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Application of Image Processing Techniques for Pothole Detection 6

Application of Image Processing Techniques for Pothole Detec...

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6th International Conference on Intelligent computing and Control Systems, ICICCS 2022

作者： Ajay, Aketi Revathy, S. Nagavijayasivasuryaprakashreddy, Bheemireddy Posonia, Mary Ankayarkanni, B. Sathyabama Institute of Science and Technology Department of Computer Science & Engineering India Sathyabama Institute of Science and Technology School of Computing India

ISBN: (纸本)9781665410359

Roads are viewed as the fundamental method of transportation. Nonetheless, because of the substantial gridlock, these roads require framework upkeep. Frequently this support isn't done on the grounds that it is difficult to control anyplace or on account of obliviousness. This prompts a pothole, which leads to and fro and causes a great deal of mishaps. This paper examined how to recognize potholes with the assistance of a camera mounted on a light strip. The picture handling strategy utilized, was informed to BMC authorities without really wasting any time utilizing an electronic framework to diminish difficult work. To test its presentation, the gave framework was carried out in Windows utilizing a CV library. To recognize potholes, straightforward picture handling innovation is utilized, for example, form discovery and Hough changes. © 2022 IEEE.

关键词： Python

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Transformer-Enhanced U-Net with Transfer Learning for Accurate Detection of Eye Coloboma

Transformer-Enhanced U-Net with Transfer Learning for Accura...

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International Conference on Power, Energy, Control and Transmission Systems (ICPECTS)

作者： A Dhayanithi K. Bala Department of Computer Science and Engineering School of Computing Bharath Institute of Higher Education and Research Chennai Tamil Nadu India Department of Computer Science and Engineering School of Computing Bharath Institute of Higher Education and Research Chennai Tamil Nadu

ISBN: (数字)9798331508845

ISBN: (纸本)9798331508852

Eye coloboma is a type of congenital eye abnormality that causes developmental abnormalities of the eye which may culminate in eye disorders that fundamentally derange eyesight. Fractional and timely diagnosis remains highly important to its proper managing and treating. This work proposes a model with a Transformer-Enhanced U-Net design implemented with Transfer Learning to enhance the ability of the model to detect and segment eye coloboma from medical imaging data. This work builds upon the benefits of U-Net which has shown promising results especially in the biomedical image segmentation problem through addition of transformer modules that introduce long range dependencies and contextual information. This enhancement makes helps the model to pay more attention to such minor aspects as coloboma and general diagnostic accuracy consequently. Thanks to Transfer Learning with a pretrained backbone, for example Efficient Net the system is provided with Feature Representations recovered from large datasets that are already available and training time is drastically cut short while the performance of the system is not compromised at *** approach includes fine-tuning of Transformer-Enhanced U-Net model using a selected set of eye images, labelled for coloboma existence. By including the self-attention mechanism, the model is capable of focusing on the critical regions in the image, increasing its responsiveness to any form of coloboma. Preliminary tests show that this scheme performs better than the standard convolutional networks for segmenting complex regions by minimizing false negatives. Further, the model’s flexibility to different imaging conditions demonstrates its suitability in realistic clinical applications. The objective of this research is to come up with a dependable, Auto-Generated diagnostic tool that will help the ophthalmologists in the early diagnosis of the eye related illnesses hence making increased positive outcomes for eye patients. The Transf

关键词： Training Image segmentation Accuracy Sensitivity Biological system modeling Transfer learning Transformers Data models Standards Biomedical imaging

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Automation of Short Answer Grading Techniques: Comparative Study using Deep Learning Techniques

Automation of Short Answer Grading Techniques: Comparative S...

