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An Energy-Efficient Protocol for Internet of Things Based Wireless Sensor Networks

computers, Mater.a.s & Continua 2023年第5期75卷 2397-2412页

作者： Mohammed Mubarak Mustafa Ahmed Abelmonem Khalifa Kor.a. Cengiz Nikola Ivkovic Department of Information Technology Sri Krishna College of Engineering&TechnologyCoimbatoreIndia Department of Networking and Security College of Information TechnologyUniversity of FujairahFujairahUAE Switching and Traffic Department National Telecommunication InstituteCairoEgypt Department of Computer Engineering Istinye UniversityZeytinburnuIstanbulTurkey Faculty of Organization and Informatics University of ZagrebVarazdinCroatia

The perfor.a.ce of Wireless Sensor Networks(WSNs)is an impor.a.t fragment of the Internet of Things(IoT),where the current WSNbuilt IoT network’s sensor hubs are enticing due to their critical *** grouping hubs,a clustering convention offers a useful solution for ensuring energy-saving of hubs andHybridMedia Access Control(HMAC)during the course of the ***,current grouping standards suffer from issues with the grouping structure that impacts the exhibition of these conventions *** this investigation,we recommend an Improved Energy-Proficient Algorithm(IEPA)for HMAC throughout the lifetime of the WSN-based *** consecutive segments are *** the covering of adjusted clusters,an ideal number of clusters is determined ***,fair static clusters are shaped,based on an updated calculation for fluffy cluster heads,to reduce and adapt the energy use of the sensor *** heads(CHs)are,ultimately,selected in optimal locations,with the pivot of the cluster heads working among cluster ***,the proposed convention diminishes and balances the energy utilization of hubs by improving the grouping structure,where the IEPAis r.a.onable for systems that need a long *** assessment results demonstrate that the IEPA performs better than existing conventions.

关键词： Energy consumption improved energy-proficient algorithm internet of things wireless sensor network

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A hybrid deep learning appr.a.h for phenotype prediction from clinical notes

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Jour.a. of Ambient Intelligence and Humanized Computing 2023年第4期14卷 4503-4513页

作者： Khalafi, Sahar Ghadiri, Nasser Moradi, Milad Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan84156-83111 Iran Institute for Artificial Intelligence Center for Medical Statistics Informatics and Intelligent Systems Medical University of Vienna Vienna Austria

Identifying patient cohorts from clinical notes in secondary use of electronic health records is a fundamental task in clinical infor.a.ion management. The patient cohort identification process requires identifying the patient phenotypes. However, the growing number of clinical notes makes it difficult to analyze the data manually. Therefore, automatically extracting clinical concepts is crucial to identify patient phenotypes correctly. This paper’s proposed hybrid model is based on a neural bidirectional sequence model (BiLSTM or BiGrU) and a Convolutional Neural Network (CNN) for identifying patients’ phenotypes in clinical notes. Furthermore, an extra CNN layer is run parallel to the hybrid proposed model to extract more features related to each phenotype. We used pre-trained embeddings such as FastText and Word2vec separately as the input layers to evaluate other embedding’s perfor.a.ce in identifying patient phenotypes. We also measured the effect of adding additional data cleaning steps on discharge reports to identify patient phenotypes using deep learning models. results demonstrated the proposed hybrid model extracts more features than existing methods of patient phenotype extraction and provides a better F1-score. We show that complementing the proposed hybrid model with an extra CNN in identifying different phenotypes improves the F1 scores. In addition, eliminating punctuation, numbers, and stop words in discharge reports before training hybrid models incr.a.ed model perfor.a.ce. © 2023, The Author(s), under exclusive licence to Springer-Ver.a. GmbH Ger.a.y, part of Springer Nature.

关键词： Embeddings

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Question Answering FAQ Bot Using Natural Language Processing 11th

Question Answering FAQ Bot Using Natural Language Processing

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11th Inter.a.ional Conference on Intelligent Computing and Applications, ICrTC 2023

作者： Vijay, K. Sowmia, K.r. ravikumar, S. Arockia raj, Y. Department of Computer Science and Engineering Rajalakshmi Engineering College Chennai India Department of Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Chennai India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Computer Science and Engineering PSNA College of Engineering and Technology Dindigul India

