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Intrusion detection in big data using hybrid feature fusion and optimization enabled deep learning based on spark architecture

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computers and Security 2022年 116卷

作者： M .P . , Ramkumar Reddy, P.V. Bhaskar Thirukrishna, J.T. Vidyadhari, Ch. Assistant Professor Department of Computer Science and Engineering Thiagarajar College of Engineering Madurai625015 India Professor School of Computer Science and Engineering REVA University Karnataka Bengalore India Associate Professor Department of Information Science and Engineering Dayananda Sagar Academy of Technology and Management Karnataka Bangalore560082 India Assistant Professor Department of Information Technology Gokaraju Rangaraju Institute of Engineering and Technology Bachupally Hyderabad India

With the rapid expansion of Internet services and increasing intrusion issues, conventional intrusion detection techniques cannot work well with several difficult intrusions. Though several intrusion detection methods have been introduced in the past years, developing an Intrusion Detection Systems (IDS) with prevailing intrusion detection strategy is still very much desirable. Hence, a robust and effective intrusion detection approach, named RV coefficient+Exponential Sea Lion Optimization-enabled Deep Residual Network (ExpSLO-enabled DRN) using spark is devised for the intrusion detection. Here, the unique features are selected using proposed RV coefficient-based hybrid feature fusion, which is designed by the incorporation of wrapper, class-wise information gain (CIG), and Canberra distance in slave node. With the distinctive features selected, the process of data augmentation is done using oversampling for making the data more appropriate to perform the further process in the slave node. Moreover, DRN classifier is utilized for detecting the intrusions in the master node where the DRN training is done using devised ExpSLO algorithm, which is the hybridization of Exponentially Weighted Moving average (EWMA) and Sea Lion Optimization (SLnO). Furthermore, the devised method obtained better performance by considering the evaluation metrics, such as precision, recall, and F-measure with the higher values of 0.8800, 0.8845, and 0.8822 based on without attacks using dataset-2. © 2022

关键词： Intrusion detection

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Emotion Recognition in Real Time for Personalized Healthcare Using Digital Twin Approach

Emotion Recognition in Real Time for Personalized Healthcare...

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International Conference on Advances in Information Technology (ICAIT)

作者： B. Karthikeyani D. Yuvaraj T. Parimalam P. Parthasarathi Nandha Arts and Science College & Assistant Professor @ Shree Venkateshwara Arts and Science (Co Education) CollegeGobi Department of Computer Science and Design Kongu Engineering College Perundurai Department of Computer Science Nandha Arts and Science College Perundurai Department of Computer Science and Engineering Bannari Amman Institute of Technology Sathyamangalam

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383874

Facial Emotional Recognition (FER) in healthcare can help to monitoring the patient by analyzing the emotional state of the patient through various emotional states. In medical industry it can help to trace the emotional state changes and diagnosis the pattern which are mapped respective to the patient's history. Digital twin technology is transforming healthcare systems and improving patient care, clinical operations, predictive analytics, and training and simulation by integrating virtual simulations and emerging analytic mechanism through ML and DL techniques. By allocating resources, monitoring workflow, and providing individualized patient care, these algorithms reduce healthcare operations and assist in determining an individual's risk. Since FER has lot of technical challenges like illumination, obstruction issues also high implementation cost. To solve these issues, the FER system will embed with the digital twin technology to provide a better result from real time sensing cameras also analyzing the result and guiding for a better treatment of an individual. We propose a system of end to end framework which produces best results in minimum amount of time with maximum accuracy. This real time implementation will help all the healthcare centers/industry to study an individual's health status as well as early prediction of diseases with effective treatment steps.

