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检索条件"任意字段=Proceedings of the 2023 4th International Conference on Machine Learning and Computer Application"

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Analysing BP Disease Prediction through Transfer learning (machine learning) - A Conceptual Framework and Case Study

Analysing BP Disease Prediction through Transfer Learning (m...

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Advance Computing and Innovative Technologies in Engineering (ICACITE), international conference on

作者： Rajendra Singh Bisht Hassan M. Al-Jawahry H. A. Alsailawi Anil Kumar Lamba Dharmesh Dhabliya Abdul Hussein Mohsen Mohammed Y. Al-Khuzaie Ali Abdulkareem Mezaal Department of Computer Science and Engineering Graphic Era Hill University Bhimtal Uttarakhand India Department of computers Techniques engineering College of technical engineering The Islamic university Najaf Iraq Department of Chemistry and Biochemistry College of Medicine University of Kerbala Kerbala Iraq School of Computer Science and Engineering Geeta University Delhi NCR Panipat Vishwakarma Institute of Information Technology Pune India Computer Engineer Department Alkunooze University College Basra Iraq College of Technical Engineering National University of Science and Technology DhiQar Iraq ‘Business Administration Department Kut University College Al kut wasit Iraq

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

In the evolving landscape of the clever healthcare industry, the detection and prognostication of ailments thru technological improvements present each possibility and demanding situations, mainly within the realm of lethal tumors. this paper delves into the application of switch getting to know in the framework of the Internet of Medical things (IoMT) to enhance the accuracy and reliability of disease predictions. Utilizing the modernA capabilities of the Google Net deep device-learning version, we introduce a relaxed IoMT-based totally technique that substantially outperforms conventional methodologies in predicting lethal most cancers ailments. Our have a study demonstrates a incredible accuracy price of 90 seven.5%, underscoring the ability of switch learning to revolutionize predictive diagnostics within the clever healthcare area. By integrating superior IoMT gadgets and secure information protocols, this method no longer most effective improves illness prediction quotes but moreover addresses important privacy and protection issues inherent in modern scientific information handling.

关键词： Privacy Accuracy Protocols Transfer learning Switches Internet of Medical things Reliability

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End-to-End Network Traffic Examination for Network Intrusion Detection using Feature Embedding learning

End-to-End Network Traffic Examination for Network Intrusion...

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Intelligent Technologies (CONIT), international conference on

作者： J Arthy Siddharth D Variar N tharakeshwar R Ashwatha Narayan Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Tamil Nadu India

ISBN: (数字)9798350349900

ISBN: (纸本)9798350349917

the rapid evolution of information sharing and portable device technologies has led to a drastic increase in the number of smart devices, which helps in enhancing the level of comfort and intelligence in every household. It also drives the progress and innovation in different fields, including transportation and healthcare industries. Industrial Control Systems help in the development of society as a whole, by controlling and monitoring the flow of communication between the systems in a industrial network. ICSs have also advanced further due to the presence of smart devices. However, this has also led to a alarming increase in network security concerns. Data security is of the highest priority now. Attacks to the networks present in an ICS also lead to very dangerous outcomes, which may sometimes prove to be fatal. Anomaly detection techniques are mainly used in traditional intrusion detection systems. this is done by detecting any abnormal patterns in the network traffic. To develop an effective and flexible IDS, we are going to examine the Deep Neural Network in this paper. the continuous development of network behaviour and rapid increase in attacks call for development of IDS and evaluating many datasets formed over time using static and dynamic methods. this type of research leads to the identification of the most efficient algorithm for identifying any cyber-attacks in the future. the proposed model achieves higher accuracy than the previous machine learning models.

关键词： Technological innovation machine learning algorithms Industrial control Transportation Network intrusion detection Telecommunication traffic Medical services

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Survivability Prediction from Multiomics Cancerous Data with 1DCNN-LSTM-Attention Mechanism

Survivability Prediction from Multiomics Cancerous Data with...

