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2023 International Conference on Advancement in Computation and computer Technologies, InCACCT 2023

作者： Rana, Sumit Kumar Rana, Arun Kumar Naz, Sardar M N Islam Singh, Ashutosh Kumar Bhushan, Bharat Panipat Institute of Engineering and Technology Department of Computer Science and Engineering Haryana Panipat India Galgotia College of Engineering Knowledge Park I Department of Computer Science Engineering Uttar Pradesh Greater Noida201310 India ISILC Victoria University Melbourne Australia Kurukshetra University NIT Department of Computer Applications Kurukshetra India Sharda Universi Knowledge Park III Department of Computer Science Engineering Greater Noida201310 India

ISBN: (纸本)9798350396485

The most prevalent issue in the supply chain ecosystem is introducing transparency to the system. Another issue is fostering trust among various stakeholders to support successful process execution. Depending on the domain and the stakeholders, the supply chain process may change. Thus, solving the issue is challenging. Blockchain technology, sometimes referred to as distributed ledger technology, is beneficial in many ways in a variety of industries, including finance and healthcare. Blockchain based model can resolve the issues of traditional supply chain ecosystem. It creates a decentralized and transparent system where every decision is taken with the help of consensus of the associated stakeholders. This supports an immutable ledger so unauthorized modification is not supported. In this article, we address the issues that the supply chain ecosystem faces as well as the benefits of blockchain technology in resolving these issues. As well as some of the remaining challenges and opportunities, other aspects of blockchain technology are explored that will enable it flourish in future supply chains. © 2023 IEEE.

关键词： Supply chains

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Enhanced Audio-Visual Speech Enhancement via Attention-based Sparse Graph Convolutional Neural Networks with Fire Hawk Optimization 3

Enhanced Audio-Visual Speech Enhancement via Attention-based...

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3rd International Conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Hebri, Dheeraj Joon, Rakesh Kumar Mohan, M. Ramapraba, P.S. Dhulipudi, Venkata Satish Narasimharao, Jonnadula Department of Master of Computer Applications Srinivas Institute of Technology Karnataka Mangalore574143 India Department of Electronics and Communication Engineering Ganga Institute of Technology and Management Haryana 124104 India Department of Computer Science and Engineering KPR Institute of Engineering and Technology Tamil Nadu Chennai641407 India Department of Electrical and Electronics Engineering Panimalar Engineering College Tamil Nadu Chennai600123 India Department of Electronics and Communication Engineering BVC Institute of Technology and Science Andhra Pradesh 533221 India Department of Computer Science and Engineering CMR Technical Campus Telangana Hyderabad501401 India

ISBN: (纸本)9798331532420

Audio-Visual Speech Enhancement (AVSE) has the vision to enhance speech quality and requite intelligibility by using both audio and video inputs, which is quite useful in noisy conditions. This approach acknowledges the fact that modalities such as lip movement may be equally informative of the auditory signals as the actual sound in cases where audio signals are distorted. In this research, an innovation approach to the AVSE is presented, which involves several state-of-the-art techniques. The data preprocessing step uses Z-score Min-Max Normalization to normalize the input data to help enhance the feature extraction process. Differentiable adaptive short-time Fourier transform (DA-STFT) is employed as the method of audio feature extraction that provides key temporal information. In the feature extraction process, a local directional structural pattern (LDSP) is utilized for the extraction of visual features since it examines the moving patterns on the face effectively. The fusion of data is done using a quadruplet depth-wise separable fusion convolutional neural network (QDSFCNN), which enhances the amalgamation of the audio and visual cues. Classification is done using an attention-based sparse graph convolutional neural network, known as ASGCNN, since the focus is on the relevant stipulations. Finally, to achieve the highest performance level, the fire hawk optimizer (FHO) is used for tuning parameters in the presented model. This combined approach presents advanced developments in AVSE, which provide improved speech quality in difficult conditions. © 2024 IEEE.

