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A Communication System for the Incapacitated using Eye Blinks 9

A Communication System for the Incapacitated using Eye Blink...

引用

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

作者： Nalini, N. Harshil Reddy, K.H. Tadkal, Dhruv R. Singh, Aditi Nitte Meenakshi Institute of Technology Computer Science & Engineering Bangalore India

ISBN: (纸本)9798350314090

In today's society, many differently abled people, especially ones with completely impaired movement, depend on others for their daily activities. Although their movement is restricted they are certainly capable of high thought. If only this ability was tapped, they will finally have a means to communicate and even end up doing more than ones who are not differently abled. The only major challenge being that the provision of a device that is of low cost, is affordable and interprets a person's choice accurately. Although techniques such as electroencephalography (EEG) signals for blink detection can be an effective solution for detecting brain activity and eye movement, and translating their choices, EEG-based devices can cause seizures. Instead, a person's series of eye blinks can be detected and converted into a meaningful sentence. By providing a predefined look up table with meaning sentences corresponding to various blink patterns, the blinks can act as a bridge and thus help the person not only communicate but also extend the application to control a variety of things such as the wheelchair, home appliances, etc without depending on others. © 2023 IEEE.

关键词： Electroencephalography

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Smart Management of Leaks in Underground Pipelines Using Machine Learning 9

Smart Management of Leaks in Underground Pipelines Using Mac...

引用

9th IEEE International Smart Cities Conference, ISC2 2023

作者： Dixit, Anubhav Tripathi, Shaijal Gupta, Bhavya Sharma, Navneet Chaitanya, Sana Bagade, Priyanka Indian Institute of Technology Department of Computer Science and Engineering Kanpur India

ISBN: (纸本)9798350397758

Undetected leakages in water distribution pipelines lead to a significant loss of freshwater supplies. Effective monitoring of these pipelines to curb water loss is a global concern. To this end, numerous works have been done previously. However, underground deployment of water pipelines, sensor sensitivity, and complexity of the pipeline network introduce challenges for accurate water leakage detection and localization. Leakages can be categorised as one of two types: reported or unreported leakages. While reported leakages like pipe bursts could be easily spotted with visual inspections, hidden and noiseless leaks creeping through joints are background leakages that require more sophisticated monitoring. Some of the advanced works relied on optical fibres and magnetic induction techniques. Being expensive, they were limited to high-risk areas and could not be generalised for larger deployment. Some other works use field sensors like temperature, pressure, and acoustic sensors. These sensor-based methods have gained sufficient accuracy for detecting leaks, but localizing the exact leaking part is still an open problem. This work proposes a novel leakage detection and localization methodology that applies machine learning to the data generated by water flow sensors. A simple pipeline network was set up consisting of three water flow sensors across different pipeline segments. The readings from the flow sensors were recorded and used to train a machine learning classifier. The results demonstrate that our method can efficiently detect and localize background leakages with 99.3% accuracy. © 2023 IEEE.

关键词： Machine learning

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Categorical Boosting Machine for Tamil Character Recognition Using Shape Based Features 7

Categorical Boosting Machine for Tamil Character Recognition...

引用

2023 7th International Conference On Computing, Communication, Control And Automation, ICCUBEA 2023

作者： T.m, Vishnu Mukundan Nevatia, Isha Mishra, Tusar Kanti School of Computer Science & Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350304268

Tamil is one of the world's earliest surviving languages, from which modern Indian scripts draw inspiration. However, there has been limited development of Optical Character Recognition techniques for Tamil characters. This paper describes a model for Tamil vowels-based Optical Character Recognition. The strategy seeks to utilise a CHFR feature extraction technique to extract three distinct feature vectors from the contours of each individual character's centroid. These feature vectors are condensed into a single vector before being sent to the CatBoost classifier for classification. Using the Optuna framework, hyperparameter optimisation and comparative analysis of existing models are performed. The proposed scheme is applied to a dataset of 6,972 handwritten samples evenly divided into 12 classes representing the vowels of the Tamil alphabet. The primary evaluation metric is accuracy, and 84.36% testing accuracy is observed. © 2023 IEEE.

关键词： CatBoost CHFR feature extraction handwritten character recognition OCR pattern recognition Tamil characters

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Comparative Analysis of Glaucoma Diagnosis on Retinal Fundus Images Using AI Techniques 4

Comparative Analysis of Glaucoma Diagnosis on Retinal Fundus...

