International Journal of Intelligent Systems and Applications @ijisa
Статьи журнала - International Journal of Intelligent Systems and Applications
Все статьи: 1173
Статья научная
Along with the growth of the Internet, social media usage has drastically expanded. As people share their opinions and ideas more frequently on the Internet and through various social media platforms, there has been a notable rise in the number of consumer phrases that contain sentiment data. According to reports, cyberbullying frequently leads to severe emotional and physical suffering, especially in women and young children. In certain instances, it has even been reported that sufferers attempt suicide. The bully may occasionally attempt to destroy any proof they believe to be on their side. Even if the victim gets the evidence, it will still be a long time before they get justice at that point. This work used OCR, NLP, and machine learning to detect cyberbullying in photos in order to design and execute a practical method to recognize cyberbullying from images. Eight classifier techniques are used to compare the accuracy of these algorithms against the BoW Model and the TF-IDF, two key features. These classifiers are used to understand and recognize bullying behaviors. Based on testing the suggested method on the cyberbullying dataset, it was shown that linear SVC after OCR and logistic regression perform better and achieve the best accuracy of 96 percent. This study aid in providing a good outline that shapes the methods for detecting online bullying from a screenshot with design and implementation details.
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Статья научная
Error pattern recognition is a routine job in the military to provide corrective guidelines to the shooter. Errors can be recognized with a visual approach based on the spreading pattern of bullets on the target board, which are categorized into four categories: long horizontal error, long vertical error, bi-focal error, and scattered error. Currently, this process is performed manually and requires active human involvement. Similarly, an automated system to predict the future performance of a shooter is not available in the military domain. Moreover, the performance of a shooter depends on several factors, including age, weather, ammunition type, availability of light, previous scores, shooting range, classification of firing, and other factors. The military domain has not addressed the automatic prediction of such performance. While error correction and performance analysis have been extensively explored in the field of sports, their application within the military domain remains an untapped area of research and investigation. Numerous recent endeavors have suggested the utilization of deep learning to tackle this challenge. However, the absence of real-time data poses a significant obstacle, rendering these solutions seemingly impractical. In this paper, we have applied machine- learning approaches and adopted the best algorithm to automate the error pattern recognition system within a military domain. Our proposed methodology has two modules. The first module uses various algorithms and finds a random forest classifier that can do better to recognize the pattern of error and in the second phase, we used the AdaBoost classifier to predict the score and performance of a firer. Several experiments have been conducted, and the results show an average accuracy of 0.968 using Random Forest to recognize the pattern of error and an accuracy of 0.69 using AdaBoost to predict score performance. The data has been collected from the real-time environment of the military domain and experiments have been carried out using real-time scenarios with the military in mind.
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Managing Lexical Ambiguity in the Generation of Referring Expressions
Статья научная
Most existing algorithms for the Generation of Referring Expressions (GRE) tend to produce distinguishing descriptions at the semantic level, disregarding the ways in which surface issues (e.g. linguistic ambiguity) can affect their quality. In this article, we highlight limitations in an existing GRE algorithm that takes lexical ambiguity into account, and put forward some ideas to address those limitations. The proposed ideas are implemented in a GRE algorithm. We show that the revised algorithm successfully generates optimal referring expressions without greatly increasing the computational complexity of the (original) algorithm.
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Margin Based Learning: A Framework for Acoustic Model Parameter Estimation
Статья научная
Statistical learning theory has been introduced in the field of machine learning since last three decades. In speech recognition application, SLT combines generalization function and empirical risk in single margin based objective function for optimization. This paper incorporated separation (misclassification) measures conforming to conventional discriminative training criterion in loss function definition of margin based method to derive the mathematical framework for acoustic model parameter estimation and discuss some important issues related to hinge loss function of the derived model to enhance the performance of speech recognition system.
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Статья научная
We analyzed the dynamic behavior of the damping system with a two-mass damper pendulum. The equations of motion of nonlinear systems were built. AFC equation systems have been identified in the linear formulation. Proposed and implemented a new numerical method of determining the optimum parameters of optimal settings two-mass damper.
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Mathematical model of the damping process in a one system with a ball vibration absorber
Статья научная
The forced oscillations of the damping mechanical system of solids "Ball Vibration Absorber (BVA) with linearly viscous resistance – a movable carrier body" under the influence of external harmonic excitation are considered. Based on Appell's formalism, the dynamic equations for the joint motion of a heavy ball without sliding into a spherical cavity of a carrier body are formulated and numerically studied. The amplitude-frequency characteristic of the damping mechanical system and the curves of the dependences of the maximum amplitude of the oscillations of the carrier body on the values of the radius of the spherical cavity and the coefficient of viscous resistance of the BVA are obtained. The conditions and restrictions on the rolling of a heavy ball in the spherical recess of the absorber without sliding are determined.
