International Journal of Information Technology and Computer Science @ijitcs
Статьи журнала - International Journal of Information Technology and Computer Science
Все статьи: 1227
SDRED: smart data retrieval engine for databases
Статья научная
Today computers are continuously betrothed in almost all domains and organizations. Thus, databases act as the heart for storing and retrieving information that contain huge digital data. However, in order to interact with such databases, it is necessary to have knowledge about the Structured Query Language (SQL), which is difficult for non-expert users to understand and manipulate. So, there is an emergent need to develop a smart and a user friendly computational technique to interact with databases. The current work proposed a smart technique that can handle such context. The proposed “Smart Data Retrieval Engine for Databases (SDRED)” provided an environment that allows a non-expert user to write and to execute the database queries easily. Furthermore, it retrieved the data stored in databases without a prior knowledge of the SQL. SDRED, which enables the non-expert user to write database queries in natural language (such as English) and to convert them to their SQL query equivalents. The current work presented a detailed design and evaluation for the proposed system by executing different database queries in English. The results established that SDRED successfully converted the non-expert user’s natural language queries into their equivalent SQL queries, thereby providing an easy and user-friendly environment to interact with databases.
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SQL Versus NoSQL Movement with Big Data Analytics
Статья научная
Two main revolutions in data management have occurred recently, namely Big Data analytics and NoSQL databases. Even though they have evolved with different purposes, their independent developments complement each other and their convergence would benefit businesses tremendously in making real-time decisions using volumes of complex data sets that could be both structured and unstructured. While on one hand many software solutions have emerged in supporting Big Data analytics, on the other, many NoSQL database packages have arrived in the market. However, they lack an independent benchmarking and comparative evaluation. The aim of this paper is to provide an understanding of their contexts and an in-depth study to compare the features of four main NoSQL data models that have evolved. The performance comparison of traditional SQL with NoSQL databases for Big Data analytics shows that NoSQL database poses to be a better option for business situations that require simplicity, adaptability, high performance analytics and distributed scalability of large data. This paper concludes that the NoSQL movement should be leveraged for Big Data analytics and would coexist with relational (SQL) databases.
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SQUIREL: Semantic Querying Interlinked OWL-S traveling Process Models
Статья научная
With the advent of new forms of information and communication technologies, the consumer needs to combine and customize different travel components as a complete travel package, namely: Dynamic Packaging Technology. Nevertheless, disparate tourist offers and services make it difficult for consumer to use them effectively. Therefore, our paper presents an intelligent querying framework of OWL-S travel services, called SQUIREL composition engine. It uses Semantic Web Services (SWSs) technologies combined with the useful of Linked e-tourism Data concept to fulfill the preferences and constraints of the e-tourist any time. This purpose supports SWSs pre-selection through the valuation of the rewritten SPARQL consumer query at runtime that manages dynamic service dependencies extracted from Linked e-tourism Data and returns the SWSs endpoint. Then, SQUIREL catches this endpoint and makes the necessary optimizations to refine it to its relevant atomic processes needed to be composed using matrix computation. However, the experimental results indicate that this method owns both lower computation cost and higher success ratio of fine-grained discovery-based atomic processes composition.
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SSKHOA: Hybrid Metaheuristic Algorithm for Resource Aware Task Scheduling in Cloud-fog Computing
Статья научная
Cloud fog computing is a new paradigm that combines cloud computing and fog computing to boost resource efficiency and distributed system performance. Task scheduling is crucial in cloud fog computing because it decides the way computer resources are divided up across tasks. Our study suggests that the Shark Search Krill Herd Optimization (SSKHOA) method be incorporated into cloud fog computing's task scheduling. To enhance both the global and local search capabilities of the optimization process, the SSKHOA algorithm combines the shark search algorithm and the krill herd algorithm. It quickly explores the solution space and finds near-optimal work schedules by modelling the swarm intelligence of krill herds and the predator-prey behavior of sharks. In order to test the efficacy of the SSKHOA algorithm, we created a synthetic cloud fog environment and performed some tests. Traditional task scheduling techniques like LTRA, DRL, and DAPSO were used to evaluate the findings. The experimental results demonstrate that the SSKHOA outperformed the baseline algorithms in terms of task success rate increased 34%, reduced the execution time by 36%, and reduced makespan time by 54% respectively.