关键词： Training Emotion recognition Face recognition Medical services Speech recognition Cameras Real-time systems Digital twins Medical diagnostic imaging Monitoring

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Optimization of machining parameters in EDM using Taguchi based grey relational analysis

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Materials Today: Proceedings 2022年 82卷 43-46页

作者： Ganapathy S. Palanivendhan M. Susitra K. Balasubramanian P. Department of Mechanical Engineering Sri Sai Ram Engineering College Chennai 600044 India Centre for Automotive Materials Assistant Professor Department of Automobile Engineering SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu 603203 India Department of Computer Science and Engineering Sri Sai Ram Engineering College Chennai 600044 India Department of Mechanical Engineering A.V.C College of Engineering Mayiladuthurai 609305 India

The present investigation optimizes process parameters such tool size, peak current, pulse on time, and dielectric pressure in the EDM process. The material removal rate affects the machining operation's quality. The goal of the present work is to maximize material removal rate using Taguchi technique-based optimization of machining parameters. On an EDM, the experimental study for machining operations was carried out. For EN-31, machining operations are carried out using an orthogonal array with L9. The ANOVA technique was used to evaluate the experimental results. Using a technique called as grey relational analysis, the optimum conditions for material removal rate are determined. The optimum approach for a set of linked parameters is evaluated using the grey relation grade. © 2023 Elsevier Ltd. All rights reserved.

关键词： Anova EDM EN-31 GRA MRR

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Nanosensor and Actuator Technologies for Wearable Mobile Patient Monitoring Systems: A Review

Nanosensor and Actuator Technologies for Wearable Mobile Pat...

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International Conference on Computational Mathematics in Nanoelectronics and Astrophysics, CMNA 2018

作者： Paliwal, Gaurav Bunglowala, Aaquil Assistant Professor Department of Computer Engineering R.C. Patel Institute of Technology Shirpur India Professor School of Technology Management and Engineering SVKM’s NMIMS Deemed to be University Mumbai India

ISBN: (纸本)9789811597077

Currently India’s population is increasing at the rate of 1.2% and to provide good healthcare services to these people healthcare sector needs a transformation. Information and Communication Technology (ICT) along with nanotechnology revolution has opened up whole new opportunities for developing countries like India to improve the healthcare for the betterment of 1.34 billion lives. With the use of ICT and nano-electronics, we can provide our patients better and specialized healthcare services at a reduced cost. ICT-based healthcare initiatives like eHealth and mHealth includes healthcare centers, mobile telemedicine, electronic patient records, remote patient monitoring, mobile patient monitoring, health surveys, awareness raising, and decision support systems that can play a crucial role towards the accomplishment of development goals to enhance Healthcare services in India. Nanosensors and actuators will play a critical role in the success of these technologies. In this paper, we are going to present a comprehensive review of existing and future wearable healthcare sensor and actuator technologies along with their merits and demerits. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Actuators

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Secure Localization Based Authentication (SLA) Strategy for Data Integrity in WNS

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computers, Materials & Continua 2021年第6期67卷 4005-4018页

作者： V.Manikandan M.Sivaram Amin Salih Mohammed V.Porkodi K.Shankar Assistant Professor Research Research CenterLebanese French UniversityErbil44001Iraq Vice President Lebanese French UniversityErbil44001Iraq Department of Information Technology College of Engineering and Computer ScienceLebanese French UniversityErbil44001Iraq Department of Computer Applications Alagappa UniversityKaraikudi630003India

Wireless Sensor Networks(WSN)has been extensively utilized as a communication model in Internet of Things(IoT).As well,to offer service,numerous IoT based applications need effective transmission over unstable *** ensure reliability,prevailing investigations exploit multiple candidate forwarders over geographic opportunistic routing in ***,these models are affected by crucial denial of service(DoS)attacks,where huge amount of invalid data are delivered intentionally to the receivers to disturb the functionality of ***,secure localization based authentication(SLA)is presented to fight against DoS attack,and to fulfil the need of reliability and *** examining state information,SLA projects a trust model to enhance efficacy of data ***,of the prevailing opportunistic protocols,SLA guarantees data integrity by modelling a trust based authentication,providing protection against DoS attackers and diminishing computational ***,this model acts as a verification strategy to accelerate?attackers and to handle *** strategy helps SLA in eliminating duplicate transmission and by continuous verification that results from conventional opportunistic *** is performed in a MATLAB environment that offers authentic and reliable delivery by consuming approximately 50%of the cost in contrast to other *** anticipated model shows better trade off in comparison to the prevailing ones.

