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Structural Invariant Feature Segmentation Based Apple Fruit Disease Detection Using Deep Spectral Generative Adversarial Networks

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SN computer science 2024年第5期5卷 635页

作者： Subha, V. Kasturi, K. Department of Computer Science School of Computing Sciences Vels Institute of Science Technology and Advanced Studies Tamilnadu Chennai India

In Indian economy agriculture, Crop cultivation plays an important role in the fruit production agricultural sector. At present, crop loss is primarily due to infested crops, resulting in reduced production rates for affecting various diseases. Manual monitoring of the disease is very difficult to analyze the type. It requires a huge amount of work, expertise, and excessive processing *** tackle this problem, we introduce Deep Spectral Generative Adversarial Networks (DSGANs) algorithm to categorize the apple disease. Initially, the preprocessing was carried out through Median and Gabor Filters to enhance the frequency of the Image. Then the boundary regions are adaptively filtered with a canny edge detector. This supports the exact boundary regions of the object to get the affected region. The color variance and the contours are different from the affected and non-effected regions. To optimize this, the Self-Adaptive Plateau Histogram Equalization (SAPHE) technique is applied to find the difference between affected and non-affected regions. Modified Gabor kernels are applied to choose the invariance of the affected region which supports segmentation using Invariant Sliding Window Segmentation (ISWS). This makes optimized segmentation by extracting the features in the affected boundary region. By intent, a ReLu activation for Logical decision to activate the logical decision depends on max successive threshold weights from convoluted margins. The non-linearity substitutive feature margins are extracted to enhance the performance of the output to process the adaptive GAN output layers. Finally, the classification part uses the DSGANs. The gated features get the marginal threshold values based on the feature extraction weights to get trained into classifier layers. This iteratively verifies the feature margins on affected regions get trained in Deep SpectralGenerative Adversarial Networks (DSGANs). © The Author(s), under exclusive licence to Springer Nature Singap

关键词： Apple disease Boundary region Classification Histogram Pre-processing Segmentation Segmentation

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A study on artificial intelligence for monitoring smart environments

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Materials Today: Proceedings 2023年 80卷 2009-2013页

作者： D., Karthika Department of Computer Science School of Computing Science Vels Institute of Science Technology & Advanced Studies Tamil Nadu Chennai India

Wireless networking has made enormous improvements. These developments have brought about new paradigms of wireless networking and communications Environmental protection has been in recent years a more intelligent and linked system for all facets of a global city. With the rise in data gathering, machine learning (ML) approaches can be used to boost the knowledge and the skill of an application. As the numbers increase and technology develops, the number of available data increases. Smart collection and interpretation of these Big Data is the underground to the rising of smart Internet of Things IoT apps. This study discovers the diverse methods of machine learning that resolve data difficulties in smart cities. The discussion takes place on applications such as air quality, water pollution, radiation pollution, smart buildings, smart transport, etc., which pose genuine environmental challenges. Adequate monitoring is needed to ensure sustainable growth in the world by safeguarding a healthy society. The potential and challenges in particular the role of machine learning technology for the Internet of Things and Big Data Analytics. © 2021

关键词： Data Analytics

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A Novel Framework for Human Chronic Disease Prediction Using CNN and Adaptive Dragon Fly Optimization (ADFO) Algorithm

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SN computer science 2025年第4期6卷 1-18页

作者： Ragu, B. Perumal, S. Department of Computer Science School of Computing Sciences Technology and Advanced Studies (VISTAS) Vels Institute of Science Tamil Nadu Chennai 600117 India

