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6th International Conference on Contemporary computing and Informatics, IC3I 2023

作者： Krishnasamy, Lalitha Kiruthika, M. Manjunatha Swamy, C. Vigneshwaran, T. Sharon Roji Priya, C. Vijay Anand, D. School of Engineering and Technology Department of CSE Christ University Bengaluru India School of Computing Department of Computational Intelligence SRM Institute of Science and Technology Tamilnadu Kattankulathur India Kongu Engineering College Department of Information Technology Erode India

ISBN: (纸本)9798350304480

Numerous cybercriminals are active in the online realm, carrying out cyber-crimes according to predetermined and preplanned agendas. Cyberbullying, which was formerly limited to physical limits, has now expanded online as a result of technology advancements. One type of cyberbullying is denigration or insult. The cyberbullying cases are in exponential rise in social media as per the reports of Computer Emergency Team by Sri Lanka. Insulting words are changeable in dynamic and the same terminology may have numerous meanings depending on the context. Bullying cannot be defined just because a statement comprises such a term. As a result, when classifying comments, standard keyword detecting approaches are insufficient. Other languages also may have dealt with this issue by utilizing lexical databases like WordNet, which might give synonyms as well as homonyms for words. Because no adequate lexical database mainly for the English language has been built, recognizing a word like bullying is difficult. As a result, employed rules to solve the problem. Facebook comments containing profanity were gathered, outliers were eliminated, and the remaining messages were pre-processed. Five feature extraction rules were employed to assess insult in the text. Following that, used the Support Vector Machine (SVM) technique. Using an F1-score of 85%, the findings demonstrate that when compared to existing works, SVM performs better. The focus on English language cyberbully identification, which has never been addressed earlier, distinguishes this study. © 2023 IEEE.

关键词： bag of words cyberbullying feature extraction sentiment analysis SVM

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Integrating digital twins and deep learning for medical image analysis in the era of COVID-19

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Virtual Reality & Intelligent Hardware 2022年第4期4卷 292-305页

作者： Imran AHMED Misbah AHMAD Gwanggil JEON School of Computing and Information Science Faculty of Science and EngineeringAnglia Ruskin UniversityEast RoadCambridgeEnglandUK Center of Excellence in IT Institute of Management SciencesHayatabadPeshawarPakistan Department of Embedded Systems Engineering Incheon National UniversityIncheonKorea

Background Digital twins are virtual representations of devices and processes that capture the physical properties of the environment and operational algorithms/techniques in the context of medical devices and *** twins may allow healthcare organizations to determine methods of improving medical processes,enhancing patient experience,lowering operating expenses,and extending the value of *** the present COVID-19 pandemic,various medical devices,such as X-rays and CT scan machines and processes,are constantly being used to collect and analyze medical *** collecting and processing an extensive volume of data in the form of images,machines and processes sometimes suffer from system failures,creating critical issues for hospitals and *** To address this,we introduce a digital-twin-based smart healthcare system in-tegrated with medical devices to collect information regarding the current health condition,configuration,and maintenance history of the device/machine/***,medical images,that is,X-rays,are analyzed by using a deep-learning model to detect the infection of *** designed system is based on the cascade recurrent convolution neural network(RCNN)*** this architecture,the detector stages are deeper and more sequentially selective against small and close false *** architecture is a multi-stage extension of the RCNN model and sequentially trained using the output of one stage for training the *** each stage,the bounding boxes are adjusted to locate a suitable value of the nearest false positives during the training of the different *** this manner,the arrangement of detectors is adjusted to increase the intersection over union,overcoming the problem of *** train the model by using X-ray images as the model was previously trained on another *** The developed system achieves good accuracy during the detection phase of *** experimental o

关键词： Digital twins Deep learning Healthcare COVID-19 Chest X-rays Artificial intelligence

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Benchmarking Hallucination in Large Language Models based on Unanswerable Math Word Problem 30

Benchmarking Hallucination in Large Language Models based on...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Sun, Yuhong Yin, Zhangyue Guo, Qipeng Wu, Jiawen Qiu, Xipeng Zhao, Hui Software Engineering Institute East China Normal University China School of Computer Science Fudan University China Shanghai AI Laboratory China Shanghai Key Laboratory of Trustworthy Computing Shanghai China

