Статьи журнала - International Journal of Information Technology and Computer Science
Все статьи: 1211
Low power and area optimized architectures for BPSK modulator
Статья научная
Low power modulators are most efficient for wireless communication. The conventional BPSK modulator consumes more power and area. In this work, the new approaches for BPSK modulator are discussed and recorded. The four new approaches consume less area and power than the conventional design of BPSK Modulator. The power and area consumed by new approaches are compared with the conventional method. Cadence software is used for the simulation and synthesis, the power and area reduction in 180nm, 90nm and 45nm CMOS Technology is reported, MATLAB/SIMULINK is used to do BER analysis of BPSK modulator with AWGN channel. The new architectures enhance the performance of BPSK Modulator in consuming less power and utilizing less area than the conventional design.
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Low-power Wireless Pressure Transmitter and Net of Oil-well Based on Zigbee
Статья научная
The paper introduced a low-power wireless pressure transmitter for measure pressure and temperature of oil-well. To avoid wiring cables onsite, ZICM2410 was adopted to construct Wireless Sensor Network (WSN), and powered by Li-Ion battery. To reduce the power consumption, MSP430F477 and other low-power chips were used to construct the hardware, and energy conservation strategies were designed in the software. One strategy was turning off the power when some chips or modules were not on duty. Another strategy was working, sleeping and timer waking up. The SNAP wireless network solution of Zigbee was used to compose WSN. By analysis and test, the power consumption of the transmitter is very low, and the transmission range of it could be up to 300m. It could longtime continuously work for the low power consumption ability. The transmitter could fully meet the actual requirements, and could be applied to other industry situations easily.
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L–Diversity-Based Semantic Anonymaztion for Data Publishing
Статья научная
Nowadays, publishing data publically is an important for many purposes especially for scientific research. Publishing this data in its raw form make it vulnerable to privacy attacks. Therefore, there is a need to apply suitable privacy preserving techniques on the published data. K-anonymity and L-diversity are well known techniques for data privacy preserving. These techniques cannot face the similarity attack on the data privacy because they did consider the semantic relation between the sensitive attributes of the data. In this paper, a semantic anonymization approach is proposed. This approach is based on the Domain based of semantic rules and the data owner rules to overcome the similarity attacks. The approach is enhanced privacy preserving techniques to prevent similarity attack and have been implemented and tested. The results shows that the semantic anonymization increase the privacy level and decreases the data utility.
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MADLVF: An Energy Efficient Resource Utilization Approach for Cloud Computing
Статья научная
Last few decades have remained the witness of steeper growth in demand for higher computational power. It is merely due to shift from the industrial age to Information and Communication Technology (ICT) age which was marginally the result of digital revolution. Such trend in demand caused establishment of large-scale data centers situated at geographically apart locations. These large-scale data centers consume a large amount of electrical energy which results into very high operating cost and large amount of carbon dioxide (CO_2) emission due to resource underutilization. We propose MADLVF algorithm to overcome the problems such as resource underutilization, high energy consumption, and large CO_2 emissions. Further, we present a comparative study between the proposed algorithm and MADRS algorithms showing proposed methodology outperforms over the existing one in terms of energy consumption and the number of VM migrations.
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MAROR: Multi-Level Abstraction of Association Rule Using Ontology and Rule Schema
Статья научная
Many large organizations have multiple databases distributed over different branches. Number of such organizations is increasing over time. Thus, it is necessary to study data mining on multiple databases. Most multi-databases mining (MDBM) algorithms for association rules typically represent input patterns at a single level of abstraction. However, in many applications of association rules – e.g., Industrial discovery, users often need to explore a data set at multiple levels of abstraction, and from different points of view. Each point of view corresponds to set of beliefs (and representational) commitments regarding the domain of interest. Using domain ontologies, we strengthen the integration of user knowledge in the mining and post-processing task. Furthermore, an interactive and iterative framework is designed to assist the user along the analyzing task at different levels. This paper formalizes the problem of association rules using ontologies in multi-database mining, describes an ontology-driven association rules algorithm to discoverer rules at multiple levels of abstraction and presents preliminary results in petroleum field to demonstrate the feasibility and applicability of this proposed approach.
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MASHUP of linked data and Web API
Статья научная
Mashups are an important way to allow normal users to build their own applications responding to the specific needs of each one. The basic components of mashups are Data and Web APIs especially Restful ones, but it is difficult for an unexperienced user to combine manually APIs with Data. Therefore, there is a need to predefine links between these resources to permit an easy combination. In this paper, we propose a new approach to make Restful Web APIs adhere to Linked Data principles, which facilitate their combination in mashup applications. The advantage of the proposed approach is the fact that it allows integrating linked data with the composition of Restful APIs, It also uses an algorithm to automatically create links between APIs.