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international conference on computer, Communication, Control and Information Technology (C3IT)

作者： Ambika Hazarika Ansuman Kumar Anindya Halde Dept.of Computer Application North-Eastern Hill University Meghalaya

ISBN: (数字)9798350389128

ISBN: (纸本)9798350389135

the possibilities for survivability prediction to enhance patient treatment and outcomes is gaining attention in the field of health care. A human being's biological composition can be fully understood by utilizing multiomics data, which combines various biological information from different omics viz., genomics, transcriptomics, proteomics, and metabolomics. Considering the increasing popularity of Deep learning (DL) algorithms as a means for recognizing significant patterns in complex and multi-dimensional information, in this study, we propose a novel method for predicting survivability using multiomics data by utilizing one dimensional convolutional neural network(1DCNN),Long Short-Term Memory (LSTM) networks and attention mechanism. Preprocessing the multiomics data using 1DCNN to extract relevant features and integration of the omics data is the first step in our approach. then, in order to extract the complex patterns and temporal relationships present in multiomics data, we use LSTM networks. Moreover, in order to improve model understanding and concentrate on informative characteristics, we include an attention mechanism into LSTM output. Experimental results of the proposed method when compared to LSTM and other machine learning classifiers utilized here, indicate that LSTM with attention mechanism performs better in terms of accuracy and other performance evaluation measure (precision, recall and F1-score)on three different types of cancerous multiomics data viz., Breast, Kidney, and Lung.

关键词： Attention mechanisms Accuracy Transcriptomics Predictive models Breast Feature extraction Biology Data mining Long short term memory Kidney

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Computing Science, Communication and Security - 4th international conference, COMS2 2023, Revised Selected Papers

Computing Science, Communication and Security - 4th Internat...

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Computing Science, Communication and Security - 4th international conference, COMS2 2023, Revised Selected Papers

ISBN: (纸本)9783031405631

the proceedings contain 20 papers. the special focus in this conference is on Computing Science, Communication and Security. the topics include: A Comparative Study of DWT and DCT Along with AES Techniques for Safety Transmission of Digital Bank Cheque Image;improvement of Network Protocol and Analysis of Security Using Aspect of Cryptography;Gaussian Mixture Model-Based Clustering for Energy Saving in WSN;advancement of Non-coherent Spectrum Sensing Technique in Cognitive Radio Networks - A Simulation-Based Analysis;DEEC Protocol with ACO Based Cluster Head Selection in Wireless Sensor Network;Relational Model and Improvised DSR Assisted Framework for Secure Wireless Communication;Power Optimization for Millimeter Wave MIMO System;An Innovative AI Architecture for Detecting the Primary User in the Spectrum;web Browser Forensics: A Comparative Integrated Approach on Artefacts Acquisition, Evidence Collections and Analysis of Google Chrome, Firefox and Brave Browser;Physical Layer Security Optimisation for NOMA Based UAV Communication for Optimal Resource Allocation;DBFEH: Design of a Deep-learning-Based Bioinspired Model to Improve Feature Extraction Capabilities of Healthcare Device Sets;design and Analysis of Wide-Band Planar Antenna Using Meta-material for S-Band applications;performance Analysis of Smart Antenna in Wireless Communication System;low-Light Video Enhancement on a Mobile Device Using Illumination-Reflection Model Based Retinex Envelope;An Efficient Signal Detection Technique for STBC-OFDM in Fast Fading Channel;achieving Accurate Trajectory Control While Training Legged Robots Using machine learning;Performance Evaluation of VBF, DBR and Modified CARP for Underwater Acoustic Sensor Network;Protecting OT Hosts with Intelligent Model-Based Defense System Against Malware Families.

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Time Complexity in Deep learning Models 4th

Time Complexity in Deep Learning Models

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4th international conference on Innovative Data Communication Technologies and application, ICIDCA 2022

作者： Shah, Bhoomi Bhavsar, Hetal Department of Computer Science and Engineering Faculty of Technology and Engineering The Maharaja Sayajirao University of Baroda Gujarat Vadodara390001 India

the convolution neural network is gaining a lot of popularity in image classification problems nowadays. It has been used in many different classification problems, like medical imaging, handwritten digits, image classification, etc. It is very critical to estimate the time required by the model to achieve the desired task. Earlier studies have used CNN for different perspectives and given the depth of each layer of CNN, but estimating the time and space taken by these layers is missing. this paper's prime idea is to find a CNN model's time complexity. the present work involves computational studies to find the factors that affect the model's performance, the time each layer takes to run, and how it affects the model's overall performance. Time complexity has been discovered on eight different models, varying by the size of filters, number of convolutional layers, number of filters, number of fully connected layers, and kernel size. the result shows that factors like an optimizer, batch size, filter, and neurons greatly impact the time taken by the model. the convolutional layers, max pool, and fully connected layers directly affect the performance of the model. © 2023 the Authors. Published by Elsevier B.V.

