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Examination of fake news from a viral perspective: an interplay of emotions, resonance, and sentiments

Journal of Systems and Information Technology 2022年第2期24卷 131-155页

作者： Nanath, Krishnadas Kaitheri, Supriya Malik, Sonia Mustafa, Shahid Department of Computer Engineering and Informatics Middlesex University – Dubai Campus Dubai United Arab Emirates Middlesex University – Dubai Campus Dubai United Arab Emirates

Purpose: The purpose of this paper is to examine the factors that significantly affect the prediction of fake news from the virality theory perspective. The paper looks at a mix of emotion-driven content, sentimental resonance, topic modeling and linguistic features of news articles to predict the probability of fake news. Design/methodology/approach: A data set of over 12,000 articles was chosen to develop a model for fake news detection. Machine learning algorithms and natural language processing techniques were used to handle big data with efficiency. Lexicon-based emotion analysis provided eight kinds of emotions used in the article text. The cluster of topics was extracted using topic modeling (five topics), while sentiment analysis provided the resonance between the title and the text. Linguistic features were added to the coding outcomes to develop a logistic regression predictive model for testing the significant variables. Other machine learning algorithms were also executed and compared. Findings: The results revealed that positive emotions in a text lower the probability of news being fake. It was also found that sensational content like illegal activities and crime-related content were associated with fake news. The news title and the text exhibiting similar sentiments were found to be having lower chances of being fake. News titles with more words and content with fewer words were found to impact fake news detection significantly. Practical implications: Several systems and social media platforms today are trying to implement fake news detection methods to filter the content. This research provides exciting parameters from a viral theory perspective that could help develop automated fake news detectors. Originality/value: While several studies have explored fake news detection, this study uses a new perspective on viral theory. It also introduces new parameters like sentimental resonance that could help predict fake news. This study deals with an extens

关键词： Forecasting

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Heart and Lung Sound Monitoring System Based Embedded System 7

Heart and Lung Sound Monitoring System Based Embedded System

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7th International Conference on Sustainable Information engineering and Technology, SIET 2022

作者： Endrawan, Nadi Rahmat Prasetio, Barlian Henryranu Maulana, Rizal Department of Informatics Engineering Brawijaya University Indonesia Faculty of Computer Science University of Brawijaya Indonesia

ISBN: (纸本)9781450397117

Every year, Indonesians still suffer from heart and lung diseases. To anticipate many lethal effects caused by these diseases, an early diagnosis solution is needed. This research discusses the implementation of a heart and lung auscultation monitoring system that aims to facilitate doctors in diagnosing patients. The system has three main components: sampler, signal conditioner, and interface. The sampler samples the signal with a period of 250 μs which is also the deadline of task completion in the system. The signal conditioner will filter the signal according to the doctor's wishes with a choice of two modes (heart and lung). Then the interface can display heart rate (HR) or respiration rate (RR) parameters according to the selected mode and other features such as sound output and signal visualization. The test results show that the system completes the task faster than the deadline with an average of each sampler, signal conditioner, and interface: 21.6 μs, 185.13 μs, and 122.62 μs. There is an increase in the signal-To-noise ratio value of both filters after filtering with a value of 35.05 for the heart and 23.64 for the lungs. Finally, the performance of the Smoothed Z score algorithm in predicting HR and RR gives an accuracy of 94% and 82%. © 2022 ACM.

关键词： FIR filters

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Large Language Models versus Natural Language Understanding and Generation 23

Large Language Models versus Natural Language Understanding ...

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Proceedings of the 27th Pan-Hellenic Conference on Progress in Computing and informatics

作者： Nikitas Karanikolas Eirini Manga Nikoletta Samaridi Eleni Tousidou Michael Vassilakopoulos Department of Informatics and Computer Engineering University of West Attica Greece Department of Electrical and Computer Engineering University of Thessaly Greece

ISBN: (纸本)9798400716263

In recent years, the process humans adopt to learn a foreign language has moved from the strict "Grammar –Translation" method, which is based mainly on grammar and syntax rules, to more innovative processes, resulting to the more modern "Communicative approach". As its name states, this approach focuses on the coherent communication with native speakers and the cultivation of oral skills, without taking into consideration, at least at the first stages, the rules that govern the language. The same trend seems to have been applied to the way machinery can be "educated" to comprehend and reproduce the unfamiliar, human language. The "rule based" Natural Language Generation (NLG) and Natural Language Understanding (NLU) algorithms, on one hand, and the "text based" Large Language Models (LLMs), on the other, are two, analogous to the two human foreign language learning processes, subareas of Natural Language Processing (NLP). This paper presents these two alternative approaches, LLMs (a technology having surfaced as an influential catalyst of NLP, during last years) on the one hand and NLG/NLU on the other, highlighting their applications, their technologies, their capabilities, their differences, their strengths and weaknesses and the challenges they present, contributing to a deeper comprehension of the evolving landscape of Artificial Intelligence and human-computer communication.

