Road safety is a critical global concern, with manual enforcement of helmet laws and vehicle safety standards (e.g., rear-view mirror presence) being resource-intensive and inconsistent. This paper presents an AI-powered system to automate traffic violation detection, significantly enhancing enforcement efficiency and road safety. The system leverages YOLOv8 for robust object detection and EasyOCR for license plate recognition. Trained on a custom dataset of annotated images (augmented for diversity), it identifies helmet non-compliance, the absence of rear-view mirrors on motorcycles, an innovative contribution to automated checks, and extracts vehicle registration numbers. A Streamlit-based interface facilitates real-time monitoring and violation logging. Advanced image preprocessing enhances license plate recognition, particularly under challenging conditions. Based on evaluation results, the model achieves an overall precision of 0.9147, a recall of 0.886, and a mean Average Precision (mAP@50) of 0.843. The mAP@50 95 of 0.503 further indicates strong detection capability under stricter IoU thresholds. This work demonstrates a practical and effective solution for automated traffic rule enforcement, with considerations for real-world deployment discussed.

M1: Chunking long documents into max-context-length chunks, and averaging all-mini-LM-v6 embeddings across chunks to produce a final document embedding.

Road accidents pose significant concerns globally. They lead to large financial losses, injuries, disabilities, and societal challenges. Accurate and timely accident data is essential for predicting and mitigating these events. This paper presents a novel framework named 'Durghotona GPT' that integrates web scraping and Large Language Models (LLMs) to automate the generation of comprehensive accident datasets from prominent national dailies in Bangladesh. The authors collected accident reports from three major newspapers: Prothom Alo, Dhaka Tribune, and The Daily Star. The collected news was then processed using the newest available LLMs: GPT-4, GPT-3.5, and Llama-3. The framework efficiently extracts relevant information, categorizes reports, and compiles detailed datasets. Thus, this framework overcomes limitations of manual data collection methods such as delays, errors, and communication gaps. The authors' evaluation demonstrates that Llama-3, an open-source model, performs comparably to GPT-4. It achieved 89% accuracy in the authors' evaluation. Therefore, it can be considered a cost-effective alternative for similar tasks. The results suggest that the framework developed by the authors can drastically enhance the quality and availability of accident data. As a result, it can support critical applications in traffic safety analysis, urban planning, and public health. The authors also developed an interface for 'Durghotona GPT' for ease of use as part of this paper. Future work will focus on expanding data collection methods and refining LLMs to further increase dataset accuracy and applicability.

This paper surveys the potential of contextualized AI in enhancing cyber defense capabilities, revealing significant research growth from 2015 to 2024. We identify a focus on robustness, reliability, and integration methods, while noting gaps in organizational trust and governance frameworks. Our study employs two LLM-assisted literature survey methodologies: (A) ChatGPT 4 for exploration, and (B) Gemma 2:9b for filtering with Claude 3.5 Sonnet for full-text analysis. We discuss the effectiveness and challenges of using LLMs in academic research, providing insights for future researchers.

Distributed Denial of Service (DDoS) attacks are a major concern in network security, as they overwhelm systems with excessive traffic, compromise sensitive data, and disrupt network services. Accurately detecting these attacks is crucial to protecting network infrastructure. Traditional approaches, such as single Convolutional Neural Networks (CNNs) or conventional Machine Learning (ML) algorithms like Decision Trees (DTs) and Support Vector Machines (SVMs), struggle to extract the diverse features needed for precise classification, resulting in suboptimal performance. This research addresses this gap by introducing a novel approach for DDoS attack detection. The proposed method combines three distinct CNN architectures: SA-Enabled CNN with XGBoost, SA-Enabled CNN with LSTM, and SA-Enabled CNN with Random Forest. Each model extracts features at multiple scales, while self-attention mechanisms enhance feature integration and relevance. The weighted ensemble approach ensures that both prominent and subtle features contribute to the final classification, improving adaptability to evolving attack patterns and novel threats. The proposed method achieves a precision of 98.71%, an F1-score of 98.66%, a recall of 98.63%, and an accuracy of 98.69%, outperforming traditional methods and setting a new benchmark in DDoS attack detection. This innovative approach addresses critical limitations in current models and advances the state of the art in network security.