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MCCM: Multilevel Congestion Avoidance and Control Mechanism for Mobile Ad Hoc Networks
Статья научная
Congestion in Mobile Ad Hoc Network causes packet loss, longer end-to-end data delivery delay which affects the overall performance of the network significantly. To ensure high throughput, the routing protocol should be congestion adaptive and should be capable of handling the congestion. In this research work, we propose a Multilevel Congestion avoidance and Control Mechanism (MCCM) that exploits both congestion avoidance and control mechanism to handle the congestion problem in an effective and efficient way. MCCM is capable of finding an energy efficient path during route discovery process, provide longer lifetime of any developed route. The efficient admission control and selective data packet delivery mechanism of MCCM jointly overcome the congestion problem at any node and thus, MCCM improves the network performance in term of packet delivery ratio, lower data delivery delay and high throughput. The result of performance evaluation section shows that, MCCM outperforms the existing routing protocols carried out in Network Simulator-2(NS-2).
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MF-NB learning based approach for recommendation system
Статья научная
The Multi Factor-Naive Bayes classifier based recommendation system is analyzed with respect to the traditional KNN classifier based recommendation system. The classification of the web usage data is done on the basis of the keyword name, keyword count, inbound links and age group of the users. Whereas, in traditional KNN the URL was the only factor that was considered for the purpose of classification. The performance evaluation is done in the terms of RMSE, Error Rate, Accuracy Rate and Precision. The MF-NB is observed to be outperforming the KNN classifier in all respective terms.
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MGF analysis of spatial diversity combiner over composite fading channel
Статья научная
The worldwide acceptability of wireless communication is due to its portability and flexibility. However, its performance is governed by the multipath propagation effects which make wireless communication modelling challenging. The existing technique being used to solve this propagation effects is based on Probability Density Function (PDF) which is inefficient in addressing diversity over combined Rayleigh and Rician (C_(Ray-Ric)) fading due to its complexity. Therefore, this paper aims to develop an approximated Moment Generating Function (MGF) for spatial diversity combining such as Equal Gain Combining (EGC) and Maximal Ratio Combining (MRC) over C_(Ray-Ric) fading channel. A MGF model in form of Taylor’s series is generated from the expected value of the C_(Ray-Ric) fading channels. The MGF is characterized using Amount of Fading (AF) and Bit Error Rate (BER) in term of Line of Sight (LOS) component ‘k’. The MGF is transformed into EGC and MRC, and were measured in terms of propagation paths (L). These are approximated using the Pade ́ Approximation (PA). The approximates obtained are used in the derivation of BER expression of M-ary Quadrature Amplitude Modulation (MQAM) and M-ary Phase Shift Keying (MPSK) in terms of Signal to Noise Ratio (SNR). The models are evaluated using AF and BER at different values of LOS to determine the performance of the diversity techniques. The results obtained show that as LOS component ‘k’ increases from 0, the Af and BER reduce indicating reduction in fading effects. Therefore, the models developed are effective in predicting the performance of diversity techniques and overcome the multipath effects associated with the wireless communication.
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Статья научная
Multiple Input Multiple Output(MIMO) has been in much importance in recent past because of high capacity gain over a single antenna system. In this article, analysis over the capacity of the MIMO channel systems with spatial channel with modified precoder and decoder has been considered when the channel state information (CSI) is considered partial. Due to delay in acquiring transmitted information at the receiver end, the time selective fading wireless channel often induces incomplete or partial CSI. The dynamic CSI model has also been implemented consisting channel mean and covariance which leads to extracting of channel estimates and error covariance which then further applied with the modified precoder and decoder utilizing both the parameters indicating the CSI quality since these are the functions of temporal correlation factor, and based on this, the model covers data from perfect to statistical CSI, either partially or fully blind. It is found that in case of partial and imperfect CSI, the capacity depends on the statistical properties of the error in the CSI which has been manipulated according to the precoder and decoder conditions. Based on the knowledge of statistical distribution of the deviations in CSI knowledge, a new approach which maximizes the capacity of spatial channel model with modified precoder and decoder has been tried. The interference then interactively reduced by employing the iterative channel estimation and data detection approach, where by utilizing the detected symbols from the previous iteration, multiuser/MIMO channel estimation and symbol detection is improved.
