VFAST - Virtual Foundation for Advancement of Science and Technology (Pakistan)
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The Comparison of Routing Algorithm for SDN Network using AI for Future Network
No full textAfter the invention of new IoT devices the new connection has been added in the network and bulk of data flow in the system. AI is very famous algorithum and software-defined networking (SDN) is new fast network controlling method both can be integrate for fast and secure network-related scenarios and finding great experience and growing interest in the research community. The present network connected to many number of objects over the Internet and present a complex scenario. To ensure security, privacy, confidentiality, and programmability, the architecture of AI and blockchain for SDN network is proposed in this paper. For network service provider this is ample time to apply artificial intelligence (AI) and blockchain (BC) in a software-defined network controller to ensure a secure network control, traffic management and system optimimum performnce. Also for shortest path to reduce delay in the network, the best routing algorithum must be apply in the network. The AI (artificial intelligence) algorithum can help SDN controller to fastdecision to reduced delay and can provide security in the network. In this paper routing algorithm also will be investigate faster Network
Transition Strategies from Monolithic to Microservices Architectures: A Domain-Driven Approach and Case Study
Get PDFThis paper focuses on the systematic mapping of monolithic applicationsto microservices architecture, a popular alternative in the software development industry.The research examines the advantages of microservices and the challenges organizationsencounter during the transition from monolithic systems. A case study of a financialapplication is presented, along with proposed techniques for identifying microserviceswithin monolithic systems using domain-driven development concepts.The study highlights the difficulties and benefits of migrating from monolithic to microservicesarchitecture, offering valuable insights for software architects and developers. Practicalimplications include a technique for identifying microservices on monolithic systemsusing domain-driven concepts and various communication protocols for service interaction.The findings suggest that this proposed technique can enhance work performanceand establish clear models, particularly for complex systems. However, it may have limitedeffectiveness in less complex systems. The paper contributes to the field of softwaredevelopment by providing practical solutions for companies considering a shift tomicroservices architecture and comparing the two architectural styles
DeepCervixNet: An Advanced Deep Learning Approach for Cervical Cancer Classification in Pap Smear Images
Get PDFCervical cancer is among the leading causes of female mortality, emphasizing the significance of early detection and treatment to prevent its spread. While Pap smear images are widely used for cervical cancer screening, the manual diagnostic method is time-consuming and prone to error. The research article introduces DeepCervixNet, an innovative automated computerized approach designed for detecting cervical cancer in Pap smear images. In this study, we enhance ResNet101 and DenseNet169, state-of-the-art Convolutional Neural Network (CNN) architectures, by integrating the sequence and excitation (SE) blocks. Subsequently, Ensemble learning is employed to utilize the extracted features and classify the final output. The Harlev dataset was employed to test our model, with Gaussian smoothing and median filtering applied for image enhancement. This resulted in an overall improvement in the performance of the model. DeepCervixNet had an accuracy of 99.89\% in cervical cells. The study\u27s findings validate our model\u27s robustness and efficacy, proving its superiority over a majority of current state-of-the-art models used to classify cervical cells, including standard ResNet and DenseNet architectures without SE blocks
Analyzing Digital Cultural Markers in Pakistan’s Web Environment
Get PDFIn the evolving landscape of web development, the incorporation of cultural markers has emerged as a pivotal strategy to enhance user experience (UX) and foster meaningful engagement. Cultural markers in web development encompass various elements such as language localization, visual aesthetics, content relevance, and more. Developers employ different types of digital cultural markers to facilitate more intuitive and engaging browsing experiences and online diversity. This paper explores the importance of digital cultural markers in web design, emphasizing their impact on user perception and overall effectiveness. To analyze the effectiveness of cultural markers, usability tests were conducted across four categories: Education, E-commerce, Government, and Media-News. We selected four websites from each category representing different cultural backgrounds of Pakistan, emphasizing their impact on user perception, inclusively, and overall effectiveness
Optimisation of Sentiment Analysis for E-Commerce
Get PDFSentiment analysis is widely used today to make data-driven decisions in different industries, starting from marketing and including brand management, reputation monitoring, and customer satisfaction analysis. Its growing importance is closely linked with so-called ‘word-of-mouth’ communication, from reading online reviews to writing comments on social networks. Effective separation of sentiments ensures that companies\u27 responses are timely and critical patterns are seen in big data sets. Statistical measures, information gain, correlation-based approaches, etc, have been employed for the feature selection. Still, the problem associated with text data mining is that they don’t convey the text\u27s relative difficulty and additional features. To fill this gap, our research proposes a new feature selection technique through Ant Colony Optimization (ACO) and K Nearest Neighbour (KNN) performed on 28,000 customer reviews in different product categories. The results, therefore, showed an overall accuracy of 80.1%, with the Support Vector Machine (SVM) set at 80.5% on each selected feature, which was slightly higher than the Convolutional Neural Network (CNN), which scored a 78.41% accuracy. SVM remains on the mark of 83%, and for CNN, the rate achieved on the same was 80.8% when both were applied to the entire dataset. These facts rejected the infallibility of the simple and complex algorithms used singly in the sentiment classification, indicating that more sophisticated algorithms like ACO and KNN can provide business solutions to improve their service delivery based on customers’ feedback
BERT Model Adoption for Sarcasm Detection on Twitter Data
