International Journal of Information Technology and Computer Science @ijitcs
Journal articles - International Journal of Information Technology and Computer Science
All articles: 1304
Scientific article
Muscles in a human body consists of a pair. Musculoskeletal imbalances caused by repetitive usage of one part of the muscle in this pair and incorrect posture a human body takes on regular basis lead to severe injuries in terms of neuro musculoskeletal problems, hamstring strains, lower back tightness, repetitive stress injuries, altered movement patterns, postural dysfunctions, trapped nerves and etc. and both neurological and physical performances are severely affected when time progresses. In clinical domain, muscle imbalances are determined by gait and posture analysis, Movement analysis, Joint range of motion analysis and muscle length analysis which require expertise knowledge and experience. X-Rays and CT scans in the medical domain also require domain experts to interpret the results of a checkup. Kinect is a motion capturing device which is able to track human skeleton, its joints and body movements within its sensory range. The purpose of this research is to provide a mechanism to identify muscle imbalances based on gait analysis tracked via Kinect motion capture device by differentiate the deviation of healthy person’s gait patterns. Primarily, the outcome of this study will be a self-identification method of human skeletal imbalance.
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A parallel evolutionary search for shortest vector problem
Scientific article
The hardness assumption of approximate shortest vector problem (SVP) within the polynomial factor in polynomial time reduced to the security of many lattice-based cryptographic primitives, so solving this problem, breaks these primitives. In this paper, we investigate the suitability of combining the best techniques in general search/optimization, lattice theory and parallelization technologies for solving the SVP into a single algorithm. Our proposed algorithm repeats three steps in a loop: an evolutionary search (a parallelized Genetic Algorithm), brute-force of tiny full enumeration (in role of too much local searches with random start points over the lattice vectors) and a single main enumeration. The test results showed that our proposed algorithm is better than LLL reduction and may be worse than the BKZ variants (except some so small block sizes). The main drawback for these test results is the not-sufficient tuning of various parameters for showing the potential strength of our contribution. Therefore, we count the entire main problems and weaknesses in our work for clearer and better results in further studies. Also it is proposed a pure model of Genetic Algorithm with more solid/stable design for SVP problem which can be inspired by future works.
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A reliable solution to load balancing with trust based authentication enhanced by virtual machines
Scientific article
Vehicular Ad hoc network is the most fast growing which shape fresh engineering opportunities like controlling traffic smartly, optimal resource maintenance and improved service for customers. Vehicular Ad hoc Network (VANET) is one of the most popular ad hoc networks. A vehicular ad hoc network generally faces the problems like trust modeling, congestion, and battery optimization issues. If the nodes are comparatively less than it can handle the traffic well when it comes to transferring the data at a rapid rate. But, when it comes to high-density traffic than a Vehicular network always faces congestion problem. This paper tried to find the reliable solution to the traffic management by adding up the virtual gears into the network and optimizes the congestion problem by using a trust queue which is updated with the broadcast concept of the hello packets in order to remove the unwanted nodes in the list. The network performance has been measured with QOS Parameters like delay, throughput, and other parameters to prove the authentication of the research.
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A robust functional minimization technique to protect image details from disturbances
Scientific article
Image capturing using faulty systems or environmental vulnerabilities always degrade the image quality and causes the distortion of true details from the original imaging signals. Thus a robust way of image enhancement and edge preservation is an enormously requirement for smooth imaging operations. Although, many techniques have been deployed in this area during the decades for its betterment. However, the key challenges are remain towards better tradeoff between image enhancement and details protection. Therefore, this study inspects the existing limitations and proposes a robust technique based on functional minimization scheme in variational framework for ensuring better performance in case of image enhancement and details preservation simultaneously. A vigorous way to solve the minimization problem is also develop to make sure the efficiency of the proposed technique than some other traditional techniques.
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A stochastic model for simple document processing
Scientific article
This work focuses on the stationary behavior of a simple document processing system. We mean by simple document, any document whose processing, at each stage of its progression in its graph of processing, is assured by a single person. Our simple document processing system derives from the general model described by MOUKELI and NEMBE. It is about an adaptation of the said general model to determine in terms of metrics and performance, its behavior in the particular case of simple document processing. By way of illustration, data relating to a station of a central administration of a ministry, observed over six (6) years, were presented. The need to study this specific case comes from the fact that the processing of simple documents is based on a hierarchical organization and the use of priority queues. As in the general model proposed by MOUKELI and NEMBE, our model has a static component and a dynamic component. The static component is a tree that represents the hierarchical organization of the processing stations. The dynamic component consists of a Markov process and a network of priority queues which model all waiting lines at each processing unit. Key performance indicators were defined and studied point by point and on average. As well as issues specific to the hierarchical model associated with priority queues have been analyzed and solutions proposed; it is mainly infinite loops.
