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3rd International Conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2024

作者： Nithiyasree, P. Subramani, Kavitha Soorya, S. Arundhathi, T. Harshitha, Vatturi Panimalar Engineering College Dept of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350391565

Detecting mental health problems at the earliest can help professionals provide correct treatment and increase the value of patients. One of the most prevalent types of impairment in the world is depression. It is important to deal adolescent mental health issues which can escalate to more serious problems if not addressed early. Machine learning techniques are ideal for estimating and diagnosing medical data. Feature selection techniques have been applied to the entire attribute dataset to reduce attributes. Machine Learning accuracy is associated with attributes from the dataset. Three algorithms are employed in this work, Logistic Regression, Random Forest Classifiers, and Decision Tree Classifiers, in data science techniques. Sensitivity and Specificity are measures that estimate a model's capability to predict true positive and negative results. When these algorithms are compared the random forest classifier yields 95% accuracy when compared with other algorithms. © 2024 IEEE.

关键词： Adversarial machine learning

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Detection and Categorization of Conflict Flows Within SDN Environments using Machine Learning Approach 2

Detection and Categorization of Conflict Flows Within SDN En...

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2nd International Conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Kousalya, S. Sundararajan, Kanthimathi Vasudevan, I. Jaichitra Sheela, L. Jaba Thyagarajan, Sarunya Panimalar Engineering College M.E. Computer Science and Engineering Tamil Nadu Chennai India Panimalar Engineering College B.E. Computer Science and Engineering Tamil Nadu Chennai India Panimalar Engineering College Dept of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350328202

Software-Defined Networking (SDN) is known for its remarkable versatility and adaptability. SDN applications encompass a wide range of network management and control functionalities, including load balancing, access control, and routing. However, SDN can encounter issues stemming from conflicting traffic patterns that may degrade network performance in relation of efficiency and optimization. These conflicts arise when factors such as priority and action settings are adjusted. Furthermore, IP spoofing, commonly employed for anonymity and amplification, frequently leads to widespread distributed denial-of-service (DDoS) attacks. We've established a sufficient condition based on the duration and frequency of DDoS attacks. Notably, our sophisticated attack method generates legitimate traffic within the SIP network using reflection techniques. 'As a result, it has the ability to bypass security measures such as blacklists, IP blocking, rate limiting based on packet counts or session/transaction volumes, and automated systems designed to detect unnatural message generation. These security mechanisms are commonly employed in advanced security setups, but the system or technique in question can effectively avoid detection or restriction when interacting with digital environments.' © 2024 IEEE.

关键词： Network security

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Autism spectrum disorder based on squeezenet with fractional tasmanian rat swarm optimization

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Multimedia Tools and Applications 2024年第41期83卷 89029-89054页

作者： Muppidi, Satish Anuradha, G. Valarmathi, K. Department of Computer Science and Engineering GMR Institute of Technology Andhra Pradesh Rajam India Computer Science and Engineering Velagapudi Ramakrishna Siddhartha Engineering College Vijayawada Kanuru India Department of Computer Science and Engineering Panimalar Engineering College Bangalore Trunk Road Chennai Pooñamallee600123 India

A complicated neuro-developmental disorder called Autism Spectrum Disorder (ASD) is abnormal activities related to brain development. ASD generally affects the physical impression of the face as well as the growth of the brain in children. An early and proper medical diagnosis is essential for ASD affected children to enhance their quality of life. However, the clinical detection of ASD is a difficult task and time-consuming, hence it is essential to design an ASD detection approach for precise diagnosis of ASD children. In this research, an algorithmic approach called Fractional Tasmanian Rat Swarm Optimization driven SqueezeNet (FTRSO-SqueezeNet) is designed for the detection of ASD. The median filter and Region of Interest (RoI) extraction are used to de-noise the input image initially and extract a particular region from the filtered image. Later, the nub regions are extracted by choosing the optimal grid from the pre-processed image and using different feature extractors the features from the input images are determined. Finally, the detection of ASD is done by using SqueezeNet, which is trained using the FTRSO approach. The performance of the method is estimated, where the designed model achieved higher performance with an accuracy of 94.55%, sensitivity of 92.53%, and specificity of 95.22% than other prevailing approaches. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Rats