关键词： Wireless sensor networks opportunistic routing secure localization based authentication denial of service computational cost

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Elicitation of Intracerebral Hemorrhage Using Deep Learning

Elicitation of Intracerebral Hemorrhage Using Deep Learning

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International Conference on Signal and Information Processing (IConSIP)

作者： Saraswati Patil Sangita M. Jaybhaye Ashwini Mamulwar Ayush Rambhad Mihir Prajapati Suresh Mante Department of Computer Engineering Assistant professor Vishwakarma Institute of Technology Pune India Department of Computer Engineering Associate Professor Vishwakarma Institute of Technology Pune India Department of Computer Engineering Btech Student Vishwakarma Institute of Technology Pune India

ISBN: (纸本)9781728168869

The paper presents modeling and prediction for Intracerebral hemorrhage. The elicitation of intracerebral bleeding from veins and arteries that damages the internal stability and mental control of an individual could help in life saving situations, thus reducing the risk of intracerebral hemorrhage to a great extent. Vascular bleeding involves a lot of fragilities, and therefore if not treated and diagnosed soon, this can cause life-threatening situations. Deep learning algorithms shows indubitable results for medical image recognition and classification in the healthcare sector. Neuro physicians face difficulties in diagnosing hemorrhage in the beginning stages of intracranial bleeding, thereby adding to possibilities of misdiagnosis. This challenge faced by Neuro physicians worldwide is considered for the research work in the present paper. It provides a solution model for elicitation of ICH within a CT scan with the support of CNN. An AI-based Web platform is developed to classify CT scans and predict ICH type using the model.

关键词： Deep learning Visualization Computed tomography Computational modeling Veins Medical services Predictive models

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Predictive Model for Accident Severity

IAENG International Journal of Computer Science

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IAENG International Journal of computer Science 2022年第1期49卷 110-124页

作者： Mostafa, Solwan M. Salem, Sameh A. Habashyis, Shahira M. Teaching Assistant and a Master Student of Computer and Systems Department Faculty of Engineering Helwan University Cairo11792 Egypt Professor of Computer and Systems Department Faculty of Engineering Helwan University Cairo11792 Egypt Associate Professor of Computer and Systems Department Faculty of Engineering Helwan University Cairo11792 Egypt

In the development of sustainable transportation, traffic safety is a significant matter, and predicting the severity of traffic accidents is still a critical problem in the traffic safety field. However, the utilized road traffic accidents (RTAs) datasets suffer from imbalance distribution. This problem leads to a decrease in classification performance, specifically in predicting the minority classes. This paper aims to treat the class imbalance problem through Synthetic Minority Over-sampling Technique (SMOTE), Support Vector Machine-SMOTE (SVM-SMOTE), Borderline-SMOTE(BL-SMOTE), and Adaptive Synthetic (ADASYN), along with proposing an accurate predictive model for accident severity. Different accident severity models are employed, namely Random Forest (RF), K-Nearest Neighbor (KNN), Naïve Bayes (NB) classifiers, Decision Tree (DT), and Extra Trees (ET). These models are tested using real-world datasets. Several evaluation metrics are used to evaluate the proposed model. Experimental results show that the proposed model significantly improves predicting both the minority and majority classes. These results indicate the robustness and reliability of the proposed predictive model in enhancing road traffic safety and management. © 2022. All Rights Reserved.

关键词： Support vector machines

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Developing a New MapReduce Framework with SVM-based Software Solution for Lyme Lesion Impact on Alzheimer's Disease Prevention

Developing a New MapReduce Framework with SVM-based Software...

