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Evaluation of Multiple Apache Spark Applications using Kubernetes as a Cluster manager on Google Cloud

Procedia computer science 2025年 252卷 576-582页

作者： M. Jayanthi K. Ram Mohan Rao Vuppala Sukanya Assistant Professor Dept of Computer Science and Informatics Mahatma Gandhi University Nalgonda Professor Dept of Information Technology Vasavi College of Engineering Hyderabad Assistant Professor Dept of CSE OU Hyderabad

Big data processing frameworks demands for scalable and efficient cluster management. Apache Spark has emerged as prominent big data processing framework providing high-speed data processing and analytics capabilities for multiple applications. This paper explores the integration of Kubernetes as a cluster manager for Apache Spark applications leveraging its containerization capabilities to improve resource utilization and simplify deployment. In this paper the challenges of deploying spark applications on traditional cluster managers and showcase the advantages of adopting Kubernetes are analysed. The experimental evaluation demonstrates the benefits of Kubernetes as a cluster manager for Apache Spark framework. To execute the multiple Apache Spark applications on Kubernetes a homogenous cluster on Google Cloud is created by History bucket and service account. Finally multiple applications are executed on Google Kubernetes Engine. Output can be shown as the number of executor pods created with the performance metrics can be viewed. In conclusion, this paper compares the performance metrics such as job execution time and resource utilization with the different cluster.

关键词： Homogenous Apache Spark Kubernetes Google Kubernetes Engine Containerization

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Design and analysis of Circular microstrip patch antenna with SRR on modified and defected ground structures for UWB applications

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Materials Today: Proceedings 2023年 80卷 3244-3251页

作者： Subitha, D. Chandra, I. Raja, A. Assistant Professor Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Vel Nagar Tamil Nadu Chennai600062 India Professor Department of ECE Rajalakshmi Institute of Technology Tamil Nadu Kuthambakkam Chennai600124 India Assistant Professor Department of ECE Saveetha School of Engineering SIMATS Tamil Nadu Chennai602105 India

Modified split-ring resonators (SRR) are used develop a high gain UWB antenna for microwave imaging applications. This paper analyzes two different ground designs for the proposed patch radiator with modified SRR;defected ground structure and the second one is rectangular slotted ground. Two SRRs are placed on the circular radiator patch element, each in the shape of alphabet letters ‘S’ and ‘V’. The results of the first design with DGS demonstrate that the reflection coefficient is well below −10 dB in the frequency range from 1.9 GHZ −11.9 GHz with the fractional bandwidth of 110%. Further modification in the resonant modes of UWB is obtained by introducing single rectangular slot on the ground plane. This helps in eliminating the lower band from 1.9 GHz to 6 GHz. This band rejection helps in alleviating the interferences caused by other unlicensed wireless applications lying in sub 6GHZ band. © 2021

关键词： Defected ground structures

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Deep Learning Techniques Advancements in Apple Leaf Disease Detection

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Procedia computer science 2024年 235卷 713-722页

作者： E Kannan Carmel Mary Belinda M J Alex David S Ruth Naveena N Almas Begum Hemalatha D Professor Assistant Professor Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India 600062 Professor Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical And Technical Sciences Chennai Tamilnadu India 602105 Assistant Professor (SG) Hindustan Institute of Technology and Science Chennai Tamilnadu India 603103 Assistant Professor Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India 600062

