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Identification of Key Genetic Drivers in Multidrug Resistance: A Co-Expression Network Analysis of Pseudomonas aeruginosa

Identification of Key Genetic Drivers in Multidrug Resistanc...

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Ambient Intelligence in Health Care (ICAIHC), International Conference on

作者： Sweta Padma Routray Saswati Mahapatra Debasish Swapnesh Kumar Nayak Enketeswara Subudhi Tripti Swarnkar Centre for Biotechnology Siksha ‘O’ Anusandhan (Deemed to be) University Bhubaneswar India Department of Computer Application Siksha ‘O’ Anusandhan (Deemed to be) University Bhubaneswar India Department of Computer Science and Engineering Centurion University of Technology and Management Bhubaneswar India Department of Computer Application National Institute of Technology Raipur India

ISBN: (数字)9798331530259

ISBN: (纸本)9798331530266

Antimicrobial resistance (AMR) in Pseudomonas aeruginosa, a critical pathogen in healthcare-associated infections, demands new insights into underlying genetic mechanisms. This study aimed to identify key regulatory genes contributing to multidrug resistance (MDR) through a comprehensive analysis of gene expression in clinical isolates. RNA-Seq data from 246 clinical isolates of P. aeruginosa was analyzed using Weighted Gene Co-Expression Network Analysis (WGCNA) to construct co-expression networks. Modules of co-expressed genes were identified, and hub genes with high connectivity were selected, focusing on those within and across modules, including the largest module enriched with resistance-related genes. Pathway enrichment was performed using STRING and ShinyGO, highlighting key resistance-associated pathways. Pathway enrichment analysis of hub genes identified polA as a central player in DNA repair and replication processes, crucial for bacterial survival under antibiotic stress. In addition to polA, other highly connected hub genes, such as dnaB, tatC, and ppnK, were enriched in pathways including protein transport and mismatch repair. Within the turquoise module, hub genes like dnaA, uvrD, and gyrA were primarily involved in ATP-binding, ligase activity, and biosynthetic processes, essential for antibiotic resistance. Network analysis further emphasized the centrality of polA and its role in bridging multiple resistance pathways, highlighting its potential as a therapeutic target. This research highlights polA and other critical hub genes as key regulatory elements within the MDR networks of P. aeruginosa, playing essential roles in pathways vital for bacterial survival. The identification of these genes provides promising biomarkers and potential therapeutic targets to counteract MDR in clinical settings.

关键词： Proteins Pathogens Microorganisms Antibiotics Network analyzers Medical services Maintenance engineering Gene expression Stress Immune system

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Integration of AI and Cloud-Native Technologies for Personalized Mobile Banking Experiences

Integration of AI and Cloud-Native Technologies for Personal...

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2024 International Conference on Recent Innovation in Smart and Sustainable Technology, ICRISST 2024

作者： Legapriyadharshini, N. Thirumalaikumari, T. Senbagam, K. Sarasu, R. Basha, H. Anwer Malathi, P. Saveetha Institute of Medical and Technical Sciences Saveetha College of Liberal Arts and Sciences Department of Computer Application Tamil Nadu Chennai India Saveetha College of Liberal Arts and Sciences Simats Deemed to Be University Department of Computer Science Tamil Nadu Chennai India Dhanalakshmi Srinivasan College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Mamallapuram India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Department of Computer Science Saveetha College of Liberal Arts and Sciences Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha College of Liberal Arts and Sciences Department of Data Analytics Tamil Nadu Chennai India

ISBN: (纸本)9798350309140

In today's rapidly evolving digital landscape, using Artificial Intelligence (AI) and Cloud-Native Technologies is essential for improving personalized mobile banking experiences. The limitations and errors of existing financial systems necessitate such integration. Existing systems often struggle to adapt to modern consumers dynamic tastes and expectations, resulting in unsatisfactory customer experiences. Identifying the gap, the proposed system revolutionizes mobile banking applications by leveraging AI algorithms such as machine learning and natural language processing within a microservices-based cloud infrastructure. As a result, the proposed system solves the limitations of outdated architectures by providing personalized and efficient services while addressing security issues. The study indicates considerable gains in terms of user pleasure, system scalability, and security robustness through an in-depth evaluation of the results. The results highlight AI's and Cloud-Native Technologies transformational potential in revolutionizing the landscape of tailored mobile banking experiences. The study provides helpful information to financial institutions looking to adopt innovation and fulfill the evolving needs of modern consumers. © 2024 IEEE.