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A study and performance comparison of MapReduce and apache spark on twitter data on Hadoop cluster
Scientific article
We explore Apache Spark, the newest tool to analyze big data, which lets programmers perform in-memory computation on large data sets in a fault tolerant manner. MapReduce is a high-performance distributed BigData programming framework which is highly preferred by most big data analysts and is out there for a long time with a very good documentation. The purpose of this project was to compare the scalability of open-source distributed data management systems like Apache Hadoop for small and medium data sets and to compare it’s performance against the Apache Spark, which is a scalable distributed in-memory data processing engine. To do this comparison some experiments were executed on data sets of size ranging from 5GB to 43GB, on both single machine and on a Hadoop cluster. The results show that the cluster outperforms the computation of a single machine by a huge range. Apache Spark outperforms MapReduce by a dramatic margin, and as the data grows Spark becomes more reliable and fault tolerant. We also got an interesting result that, with the increase of the number of blocks on the Hadoop Distributed File System, also increases the run-time of both the MapReduce and Spark programs and even in this case, Spark performs far more better than MapReduce. This demonstrates Spark as a possible replacement of MapReduce in the near future.
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A study on diagnosis of Parkinson’s disease from voice dysphonias
Scientific article
Parkinson disease that occurs at older ages is a neurological disorder that is one of the most painful, dangerous and non-curable diseases. One symptom that a person may have Parkinson’s disease is trouble that can occur in the voice of a person which is so-called dysphonia. In this study, an application based on assessing the importance of features was carried out by using multiple types of sound recordings dataset for diagnosis of Parkinson disease from voice disorders. The sub-datasets, which were obtained from these records and were divided into 70-30% training and testing data respectively, include the important features. According to the experimental results, the Random Forest and Logistic Regression algorithms were found successful in general. Besides, one or two of these algorithms were found to be more successful for each sound. For example, the Logistic Regression algorithm is more successful for the ‘a’ voice. The Artificial Neural Networks algorithm is more successful for the ‘o’ voice.
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A study on the diagnosis of parkinson’s disease using digitized wacom graphics tablet dataset
Scientific article
Parkinson Disease is a neurological disorder, which is one of the most painful, dangerous and non-curable diseases, which occurs at older ages. The Static Spiral Test, Dynamic Spiral Test and Stability Test on Certain Point records were used in the application which was developed for the diagnosis of this disease. These datasets were divided into 80-20% training and testing data respectively within the framework of 10-fold cross validation technique. Training data as the input data were sent to the Random Forest, Logistic Regression and Artificial Neural Networks classifier algorithms. After this step, performances of these classifier algorithms were evaluated on testing data. Also, new data analysis was carried out. According to the results obtained, Artificial Neural Networks is more successful than Random Forest and Logistic Regression algorithms in analysis of new data.
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A systematic study of data wrangling
Scientific article
The paper presents the theory, design, usage aspects of data wrangling process used in data ware housing and business intelligence. Data wrangling is defined as an art of data transformation or data preparation. It is a method adapted for basic data management which is to be properly processed, shaped, and is made available for most convenient consumption of data by the potential future users. A large historical data is either aggregated or stored as facts or dimensions in data warehouses to accommodate large adhoc queries. Data wrangling enables fast processing of business queries with right solutions to both analysts and end users. The wrangler provides interactive language and recommends predictive transformation scripts. This helps the user to have an insight of reduction of manual iterative processes. Decision support systems are the best examples here. The methodologies associated in preparing data for mining insights are highly influenced by the impact of big data concepts in the data source layer to self-service analytics and visualization tools.