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Sample of Groups: A New Strategy to Find a Representative Point for Each Undisclosed Cluster
Статья научная
Some problems involving the selection of samples from undisclosed groups are relevant in various areas such as health, statistics, economics, and computer science. For instance, when selecting a sample from a population, well-known strategies include simple random and stratified random selection. Another related problem is selecting the initial points corresponding to samples for the K-means clustering algorithm. In this regard, many studies propose different strategies for choosing these samples. However, there is no consensus on the best or most effective ap-proaches, even when considering specific datasets or domains. In this work, we present a new strategy called the Sam-ple of Groups (SOG) Algorithm, which combines concepts from grid, density, and maximum distance clustering algo-rithms to identify representative points or samples located near the center of the cluster mass. To achieve this, we create boxes with the right size to partition the data and select the representatives of the most relevant boxes. Thus, the main goal of this work is to find quality samples or seeds of data that represent different clusters. To compare our approach with other algorithms, we not only utilize indirect measures related to K-means but also employ two direct measures that facili-tate a fairer comparison among these strategies. The results indicate that our proposal outperforms the most common-ly used algorithms.
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ScrumFall: a hybrid software process model
Статья научная
Every software project is unique in its own way. As a consequence, a single software process model cannot be suitable for all types of projects. In the real world, practitioners face different difficulties with the existing process models during development. Still, they cope up with the challenges by tailoring the software development lifecycle according to their needs. Most of these custom-tailored practices are kept inside the walls of the organizations. However, sharing these proven and tested practices as well as acquired knowledge and experience would be highly beneficial for other practitioners as well as researchers. So in this paper, we have presented a software process model which contains the characteristics of both Scrum and Waterfall model and named it “ScrumFall”. This model has been practicing in an Anonymous Software Development Company, Bangladesh to solve the shortcomings of Scrum and Waterfall models. Moreover, we have analyzed the performance and suitability for applying this process model. The result shows that this process model is highly effective for the certain projects.
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ScrumSpiral: An Improved Hybrid Software Development Model
Статья научная
In the lightning-quick world of software development, it is essential to find the most effective and efficient development methodology. This thesis represents "Scrum Spiral" which is an improved hybrid software development model that combines the features of Scrum and Spiral approach to enhance the software development process. This thesis aims to identify the usefulness of "ScrumSpiral" methodology and compare it with other hybrid software development models to encourage its use in software development projects. To develop this hybrid model, we did extensive research on the software engineering domain and decided to create a hybrid model by using Scrum and Spiral, named "Scrum Spiral" which is suitable for complicated projects and also for those projects whose requirements are not fixed. Traditional software development models face numerous challenges in rapidly changing markets. By developing this kind of hybrid model, we want to overcome these kinds of limitations and present the software development community with a novel concept for better project results. Final outcome of this thesis was that we developed a model that should be able to complete the project according to the expected schedule, satisfy customer requirements, and obtain productivity through team coordination. The significance of the hybrid model "Scrum Spiral" is reflected in its ability to offer flexibility towards various size projects, proactive risk management to identify all risks before developing the system, and result in higher-quality outcomes for those projects whose requirements are not properly described initially in the project.
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Scrutable Mobile Client-side Personalization
Статья научная
Personalization has become an essential feature of mobile services of different domains. On the other hand, users have conflicting needs of personalized experience and privacy. This leads to the question of how to maximize the user’s experience of personalized mobile services while keeping privacy. One possible solution is to provide user’s control of their personal data by keeping their user model on their personal mobile devices. In this way, a user can scrutinize the data while sharing with service providers depending on her/his requirements. The client-side personalization approach can shift the control of privacy to the users and can involve them in personalization process. Transparency and user control can increase the user’s trust. In this paper, we have proposed a solution with the objective of scrutable client-side personalization while keeping the user in control of both privacy and personalization process.