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International Conference on Electronics, Communication and computing Technologies (ICECCT)

作者： Arunima Divya Vivek Haridas Jayasree Narayanan Deportment of Computer Science and Engineering Amrita School of Computing Amritapuri India

Automatic short answer grading (ASAG) techniques have been shown to cut down on the time and work needed to grade exams, and it is a method that is becoming more and more common, especially with the rise in popularity of online courses. This study compares the results of 7 pre-trained embedding models using just one feature to automatically grade brief responses: the similarity between the model answer's and the student answer's embeddings. Regression models are developed and evaluated to predict a short answer's score based on the similarities between all pairs of answers in the Mohler dataset. The predictions are evaluated by comparing the Root Mean Squared Error (RMSE) and Pearson correlation scores of each model.

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Brain Tumor Classification Using ML and DL Approaches 5

Brain Tumor Classification Using ML and DL Approaches

引用

5th IEEE International Conference on Cybernetics, Cognition and Machine Learning Applications, ICCCMLA 2023

作者： Solanki, Shubhangi Singh, Uday Pratap Chouhan, Siddharth Singh LNCT University Department of Computer Science Engineering Bhopal462042 India School of Mathematics Shri Mata Vaishno Devi University Kakrayl Katra182320 India School of Computing Science and Engineering VIT Bhopal University Madhya Pradesh Sehore466114 India

ISBN: (纸本)9798350338287

In medical image processing, categorizing brain tumors is one of the most critical and challenging challenges that must be solved. Because manual classification carried out with the assistance of humans often leads to inaccurate diagnoses and projections, we have turned to machine learning to help us. The proposed work includes a performance evaluation of autonomous brain tumor identification from MR imaging as well as a CT scan utilizing basic image processing approaches depending on multiple hard and soft computing techniques. Furthermore, six classical classifiers were used to identify brain tumors in the photos. Then, to incorporate deep learning methods into our study, we used Convolutional Neural Networks and Deep Convolutional Neural Networks for brain tumor identification. The outcome of the conventional ML with the best accuracy method SVM was analyzed to the outcome of the conventional CNN. The performance of Deep CNN models is assessed, and it is discovered that VGG16 outperforms all other Deep learning models. When contrasted with these traditional classification methods, the SVM's findings were the most accurate. After that, a CNN is used, which, in contrast to the conventional classifiers, shows a discernible rise in overall performance that is much superior. It was decided to test several different layers of CNN with a variety of split ratios for the dataset. The outcomes of the experiments show that a five-layered CNN with an 80:20 split ratio is capable of achieving the greatest performance accuracy possible, which is 97.86%. © 2023 IEEE.

关键词： Convolutional neural networks

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Interpretable Drug Response Prediction through Molecule Structure-aware and Knowledge-Guided Visible Neural Network

Interpretable Drug Response Prediction through Molecule Stru...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Xie, Jiancong Zhang, Zhe Li, Youyou Rao, Jiahua Yang, Yuedong Sun Yat-sen University School of Computer Science and Engineering China University of Chicago Department of Chemistry IL United States Sun Yat-sen University Key Laboratory of Machine Intelligence and Advanced Computing of Moe China

ISBN: (纸本)9798350386226

Precise prediction of anti-cancer drug responses has become a crucial obstruction in anti-cancer drug design and clinical applications. In recent years, various deep learning methods have been applied to drug response prediction and become more accurate. However, they are still criticized as being non-transparent. To offer reliable drug response prediction in real-world applications, there is still a pressing demand to develop a model with high predictive performance as well as interpretability. In this study, we propose DrugVNN, an end-to-end interpretable drug response prediction framework, which extracts gene features of cell lines through a knowledge-guided visible neural network (VNN) and learns drug representation through a node-edge communicative message passing network (CMPNN). Additionally, between these two networks, a novel drug-aware gene attention gate is designed to direct the drug representation to VNN to simulate the effects of drugs. By evaluating on the GDSC dataset, DrugVNN achieved state-of-the-art performance. Moreover, DrugVNN can identify active genes and relevant signaling pathways for specific drug-cell line pairs with supporting evidence in the literature, implying the interpretability of our model. © 2024 IEEE.

