Demystifying Emerging Trends in Machine Learning E-Book
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- Herausgeber: Bentham Science Publishers
- Kategorie: Wissenschaft und neue Technologien
- Serie: Emerging Trends in Computation Intelligence and Disruptive Technologies
- Sprache: Englisch
Demystifying Emerging Trends in Machine Learning (Volume 2) offers a deep dive into emerging and trending topics in the field of machine learning (ML). This edited volume showcases several machine learning methods for a variety of tasks. A key focus of this volume is the application of text classification for cybersecurity, E-commerce, sentiment analysis, public health and web content analysis.
The 49 chapters highlight a wide variety of machine learning methods including SVNs, K-Means Clustering, CNNs, DCNNs, among others. Each chapter includes accessible information through summaries, discussions and reference lists. This comprehensive volume is essential for students, researchers, and professionals eager to understand the emerging trends reshaping machine learning today.
Readership
Scholars and professionals interested in machine learning trends and research.
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Seitenzahl: 941
Veröffentlichungsjahr: 2025
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PREFACE
Artificial intelligence (AI) and machine learning (ML) are revolutionizing the way we approach healthcare and various industries. AI and ML are being used to improve patient outcomes, reduce costs, and increase efficiency in the healthcare industry. AI is also used in medical devices to predict and identify diseases, classify data for disease outbreaks, and optimize medical therapy.
In this book, we explore the role of neural networks in AI and ML in the medical and health sector. Neural networks are being used in oncology to train algorithms that can identify cancerous tissues at the microscopic level with the same accuracy as trained physicians. Various rare diseases may manifest in physical characteristics that can be identified in their premature stages by using facial analysis on patient photos.
The book also explores the role of AI and ML in various industries such as finance, retail, manufacturing, and more. AI is being used to improve customer experience by providing personalized recommendations based on customer data. In manufacturing, AI is being used to optimize supply chain management by predicting demand and reducing waste.
This book is a comprehensive guide for anyone interested in learning about the role of AI and ML in medical, health sectors, and various industries.
List of Contributors
A Method Based on Machine Learning to Classify Text for the Field of Cybersecurity
Siddharth Sriram1,*Abstract
Rapid advancements in networks and computer systems have opened a new door for immoral acts like cybercrime, which threaten public safety, and security, as well as the global economy. The purpose of this proposal is to analyse IP fraud and cyberbullying as two distinct types of cybercrime. The primary goals of this study are to use instances of cybercrime to provide a short examination of cybercrime activities, and the family member principles, and propose a pairing schema. Using the Naive Bayes (NB) & Support Vector Machine (SVM) artificial intelligence techniques, cybercrime instances are categorised according to their ideal qualities. The Twitter data in the Kaggle database has been clustered using K-means. User ID, sign-up date, referral, browser, gender, and age as well as IP address are just a few of the most useful information used to educate the computer. Total 151,113 datasets were used for experimental analysis of the suggested algorithm's performance. The accuracy of the suggested approach, 97%, is higher than that of the current method (NB). The challenge of regression may be easily surmounted with the use of the random forest method for the categorization of the resultant cybercrimes. The planned study uses age categories as the foundation for identifying the different offenses.
Introduction
In order to collect energy from the wake created by oscillating foils, a machine-learning model is constructed. For array deployments of oscillating foils, the function of the wake structure is crucial because the unstable wake significantly affects the performance of downstream foils. User sentiment is gleaned from social media using sentiment analysis [1, 2]. It is a way of organizing the text's ideas into positive, negative, and neutral categories. The outcomes of training and classifying the Twitter dataset have varied depending on the strategy utilized by
the researchers. The algorithm is crucial, especially in time-sensitive industries like airlines and retail [3, 4]. Smartphones, tablets, and other Internet of Things (IoT) devices are frequently used in settings as diverse as the household and the factory. Bluetooth Low Energy (BLE) is used by many of these gadgets as a control or data transmission mechanism [5, 6]. These devices are susceptible to simple attacks because of their lack of robust security mechanisms and the inherent weaknesses in their software as well as communication components. Machine learning is often used for classification purposes [7]. In order to classify images and other remote sensing data, GIS experts often use deep neural network-based classification methods. Graph neural networks (GNNs) can be utilized for identifying geographical features by taking their topology into account, which is useful for data represented as a graph, such as line or polygonal spatial data [8, 9]. Using GNNs to group spatial objects into several categories is suggested in this article. Three alternative methods were tested, two of which depended only on the classification of text and one which combined text classification with a matrix of adjacency. The suggested method's application case was the categorization of planning zones in LSPs [9]. The outcomes of the trials demonstrated the importance of object topological information in enhancing GNNs' classification accuracy. Input characteristics including document length, training data representation format, and network architecture all need to be considered for optimal model performance [10].
