International Journal of Information Technology and Computer Science @ijitcs
Journal articles - International Journal of Information Technology and Computer Science
All articles: 1304
Scientific article
The advancements of Information Technology have led to many developments that make life easier and faster with high reachability and efficiency. There is a drastic improvement in the area of distributed applications with the advent of mobile agent technology over the usual client/server framework. Applying mobile agent technology in the area of distributed applications improves the performance and quality of service. Mobile agent technologies in particular have taken a prominent place in handling effective road and vehicle traffic (VANET). This paper highlights on the integration of previously defined MATLB, PCM and MSA Agent and proposes an approach for reducing the size of the mobile agent that helps in data collection of vehicles in the VANET for effective and efficient traffic control. This paper is an advancement of agent load shedding algorithm and an attempt to optimize the size reduction process. This reduction in size of mobile agents will enhance the performance of VANET making the agents more acceptable by the hosts and correspondingly building an effective co-operative vehicular network.
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An Integrated Approach to Drive Ontological Structure from Folksonomie
Scientific article
Web 2.0 is an evolution toward a more social, interactive and collaborative web, where user is at the center of service in terms of publications and reactions. This transforms the user from his old status as a consumer to a new one as a producer. Folksonomies are one of the technologies of Web 2.0 that permit users to annotate resources on the Web. This is done by allowing users to use any keyword or tag that they find relevant. Although folksonomies require a context-independent and inter-subjective definition of meaning, many researchers have proven the existence of an implicit semantics in these unstructured data. In this paper, we propose an improvement of our previous approach to extract ontological structures from folksonomies. The major contributions of this paper are a Normalized Co-occurrences in Distinct Users (NCDU) similarity measure, and a new algorithm to define context of tags and detect ambiguous ones. We compared our similarity measure to a widely used method for identifying similar tags based on the cosine measure. We also compared the new algorithm with the Fuzzy Clustering Algorithm (FCM) used in our original approach. The evaluation shows promising results and emphasizes the advantage of our approach.
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Scientific article
The incorporation of distributed generation (DG) in radial distribution systems (RDS) has recently garnered much attention. The prime goal of DG integration is to generate power locally and cut down the total power losses (PL) of RDS to increase the overall efficiency. The present work suggests a hybrid optimization approach integrating loss sensitivity factor (LSF) with a whale optimization algorithm (WOA) to optimize different categories of DG. The LSF locates the ideal site, and WOA optimizes the size. The present study optimizes DG units to minimize the total active power losses (APLT) and enhance the bus voltages (BV). The present work investigates the adaptability of the proposed integrated technique on the small 33-bus and a large 118-bus RDS. The APLT of the 33-bus RDS is minimized from 210.98 kW to 101.3 kW, 124.3 kW, 64.56 kW, and 86.5 kW for Type I, Type II, Type III, and Type IV DG placements, respectively. Correspondingly, the minimum bus voltage (BVmin) is increased from 0.9038 p.u. to 0.9511 p.u., 0.9503 p.u., 0.9608 p.u., and 0.9579 p.u. Likewise, significant PL reduction and bus voltage enhancement are obtained in 118-bus for three units of Type I and Type III DG placements. Further, the adequateness of the hybrid technique is examined for varying power demand on the IEEE 33-bus RDS. The integrated technique effectively narrows the search space of the optimization problem and helps the WOA to find the optimal solution. The simulation outcomes are compared to examine the sovereignty of the proposed optimization technique.
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An Integrated CEA Approach for Color Light Source Estimation
Scientific article
Color constancy is an element of human vision framework which guarantees that the apparent color of items under fluctuating light conditions generally remains constant. It is fundamentally used to eliminate the color cast in the picture. Color Cat is a quick and precise learning-based methodology for accomplishing computational color constancy. However, despite everything it confronts a few limitations like poor brightness due to normalization used. Furthermore it doesn't promise edge preservation. So to overcome these issues a CEA strategy has been proposed which is a hybrid model based on Color Cat, Edge preservation filter and Adaptive histogram Equalization. As Adaptive histogram Equalization is exceptionally valuable for contrast improvement and edges are protected by edge preservation filter. Experimental results show that the proposed CEA approach outperforms over existing techniques.
