Turkish Journal of Computer and Mathematics Education (TURCOMAT)
Not a member yet
9094 research outputs found
Sort by
DETECTING AND CLASSIFYING FRAUDULENT SMS AND EMAIL WITH A ROBUST MACHINE LEARNING APPROACH
No full textSpam is an unwanted message or SMS sent onmobile phones whose content may bemalicious. Scammers sendfake text messages to trick people into responding to their SMSand they may hack personal information, password, accountnumber, etc. To avoid being tricked by scammers, proposed amodel based on Machine learning Algorithms. The proposedmodel is implemented using the Naïve Bayes algorithm and termfrequency-inverse document frequency vectorizer. Obtainedthe dataset from Kaggle and trained the model using it. Thismodel consists of a local host website which is obtained throughPyCharm IDE. Obtained results show that the model accuracyof 95% and a precision of 100
A Survey on Deep Learning Approaches for Crop Disease Analysis in Precision Agriculture
No full textPrecision agriculture has emerged as a transformative paradigm in modern farming, leveraging advanced technologies to optimize crop management. This paper presents a comprehensive survey of deep learning approaches for crop disease analysis in precision agriculture. The investigation focuses on four key aspects: leaf disease detection through deep learning techniques, leaf shape-based disease analysis, crop weed detection utilizing deep learning methods, and crop damage detection using aerial images. The survey encompasses a review of recent advancements, methodologies, challenges, and future prospects in each of these domains. By exploring the intersection of deep learning and precision agriculture, this paper aims to provide a holistic understanding of the current state-of-the-art and inspire further research initiatives to enhance crop health monitoring and management
Enhancing CRM Systems with AI-Driven Data Analytics for Financial Services
No full textThis research paper is aimed to provide the analysis of the phenomenon of artificial intelligence and data analytics in the context of the financial services industry with focus on the incorporation of these two concepts in Customer Relationship Management systems. The work takes a detailed look at the present and the contemporary developments in the field of CRM systems, the immense opportunities of applying artificial intelligences in the field of customer analytics, as well as the complex issues of implementation. In the case study, analysing machine learning algorithms, natural language processing, and complex predictive analyses, I show how AI improves customer information and personalisation operations, as well as decision-making. Lack of hard evidence of the performance of the AI-CRM system is an area that needs some improvement, Real-life examples taken from retail banking, wealth management businesses, and insurance industries show the effective adoption of the AI -CRM system. The research also incorporates invaluable questions concerning data privacy, compliance, and the ethical use of AI in the financial service industry. Last but not the least, it speaks about the current trends and offers a literature-backed guideline for the financial service providers who want to use the AI in the CRM and create possibilities for the future of the AI in the CRM system
Comprehensive Data Corruption Identification Using Machine Learning Algorithms (PAACDA)
No full textData and analysis have evolved from being scattered numbers and qualities in spreadsheets to being seen as a means to revolutionize any substantial industry, thanks to the rise of technology. There are many unethical and unlawful ways that data may get corrupted; thus, it\u27s important to find a way to effectively detect and highlight all the corrupted data in the dataset. It is not an easy task to detect damaged data or to restore information from a corrupted dataset. This is crucial and could cause issues with data processing using machines or deep learning methods later on if not handled early enough. Rather than focusing on outlier identification, this study introduces its PAACDA: Presence-driven Adamic Adar Corruption Identification Algorithm and then consolidates the findings. Even though they rely on parameter tuning to achieve high accuracy, and remember, current state-of-the-art models like Isolation Forest and DBSCAN (which stands for "Density-Based the Spatial Process of Clustering of the Applications with Noise") have a lot of uncertainty when they factor in corrupted data. This study investigates the specific performance problems with several unsupervised learning methods on corrupted linear and clustered datasets. In addition, we provide a new PAACDA technique that achieves a higher precision of 96.35% for cluster data and 99.04% for linear data compared to previous unsupervised training benchmarks on 15 prominent baselines, including K-means clustering, isolation forest, and LOF (local outlier factor). From the aforementioned angles, this essay delves deeply into the relevant literature as well. In this study, we identify all the problems with current methods and suggest ways forward for research in this area
Darknet Traffic Analysis: Examining How the ADABOOST Algorithm Affects the Classification of Onion Service Traffic Given Modified Tor Traffic
No full textIn order to shape and monitor traffic, it is necessary to classify network traffic. The significance of privacy-preserving technology has increased in the last twenty years due to the growth of privacy concerns. One common method of remaining anonymous while surfing the web is to join the Tor network. This will allow you to remain anonymous while also supporting anonymous services called Onion Services. The problem is that government and law enforcement organizations often take advantage of this anonymity, particularly with Onion Services, and end up de-anonym zing its users. This paper\u27s emphasis is on three primary contributions in an effort to discover the capability to categorize Onion Service traffic. Separating Onion Service communication from regular Tor traffic is our first objective. With over 99% accuracy, our methods can detect Onion Service traffic. On the other hand, Tor traffic may have its information leaking concealed by making a
