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arXiv 2024年

作者： Su, Ran Shi, Rui Cui, Hui Xuan, Ping Fang, Chengyan Feng, Xikang Jin, Qiangguo School of Computer Software College of Intelligence and Computing Tianjin University Tianjin China School of Software Northwestern Polytechnical University Shaanxi China Department of Computer Science and Information Technology La Trobe University Melbourne Australia Department of Computer Science School of Engineering Shantou University Guangdong China Yangtze River Delta Research Institute of Northwestern Polytechnical University Taicang China

Molecular subtyping of cancer is recognized as a critical and challenging upstream task for personalized therapy. Existing deep learning methods have achieved significant performance in this domain when abundant data samples are available. However, the acquisition of densely labeled samples for cancer molecular subtypes remains a significant challenge for conventional data-intensive deep learning approaches. In this work, we focus on the few-shot molecular subtype prediction problem in heterogeneous and small cancer datasets, aiming to enhance precise diagnosis and personalized treatment. We first construct a new few-shot dataset for cancer molecular subtype classification and auxiliary cancer classification, named TCGA Few-Shot, from existing publicly available datasets. To effectively leverage the relevant knowledge from both tasks, we introduce a task-specific embedding-based meta-learning framework (TSEML). TSEML leverages the synergistic strengths of a model-agnostic meta-learning (MAML) approach and a prototypical network (ProtoNet) to capture diverse and fine-grained features. Comparative experiments conducted on the TCGA Few-Shot dataset demonstrate that our TSEML framework achieves superior performance in addressing the problem of few-shot molecular subtype classification. Copyright © 2024, The Authors. All rights reserved.

关键词： Zero-shot learning

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Implementation of IoT and WSN Technologies for Health Monitoring 1st

Implementation of IoT and WSN Technologies for Health Monito...

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1st International Conference on computing and Emerging Technologies, ICCET 2023

作者： Mazhar, Tehseen Iqbal, Muhammad Iqbal, Muhammad Ghadi, Yazeed Yasin Rehman, Ateeq Ur Abbasi, Waseem Department of Computer Science Virtual University of Pakistan Lahore54000 Pakistan Institute of Computing and Information Technology Gomal University Dera Ismail Khan29220 Pakistan Department of Computer Science and Software Engineering Al Ain Universit Abu Dhabi15322 United Arab Emirates Department of Electrical Engineering Government College University Lahore54000 Pakistan Department of Computer Science and IT Superior University Sargodha Pakistan

ISBN: (纸本)9783031776168

IoT-based applications make innovative use of millions of sensors. Research has risen in the medical and technological fields in the previous ten years. The Internet of Things impacts the development of medical research. Improved medical care is possible because the Internet of Things can link sensors, medical equipment, and remote healthcare professionals. Patient treatment has been enhanced in terms of effectiveness, cost, accessibility, and safety. Due to COVID-19, receiving medical treatment in the convenience of one’s home is more important than ever. To enhance healthcare, internet of things devices is utilized to monitor patients. His article examines how the Internet of Things has improved, enabling technology, healthcare services, and healthcare-related challenges. IoT system concerns are also taken into consideration. This effort aims to help future researchers understand the topic better by giving a thorough overview of all of HIoT’s many uses. The applications, technology, and challenges of the healthcare industry are all examined in this article. As a result, the IoT has the potential to improve healthcare’s effectiveness and ability to treat patients. The idea put out in this article is that the Internet of Things may be made widely used, lucrative, and easily available everywhere. The integration provides a faster calculation. The last section includes a number of practical recommendations and suggestions for implementing the IoT healthcare system during COVID-19 and future severe pandemics. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： COVID-19

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Multi Sensor-Based Implicit User Identification

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Computers, Materials & Continua 2021年第8期68卷 1673-1692页

作者： Muhammad Ahmad Rana Aamir Raza Manuel Mazzara Salvatore Distefano Ali Kashif Bashir Adil Khan Muhammad Shahzad Sarfraz Muhammad Umar Aftab Department of Computer Science National University of Computer and Emerging SciencesChiniot35400Pakistan Department of Computer Science Bahauddin Zakariya UniversityMultan66000Pakistan Institute of Software Development and Engineering Innopolis UniversityInnopolis420500Russia Dipartimento di Matematica e Informatica—MIFT University of MessinaMessina98121Italy Department of Computing and Mathematics Manchester Metropolitan UniversityManchesterUK