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An Integrated Knowledge Base System Architecture for Histopathological Diagnosis of Breast Diseases
Scientific article
The histopathological diagnosis of breast diseases requires highly trained and experienced experts, and often strains pathologists’ cognitive capabilities. Accurate and timely diagnosis of breast diseases is essential for the appropriate management of the patients. The paper presents a knowledge base system that uses a combination of rule-based and case-based techniques to achieve the diagnosis. Rule-based systems handle problems with well-defined knowledge bases this limits the flexibility of such system. Case-based reasoning has been adopted to overcome this inherent weakness of rule-based systems by incorporating previous cases in the generation of new cases to improve the performance of the system. The result of this research shows that the system is capable of assisting pathologists in making accurate, consistent and timely diagnoses. The system also aid in eliminating errors of omission that have been viewed as a prominent cause of medical errors. In conclusion this paper investigated the histological features used in the diagnosis of breast diseases and proposed an integrated knowledge base system based on the features.
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An Integrated Knowledge Management Capabilities Framework for Assessing Organizational Performance
Scientific article
In the present aggressive world of competition, knowledge management strategies are becoming the major vehicle for the organizations to achieve their goals; to compete and to perform well. Linking knowledge management to business performance could make a strong business case in convincing senior management of any organization about the need to adopt a knowledge management strategy. Organizational performance is, therefore, a key issue and performance measurement models provide a basis for developing a structured approach to knowledge management. In this respect, organizations need to assess their knowledge management capabilities and find ways to improve their performance. This paper takes these issues into account when study the role of knowledge management in enhancing the organizational performance and consequently, developed an integrated knowledge management capabilities framework for assessing organizational performance. The results show that there is positive correlation between knowledge management capabilities and organizational performance. The results also show that the proposed framework can be used to assess organizational performance and also can be used as decision tool to decide which knowledge management capability should be improved.
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An Investigation on the Characteristics of Mobile Applications: A Survey Study
Scientific article
Swift advances in mobile communication technology have spawned almost unlimited new mobile applications. Mobile application development is an extremely well growing industry across the globe that created new opportunities of modern businesses and pioneered new technologies in the area. In order to build high quality mobile applications, it is imperative to understand the key characteristics that define mobile applications, which if wisely considered and implemented, can facilitate the delivery of truly exceptional, valuable and user friendly mobile apps that satisfy users’ needs. Only few scientific publications can be found which specifically identify the key characteristics and what makes mobile applications different from traditional software. For this purpose, we conducted an online survey from the mobile research and development community. The survey questions covered the entire mobile application development lifecycle starting from inception to the maintenance stage. This paper presents the survey results by classifying the key characteristics that differentiate mobile applications from traditional ones into three categories: Hardware, Software (application interaction, application development, and application security) and Communication. The study contributes towards a greater understanding of mobile software and the current trends in the mobile application development. It also highlights various features and attributes that assist in developing high quality mobile software applications.
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An Iterated Function System based Method to Generate Hilbert-type Space-filling Curves
Scientific article
Iterated function system has been found to be an important method to generate fractal sets. Hilbert space-filling curve is one kind of fractal sets which has been applied widely in digital image processing, such as image encoding, image clustering, image encryption, image storing/retrieving, and pattern recognition. In this paper, we will explore the generation of Hilbert-type space-filling curves via iterated function system based approach systematically. Cooperating a recursive calling of the common Hilbert's original space-filling curve at resolution n-1 and an IFS consisting of four affine transformations, one can generate the vertices for Hilbert-type space-filling curves at any resolution n. The merit is that the recursive algorithm is easy to implement and can be generalized to produce any other Hilbert-type space-filling curves and their variation versions.