Chronic diseases in human beings are long-lasting health disorders that will progress slowly over months or a lifetime. The chronic diseases can affect various parts or systems of the body and are non-acute in nature. The chronic disease does not occur suddenly and shall not be resolved quickly. However, the spreading of the disease to other parts of the body and the effectiveness of the disease shall be suppressed by providing proper medical treatment from the earlier stages. The World Health Organization has expressed the significance of the treatment of chronic disease, stating that around 41 million deaths, representing 71% of all deaths occurring every year due to chronic diseases. This necessitates the earlier detection of human chronic diseases like cancer, cardiovascular disease and diabetes mellitus. The major challenge associated with chronic disease is that it does not express valid symptoms at the earlier stage and needs advanced technology for the earlier detection process with high accuracy. The existing system of chronic disease predictions falls short in diagnosis and detection at the earlier stages, whereas capable of detecting at stage 3 or stage 4, which were considered the advanced stage of the chronic disease. To overcome this challenge, this proposed work introduces a novel framework encompassing the Convolutional Neural Networks (CNN) and the Adaptive Dragon Fly Optimization algorithm for the earlier prediction process. The proposed work was trained with datasets of various chronic diseases individually to prove the system’s adaptability. In addition, the performance of the proposed work is analyzed in terms of accuracy, Precision, recall and F score, which were compared with the performance of the existing state-of-the-art methodologies. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adaptive dragon fly optimization algorithm Cancer Cardiovascular disease Convolutional neural networks Human chronic disease

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Detection of Brain Tumor using Transfer Learning using Conventional Autoencoder with Long Short Term Memory Method 6

Detection of Brain Tumor using Transfer Learning using Conve...

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6th International Conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Agalya, D. Kamalakkannan, S. Department of Computer Science School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Pallavaram Chennai India Department of Information Technology School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Pallavaram Chennai India

ISBN: (纸本)9798331522667

Brain tumor (BT) has generate a significant health challenge by putting pressure on healthy brain parts or spreading into other areas as well as blocking the flow of fluid around the brain. BT diagnosis is an extensive and time consumption process that depends primarily on radiologists experience and interpretive capabilities. For the area of medical imaging, automatic segmentation of images by means of Deep Learning (DL) approaches. The DL approaches assist in transforming the sector, resulted in improving precision and effectiveness of investigations. However, there are various feature extraction mechanisms available. Therefore, the Recurrent Neural Network (RNN) is utilized for feature extraction and image classification. In model execution, the data have trained in transforming the test image as well as data features for minimizing the domain shift is calculated through the Convolutional Autoencoder (CAE) for reconstruction loss. This research has concentrated in building a model with VGG16 as a single test that subjected at inference and existing method is adopted as neural networks for AE as Transfer Learning (TL) that performs an image analysis task such as segmentation and even set as an adopter for pre training the model. The AE used to train from the source dataset and perform as the adaptors in optimizing during testing using a test subject for effective computation. Moreover, the Long Short Term Memory (LSTM) is utilized as RNN model with CAE for providing improved detection of BT in healthcare industries. Hence, the proposed CAE with LSTM is compared with AE with Convolutional Neural Network (CNN) for evaluating BT detection using MRI dataset with various BT type classifications. © 2025 IEEE.

关键词： Image segmentation

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Enhancement of Containerization-based Cloud computing Performance using Hybrid Metaheuristic Optimization Algorithm: A Review 3

Enhancement of Containerization-based Cloud Computing Perfor...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Ramasamy, G. Kamalakkannan, S. Vels Institute of Science Technology & Advanced Studies Department of Computer Science School of Computing Sciences Pallavaram Chennai India Vels Institute of Science Technology & Advanced Studies Department of Information Technology School of Computing Sciences Pallavaram Chennai India

ISBN: (纸本)9798331509675

Container-based virtualisation has become a cornerstone of Cloud computing (CC) due to its lightweight and scalable properties when compared to traditional virtual machines. However, it is still difficult to optimising resource allocation in containerised systems, and current approaches frequently fall short in addressing problems like high computational costs, scalability, cold-start scenarios, and Graphics Processing Unit (GPU) management. Container resource allocation is critical for effectively managing computing workloads in cloud settings, enabling scalability, performance optimization, and cost effectiveness. It is extensively used in a various industry, including e-commerce, healthcare, and finance, where dynamic workloads call for efficient resource management. Traditional resource allocation techniques, while their importance, frequently face difficulties such as unequal load distribution and resource underutilization. To address these constraints, this paper suggests a hybrid metaheuristic optimization algorithm that addresses limitations such as network distance, execution time, and migration costs to provide balanced load distribution. This study attempts to analyse the efficiency, time consumption and latency of resource allocation in dynamic cloud systems by addressing existing limitations. © 2025 IEEE.

