Botnets pose a significant threat to network security, especially to the potential server crashes they can cause. The proposed study focuses on methods for identifying the presence of and the type of botnet activity, ...
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ISBN:
(数字)9798350394474
ISBN:
(纸本)9798350394481
Botnets pose a significant threat to network security, especially to the potential server crashes they can cause. The proposed study focuses on methods for identifying the presence of and the type of botnet activity, by implementing machine learning techniques, and consecutively comparing the black-box and white-box approaches employed. Utilizing the CTU-13 and N-BaIoT datasets, the study involves data preprocessing and a comparative analysis of various models. The LGBM Classifier emerges as the top-performing model for detecting the presence of a botnet, while the XGBoost Classifier excels in identifying the specific type of botnet attack based on different performance evaluation metrics.
Sentiment analysis, an essential tool for deciphering public sentiment from vast internet data, offers valuable insights to businesses and policymakers. Its adoption is driven by the ability to interpret emotions, pro...
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ISBN:
(数字)9798350349900
ISBN:
(纸本)9798350349917
Sentiment analysis, an essential tool for deciphering public sentiment from vast internet data, offers valuable insights to businesses and policymakers. Its adoption is driven by the ability to interpret emotions, providing guidance for product refinement and decision-making. This tool is crucial for understanding user sentiments, empowering businesses to improve customer experiences, shape brand perception, and refine marketing strategies. Organizations benefit from sentiment analysis by leveraging customer insights for informed decision-making and product enhancement, thereby safeguarding positive relationships and brand reputation. The proposed system utilizes both formal and informal datasets and works on models like Logistic Regression, Naive bayes and SVM. Additionally, feature extraction techniques such as hashing and TF-IDF enhance data representation, contributing to the accuracy and F1-score of the employed models. The proposed system makes use of the PySpark platform to elevate the *** the three models utilized, SVM achieved the highest accuracy and F1-score for the Twitter dataset, with an accuracy of 0.76 and an F1-score of 0.76. Similarly, for the Amazon dataset, Logistic Regression exhibited the highest accuracy and F1-score, recording values of 0.89 for both metrics. For comparison and experimental evaluation, the models were executed on a merged dataset comprising both Twitter and Amazon data. In this merged dataset, Logistic Regression outperformed the other models, achieving an accuracy and F1-score of 0.83 for both metrics.
Rainfall forecasting is important for several uses, such as agriculture, water supply, and flooding emergencies. This is because rainfall, which is a type of precipitation, is not easily predictable owing to several f...
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ISBN:
(数字)9798350354379
ISBN:
(纸本)9798350354386
Rainfall forecasting is important for several uses, such as agriculture, water supply, and flooding emergencies. This is because rainfall, which is a type of precipitation, is not easily predictable owing to several factors that include atmospheric, oceanic, and geographical factors. Therefore, a new Optune CatBoost prediction model has been developed here that can be used to predict rainfall. Some of the research techniques used include; data preprocessing, outlier analysis, normalization, SMOTE, feature engineering, and correlation analysis. This Optune CatBoost prediction gives a maximum accuracy of 96% a precision of 97% F1 score of 96.7%, and recall of 95.5%. The discrimination between rainfall and non-RA events is evaluated to be strong with the area under the ROC curve equalling 0.96. The study implies that the developed model could be used to enhance rainfall prediction, which in turn aids in decision-making on appropriate resource utilization in weather-sensitive areas.
A traffic prediction system might help drivers in making better travel choices. Minimizing carbon emissions, reducing traffic congestion, and improving the effectiveness of traffic management to deliver such traffic f...
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This research introduces a new and improved model for a financial chatbot based on deep learning, machine learning, and natural language processing to provide suitable financial services and increase the financial lit...
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ISBN:
(数字)9798331540364
ISBN:
(纸本)9798331540371
This research introduces a new and improved model for a financial chatbot based on deep learning, machine learning, and natural language processing to provide suitable financial services and increase the financial literacy of users. It utilizes a detailed financial database, another complex machine learning structure for market forecasts., and natural language processing to parse user questions. Moreover, there is the customer resource management feature that applies machine learning in the analysis of financial data and natural language processing for understanding the user”s intent. The paper also looks into the use of transport layer security (TLS) encryption to secure the flow of the sensitive information, the data that is to be transmitted from the chatbot server to the user clients. By integrating the dimensions of concepts such as financial analysis, personal finance advice, and data transmission, this research provides an extensive and secure approach to improving one”s financial situation.
