International Journal of Information Technology and Computer Science @ijitcs
Статьи журнала - International Journal of Information Technology and Computer Science
Все статьи: 1211
Статья научная
In this paper, the performance of cellular communications based on the ambitious technology of High-Altitude Platforms (HAPs) is discussed when using tapered concentric circular arrays. The coverage cell will be described and designed with an efficient beamforming technique where the Hamming window is proposed as a tapering function and applied to the uniform concentric circular arrays (UCCA) for sidelobe reduction. Based on establishing some mapping curves, this novel tapering window is optimized in its parameters to have the lowest possible sidelobe level that can be 45 dB below the main lobe level. The optimum weights of Hamming window are found to be function of the number of elements of the innermost ring and the number of rings in the array. The cell power pattern is also discussed where the out-of-cell radiation is greatly reduced which in turns reduces the co-channel interference and improves the Carrier-to-Interference Ratio (CIR).
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Performance of Linear Block Coding for Multipath Fading Channel
Статья научная
This paper deals with the performance of linear block codes which provide a new paradigm for transmission over multipath fading channels. Multi path channel fading is the main enemy for any wireless communications system. Therefore, for any novel approach applied at any wireless communication system, its efficiency is measured according to its ability of mitigating the distortion caused by fading. It causes time dispersion to the transmitted symbols resulting in inter symbol interference (ISI). ISI inter symbol interference problem is a major impairment of the wireless communication channel. To mitigate the ISI problem and to have reliable communications in wireless channel, channel equalizer and channel coding technique is often employed. In this paper the BER (Bit Error Rate) performance is shown from analytically and by means of simulation for multipath dispersive channels. We have designed a channel equalizer using MLSE (Viterbi algorithm) in this paper for such a multipath channel (introducing inter symbol interferences) with BPSK modulation based on the assumption that the channel can be perfectly estimated at the receiver. Obviously the performance of channel coding in terms of BER is better than uncoded channel.
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Performance of Machine Learning Algorithms with Different K Values in K-fold Cross-Validation
Статья научная
The numerical value of k in a k-fold cross-validation training technique of machine learning predictive models is an essential element that impacts the model’s performance. A right choice of k results in better accuracy, while a poorly chosen value for k might affect the model’s performance. In literature, the most commonly used values of k are five (5) or ten (10), as these two values are believed to give test error rate estimates that suffer neither from extremely high bias nor very high variance. However, there is no formal rule. To the best of our knowledge, few experimental studies attempted to investigate the effect of diverse k values in training different machine learning models. This paper empirically analyses the prevalence and effect of distinct k values (3, 5, 7, 10, 15 and 20) on the validation performance of four well-known machine learning algorithms (Gradient Boosting Machine (GBM), Logistic Regression (LR), Decision Tree (DT) and K-Nearest Neighbours (KNN)). It was observed that the value of k and model validation performance differ from one machine-learning algorithm to another for the same classification task. However, our empirical suggest that k = 7 offers a slight increase in validations accuracy and area under the curve measure with lesser computational complexity than k = 10 across most MLA. We discuss in detail the study outcomes and outline some guidelines for beginners in the machine learning field in selecting the best k value and machine learning algorithm for a given task.
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Performance of a Slantlet Based OFDM Transceiver under Different Channel Conditions
Статья научная
A major goal of the next-generation wireless communication systems is the development of a reliable high-speed wireless communication system that supports high user mobility. Orthogonal Frequency Division Multiplexing (OFDM) system is one of the most promising technologies for current and future wireless communications that has drawn a lot of attention. OFDM usually achieved by Fast Fourier Transform (FFT). In this paper, Fast Fourier Transform (FFT) is replaced by SlantLet Transform (SLT) in order to reduce Inter-Carrier Interference (ICI), Inter-symbol Interference (ISI), and to improve the bandwidth efficiency by removing the Guard Interval (GI) needed in FFT-OFDM. The new structure was tested and compared with conventional FFT-based OFDM for Additive White Gaussian Noise (AWGN) channel, Flat Fading Channel (FFC), and multi-path Selective Fading Channel (SFC). Simulation tests were generated for different channels parameters values. The obtained results showed the proposed system has an improved Bit Error Rate (BER) performance compared with the reference system. For SFC the SLT-OFDM performs better than the FFT-OFDM on the lower SNR region, while the situation reverses with increasing SNR values.