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A task scheduling model for multi-CPU and multi-hard disk drive in soft real-time systems
Scientific article
In recent years, by increasing CPU and I/O devices demands, running multiple tasks simultaneously becomes a crucial issue. This paper presents a new task scheduling algorithm for multi-CPU and multi-Hard Disk Drive (HDD) in soft Real-Time (RT) systems, which reduces the number of missed tasks. The aim of this paper is to execute more parallel tasks by considering an efficient trade-off between energy consumption and total execution time. For study purposes, we analyzed the proposed scheduling algorithm, named HCS (Hard disk drive and CPU Scheduling) in terms of the task set utilization, the total execution time, the average waiting time and the number of missed tasks from their deadlines. The results show that HCS algorithm improves the above mentioned criteria compared to the HCS_UE (Hard disk drive and CPU Scheduling _Unchanged Execution time) algorithm.
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ADPBC: Arabic Dependency Parsing Based Corpora for Information Extraction
Scientific article
There is a massive amount of different information and data in the World Wide Web, and the number of Arabic users and contents is widely increasing. Information extraction is an essential issue to access and sort the data on the web. In this regard, information extraction becomes a challenge, especially for languages, which have a complex morphology like Arabic. Consequently, the trend today is to build a new corpus that makes the information extraction easier and more precise. This paper presents Arabic linguistically analyzed corpus, including dependency relation. The collected data includes five fields; they are a sport, religious, weather, news and biomedical. The output is CoNLL universal lattice file format (CoNLL-UL). The corpus contains an index for the sentences and their linguistic meta-data to enable quick mining and search across the corpus. This corpus has seventeenth morphological annotations and eight features based on the identification of the textual structures help to recognize and understand the grammatical characteristics of the text and perform the dependency relation. The parsing and dependency process conducted by the universal dependency model and corrected manually. The results illustrated the enhancement in the dependency relation corpus. The designed Arabic corpus helps to quickly get linguistic annotations for a text and make the information Extraction techniques easy and clear to learn. The gotten results illustrated the average enhancement in the dependency relation corpus.
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AI-based Secure Cluster Formation and Reliable Data Transmission for Wireless Sensor Networks
Scientific article
Clustering in wireless sensor networks (WSNs) offers numerous desirable properties, including load balancing, energy conservation, and distributed key management. Secure Clustering requires it to detect compromised nodes and remove them from clusters during setup. Suppose some nodes are attacked and pass the filtering. In that case, they can modify some nodes to adopt a different clustering perspective, as well as initiate new clusters to degrade the overall cluster quality. To address these issues, a new method, Secretary Bird with Self-Organizing Maps (SBWSOM), has been designed to detect and eliminate malicious nodes while efficiently providing data. First, the appropriate sensor nodes were constructed in Python. Second, the malicious node was located and destroyed, and the Cluster Head (CH) was picked based on parameters such as remaining energy, network level, and base station (BS) location. Furthermore, the data rates of chosen CHs have been confirmed and sent to empty nodes. Lastly, the values compared and studied were Latency, throughput, packet delivery ratio (PDR), energy consumption, and transmission loss. The evaluation of this proposal demonstrated improved data transfer, with a throughput of 0.91, an energy consumption of 0.46 mJ, and a packet delivery ratio of 96.3%. Also, the transmit loss was 4.20%, and Latency was 6.04 ms. Overall, this method performed well, with significant improvement over previous models.
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APESS - A Service-Oriented Data Mining Platform: Application for Medical Sciences
Scientific article
The domain medical and public health remains the principal preoccupation of all world population. It makes recourse at several means from various disciplines, including for instance epidemiology, to help clinicians in decision processes. This paper proposes an Assistance Platform for Epidemiological Searches and Surveillance (APESS) for service-oriented data mining in the field of epidemiology. The main aim of the present platform is to build a system that enables extracting predictive rules, flexible and scalable for aid in decision-making by trades' experts. Results showed that the current system provides prediction models of chronic diseases (epidemiological prediction rules), using classification algorithms.
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AQUAZONE: A Spatial Decision Support System for Aquatic Zone Management
Scientific article
During the last years, the Sebkha Lake of Oran (Algeria) has been the subject of many studies for its protection and recovery. Many environmental and wetlands experts are a hope on the integration of this rich and fragile space, ecologically, as a pilot project in "management of water tides". Support the large of Sebkha (Lake) of Oran is a major concern for governments looking to make this a protected natural area and viable place. It was a question of putting in place a management policy to respond to the requirements of economic, agricultural and urban development and the preservation of this natural site through management of its water and the preservation of its quality. The objective of this study is to design and develop a Spatial Decision Support System, namely AQUAZONE, able to assist decision makers in various natural resource management projects. The proposed system integrates remote sensing image processing methods, from display operations, to analysis results, in order to extract useful knowledge to best natural resource management, and in particular define the extension of Sebkha Lake of Oran (Algeria). Two methods were applied to classify LANDSAT 5 TM images of Oran (Algeria): Fuzzy C-Means (FCM) applied on multi spectral images, and the other that comes with the manual which is the Ordered Queue-based Watershed (OQW). The FCM will serve as initialization phase, to automatically discover the different classes (urban, forest, water, etc..) from a LANDSAT 5 TM images, and also minimize ambiguity in grayscale and establish Land cover map of this region.
