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Vulnerability in Cell Scheduling Function for 6TiSCH Networks: Impact and its Mitigation 20

Vulnerability in Cell Scheduling Function for 6TiSCH Network...

引用

20th IEEE India Council International Conference, INDICON 2023

作者： Tapadar, Karnish N A Sakre, Aditya Kumar Khatua, Manas Tamarapalli, Venkatesh Indian Institute of Technology Guwahati Dept. of Computer Science and Engineering India

ISBN: (纸本)9798350305593

The 6TiSCH standard is widely adopted for Industrial IoT applications, offering low latency and high reliability. Cell scheduling plays a critical role in 6TiSCH networks by determining packet transmission schedules in terms of timing and channel allocation. The IETF introduced the Minimal Scheduling Function (MSF) and the 6top protocol to facilitate schedule management dynamically. This paper investigates the vulnerabilities associated with cell scheduling and the 6top protocol in 6TiSCH networks. We have demonstrated the existence of a cell depletion attack which can be launched by a compromised node, and its potential to disrupt the proper functioning of cell scheduling. This attack adversely affects transmission reliability, end-to-end latency, and energy consumption of victim nodes and their child nodes. To address the impact of this attack, we have proposed a defence mechanism which can be easily incorporated into the MSF scheduling function. Our approach incorporates parent-child authentication and validation measures to ensure that only legitimate nodes participate in the scheduling process. Through extensive simulations, we have verified that the proposed cell depletion attack significantly degrades network performance and increases the energy consumption of victim nodes by 41% in a 60-node network compared to normal functioning of scheduling function, leading to a significant decrease in device lifespan. However, when our defence mechanism is implemented, we observe a significant improvement in network performance even in the presence of the attacker. © 2023 IEEE.

关键词： 6P 6P Vulnerabilities 6TiSCH Network Link Scheduling Minimal Scheduling Function

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A Review on Deep Learning Based Question Answering with Natural Language Processing in Healthcare

A Review on Deep Learning Based Question Answering with Natu...

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

作者： Javeed, Abdul Saleem Gopala Krishnan, C. Gitam School of Technology Dept. of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798350359688

Over the Internet, an efficient approach and promising solution to retrieve significant information envisages the beginning of Question Answering Systems (QAS). Because of data sources availability, the deep learning model based on open domain QAS focuses a substantial quantity of research work. Because of the lack of medical datasets, less attention is received in the medical domain. The deep learning (DL) utility increases the development of huge data in the healthcare domain. In the past years, popularity has grown in hardware parallelism and quick data storage availability. The challenge, pipelines, and tasks involved in medical NLP and medical imaging deploy the existing review of research in this work. The medical NLP and imaging fields deploy DL structures with its extensive survey is presented here. Over all 39 research papers are collected for this survey analysis from the years 2017 to 2023. The diagnosis is enhanced with medical imaging, Natural language processing, and DL combination to detect the appropriate combination. Each of the outcomes is shown graphically. The information provided in this survey is quite useful for newcomers carrying out research in medical informatics. © 2024 IEEE.

关键词： and Medical Data Deep Learning Healthcare Natural Language Processing Question Answering

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Multimodal biometric authentication using Fully Homomorphic Encryption 2

Multimodal biometric authentication using Fully Homomorphic ...

引用

2nd International Conference on Paradigm Shifts in Communications Embedded Systems, Machine Learning and Signal Processing, PCEMS 2023

作者： Vallabhadas, Dilip Kumar Sandhya, Mulagala Sarkar, Soumyadip Chandra, Y Rupesh National Institute of Technology Dept. of Computer Science and Engineering Warangal India

ISBN: (纸本)9798350310719

In this paper multimodal biometric system is developed using two traits iris and fingerprint. The features generated by iris and fingerprint images are fused at the feature level. The generated fused feature vector template cannot be stored directly on the server, if stored directly can lead to various privacy and security concerns. So, these templates are encrypted in such a way that even when applying any operations on the templates, the templates should be in encrypted form. So, the operations need to be performed in the encrypted domain without decrypting it, and the final result, when decrypted should again give back the correct result as if the operations are performed on the original data. Fully Homomorphic encryption (FHE) scheme is designed to satisfy the above conditions. FHE is used to compute the hamming distance between the reference and probe template in an encrypted domain. To improve accuracy rotational invariant technique is used, which solves rotational inconsistency problems. The computational speed is increased by using a batching scheme to reduce the number of operations during homomorphic multiplication. We have conducted our experiment on the IITD and CASIA dataset. The best EER is obtained in CASIA dataset of 0.01% with a computational time of 0.0152 sec per template. © 2023 IEEE.

关键词： Hamming distance

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Cognate Transformer for Automated Phonological Reconstruction and Cognate Reflex Prediction

Cognate Transformer for Automated Phonological Reconstructio...

