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Internet of Things (IoT) Insights in the Healthcare Sector: A Topic Modelling Approach 10

Internet of Things (IoT) Insights in the Healthcare Sector: ...

10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and computer engineering, UPCON 2023

作者： Ingale, Nilesh Vasant Nassa, Vinay Kumar Kumarbha, Modi Bhumika Jamalpur, Bhavana Gehlot, Anita Chakravarthi, M. Kalyan G.H. Raisoni Institute of Engineering and Business Management Coet North Maharashtra Knowledge Jalagaon Department of Computer Engineering Jalgaon India India Shri. C. J. Patel College of Computer Studies Sankalchand Patel University Visnagar India School of Computer Science and Artificial Intelligence Sr University Warangal Telangana India Uttaranchal Institute of Technology Uttaranchal University India School of Electronics Engineering VIT-AP University Amaravathi522237 India

ISBN: (纸本)9798350382471

With an invention of Internet of Things (IoT), both traditional and digital realms are rapidly integrating, thereby changing the livelihood of people in such a manner, that were unimaginable merely just a decade ago. IoTs have created a big effect so far despite being in their nascent stage, especially in the healthcare (HC) sector. This research seeks to uncover essential themes hidden within limited, yet expanding scholarly papers on IoT applications in HC. By performing topic modeling, we were able to pinpoint 5 key experiment groups, including confidentiality and safety, wirelessly networking techniques, apps, dataset, smarter well-being and cloud. Study findings demonstrate that societal issues have not received much emphasis as compared to technological issues in HC IoT exploration. With regard to classifying and analyzing the selected topics, our paper offers suggestions for further investigation. © 2023 IEEE.

关键词： Healthcare sector IoT Topic modeling wireless network technique

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Brain-Controlled computer Tasks for Paralyzed Persons: Framework Overview

Brain-Controlled Computer Tasks for Paralyzed Persons: Frame...

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2023 International Conference on Industry sciences and computer science Innovation, iSCSi 2023

作者： Imran, Amna Chaudhary, Haider Tariq, Maimoona Ali, Zahra Ahad, Abdul Naqvi, Hammad Coelho, Paulo Jorge Pires, Ivan Miguel Knowledge Unit of Science and Technology University of Management and Technology Sialkot Pakistan School of Software Northwestern Polytechnical University Shaanxi Xian710072 China Department of Computer Science Istanbul Technical University Turkey Polytechnic of Leiria Leiria Portugal Institute for Systems Engineering and Computers at Coimbra Coimbra Portugal Instituto de Telecomunicações Universidade da Beira Interior Covilhã Portugal

To bridge the digital barrier between those with disabilities and those without impairments, several applications can be operated by brain signals. It highlights the importance of employing brain-computer interfaces (BCI) and mentions a specific system called the "Brain-Controlled computer System" that can control a computer cursor and perform mouse actions using brain signals.. The system can control the cursor and provide mouse left/right click with brain signals using a low-cost Brainlink Lite device. Based on this system, we employed machine learning and deep learning methodologies to develop a system that classifies brain signals for cursor movement. The methodology involved gathering user input through surveys, interviews, and comparison studies, as well as utilizing pre-developed datasets collected using a mind sensor. We initially trained a model on a previous dataset, achieving approximately 70% accuracy. However, when applied to the new dataset, the accuracy dropped to 49%. To improve the results, we experimented with more complex models, including additional layers and a dropout layer to prevent overfitting. Despite these enhancements, the accuracy only reached a maximum of 52%. The research team collected a dataset tailored to their specific application. They made a groundbreaking discovery by applying a convolutional neural network (CNN), typically used for image analysis, to process continuous brainwave data. Despite this non-standard use, their hypothesis proved correct, with the CNN model achieving an impressive 80% accuracy in classifying brain signals for controlling a cursor. © 2024 The Authors. Published by ELSEVIER B.V.

关键词： Brain computer interface

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Analyzing P300 Signals to Assess Undergraduate Attention Span Using Impulse Neiry BCI

Analyzing P300 Signals to Assess Undergraduate Attention Spa...

