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Adaptive pelican optimization with optimized mask RCNN for automatic lung cancer detection

Multimedia Tools and Applications 2025年 1-21页

作者： Sudha, R. Maheswari, K.M. Uma Department of Computer Science and Engineering SRM Institute of Science and Technology Chengalpattu India Department of Computing Technologies SRM Institute of Science and Technology Chengalpattu India

Lung cancer is a prevalent and deadly disease worldwide, necessitating accurate and timely detection methods for effective treatment. Deep learning-based approaches have emerged as promising solutions for automated medical image analysis. This study proposes an enhanced Mask R-CNN framework tailored specifically for the automatic detection and severity analysis of lung cancer from CT images. The proposed approach consists of three stages namely, pre-processing, lung nodule detection, and segmentation using enhanced Mask R-CNN and severity analysis. Our framework employs a deep convolutional neural network architecture trained on a comprehensive dataset of annotated lung images. By incorporating a region-based convolutional neural network (R-CNN) with a mask prediction branch, our model accurately localizes lung tumors while providing precise pixel-level segmentation masks. To enhance the performance of mask RCNN, the parameter present in the classifier is optimally selected using the adaptive pelican optimization (APO) algorithm. The proposed framework detects lung tumors and provides a comprehensive severity analysis, enabling clinicians to assess cancer stage and progression accurately. Evaluation on a benchmark dataset demonstrates superior detection accuracy and robustness compared to existing methods. Our enhanced Mask R-CNN approach shows promise as a valuable tool for early diagnosis and severity assessment of lung cancer, potentially improving patient outcomes and healthcare efficiency. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Lung cancer

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A reinforcement learning based backup queue strategy for reliable and fault tolerant scheduling

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International Journal of Information technology (Singapore) 2025年第5期17卷 2613-2618页

作者： Archana, R. Kumar, K. Pradeep Mohan Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India

Fog computing has the capability to perform tasks in the local distributed environment within the expected time period. The approaches used for managing the faults in a fog computing environment are not competent to resolve the issues of latency and energy consumption in the network. Allocating and executing all the tasks regardless of how they scale up and completing them within the allotted time frame is crucial for achieving a higher throughput and a lower drop rate. In order to achieve higher scalability, timely execution, and dependability, the given paper introduces a reinforcement learning (RL) backup queue strategy (SEEDBACK_RL) that ensures every task is executed irrespective of its size. The proposed strategy is enhanced to manage the scalability and reliability by identifying the best nodes depending on the type of task. With the use of a simulative environment, the algorithm's efficacy is assessed, and the results show a notable improvement in load balancing, reliability, and network efficiency. © Bharati Vidyapeeth's institute of Computer Applications and Management 2025.

关键词： Asynchronous advantage actor critic (A3C) Distributed reinforcement learning Fog computing Internet of things (IoT) Load balancing

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Transforming Education with Photogrammetry:Creating Realistic 3D Objects for Augmented Reality Applications

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Computer Modeling in Engineering & sciences 2025年第1期142卷 185-208页

作者： Kaviyaraj Ravichandran Uma Mohan Department of Computational Intelligence School of ComputingSRM Institute of Science and TechnologyKattankulathur603203India

Augmented reality(AR)is an emerging dynamic technology that effectively supports education across different *** increased use of mobile devices has an even greater *** the demand for AR applications in education continues to increase,educators actively seek innovative and immersive methods to engage students in ***,exploring these possibilities also entails identifying and overcoming existing barriers to optimal educational ***,this surge in demand has prompted the identification of specific barriers,one of which is three-dimensional(3D)*** 3D objects for augmented reality education applications can be challenging and time-consuming for the *** address this,we have developed a pipeline that creates realistic 3D objects from the two-dimensional(2D)*** for augmented and virtual reality can then utilize these created 3D *** evaluated the proposed pipeline based on the usability of the 3D object and performance ***,with 117 respondents,the co-creation team was surveyed with openended questions to evaluate the precision of the 3D object created by the proposed photogrammetry *** analyzed the survey data using descriptive-analytical methods and found that the proposed pipeline produces 3D models that are positively accurate when compared to real-world objects,with an average mean score above *** study adds new knowledge in creating 3D objects for augmented reality applications by using the photogrammetry technique;finally,it discusses potential problems and future research directions for 3D objects in the education sector.

关键词： Augmented reality education immersive learning 3D object creation photogrammetry and StructureFromMotion

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Anomaly graph: leveraging dynamic graph convolutional networks for enhanced video anomaly detection in surveillance and security applications

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Neural computing and Applications 2024年第20期36卷 12011-12028页

作者： Chiranjeevi, V. Rahul Malathi, D. Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur Chennai603203 India