Understanding the emotion and confidence level of a respondent’s answer helps formulate the response as well as the next question in an oral examination or viva. Automatic detection of these parameters is essential t...
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ISBN:
(数字)9798350376685
ISBN:
(纸本)9798350376692
Understanding the emotion and confidence level of a respondent’s answer helps formulate the response as well as the next question in an oral examination or viva. Automatic detection of these parameters is essential to develop appropriate next statements, provided the relevant tone of a Viva chatbot curated for assistance and analysis of human speech in interview preparations. Supporting this motivation, this work analyzes the effectiveness of using various spectral attributes such as Mel Frequency Cepstral Coefficients (MFCC), Chroma, Mel Spectrogram & Spectral Contrast alongside CNN, RNN, and Bi-LSTM networks to identify confidence & emotion in speech signals. It is observed that MFCC features along with a BiLSTM network provide the best results with 63.76% test accuracy and 58.82% train accuracy by considering minimal variance.
In recent years, bearing fault diagnosis based on deep learning has gradually become the mainstream. However, the existing studies still have some defects, such as unreasonable sampling and incomplete utilization of b...
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Diabetic foot ulcers (DFUs) are a serious consequence for diabetes individuals, frequently resulting in amputation. Early identification is critical in averting such results. This study looks at the effectiveness of d...
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ISBN:
(数字)9798350370249
ISBN:
(纸本)9798350370270
Diabetic foot ulcers (DFUs) are a serious consequence for diabetes individuals, frequently resulting in amputation. Early identification is critical in averting such results. This study looks at the effectiveness of deep learning models in recognizing DFUs from foot thermography photos. We evaluate the performance of several deep learning architectures against more contemporary models. The models will be trained and assessed using a publicly accessible foot thermography picture collection that includes both healthy and DFU patients. Each model’s performance in DFU identification will be evaluated based on accuracy, precision, recall, and $\mathbf{f 1}$ score. We intend to find the deep learning model with the greatest accuracy,f1 score, and robustness in DFU classification, which would pave the way for a computer-aided diagnostic tool that uses thermography to detect DFU in diabetes patients early. This research shows potential for enhancing patient care and lowering amputation rates.
The majority of people on the planet now have access to the Internet for text, image, audio, and video communication. Through social media, people from many backgrounds share knowledge about current events and express...
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ISBN:
(数字)9798350359688
ISBN:
(纸本)9798350359695
The majority of people on the planet now have access to the Internet for text, image, audio, and video communication. Through social media, people from many backgrounds share knowledge about current events and express their own opinions on them. Analyzing people’s emotions is necessary to comprehend and identify the behavior of such vast amounts of textual data about them. The study focuses on information gathered from Twitter, one of the most widely used social media platforms, by examining both historical and real-time feeds and extracting emotions from them. The necessary English-language Twitter data is transformed into a vector of six emotions, and supervised learning methods like Logistic Regression, Naive Bayes, SVM, Decision Tree, Random Forest, Gradient Boosting, and Multi-Layer Perceptron are utilized to identify the label of one of the basic human emotions. Gradient Boosting and SVM are the top-performing models that reported the highest F1-Score of 0.88 among the other models. Sadness and joy came out to be the best-performing emotions whereas it was comparatively difficult to predict surprise emotion.
Audio fingerprinting is an essential technique for copyright protection and music recognition. The capacity to accurately categorize audio content and spot possible copyright violations is vital for musicians, content...
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ISBN:
(数字)9798350396157
ISBN:
(纸本)9798350396164
Audio fingerprinting is an essential technique for copyright protection and music recognition. The capacity to accurately categorize audio content and spot possible copyright violations is vital for musicians, content creators, and streaming services. With its valuable solutions for copyright protection, content monitoring, and music recommendation, this study has significant implications for the music industry. In the research, the research focused on developing robust audio fingerprinting methods for precise audio content recognition. These tactics have shown to be remarkably accurate and quite effective. The approach is based on the use of strong hashing techniques and the extraction of various audio aspects. Long Short-Term Memory (LSTM) along with SVM, KNN, and CNN were employed in the research. Notably, LSTM outperformed other models, showcasing an outstanding accuracy rate of 93 %. The results hold particular promise for massive music platforms and instantaneous content matching. The proposed methods have demonstrated the effectiveness of these strategies in identifying audio material, even under challenging conditions.
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