关键词： Cloud platforms

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Blockchain Security Using Q-Pragmatic-Sensitive Feature Analysis to Preserve Privacy 3

Blockchain Security Using Q-Pragmatic-Sensitive Feature Anal...

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3rd IEEE International Conference on Distributed computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Punithavathi, S. Jeyalaksshmi, S. Vels Institute Of Science Technology & Advanced Studies Department Of Computer Science Chennai India School Of Computing Science Vels Institute Of Science Technology & Advanced Studies Department Of Information Technology Chennai India

ISBN: (纸本)9798350318609

Healthcare data contains large amounts of personal information and is privacy sensitive. The healthcare sector needs privacy protection to keep private and sensitive information safe from unauthorized access. Security problems are raised by fundamental sharing difficulties involving personal information setting to securely forecast sensitive medical data using blockchain technology to address this issue. To solve the problem issue, propose a quasi-sensitive identification of blockchain security using tried-and-true partition fold padding encryption (PFPE) to improve the security of healthcare transactions. The Q-Pragmatic-Sensitive Feature Analysis (QPSFA) determines the terms related to sensitive privacy features. Finally, the Provable Fold Padding Encryption, and the authentication procedure is completed by using an Access Verification-based role to verify it. To ensure that, in a blockchain, the private data are cleansed and placed into data blocks. This suggests that the proposed system outperforms high accuracy up to 90%, as well security up to 94 % high insecurity compared to previous approaches regarding sensitivity and specificity rate performance and prediction accuracy. © 2024 IEEE.

关键词： Blockchain

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Secure Block Chain-Based Healthcare Sensitive Data Prediction Using Pragmatic Quasi-Identifiers in a Decentralized Cloud Environment

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SN computer science 2024年第1期5卷 41页

作者： Punithavathi, S. Jeyalaksshmi, S. Department of Computer Science Vels Institute of Science Technology and Advanced Studies Chennai India Hindustan College of Arts and Science Chennai India Department of Information Technology School of Computing Science Vels Institute of Science Technology and Advanced Studies Chennai India

Security is essential for all facts, information sharing around the internet and maintaining personalized information. In recent days, the healthcare industry needs privacy-preserving to keep personalized data from others containing sensitive information. Due to increasing security breaches, fundamental sharing problems with personalized details shared on the internet lead to security problems. To resolve this problem, we propose a Secure Blockchain-based Healthcare-sensitive data prediction using a Pragmatic quasi identifier in a decentralized cloud environment. Pragmatic quasi-sensitivity Identification (PQSI) based Privacy-preserving for securing personalized records Using Hyper Recurrent feature classification for improved cloud security. Initially, the sensitivity fitness value of the feature is estimated through Intensive feature success rate (IFSR) and clustered by marginal subset features. By marginalizing the sensitivity threshold frequency rate, the components are extracted by pragmatic quasi-sensitive identifier and classified using Hyper recurrent Neural classification (HRNC) to find the sensitive and non-sensitive records based on frequency fitness weight to split and store perception as individually in the private cloud. Further, to improve the security level Elliptic Curve Cryptography (ECC) based master node authentication technique is used in the blockchain concept. Peer end Block chain principle is applied to secure the sensitive data. To ensure the sensitive records are sanitized into data blocks to assure in a blockchain environment. This proposed system produces higher prediction accuracy than other methods and achieves higher sensitivity and specificity rate performance. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Cloud computing ECC Hyper recurrent feature classification Privacy preservation Security Sensitive data analysis