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Phoenix: a framework to support transient overloads on cloud computing environments
Статья научная
This paper aims to present a computational framework capable of withstanding the effects produced by transient overloads on physical and virtual servers hosted on cloud computing environment. The proposed framework aims at automating management of virtual machines that are hosted in this environment, combining a proactive strategy, which performs load balancing when there is not overload of physical and/or virtual machines with a reactive strategy, which is triggered in the event of overload in these machines. On both strategies, it is observed the service level agreement (SLA) established for each hosted service according to the infrastructure as a service (IaaS) model. The main contribution of this paper is the implementation of a computational framework called Phoenix, capable of handling momentary overloads, considering the CPU, memory and network resources of physical and virtual machines and guaranteeing SLAs. The results demonstrate that Phoenix framework is effective, and it has outstanding performance in handling overloads virtual machine network, which has achieved the isolation of momentary overload on the physical machine preventing the propagation of their effects on the cloud.
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Phoneme concatenation method for myanmar speech synthesis system
Статья научная
This paper discusses the approach used to develop a Text-to-Speech (TTS) synthesis system for the Myanmar language. Concatenative method has been used to develop this TTS system using phoneme as the basic units for concatenation. In this proposed system, phoneme plays an important role so that Myanmar phoneme inventory is presented in detail. In Myanmar language, schwa is the only vowel that is allowed in a minor syllable or consonant that has half-sound of the original one. If these half sound can be handled, the TTS quality will be high. After analyzing the number of phoneme and half-sound consonant to be recorded, create the Myanmar phoneme speech database which contains total 157 phoneme speech sounds that can speech out for all Myanmar texts. These phonemes are fetched according to the result from the phonetic analysis modules and concatenated them by using proposed new phoneme concatenation algorithm. According to the experimental results, the system achieved the high level of intelligibility and acceptable level of naturalness. As the application area, it is intended for the resource limited device to use as language learning app and so on.
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Plagiarism detection system for the kurdish language
Статья научная
One of the serious issues is plagiarism, especially in the education field. Detecting the plagiarism became a challenging task, particularly in natural language texts. In the past years, some plagiarism detection tools have been developed for diverse natural languages, mainly English. Language-independent tools exist as well but are considered as too restrictive as they usually do not consider specific language features. The problem is there is no plagiarism Detection system for the Kurdish language. In this paper, we introduce a new system for plagiarism detection for Kurdish Language, based on n-gram algorithm, our system can detect the word, phrases, and paragraphs. Moreover, our system effectiveness for detect plagiarist texts in localhost and online especially in Google search engine. This system is more useful for the academic organizations such as schools, institutes, and universities for finding copied texts from another document.
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Plant disease detection system using bag of visual words
Статья научная
Plants are important to human life since plants provide the food, shelter, rain, building material, medicine, fuel such as coal, wood, etc. Therefore, planting, growing, and protecting the plants is essential for sustainable development of any nation. The plant disease can affect the growth of the plats that is caused by pathogens, living microorganisms, bacteria, fungi, nematodes, viruses, and living agents. Hence, identifying the plant disease is very essential to protect the plants in the early stage. Moreover, the plant diseases are identified from the symptoms that appear in stem, fruit, leaf, flower, root, etc. The common symptom of the plant disease can be predicted from the appearance of leaf since the appearance of leaves highly depends on the healthiness of the plant. Therefore, this paper presents a system to identify the lesion leaf from the plants in order to detect the disease occurred in the plant. This system is developed using the bag of visual words model. Moreover, the real time images are collected for various plants and tested with this system and the system produces better results for the given set of images.
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Platform-Independent Courseware Sharing
Статья научная
Courseware distribution between different platforms is the major issue of current e-Learning. SCORM (Sharable Content Object Reference Model) is one of the solutions for courseware sharing. However, to make SCORM-conformable courseware, some knowledge about HTML and JavaScript is required. This paper presents a SWF (Sharable Web Fragment)-based e-Learning system, where courseware is created with sharable Web fragments such as Web pages, images and other resources, and the courseware can be distributed to another platform by export and import facilities. It also demonstrates how to export a subject that contains assignments and problems and how to import the whole subject, only the assignments, or only the problems. The exported meta-information is architecture-independent and provides a model of courseware distribution.
