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Detection and Classification of Types of Cracks Using Deep Learning

Detection and Classification of Types of Cracks Using Deep L...

引用

2024 IEEE International Conference on Information Technology, Electronics and Intelligent Communication Systems, ICITEICS 2024

作者： Kadagala, Siva Charan Konda, Harshith Reddy Krovvidi, Prudhvi Sri Harsha Sampath, Nalini Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350382693

The identification and classification of cracks are vital for maintaining the safety and integrity of buildings and transportation infrastructure. Conventional manual inspection methods are often time-consuming and labor-intensive, underscoring the necessity for automated systems leveraging computer vision techniques. This study proposes two distinct methodologies: one based on digital image processing and the other on deep learning, to facilitate the development of crack inspection systems. The deep learning approach employs a pre-trained model serving as a feature extractor, coupled with a fully connected neural network classifier, to facilitate the detection and classification of cracks. Specifically, detection is accomplished utilizing Convolutional Neural Networks (CNNs), while classification is conducted employing ResN et, addressing the distinct tasks of identifying cracks and categorizing them accurately. Evaluation on publicly available crack datasets demonstrates the superior accuracy of the proposed method in detecting surface cracks, surpassing existing state-of-the-art techniques. Furthermore, a supervised deep learning model is introduced for the detection and classification of pavement cracks, adept at tackling the complexities associated with diverse backgrounds and variations in crack patterns. Results from this model exhibit promise accurately classifying individual image patches within collected datasets. These methodologies hold considerable potential for practical implementation in non-destructive testing, enabling the detection and classification of various types of cracks across diverse structural components. © 2024 IEEE.

关键词： Convolutional neural networks

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Precise Lake: A Feedback and Visualization System For Optimizing Code Health 15

Precise Lake: A Feedback and Visualization System For Optimi...

引用

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

作者： Shreya Shree, S. Katariya, Priyanka Prakash Sruthi, S. Belwal, Meena Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

The creation of efficient and maintainable code is a crucial aspect of software development. It is a daunting task to write code that does not smell, especially for novice programmers who lack familiarity with best practices for writing clean code. Despite ongoing research into code optimization, there is a significant gap in teaching developers how to write code that minimizes runtime and memory usage. With Precise Lake, a novel code analysis tool, these challenges can be solved with the identification and optimization of latent code like unused imports, functions and variables, inefficient patterns, and much more in the form of a code health analyzer and optimizer. Obtaining an experimental average gain of 56% in terms of memory usage and 21% with respect to runtime, it aims at bridging the gap between the writing of inefficient code and the manual reviewing step done at the end by automating the process for programmers to scan their code files and receive a report on the areas of improvement. © 2024 IEEE.

关键词： Software design

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Fine Grain Image classification using Fine-tuned MobileNet model 5

Fine Grain Image classification using Fine-tuned MobileNet m...

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5th IEEE International Conference for Emerging Technology, INCET 2024

作者： Anvitha, Sagala Sai Reddy, Sagam Prashamsa Sunaini, Yerramachu Singh, Rimjhim Padam Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350361155

The paper focuses on the development of an image classifier, which classifies variousfine-grained floral images based on their species. Classification of flowers has many advantages in various fields like science, Medical, Industries *** classifying various types of flowers, one can study their characteristics, etiquette, considering to our knowledge of ecosystems and plant life. Our focal point rests upon the image features woven into the pixels. Through various Convolutional neural network models, the work is able to propose an efficient model based on fine-tuned MobileNetv3 architecture and sel-attention mechanismsthat classifies fine-grained flower images into their respective species. © 2024 IEEE.

关键词： Image classification

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Continuous Sign Language Recognition using Convolutional Neural Network 2

Continuous Sign Language Recognition using Convolutional Neu...

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2nd International Conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Shankara Narayanan, V. Sneha Varsha, M. Padmavathi, S. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350328202

Automatic Continuous Sign Language Recognition is a task that aims at associating words/phrases for sign language gestures. The scope of building a CSLR framework extends beyond bridging the communication barrier between the deaf community and the hearing majority but includes profound benefits such as bootstrapping translation systems, human-computer interaction, virtual reality environments, etc. In this study, a continuous sign language recognition framework based on a CNN architecture is proposed. Utilizing the Argentina Sign Language dataset, sign language gestures are preprocessed to extract training and testing frames. Employing transfer learning with the ResNet-50 model, a CNN is trained to classify each frame into one of 64 different words present in the dataset. The paper also conducts a thorough post-training data analysis, offering insights into areas where the model performed less effectively, identifying commonly confused pairs of sign language gestures. Additionally, a heatmap overlay technique is employed to elucidate the black-box CNN model. © 2024 IEEE.

