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International Artificial Intelligence and Data Processing Symposium (IDAP)

作者： Rahmi Kavak Selma Ayşe Özel Sema Polat Sinan Özler Department of Software Engineering Adana Alparslan Türkeş Science and Technology University Adana Turkey Department of Computer Engineering Çukurova University Adana Turkey Department of Anatomy Faculty of Medicine Çukurova University Adana Turkey Department of Mental and Nervous Diseases The Seyhan State Hospital Adana Turkey

ISBN: (数字)9798331531492

ISBN: (纸本)9798331531508

Depression is a psychiatric condition characterized by a persistent feeling of sadness and diminished interest in significant activities. Diagnosing this health problem is challenging because it relies on several social and physiological factors. The timely identification of depression aids in the prevention of severe outcomes, such as suicide. Early detection of depression levels is crucial to prevent adverse effects and enhance the quality of everyday life. The purpose of this work is to use data preprocessing techniques to create a clean dataset from noisy and incomplete medical data that includes blood biomarker values of patients and then apply data mining techniques to this dataset to predict the degree of depression. Adana Dr. Ekrem Tok Mental Health Hospital supplied the raw data for the study, with consent from the ethics committee. Missing data are completed by filling with the constant value and the most frequent value methods (i.e., 0 and mode values) in the relevant column. The classification of the dataset is performed using AdaBoost, Decision Tree (DT), and Logistic Regression (LR) classifiers, which have been previously used in medical datasets and demonstrated to be effective. The Logistic Regression classifier achieved the highest success rate (Accuracy: 0.541 and weighted F-score: 0.481).

关键词： Logistic regression Ethics Hospitals Prevention and mitigation Mental health Depression Noise measurement Decision trees Medical diagnostic imaging Blood

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Designing Quantum-Safe Lattice-Based Multi-Authority CP-ABE Scheme for Blockchain-Enabled IoT-Based Consumer Healthcare Electronics

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IEEE Transactions on Consumer Electronics 2025年

作者： Bagchi, Prithwi Bisht, Abhishek Das, Ashok Kumar Saxena, Neetesh Hossain, M. Shamim International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500 032 India Department of Computer Science and Engineering College of Informatics Korea University 145 Anam-ro Seongbuk-gu Seoul02841 Korea Republic of Cardiff University School of Computer Science and Informatics CardiffCF24 4AG United Kingdom King Saud University College of Computer and Information Sciences Department of Software Engineering Riyadh13273 Saudi Arabia

Despite edge computing reducing communication delays associated with cloud computing, privacy concerns remain a significant challenge when sharing data from edge-based consumer electronics (CE) or Internet-of-Things (IoT) devices. Ciphertext policy attribute-based encryption (CP-ABE) is a cryptographic tool that facilitates intricate and refined access control. It can deliver more flexible, secure, compact, and effective access control policies for the cloud data. In the case of a conventional CP-ABE scheme, the keys can only be issued and disseminated by a trusted central authority (CA). However, if the CA is compromised, the entire system becomes more susceptible to assaults or failures, which leads to a single-point failure. In this article, we propose a Ring-Learning with Errors (Ring-LWE)-based MA-CP-ABE scheme that is effectively resolved by the multi-authority CP-ABE (MA-CP-ABE), and it ensures the security against quantum attacks. The threshold secret sharing by Shamir and the Lagrange interpolation formula are applied in the key-generation and decryption procedures of the proposed scheme, which make it easier to segment and restore the private keys. The proposed scheme is implemented in the CE-enabled IoT-based smart healthcare applications using the blockchain technology as a secure storage. A detailed comparative study, security analysis and experimental results with the existing relevant schemes shows that the proposed scheme exhibits superior security and better efficiency as compared to other schemes, demonstrating its feasibility in practical IoT-based healthcare applications. © 2025 IEEE.

关键词： Quantum cryptography

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Contrastive Learning Based Human Activity Recognition Using Body Sensors 9

Contrastive Learning Based Human Activity Recognition Using ...

