International Journal of Information Technology and Computer Science @ijitcs
Статьи журнала - International Journal of Information Technology and Computer Science
Все статьи: 1211
Mobile Phone Based RIMS for Traffic Control a Case Study of Tanzania
Статья научная
Vehicles saturation in transportation infrastructure causes traffic congestion, accidents, transportation delays and environment pollution. This problem can be resolved with proper management of traffic flow. Existing traffic management systems are challenged on capturing and processing real-time road data from wide area road networks. The main purpose of this study is to address the gap by implementing a mobile phone based Road Information Management System. The proposed system integrates three modules for data collection, storage and information dissemination. The modules works together to enable real-time traffic control. Disseminated information from the system, enables road users to adjust their travelling habit, also it allows the traffic lights to control the traffic in relation to the real-time situation occurring on the road. In this paper the system implementation and testing was performed. The results indicated that there is a possibility to track traffic data using Global Positioning System enabled mobile phones, and after processing the collected data, real-time traffic status was displayed on web interface. This enabled road users to know in advance the situation occurring on the roads and hence make proper travelling decision. Further research should consider adjusting the traffic lights control system to understand the disseminated real-time traffic information.
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Статья научная
Model-driven testing is a method to verify the requirement specification of the system through UML models. Cuckoo search (CS) algorithm is based on the brooding characteristics of cuckoo birds. The test case generation process is used to identify the test cases with resources with critical domain requirements. This proposed paper emphasizing on the generation and optimization of test cases or test data using cuckoo search technique through a case study, i.e., the withdrawal operation in a Bank ATM and it also describes the generation of test cases from UML behavioral diagram like activity diagram, possible test paths are also generated through activity diagram graph.
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Modeling Truncated Loss Data of Operational Risk in E-banking
Статья научная
Operational risk is an important risk component for financial institutions, especially in E-banking. Large amount of capital that are assigned to decrease this risk are evidence to this subject. One of the most important factors for modeling operational risk to estimate capital charge is loss data collections of banks. But sometimes for reasons like decreasing the costs, banks save only the losses above some determined thresholds at their database. For achieving accurate capital charge, this threshold should be considered in determining capital charge. This paper focuses on modeling truncated loss data above some given threshold. We discuss several statistical methods for modeling truncated data, and suggest the best one for modeling truncated loss data. We have tested our suggested model for some operational loss data samples. Our results indicate that our approach can be useful for increasing accuracy of estimating operational risk capital charge in E-banking.
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Modeling of seamless vertical handover mechanism using demanded resource identification & mapping
Статья научная
One of the prominent challenges for offering seamless communication system while performing vertical handover in heterogeneous network is to relay the communication without identifying the accurate demands of the resources as well as quality of services for the newly moved node. After reviewing the existing literatures, it was found that there is a potential research gap in addressing this problem of seamless vertical handover. Therefore, the proposed manuscript addresses this problem by introducing a novel analytical model which is capable of formulating a precise decision for controlling the selection /dropping of the data packets on the basis of dynamic state of the network condition. The proposed system contributes faster processing by arbitrarily selecting the packets to be forwarded with a very unique and simple resource management. The study outcome of proposed system highlights an increased throughput and reduced length of queue along with better fairness control to offer seamless vertical handover.
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Modelling Misinformation in Swahili-English Code-switched Texts
Статья научная
Code-switching, which is the mixing of words or phrases from multiple, grammatically distinct languages, introduces semantic and syntactic complexities to sentences which complicate automated text classification. Despite code-switching being a common occurrence in informal text-based communication among most bilingual or multilingual users of digital spaces, its use to spread misinformation is relatively less explored. In Kenya, for instance, the use of code-switched Swahili-English is prevalent on social media. Our main objective in this paper was to systematically re- view code-switching, particularly the use of Swahili-English code-switching to spread misinformation on social media in the Kenyan context. Additionally, we aimed at pre-processing a Swahili-English code-switched dataset and developing a misinformation classification model trained on this dataset. We discuss the process we took to develop the code- switched Swahili-English misinformation classification model. The model was trained and tested using the PolitiKweli dataset which is the first Swahili-English code-switched dataset curated for misinformation classification. The dataset was collected from Twitter (now X) social media platform, focusing on text posted during the electioneering period of the 2022 general elections in Kenya. The study experimented with two types of word embeddings - GloVe and FastText. FastText uses character n-gram representations that help generate meaningful vectors for rare and unseen words in the code-switched dataset. We experimented with both the classical machine learning algorithms and deep learning algo- rithms. Bidirectional Long Short-Term Memory Networks (BiLSTM) algorithm showed the best performance with an f-score of 0.89. The model was able to classify code-switched Swahili-English political misinformation text as fake, fact or neutral. This study contributes to recent research efforts in developing language models for low-resource languages.
