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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Latha, B. Mridhula, S. Aishvarya, R. Darshna, S.Shreeya Sri Sairam Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331529635

Intensive care units (ICUs) are dedicated to the treatment of patients with severe or life-threatening illnesses and injuries, necessitating constant care, close supervision, and the use of life support equipment and medications to maintain normal bodily functions. In ICUs, the monitoring of patients' physiological parameters has significantly advanced patient safety by alerting ICU staff through visual or audible alarms. However, due to understaffing during night shifts, nurses may not be able to respond promptly to these alarms, potentially endangering patient lives. Moreover, the continuous exposure of medical staff to alarms may lead to decreased concentration and errors, as well as desensitization to critical alarms, further compromising patient safety. The proposed solution utilizes a neural network to classify distress alarms in the ICU and direct them to a nurse station for timely response. © 2024 IEEE.

关键词： Nursing

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Propagation of computer Worms—A Study 6th

Propagation of Computer Worms—A Study

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6th International Conference on Soft Computing and Signal Processing, ICSCSP 2023

作者： Rao, Mundlamuri Venkata Midhunchakkaravarthy, Divya Dandu, Sujatha Department of Computer Science and Multimedia Lincoln University College Kota Bharu Malaysia Department of Computer Science and Engineering Malla Reddy College of Engineering and Technology Hyderabad India

ISBN: (纸本)9789819984503

Worms are commonly disseminated through two methods: scanning vulnerable machines in a network as well as spreading through topological neighbors. Modeling worm propagation can assist us in understanding how worms propagate and develop efficient defense tactics. However, most past studies either focused on their intended task or explored detection systems as well as defense systems. Few provide very detailed study in worm propagation modeling, that is useful in building defense mechanisms to deal with worm spread. This work includes a survey as well as comparison of worm propagation models based on two independent worm-spreading strategies. We first define worm features based on their spreading behavior and then classify the numerous target discovery approaches they deploy. In addition, we examine several topologies for modeling worm spreading, analyze numerous models of worm propagation and their effectiveness. On the basis of the analysis of worm spread and current studies, future directions for modeling worm propagation models are offered. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Network security

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On generalization of the sense retrofitting model

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Natural Language engineering 2023年第4期29卷 1097-1125页

作者： Lee, Yang-Yin Yen, Ting-Yu Huang, Hen-Hsen Shiue, Yow-Ting Chen, Hsin-Hsi Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan Institute of Information Science Academia Sinica Taipei Taiwan

With the aid of recently proposed word embedding algorithms, the study of semantic relatedness has progressed rapidly. However, word-level representations are still lacking for many natural language processing tasks. Various sense-level embedding learning algorithms have been proposed to address this issue. In this paper, we present a generalized model derived from existing sense retrofitting models. In this generalization, we take into account semantic relations between the senses, relation strength, and semantic strength. Experimental results show that the generalized model outperforms previous approaches on four tasks: semantic relatedness, contextual word similarity, semantic difference, and synonym selection. Based on the generalized sense retrofitting model, we also propose a standardization process on the dimensions with four settings, a neighbor expansion process from the nearest neighbors, and combinations of these two approaches. Finally, we propose a Procrustes analysis approach that inspired from bilingual mapping models for learning representations that outside of the ontology. The experimental results show the advantages of these approaches on semantic relatedness tasks. © The Author(s), 2023. Published by Cambridge University Press.

关键词： Retrofitting

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Enhancing Sentiment Analysis on Amazon Product Reviews using LSTM Networks 15

Enhancing Sentiment Analysis on Amazon Product Reviews using...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Dubey, Shivani Singhal, Vikas Sahu, Ajay Kumar Kumar, Sunil Saraswat, Bal Krishna Tomer, Anoushka Kumar, Abhishek Ranjan, Alok Singh, Sanskriti Greater Noida India Department of Computer Science & Engineering Galgotias College of Engineering & Technology Greater Noida India Department of Computer Science & Engineering Sharda University Greater Noida India

ISBN: (纸本)9798331300579

In recent years, the explosive growth of online shopping and e-commerce platforms has resulted in an unprecedented amount of user-generated reviews, making sentiment Analysis a crucial task for a business to understand customer feedback and improve product offerings. This research study centers on the implementation of the Long Short Term Memory (LSTM) algorithm in sentiment analysis of the Amazon dataset. The dataset consists of customer reviews and ratings of various products on Amazon. Sentimental analysis is the procedure of discerning the sentiment or emotion expressed in the text. The primary objective of this research is to assess the effectiveness of the LSTM algorithm in analyzing sentiments and to contrast its outcomes with those of other frequently utilized algorithms. The paper discusses the pre-processing steps taken to clean the dataset and convert suitable formats of analysis. Python programming language is utilized for implementing the LSTM algorithm, and its performance is gauged based on multiple metrics like accuracy, precision, recall, and F-1 Score. The findings indicate that the LSTM algorithm outperforms other algorithms with regard to Accuracy, F-1 Score, Recall, and Precision. The study provides valuable perspectives on the efficiency of the LSTM algorithm for sentiment analysis, as well as its potential utilization in the field of natural language processing. © Grenze Scientific Society, 2024.