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Secluding Efficient Geographic Multicast Protocol against Multicast Attacks
Статья научная
A Mobile Ad-hoc Network (MANETs) is composed of Mobile Nodes without any infrastructure. The network nodes in MANETs, not only act as ordinary network nodes but also as the routers for other peer devices. The dynamic topology, lack of a fixed infrastructure and the wireless nature make MANETs susceptible to the security attacks. To add to that, due to the inherent, severe constraints in power, storage and computational resources in the MANET nodes, incorporating sound defense mechanisms against such attacks is also non-trivial. Therefore, interest in research of Mobile Ad-hoc NETworks has been growing since last few years. Security is a big issue in MANETs as they are infrastructure-less and autonomous. The main objective of this paper is to address some basic security concerns in EGMP protocol which is a multicast protocol found to be more vulnerable towards attacks like blackhole, wormhole and flooding attacks. The proposed technique uses the concepts of certificate to prevent these attacks and to find the malicious node. These attacks are simulated using NS2.28 version and the proposed proactive technique is implemented. The following metrics like packet delivery ratio, control overhead, total overhead and End to End delay are used to prove that the proposed solution is secure and robust.
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Secure Hajj Permission Based on Identifiable Pilgrim’s Information
Статья научная
Event management of large international events is attracting interest from researchers, not least due to the potential use of technology to provide support throughout the different stages of the event. Some events, such as major sports or religious events, can involve millions of people from different countries, and require active management to control access (e.g. many popular events can be oversubscribed) and to reduce risks for the participants, local communities and environment. This paper explores the context of a large event - the Hajj pilgrims in Saudi Arabia - which involves up to three million pilgrims, many of whom are international. The paper presents a novel identification system - the Identification Wristband Hajj Permission (IWHP) - which uses encryption technologies and biometric attributes to identify pilgrims, whilst remaining sensitive to the context of the Hajj. The suggested solution has many attributes of relevance that could support its use in other large-crowd events.
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Secured and Optimized AODV for Wireless Sensor Networks
Статья научная
Similar to conventional wireless networks, WSNs are based on multi hop routing to ensure connectivity and data forwarding which makes the routing service a challenging task due to the nature of sensors usually limited in memory, battery and computing capacities as well as the nature of the environment which is hostile and unpredictable making the routing protocols developed for conventional wireless network useless for WSNs without modifications and adaptations for the new context of WSNs. Thus in this paper we present an optimized version of AODV protocol for WSNs which takes into consideration the traffic pattern of WSNs and sensors’ constraints. In the proposed protocol we affect the task of route discovery to the base station which periodically informs sensors about its location instead of letting this task to sensors which consumes the network resources due to the broadcasting nature of the route discovery. We have also proposed a key distribution scheme destined to establish a symmetric encrypting key between each sensor and the base station, the proposed key management scheme uses the underlying routing requests to execute handshakes and key update which have greatly saved the network resources and ensured a good threshold of security.
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Secured biometric identification: hybrid fusion of fingerprint and finger veins
Статья научная
The goal of this work is the improvement of the performance of a multimodal biometric identification system based on fingerprints and finger vein recognition. This system has to authenticate the person identity using features extracted from his fingerprints and finger veins by multimodal fusion. It is already proved that multimodal fusion improves the performance of biometric recognition, basically the fusion at feature level and score level. However, both of them showed some limits and in order to enhance the overall performance, a new fusion method has been proposed in this work; it consists on using both features and scores fusion at the same time. The main contribution of investigation this technique of fusion is the reduction of the template size after fusion without influencing the overall performance of recognition. Experiments were performed on a real multimodal database SDUMLA-HMT and obtained results showed that as expected multimodal fusion strategies achieved the best performances versus uni-modal ones, and the fusion at feature level was better than fusion at score level in recognition rate (100%, 95.54% respectively) but using more amounts of data for identification. The proposed hybrid fusion strategy has overcome this limit and clearly preserved the best performance (100% as recognition rate) and in the same time it has reduced the proportion of essential data necessary for identification.
