In the age of information overload, where the world is getting increasingly digital, traditional methods of learning are becoming tedious and extensively outdated. Imagine a system with just a few clicks that can quic...
In the age of information overload, where the world is getting increasingly digital, traditional methods of learning are becoming tedious and extensively outdated. Imagine a system with just a few clicks that can quickly generate perplexing questions and enlightening solutions from a given text. Likewise, this paper represents a groundbreaking system that uses stateof-the-art of natural language processing techniques to analyze subject-specific chapters to create questions and corresponding solutions of varying lengths. The system’s versatility as an ideal tool for a wide range of users, including students, researchers, and educators, is a result of its capability to handle a wide range of domains. By offering questions of insight along with appropriate answers, this aforementioned structure demonstrated its extraordinary accuracy and competency in our studies on an array of informational datasets. Altering the learning process and promoting knowledge discovery, this program is flexible in delivering brief or comprehensible solutions to inquiries, having the potential to completely change how individuals interact with written material, whether they are reading for short reference or conducting any depth research. Our suggested framework establishes a dynamic platform for immediate information that enables people to learn substantially more and comprehend any topic in depth. With this leading-edge method, bid goodbye to exhausting manual question generation and get ready to embrace a new era of seamless and fruitful learning with this cutting-edge system.
Multi-modal sequential recommendation (SR) leverages multi-modal data to learn more comprehensive item features and user preferences than traditional SR methods, which has become a critical topic in both academia and ...
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Fruit flies pose a significant threat to fruit yields, necessitating immediate detection solutions for effective pest management. In this study, we present our approach using YOLOv7 and the Jetson Nano 4GB for rapid a...
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According to initial data, individuals who have been diagnosed with type 2 diabetes (T2DM) appear to be at a more chances of evolving breast cancer compared to those who have not received a T2DM diagnosis. The primary...
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In recent years, remarkable progress has been made in remote sensing image-text retrieval (RSITR), which has transitioned from relying on compact global features to more fine-grained local features representing salien...
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ISBN:
(纸本)9798400718779
In recent years, remarkable progress has been made in remote sensing image-text retrieval (RSITR), which has transitioned from relying on compact global features to more fine-grained local features representing salient objects in images. However, existing methods typically concentrate only on significant entity information in remote sensing images as local features, overlooking the correlations between entities, resulting in isolated entity information. Moreover, there is ample room for exploring text local *** address these issues, this paper presents an Entity Spatial Relation enhancement Network (ESRN), leveraging global and local entity features in remote sensing images and texts. For local feature processing, Graph Convolutional Network (GCN) is employed to aggregate the correlation between entity features and entity information in remote sensing images, enhancing entity information learning. The self-attention mechanism is used to model the remote dependency of entity keywords and spatial orientation semantic relations in text, strengthening the text representation ability. A strategy of proportionally adding different levels of features is proposed to enhance the representation of salient features and reduce noise *** approach was evaluated on two renowned remote sensing datasets, RSICD and RSITMD, validating the model's capacity to perceive the semantics of remote sensing images and text entities. Performance comparison, ablation experiments, and visualization analysis convincingly demonstrate the state-of-the-art performance of the ESRN method in the RSITR task.
Reviews can significantly impact a company’s reputation in the market, potentially influencing its overall business outcomes, either positively or negatively. This is especially crucial for companies that operate pri...
Reviews can significantly impact a company’s reputation in the market, potentially influencing its overall business outcomes, either positively or negatively. This is especially crucial for companies that operate primarily through e-commerce platforms. Hence, it is vital for companies to pay close attention to customer reviews. Sentiment Analysis, often referred to as "opinion mining," is a significant procedure in Natural Language Processing (NLP) which serves the purpose of ascertaining the emotional tone of a provided text and categorizing it into positive, negative, or neutral perspectives. In this paper, sentiment analysis methodology is presented for classifying Amazon reviews which utilizes a large dataset of reviews and employs Multinomial Naïve Bayesian (MNB), Support Vector Machine (SVM), Maximum Entropy (ME), and Logistic Regression as the primary classifiers by the authors. With the aid of machine learning, we employed a supervised learning approach to an extensive Amazon dataset in order to categorize it based on sentiment polarity, achieving a high level of accuracy for the results. Here, we utilized the Kaggle dataset that includes a substantial volume of reviews and associated metadata which comprises customer reviews and ratings on Amazon products.