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Analyzing Brain Damages in Children with Image Processing Using KNN and FCM Algorithms

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SN computer science 2024年第1期5卷 62页

作者： Kayathri, B. Prasanna, S. School of Computing Sciences Vels Institute of Science Technology and Advanced Studies Tamil Nadu Chennai India Department of Computer Applications Vels Institute of Science Technology and Advanced Studies Tamil Nadu Chennai India

In current scenario, children are addicted towards mobile communication devices such as mobile phone, tablet and various wireless devices. Exploring more radio frequencies with these devices causes brain damage in children aged 5–10 years. This paper uses an original calculation for the examination of the cerebral image. Typically, an MRI scan or CT scan will show the Cerebrum’s living structures. The brain of children is scanned in MRI and analyzed through image processing to know whether the children put their lives in danger or not, parents should be aware of their kids’ true health. The analysis of training data selected from MRI scan pictures yields 96.3% accuracy in determining the outcomes through examination of the children’s brains. Hence, the findings of brain analyses on various children are examined. Thus, the existing methods to analysis on finding brain damage which is not enough to sort out the problem still, so proposed method is needed to find Brain Damage Findings (BDF) which has to work prominently compared with the existing methods of FCM and KNN with unique test and trained data of brain. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Communication devices CT (computerized Tomography) Damage FCM (Fuzzy C-Means) KNN (K-Nearest Neighbour) MRI (Magnetic Resonance Imaging)

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Improving agility in projects using machine learning algorithm

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Multimedia Tools and Applications 2024年第38期83卷 85987-86005页

作者： Varun, Janani Karthika, R.A. Department of Computer Science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai India Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai India

All the software products developed will need testing to ensure the quality and accuracy of the product. It makes the life of testers much easier when they can optimize on the effort spent and predict defects for the upcoming modules in the Agile era. The functionality being discussed in this paper is to predict the defects using Random Forest Algorithm. Predictive analytics draws on information from the past to create forecasts about the outcomes of future events. Product team always have the difficulty in delivering the product as per schedule. As we are in the agile era, the requirement keeps changing and team is unsure on upcoming releases. Prediction helps the team to focus on the complex and error prone modules in upcoming releases. The Predictive analytics model designed, can predict defects with an accuracy rate of 88% with the help of historical data. By predicting, testers can focus on the module where there are a greater number of defects predicted by the model and left shift the delivery. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Predictive analytics

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An Ensemble Machine Learning Model for Osteoporosis Risk Prediction from Medical Data 2

An Ensemble Machine Learning Model for Osteoporosis Risk Pre...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Thulasi, M. Thailambal, G. Vels Institute of Science Technology & Advanced Studies Department of Advanced Computing and Analytics Tamil Nadu Chennai India

ISBN: (纸本)9798331505745

The metabolic condition osteoporosis afflicted a large number of people worldwide. Osteoporosis causes major health and its financial implications worldwide. However, the advancement of osteoporosis-related methods for forecasting is inadequate. Machine Learning (ML), a subfield of Artificial Intelligence (AI)I allows computers to 'learn' using programs. In comparison to classic statistical methods, ML places a greater focus on prediction accuracy and can find patterns in multidimensional data sets. This study propose an ensemble model employing Random Forest(RF), XGBoost(XGB) and Light Gradient Boosting(LGBM) techniques for the prediction of osteoporosis from the medical data of an individual. The accuracy, precision, recall, and f1-score were computed to compare the model with four alternative machine learning models: Logistic Regression (LR), Decision Tree (DT), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN). The findings show that the ensemble model outperforms with an accuracy, precision, recall and F1-score of 93.5%, 92.8%, 94.2% and 93.5% respectively. © 2025 IEEE.

关键词： Support vector machines

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