关键词： Legacy systems

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A Deep Learning Framework for Classifying Autism Spectrum Disorder from fMRI Images

A Deep Learning Framework for Classifying Autism Spectrum Di...

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Cybernation and Computation (CYBERCOM), International Conference on

作者： Rajat Kishor Varshney Alok Katiyar Prashant Johri School of Computing Science & Engineering Galgotias University Greater Noida India School of Computer Application & Technology Galgotias University Greater Noida India

ISBN: (数字)9798331542344

ISBN: (纸本)9798331542351

The objective of this work is to propose a new machine learning based approach for ASD classification from fMRI data employing deep learning architecture and novel preprocessing method. Main datasets included in ABIDE I, ABIDE II, Human Connectome Project (HCP), and Social Functions and Autism Research Interface (SFARI). Many supplements include graph neural networks for brain segmentation, 3D-BRA for accurate mapping of the brain than just using conventional registration technique, EN-CC for feature selection. The approach incorporated graph neural network for segmentation of the brain, 3DBRA mapping that translates and rotates the brain precisely unlike the general technique of registration, EN-CC for selecting the features. The deep learning models under consideration were ResNet-50, VGG-16, Inception-v3, and Efficient Net which were improved through ABSC and MSTC. Classification accuracy of the proposed models was higher than 95% on all the datasets with Efficient Net scoring 99% on the SFARI dataset. This means that only the Stratified Group Cross-Validation (SGCV) was employed to make sure the results would be more reliable. The results present a considerable improvement in both accuracy and the interpretability of the derived connectivity profiles of the ASD-related brain.

关键词： Deep learning Autism Accuracy Three-dimensional displays Functional magnetic resonance imaging Brain modeling Feature extraction Graph neural networks Reliability Testing

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A Unified Framework for Image Caption Generation Using Deep Reinforcement Learning

A Unified Framework for Image Caption Generation Using Deep ...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Ritu Shree T Narmadha Vishakha Joseph Department of Computer Science & Application Vivekananda Global University Jaipur India Department of Computer Science Engineering School of Engineering and Technology JAIN (Deemed to be University) Bangalore Karnataka India Department of Management ARKA JAIN University Jamshedpur Jharkhand India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

This paper introduces a unified framework for picture caption generation with the use of deep reinforcement mastering (RL) that combines convolution neural networks (CNNs) with RL. The paper first introduces a country representation as a remark for RL by means of combining CNN-primarily based photo representation and natural language processing (NLP) natural language representation. Then, the paper describes an RL-based reinforcement mastering (RL-RL) set of rules which uses the statement to generate captions. Eventually, the paper shows the experimental consequences of the proposed RL-RL set of rules on real-world datasets and shows that the RL-RL set of rules outperforms the present RL-NLP and CNN-RL algorithms. The paper additionally demonstrates the very best overall performance on the benchmark dataset COCO with a BLEU score of 0.645 and a METEOR score of 0.348. The proposed framework affords a sensible technique to generate interesting and affordable captions for photographs.

关键词： Knowledge engineering Convolution Neural networks Switches Semisupervised learning Benchmark testing Deep reinforcement learning Natural language processing Meteors Convolutional neural networks

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Towards A Goal-driven Dynamic Configuration approach of Business Process Models 4

Towards A Goal-driven Dynamic Configuration approach of Busi...

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4th IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2021

作者： Liang, Yan Wan, Rui Wen, Zepeng Li, Gongliang China Academy of Engineering Physics Institute of Computer Application Mianyang China Sichuan University College of Computer Science Chengdu China

ISBN: (纸本)9781728185347

Along with the rapid development of business change and innovation, the on-demand dynamic configuration of business process becomes more and more urgent. To address the deviation problem between process models and user demand intention, a goal-driven dynamic business process model configuration method is proposed. Firstly, the explicit expression of user requirement intention is realized by decomposing and formalizing the business objectives layer by layer. Then, according to the goal association rules, the business activities are associated with the corresponding business goals and data objects to realize the dynamic configuration. In addition, the consistency between user requirements and configurable business processes is verified. Experimental results show the proposed method can effectively improve the flexibility of business process models to adjust the rapid business changes. © 2021 IEEE.