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Polynomial Differential-Based Information-Theoretically Secure Verifiable Secret Sharing
Статья научная
In Pedersen’s VSS scheme the secret is embedded in commitments. And the polynomial used is of degree at most (t-1). In strong – (t, n) VSS which based on Pedersen’s scheme that polynomial in verification purpose is public polynomial. The public polynomial in their scheme which acts in verification purpose is not secure. And the secret is secure if the dealer cannot solve the discrete logarithm problem. In our propose scheme we will satisfy the security requirements in strong t-consistency and consider the security on verification polynomial used. We will show in shares verification algorithm the participants can verify that their shares are consistent and the dealer is honest (i.e. the dealer cannot success in distributing incorrect shares even the dealer can solve the discrete logarithm problem.) before start secret reconstruction algorithm. The security strength of the proposed scheme lies in the fact that the shares and all the broadcasted information convey no information about the secret.
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Статья научная
The recommendation is playing an essential part in our lives. Precise recommendations facilitate users to swiftly locate desirable items without being inundated by irrelevant information. In the last few years, the amount of customers, products and online information has raised speedily and results out into the huge data analysis problem for recommender systems. While handling and evaluating such large-scale data, usual service recommender systems regularly undergo scalability and inefficiency problems. Nowadays, in multimedia platform such as movie, music, games, the use of Recommender System is increased. Collaborative Filtering is a dominant filtering technique used by many RSs. CF utilizes the rating history of the user to find out "like minded" users and this set of like-minded user is then used to recommend the movies which are liked by these like-minded users but did not watch by the active user. Thus, in CF, to find out the "neighborhood" the rating history of a user is used, but the reason behind the rating is not considered at all. This will lead to inaccuracy in finding a neighborhood set and subsequently in recommendation also. To cope with these scalability and accuracy challenges, this paper proposes an innovative solution, Clustering and Review based Approach for Collaborative Filtering based Recommendation. This innovative approach is enacted with the two stages; in the first stage the clustering of the available movies for recommendation is clustered into the subclasses for further computation. In the succeeding stage, the methodology based on reviews is utilized for finding neighborhood set in User Based Collaborative Filtering.
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Prediction Model of the Stock Market Index Using Twitter Sentiment Analysis
Статья научная
Stock market prediction has been an interesting research topic for many years. Finding an efficient and effective means of predicting the stock market found its way in different social networking platforms such as Twitter. Studies have shown that public moods and sentiments can affect one's opinion. This study explored the tweets of the Filipino public and its possible effects on the movement of the closing Index of the Philippine Stock Exchange. Sentiment Analysis was used in processing individual tweets and determining its polarity - either positive or negative. Tweets were given a positive and negative probability scores depending on the features that matched the trained classifier. Granger causality testing identified whether or not the past values of the Twitter time series were useful in predicting the future price of the PSE Index. Two prediction models were created based on the p-values and regression algorithms. The results suggested that the tweets collected using geo location and local news sources proved to be causative of the future values of the Philippine Stock Exchange closing Index.
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Prediction Models for Diabetes Mellitus Incidence
Статья научная
Diabetes mellitus is an incurable disease with global prevalence and exponentially increasing incidence. It is one of the greatest health hazards of the twenty-first century which poses a great economic threat on many nations. The premise behind effective disease management in healthcare system is to ensure coordinated intervention targeted towards reducing the incidence of such disease. This paper presents an approach to reducing the incidence of diabetes by predicting the risk of diabetes in patients. Diabetes mellitus risk prediction model was developed using supervised machine learning algorithms of Naïve Bayes, Support Vector Machine and J48 Decision Tree. The decision tree was able to give a prediction accuracy of 95.09% using rules of prediction that give acceptable results, that is, the model was approximately 95% accurate. The easy-to-understand rules of prediction got from J48 decision tree make it excellent in developing predictive models.
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Prediction and monitoring agents using weblogs for improved disaster recovery in cloud
Статья научная
Disaster recovery is a continuous dilemma in cloud platform. Though sudden scaling up and scaling down of user’s resource requests is available, the problem of servers down still persists getting users locked at vendor’s end. This requires such a monitoring agent which will reduce the chances of disaster occurrence and server downtime. To come up with an efficient approach, previous researchers’ techniques are analyzed and compared regarding prediction and monitoring of outages in cloud computing. A dual functionality Prediction and Monitoring Agent is proposed to intelligently monitor users’ resources requests and to predict coming surges in web traffic using Linear Regression algorithm. This solution will help to predict the user’s future requests’ behavior, to monitor current progress of resources’ usage, server virtualization and to improve overall disaster recovery process in Cloud Computing.
