International Journal of Intelligent Systems and Applications @ijisa
Journal articles - International Journal of Intelligent Systems and Applications
All articles: 1277
An Automated Real-Time System for Opinion Mining using a Hybrid Approach
Scientific article
In this paper, a novel idea is being presented to perform Opinion Mining in a very simple and efficient manner with the help of the One-Level-Tree (OLT) based approach. To recognize opinions specific for features in customer reviews having a variety of features commingled with diverse emotions. Unlike some previous ventures entirely using one-time structured or filtered data but this is solely based on unstructured data obtained in real-time from Twitter. The hybrid approach utilizes the associations defined in Dependency Parsing Grammar and fully employs Double Propagation to extract new features and related new opinions within the review. The Dictionary based approach is used to expand the Opinion Lexicon. Within the dependency parsing relations a new relation is being proposed to more effectively catch the associations between opinions and features. The three new methods are being proposed, termed as Double Positive Double Negative (DPDN), Catch-Phrase Method (CPM) & Negation Check (NC), for performing criteria specific evaluations. The OLT approach conveniently displays the relationship between the features and their opinions in an elementary fashion in the form of a graph. The proposed system achieves splendid accuracy across all domains and also performs better than the state-of-the-art systems.
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An Automatic Number Plate Recognition System under Image Processing
Scientific article
Automatic Number Plate Recognition system is an application of computer vision and image processing technology that takes photograph of vehicles as input image and by extracting their number plate from whole vehicle image , it display the number plate information into text. Mainly the ANPR system consists of 4 phases: - Acquisition of Vehicle Image and Pre-Processing, Extraction of Number Plate Area, Character Segmentation and Character Recognition. The overall accuracy and efficiency of whole ANPR system depends on number plate extraction phase as character segmentation and character recognition phases are also depend on the output of this phase. Further the accuracy of Number Plate Extraction phase depends on the quality of captured vehicle image. Higher be the quality of captured input vehicle image more will be the chances of proper extraction of vehicle number plate area. The existing methods of ANPR works well for dark and bright/light categories image but it does not work well for Low Contrast, Blurred and Noisy images and the detection of exact number plate area by using the existing ANPR approach is not successful even after applying existing filtering and enhancement technique for these types of images. Due to wrong extraction of number plate area, the character segmentation and character recognition are also not successful in this case by using the existing method. To overcome these drawbacks I proposed an efficient approach for ANPR in which the input vehicle image is pre-processed firstly by iterative bilateral filtering , adaptive histogram equalization and number plate is extracted from pre-processed vehicle image using morphological operations, image subtraction, image binarization/thresholding, sobel vertical edge detection and by boundary box analysis. Sometimes the extracted plate area also contains noise, bolts, frames etc. So the extracted plate area is enhanced by using morphological operations to improve the quality of extracted plate so that the segmentation phase gives more successful output. The character segmentation is done by connected component analysis and boundary box analysis and finally in the last character recognition phase, the characters are recognized by matching with the template database using correlation and output results are displayed. This approach works well for low contrast, blurred, noisy as well as for dark and light/bright category images. The comparison is done between the ANPR with Adaptive Histogram Equalization and Iterative Bilateral Filtering that is the proposed approach and the existing ANPR approach using metrics: MSE, PSNR and Success rate.
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An Efficient Algorithm for Mining Weighted Frequent Itemsets Using Adaptive Weights
Scientific article
Weighted frequent itemset mining is more practical than traditional frequent itemset mining, because it can consider different semantic significance (weight) of items. Many models and algorithms for mining weighted frequent itemsets have been proposed. These models assume that each item has a fixed weight. But in real world scenarios, the weight (price or significance) of the items may vary with time. Therefore, reflecting these changes in item weight is necessary in several mining applications, such as retail market data analysis and web click stream analysis. Recently, Chowdhury F. A. et al. have introduced a novel concept of adaptive weight for each item and propose an algorithm AWFPM (Adaptive Weighted Frequent Pattern Mining). AWFPM can handle the situation where the weight (price or significance) of an item may vary with time. In this paper, we present an improved algorithm named AWFIMiner. Experimental computations show that our AWFIMiner is more efficient and scalable for mining weighted frequent itemsets using adaptive weights. Moreover, because it only requires one single database scan, the AWFIMiner is applicable for mining these itemsets on data streams.
