Semantic web for next generation of e-commerce

Web technology left a significant impact for business transaction.The role of buyers and vendors has been replaced by informative websites where the available information of products and services could improve supply chain and delivery cycles.As the market segment grows, the need of having organized and thoughtful web content is increasing.Search functions using keyword-based search are known for its inability for the machine to interpret different terminology with the same meaning.Information needs to be structured for parametric search to locate products with certain combination of traits.Ontology is the solution to structure semantic of product data.It allows computer to process content with meaning for human based consensual terminologies.Ontology provides a shared platform and common understanding of a domain that can be communicated between user and application systems.The purpose of this paper is to highlight the importance of exploiting ontology based e-commerce for Semantic Web. The ontology is mediator for software agents to communicate and exchange data.These agents can search products with certain traits, negotiate products or automatically configure product or services according to the required specifications.The semantic combination of product data elevate full potential of e-commerce and development of many specialized reasoning services bring full power of Semantic Web Based E-Commerce

A Comparative Performance Analysis of Hybrid and Classical Machine Learning Method in Predicting Diabetes

Diabetes mellitus is one of medical science’s most important research topics because of the disease’s severe consequences. High blood glucose levels characterize it. Early detection of diabetes is made possible by machine learning techniques with their intelligent capabilities to accurately predict diabetes and prevent its complications. Therefore, this study aims to find a machine learning approach that can more accurately predict diabetes. This study compares the performance of various classical machine learning models with the hybrid machine learning approach. The hybrid model includes the homogenous model, which comprises Random Forest, AdaBoost, XGBoost, Extra Trees, Gradient Booster, and the heterogeneous model that uses stacking ensemble methods. The stacking ensemble or stacked generalization approach is a meta-classifier in which multiple learners collaborate for prediction. The performance of the homogeneous hybrid models, Stacked Generalization and the classic machine learning methods such as Naive Bayes and Multilayer Perceptron, k-Nearest Neighbour, and support vector machine are compared. The experimental analysis using Pima Indians and the early-stage diabetes dataset demonstrates that the hybrid models achieve higher accuracy in diagnosing diabetes than the classical models. In the comparison of all the hybrid models, the heterogeneous model using the Stacked Generalization approach outperformed other models by achieving 83.9% and 98.5%. Doi: 10.28991/ESJ-2023-07-01-08 Full Text: PD

IT governance framework for e-government initiatives

As the penetration of electronic commerce (e-commerce) and electronic business (e-business) occurs in our daily lives, the overall stakeholders of economic growth, including private sector enterprises, governments and society as a whole are beginning to realize the true potential of information technology (IT) and the internet. While the private sector has always ensured that they keep in line with emerging trends, now governments around the globe are also aiming to ensure that all public sector products and services are offered online.Many citizens have a minimal understanding of how government processes are executed or how decisions are made.This lack of awareness and trust can prevent the citizens from actively participating in government services.Thus, E-government security and assurance has become a serious concern of the citizens and private companies who put more reliance on the distributed computing processes in their daily operations.In order to make citizens and private organizations to trust and involves in e-government services, IT Governance should be implemented. As the IT Governance Institute defined that IT governance is the responsibility of the board of directors and executive management. It is an integral part of enterprise governance and consists of the leadership and organisational structures and processes that ensure that the organisation’s IT sustains and extends the organisation’s strategies and objectives.This paper presents an IT governance framework for e-government and introduces an assessment tool designed to measure its effectiveness.The framework builds on the integration between the structural and processes perspectives of IT governance, public services-IT alignment, and senior government executives’ needs

Data location aware scheduling for virtual Hadoop cluster deployment on private cloud computing environment

With the advancements of Internet-of-Things (IoT) and Machine-to-Machine Communications (M2M), the ability to generate massive amount of streaming data from sensory devices in distributed environment is inevitable. A common practice nowadays is to process these data in a high-performance computing infrastructure, such as cloud. Cloud platform has the ability to deploy Hadoop ecosystem on virtual clusters. In cloud configuration with different geographical regions, virtual machines (VMs) that are part of virtual cluster are placed randomly. Prior to processing, data have to be transferred to the regional sites with VMs for data locality purposes. In this paper, a provisioning strategy with data-location aware deployment for virtual cluster will be proposed, as to localize and provision the cluster near to the storage. The proposed mechanism reduces the network distance between virtual cluster and storage, resulting in reduced job completion times

Smart Farm-Care using a Deep Learning Model on Mobile Phones

Deep learning and its models have provided exciting solutions in various image processing applications like image segmentation, classification, labeling, etc., which paved the way to apply these models in agriculture to identify diseases in agricultural plants. The most visible symptoms of the disease initially appear on the leaves. To identify diseases found in leaf images, an accurate classification system with less size and complexity is developed using smartphones. A labeled dataset consisting of 3171 apple leaf images belonging to 4 different classes of diseases, including the healthy ones, is used for classification. In this work, four variants of MobileNet models - pre-trained on the ImageNet database, are retrained to diagnose diseases. The model’s variants differ based on their depth and resolution multiplier. The results show that the proposed model with 0.5 depth and 224 resolution performs well - achieving an accuracy of 99.6%. Later, the K-means algorithm is used to extract additional features, which helps improve the accuracy to 99.7% and also measures the number of pixels forming diseased spots, which helps in severity prediction. Doi: 10.28991/ESJ-2023-07-02-013 Full Text: PD

