International Journal of Intelligent Systems and Applications @ijisa
Статьи журнала - International Journal of Intelligent Systems and Applications
Все статьи: 1238
Influence of GUJarati STEmmeR in Supervised Learning of Web Page Categorization
Статья научная
With the large quantity of information offered on-line, it's equally essential to retrieve correct information for a user query. A large amount of data is available in digital form in multiple languages. The various approaches want to increase the effectiveness of on-line information retrieval but the standard approach tries to retrieve information for a user query is to go looking at the documents within the corpus as a word by word for the given query. This approach is incredibly time intensive and it's going to miss several connected documents that are equally important. So, to avoid these issues, stemming has been extensively utilized in numerous Information Retrieval Systems (IRS) to extend the retrieval accuracy of all languages. These papers go through the problem of stemming with Web Page Categorization on Gujarati language which basically derived the stem words using GUJSTER algorithms [1]. The GUJSTER algorithm is based on morphological rules which is used to derived root or stem word from inflected words of the same class. In particular, we consider the influence of extracted a stem or root word, to check the integrity of the web page classification using supervised machine learning algorithms. This research work is intended to focus on the analysis of Web Page Categorization (WPC) of Gujarati language and concentrate on a research problem to do verify the influence of a stemming algorithm in a WPC application for the Gujarati language with improved accuracy between from 63% to 98% through Machine Learning supervised models with standard ratio 80% as training and 20% as testing
Бесплатно
Статья научная
In this paper, motion differential equation of the two degrees of freedom(2-DOF) vehicle is established based on the linear two degrees of freedom vehicle model and is derived without simplifying the front wheel steering angle(FWSA), then we analyze the vehicle's steady-state response , transient response and the amplitude-frequency characteristic of yaw velocity under different FWSA with the help of the matlab software and finally compare the results with the simplified ones to determine how the FWSA influences the level of the vehicle handling and stability(VHS). At the same time in order to better improve the VHS, this paper proposes a set of active control theory to optimize vehicle’s steady-state performance.The results show that: while the FWSA is small, it has a less influence on vehicle handling and stability, the FWSA is large,it has a greater influence on vehicle handling and stability and the active control can make the vehicle in the best response state when it is in the steady-state.
Бесплатно
Статья научная
The goal of designing and implementing an intelligent information system for the recognition and classification of sound signals is to create an effective solution at the software level, which would allow analysis, recognition, classification and forecasting of sound signals in megacities and smart cities using machine learning methods. This system can help people in various fields to simplify their lives, for example, it can help farmers protect their crops from animals, in the military it can help with the identification of weapons and the search for flying objects, such as drones or missiles, in the future there is a possibility for recognizing the distance to sound, also, in cities can help with security, so a preventive response system can be built, which can check if everything is in order based on sounds. Also, it can make life easier for people with impaired hearing to detect danger in everyday life. In the part of the comparison of analogues of the developed product, 4 analogues were found: Shazam, sound recognition from Apple, Vocapia, and SoundHound. A table of comparisons was made for these analogues and the product under development. Also, after comparing analogues, a table for evaluating the effects of the development was built. During the system analysis section, a variety of audio research materials were developed to indicate the characteristics that can be used for this design: period, amplitude, and frequency, and, as an example, an article on real-world audio applications is shown. A precedent scenario is described using the RUP methodology and UML diagrams are constructed: Diagram of use cases; Class diagram; Activity chart; Sequence diagram; Diagram of components; and Deployment diagram. Also, sound data analysis was performed, sound data was visualized as spectrograms and sound waves, which clearly show that the data are different, so it is possible to classify them using machine learning methods. An experimental selection of the machine learning method as staandart clasificers for building a sound recognition model was made. The best method turned out to be SVC, the accuracy of which reflects more than 30 per cent. A neural network was also implemented to improve the obtained results. The result of training a model based on a neural network during 100 epochs achieved a result of 97.7% accuracy for training data and 47.8% accuracy when checking performance on test data. This result should be higher, so it is necessary to consider improving recognition algorithms, increasing the amount of data, and changing the recognition method. Testing of the project was carried out, showing its operation and pointing out shortcomings that need to be corrected in the future.