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Статья научная
Pharmacy handles all the medicine needed in the hospital that consists of vast amount of records. These produce large scale of datasets that are complex to manage and thereby need tools and technique to easily process, interpret, forecast and predict future consumption. Due to this, the method of predicting and forecasting stock consumption using Data Mining technique in hospital pharmacy is not be a surprising issue. Thus, this research investigated the potential applicability of data mining technology to predict the Anti-Retroviral drugs consumption for pharmacy based up on patient's history datasets of Jugal hospital, Harar, Ethiopia. The methodology used for this research is based on Knowledge Discovery in Database which had mostly relied on using the decision tree algorithms specifically M5P model tree. WEKA software, a data-mining tool were used for interpreting, evaluating and predicting from large datasets. Result with the data set suggests that tree based modeling approach can effectively be used in predicting the consumption of ARV drugs.
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Prediction of Defect Prone Software Modules using MLP based Ensemble Techniques
Статья научная
Prediction of defect prone software modules is now considered as an important activity of software quality assurance. This approach uses the software metrics to predict whether the developed module is defective or not. This research presents MLP based ensemble classification framework to predict the defect prone software modules. The framework predicts the defective modules by using three dimensions: 1) Tuned MLP, 2) Tuned MLP with Bagging 3) Tuned MLP with Boosting. In first dimension only the MLP is used for the classification after optimization. In second dimension, the optimized MLP is integrated with bagging technique. In third dimension, the optimized MLP is integrated with boosting technique. Four publically available cleaned NASA MDP datasets are used for the implementation of proposed framework and the performance is evaluated by using F-measure, Accuracy, Roc Area and MCC. The performance of the proposed framework is compared with ten widely used supervised classification techniques by using Scott-Knott ESD test and the results reflects the high performance of the proposed framework.
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Prediction of Missing Values for Decision Attribute
Статья научная
The process of determining missing values in information system is an important issue for decision making especially when the missing values are in the decision attribute. The main goal for this paper is to introduce algorithm for finding missing values of decision attribute. Our approach is depending on distance function between existing values. These values can be calculated by distance function between the conditions attributes values for the complete information system and incomplete information system. This method can deal with the repeated small distance by eliminating a condition attribute which has the smallest effect on the complete information system. This algorithm will be discussed in detail with an example of a case study.
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Preference-Based Web Service Composition: Case-Based Planning Approach
Статья научная
Web service selection is an indispensable process for web service composition. However it became a difficult task as many web services are increased on the web and mostly they offer similar functionalities, which service will be the best. User preferences are the key to retain only the best services for the composition. In this paper, we have proposed a web service composition model based on user preferences. To improve the process of web service composition we propose a case-based planning approach with user preferences which uses successful experiences in past to solve similar problems. In this paper we integrate user preferences in the phase of selection, adaptation and planning. Our main contributions are a new method of case retrieval, an extended algorithm of adaptation and planning with user preferences. Results obtained offer more than a solution to the user and taking both functional and non-functional requirements.
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Primary-Backup Access Control Scheme for Securing P2P File-Sharing Systems
Статья научная
Peer-to-peer (P2P) file-sharing systems have gained large interests among the internet users. However, wide-scale applications of P2P file-sharing technologies are constrained by the limitations associated with the sophisticated control mechanisms. Moreover, the decentralized and anonymous characteristics of P2P environments make it more difficult to control accesses on the shared resources, especially for using traditional access control methods. To overcome these limitations, we propose a role-based access control architecture for P2P file-sharing systems that supports autonomous decisions and centralized controls. The architecture integrates policies of credential, identity and role-based access control models to provide scalable, efficient and fault-tolerant access control services. Furthermore, we employ the primary-backup (PB) scheme to preserve P2P decentralized structure and peers’ autonomy property while enabling collaboration between peers. In particular, we propose a method for setting up interoperating relationships between domains by role mappings and resolve two kinds of interoperability conflicts while mapping roles from foreign domain to local domain without centralized authority. We believe that the proposed architecture is realistic, efficient and can provide controlled communications between peers.
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Priority Based New Approach for Correlation Clustering
Статья научная
Emerging source of Information like social network, bibliographic data and interaction network of proteins have complex relation among data objects and need to be processed in different manner than traditional data analysis. Correlation clustering is one such new style of viewing data and analyzing it to detect patterns and clusters. Being a new field, it has lot of scope for research. This paper discusses a method to solve problem of chromatic correlation clustering where data objects as nodes of a graph are connected through color-labeled edges representing relations among objects. Purposed heuristic performs better than the previous works.
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