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2023 Conference on Empirical Methods in Natural Language Processing, EMNLP 2023

作者： Mahesh Akavarapu, V.S.D.S. Bhattacharya, Arnab Dept. of Computer Science and Engineering Indian Institute of Technology Kanpur India

ISBN: (纸本)9798891760608

Phonological reconstruction is one of the central problems in historical linguistics where a proto-word of an ancestral language is determined from the observed cognate words of daughter languages. Computational approaches to historical linguistics attempt to automate the task by learning models on available linguistic data. Several ideas and techniques drawn from computational biology have been successfully applied in the area of computational historical linguistics. Following these lines, we adapt MSA Transformer, a protein language model, to the problem of automated phonological reconstruction. MSA Transformer trains on multiple sequence alignments as input and is, thus, apt for application on aligned cognate words. We, hence, name our model as Cognate Transformer. We also apply the model on another associated task, namely, cognate reflex prediction, where a reflex word in a daughter language is predicted based on cognate words from other daughter languages. We show that our model outperforms the existing models on both tasks, especially when it is pre-trained on masked word prediction task. © 2023 Association for Computational Linguistics.

关键词： Forecasting

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Covid-19 crowd detection and alert system using image processing

Covid-19 crowd detection and alert system using image proces...

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2023 International Conference on Advances in Intelligent Computing and Applications, AICAPS 2023

作者： Lodha, Nitin Singh Gahlaut, Harshvardhan Vellore Institute of Technology Dept. of Computer Science and Engineering Vellore India

ISBN: (纸本)9798350333817

In this paper, we aim to help in identifying the people that are violating social distancing norms set by the government (necessary during the COVID-19 pandemic in public places), by providing an efficient real-time deep learning-based framework to automate the process of monitoring the social distancing via object detection and tracking approaches. Our system is divided into two subsystems: one that deals with crowd detection and control, and the other that sends information to the police authorities. Our system technologies, including as IoT, image processing, web cams, BLE, OpenCV, and Cloud, are being considered for inclusion in the proposed framework. The image processing is divided into two sections, the first of which is the extraction of frames from real-time movies, and the second of which is the processing of the frame to determine the number of individuals in the crowd. Even in a crowd, dissemination may be restricted if people adhere to social distancing standards. As a result, the image processing model primarily targets the number of people who do not adhere to social distancing norms and stand too close together. © 2023 IEEE.

关键词： Deep learning

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Thyroid Nodule Detection and Classification Using Deep Learning 1

Thyroid Nodule Detection and Classification Using Deep Learn...

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1st International Conference on Computational Intelligence for Information, Security and Communication Applications, CIISCA 2023

作者： Gowda, Samarth Thrishul, D. Sheth, Vaibhav B Vishwas, R. Gargi, N. Hemavathi, P. Bangalore Institute of Technology Dept. of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350339727

Cells within the thyroid gland can grow abnormally, leading to the formation of radiographically identifiable lumps termed nodules. The latter is a little mass that has developed at the thyroid gland's level and necessitatesa medical examination in order to accurately diagnose the nodule's source and decide any potential treatments. Malignant nodules can be found using the Fine Needle Aspiration Biopsy (FNAB) technique. Yet, FNAB is an expensive, intrusive procedure that significantly increases patient anxiety. As a substitute, thyroid nodule ultrasound imaging is performed to classify the nodules. Even for experienced radiologists, accurately diagnosing thyroid nodules by ultrasonography is challenging due to the varied appearances of both cancerous and non-cancerous nodules. This paper suggests deep learning-based system that is capable of detecting and classifying thyroid nodules that may be able to offer radiologists unbiased assistance. © 2023 IEEE.

关键词： Convolutional neural networks

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Identification and Classification of Medicinal Leaves and their Medicinal Values 4

Identification and Classification of Medicinal Leaves and th...

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4th IEEE Global Conference for Advancement in technology, GCAT 2023

作者： Saunshi, Girish Chini, Shridhar Ganvatkar, Prerana Nayak, Rahul Dept. of Computer Science and Engineering KLE Institute of Technology Hubballi India

ISBN: (纸本)9798350305258

The traditional knowledge systems about medicinal properties of plant species have started to disappear with the passage of time due to the scarcity of written documents. Due to resemblance in the visual properties among plants, sorting and classification are complicated to carry out, especially in large area. The main objective of this work is to develop a machine learning based system for classifying different Indian medicinal plants, which will be useful in drug discovery process and provide a platform for future research. This work on identifying plant species and determining their medicinal value using the VGG-16 convolutional neural network model. We embarked on the task of collecting a comprehensive dataset by conducting fieldwork. The work involved visiting botanical gardens to collect plant specimens along with their associated information, such as species, habitat, and traditional medicinal uses. The collected images were labelled ensuring its quality and relevance for training our model. The collected dataset was then utilized to train the VGG-16 model. Techniques such as data augmentation and transfer learning to enhance the model's performance and optimize training efficiency. Throughout the training process the model's parameters and hyper parameters were fine-tuned, striving to achieve optimal accuracy and robustness. After conducting extensive research and comparison between VGG 16, Resnet 50, Sequential Model. VGG-16 yielded superior results based on our findings. The VGG-16 model achieved an impressive accuracy of 98%, outperforming other models. © 2023 IEEE.

