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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Sravanthi, Jakkula Reddy, Chada Sampath Mahendar, A. Kumar, V. Ravi Buragadda, Swathi Ghantasala, G. S. Pradeep Gupta, Gaurav Vnr Vignana Jyoti Institute of Engineering and Technology Department of Information Technology Telangana India Sree Chaitanya Institute of Technological Sciences Department of Computer Science Engineering Telangana Karimnagar India Telangana Hyderabad India Ace Engineering College Department of Computer Science Engineering Ghatkesar India Lakireddy Balireddy College of Engineering Department of Computer Science Engineering Kakinada India Alliance University Department of Computer Science and Engineering Bengaluru India Shoolini University Yogananda School of Artificial Intelligence Computers and Data Sciences Solan India

ISBN: (纸本)9789380544519

This paper discusses the significance of Machine Learning (ML) and Deep Learning (DL) techniques for structured and unstructured healthcare data. As healthcare data is increasing tremendously, it is difficult to identify hidden patterns in huge amounts of data. DL handles a massive amount of clinical data and provides better outcomes. A novel competitive ensemble deep learning model has been proposed to improve the classification performance of structured data. However, dealing with unstructured data, the proposed work highlights a competitive DL model for Twitter sentiment analysis. In addition, this paper discusses the proposed Competitive Ensemble Deep Learning (CEPL) algorithm for text data. The proposed model is compared with a traditional model to evaluate the model's performance in the range of 0.2%-0.5%. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Accuracy CEPL Deep Learning Healthcare data Machine learning Methods

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The influence of culture on creativity in ideation: a review

The influence of culture on creativity in ideation: a review

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2024 International design Society Conference, design 2024

作者： Gong, Zhengya Gonçalves, Milene Nanjappan, Vijayakumar Georgiev, Georgi V. Center for Ubiquitous Computing University of Oulu Finland Department of Design Organisation and Strategy Faculty of Industrial Design Engineering Delft University of Technology Netherlands School of Computer Science and Information Technology University College Cork Ireland

The relationship between culture and creativity has sparked the interest of researchers for decades. Although researchers have attempted to establish a connection between culture and creativity, the precise relationship between the two remains ambiguous. The current paper examined extant literature on the subject matter and synthesized the relations between culture and creativity in ideation over the past twenty years. The present study expounds upon the utilized samples, measures implemented to assess creativity and culture, and the study results. © 2024 Proceedings of the design Society. All rights reserved.

关键词： Behavioral research

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Efficient Software Vulnerability Detection with Minimal Data Size in 5G-IoT 1

Efficient Software Vulnerability Detection with Minimal Data...

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1st IEEE International Conference on Emerging Research in Computational Science, ICERCS 2023

作者： Kushwaha, Sumit Asha, V. Kumar, B Santhosh Singh, Navdeep Prabagar, S. Supriya, B Yamini University Institute of Computing Chandigarh University Department of Computer Applications Punjab India New Horizon College of Engineering Department of Computer Applications Bangalore India Institute of Aeronautical Engineering Hyderabad India Lovely Professional University Phagwara India Alliance University Alliance College of Engineering and Design Department of Computer Science and Engineering Karnataka Bangalore562106 India Koneru Lakshmaiah Education Foundation Department of Cse AP Vaddeswaram India

ISBN: (纸本)9798350359756

This research addresses the security challenges arising from the widespread implementation of Internet of Things (IoT) networks in the industry, a situation exacerbated by the integration of artificial intelligence (AI) and 5G mobile communication technologies. While these IoT networks enhance industrial efficiency and convenience, their vulnerability to security breaches is heightened due to nonstandard protocol stacks and open-source software that lacks thorough vetting. Traditional AI-based vulnerability detection methods, with their high memory and computational demands, are unsuitable for IoT applications. In response, this study introduces a novel approach utilizing the Sampling Threshold Optimization Strategy. This method maximizes the training data size while preserving learning accuracy, enabling swift identification of software vulnerabilities in IoT systems. Experiments conducted on a software vulnerability classification dataset reveal that the effectiveness of the models is not significantly impacted by variations in ideal data sizes. Notably, the research establishes the minimum amount of data required to train a model without compromising performance. For instance, employing the random forest model with the sampling threshold optimization technique resulted in a 97.9% improvement in latency and conserved 85.2% of memory, 1% of the data while still achieving learning accuracy that is on par with 100% of the data. This innovative method provides a practical means to enhance the security of IoT networks without the need for extensive processing power. © 2023 IEEE.

