Potholes are a common problem in roads and highways around the world, which can cause severe damage to vehicles and create safety hazards for drivers.In recent years, deep learning algorithms have been increasingly used for automated pothole detection.This research offers a deep learningbased algorithm that can detect potholes early using photos and videos, reducing the likelihood of an accident.This model is basically based Faster Region-based Convolutional Neural Network(F-RCNN) and You Only Look Once Version 3(YOLO V3).It also discuss the challenges in detecting potholes, such as variable lighting conditions and noise in the data, and how these challenges have been addressed in previous research.Finally, we provide a comparative analysis of the performance of different deep learning algorithms for pothole detection based on accuracy.There are various pothole identification models that combine the accelerometer with machine learning techniques, but there are fewer pothole detection models that use simply machine learning techniques to detect potholes.The findings of this study suggest that deep learning algorithms can provide accurate and efficient pothole detection solutions that can help road authorities to maintain and repair roads, reduce vehicle damage, and enhance road safety.
The widespread dissemination of misinformation, commonly known as fake news, has been facilitated by the rapid expansion of social networks as platforms for news distribution.This stands in contrast to traditional mass media channels such as newspapers, magazines, radio, and television.The challenges arise from human limitations in discerning between true and false information, which poses a significant threat to logical coherence, democratic processes, journalistic integrity, and the credibility of government institutions.The lack of reliable and trustworthy information on social media further compounds the issues associated with this phenomenon.To address this pressing problem, we have proposed an integrated system that incorporates various pre-processing techniques and classification models.This system aims to detect and combat fake news by evaluating their efficacy on a specific dataset of labeled news statements.By utilizing metrics such as precision, F1 scores, and recall, we can determine the most effective model.The primary objective of this system is to develop an efficient and accurate model capable of predicting and identifying instances of fake news within social media networks.
Software Engineering is a Comprehensive domain since software system have become larger and complex than ever.Such software characteristics make it very complex to develop fault free software.Therefore, identifying defects automatically and fixing them is challenging task.Improper modelling, lack of requirement specifications, wrong coding, poor configuration management may cause defects which leads to failure of software system.Such defects must be detected and fixed at early stage of software development to minimize cost.Machine learning algorithms widely used in the software defect prediction also achieves good results in predicting software defects using deep learning techniques.In this paper, we are providing comparative study of various algorithms like convolutional neural networks, multi-layer Perceptrons in identifying defects in software.Moreover, the experiments conducted on NASA datasets.
Wouldn't it be cool for your device to translate English language into the language you speak in real life and vice versa?Understanding multiple languages can be a challenging task for humans.In a country like India, where there are over 22 recognized languages, it can be nearly impossible for an individual to learn and comprehend all of them.Consequently, communicating effectively in such a diverse linguistic environment can pose significant obstacles, forcing the need of human interpreters.An interpreter is a platform which translates what is being said in one language into another language without changing its original meaning.Google Translate is one such existing platform but it has drawbacks like not ideal for confidential documents, cannot provide a perfectly accurate translation and requires internet connection to translate.Our goal is to address the limitations mentioned earlier, and to accomplish this objective, we have come up with a project idea that involves real-time translation of spoken languages.The project aims to achieve this by using audio as an input.The proposed system will take real-time English audio as an input, translate it into Telugu and then will produce audio output in Telugu and vice versa.As English is the third most spoken native language in the world after Chinese and Spanish and also the most widely used, we chose to convert the text from English to Telugu and vice versa.Our intention is to utilize a Sequence-to-Sequence model as it is proficient in performing language translations.This model works by transforming a sequence of words from one language into a sequence of different words in another language.It uses neural network for sequence learning.