Related Work
In this study, we look at 46 different kinematics of oscillation foils to find vortex wakes in pictures of vorticity fields and to adjust the wake parameters according to the input kinematic elements. There are three types of wakes that are classified using a network of convolutional neural networks (CNN) that has lengthy short-term memory units. Utilizing an unsupervised convolutional auto-encoder with [Formula: see text]-means++ clustering, four separate wake patterns are identified, which corroborate the differences in foil kinematics. Future research might use these patterns to predict how foils positioned in the wake would behave and to build optimal foil combinations for tidal energy collecting [11].
In this paper, we provide an optimisation-based machine-learning method for tweet classification. The process consisted of three distinct steps. The process begins with gathering and organising data, then moves on to improving it via feature extraction, and then concludes with reclassifying the revised training set using machine learning methods. Different algorithms provide different results. Sequential minimal optimising with decision trees has been proven to have a high degree of accuracy (89.47%) when compared to other machine learning techniques [12].
In the first part of this essay, we saw how to get unprocessed information on network traffic while simultaneously launching a MitM assault on BLE devices. Second, we investigate the possibility of using machine learning—and specifically a combination of unsupervised and supervised methods—to identify this kind of assault. We reconstructed the model of BLE interactions using two unsupervised approaches and utilised those models to spot suspicious data batches. The packets within each batch were then categorised as either normal or attack using a classification approach based on the Text-CNN technology. The results of our model reconstruction demonstrate that our classification approach is very accurate (0.99), with a low false positive rate (0.03) [13].
We used LIME to explain the model's predictions and a conglomeration of deep learning algorithms (CNN-GRU) to categorize four distinct cardiac arrhythmia kinds as part of this study. A 1D convolutional neural network (CNN) served as the basis for training the model. A well-liked local explanation method, LIME can simulate the behavior of every machine learning system and provide an explanation for it. Unfortunately, LIME is limited to explaining data in tabular, textual, and visual formats. In order to better illustrate LIME on the signal dataset, we advocated for a heat map to be used to draw attention to relevant regions of the heartbeat signals. Our approach also allows for more accurate heartbeat segmentation by accurately extracting characteristics such as the QRS Complex, P Wave, and T Wave from electrocardiogram, or ECG, records. Tests for recall, accuracy, precision, f1 score, as well as area under the receiver operator curve of characteristic (AUC-ROC) were conducted using ECG lead II from the MIT-BIH datasets to evaluate the proposed hybrid model. We directly compare the suggested model to the independent CNN and GRU algorithms to show that it is more accurate and has a better ROC [14].
Corona Virus and Conspiracies Multimedia Analysis Task of the MediaEval 2021 Challenge is dedicated to investigating claims of wrongdoing in relation to the COVID-19 pandemic. Our HCMUS group takes several methods based on various pre-trained models to handle 2 separate jobs. We provide 5 iterations of Task 1 and 1 of Task 2 based on our experiments. While the BERT [5] trained model is included in both runs, the first run also incorporates a subjective assessment for acquiring semantic features prior to training. Our third and fourth runs will be more method-diversified with the use of naïve Bayes classifiers [4] as well as a long short-term memory framework [8]. By combining many ML and DL models, Run 5 is able to perform a multimodal analysis of textual data [3]. A single-run Bayesian categorization approach is used in the final phase of subtask 2. In the end, our approach yields scores of 0.5987 on task 1 as well as 0.3136 on task 2. The author's copyright for this article is valid until 2021 [15].
Proposed Work
Preliminary Knowledge
As a machine learning strategy, text classification aims to automatically classify texts using tags or categories. NLP (natural language processing) text classifiers can quickly sort through massive volumes of text according to user intent, emotions, and themes, far faster than humans can. Emails, chats, websites, social media, reviews on the internet, support tickets, survey results, you name it: they all contribute to an overwhelming amount of data that is difficult for people to process. Imagine attempting to manually handle even a small percentage of the staggering 20 billion messages sent each month between businesses and individuals on Facebook Messenger. It would take an extremely long time and a lot of money. Businesses may get a wealth of knowledge from any and all communication channels, but it can be difficult to sift through the information they receive and identify actionable insights. This is prompting businesses to look at automated options, such as text classification. Text categorization, driven by machine learning, allows you to categorise text in a dependable, scalable, accurate, as well as economical manner. Fig. (1) represents the System Architecture.