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Статья научная
Industry heavyweights like Microsoft, Amazon, and Google are at the forefront of the development and provision of cutting-edge and affordable cloud computing solutions, contributing to the widespread recognition of cloud computing. Without requiring direct human control, this technology provides network services, including data storage and computational power. But security becomes apparent as a major issue, hindering widespread adoption. The present study performs an extensive investigation to investigate security concerns related to cloud computing at several infrastructure levels, including application, network, host, and data. It examines significant issues that could impact the business model for cloud computing and discuss ways to solve security issues at every level that have been documented in the literature. The study identifies open problems, especially when considering cloud capabilities like elasticity, flexibility, and multi-tenancy, which create new problems at every infrastructure tier. Notably, it is found that multi-tenancy has a significant influence, contributing to security issues at all levels including abuse, unavailability, data loss, and privacy violations. The research ends with practical recommendations for additional studies targeted at improving overall cloud computing security. The results highlight the necessity of concentrated effort on mitigating security vulnerabilities resulting from multi-tenancy. This study makes a valuable contribution to the wider discussion on cloud security by identifying particular issues and supporting focused initiatives to strengthen the resilience of cloud infrastructure.
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Статья научная
An increase in cyber threats directed at interconnected devices has resulted from the proliferation of the Internet of Things (IoT), which necessitates the implementation of comprehensive defenses against evolving attack vectors. This research investigates the utilization of machine learning (ML) prediction models to identify and defend against cyber-attacks targeting IoT networks. Central emphasis is placed on the thorough examination of the CIC-IoT2023 dataset, an extensive collection comprising a wide range of Distributed Denial of Service (DDoS) assaults on diverse IoT devices. This ensures the utilization of a practical and comprehensive benchmark for assessment. This study develops and compares four distinct machine learning models Logistic Regression (LR), K-Nearest Neighbors (KNN), Decision Tree (DT), and Random Forest (RF) to determine their effectiveness in detecting and preventing cyber threats to the Internet of Things (IoT). The comprehensive assessment incorporates a wide range of performance indicators, such as F1-score, accuracy, precision, and recall. Significantly, the results emphasize the superior performance of DT and RF, demonstrating exceptional accuracy rates of 0.9919 and 0.9916, correspondingly. The models demonstrate an outstanding capability to differentiate between benign and malicious packets, as supported by their high precision, recall, and F1 scores. The precision-recall curves and confusion matrices provide additional evidence that DT and RF are strong contenders in the field of IoT intrusion detection. Additionally, KNN demonstrates a noteworthy accuracy of 0.9380. On the other hand, LR demonstrates the least accuracy with a value of 0.8275, underscoring its inherent incapability to classify threats. In conjunction with the realistic and diverse characteristics of the CIC-IoT2023 dataset, the study's empirical assessments provide invaluable knowledge for determining the most effective machine learning algorithms and fortification strategies to protect IoT infrastructures. Furthermore, this study establishes ground-breaking suggestions for subsequent inquiries, urging the examination of unsupervised learning approaches and the incorporation of deep learning models to decipher complex patterns within IoT networks. These developments have the potential to strengthen cybersecurity protocols for Internet of Things (IoT) ecosystems, reduce the impact of emergent risks, and promote robust defense systems against ever-changing cyber challenges.
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Статья научная
The rise of the Internet accelerates the creation of various large-scale online social networks, which can be described the relationships and activities between human beings. The online social networks relationships in real world are too big to present with useful information to identify the criminal or cyber-attacks. This research proposed new information security analytic model for online social networks, which called Security Visualization Analytics (SVA) Model. SVA Model used the set of algorithms (1) Graph-based Structure algorithm to analyze the key factors of influencing nodes about density, centrality and the cohesive subgroup to identify the influencing nodes of anomaly and attack patterns (2) Supervised Learning with oneR classification algorithm was used to predict new links from such influencing nodes in online social networks on discovering surprising links in the existing ones of influencing nodes, which nodes in online social networks will be linked next from the attacked influencing nodes to monitor the risk. The results showed 42 influencing nodes of anomaly and attack patterns and can be predict 31 new links from such nodes were achieved by SVA Model with the accuracy of confidence level 95.0%. The new proposed model and results illustrated SVA Model was significance analysis. Such understanding can lead to efficient implementation of tools to links prediction in online social networks. They could be applied as a guide to further investigate of social networks behavior to improve the security model and notify the risk, computer viruses or cyber-attacks for online social networks in advance.