关键词： Image classification

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Medicinal Plants Attribute Detection by Deep learning Image Processing Techniques

Medicinal Plants Attribute Detection by Deep learning Image ...

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Communication, Computing and Industry 4.0 (C2I4), international conference on

作者： Chamanth S M. Farida Begam Chandan N S Girish G S Department of Information Science and Engineering CMR Institute of Technology Bengaluru India

the identification of medicinal plant species leads to many benefits to wide range of stakeholders such as, forestry services, taxonomists, doctors, and pharmaceutical labs, organizations that protect endangered species, the government, and the general public. Important plant species are disappearing around the world on a daily basis. Steps include plant identification, restoration, and protection in order to control the phenomenon. Typically, leaves are used to identify plants. Convolutional Neural Network which is the widely used architecture for image base classification motivated us to creating automated methods for classifying various plant species and identify medicinal attributes of the plants. In this paper, using computer vision and machine learning approaches, a fully automated front end based software system has been designed and described for achieving this. Each leaf's length, width, perimeter, area, number of vertices, color, perimeter, and hull area were all just a few of the many features that were taken out. then, from these properties, a number of derived features were calculated. the native people are expected to get more knowledge about medicinal leaves with the use of this work for the automatic recognition of medicinal leaves. Additionally, it helps taxonomists in the development of more effective methods for identifying species, which will greatly help in the preservation of endangered species. the utilization of various plant traits ranges from the most basic, such as leaf color and form, to the most complicated, such as tissue and cell structure are applied. the results obtained with these analytics gives 98% accuracy in 100 epochs.

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RDF-ML: A Proposed SPARQL Tool for machine learning on Semantic Web Data 22

RDF-ML: A Proposed SPARQL Tool for Machine Learning on Seman...

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proceedings of the 4th international conference on Information Management & machine Intelligence

作者： Rupal Gupta Sanjay Kumar Malik University School of Information Communication & Technology Guru Gobind Singh Indraprastha University India and College of Computing Sciences and IT Teerthanker Mahaveer University India University School of Information Communication & Technology Guru Gobind Singh Indraprastha University India

ISBN: (纸本)9781450399937

Semantic web data is a core data representation format in different technologies over web like linked open data, social networks, and knowledge graph databases. It will give the strength to these technologies while incorporating machine learning power on those data. W3C community is also working in this direction while constructing a group of SPARQL-ML. RDF-ML is a proposed web based tool which contains the capability of SPARQL processing engine. through this tool machine learning can be applied on the built-in as well as user defined datasets in the form of RDF. this tool will produce faster results for the research community, working in the domain of semantic web and wants to produce results while applying machine learning and interpret them. this paper brief about the tool, it's utility with the aspect for SPARQL query processing along with machine learning. this paper proposed a framework for RDF-ML tool and represents the working of tool with snapshots and comparison for machine learning model on RDF data.

关键词： Knowledge Graph

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4th international conference on Innovative Intelligent Industrial Production and Logistics, IN4PL 2023

4th International Conference on Innovative Intelligent Indus...

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4th international conference on Innovative Intelligent Industrial Production and Logistics, IN4PL 2023