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Статья научная
A new form of wireless sensor networks is emerging as an important component of the Internet of Things (IoT) where camera devices are interconnected and endowed with an IP address to form visual sensor networks. The applications of these networks span from smart parking systems in smart cities, video surveillance for security systems to healthcare monitoring and many others which are emerging from niche areas. The management of such sensor networks will require meeting a higher quality of service (QoS) constraints than demanded from traditional sensor networks. While many works have focused only on energy efficiency as a way of providing QoS in sensor networks, we consider a novel modelling approach where local optimizations implemented on the sensor nodes are translated into pheromone distribution used in ant colony optimization for path computation. We propose a routing protocol called the multipath ant colony optimization (MACO) that finds QoS-aware routing paths for the sensor readings from source nodes to the sink by relying on four local parameters: the link cost, the remaining energy of neighboring nodes, sensor nodes location information and the amount of data a neighbor node is currently processing. Finally, we propose an architecture for integrating sensor data with the cloud computing. Simulation results reveal the relative efficiency of the newly proposed approach compared to selected related routing protocols in terms of several QoS metrics. These include the network energy efficiency, delay and throughput.
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Modern Platform for Parallel Algorithms Testing: Java on Intel Xeon Phi
Статья научная
Parallel algorithms are popular method of increasing system performance. Apart from showing their properties using asymptotic analysis, proof-of-concept implementation and practical experiments are often required. In order to speed up the development and provide simple and easily accessible testing environment that enables execution of reliable experiments, the paper proposes a platform with multi-core computational accelerator: Intel Xeon Phi, and modern programming language: Java. The article includes the description of integration Java with Xeon Phi, as well as detailed information about all of the software components. Finally, the set of tests proves, that proposed platform is able to prepare reliable experiments of parallel algorithms implemented in modern programming language.
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Modified Binary Exponential Backoff Algorithm to Minimize Mobiles Communication Time
Статья научная
The field of Wireless Local Area Networks (LANs) is expanding rapidly as a result of advances in digital communications, portable computers, and semiconductor technology. The early adopters of this technology have primarily been vertical application that places a premium on the mobility offered by such systems. Binary Exponential Backoff (BEB) refers to a collision resolution mechanism used in random access MAC protocols. This algorithm is used in Ethernet (IEEE 802.3) wired LANs. In Ethernet networks, this algorithm is commonly used to schedule retransmissions after collisions. The paper’s goal is to minimize the time transmission cycle of the information between mobiles moving in a Wi-Fi by changing the BEB algorithm. The Protocol CSMA / CA manage access to the radio channel by performing an arbitration based on time. This causes many problems in relation to time transmission between mobiles moving in a cell 802.11. what we have done show that the protocol using CSMA / CA access time believed rapidly when the number of stations and / or the network load increases or other circumstances affects the network..
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Morphotactics of Manipuri Adjectives: A Finite-State Approach
Статья научная
This paper presents a constrained finite-state model to represent the morphotactic rule of Manipuri adjective word forms. There is no adjective word category in Manipuri language. By rule this category is derived from verb roots with the help of some selected affixes applicable only to verb roots. The affixes meant for the purpose and the different rules for adjective word category formation are identified. Rules are composed for describing the simple agglutinative morphology of this category. These rules are combined to describe the more complex morphotactic structures. Finite-state machine is used to describe the concatenation rules and corresponding non-deterministic and deterministic automaton are developed for ease of computerization. A root lexicon of verb category words is used along with an affix dictionary in a database. The system is capable to analyze and recognize a certain word as adjective by observing the morpheme concatenation rule defined with the help of finite-state networks.
