Spam Filtering Research Articles

Email Spam Detection Using a Hybrid Approach of Feedforward Neural Network and Penguin Optimization Algorithm

The electronic communication of today is beset by unwanted junk messages. These are not just a nuisance; they waste our time and energy and clog up our inboxes, which are supposed to be, in many ways, our digital front doors. But how do we keep them from coming? Well, as today's article makes clear, the situation is hardly straightforward. There are various methods that can be used, both traditional and modern, and each has its own set of pros and cons. One method, however, stands out in today's article as one that we think might work well in practice, the good old-fashioned neural network. The method's novelty derives from its use of neural networks, a type of machine learning, in conjunction with the POA. The POA is a powerful optimization algorithm that can perform very well when fine-tuning the weights and biases of a neural network. We therefore used the POA to optimize these two crucial components of our model and achieved a surprising level of accuracy—98.7%. This figure surpasses the next nearest competitor by 2% and demonstrates the efficacy of our method in spam detection while also highlighting the POA's potential in this field. The overall proposed method pushes the spam-detection field forward, but it also demonstrates something even broader: the power of hybrid approaches in machine learning. Neural networks are great—but you have to train them properly, and the use of a particle swarm for that purpose is a neat idea. But in the end, a hybrid spam filter is still a spam filter, and this one does what it's supposed to do better than many rivaling methods.

An enhanced algorithm for semantic-based feature reduction in spam filtering.

With the advent and improvement of ontological dictionaries (WordNet, Babelnet), the use of synsets-based text representations is gaining popularity in classification tasks. More recently, ontological dictionaries were used for reducing dimensionality in this kind of representation (e.g., Semantic Dimensionality Reduction System (SDRS) (Vélez de Mendizabal et al., 2020)). These approaches are based on the combination of semantically related columns by taking advantage of semantic information extracted from ontological dictionaries. Their main advantage is that they not only eliminate features but can also combine them, minimizing (low-loss) or avoiding (lossless) the loss of information. The most recent (and accurate) techniques included in this group are based on using evolutionary algorithms to find how many features can be grouped to reduce false positive (FP) and false negative (FN) errors obtained. The main limitation of these evolutionary-based schemes is the computational requirements derived from the use of optimization algorithms. The contribution of this study is a new lossless feature reduction scheme exploiting information from ontological dictionaries, which achieves slightly better accuracy (specially in FP errors) than optimization-based approaches but using far fewer computational resources. Instead of using computationally expensive evolutionary algorithms, our proposal determines whether two columns (synsets) can be combined by observing whether the instances included in a dataset (e.g., training dataset) containing these synsets are mostly of the same class. The study includes experiments using three datasets and a detailed comparison with two previous optimization-based approaches.

Supervised Learning: An InDepth Analysis

Supervised learning pivotal machine learning paradigm wherein models are trained on labeled datasets. They predict outcomes or classify data. It includes methodologies and diverse applications of supervised learning. Emphasizing significance in modern technology. Key methodologies encompass linear regression logistic regression. Also decision trees, support vector machines neural networks. Each with unique advantages for specific tasks. Versatility is demonstrated through applications in image and speech recognition. Natural language processing, medical diagnosis and financial forecasting also highlighted. Challenges include overfitting data quality, interpretability. Scalability discussed emphasizing areas for ongoing research. Future directions point towards transfer learning. Integration with semisupervised and unsupervised methods. Explainable AI and quantum machine learning promise further enhancements. They will impact supervised learning across various domains. Key words: Supervised learning, Machine learning, Labeled datasets, Algorithm training, Outcome prediction, Data classification, Input-output pairs, Model generalization, Accurate predictions, Real-world applications, Spam filters, Recommendation systems, Medical diagnosis, Autonomous driving, Efficiency enhancement, Decision-making, User experience, Data handling.

