. In this paper, we introduce a method based on genetic algorithms for scheduling and laod balancing in parallel heterogeneous multi-processor systems. The results of the simulations indicate Genetic algorithm for scheduling at in systems is better than LPT, SPT and FIFO. Simualation results indicate Genetic Algorithm reduce total response time and also it increase utilization.
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