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Mathematical model of the dynamics in a one nonholonomic vibration protection system
Статья научная
Dynamic behavior of a heavy homogeneous sphere in a spherical cavity of a supporting body that performs specified translational movements in space has been studied. Using the Appel formalism, the equations of ball motion in a moving spherical cavity without slip are constructed and a numerical analysis of the evolution of the ball motion is carried out.
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Measuring and Evaluation on Priority Lanes
Статья научная
Along with economic development, cities are increasingly congested in China. In order to eliminate peak-hour congestion, many cities establish priority lanes, commonly bus lanes. Although priority lanes could help Local Authorities gain its short-term management objectives, at the same time, it would greatly infringe on the legitimate rights of other vehicles and waste the scarce road resources, which is rigorously proved by mathematical models in this paper. In the long run, priority lanes would make social conflicts more intensified, and therefore highly undesirable. On the contrary, the social system engineering, combined with High Occupancy Vehicle (HOV) lanes and High Occupancy Toll (HOT) lanes, is the right way to alleviate overcrowding and build a Low-Carbon harmonious society.
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Mechatronics Design of a Mobile Robot System
Статья научная
Mobile robot motion control is simplified to a DC motor motion control that may include gear system. The simplest and widespread approach to control the mobile robot motion is the differential drive style, it consists of two in-lines with each a DC motor. Both DC motors are independently powered so the desired movements will rely on how these two DC motors are commanded. Thedevelop design, model and control of Mechatronics mobile robotic system is presented in this paper. The developed robotic system is intended for research purposes as well as for educational process. The model of proposed mobile robot was created and verified using MATLAB-Simulink software.
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MediBERT: A Medical Chatbot Built Using KeyBERT, BioBERT and GPT-2
Статья научная
The emergence of chatbots over the last 50 years has been the primary consequence of the need of a virtual aid. Unlike their biological anthropomorphic counterpart in the form of fellow homo sapiens, chatbots have the ability to instantaneously present themselves at the user's need and convenience. Be it for something as benign as feeling the need of a friend to talk to, to a more dire case such as medical assistance, chatbots are unequivocally ubiquitous in their utility. This paper aims to develop one such chatbot that is capable of not only analyzing human text (and speech in the near future), but also refining the ability to assist them medically through the process of accumulating data from relevant datasets. Although Recurrent Neural Networks (RNNs) are often used to develop chatbots, the constant presence of the vanishing gradient issue brought about by backpropagation, coupled with the cumbersome process of sequentially parsing each word individually has led to the increased usage of Transformer Neural Networks (TNNs) instead, which parses entire sentences at once while simultaneously giving context to it via embeddings, leading to increased parallelization. Two variants of the TNN Bidirectional Encoder Representations from Transformers (BERT), namely KeyBERT and BioBERT, are used for tagging the keywords in each sentence and for contextual vectorization into Q/A pairs for matrix multiplication, respectively. A final layer of GPT-2 (Generative Pre-trained Transformer) is applied to fine-tune the results from the BioBERT into a form that is human readable. The outcome of such an attempt could potentially lessen the need for trips to the nearest physician, and the temporal delay and financial resources required to do so.
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Medical Image Segmentation through Bat-Active Contour Algorithm
Статья научная
In this research work, an improved active contour method called Bat-Active Contour Method (BA-ACM) using bat algorithm has been developed. The bat algorithm is incorporated in order to escape local minima entrapped into by the classical active contour method, stabilize contour (snake) movement and accurately, reach boundary concavity. Then, the developed Bat-Active Contour Method was applied to a dataset of medical images of the human heart, bone of knee and vertebra which were obtained from Auckland MRI Research Group (Cardiac Atlas Website), University of Auckland. Set of similarity metrics, including Jaccard index and Dice similarity measures were adopted to evaluate the performance of the developed algorithm. Jaccard index values of 0.9310, 0.9234 and 0.8947 and Dice similarity values of 0.8341, 0.8616 and 0.9138 were obtained from the human heart, vertebra and bone of knee images respectively. The results obtained show high similarity measures between BA-ACM algorithm and expert segmented images. Moreso, traditional ACM produced Jaccard index values 0.5873, 0.5601, 0.6009 and Dice similarity values of 0.5974, 0.6079, 0.6102 in the human heart, vertebra and bone of knee images respectively. The results obtained for traditional ACM show low similarity measures between it and expertly segmented images. It is evident from the results obtained that the developed algorithm performed better compared to the traditional ACM.