关键词： Large Language Models Natural Language Generation Natural Language Processing Natural Language Understanding

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A Reduced overhead Accumulator-based BIST scheme for Two-pattern generation

A Reduced overhead Accumulator-based BIST scheme for Two-pat...

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South Eastern European Design Automation, computer engineering, computer Networks and Social Media Conference (SEEDA-CECNSM)

作者： E. Fotopoulos S. Fatouros A. Milidonis I. Voyiatzis Department of Informatics and Computer Engineering University of West Attica Athens Greece

ISBN: (数字)9798331504489

ISBN: (纸本)9798331504496

Two-pattern tests target the detection of common failure mechanisms in CMOS VLSI circuits, modelled as stuck-open or delay faults. In this paper, a Reduced-overhead Accumulator-Based BIST scheme for Two-pattern generation (RABIT) is presented, that generates an exhaustive n-bit two-pattern test. RABIT is implemented in hardware utilizing an accumulator whose inputs are driven by a binary counter. An important advantage of the presented scheme is that it can be implemented by augmenting existing data path components, rather than building a new pattern generation structure. Furthermore, with the proposed scheme, the requirement for additional (i.e. control and/or accompanying) circuitry is eliminated. Comparisons with previously proposed schemes reveal that the proposed here scheme presents lower hardware overhead for the implementation of the BIST structure.

关键词： Semiconductor device modeling Design automation Social networking (online) Failure analysis Europe Built-in self-test Very large scale integration Hardware Circuit faults Integrated circuit modeling

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Federated Feature Concatenate Method for Heterogeneous Computing in Federated Learning

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computers, Materials & Continua 2023年第4期75卷 351-371页

作者： Wu-Chun Chung Yung-Chin Chang Ching-Hsien Hsu Chih-Hung Chang Che-Lun Hung Department of Information and Computer Engineering Chung Yuan Christian UniversityTaoyuanTaiwan Department of Computer Science and Information Engineering Asia UniversityTaichungTaiwan Department of Medical Research China Medical University HospitalChina Medical UniversityTaichungTaiwan Department of Computer Science&Communication Engineering Providence UniversityTaichungTaiwan Institute of Biomedical Informatics National Yang Ming Chiao Tung UniversityTaipeiTaiwan

Federated learning is an emerging machine learning techniquethat enables clients to collaboratively train a deep learning model withoutuploading raw data to the aggregation server. Each client may be equippedwith different computing resources for model training. The client equippedwith a lower computing capability requires more time for model training,resulting in a prolonged training time in federated learning. Moreover, it mayfail to train the entire model because of the out-of-memory issue. This studyaims to tackle these problems and propose the federated feature concatenate(FedFC) method for federated learning considering heterogeneous *** leverages the model splitting and feature concatenate for offloadinga portion of the training loads from clients to the aggregation server. Eachclient in FedFC can collaboratively train a model with different cutting ***, the specific features learned in the deeper layer of the serversidemodel are more identical for the data class classification. Accordingly,FedFC can reduce the computation loading for the resource-constrainedclient and accelerate the convergence time. The performance effectiveness isverified by considering different dataset scenarios, such as data and classimbalance for the participant clients in the experiments. The performanceimpacts of different cutting layers are evaluated during the model *** experimental results show that the co-adapted features have a criticalimpact on the adequate classification of the deep learning model. Overall,FedFC not only shortens the convergence time, but also improves the bestaccuracy by up to 5.9% and 14.5% when compared to conventional federatedlearning and splitfed, respectively. In conclusion, the proposed approach isfeasible and effective for heterogeneous clients in federated learning.

关键词： Federated learning deep learning artificial intelligence heterogeneous computing

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Prototyping a Digital Twin System for Environmental Education 22

Prototyping a Digital Twin System for Environmental Educatio...

