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IEEE Signal Processing and Communications Applications (SIU)

作者： Nur Sebnem Ersoz Cihan Kuzudisli Malik Yousef Burcu Bakir-Gungor Department of Bioengineering Faculty of Life and Natural Science Abdullah Gul University Kayseri Turkey Department of Computer Engineering Faculty of Engineering Hasan Kalyoncu University Gaziantep Turkey Department of Information Systems Galilee Digital Health Research Center Zefat Academic College Zefat Israel Department of Computer Engineering Faculty of Engineering Abdullah Gul University Kayseri Turkey

ISBN: (数字)9798350388961

ISBN: (纸本)9798350388978

The human gut microbiome comprises over 10 trillion microbes and plays important roles in maintaining metabolism, body homeostasis, impacting immune function. Metagenomics which studies genomic data from clinical and environmental samples is crucial in understanding the interplay between the host and the gut microbiome. Recently, functional profiling of metagenomes helps to identify alterations in microbial functions, particularly enzyme-encoding genes. Colorectal cancer (CRC) is known as one of the leading causes of cancer-related deaths. In this study, we aimed to find the CRC-associated enzymes by analyzing metagenomic data with different machine learning methods. A total of 1262 samples including CRC and control groups from different countries were used in this study. This dataset was obtained by functionally profiling metagenomics data and estimating community level enzyme commission (EC) abundance values. For the analysis of this dataset, RCE-IFE and SVM-RCE machine learning methods, which are group-based feature selection methods, were compared with 6 different individual feature selection methods. 10 times Monte-Carlo Cross Validation was used in our experiments. It was observed that RCE-IFE, Extreme Gradient Boosting and Select K Best methods similarly provided the best performances. Especially in this study, besides the its high performance, the group-based feature selection method RCE-IFE grouped enzymes into clusters unlike TFS, and then identified biologically relevant CRC-associated enzymes.

关键词： Enzymes Metagenomics Microorganisms Monte Carlo methods Colorectal cancer Signal processing Homeostasis

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Evaluating Machine Learning and Deep Learning Analytics for Predicting Bankruptcy of Companies

Evaluating Machine Learning and Deep Learning Analytics for ...

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International Conference on Intelligent Manufacturing and Robotics, ICIMR 2023

作者： Sen, Prasenjit Assi, Sulaf Assi, Jolnar Liatsis, Panos Jayabalan, Manoj Al-Jumeily, Dhiya Faculty of Engineering and Technology Liverpool John Moores University Byrom Street LiverpoolL3 3AF United Kingdom Faculty of Science Liverpool John Moores University Byrom Street LiverpoolL3 3AF United Kingdom Traders Island Ltd London United Kingdom Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi United Arab Emirates

ISBN: (纸本)9789819984978

Corporate bankruptcy is a global issue that has increased over the last few years. Due to lack of adequate historical data, current models have not been able to correctly predict cases of bankruptcy. This research proposed a composite procedure at four stages which includes pre-processing and data rebalancing methods to curate the data, perform feature selection, use various machine learning and deep learning models to construct a robust predictive bankruptcy model and the use of explainable AI to understand the various features that contribute to the model prediction. Models were based on the labelled historic data of various bankrupted and non-bankrupted Polish companies between the period of 2007–2013. Prior to models’ application, data were split to training and test sets that consisted of 25,122 and 12,375 datapoints, respectively. Models were evaluated on various metrics: ROC-AUC, recall, and F-Beta score to determine the best predictive model. Our comparative study showcases that missing data imputation performed using KNN Imputer, skewness reduction using Yeo-Johnson transformation, feature elimination using Recursive Feature Elimination technique along with cost sensitive learning used in tandem with XGBoost algorithm produces the best model with test AUC score of 96.1%, recall score as 96%, and F-Beta score of 92.42%. Implementation of Explainable AI has also helped in realizing top four significant features that impact negatively to bankruptcy prediction globally and locally across all the models created are the ratios "total_cost_overtotal_sales", "gross_profit_in_3_years_over_total_assests", "profit_on_sales_over_sales", and "profit_on_sales_over_total_assests". Such insights on the classification outcome which instils confidence amongst the decision makers about the validity of the model and its prediction capabilities. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Metadata

