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An Investigation of Deep Neural Network based Techniques for Object Detection and Recognition Task in computer Vision 2

An Investigation of Deep Neural Network based Techniques for...

引用

2nd International Conference on Edge Computing and applications, ICECAA 2023

作者： Sunil Kumar, Aithal S Mahesh, Kavitha St Joseph Engineering College Dept. of Computer Science and Engineering Mangalore India Nmam Institute of Technology Nitte Dept. of Computer Science and Engineering India Science and Engineering Nagarjuna College of Engineering & Technology Mudugurki Bengaluru India

ISBN: (纸本)9798350347579

Detection of objects and its recognition in visual sequences are the two critical tasks in the computer vision field. Various real-time applications such as autonomous vehicles, face recognition, health-care systems and space exploration requires highly reliable and precise object detection models. Traditional object detection and recognition algorithms are based on hand crafted and are considered to be erroneous, time consuming and expensive leading to the significant reduction of accuracy rate for object detection in large datasets. Recently, large number of promising deep neural networks models have been emerged for facilitating automated and accurate detection of varying scale objects and its precise recognition across various computer vision applications. Several GPU based neural models thereby incorporating context-aware capabilities have shown effective performance, which overcomes the drawbacks of traditional techniques. This research study provides an investigation of several popular deep learning models that exists for accurate object detection in various forms of visual sequences. Varying scale objects are detected frequently available in popular MS COCO and PASCAL datasets and their performance are evaluated utilizing one stage Yolo family and two stage Faster RCNN deep learning object detectors. At the end of the study, several future research directions for object detection task are discussed. © 2023 IEEE.

关键词： Object detection

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Recent Advancements and Challenges in Bangla Textual Document Classification: A Review 26

Recent Advancements and Challenges in Bangla Textual Documen...

引用

26th International Conference on computer and Information Technology, ICCIT 2023

作者： Nahar, Khairun Al Mahfuz, Mohammed Abdullah Haque Sweety, Mahmuda Comilla University Faculty of Engineering Dept. of Computer Science and Engineering Cumilla Bangladesh

ISBN: (纸本)9798350359015

Bengali is the world's sixth-most frequently used dialect. Natural language processing (NLP), which enables data scientists to extract useful information from the constantly growing quantity of text data available on the net, includes text categorization as a key component. It is the act of organizing already-created groups or classes of text documents. But compared to other languages, like English, Bengali does not have as many capabilities for classifying texts. This study offers an extensive analysis of the current categorization of Bengali documents. Also gives a comprehensive summary of recent advances in ML and DL, covering datasets, methods, performance, accuracy rate, classification category, strengths, and limits. The purpose of this discussion is to identify current challenges that must be addressed and recommend some guidelines for future studies that might be explored. © 2023 IEEE.

关键词： Bangla Language Bengali text classification Classification Document classification Natural Language processing Text categorization

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Vulnerability Scanning and Analysis Tool (VSAT) 9

Vulnerability Scanning and Analysis Tool (VSAT)

引用

9th International Conference on Smart Computing and Communications, ICSCC 2023

作者： Nair, Aarya Anil, Abhinav Pallivathuckal, Anna Rose Daiju, Dijo John, Jomina Rajagiri School of Engineering and Technology Dept. of Computer Science and Engineering Kochi India

ISBN: (纸本)9798350314090

Due to exponential technological advancement, the number of cyber attacks has been steadily increasing and leading to substantial loss of profits, productivity losses, reputational damage as well as judicial liability. To ensure that cyber attacks do not occur, it is important to identify vulnerabilities in web applications. This paper presents a vulnerability scanning tool that involves continuous monitoring and testing of Web applications which analyses the various vulnerabilities that are present in a system. Different types of scans that are performed to detect these vulnerabilities are also explained. © 2023 IEEE.

关键词： Network security

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IoT-Enabled Remote-Controlled Raft for Enhanced Water Quality Assessment 20

IoT-Enabled Remote-Controlled Raft for Enhanced Water Qualit...

