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Comparing Traditional computer Vision Algorithms and Deep Convolutional Neural Networks as Self Driving Algorithms for Use in Dynamic Conditions

Comparing Traditional Computer Vision Algorithms and Deep Co...

引用

2023 IEEE MIT Undergraduate Research Technology Conference, URTC 2023

作者： Shah, Shilpi Franz, Brendan Forgach, Travis Jostes, Milan Siegel, Joshua Chung, Chan-Jin Rutgers University Dept. of Computer Science New BrunswickNJ United States Harvard University Dept. of Computer Science CambridgeMA United States University of Michigan Dept. of Computer Science and Engineering Ann ArborMI United States Lawrence Technological University Dept. of Math and Computer Science SouthfieldMI United States Michigan State University Dept. of Computer Science and Engineering East LansingMI United States

ISBN: (纸本)9798350308600

This research develops, compares, and analyzes both a traditional algorithm using computer vision and a deep learning model to deal with dynamic road conditions. In the final testing, the deep learning model completed the target of five laps for both the inner and outer lane, whereas the computer vision algorithm only completed almost three laps for the inner lane and slightly over four laps for the outer. After conducting statistical analysis on the results of our deep learning model by finding the p-value between the absolute error and squared error of the self-driving algorithm in the outer lane and inner lane, we find that our results are statistically significant based on a two-Tailed T test with unequal variances where the p-value for absolute error is 0.009, and 0.001 for squared error. Self-driving vehicles are not only complex, but they are growing in necessity-therefore, finding an optimal solution for lane detection in dynamic conditions is crucial to continue innovation. © 2023 IEEE.

关键词： Convolutional neural networks

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Transcribing Natural Language to Python Code: A Compiler Based Approach 15

Transcribing Natural Language to Python Code: A Compiler Bas...

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Pandey, Mayank Kunda, Naga Sai Shreya Pranave, K.C. Belwal, Meena Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

Coding in a programming language can indeed be a meticulous and a less interesting task, especially when compared to the fluidity of spoken or written communication. Each programming language has its own set of syntax and rules, demanding expertise to effectively write code in it. Through this study, a compiler based approach to translate Natural Language into Python code is proposed. This endeavor could revolutionize access to programming by making it more intuitive and inclusive. The research involves working with different phases of the compiler including the lexical analyser, syntax and the semantic analyser phase in combination with Natural Language Processing techniques. By automating this process, we aim to streamline programming for a wider audience, removing barriers to entry and making coding more accessible to all. © 2024 IEEE.

关键词： Code Generation Compiler Architecture Compiler-Based Translation Natural Language Processing (NLP) Semantic Understanding

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Performance Assessment of Algorithms for Ship Routing through AnyLogic Simulations 1

Performance Assessment of Algorithms for Ship Routing throug...

引用

1st International Conference for Women in Computing, InCoWoCo 2024

作者： Sridhar, Swetha Shendre, Sanjivani P. Kamble, Samrudhi S. Priya, M. Yagnasri Apurvanand, S. Amrita School of Computing Bengaluru Amrita Vishwa Dept. of Computer Science and Engineering Vidyapeetham India

ISBN: (纸本)9798331518943

With constant improvement in intelligent technolo- gies across domains, improving intelligence in the maritime sector is very important. Optimal routing of ships in the vast ocean is a tedious task. Unmanned ships need a strong route planning system for easy operation on the field. The proposed work compares various shortest-path algorithms to find the optimal route between the source and the destination, based on cost. The algorithms implemented for the study are D-star, Delta Stepping, Theta-star, and AO-star. The practical implementation of the simulation has been performed using the Anylogic platform and implemented using the Python programming language. The time complexities of each algorithm have also been taken into account. From this, it has been analyzed that though the Theta-star algorithm seems to be efficient in terms of execution time with an average time 0.946 seconds, of the test cases chosen, the Delta Stepping algorithm proves to be more efficient as compared to the other algorithms taken for a larger sample size of scenarios of 0.97 seconds. On the other hand, the AO-star algorithm is found to be the least efficient algorithm for ship routing applications in terms of execution time with an average of 1.093 seconds. © 2024 IEEE.

关键词： Routing algorithms

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Estimation of Wetland Area Change Using Sentinel-2 Satellite Images 5

Estimation of Wetland Area Change Using Sentinel-2 Satellite...