Бесплатно
Статья научная
A concept targeting information technology is considered. The model of selection of the optimal amount of advertising on various Internet resources, in order to maximize the desired reach to the target audience is analyzed which. This model is different from traditional. A chance constrained target programming model was developed after considering the parameter that corresponds to reach for different media as random variables. The random variables in this case has been considered as the values with known mean and standard deviation. The reachability parameter can be determined by finding the ideal solution and the law on which the parameter values change. The method of multicriteria optimization is examined with determination of resulting objective function, which allows to consider various aspects of the problems of media choice and optimal budgeting and budget allocation simultaneously to get a satisfactory solution of the problem.
Бесплатно
Infrared Images Spectra Multi-class Classification Model Based on Deep Learning
Статья научная
The classification of Fourier Transform Infrared spectra images is crucial in chemometrics. This paper proposes an efficient model based on deep learning approaches for enhancement and classification of the Fourier Transform Infrared Spectra (FTIR) images. The proposed model integrates three deep learning models including ResNet101, EfficientNetB0, and Wavelet Scattering transform (WST) to extract several features from FTIR. Then the obtained features were fused in conjunction with standard statistical feature extraction. It followed by a subsequent classification phase that employs a Convolutional Neural Network (CNN) architecture, which demonstrates high accuracy in classifying the infrared spectra images into six different classes of ligands and their metal complexes. During the training phase, the network’s weights are iteratively updated using the Adam optimization algorithm. This model addresses the challenge of small and imbalanced datasets through an image oversampling process. Using random over-sampling technique, it enhances the training process and overall classification performance. The extracted features were analyzed using t-distributed Stochastic Neighbor Embedding (t-SNE) to visualize high-dimensional data in two dimensions. The results of the proposed model show high classification accuracy of 0.91%, low error rate of 0.08%, a sensitivity of 0.89% and a precision of 0.89%, false positive rate of 0.01%, F1 score of 0.89, Matthews Correlation Coefficient of 0.87 and Kappa of 0.68.
Бесплатно
Статья научная
The rapid rise of mobile technology paired with the steady growth of the internet, has led to a massive increase in the amount of user generated content, such as online consumer reviews, accessible through the browser. As the volume of user-generated content continues to rise, it becomes increasingly important to develop sophisticated methods for performing sentiment analysis on the texts collected from users, especially those that have been generated in relation to restaurants and similar types of service establishments. In this paper, we will present a new approach to sentiment analysis which incorporates Latent Dirichlet Allocation topic models, Term Frequency- Inverse Document Frequency vector representations and XGBoost Classifiers into a unified framework. Unlike conventional implementations, this study integrates probabilistic topic distributions from LDA with multi-level n-gram TF-IDF features and evaluates their combined impact using XGBoost for enhanced classification performance. Using three distinct n-gram levels (unigrams, bigrams, and trigrams), we will evaluate various aspects of text-based data including common linguistic patterns and sentiment trends. Higher-order n-grams were included to capture contextual dependencies beyond single-word features. Overall, our results demonstrate that the performance of our proposed framework is superior to traditional corpus-based models on multiple evaluation metrics, including: classification accuracy 96.07%, classification sensitivity 95.43%, classification specificity 97.12% and F1-Score 96.16%.
Бесплатно
Статья научная
Owing to its outstanding performance and rich features, Smart phones have been the rapid development over the past decade, more and more people love to use its mobile Application to process the day-to-day affairs instead of PC, including the normal call and Short Messaging Service, Personal Information Management, send and receive e-mail, browse Web, multimedia Applications and online shopping. In April 2011, the Taipei people began to use the free smart phone Apps “Good Travel in Taipei” to check the real-time traffic information of Taipei City Department of Transportation and get the best route plans according to your location, the Apps software brings together road, bus, subway, bike, high-speed rail, airport, parking and other traffic information, can be easily, simple and fast delivery to the public. The papers will introduce the case of “Good Travel in Taipei” firstly, then Zhengzhou is as an example in China to illustrate the Application of integrated traffic information service system for public travel based on smart phones, we hope it can provide a reference for the future construction of the similar mobile App of traffic information service system in the other cities for public travel.
Бесплатно
Integrating Face and the Both Irises for Personal Authentication
Статья научная
The biometric authentication, which use the characteristic of persons to verify their identity by using their behavioral and physiological characteristics are an important application of the pattern recognition. There are different biometric modalities used to achieve the task of recognition. Among the most popular traits biometric currently used in several applications are the face and the iris. This paper proposes a multi-biometric technique which combines the face modality with the both irises (the left and the right irises) to authenticate the persons. The fusion of these two traits biometrics combines the advantages of the both instances of the iris modality with the face modality. The wavelets are used for the extraction of the biometrics features and the Support Vector Machine is used to obtain scores for fusion. Then, the Min-Max operator is used to normalize these scores. The fusion is operated at score level by the combination of two methods: a combination method and a classification method. So, we used the five rules (Sum, Product, Max, Min, Mean) combined with a classification method for the fusion. The Fusion is tested using the SDUMLA-HMT database. The experimental results show that multi-biometric systems achieve the task of recognition better than the mono-modal systems.
