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

作者： Kumar, e. Saravana Ramamoorthy, Raghu Kesavan, Selvaraj Shobha, T. Patil, Seema Vighneshwari, B. The Oxford College of Engineering Department of Computer Science and Engineering Bengaluru India Dxc Technology Bengaluru India B.M.S.C.E. Department of Ise Bengaluru India B.M.S.C.E. Department of Cse Bengaluru India The Oxford College Engineering Department of Electrical and Electronics Engineering Bengaluru India

ISBN: (纸本)9789380544519

The advent of digital transformation has revolutionized the way businesses operate. Applications have become the focal point of this transformation, shifting the focus from being organization-centric to user-centric. To realize the full potential of businesses, high-quality, secure, and agile applications are essential. Containers are a cutting-edge invention in the world of virtualization, gaining im.e.se popularity in recent years. They have replaced traditional business continuity solutions and are now used to address highly demanding business needs. multiple containers and orchestration frameworks are available, both as standalone and cloud-based services. However, developers and industry experts face challenges in identifying and evaluating the appropriate containers and orchestration frameworks for their application needs. The selected tools may not always be feasible due to a lack of available features, inability to provide agility, and lack of platform support. When applications are deployed across multiple containers, coordination and management among container clusters become critical. Since containers play a pivotal role in edge deployment, cloud-native, continuous integration, and continuous deployment, it is vital to have centralized orchestration for proper re-source management and scheduling. This paper discusses and compares emerging container platforms and cloud-centric orchestration frameworks, highlighting the challenges involved. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Cloud Containers Digital transformation framework Orchestration

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A comparative analysis of paddy crop biotic stress classification using pre-trained deep neural networks

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Artificial Intelligence in Agriculture 2022年第1期6卷 167-175页

作者： Naveen N.malvade Rajesh Yakkundimath Girish Saunshi mahantesh C.elemmi Parashuram Baraki Department of Information Science and Engineering Smt.Kamala and Sri.Venkappa M.Agadi College of Engineering and TechnologyLakshmeshwar582116KarnatakaIndia Department of Computer Science and Engineering K.L.E.Institute of TechnologyHubballi580030KarnatakaIndia Department of Computer Science and Engineering Navkis College of EngineeringHassan573217KarnatakaIndia Department of Computer Science and Engineering Smt.Kamala and Sri.Venkappa M.Agadi College of Engineering and TechnologyLakshmeshwar582116KarnatakaIndia Visvesvaraya Technological University Belagavi 590018KarnatakaIndia

The agriculture sector is no exception to the widespread usage of deep learning tools and *** this paper,an automated detection method on the basis of pre-trained Convolutional Neural Network(CNN)models is proposed to identify and classify paddy crop biotic stresses from the field *** proposed work also provides the empirical comparison among the leading CNN models with transfer learning from the ImageNet weights namely,Inception-V3,VGG-16,ResNet-50,DenseNet-121 and *** spot,hispa,and leaf blast,three of the most common and destructive paddy crop biotic stresses that occur during the flowering and ripening growth stages are considered for the *** experimental results reveal that the ResNet-50 model achieves the highest average paddy crop stress classification accuracy of 92.61%outperforming the other considered CNN *** study explores the feasibility of CNN models for the paddy crop stress identification as well as the applicability of automated methods to non-experts.

关键词： Paddy crop Stress classification Biotic stress PlantVillage ImageNet Pre-trained CNN models

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enhancing Prostate Cancer Diagnosis with Deep Learning: A Study Using mpmRI Segmentation and Classification

Enhancing Prostate Cancer Diagnosis with Deep Learning: A St...

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Control Instrumentation System Conference, CISCON 2023

作者： Gavade, Anil B. Kanwal, Neel Gavade, Priyanka A. Nerli, Rajendra Department of E&C KLS Gogte Institute of Technology Belagavi590008 India Department of Electrical Engineering and Computer Science University of Stavanger Stavanger4036 Norway Department of Computer Science and Engineering Dr. M.S. Sheshgiri College of Engineering and Technology Belagavi590008 India Department of Urology JN Medical College Belagavi590008 India

ISBN: (纸本)9789819758654

Prostate cancer (PCa) is a severe disease among men globally. It is important to identify PCa early and do a precise diagnosis for effective treatment. For PCa diagnosis, multi-parametric magnetic resonance imaging (mpmRI) emerged as an invaluable imaging modality that offers a precise anatomical view of the prostate gland and its tissue structure. Deep learning (DL) models can enhance existing clinical systems and improve patient care by locating regions of interest for physicians. Recently, DL techniques have been employed to develop a pipeline for segmenting and classifying different cancer types. These studies show that DL can be used to increase diagnostic precision and give objective results without variability. This work uses well-known DL models for the classification and segmentation of mpmRI images to detect PCa. Our implementation involves four pipelines;Semantic DeepSegNet with ResNet-50, DeepSegNet with recurrent neural network (RNN), U-Net with RNN, and U-Net with a long short-term memory (LSTm). each segmentation model is paired with a different classifier to evaluate the performance using different metrics. The results of our experiments show that the pipeline that uses the combination of U-Net and the LSTm model outperforms all other combinations, excelling in both segmentation and classification tasks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Urology

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A multimodal Content moderation System using Adversarial machine Learning 3

A Multimodal Content Moderation System using Adversarial Mac...