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9th International Conference on Big Data Computing and Communications, BigCom 2023

作者： Xie, Chuanshi Zhou, Yu Yu, Dandan Sun, Shilong Zhang, Xiao Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China The First Affiliated Hospital of Dalian Medical University Information Center Dalian China Harbin Institute of Technology School of Mechanical Engineering and Automation Shenzhen518055 China South-Central Minzu University Department of Computer Science China South-Central Minzu University Key Laboratory of Cyber-Physical Fusion Intelligent Computing State Ethnic Affairs Commission Wuhan430074 China

ISBN: (纸本)9798350331240

Pervasive sensing and wearable sensor techniques have been increasingly employed to monitor and recognize human activities through body sensors in areas of smart healthcare and manufacturing. However, conventional machine learning or deep learning based human activity recognition (HAR) requires a large amount of labeled data, which is cost-expensive in real-world scenarios. To tackle this issue, we propose a contrastive learning framework for HAR (CL-HAR), which utilizes the generated unlabeled data as the input and explores the supervised features of the unlabeled data under the principle of self-supervised learning. A simple yet effective backbone network as a feature extractor for subsequent activity recognition is proposed. By using a small portion of the labeled samples as the training set which is fed into our learned feature extractor, we build a classifier and use the rest of the data to verify the feasibility and effectiveness of our CL method. Extensive experiments on three benchamark datasets and one real-world dataset demonstrate that CL-HAR can achieve better classification accuracy than compared supervised and semi-supervised methods with less labelled samples, which is of practical use. © 2023 IEEE.

关键词： Wearable sensors

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A Novel Preprocessing Technique for Toxic Comment Classification

A Novel Preprocessing Technique for Toxic Comment Classifica...

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2021 International Conference on Artificial Intelligence, ICAI 2021

作者： Husnain, Muhammad Khalid, Adnan Shafi, Numan National University of Computer and Emerging Science Islamabad Chiniot-Faisalabad Campus Department of Computer Science Chiniot35400 Pakistan Northeastern University College of Software Engineering Shenyang China

ISBN: (纸本)9781665432931

The threat of online abuse and harassment is increasing day by day in the cyber community. To tackle this problem, many platforms have devised policies. But these policies require prior identification of the content that is inappropriate and offensive. Furthermore, the data contains various aspects of negativity, for example, a particular piece of comment can express, disgust, disbelief, and threat at the same time. It points that even the negativity/toxicity exhibited in a comment can have various facets. Hence, the challenge is to identify what exactly is exhibited in comments so that respective policies can be formulated and applied to penalize the offender. This study makes use of two approaches to identify these underlying toxicities in the comments. The first approach is to train separate classifiers against each facet of the toxicity in comments. The second approach deals with the problem as a multi-label classification problem. Different machine learning approaches including logistic regression, Naïve Bayes, and decision tree classification are employed to carry out this study. The dataset is taken from Kaggle and 10-fold cross-validation is used to report the robustness of the model. The study uses a novel preprocessing scheme that transforms the multi-label classification problem into the multi-class classification problem. The preprocessing strategy has shown a significant improvement in the accuracies when employed for simple classification models encouraging its use for other sophisticated models as well. Experimental results show that in both the binary classification and the multi-classification, logistic regression turns out to be a better performer. This indicates the potential use of the preprocessing for the neural classification models. © 2021 IEEE.

关键词： Classification (of information)

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Owl Ontology for Liver Diseases Using Protégé & WEB-V-OWL

Owl Ontology for Liver Diseases Using Protégé & WEB-V-OWL

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International Conference on Open Source Systems and Technologies (ICOSST)

作者： Zaeem Anwaar Farasat Ullah Khan Mehwish Naz Department of Computer and Software Engineering College of Electrical and Mechanical Engineering National University of Sciences and Technology (NUST) Islamabad Pakistan

ISBN: (纸本)9781665464789

Science and health care systems are working hand in hand to cater and support each other in the current era. The liver is one of the important body parts that need to work appropriately for a human body. But sometimes the most hazardous reasons for liver problems are infections or diseases like Hepatitis because they remain for an extended time and direct to dangerous difficulties (liver swelling, liver cancer, etc.). Liver hepatitis contains a diversity of different types of viruses: hepatitis A, B, C, and D. This study presents the design and implementation of liver hepatitis ontology. Protege-Owl and WEB-V-OWL is used for implementing and its visualization. The proposed ontology is portable and can be edited for further addition of concepts in the future. Web ontology language is used to implement this proposed ontology. By developing the Hepatitis ontology, both Intelligent health care systems and physicians can share, reason, and exploit this knowledge in different ways.