关键词： Internet of things

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Efficient Route Analysis and Content Delivery in IoT-Fog Networks with ETLO 7

Efficient Route Analysis and Content Delivery in IoT-Fog Net...

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7th International Conference on Image Information Processing, ICIIP 2023

作者： Radhika, M. Jayavadivel, R. Prabhu, M. Ramkumar Kumar, Vijay Pardey, Minal A. Anil Kumar, N. Bharathiyar Institute of Engineering For Women Department of Ece Tamilnadu Salem India Alliance University Alliance College of Engineering And Design Department of Computer Science And Engineering Karnataka Bangalore562106 India Saveetha Institute of Medical And Technical Sciences Saveetha School of Engineering Department of Ece Tamil Nadu Chennai India Graphic Era Hill University Dehradun Department of Physics India Jagadambha College of Engineering & Technology Department of Computer Engineering Maharashtra Yavatmal India Mohan Babu University School of Engineering And Technology Department of Electronics And Communication Engineering Andhra Pradesh Tirupati India

ISBN: (纸本)9798350371406

Efficient content delivery in fog computing networks is crucial for reducing the internet access points and long-distance cloud network traffic. Optimizing route analysis for forwarding content using cache is a difficult task because fog networks are dynamic and the need to minimize computation and source overhead. In this study, we discourse the route research problem in fog computing networks with an optimised technique called Enhanced Teaching Learning Optimisation (ETLO). The ETLO algorithm is made to reflect system conditions as accurately as possible and achieve optimal coverage while minimizing the number of iterations and resource overhead. Through extensive simulations and comparisons with existing Teaching Learning Based Optimization (TLBO) and Simulated Annealing (SA) algorithms, we demonstrate that ETLO outperforms TLBO in terms of memory overhead and network size needed to distribute material to far-off places. Moreover, ETLO exhibits better execution time, memory overhead, and scalability with respect to network size compared to SA. The analysis reveals that the moderation rate plays a critical part in determining the amount of memory and execution time, with an inverse proportional relationship. The average memory usage of ETLO is found to be 0.186% less than TLBO, while TLBO performs slightly better in execution time. Thereby, the proposed ETLO algorithm significantly enhances the efficiency of content delivery in fog computing networks, leading to an optimized and resource-efficient routing process. The findings underscore the potential of ETLO in improving network performance and content caching strategies, thereby contributing to the advancement of fog computing technologies. © 2023 IEEE.

关键词： Fog computing

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Machine learning with Monarch Butterfly Optimization for Prediction of Emergency Patient Admission Status 5

Machine learning with Monarch Butterfly Optimization for Pre...

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5th IEEE International Conference on Electrical, computer and Communication Technologies, ICECCT 2023

作者： Vijayarangam, S. Vasundhara, S. Beherac, Nihar Ranjan Das, Shyamali Chandre, Shanker Rajagopal, R. Sri Indu College of Engineering and Technology Department of Computer Science and Engineering Telangana Hyderabad India G.Narayanamma Institute of Technology and Science Department of Humanities and Mathematics Telangana Hyderabad India Swiss School of Business and Management Geneva Petit-Lancy1213 Switzerland CMR Institute of Technology AECS Layout Department of Artificial Intelligence and Machine Learning Karnataka Bangalore India SR University Department of Computer Science & Artificial Intelligence Telangana Warangal India Alliance College of Engineering & Design Department of Computer Science & Engineering Alliance University Karnataka Bengaluru India