关键词： Convolutional neural networks

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Face Recognition using Adaboost Classifier For The Visually Impaired 8

Face Recognition using Adaboost Classifier For The Visually ...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Avinash, N.J. Krishnaraj Rao, N.S. Rama Moorthy, H. Mangalore Institute of Technology and Engineering Department of Electronics and Communication Engineering Mangalore Karnataka Moodabidre India Nmam Institute of Technology Department of Information Science and Engineering Karnataka Nitte India Nitte Institute of Professional Education Department of Computer Applications Karnataka Mangaluru India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376425

The paper suggests an approach to facial recognition of physical challenges. The most difficult challenge for blind persons is identifying impediments outside and behind them. They mostly rely on other people to help them make up for their blindness. To overcome this, they need to be supported by someone else people. In their daily lives, they face many problems when it comes to moving from one place to another. There is no guidance to help them when they need it. This study suggests a facial recognition system for those with vision impairments utilizing the Raspberry Pi to help prevent these situations. There is a camera designed for face recognition and a microphone designed for voice recognition in this case, so these components can be combined and people with visual disabilities can use it easily and behaves reasonably. In this work, a method is presented for detecting, classifying, and locating objects within complex environments. The paper applies the techniques of image processing and Adaboot classifiers to achieve an accuracy of more than 90 © 2024 IEEE.

关键词： Face recognition

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Deep Learning Empowered IoT Toothbrush: A Paradigm Shift in Dental Health Monitoring 1

Deep Learning Empowered IoT Toothbrush: A Paradigm Shift in ...

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1st International Conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Lodha, Nikita Pal, Ankit Das, Sayan Roy, Shreyashi Chakraborty, Sanjay Pandey, Saroj Kumar Techno International New Town Computer Science and Engineering Kolkata India Techno International New Town Aiml Department Kolkata India Gla University Computer Engineering and Applications Mathura India

ISBN: (纸本)9798350330915

The field of dentistry plays a crucial role in maintaining oral health and preventing dental problems. However, many people neglect their dental well-being because of busy schedules or limited access to dental care. Dental issues may go unnoticed until they become more serious and require complex and expensive treatments. To address this issue, we propose an innovative solution that uses dental selfies and advanced machine learning algorithms to identify and classify various dental conditions, such as cavities and gum diseases. These conditions will be categorized as either 'Healthy' or 'Unhealthy.' The main objective of this project is to create a convenient and affordable tool that empowers individuals to detect and diagnose dental problems early on. This tool encourages proactive measures to maintain oral health and seek timely treatment. Our project focuses on developing a technologically advanced toothbrush that incorporates a camera and sensors to classify dental selfies as either indicative of 'Healthy' or 'Unhealthy' oral conditions. To ensure accurate results, we conducted extensive experiments using various Convolutional Neural Network (CNN) models such as VGG16, MobileNet, DenseNet, and VGG19. Through rigorous testing, we achieved a maximum accuracy rate of 88%. This outcome demonstrates the effectiveness of our approach in accurately evaluating oral health conditions based on dental selfies. © 2023 IEEE.

关键词： Convolutional neural network Deep Learning Dentalcare IoT Smart Toothbrush

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Diabetes Mellitus Prediction and Classification Using Firefly Optimization Based Support Vector Machine 2

Diabetes Mellitus Prediction and Classification Using Firefl...

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2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Chitradevi, B. Supriya Chandra, N. Subhash Chitradevi, T.N. Alabdeli, Haider Department of Computer Applications Trichy India Nitte Meenakshi Institute of Technology Dept of ISE Bengaluru India Cvr College of Engineering Department of Cse Telangana Hyderabad India Bannari Amman Institute of Technology Department of Computer Science and Engineering Erode India The Islamic University Najaf Iraq

ISBN: (纸本)9798350382952

The Diabetes mellitus is the most dangerous disease all over the world and it is deadly disease determined by the rapid growth levels of the glucose in a blood. The Machine Learning (ML) approaches supports in the prediction and detection of the diabetes. This research aims to propose a Firefly Optimized-based Support Vector Machine (FSVM) for the prediction and classification of diabetes. The datasets utilized for diabetic prediction are PIMA Indian Diabetic dataset. The pre-processing is done by using the data normalization, cleaning as well as data splitting techniques. The Principal Component Analysis (PCA)-based feature selection approach is used for selecting the features in a acquired dataset. Finally, the ML-based algorithms are used for the classification of the diabetes disease. The effectiveness of proposed method is evaluated by PIMA dataset with the various performance metrices such as accuracy, precision, recall, and F1-score of values about 98.45%, 98.69%, 99.37%, and 98.25% respectively when compared to previous approaches such as Naïve Bayes, Integrated kernel as well as K-Nearest Neighbour. © 2024 IEEE.

