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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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Meander-Line Slots Based Miniaturized Inset-Fed Patch-Sensor Antenna for Water Characterization Technique 13

Meander-Line Slots Based Miniaturized Inset-Fed Patch-Sensor...

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13th IEEE International Conference on System engineering and Technology, ICSET 2023

作者： Hadi, Aya Khalid Mosleh, Mahmood Farhan Azeez, Adham R. Abd-Alhameed, Raed A. University of Middle Technique Department of Computer Techniques Engineering Baghdad Iraq University of Information Technology and Communications Department of Media Technology and Communications Engineering College of Engineering Baghdad Iraq University of Bradford Faculty of Engineering and Informatics Bradford United Kingdom

ISBN: (纸本)9798350340891

This article presents a miniaturized size Microstrip-patch sensor structure based on meander-line slot for water-quality and salinity measurement applications. The HFSS-software tool is employed to design and simulate the proposed sensor. The proposed sensor is printed on 60× 50× 1mm3 a dielectric material of FR-4 type. The meander-line slot resonator is inserted in the patch-sensor for miniaturize with the sensitivity enhancement. In order to contain the water sample that wants to be tested, a receptacle with dimensions of 47.75× 50× 4mm3 is mounted on the sensor structure. The receptacle is made from PTFE-Teflon dielectric material. The miniaturize sensor is noticed resonant at 2.74 GHz for unloaded-state. Furthermore, it's found resonant at;2.44 GHz, 2.49 GHz, and 2.5 GHz for DI-water, fresh-water and sea-water respectfully. The miniaturized-sensor is showed good sensing values that equals to 0.901 for DI-water, 0.751 for fresh-water, and 0.721 for Sea-water. Finally, a good agreement is showed between the measurement results and those from simulation. © 2023 IEEE.

关键词： Dielectric materials

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A Framework for Detection of MitM Cyberattacks in Smart Grid Networks Based on the Application of the Ensemble Process for Feature Selection 13

A Framework for Detection of MitM Cyberattacks in Smart Grid...

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13th International Conference on Dependable Systems, Services and Technologies, DESSERT 2023

作者： Nicheporuk, Andrii Savenko, Oleg Sachenko, Anatoliy Nicheporuk, Anastasiia Khmelnytskyi National University Department of Computer Engineering and Information Technologies Khmelnytskyi Ukraine Kazimierz Pulaski University of Technology and Humanities Department of Informatics and Tele infomatics Radom Poland

ISBN: (纸本)9798350396119

Recent years have shown an increase in the number and complexity of cyberattacks not only on traditional IT infrastructures, but also on smart energy supply networks. Cybercriminals and cyberspies are becoming more sophisticated and persistent in their actions, which requires increased cyber security measures and the development of new technologies to protect infrastructure. In this work we present a framework for detection of Man-in-the-Middle cyberattacks in smart grids based on the application of the ensemble process for feature selection. The workflow of the framework consists of two main stages: model creation and performance evaluation. To create the model, a study was carried out, which involved the selection of the optimal set of features by applying various methods of feature selection. In addition to the sets of features that were formed by feature selection methods, another set of features was formed, which is the result of the process of assembling features that were formed by feature selection methods. A feature score was proposed, which allows weighting of each feature depending on its frequency of selection in all selection methods and the relative number of selected features in the entire set. To check the effectiveness of detecting MitM cyberattacks, SVM and Decision Tree classifiers were used, the test data for which were generated according to the obtained sets of features. © 2023 IEEE.

关键词： Smart power grids

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SMOTE Oversampling and Near Miss Undersampling Based Diabetes Diagnosis from Imbalanced Dataset with XAI Visualization 28

SMOTE Oversampling and Near Miss Undersampling Based Diabete...

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28th IEEE Symposium on computers and Communications, ISCC 2023

作者： Nayan, Nasim Mahmud Islam, Ashraful Islam, Muhammad Usama Ahmed, Eshtiak Hossain, Mohammad Mobarak Alam, Md Zahangir University of Information Technology and Sciences Department of Computer Science and Engineering Bangladesh Independent University Center for Computational and Data Sciences Bangladesh School of Computing and Informatics University of Louisiana at Lafayette United States Independent University Department of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798350300482

This study investigated the predictive ability of ten different machine learning (ML) models for diabetes using a dataset that was not evenly distributed. Additionally, the study evaluated the effectiveness of two oversampling and undersampling methods, namely the Synthetic Minority Oversampling Technique (SMOTE) and the Near-Miss algorithm. Explainable Artificial Intelligence (XAI) techniques were employed to enhance the interpretability of the model's predictions. The results indicate that the extreme gradient boosting (XGB) model combined with SMOTE oversampling technique exhibited the highest accuracy and an F1-score of 99% and 1.00 respectively. Furthermore, the utilization of XAI methods increased the dependability of the model's decision-making process, rendering it more appropriate for clinical use. These results imply that integrating XAI with ML and oversampling techniques can enhance the early detection and management of diabetes, leading to better diagnosis and intervention. © 2023 IEEE.