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Статья научная
To achieve successful reusability of components a disciplined development approach is required which is the component based software engineering(CBSE).The software component selection is a vital part of this approach. It consists of defining an evaluation criteria based on user requirements and depending on this the repository is searched and shortlisted components are presented to the user. Due to availability of large number of components offering same type of functionality it is difficult to select a particular component based on available description. This paper presents a multiobjective optimization model for component selection purpose and solves it using preemptive goal programming approach by using an optimization tool LINDO. Subsequently, an illustrative case study is given where the components are taken from an online repository and goal programming is applied for getting the most optimal component. However, this model is applicable when the repository is small but for larger set of components it needs to be validated.
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Multi Objective Test Suite Reduction for GUI Based Software Using NSGA-II
Статья научная
Regression Testing is a performed to ensure modified code does not have any unintended side effect on the software. If regression testing is performed with retest-all method it will be very time consuming as testing activity. Therefore test suite reduction methods are used to reduce the size of original test suite. Objective of test suite reduction is to reduce those test cases which are redundant or less important in their fault revealing capability. Test suite reduction can only be used when time is critical to run all test cases and selective testing can only be done. Various methods exist in the literature related to test suite reduction of traditional software. Most of the methods are based of single objective optimization. In case of multi objective optimization of test suite, usually researchers assign different weight values to different objectives and combine them as single objective. However in test suite reduction multiple Pareto-optimal solutions are present, it is difficult to select one test case over other. Since GUI based software is our concern there exist very few reduction techniques and none of them consider multiple objective based reduction. In this work we propose a new test suite reduction technique based on two objectives, event weight and number of faults identified by test case. We evaluated our results for 2 different applications and we achieved 20% reduction in test suite size for both applications. In Terp Paint 3.0 application compromise 15.6% fault revealing capability and for Notepad 11.1% fault revealing capability is reduced.
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Multi Population Hybrid Genetic Algorithms for University Course Timetabling Problem
Статья научная
University course timetabling is one of the important and time consuming issues that each University is involved with it at the beginning of each. This problem is in class of NP-hard problem and is very difficult to solve by classic algorithms. Therefore optimization techniques are used to solve them and produce optimal or near optimal feasible solutions instead of exact solutions. Genetic algorithms, because of multidirectional search property of them, are considered as an efficient approach for solving this type of problems. In this paper three new hybrid genetic algorithms for solving the university course timetabling problem (UCTP) are proposed: FGARI, FGASA and FGATS. In proposed algorithms, fuzzy logic is used to measure violation of soft constraints in fitness function to deal with inherent uncertainly and vagueness involved in real life data. Also, randomized iterative local search, simulated annealing and tabu search are applied, respectively, to improve exploitive search ability and prevent genetic algorithm to be trapped in local optimum. The experimental results indicate that the proposed algorithms are able to produce promising results for the UCTP.
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Multi-Factor Authentication for Improved Enterprise Resource Planning Systems Security
Статья научная
Universities across the globe have increasingly adopted Enterprise Resource Planning (ERP) systems, a software that provides integrated management of processes and transactions in real-time. These systems contain lots of information hence require secure authentication. Authentication in this case refers to the process of verifying an entity’s or device’s identity, to allow them access to specific resources upon request. However, there have been security and privacy concerns around ERP systems, where only the traditional authentication method of a username and password is commonly used. A password-based authentication approach has weaknesses that can be easily compromised. Cyber-attacks to access these ERP systems have become common to institutions of higher learning and cannot be underestimated as they evolve with emerging technologies. Some universities worldwide have been victims of cyber-attacks which targeted authentication vulnerabilities resulting in damages to the institutions reputations and credibilities. Thus, this research aimed at establishing authentication methods used for ERPs in Kenyan universities, their vulnerabilities, and proposing a solution to improve on ERP system authentication. The study aimed at developing and validating a multi-factor authentication prototype to improve ERP systems security. Multi-factor authentication which combines several authentication factors such as: something the user has, knows, or is, is a new state-of-the-art technology that is being adopted to strengthen systems’ authentication security. This research used an exploratory sequential design that involved a survey of chartered Kenyan Universities, where questionnaires were used to collect data that was later analyzed using descriptive and inferential statistics. Stratified, random and purposive sampling techniques were used to establish the sample size and the target group. The dependent variable for the study was limited to security rating with respect to realization of confidentiality, integrity, availability, and usability while the independent variables were limited to adequacy of security, authentication mechanisms, infrastructure, information security policies, vulnerabilities, and user training. Correlation and regression analysis established vulnerabilities, information security policies, and user training to be having a higher impact on system security. The three variables hence acted as the basis for the proposed multi-factor authentication framework for improve ERP systems security.