ISBN: (纸本)9782493814104

Large language models (LLMs) are highly effective in various natural language processing (NLP) tasks. However, they are susceptible to producing unreliable conjectures in ambiguous contexts called hallucination. This paper presents a new method for evaluating LLM hallucination in Question Answering (QA) based on the unanswerable math word problem (MWP). To support this approach, we innovatively develop a dataset called Unanswerable Math Word Problem (UMWP) which comprises 5200 questions across five categories. We developed an evaluation methodology combining text similarity and mathematical expression detection to determine whether LLM considers the question unanswerable. The results of extensive experiments conducted on 31 LLMs, including GPT-3, InstructGPT, LLaMA, and Claude, demonstrate that in-context learning and reinforcement learning with human feedback (RLHF) training significantly enhance the model's ability to avoid hallucination. We show that utilizing MWP is a reliable and effective approach to assess hallucination. Our code and data are available at https://***/Yuki-Asuuna/UMWP. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Reinforcement learning

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IoT-Enabled Automated Smart Irrigation System Incorporating Sensor Parameters 5th

IoT-Enabled Automated Smart Irrigation System Incorporating ...

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5th Doctoral Symposium on Computational Intelligence, DoSCI 2024

作者： Singh, Jagendra Neeraj Srinivas Reddy, P. Hasan, Dler Salih Alsahlanee, Abbas Thajeel Rhaif Shaik, Nazeer Alliance School of Advanced Computing Alliance University Bangalore India School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of Mechanical Engineering CVR College of Engineering Hyderabad India Department of Computer Science and Information Technology University of Salahaddin-Erbil Erbil Iraq Development and Continuous Education Center University of Thi-Qar Thi-Qar Iraq Department of Computer Science and Engineering Srinivasa Ramanujan Institute of Technology—Autonomous Anantapur India

ISBN: (纸本)9789819763177

This article presents the experimental findings of a smart irrigation system evaluated in two separate agricultural settings—small-scale farming and large-scale agriculture. The system combines sensors, microcontrollers, and actuators to precisely monitor and regulate the irrigation process in order to optimum water use and crop output. Water savings range from 20 to 45% when using smart irrigation technology in small-scale agriculture. By only turning on the irrigation pump when necessary, the technology optimizes water use and fosters sustainable farming methods. Furthermore, by sustaining perfect growing conditions, the approach boosts crop output, with yield benefits ranging from 10 to 22%. These improvements assist small-scale farmers in becoming more profitable and raising their level of living. In large-scale agriculture, smart irrigation systems allow excellent water management and resource optimization across huge agricultural fields. The technology consistently saves a large quantity of water, often between 25 and 50%, addressing water scarcity issues. Additionally, this method has led to a 12–25% increase in aggregate crop yields, thereby raising farm income and increasing agricultural firm profitability. Experiments shows that this smart irrigation system could bring cost-effective, sustainable techniques to extensive and intensive agriculture alike. Intelligence-guided decision-making technology is applied here, and cutting-edge technologies are brought to bear on the water use problem. The system conserves precious resources and increases agricultural output at the same time. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Smart agriculture

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Arrhythmia Classification using Supervised Machine Learning Algorithms

Arrhythmia Classification using Supervised Machine Learning ...

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2024 IEEE International Conference on Electronic Systems and Intelligent computing, ICESIC 2024

作者： Sriram, Suthir Vyshnavi, P. Nivethitha, V. Thangavel, M. Ravikumar, S. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India United Arab Emirates University College of Information and Technology Al Ain United Arab Emirates Vel Tech Rangarajan Dr.Sagunthala R and D Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331522988

Heart related disorders remain as one of the most chronic health issues in the world;generally, cardiovascular diseases are rated as a prime factor for morbidity and mortality worldwide. The early diagnosis and detection of such conditions may lead to imperative treatment measures and prevent unpleasant complications. This would considerably reduce the risk of serious health complications. One of the diagnostic tools for cardiac abnormalities is an electrocardiogram, a noninvasive, more generally adopted clinical investigative tool that captures over a period of time the heart's electrical function. Due to the fact that it is very accessible and easy to use, an ECG would normally be done first in diagnosing many cardiac conditions, including arrhythmias. The paper represents the detailed classification of arrhythmia, which is an irregularity in heart rhythm and can be a symptom of severe underlying cardiac problems based on ECG data. We have explored the efficacy of machine learning algorithms and Ensemble Method that combined the former for better predictions. Our results indicate that this ensemble learning approach performs better compared to all of the other individual algorithms used in this study, with an accuracy of 72.15%. The present study not only endorses the effort being made in better cardiac diagnostic tools but also calls for further attention toward advanced computational methods in health care to resolve some of the pivotal, complex challenges in medicine. © 2024 IEEE.