Video abnormality behavior identification plays a pivotal role in improving the safety and security of surveillance systems by identifying unusual events within video streams. However, existing methods face challenges in capturing complex spatiotemporal anomalies effectively. To address this, we propose an anomaly graph approach that leverages dynamic graph representations and dynamic graph convolutional networks (GCN) for video anomaly detection. Anomaly graph constructs dynamic graphs where nodes represent objects or regions of interest within video frames while the edges encode spatial and temporal relationships. The GCN architecture extracts spatiotemporal embeddings from the dynamic graphs and allows the model to identify anomalies involving both spatial and temporal cues. Anomaly graph introduces uniqueness scores to quantify frame distinctiveness for precise anomaly detection while it employs adaptive reconstruction errors to pinpoint spatially localized anomalies. Real-time alerts are generated for detected anomalies to ensure timely responses to security incidents, and online fine-tuning (OLT) is incorporated as a dynamic learning mechanism to adapt to evolving anomaly patterns existing in dynamic environments. The extensive experimentation on benchmark datasets including UCSD Ped2 and CUHK Avenue is carried out for diverse performance measures, and the anomaly graph exhibits enhanced performance in the detection of anomalous events by achieving a detection accuracy of 98.72% and precision of 97.18%. Moreover, it is found to have achieved better values compared to other related methods for video anomaly detection. Overall, the anomaly graph introduces a robust video anomaly detection framework that excels in identifying complex spatiotemporal anomalies and empowers surveillance and security systems to detect anomalies more effectively. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Anomaly detection

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Hybrid Product Recommendation System Using Popularity Based and Content-Based Filtering 3

Hybrid Product Recommendation System Using Popularity Based ...

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3rd International Conference on Intelligent and Innovative technologies in computing, Electrical and Electronics, IITCEE 2025

作者： Anchalia, Chayan Sinha, Utkarsh Rajalakshmi, M. Srm Institute of Science and Technology School of Computing Department of Computing Technologies Chennai India

ISBN: (纸本)9798331515911

The paper introduces a hybrid product recommendation system that combines popularity-based and content-based filtering methods. The aim is to enhance the accuracy and relevance of product suggestions by utilizing both the intrinsic features of products (content-based filtering) and their popularity among users (popularity-based filtering). We assess the system's performance using standard metrics for recommendations, including precision and recall showcasing its effectiveness in delivering improved results. © 2025 IEEE.

关键词： Wiener filtering

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TRS Scheduling for Improved QoS Performance in Cloud System

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Computers, Materials & Continua 2023年第4期75卷 1547-1559页

作者： G.John Samuel Babu M.Baskar Department of Computer Science and Engineering School of ComputingCollege of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathurChengalpattuTamilnadu603203India Department of Computing Technologies School of ComputingCollege of Engineering andTechnologySRM Institute of Science and TechnologyKattankulathurChengalpattuTamilnadu603203India

Numerous methods are analysed in detail to improve task schedulingand data security performance in the cloud environment. The methodsinvolve scheduling according to the factors like makespan, waiting time,cost, deadline, and popularity. However, the methods are inappropriate forachieving higher scheduling performance. Regarding data security, existingmethods use various encryption schemes but introduce significant serviceinterruption. This article sketches a practical Real-time Application CentricTRS (Throughput-Resource utilization–Success) Scheduling with Data Security(RATRSDS) model by considering all these issues in task scheduling anddata security. The method identifies the required resource and their claim timeby receiving the service requests. Further, for the list of resources as services,the method computes throughput support (Thrs) according to the number ofstatements executed and the complete statements of the service. Similarly, themethod computes Resource utilization support (Ruts) according to the idletime on any duty cycle and total servicing time. Also, the method computesthe value of Success support (Sus) according to the number of completions forthe number of allocations. The method estimates the TRS score (ThroughputResource utilization Success) for different resources using all these supportmeasures. According to the value of the TRS score, the services are rankedand scheduled. On the other side, based on the requirement of service requests,the method computes Requirement Support (RS). The selection of service isperformed and allocated. Similarly, choosing the route according to the RouteSupport Measure (RSM) enforced route security. Finally, data security hasgets implemented with a service-based encryption technique. The RATRSDSscheme has claimed higher performance in data security and scheduling.

关键词： Cloud task scheduling TRS quality of service RSM route security data security SDE RATRSDS

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Real-Time Multi Fractal Trust Evaluation Model for Efficient Intrusion Detection in Cloud

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Intelligent Automation & Soft computing 2023年第8期37卷 1895-1907页

作者： S.Priya R.S.Ponmagal Department of Computer Science and Engineering School of ComputingSRM Institute of Science and TechnologyChengapattu603203Tamil NaduIndia Department of Computing Technologies School of ComputingSRM Institute of Science and TechnologyChengapattu603203Tamil NaduIndia

Handling service access in a cloud environment has been identified as a critical challenge in the modern internet world due to the increased rate of intrusion *** address such threats towards cloud services,numerous techniques exist that mitigate the service threats according to different *** rule-based approaches are unsuitable for new threats,whereas trust-based systems estimate trust value based on behavior,flow,and other ***,the methods suffer from mitigating intrusion attacks at a higher *** article presents a novel Multi Fractal Trust Evaluation Model(MFTEM)to overcome these *** method involves analyzing service growth,network growth,and quality of service *** process estimates the user’s trust in various ways and the support of the user in achieving higher service performance by calculating Trusted Service Support(TSS).Also,the user’s trust in supporting network stream by computing Trusted Network Support(TNS).Similarly,the user’s trust in achieving higher throughput is analyzed by computing Trusted QoS Support(TQS).Using all these measures,the method adds the Trust User Score(TUS)value to decide on the clearance of user *** proposed MFTEM model improves intrusion detection accuracy with higher performance.