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An Ontology Driven Machine Learning Framework for Early Prediction in Children with Cerebral Palsy
Scientific article
Cerebral palsy (CP) is a neurological disorder that affects 2-3 in every 1,000 births worldwide. Early prediction of severity is vital for optimizing therapeutic interventions. This study introduces OntoML-CP, a novel hybrid intelligence framework that combines inductive machine learning with deductive ontology-based reasoning to predict Gross Motor Function Classification System (GMFCS) levels in children with CP. We present a hybrid architecture combining semantic features from a CP ontology and clinical data for machine learning, using ontological reasoning to refine predictions and improve clinical validity and interpretability. The clinical ontology built using OWL captures the relationships between symptoms of cerebral palsy, developmental disorders, and motor functions, enriched with clinical knowledge and FOAF to represent key stakeholders like patients, parents, and therapists. Using a synthetic dataset of 1,695 children with CP, generated by physical medicine and rehabilitation specialists based on real clinical cases and validated through expert review, we address demographic diversity and missing data through preprocessing techniques to correct class imbalance during model evaluation and selection. Seven supervised algorithms were evaluated, among which Random Forest and Gradient Boosting models achieved superior performance (accuracy: 85% and 83%), when augmented with our ontological framework. The models showed consistent performance across all GMFCS levels with macro-averaged F1-scores of 0.81 and 0.79, respectively, and maintained high sensitivity for severe cases (levels 4-5), significantly outperforming baseline models. The semantic layer enhances predictions with logical explanations and presents them through SPARQL queries and intuitive visual formats designed for healthcare professionals. Our ontology-driven approach provides medicine with not only accurate predictions but also context-aware, clinically interpretable explanations that support informed decisions and enable personalized, actionable CP severity predictions.
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An Optimization Model and DPSO-EDA for Document Summarization
Scientific article
We model document summarization as a nonlinear 0-1 programming problem where an objective function is defined as Heronian mean of the objective functions enforcing the coverage and diversity. The proposed model implemented on a multi-document summarization task. Experiments on DUC2001 and DUC2002 datasets showed that the proposed model outperforms the other summarization methods.
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An Optimization of Feature Selection for Classification using Modified Bat Algorithm
Scientific article
Data mining is the action of searching the large existing database in order to get new and best information. It plays a major and vital role now-a-days in all sorts of fields like Medical, Engineering, Banking, Education and Fraud detection. In this paper Feature selection which is a part of Data mining is performed to do classification. The role of feature selection is in the context of deep learning and how it is related to feature engineering. Feature selection is a preprocessing technique which selects the appropriate features from the data set to get the accurate result and outcome for the classification. Nature-inspired Optimization algorithms like Ant colony, Firefly, Cuckoo Search and Harmony Search showed better performance by giving the best accuracy rate with less number of features selected and also fine f-Measure value is noted. These algorithms are used to perform classification that accurately predicts the target class for each case in the data set. We propose a technique to get the optimized feature selection to perform classification using Meta Heuristic algorithms. We applied new and recent advanced optimized algorithm named Modified Bat algorithm on University of California Irvine datasets that showed comparatively equal results with best performed existing firefly but with less number of features selected. The work is implemented using JAVA and the Medical dataset has been used. These datasets were chosen due to nominal class features. The number of attributes, instances and classes varies from chosen dataset to represent different combinations. Classification is done using J48 classifier in WEKA tool. We demonstrate the comparative results of the presently used algorithms with the existing algorithms thoroughly. The significance of this research is it will show a great impact in selecting the best features out of all the existing features which gives best accuracy rates which helps in extracting the information from raw data in Data Mining Domain. The Value of this research is it will manage main fields like medical and banking which gives exact and proper results in their respective field. The best quality of the research is to optimize the selection of features to achieve maximum predictive accuracy of the data sets which solves both single variable and multi-variable functions through the generation of binary structuring of features in the dataset and to increase the performance of classification by using nature inspired and Meta Heuristic algorithms.
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An Optimization-Based Framework for Feature Selection and Parameters Determination of SVMs
Scientific article
In this paper, feature selection and parameters determination in SVM are cast as an energy minimization procedure. The problem of feature selection and parameters determination is a very difficult problem where the number of feature is very large and where the features are highly correlated. We define the problem of feature selection and parameters determination in SVM as a combinatorial problem and we use a stochastic method that, theoretically, guarantees to reach the global optimum. Several public datasets are employed to evaluate the performance of our approach. Also, we propose to use the DNA Microarray Datasets which are characterized by the large number of features. To validate our approach, we apply it to image classification. The feature descriptors of the images were extracted by using the Pyramid Histogram of Oriented Gradients. The proposed approach was compared with twenty feature selection methods. Experimental results indicate that the classification accuracy rates of the proposed approach exceed those of other approaches.