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20th IEEE International Colloquium on Signal Processing and Its applications, CSPA 2024

作者： Surasak, Thattapon Kitchat, Kotcharat Jiteurtragool, Nattagit King Mongkut's University Of Technology North Bangkok Faculty Of Applied Science Dept. Of Computer And Information Science Bangkok Thailand National Central University Dept. Of Computer Science And Information Engineering Taoyuan Taiwan

ISBN: (纸本)9798350382310

Water is vital for all life, serving as the foundation for plants and aquatic creatures and promoting abundance among all living beings. This study introduces an innovative approach by combining the Internet of Things (IoT) with advanced sensing techniques to assess water quality. The IoT-powered water quality testing raft has been developed to significantly enhance the monitoring capabilities. Equipped with sensors measuring key parameters such as turbidity, pH, and temperature, the raft provides a comprehensive understanding of water quality. Its GPS module enables real-time tracking and maintains location history, ensuring precise sampling. The collected data undergoes robust processing by a Raspberry Pi, ensuring integrity before being showcased on the Thingsboard. The system's uniqueness lies in its capacity to promptly issue alerts via LINE Notify upon detecting water quality abnormalities, ensuring timely intervention. This IoT-powered raft represents a paradigm shift in water quality assessment, characterised by its precision, remote capabilities, and user-focused approach. Practical applications have been demonstrated through real-world testing in a private lake in Cha-choengsao province, Thailand. The seamless integration of IoT into environmental monitoring marks a significant advancement in ensuring water quality and ecosystem health. © 2024 IEEE.

关键词： Internet of things

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Sign Language Identification using Skeletal Point-based Spatio-Temporal Recurrent Neural Network 9

Sign Language Identification using Skeletal Point-based Spat...

引用

9th International Conference on Smart Computing and Communications, ICSCC 2023

作者： Johnson, Jans Joseph, Jisha Reji, Maris George, Megha Elisa Sajanraj, T.D. Rajagiri School of Engineering and Technology Dept. of Computer Science and Engineering Kochi India

ISBN: (纸本)9798350314090

Sign language is a visual language that serves as the primary means of communication in the non-hearing community. It is essential to research and develop sign language translation methods to enable smooth communication between the non-hearing and the hearing communities. The non-hearing community faces several challenges that hinder their interaction with the general public, such as the lack of knowledge of sign language and the availability of interpreters. A Sign Language Converter would thus be an important tool in breaking the communication barrier between the non-hearing and hearing communities. The paper aims to build a model that converts Indian Sign Language into corresponding words. We plan on using a skeletal-point feature extraction framework to identify hand landmarks from sequences containing distinct signs and use these landmarks to build a model for recognizing hand gestures using various Long Short-Term Memory (LSTM) Networks. [1] This approach can produce an accurate result compared to the traditional approach. The dataset is made by the co-authors of this paper due to the insufficiency of pre-existing ISL datasets. The user will be monitored and using the machine learning techniques discussed above, which will perform the real-time translation to display the final result. © 2023 IEEE.

关键词： Audition

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Stock Price Prediction using Various LSTMs 2

Stock Price Prediction using Various LSTMs

引用

2nd International Conference on Sustainable Computing and Data Communication Systems, ICSCDS 2023

作者： Narayana, Satyala Keerthi, Kokkiligadda Stuthi Srinivasulu, Anneboina Keerthika, Vampugani Prakash, Teki Veera Venkata Satya Sr Gudlavalleru Engineering College Dept. of Computer Science & Engineering Gudlavalleru India

ISBN: (纸本)9781665491990

In the world of the stock market, everyone wants to make money with fewer risks. Owing to the stock market's complexity, even if you do manage to understand it, you can never truly say that you do. For predicting share prices, a wide range of estimation techniques, which include technical analysis and fundamental analysis, have been employed. Due to its uncertainty, people still find it challenging to predict even after that. Additionally, new investors will continually enter the stock market at regular intervals. The fundamental issue faced with the current models, especially recurrent neural networks, is disappearing gradient. Consequently, the process of fully grasping the stock market and aiding investors in generating profits continues to insist on the use of varied deep learning methodologies to anticipate future stock prices. The five Deep Learning (DL) Long Short-Term Memory (LSTM) models used in this study, each demonstrated promising forecasting results for the same time series when one-step ahead predictability for stock market prices was performed. The findings are confirmed using RMSE computation. © 2023 IEEE.

关键词： Long short-term memory

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Tech-It-Easy: An Application for Physically Impaired People Using Deep Learning

Tech-It-Easy: An Application for Physically Impaired People ...