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Proceedings of the 26th Pan-Hellenic Conference on informatics

作者： Andreas Komninos Georgios Tsigkas Computer Engineering & Informatics University of Patras Greece

ISBN: (纸本)9781450398541

Our time is characterized by big sprawling urban complexes that a majority of the human population chooses to live where interactions with the environment seem to be vanishing from our day to day lives. Our relationship with the planet’s natural ecosystem is paramount to our survival and well-being, a relationship that begins to form during our childhood years. Considering the above we propose a novel system which aims to give the ability to children to interact with the natural environment even when they have no access to it, from the classrooms in which they spend considerable parts of their lives. Specifically the proposed system is based on the technology of the Digital Twin, a core pillar of the 4th Industrial Revolution, and is comprised of a smart birdhouse and an electronic Digital Twin. The smart birdhouse can be deployed in a natural environment and collect atmospheric data like humidity and temperature values as well as record audio and pictures of possible bird visitors. At the same time the collected data will be available to the end user through a dashboard platform for atmospheric parameters and the digital electronic twin which will play the collected sound or pictures. With the above system we aim to achieve a greater and meaningful engagement of children with their natural habitat even when their urban environment makes it impractical or unfeasible.

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Cardiacnet: Cardiac Arrhythmia Detection and Classification Using Unsupervised Learning Based Optimal Feature Selection with Custom CNN Model

Informatica (Slovenia)

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Informatica (Slovenia) 2024年第2期48卷 147-158页

作者： Srinivas, Kalyanapu Ch, Vijayalakshmi Borra, Subba Reddy Raju, K. Srujan Rao, Ganga Rama Koteswara Satyanarayana, K.N.V. Kumar, Pala Mahesh Department of Artificial Intelligence and Data Science Seshadri Rao Gudlavalleru Engineering College Andhra Pradesh Gudlavalleru India Department of Electronics and Communication Engineering St. Peter’s Engineering College Maisammaguda Telangana Hyderabad India Maisammaguda Telangana Hyderabad India Department of Computer Science and Engineering CMR Technical Campus Kandlakoya Medchal Road Telangana Hyderabad India Department of Computer Science and Information Technology Koneru Lakshmaiah Education Foundation Guntur Andhra Pradesh Vaddeswaram India Department of Electronics and Communication Engineering Sagi Rama Krishnam Raju Engineering College Andhra Pradesh Bhimavaram India SAK Informatics Telangana Hyderabad India

The irregularity in the heartbeats caused cardiac arrhythmia, which resulted in serious health problems. This cardiac arrhythmia is monitored by electrocardiogram (ECG) signals. As a result, an accurate and timely analysis of ECG data can prevent serious health problems. However, the conventional manual prediction systems and artificial intelligence (AI) methods failed to detect cardiac arrhythmia because they failed to extract the deep salient features from the ECG dataset. So, this research work implements a model named CardiacNet, which is used to identify and classify cardiac arrhythmias from an MIT-BIH-based dataset. Initially, the pre-processing operation is performed to remove the non-linearities from the dataset. Then, unsupervised machine learning algorithm-based principal component analysis (UML-PCA) is used to extract the features of the pre-processed dataset. Further, the optimal feature selection operation is carried out using improved Harris Hawk's optimization (IHHO), which is a naturally inspired model. Moreover, a customized convolutional neural network (CCNN) model performs the classification of various cardiac arrhythmia diseases using IHHO features. The simulation results show that the proposed CardiacNet resulted in an accuracy of 97.57%, sensitivity of 98.29%, specificity of 97.97%, F-measure of 97.40%, precision of 98.66%, Matthew's correlation coefficient (MCC) of 98.17%, dice of 98.96%, and Jaccard of 97.12%. The performance comparisons show that the proposed CardiacNet resulted in improved metrics over all existing methods. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Convolutional neural networks

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Maternal Health Risk Classification - A Comparison between Algorithm Decision Tree and k-Nearest Neighbor (kNN)

Maternal Health Risk Classification - A Comparison between A...

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2023 International Conference on Sustainable Emerging Innovations in engineering and Technology, ICSEIET 2023

作者： Fatmawati, Maylia Novita, Mega Saputro, Nugroho Dwi Chauhan, Alok Singh Nada, Noora Qotrun Sinha, Amit Herlambang, Bambang Agus Gupta, Shruti Universitas Pgri Semarang Faculty of Engineering and Informatics Department of Informatics Semarang Indonesia Universitas Pgri Semarang Postgraduate Program of Science Education Semarang Indonesia School of Computing Science & Engineering Galgotias University Greater Noida India ABES Engineering College Department of Computer Science & Engineering Uttar Pradesh Ghaziabad201009 India