Fig. (1)) System Architecture.Dataset Description
Since we could not find a Spanish-language dataset, we used Facebook's API to compile data on three forms of cyber-aggression in Latin America: racism, violence against women, and violence according to gender identity. We gathered a total of 5,000 comments, but only 2000 were usable since they were free of spam (which we define as texts including uncommon characters, visuals of expression, or hilarious ideas like memes, blank spaces, or irrelevant remarks). Afterward, we classified the feedback (instances) as follows: 700 comments related to sexual orientation-based violence; 700 comments related to violence against women; and 600 comments related to racism.
The act of labelling. We noticed that some academics utilised the Amazon Mechanical Turk website to hire unidentified online employees to manually categorise various types of data (e.g., comments, reviews, words, photographs). Amazon's Mechanical Turk employees found that 2.5% of reviews flagged as “no cyber-bullying” should really be labelled as such, therefore the company reached out to psychology majors at the graduate and undergraduate levels for help with the tagging process.
In light of the above, we opted to have a team of three educators versed in machine-learning algorithms to manually tag the remarks with the help of psychologists versed in assessing and treating incidents of bullying in secondary schools. The psychologists went through the three types of cyber-aggression with the faculty and how to classify comments into the appropriate categories. Each remark was assigned an offensive value based on the criteria of cyber-aggression and a predetermined number scale as part of the labelling procedure. We utilised a scale from 0 to 2 to rank the offensiveness of statements that included sexual assault against women. The same four-point-six-point scale was applied for statements concerning sexual orientation-based violence, with the lowest number indicating the least obscene remarks. Last but not least, racist statements were ranked from least offensive to most offensive using a scale from zero to 10. To this end, we collected a dataset of abusive posts and fed it into our feature selection and training processes. The first column of this data set held the comment or instance, while the second column provided an objectionable value for each comment. In the following, we detail the feature-selection technique and training process algorithms and methodologies employed in this study.
Machine Learning Algorithms for Text Classification
Machine learning classifier training begins with converting text into a machine-readable format. In many cases, this is accomplished with the help of a “bag of words,” a set of words whose frequency in a given set of words is represented by a vector. After information is vectorized, vectors of features for every word sample and tag are used to train a text classification model. The more data it has to learn from, the more precise its forecasts will be. The most popular algorithms for text categorization are waiting, so let's have a look at them:
Naive Bayes
The Naive Bayes approach uses Bayes' Theorem, a classifier based to forecast a text's label using prior information about potentially related circumstances. After determining the likelihood of every tag for the input text, it makes a prediction based on the tag having the greatest likelihood.
Naive Bayes may also be made more effective with the use of other methods:
Getting rid of filler words that nobody ever uses. Words like “able to,” “either,” “else,” “ever,” “etc”.To lemmatize a term is to classify its many derivations under the same heading. Words like “draught,” “drafted,” “draughts,” “drafting,” etc.N-grams: The n-gram denotes the likelihood of occurrence of a single word or a series of words of 'n length' inside a text.TF-IDF: A measure of a word's significance in a text or group of documents, TF-IDF is an abbreviation for “term frequency-inverse document frequency.” It has great potential for scoring words, in that its value rises with the frequency with which a given word occurs in a document rises, but its value falls as the quantity of documents containing that word falls.Forecasters: [X1, X2] and Board: Y To get the posterior probability, one uses the following formula -(1)Accepting the Offer of Limited Responsibility for P(X1,X2/Y=1),(2)Naive Bayes Classifier's posterior probability calculation formula.Support Vector Machines
The classification system known as Support Vector Machines (SVM) excels in situations when there is a small quantity of data to work with. For each given category, it evaluates all possible vectors and selects the best one. For example, let us pretend that there are two attributes (x and y) and two labels (expensive and cheap) in the data set. There has to be a way to tell which of the two sets of variables (x, y) is more expensive. The support vector machine (SVM) does this by creating a boundary (the decision boundary) between the data points and classifying everything on either side as expensive or cheap. The subspace of a group and the subspace of an outgroup are created when a space is divided in half by a decision boundary. Training text is represented by vectors, and each tag is represented by a set. SVM has the advantage of not needing a large amount of training information to provide correct results, but it does demand more processing power than Naive Bayes to do so. It is a machine learning method that may be used for Regression and Classification, and it is supervised. The number of features, n, determines the number of dimensions in which data points are shown in SVM. The next step in classification is to choose an appropriate hyper-plane that clearly separates the two groups. The hyper-plane has (n minus 1) dimensions in n-dimensional space.