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Статья научная
An efficient classification algorithm used recently in many big data applications is the Random forest classifier algorithm. Large complex data include patient record, medicine details, and staff data etc., comprises the medical big data. Such massive data is not easy to be classified and handled in an efficient manner. Because of less accuracy and there is a chance of data deletion and also data missing using traditional methods such as Linear Classifier K-Nearest Neighbor, Random Clustering K-Nearest Neighbor. Hence we adapt the Random Forest Classification using K-means clustering algorithm to overcome the complexity and accuracy issue. In this paper, at first the medical big data is partitioned into various clusters by utilizing k- means algorithm based upon some dimension. Then each cluster is classified by utilizing random forest classifier algorithm then it generating decision tree and it is classified based upon the specified criteria. When compared to the existing systems, the experimental results indicate that the proposed algorithm increases the data accuracy.
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Memory Enhancer Games: A General-purpose Game-based Intelligent Tutoring System
Статья научная
Students of today are exposed to technologies that are either educationally effective or distractive. Most of them are having a hard time learning in a traditional classroom setup, are easily distracted, and have difficulty remembering lessons just learned and prerequisite skills needed in learning new lessons. Game-Based Intelligent Tutoring System (GB-ITS) is a technology that provides an individualized learning experience based on student’s learning needs. GB-ITS mimics a teacher doing one-on-one teaching, also known as tutoring, which is more cost-efficient than human tutors. This study developed a general-purpose Memory Enhancer Games system, in a form of a GB-ITS. This study was conducted at Calasiao Comprehensive National High School, identified the game type that best enhances memory and the game features for this proposed system through a questionnaire by (9) ICT teacher respondents. The developed system in this study has undergone validity testing by (8) ICT teachers and professors from Schools Division I of Pangasinan, and of a University in Dagupan City, and acceptability testing by (100) senior high school students of Calasiao Comprehensive National High School, 1st semester of school year 2022-2023, using Likert scale to determine its appropriateness as an intelligent learning tool. The results of the game design questionnaire confirmed the studies of which elements were ideal for a GB-ITS, and both the validity and acceptability survey questionnaires with overall weighted means of 4.57 and 4.08, show that the system is a valid and acceptable intelligent learning tool. The developed MEG can also be of use for testing game features for educational effectiveness and can also contribute to any future study which will conduct to test whether a general-purpose GBL or GB-ITS model would compare; if won’t equal the effectiveness of GBLs designed for delivering specific contents or subjects.
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Metadata based Classification Techniques for Knowledge Discovery from Facebook Multimedia Database
Статья научная
Classification is a parlance of Data Mining to genre data of different kinds in particular classes. As we observe, social media is an immense manifesto that allows billions of people share their thoughts, updates and multimedia information as status, photo, video, link, audio and graphics. Because of this flexibility cloud has enormous data. Most of the times, this data is much complicated to retrieve and to understand. And the data may contain lot of noise and at most the data will be incomplete. To make this complication easier, the data existed on the cloud has to be classified with labels which is viable through data mining Classification techniques. In the present work, we have considered Facebook dataset which holds meta data of cosmetic company’s Facebook page. 19 different Meta Data are used as main attributes. Out of those, Meta Data ‘Type’ is concentrated for Classification. Meta data ‘Type’ is classified into four different classes such as link, status, photo and video. We have used two favored Classifiers of Data Mining that are, Bayes Classifier and Decision Tree Classifier. Data Mining Classifiers contain several classification algorithms. Few algorithms from Bayes and Decision Tree have been chosen for the experiment and explained in detail in the present work. Percentage split method is used to split the dataset as training and testing data which helps in calculating the Accuracy level of Classification and to form confusion matrix. The Accuracy results, kappa statistics, root mean squared error, relative absolute error, root relative squared error and confusion matrix of all the algorithms are compared, studied and analyzed in depth to produce the best Classifier which can label the company’s Facebook data into appropriate classes thus Knowledge Discovery is the ultimate goal of this experiment.