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An Optimized Convolutional Neural Network Model for Detecting Depressive Symptoms from Image Posts
Scientific article
This paper presents an optimized model that uses an optimized CNN to detect depressive symptoms from image posts. This is with a view to detecting depression symptoms in individuals. Visual data were collected in their raw form and assessed as having or not having a mental condition. The images were processed, and the relevant features retrieved from them. An optimized convolutional neural network (CNN) was used to simulate the defined classification model of the image posts. The model was implemented using Python Programming Language. Precision, recall, accuracy, and the area under the Receiver Operating Characteristics (ROC) curve were used as performance indicators to assess the model's efficacy. The collected findings indicate that 77% accuracy is achieved by the optimized model. As a result, 77% of the cases were accurately predicted by the model, suggesting that the model is generally accurate in its predictions. The research will contribute to a decrease in the incidence, prevalence, and recurrence of mental health illnesses as well as the disabilities they cause.
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An Optimized Graph-based Deep Learning Framework for Depression Detection Using EEG Signals
Scientific article
Depression is a serious psychiatric disorder that greatly impacts the quality of life and daily functioning of a person. Accurate diagnosis at the early stage is critical for success with intervention. Electroencephalography (EEG) offers a non-invasive technique to assess neurophysiological activity and is thus an important instrument for diagnosis of depression. Current EEG-based deep learning approaches are beset by high-dimensional data, poor feature selection, and poor classification performance owing to the nature of the EEG signal. To address these issues, we introduce EEGEffV2-SpikeNet a new framework for depression detection that combines statistical feature extraction with deep feature extraction through a Graph Convolutional Network (GCN) approach. The proposed model incorporates a new fusion of statistical feature extraction and GCN-based deep feature learning for the extraction of both spatial and temporal EEG features. The extracted features are then optimized by Modified Addax Optimization Algorithm (MAOA), which is a cutting-edge bio-inspired optimization algorithm for optimizing feature selection efficiency by discarding redundant information and improving classification accuracy. For depression classification, EfficientNetV2, Deep Belief Network (DBN) and a Spiking Neural Network (SNN) are utilized based on the computational efficiency of EfficientNetV2 and the biologically simulated processing of SNN for enhancing feature representation and decision-making. Experimental results on two standard EEG datasets validate the better performance of the model, achieving 98.74% accuracy on Dataset 1 and 97.88% accuracy on Dataset 2, outperforming baseline models like DBN, EfficientNet, and SNN. The results prove the framework's promise as a dependable tool for objective and early depression diagnosis, with clinical application and mental health monitoring implications.
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An Overview of Automatic Audio Segmentation
Article
In this report we present an overview of the approaches and techniques that are used in the task of automatic audio segmentation. Audio segmentation aims to find changing points in the audio content of an audio stream. Initially, we present the basic steps in an automatic audio segmentation procedure. Afterwards, the basic categories of segmentation algorithms, and more specific the unsupervised, the data-driven and the mixed algorithms, are presented. For each of the categorizations the segmentation analysis is followed by details about proposed architectural parameters, such us the audio descriptor set, the mathematical functions in unsupervised algorithms and the machine learning algorithms of data-driven modules. Finally a review of proposed architectures in the automatic audio segmentation literature appears, along with details about the experimenting audio environment (heading of database and list of audio events of interest), the basic modules of the procedure (categorization of the algorithm, audio descriptor set, architectural parameters and potential optional modules) along with the maximum achieved accuracy.
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An analysis of the Intelligent Predictive String Search Algorithm: A Probabilistic Approach
Scientific article
Due to the huge surge of digital information and the task of mining valuable information from huge amount of data, text processing tasks like string search has gained importance. Earlier techniques for text processing relied on following some predetermined sequence of steps or some hard coded rules. However, these techniques might soon prove to be inefficient as the amount of data generated by modern computer systems in increasing more and more. One solution to this problem lies in the development of intelligent algorithms that incorporate a certain degree of intelligence and unlike traditional algorithm are able to cope up with changing scenarios. This paper presents a string searching algorithm that incorporates a certain degree of intelligence to search for a string in a text. In the search of a string, the algorithm relies on a chance process and a certain probability at each step. An analysis of the algorithm based on the approach suggested by A. A. Markov is also presented in the paper. The expected number of average comparisons required for searching a string in a text is computed. Based on the varieties of applications that are coming up in the area of text processing and the related fields, this new algorithm aims to find its use.