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Secure container Orchestration: A framework for detecting and mitigating Orchestrator - level vulnerabilities

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Multimedia Tools and Applications 2025年第17期84卷 18351-18371页

作者： Mahavaishnavi, V. Saminathan, R. Prithviraj, R. Department of Artificial Intelligence and Data Science Panimalar Engineering College Tamil Nadu Chennai600123 India Department of Computer Science and Engineering Annamalai University Chidambaram India Department of Computer Science and Engineering Annamalai University Chidambaram India

This paper presents Secure Orchestration, a novel framework meticulously planned to uphold rigorous security measures over the profound security concerns that lie within the container orchestration platforms, especially in Kubernetes. The widespread adoption of containerized environments has raised concerns about the vulnerabilities at the level of the orchestrator. Secure Orchestration delivers a comprehensive approach by leveraging cutting-edge methods to detect and prevent the exposure of vulnerabilities from orchestrator misconfigurations, privilege escalation exploits that can thwart the orchestration security layer, and unauthorized access attempts targeting the orchestration system itself. In addition, it is further strengthened by deploying an Intrusion Detection and Prevention System (IDPS), which is responsible for actively monitoring the orchestration infrastructure. The IDPS utilizes sophisticated algorithms and mechanisms for detecting anomalies to continuously assess the orchestration environment for indications of intrusion, vulnerabilities, and anomalous activities. By using this proactive approach, the framework is able to rapidly identify potential threats and respond through countermeasures in a timely manner, thus greatly reducing the risks associated with orchestrator level vulnerabilities. Conducting a set of real-world experiments that closely simulate a variety of threat scenarios is imperative for empirically validating the efficiency of this framework. The obtained empirical results yield insightful revelations on the efficacy of the framework in accurately identifying and mitigating vulnerabilities, thereby ensuring the privacy and integrity of container orchestration systems. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Containers

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A QoS-aware routing approach for Internet of Things-enabled wireless sensor networks in smart cities

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Multimedia Tools and Applications 2025年第17期84卷 17951-17977页

作者： Karunkuzhali, D. Meenakshi, B. Lingam, Keerthi Department of Information Technology Panimalar Engineering College Chennai India Department of Electrical and Electronics Engineering Sri Sairam Engineering College Chennai India Department of Computer Science and Engineering Gitam University Visakhapatnam India

In the Internet of Things (IoT), optimizing machine performance through data analysis and improved connectivity is pivotal. Addressing the growing need for environmentally friendly IoT solutions, we focus on "green IoT." Quality in smart cities heavily relies on efficient data gathering and communication. In this study, we propose a novel software architecture tailored for data collection and communication in IoT-enabled smart applications, emphasizing sustainability from the outset. Specifically, for smart cities utilizing IoT-enabled wireless sensor networks, we introduce a Quality of Service (QoS)-aware routing strategy. In the data collection phase, we employ Chaotic Bird Swarm Optimization (CBSO) for creating IoT sensor clusters, accompanied by Improved Differential Search (IDS) to assess the reliability of individual sensor nodes, ultimately designating the most trusted node as the Cluster Head (CH). In the data transport phase, we implement lightweight signcryption to enhance the security of IoT sensor data. Furthermore, we employ an Optimal Data Routing (ODM) approach to compute the most efficient data transmission paths between source and destination nodes within the IoT network. Finally, the performance of our QoS-aware routing strategy is evaluated through network simulations using NS2 and compared against existing approaches, offering valuable insights into its effectiveness and suitability for smart city IoT applications. The main findings of the study indicate that the proposed QoS-aware routing strategy (QOS-AWARE ROUTING) consistently outperforms existing routing algorithms in terms of energy efficiency, network longevity, and latency, both in scenarios with varying sensor node density and over multiple cycles. Additionally, it demonstrates significant advantages, such as up to 90.8% less energy consumption, 66.8% longer network lifespan, and 80.1% reduced latency when compared to the benchmarked methods. © The Author(s), under exclusive licence to S

关键词： Internet of things

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Morphological Dilated Preprocessing with Depthwise Separable Visual Geometry Group Model for Classification of Minerals 5

Morphological Dilated Preprocessing with Depthwise Separable...

