This investigation studies the effectiveness of Natural Language Processing (NLP) in analyzing and interpreting trends in social media. Because social media platforms generate enormous amounts of data daily, extractin...
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As the Internet continues to be the primary driver of modern technology, the need for robust safety procedures increases. Distributed Denial of Service (DDoS) assaults represent an essential threat to network availabi...
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This abstract gives a complete review of GPS Integrated Toll Collection' a novel GPS-based toll series gadget, highlighting the important things studies query, primary research reviewed, and the conclusions *** ev...
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Sentiment analysis is becoming increasingly important in today’s digital age, with social media being a significantsource of user-generated content. The development of sentiment lexicons that can support languages ot...
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Sentiment analysis is becoming increasingly important in today’s digital age, with social media being a significantsource of user-generated content. The development of sentiment lexicons that can support languages other thanEnglish is a challenging task, especially for analyzing sentiment analysis in social media reviews. Most existingsentiment analysis systems focus on English, leaving a significant research gap in other languages due to limitedresources and tools. This research aims to address this gap by building a sentiment lexicon for local languages,which is then used with a machine learning algorithm for efficient sentiment analysis. In the first step, a lexiconis developed that includes five languages: Urdu, Roman Urdu, Pashto, Roman Pashto, and English. The sentimentscores from SentiWordNet are associated with each word in the lexicon to produce an effective sentiment score. Inthe second step, a naive Bayesian algorithm is applied to the developed lexicon for efficient sentiment analysis ofRoman Pashto. Both the sentiment lexicon and sentiment analysis steps were evaluated using information retrievalmetrics, with an accuracy score of 0.89 for the sentiment lexicon and 0.83 for the sentiment analysis. The resultsshowcase the potential for improving software engineering tasks related to user feedback analysis and productdevelopment.
Cataract, a common eye disease characterized by clouding of the natural lens of the eye, is a serious threat to visual health. If left untreated, they can lead to blurred vision and even blindness, underscoring the im...
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Machine Learning (ML) has significantly impacted daily life by automating tasks and enhancing decision-making across diverse sectors such as healthcare, finance, and transportation. However, concerns regarding data pr...
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Irregularly Sampled Medical Time Series (ISMTS) are commonly found in the healthcare domain, where different variables exhibit unique temporal patterns while interrelated. However, many existing methods fail to effici...
Traditional rescue efforts encounter difficulties in challenging terrain, hindered by obstacles and natural calamities. Unmanned Aerial Vehicles (UAVs) integrated with Vehicular Ad Hoc Networks (VANETs) present a viab...
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To train sentiment classifiers, a collective multi-Trends sentiment classification approach is proposed for numerous tweets simultaneously. This technique uses sentiment facts from exceptional tweets to train accurate...
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With the development of science and technology, all kinds of data are generated in our daily life. While data analysis brings convenience, it also poses threat to users' privacy. This paper proposes a data securit...
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