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9th IEEE International Women in engineering (WIE) Conference on Electrical and computer engineering, WIECON-ECE 2023

作者： Routray, Sweta Padma Nayak, Amlan Deep Nayak, Debasish Swapnesh Kumar Sahoo, Swayamprabha Swarnkar, Tripti Center for Biotechnology Siksha 'O' Anusandhan Deemed to be University Bhubaneswar751003 India Institute of Technical Education and Research Siksha 'O' Anusandhan Deemed to be University Department of Computer Application Bhubaneswar751030 India Institute of Technical Education and Research Siksha 'O' Anusandhan Deemed to be University Department of Computer Science & Engineering Bhubaneswar751030 India

ISBN: (纸本)9798350319651

Pseudomonas aeruginosa, a multidrug-resistant organism with clinical significance, complicates the treatment of critically ill patients. Since traditional susceptibility testing takes at least 24 hours, timely identification and treatment are crucial in the face of rising Antimicrobial Resistance (AMR). In this study, we present the development of ANTIBIOLOG, a cutting-edge Machine Learning (ML) model. This model predicts P. aeruginosa antibiotic susceptibility based on patient demographics and antibiotic susceptibility testing results, using a dataset of 49 patient samples from IMS & SUM Hospital. Multinomial logistic regression was selected for dealing with multiple classes and providing probabilistic interpretations. The performance of the model is evaluated using various metrics such as accuracy, precision, recall, specificity, F1 score, and MCC. Through a user-friendly web interface, it simplifies resistance prediction and guides empirical antibiotic treatment. Unlike generic tools, it focuses on P. aeruginosa and provides accurate therapy selection. ANTIBIOLOG predicted antibiotic susceptibility with a promising 77.77% accuracy. This ML-based tool enables healthcare professionals to combat multidrug-resistant P. aeruginosa infections and address AMR issues. © 2023 IEEE.

关键词： Statistical tests

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Authentication using Biometric data from Mobile Cloud Computing in Smart Cities 3

Authentication using Biometric Data from Mobile Cloud Comput...

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3rd International Conference on Advancement in Electronics and Communication engineering, AECE 2023

作者： Salama, Ramiz Al-Turjman, Sinem Altrjman, Chadi Al-Turjman, Fadi Prakash, R.Om Yadav, Satya Prakash Vats, Satvik Ai and Robotics Institute Research Center for Ai and IoT Department of Computer Engineering Near East University Near East Boulevard TRNC Mersin 10 Nicosia99138 Turkey Ai and Robotics Institute Near Artificial Intelligence Engineering Dept Near East University Mersin10 Nicosia Turkey Waterloo University Department of Chemical Engineering ONN2L 3G1 Canada Noida Instue Engineering and Technology U.P Greator Noida201306 India Department of Computer Science and Engineering Greater Noida201306 India Graphic Era Hill University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350330724

Smart cities are cities that are designed to be more efficient, sustainable, and connected. As our cities grow and become more complex, it's important to find new ways to address the challenges that arise, such as security, privacy, and mobility. This is where mobile cloud computing and biometric authentication come in. Mobile cloud computing is a way to provide cloud computing services to mobile devices, which means people can access services and data from anywhere and at any time. Biometric authentication is a method of identifying people based on their unique physical or behavioral characteristics, like fingerprints, facial recognition, or voice recognition. These two technologies can be combined to create a secure and personalized environment for residents and visitors in smart cities. By using mobile devices, IoT devices, wireless sensor networks, and edge computing, we can collect real-time data that can be used for big data analytics and machine learning. This means we can optimize city services, like traffic management, waste management, or energy consumption, and enhance the quality of life for people living in smart cities. However, the use of these technologies also poses significant security and privacy risks, so it's important to design systems that are secure and privacy-preserving. This requires interdisciplinary research and collaboration between experts in different fields. In summary, mobile cloud computing and biometric authentication have the potential to transform smart cities by creating a more secure and personalized environment for residents and visitors. But we must also be mindful of the potential risks and work together to address them. © 2023 IEEE.

