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| Title | Abstract | Action(s) |
|---|---|---|
| EXPLOITING SEMANTIC KNOWLEDGE FOR IMAGE CAPTIONING USING DEEP LEARNING | The technique of generating textual explanations for images is commonly referred to as image captioning. It has attracted a lot of attention recently because it may be used in a variety of fields. There are some challenges in image captioning, one of them is the lack of incorporating semantic knowledge in generating image captions. Semantic knowledge can be helpful in object detection by exploiting relationships among objects and in language semantics. In this study, the issue of image captioning is investigated by combining two efficient models, the vision transformer (ViT) and the generative pre-trained transformer 2 (GPT-2). The ViT uses self-attention techniques that are applied to image patches to capture visual elements and overall context from images. The GPT-2 model complements ViT with extraordinary language production abilities that enable it to produce content that is cohesive and related to the situation. An encoder-decoder-based deep learning model is proposed where the ViT performs the encoder function, extracting meaningful visual representations from images, while the GPT-2 model performs the decoder function, producing descriptive captions based on the retrieved visual features. This method makes it possible to seamlessly combine textual and visual information, producing captions that faithfully reflect the content of the input images. The potential of this combination is demonstrated through empirical analyses, highlighting the advantages of utilizing both language and visual components in the ‘image captioning’ process. My research strengthens multimodal AI systems by bridging the gap between visual and language comprehension. The experiments were performed on the MS COCO dataset and Flicker 30k dataset. The model was validated using various evaluation metrics. Results show an improvement as Bleu-1, Bleu-2, Bleu-3, Bleu-4, Rogue, and Meteor by 10.58, 20.45, 21.07, 34.19, 0.3, and 11.16 respectively. The other evaluation metrics like Meteor improved by 11.16 and the Rogue metric improved by 0.3 on the MS COCO dataset. |
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| Analysis of Scrum based Software Development to Improve Risk Management in Pakistani Software Industry | Software evolves continuously to accommodate market volatility, posing danger to the project. Agile approaches have been suggested to handle these continuous changes in software requirements. Although, where there is a considerable amount of academic literature on the process of projects, a very negligible amount of research considered proper process for risk management in scrum projects in Pakistani Software Industry. The process of risk management involves seven processes such as planning, identification, qualitative analysis, quantitative analysis, risk response planning, risk response implementation, and monitoring. While adopting agile, many risks arise so proper mitigation strategies should be established by incorporating all risk management processes to overcome these risks. Existing literature lacks the implementation of proper processes for risk management that could lead the software toward failure. The major reason of failure of software projects is limited application of proper risk management. Agile methods like scrum do not propose particular activities for risk management. Due to this practitioner are not completely aware of these uncertain events. Keeping in mind this weakness, this study tried to provide mitigation strategies for a proper risk management process based on the scrum method. For that purpose, systematic literature review was conducted for identifying the challenges that can arise in agile software development. The practicality of these challenges was found by conducting survey in different software development companies. Based on these challenges mitigation strategies were proposed by conducting interviews from industry practitioners for mitigating these challenges. To validate these proposed mitigation strategies, a focus group methodology is applied. The mitigation strategies provide recommendations to mitigate the identified risk management challenges in scrum development. The proposed mitigation strategies will be helpful in reducing risks as well as in facilitating teams to handle them more easily in agile projects that use the scrum methodology and to enhance scrum project success rate. |
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| Sentiment Analysis of Toxic Comment on Social Media using Deep Learning | In the rapidly evolving field of natural language processing, accurately predicting sentiments in text remains a critical challenge. This thesis addresses the problem by developing a novel multi-head model combining transformer-based architectures, DistilBERT and RoBERTa, with Bi-LSTM layers. Leveraging their complementary strengths, the model captures both global context and sequential dependencies in textual data. The research methodology involves extensive data preprocessing, model training, and evaluation using accuracy and F1-scores. Results demonstrate that the multi-head model outperforms traditional approaches, achieving a notable accuracy of 90.02%. This advancement offers significant benefits, including improved sentiment-driven decision-making and valuable insights across various industries, such as social media monitoring, customer feedback analysis, and market research. |
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