ISBN: (纸本)9798350329186

Maternal health is a critical concern, particularly for individuals who are pregnant and will shape the future generations. However, not all expectant mothers receive tailored attention and care for their unique health needs. Consequently, many mothers face high levels of health risks during pregnancy, highlighting the importance of classifying maternal health risks. Classification can help identify influential factors affecting maternal health and predict risk levels for individual mothers. In the field of data mining, various classification methods exist for analyzing complex datasets. This study focuses on two widely used algorithms: decision tree and k-Nearest Neighbor (kNN). By employing these algorithms, we aim to classify a dataset comprising 1,014 records of maternal health risk levels. Through cross-validation and T-tests, we rigorously evaluate the performance of the decision tree and kNN algorithms in this context. Our findings indicate that the kNN algorithm is more suitable for classifying maternal health risks, achieving a significantly higher accuracy of 71.50% compared to the decision tree algorithm's accuracy of 70.71%. This result establishes the superiority of the kNN algorithm in accurately classifying maternal health risks. These findings have implications for developing targeted interventions and tailored healthcare strategies to mitigate identified risks. © 2023 IEEE.

关键词： Health risks

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Mobile Crowd Sensing with Secure Coin Rewarding in Human Activity Recognition for Elderly People System

Mobile Crowd Sensing with Secure Coin Rewarding in Human Act...

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IEEE International Symposium on Consumer Electronics (ISCE)

作者： Erita Cicilia Febrianti Amang Sudarsono Tri Budi Santoso Informatics and Computer Engineering Politeknik Elektronika Negeri Surabaya Surabaya Indonesia

ISBN: (数字)9798350365191

ISBN: (纸本)9798350365207

Mobile Crowd Sensing (MCS) is a promising approach that harnesses the collective intelligence of mobile devices to gather data, particularly beneficial for monitoring the daily activities of elderly individuals. This paper proposes a system that integrates secure coin rewarding with MCS for Human Activity Recognition in elderly care. By addressing challenges such as data security, privacy attacks, and incentivizing healthy behaviors, the system aims to enhance the well-being of elderly individuals. Pairing-based cryptography is employed to ensure privacy preservation, while a reward system motivates active participation and data accuracy. The proposed system operates through a series of steps involving key generation, challenge authentication, receipt issuance, and reward claiming. Latency measurements demonstrate the efficiency of the system in issuing receipts and claiming rewards. Overall, this research contributes to the advancement of monitoring systems for elderly care by combining technology, community engagement, and personalized recommendations to promote healthy living among the elderly population.

关键词： Data privacy Accuracy Atmospheric measurements Particle measurements Sensors Human activity recognition Cryptography Older adults Monitoring Mobile computing

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Customer Segmentation in Steel Sales : Optimizing Profitability with RFM Model and K-Means Algorithm

Customer Segmentation in Steel Sales : Optimizing Profitabil...

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Artificial Intelligence, Blockchain, Cloud Computing, and Data Analytics (ICoABCD), International Conference on

作者： Mera Kartika Delimayanti Anita Hidayati Dewi Kurniawati Nadya Zahra Rahmadani Mas Brillianesa Faydhurrahman Department of Computer and Informatics Engineering Politeknik Negeri Jakarta Depok Indonesia

ISBN: (数字)9798350368451

ISBN: (纸本)9798350368468

This paper is on customer segmentation in steel sales, whose objective is to improve profitability and customer relationship management. The current research categorizes customers based on purchasing behaviors by using the RFM model and the K-Means clustering algorithm. Due to this approach, the company will be able to formulate stratified marketing plans for the requirements and Preferences of various customer segments identified and hence ensure high retention and satisfaction. The RFM model assesses the three main customer behaviors of purchase recency, purchase frequency, and monetary value. This gives a set of metrics that provide the overall depth of customer engagement, which is important in segmentation. Then, K-Means is applied to cluster these similar patterns of customer purchasing, to precisely target market customers. The goodness of the segmentation strategy is measured through metrics such as the Elbow Method, Davies-Bouldin Index, and Silhouette Coefficient, which provide proper cluster selection and meaningful segmentation. This eventual outcome has proved that this combined approach will maximize the revenue potential and enhance customer satisfaction tremendously through personalized marketing efforts. Eventually, the embedding of the RFM model within K-Means clustering offers a robust framework for strategic decision-making and realizing competitive advantage within the steel industry.

关键词： Measurement Profitability Clustering algorithms Steel industry Filtering algorithms Data models Steel Indexes Elbow Standards

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