Air quality is a critical concern in urban environments, impacting public health and quality of life. In order to keep track of the trend of air quality, it is especially important to forecast it accurately. The Air Quality Index (AQI) is used for reporting daily air quality. It tells you how clean or polluted your air is, and what associated health effects might be a concern for you. The daily average value of the mass concentration of PM2.5, PM10,SO2, NO2, CO, O3 was obtained for this study.By integrating meteorological factors, pollutant concentrations, and historical AQI
trends. This model offers a comprehensive and accurate forecast of AQI levels. The model not only improves the accuracy of short-term AQI predictions but also enhances our understanding of the complex interactions between various environmental factors affecting air quality, ultimately contributing to better-informed decision-making for pollution control and public health management. Air Quality Index (AQI) is the crucial foundation for measuring air quality, which reflects the influence of air quality on people’s health and life to a certain extent.
Spam emails continue to pose significant challenges to users and organizations, necessitating robust classification techniques for effective filtering. This paper presents an overview of spam classification methods leveraging unsupervised learning algorithms. By eschewing the need for labeled data, unsupervised learning offers a promising avenue for identifying and categorizing spam without explicit human intervention. The abstract discusses the key concepts and methodologies employed in unsupervised spam classification, encompassing clustering algorithms such as K-means, hierarchical clustering, and density-based methods.
Cyberattacks and the use of malware are more and more omnipresent nowadays. Targets are as varied as states or publicly traded companies. Malware analysis has become a very important activity in the management of computer security incidents. Organizations are often faced with suspicious files captured through their antiviral and security monitoring systems, or during forensics analysis. Most solutions funnel out suspicious files through multiple tactics correlating static and dynamic techniques in order to detect malware. However, these mechanisms have many practical limitations giving rise to a new research track. The aim of this paper is to tackle the use of machine learning algorithms to analyse malware and expose how data science is used to detect malware. Training systems to find attacks allows to develop better protection tools, capable of detecting unprecedented campaigns. This study reveals that many models can be employed to evaluate their detectability. Our demonstration results illustrate the possibility to analyze malware leveraging several machine learning (ML) algorithms comparing them.
This project aims to generate word embeddings for Sanskrit shlokas by employing a hybrid approach that combines traditional linguistic knowledge with modern computational techniques. Sanskrit, an ancient Indo-Aryan language, holds significant cultural and religious importance, with shlokas serving as fundamental units of expression in various texts. The proposed methodology involves collecting a diverse dataset of Sanskrit shlokas and preprocessing them through tokenization, sandhi splitting, and normalization. Traditional linguistic features, including morphological, phonological, and semantic aspects, are extracted from the shlokas. These features are then integrated with modern natural language processing (NLP) techniques. The project aims to contribute to the preservation and understanding of Sanskrit language and literature while also showcasing the potential of hybrid approaches in computational linguistics.
This letter presents a methodology for urban area mapping with density-based spatial clustering of applications with noise (DBSCAN) using the Advanced Synthetic Aperture Radar (ASAR), Sentinel-1A, and HuanJing-1C data. Urban areas have a diversity of shapes, including circles, squares, strips, and other irregular shapes, and the DBSCAN clustering algorithm is suitable for identifying clusters of arbitrary shapes. Exploiting DBSCAN to extract urban areas is a key aspect of this method, and improvements via the incorporation of synthetic aperture radar data preprocessing and postprocessing also play important roles in optimizing the extractions. Different test site sizes were chosen to demonstrate the effectiveness and feasibility of the proposed method, and the validation results showed that the method is efficient and accurately extracts urban areas ranging from small towns to super metropolitan areas. Index Terms Density-based spatial clustering of applications with noise (DBSCAN), synthetic aperture radar (SAR), urban area mapping.
Platforms for social media like Facebook, Twitter, Instagram, and others have a big impact on our lives. Our lives nowadays rely heavily on social media. Everyone uses social media, whether it be to share beautiful, expensive photos, follow celebrities, or talk with nearby and distant pals. It is a fantastic place for exchanging knowledge and interacting with others. However, everything has a drawback. Social media has a significant role in our lives, yet there have been times when it has become problematic. People are actively participating in it the world over. However, it also has to deal with the issue of bogus profiles. False accounts are frequently created by humans, bots, software, or machines. They are employed in the spread of rumors and illegal actions like phishing and identity theft. The aim of this project is to use several machine learning techniques to discriminate between fake and authentic profiles. It is possible to disable or delete a fake profile when it is found, preventing cyber security issues.
Plagiarism involves stealing someone’s work and presenting it as one’s own. This system analyzes plagiarism data by detecting similarities in text, including paraphrased works and keyword overlaps. It utilizes techniques like WordNet to identify similar content. The system measures text similarity and detects plagiarism.The internet has revolutionized students’ lives and learning styles, allowing for deeper engagement with learning materials. Text mining methods are employed for plagiarism detection. Users register with basic details to create a login. Students upload assignments, which are divided into content and reference links. The system processes content, visits each reference link, and compares it with the original content. Students can view their document history and check for grammar mistakes.
With the increasing prevalence of online fraud and the use of fake logos to deceive consumers, there is a need for effective methods to detect and prevent the use of fake logos on the internet. In this paper, we propose a method for detecting fake logos using machine learning techniques. Our approach involves extracting features from the logos and training a classifier to distinguish between real and fake logos. We evaluate the performance of our method on a dataset of real and fake logos and demonstrate its effectiveness in detecting fake logos with high accuracy. Every day, hundreds of domain names, websites and logos are being cloned by cyber criminals who want to gain your trust so they can steal your data. It is becoming a big issue in the online world and needs to be addressed. This article will discuss the initial project background of our new Online Fake Logo Detection System.
This project focuses on automating the detection of middle ear pathologies using the VGG16 convolutional neural network. Trained on a diverse dataset of annotated images from various imaging modalities, including otoscopy, CT, and MRI, the fine-tuned VGG16 model shows promising results. Its accuracy in identifying and classifying middle ear disorders is evaluated against traditional methods, showcasing advantages such as rapid image analysis and potential integration into healthcare systems. The VGG16-based approach holds promise for improving diagnostic accuracy, enabling early intervention, and enhancing patient outcomes in managing middle ear disorders. Further research is needed to validate its performance on larger datasets and explore integration into clinical practice.