An exhaustive and comprehensive investigation was undertaken to address the critical issue of disease detection on apple leaves using cutting-edge deep learning techniques. The research delved into an array of diverse approaches, meticulously examining their efficacy and performance in disease detection, ultimately offering valuable insights into this vital domain. The research effort was marked by the exploration and application of a wide spectrum of deep learning models, each chosen for its distinct characteristics and potential advantages. The results of this extensive work were nothing short of remarkable. This study uses state-of-the-art deep learning techniques to present a thorough and rigorous analysis into the important problem of disease detection on apple leaves. Our research covers a wide range of approaches, all of which have been thoroughly assessed for their efficacy in the diagnosis of disease. We used a wide range of deep learning models, selected for their special qualities and possible benefits. The results of this extensive study are impressive and measurable. VGG-INCEP, the top approach, showed exceptional performance with a measured accuracy rate of 97%. The quantification of precision, recall, and F1 scores were 0.94, 0.92, and 0.92, respectively. Similarly, InceptionV3 yielded an F1 score of 0.93, precision of 0.95, and recall of 0.91, in addition to a measured accuracy of 97%. AlexNet consistently demonstrated measurable high precision (0.95) and recall (0.93), resulting in an F1 score of 0.93, despite a somewhat lower accuracy of 87%. The method's balanced performance is highlighted by these metrics. The study also evaluated the effectiveness of SVM, MobileNet, RCNN, and a recommended method. With quantifiable accuracy of 98% and quantifiable precision, recall, and F1 scores of 0.96, the suggested technique stood out. This assessment unequivocally shows that the suggested approach produces the best accuracy and overall performance and is di

关键词： Apple leaf diseases Deep learning Disease detection Precision agriculture Performance evaluation

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Investigation of Security Attacks in IoMT Devices and Federated Learning as a Mitigation Strategy

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Procedia computer science 2025年 258卷 3426-3435页

作者： Kiruthika M T Poongodi Assistant Professor Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Tamilnadu 603203 India Professor Department of Computer Science and Engineering School of Engineering Dayananda Sagar University Bangalore Karnataka India

Internet of Medical Things (IoMT) is transforming the healthcare business with an increasing number of wearables, gadgets, and sensing devices. As a result, several patients are under daily monitoring for their cardio health, blood glucose level, blood pressure, etc. The data generated by these devices are very voluminous, and there is an increasing need for safeguarding these sensitive data in order to protect them from malicious attacks. This paper suggests to incorporate Federated learning model to IoMT as a mitigative measure. It is apt to induct FL into IoMT as it operates on a collaborative environment which do not allow transfer of the raw data. Patients’ data are stored at the client end itself and only model updates are shared to the server. In this way, the data and network attacks can be reduced to a great extent. This work proposes an integrated architecture where the layers of FL and IoMT can be knotted up for ensuring privacy and security. This proposed architecture model, further elaborates a distributed working environment. This paper illustrates how FL helps in securing the data produced by the different IoMT devices through the proposed architecture. The challenges involved in the deployment of FL in the framework of IoMT along with potential research directions are discussed further in this research.

关键词： : Federated Learning IoMT Security Internet of Medical Things IoMT Data Attacks Optimization

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Characterization and Micro Structural Analysis of Concrete Using Industrial By-Products and Incinerated Ash

Characterization and Micro Structural Analysis of Concrete U...

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International Conference on Recent Trends in Infrastructural Development and Sustainable Materials, IC-RTIDSM 2023

作者： Dambhare, Pranita S. Dhale, S.A. Das, A. Tajne, K.M. Yadav, A. Ph.D Research Scholar Department of Electronics and Computer Science R T M Nagpur University Nagpur India Principal & Professor Priyadarshini College of Engineering Nagpur India Assistant Professor Priyadarshini College of Engineering Nagpur India Associate Professor Government College of Engineering Nagpur India Assistant Professor Yashwantrao Chavan College of Engineering Nagpur440033 India

ISBN: (纸本)9783031821325

For the last vicennial, scientists have been focusing attention on mitigating the risks of environmental contamination brought on by the manufacturing of industrial waste and cementitious materials. Finding efficient waste to effectively incorporate into concrete production is the most feasible approach. Incinerated ash, an industrial by-product, offers unique qualities and long-term advantages that make it suitable as a building material. As a result, the characteristics of waste material are thoroughly covered in this paper to assess how effective it is as filler or cement substitute in concrete. This study examines the impact of replacing silica fume in concrete with incinerated ash on the material's strength and penetration capabilities. To achieve this, four concrete mixes containing four different percentages of silica fume (6, 8, 10, and 20%) and incinerated ash (4, 6, 8, and 10%) were prepared, along with a control concrete mix. It was noted that the control-mix concrete was easier to work than the waste-mix concrete. These investigations verified that 4% incinerated ash replacement combined with 8% silica fume results in improved mechanical properties and elevated pozzolanic reactivity. In the meanwhile, further study is required, particularly in the area of durability details, to assess the comprehensive performance of concrete made from industrial and incinerated ash. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Concrete mixtures