关键词： Support vector machines

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Enhanced satellite imagery analysis for post-disaster building damage assessment using integrated ResNet-U-Net model

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Multimedia Tools and applications 2025年第5期84卷 2689-2714页

作者： Bhardwaj, Diwakar Nagabhooshanam, N. Singh, Ajeet Selvalakshmi, B. Angadi, Sanjeevkumar Shargunam, S. Guha, Tapas Singh, Gurkirpal Rajaram, A. Computer Engineering and Applications GLA University Chaumuhan India Department of Research Analytics Saveetha Dental College and Hospitals Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India Aditya University Surampalem Andhra Pradesh India VIT Bhopal University Madhya Pradesh Bhopal466114 India Department of Computing Science and Engineering Tagore Engineering College Rathinamangalam Tamil Nadu Chennai600127 India Department of Computing Science and Engineering Nutan College of Engineering and Research Talegaon Dabhade Maharashtra 410507 India Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Tamil Nadu Srivilliputhur626126 India Department of CSE Techno International New Town West Bengal Kolkata700156 India Department of Architecture PTU Punjab Mohali140307 India Department of Electronics and Communication Engineering E.G.S. Pillay Engineering College Tamil Nadu Nagapattinam611002 India

In the aftermath of natural disasters, timely and accurate building damage assessment is crucial for effective disaster response. This research introduces an innovative integrated ResNet-U-Net ensemble model that significantly enhances the accuracy and efficiency of damage detection from satellite imagery. The model combines the deep feature extraction capabilities of ResNet with the precise localization advantages of U-Net, supplemented by attention mechanisms to focus on critical damage indicators. Methodologically, the research employs a dataset of high-resolution satellite images from post-Hurricane Iota, processed through normalization and augmentation techniques. The model demonstrates exceptional accuracy, achieving 96.91% on an unbalanced dataset representative of real-world conditions, and 89.35% on a balanced dataset. This performance indicates a substantial improvement over existing approaches, offering a rapid and reliable tool for aiding disaster relief efforts. The proposed model's computational efficiency and generalizability underscore its potential to transform disaster management practices. Future work will explore the model's application to various disaster types and integration with real-time data processing systems. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Satellite imagery

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Utilizing Random Forests for High-Accuracy Classification in Medical Diagnostics 7

Utilizing Random Forests for High-Accuracy Classification in...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Maindola, Meenakshi Al-Fatlawy, Ramy Riad Kumar, Rakesh Boob, Nandini Shirish Sreeja, S.P. Sirisha, N. Srivastava, Abhinav Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India The Islamic university College of technical engineering Department of computers Techniques engineering Najaf Iraq Gla University Department of Computer Engineering & Applications Mathura India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Tamilnadu Chennai India New Horizon College of Engineering Master of Computer Application Bangalore India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India Fs University Shikohabad Firozabad India

ISBN: (纸本)9798350350067

In recent years, medical diagnostics has increasingly relied on machine learning techniques to improve accuracy and efficiency. Among these, the Random Forest algorithm has emerged as a powerful tool for classification tasks due to its robustness and ability to handle complex datasets. This paper explores the application of Random Forests in medical diagnostics, specifically focusing on enhancing the accuracy of disease classification. By analyzing large, heterogeneous medical datasets, we demonstrate how Random Forests can outperform traditional classification algorithms, offering high precision in identifying and classifying diseases. Key features, such as decision tree ensemble methods and bootstrap aggregation, are utilized to minimize overfitting and improve model generalization. The research compares Random Forests to other machine learning models, such as Neural Networks and Support Vector Machines, and finds that Random Forests perform better in terms of dependability and classification accuracy. We further emphasise that the technique can scale to big datasets and that it can manage missing data. The findings show that Random Forests might be a useful tool for accurate and early diagnosis when incorporated into clinical procedures. The findings of this study highlight the promise of machine learning, and Random Forests in particular, to revolutionise healthcare diagnostics and enhance patient results. © 2024 IEEE.

关键词： Random forests

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Quantum-Inspired Neural Network For Enhanced Image Recognition

Quantum-Inspired Neural Network For Enhanced Image Recogniti...