关键词： computer vision

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Exploring the Techniques for Option Pricing: An Empirical Study and Performance Assessment

Exploring the Techniques for Option Pricing: An Empirical St...

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2024 International Conference on electrical, Electronics and computing Technologies, ICEECT 2024

作者： Anand, R.N. Supriya, M. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science & Engineering Bengaluru India

ISBN: (数字)9798350378092

ISBN: (纸本)9798350378092

The accurate pricing of financial options is crucial for effective risk management and decision-making in financial markets. Traditional option pricing models, such as the Black-Scholes model, rely on simplifying assumptions that may not fully capture the complexities of real-world market dynamics. This study aims at exploring machine learning (ML) and deep learning (DL) models in different scenarios and evaluating their performances against the conventional methods. The methodology harnesses the power of neural networks within deep learning models to discern and learn intricate patterns and relationships inherent in historical market data. By training on a diverse dataset comprising stock prices, strike prices, time to expiration, and volatility, DL models aim to capture the nuanced interactions among these variables, thus enabling more accurate and robust option pricing compared to traditional methods. Moreover, the analysis is extended to incorporate ML models, which complement DL techniques by leveraging a variety of algorithms to discern patterns and make predictions based on historical data. Through the evaluation and comparison of both DL and ML models, we aim to ascertain the efficacy and suitability of each approach for option pricing tasks. Among the models evaluated, the Multi-Layer Perceptron (MLP) demonstrated superior performance in option pricing tasks, achieving high accuracy and robustness across synthetic and real-world datasets. Additionally, ensemble learning techniques such as XGBoost and Gradient Boosting Tree Regression (GBTR) exhibited strong predictive capabilities, offering valuable insights into option pricing dynamics with R2 values of 0.9694 and 0.9474 in S&P 500 dataset respectively. Overall, the comparative analysis highlights the efficacy of machine learning and deep learning models in option pricing, with MLP emerging as a standout performer. © 2024 IEEE.

关键词： Long short-term memory

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A Collaborative Elevator System Optimization: A Finite Automata Approach 5

A Collaborative Elevator System Optimization: A Finite Autom...

引用

5th IEEE Global Conference for Advancement in Technology, GCAT 2024

作者： Sivakumar, Amrutha Reddy, G. Bhavya Tanvi, Yalakanti Sidhu, Samreen Radha, D. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350376685

Modern high-rises and metropolitan areas rely largely on efficient elevator systems to control the vertical movement of people. However, traditional elevator control systems frequently fail to fulfill the needs of a growing population and changing usage patterns. The proposed paper seeks to evaluate how automata theory can be used to establish an elevator system that works efficiently. Each elevator is represented as an individual finite automaton. A composite automaton to coordinate the actions, the system's behaviour and interactions is modelled. The Deterministic Finite Automaton (DFA) model specifies the states and transitions of the two cooperating lifts to optimise passenger service. The model considers numerous situations that include passenger requests through the floor, emergencies, maintenance, and the adaptation for power failures. This DFA-based design is targeted to increase elevator efficiency many times over, reduce waiting time, save energy, and provide a better passenger service experience before the implementation. © 2024 IEEE.

关键词： Finite automata

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Visio-Voice Transforming Images into Sound for the Visually Impaired

Visio-Voice Transforming Images into Sound for the Visually ...

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2024 IEEE International Conference on Information Technology, Electronics and Intelligent Communication Systems, ICITEICS 2024

作者： Sai, Gogineni Ashrith Reddy, Kondareddy Balaji Gurunadh, Kothuru Madhav, Thanakanti Ganesh Santhanalakshmi, S. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798350382693

The integration of deep learning and computer vision has led to groundbreaking innovations, notably in Image-to-Audio technologies. This transformative paradigm aims to provide comprehensible audio descriptions for visual content, offering increased independence and inclusivity for individuals with visual impairments. Rooted in intricate computational methodologies, this approach seamlessly translates visual data into auditory representations. The paper explores the technical aspects of Image-to-Audio conversion, emphasizing its potential applications in digital media, education, and everyday environments. Technology is seen as a societal catalyst, empowering individuals with visual impairments and fostering newfound self-sufficiency and autonomy. © 2024 IEEE.