ISBN: (纸本)9783031493386

the proceedings contain 25 papers. the special focus in this conference is on Innovative Intelligent Industrial Production and Logistics. the topics include: Virtual Try-On Networks Based on Images with Super Resolution Model;assigning Products in a Vertical Lift Module Supermarket to Supply Production Lines;Bridging the Operationalization Gap: Towards a Situational Approach for Data Analytics in Manufacturing SMEs;AutoPose: Pose Estimation for Prevention of Musculoskeletal Disorders Using LSTM;towards Circular Systems: the Role of Digital Servitization in an Italian Extended Partnership;Industrial application Use Cases of LiDAR Sensors Beyond Autonomous Driving;When the learning Lab Embraces Digitalisation: the Development of a Digital learning Lab for the SMILE Project;case Fill Rate Prediction;a Continuous Review Policy for Warehouse Inventory Management;Measuring the Phase Shift Between Hall Signals and Phase Voltages for Purpose of End Quality Control of BLDC Motor Production;industrial Communication with Semantic Integration Patterns;strategic Roadmap for Digital Transformation Based on Measuring Industry 4.0 Maturity and Readiness;MBSE- Based Construction Method of Unified Information Model for Production Equipment;decentralized, Autonomous Setup and Access to Data of Independent Partners in a Hyper Connected Ecosystem;human-Centric Digital Twins: Advancing Safety and Ergonomics in Human-Robot Collaboration;cost-Benefit Evaluation of Digital Twin Implementation for Pharmaceutical Lyophilization Plant;5G and MEC Based Data Streaming Architecture for Industrial AI;a Classification of Data Structures for Process Analysis in Internal Logistics;harmonizing Heterogeneity: A Novel Architecture for Legacy System Integration with Digital Twins in Industry 4.0;approach of a Ticket-Based Test Strategy for Industry 4.0 Components with Asset Administration Shells;balancing Risks and Monetary Savings When the Crowd is Involved in Pickups and Deliveries.

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Enhancing Cloud Computing Security through Deep learning and Attention Mechanism Intrusion Detection Systems

Enhancing Cloud Computing Security through Deep Learning and...

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Intelligent Technologies (CONIT), international conference on

作者： Sunil Shukla Jagendra Singh T. Ramya Satyakam Rahul Arnab Kumar Mallick Pawan Pandey School of Computing Graphic Era Hill University Dehradun India School of Computer Science Engineering & Technology Bennett University Greater Noida India Department of CSE-DS CVR College of Engineering Hyderabad India School of Computer Science Engineering MIT ADT University Pune India Department of Computer Science Engineering Indian Institute of Technology Madras Chennai India Department of Computer Science Raj Kumar Goel Institute of Technology Ghaziabad India

ISBN: (数字)9798350349900

ISBN: (纸本)9798350349917

this research investigates the enhancement of cloud computing security through adaptive neural network intrusion detection systems, employing Deep Belief Networks (DBN) and Attention Mechanism. the study aims to address the growing concerns surrounding security threats in cloud environments by leveraging advanced machine learning techniques. Experimental evaluations were conducted to assess the performance of the proposed approach, with specific emphasis on detection accuracy, false positive rate, and computational efficiency. Results indicate that the DBN-based intrusion detection system achieved an average detection accuracy of $94.4 \%$, with a false positive rate of $2.2 \%$ and a computational efficiency of $\mathbf{1 5 8. 4}$ milliseconds. In comparison, the IDS utilizing Attention Mechanism demonstrated superior performance, with an average detection accuracy of $96.2 \%$, a false positive rate of $1.1 \%$, and a computational efficiency of 153.2 milliseconds. these findings underscore the effectiveness of DBN and Attention Mechanism in bolstering the security posture of cloud computing environments. By integrating adaptive neural network-based intrusion detection systems, organizations can enhance threat detection capabilities, mitigate risks, and safeguard sensitive data and critical assets in the cloud.

关键词： Attention mechanisms Accuracy Adaptive systems Cloud computing security Intrusion detection Clustering algorithms threat assessment

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Plant Disease Detection Over Multiple Datasets Using AlexNet 22

Plant Disease Detection Over Multiple Datasets Using AlexNet

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proceedings of the 4th international conference on Information Management & machine Intelligence

作者： Palika Jajoo Mayank Kumar Jain Sarla Jangir CSE Swami Keshvanand Institute of Technology Management & Gramothan Jaipur Rajasthan India CSE Manipal University Jaipur Rajasthan India

ISBN: (纸本)9781450399937

Plants diseases are responsible for huge loss of crop yield. Manual inspection of plant disease is a time taken and inefficient process. Image processing and machine learning-based approaches have been offered as a solution for creating such automated plant disease detection systems. Plant diseases leads to change in color and texture of leave, this property is used for developing plant disease detection systems. Deep learning models such as Visual Geometry Group (VGG) and ResNET are extensively used in this field. However, most of these models are not scalable as they are either focused on disease classification on a particular crop or dataset. the focus of this study is to showcase a new method for identifying leaf diseases. AlexNet is used in the system's development, and it is trained and verified using data from many sources. Results indicate improved performance as compared to previously published works.

关键词： Image Processing Leaf Disease machine learning Deep Neural Network

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