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Scientific article
In recent years, the mining research over data stream has been prominent as they can be applied in many alternative areas in the real worlds. In [20], a framework for mining frequent itemsets over a data stream is proposed by the use of weighted slide window model. Two algorithms of single pass (WSW) and the WSW-Imp (improving one) using weighted sliding model were proposed in there to solve the data stream problems. The disadvantage of these algorithms is that they have to seek all data stream many times and generate a large set of candidates. In this paper, we have proposed a process of mining frequent itemsets with weights over a data stream. Based on the downward closure property and FP-Growth method [8,9] an alternative algorithm called WSWFP-stream has been proposed. This algorithm is proved working more efficiently regarding to computing time and memory aspects.
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An Efficient Method of Steganography using Matrix Approach
Scientific article
A large number of the world business is going on using “INTERNET” and the data over the internet which is vulnerable for attacks from the hackers. Thus, uses of highly efficient methods are required for sensitive data transmission over the internet to ensure data security. One of the solutions to data security is to use an efficient method of steganography. The goal of steganography is to hide messages inside other ‘harmless’ messages in a way that does not allow any enemy to even detect that there is a second message present. Steganography can be used with a large number of file formats most commonly used in the digital world of today. The different file formats popularly used are .bmp, .gif, .txt etc. Thus the techniques of steganography are going to play a very important part in the future of data security and privacy on open systems such as the Internet. This paper presents an efficient method for hiding data into an image and send to the destination in a safe manner. This technique does not need any key for embedding and extracting data. Also, it allows hiding four bits in a block of size 5×5 with minimal distortion. The proposed algorithm ensures security and safety of the hidden information. The experimental results presented in this paper show the efficacy of the proposed method.
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Scientific article
With the proliferation of Internet of Things (IoT) applications, a massive amount of data has been produced, requiring an efficient platform to store and process this data. Cloud computing has the ability to tackle such enormous data, but cannot provide real-time response to latency sensitive IoT applications. Fog computing delivers the cloud service at network edge with rapid response to IoT applications, but the computation offloading decision, unpredictable demands, unbalanced workload among fog servers and heterogeneity become challenging issues. Hence, this study applied an effective approach named Spider Monkey Optimization (SMO) to address the mentioned challenges and employs the proposed framework to ensure the Quality of Service (QoS) parameters performance. This framework uses an adaptive approach to allocate the workload based upon the computation ability of the resource, and continuously monitoring the workload among the fog nodes avoids the possibility of overloading and underloading the fog nodes. Exploration and exploitation ability of the SMO algorithm reduces the chances of being trapped in the local optimum and decides the optimal offloading destination. The performance of the proposed approach is assessed in a simulation environment, showing that the proposed algorithm reduces parameters such as latency, communication overhead, cost and energy consumption compared to baseline approaches Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO) and Whale Optimization Algorithm (WOA) using the same experimental environments.
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An EfficientNetB3 Approach for Retinal Disease Classification with XAI and Online Interface
Scientific article
In real-world clinical settings, the growing number of patients and the shortage of experienced ophthalmologists make early and accurate diagnosis of retinal diseases increasingly challenging. Cataracts, diabetic retinopathy, and glaucoma are some of the most common causes of lifelong blindness around the world. This is why there is a need for automated diagnostic systems that can accurately diagnose and interpret clinical data. The major goal of this work is to find out if a deep learning architecture based on EfficientNetB3 and Explainable Artificial Intelligence (XAI) can accurately classify multiple types of retinal diseases while still being clear to doctors. The proposed system categorizes retinal fundus images into four groups: cataract, diabetic retinopathy, glaucoma, and normal. The dataset consisted of a balanced and publicly accessible collection of 4,217 retinal fundus pictures, processed using standard preprocessing techniques to enhance their generalizability. We chose EfficientNetB3 as the main architecture since it is better at extracting features, and we compared it to the standard convolutional neural network baselines to show how useful it is. The suggested model was 97% accurate in classifying better than Residual Network 50 (ResNet50) is 91% and Visual Geometry Group 16 (VGG 16) is 87%. The high precision, recall, and F1-scores (0.94 – 1.00), the Cohen’s kappa of 0.95, and the low logarithmic loss of 0.10 all point to reliable predictions. The receiver operating characteristic analysis yielded an AUC of 1.00 across all illness categories. Gradient-weighted Class Activation Mapping (Grad-CAM) was utilized to address the interpretability deficit in deep learning-based medical systems and to pinpoint clinically significant retinal regions that influence model predictions. The results indicate that employing XAI alongside EfficientNetB3 enhances both diagnostic precision and interpretability, hence validating its suitability as a transparent decision-support system for the automated screening of retinal disorders.