关键词： Long short-term memory

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Reliability Analysis of Correlated Competitive and Dependent Components Considering Random Isolation Times

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computers, Materials & Continua 2023年第9期76卷 2763-2777页

作者： Shuo Cai Tingyu Luo Fei Yu Pradip Kumar Sharma Weizheng Wang Lairong Yin School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China Department of Computing Science University of AberdeenAberdeenUK School of Automotive and Mechanical Engineering Changsha University of Science and TechnologyChangsha410114China

In the Internet of Things(IoT)system,relay communication is widely used to solve the problem of energy loss in long-distance transmission and improve transmission *** Body Sensor Network(BSN)systems,biosensors communicate with receiving devices through relay nodes to improve their limited energy *** the relay node fails,the biosensor can communicate directly with the receiving device by releasing more transmitting ***,if the remaining battery power of the biosensor is insufficient to enable it to communicate directly with the receiving device,the biosensor will be isolated by the ***,a new combinatorial analysis method is proposed to analyze the influence of random isolation time(RIT)on system reliability,and the competition relationship between biosensor isolation and propagation failure is *** approach inherits the advantages of common combinatorial algorithms and provides a new approach to effectively address the impact of RIT on system reliability in IoT systems,which are affected by competing ***,the method is applied to the BSN system,and the effect of RIT on the system reliability is analyzed in detail.

关键词： BSN Internet of Things reliability analysis random isolation time

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Energy and Bandwidth Based Link Stability Routing Algorithm for IoT

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computers, Materials & Continua 2022年第2期70卷 3875-3890页

作者： D.Kothandaraman A.Balasundaram R.Dhanalakshmi Arun Kumar Sivaraman S.Ashokkumar Rajiv Vincent M.Rajesh School of Computer Science and Artificial Intelligence SR UniversityWarangalTSIndia School of Computer Science and Engineering Center for Cyber Physical SystemsVellore Institute of Technology(VIT)ChennaiIndia Department of Computer Science and Engineering KCG College of TechnologyChennaiIndia School of Computer Science and Engineering Vellore Institute of Technology(VIT)ChennaiIndia Department of Computer Science and Engineering Saveetha School of EngineeringSIMATSChennaiIndia

Internet of Things(IoT)is becoming popular nowadays for collecting and sharing the data from the nodes and among the nodes using internet ***,some of the nodes in IoT are mobile and dynamic in *** maintaining the link among the nodes,efficient bandwidth of the links among the mobile nodes with increased life time is a big challenge in IoT as it integrates mobile nodes with static nodes for data *** such networks,many routing-problems arise due to difficulties in energy and bandwidth based quality of *** to the mobility and finite nature of the nodes,transmission links between intermediary nodes may fail frequently,thus affecting the routing-performance of the network and the accessibility of the *** existing protocols do not focus on the transmission links and energy,bandwidth and link stability of the nodes,but node links are significant factors for enhancing the quality of the *** stability helps us to define whether the node is within or out of a coverage *** paper proposed an Optimal Energy and bandwidth based Link Stability Routing(OEBLS)algorithm,to improve the link stable route with minimized error rate and *** this paper,the optimal route from the source to the sink is determined based on the energy and bandwidth,link stability *** the existing routes,the sink node will choose the optimal route which is having less link stability *** stable link is determined by evaluating link stability value using distance and ***-energy of the node is estimated using the current energy and the consumed *** energy is estimated using transmitted power and the received *** bandwidth in the link is estimated using the idle time and channel capacity with the consideration of probability of collision.

关键词： Link stability internet of things optimal energy optimal bandwidth residual energy

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Identification of Tamil Sign Language using Hamiltonian deep neural network

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International Journal of Systems, Control and Communications 2025年第1期16卷 62-78页

作者： Lingeswari, Ponnusamy Pavunraj, Daniel Kanagavalli, Mariappan Manikandan, Karuppasamy Department of Electronics and Communication Engineering P.S.R. Engineering College Tamil Nadu Sivakasi India Department of Artificial Intelligence and Data Science Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Tamil Nadu Chennai India Department of Electronics and Communication Engineering P.S.R.R. College of Engineering Tamil Nadu Sivakasi India Department of Computer Science and Engineering P.S.R. Engineering College Tamil Nadu Sivakasi India

Sign language consisting of various hand patterns, serves as a vital communication medium for conveying messages, sharing knowledge, and expressing ideas among the deaf community. This research addresses the challenge of recognising Tamil Sign Language (TSL) by proposing a novel identification method using a Hamiltonian deep neural network (HDNN). The primary objective is to maximise the accuracy and reliability of TSL recognition, especially for real-time applications. The dataset comprises real-time images of 12 vowels, 1 Aayutha Ezhuthu, and 18 consonants, collected from 110 different signers. To address noise in the images, an iterative guided filtering (IGF) method is employed for preprocessing. Subsequently, feature extraction is performed using residual exemplars local binary pattern (RELBP). HDNN is then applied to classify the signs. The performance of the proposed approach achieves 23.32%, 25.07%, 21%, 27.53%, and 30% higher accuracy and 14.09%, 19.63%, 28%, 18.45%, and 10.54% lower error rates compared to existing methods. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Local binary pattern

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dataHarbour: Enabling Decentalized AI data Marketplace using Blockchain 3

DataHarbour: Enabling Decentalized AI Data Marketplace using...