关键词： Systems engineering

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A Multi-criteria Decision-Making Approach to Analyze Python Code Smells 1

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International Conference on Advances in Electrical and computer Technologies, ICAECT 2021

作者： Gupta, Aakanshi Sharma, Deepanshu Phulli, Kritika Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Uttar Pradesh Noida India Executive Branch-IT Indian Navy New Delhi India Application Development Associate Accenture India

ISBN: (数字)9789811911118

ISBN: (纸本)9789811911101

Frequent modifications in the software leads to the issues in the code which deteriorates software quality. The suitable approach to rectify it is to refactor it. Consequently, this research devises a ranking using the MCDM approach for prioritizing Python-based code smells. This order ranking has been studied over 10,552 classes for five generic code smells diffused in 20 Python software. The software metrics extracted over these python classes have been considered as multiple criteria which are processed using rule-based machine learning approach. In this study, an approach providing compromised solution—VIKOR have been practiced along with TOPSIS and WASPAS methods. Additionally, the MCDM approaches have utilized Shannon’s Entropy and CRITIC method as weight estimation techniques. The extracted vital software metrics achieved over 90% accuracy. The prominent outcome portrays that the most dominant smell is Collapsible "IF" which needs to be refactored at the early stage. This should be accompanied by Cognitive Complexity smell so as to attain sustainable development environment. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Python

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Dynamic Load Balancing with Task Migration: A Genetic Algorithm Approach for Optimizing Cloud Computing Infrastructure

Dynamic Load Balancing with Task Migration: A Genetic Algori...

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Advancements in Smart, Secure and Intelligent Computing (ASSIC), International Conference on

作者： Aliva Priyadarshini Sateesh Kumar Pradhan Suprava Ranjan Laha Soumen Nayak Bikash Chandra Pattanaik Department of Computer Science & Application Utkal University Bhubaneswar Odisha India Department of Computer Science & Engineering ITER Siksha ‘O’ Anusandhan University Bhubaneswar Odisha India Department of Computer Science & Engineering Gandhi Institute for Education and Technology Affiliated to Biju Patnaik University of Technology Rourkela Odisha India

The advancement of cloud computing is contingent upon the effective establishment of infrastructure and the flexible use of accessible resources. An essential aspect of this evolutionary process is load balancing, a critical task involving fair allocation of dynamic workloads among different nodes. The primary aim is to mitigate the issues of resource overload or underutilization issues, which presents a complex challenge in optimization. A proficient load balancer must be able to adapt its method to the dynamic environment and work demands. This research presents a novel load-balancing technique incorporating task migration using a Genetic Algorithm (GA). The objective is to achieve optimal load distribution within the cloud infrastructure while minimizing the makespan for a given set of tasks. By utilizing the inherent optimization capabilities of genetic algorithms and the flexibility of task migration, the suggested approach demonstrates dynamic adaptation to the constantly evolving demands of cloud computing. In order to evaluate the effectiveness of the plan, it is implemented and assessed using simulations carried out on the Cloud Analyst simulator. The findings from the simulation outcomes, as demonstrated in a representative application scenario, provide evidence for the effectiveness of the task migration-integrated genetic algorithm-based approach compared to existing methods such as First Come, First Serve (FCFS), Round Robin (RR), and the local search algorithm Stochastic Hill Climbing (SHC). This comprehensive method not only improves resource utilization efficiency but also showcases the capacity to mitigate performance limitations through intelligent task reallocation. The proposed technique, which incorporates task migration capabilities, shows potential in facilitating the development of a more efficient and adaptable cloud infrastructure as the cloud landscape undergoes ongoing changes.

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Imaging Detection and Classification for Near-space Dim Targets 7

Imaging Detection and Classification for Near-space Dim Targ...