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MOTIFSM: Cloudera Motif DNA Finding Algorithm
Статья научная
Many studying systems of gene function work depend on the DNA motif. DNA motifs finding generate a lot of trails which make it complex. Regulation of gene expression is identified according to Transcription Factor Binding Sites (TFBSs). There are different algorithms explained, over the past decades, to get an accurate motif tool. The major problems for these algorithms are on the execution time and the memory size which depend on the probabilistic approaches. Our previous algorithm, called EIMF, is recently proposed to overcome these problems by rearranging data. Because cloud computing involves many resources, the challenge of mapping jobs to infinite computing resources is an NP-hard optimization problem. In this paper, we proposed an Impala framework for solving a motif finding algorithms in single and multi-user based on cloud computing. Also, the comparison between Cloud motif and previous EIMF algorithms is performed in three different motif group. The results obtained the Cloudera motif was a considerable finding algorithms in the experimental group that decreased the execution time and the Memory size, when compared with the previous EIMF algorithms. The proposed MOTIFSM algorithm based on the cloud computing decrease the execution time by 70% approximately in MOTIFSM than EIMF framework. Memory size also is decreased in MOTIFSM about 75% than EIMF.
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MRI of the Brain in Moving Subjects Application to Fetal, Neonatal and Adult Brain
Статья научная
Imaging in the presence of subject motion has been an ongoing challenge for magnetic resonance imaging (MRI). In this paper some of the important issues regarding the acquisition and reconstruction of anatomical and DTI imaging of moving subjects are addressed; methods to achieve high resolution and high Signal to Noise Ratio (SNR) volume data. Excellent fetal brain 3D Apparent Diffusion Coefficient maps in high resolution have been achieved for the first time as well as promising Fractional Anisotropy maps. Growth curves for the normally developing fetal brain have been devised by the quantification of cerebral and cerebellar volumes as well as someone dimensional measurements. A Verhulst model is to describe these growth curves, and this approach has achieved a correlation over 0.99 between the fitted model and actual data.
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Machine Learning based Wildfire Area Estimation Leveraging Weather Forecast Data
Статья научная
Wildfires are increasingly destructive natural disasters, annually consuming millions of acres of forests and vegetation globally. The complex interactions among fuels, topography, and meteorological factors, including temperature, precipitation, humidity, and wind, govern wildfire ignition and spread. This research presents a framework that integrates satellite remote sensing and numerical weather prediction model data to refine estimations of final wildfire sizes. A key strength of our approach is the use of comprehensive geospatial datasets from the IBM PAIRS platform, which provides a robust foundation for our predictions. We implement machine learning techniques through the AutoGluon automated machine learning toolkit to determine the optimal model for burned area prediction. AutoGluon automates the process of feature engineering, model selection, and hyperparameter tuning, evaluating a diverse range of algorithms, including neural networks, gradient boosting, and ensemble methods, to identify the most effective predictor for wildfire area estimation. The system features an intuitive interface developed in Gradio, which allows the incorporation of key input parameters, such as vegetation indices and weather variables, to customize wildfire projections. Interactive Plotly visualizations categorize the predicted fire severity levels across regions. This study demonstrates the value of synergizing Earth observations from spaceborne instruments and forecast data from numerical models to strengthen real-time wildfire monitoring and postfire impact assessment capabilities for improved disaster management. We optimize an ensemble model by comparing various algorithms to minimize the root mean squared error between the predicted and actual burned areas, achieving improved predictive performance over any individual model. The final metric reveals that our optimized WeightedEnsemble model achieved a root mean squared error (RMSE) of 1.564 km2 on the test data, indicating an average deviation of approximately 1.2 km2 in the predictions.
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Machine learning application to improve COCOMO model using neural networks
Статья научная
Millions of companies expend billions of dollars on trillions of software for the development and maintenance. Still many projects result in failure causing heavy financial loss. Major reason is the inefficient effort estimation techniques which are not so suitable for the current development methods. The continuous change in the software development technology makes effort estimation more challenging. Till date, no estimation method has been found full-proof to accurately pre-compute the time, money, effort (man-hours) and other resources required to successfully complete the project resulting either over-estimated budget or under-estimated budget. Here a machine learning COCOMO is proposed which is a novel non-algorithmic approach to effort estimation. This estimation technique performs well within their pre-specified domains and beyond so. As development methods have undergone revolutionaries but estimation techniques are not so modified to cope up with the modern development skills, so the need of training the models to work with updated development methods is being satiated just by finding out the patterns and associations among the domain specific data sets via neural networks along with carriage of desired COCOMO features. This paper estimates the effort by training proposed neural network using already published data-set and later on, the testing is done. The validation clearly shows that the performance of algorithmic method is improved by the proposed machine learning method.
