The Wonders of RAG: Streamlining Knowledge with Advanced Techniques, 2024
The RAG addresses the limitations of standard Large Language Models (LLMs) by incorporating exter... more The RAG addresses the limitations of standard Large Language Models (LLMs) by incorporating external data through Information Retrieval, thereby enhancing their generation ability. As a recent advancement, RAG improves the selection of knowledge sources for response generation in dialogues. Although LLMs generate answers to questions, these answers may sometimes be of suboptimal quality and contain inaccuracies. The RAG framework includes a fine-tuning process that refines models using feedback and examples based on relevance. This process further enhances Open Domain Question Answering by incorporating external data through Information Retrieval. The RAG end2end extension dynamically updates external data during the training of both the retriever and generator, as well as during the training of Dense Passage Retrieval (DPR) models with QA pairs. This process eliminates the need for large continuous improvements in prediction. RAG goes beyond merely creating a smarter ChatGPT; it enables conversations by integrating external sources, adding personalized external sources, and implementing metrics to evaluate these sources, thereby generating beneficial sources. The framework also employs metrics to evaluate answers, refines them through dialogue and feedback, and reduces hallucinations by augmenting with up-to-date knowledge. In summary, these instances highlight the power of RAG and its potential applications for optimizing language models. However, RAG has some limitations. The quality of the generated responses may be impacted by the quality of the incorporated external data. If the data is inaccurate or biased, this could negatively affect the responses. Furthermore, hallucinations remain a challenge because inaccuracies can arise if the input does not contain sufficient information or metrics for evaluation. Future work should focus on enhancing data integration, educating the prompt query, developing real-time correction mechanisms, and adapting RAG for specific domains.
Comprehensive Review Sarcasm detection using hybrid approach , 2024
On social media platforms, it is necessary for users to post, publish, catalog, and comment for o... more On social media platforms, it is necessary for users to post, publish, catalog, and comment for organizations to understand customers' feedback on their services. One commonly used linguistic tool is sarcasm. This survey endeavors to discourage the sole use of grammatical features in sarcasm detection as opposed to contextual features, where the text has overt and hidden meanings in that the polarity of sentiment notifies the reader that the text is sarcastic. This paper represents approaches of a hybrid system that combines Lexicon-based preprocessing techniques (tokenization, stop words production, conversion of uppercase to lowercase, stemming, lemmatization, and conciseness) with neural network methods: machine learning supervised layer to produce the outputs, the semi-supervised machine learning method the middle layer which has the inputs and their labels or the generated output. This middle layer is the bag of words. After significant training by unsupervised machine learning, a positive or negative outcome is generated. Through weighting the outcome, the machine will reduce the amount of data in each post and determine which posts/comments are sarcastic. The system's architecture of weight classifiers is an ensemble of rules based on Gaussian Naive Bayes, BERT, Mar BERT and Mazajak text blob, Sent WordNet and Vader libraries. Furthermore, some steps of enhanced system are labels and features of each input by applying machine learning classifiers and use Bow to obtain the high performance and accuracy. And the effective created system should take the inference there is relation between positive sentiment, and trend will sale. When there is sarcasm, a decision will be made to stop or enhance the product.
Survey of Data Mining in Elearning Managment System of Course Selecting, 2024
This paper presents Data mining methods extracted from previous related papers, that would be app... more This paper presents Data mining methods extracted from previous related papers, that would be applied by Education University to predict courses on student selecting, in summary, these researches create a system that is an integration of a group of algorithms that are implemented sequentially until the system finds the necessary courses for the student. The created models utilizes k-means cluster unsupervised machine learning method, that takes the entry and groups them by taking their mean and produces a vector of selected courses. Further, to predict new results, the model can use this vector as an input and utilize the classification Apriori rule Algorithm to calculate the minimum support of predicted items then eliminate it, then repeat the calculation and the elimination till two items have same minimum support, next take the result and a rule of confidence to eliminate other courses, after that repeat the calculation and the elimination till reach number required of courses. Sometimes after the label is predicted by using aprori algorithm, the recommended system uses the supervised method weka and enhances the results. On the other hand, the open source data mining tool Weka is used for transforming datasets and even for analyzing the result and its performance. The selected courses are typically optimal but in some cases there is huge amount of data (the solution). To resolve this, we should use parallel k-means combined with Approri algorithm.