(3)We presume that categories may be divided into linear subsets. Classification is facilitated by the sign in the equation, while the scale of the equation clarifies the extent to which the observation deviates from the hyper-plane. If the magnitude is large, we may more confidently place it in a certain category. Margin is defined as the minimal distance of points of data from the hyper-plane to either class. For it to be very significant, we need a large safety margin. Therefore, the name “Maximum Margin Classifier” is applied to this hyper-plane. The observations that fall on or outside the boundary and cause the hyper-plane to deform are known as support vectors. The hyper-plane is propped up by the support vectors.
Results and Discussion
Seventy percent, ten percent, and twenty percent of the whole sequence dataset were used for training, validation, and testing, respectively.
To evaluate the models, we used accuracy and macro-weighted F1. The article's claimed accuracy is based on conclusions drawn from the test set. Since it gives an average F1 score while considering the corresponding frequency of each label, the macro-weighted F1 statistics is used when assessing the efficacy of a model on a dataset. Fig. (2) displays comparison analysis of accuracy.
Fig. (2)) Comparison analysis of accuracy. Fig. (3)) Accuracy (a). Sample Size, and (b). Feature Size. Fig. (4)) Comparison analysis of precision. Fig. (5)) Precision (a). Sample Size, and (b). Feature Size.Fig. (3) depicts accuracy (a). sample size and (b). and feature size. Fig. (4) displays comparison analysis of precision. Fig. (5) represents precision (a). sample size, and (b). feature size, Fig. (6) portrays comparison analysis of recall. Fig. (7) displays recall (a). sample size, and (b). feature size, Fig. (8) shows comparison analysis of F-score, Fig. (9) shows F-score (a). sample size, and (b). feature size. Cross-validation is often used as a measure of a text classifier's effectiveness. The training data are randomly split into sets of equal size. The remaining sets are used to train the text classifier and test its predictions for each set of the same length. By comparing their predictions with human-tagged data, classifiers may reduce the likelihood of producing false positives and negatives.
Fig. (6)) Comparison analysis of Recall. Fig. (7)) Recall (a). Sample Size, and (b). Feature Size. Fig. (8)) Comparison analysis of F-score. Fig. (9)) F-score (a). Sample Size, and (b). Feature Size.Measures of a classifier's efficacy may be derived from these outcomes:
Accurateness: Proportion of correctly predicted texts with tags.Exactness: Numbers of texts the classifier's prediction for a tag's total number of instances was within the margin of error.Recallotion: The proportion of instances out of the overall amount of cases for which it might have made a prediction, the classifier made a prediction for a given tag.F1 Score: The optimal balance between precision and recollection.Conclusion
Cybercrime poses a danger to humanity's security, and the global economy because of the quick advancements in networks and computer networks that have opened up new avenues for immoral actions. The proposed work's objective is to categorise cybercrimes into IP fraud analysis and cyberbullying. The main goals of this work are to provide a brief inspection of cyber activities, and their respective underpinnings, and supplying a matching schema via cybercrime occurrences. Cybercrime episodes are grouped based on their ideal qualities using data mining methods called Naive Bayes (NB) as well as Support Vector Machine (SVM). Data from Twitter is extracted from the Kaggle databases using K-means clustering. In order to provide appropriate computer training, it is necessary to collect the majority of relevant data, including customers ID, sign-up time, source, web browser, gender, age, as well as IP address. There are 151,113 data points used in the studies that test how well the approach works. With an accuracy rate of 97%, the suggested approach outperforms the current technique (NB). When it comes to cybercrime categorization, the random forest method is superior to novelty regression. The proposed method classifies the different crimes according to a number of age groups.
References
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Sidhant Das1,*Abstract
This article examines the features of the dealer's brush list evaluation material in light of research findings on misleading assessment and identification of online purchasing. A Gated Recurrent Unit (GRU) model using keyword weighting is presented as a solution to the issue that it is challenging for the DL model to collect the feature data of the whole assessment text in a false evaluation identifying job. The TF-IDF technique is first used to generate the list of keywords, and then that list's weight is applied to the word vector. Finally, a weighted vector of words is categorised using this method of the model to finish the recognition job of erroneous evaluation, replacing the pooling component of the GRU model with a constrained Boltzmann machine. By using a variety of text categorization algorithms and comparing their results in terms of correctness and performance, this research aspires to represent the practical benefits of applications that use machine learning in the real world. We built a system that can run several text classification algorithms, and we used that system to create models that were educated using actual data taken from E-Commerce, a virtual fashion e-commerce platform. The Convolutional Neural Network technique achieved the greatest mean accuracy of 96.08% (with a range of 85.44% to 99.99%) with an average deviation of 5.65%.