ВИКОРИСТАННЯ МАШИННОГО НАВЧАННЯ ДЛЯ ОБРОБКИ ДАНИХ НА МІКРОКОМП’ЮТЕРАХ

Machine learning is a data analysis method that automates the construction of an analytical model. It is a branch of artificial intelligence based on the idea that machines should be able to learn and adapt through experience. It is closely related to computational statistics, which makes predictions based on statistical data collected by a computer. It is sometimes confused with data mining, but that is more focused on analyzing intelligence data, while machine learning contains complex algorithms that are used mainly for prediction, where machine learning concentrates on predicting based on already known attributes obtained through training data, then data mining focuses more on finding unknown attributes in any data. attributes in any data. Machine learning applications may be associated with spam filtering, optical character recognition, and search engines. Machine learning uses data to determine which algorithm is best for generating results based on the quantity, quality, and nature of the data. This data is then used for intelligent analysis in various ways, such as recommendation systems such as similar products on eBay, personalized content on google plus pages, video ads on sites such as YouTube, and last but not least, suggestions from friends on Facebook. Machine learning and artificial intelligence can be used to solve business problems, namely: Automating repetitive tasks such as data entry, document processing and customer service. This frees up employees' time for more complex work. Optimization of business processes. ML/AI can analyze data to identify inefficiencies and optimize business processes, increasing company productivity. Service personalization. ML/AI helps analyze customer data to provide them with personalized offers, recommendations and support. Automation of answers to questions and anticipation of customer needs. Detection of fraudulent transactions and cyber security. Risk management.

Machine Learning-Based Email Spam Filter

The surge in email usage has been accompanied by an unwelcome increase in spam, posing challenges for individuals and organizations. This study addresses the urgent need for efficient spam detection by evaluating the performance of three widely-used machine learning classifiers: Naive Bayes, Random Forest, and Logistic Regression. Our approach includes comprehensive data preparation, feature extraction, model training, and rigorous performance evaluation using metrics such as Accuracy, Precision, Recall, and F1-Score. Furthermore, we analyze the trade-off between accuracy and computational efficiency, essential for real-time spam detection systems. A user-friendly interface developed with Flask showcases the practical application of our findings. The Random Forest Classifier outperforms its counterparts, proving to be the most effective in accurately classifying emails and maintaining a balance between sensitivity and specificity. The study’s implications highlight the potential for sophisticated machine learning-based spam filters to enhance email security.

Unveiling the Power of Data: A Journey Through Machine Learning Techniques

Machine Learning Techniques: Unveiling the Power of Data-Driven Solutions Machine learning (ML) has become a transformative force across disciplines, revolutionizing how computers extract knowledge from data and tackle complex tasks without explicit programming. This research paper delves into the core principles of machine learning, exploring its fundamental techniques and their far-reaching applications. We embark on a journey through the two main paradigms that govern learning: supervised and unsupervised. Supervised learning equips algorithms to make informed predictions based on labeled data, encompassing tasks like spam filtering, disease diagnosis, and stock market forecasting. The algorithm ingests this data, which includes both the input features and the corresponding desired output, and learns the intricate relationships between them. This empowers it to generalize this knowledge to unseen data, enabling accurate predictions. Unsupervised learning, in contrast, thrives on unlabeled data, where the data points lack predefined categories or target variables. Here, the objective is to uncover the inherent structures or patterns within the data itself. Unsupervised learning techniques are invaluable for tasks like data clustering, where similar data points are grouped together to reveal underlying relationships. Additionally, they play a crucial role in dimensionality reduction, a process of compressing high-dimensional data into a lower-dimensional space for efficient processing and analysis. This is particularly beneficial when dealing with massive datasets that may overwhelm traditional computational methods. Beyond these fundamental paradigms, the landscape of machine learning extends further. Deep learning, a subfield inspired by the structure and function of the human brain, utilizes artificial neural networks with multiple layers. These complex networks are adept at learning intricate patterns from vast amounts of data, achieving remarkable success in areas like computer vision, natural language processing, and speech recognition. Deep learning models, for instance, power facial recognition technology, enabling our devices to identify individuals with exceptional accuracy. In the realm of natural language processing, deep learning algorithms fuel machine translation systems, allowing seamless communication across languages. Reinforcement learning presents another powerful approach. Here, an agent interacts with an environment, learning through trial and error. The agent receives rewards for desired actions and penalties for undesired ones, constantly refining its decision-making process. This approach has applications in robotics, where robots can learn to navigate complex environments and perform tasks with increasing autonomy. Reinforcement learning is also used in game playing, where AI agents can learn optimal strategies through self-play and achieve superhuman levels of performance. As machine learning continues its exponential growth, ethical considerations become paramount. Potential biases within the training data can lead to discriminatory outcomes in areas like loan approvals or facial recognition software. Additionally, the explainability and transparency of complex models, particularly deep learning models, raise concerns. To ensure responsible development and deployment of ML, addressing these issues is crucial. The field requires robust methodologies to mitigate bias in training data and develop techniques that make complex models interpretable, allowing us to understand the rationale behind their predictions. Looking towards the horizon, the future of machine learning is brimming with potential. From personalized healthcare with tailored treatment plans to self-driving cars that navigate roads with exceptional precision, ML promises to revolutionize various aspects of our lives. Continuous research and development will enhance the capabilities and robustness of machine learning models, further expanding their reach and impact. By harnessing the power of data and fostering responsible development practices, machine learning will undoubtedly play a pivotal role in shaping a more intelligent and data-driven future