International Journal of Engineering and Computer Science, 2019
Image processing contributes on many of the technological advancements today. It is widely used b... more Image processing contributes on many of the technological advancements today. It is widely used by the developing institutes as well as a source of important data. One of the main things that are considered through this process would be the length of time on dealing with the application of different routines, on these images. Thus, time, being a valuable criterion for the efficiency of the systems, demands a better way for how these images will be managed. With the given situation, the idea of integrating a number of computers to perform image manipulation is considered. This uses the idea of parallel computing.
Abstract:
Today, several remote sensing sensor technologies are utilized to the purpose visualizi... more Abstract: Today, several remote sensing sensor technologies are utilized to the purpose visualizing remotely sensed data, with special developed software sets. Hence, this essay provides Remote sensing techniques that expose range of applications to everything can be imagined, in the space, as soil investigations, human detection, weather, mineral discover, maladies symptom and whatever you imagine. This analysis uses aerial, wireless, satellite, and microwaves.
International Journal Of Engineering And Computer Science
There are many divisions of philosophy named the philosophy of a domain where domains going from ... more There are many divisions of philosophy named the philosophy of a domain where domains going from history to physics. This essay proposes some issues that might constitute the philosophy of computer science as architecture of computer, divide the science into hardware and software, programming languages, operating systems, network architecture, security, privacy, and of protect the machine by anti-virus
The Wonders of RAG: Streamlining Knowledge with Advanced Techniques, 2024
The RAG addresses the limitations of standard Large Language Models (LLMs) by incorporating exter... more The RAG addresses the limitations of standard Large Language Models (LLMs) by incorporating external data through Information Retrieval, thereby enhancing their generation ability. As a recent advancement, RAG improves the selection of knowledge sources for response generation in dialogues. Although LLMs generate answers to questions, these answers may sometimes be of suboptimal quality and contain inaccuracies. The RAG framework includes a fine-tuning process that refines models using feedback and examples based on relevance. This process further enhances Open Domain Question Answering by incorporating external data through Information Retrieval. The RAG end2end extension dynamically updates external data during the training of both the retriever and generator, as well as during the training of Dense Passage Retrieval (DPR) models with QA pairs. This process eliminates the need for large continuous improvements in prediction. RAG goes beyond merely creating a smarter ChatGPT; it enables conversations by integrating external sources, adding personalized external sources, and implementing metrics to evaluate these sources, thereby generating beneficial sources. The framework also employs metrics to evaluate answers, refines them through dialogue and feedback, and reduces hallucinations by augmenting with up-to-date knowledge. In summary, these instances highlight the power of RAG and its potential applications for optimizing language models. However, RAG has some limitations. The quality of the generated responses may be impacted by the quality of the incorporated external data. If the data is inaccurate or biased, this could negatively affect the responses. Furthermore, hallucinations remain a challenge because inaccuracies can arise if the input does not contain sufficient information or metrics for evaluation. Future work should focus on enhancing data integration, educating the prompt query, developing real-time correction mechanisms, and adapting RAG for specific domains.