ISBN: (纸本)9781665493604

Emergency admission is one of the most important resources of healthcare expenditure. The accessibility of predictive methods is support to recognize the admission status of arriving patients and the patient mix that supports managing downstream resources and decreasing overpopulation at emergency department (ED) by restriction boarding delays. In recent times, machine learning (ML) techniques are well enhanced and executed in several applications like health protection, cancer detection, and hospital operations. But, most difficult problem with ML is that each technique has parameter and without optimizing the parameter, attaining a higher accuracy method develops very complex. This study introduces Machine learning with Monarch Butterfly Optimization for Prediction of Emergency Patient Admission Status (MLMBO-PEP A) technique. The presented MLMBO-PEP A technique can predict the admission status of the emergency patient. In the presented MLMBO-PEP A technique, data normalization is initially performed to scale the medical data. For predictive process, the MLMBO-PEP A technique uses Dendritic Neuron Model (DRN). The MBO technique is used to optimize the forecasting outcomes of the DRN model. A widespread simulation study is performed for ensuring the better performance of the MLMBO-PEP A technique. The resultant values implied the improved efficacy of the MLMBO-PEP A technique under different evaluation metrics. © 2023 IEEE.

关键词： Forecasting

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Novel soft robotic finger model driven by electrohydrodynamic(EHD)pump

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Journal of Zhejiang University-Science A(Applied Physics & engineering) 2024年第7期25卷 596-604页

作者： Xuehang BAI Yanhong PENG Dongze LI Zhuochao LIU Zebing MAO Department of Mechanical Automotive and Materials EngineeringFaculty of EngineeringUniversity of WindsorWindsorN9B 3P4Canada Department of Information and Communication Engineering Graduate School of EngineeringNagoya UniversityNagoya464-8601Japan Department of Intelligent Science and Technology College of Computer Science and TechnologyQingdao UniversityQingdao266071China Design and Research Institute of University of Science and Technology Beijing Co.Ltd. Beijing100083China Faculty of Engineering Yamaguchi UniversityYamaguchi755-8611Japan

1Introduction In the field of robotic-human interactions,soft robotics offers enhanced safety and adaptability.A major challenge in this area is the integration of soft actuators with pump systems,which often increases the system volume and *** study presents the development and testing of a robotic finger powered by electrohydrodynamic(EHD)*** leveraging the electric field-induced flow of dielectric fluids.

关键词： robotic complexity. pump

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Next Generation Sequencing Data Platform Optimization Framework 15

Next Generation Sequencing Data Platform Optimization Framew...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Maruthi, P.Baby Yamuna, R. Kumar, Rajath P. Nair, Rekha R. Mohan Babu University Tirupati India Vemu Institute of Technology Chittoor India Upgrad Education Pvt.Ltd. CAE Simulations Pvt.Ltd. India Department of CSE Alliance College of Engineering and Design Alliance University Bangalore India

ISBN: (纸本)9798331300579

Next generation sequencing (NGS) technology has recently been introduced into clinical diagnostics, having already profoundly changed the nature and scope of genomic research in previous *** large volume of data generated at relatively low cost makes NGS an ideal platform for comprehensive mutation analysis of both constitutional disorders (*** alterations) and cancer diagnostics (*** alterations).However, with the advent of NGS, clinical laboratories can offer cost-effective comprehensive gene panels for several complex inherited *** main aim of this research paper is to process and analyze large-scale genomic data, enabling valuable insights into the genomic landscape of cancer and facilitating precision oncology initiatives. © Grenze Scientific Society, 2024.

关键词： Genome

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An Evaluation of Machine Learning Techniques for Detecting Banking Frauds

An Evaluation of Machine Learning Techniques for Detecting B...