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Customer behavior in e-commerce purchase from learning style 9th

Customer behavior in e-commerce purchase from learning style

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9th International Conference on Information Technology and Quantitative Management, ITQM 2022

作者： Córdova, Felisa M. Cifuentes, Fernando Diaz, Hernán Castro, Catalina Hinostroza, Cristofer School of Engineering Faculty of Engineering University San Sebastián Bellavista 7 Recoleta Santiago8420000 Chile Departament of Industrial Engineering Faculty of Engineering Department of Mathematics and Computer Science Faculty of Science University of Santiago de Chile Av. Alameda Libertador Bernardo O'Higgins 3363 Estación Central Santiago9170002 Chile School of Engineering Faculty of Engineering University Finis Terrae Pedro de Valdivia 1509 Providencia Santiago7500000 Chile

This research aims to analyze and interpret the behavior of customers who purchase via e-commerce, initially using traditional marketing tools, and then comparing them with neuromarketing tools such as EEG, VAK tests and Kolb learning styles. It begins with an online survey on e-commerce purchasing preferences, obtaining the classification by gender, socioeconomic group and other classic classifications, adding the VAK and Kolb learning styles as classifiers, both in the perception of an input message (VAK) and in its interpretation within potential internet buyers. It concludes with the promising usage of these new tools as contributions to classic marketing. Learning styles complement traditional marketing and especially online marketing, as they allow to better define the market segments to target. © 2022 The Authors. Published by Elsevier B.V.

关键词： Sales

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Cognitive Computing for HR Decision-Making 2

Cognitive Computing for HR Decision-Making

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2nd International Conference Computational and Characterization Techniques in engineering and sciences, IC3TES 2024

作者： Manoharan, Geetha Sawant, Pravin D Vanitha, J. Lourens, Melanie Anusuya, R. Bhati, Indira Sr University School of Business Telangana Warangal India Narayan Zantye College of Commerce Goa India E.G.S Pillay Engineering College Department of Mca India Durban University of Technology Faculty of Management Sciences South Africa Modern Institute of Technology and Research Centre Department of Computer Science and Engineering Rajasthan Alwar India Mmim Department of Management India

ISBN: (纸本)9798350364699

Cognitive computing is becoming an indispensable component of HR decision-making. By utilising AI, ML, NLP, and data analytics, cognitive computing solutions can efficiently process substantial quantities of HR data, including both structured and unstructured data. This function allows human resources professionals to make more informed decisions on employee engagement, talent management, and recruitment, as well as to predict future workforce patterns and obtain deeper insights into employee behaviours. We can enhance the accuracy of decision-making, reduce biases, and create a more personalised experience for employees by integrating sophisticated data analysis with the automation of routine duties. It is anticipated that cognitive computing will be instrumental in the advancement of strategic HR initiatives as organisations fully integrate digital transformation. It will guarantee that decisions are made with the assistance of factual information, are impartial, and are consistent with the organization's overarching objectives. © 2024 IEEE.

关键词： Data Analytics

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Multi-Quasi-Proportional-Resonant Control for Capacitive-Coupling Grid-Connect Inverter (CGCI) with Accurate Active Power Injection Technique 49

Multi-Quasi-Proportional-Resonant Control for Capacitive-Cou...

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49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Chan, Pak-Ian Sou, Wai-Kit Lam, Chi-Seng University of Macau State Key Laboratory of Analog and Mixed-Signal Vlsi China Institute of Microelectronics University of Macau China University of Macau Faculty of Science and Technology Department of Electrical and Computer Engineering China

ISBN: (纸本)9798350331820

In this paper, a multi-quasi-proportional-resonant control (MQPRC) for a three-phase capacitive-coupling grid-connect inverter (CGCI) with accurate active power injection technique is proposed to mitigate the current harmonics and improve the active power injection accuracy. Then, a detail parameter design of the proposed MQPRC is provided. And the proposed accurate active power injection is verified by the simulation. Subsequently, to verify the effectiveness of the proposed MQPRC, the simulation results are presented in comparison to the quasi-proportional-resonant control (QPRC). © 2023 IEEE.