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Brain computer Interface & Healthcare Technologies (iCon-BCIHT), International Conference on

作者： Kavitha Sooda Indiramma M Sanjeev Kubakaddi Department of Computer Science and Engineering B.M.S. College of Engineering Bangalore India Department of AI and DS B.M.S. College of Engineering Bangalore India itie Knowledge Solutions Bangalore India

ISBN: (数字)9798331540067

ISBN: (纸本)9798331540074

The study on brain signals to understand working memory in undergraduate students has yielded promising results. However, the challenge remains in accurately assessing different brain activities. This work seeks to measure concentration in cognitive performance, offering a detailed dataset that tracks how individuals respond to cognitive tasks. A virtual reality-based solution has proven effective in capturing the P300 signal, providing a more focused and accurate measurement platform. The study aims to evaluate the efficacy of P300 signals as indicators of attention and cognitive engagement in educational settings. A brain-computer interface system, Impulse Neiry, captured and analyzed P300 signals from undergraduate students while performing various cognitive tasks. User attention span was evaluated by measuring parameters such as session duration and interaction frequency with the software, offering insights into the system's usability. The results indicated that participants experienced about 35% deeper relaxation, and their attention spans increased by 18.97% compared to baseline engagement levels.

关键词： Headphones Medical services Games Particle measurements Software Electroencephalography Frequency measurement Cognitive science Software measurement Usability

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Advanced Deep Reinforcement Learning Strategies for Enhanced Autonomous Vehicle Navigation Systems

Advanced Deep Reinforcement Learning Strategies for Enhanced...

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computer, Communication and Control (IC4), International Conference on

作者： M Dhinakaran R.Thalapathi Rajasekaran V. Balaji V. Aarthi S. Ambika Department of Electronics and Communication Engineering Galgotias College of Engineering & Technology Knowledge Park II Greater Noida Uttar Pradesh India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India Department of Computer Science and Engineering Easwari Engineering College Chennai India Department of Computer Science and Engineering GRT Institute of Engineering and Technology Tiruttani Tamil Nadu India Department of Information Technology E.G.S. Pillay Engineering College Nagapattinam Tamil Nadu India

The objective of this research is to investigate more sophisticated techniques in Deep Reinforcement Learning (DRL) for the purpose of improving navigation systems for autonomous vehicles. The results of our research indicate that the success rates of DRL algorithms are encouraging: DQN (75.2%), PPO (82.6%), and TRPO (78.1%). The success of navigation is highly influenced by optimisation factors such as parameter tuning (87.4%), exploration-exploitation balance (85.1%), and reward shaping (88.9%). The generalisation score for transfer learning is rather good (92.3%), while the safety modules are designed to make the system safer. These findings highlight the significance of algorithm selection, parameter optimisation, and advanced techniques in the process of optimising navigation for autonomous vehicles, hence establishing essential benchmarks for future developments.

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Secure Pool Mining Through SVM-Based Miner Classification and Computation Validation in Blockchain Networks

Secure Pool Mining Through SVM-Based Miner Classification an...

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Information Technology (OCIT), OITS International Conference on

作者： Srinuvas Kumar Battina Ravi Kumar Suggala Srinivasa Rao Dangeti Meriga Kiran Kumar Pavan Kumar Vadrevu Naga Sravanthi Puppala Information Technology SR Gudlavalleru Engineering College Gudlavalleru AP Seshadri Rao Knowledge Village India Department of Information Technology Shri Vishnu Engineering College for Women Bhimavaram India Department of Computer Science and Engineering Lakireddy Bali Reddy College of Engineering Mylavaram India Department of Computer Science and Engineering School of Engineering and Applied Sciences SRM University AP Amaravati India

ISBN: (数字)9798331510404

ISBN: (纸本)9798331510411

Since the adoption of the Blockchain network by modern technologies, which benefit from its immutability and secure access provided by its consensus process, blockchain mining has gained popularity. Blockchain networks' miners rely on the consensus procedure to contribute data to the network. Miners use their resources to publish new blocks and receive rewards, and they participate in the mining process. Since solo mining might be difficult, most miners decided to join a mining pool. Several issues surrounding the miner's choice with pool mining remain unanswered because of the range of many mining pools and the potential adoption of various incentive systems. There are a few issues to consider, though, about maintaining a miner's trust throughout the consensus and mining processes. As part of the consensus, miners that are part of a mining pool contribute their processing power toward the task of adding a block. The pool will be rewarded if they are successful in their endeavors. To guarantee the execution of the consensus protocol in pool mining, a trust evaluation model is presented in our study. As a result, trust assessment among miners is formulated in this research it is evaluated as a classification problem, and an innovative approach using machine learning is offered to classify trust. First, the vector for the trust features is built using the parameters of the Blockchain. A trust classifier can then be created by training it with samples of data that miners have acquired and that contain transaction activities. Also, the provided Trust model verifies that the total computing power of the pool for which miners are requested and the miner's computation power should be 50 percent greater than or equal to the total computation power this is for to mitigate attacks brought on by untrusted miners through forks in the Blockchain. The Hyperledger tool was used as the framework to assure robustness in analyzing transaction delay, and transaction throughput, and to