Smartphones have ubiquitously integrated into our home and work environments,however,users normally rely on explicit but inefficient identification processes in a controlled ***,when a device is stolen,a thief can have access to the owner’s personal information and services against the stored *** a result of this potential scenario,this work proposes an automatic legitimate user identification system based on gait biometrics extracted from user walking patterns captured by smartphone sensors.A set of preprocessing schemes are applied to calibrate noisy and invalid samples and augment the gait-induced time and frequency domain features,then further optimized using a non-linear unsupervised feature selection *** selected features create an underlying gait biometric representation able to discriminate among individuals and identify them *** classifiers are adopted to achieve accurate legitimate user *** experiments on a group of 16 individuals in an indoor environment show the effectiveness of the proposed solution:with 5 to 70 samples per window,KNN and bagging classifiers achieve 87–99%accuracy,82–98%for ELM,and 81–94%for *** proposed pipeline achieves a 100%true positive and 0%false-negative rate for almost all classifiers.

关键词： Sensors smartphone legitimate user identification

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SUPPLYDECK – A Blockchain-Based Pharmaceutical Supply Chain Management

SUPPLYDECK – A Blockchain-Based Pharmaceutical Supply Chain...

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Advancements in computing (ICAC), International Conference on

作者： A.A.B. Amarasinghe G G.M.H. Gamagedara S Pitigala K.K H.D.A. Subawickrama E Kavinga Yapa Abeywardena Kanishka Yapa Department of Computer Science and Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Information Technology Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350398106

With the rising competition among pharmaceutical companies, the current drug supply chain market has become more competitive with high-quality product segments. The rapid growth of internet pharmacies has made it more difficult to standardize drug safety throughout in complex distributed supply chain networks. There is a high chance of introducing counterfeit drugs which are almost the same as the original pharmaceuticals. Due to the lack of transparency the possibility of tampering with drugs in the current manual and web-based pharmaceutical systems is extremely high. Throughout the past recent years, these counterfeit drugs were recognized as the one of major worldwide problems. This situation is worse in developing countries. The unavailability of verifying the legitimacy of drug suppliers and poor mechanisms to trace the drugs were identified as critical points that need to be resolved. SUPPLYDECK comes as a solution for the critical scenario mentioned. This solution addresses the above problems using blockchain technology, as a distributed digital ledger that ensures transparency, traceability, and security. This can show promise for solving some global supply chain management problems using smart contracts and user authentication along with IoT technology and machine learning concepts that are critically examined with this potential application.

关键词： Drugs Supply chain management Supply chains Smart contracts Manuals Machine learning Companies

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Computational network analysis of two popular skin cancers provides insights into the molecular mechanisms and reveals common therapeutic targets

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Heliyon 2025年第1期11卷 e41688页

作者： Mahmud, Md Sujan Paul, Bikash Kumar Hasan, Md. Rakibul Islam, K.M. Tanjida Mahmud, Imran Mahmud, Shahin Department of Software Engineering Daffodil International University Bangladesh Department of Information and Communication Technology Mawlana Bhashani Science and Technology University Santosh Tangail 1902 Bangladesh Department of Biotechnology and Genetic Engineering Mawlana Bhashani Science and Technology University Santosh Tangail 1902 Bangladesh Department of Computing and Information System (CIS) Daffodil International University Dhaka Bangladesh

Basal Cell Carcinoma (BCC) and Actinic Keratosis (AK) are prevalent skin conditions with significant health complications. The molecular mechanisms underlying these conditions and their potential shared pathways remain ambiguous despite their prevalence. Therefore, this study aims to elucidate the common molecular pathways and potential therapeutic targets for BCC and AK through comprehensive computational network analysis. Linkage analysis was performed to identify common liable genes between BCC and AK. Protein-protein interactions (PPIs), Topological properties, GO enrichment, pathway enrichment, and gene regulatory network analyses were also performed to reveal potential molecular mechanisms and pathways. Furthermore, we evaluated protein-drug interactions (PDIs) to identify potential therapeutic targets. Our analysis revealed 22 common genes between BCC and AK: TP53, EGFR, CDKN2A, MMP9, PTGS2, VDR, BCL2, MMP2, EZH2, TP63, FOXP3, MSH2, MMP14, FLG, MC1R, CDKN2B, TIMP3, TYR, SOX10, IRF4, KRT17, and NID1. PPI network analysis highlighted TP53 and EGFR as central hubs, validated using RNA-seq data. Co-expression and physical interaction analysis revealed a strong interplay between the common genes at the transcriptional and functional levels. GO analysis identified skin cancer-relevant terms: "skin development", "immune system development", and “response to radiation” as significantly enriched biological processes, while pathway enrichment analysis highlighted several cancer-related pathways enrichment. Gene regulatory network analysis revealed complex interactions between genes, miRNAs, and transcription factors, with TP53, BCL2, and EGFR playing central roles. PDI network analysis identified ibuprofen as a potential therapeutic agent targeting PTGS2 and BCL2, while other proteins VDR, MMP2, MMP9, and TYR showed interactions with multiple drugs. This computational analysis provides valuable insights into the shared molecular mechanisms of BCC and AK, revealing comm

