With the growth of computer technology, 3D animation character modeling technology has become an important part in the field of animation production. However, the traditional modeling method needs a lot of time and ma...
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Harmful Algal Blooms (HAB), a significant environmental concern on a global scale, are damaging to ecosystem functions and pose challenges to environmental management. The key to HAB management is to classify the loca...
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Social media platforms, especially Twitter, have become a popular channel for customers to express their grievances related to products or services. Companies need to have an efficient grievance redressal system in pl...
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ISBN:
(纸本)9789819998104
Social media platforms, especially Twitter, have become a popular channel for customers to express their grievances related to products or services. Companies need to have an efficient grievance redressal system in place to address these complaints in real-time to ensure customer satisfaction and loyalty. This research paper presents the development of a cloud-based integrated real-time Twitter grievance redressal system using Amazon Web Services (AWS) and machine learning approach. The proposed system uses AWS cloud infrastructure to lever the huge volume of dataset created by tweets and machine learning algorithms to classify and prioritize them based on their severity. The system includes a web application that allows the grievance redressal team to view, categorize, and respond to the tweets efficiently. The efficiency of the planned system is evaluated via a case study. The results show that the system can effectively handle a huge volume of tweets and improve the grievance redressal process. The system’s response time is significantly reduced, and the team can prioritize the tweets based on their severity and importance, leading to better customer satisfaction. Data security is a critical aspect of the proposed real-time application as it will be handling sensitive data of the users. Therefore, security measures such as encryption, MFA, and disaster recovery must be properly implemented and configured, in order to ensure the security of data of the suggested grievance redressal system. The suggested system has achieved an accuracy of 89.5%. This research paper contributes to the development of efficient social media grievance redressal systems using cloud infrastructure and machine learning algorithms. The proposed system can be easily integrated into existing customer relationship management systems, making it a viable solution for companies of all sizes. The system’s ability to handle real-time data and provide quick responses can improve customer trust and
Stroke and related neurological disorders are difficult to classify accurately, hence new computational approaches are needed to enhance diagnosis. In order to transform the categorization of brain strokes, this study...
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This experiment focuses on employing machine learning techniques to create an automated system for classifying palm leaf manuscripts into three distinct categories based on their degradation levels: good, bad, and med...
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The proposed study predicts Caffeine inflorescence diseases employing strong ML, and forecasting technologies. This research increases prediction abilities, evaluates cutting-edge technologies, and studies unique trai...
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The paper presents a study on task offloading migration decisions. The aim is to minimize the total latency of the offloaded computational tasks. Actor-Critic algorithm was employed to solve the problem, and real worl...
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A complicated neurological illness, autism spectrum disorder (ASD) impacts social skills, communication, and other behavioral elements. Because the symptoms of ASD might be confused with those of other mental health c...
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Self-supervised heterogeneous graph learning (SHGL) has shown promising potential in diverse scenarios. However, while existing SHGL methods share a similar essential with clustering approaches, they encounter two sig...
In this paper, we introduce InternVL 1.5, an open-source multimodal large language model(MLLM) to bridge the capability gap between open-source and proprietary commercial models in multimodal understanding. We introdu...
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In this paper, we introduce InternVL 1.5, an open-source multimodal large language model(MLLM) to bridge the capability gap between open-source and proprietary commercial models in multimodal understanding. We introduce three simple improvements.(1) Strong vision encoder: we explored a continuous learning strategy for the large-scale vision foundation model — InternViT-6B, boosting its visual understanding capabilities, and making it can be transferred and reused in different LLMs.(2) Dynamic high-resolution: we divide images into tiles ranging from 1 to 40 of 448×448 pixels according to the aspect ratio and resolution of the input images, which supports up to 4K resolution input.(3) High-quality bilingual dataset: we carefully collected a high-quality bilingual dataset that covers common scenes, document images,and annotated them with English and Chinese question-answer pairs, significantly enhancing performance in optical character recognition(OCR) and Chinese-related tasks. We evaluate InternVL 1.5 through a series of benchmarks and comparative studies. Compared to both open-source and proprietary commercial models, InternVL 1.5 shows competitive performance, achieving state-of-the-art results in 8 of 18 multimodal benchmarks. Code and models are available at https://***/OpenGVLab/InternVL.
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