Cyber security Vulnerabilities and Remediation Through Cloud Security Tools

The proliferation of internet usage has surged dramatically, prompting individuals and businesses to conduct myriad transactions online rather than in physical spaces. The onset of the COVID-19 pandemic has further propelled this trend. Consequently, traditional forms of crime have migrated to the digital realm alongside the widespread adoption of digital technologies such as cloud computing, the Internet of Things (IoT), social media, wireless communication, and crypto currencies, amplifying security concerns in cyberspace. Notably, cybercriminals have begun offering cyber attacks as a service, automating attacks to magnify their impact. These attackers exploit vulnerabilities across hardware, software, and communication layers, perpetrating various forms of cyber attacks including distributed denial of service (DDoS), phishing, man-in-the-middle, password, remote, privilege escalation, and malware attacks. The sophistication of these attacks renders conventional protection systems, such as firewalls, intrusion detection systems, antivirus software, and access control lists, ineffective in detection. Consequently, there is an urgent imperative to devise innovative and pragmatic solutions to thwart cyber attacks. This paper elucidates the primary drivers behind cyber attacks, surveys recent attack instances, patterns, and detection methodologies, and explores contemporary technical and non-technical strategies for preemptively identifying and mitigating attacks. Leveraging cutting-edge technologies like machine learning, deep learning, cloud platforms, big data analytics, and blockchain holds promise in combating present and future cyber threats. These technological interventions can aid in malware detection, intrusion detection, spam filtering, DNS attack classification, fraud detection, identification of covert channels, and discernment of advanced persistent threats. Nonetheless, it's crucial to acknowledge that some promising solutions, notably machine learning and deep learning, are susceptible to evasion techniques, necessitating careful consideration when formulating defenses against sophisticated cyber attacks.

Cyber security Vulnerabilities and Remediation Through Cloud Security Tools

The proliferation of internet usage has surged dramatically, prompting individuals and businesses to conduct myriad transactions online rather than in physical spaces. The onset of the COVID-19 pandemic has further propelled this trend. Consequently, traditional forms of crime have migrated to the digital realm alongside the widespread adoption of digital technologies such as cloud computing, the Internet of Things (IoT), social media, wireless communication, and crypto currencies, amplifying security concerns in cyberspace. Notably, cybercriminals have begun offering cyber attacks as a service, automating attacks to magnify their impact. These attackers exploit vulnerabilities across hardware, software, and communication layers, perpetrating various forms of cyber attacks including distributed denial of service (DDoS), phishing, man-in-the-middle, password, remote, privilege escalation, and malware attacks. The sophistication of these attacks renders conventional protection systems, such as firewalls, intrusion detection systems, antivirus software, and access control lists, ineffective in detection. Consequently, there is an urgent imperative to devise innovative and pragmatic solutions to thwart cyber attacks. This paper elucidates the primary drivers behind cyber attacks, surveys recent attack instances, patterns, and detection methodologies, and explores contemporary technical and non-technical strategies for preemptively identifying and mitigating attacks. Leveraging cutting-edge technologies like machine learning, deep learning, cloud platforms, big data analytics, and blockchain holds promise in combating present and future cyber threats. These technological interventions can aid in malware detection, intrusion detection, spam filtering, DNS attack classification, fraud detection, identification of covert channels, and discernment of advanced persistent threats. Nonetheless, it's crucial to acknowledge that some promising solutions, notably machine learning and deep learning, are susceptible to evasion techniques, necessitating careful consideration when formulating defenses against sophisticated cyber attacks.