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Precaution Chatbot for Epilepsy Patients using Natural Language Processing and Deep Learning Sequential Model

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Procedia computer science 2024年 233卷 537-546页

作者： E Kasthuri S. Subbulakshmi Rajasree Sreedharan Research Scholar Department of Computer Science and Engineering Assistant Professor Department of Computer Science and Applications Amrita School of Computing Amritapuri India Associate Professor Division of Nephrology Department of Pediatrics Medical College of Wisconsin Milwaukee Wisconsin USA

Epilepsy is a brain disorder that can affect people of all ages which is one of the most common neurological disorders affecting around 50 million individuals. Healthcare Chatbots schedule appointments, respond to patient inquiries, and offer relevant information, freeing medical staff to handle life crises. This paper implements a chatbot for epilepsy patients to increase awareness about epilepsy. Patients may easily access this AI-based healthcare chatbot whenever they have queries regarding precautions to be taken by epilepsy patients. Chatbot uses a rich set of epilepsy-related datasets collected from trusted websites which are preprocessed with NLP techniques to prepare the data for model ***, LSTM, and RNN models are created and evaluated to produce answers to user queries related to possible precautions to avoid seizures, safety measures to be taken by patients with seizure history, etc. Evaluation of models shows that the RNN model generates more accurate answers with 94% accuracy. Finally, the epilepsy question answering Chatbot for precaution is concluded with the RNN model.

关键词： Epilepsy Seizure Chatbot Natutal Language Processing LSTM Long short-term memory Recurrent neural network Deep neural network

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Insightful Clinical Assistance for Anemia Prediction with Data Analysis and Explainable AI

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Procedia computer science 2024年 233卷 45-55页

Anemia among young people has positive correlation with susceptibility and impede cognitive development. As per WHO 2019 statistics global prevalence rate of anemia is 33 percent. This work aims to predict anemia with inferences to identify the root cause of anemia. Prediction is done with machine learning algorithms with proper pre-processing, data normalization, and class imbalance approaches. Explainable AI, a transparent model, is used to enhance the prediction which enables to draw inferences. LGM Boost learning model prediction has a 91% accuracy and explainable AI framework predictions of most contributing attribute is accurate when compared to other models. This approach helps healthcare professionals make informed decisions on the most optimal treatment for anemia.

关键词： Explainable Artificial Intelligence Machine learning Anemia Data preprocessing

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Performance analysis of machine learning algorithms on automated sleep staging feature sets

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CAAI Transactions on Intelligence Technology 2021年第2期6卷 155-174页

作者： Santosh Satapathy D Loganathan Hari Kishan Kondaveeti RamaKrushna Rath Puducherry Research Scholar of Computer Science and EngineeringPondicherry Engineering CollegePuducherryIndia Professor of Computer Science and Engineering Pondicherry Engineering CollegePuducherryPuducherryIndia Assistant Professor of Computer Science and Engineering VIT UniversityAmaravatiAndhra PradeshIndia Research Scholar of Computer Science and Engineering Anna UniversityChennaiIndia

With the speeding up of social activities,rapid changes in lifestyles,and an increase in the pressure in professional fields,people are suffering from several types of sleep-related *** is a very tedious task for clinicians to monitor the entire sleep durations of the subjects and analyse the sleep staging in traditional and manual laboratory environmental *** the purpose of accurate diagnosis of different sleep disorders,we have considered the automated analysis of sleep epochs,which were collected from the subjects during sleep *** complete process of an automated approach of sleep stages5 classification is majorly executed through four steps:pre-processing the raw signals,feature extraction,feature selection,and *** this study,we have extracted 12 statistical properties from input *** proposed models are tested in three different combinations of features *** the first experiment,the feature set contained all the 12 *** second and third experiments were conducted with the nine and five best *** patient records come from the ISRUC-Sleep *** highest classification accuracy was achieved for sleep staging through combinations with the five feature *** the categories of the subjects,the reported accuracy results were found to exceed above 90%.As per the outcome from the proposed system the random forest classification techniques achieved best accuracy incomparable to that of the other two classifiers.