关键词： Mobile cloud computing

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research on Mining Association Rules for Classroom Concentration Based on Apriori Algorithm

Research on Mining Association Rules for Classroom Concentra...

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IEEE Information Technology and Mechatronics engineering Conference (ITOEC)

作者： Jingqiu Bai Huafeng Kong Wenhan Hu Department of Information Engineering Wuhan Business University Hubei China Major in Data Science and Big Data Technology The Main Research Direction is the Computer Application

This paper studies the correlation between students' concentration in class and learning interest, emotional state and other influencing factors. By collecting students' classroom status data, a data set suitable for data mining is constructed. Based on the idea of Apriori algorithm, the Anaconda Navigator programming tool is used to mine the association rules of students' classroom concentration. Through data mining based on Apriori algorithm, and the association rules between students' emotional state, learning interest and other factors and in-class test scores are output. The results of strong association rules show that the better students' emotional state, learning interest and other states are, the better their classroom concentration performance is. Mining the potential influencing factors of students' classroom concentration provides valuable reference theory for students to improve classroom concentration and teachers to improve classroom teaching effect, which is helpful for teachers and students to build a more efficient classroom together in the future.

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Joint Self-Supervised Image-Volume Representation Learning with Intra-inter Contrastive Clustering 37

Joint Self-Supervised Image-Volume Representation Learning w...

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37th AAai Conference on Artificial Intelligence, AAai 2023

作者： Nguyen, Duy M.H. Nguyen, Hoang Mai, Truong T.N. Cao, Tri Nguyen, Binh T. Ho, Nhat Swoboda, Paul Albarqouni, Shadi Xie, Pengtao Sonntag, Daniel Department of Computer Science University of Stuttgart Germany AISIA Lab. University of Science - VNU HCM Viet Nam Department of Multimedia Engineering Dongguk University Korea Republic of Department of Statistics and Data Sciences University of Texas Austin United States Max Planck Institute for Informatics Germany Helmholtz AI Helmholtz Munich Germany Clinic for Diagnostic and Interventional Radiology University of Bonn Germany Department of Electrical and Computer Engineering University of California San Diego United States Department of Computer Science Oldenburg University Germany German Research Center for Artificial Intelligence Germany

ISBN: (纸本)9781577358800

Collecting large-scale medical datasets with fully annotated samples for training of deep networks is prohibitively expensive, especially for 3D volume data. Recent breakthroughs in self-supervised learning (SSL) offer the ability to overcome the lack of labeled training samples by learning feature representations from unlabeled data. However, most current SSL techniques in the medical field have been designed for either 2D images or 3D volumes. In practice, this restricts the capability to fully leverage unlabeled data from numerous sources, which may include both 2D and 3D data. Additionally, the use of these pre-trained networks is constrained to downstream tasks with compatible data dimensions. In this paper, we propose a novel framework for unsupervised joint learning on 2D and 3D data modalities. Given a set of 2D images or 2D slices extracted from 3D volumes, we construct an SSL task based on a 2D contrastive clustering problem for distinct classes. The 3D volumes are exploited by computing vectored embedding at each slice and then assembling a holistic feature through deformable self-attention mechanisms in Transformer, allowing incorporating long-range dependencies between slices inside 3D volumes. These holistic features are further utilized to define a novel 3D clustering agreement-based SSL task and masking embedding prediction inspired by pre-trained language models. Experiments on downstream tasks, such as 3D brain segmentation, lung nodule detection, 3D heart structures segmentation, and abnormal chest X-ray detection, demonstrate the effectiveness of our joint 2D and 3D SSL approach. We improve plain 2D Deep-ClusterV2 and SwAV by a significant margin and also surpass various modern 2D and 3D SSL approaches. Copyright © 2023, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Embeddings

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Security and Privacy Concerns in 'Communication Systems' Blockchain Technology and the Internet of Things 3

Security and Privacy Concerns in 'Communication Systems' Blo...