关键词： Actinic keratosis Basal cell carcinoma Bioinformatics Drug repurposing Protein-drug interaction Protein-protein interaction Skin cancers

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An Enhanced and Robust Data Publishing Scheme for Private and Useful 1:M Microdata

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IEEE Transactions on Big Data 2024年

作者： Rizwan, Muhammad Hawbani, Ammar Xingfu, Wang Anjum, Adeel Angin, Pelin Sever, Yigit Chen, Sanchuan Zhao, Liang Al-Dubai, Ahmed University of Science and Technology of China School of Computer Science and Technology China Shenyang Aerospace University School of Computer Science China Quaid i Azam University Institute of Information Technology Islamabad Pakistan Middle East Technical University Department of Computer Engineering Turkey Auburn University Department of Computer Science and Software Engineering United States Computing School of Edinburgh Napier University United Kingdom

A data publishing deal conducted with anonymous microdata can preserve the privacy of people. However, anonymizing data with multiple records of an individual (1:M dataset) is still a challenging problem. After anonymizing the 1:M microdata, the vertical correlation can be exploited to launch privacy attacks. In this paper, a novel privacy preserving model lc, ls-ANGEL is proposed. To validate the new model, two privacy attacks are presented, namely, a Vertical correlation attack (Vc0) and a Vulnerable sensitive attribute attack (Vsa) on 1:M datasets, which breach the privacy of individuals. Furthermore, the proposed model is examined through High-Level Petri Nets (HLPNs). Our experiments on three real-world datasets;'INFORMS','YOUTUBE', and 'IMDb' demonstrate that the proposed model outperforms the state-of-the-art models. Our practices and lessons learned in this work can direct future concrete steps towards Multiple Sensitive Attributes, where we can expand the proposed model to dynamic datasets © 2024 IEEE.

关键词： Anonymity

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Development of Low Resource Machine Learning Models for Child Cognitive Ability Assessments

Development of Low Resource Machine Learning Models for Chil...

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Advancements in computing (ICAC), International Conference on

作者： Adithya Kahawanugoda Kulanika Gnanarathna Nilan Meegoda Randika Monarawila Pradeepa Samarasinghe Asiri Gawesha Lindamulage Department of Computer Science and Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Information Technology Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350398106

Automated cognitive assessment tools are state-of-the-art in assessing cognition development. Due to the low availability of resources, building automated cognitive ability evaluation tools is challenging. This study focuses on developing machine learning models using a limited amount of data to assess Reasoning IQ, Knowledge IQ, Mental Chronometry and Attention-levels of Sinhala-speaking children between the age of 7 to 9 years. Our solution includes Sinhala speech recognition systems, image classification models, gaze estimation, blink count detection and facial expression recognition models to evaluate the above four cognitive ability measuring factors. Open domain speech recognition has been used to evaluate complex Sinhala child verbal responses and limited vocabulary responses were assessed using an end-to-end speech recognition system, respectively achieving 40.1% WER and 97.14% accuracy. Additionally, the image classification models for handwritten Sinhala letter recognition and two shape recognition models have gained 97%, 89% and 99% accuracy. The linear regression model for attention level evaluation that utilizes the inputs from a combination of eye-gaze estimation, facial expression recognition and blink rate detection models has gained 85% accuracy.

关键词： Handwriting recognition Vocabulary Image recognition Computational modeling Face recognition Estimation Speech recognition

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TSEML: A task-specific embedding-based method for few-shot classification of cancer molecular subtypes