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2023 International Conference on Artificial Intelligence and applications, ICAIA 2023 and Alliance Technology Conference, ATCON-1 2023

作者： Naveen, Devi Nirmala, M.D. Tejas Maladhkar, T. Serena, M. Mohis, Rahmath New Horizon College of Engineering Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9781665456272

Physical limitations are and always will be a barrier to daily progress. Technology advancements are assisting everyone in leading simpler lives. Using the same technologies, we can provide a significant and beneficial answer to the issues associated with physical impairment. This essay discusses the use of technology to improve daily life for those who have physical or sensory disabilities. Not just for those who have hearing loss, but also as a tool for those who have speech disability, sign language is a vital means of communication. People without disabilities have a hard time understanding sign language, and specialists are frequently the only ones who can. Hence, a tool for sign language interpretation becomes necessary. Although Braille is a reading and writing system used by people who are blind. Braille is less popular among persons who are visually impaired, as it is time-consuming to manually translate every text into braille. Our study examines the issues raised by these two deficits and looks for technical remedies. Text to audio conversion is a piece of technology that can revolutionize the way visually impaired individuals communicate currently. It is simple and has been done effectively for the past ten years to convert written text to audio. In addition to sign language interpreters, a relatively new concept for assisting the education of the blind is to translate speech into sign language. The technologies stated above are anticipated to significantly improve the daily lives of people with physical disabilities, and this project can be further customized to match any suitable smart object. © 2023 IEEE.

关键词： Convolutional neural networks

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Hazardous Asteroid Prediction Using Machine Learning 2

Hazardous Asteroid Prediction Using Machine Learning

引用

2nd IEEE International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies, ViTECoN 2023

作者： Babu Rao, M. Alekhya, G. Aakanksha, J. Naga Jyothi, K. SR Gudlavalleru Engineering College Dept. of Computer Science and Engineering Gudlavalleru India

ISBN: (纸本)9798350347982

With the help of machine learning algorithms, make a prediction of potentially hazardous asteroids (PHAs) which are having different combinations of orbital parameters. The suggested method intends to identify all major groupings of PHA-rich near-Earth asteroids (NEAs) and their subgroups. we applied different machine learning methods namely Random Forest, decision tree, k nearest neighbours, Naive bayes, Logistic Regression, Support Vector Machine (SVM) - for identifying potential hazardous and non-hazardous asteroids and features responsible for claiming an asteroid to be hazardous. so far as, asteroids are considered, there are many asteroids which are nearer to the earth, but all are not hazardous. So, our target in this project is to identify the asteroids which are hazardous and classify them with non-hazardous types. For this, we choose machine learning models. We used the data attributes to train these models, and then we compared the results to choose the model that provided the greatest accurate categorization. © 2023 IEEE.

关键词： Support vector machines

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'Unlocking the Future of Stock Market Trend Prediction: Advancements with Long Short-Term Memory (LSTM) Models' 14

'Unlocking the Future of Stock Market Trend Prediction: Adva...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Rajan, Rajeswary Soni, P. Government Engineering College Dept. of Computer Science and Engineering Kerala Thrissur India

ISBN: (纸本)9798350335095

The stock marketplace is a complex and dynamic environment where traders attempt to make informed choices to maximize their returns. Predicting the destiny tendencies of stock prices is a difficult project due to various factors influencing marketplace conduct. In recent years, deep mastering strategies, along with long short-term memory (LSTM), have won enormous interest for their capacity to capture elaborate styles and dependencies in time-collection information. This paper aims to develop an LSTM-based stock market trend prediction model with the objective of achieving significantly higher accuracy compared to existing approaches. The goal is to generate valuable insights from the predictions, enabling investors and traders to make more informed decisions in the stock market. These insights could include identifying potential trends, forecasting market movements, or suggesting optimal trading strategies. © 2023 IEEE.

关键词： Long short-term memory

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Wildfire Spread Prediction using Machine Learning Algorithms 14

Wildfire Spread Prediction using Machine Learning Algorithms

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Singh, Aman Yadav, Rakesh Sudhamshu, Gadug Basnet, Aryan Ali, Rahmat University Faculty of Engineering and Technology Dept. of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350335095

Wildfires are one of the most destructive natural disasters that cause significant harm to both humans and the environment. Predicting their spread is critical for disaster management and preparedness. In this study, we have utilized machine learning algorithms, including Decision Tree Regression, XG Boost Regression, and Artificial Neural Networks, to predict the spread of wildfires using the Next Day Wildfire dataset. The dataset includes satellite images, weather, and geography conditions aggregated across the United States from 2012 to 2020. We preprocessed and engineered the dataset which includes the features such as elevation, wind direction and speed, temperature, humidity, precipitation, drought index, vegetation index, energy release component, and population density. We evaluated the models using the Root Mean Squared Error (RMSE) metric and found that the Decision Tree Regression algorithm performed the best with the lowest RMSE score. Our study highlights the potential of machine learning algorithms in predicting the spread of wildfires, which can aid in better disaster management and preparedness efforts. © 2023 IEEE.

关键词： Forecasting

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