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Scientific article
Automated, i.e. machine vision based fabric defect inspection systems have been drawing plenty of attention of the researchers in order to replace manual inspection. Two difficult problems are mainly posed by automated fabric defect inspection systems. They are defect detection and defect classification. Counterpropagation neural network (CPN) is a robust classifier and very promising for defect classification. In general, works reported to date have claimed varying level of successes in detection and classification of different types of defects through CPN; but in particular, no claimed has been made for successful application of CPN for fabric defects detection and classification. In those published works, no investigation has been reported regarding to the variation of major performance parameters of NN based classifiers such as learning time and classification accuracy based on network topology and training parameters. As a result, application engineer has little or no guidance to take design decisions for reaching to optimum structure of NN based defect classifiers in general and CPN based in particular. Our work focuses on empirical investigation of interrelationship between design parameters and performance of CPN based classifier for fabric defect classification. It is believed that such work will be laying the ground to empower application engineers to decide about optimum values of design parameters for realizing most appropriate CPN based classifier.
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Scientific article
This research paper propose experimental support to secure Round Trip Engineering and use of security performance flexibility trusted operating systems for the designing of secure web applications. In this research paper, for security concern, we suggest use of trusted operating systems as a platform to run these web applications. In this regard, a number of trusted operating systems like Argus, Trusted Solaris, and Virtual Vault have been developed by various companies to handle the increasing need of security. For improving the performance of same web applications, we observe that all security checks in a Trusted Operating System are not necessary. As per our suggestion, various unnecessary security checks can be skipped by administrator, so that system performance of these web applications can improve. These unnecessary security checks, system calls and operations can be easily identified at the time of requirement elicitation and Requirement Engineering. For example, as we know, the popular web servers deal with public information. In this web application, the need for security checks during reads from disk seems like a waste of CPU cycles. On the other hand the real security need for servers seems to be of the write accesses. This research paper aims to support the efficiency of object-oriented class-based programming and object oriented modeling in secure software development.
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An Enhanced Adaptive B-spline Smoothing Approach for UAV Path Planning
Scientific article
This paper presents an Enhanced Adaptive B-Spline Smoothing approach for UAV path planning in complex three-dimensional environments. By leveraging the inherent local control and smoothness properties of cubic B-Splines, the proposed method integrates an adaptive knot selection mechanism—optimized via a genetic algorithm—with curvature-aware control point refinement to generate dynamically feasible and smooth flight paths. Simulation studies in a cluttered 3D airspace show that the proposed technique reduces path length and lowers maximum curvature compared to uniform and chord-length-based B-Spline strategies. Despite a moderate computational overhead, the results demonstrate smoother, more stable flight trajectories that adhere to aerodynamic constraints and ensure safe obstacle avoidance. This approach is particularly valuable for near-real-time missions, where flight stability, rapid re-planning, and energy efficiency are paramount. Results emphasize the potential of the proposed method for improving UAV navigation in various applications—such as urban logistics, infrastructure inspection, and search-and-rescue—by providing better maneuverability, reduced energy consumption, and increased operational safety to the UAV agents.
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An Enhanced Adaptive k-Nearest Neighbor Classifier Using Simulated Annealing
Scientific article
The k-Nearest Neighbor classifier is a non-complex and widely applied data classification algorithm which does well in real-world applications. The overall classification accuracy of the k-Nearest Neighbor algorithm largely depends on the choice of the number of nearest neighbors(k). The use of a constant k value does not always yield the best solutions especially for real-world datasets with an irregular class and density distribution of data points as it totally ignores the class and density distribution of a test point’s k-environment or neighborhood. A resolution to this problem is to dynamically choose k for each test instance to be classified. However, given a large dataset, it becomes very tasking to maximize the k-Nearest Neighbor performance by tuning k. This work proposes the use of Simulated Annealing, a metaheuristic search algorithm, to select optimal k, thus eliminating the prospect of an exhaustive search for optimal k. The results obtained in four different classification tasks demonstrate a significant improvement in the computational efficiency against the k-Nearest Neighbor methods that perform exhaustive search for k, as accurate nearest neighbors are returned faster for k-Nearest Neighbor classification, thus reducing the computation time.