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7th International Conference on Vision, Image and Signal Processing, ICVISP 2023

作者： Yuan, Yupu Xi, Runping Chen, Qian School of Computer Science Northwestern Polytechnical University Xi'an China Shaanxi Province Key Laboratory of Speech & Image Processing Xi'an China National Engineering Laboratory of Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xi'an China

ISBN: (纸本)9781839539848

The motion mode of near-space targets is complex due to their high threat level. The target imaging faces low SNR and susceptibility to background noise. The existing detection and classification algorithms struggle to meet the demands of performance and speed on such targets. To address these issues, a YOLOv5-based target detection algorithm utilizes CARAFE upsampling and the small detection head to extract internal features and edge information of dim target. The algorithm adeptly captures dim target features, enhancing real-time performance and detection accuracy while overcoming subtle feature granularity and extraction difficulties. Aiming at the problem that there is no existing trajectory classification algorithm in the near-space field, we propose an infrared dim target trajectory classification algorithm. The improved attention module is used to enhance the extraction of motion features, and the target category is accurately identified by the prediction module, improving the capacity to extract dependencies between trajectory fragments. We execute comparative experiments on common infrared dim target dataset, near-space infrared dim target dataset and real dataset to confirm the viability of our methods. The outcomes indicate that our proposed method can be applied to the above three types of dim targets and significantly outperforms state-of-the-art methods. © The Institution of engineering & Technology 2023.

关键词： Extraction

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Using Neural Network-Based Time Series Analysis in Wireless Sensor Networks

Using Neural Network-Based Time Series Analysis in Wireless ...

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Artificial Intelligence and Machine Learning applications Theme: Healthcare and Internet of Things (AIMLA), International Conference on

作者： Manish Kumar Goyal Nagaraj Patil Akash Kumar Bhagat Department of Computer Science & Application Vivekananda Global University Jaipur India Department of Mechanical Engineering School of Engineering and Technology JAIN (Deemed to be University) Bangalore Karnataka India Department of Computer Science & IT ARKA JAIN University Jamshedpur Jharkhand India

ISBN: (数字)9798350349221

ISBN: (纸本)9798350349238

Neural community-primarily based time series analysis (NNTSA) is an effective technique for research records collected from wireless sensor networks (WSNs). NNTSA uses models stimulated by means of biological neural networks to capture complicated time-various sensor statistics. It requires minimum training of the enter facts and offers a high degree of modelling accuracy. This method has turned out to be increasingly famous for WSN facts evaluation when you consider that it is miles appropriate for streaming statistics and seizing complicated patterns that might not be effortlessly detected with the aid of other techniques. NNTSA fashions are commonly carried out the use of supervised gaining knowledge of processes, together with lower back-propagation, wherein education styles are fed to a neural network at the same time as its parameters are adjusted the use of a fixed of desired output values, together with an average temperature over some time. After the NNTSA c section is complete, new patterns can be evaluated with the trained model to analyze their temporal conduct. NNTSA techniques have been applied in diverse areas, inclusive of fitness monitoring, site visitors management, and weather tracking, with suitable result.

关键词： Training Wireless sensor networks Analytical models Biological system modeling Scalability Time series analysis Artificial neural networks

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Email Spam Detection Ensemble Methods

Email Spam Detection Ensemble Methods

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Disruptive Technologies (ICDT), International Conference on

作者： Sudhir Prakash Gond Sudeept Singh Yadav Md Arfat Alam Satyajit Sahoo School of Computer Application and Technology (SCAT) Galgotias University Greater Noida Uttar Pradesh India School of Computer Science and Engineering (SCSE) Galgotias University Greater Noida Uttar Pradesh India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

Finding on-demand messages has become a key task in today's generation of virtual verbal exchanges as emails and unsolicited messages flood inboxes. Spammers maximize email messaging system provisioning, especially for the scope of unwanted content that can dampen net overall performance, staff. Loan the records can catch up, and degrade the user experience. This paper provides a thorough look at using the device for unwanted mail detection methods to identify models. Several styles were observed including Naive Bayes, Support Vector Machine (SVM), Decision Trees, and Random Forest, Voting Classifier, and other ensemble methods. The data set used consisted of 1,00,000 text messages, equally cut into spam and ham (unsolicited mail) between. Our experimental results show that ensemble methods, especially random forest and alternative classification trees, provide the highest accuracy and precision in junk mail detection, achieving an accuracy of 0.97975. The experiment demonstrates the tool's knowledge-enhancing capabilities; it will likely enhance the security of e-mail messages with powerful junk mail detection.

关键词： Support vector machines Accuracy Filtering Unsolicited e-mail Clustering algorithms Feature extraction Classification algorithms Ensemble learning Random forests Postal services

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