关键词： Diseases

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Short-Term Electricity Load Forecasting Using Modified Hidden Markov Model 1

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2nd International Conference on Renewable Power, ICRP 2023

作者： Khetarpal, Poras Nagpal, Neelu Kumar, Mahesh Lakshmi, D. Kassarwani, Neelam IT Department Bharati Vidyapeeth’s College of Engineering New Delhi110063 India EEE Department Maharaja Agrasen Institute of Technology Delhi110086 India School of Computing Science and Engineering VIT Bhopal University MP Bhopal466114 India

ISBN: (数字)9789819967490

ISBN: (纸本)9789819967483

This study proposes a modified Hidden Markov Model (HMM) as a method for predicting electricity on an hourly basis in the Delhi region. Typically, load prediction involves utilizing statistical techniques that need significant modifications in the data to adapt to the random nature of the energy demand. Alternatively, data-based methods like artificial neural networks (ANNs) rely heavily on data to deliver reliable findings. An attempt is made to implement HMM taking into account short-term electricity demand as a non-stationary time series. This provides satisfactory prediction results even with limited data. Furthermore, the proposed modified HMM technique outperforms alternative techniques in terms of computational time and complexity. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Hidden Markov models

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A Stack Ensemble Approach for Early Alzheimer Classification Using Machine Learning Algorithms 6th

A Stack Ensemble Approach for Early Alzheimer Classification...

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6th International Conference on Recent Trends in Image Processing and Pattern Recognition, RTIP2R 2023

作者： Kumar, Amit Sharma, Neha Chauhan, Rahul Khare, Akhilendra Anand, Abhineet Sharma, Manish Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India Department of CSE Graphic Era Hill University Dehradun India School of Computing Science and Engineering Galgotias University Noida India Chandigarh University Mohali India Computer Science and Engineering Graphic Era Deemed to Be University Uttarakhand Dehradun India

ISBN: (纸本)9783031530845

The incorporation of machine learning techniques in medical research has facilitated the exploration of novel avenues for the timely identification of diseases. The continuous progress in medical technology has facilitated the acquisition of complex and complete datasets, which in turn enhances the ability to identify medical diseases in their early stages. Alzheimer’s disease, a significant and hard problem, is characterised by the slow degeneration of brain cells and has a profound impact on cognitive functions, namely memory. It occupies a prominent position within this domain. In the middle of these exciting promises, there remains a significant research gap that pertains to the absence of thorough empirical evidence about the effectiveness of machine learning algorithms in the early identification of Alzheimer’s disease. The primary objective of this study is to address the existing research gap by conducting a comprehensive and meticulous series of experiments. A comprehensive examination of data obtained from sophisticated neuroimaging technologies is performed by utilising a wide range of machine learning models, such as Logistic Regression, Naive Bayes, Neural Networks, Random Forest, and the Stack ensemble. The primary objective is to facilitate the prompt detection of Alzheimer’s disease, hence enabling expedited interventions and therapeutic approaches. As one embarks on the journey of research, the unfolding narrative is shaped by the use of empirical evidence, establishing a strong foundation in the convergence of state-of-the-art technology and the urgent healthcare need to detect early stages of Alzheimer’s disease. Furthermore, this research not only addresses existing gaps in the literature but also ends in the identification of the most effective machine learning model, specifically the Neural Network, which has an accuracy rate of 87%. This significant advancement represents a critical juncture in the diagnosis of Alzheimer’s disease and sets a h

关键词： Machine learning

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Deep Learning Approach for Analysis and Characterization of COVID-19

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Computers, Materials & Continua 2022年第1期70卷 451-468页

作者： Indrajeet Kumar Sultan S.Alshamrani Abhishek Kumar Jyoti Rawat Kamred Udham Singh Mamoon Rashid Ahmed Saeed AlGhamdi School of Computing Graphic Era Hill UniversityDehradun(UK)India Department of Information Technology College of Computer and Information TechnologyTaif UniversityTaif21944Saudi Arabia School of Computer Science&IT JAIN(to be Deemed University)BangaloreIndia School of Computing Dehradun Institute of TechnologyUttarakhandIndia Department of Computer Engineering Faculty of Science and TechnologyVishwakarma UniversityPuneIndia Department of Computer Engineering College of Computer and Information TechnologyTaif UniversityTaif21944Saudi Arabia