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Man-made Object Detection Based on Texture Clustering and Geometric Structure Feature Extracting
Статья научная
Automatic aerial image interpretation is one of new rising high-tech application fields, and it’s proverbially applied in the military domain. Based on human visual attention mechanism and texture visual perception, this paper proposes a new approach for man-made object detection and marking by extracting texture and geometry structure features. After clustering the texture feature to realize effective image segmentation, geometry structure feature is obtained to achieve final detection and marking. Thus a man-made object detection methodology is designed, by which typical man-made objects in complex natural background, including airplanes, tanks and vehicles can be detected. The experiments sustain that the proposed method is effective and rational.
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Статья научная
Both fuzzy logic and sliding mode can compensate the steady-state error of proportional-derivative (PD) method. This paper presents parallel sliding mode optimization for fuzzy PD management. The asymptotic stability of fuzzy PD management with first-order sliding mode optimization in the parallel structure is proven. For the parallel structure, the finite time convergence with a super-twisting second-order sliding-mode is guaranteed.
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Management of Possible Roles for Distributed Software Projects Using Layer Architecture
Статья научная
Several members are involved in development and management of the Distributed Software Projects. Each member needs to know the responsibilities of each other for proper management of the activities of such distributed projects to produce coherent outcomes. Distribution middleware software has higher-level distributed programming models whose reusable APIs (application programming interface) and components automate and extend native operating system capabilities. Software management tools like Work break-down structure (WBS), Gantt chart, Critical Path Method, and Critical Chain Method etc. does not fully help the managers to manage the member's responsibilities during the development of distributed applications. The layered architecture can help to do so. This style not only gives the layer level description of the activity involved, it also defines and directs the group of workforce. By listing the groups of workforce, the development team as well as the customer can know the activity and the member involved to work on those specific activities. This layered architecture is much benefited to development team and also to numbers of stakeholder of the large distributed project. The extended new approach of layer pattern with 'Responsibility Index' adds extra value to manage all the members' responsibilities. Managers, stakeholders and others can have an easy management system. The request or complaint from the customer can be passed to appropriate team without much delay. Most importantly this will give facility to collect timely feedback from all levels of customers.
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Managing Data Diversity on the Internet of Medical Things (IoMT)
Статья научная
In the healthcare industry, the Internet of Medical Services (IOMT) plays a vital role throughout the increasing performance, reliability, and efficiency of an electronic device. Healthcare is also characterized as being complicated due to its highly diverse and large number of shareholders. Data diversity refers to the continuum of various types of elements in the data. The integration of data is difficult where different sources can adopt different identification for the same entity, but there is no explicit connection. Researches are contributing to a digitized Health care system through interconnections available medical resources and health care services. This Research presents the contribution of IoT to people in the field of Healthcare, highlighting the issues in different data integration, analysis of the existing algorithms and models, applications, and future challenges of IoT in terms of healthcare medical services. Big data analytics that incorporates millions of fragmented, organized, and unstructured sources of data will play a key role in how health care will be delivered in the future.
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Markov Models Applications in Natural Language Processing: A Survey
Статья научная
Markov models are one of the widely used techniques in machine learning to process natural language. Markov Chains and Hidden Markov Models are stochastic techniques employed for modeling systems that are dynamic and where the future state relies on the current state. The Markov chain, which generates a sequence of words to create a complete sentence, is frequently used in generating natural language. The hidden Markov model is employed in named-entity recognition and the tagging of parts of speech, which tries to predict hidden tags based on observed words. This paper reviews Markov models' use in three applications of natural language processing (NLP): natural language generation, named-entity recognition, and parts of speech tagging. Nowadays, researchers try to reduce dependence on lexicon or annotation tasks in NLP. In this paper, we have focused on Markov Models as a stochastic approach to process NLP. A literature review was conducted to summarize research attempts with focusing on methods/techniques that used Markov Models to process NLP, their advantages, and disadvantages. Most NLP research studies apply supervised models with the improvement of using Markov models to decrease the dependency on annotation tasks. Some others employed unsupervised solutions for reducing dependence on a lexicon or labeled datasets.
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Mask R-CNN for Geospatial Object Detection
Статья научная
Geospatial imaging technique has opened a door for researchers to implement multiple beneficial applications in many fields, including military investigation, disaster relief, and urban traffic control. As the resolution of geospatial images has increased in recent years, the detection of geospatial objects has attracted a lot of researchers. Mask R-CNN had been designed to identify an object outlines at the pixel level (instance segmentation), and for object detection in natural images. This study describes the Mask R-CNN model and uses it to detect objects in geospatial images. This experiment was prepared an existing dataset to be suitable with object segmentation, and it shows that Mask R-CNN also has the ability to be used in geospatial object detection and it introduces good results to extract the ten classes dataset of Seg-VHR-10.
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