Бесплатно
Статья научная
We report on the development of an automated acoustic bird recognizer with improved noise robustness, which is part of a long-term project, aiming at the establishment of an automated biodiversity monitoring system at the Hymettus Mountain near Athens, Greece. In particular, a typical audio processing strategy, which has been proved quite successful in various audio recognition applications, was amended with a simple and effective mechanism for integration of temporal contextual information in the decision-making process. In the present implementation, we consider integration of temporal contextual information by joint post-processing of the recognition results for a number of preceding and subsequent audio frames. In order to evaluate the usefulness of the proposed scheme on the task of acoustic bird recognition, we experimented with six widely used classifiers and a set of real-field audio recordings for two bird species which are present at the Hymettus Mountain. The highest achieved recognition accuracy obtained on the real-field data was approximately 93%, while experiments with additive noise showed significant robustness in low signal-to-noise ratio setups. In all cases, the integration of temporal contextual information was found to improve the overall accuracy of the recognizer.
Бесплатно
Статья научная
Biological systems, including agriculture and allied sectors are very complex and nonlinear in nature. The pace of current climate change, which is unique about it, makes the biological system more and more complicated and unpredictable. The novelty or ambiguity that the variable environment presents, demands for the development of self-adaptive intelligent systems in agriculture and allied sectors. Agriculture is emerging as knowledge-based enterprise that demands efficient need-based information retrieval systems and smart actions. Intelligence is that resource that guides actions and provide options under variable, uncertain and unseen conditions. The objective of the current paper is to analyze the attributes that are considered to be characteristics of intelligence having wide potential for the development of intelligent system and technologies for agricultural applications. The intelligent techniques like forecasting, database management, knowledge discovery, deception, simulation, contingency planning etc. revolutionize the whole agricultural sector opening new and competent options and dimensions. Sustainable agricultural development demands multidisciplinary holistic approach and intelligence should be the guiding principle that demands study of human cognitive psychology.
Бесплатно
Intelligent Application for Predicting Diabetes Spread Risk in the World Based on Machine Learning
Статья научная
This paper presents the development and implementation of an intelligent system for predicting the risk of diabetes spread using machine learning techniques. The core of the system relies on the analysis of the Pima Indians Diabetes dataset through k-nearest neighbours (k-NN), Random Forest, Logistic Regression, Decision Trees and XGBoost algorithms. After pre-processing the data, including normalization and handling missing values, the k-NN model achieved an accuracy of 77.2%, precision of 80.0%, recall of 85.0%, F1-score of 83.0% and ROC of 81.9%. The Random Forest model achieved an accuracy of 81.0%, precision of 87.0%, recall of 91.0%, F1-score of 89.0% and ROC of 90.0%. The Logistic Regression model achieved an accuracy of 60.0%, precision of 93.0%, recall of 61.0%, F1-score of 74.0% and ROC of 69.0%. The Decision Trees model achieved an accuracy of 79.0%, precision of 87.0%, recall of 89.0%, F1-score of 88.0% and ROC of 83.0%. In comparison, the XGBoost model outperformed with an accuracy of 83.0%, precision of 85.0%, recall of 96.0%, F1-score of 90.0% and ROC of 91.0%, indicating strong prediction capabilities. The proposed system integrates both hardware (continuous glucose monitors) and software (AI-based classifiers) components, ensuring real-time blood glucose level tracking and early-stage diabetes risk prediction. The novelty lies in the proposed architecture of a distributed intelligent monitoring system and the use of ensemble learning for risk assessment. The results demonstrate the system's potential for proactive healthcare delivery and patient-centred diabetes management.