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3rd International Conference on Advancement in Electronics and Communication engineering, AECE 2023

作者： Salama, Ramiz Al-Turjman, Sinem Altrjman, Chadi Al-Turjman, Fadi Prakash, R.Om Yadav, Satya Prakash Vats, Satvik Ai and Robotics Institute Research Center for Ai and IoT Near East University Department of Computer Engineering Near East Boulevard TRNC Mersin 10 Nicosia99138 Turkey Ai and Robotics Institute Near East University Artificial Intelligence Engineering Dept Mersin10 Nicosia Turkey Waterloo University Department of Chemical Engineering ONN2L 3G1 Canada Noida Instue Engineering and Technology U.P Greator Noida201306 India Department of Computer Science and Engineering Greater Noida201306 India Graphic Era Hill University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350330724

This article examines the security and privacy concerns associated with the integration of blockchain technology and the Internet of Things (IoT) in communication systems. As the IoT continues to grow and evolve, there is an increasing need to ensure the security and privacy of the vast amounts of data generated and exchanged between connected devices. Blockchain technology offers promising solutions for enhancing security and privacy in communication systems;however, it also presents its own set of challenges and vulnerabilities. This article explores the existing literature on the topic and analyzes the materials and methods used in previous studies. The results and discussions delve into the key security and privacy concerns in communication systems with blockchain and IoT, including data integrity, authentication, and confidentiality, scalability, and consensus mechanisms. Finally, the article concludes with recommendations and future directions for addressing these concerns and advancing the security and privacy of communication systems in the context of blockchain technology and the IoT. © 2023 IEEE.

关键词： Internet of things

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Synergistic Low-Carbon Strategies for Robust Electricity-Gas System Dispatch Under Uncertainty

SSRN

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SSRN 2025年

作者： Fu, Zezheng Yang, Degang Lin, Wenting Liu, Kangming School of Computer and Information Science Chongqing Normal University Chongqing401331 China Chongqing Engineering Research Center of Educational Big Data Intelligent Perception and Application Chongqing401331 China

Reducing carbon emissions in energy systems remains a critical concern, giving rise to a variety of low-carbon initiatives. Current research largely focuses on the application of individual or dual low-carbon technologies (such as carbon capture, carbon trading, renewable energy, etc.), with limited exploration of the synergistic effects of integrating multiple technologies. To address this, we propose a low-carbon economic dispatch model for an electricity-gas system designed to maintain system stability under extreme conditions in renewable energy scenarios. This study integrates several low-carbon technologies—including carbon capture systems, carbon trading mechanisms, demand response strategies, power-to-gas technology, and renewable energy generation—and examines their collective impact on the system. To address uncertainties in renewable energy output, a two-stage robust optimization framework with a min-max-min structure is developed, aiming to determine optimal renewable energy scheduling under extreme scenarios. By employing a column-and-constraint generation algorithm and the KKT conditions, the problem is decomposed into a primary mixed-integer linear programming problem and a series of subproblems, which are solved iteratively to achieve the optimal solution. Simulation results verify the proposed model's effectiveness. © 2025, The Authors. All rights reserved.

关键词： Carbon capture and utilization

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Satellite Image–Based Ecosystem Monitoring with Sustainable Agriculture Analysis Using Machine Learning Model

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Remote Sensing in Earth Systems sciences 2024年第4期7卷 764-773页

作者： Mulakaledu, Ajjanna Swathi, Baswaraju Jadhav, Makarand Mohan Shukri, Shakeerah Mohd Bakka, Vinod Jangir, Pradeep Department of English Koneru Lakshmaiah Education Foundation AP Vaddeswaram India Department of Computer Science and Engineering (Data Science) New Horizon College of Engineering Bengaluru India Department of Electronics and Telecommunication NBN Sinhgad Technical Institutes Campus Pune 411041 India Management and Science University Selangor Shah Alam Malaysia Koneru Lakshmaiah Education Foundation AP Vaddeswaram India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai 602 105 India Applied Science Research Center Applied Science Private University Amman Jordan