ISBN: (纸本)9789819717231

The BOT retorts with the precise results that help the user for the query and also requests them to rate the response to provide better results. The [frequently answered questions] BOT is a program developed through machine learning and natural language processing. It classifies the queries and results as key-value pairs and maps the response to the user query. It extricates itself by providing additional capabilities such as NLU processes and reinforcement learning. NLU helps the bot to make it convenient for.a. ar.a. of languages to meet the diverse expectations. reinforcement learning retrieves user rating to provide better perfor.a.ce and search results. The project was developed due to a concern about fetching meticulous responses for specific service-related queries. The overall satisfaction rate of users in getting responses to the questions they post is devastating. The root cause for the proposed project has aimed at providing an environment for.a.people to find their.a.swers at better.a.curacy and also respond to their satisfaction. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： reinforcement learning

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Machine learning based Development and Evaluation of a Hand Exoskeleton rehabilitation robot for Patients with Partial Paralysis 2

Machine learning based Development and Evaluation of a Hand ...

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2nd Inter.a.ional Conference on Disruptive Technologies, ICDT 2024

作者： Singh, Durgesh Kumar Srivastava, Anand Prakash Singh, Shivanshu Paliwal, Sanjay Singh, Anupam Singh, Jagendra Hi-Tech Institute of Engineering & Technology Mechanical Engineering Department Ghaziabad India Nitra Technical Campus Department of Computer Science Ghaziabad India R D Engineering College Mechanical Engineering Department Ghaziabad India Graphic Era Hill University Department of Computer Science and Engineering Dehradun India School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (纸本)9798350371055

This work focuses on the estimation of elbow joint angles from electromyography (EMG) signals and investigates the use of machine learning models in rehabilitation robotics. Four distinct machine learning algorithms were tested for their capacity to precisely translate EMG inputs into actuator commands: Artificial Neural Network (ANN), Naive Bayes (NB), Linear regression (Lr), and Decision Trees (DT). The models were trained and tested using a dataset generated by a wireless EMG sensor.a.d motion capture system that recorded the dynamic relationship between elbow movements and related muscle activations. The results show the remar.a.le 98.7% accuracy of the ANN model, indicating that it can be a valuable tool for r.a.-time rehabilitation robotics applications. Additionally, NB, Lr, and DT displayed competitive results that demonstrated their unique capacities to manage a range of sensor inputs. The discussion highlights how impor.a.t these models' interpretability, computational efficiency, and continuous adaptation are to their successful integration into r.a.-world rehabilitation settings. This work contributes to the field of rehabilitation robotics by applying machine learning models for.a.curate actuation control based on EMG signals. The findings demonstrate how these models could enhance the accuracy and responsiveness of assistive technology, offering individuals with partial paralysis customized and adaptable rehabilitation regimens. © 2024 IEEE.

关键词： assistive devices electromyography joint angle prediction machine learning rehabilitation robotics

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Alapana Generation using Finite State Machines and Generative Adver.a.ial Networks

Alapana Generation using Finite State Machines and Generativ...

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2023 Inter.a.ional research Conference on Smart Computing and Systems engineering, SCSE 2023

作者： Jayatharan, Vithushigan Alwis, Dileeka University of Westminster Computer Science and Engineering London United Kingdom Informatics Institute of Technology Department of Computing Colombo Sri Lanka

ISBN: (纸本)9798350341454

The recent advancements in deep learning techniques and computational power have promoted the development of novel appr.a.hes for music generation. In this study, generating alapana, an improvisational form of Car.a.ic music was proposed, by leveraging Generative Adver.a.ial Networks (GANs) and Finite State Machines (FSM). The goal is to cr.a.e melodious alapana sequences that follow a given input raga, ensuring continuity and coherence throughout the generated musical piece. The proposed appr.a.h incorporates Car.a.ic music theory rules into the generation process to enhance the structural coherence of the generated alapana. Additionally, various hyper.a.ameter settings were explored to achieve the best perfor.a.ce. The Fréchet Audio Distance, Percentage of Correct Pitches and the Subjective evaluation through human listeners are the evaluation metrics of this appr.a.h. The result of this study demonstrates the potential of using GANs and FSM for generating continuous and pleasing alapana sequences in Car.a.ic music, contributing to the growing body of research in computational music generation. © 2023 IEEE.

关键词： Music

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Firefighter Skill Advancement through IoT-Enabled Virtual r.a.ity and CNN-Based Training 11

Firefighter Skill Advancement through IoT-Enabled Virtual Re...