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Статья научная
Alzheimer’s Disease (AD) is the neuro-degenerative dementia, where the precise and early recognition of AD is vital for timely treatment to reduce mortality rate. A new automated model is implemented in this work for early discovery of AD in the Magnetic Resonance Imaging (MRI) brain scans. Initially, the input brain scans are taken from the Alzheimer's disease Neuroimaging Initiative (ADNI) database. Further, the acquired raw brain scans are visually improved by employing the binary normalization technique. The denoised brain scans are fed to the pre-trained Convolutional Neural Network (CNN) named GoogleNet for feature extraction. Next, the extracted richer feature values are fed to the Long Short Term Memory (LSTM) network for classifying the brain scan as Normal Control (NC), Mild Cognitive Impairment (MCI) and AD. In this manuscript, a Honey Badger Optimization Algorithm (HBOA) technique is incorporated with the LSTM networks for hyper-parameters optimization, where this procedure helps in diminishing the LSTM network’s complexity and computational time. The experimental results conducted on the ADNI database underscore the HBOA-based LSTM network's effectiveness, showcasing a remarkable mean classification accuracy of 97.83% in multi-class classification. Moreover, the sensitivity of HBOA based LSTM for AD/NC is 96.73% which is high when compared to the existing methodologies such as SVM with radial basis kernel function and NCSINs. This performance surpasses that of other comparative models for AD detection, emphasizing the superior capabilities and potential of the proposed method in the early detection.
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Method for determination of cyber threats based on machine learning for real-time information system
Статья научная
This work is about the definition of cyber threats in the information system. The cyber threats lead to significant loss of network resources and cause the system disability as a whole. Detecting countermeasures in certain threats can reduce the impact on the system by changing the topology of the network in advance. Consequently, the interruption of a cyberattack forces the intruders to seek for alternative ways to damage the system. The most important task in the information system work is the state of network equipment monitoring. Also it’s the support of the network infrastructure in working order. The purpose of the work is to develop a method for detecting cyber threats for the information system. The system can independently detect cyber threats and develop countermeasures against them. The main feature of the counteractions is to protect network nodes from compromising. To ensure the functional stability, the most important issues are providing safety metrics. This technique allows to increase the functional stability of the system, which works in real time.
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Method for optimization of information security systems behavior under conditions of influences
Статья научная
The paper analyzes modern methods of modeling impacts on information systems, which made it possible to determine the most effective approaches and use them to optimize the parameters of security systems. And also as a method to optimize data security, taking in the security settings account (number of security measures, the type of security subsystems, safety resources and total cost information) allows to determine the optimal behavior in the “impact-security”. Also developed special software that allowed to verify the proposed method.
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Method for unit self-diagnosis at system level
Статья научная
This paper suggests unconventional approach to system level self-diagnosis. Traditionally, system level self-diagnosis focuses on determining the state of the units which are tested by other system units. In contrast, the suggested approach utilizes the results of tests performed by a system unit to determine its own state. Such diagnosis is in many respects close to self-testing, since a unit evaluates its own state, which is inherent in self-testing. However, as distinct from self-testing, in the suggested approach a unit evaluates it on the basis of tests that it does not performs on itself, but on other system units. The paper considers different diagnosis models with various testing assignments and diferent faulty assumptions including permanent and intermittent faults, and hybrid- fault situations. The diagnosis algorithm for identifying the unit’s state has been developed, and correctness of the algorithm has been verified by computer simulation experiments.
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Method of medical images similarity estimation based on feature analysis
Статья научная
The paper presents the method of medical images similarity estimation based on feature extraction and analysis. The proposed method has been developed for and tested on rat brain histological images, however, it can be applied for other types of medical images, since the general approach is based on consideration of the shape of core components present in a given template image. The proposed method can be used in image analysis tools in a wide range of image-based medical investigations, in particular, in the brain researches. The theoretical background of the proposed method is presented in the paper. The expert evaluation approach used for assessment of the proposed method effectiveness is explained and illustrated by examples. The method of medical images similarity estimation based on feature analysis consists of several stages: colour model conversion, image normalization, anti-noise filtering, contours search, conversion, and feature analysis. The results of the proposed method algorithmic realization are demonstrated and discussed.
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Metrics for Evaluating Pervasive Middleware
Статья научная
Pervasive computing aims at developing smart environments which enable user to interact with other devices. Pervasive computing includes a middleware to support interoperability, heterogeneity and self-management among different platforms. It provides efficient communications and context awareness among devices. Middleware for pervasive computing provides much more attention to coordinate the devices in the smart environment. The evaluation of the pervasive middleware is a challenging endeavor. The scope of evaluating smart environment is mainly increasing due to various devices involved in that environment. In this paper evaluation metrics are proposed based on the contexts available in the environment, how the devices are used, security and autonomy of smart applications. These metrics are used for evaluating different kind of smart applications.
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