The Eye: A Light Weight Mobile Application for Visually Challenged People Using Improved YOLOv5l Algorithm

The eye is an essential sensory organ that allows us to perceive our surroundings at a glance. Losing this sense can result in numerous challenges in daily life. However, society is designed for the majority, which can create even more difficulties for visually impaired individuals. Therefore, empowering them and promoting self-reliance are crucial. To address this need, we propose a new Android application called “The Eye” that utilizes Machine Learning (ML)-based object detection techniques to recognize objects in real-time using a smartphone camera or a camera attached to a stick. The article proposed an improved YOLOv5l algorithm to improve object detection in visual applications. YOLOv5l has a larger model size and captures more complex features and details, leading to enhanced object detection accuracy compared to smaller variants like YOLOv5s and YOLOv5m. The primary enhancement in the improved YOLOv5l algorithm is integrating L1 and L2 regularization techniques. These techniques prevent overfitting and improve generalization by adding a regularization term to the loss function during training. Our approach combines image processing and text-to-speech conversion modules to produce reliable results. The Android text-to-speech module is then used to convert the object recognition results into an audio output. According to the experimental results, the improved YOLOv5l has higher detection accuracy than the original YOLOv5 and can detect small, multiple, and overlapped targets with higher accuracy. This study contributes to the advancement of technology to help visually impaired individuals become more self-sufficient and confident. Doi: 10.28991/ESJ-2023-07-05-011 Full Text: PD

Learning domain semantics for knowledge management

Knowledge management enables organisat ion to gain strategic economic competit iveness by managing its intellectual assets. In recent years, Semantic Web technology has been used to discovers, capture, store, disseminate, share and reuse knowledge. Since most of organisat ional knowledge in the form of unstructured text documents, ontology learning methodologies are used to extract and model the concepts and its relationship, and present it as ontologies. However, most of ontology learning approaches used in knowledge management init iatives are centralised and developed by a small group of domain expert and knowledge engineers. The end users of domain ontology based application are neglected in building ontology. This isolated ontology development has hindered the mass adoption of semant ic technology in knowledge management communit ies. The complexit y in ontology learning procedures and techniques is another barrier that has hindered user participation. User involvement in ontology building is important as they are the originators and benefactors of domain knowledge. In order to allow mass adoption and participation in ontology building, users need to be empowered with simpler tools and techniques. In this research, a decentralised user based hybrid ontology learning framework is introduced which combine lexico-syntactic techniques and XML based techniques wit h the use of an integrated ontology development environment (IODE). The lexico-syntactic method utilises the subject-predicate-object pattern. The XML technique is used to provide a transit ion model to structure the extracted semant ics to support smoother ontologies translation. The use of IODE is to generate the ontologies using a predefined built in ontology that converts XML to OWL translation

The Impact of the Covid-19 Pandemic on Air Traffic in the Asian Region: How the Aviation Industry Adapted Throughout the Pandemic

The COVID-19 pandemic continues to pose a global threat, profoundly impacting various sectors, including the aviation industry. This research aims to uncover the impact of the pandemic on the aviation sector in the Asian region. Specifically, it explores the mandatory compliance of social measures and vaccination requirements imposed by governments, which have become crucial factors determining the feasibility of direct or international flights. The analysis focuses on several key aspects, including air traffic data, daily COVID-19 cases, vaccination rates, and implemented social measures within the Asian region. By examining the air traffic of each successful flight from departure to arrival, valuable insights into the aviation industry's perseverance throughout the pandemic are generated. Preliminary findings reveal a sharp decline in air traffic volume by -58.68% in 2020 compared to the previous year, attributed to the significant number of COVID-19 cases reported by December 31, 2020 (totaling 19,892,098 cases). However, the deployment of vaccination doses in 2021 has resulted in a modest recovery, with air traffic volume increasing by 12.64% compared to 2020. This recovery has prompted several countries to cautiously reopen borders and transition towards the recovery phases of the pandemic, despite the ongoing prevalence of COVID-19 cases. The research provides insights into the resilience and adaptability of the aviation industry, shedding light on the challenges faced and opportunities presented during the COVID-19 pandemic. By understanding the impact of social measures and vaccination requirements, stakeholders in the aviation sector can formulate strategies to navigate the current crisis and prepare for potential future disruptions. The findings contribute to the sustainable recovery of the aviation industry by informing policymakers, airlines, and other stakeholders about the factors influencing air travel in the Asian region. The analysis of air traffic, COVID-19 cases, vaccination rates, and social measures provides a comprehensive understanding of the aviation industry's response to the pandemic

Empowering education managers in schools via a multi agent system

With the recent advances in emerging technology, the study of intelligent agents has become one of the most important fields in education. The voluminous information available in the education management field has given rise to the exploration of agent technology to analyse data and make critical business decisions. In this paper, we propose a framework of an intelligent multi-agent based information retrieval for education management. This framework consists of two main parts – the multi-agent education management system and an ontology model. The proposed framework was implemented using the Jade Multi-Agent System and the ontology was designed using Protégé. In general, the proposed system helps school administrators to search for information precisely and rapidly. The system searches for relevant documents from various databases, parses and presents them in an XML format. This will free education administrators from relatively tedious tasks to focus more on decision-making processes.