The proper planning of different types of public transportation such as metro, highway, waterways, and so on, can increase the efficiency, reduce the congestion and improve the safety of the country. There are certain challenges associated with route planning, such as high cost of implementation, need for adequate resource & infrastructure and resistance to change. The goal of this research is to examine the working, applications, complexity factors, advantages & disadvantages of Floyd- Warshall, Bellman-Ford, Johnson, Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), & Grey Wolf Optimizer (GWO), to find the best choice for the above application. In this paper, comparative analysis of above-mentioned algorithms is presented. The Floyd-Warshall method and ACO algorithm are chosen based on the comparisons. Also, a combination of modified Floyd-Warshall with ACO algorithm is proposed. The proposed algorithm showed better results with less time complexity, when applied on randomly structured points within a boundary called quasi-structured points. In addition, this paper also discusses the future works of integrating Floyd-Warshall with ACO to develop a real-time model for overcoming above mentioned-challenges during transportation route planning.

Automatic summarization is the process of reducing a text document in order to generate a summary that retains the most important points of the original document. In this work, we study two problems - i) summarizing a text document as set of keywords/caption, for image recommedation, ii) generating opinion summary which good mix of relevancy and sentiment with the text document. Intially, we present our work on an recommending images for enhancing a substantial amount of existing plain text news articles. We use probabilistic models and word similarity heuristics to generate captions and extract Key-phrases which are re-ranked using a rank aggregation framework with relevance feedback mechanism. We show that such rank aggregation and relevant feedback which are typically used in Tagging Documents, Text Information Retrieval also helps in improving image retrieval. These queries are fed to the Yahoo Search Engine to obtain relevant images 1. Our proposed method is observed to perform better than all existing baselines. Additonally, We propose a set of submodular functions for opinion summarization. Opinion summarization has built in it the tasks of summarization and sentiment detection. However, it is not easy to detect sentiment and simultaneously extract summary. The two tasks conflict in the sense that the demand of compression may drop sentiment bearing sentences, and the demand of sentiment detection may bring in redundant sentences. However, using submodularity we show how to strike a balance between the two requirements. Our functions generate summaries such that there is good correlation between document sentiment and summary sentiment along with good ROUGE score. We also compare the performances of the proposed submodular functions.

Sugarcane, a key crop for the world's sugar industry, is prone to several diseases that have a substantial negative influence on both its yield and quality. To effectively manage and implement preventative initiatives, diseases must be detected promptly and accurately. In this study, we present a unique model called sugarcaneNet2024 that outperforms previous methods for automatically and quickly detecting sugarcane disease through leaf image processing. Our proposed model consolidates an optimized weighted average ensemble of seven customized and LASSO-regularized pre-trained models, particularly InceptionV3, InceptionResNetV2, DenseNet201, DenseNet169, Xception, and ResNet152V2. Initially, we added three more dense layers with 0.0001 LASSO regularization, three 30% dropout layers, and three batch normalizations with renorm enabled at the bottom of these pre-trained models to improve the performance. The accuracy of sugarcane leaf disease classification was greatly increased by this addition. Following this, several comparative studies between the average ensemble and individual models were carried out, indicating that the ensemble technique performed better. The average ensemble of all modified pre-trained models produced outstanding outcomes: 100%, 99%, 99%, and 99.45% for f1 score, precision, recall, and accuracy, respectively. Performance was further enhanced by the implementation of an optimized weighted average ensemble technique incorporated with grid search. This optimized sugarcaneNet2024 model performed the best for detecting sugarcane diseases, having achieved accuracy, precision, recall, and F1 score of 99.67%, 100%, 100%, and 100% , respectively.

Formality plays a significant role in language communication, especially in low-resource languages such as Hindi, Japanese and Korean. These languages utilise formal and informal expressions to convey messages based on social contexts and relationships. When a language translation technique is used to translate from a source language that does not pertain the formality (e.g. English) to a target language that does, there is a missing information on formality that could be a challenge in producing an accurate outcome. This research explores how this issue should be resolved when machine learning methods are used to translate from English to languages with formality, using Hindi as the example data. This was done by training a bilingual model in a formality-controlled setting and comparing its performance with a pre-trained multilingual model in a similar setting. Since there are not a lot of training data with ground truth, automated annotation techniques were employed to increase the data size. The primary modeling approach involved leveraging transformer models, which have demonstrated effectiveness in various natural language processing tasks. We evaluate the official formality accuracy(ACC) by comparing the predicted masked tokens with the ground truth. This metric provides a quantitative measure of how well the translations align with the desired outputs. Our study showcases a versatile translation strategy that considers the nuances of formality in the target language, catering to diverse language communication needs and scenarios.