Understanding the variations in soil fertility and crop growth across time and geography is crucial for understanding the agricultural environment. Satellite and unmanned aerial remote sensing are the two main types of remote sensing methods used in agroecosystem monitoring. Through the collection of remote sensing photos, it is able to monitor as well as control agro-ecosystem environment in real-time. Spatial choice constraints are lessened by high-decision satellites, and satellite imagery processing is improved by use of artificial intelligence (ai) and machine learning methods, which enables computerised crop identification, disease diagnosis, and yield calculation. This research proposes novel technique in satellite image–based sustainable agriculture analysis with ecosystem monitoring using machine learning techniques. Here, the input is collected as satellite image and processed for noise removal and normalisation. Then, this image feature has been selected for analysis of crop growth using Gaussian belief kernel component analysis and classified using recursive Bayes probabilistic vector neural networks. Experimental analysis has been carried out in terms of training accuracy, precision, sensitivity, F-1 score, and AUC for various satellite image dataset. Proposed technique attained precision of 94%, sensitivity of 96%, AUC of 93%, training accuracy of 97%, and F1-score of 95%. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Crop Growth Analysis Ecosystem Monitoring Machine Learning Techniques Satellite Image Sustainable Agriculture Analysis

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Enhancing the Prediction of Breast Cancer Progression Through Multi-modal data Transformation

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Cognitive Computation 2025年第3期17卷 1-18页

作者： Abdullakutty, Faseela Akbari, Younes Al-Maadeed, Somaya Bouridane, Ahmed Rifat Hamoudi, Rifat Department of Computer Science and Engineering Qatar University Doha Qatar Center for Data Analytics and Cybernetics University of Sharjah Sharjah United Arab Emirates Research Institute for Medical and Health Sciences University of Sharjah Sharjah United Arab Emirates Clinical Sciences Department College of Medicine University of Sharjah Sharjah United Arab Emirates BIMAI-Lab Biomedically Informed Artificial Intelligence Laboratory University of Sharjah Sharjah United Arab Emirates

The ability to predict breast cancer metastases is essential for making effective clinical decisions and managing patients. Traditional models predominantly rely on structured clinical data, which often lacks essential contextual details, limiting their predictive accuracy. In order to address this limitation, a multi-modal approach is introduced in which structured data is transformed into unstructured text, while contextual richness is preserved. Using this text, synthetic images are generated across three key diagnostic modalities, histopathology, mammography, and ultrasound, to enhance predictive capabilities. Based on converted text data, a pre-trained diffusion model was used to generate synthetic medical images in histopathology, mammography, and ultrasound modalities. The impact of a variety of text description variants on image quality and metastasis prediction was assessed. Comprehensive tumor descriptions or a combination of histological type and differentiation status were the most effective generation strategies. A comparison was conducted between three prediction approaches: a unimodal approach, an early fusion approach based on concatenation, and the Multi Co-Guided Attention (MCGA) approach. Through mutual attention, MCGA enhances feature alignment by addressing inter- and intra-modal heterogeneity and capturing complex relationships. Unimodal and multi-modal methods were evaluated with the application of SMOTE to mitigate the impact of data imbalance. Multi-modal fusion significantly outperforms unimodal methods, especially when class imbalances are mitigated by using SMOTE. When SMOTE was applied, Ultrasound+BERT achieved the highest level of accuracy (0.90), followed by Histopathology+BERT (0.88), and Mammogram+BERT (0.88). As compared to early fusion, MCGA demonstrated better class balance and improved minority class detection. Incorporating unstructured text with synthetic imaging modalities improves the accuracy of metastasis prediction by pres

关键词： Modal analysis

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Identification of Key Genes as Potential Drug Targets for Gastric Cancer