关键词： Visualization Liver cancer Liver diseases OWL Medical services Ontologies Diseases

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Breast Cancer Detection in Underserved Areas Using Deep Transfer Learning: The Case of Palestine

SSRN

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SSRN 2024年

作者： Obaid, Mahmoud Odeh, Suhail M. Ashqar, Huthaifa I. Abumwais, Allam Hodrob, Rami Computer System Engineering Department Arab American University 13 Zababdeh P.O Box 240 Jenin Palestine Software Engineering Department Bethlehem University Bethlehem Palestine Civil Engineering Department Arab American University 13 Zababdeh P.O Box 240 Jenin Palestine

Background and objective: Breast cancer is the most common form of cancer among women in Palestine, accounting for over 34% of cancer diagnoses and 12% of cancer-related deaths. The Palestinian healthcare system faces challenges, including limited resources and inadequate access to advanced diagnostic tools. Traditional diagnostic techniques often struggle to differentiate between benign and malignant tumors, leading to potential misdiagnosis. The study aims to improve breast cancer detection accuracy and efficiency by introducing a novel two-step deep learning model designed to identify and characterize cancerous areas in breast tissue. This model, which distinguishes between benign and malignant tumors, is intended as a scalable, cost-effective solution for low-resource settings like Palestine. Methods: The study employs a two-step deep learning approach: first, a U-Net architecture combined with VGG16 is used for segmenting abnormalities from mammography images, trained using the CBIS-DDSM dataset, which includes 1592 mammograms showing mass abnormalities. Second, VGG16 classifies segmented regions as benign or malignant, using a dataset from Palestine Hospital, comprising images from 34 patients, for testing. The CBIS-DDSM dataset provides annotations and ground truth binary masks, while the Palestine dataset consists of raw images without ground truth masks. Results: The proposed model achieved a 91% accuracy rate in classifying masses in the Palestine Hospital dataset and demonstrated robust performance with an average Intersection over Union (IoU) score of 0.70 on a diversified public dataset. Additionally, the U-Net model achieved a mean IoU score of 0.7 on the test set, indicating precise tumor region outlines. Conclusion: This study successfully presents a deep learning framework for breast cancer detection and classification, showing excellent performance in both segmentation and classification tasks. The model's high accuracy and robustness confirm its p

关键词： Image segmentation

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Extending assessment of engineering professional skills to include the local indonesia higher education competency

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Australasian Journal of engineering Education 2020年第1期25卷 90-102页

作者： Hadisantono, Hadisantono Rowe, Gerard Giacaman, Nasser Department of Industrial Engineering Universitas Atma Jaya Yogyakarta Yogyakarta Indonesia Department of Electrical Computer and Software Engineering The University of Auckland Auckland New Zealand

While engineering Professional Skills (EPS) are critical for today’s engineering careers, their assessment is much more subjective and significantly harder to measure than quantitative disciplinary skills. The differing graduate mobilities required for global, regional, and local engineering have led to the development of several accreditation standards (e.g. the Accreditation Board for engineering and Technology, Washington Accord, Bologna agreement, the Indonesia Accreditation Board for engineering Education). Although these standards share most of their EPS features, a significant difference exists in the extent to which cultural competency development in relation to local populations is required in the education of engineers. This paper addresses EPS assessment in an Indonesian setting where the curriculum must simultaneously cover both the national (core) curriculum and the local (institutional) curriculum, with the latter requiring (by government decree) awareness of and sensitivity to local requirement issues. The modification of an existing engineering Professional Skills Assessment (EPSA) rubric to include assessment of the local Indonesia higher education competency is described, and the results of two cycles of action research with Indonesian tertiary students are presented. The curricula activities introduced involved complex scenarios to develop competency with diverse ethical, cultural, ethnic, religious, political, and citizenship views. © 2020 Engineers Australia.