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International Conference on Trends in Electronics and Informatics (ICEI)

作者： Saravanan. M.S G. Sreehitha Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamilnadu India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences and Assistant Professor Sri Padmavati Mahila Visvavidyalayam Tirupathi Andhra Pradesh India

ISBN: (数字)9798331544607

ISBN: (纸本)9798331544614

Alzheimer's disease has many symptoms to occur, one of the latest research studies identified that Lyme disease, caused by “Borrelia burgdorferi” also one of the reasons indirectly affect the neuroinflammation. The dataset used for this research study is supplied by many web sources, that are involved in feature extraction to apply on machine learning techniques for extracting meaningful knowledge. This work proposing a new MapReduce framework-based Support Vector Machine (SVM) model for better prediction of impact of Lyme on Alzheimer's disease. The MapReduce based framework using SVM model creates more impact on prediction of Alzheimer's disease at the earliest. The clinical data with images are collected from distributed environments and applied on preprocessing and feature extraction stages then applied to proposed MapReduce based SVM model for better accuracy and optimal classification for complex patters on Lyme image dataset. From these results the Alzheimer's risk factors are predicted for early treatment process. The results obtained by this study will give early decision making for highly available sources of treatment procedures and gives more insight on preventive care on each and every patient involved in diagnosis of Lyme disease prediction, lead to identify the Alzheimer's disease prediction.

关键词： Support vector machines Accuracy Machine learning algorithms Software algorithms Machine learning Predictive models Feature extraction Software Classification algorithms Alzheimer's disease

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Evaluative Analysis of Secure approaches in Simulated Real-World Contexts

Evaluative Analysis of Secure approaches in Simulated Real-W...

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International Conference on ICT in Business Industry & Government (ICTBIG)

作者： A Kannagi Pavan Chaudhary Muthupandi G Associate Professor Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India Assistant Professor Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Associate Professor Department of Electronics and Communication Engineering Presidency University Bangalore Karnataka India

This paper introduces a novel methodological approach to evaluating security measures within simulated real-world contexts. By leveraging three distinct algorithms Vulnerability Scoring Algorithm (VSA), the Attack Simulation Algorithm (ASA), and the Impact Assessment Algorithm (IAA) this provides an exhaustive analysis of potential vulnerabilities, simulate cyberattacks based on these vulnerabilities, and gauge the consequences of successful breaches. The Proposed method's foundational algorithm, VSA, quantifies vulnerabilities' severity, whereas ASA simulates realistic attacks and calculates the likelihood of their success. Lastly, the IAA assesses the potential impact of successful attacks. Collectively, these algorithms offer a rigorous and holistic evaluation of security measures. The findings visualized and underscored the superior performance of our proposed method compared to traditional approaches. This research contributes a valuable tool set for organizations seeking to fortify their security posture in an ever-evolving threat landscape.

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Message Communication Challenges in Heterogeneous Architecture of Internet of Vehicles: A Review

Message Communication Challenges in Heterogeneous Architectu...

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International Conference on System Modeling & Advancement in Research Trends (SMART)

作者： Jitendra Kumar Y.D.S. Arya Akash Sanghi Inverters University Bareilly & Assistant Professor Teerthanker Mahaveer University Moradabad UP India Invertis University Bareilly UP India Department of Computer Science & Engineering Invertis University Bareilly UP India

ISBN: (数字)9798350380583

ISBN: (纸本)9798350380590

Inclusion of Internet of vehicles (IoV) an emerging technology in smart city for vehicular communication is covering the Internet of things (IoT) technology into vehicular networks for benefiting from omnipresent Internet connectivity. Smart city as a demand of new way of living of current and coming generation by gathering the information through the various electronic devices. The one of the key point in smart city is making traffic planning more secure and accurate by using the better message communication between vehicles. A huge number of researchers have been worked on vehicular networks for message communication but researchers are still having to do work on to achieve the higher data rate, faultless network connectivity, message scalability, message security and improved quality of services for message communication for making IoV more effective. IoV is more capable for comprehensively information processing that is collected from the vehicles, roads and surroundings and helps the drivers for driving the vehicles in safer zone. The Internet of Vehicles (IoV) model integrates vehicular intelligence in addition to leveraging ubiquitous vehicle connection for a multitude of applications. This research technology used in network communication including the smart city requirements, traffic control for increasing the life security.

关键词： Wireless communication Accuracy Smart cities Scalability Roads Traffic control Security Standards Internet of Vehicles Systematic literature review

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