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2023 Intelligent Computing and Control for engineering and Business Systems, ICCEBS 2023

作者： Michael, Raj. T.F. Rmesh, G. Agoramoorthy, Moorthy Ahamed Ali, S. Sujatha, D. Sclas Simats Department of Computer Applications Tamilnadu Chennai India K.L.N.College of Engineering Department of Information Technology Sivagangai Tamilnadu Pottapalayam India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India Computer Science and Engineering Easwari Engineering College Chennai Ramapuram India St.Peters College of Engineering and Technology Department of Computer Science and Engineering Chennai Avadi India

ISBN: (纸本)9798350394580

The field of quantum computing has seen significant progress in recent times, leading to the emergence of innovative methodologies across several areas, particularly in machine learning and neural networks. This research study introduces a quantum-inspired neural network (QINN) that has been particularly developed to improve image recognition capabilities. In contrast to conventional neural networks, the Quantum-inspired Neural Network (QINN) utilizes concepts derived from quantum mechanics, including superposition and entanglement, to process and encode information effectively. The paradigm we propose incorporates quantum bits (qubits) as computing units instead of traditional binary nodes, simultaneously enabling the representation of many states. Moreover, the use of the intrinsic parallelism of quantum mechanics is harnessed to provide expedited and more effective data processing, particularly within the concealed levels of the network. The researchers ran benchmark tests using well-recognized image datasets, including CIFAR-10 and ImageNet. The study's findings indicated that QINN displayed superior accuracy rates compared to traditional methods and revealed faster convergence throughout the training process. Additionally, our quantum-inspired methodology demonstrated significant efficacy in analyzing pictures containing nuanced characteristics, highlighting its potential in practical scenarios where precise identification is of utmost importance. In summary, integrating quantum principles with neural network topologies has the potential for groundbreaking advancement in image recognition skills. QINN is a groundbreaking advancement in this domain, emphasizing the unexplored capacity to use quantum phenomena for sophisticated machine learning objectives. Potential areas for future study are the expansion of network scalability to accommodate bigger datasets, exploration of security measures that are resilient against quantum threats, and investigating hybrid mo

关键词： image recognition neural network Quantum-inspired Qubits

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Enhanced Machine Learning Algorithms for Validating the Biometrics in VANET 1

Enhanced Machine Learning Algorithms for Validating the Biom...

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1st International Conference on Machine Learning, computer Systems and Security, MLCSS 2022

作者： Chithaluru, Premkumar Jena, Lambodar Patra, Bichitrananda Panda, Niranjan Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram522302 India University Department of Computer Applications Odisha Bhubaneswar India University Department of Computer Science and Engineering Odisha Bhubaneswar India

ISBN: (纸本)9781665454933

In this rapidly sprouting technological era, trans-portation is also becoming intelligent and driverless. Ad hoc networking plays an essential role in making the vehicle to vehicle and vehicle to infrastructure communication environment compatible with all types of users. As technology grows, the risk of security and privacy also grows side by side. However, the intruders can easily find out holes in the security network and gather information from them. The scope of the paper is to propose a methodology that inflates the security of stored data in the hardware of vehicular networks and identity verification of vehicles in communication easier. The importance of the present research is that these proposed simulation techniques help in maintaining the security of temper proof devices used in VANETs for the storage of private data. Authors communicate all sensitive data like bank details, vehicle details and personal details over wireless networks during communication. Biometric data and many other cryptographic techniques are used to secure the communication. Proposed methodologies are very efficient, less time consuming and less cost effective too which can be used in any type of intelligent vehicle with no extra hardware requirement. © 2022 IEEE.

关键词： Vehicles

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Smart vegetable cutter for Smart Home 1

Smart vegetable cutter for Smart Home

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1st International Conference in Advanced Innovation on Smart City, ICAISC 2023

作者： Singhal, Saurabh Mangal, Deepak Kumar, Rakesh Sharma, Bhisham Dhaou, Imed Ben GLA University Department of Computer Engineering & Applications Mathura India Jain University Department of Computer Science and IT Bangalore India Dar Al-Hekma University Hekma School of Engineering Computing and Informatics Department of Computer Science Jeddah Saudi Arabia Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9781665472753

Modern society strives for simpler, more efficient, dependable, speedier, and more automated lifestyle. Existing vegetable cutters in the market have drawbacks such as high investment costs, additional manpower, and time consumption. Moreover, none of them provide the feature of cleaning, peeling, and cutting in a single machine. The veggies offered in the market have pesticides and chemicals, and many people, in their haste, do not wash them properly, endangering their health. To reduce human labor while also providing good health care, a solution to this problem is required. This paper offers a novel method for developing a vegetable cutter and chopper using an Android application and IoT devices. In addition, smart vegetable cutters clean, peel, and cut veggies all in one piece of equipment. This strategy will assist human society in reducing the amount of additional manpower needed and would save them time. © 2023 IEEE.

关键词： Real time systems

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