引用

4th International Conference on Signal Processing and Communication, ICSPC 2023

作者： Abraham, Bibiya Susan Bhanu, Dalaali Gousiya Thomas, Anna Anitha, J. Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (纸本)9798350300772

Glaucoma is a retinal condition that harms the eye as a result of High Intraocular Pressure (IOP). Untreated glaucoma damages the Optic Nerve Head (ONH), which results in visual loss. This issue can be resolved by automating the glaucoma diagnosis so that it can be identified at an early stage and minimize damage to the patient's eyes by offering the right treatment early on. Detecting the advancement of glaucoma manually is a laborious and time-consuming process. HOG and GLCM features are utilized in this study to extract features, which are then passed on to machine learning classifiers including SVM, MLP, and Random Forest to evaluate the classification accuracy. Deep learning approaches like CNN, and pre-trained models such as Xception and EfficientNet are also implemented for automatic feature extraction and classification to categorize the image as glaucoma or normal. The results of the analysis indicate that the deep learning models outperform machine learning classifiers in the diagnosis of glaucoma using the DRISHTI-GS dataset with EfficientNet producing the highest accuracy of 99.72%. © 2023 IEEE.

关键词： Support vector machines

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From Whitepaper to Market Trends: Unraveling Bitcoin's Evolution and Business Implications 9

From Whitepaper to Market Trends: Unraveling Bitcoin's Evolu...

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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Panwar, Amisha Chauhan, Rahul Kumari, Kirti Devliyal, Swati Chanti, Yerrolla Singh, Retinderdeep Graphic Era Hill University Computer Science And Engineering Uttarakhand Dehradun India Graphic Era Deemed To Be University Computer Science And Engineering Uttarakhand Dehradun India Sr University School of Computer Science & Artificial Intelligence Warangal India Chitkara University Institute of Engineering And Technology Chitkara University Punjab India

ISBN: (纸本)9798350394474

The emergence of Bitcoin as a disruptive force in the business and finance space, starting an extensive examination of its origin, technology foundations, effects on trade, and where it will go from here, has set off a chain reaction. This research paper takes us through the broad range of Bitcoin landscapes, examining its past context, fundamental features, and underpinning technologies for a decentralized framework. The exploration begins with the introduction, which serves as a starting point for an in-depth analysis that explains the background, scope, and significance of the study. Before a dedicated section on Bitcoin, which discusses its roots, developmental evolution and fundamental features that distinguish it from the digital asset field, is an introductory overview of cryptocurrencies. Regulatory complexity, security risks and the risk to business in an evolving cryptocurrencies ecosystem are highlighted by a comprehensive assessment of challenges and concerns. Anticipating the future of Bitcoin occupies the eighth section, where scaling solutions like Segregated Witness (SegWit) and the Lightning Network are discussed. In addition, potential developments which might occur in the Bitcoin landscape such as integration of smart contracts and collaboration with other technologies can also be seen. This research project is intended to provide valuable insight into the current discussion about how bitcoin has fundamentally changed the business landscape and its future development. © 2024 IEEE.

关键词： Smart contract

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Multi-Lingual Sentiment Analysis of Urdu and English Tweets Using RNN with Bidirectional LSTM 6

Multi-Lingual Sentiment Analysis of Urdu and English Tweets ...

引用

6th International Conference on Recent Trends in Advance Computing, ICRTAC 2023

作者： Deole, Yash Jagadish, Tarun Shabrez, Md Premalatha, M. Anusooya, G. Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350394702

In this research paper, we introduce a cross-lingual sentiment analysis model designed to evaluate sentiments expressed in tweets associated with Pakistan's political parties, across three languages: Urdu, transliterated Urdu, and English. Our model leverages a recurrent neural network (RNN) featuring a bidirectional Long Short-Term Memory (BiLSTM) architecture. Demonstrating remarkable proficiency, our model achieved an impressive accuracy rate of 95.64% on the test dataset, surpassing the performance of two other prominent cross-lingual sentiment analysis models, namely mBERT and RoBERTa. This study's outcomes underscore the effectiveness of our proposed RNN-BiLSTM model for cross-lingual sentiment analysis. The model's application extends to sentiment assessment of tweets concerning Pakistan's political landscape, irrespective of language, be it Urdu, transliterated Urdu, or English. This analytical tool holds substantial potential for enhancing our comprehension of public sentiment and tracking evolving political trends within Pakistan. © 2023 IEEE.

关键词： Sentiment analysis

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Blockchain based on Reliable Task Offloading Strategy for Edge Computing in Smart Home 8

Blockchain based on Reliable Task Offloading Strategy for Ed...