关键词： Training Distributed ledger Computational modeling Machine learning Throughput Vectors Robustness Blockchains Data mining Security

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ANFIS-based Multilayered Algorithm for Botnet Detection

ANFIS-based Multilayered Algorithm for Botnet Detection

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Recent Advances in Electrical, Electronics, Ubiquitous Communication, and Computational Intelligence (RAEEUCCI), International Conference on

作者： T Thamaraimanalan C Venkatesan M Ramkumar A Sivaramakrishnan M Marimuthu Department of Electronics and Communication Engineering Sri Eshwar College of Engineering Coimbatore India Department of Electronics and Communication Engineering HKBK College of Engineering Bangalore India Department of Computer Science and Business System Knowledge Institute of Technology Salem India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra pradesh India Department of Computer Science and Engineering Sona College of Technology Salem India

In this study, a multilayered botnet detection system for network-based botnet detection based on ANFIS is suggested. Two layers make up the suggested system, one of which is utilized for feature extraction and the other for botnet detection. Using mean and variance, the feature extraction layer pulls features from network traffic data. The botnet detection layer employs ANFIS to identify botnet activity based on the collected features. Using the CICIDS2017 dataset, the suggested system's performance is assessed. The experimental findings demonstrate that the suggested algorithm works better than other current botnet detection techniques regarding accuracy, precision, and recall. The proposed system obtained 99.83% accuracy, 99.75% precision, and 99.91 % recall. The proposed multilayered ANFIS-based botnet detection system offers an excellent way to identify botnet activity in a network. It can be utilized in various network security applications, such as threat intelligence, malware detection, and intrusion detection.

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Machine Learning-Driven Blockchain for Enhanced Drug Discovery and Development in Pharmaceutical Research

Machine Learning-Driven Blockchain for Enhanced Drug Discove...

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2024 OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Asha, V. Anjimoon, Shaik Verma, Meenu Rani Singla, Atul Khan, Irfan Alkhafaji, Mohammed Ayad New Horizon College of Engineering Department of Computer Applications Bangalore India Institute of Aeronautical Engineering Hyderabad Dundigal India GyanGanga Institute of Technology and Sciences DoCSBS M.P Jabalpur India Lovely Professional University Phagwara India Lloyd Institute of Engineering & Technology Knowledge Park II Uttar Pradesh Greater Noida201306 India National University of Science and Technology College of Technical Engineering Dhi Qar64001 Iraq

ISBN: (纸本)9798350373783

Machine learning (ML) and blockchain technologies have made huge steps forward in the pharmacy business by being used in the process of creating and discovering new medicines. Both breakthroughs were made possible by using both tools together. With the help of a blockchain design powered by machine learning, the study's main goal is to come up with a new way to make drug research more effective and efficient. This method makes it possible to make molecules with a wide range of structure features that have the right pharmacological qualities. You can also make these molecules, improve the plans of clinical trials, and guess how drugs will interact with their targets. Technologies like generative adversarial networks, reinforcement learning methods, and deep learning algorithms have made it possible for these things to be used. If blockchain technology is used throughout the whole process of making medicines, it improves global openness, traceability, and data security. This is because it makes sure that managing data is safe. The tests show that the suggested method works better than the others when it comes to the process of finding and developing new drugs. This is better because the creation process is more precise, accurate, and efficient. © 2024 IEEE.

关键词： Drug discovery

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Advancing Wire Arc Additive Manufacturing: A New Era of Precision and Efficiency in Large Metal Component Production

Advancing Wire Arc Additive Manufacturing: A New Era of Prec...