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Security in Fog Computing through Encryption
Статья научная
Cloud computing is considered as one of the most exciting technology because of its flexibility and scalability. The main problem that occurs in cloud is security. To overcome the problems or issues of security, a new technique called fog-computing is evolved. As there are security issues in fog even after getting the encrypted data from cloud, we implemented the process of encryption using AES algorithm to check how it works for the fog. So far, to our analysis AES algorithm is the most secured process of encryption for security. Three datasets of different types are considered and applied the analysed encryption technique over those datasets. On validation, entire data over datasets is being accurately encrypted and decrypted back as well. We took android mobile as an edge device and deployed the encryption over datasets into it. Further, performance of encryption is evaluated over selected datasets for accuracy if the entire data is correctly encrypted and decrypted along with the time, User load, Response time, Memory Utilization over file size. Further best and worst cases among the datasets are analysed thereby evaluating the suitability of AES in fog.
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Segmentation of Isolated and Touching Characters in Offline Handwritten Gurmukhi Script Recognition
Статья научная
Segmentation of a word into characters is one of the important challenges in optical character recognition. This is even more challenging when we segment characters in an offline handwritten document. Touching characters make this problem more complex. In this paper, we have applied water reservoir based technique for identification and segmentation of touching characters in handwritten Gurmukhi words. Touching characters are segmented based on reservoir base area points. We could achieve 93.51% accuracy for character segmentation with this method. If the characters are neither broken nor overlapping, then this technique shall produce even better results.
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Selecting Appropriate Requirements Management Tool for Developing Secure Enterprises Software
Статья научная
This paper discusses about the significance of selecting right requirements management tools. It’s no secret that poorly understood user requirements and uncontrolled scope creep to many software project failures. Many of the application development professionals buy wrong tools for the wrong reasons. To avoid purchasing the more complex and expensive tool, the organization needs to be realistic about the particular problem for which they opt. Software development organizations are improving the methods, they use to gather, analyze, trace, document, prioritize and manage their requirements. This paper considers four leading Requirements Management tools; Analyst Pro, CORE, Cradle and Caliber RM, the focus is to select the appropriate tool according to their capabilities and customers need.
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Selecting appropriate metrics for evaluation of recommender systems
Статья научная
The abundance of information on the web makes it difficult for users to find items that meet their information need effectively. To deal with this issue, a large number of recommender systems based on different recommender approaches were developed which have been used successfully in a wide variety of domains such as e-commerce, e-learning, e-resources, and e-government among others. Moreover, in order for a recommender system to generate good quality of recommendations, it is essential for a researcher to find the most suitable evaluation metric which best matches a given recommender algorithm and a recommender's task. However, with the availability of several recommender tasks, recommender algorithms, and evaluation metrics, it is often difficult for a researcher to find their best combination. This paper aims to discuss various evaluation metrics in order to help researchers to select the most appropriate metric which matches a given task and an algorithm so as to provide good quality of recommendations.
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Статья научная
The process of health insurance policy selection is a critical decision with far–reaching financial implications. The complexity of health insurance policy selection necessitates a structured approach to facilitate informed decision-making amidst numerous criteria and provider options. This study addresses the health insurance policy selection problem by employing a comprehensive methodology integrating Spherical Fuzzy Analytic Hierarchy Process (SF–AHP) and Combined Compromise Solution (CoCoSo) Algorithm. Eight experienced experts, four from academia and industry each, were engaged, and eleven critical factors were identified through literature review, survey, and expert opinions. SF–AHP was utilized to assign weights to these factors, with Claim settlement ratio (C9) deemed the most significant. Subsequently, CoCoSo Algorithm facilitated the ranking of insurance service providers, with alternative A6 emerging as the superior choice. The research undertakes sensitivity analysis, confirming the stability of the model across various scenarios. Notably, alternative A6 consistently demonstrates superior performance, reaffirming the reliability of the decision-making process. The study’s conclusion emphasizes the efficacy of the joint SF–AHP and CoCoSo approach in facilitating informed health insurance policy selection, considering multiple criteria and their interdependencies. Practical implications of the research extend to individuals, insurance companies, and policymakers. Individuals benefit from making more informed choices aligned with their healthcare needs and financial constraints. Insurance companies can tailor policies to customer preferences, enhancing competitiveness and customer satisfaction. Policymakers gain insights to inform regulatory decisions, promoting fair practices and consumer protection in the insurance market. This study underscores the significance of a structured approach in navigating the intricate health insurance landscape, offering practical insights for stakeholders and laying a foundation for future research advancements.
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