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3rd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Dave, Maheak Saraf, Awantika Kumar, Rishabh Bhattacharya, Rituparna Techno India University Department of Computer Science and Engineering-Artificial Intelligence West Bengal 700091 India Techno India University Department of Computer Science and Engineering India

ISBN: (纸本)9798350354348

Artificial Intelligence revolutionizes digital applications through simulation of human intelligence in machines, driving unprecedented progress in technology and decision-making. By adeptly processing large volumes of data, AI systems excel in pattern recognition, learning from experience, and autonomously making decisions. However, the present AI ecosystem suffers from data inequity, with major corporations dominating the market while smaller businesses still struggling. Access to AI data is the privilege of tech giants. In this paper, we propose dataHarbour, a blockchain-based marketplace that addresses issues with AI data access. dataHarbour's decentralized platform seeks to democratize data access and promote cooperation. Built on blockchain- a disruptive emerging technology that buttresses decentralized systems, it leverages smart contracts, incorporates data verification, and a reputation system. This paper also delineates the design and operation of AI data market place and portrays the system's ability to foster a more inclusive AI environment. dataHarbour aims to accelerate AI innovation and close the data divide by facilitating safe data interchange and fostering integrity. © 2024 IEEE.

关键词： Smart contract

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Deep Learning into the Future: Hybrid CNN-RNN for Early Detection of Alzheimer's Disease 5

Deep Learning into the Future: Hybrid CNN-RNN for Early Dete...

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Kingsy Grace, R. Kowshiga, A. Pavithra, T. Priyanka, V. Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379945

Alzheimer's Disease (AD), which is characterized by cognitive decline and memory loss, affects millions of people worldwide and poses a significant challenge to modern healthcare. Detecting AD at its earliest stages is paramount for initiating timely interventions and optimizing management strategies. In this innovative project, electroencephalogram (EEG) signals in CSV format are utilized as a non-invasive and accessible modality for AD detection. The methodology integrates Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), renowned for their ability to extract spatial and temporal features from multidimensional data, respectively. EEG signals undergo preprocessing and transformation into spectrogram representations, facilitating the extraction of relevant features for deep learning models. The CNN architecture excels in capturing hierarchical spatial patterns indicative of AD-related alterations in brain activity, allowing the model to discern subtle nuances within EEG signals. Moreover, the incorporation of RNNs enhances the model's capability to capture temporal dependencies inherent in EEG data, thereby augmenting sensitivity to early signs of AD. An extensive comparative study between standalone CNN and CNN-RNN hybrid models is required for performance evaluation. Using important criteria including Fl-score, recall, accuracy, and precision. Using deep learning and EEG signals to its full potential, this research project could revolutionize AD diagnosis and care. It offers a non- invasive, cost-effective approach to AD detection, addressing critical diagnostic gaps and paving the way for future advancements in neurology and health care. The ultimate goal of this multidisciplinary endeavor is to change the way AD is identified, treated, and managed in order to improve patient outcomes and quality of life. © 2024 IEEE.

关键词： Electroencephalography

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A 0.75 to 1.5 GHz Reconfigurable Diplexer With Independently Controllable Center Frequency and Insertion Phase

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IEEE Transactions on Circuits and Systems II: Express Briefs 2024年第4期71卷 1924-1928页

作者： Yi, Chengfei Chen, Xiong Chi, Pei-Ling Yang, Tao The School of Electronic Science and Engineering University of Electronic Science and Technology of China Sichuan Chengdu611731 China The Department of Electrical and Computer Engineering National Chiao Tung University Hsinchu300 Taiwan

A novel reconfigurable diplexer with independently controllable center frequency and insertion phase is proposed in this brief. It simply consists of six coupled resonators and two non-resonating-nodes (NRNs). These six resonators are arranged in two triplet topologies and construct two typical third-order filters, forming the lower and upper channels in the diplexer, respectively. The NRNs are loaded at the receiver (RX) and transmitter (TX) ports. With the utilization of NRNs, the insertion phases of the upper and lower channels in the diplexer can be independently controlled. Optimization technique is used to obtain the coupling matrix of the diplexer, in which the NRN is considered to achieve a prespecified insertion phase. To validate the proposed idea, a microstrip prototype is fabricated and measured. Experiment results show that the center frequencies of lower and upper channels can be tuned from 0.75 GHz to 0.98 GHz, and from 1.23 GHz to 1.5 GHz, respectively. The insertion phases of both channels can be independently tuned from 0 to 120 degrees at each frequency tuning state. © 2023 IEEE.

关键词： Resonators

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