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Self Sustainable Artificial Intelligence Systems (ICSSAS), International Conference on

作者： R Rajagopal M Senbagavalli Saswati Debnath Deepu K Darshan K K S Varun Tejas Department of Computer Science and Engineering Alliance College of Engineering and Design Alliance University Bangalore India

Credit cards are an essential part of every person's daily life in the modern world. Customers use their credit cards and online transactions to make purchases for their requirements. To reduce the risk of defaulters, banks and other financial institutions consider customer credit applications. Credit risk is the increase in debt owed by a customer who repeatedly misses payments on their bills. The precision and recall measures received greater attention than the metrics for accuracy. Based on the False Negative value of the confusion metrics, logistic regression is the best model after comparison with the precision-recall curve. Additionally, a GUI (Graphical User Interface) was built after altering the logistic regression's threshold value and it successfully forecasted whether a customer was a defaulter or not. It is a problem to categorize clients' credit payment defaulters and non-defaulters. The basic objective of risk prediction is to evaluate each customer's credit risk and reduce it using information from financial statements, customer transactions, and repayment history. The damage and unpredictable nature. Different methods, including logistic regression and decision trees, are used to create risk prediction models.

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Optimizing Drug Discovery for Breast Cancer in a Laboratory Environment Using Machine Learning 9

Optimizing Drug Discovery for Breast Cancer in a Laboratory ...

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9th International Conference on Wireless Communications, Signal Processing and Networking, WiSPNET 2024

作者： Borkhade, Gauri Singh, Jagendra Shelke, Nitin Arvind Upreti, Kamal Kuwar, Vishakha Tiwar, Mohit Bharati Vidyapeeth College of Engineering Navi Mumbai India Alliance School of Advanced Computing Alliance University Bangalore India School of Computer Science Engineering and Technology Bennett University Greater Noida India Delhi-NCR Department of Computer Science Ghaziabad India Dr. D y Patil Vidyapeeth Centre for Online Learning Pune India Bharati Vidyapeeth's College of Engineering Computer Science and Engineering Delhi India

ISBN: (纸本)9798350350845

Breast cancer therapy can be greatly enhanced by the proposed method that combines experimental and computational techniques. Employing a state-of-the-art in vitro system, we evaluated biopsy tissues at different cancer stages, monitoring them for 48 hours. Later on, our investigation involved the application of machine learning models including naïve Bayes (NB), artificial neural networks (ANN), random forest (RF), and decision trees (DT). Surprisingly, these models reached high test accuracies - ANN 93.2%, NB 90.4%, DT 87.8%, and RF 85.9%. The dataset's impedance dynamics data provide evidence for treatment efficacy. Therapeutic strategies need to be adjusted for particular patients and their stage of cancer since the results underscore the usefulness of personalized breast cancer therapy. This study will significantly contribute to new tailored treatment options available for breast cancer patients. © 2024 IEEE.

关键词： Decision trees

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Network-Based Rail Running Band Anomaly Recognition via Recurrent Attention Graphs

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IEEE Transactions on Automation Science and engineering 2025年 22卷 15381-15395页

作者： Ni, Xuefeng Fieguth, Paul W. Ma, Ziji Shi, Bo Liu, Hongli Hunan University College of Electrical and Information Engineering Changsha410012 China University of Waterloo Department of Systems Design Engineering WaterlooONN2L 3G1 Canada The Hong Kong University of Science and Technology Department of Computer Science and Engineering Sai Kung Hong Kong

Anomaly detection for rail running bands, the pattern of wheel-rail contact area, is crucial to analyze composite rail irregularities. This paper presents an all-weather vision-based solution for running-band inspection. However, two major algorithmic challenges restrict inspection effectiveness: 1) the identification of high-quality features for diversified and imbalanced data subject to noise and outliers, and 2) inferring implicit anomaly co-occurrence patterns. We regard overall running-band anomaly detection as a multi-label classification problem and develop a novel deep multi-anomaly recognition network via recurrent attention graphs (RAGRN). © 2004-2012 IEEE.

关键词： Network theory (graphs)

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