关键词： Accreditation

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The Development of the Optical-Electronic Images Segmentation Method Based on the Canny Edge Detector 18

The Development of the Optical-Electronic Images Segmentatio...

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18th IEEE International Conference on computer Science and Information Technologies, CSIT 2023

作者： Khudov, Hennadii Makoveichuk, Oleksandr Khudov, Vladyslav Khizhnyak, Irina Kalimulin, Temir Khudov, Rostyslav Ivan Kozhedub Kharkiv National Air Force University Department of Radar Troops Tactic Kharkiv Ukraine Academician Y. Bugay International Scientific and Technical University Department of Computer Science and Software Engineering Kyiv Ukraine Kharkiv National University of Radio Electronics Department of Electronic Computers Kharkiv Ukraine V.N.Karazin Kharkiv National University Department of Theoretical and Applied Informatics Kharkiv Ukraine

ISBN: (纸本)9798350360462

The article deals with the problem associated with the segmentation of color images. The main groups of color image segmentation methods operation, are considered. The main advantages and disadvantages of each group of methods are shown. The paper suggests the use of the Canny edge detector for optical-electronic images obtained from space observation systems. It is proposed to regulate the parameters of the Canny edge detector in order to improve the segmentation result. It's determined that the value of the lower threshold should be no more than 10-20 unit, and the value of the upper threshold should be within 60-100 unit. © 2023 IEEE.

关键词： Image segmentation

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A face recognition algorithm based on feature fusion

A face recognition algorithm based on feature fusion

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作者： Zhang, Jiwei Yan, Xiaodan Cheng, Zelei Shen, Xueqi School of Software Engineering Beijing University of Posts and Telecommunications Beijing China School of Cyberspace Security Beijing University of Posts and Telecommunications Beijing China Department of Computer and Information Technology Purdue University West LafayetteIN United States

In the process of building a smart city, face recognition can be applied to the transformation of enterprises, communities, and parks. The combination of building security system and face recognition technology can improve the security experience of enterprises and citizens through the solution of hardware and software integration. Face recognition is still facing the challenges of illumination, occlusion, and attitude change in the actual application process. In addition, the end-to-end convolutional neural networks (CNN) seldom make use of the hierarchical feature of the network. So, we propose a hierarchy feature fusion method for face recognition, which uses supervisory information to learn shallow and deep facial features. The features are fused to enhance the recognition accuracy of face recognition against illumination and occlusion. The method is applied to the transformation of the visual geometry group network and Lightened CNN. The face recognition experiments are carried out using the hierarchy network. Our method has achieved good recognition results in the labeled faces in the wild (LFW) and AR face databases. © 2020 John Wiley & Sons, Ltd.

关键词： Face recognition

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A Novel Rigorous Measurement Model for Big Data Quality Characteristics

A Novel Rigorous Measurement Model for Big Data Quality Char...

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IEEE International Conference on Big Data

作者： Haochen Zou Kun Xiang Department of Computer Science and Software Engineering Concordia University Montreal Quebec Canada Department of Science and Engineering Hosei University Tokyo Japan

ISBN: (纸本)9781665480468

Satisfiable data quality is the basic guarantee for data-based research, decision-making, and service. Today, new trends in the creation, collection, and utilization of data are constantly emerging. With the usage of massive data, the problem of data quality is highlighted. Several studies on the measurement, evaluation, and management of big data quality have been proposed, and the data quality problem in the big data environment has received attention. The big data characteristics Vs model describes the dimensions and attributes information of data sources in detail, which can be implemented in big data quality measurement. In this paper, a novel rigorous big data quality measurement architecture is proposed for automatically and parallelly quantifying the value of six big data Vs, which are Volume, Variety, Velocity, Veracity, Validity, and Vincularity according to the developed algorithms in every big data process step and time phase of the big data pipeline. Thresholds for the six big data Vs are provided correspondingly for analyzing the result values. The hierarchical measurement model is constructed with multiple-based measures, derived measures, and indicators. The model is verified by comparative experiments and experiments results indicate that the designed architecture can improve the outcomes of data source implementation.

关键词： Soft sensors Data integrity Volume measurement Pipelines Big Data Data models Mathematical models

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