Innovative technology solutions have been developed in response to the growing need for effective and customized client contact on e-commerce platforms. This work introduces an intelligent chatbot system that uses mac...
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ISBN:
(数字)9798331541217
ISBN:
(纸本)9798331541224
Innovative technology solutions have been developed in response to the growing need for effective and customized client contact on e-commerce platforms. This work introduces an intelligent chatbot system that uses machine learning and text mining techniques to improve user satisfaction and answer customer concerns. Advanced Natural Language Processing (NLP) models have been trained on large datasets, allowing for precise user intent interpretation and pertinent product recommendations. A strong recommendation engine is included into the system architecture to aid in decision-making, ultimately enhancing the customer experience by providing individualized, real-time help. The study also highlights the significance of data privacy by using privacy-preserving techniques to safeguard user data. The system’s scalability and performance benefits are highlighted through thorough comparisons with current models. By showcasing the useful uses of machine learning and text mining in enhancing customer contact and service delivery, this study makes a substantial contribution to the development of AI-driven solutions in e-commerce.
WiFi Channel State Information (CSI)-based gesture recognition offers unique advantages, including cost-effectiveness and enhanced privacy protection, and has garnered significant attention in recent years. However, e...
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The use of information technology by companies and individuals in cloud computing revolutionizes. Cloud computing offers virtualized tools over the Internet for dynamic services. This ensures that end-users with resou...
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Pancreatic cancer poses a significant challenge in early detection and treatment due to its malignant nature within the digestive tract. Recent studies, such as those conducted by the Pancreatic Cancer Action Network,...
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Recent advances in Large Language Models (LLMs) have been changing the paradigm of Recommender Systems (RS). However, when items in the recommendation scenarios contain rich textual information, such as product descri...
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Recently, the coronavirus disease of 2019 (COVID-19), as named by the World Health Organization (WHO), has spread to over 200 countries. The WHO has declared this disease as a worldwide public health emergency. One of...
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Recently, the coronavirus disease of 2019 (COVID-19), as named by the World Health Organization (WHO), has spread to over 200 countries. The WHO has declared this disease as a worldwide public health emergency. One of the most difficult tasks in combating this epidemic is to identify and segregate the afflicted people. The reverse transcription-polymerase chain reaction test (RT-PCR) is the most common pathology test used to diagnose this infection. Studies show that the RT-PCR test has a low-positive rate and sometimes becomes ineffective in diagnosing infection. In some cases, computed tomography (CT) scans reveal acute pneumonia and pulmonary anomalies. Therefore, CT scans are used together with RT-PCR tests to confirm infected people. Existing artificial intelligence and machine learning techniques require a large number of CT scans for training, which is a time-consuming process. Visual inspection shows that most CT scans of COVID-19 cases have broken, blurred, and ambiguous edges for infectious areas. Another major issue with these images is the heterogeneous intensity of the pixels, high noise, and low resolution. As a result of all these issues, the problem of effective edges/boundaries of various areas of CT scans of COVID-19 cases cannot be resolved by the current edge detection approach. Indeed, improper selection of edges can lead to an incorrect diagnosis of diseases through CT scans of COVID-19 cases. Therefore, there is an urgent need for a diagnostic method in addition to the RT-PCR test that can extract useful information from the minimum number of chest CT scans of suspected COVID-19 cases. This study introduces a new ambiguous edge detection method (AEDM) for identifying the edges/boundaries of different regions in CT scans of COVID-19 cases. The proposed AEDM is developed on the basis of ambiguous set (AS) theory, which is highly efficient in processing ambiguous pixel information. For simulation purposes, various CT scans of COVID-19 cases are c
The contagious Corona Virus (COVID-19) transmission can be reduced by following and maintaining physical distancing (also known as COVID-19 social distance). The World Health Organisation (WHO) recommends preventing C...
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Breast and cervical cancers account for more than 85 percent of all cancer-related fatalities in developing nations, according to the World Cancer Research Fund. As a result, breast and cervical cancer have become one...
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Breast and cervical cancers account for more than 85 percent of all cancer-related fatalities in developing nations, according to the World Cancer Research Fund. As a result, breast and cervical cancer have become one of the leading causes of mortality among women worldwide. This field is still in its infancy, with only a few studies in gynaecology and computerscience looking into the detection of breast and cervical cancer. According to the researchers, medical records and early testing from individuals with breast and cervical cancer will be used in this study to determine the prognosis of those suffering from the diseases. To assess our cervical cancer predictions, we employed machine learning models such as Optimized Hybrid Ensemble Classifier (OHEC), which were trained on patient behavior and variables revealed to be associated with patient behavior. The datasets in this study have a substantial number of missing values, and the distribution of those values has been altered as a function of the missing values. OHEC classifier performance has been shown to improve when the number of features is reduced and the problem of high-class imbalance is resolved, because the accuracy, sensitivity, and specificity of the classifier, as well as the number of false positives, were used to demonstrate the success of feature selection in the suggested model's predictive analysis. This has been demonstrated through the use of numerous tests involving categorization challenges. The study underscores the critical significance of early detection and prognosis in combating breast and cervical cancers, which remain leading causes of mortality worldwide. Through the utilization of machine learning models like the OHEC, the authors have demonstrated the potential for improved predictive accuracy and clinical outcomes. The findings highlight the importance of addressing challenges such as missing data and class imbalance in enhancing the performance of predictive models for effective
Deep neural networks, particularly in vision tasks, are notably susceptible to adversarial perturbations. To overcome this challenge, developing a robust classifier is crucial. In light of the recent advancements in t...
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Generative Large Language Models (LLMs) stand as a revolutionary advancement in the modern era of artificial intelligence (AI). However, scaling down LLMs for resource-constrained hardware, such as Internet-of-Things ...
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