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2024 OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Khan, Irfan Anjimoon, Shaik Asha, V. Singla, Atul Akhil, P. Alsultany, Nahed Mahmood Ahmed Lloyd Institute of Engineering & Technology Knowledge Park II Uttar Pradesh Greater Noida201306 India Institute of Aeronautical Engineering Hyderabad Dundigal India New Horizon College of Engineering Department of Computer Applications Bangalore India Lovely Professional University Phagwara India St. Martin's Engineering College Dept. of Cse Telangana Secunderabad India National University of Science and Technology Collage of Dentist Dhi Qar64001 Iraq

ISBN: (纸本)9798350373783

A comprehensive Wire Arc Additive Manufacturing (WAAM) approach that employs five algorithms to improve massive metal components is described in this research. The adaptive layer thickness control algorithm adjusts layer thickness based on real-time complexity. This ensures additive manufacturing accuracy throughout. Also, the Material Versatility Optimization Algorithm simplifies working with multiple materials, making casting more efficient. The Intelligent Path Planning Algorithm allocates the robotic arm's movement to traverse the shortest distance and uniformly distribute heat during deposition. The MultiMaterial-Materialon Strategy Algorithm improves material amounts, making WAAM more usable for additional materials. The Real-Time Excellent Assurance Algorithm constantly examines and adjusts parameters to ensure excellent production. A complete analysis of research reveals how algorithms are related and how vital they are for accuracy, speed, and flexibility. Testing shows that the recommended technique controls layer thickness, material variety, deposition velocity, accuracy, energy efficiency, and real-time tracking better than typical WAAM methods. Figures 6, 7, and 8 indicate that the proposed method delivers superior results across several criteria. The recommended WAAM approach can create more massive metal components by fundamentally changing how things are done. This research provides a comprehensive additive manufacturing approach to solve numerous large-scale metal production issues. © 2024 IEEE.

关键词： Quality control

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Chinese Lexical Simplification

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IEEE/ACM Transactions on Audio Speech and Language Processing 2021年 29卷 1819-1828页

作者： Qiang, Jipeng Lu, Xinyu Li, Yun Yuan, Yunhao Wu, Xindong Department of Computer Science Yangzhou China Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei University of Technology Hefei China

Lexical simplification has attracted much attention in many languages, which is the process of replacing complex words in a given sentence with simpler alternatives of equivalent meaning. Although the richness of vocabulary in Chinese makes the text very difficult to read for children and non-native speakers, there is no research work for the Chinese lexical simplification (CLS) task. To circumvent difficulties in acquiring annotations, we manually create the first benchmark dataset for CLS, which can be used for evaluating the lexical simplification systems automatically. To acquire a more thorough comparison, we present five different types of methods as baselines to generate substitute candidates for the complex word that includes synonym-based approach, word embedding-based approach, BERT-based approach, sememe-based approach, and a hybrid approach. Finally, we design the experimental evaluation of these baselines and discuss their advantages and disadvantages. To our best knowledge, this is the first study for CLS task. © 2014 IEEE.

关键词： BERT Lexical simplification pretrained language model unsupervised

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PSDNet: A Breakthrough Parking Space Detection Network Powered by YOLOv8

PSDNet: A Breakthrough Parking Space Detection Network Power...

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Advances in Computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： Prabu Selvam P. Saravanan M. Marimuthu M. R. Nithya V. Sathiyapriya V. Nareen Harsha Department of Computer Science and Engineering SRM Institute of Science & Technology Tiruchirappalli India School of Computer Science and Engineering VIT University Chennai India Dept. of CSBS K. Ramakrishnan College of Engineering Trichy India Department of CSE Knowledge Institute of Technology Salem India Dept. of CSE (AIE) Amrita VishwaVidyapeetham Chennai India

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

Parking congestion presents a significant challenge in urban areas worldwide, spanning both developed and developing nations. The escalating volume of vehicles compounds the scarcity of parking spots, particularly in downtown areas and adjacent streets. Local authorities are tasked with enforcing regulations to mitigate this issue, yet the current state of affairs often falls short of meeting the needs of numerous residents. Consequently, this article advocates using the YOLOv8 algorithm to identify vacant parking spaces. The proposed YOLOv8 comprises three pivotal blocks: the Backbone, Neck, and Head. The Backbone segment employs an enhanced version of the CSPDarknet53 network, integrating cross-stage partial connections to strategically strengthen information exchange across its diverse layers. The Neck section merges features with varying scales and incorporates contextual information from the feature maps. The Head section predicts class, objectness scores, and bounding boxes of identified objects. The performance of the YOLOv8 algorithm in identifying vacant parking spaces is evaluated using the parking space availability dataset. It achieves an F1-score of $98.7 \%$, which is $8.7 \%$ higher than the existing object detector.

关键词： Local government Urban areas Detectors Prediction algorithms Regulation Neck Object recognition

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