Cyber security Vulnerabilities and Remediation Through Cloud Security Tools

The proliferation of internet usage has surged dramatically, prompting individuals and businesses to conduct myriad transactions online rather than in physical spaces. The onset of the COVID-19 pandemic has further propelled this trend. Consequently, traditional forms of crime have migrated to the digital realm alongside the widespread adoption of digital technologies such as cloud computing, the Internet of Things (IoT), social media, wireless communication, and crypto currencies, amplifying security concerns in cyberspace. Notably, cybercriminals have begun offering cyber attacks as a service, automating attacks to magnify their impact. These attackers exploit vulnerabilities across hardware, software, and communication layers, perpetrating various forms of cyber attacks including distributed denial of service (DDoS), phishing, man-in-the-middle, password, remote, privilege escalation, and malware attacks. The sophistication of these attacks renders conventional protection systems, such as firewalls, intrusion detection systems, antivirus software, and access control lists, ineffective in detection. Consequently, there is an urgent imperative to devise innovative and pragmatic solutions to thwart cyber attacks. This paper elucidates the primary drivers behind cyber attacks, surveys recent attack instances, patterns, and detection methodologies, and explores contemporary technical and non-technical strategies for preemptively identifying and mitigating attacks. Leveraging cutting-edge technologies like machine learning, deep learning, cloud platforms, big data analytics, and blockchain holds promise in combating present and future cyber threats. These technological interventions can aid in malware detection, intrusion detection, spam filtering, DNS attack classification, fraud detection, identification of covert channels, and discernment of advanced persistent threats. Nonetheless, it's crucial to acknowledge that some promising solutions, notably machine learning and deep learning, are susceptible to evasion techniques, necessitating careful consideration when formulating defenses against sophisticated cyber attacks.

Hybrid Genetic Algorithm Trained Bayesian Ensemble for Short Messages Spam Detection

Today’s popularity of the short messages services (SMS) has created a propitious environment for spamming to thrive. Spams are unsolicited advertising, adult-themed or inappropriate content, premium fraud, smishing and malware. They are a constant reminder of the need for an effective spam filter. However, SMS limitations of 160-charcaters and 140-bytes size as well as its being rippled with slangs, emoticons and abbreviations further inhibits effective training of models to aid accurate classification. The study proposes Genetic Algorithm Trained Bayesian Network solution that seeks to normalize noisy feats, expand text via use of lexicographic and semantic dictionaries that uses word sense disambiguation technique to train the underlying learning heuristics. And in turn, effectively help to classify SMS in spam and legitimate classes. Hybrid model comprises of text preprocessing, feature selection as well as training and classification section. Study uses a hybrid Genetic Algorithm trained Bayesian model for which the GA is used for feature selection; while, the Bayesian algorithm is used as classifier. Keywords: Hybrid Genetic Algorithm, Trained Bayesian Ensemble, Short Messages Spam Detection Odiakaose, C. Emordi, F. Ejeh, P., Ashioba, N., Odeh, C., Attoh, O. & Azaka, M. (2024): Hybrid Genetic Algorithm Trained Bayesian Ensemble for Short Messages Spam Detection. Journal of Advances in Mathematical & Computational Science. Vol. 12, No. 1. Pp 37-52. Available online at www.isteams.net/mathematics-computationaljournal. dx.doi.org/10.22624/AIMS/MATHS/V12N1P4

Spam email classification based on SVM, Transformer and Naive Bayes

As a matter of fact, with the booming of information and big data, there are too many unwanted e-mails called spam sent to peoples e-mail account in recent years. On this basis, it could lead to a lot of problems including occupying the public resources, causing financial loss and so on. With this in mind, spam filtering technique is in need to solve the problem and address the issues. In reality, based on previous analysis, machine learning methods are very effective in spam filtering. On this basis, this study carried out background research of machine learning algorithms in spam filtering, and find the spam e-mail dataset of Kaggle.com, and implement 3 algorithms on the dataset. According to the analysis, Transformer and SVM work better on the dataset, and SVM is the best. At the same time, the current limitations are discussed as well. In addition, the prospects are demonstrated in the meantime.

Survey on Fraud App Detection Using Sentimental Analysis and Machine Learning

Abstract: Ranking fraud is the practice of acting dishonestly or deceitfully to artificially improve an App's position on a popularity list in the mobile app market. In actuality, app makers are increasingly using ranking fraud. These behaviors include posting phoney app reviews or exaggerating the sales of their apps. Although the importance of combating ranking fraud has long been recognized, there is little literature and research available in this area. To achieve this, we give a thorough examination of fraud app detection in this work utilizing sentiment analysis and spam filtering and propose a technique for identifying it in mobile apps. We specifically suggest mining the active times, or leading sessions, of mobile Apps to precisely locate the ranking scam in the first place.