Бесплатно
Статья научная
During the development and implementation of the software system for text analysis, attention was focused on the morphological, syntactic and stylistic levels of the language, which made it possible to develop detailed profiles of authorship for various writers. The main goal of the system is to automate the process of identifying authorship and detecting plagiarism, which ensures the protection of intellectual property and contributes to the preservation of cultural heritage. The scientific novelty of the research was manifested in the development of specific algorithms adapted to the peculiarities of the natural language, as well as in the use of advanced technologies, such as deep learning and big data. The introduction of the interdisciplinary approach, which combines computer science, linguistics, and literary studies, has opened up new perspectives for the detailed analysis of scholarly works. The results of the work confirm the high efficiency and accuracy of the system in authorship identification, which can serve as an essential tool for scientists, publishers, and law enforcement agencies. In addition to technical aspects, it is vital to take into account ethical issues related to confidentiality and copyright protection, which puts under control not only the technological side of the process but also moral and legal norms. Thus, the work revealed the importance and potential of using modern text processing methods for improving literary analysis and protecting cultural heritage, which makes it significant for further research and practical use in this area.
Бесплатно
Intelligent Approaches to Real Time Level Control
Статья научная
Liquid level control is important for ensuring energy and material balance in many installations but it also difficult as the plant is nonlinear, inertial and with model uncertainties. Fuzzy logic controllers (FLCs) are successfully applied to ensure system stability and robustness by simple means and a model-free design. This paper suggests a procedure for off-line tuning of the many FLC parameters based on optimization of a suggested multi-objective function defined on several system performance indices using genetic algorithms (GAs). First, a model-free FLC is empirically tuned, then applied for real time control of the plant and the necessary data recorded and used to GA parameter optimize a TSK plant model of an accepted structure. The validated on different set of experimental data model is employed in FLC closed loop system simulation experiments to evaluate the fitness function in the GA optimization of the FLC pre-processing and post-processing parameters. The procedure is applied for the real time PI/PID FLC level control in a laboratory-scale tank system. The improvement of the system performance indices due to the GA optimization is estimated in level real time control.
Бесплатно
Intelligent Identification of Cryptographic Ciphers using Machine Learning Techniques
Статья научная
This research work demonstrates cipher-type identification methods using machine learning algorithms. Cipher-type identification is a recent research interest to do better cryptanalysis of an encryption algorithm in a minimal time. Along with the increased security issues, obfuscation is being used with encryption algorithms to keep them hidden. This is when the ciphertext identification challenge came into play. The ciphertext classification challenge was performed using both image processing and natural language processing methods. For image processing purposes, CNN was utilized; whereas text-CNN, transformers and BERT models were used as natural language processing tools. In order to train the proposed machine learning based classification models, two types of datasets were generated: image data and text data. This study compares the experimental outcomes derived from various architectural CNN, Transformer, and BERT models. We also present a comparative study of our research work with another research works which are done in the recent past. The proposed BERT model is found to be the most efficient model for the correct classification of ciphertext over other transformer and CNN-based classification models. This work will surely help the cryptanalyst to perform cryptanalysis of an encryption algorithm in a minimal time.
Бесплатно
Intelligent PI Fuzzy Control of An Electro-Hydraulic Manipulator
Статья научная
The development of a fuzzy-logic controller for a class of industrial hydraulic manipulator is described. The main element of the controller is a PI-type fuzzy control technique which utilizes a simple set of membership functions and rules to meet the basic control requirements of such robots. Using the triangle shaped membership function, the position of the servocylinder was successfully controlled. When the system parameter is altered, the control algorithm is shown to be robust and more faster compared to the traditional PID controller. The robustness and tracking ability of the controller were demonstrated through simulations.
Бесплатно
Intelligent Routing Techniques for Mobile Ad hoc Networks using Swarm Intelligence
Статья научная
A Mobile Ad hoc Network (MANET) is a collection of autonomous self-organized nodes. They use wireless medium for communication, thus two nodes can communicate directly if and only if they are within each other’s transmission radius in a multi-hop fashion. Many conventional routing algorithms have been proposed for MANETs. An emerging area that has recently captured much attention in network routing researches is Swarm Intelligence (SI). Besides conventional approaches, many new researches have proposed the adoption of Swarm Intelligence for MANET routing. Swarm Intelligence (SI) refers to complex behaviors that arise from very simple individual behaviors and interactions, which is often observed in nature, especially among social insects such as ants, bees, fishes etc. Although each individual has little intelligence and simply follows basic rules using local information obtained from the environment. Ants routing resembles basic mechanisms from distributed Swarm Intelligence (SI) in biological systems and turns out to become an interesting solution where routing is a problem. Ants based routing is gaining more popularity because of its adaptive and dynamic nature. A number of Swarm Intelligence (SI) based algorithms were proposed by researchers. In this paper, we study bio-inspired routing protocols for MANETs.