Few adjustments. We assess the efficacy of our methods in light of these changes to Tor traffic in our second contribution. According to our experiments, under these circumstances, the Onion Services traffic becomes less distinct, with an accuracy decrease of over 15% seen in some instances. We conclude by determining and assessing the effect of the most important feature combinations on our classification task
PREDICTING FLIGHT DELAYS WITH ERROR CALCULATION USING MACHINE LEARNED CLASSIFIERS
No full textWorldwide, flight delays are becoming a major issue for the airline business. Due to air traffic congestion brought on by the airline industry\u27s expansion over the last 20 years, flights have been delayed. In addition to hurting the economy, flight delays also have a detrimental effect on the environment since they increase fuel consumption and gas emissions. Thus, it is essential to take all reasonable precautions to avoid flight delays and cancellations. This paper\u27s primary goal is to forecast an airline\u27s delay utilizing a variety of variables. Thus, it is necessary to perform forward-looking analysis, which encompasses a variety of algorithmic predictive analytics approaches that use historical and current data to create models that are used for forecasts or simply to look at future delays using machine learning algorithms like Python 3\u27s Gradient Boosting Regression technique, Bayesian Ridge, Random Forest Regression, and Logistic Regression. This will make it easier for the user to forecast whether an aircraft will arrive on time or not. Additionally, delay prediction analysis will assist airline industries in reducing their losses
Advancements and Applications of Deep Learning: A Comprehensive Review
No full textArtificial intelligence\u27s significant branch of deep learning has grown tremendously in the past few years. It has integrated into various fields due to its enhanced predictive and analytical features. Due to this technology\u27s capability of making sense of massive amounts of data, it has found application across a broad spectrum of industries, including the healthcare and financial sectors, to name but a few. This review aims to identify and present the significant developments in deep learning methods and the wide range of fields to which they are applied. The key concern is what has happened to these processes, how these changes have affected professional practices, and the nature of future technological environments. It systematically identifies the literature based on scientific databases like IEEE Xplore, PubMed, and Google Scholar. The search used the following keywords: \u27deep learning advancements,\u27 \u27neural networks in practice,\u27 and \u27AI applications in industry. \u27Applying strict inclusion and exclusion criteria helped to choose more relevant and reliable works. The presented results focus on the main achievements in deep learning structures, especially convolutional and recurrent neural networks, and their application in real-life use cases. Some areas where deep learning is applied include diagnosing medical conditions, self-driving cars, and even predicting the trends in financial markets. Deep learning has dramatically impacted technological advancement and set new standards by bringing significant economic and social value improvements. The field has not been out of finding data privacy and the lack of model interpretability, which must be solved to ensure future growth
A Review on Methods used to determine Fractal Dimension Analysis of AFM Images of Thin Film
No full textThis aims the applications in the field of surface analysis and study of structure of different materials. The purposeof this examination is to additionally research the ultra-structural details of the surface of thin film using atomicforce microscopy (AFM) images. The fractal dimension, gave quantitative qualities that describe the scaleproperties of surface geometry. Detailed identification of the surface geometry was obtained using statisticalparameters. The analyzed AFM images confirm a fractal nature of the surface, which is not taken into account byclassical surface statistical parameters. In this paper, we present a review on different methods used to determinefractal dimension of AFM images of thin film
Depth reduction of RGB image data and reduction of point noise based on metric learning method
No full textIn this paper, a method of data depth reduction based on metric learning method in reducing point noise in different images is proposed. In order to be more accurate in reviving depth from data, noise variance is also calculated for each separate scale. In this way, our method becomes more sensitive to noise detection. The quantitative and qualitative results obtained from the implementation and calculation of the PSNR parameter of this method show that the proposed method of this paper has given a good answer compared to previous methods for noise elimination and has performed better in maintaining sharp corners and sharp features
MACHINE LEARNING AND BLOCKCHAIN-BASED REAL-TIME FACIAL RECOGNITION ATTENDANCE SYSTEM
No full textIn a vast majority of fields, the use of facial recognition for authentication is expanding. In this information age, authentication has become vital, and the need for faster and more secure methods of user authentication has been on the rise. The introduction of image processing technologies such as OpenCV has increased society’s reliance on face recognition. Using blockchain, information could be stored in blocks throughout the blockchain network. Blockchain is an extremely secure means for storing and protecting data from intruders. It is a highly disruptive technology that has the ability to alter every plane of society. This paper intends to implement opensource computer vision (OpenCV) to construct a facial detection model that will be employed in a blockchain-secured Attendance Monitoring System. It will not only automate the attendance procedure but also give the system unassailable security. This system will take a live video feed from a camera using OpenCV and identify the faces of students and record their attendance along with the entry time. The data will be kept in a distributed way over the blockchain network that will be accessible to everyone, but data cannot be manipulated