关键词： Prediction models

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A Comparative Analysis of Joomla, Drupal, Wordpress and ***: Exploring Features, Performance, and Suitability

A Comparative Analysis of Joomla, Drupal, Wordpress and ***:...

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2023 IEEE Asia-Pacific Conference on computer Science and Data engineering, CSDE 2023

作者： Prasad, Ashwin Ashika Sharma, Neeraj Anand Kumar, Anal School of Science and Technology The University of Fiji Department of Computer Science and Mathematics Fiji College of Engineering Science and Technology Fiji National University Department of Computing Sciences and Information System Fiji

ISBN: (纸本)9798350341072

In the dynamic landscape of web development, the judicious choice of platform and structure plays a pivotal role. This comprehensive study conducts a thorough comparison of four prominent web development technologies: ***, Joomla, Drupal, and WordPress. With a primary focus on scrutinizing features, performance, and overall suitability, the research aims to provide valuable insights for developers, businesses, and decision-makers. By objectively evaluating the merits and limitations of each technology, this study seeks to empower stakeholders with essential knowledge, enabling them to make informed decisions at the onset of web development projects. © 2023 IEEE.

关键词： Decision making

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Indian Sign Language Generation from Live Audio or Text for Tamil

Indian Sign Language Generation from Live Audio or Text for ...

引用

International Conference on Advanced computing and Communication Systems (ICACCS)

作者： Bandi Rupendra Reddy Daka Chandra Rup Mathi Rohith Meena Belwal Department of Computer Science and Engineering Amrita School of Computing Bengaluru India

Communication is the key behind the whole human evolution, and there would be nothing possible if the stream for communicating our thoughts to one another were cut off. Language is a significant factor when it comes to conveying one's ideas, thoughts, and information to other people. Sign language is used to communicate hard-of-hearing people's opinions. Every country has different sign languages. In this paper, we have used ISL (Indian Sign Language). To ease communication between ordinary people and deaf/dumb people, we propose the design and implementation of a model that translates a live voice, audio recordings, or text of a native Indian regional language (Tamil) to text and then further matches it to sign language animations from the video animation dataset. The speech is converted to text using two deep learning models LSTM(Long Short Term Memory), Bi-LSTM, and Google API. Then the text is transformed into a sign using ISL (Indian Sign Language) dataset. The proposed models achieved 45%, 65%, and 95% accuracy for LSTM, Bi-LSTM, and Google API, respectively.

关键词： Deep learning Computational modeling Gesture recognition Assistive technologies Animation Audio recording Internet

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Design and Develop an Efficient Load Balancing Approach with AOMDV Routing in 7G-FANET 4

Design and Develop an Efficient Load Balancing Approach with...

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4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Soni, Gaurav Chandravanshi, Kamlesh Deoskar, Devashri Parashar, Sadhana Thakur, Ruchi Vit Bhopal University School of Computing Science and Engineering Kothrikalan Bhopal-Indore Highway Madhya Pradesh Sehore India Lakshmi Narain College of Technology Dept. of Computer Science and Engineering Raisen Road Madhya Pradesh Bhopal India Bansal Institute of Research and Technology Dept. of Computer Science and Engineering Kokta Bypass Rd Anandnagar Madhya Pradesh Bhopal India Sati Dept. of Computer Science and Engineering Madhya Pradesh Vidisha India