关键词： CNN Dataset Medicinal Plants Resnet 50 Sequential Model VGG16

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Data Visualisation of Time Tradable Assets Using Machine Learning

Data Visualisation of Time Tradable Assets Using Machine Lea...

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2023 IEEE International Conference on Network, Multimedia and Information technology, NMITCON 2023

作者： Mallikarjunaiah, Nirupama Jain, Rishab S. Vinay, K.R. Navada, Sharan S. Pramod, T.C. Siddaganga Institute of Technology Dept. of Computer Science and Engineering Tumkur India

ISBN: (纸本)9798350300826

Forecasting financial instruments accurately is a complex task due to the volatile, non-linear and dynamic nature of financial markets. Numerous factors, including political events, global economic conditions, and company performance, contribute to the difficulty of predicting outcomes. However, this challenge also provides a wealth of data that can be leveraged to uncover patterns. With the advancement of AI and increased computational power, programmable prediction techniques have emerged as more effective tools for forecasting tradable assets. This paper explores the application of various regression techniques, such as Linear, Support Vector, Polynomial, and Tree Regression, in financial prediction. Additionally, the study incorporates Long Short-Term Memory (LSTM), a method of recurrent neural network model, to analyse an asset's historical behaviour and generate reliable conclusions. The proposed approach aims to address the practical implementation of prediction systems in real-world scenarios while addressing challenges related to the accuracy of the provided values. Furthermore, appropriate visualization techniques are employed to enhance the interpretation and understanding of the results. By combining advanced prediction models and visualization methods, this research endeavours to improve the accuracy of financial predictions and provide valuable insights for decision-making infinancial markets. © 2023 IEEE.

关键词： Data visualization

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Operating System Command Execution Using Voice Command 3

Operating System Command Execution Using Voice Command

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3rd IEEE Asian Conference on Innovation in technology, ASIANCON 2023

作者： Singh, Paras Nath Navaneetha, M. Tijare, Poonam Vijay Cmr Institute of Technology Dept. of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350302288

Recent years have seen a considerable increase in the use of voice commands as a result of the growing popularity of smart gadgets, virtual assistants, and other related technologies. In this study, we investigate the viability of voice commands for operating system commands. In this paper a system is proposed and demonstrated that enables voice commands to be recognized by the operating system and executed according to parameters. This way it is offering a fresh and more usable voice assistant to carry out typical computer chores. Python tools are used to go over how the system is put into practice, including how speech recognition and natural language processing are used, and how well it executes in terms of accuracy, speed, and usefulness. The main impact factor of this paper is to provide voice assistance to disadvantageous people whose hands and fingers are not working well. © 2023 IEEE.

关键词： Speech recognition

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An Ensemble-based Deep Learning Model for Multi-class Flower Recognition

An Ensemble-based Deep Learning Model for Multi-class Flower...

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2023 International Conference on Next-Generation Computing, IoT and Machine Learning, NCIM 2023

作者： Rabbi, Md Fozle Dola, Sraboni Das Taha, Noorjahan Binte Kader Al Mahmud, Tamim Islam, Muhammad Nazrul Razzaque, Md. Abdur Al Faisal, Faiz Green University of Bangladesh Dept. of Computer Science and Engineering Dhaka Bangladesh Military Institute of Science and Technology Dept. of Computer Science and Engineering Dhaka Bangladesh University of Dhaka Dept. of Computer Science and Engineering Dhaka1000 Bangladesh

ISBN: (纸本)9798350316001

Many species around the globe heavily depend on flowers, and even the use of flowers for medical purposes is enormous, which has been in practice since ancient times. In modern botany, agronomy, and species research, the identification and classification of flowers mainly depend on human intervention, which requires a lot of human resources, and the recognition accuracy rate is low. On the other hand, due to the vast number of flowers with different types, it is quite a challenge and increases the complexity of the accurate detection and classification process. Moreover, the traditional CNN model and other pre-trained models like- VGG16, VGG19, Xception, and MobileNet-V2 failed to ensure high validation accuracy. In this research, we have designed an ensemble-based deep learning model consisting of two pre-trained models DenseNet-201 and EfficientNet-B7, which we renamed 'FlowerConvNet'. This paper considers classifying five categories of flowers consisting of 4242 images, collected from the Kaggle flower recognition dataset. In this research study, the proposed deep learning model FlowerConvNet using transfer learning can obtain a competitive result with 95% validation accuracy. © 2023 IEEE.

关键词： Learning systems

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