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11th Inter.a.ional Conference on reliability, Infocom Technologies and Optimization, ICrITO 2024

作者： Asha, P. Muthalakshmi, M. Jeslin, J Gnana Nithisha, J. Vijayakumar, S. Srinivasan, C. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Veltech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Biomedical Engineering Tamil Nadu Chennai India Rmk College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Srm Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Department of Biomedical Engineering Tamil Nadu Namakkal India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350350357

To maintain the safety and efficacy of firefighters in various circumstances, modern firefighting necessitates constantly improving skills and training techniques. Utilizing the Internet of Things (IoT), virtual r.a.ity (Vr), and convolutional neural networks (CNN), this paper details a novel method for training firefighters. The proposed system collects r.a.-time data on ambient var.a.les, equipment state, and firefighter biometrics via integrating IoT sensors into firefighting equipment and training settings. Using this infor.a.ion, it can develop lifelike Vr training simulations of difficult and potentially dangerous scenarios. To make the training settings more r.a.istic and malleable, CNN-based algorithms are used to assess the data. The capacity to simulate a wide variety of firefighting situations, customize training difficulty depending on individual and team perfor.a.ce, and provide instant feedback and perfor.a.ce metrics to trainees are all major benefits of this method. The method also allows teachers to check in and evaluate their learners remotely, improving instruction quality. An IoT-enabled Vr.a.d CNN-based training technique has shown promising preliminary results in pilot tr.a.s, suggesting it might gr.a.ly enhance firefighter competence, situational awareness, and decision-making ability. Because of this, it has the potential to completely alter the way firefighters are informed and prepared for the ever-changing dangers users may encounter on the job. © 2024 IEEE.

关键词： Internet of things

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The Effects of Weather Conditions on Data Transmission in Free Space for Wireless Sensor Networks 3rd

The Effects of Weather Conditions on Data Transmission in Fr...

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3rd Inter.a.ional Conference on Computing and Communication Networks, ICCCN 2023

作者： Kadhim, Aqeel A. Naif, Jolan r. Salman, Muna M. Hoomod, Haider K. Computer Techniques Engineering Department Imam AlKadhim University College Baghdad Iraq Informatics Institute for Postgraduate Studies Iraqi Commission for Computers and Informatics Baghdad Iraq Information Technology Directorate Ministry of Science and Technology Baghdad Iraq College of Education Computer Science Department Al-Mustansiriya University Baghdad Iraq

ISBN: (纸本)9789819708918

It is necessary to collect data measured in open lands, whether for example for monitoring purposes or for border protection. The process of data transmission from active nodes and its next level to the cluster head (CH), followed by follow-up to arrive at the base station (BS), with safety and acceptability, are the main objectives of wireless sensor networks (WSNs). The key benefits of wireless systems are low-cost system installation and loss-free data transfer. Weather conditions in the spacious ar.a.affect wireless data transmission that is done between sensor nodes in free space. This study attempted to ascer.a.n the influence of atmospheric and meteorological conditions on the loss of data when utilizing the wireless data transmission systems in a wireless sensor network. For this r.a.on, the volume of data transfer losses that happen in free space because of three types of weather conditions is the main discussion point. The meteorological criter.a.that can be considered in this research are temperature, clear weather, dust, and rain as the weather elements that affect the Iraqi climate. The correlation analysis's outcomes show that the amount of precipitation and the size of raindrops have a main effect on the losses of data transmission. From a practical point of view, there is no confirmed statistical evidence of the amount of loss in the volume of transmitted data as a result of the amount of rainfall with the use of different transmission frequencies. In this research, we designed an objective routing protocol called Fuzzy Clustering with Ant Colony System FCACS, which has been utilized as a simulation program using the MATLAB 2018b application. The practical results of the proposed protocol for data transmission in free space and under different weather conditions were examined, and the simulation results were compared with those of two well-known routing protocols, which are DEEC and Z-SEP. In terms of the quantity of data received from the CH or BS a

关键词： Data transfer

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Price Prediction of Bitcoin using Social Media Activities and Past Trends 13

Price Prediction of Bitcoin using Social Media Activities an...