Random Forest Machine Learning for Spam Email Classification

This research discusses the crucial role of email as a main element in digital communication, facilitating information transfer and serving as an advertising platform. However, the problem of email spam, which involves sending unsolicited commercial messages, has had negative impacts such as consuming large amounts of resources and disrupting user experience. With its affordable cost and ease of sending messages to thousands of recipients, email spam includes product promotions, pornographic material, viruses and irrelevant content. The impact includes loss of time and damage to the user's computer resources. To address this problem, email services provide advanced spam filters that use email content analysis and machine learning techniques. This research focuses on the use of the Random Forest Classification algorithm as a basis for filtering spam emails. Although Random Forest is known to have strong classification capabilities, the risk of overfitting is a challenge. Therefore, this study adopts the Randomized Search CV method to identify the best parameter combination, ensuring the reliability of the model in dealing with the complexity of diverse email datasets. With this approach, this research contributes to the development of effective solutions to reduce the impact of email spam in digital communications.

Text classification supervised algorithms with term frequency inverse document frequency and global vectors for word representation: a comparative study

Over the course of the previous two decades, there has been a rise in the quantity of text documents stored digitally. The ability to organize and categorize those documents in an automated mechanism, is known as text categorization which is used to classify them into a set of predefined categories so they may be preserved and sorted more efficiently. Identifying appropriate structures, architectures, and methods for text classification presents a challenge for researchers. This is due to the significant impact this concept has on content management, contextual search, opinion mining, product review analysis, spam filtering, and text sentiment mining. This study analyzes the generic categorization strategy and examines supervised machine learning approaches and their ability to comprehend complex models and nonlinear data interactions. Among these methods are k-nearest neighbors (KNN), support vector machine (SVM), and ensemble learning algorithms employing various evaluation techniques. Thereafter, an evaluation is conducted on the constraints of every technique and how they can be applied to real-life situations.

Neural Networks and Cyber Resilience: Deep Insights into AI Architectures for Robust Security Framework

With the goal of applying artificial intelligence architectures to strengthen security frameworks, this paper explores the integration of Neural Networks into cybersecurity. The review of the literature highlights the advancements made in the use of neural networks for a range of cyber defense uses, with a focus on spam filtering and intrusion detection. Notable obstacles highlight the changing cybersecurity scene, such as the interpretability of AI and the need for explainable models.The study paper that goes along with it offers helpful advice on how to put AI architectures for strong security frameworks into practice. It emphasizes the methodical fusion of various AI techniques to strengthen defenses against the ever-changing array of cyberthreats. Tabulated performance ratings of several Neural Network types provide a comprehensive grasp of their strengths and capabilities across a range of metrics. These evaluations show that hybrid RCNN-ML performs very well. The potential and difficulties presented by the changing cybersecurity landscape are explored in discussions of AI technologies and their effects, with a focus on how traditional defenses must evolve in order to effectively use AI-driven solutions. At the end this work offers a nuanced viewpoint on the use of neural networks to improve cybersecurity resilience. Understanding the nuances of various Neural Network types is crucial for creating flexible and reliable security frameworks in the face of constantly evolving cyber threats as AI continues to advance.

Tweet Spam Detection Using Machine Learning and Swarm Optimization Techniques

Social media networking platforms connect people living in every corner of the world. Twitter has now become a popular microblogging service, allowing users to express themselves and keep up with current events. Twitter has attracted spammers because to its popularity and ease of use. As a result, spam identification (ID) has become one of the most pressing issues. It is vital to detect and filter spam tweets as well as their owners in order to provide a spam-free environment. In this article, a spam detection method is proposed using a swarm optimization approach on a tweet-by-tweet basis. A spam tweet detection dataset is used to train the machine learning (ML) model. Metaheuristic features are created based on the input features in the dataset. Whale swam optimization algorithm (WOA) is used to select the important features before classification. The conventional objective function of WOA is modified into stochastic gradient descent (SGD) to perform feature selection. The selected subset of features is used to train the Adaboost (AB) classifier to detect the spam in the tweets. The AB classifier produced the best results in combination with WOA and SGD. The obtained accuracy is 99.85% in testing with a minimum subset of seven features and in the least possible minimum time of 17.9 s.