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Multi-Feature Segmentation and Cluster based Approach for Product Feature Categorization
Статья научная
At a recent time, the web has become a valuable source of online consumer review however as the number of reviews is growing in high speed. It is infeasible for user to read all reviews to make a valuable or satisfying decision because the same features, people can write it contrary words or phrases. To produce a useful summary of domain synonyms words and phrase, need to be a group into same feature group. We focus on feature-based opinion mining problem and this paper mainly studies feature based product categorization from the number of users - generated review available on the different website. First, a multi-feature segmentation method is proposed which segment multi-feature review sentences into the single feature unit. Second part of speech dictionary and context information is used to consider the irrelevant feature identification, sentiment words are used to identify the polarity of feature and finally an unsupervised clustering based product feature categorization method is proposed. Clustering is unsupervised machine learning approach that groups feature that have a high degree of similarity in a same cluster. The proposed approach provides satisfactory results and can achieve 100% average precision for clustering based product feature categorization task. This approach can be applicable to different product.
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Статья научная
The focus of this work is on how the congestion experienced on the GSM network can be minimized. The voice calls is broken into sub-classes of services and a level of priority is established among the classes so that the most urgent and important service will have access to the channel by preempting the lower priority services during congestion. The voice communications over the GSM network using the different classes of subscribers were analyzed with Markov chain’s model. The steady state probabilities for voice services were derived. The blocking and dropping probabilities models for the different services were developed using the Multi-dimensional Erlang B. To give a new call a fair sharing of the channel, Time-Threshold scheme is employed. This scheme classifies handoff call as either prioritised call or new call according to its associated elapsed real time value. The models were implemented based on the blocking and dropping probabilities models to show how the congestion can be minimised for different subscribers based on their priority levels. The work shows that the models used gave significant reduction in congestion when compared to the traditional Erlang-B model used in GSM.
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Статья научная
This paper presents solution of multi-objective optimal dispatch (MOOD) problem of solar-wind-thermal system by improved stochastic fractal search (ISFSA) algorithm. Stochastic fractal search (SFSA) is inspired by the phenomenon of natural growth called fractal. It utilizes the concept of creating fractals for conducting a search through the problem domain with the help of two main operations diffusion and updating. To improve the exploration and exploitation capability of SFSA, scale factor is used in place of random operator. The SFSA and proposed ISFSA is implemented and tested on six different multi objective complex test systems of power system. TOPSIS is used here as a decision making tool to find the best compromise solution between the two conflicting objectives. The outcomes of simulation results are also compared with recent reported methods to confirm the superiority and validation of proposed approach.
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Multi-Objective Task Scheduling in the Cloud Computing based on the Patrice Swarm Optimization
Статья научная
Cloud computing is the latest emerging trend in distributed computing, where shared resources are provided to end-users in an on demand fashion that brings many advantages, including data ubiquity, flexibility of access, high availability of resources, and flexibility. In this type of systems many challenges are existed that the task scheduling problem is one of them. The task scheduling problem in Cloud computing is an NP-hard problem. Therefore, many heuristics have been proposed, from low level execution of tasks in multiple processors to high level execution of tasks. In this paper, we propose a new algorithm based on PSO to schedule the tasks in the Cloud. The results demonstrated that the proposed algorithm has a better operation in terms of task execution time, waiting time and missed tasks in comparison of First Come First Served (FCFS), Shortest Process Next (SPN) and Highest Response Ratio Next (HRRN).