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13th Inter.a.ional Conference on Cloud Computing, Data Science and engineering, Confluence 2023

作者： Shar.a. Krishna Pal Singh, Shivam Kumar Choudhary, Ankur Goel, Himanshu Dr. B. R. Ambedkar Nit Department of Computer Science and Engineering Punjab Jalandhar India Department of Computer Science and Engineering Dehradun India R v Institute of Technology Department of Computer Science and Engineering Bijnor India

ISBN: (纸本)9781665462631

The world's most well-known cryptocurrency, Bitcoin, is much attractive to financial market players due to its recent price boom and fall. It is very difficult to anticipate because of the substantial volatility of the Bitcoin conversion scale. Forecasting of its behavior is crucial for the financial industry sectors. Some of the research works have been used machine learning techniques on past trends data of Bitcoin for its price predictions but they lack in accuracy. Therefore, the aim of this paper is to look into the global crypto-currency price movement trends about social media communication data as well as historical data trends. The thought is to analyze infor.a.ive trends in online groups and social media networks to fully understand and extract useful infor.a.ion which could be used to improve the prediction accuracy of cryptocurrency price fluctuations. In this work we examine the behavior of Artificial Neural Networks (ANN), recurrent Neural Networks (rNN) with LSTM and GrU, ArIMA model, random forests(rF), and others for price direction predictions. Similarly, the SVM and ANN are used to predict Bitcoin's minimum, maximum, and closing prices. The outcomes of the forecasts are also utilized as inputs to improve forecasts of price movement. The results showed that perfor.a.ce was gr.a.ly improved by the characteristics that were picked as well as by the effective machine learning and deep learning techniques. © 2023 IEEE.

关键词： Bitcoin

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Covid19 Disease Assessment Using CNN Architecture 3

Covid19 Disease Assessment Using CNN Architecture

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3rd Inter.a.ional Conference on Innovative Practices in technology and Management, ICIPTM 2023

作者： Mary Shiba, C. Mishra, Sumit Kumar Sandhya, S. Vidhya, K. Jaichandran, r. Manjula, G. R.M.K Engineering College Department of Computer Science Chennai India Chandigarh University Department of Computer Science Punjab India Acharya Institute of Technology Department of Master of Business Administration Karnataka India Kpr Institute of Engineering and Technology Department of Computer Science Chennai India Aarupadai Veedu Institute of Technology Department of Computer Science Chennai India

ISBN: (纸本)9798350336238

recently, the COVID-19 pandemic has emerged as one of the world's most critical public health concerns. One of the biggest problems in the present COVID-19 outbr.a. is the difficulty of accurately separating COVID-19 cases from non-COVID-19 cases at an affor.a.le price and in the initial stages. Besides the use of antigen rapid Test Kit (rTK) and reverse Transcription Polymerase Chain r.a.tion (rT-PCr), chest x-rays (CXr) can also be used to identify COVID-19 patients. Unfortunately, manual checks may produce inaccurate results, delay tr.a.ment or even be fatal. Because of differences in perception and experience, the manual method can be chaotic and imprecise. technology has progressed to the point where we can solve this problem by training a Deep Learning (DL) model to distinguish the nor.a. and COVID-19 X-rays. In this work, we choose the Convolutional Neural Network (CNN) as our DL model and train it using Kaggle datasets that include both COVID-19 and nor.a. CXr data. The developed CNN model is then deployed on the website after going through a training and validation process. The website layout is straightfor.a.d to navigate. A CXr can be uploaded and a prediction made with minimal effort from the patient. The website assists in determining whether they have been exposed to COVID-19 or not. © 2023 IEEE.

关键词： COVID-19

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Natural Language Processing: Concepts and Applications using Chatbot 7

Natural Language Processing: Concepts and Applications using...

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7th Inter.a.ional Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2023

作者： Srinivasan, r. Kavitha, M. Uma, S. Vel Tech Rangarajan Dr Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Rajalakshmi Institute of Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350341485

A chatbot is a paradigm of human-computer interaction (HCI) and artificial intelligence software. A chatbot is described as "computer software designed to simulate human-to-human interaction, particularly over the Internet."Chatbot is a popular computer programme that is used to promote human user interaction and communication, particularly via the internet. As a computer programme application, it is becoming more popular. Previously, a user would ask a query, and a computer programme would respond. By entering in the question, the user can answer it. If a user.a.ks a question that isn't answered in the database, the chat bot conducts a Google search and responds with pertinent infor.a.ion. When a user enters a query, the bot compares and correlates the query terms with words in the database using pattern matching. User's input will be used to analyses the terms in the phrase for similarity. As a result, the highest score is printed. NLP (Natural Language Processing) is a technique for helping computers understand human language. This document also offers a summary of the metrics used to evaluate bot perfor.a.ce. This contributes in the development of more effective bots. © 2023 IEEE.

关键词： Artificial intelligence

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