关键词： Caption Generation CNN CSMN google-TTS Inception V3 LSTM Neural networks Text to Speech

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An Efficient Spell Checker System for Paragraphs and Baudot Codes Using Tries 4

An Efficient Spell Checker System for Paragraphs and Baudot ...

引用

4th IEEE Asian Conference on Innovation in Technology, ASIANCON 2024

作者： Tejesh, G. Gowtham, Y. Sriharsha, P. Aditya, U. Aiswariya Milan, K. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350354218

This study describes an interactive spell- checking tool designed to improve text input accuracy and usability. Spell checking is necessary to reduce spelling errors such as phonetic errors and thus boost the overall message passing system. The purpose of this spell-checking tool is to alleviate the orthographic errors of the English language, which in most cases result in spelling mistakes and other typographical issues. It employs existing techniques, such as the trie data structure, to correct misspellings and offer adjustments thereby improving the overall user experience. In Our project we included Baudot code, is an early character encoding for telegraphical work. The project is divided into two phases: spell checking and baudot code integration. The spell-checking mechanism is designed to discover and handle misspelled words in user- provided input. If a word is determined as misspelled, the system returns the three closest suggestions. The project introduces the spell checking for Baudot code, a character encoding system represented in permutation and combination of '+' and '-' of length five for each alphabet. Words written in Baudot code are decoded and compared with the dictionary they contain, which makes it possible to transfer encoded messages, especially in limited-purpose stenographic work, like for spy- operations. HashMaps are used to store Baudot codes for each alphabet, allowing for faster and more efficient encoding and decoding mechanisms. This implemented spell-checking tool is projected to significantly improve the feasibility and practicability of text processing through the use of a two- phase technique. Spelling errors must be properly identified and eliminated. © 2024 IEEE.

关键词： Encoding (symbols)

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Investigating Fake Job Descriptions with TF-IDF and Word Embeddings 15

Investigating Fake Job Descriptions with TF-IDF and Word Emb...

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

作者： Umah, B. Gupta, Deepa Venugopalan, Manju Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

Fraudulent actions have increased as a result of the growth of online recruitment platforms, especially when it comes to the creation of false job descriptions intended to trick prospective employees. This study tackles the important problem of recognizing and categorizing fraudulent employment advertisements. By utilizing an extensive dataset of annotated fraudulent job descriptions, cutting-edge classification methods are utilized to construct a strong model that can discern between authentic and fraudulent job ***-IDF, a feature extraction method is used to assign numerical weights to words based on their frequency. Several word embeddings, such as BERT, GloVe, and Word2Vec, were employed in this study to enhance the job descriptions' feature representation for fraud detection. To overcome data imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) is also used in combination with TF-IDF. 5-fold cross validation is applied to the different classifiers to study the performance of the system. By merging numerous embeddings and resolving class imbalance, this ensemble model demonstrated a significant boost in fraud detection accuracy over extended testing, demonstrating the usefulness of doing so for robust categorization of misleading job descriptions. © 2024 IEEE.

关键词： 5-fold cross validation BERT GloVe SMOTE TF-IDF Word Embeddings-Word2vec

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Wireless ECG Monitoring and Automated Heart Diagnosis: A Mobile App Solution Using Deep Learning 15

Wireless ECG Monitoring and Automated Heart Diagnosis: A Mob...

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

作者： Sunil, M. Dhanush, Uppalapati Sai, Thippaluru Tharun Jyotsna, C. Sanjay, Thokali Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350370249

This research presents a novel solution to the serious global health issue of cardiovascular diseases. The suggested technology revolutionizes remote cardiac monitoring by combining wireless Electrocardiogram sensors and powerful deep learning algorithms. A user-friendly mobile application acts as a center for data collection, storage, and analysis in real time. More importantly, a convolutional neural network can be used for the automatic detection of cardiac problems by pattern extraction from Electrocardiogram readings. The system enables immediate action by sending real-time notifications for detected anomalies, while also keeping a detailed historical record for long-term monitoring. Prioritizing data security, messages are encrypted, and the system contains user authentication mechanisms that are in line with healthcare standards. Engaging individuals in proactive health management, this proposal aims to foster cooperation between users and healthcare providers for better patient outcomes. The suggested system shall offer accessibility, accuracy, and user-centricity, coupled with a new approach toward solving the global cardiovascular disease crisis using wireless Electrocardiogram monitoring and deep learning technology. ©2024 IEEE.

关键词： cardiovascular diseases Convolutional Neural Network Wireless Electrocardiogram Monitoring

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