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Multi-agent System for Management of Data from Electrical Smart Meters
Статья научная
The smart meter can process sensor data in a residential grid. These sensors transmit different parameters or measurement data (index, power, temperature, fluctuation of voltage and electricity, etc.) to the smart meter. All of these measurement data can come in different ways at the smart meter. The sensors transmit each measurement data to the smart meter. In addition, the collection of this data to a central system is a significant concern to ensure data integrity and protect the privacy of residents. The complexity of these data management also lies in their volume, frequency, and scheduling. This work presents a scheduling and a collection mechanism in private power consumption data between both sensors and smart meters on one hand and between smart meters and the central data collection system on other hand. We have found several approaches to intelligent meter data management in scientific researches. We propose another approach in response to this concern for the scheduling and collection of measurement data to a central system from residential areas of sensors’ network connected to smart meters. This work is also an example of a link between data collection and data scheduling in intelligent information management, transmission, and protection. We also propose a modeling of the measurement objects of smart grid and highlight the changes made to these objects throughout the process of data processing. It should be noted that this smart grid system consists of three main active systems namely sensors, smart meters and central system. In addition to these three systems, there are other systems that communicate with the smart meters and the central system. We have identified three implementation models for the smart metering system. We also present an intelligent architecture based on multi-agent systems for the smart grid. Most current electricity management systems are not adapted to the new challenges imposed by social and economic development in Africa. The objectives of this study are to initiate the design of a smart grid system for the management of electricity data.
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Статья научная
Emotions are pivotal in the learning process, highlighting the importance of identifying students' emotional states within educational settings. While neural network models, particularly those rooted in deep learning, have demonstrated remarkable accuracy in detecting primary emotions like happiness, sadness, fear, disgust, and anger from facial expressions in videos, these emotions occur infrequently in learning environments. Conversely, cognitive emotions such as engagement, confusion, frustration, and boredom are significantly more prevalent, transpiring five times more frequently than basic emotions. However, unlike basic emotions which are relatively distinct, cognitive emotions present a subtler distinction, necessitating the utilization of more sophisticated models for accurate recognition. The proposed work presents an efficient Facial Expression Recognition (FER) model for monitoring the student engagement in a learning environment by considering their facial expressions like boredom, frustration, confusion and engagement. The proposed methodology includes certain pre-processing steps followed by facial expression recognition founded on Efficient-Net B3 CNN in which the learning parameters are optimized using Circle-Inspired Optimization Algorithm (CIOA). Finally, the post processing stage estimates the frame-wise group engagement level (GEL) of students based on certain expression labels. Based on the acquired results, it is noted that the suggested Efficient-Net B3 CNN-CIOA based FER model provides promising results in terms of accuracy by 99.5%, precision by 99.2%, recall by 99.5% and f1-score by 99.6%, when compared with some state-of-art facial expression recognition approaches. Also, the suggested approach computational complexity is very much less than the compared existing approaches.
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Multi-platform code generation supported by domain-specific modeling
Статья научная
Code generation is widely used to make software development more efficient and less prone to human errors. A significant use case of code generation is processing of Domain-Specific Languages (DSLs) and Domain-Specific Models (DSMs). Sometimes, it is desired to generate semantically equivalent or similar functionality to different languages to better support multiple platforms and achieve better reuse in the tooling. For example, it is convenient if a single tool supports code generating from a DSM to either Java or C#. There has been relevant research on using modeling and model transformations for code generation to multiple platforms. The Model-Driven Architecture (MDA) inherently supports multi-platform code generation based on models. Nevertheless, the MDA standard is a high-level general framework that includes standards, notions and principles but does not specify more concrete methods or workflows about their efficient adoption. Our research focuses on the efficient and practically usable application of MDA principles to generate multi-platform code. This paper reports on our results on multi-platform code generation and the difficulties that we are about to addressed in future research. The approach and the challenges presented in the paper are useful for tool developers, such as developers of DSLs, who generates code for several platforms.
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