A Hybrid Model with New Word Weighting for Fast Filtering Spam Short Texts.

Short message services (SMS), microblogging tools, instant message apps, and commercial websites produce numerous short text messages every day. These short text messages are usually guaranteed to reach mass audience with low cost. Spammers take advantage of short texts by sending bulk malicious or unwanted messages. Short texts are difficult to classify because of their shortness, sparsity, rapidness, and informal writing. The effectiveness of the hidden Markov model (HMM) for short text classification has been illustrated in our previous study. However, the HMM has limited capability to handle new words, which are mostly generated by informal writing. In this paper, a hybrid model is proposed to address the informal writing issue by weighting new words for fast short text filtering with high accuracy. The hybrid model consists of an artificial neural network (ANN) and an HMM, which are used for new word weighting and spam filtering, respectively. The weight of a new word is calculated based on the weights of its neighbor, along with the spam and ham (i.e., not spam) probabilities of short text message predicted by the ANN. Performance evaluations on benchmark datasets, including the SMS message data maintained by University of California, Irvine; the movie reviews, and the customer reviews are conducted. The hybrid model operates at a significantly higher speed than deep learning models. The experiment results show that the proposed hybrid model outperforms other prominent machine learning algorithms, achieving a good balance between filtering throughput and accuracy.

Spam filter based on naive bayes algorithm

The widespread use of Electronic Mail (E-mail) has led to a significant increase in spam, which has severely impeded the growth and well-being of the Internet. To mitigate this issue, the implementation of email filtering techniques has become necessary, requiring the use of specific technological tools. Presently, the K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Naive Bayes (NB) algorithms are commonly used in probability statistical classification methods for email filtering. Among these, the NB algorithm is the most classical, with its rich mathematical theory as the basis, high classification efficiency, and straightforward algorithmic approach. However, the algorithm relies on the conditional independence assumption, making the accuracy susceptible to the correlation between attributes. This study focuses on email filtering techniques based on the NB algorithm, conducting experiments to evaluate the classification accuracy and proposing feasible improvements to weaken the independence assumption. The experimental results demonstrated the effectiveness of the employed method.

SecK2 – A novel machine learning algorithm for detecting data poisoning attacks

Machine learning (ML) techniques play a crucial role in producing precise predictions without the use of explicit programming by utilizing representative and unbiased data. These methods, which are a subset of artificial intelligence (AI), are used in a variety of settings, including recommendation engines, spam filtering, malware detection, classification, and predictive maintenance. While ML algorithms improve results, they also present security and privacy threats, especially in the face of adversarial ML attacks such as data poisoning assaults that can undermine data modeling applications. This study introduces SecK2, a cutting-edge ML method developed to stop dangerous input from entering ML models. The scalability of SecK2 is proved through meticulous experimental research, revealing its astonishing capacity to identify data poisoning attacks at a previously unheard-of pace. As a result, SecK2 becomes a valuable tool for guaranteeing the reliability and security of ML models. Our suggested method produces outstanding results by a variety of criteria. Notably, it achieves a noteworthy 61% convergence rate and an exceptional 89% attack detection rate. Additionally, it offers a phenomenal 96% throughput while protecting data integrity at 53%. The technique also boasts impressive Validation accuracy of 96% and Training accuracy of 92%. The suggested technology offers a strong and reliable barrier against the rising danger of data poisoning attacks. ML practitioners can have more faith in their models, thanks to SecK2’s capabilities, protecting against potential adversarial assaults and preserving the dependability of ML-based applications.

Analyzing Cognitive Load Associated with Manual Text Classification Task Using Eye Tracking

The accuracy of machine learning-based automated text classification systems, such as spam filters and search engine results, heavily depends on the quality of manual text classification. However, the cognitive demands of manual text classification tasks, particularly when dealing with challenging or difficult-to-comprehend texts, have not been extensively explored in previous studies. This research aims to address this gap by investigating the cognitive load associated with manual text classification tasks through analyzing eye tracking data. In this study, 30 participants performed manual text classification tasks while their ocular parameters were recorded using an eye tracker. The findings of this study revealed that ocular parameters recorded through eye tracking provided valuable insights into the cognitive load experienced during manual text classification tasks. Furthermore, it was observed that complex narratives led to higher cognitive load estimation. Moreover, native English-speaking participants exhibited lower cognitive load, compared to non-native English speakers.