关键词： Power quality

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A Hybrid Learning Approach for Automatic Data Labelling and Anomaly Detection in IoT Networks 3

A Hybrid Learning Approach for Automatic Data Labelling and ...

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3rd IEEE International Conference on Artificial Intelligence, ICAI 2023

作者： Kanwal, Rimsha Noor, Umara Rashid, Zahid International Islamic University Faculty of Computing and Information Technology Department of Computer Science Islamabad Pakistan College of Engineering Technology Management Economics and Policy Program Seoul National University Seoul Korea Republic of

ISBN: (纸本)9798350322125

Internet of Things (IoT) is an environment in which digital equipment is augmented with sensors to share and receive data through network. When devices share data it can be effected by anomalies or any attack due to corrupted data or by any other uncertainty and ambiguity in data. The data can also be corrupted through a damage in device. These attacks or anomalies damage the working of the IoT networks. The anomalous data can be detected through detection techniques however most anomaly detection techniques depend upon labelled data but for IoT datasets, usually class labels are not available. Labeling is performed by a manual process which is time consuming and also costly. As data in IoT increases day by day so there is a need to label and classify data for future unseen data. In this paper a hybrid algorithm is proposed in which both clustering and classification techniques are applied for automatic labeling and classifying on IoT dataset. The model contains two function. In the first phase k-means clustering is employed for labelling dataset instances as normal or anomalous. In the second phase labelled dataset is used to train Random Forest model to detect anomalies in IoT networks. The results show that the proposed model is detecting anomalies in IoT networks with an accuracy 98%, precision 98 %, recall 98%, and F-meausre 0.98%. © 2023 IEEE.

关键词： Classification (of information)

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Bridging the Gap: Transfer Learning for Dialect Identification in Low-Resource Settings A Case Study with Algerian Arabic

Bridging the Gap: Transfer Learning for Dialect Identificati...

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iNnovative Informatics of Biskra (ISNIB), International Symposium on

作者： Mafaza Chabane Fouzi Harrag Khaled Shaalan Skander Hamdi Department of computer science University of Ferhat Abbes Setif Algeria Faculty of Engineering and IT The British University in Dubai Dubai UAE

ISBN: (数字)9798331509576

ISBN: (纸本)9798331509583

This paper explores bridging the gap between well- resourced languages and low-resource languages in Natural Language Processing (NLP). We investigate leveraging Modern Standard Arabic (MSA) as a source for transferring knowledge to a low-resource dialect, Algerian Arabic, for the task of dialect identification. We compare the performance of three pre-trained models: AraBERT, DziriBERT, and mBERT. Our evaluation uses the Madar Corpus, a collection of parallel sentences from various languages and dialects. For this study, we focus on the Algerian Arabic and their corresponding MSA counterparts, comprising a dataset of 4k sentences. Our findings demonstrate the effectiveness of transferring knowledge from MSA and other well-resourced languages to enhance NLP tasks for low-resource languages.

关键词： Accuracy Navigation Transfer learning Linguistics Natural language processing Complexity theory Informatics Standards

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Usability Evaluation Through Fuzzy AHP-TOPSIS Approach: Security Requirement Perspective

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computers, Materials & Continua 2021年第7期68卷 1203-1218页

作者： Yoosef BAbushark Asif Irshad Khan Fawaz Jaber Alsolami Abdulmohsen Almalawi Md Mottahir Alam Alka Agrawal Rajeev Kumar Raees Ahmad Khan Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Electrical and Computer Engineering Faculty of EngineeringKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information Technology Babasaheb Bhimrao Ambedkar UniversityLucknow226025India Department of Computer Application Shri Ramswaroop Memorial UniversityBarabanki225003India