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Scientific article
Data Structures and Algorithms (DSA) is a widely explored domain in the world of computer science. With it being a crucial topic during an interview for a software engineer, it is a topic not to take lightly. There are various platforms available to understand a particular DSA, several programming problems, and its implementation. Hacckerank, LeetCode, GeeksForGeeks (GFG), and Codeforces are popular platforms that offer a vast collection of programming problems to enhance skills. However, with the huge content of DSA available, it is challenging for users to identify which one among all to focus on after going through the required domain. This work aims to use a Content-based filtering (CBF) recommendation engine to suggest users programming-based questions related to different DSAs such as arrays, linked lists, trees, graphs, etc. The recommendations are generated using the concept of Natural Language Processing (NLP). The data set consists of approximately 500 problems. Each problem is represented by the features such as problem statement, related topics, level of difficulty, and platform link. Standard measures like cosine similarity, accuracy, precision, and F1-score are used to determine the proportion of correctly recommended problems. The percentages indicate how well the system performed regarding that evaluation. The result shows that CBF achieves an accuracy of 83 %, a precision of 83 %, a recall of 80%, and an F1-score of 80%. This recommendation system is deployed on a web application that provides a suitable user interface allowing the user to interact with other features. With this, a whole E-learning application is built to aid potential software engineers and computer science students. In the future, two more recommendation systems, Collaborative Filtering (CF) and Hybrid systems, can be implemented to make a comparison and decide which is most suitable for the given problem statement.
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An Evolving Cascade System Based on a Set of Neo - Fuzzy Nodes
Scientific article
Neo-fuzzy elements are used as nodes for an evolving cascade system. The proposed system can tune both its parameters and architecture in an online mode. It can be used for solving a wide range of Data Mining tasks (namely time series forecasting). The evolving cascade system with neo-fuzzy nodes can process rather large data sets with high speed and effectiveness.
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An Expert GIS-Based ANP-OWA Decision Making Framework for Tourism Development Site Selection
Scientific article
The selection of a tourism development site involves a complex array of decision criteria that may have interdependence relationships within and between them. In the process of finding the optimum location that meet desired conditions, the analyst is challenged by the tedious manipulation of spatial data and the management of multiple decision-making criteria. This paper presents a novel decision making framework in which expert systems (ES), and geographic information systems–based multicriteria evaluation techniques (Analytical Network Process and fuzzy quantifiers-guided ordered weighted averaging operators (GIS-based ANP-OWA)) are integrated systematically to facilitate the selection of suitable sites for building new tourism facilities. First, ES is used for recommending the proper site selection criteria and their interdependence relationships. Then, the GIS-based ANP-OWA is used to perform the spatial data analysis necessary to generate a wide range of possible candidate sites’ scenarios taking into accounts both the interdependence relationships between sitting criteria and the level of risk the decision-makers wish to assume in their multicriteria evaluation. A typical case study is presented to demonstrate the application of the proposed decision making framework.
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An Exploratory Study on Simulated Annealing for Feature Selection in Learning-to-rank
Scientific article
Learning-to-rank is an applied domain of supervised machine learning. As feature selection has been found to be effective for improving the accuracy of learning models in general, it is intriguing to investigate this process for learning-to-rank domain. In this study, we investigate the use of a popular meta-heuristic approach called simulated annealing for this task. Under the general framework of simulated annealing, we explore various neighborhood selection strategies and temperature cooling schemes. We further introduce a new hyper-parameter called the progress parameter that can effectively be used to traverse the search space. Our algorithms are evaluated on five publicly benchmark datasets of learning-to-rank. For a better validation, we also compare the simulated annealing-based feature selection algorithm with another effective meta-heuristic algorithm, namely local beam search. Extensive experimental results show the efficacy of our proposed models.