TSEML: A task-specific embedding-based method for few-shot c...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Ran Su Rui Shi Hui Cui Ping Xuan Chengyan Fang Xikang Feng Qiangguo Jin School of Computer Software College of Intelligence and Computing Tianjin University Tianjin China School of Software Northwestern Polytechnical University Shaanxi China Department of Computer Science and Information Technology La Trobe University Melbourne Australia Department of Computer Science School of Engineering Shantou University Guangdong China Yangtze River Delta Research Institute of Northwestern Polytechnical University Taicang China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Molecular subtyping of cancer is recognized as a critical and challenging upstream task for personalized therapy. Existing deep learning methods have achieved significant performance in this domain when abundant data samples are available. However, the acquisition of densely labeled samples for cancer molecular subtypes remains a significant challenge for conventional data-intensive deep learning approaches. In this work, we focus on the few-shot molecular subtype prediction problem in heterogeneous and small cancer datasets, aiming to enhance precise diagnosis and personalized treatment. We first construct a new few-shot dataset for cancer molecular subtype classification and auxiliary cancer classification, named TCGA Few-Shot, from existing publicly available datasets. To effectively leverage the relevant knowledge from both tasks, we introduce a task-specific embedding-based meta-learning framework (TSEML). TSEML leverages the synergistic strengths of a model-agnostic meta-learning (MAML) approach and a prototypical network (ProtoNet) to capture diverse and fine-grained features. Comparative experiments conducted on the TCGA FewShot dataset demonstrate that our TSEML framework achieves superior performance in addressing the problem of few-shot molecular subtype classification.

关键词： Metalearning Deep learning Knowledge engineering Medical treatment Benchmark testing Bioinformatics Cancer

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Design of an energy-efficient IOT device-assisted wearable sensor platform for healthcare data management

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Measurement: Sensors 2023年 30卷

作者： Ahamed, Bazeer Sellamuthu, Suseela Karri, Purushotam Naidu Srinivas, Inumarthi V. Mohammed Zabeeulla, A.N. Ashok Kumar, M. Department of IT University of Technology And Applied Sciences Al Musanna Oman Senior Grade School of Computing Sciences and Engineering Vellore Institute of Technology TamilNadu Chennai India Department of CSE Gayatri Vidya Parishad College of Engineering for Women Andhra Pradesh Visakhapatnam535546 India Mumbai400077 India Bangalore India Department of Computer Science and Software Engineering Skyline University Nigeria.Kano Nigeria

Advances in health-related behaviors, technical breakthroughs, and the spread of healthy living activities have expanded personal health evaluation on a broad scale. In medical applications, networks, data, and communication systems are extensively utilized and further expanding. Real-time applications for remote health monitoring generate massive amounts of data in a short period. Internet of Things (IoT) and cloud systems, resulting in unprocessed and inadequate data for end-users. Since cloud technology is a dispersed intermediary layer between the edge network and the cloud infrastructure, the delay is significantly decreased, and reliable communications can be established using cloud computing on an IoT-enabled healthcare device. This study presents an Energy Efficient Healthcare Data Management Method (EE-HDMM), which enhances health monitoring using an IoT-assisted wearable sensor infrastructure. The difficulty of constrained sources and the energy consumption is minimized by proposing the optimum energy resource allocation method. Wearable sensor technology supported by cloud computing is the ideal and dependable platform for powerful life-critical systems that are unlikely to have communication delays. Compared to conventional techniques, the suggested EE-HDMM has a high precision rate of 97%, a sensitivity rate of 94%, and a system efficiency of 96%. © 2023

关键词： Internet of things

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The Future of Crime Prevention: Police Case Analysis Using Machine Learning

The Future of Crime Prevention: Police Case Analysis Using M...

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Advancements in computing (ICAC), International Conference on

作者： Dharsan. R Krishanthini M Traveena C Anubama L Mahaadikara M.D.J.T. Hansika Sanjeevi Shahikala Chandrasiri Department of Computer Science and Software Engineering (CSSE) Sri Lanka Institute of Information Technology (SLIIT) Malabe Sri Lanka Faculty of Computing Sri Lanka Institute of Information Technology (SLIIT) Malabe Sri Lanka

Law enforcement agencies and police departments face challenges in analyzing and preventing accidents and crimes. This research presents a comprehensive machine learning system, “The Future of Crime Prevention: Police Case Analysis,” to enhance public safety. The system comprises components like accident case analysis, crime analysis, case document classification, and crimes against women analysis. It leverages various machine learning techniques to predict accident causes, analyze criminal cases, automate document classification, and cluster crimes against women. The objectives encompass bolstering public safety, enabling proactive decision-making, anticipating future crime rates, and optimizing document analysis. This research strives to provide effective tools for crime and accident prevention, addressing traditional challenges faced by police departments and crime divisions. In an era of advancing technology, the need for innovative solutions to ensure public safety has never been more crucial. Furthermore, it seeks to bridge the gap between law enforcement agencies and cutting-edge technology, revolutionizing the landscape of crime prevention and public safety.

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