Most of the security strategies today are primarily designed to provide security protection,rather than to solve one of the basic security issues related to adequate software product *** models,frameworks and methodologies have been introduced by the researchers for a secure and sustainable software development life *** it is important to assess the usability of the popular security requirements engineering(SRE)approaches.A significant factor in the management and handling of successful security requirements is the assessment of security requirements engineering method *** assessment will allow changes to the engineering process of security *** consistency of security requirements depends heavily on the usability of security requirements *** SRE approaches are available for use and each approach takes into account several factors of usability but does not cover every element of *** seems to be no realistic implementation of such models because the concept of usability is not *** paper aims at specifying the different taxonomy of usability and design hierarchical usability *** taxonomy takes into account the common quality assessment parameters that combine variables,attributes,and characteristics identified in different approaches used for security requirements *** multiple-criteria decision-making(MCDM)model used in this paper for usability evaluation is called the fuzzy AHP-TOPSIS model which can conveniently be incorporated into the current approach of software *** significant usability criteria are identified and used to evaluate the six different *** strategies are graded as per their expected values of usability.

关键词： Security requirements engineering cyber-security usability fuzzy logic MCDM

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Facial Surgery Preview Based on the Orthognathic Treatment Prediction

arXiv

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arXiv 2024年

作者： Han, Huijun Zhang, Congyi Zhu, Lifeng Singh, Pradeep Hsung, Richard Tai-Chiu Leung, Yiu Yan Komura, Taku Wang, Wenping Gu, Min Faculty of Dentistry The University of Hong Kong Hong Kong Department of Computer Science Faculty of Engineering The University of Hong Kong Hong Kong School of Instrument Science and Engineering Southeast University Nanjing China The University of Hong Kong Hong Kong Department of Computer Science and Engineering Texas A&M University TX United States

Orthognathic surgery consultation is essential to help patients understand the changes to their facial appearance after surgery. However, current visualization methods are often inefficient and inaccurate due to limited pre- and post-treatment data and the complexity of the treatment. To overcome these challenges, this study aims to develop a fully automated pipeline that generates accurate and efficient 3D previews of postsurgical facial appearances for patients with orthognathic treatment without requiring additional medical images. The study introduces novel aesthetic losses, such as mouth-convexity and asymmetry losses, to improve the accuracy of facial surgery prediction. Additionally, it proposes a specialized parametric model for 3D reconstruction of the patient, medical-related losses to guide latent code prediction network optimization, and a data augmentation scheme to address insufficient data. The study additionally employs FLAME, a parametric model, to enhance the quality of facial appearance previews by extracting facial latent codes and establishing dense correspondences between pre- and post-surgery geometries. Quantitative comparisons showed the algorithm’s effectiveness, and qualitative results highlighted accurate facial contour and detail predictions. A user study confirmed that doctors and the public could not distinguish between machine learning predictions and actual postoperative results. This study aims to offer a practical, effective solution for orthognathic surgery consultations, benefiting doctors and *** Codes 68U99 © 2024, CC BY.

关键词： Transplantation (surgical)

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An Array of Bandpass Detectors for Measuring Beam Spectral Components

An Array of Bandpass Detectors for Measuring Beam Spectral C...

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2023 IEEE Applied Sensing Conference, APSCON 2023

作者： Jahangiri, Mojtaba Sberna, Paolo Sammak, Amir Nihtianov, Stoyan Department of Microelectronics Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Mekelweg 4 Delft2628CD Netherlands Stieltjesweg 1 Delft2628CK Netherlands

ISBN: (纸本)9781665461634

A clear understanding of the spectral components of an irradiated beam, or captured optical emission, is essential to optimize an optical system and increase its performance. Logically, for this purpose a grating-based spectrometer could be the first choice. However, in the case of a wide range spectrum, and for radiation with one dominant wavelength, this option may not work well. In this paper, we present a technique based on an array of bandpass detectors to measure accurately the power of a number of beam-specific spectral components in a wide spectrum range: from soft X-ray to infrared. The main unique features of this technique are: customization for specific wavelengths of interest;vacuum compatibility;and high sensitivity to low-energy spectral components in the presence of one or more dominant highpower spectral components. © 2023 IEEE.

关键词： Optical systems

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