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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Barveen, A. mohamed Faizal, m.K. Geetha, S. M.I.E.T. Engineering College Department of Artificial Intelligence and Data Science Tamilnadu Trichy India K.Ramakrishnan College of Engineering Department of Artificial Intelligence and Data Science Tamilnadu Trichy India University College of Engineering Department of Computer Applications Tamilnadu Trichy India

ISBN: (纸本)9798350375190

memes are a visual representation of images with text em.e.ded in them that conveys the thoughts and feelings of a peculiar audience and is mainly intended to elicit humor. It is widely spread across social media platforms in the guise of sardonic images, humorous jokes, and several other viral sensations. After the profound success of the late fusion technique that combines multimodal features, researchers have used stacked LSTm to extract textual features, whereas VGG16 has been used to extract visual features. Apart from this, BiLSTm and CNN have been used to extract contextual features. This study suggests a unique deep learning-based method for classifying objectionable memes within a multimodal dataset to address this problem. The proposed system uses machine learning to train the classifier on labeled data, which allows it to categorize the information accurately. The proposed system uses both text and image data. To improve user experience and safety, offensive images are blurred, and offensive words are shuffled using adversarial training. © 2024 Ieee.

关键词： Image enhancement

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Design of a Smart energy meter and monitoring System for Industrial and Residential Use Based on the Internet of Things

Design of a Smart Energy Meter and Monitoring System for Ind...

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2023 International Conference on System, Computation, Automation and Networking, ICSCAN 2023

作者： moorthy, Vijai meyyappan muthukumaran mansingh, Benson manoj, Britto Sateesh Kumar Reddy, C. elango, Arul Vignan Foundation of Science Research and Technology Department of Advanced Computer Science and Engineering Andhra Pradesh Guntur India M. I. E. T. Engineering College Department of Electrical and Electronics Engineering Tamilnadu Trichy India

ISBN: (纸本)9798350315127

electronic meters have become increasingly important for efficient energy use, and the proposed research aims to develop an IoT-based architecture for metering and monitoring. This smart monitoring and control system (SmACS) for home appliances is suggested to enhance household electricity control. The technology uses a wireless control module with an eSP8266 microcontroller and Wi-Fi channel to monitor current consumption and send measured amounts over Wi-Fi. The system calculates current consumption, counts units, and displays results on the user's website and smartphone. It avoids using traditional analog and digital meter reading techniques and allows remote access to electronic meters. The proposed work offers lower total costs, greater energy savings, and reduced time and labour requirements. © 2023 Ieee.

关键词： Domestic appliances

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Poster: Hardware Security for IoT Wireless Network

Poster: Hardware Security for IoT Wireless Network

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Ieee Conference on Network Softwarization (NetSoft)

作者： Uday Kiran Sunkara Abdulquadri Abiru Kyle R morman Aaron Costin Department of Electrical and Computer Engineering University of Florida Gainesville FL USA M.E. Rinker Sr. School of Construction Management University of Florida Gainesville FL USA

ISBN: (数字)9798350369588

ISBN: (纸本)9798350369595

This composition describes an Internet of Things (IoT) system designed for applications typical to the construction industry, focusing on the protection of IoT devices against hardware attacks. An experimental test bed was designed, consisting of a heterogeneous set of sensors on five wireless nodes. The main goals of this work are to test the network’s functionality and performance before and after incorporation of a hardware security module. Observations were made regarding how the hardware security enhancements impacted the network’s efficiency. Specifically, the integration of the Hardware Security module (HSm) has resulted in a 6.08% reduction in network latency; a 5.97% reduction in network connection time; and most notably, a reduction in encryption and decryption delay of 44.69% and 57.17%, respectively, over related techniques.

关键词： Performance evaluation Wireless sensor networks Hardware security Wireless networks Logic gates Sensors Internet of Things

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Investigation of Graph Neural Networks for Instance Segmentation of Industrial Point Cloud Data 16th

Investigation of Graph Neural Networks for Instance Segmenta...

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16th International Conference on Learning and Intelligent Optimization, LION 16 2022

作者： Jalui, Sandeep Agapaki, evangelia Electrical and Computer Engineering Herbert Wertheim College of Engineering University of Florida GainesvilleFL32611 United States Construction Management M.E. Rinker Sr. School of Construction Management University of Florida GainesvilleFL32603 United States