Comprehensive Review Sarcasm detection using hybrid approach , 2024
On social media platforms, it is necessary for users to post, publish, catalog, and comment for o... more On social media platforms, it is necessary for users to post, publish, catalog, and comment for organizations to understand customers' feedback on their services. One commonly used linguistic tool is sarcasm. This survey endeavors to discourage the sole use of grammatical features in sarcasm detection as opposed to contextual features, where the text has overt and hidden meanings in that the polarity of sentiment notifies the reader that the text is sarcastic. This paper represents approaches of a hybrid system that combines Lexicon-based preprocessing techniques (tokenization, stop words production, conversion of uppercase to lowercase, stemming, lemmatization, and conciseness) with neural network methods: machine learning supervised layer to produce the outputs, the semi-supervised machine learning method the middle layer which has the inputs and their labels or the generated output. This middle layer is the bag of words. After significant training by unsupervised machine learning, a positive or negative outcome is generated. Through weighting the outcome, the machine will reduce the amount of data in each post and determine which posts/comments are sarcastic. The system's architecture of weight classifiers is an ensemble of rules based on Gaussian Naive Bayes, BERT, Mar BERT and Mazajak text blob, Sent WordNet and Vader libraries. Furthermore, some steps of enhanced system are labels and features of each input by applying machine learning classifiers and use Bow to obtain the high performance and accuracy. And the effective created system should take the inference there is relation between positive sentiment, and trend will sale. When there is sarcasm, a decision will be made to stop or enhance the product.
Survey of Data Mining in Elearning Managment System of Course Selecting, 2024
This paper presents Data mining methods extracted from previous related papers, that would be app... more This paper presents Data mining methods extracted from previous related papers, that would be applied by Education University to predict courses on student selecting, in summary, these researches create a system that is an integration of a group of algorithms that are implemented sequentially until the system finds the necessary courses for the student. The created models utilizes k-means cluster unsupervised machine learning method, that takes the entry and groups them by taking their mean and produces a vector of selected courses. Further, to predict new results, the model can use this vector as an input and utilize the classification Apriori rule Algorithm to calculate the minimum support of predicted items then eliminate it, then repeat the calculation and the elimination till two items have same minimum support, next take the result and a rule of confidence to eliminate other courses, after that repeat the calculation and the elimination till reach number required of courses. Sometimes after the label is predicted by using aprori algorithm, the recommended system uses the supervised method weka and enhances the results. On the other hand, the open source data mining tool Weka is used for transforming datasets and even for analyzing the result and its performance. The selected courses are typically optimal but in some cases there is huge amount of data (the solution). To resolve this, we should use parallel k-means combined with Approri algorithm.
International Journal of Engineering and Computer Science, 2019
Image processing contributes on many of the technological advancements today. It is widely used b... more Image processing contributes on many of the technological advancements today. It is widely used by the developing institutes as well as a source of important data. One of the main things that are considered through this process would be the length of time on dealing with the application of different routines, on these images. Thus, time, being a valuable criterion for the efficiency of the systems, demands a better way for how these images will be managed. With the given situation, the idea of integrating a number of computers to perform image manipulation is considered. This uses the idea of parallel computing.
Abstract:
Today, several remote sensing sensor technologies are utilized to the purpose visualizi... more Abstract: Today, several remote sensing sensor technologies are utilized to the purpose visualizing remotely sensed data, with special developed software sets. Hence, this essay provides Remote sensing techniques that expose range of applications to everything can be imagined, in the space, as soil investigations, human detection, weather, mineral discover, maladies symptom and whatever you imagine. This analysis uses aerial, wireless, satellite, and microwaves.
International Journal Of Engineering And Computer Science
There are many divisions of philosophy named the philosophy of a domain where domains going from ... more There are many divisions of philosophy named the philosophy of a domain where domains going from history to physics. This essay proposes some issues that might constitute the philosophy of computer science as architecture of computer, divide the science into hardware and software, programming languages, operating systems, network architecture, security, privacy, and of protect the machine by anti-virus
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Today, several remote sensing sensor technologies are utilized to the purpose visualizing remotely sensed data, with special developed software sets. Hence, this essay provides Remote sensing techniques that expose range of applications to everything can be imagined, in the space, as soil investigations, human detection, weather, mineral discover, maladies symptom and whatever you imagine. This analysis uses aerial, wireless, satellite, and microwaves.
Today, several remote sensing sensor technologies are utilized to the purpose visualizing remotely sensed data, with special developed software sets. Hence, this essay provides Remote sensing techniques that expose range of applications to everything can be imagined, in the space, as soil investigations, human detection, weather, mineral discover, maladies symptom and whatever you imagine. This analysis uses aerial, wireless, satellite, and microwaves.