The Arduino-controlled radar systems constituting the fundamental elements of this RADAR system, an ultrasonic sensor and servo motor are employed. The fundamental operation of the system is to detect objects within t...
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The paper Augmenting Content Retrieval Using NLP in AIML describes using Natural Language Processing (NLP) with Artificial Intelligence Markup Language (AIML) to enhance content retrieval. Among Markup languages, AIML...
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The Arduino-controlled radar systems constituting the fundamental elements of this RADAR system, an ultrasonic sensor and servo motor are employed. The fundamental operation of the system is to detect objects within t...
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ISBN:
(数字)9798350367720
ISBN:
(纸本)9798350367737
The Arduino-controlled radar systems constituting the fundamental elements of this RADAR system, an ultrasonic sensor and servo motor are employed. The fundamental operation of the system is to detect objects within the range specified. Attached to the servo motor is an ultrasonic sensor that rotates approximately 180 degrees and provides a graphical representation on the processing IDE software. In addition to providing a graphical representation, the Processing IDE also provides the object's angle, position, and distance. The administration of this system is facilitated by Arduino. The Arduino UNO board is adequate for both interface development and ultrasonic sensor and display device control. We learned about extant navigation and obstacle detection innovations as well as various systems that make efficient use of ultrasonic sensors through our research. This RADAR system is primarily utilized in the fields of object identification, mapping, surveillance, and monitoring, in addition to navigation and positioning. Additionally, these low-cost systems are appropriate for implementation indoors.
Low and middle-income countries (LMICs) grapple with various difficulties in their campaign to curb the tuberculosis (TB) outbreak, including persistent socioeconomic health inequities, a lack of medical professionals...
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ISBN:
(数字)9798331518523
ISBN:
(纸本)9798331518530
Low and middle-income countries (LMICs) grapple with various difficulties in their campaign to curb the tuberculosis (TB) outbreak, including persistent socioeconomic health inequities, a lack of medical professionals in the field, and inadequate healthcare systems in underdeveloped areas. The rate of tuberculosis-related deaths is rising, and the causes of treatment failure remain unclear. In the healthcare industry, predictive algorithms also known as machine learning and information analysis approaches have shown to be helpful in identifying correlations between various characteristics that may influence a disease's course. The procedure of diagnosing tuberculosis has accelerated due to the advancement of computer technology. Machine learning and data analytics approaches have proven useful in the healthcare sector for determining connections between different features that could impact the course of a disease. Our work proposes a Hybrid strategy to handle unbalanced, less-category X-ray images, utilizing deep Convolutional Neural Networks (CNNs) in conjunction with transfer learning. We examine the efficacy and efficiency of random sampling method for training neural networks and discover that it has a remarkable influence on the classification of medical images. Our techniques and findings point to a possible route for quicker and more precise TB diagnosis in LMIC medical facilities.
The paper Augmenting Content Retrieval Using NLP in AIML describes using Natural Language Processing (NLP) with Artificial Intelligence Markup Language (AIML) to enhance content retrieval. Among Markup languages, AIML...
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ISBN:
(数字)9798350364729
ISBN:
(纸本)9798350364736
The paper Augmenting Content Retrieval Using NLP in AIML describes using Natural Language Processing (NLP) with Artificial Intelligence Markup Language (AIML) to enhance content retrieval. Among Markup languages, AIML is most commonly used in chatbots or conversational UI. On the other hand, NLP is a subfield of artificial intelligence that handles reading and understanding human language. Their abstract underscores the importance of improved content extraction for AIML systems that rely on a keyword-based approach, which wouldn't work very well as it fails to capture all elements in human language. Here, wide-scale use of the NLP techniques provides a breakthrough in the analysis. It allows understanding the meaning inherent in natural language and interpreting semanticity through context-sensitive, syntactic rule-based arrangement. The paper proposes a mix of NLP and AIML for better retrieval. Approach: In this technique, we first preprocess the user input using NLP techniques, i.e., POS tagging & Named Entity Recognition, to extract context-specific Keywords/Concepts. Then, these keywords and concepts are mapped with answers in the AIML-based system. The abstract also presents the experimental evaluation of the proposed strategy, which significantly outperforms conventional keyword-based retrieval for content. This proves that applying NLP in AIML systems can even increase the capability to understand and recover user inputs.
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