关键词： Machine learning

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SGNR: A Social Graph Neural Network Based Interactive Recommendation Scheme for E-Commerce

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Tsinghua Science and Technology 2023年第4期28卷 786-798页

作者： Dehua Ma Yufeng Wang Jianhua Ma Qun Jin School of Communications and lnformation Engineering Nanjing University of Posts and TelecommunicationsNanjing 210003China Department the Faculty of Computer and Information SciencesHosei UniversityTokyo 194-0298Japan Networked Information Systems Laboratory Department of Human Informatics and Cognitive SciencesFaculty of Human SciencesWaseda UniversityTokyo 169-8050Japan

Interactive Recommendation(IR)formulates the recommendation as a multi-step decision-making process which can actively utilize the individuals’feedback in multiple steps and optimize the long-term user benefit of *** Reinforcement Learning(DRL)has witnessed great application in IR for ***,user cold-start problem impairs the learning process of the DRL-based recommendation ***,most existing DRL-based recommendations ignore user relationships or only consider the single-hop social relationships,which cannot fully utilize the social *** fact that those schemes can not capture the multiple-hop social relationships among users in IR will result in a sub-optimal *** address the above issues,this paper proposes a Social Graph Neural network-based interactive Recommendation scheme(SGNR),which is a multiple-hop social relationships enhanced DRL *** this framework,the multiple-hop social relationships among users are extracted from the social network via the graph neural network which can sufficiently take advantage of the social network to provide more personalized recommendations and effectively alleviate the user cold-start *** experimental results on two real-world datasets demonstrate that the proposed SGNR outperforms other state-of-the-art DRL-based methods that fail to consider social relationships or only consider single-hop social relationships.

关键词： Interactive Recommendation(IR) Deep Reinforcement Learning(DRL) Graph Neural Network(GNN)

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

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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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eCeLLM: Generalizing Large Language Models for E-commerce from Large-scale, High-quality Instruction Data 41

eCeLLM: Generalizing Large Language Models for E-commerce fr...

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41st International Conference on Machine Learning, ICML 2024

作者： Peng, Bo Ling, Xinyi Chen, Ziru Sun, Huan Ning, Xia Department of Computer Science and Engineering The Ohio State University United States Translational Data Analytics Institute The Ohio State University United States Department of Biomedical Informatics The Ohio State University United States

With tremendous efforts in developing effective e-commerce models, conventional e-commerce models show limited success in generalist e-commerce modeling, and suffer from unsatisfactory performance on new users and new products - a typical out-of-domain generalization challenge. Meanwhile, large language models (LLMs) demonstrate outstanding performance in generalist modeling and out-of-domain generalizability in many fields. Toward fully unleashing their power for e-commerce, in this paper, we construct ECInstruct, the first open-sourced, large-scale, and high-quality benchmark instruction dataset for e-commerce. Leveraging ECInstruct, we develop eCeLLM, a series of e-commerce LLMs, by instruction-tuning general-purpose LLMs. Our comprehensive experiments and evaluation demonstrate that eCeLLM models substantially outperform baseline models, including the most advanced GPT-4, and the state-of-the-art task-specific models in in-domain evaluation. Moreover, eCeLLM exhibits excellent generalizability to out-of-domain settings, including unseen products and unseen instructions, highlighting its superiority as a generalist e-commerce model. Both the ECInstruct dataset and the eCeLLM models show great potential in empowering versatile and effective LLMs for e-commerce. ECInstruct and eCeLLM models are publicly accessible through https://***/eCeLLM/. Copyright 2024 by the author(s)

关键词： Marketplaces

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Analyzing Public Sentiment Towards the Covid-19 Pandemic: A Twitter-Based Sentiment Analysis and Machine Learning Approach 18

Analyzing Public Sentiment Towards the Covid-19 Pandemic: A ...

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18th International Workshop on Semantic and Social Media Adaptation and Personalization, SMAP 2023

作者： Kanavos, Andreas Antonopoulos, Nikos Mohasseb, Alaa Mylonas, Phivos Ionian University Department of Informatics Corfu Greece Ionian University Department of Digital Media and Communication NeMeCULAB Argostoli Greece School of Computing University of Portsmouth Portsmouth United Kingdom University of West Attica Department of Informatics and Computer Engineering Athens Greece

ISBN: (纸本)9798350327717

The Covid-19 pandemic has significantly reshaped societies, prompting an unprecedented surge in digital interactions and communication via social media platforms. Amidst this evolving landscape, the analysis of public sentiment towards pandemic management strategies has emerged as a critical avenue for understanding societal responses. This paper delves into the intricate landscape of sentiment dynamics surrounding the Covid-19 pandemic, with a focus on sentiments expressed in Twitter posts. Leveraging sentiment analysis techniques, the study provides nuanced insights into societal attitudes, concerns, and perceptions across distinct phases of the pandemic. The investigation not only employs traditional lexicon-based sentiment analysis but also explores the intersection of sentiment analysis and machine learning algorithms. The research contributes to the discourse by presenting a comprehensive analysis of public sentiment dynamics during various pandemic stages, shedding light on evolving emotional responses and offering insights into the effectiveness of measures. © 2023 IEEE.

关键词： Sentiment analysis

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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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