Early diagnosis of a pandemic disease like COVID-19 can help deal with a dire situation and help radiologists and other experts manage human resources more *** a recent pandemic,laboratories perform diagnostics manually,which requires a lot of time and expertise of the laboratorial technicians to yield accurate ***,the cost of kits is high,and well-equipped labs are needed to perform this ***,other means of diagnosis is highly *** is one of the existing methods that finds its use in the diagnosis of *** radiography observes change in Computed Tomography(CT)chest images of patients,developing a deep learning-based method to extract graphical features which are used for automated diagnosis of the disease ahead of laboratory-based *** proposed work suggests an Artificial Intelligence(AI)based technique for rapid diagnosis of COVID-19 from given volumetric chest CT images of patients by extracting its visual features and then using these features in the deep learning *** proposed convolutional neural network aims to classify the infectious and non-infectious SARS-COV2 *** proposed network utilizes 746 chests scanned CT images of 349 images belonging to COVID-19 positive cases,while 397 belong to negative cases of *** experiment resulted in an accuracy of 98.4%,sensitivity of 98.5%,specificity of 98.3%,precision of 97.1%,and F1-score of 97.8%.The additional parameters of classification error,mean absolute error(MAE),root-mean-square error(RMSE),and Matthew’s correlation coefficient(MCC)are used to evaluate our proposed *** obtained result shows the outstanding performance for the classification of infectious and non-infectious for COVID-19 cases.

关键词： Coronavirus covid-19 respiratory infection computed tomography deep neural network

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An Immutable Framework for Smart Healthcare Using Blockchain Technology

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Computer Systems science & engineering 2023年第7期46卷 165-179页

作者： Faneela Muazzam A.Khan Suliman A.Alsuhibany Walid El-Shafai Mujeeb Ur Rehman Jawad Ahmad Department of Computer Science Quaid-i-Azam UniversityIslamabad45320Pakistan Department of Computer Science College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Computer Science Security Engineering LaboratoryPrince Sultan UniversityRiyadh11586Saudi Arabia James Watt School of Engineering University of GlasgowG128QQUK School of Computing Edinburgh Napier UniversityEdinburghEH105DTUK

The advancements in sensing technologies,information processing,and communication schemes have revolutionized the healthcare *** Healthcare Records(EHR)facilitate the patients,doctors,hospitals,and other stakeholders to maintain valuable data and medical *** traditional EHRs are based on cloud-based architectures and are susceptible to multiple cyberattacks.A single attempt of a successful Denial of Service(DoS)attack can compromise the complete healthcare *** article introduces a secure and immutable blockchain-based framework for the Internet of Medical Things(IoMT)to address the stated *** proposed architecture is on the idea of a lightweight private blockchain-based network that facilitates the users and hospitals to perform multiple healthcare-related operations in a secure and trustworthy *** efficacy of the proposed framework is evaluated in the context of service execution time and *** experimental outcomes indicate that the proposed design attained lower service execution time and higher throughput under different control parameters.

关键词： Blockchain technology healthcare applications cybersecurity services IoMT DoS EHR

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UAV Deployment Optimization for Efficient Data Forwarding in UAV-assisted Wireless Networks

UAV Deployment Optimization for Efficient Data Forwarding in...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Zhang, Xueqi Wang, Aimin Sun, Geng Liu, Lingling Zhang, Jing Wang, Jiacheng Shi, Wenxiao Jilin University College of Computer Science and Technology Changchun130012 China Hainan University School of Information and Communication Engineering Haikou570000 China Jilin University College of Communication Engineering Changchun130012 China Nanyang Technological University College of Computing and Data Science Singapore639798 Singapore

ISBN: (纸本)9798350351255

Given that the locations of base stations (BSs) remain fixed after installation, direct data forwarding to remote user equipment (UE) becomes challenging. Unmanned aerial vehicles (UAVs) offer a hopeful solution as mobile relays for next generation wireless communications to realize data forwarding with the flexible and cost-effective deployment. However, the limited onboard energy of UAVs and slow progress in energy storage technology pose significant challenges to achieving energy-efficient communication. Therefore, in this article, we investigate a wireless communication network utilizing a UAV as a high-altitude relay for data forwarding, and formulate a UAV relay deployment optimization problem (URDOP) to minimize the energy consumption of data forwarding and UAV hovering by optimizing UAV deployment, including the locations and number of UAV hover points. Given that the URDOP is a mixed-integer programming problem, conventional gradient-based approaches face limitations. To address this, we propose a self-adaptive differential evolution with a variable population size (SaDEVPS) algorithm to solve the URDOP. The performance of proposed SaDEVPS is verified through simulations, and the results show that it can successfully decrease the energy consumption of system when compared to other benchmark algorithms. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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