关键词： sleep staging diagnosis

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Deep Learning Cerebral Splitting: Adaptation for Spine Elimination

Deep Learning Cerebral Splitting: Adaptation for Spine Elimi...

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Communication, Computing and Energy Efficient Technologies (I3CEET), International Conference on

作者： M. Balasubramani B. J. Job Karuna Sagar K. C. Rajheshwari E. Saranya B. Aishwarya P. Murugeswari Assistant Professor Department of Computer Science and Engineering V.S.B. Engineering College Professor Department of Computer Science and Engineering Joginpally B.R Engineering College Hyderabad India Department of Computer Science and Engineering Sona College of Technology Assistant Professor Department of Computer Science and Engineering Sri Eshwar College of Engineering Coimbatore Assistant Professor Department of Computer Science and Engineering(Cyber Security) Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Department of Electronics and Communication Engineering Theni Kammavar Sangam College of Technology

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

The posterior fossa volume and the total intracranial volume are important factors to consider when doing a brain volumetric analysis using structural electromagnetic resonance imaging. The thorough segmentation of the whole cortex offers a method for non-invasively evaluating different brain regions. In addition, the extraction of skulls in a way that maintains anonymity is increasingly being used as a means of disseminating large amounts of data obtained from brain images. Therefore, generalizing machine learning brain regions for intracranial measurements after skull removal is an interesting task. However, data collecting is difficult due to the small number of handwritten and whole-brain atlases marked with his TICV/PFV. In this study, we use U-Net tiles to estimate intracranial volume and perform whole-brain segmentation with and without cranial characteristics simultaneously. In the absence of individually recorded total cortical volume, a transfer learning approach has been shown to estimate extra her TICV and PFV labeling during whole-brain delineation on T1-weighted MRI. More specifically, U-Net tiles are pre-trained using a big Brain COLOR atlas generated using multi-atlas segmentation, and do not include his PFV labels. The pre-trained model is improved to learn more His TICV and PFV labels using the limited Brain COLOR atlas. We also elaborate on his method for processing MR images of the brain in the absence of a skull. The results show that our method produces reasonable segmentation and size estimation results in terms of average contact distance, cubic similarity index, and total volumetric similarity for both brains within and outside the skull. is showing off. This technology is currently available as open source to the general public.

关键词： Deep learning Image segmentation Image color analysis Magnetic resonance imaging Transfer learning Pipelines Estimation Skull Resonance Telecommunication computing

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Resource Request Handling Mechanisms for Effective VM Placements in Cloud Environment 1

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1st International Conference on Expert Clouds and Applications, ICOECA 2023

作者： Thiruvenkadam, T. Muthusamy, A. Vijayakumar, M. Karnataka Bengaluru India Assistant Professor and Head Department of Computer Science Faculty of Science & Humanities SRM Institute of Science and Technology Tamil Nadu Tiruchirappalli India Professor School of Computer Science VET Institute of Arts and Science College Tamil Nadu Erode Tindal India

ISBN: (数字)9789819917457

ISBN: (纸本)9789819917440

In a cloud computing environment, effective resource request handling and their dynamic management can optimize the VM placement process. An efficient resource request handling can resolve the problem of increased waiting time as well as load imbalance rate during the course of running the VM resources. Load balancing techniques only helps to reschedule the VMs in the appropriate PMs based on the dynamic changes in the load. To make use of the resources in an effective way, an optimized resource scheduling and load balancing model is necessary along with the proper model to manage the resource request made by the cloud users. A rule based method utilizing the M/M/C queue model for effective management of resource request handling is presented in this work. The RB-M/M/C queue model can ensure the availability of cloud datacenter services by managing user requests. This further enriches the cloud service by increasing the physical server utilization rate and minimizing the waiting time and number of servers used to fulfill the requirements of the users. The resource request queue management algorithm is designed by utilizing some suitable modesties of queuing theory. Simulation results for performance analysis shows that the proposed model decreases the waiting time and at the same time increases the utilization rate. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Computation theory

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