引用

5th IEEE International Conference on Electrical, computer and Communication Technologies, ICECCT 2023

作者： Annavarapu, Lalitha Anupama Popuru, Sahithi Afreen, Sk Asha Sailaja, K.L. Kumar, P. Ramesh VR Siddhartha Engineering College Dept. of Computer Science & Engineering Vijayawada India

ISBN: (纸本)9781665493604

Numerous functions, such as food supply, water supply, a means of subsistence in fishing, and recreational opportunities are provided by wetlands. Wetlands are in danger of expanding and degrading as a result of anthropogenic influences and the impact of climate change. Shoreline erosion, water pollution, and drought damage all worsen when wetlands are destroyed. Therefore, it is crucial to do a wetlands inventory in a location so that we can track or keep an eye on any changes that may have occurred over time. The monitoring of crop moisture and water bodies area like lakes and ponds are the two aims. Images used as input are gathered from the Sentinel Hub Earth Observation Browser. The input images chosen for monitoring wetland crop moisture are based on NDMI(Normalized Difference Moisture Index), whereas the input images chosen for monitoring water bodies are based on NDWI (Normalized Difference Water Index). To monitor the water bodies area, pre-processing using Gaussian blur was done followed by clustering which was done using the Fuzzy-C-Means algorithm followed by edge detection using a Canny edge detector and identifying the changes in the area of the water bodies over the previous five years using Open CV approaches. To monitor crops moisture, Clustering was carried out using the K-means clustering algorithm, the resultant image and masked image were performed bitwise AND followed by a comparison of changes in moisture percentage over the previous five years using Open CV techniques. © 2023 IEEE.

关键词： Crops

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Voice Comparison Approaches for Forensic Application: A Review 3

Voice Comparison Approaches for Forensic Application: A Revi...

引用

3rd International Conference on Secure Cyber Computing and Communications, ICSCCC 2023

作者： Kruthika, S.G. Nagavi, Trisiladevi C. Mahesha, P. Sri Jayachamarajendra College of Engineering Jss Science and Technological University Dept. of Computer Science and Engineering Mysore India

ISBN: (纸本)9798350300710

Advancement in technology has led to various types of online security attacks and frauds. Evidences produced is in digital form and the field of analysis is called digital forensics. It focuses on identifying, acquiring and analyzing electronic evidence. However, there are many research works and techniques under the speech forensics domain such as Forensic Speaker Recognition (FSR) and Forensic Voice Comparison (FVC) for supporting the crime investigation. Within the broader domain of speech forensics, the paper focuses specifically on FVC, which adopts a trace sample to examine speech patterns in a recording of a known suspect voice. This article provides an overview of generic approach, traditional and semi-automated methods for FVC, including auditory, acoustic, phonetic, linguistic and spectrographic methods. In present scenario there are many automatic approach proposed using deep learning to identify the criminals using time and frequency domain methods. They present the efficient results and there is a scope for improvement with reference to FVC. The research also examines various challenges, applications and deliberations. Finally, the paper concludes with discussions and future directions. © 2023 IEEE.

关键词： Digital forensics

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Evaluation of Machine Learning Algorithms on Finding Drinking Water Quality Based on Feature Selection Methodologies 9

Evaluation of Machine Learning Algorithms on Finding Drinkin...

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9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Priya, K. Devi Sai, Sanga Monish Pagadala, Venu Gopal Reddy Kumar, Dasari Praveen Lakireddy Balireddy College of Engineering Dept. of Computer Science and Engineering Mylavaram India

ISBN: (纸本)9798350397376

Production of wastage plastics, heavy metals, pesticides from the variety of the domestic and non-domestic organizations like industries, offices, people, hospitals, agriculture etc., nearer to the surface and groundwater bodies destroyed the natural resources useful to public health. Polluted water leads to the ill health to the people and effected with several diseases. Timely monitoring of quality of the water is required for the public health. Quality of the drinking water is based on several factors like pH value, hardness, solids, chloramines. Here, analyzed the factors that decide the quality of the water using 11 AI based classifiers with feature selection techniques and evaluated performance metrics. Results shows that ensemble based random forest classifier with gradient boost feature selector produced the highest accuracy to decide the potability of the drinking water. © 2023 IEEE.

关键词： Potable water

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Cyberbullying Detection on Multiclass data Using Machine Learning and A Hybrid CNN-BiLSTM Architecture

Cyberbullying Detection on Multiclass Data Using Machine Lea...