Бесплатно
Intelligent Smart Energy Meter Reading System Using Global System for Mobile Communication
Статья научная
The innovation of e-metering (Electronic Metering) has experienced fast mechanical progressions and there is expanded interest in a solid and effective Automatic Meter Reading (AMR) framework. GSM Based shrewd vitality meter perusing framework replaces conventional meter perusing techniques. It empowers remote access to the existing vitality meter by the vitality provider. A GSM-based remote correspondence module is incorporated with the electronic vitality meter of every element to have remote access to the utilization of power. A PC with a GSM recipient at the opposite end, which contains the database goes about as the charging point. Live meter perusing from the GSM-empowered vitality meter is sent back to this charging point intermittently and these subtle elements are refreshed in a focal database. The total month-to-month utilization and the due bill are informed back to the client after handling this information. So, GSM-based remote AMR framework is a more successful approach for a traditional charging framework. This framework additionally gives specialists to power organizations to take activities against tolerant clients who have a remarkable contribution; generally, the organization has the ideal to detach the power supply, and it can reconnect the control supply after the affidavit of duty. So, we thought about building such an automatic system. This research is GSM-Based on a smart energy meter reading system to eliminate the conventional way of the reading system. In this paper, the GSM module sends reading information through SMS to the related Authority. There are no chances of any unethical mistake by using this modern technique.
Бесплатно
Intelligent scheduling of demand side energy usage in smart grid using a metaheuristic approach
Статья научная
As the global demand for electricity is growing continuously, the sources use more fossil fuels to generate electricity which in turn increases the level of carbon dioxide in the atmosphere. Moreover the electrical system becomes unreliable during the peak hours if the demand for electricity is very high. So there is a need to have a grid system which can handle these cases in a smarter way. A Smart Grid is such an electrical grid system which can control and manage electricity demand in a more reliable and economic manner using various energy efficient resources and a variety of operational measures like smart meters, smart appliances and smart communication system. The smart grid uses a technique called energy demand management at consumer side which motivates the consumers to control and reduce their demand for energy during peak hours. This makes the whole system more reliable and efficient. The demand side management (DSM) includes various methods such as increasing awareness among the consumers and giving them some financial incentives which can encourage them to be a part of the DSM program. In this paper a novel Demand Side Management technique has been proposed for a typical smart grid scenario which comprises users with energy storage devices using a metaheuristic approach to have an optimal load scheduling that results in reduced peak hour demands.
Бесплатно
Intelligent training algorithm for artificial neural network EEG classifications
Статья научная
Artificial neural networks (ANN) have been widely used in classification. They are complicated networks due to the training algorithm used to fix their weights. To achieve better neural network performance, many evolutionary and meta-heuristic algorithms are used to optimize the network weights. The aim of this paper is to implement recently evolutionary algorithms for optimizing neural weights such as Grass Root Optimization (GRO), Artificial Bee Colony (ABC), Cuckoo Search Optimization (CSA) and Practical Swarm Optimization (PSO). This ANN was examined to classify three classes of EEG signals healthy subjects, subjects with interictal epilepsy seizure, and subjects with ictal epilepsy seizures. The above training algorithms are compared according to classification rate, training and testing mean square error, average time, and maximum iteration.
Бесплатно
Internet Addiction and its Influence on University Students using Relationship Mining
Статья научная
Internet addition is a type of mental disorder. It is the result of the excessive internet usage and is concern for the physical and psychological well-being. This paper employs machine learning techniques to understand, evaluate and predict the severity of internet addiction and its impact on health. For this purpose, a real dataset of “Internet Addiction and Mental Health among College Students in Malawi” has been considered. It consists of self-assessed response of 984 university student participants. That includes demographic, behavioral and health-related information. Based on this dataset, two type of relationship have been discussed (1) relation between “demographic features” and “health complexities” and (2) relation between “Internet usage behaviors” and “health complexity”. Next, the key features were identified through comprehensive data analysis. Additionally, there machine learning algorithms namely Backpropagation Neural Network, Random Forest, and C4.5 Decision Tree— were tested to identify ‘internet addiction’ in a subject with the four severity levels (0 to 3). According to results, the Random Forest classifier achieved the highest accuracy of 91%. Additionally, C4.5 algorithm has been used for extracting rules for predicting “Internet addiction” severity level. These rules are demonstrating a relation between “Internet usages pattern” and “Internet addiction severity level”. Additionally, these rules are easy to interpret and can be utilized as a practical tool for self-assessment towards Internet addiction and additionally beneficial for healthcare professionals.
Бесплатно