ISBN: (纸本)9798331518981

The congestion in the Flying Ad hoc Network (FANET) is due to insufficient bandwidth or a heavy load on the network. The multipath routing protocol AOMDV (Ad -hoc On-demand Distance Vector) is used for balance the load in network. The multiple routes serve as backup routes for data transmission in the connection breakdown. The speed of the Seventh Generation (7G) is much higher than other generations of wireless networks. 7G has a speed of 100 Gigabits, which reduces communication latency. To optimize the bandwidth capacity, we must shift the load to other or alternative routes for handle congestion and proper load balancing. In this paper, propose a delay estimation rate-based control scheme to improve UAV-to-UAV communication in 7G-FANET. The Efficient Load Balancing AOMDV (ELAOMDV) scheme furnishes data on the number of packets that failed to reach their destination. The variation in the rate of transmission is based on the delay of successful acknowledgement. The proposed approach is to avoid the risk of congestion and balance the load on a specific link. 7G has the advantage of rapid communication. The ELAOMDV congestion management strategy is to minimize the latency and buffer overflow caused by network congestion. The delay difference confirms the existence of congestion and indicates the availability of multipath to balance the load. The performance of ELAOMDV is better than the multipath ad hoc on demand protocol (MAODV). All of the performance metrics showing improved results, indicating that the ELAOMDV approach has improved routing performance in 7G-FANET. © 2024 IEEE.

关键词： Bandwidth

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A Comprehensive Survey of Intrusion Detection System Using Machine Learning and Deep Learning Approaches 10

A Comprehensive Survey of Intrusion Detection System Using M...

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10th International Conference on Advanced computing and Communication Systems, ICACCS 2024

作者： Abhiram, Kotnur Muthusamy, Hariharan Ravindran, Sindhu Vijean, Vikneswaran Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India National Institute of Technology Uttarakhand Department of Electronis Engineering Uttarakhand Srinagar Garhwal246174 India Universiti Malaysia Perlis Faculty of Electronic Engineering & Technology Arau Pauh Putra Perlis02600 Malaysia

ISBN: (纸本)9798350384369

The necessity for computer networks' use is expanding quickly, which raises problems with preserving network secrecy, availability, and integrity. Intrusion can be defined as an intentional breach of security rules within a secured network. Such intrusions can be identified by an Intrusion Detection system which searches for any malicious actions and recognized dangers within a secured network. Intrusion detection systems patrol the traffic passing through computer systems and give out notifications when they do. Recently, an immense upsurge in cyber-attack cases on computer networks has imposed the need for an effective Intrusion detection system than ever before. Nowadays, network administrators are utilizing multivarious kinds of Intrusion Detection Systems (IDS), in order to monitor network traffic for malicious and unauthorized activities. This review paper focuses on various research works that has developed an approach for evaluating or identifying IDS using many kinds of Machine Learning (ML) and Deep Learning (DL) techniques. © 2024 IEEE.

关键词： Network intrusion

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Performance Analytics in Collaborative Learning Environments 2

Performance Analytics in Collaborative Learning Environments

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2nd IEEE International Symposium on Sustainable Energy, Signal Processing and Cyber Security, iSSSC 2022

作者： Kavitha, R.K. Jaisingh, W. Harishma, S. Kumaraguru College of Technology Department of Computer Applications Coimbatore India Vit Bhopal University School of Computing Science and Engineering India

ISBN: (纸本)9781665490566

Collaborative learning has recently become more common in educational settings and has the potential to revolutionise the way students are taught today. Pair programming is a tool for group learning in which two programmers work on the same projects simultaneously on a single computer. The pairs will work together on every aspect of programming, including algorithm development, coding, and testing. The environment that facilitates sharing of understanding and perception is provided by the continual communication that prevails among pair programmers. The goal of this study is to use pair programming as a teaching method in computer labs. To produce insightful findings, predictive analytics was applied to the data set. The study was conducted in appropriate lab settings where students are expected to learn independently and write code. During the regular lab sessions, research participants were required to conduct the lab experiments in pairs. The experiences of the students working in such surroundings were recorded using the necessary tools in order to study the effectiveness of this strategy. The students' learning was evaluated based on a number of factors, including student preferences, increased productivity, enhanced learning, and information sharing. Additionally, projections about the students' success in the final exams were made based on the analysis of the result. © 2022 IEEE.

关键词： Students

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