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Tsinghua science and Technology 2023年第4期28卷 649-664页

作者： Md.Tofazzal Hossain Md.Selim Reza Yin Peng Shengzhong Feng Yanjie Wei Center for High Performance Computing Joint Engineering Research Center for Health Big Data Intelligent Analysis TechnologyShenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China School of Computer Science and Technology University of Chinese Academy of SciencesBeijing 100049China Department of Statistics Bangabandhu Sheikh Mujibur Rahman Science and Technology UniversityGopalganj 8100Bangladesh Guangdong Provincial Key Laboratory for Genome Stability and Disease Prevention and Regional Immunity and Diseases Department of PathologyShenzhen UniversityShenzhen 518060China

Gastric cancer(GC)is one of the most common cancers and ranks the third in cancer mortality all over the *** goal of this study was to identify potential hub-genes,highlighting their functions,signaling pathways,and candidate drugs for the treatment of GC *** used publicly available next generation sequencing(NGS)data to identify differentially expressed(DE)*** top DE genes were mapped to STRING database to construct the protein-protein interaction(PPI)network and top hub genes were selected for further *** found a total of 1555 DE genes with 870 upregulated and 685 downregulated genes in *** selected the top 400(200 upregulated and 200 downregulated)genes to construct a PPI network and extracted the top 15 hub *** gene ontology(GO)term and kyoto encyclopedia of genes and genomes(KEGG)pathway enrichment analyses of the 15 hub genes exposed some important functions and signaling pathways that were significantly associated with GC *** survival analysis of the hub genes disclosed that the lower expressions of the three hub genes CDH2,COL4A1,and COL5A2 were associated with better survival of GC *** three genes might be the candidate biomarkers for the diagnosis and treatment of ***,we considered 3 key proteins(genomic biomarkers)(COL4A1,CDH2,and CO5A2)as the drug target proteins(receptors),performed their docking analysis with the 102 meta-drug agents,and found Everolimus,Docetaxel,Lanreotide,Venetoclax,Temsirolimus,and Nilotinib as the top ranked 6 candidate drugs with respect to our proposed target proteins for the treatment against GC ***,the proposed drugs might play vital role for the treatment against GC patients.

关键词： gastric cancer hub genes candidate genes molecular docking candidate drugs

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Federated privacy computing based on oblivious transfer in financial applications: A case study in credit risk management 3

Federated privacy computing based on oblivious transfer in f...

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3rd International Symposium on computer applications and Information Systems, ISCaiS 2024

作者： Liang, Jian Quan, Fan Yang, Suyi Tao, Ping Ge, Fei Li, Hualin School of Software and Artificial Intelligence Chongqing Institute of Engineering Chongqing China Intelligent Application of Financial Big Data Chongqing Colleges Universities Engineering Research Center Chongqing China Shu Yi Xin Credit Management Co LTD Chongqing China School of Information and Communication Studies University College Dublin Dublin Ireland Department of Statistics and Data Science University of California Los Angeles United States

ISBN: (纸本)9781510681316

Invoice data is critical and highly sensitive for enterprises, yet it stands as one of the most extensively used data categories in the financial credit sector. Participants in the credit market, engaged in corporate lending activities, face the dual challenge of ensuring the security of enterprise invoice data and safeguarding the integrity of institutional rules. Traditional data computation and encryption present a dilemma, as two encrypted datasets cannot directly engage in calculations, and two datasets participating in calculations must be in plaintext. This necessitates the convergence of enterprise data and institutional rules for computation. Consequently, two sets of data must be exposed to one of the parties or a mutually trusted third party. In practice, identifying a genuinely trusted third party proves to be a formidable task. This paper introduces a federated privacy computing method based on an oblivious transfer algorithm in the context of risk control admission rules within the credit risk management field. This approach enables both parties holding data to determine admission rules without disclosing their own data. © 2024 SPIE.

关键词： Risk management

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