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5th IEEE Global Conference for Advancement in Technology, GCAT 2024

作者： Devi, M. Shyamala Prateeka, K.K. Rishaliniirien, A. Reemaaasmin, A. Hemavathy, K. Akshayavarthini, Akshayavarthini Panimalar Engineering College Department of Computer Science and Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350376685

Accurate identification of minerals may have considerable influence on research and industrial applications, which makes it a crucial task in geology and materials science. Image classification challenges have shown significant results for traditional Convolutional Neural Networks (CNNs), such as VGG model. Unfortunately, because of their depth and variety of parameters, these CNN models frequently need large amounts of computational power. For enhancing the performance of mineral classification while preserving high accuracy, this work introduces a unique method that incorporates depth wise separable convolutions into the VGG architecture. mineral images to the current CNN models. By splitting the convolution process into depthwise and pointwise convolutions, depthwise separable convolutions are a viable substitute for constrained computing settings due to their ability to reduce both computational complexity and model size. So the traditional VGG model was intergrated with depthwise separable convolutions at the end to propose Depthwise Separable VGG16 (DS-VGG16) that uses Mineral Identification Dataset from KAGGLE for execution. The efficiency of the proposed DS-VGG model was done in by applying the morphological reconstructed Dilated mineral image throug preprocessing. The DS-VGG16 classifies six mineral types by comparing with existing CNN models on evaluating the mineral identification dataset. This paper makes a significant addition to the fields of CNN by demonstrating the potential of depthwise separable convolutions in strengthening deep learning models for mineral classification tasks. The execution shows that the proposed DS-VGG16 model performs well in classifying mineral types, with a high accuracy of 99.63%. © 2024 IEEE.

关键词： Convolution

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High Dynamic Range Edge Filtered LeNet based Gemstone Classification

High Dynamic Range Edge Filtered LeNet based Gemstone Classi...

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2024 Asian Conference on Intelligent Technologies, ACOIT 2024

作者： Devi, M. Shyamala Shaarmi, G. Priyadharshini, R. Priyanka, D. Sathvika, M. Prabavathi, R. Department of Computer Science and Engineering Panimalar Engineering College Tamilnadu Chennai India

ISBN: (纸本)9798350374933

The precise detection of subtle characteristics and textures in gemstone photographs is necessary for the hard process of effective gemstone categorization. This research provides a unique method of classifying gemstones using convolutional neural networks (CNNs) based on LeNet and edge-filtered images. By highlighting the edges and contrasts in gemstone images, edge filtering improves the visibility of important characteristics including cuts, facets, and defects. The HDR function reveals subtle color and texture changes that are essential for differentiating between different types of gemstones by capturing a wider range of light intensities. This paper proposes High Dynamic Range Edge Filtered LeNet (HDEF-LeNet) that categorizes the Gemstone types with high accuracy. The Gemstone Dataset that includes 800 images was used for this execution. The HDR edge filtered images are extracted from the original images. After that, a customized HDEF-LeNet architecture designed to take into account the special qualities of gemstone images that is fed with this preprocessed data. Our strategy seeks to outperform conventional approaches based on conventional imaging techniques in terms of gemstone classification and accuracy. The findings of the experiments show that combining edge filtering and HDR with LeNet improves performance in classification tasks for gemstones, providing a viable approach to more accurate and consistent gemstone identification. The experiment demonstrates that the suggested HDEF-LeNet model works better in the categorization of gemstone types, with a high accuracy of 99.68%. © 2024 IEEE.

关键词： Convolutional neural networks

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Canny Edge Filtered VGG Framework for Diamond Cut Classification 8

Canny Edge Filtered VGG Framework for Diamond Cut Classifica...

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8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Devi, M. Shyamala Sukesh Babu, R. Suganthan, K. Sarmilaa, R. Sruthi, N. Sai Rajashree, S. Panimalar Engineering College Department of Computer Science and Engineering Tamilnadu Chennai India

ISBN: (纸本)9798331505462

Precise diamond cut categorization is essential for value estimation and uniform grading in the field of diamond quality evaluation. Conventional techniques depend on heuristic algorithms and manual examination, which can be error-prone and time-consuming. In this work, we provide a novel method for diamond cut classification by proposing Canny Edge Filtered VGG16 (CEF-VGG16) that has been improved by preprocessing the diamond images with Canny edge recognition. When preprocessing diamond images, the Canny edge filter is used to highlight the edges and structural characteristics that are essential for differentiating various cut levels. The Diamond Cut Dataset having 1600 images with 8 classes of diamond cuts was used for this execution. The Original images are subjected to extract sobel Edge filtered, Otsu threshold Edge filtered and Canny Edge filtered diamond images. These edge filtered images are applied to existing CNN models. The suggested approach improves resistance to changes in illumination and image quality while simultaneously simplifying the categorization process. The experiment reveals that the proposed CEF-VGG16 model performs well in the diamond cut categorization with a high accuracy of 99.72%. © 2024 IEEE.