Cutaneous abnormalities, commonly known as skin lesions, form a broad spectrum of skin irregularities that necessitates proper identification and immediate treatment. A significant development in the utilization of ma...
Cutaneous abnormalities, commonly known as skin lesions, form a broad spectrum of skin irregularities that necessitates proper identification and immediate treatment. A significant development in the utilization of machine learning approaches for analyzing medical imagery has been observed recently - particularly its effectiveness in the automatic detection and categorization of skin lesions. This academic study discusses an extensive technique for recognizing and categorizing skin lesions using machine learning protocols. The key cornerstone is the HAM10000 dataset, which comprises 10,000 images portraying discolored skin conditions varying in types and patient demographics. Our analysis examines the efficiency of Decision Trees, Support Vector Machines (SVMs), Random Forests, and K-Nearest Neighbors (KNN) in detecting and classifying such tensions on the dermis. Stringent evaluation procedures involving Accuracy, Precision, Recall rate, along with F1-score have been employed to measure these algorithms’ efficacy alongside their possible influence within clinical settings. Overall model performance was strong, with Support Vector Machines (SVMs) acquiring the highest accuracy of 94.8%, while Decision Tree gave an accuracy of 94.3%, Random Forest coming to a close third with an accuracy of 94.1%, and finally K-Nearest Neighbors (KNN), which gave us an accuracy of 93.7%. The presented methodology contributes meaningfully to progress in dermatology by generating precise diagnostic instruments that are beneficial for both healthcare professionals as well as patients suffering from these anomalies. This inquiry underscores how machine learning could elevate health outcomes by improving early recognition processes and enabling personalized therapeutics directed at treating skin lesions effectively.
Automated records analysis strategies primarily based on artificial intelligence (AI) have become increasingly famous in recent years because of their capability to quickly and effectively method enormous quantities o...
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A computer programme that imitates and processes human interaction, either through the use of voice or text communication, is known as a chatbot. Its purpose is to be of assistance in the process of finding a solution...
A computer programme that imitates and processes human interaction, either through the use of voice or text communication, is known as a chatbot. Its purpose is to be of assistance in the process of finding a solution to a problem. The transformation brought on by advances in technology has had an effect on every industry. The chatbot provides assistance with a wide variety of tasks, including Reservations, Customer Service, and a great number of other services. The fast development of technologies relating to artificial intelligence and natural language processing has resulted in an increase in the use of chatbots in a variety of fields, most notably in the field of customer service. Customers could receive advice that is prompt, accurate, and personalised through the use of chatbots, which has the potential to completely transform customer service. Because it can automate customer service and reduce the amount of work that needs to be done by humans, it has gained a lot of popularity in the business world. Which can help businesses improve the experience they provide for their customers. The purpose of this research is to undertake a comparative review of customer service chatbots, with a particular emphasis on their efficiency, usability, and application across a variety of business sectors. The research will uncover best practises, difficulties, and potential for improvement by analysing a variety of chatbot solutions.
In this study, we conducted sentiment analysis on restaurant reviews from Bangladeshi food delivery apps using natural language processing techniques. Food delivery apps have become increasingly popular in Bangladesh,...
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In this study, we conducted sentiment analysis on restaurant reviews from Bangladeshi food delivery apps using natural language processing techniques. Food delivery apps have become increasingly popular in Bangladesh, and understanding the sentiment of customer reviews can provide valuable insights for restaurant owners and food delivery app companies. In this research, we have created a dataset named “Bangladeshi Restaurant Reviews” by gathering customer reviews of restau-rants available on Foodpanda and Hungrynaki, which are two popular food delivery apps in Bangladesh. We used Robustly Optimized BERT Pretraining Approach (RoBERTa), AFINN, and DistilBERT, a distilled version of Bidirectional Encoder Repre-sentations from Transformers (BERT) to perform the sentiment analysis. Overall, this research paper highlights the importance of sentiment analysis in the food delivery industry and demonstrates the effectiveness of different models in performing this task. It also provides insights for businesses looking to use sentiment analysis to improve their services and products. The accuracy of the models evaluated, RoBERTa, AFINN, and DistilBERT, were 74%, 73 %, and 77 % respectively.
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