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2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Balaji, Peddi Gowtham Katariya, Priyanka Prakash Sruthi, S. Venugopalan, Manju Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350359688

Cyberbullying has emerged as a significantly concerning online societal issue in the past few years with the proliferation of social media platforms. To address and overcome this pressing communal concern and successfully detect cyberbullying across various categories, this study proposes a novel approach that integrates Convolutional Neural Networks and Bidirectional Long Short-Term Memory Networks, alongside six traditional machine learning techniques, driven by the purpose of enhancing the accuracy and robustness of cyberbullying detection across diverse cultural and textual landscapes. To highlight the multiclass aspect, the model is trained and tested on a dataset containing instances categorized into Age, Religion, Ethnicity, Gender, Other Cyberbullying, and Not Cyberbullying. Through rigorous experimentation, the proposed deep learning model's proficiency results in a 0.83 accuracy and F measure, showcasing its efficacy in capturing the nuances of various categories. Meanwhile, among the six machine learning models employed, Random Forest emerges as the top performer with values 0.94 and 0.93 for the accuracy and F measure respectively. This comprehensive methodology underscores its ability to effectively discern between the different classes and offers a promising solution to combat cyberbullying across diverse demographics and thematic dimensions. © 2024 IEEE.

关键词： Adversarial machine learning

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Vision Enhancer: A Voice-Driven System For Obstacle Detection

Vision Enhancer: A Voice-Driven System For Obstacle Detectio...

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2024 IEEE International Conference on Innovation and Novelty in engineering and Technology, INNOVA 2024

作者： Supraja, P. Ganapathi, Ananya Ram, K. Sushmitha Vaishnavi, V. Jyotsna, C. Dept. of Computer Science Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331505134

The eyes serve as the primary sensory organ for individuals, providing vital visual information that aids in perception, recognition, and understanding of the surrounding environment. However, for those with visual impairments, accessing visual information poses significant challenges, limiting their freedom, particularly in navigating through obstacles. The paper attempts to address the obstacles faced by visually impaired individuals by leveraging object detection techniques such as TensorFlow to identify obstacles in their surroundings. By incorporating dept. estimation, the system aims to determine the distance to these obstacles, providing crucial spatial awareness. The system generates warnings through voice feedback, aiding users in making informed decisions to navigate safely. The implementation of this detection system is active on handheld devices, predominantly utilizing webcams or mobile devices. Through this approach, the system achieves a commendable accuracy rate of 98%, thereby enhancing the autonomy and safety of individuals with visual impairments in their daily lives. © 2024 IEEE.

关键词： Feature extraction

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Enhanced Task Scheduling in Cloud Computing: A Comparative Analysis and Hybrid Algorithm Implementation Using CloudSim 15

Enhanced Task Scheduling in Cloud Computing: A Comparative A...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Reddy, Padigela Srinithya Trisha, Kariveda Sree, Nichenametla Hima Bhaskaran, Sreebha Sampath, Nalini Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

Task scheduling is crucial in cloud environments to optimize resource allocation and performance. This research presents a comprehensive comparative analysis of task scheduling algorithms, that includes First-Come-First Serve (FCFS), Shortest Job First (SJF), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO). FCFS schedules tasks based on their arrival order, SJF prioritizes tasks based on the shortest execution time, and GA and PSO use metaheuristic approaches that use evolutionary methods for optimization. The hybrid algorithm is proposed combining SJF and GA to improve scheduling efficiency. The main objective is to improve the performance of task-scheduling algorithms in cloud environments. The performance of the individual algorithms and of the proposed hybrid algorithm are analyzed under static and dynamic workload scenarios. Metrics such as makespan and response time are evaluated using simulations. © 2024 IEEE.

关键词： Cloud Genetic Algorithm Makespan Shortest Job Task Scheduling

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Phishing Perception and Prediction 4

Phishing Perception and Prediction

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4th International Conference on Innovative Trends in Information Technology, ICITIIT 2023

作者： Ashwitha Noble, P. Hubert Veyannie, V. Summia Parveen, H. Sri Eshwar College of Engineering Dept. Computer Science and Engineering Coimbatore India

ISBN: (纸本)9781665494144

A practice known as URL phishing involves cybercriminals creating fake websites in order to lure victims and steal sensitive information. The attacker disguises themselves in an email, instant message, or text message, pose as a reliable source to persuade the recipient to open it. Once the recipient clicks the link, they realize they were deceived into clicking a potentially dangerous link. In this case, ransomware can be set up on the recipient's PC using a malware that can be used to lock it down or even worse the private data can be released. In spite of the fact that fraudulent websites often resemble the real thing, checking for warning signals alone is not enough to prevent URL phishing. A study found that around 90% of the data breach is due to phishing. The variety of phishing schemes has expanded over the years and they may now be more dangerous than ever before. © 2023 IEEE.

关键词： Machine learning

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