关键词： Intrusion detection cloud systems trusted service support trusted network support trust user score trusted QoS support

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An efficient meta-heuristic resource allocation with load balancing in IoT-Fog-cloud computing environment

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Journal of Ambient Intelligence and Humanized computing 2023年第3期14卷 2981-2992页

作者： Yakubu, Ismail Zahraddeen Murali, M. Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur India

Fog computing is considered a derivative of cloud computing that aims to reduce the huge transmission latency and CPU time, as well as the overall cost of resource usage in the cloud. The deployment of Internet-of-Things (IoT) enabled smart systems, which frequently demand real-time processing, is rapidly expanding. Following that, the volume of generated data and computation workload dramatically increased. Fog resources are limited and typically resource constrained. Therefore, it is impossible to execute all tasks at the edge network. To support the increasing amounts of data and computation, cloud computing, associated with significant delays in transmission and processing of workload, is used. The distribution of tasks between the cloud and fog layer and the allocation of layer resources to satisfy the users' demands prevents layer oversaturation, service degradation, and resource failure due to excessive workload is challenging. This paper proposes a layer fit algorithm that evenly distributes tasks between the fog and cloud, based on priority levels. Also, a Modified Harris-Hawks Optimization (MHHO) based meta-heuristic approach is proposed to assign the best available resource to a task within a layer. The key intention of this paper is to reduce the makespan time, task execution cost, and power consumption and enhance resource usage in both the fog and cloud layer. The simulations are performed using the iFogSim simulation toolkit. The proposed layer fit algorithm and the Modified Harris-Hawks Optimization (MHHO) are compared with the traditional Harris-Hawks Optimization (HHO), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and the Firefly Algorithm (FA). Based on the experimental results, the MHHO has improved the performance of the system in terms of makespan time, execution cost, and energy consumption. The ability of the MHHO to balance the load across resources yields a significant improvement when the number of tasks increases as c

关键词： Internet of things

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A Reliable Real-Time Traffic Sign Detection Model for Diverse Environmental Challenges 5

A Reliable Real-Time Traffic Sign Detection Model for Divers...

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5th IEEE International Conference on Communication, computing and Industry 6.0, C2I6 2024

作者： Jayan, Keerthi Muruganantham, B. Srm Institute of Science and Technology School of Computing Department of Computing Technologies Chengalpattu India

ISBN: (纸本)9798350379884

This paper presents the development and evaluation of the Real-Time Traffic Sign Detection Model (RTTSDM), lever-aging the YOLOv4 framework with a CSPDarknet53 backbone, trained on the German Traffic Sign Recognition Benchmark (GTSRB) dataset. The RTTSDM exhibits significant advancements in traffic sign detection, showing substantial improvements across key performance metrics including average Intersection over Union (IoU), precision, recall, F1 Score, and mean Average Precision (mAP) throughout training. Notably, the model achieves a peak mAP of 0.9685 at iteration 4000, reflecting its superior ability to accurately identify and classify traffic signs. The model demonstrates robust performance under various lighting conditions, effectively detecting prohibitory, danger, mandatory, and priority signs with high accuracy. The RTTSDM shows strong potential to improve road safety and traffic management through its precise and reliable traffic sign detection capabilities. © 2024 IEEE.

关键词： Deep learning

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Advancing Breast Cancer Prediction and Early Detection with Advanced Deep Learning Models 4

Advancing Breast Cancer Prediction and Early Detection with ...

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4th International Conference on Advances in Electrical, computing, Communication and Sustainable technologies, ICAECT 2024

作者： Garg, Parth Sharma, Pulkit Prakash, U.M. Srm Institute of Science and Technology School of Computing Department of Computing Technologies Chennai India

ISBN: (纸本)9798350343670

Breast cancer is a common disease that predominantly affects women worldwide, with the potential to be fatal. Histopathologists utilize various criteria to examine tissue samples under a microscope for diagnosing cancer through histopathological imaging. However, pathologists often disagree during the diagnostic process. To address this issue, Deep Neural Networks are employed for supervised classification, given the considerable time required for manual categorization of histological pictures. In our classification task, we utilized the Breast Histology dataset, comprising 241 training and 21 test pictures. Effective preprocessing of the dataset is crucial for successful categorization. Images were classified into four categories (Normal, Benign, Insitu carcinoma, and Invasive cancer) using transfer learning based on GoogleNet, AlexNet, and ResNet. Notably, our method achieved the highest accuracy, with ResNet reaching 97.11%. Ongoing research aims to enhance the technique's effectiveness and reduce reliance on human involvement. Furthermore, this promising approach can be adapted to automate additional medical imaging modalities, potentially advancing the automation of various aspects of medical diagnosis through improvements to the suggested framework. © 2024 IEEE.

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