关键词： accuracy canny classification CNN edge filtering otsu sobel

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Sobel Filtered UNet Prototype based Date Fruit Classification 5

Sobel Filtered UNet Prototype based Date Fruit Classificatio...

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5th IEEE Global Conference for Advancement in Technology, GCAT 2024

作者： Devi, M. Shyamala Pallavi, S. Ramya, A.B. Radhika, K. Revathi, K. Sarupriya, P. Panimalar Engineering College Department of Computer Science and Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350376685

Accurate and effective fruit classification is essential for separating and analyzing in the field of agriculture. This article design a proposed model for classifying date fruit using Sobel Filtered UNet (SFU-Net). To increase feature extraction and classification accuracy, the recommended strategy incorporates Sobel edge detection into the UNet architecture. For execution, 1200 images from the KAGGLE Date Fruit Dataset were used. The dataset contains 200 images for six classes of Date fruit for Sugaey, Shaishe, Ajwa, Galaxy, Natab Ali and Rutab date images. Date Fruit Images are preprocessed using the Sobel filter, which is renowned for its ability to identify edges. This Sobel filter highlights the characteristics that are essential for differentiating between various types of date fruit. Subsequently, the preprocessed Sobel filtered date fruit image input is fed into the UNet, a popular CNN that is popular for its efficiency in segmentation tasks. The prototype accomplishes precise date fruit categorization and high-resolution segmentation by using the encoder-decoder structure of UNet. Experiments show that integrating edge detection with state-of-the-art deep learning approaches can lead to notable gains in classification performance when compared to conventional methods. This method not only pushes the boundaries of fruit categorization but also has potential uses in automated sorting methods and agricultural quality control. The results show that the proposed SFU-Net model performs exceptionally well towards date fruit classification, with a high accuracy of 99.45%. © 2024 IEEE.

关键词： Wiener filtering

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Contour Edge Mask Filtered Residual Network based Egg Classification 8

Contour Edge Mask Filtered Residual Network based Egg Classi...

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8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Devi, M. Shyamala Sangeetha, V. Reshmi, R. Saima Nooreen, S. Rajeshwari, C. Sai Pranavi, N. Panimalar Engineering College Department of Computer Science and Engineering Tamilnadu Chennai India

ISBN: (纸本)9798331505462

In agricultural and food safety, classifying eggs is a crucial activity that requires great precision in distinguishing various kinds of egg. Traditional approaches can be expensive and error-prone since they frequently rely on manual examination or simple image processing techniques. In order to improve classification performance, this research proposes Contour Edge Mask Filtered ResNet (CEM-ResNet), which combines advanced deep learning techniques with edge-detection methods. The Egg Dataset containing 2000 egg images that was used for implementation. The suggested CEM-ResNet model uses contour edge masking to eliminate extraneous background noise and highlights key structural information while utilizing ResNet architecture to capture essential details of eggs. The suggested CEM-ResNet model uses contour edge masking to eliminate extraneous background noise and highlights key structural information while utilizing ResNet architecture to capture essential details of eggs. Initially, the CEM-ResNet model organizes the egg into two groups as Chicken egg and Duck egg. The organized eggs are performed with labeling and then subjected to data augmentation to end up with 42,000 images. The augmented egg images are converted to grayscale images. The grayscale images are processed with edge detection model to finalize the contour filtered egg images and ResNet model. The contour filtered egg images are refined to create the edges and then the mask was created to generate Contour Edge Mask Filtered (CEM) egg images. The ResNet model was refined to process the CEM egg images. With a high accuracy of 99.64%, the implementation reveals that the proposed CEM-ResNet model outperforms towards the classification task. © 2024 IEEE.

关键词： Deep learning

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