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An association prediction model: GECOL as a case study
Scientific article
Nowadays, there exists a lot of information that can be handled from business transactions and scientific data and information retrieval is simply no longer enough for decision-making. In this paper will supervised machine learning technique is applied to the mine data warehouse for Enterprise Resource Planning (ERP) of the General Electricity Company of Libya (GECOL). This technique has been applied for the first time on the data of production, transportation and distribution departments. These data are in the form of purchase and work orders of operational material strategic equipment spare parts. This technique would extract prediction rules in order to assist the decision-makers of the company to make appropriate future decisions more easily and in less time. A supervised machine learning technique has been adopted and applied for the mining data warehouse. A well-known software package for data mining which is referred to as WEKA tool was adopted throughout this work. The WEKA tool is applied to the collected data from GECOL. The conducted experiments produce prediction models in the form set of rules in order to help responsible employees make the suitable, right and accurate future decision in a simple way and inappropriate time. The collected data were preprocessed to be prepared in a suitable format to be fed to the WEKA system. A set of experiments has been conducted on those data to obtain prediction models. These models are in the form of decision rules. The produced models were evaluated in terms of accuracy and production time. It can be concluded that the obtained results are very promising and encouraging.
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An astute SNA with OWA operator to compare the social networks
Scientific article
This paper mainly focuses on the development of quantitative approach based algorithm for comparing the social networks. Firstly, comparison of social networks can be done on different parameters at all the three levels – network, group and node level characteristics. Secondly, for getting more accurate results, the paper has incorporated weights to these parameters according to their importance. For addressing these two, the paper has taken an advantage from the Ordered Weighted Averaging (OWA) operator in the proposed algorithm. This algorithm outputs one quantitative value for each of the social network, on which the comparison has to be made. This paper has also employed the Gephi tool, in order to accomplish the quantitative and graphical comparison between the social networks. The analysis has been done on multiple varied social network data sets. This paper has made an effort to analyze, which among them is better in terms of connectivity and coherency factors. The paper takes into account six vital metrics of the social networks so that there will be low complexity with high accuracy. They are average degree, network diameter, graph density, modularity, clustering coefficient and average path length. The proposed SNA approach is very advantageous for finding the potential group suited for a particular task in different areas like identification of criminal activities, and more fields like economics, cyber security, medicine etc.
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An automated parking guidance system for megacities
Scientific article
Enormous increase for vehicles in the megacities, with limited parking creates a serious issue. In order to handle the issue, many cities have adopted the guided parking as a part of Intelligent Transportation System (ITS). The current ITS is continuously evolving to incorporate the required issues. ITS communicates among vehicles and parking facilities and shares the information of interest. Thereafter ITS employs dynamic information obtained from vehicles for guiding the parking. In the current work, authors have suggested two functions for parking guidance in this study. Using these functions, central server uses this dynamic information obtained from sensory networks and uses the same to suggest parking to the driver. The driver, upon receiving the suggestion, in turn may reserve the suggested parking or may choose to decline the suggestion based on his personal experience. The proposed approach considers various parameters to evaluate effectiveness of the guided parking. During simulation, these parameters have been demonstrated and it is observed that the proposed system outperforms the existing system in literature.
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An edge based clustering technique with self-organizing maps
Scientific article
Recently, artificial neural networks are fund to be efficiently used in clustering algorithms. So, the present paper focuses on the development of a novel clustering method based on artificial neural networks. The present paper uses an enhancement filter to enhance the segments in the input image. After this, the various sub images are generated and features are computed for each sub and edge image. Finally, the Self Organizing Map (SOM) is used for clustering process. The proposed novel method is evaluated with a database of 795 leaf images. Further various Probability Distributed Functions (PDFs) are used to evaluate the efficacy of the proposed method. The performance measures of the proposed method indicate the efficiency of the extended clustering method with SOM.
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