引用

8th International Conference on Intelligent Computing and Signal Processing, ICSP 2023

作者： Zhu, Mengyuan Li, Juan Wuhan Institute of Technology School of Computer Science and Engineering Wuhan China

ISBN: (纸本)9798350302455

With the progress of the internet of things, more and more smart home devices are entering the home. When users enjoy the convenience brought by smart home devices, they also face problems such as resource constraints and insufficient privacy protection on the device. Based on this, this article proposes a blockchain based trusted offloading strategy for end-edge-cloud collaborative tasks to address these challenges. Specifically, establish a blockchain based end-edge-cloud collaboration framework to offload tasks more safely and efficiently. In addition, to address user privacy security issues, a decentralized blockchain is introduced, using consensus algorithms to achieve reliable data transmission and storage. Finally, with the goal of minimizing delay, an improved genetic algorithm based on blockchain (BWOGA) is proposed, and its effectiveness is verified through simulation experiments. © 2023 IEEE.

关键词： Blockchain

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Harris Hawk Optimization-Based Distributed Denial of Service Attack Detection in IoT Networks 4

Harris Hawk Optimization-Based Distributed Denial of Service...

引用

4th International Conference for Emerging Technology, INCET 2023

作者： Chaudhari, Shilpa S Yamini, D A Deepthi Ramaiah Institute of Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350335750

As Internet of Things (IoT) grows in popularity, cyber risks and distributed denial of service (DDoS) attacks have increased. DDoS attacks shut down genuine users by flooding a targeted network or service with a large volume of traffic from several hacked computers. DDoS attack can cause significant harm to a network and its users. By gathering benchmark network traffic statistics that come within the Bigdata category, the proposed method assesses and categories these attacks to detect DDoS network activities. DDoS Insights and trends that might not be immediately visible from the table are discovered and understood using an exploratory data analysis technique named Harris Hawk Optimization (HHO). The minimum fitness value based optimal fitness function helps to achieve optimal detection result is effectively. Using benchmark/NS3 DDoS data, the proposed technique is assessed and found to be successful in detecting attack features and offering defense-related knowledge. By providing more accurate and consistent detection and prevention of DDoS attacks, this technology will play a significant role in enhancing security for IoT networks. © 2023 IEEE.

关键词： Denial-of-service attack

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Depression Detection Using Deep Learning 11th

Depression Detection Using Deep Learning

引用

11th International Conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2023

作者： Gopichand, G. Ramesh, Anirudh Tholappa, Vasant Sridara Pandian, G. School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9789819967056

It is of utmost importance to take care of mental health. This is especially true in the ongoing COVID-19 pandemic. Depression is a crippling problem and must be treated quickly. It is useful to focus efforts on new, efficient, and accurate methods of depression detection. We intend to detect whether a person is in a depressed state of mind. We will be using deep learning models. The algorithms that have been used are feed-forward deep neural network (FNN), long short-term memory (LSTM) neural network, simple recurrent neural network (RNN), 1D convolutional neural network (1D CNN), and gated recurrent units (GRU) neural network. These models have been trained by utilizing electroencephalogram (EEG) sensor datasets. They classify individuals into either "depressed" or "non-depressed" categories. These models have been compared using confusion matrix, precision, recall score, F1 score, and accuracy. The best-performing models identified are FNN and 1D CNN. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Electroencephalography

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Privacy Preserved Effective Bill Collection System Using Blockchain 7th

Privacy Preserved Effective Bill Collection System Using Blo...

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7th International Conference on Information and Communication Technology for Competitive Strategies, ICTCS 2022

作者： Pooja, L. Madhuri, J. Department of Computer Science and Engineering Bangalore Institute of Technology Bangalore India

ISBN: (纸本)9789811993039

Electronic invoices are generated using blockchain technology. Additionally, it has the potential to fundamentally alter how invoices are produced, transactions are validated, and payments are executed. These distributed ledgers continue to be the most well known and are regarded as the cornerstone of cryptocurrencies. They are composed of blocks, and each block contains a transactional record. On a decentralized blockchain network, a document that is accessible and editable by multiple users simultaneously keeps account of who made changes and when. It is impermeable and translucent. The proposed system is used for enforced bill collection system ensuring user privacy using the smart contract. Since every transaction is visible to both parties and every record or block is linked together and encrypted, there is no need for a middleman. It will be possible for users to make automatic payments to a service provider’s digital wallet by using an invoicing system built on the blockchain. Transactions can be easily tracked and monitored, and thanks to blockchain technology, a complete history of all transactions can be downloaded. This study suggests using smart contracts and accountable ring signatures to secure blockchain transactions. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Smart contract

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