ISBN: (纸本)9783031248658

The concept of Digital Twins (DTs) was introduced 15 years ago. There have been many research methodologies and software to implement DTs in different industries including manufacturing, construction, product design, and other fields. DTs help quantify operational risks, improve production time, assist predictive maintenance, enable real-time remote monitoring and thus better financial decision-making. The generation of DTs for existing industrial sites necessitates the use of laser scanners for the acquisition of point cloud data that capture the existing (as-is) conditions. Currently, human modelers manually segment point cloud data by overlaying 3D CAD models on top of the laser scans or validating laser scanned point clouds with 2D documentation and drawings. Our previous work has achieved effective point cloud processing with techniques such as instance segmentation and class segmentation of the collected and registered industrial point cloud data. Instance Segmentation is an important method of clearly partitioning each object to a human-understandable point cluster in complex laser scanned data, creating a Geometric Digital Twin of Industrial Facilities. The industrial point cloud data consists of pipes, valves, cylinders, and various other combinations of geometric shapes. Segmenting such data is a difficult task as the data is too complex to visualize and understand. In our previous work, CLOI-NeT, which is the state-of-the-art architecture for instance segmentation of industrial point clouds, achieves instance segmentation with average accuracy of 70% using graph connectivity algorithms. This proved that there is scope for more accurate instance segmentation of complex industrial point cloud data with a focus on identifying topological connectivity between components of the point cloud (e.g., a piping network). Also, there have been many research methods, for instance segmentation in city-scale and indoor environments classifying cars, people, buildings, t

关键词： computer vision

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Requirements engineering Practices model to Incorporate 'Power' Human Value 12

Requirements Engineering Practices Model to Incorporate 'Pow...

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12th International Workshop on Quantitative Approaches to Software Quality, QuASoQ 2024

作者： minhas, Nasir mehmood Iqbal, Javed Khan, Saif Ur Rehman School of Innovation Design and Engineering Mälardalen University Västerås Sweden Department of Computer Science COMSATS University Islamabad Pakistan Department of Computing Shifa Tameer-e-Millat University Islamabad Pakistan

Integrating human values into software development, particularly in Requirements engineering (Re), is essential for ensuring systems' alignment with stakeholder concerns, yet this area has not received due attention in research. To bridge this gap,this study presents a model based on Schwartz's value framework, focusing on incorporating the human value of’Power’ into the Re process. This model addresses five sub-values: preserving public image, social power, social recognition, authority, and wealth. Leveraging two focus-group sessions, the model integrates the sub-values of Power using 53 Re practices, 48 identified through a literature review and five from a survey with practitioners. These practices operationalize the sub-values ensuring that critical aspects like public image, recognition, and authority are addressed within the Re process. Preliminary evaluations by an expert panel indicate that the model is comprehensive, practical, and effective. Further validation through real-world testing will assess its impact on Re practices and outcomes. © 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Software design

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Leveraging Deep Autoencoders for Security in Big Data Framework: An Unsupervised Cloud Computing Approach 2

Leveraging Deep Autoencoders for Security in Big Data Framew...

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2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Roy, Sourav Nijaguna, G.S. Shankar, Smitha Sharath Adnan, myasar mundher Um.e.wari, P. Department of Electronics and Communication Engineering Bangalore India S.E.A. College of Engineering and Technology Department of Information Science and Engineering Bangalore India Nitte Meenakshi Institute of Technology Dept of Ece Bengaluru India The Islamic University Najaf Iraq R.M.K. Engineering College Department of Computer Science and Business Systems Tamil Nadu Kavaraipettai India

ISBN: (纸本)9798350382952

Abnormalities recognition in bank transaction big data is the num.e. one issue for stability of financial security system. Due to the rate digital transactions are increasing it is vital to have effective ways. encryption with deep autoencoder model should be explored as it involves trained neural networks that learn such patterns from the complex transaction data. The following paper demonstrates application of anomaly detection using deep autoencoders in the banking big data transactions. It focuses on the theoretical bases, network design, preparedness and the testing measures for deep autoencoders. On the other hand, it solves problems such as high dimensionality and imbalanced dataset. This research paper shows deep autoencoders effectiveness in deep learning and how the network identifies different fraudulent big data transactions, money laundry and unauthorized access. It also encompasses recent developments of cloud environments and future methods using deep autoencoders including the fact that constant search for new possible solutions is a must. The insights delivered contribute to the discourse in financial security community, which incorporates researchers, practitioners, and policymakers involved in anomaly detection in cloud. © 2024 Ieee.

关键词： Cryptography

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Continuous monitoring of Web Server Assaults Using machine Learning

Continuous Monitoring of Web Server Assaults Using Machine L...

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emerging Trends in Information Technology and engineering (ic-eTITe), Conference on

作者： Vishali m mirudhula A Priya A m Iswarya Kavitha Subramanian M.E Computer Science and Engineering Panimalar Engineering College Chennai Tamil Nadu India Dept of Computer Science and Engineering Panimalar Engineering College Chennai Tamil Nadu India

Because of the widespread accessibility of the weband its ease of use, every organisation nowadays uses web apps to expand its company. With the growth of Due to growing online usage, attack riskhas grown as well. An effective monitoring tools that can recognise these threats Timely is necessary. Java script, PHP, and HTmL are utilisedfor website development and database use of SQL management in depth. most typical web server assaults include XSS, DOS (Denial of Service), and SQL injection Cascading Style Sheets. IDS with rules work with keywords and patterns but fail to recognise unidentified assaults. In this article, mL based model is used for leveraging web server logsto identify intrusions. It identifies the sort of assault as well as if a given log is normal or malicious. By establishing a private network with the aid of an Apache WAmP server, web server logs are produced and gathered.

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