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ATAM-based Architecture Evaluation Using LOTOS Formal Method
Scientific article
System Architecture evaluation and formal specification are the significant processes and practical endeavors in all domains. Many methods and formal descriptive techniques have been proposed to make a comprehensive analysis and formal representation of a system architecture. This paper consists of two main parts, in first we evaluated system performance, quality attribute in Remote Temperature Sensor clients-Server architecture by implementing an ATAM model, which provides a comprehensive support for evaluation of architecture designs by considering design quality attributes and how they can be represented in the architecture. In the second part, we computed the selected system architecture in ISO standards formal description technique LOTOS with which a system can be specified by the temporal relation between interactions and behavior of the system. Our proposed approach improves on factors such as ambiguity, inconsistency and incompleteness in current system architecture.
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AWG Based Optical Packet Switch Architecture
Scientific article
This paper discusses an optical packet switch (OPS) architecture, which utilizes the components like optical reflectors, tunable wavelength converters (TWCs), arrayed waveguide grating (AWG) and pieces of fiber to realize the switching action. This architecture uses routing pattern of AWG, and its symmetric nature, to simplify switch operation significantly. It is also shown that using multi-wavelengths optical reflector, length of delay lines can be reduced to half of its original value. This reduction in length is useful for comparatively larger size packets as for them. It can grow up some kilometers. The considered architecture is compared with already published architecture. Finally, modifications in the architecture are suggested such that switch can be efficiently placed in the backbone network.
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About One Model Strategic Game of Collective Choice
Scientific article
A model of dyadic non-cooperative game Γ(H) is discussed in the paper for the set of one and the same players’ strategies. The players make their choice sitting round the table and have the opportunity to coordinate only the meanings of utilities in every situation. Therefore the players’ payoffs are given by 2×2 matrixes. A notion “the equalized situation” in mixed strategies which is at the same time the equilibrium is introduced. The theorem has been proved, which establishes the conditions of existance of an equalized situation in the given game. In the case of the existence algorithm is constructed. If equalized situation doesn’t exist in the game, then there exists the equilibrium situation in the pure strategies and it is possible to find it by analysis of situations. Γ(H) game’s with bimatrix game in case of two players is given. The players’ conditions of optimal mixed strategies existence in game is written. Relevant examples are solved and Γ(H) game’s application for finite amount of players’ is discussed.
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Abuse-Free Optimistic Contract Signing Using RSA for Multiuser Systems
Scientific article
Multi-party contract signing (MPCS) is a way for signers to agree on a predetermined contract by exchanging their signature. This matter has become crucial with the growing number of communications. In this paper, we focus mainly on studying the state of the art protocols and more specifically the cryptography involved. We identify the major advances in MPCS, highlight a few gaps with the current protocols and propose an algorithm for contract signing to be abuse-free, optimistic for many signers in industrial standards.
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Accelerated K-means Clustering Algorithm
Scientific article
Optimizing K-means is still an active area of research for purpose of clustering. Recent developments in Cloud Computing have resulted in emergence of Big Data Analytics. There is a fresh need of simple, fast yet accurate algorithm for clustering huge amount of data. This paper proposes optimization of K-means through reduction of the points which are considered for re-clustering in each iteration. The work is generalization of earlier work by Poteras et al who proposed this idea. The suggested scheme has an improved average runtime. The cost per iteration reduces as number of iterations grow which makes the proposal very scalable.
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Accelerated Simulation Scheme for Solving Financial Problems
Scientific article
The Monte Carlo simulation method uses random sampling to study properties of systems with components that behave in a random state. More precisely, the idea is to simulate on the computer the behavior of these systems by randomly generating the variables describing the behavior of their components. In this paper, we propose an efficient and reliable simulation scheme based on Monte Carlo algorithm and combining two variance reduction procedures. We simulate a European option price numerically using the proposed simulation scheme.
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