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An Extended Neo-Fuzzy Neuron and its Adaptive Learning Algorithm
Scientific article
A modification of the neo-fuzzy neuron is proposed (an extended neo-fuzzy neuron (ENFN)) that is characterized by improved approximating properties. An adaptive learning algorithm is proposed that has both tracking and smoothing properties and solves prediction, filtering and smoothing tasks of non-stationary “noisy” stochastic and chaotic signals. An ENFN distinctive feature is its computational simplicity compared to other artificial neural networks and neuro-fuzzy systems.
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Scientific article
Image segmentation is a basic work in the field of image analysis and computer vision. Thresholding is one of the simplest methods of image segmentation. In general, thresholding approaches based on 1-D histogram do not make use of any space adjacent information of the image, thus it is often ruined by noise; thus, thresholding methods based on 2-D histogram are put forward. These methods have better segmentation performance, but heavy computation is required with these methods. In the paper, to improve the running efficiency of thresholding methods based 2D histogram, ant colony optimization algorithm combined with genetic algorithm are employed to speed up these methods, which view 2-D histogram based thresholding as a kind of optimization problem. The proposed method has been conducted on some images. Experiments results display that the proposed approach is able to achieve improved search performance which is an efficient method and suitable for real time applications.
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An Improved Sampling Dijkstra Approach for Robot Navigation and Path Planning
Scientific article
The task of path planning is extremely investigated in mobile robotics to determine a suitable path for the robot from the source point to the target point. The intended path should satisfy purposes such as collision-free, shortest-path, or power-saving. In the case of a mobile robot, many constraints should be considered during the selection of path planning algorithms such as static or dynamic environment and holonomic or non-holonomic robot. There is a pool of path-planning algorithms in the literature. However, Dijkstra is still one of the effective algorithms due to its simplicity and capabilities to compute single-source shortest-path to every position in the workspace. Researchers propose several versions of the Dijkstra algorithm, especially in mobile robotics. In this paper, we propose an improved approach based on the Dijkstra algorithm with a simple sampling method to sample the workspace to avoid an exhaustive search of the Dijkstra algorithm which consumes time and resources. The goal is to identify the same optimal shortest path resulting from the Dijkstra algorithm with minimum time and number of turns i.e., a smoothed path. The simulation results show that the proposed method improves the Dijkstra algorithm with respect to the running time and the number of turns of the mobile robot and outperforms the RRT algorithm concerning the path length.
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An Initilization Method for Subspace Clustering Algorithm
Scientific article
Soft subspace clustering is an important part and research hotspot in clustering research. Clustering in high dimensional space is especially difficult due to the sparse distribution of the data and the curse of dimensionality. By analyzing limitations of the existing algorithms, the concept of subspace difference and an improved initialization method are proposed. Based on these, a new objective function is given by taking into account the compactness of the subspace clusters and subspace difference of the clusters. And a subspace clustering algorithm based on k-means is presented. Theoretical analysis and experimental results demonstrate that the proposed algorithm significantly improves the accuracy.
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An Innovative System for Daily Thunderstorm Event Prediction using Deep Learning
Scientific article
Thunderstorms are weather disturbances that can cause lightning, stormy winds, dense clouds, tornadoes, and heavy rain. Thunderstorms can cause extensive damage to people's lives, property, and economies, as well as livestock and national infrastructure. Early warning of thunderstorms can save people's lives and property. Previous thunderstorm prediction research did not develop a system for daily thunderstorm prediction with high accuracy for Bangladeshi citizens by assessing a wide range of meteorological variables. To address this issue, this work develops a daily high accuracy based localized thunderstorm event prediction system that analyzes various meteorological factors, dates, and specific location information. This dataset was analyzed using a variety of machine learning models, including traditional statistical models like ARMA, ARIMA, and SARIMA, as well as XGBoost ensemble methods and some deep learning models such as ANN, LSTM, and GRU. The results show that advanced neural network models, particularly GRU and LSTM, outperform others in terms of RMSE, R2, MAE, and MAPE. The GRU model outperformed all other schemes, with an RMSE of 0.794, R2 of 0.998, MAE of 0.476, and MAPE of 3.544%. The mobile application provides users with accurate, localized thunderstorm forecasts, allowing for better safety, event planning, and environmental preparedness. User feedback-based mobile app assessment confirms that more than 55% of users are highly satisfied with the thunderstorm assistance app’s features and usefulness.
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