Get PDFSarcasm is a term used to criticize someone\u27s feelings. Sometimes, humans are not able to identify sarcastic comments, and they typically express the reverse of what they mean when they make snarky remarks. Therefore, the detection of sarcasm within a text automatically is a difficult task. Its significance in enhancing sentiment analysis has also made it an important study field. In previous studies, different approaches to deep learning (DL) and machine learning (ML) have been explored. However, previous approaches mainly depend on the lexical and linguistic aspects. Therefore, these techniques could not perform well in the context of sentiment accuracy. In this research, an efficient approach for detecting sarcasm is proposed. A Bidirectional Encoder Representation from a Transformer (BERT) is proposed to improve the sentiment accuracy in this research. This research also aims to compare the two models of deep learning, the BERT and LSTM (Long Short-Term Memory) models. This comparative analysis aims to provide a detailed overview of the pros and cons of each approach for the detection of sarcasm. The primary aim of this study is to examine the different existing ML and DL approaches for the identification of sarcasm. Apart from this, the comparison of BERT and LSTM contributes to the ongoing debate about whether models work best for sarcasm detection in social media. In this study, sentiment analysis\u27s accuracy is improved by making better decisions, especially when it concerns Twitter interactions
Enhancing User Experience: Exploring Mobile Augmented Reality Experiences
Get PDFIn recent years, the integration of augmented reality (AR) technology into mobile devices has revolutionized both human-computer interaction and computer graphics. This transformative blend of virtual elements into the physical world has led to a new era of possibilities for user experiences. However, the unique challenges posed by Mobile Augmented Reality (MAR) applications demand a closer examination of design and usability considerations to ensure optimal user engagement and satisfaction. This study investigates users\u27 experiences with Mobile Augmented Reality applications, with particular emphasis on design and usability-related concerns. Using a controlled experiment with twenty different people, this study uses thematic analysis to investigate UX improvement options. The aim is to furnish practical design principles that consider the individual characteristics and preferences of users, thereby contributing to the development of empirical insights that enhance Human-Computer Interaction (HCI) standards and best practices. Moreover, the results highlight the importance of user-centered design and assessment approaches. This work fills important gaps in the literature on UX studies of MAR applications and advances our knowledge of creating, engaging and easy-to-use augmented reality experiences
Identifying Key Learning Algorithm Parameter of Forward Feature Selection to Integrate with Ensemble Learning for Customer Churn Prediction
Get PDFThe Telecommunication has been facing fierce growth of customer data and competition in the market for a couple of decades. Due to this situation, an analytical strategy of proactive anticipation about customer churn and their profitable retention is inevitable for Telecommunication companies. To nip this problem in the bud, a lot of research work has been conducted in the past, but still the previously introduced churn prediction models possess their own limitations, such as high dimensional data with poor information and class imbalance, which turn into barriers while being implicated in real life to attain accurate and improved predictions. This study has been conducted, basically, to identify the key Learning Algorithm parameter of Forward Feature Selection (FFS) for dimensionality reduction which can be further integrated with class Imbalance Handling Technique and Ensemble Learning (EL) to attain improved accuracy. The core objective of this study is to turn an imbalanced dataset into a balanced one for Ensemble Learning (EL) Model of Customer Churn Prediction (CCP). This study concluded that Logistic Regression (LR) based Forward Feature Selection (FFS) can outperform with Oversampling Class Imbalance Handling Techniques and Ensemble Learning (EL) by scoring 0.96% accuracy, which is the highest accuracy against benchmark studies. The resulting methodology has been named as the Logistic Regression Learning based Forward Feature Selection for ensemble Learning (LRLFFSEL) and applied over Orange dataset with 20 features and 3333 instances. In future this methodology can be evaluated over a bigger dataset and combined with some data optimization techniques to improve its accuracy
Effectiveness of Game-Based Interactive Approach Using Deep Learning Framework for Dyslogia."
Get PDFTraditional speech therapy approaches have long been considered revolutionary for treating speech disorders. However, as the younger generation becomes increasingly disengaged from these methods, their effectiveness is diminishing. This study identifies the need to revitalize traditional practices by integrating them into virtual environments and incorporating gamification elements. The motivation behind this work is to enhance engagement and improve therapy outcomes by making the process more appealing to children. Our proposed solution involves converting conventional speech therapy exercises into interactive virtual modules that incorporate game-like features to sustain interest and foster a competitive spirit. The method includes developing these virtual modules and testing their effectiveness through user trials. Results indicate a significant increase in engagement and a corresponding improvement in therapy outcomes, suggesting that this approach holds promise for enhancing the effectiveness of speech therapy in the digital age
Early Detection and Prediction of Pests in Field Crops Using Transfer Learning
Get PDFThis research study addresses the problem of early detection and prediction of pests in field crops. The primary objective of this research is to identify and distinguish pest species from an open-source dataset that includes 5,494 images across 12 classes. We developed an efficient model with a high probability of detecting pests in field crops using pre-trained models such as EfficientNetV2 and deep learning techniques. We applied hyperparameter tuning to the model to enhance its accuracy. Our proposed model is designed to detect and predict pests at an early stage, thereby preventing crop damage. Experimental results demonstrate that the performance of the proposed model is more accurate and precise compared to state-of-the-art existing studies. The F1 scores of the model for different classes of pest images are as follows: Ants 0.96, Bees 0.98, Beetles 0.97, Caterpillars 0.98, Earthworms 0.95, Earwigs 0.97, Grasshoppers 0.96, Moths 0.96, Slugs 0.97, Snails 0.99, Wasps 0.99, and Weevils 0.98. The overall accuracy of the model across all classes is 97.17. These results demonstrate the improved performance of the proposed model for early pest detection. In the agricultural sector, this model can be immensely beneficial, aiding in quick, accurate, and reliable pest detection to support decision-making processes. Identification of pest occurrence at their early stages leads to actions on interventions, which helps in reducing crop losses avoids unnecessary spraying for chemicals, and ensures sustainable eco-friendly agricultural practices. An approach like this would help in maintaining food security and economic sustainability of farmer communities.