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

作者： Sivasankari, S. Aneetha, A.S. Vels Institute Of Science Technology & Advanced Studies Department Of Computer Science Chennai India

ISBN: (纸本)9798350318609

The cloud computing is a novel approach to online computing. It offers numerous benefits, but several important problems need to be fixed if the cloud environment is to become more reliable. These are the problems associated with fault tolerance, load management, and other security concerns in cloud environments. The main focus is to solve the issue in the pressure of network and improve the cloud computing efficiently and this paper runs with ant colony algorithm. The real time network loads the situation that provides and calculates the link to smallest load ad provides the dynamic stream of data forwarding strategy. The results showed the technique of load balanced ACO that can improve the response computationally. The Ant Colony Optimization Algorithm got the average response 30% lower compare to other algorithms. The ant colony optimization shows the uses and good result in optimization analysis. © 2024 IEEE.

关键词： Ant colony optimization

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Analysis of deep actor-critic methods for classifying cancer subtypes through gene expression

International Journal of Systematic Innovation

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International Journal of Systematic Innovation 2024年第4期8卷 46-66页

作者： Jayakrishnan, R. Meera, S. Department of Computer Science and Engineering Vels Institute of Science Technology and Advanced Studies Tamil Nadu Chennai India

The word “cancer” denotes a syndromes that can spread to various bodily areas and are brought on by abnormal cell proliferation. After cardiovascular illnesses, according to the World Health Organisation (WHO), cancer is the second largest cause of death in the world. To better understand molecular processes behind various cancer subdivisions, cancer categorization depends on gene expression information is essential. Conventional machine learning methods have proven helpful in this situation, but new approaches are needed for accurate and understandable categorisation due to the difficulty and dimensionality of gene expression datasets. In this article, we analyse various methods for multiclass categorising cancer subtypes using deep structured reinforcement learning (DSRL). Our methodology addresses several significant issues in cancer subtype classification by combining the strength of deep neural networks with reinforcement learning. In this research, seven different gene expression datasets are utilised to classify the cancer subtype. We also used different classification approaches in Python for the same dataset to perform a comparative study. Deep reinforcement learning for cancer subtype classification improves the accuracy of gene expression data by integrating intricate data patterns, enabling customised therapies, and expanding the field of precision medicine research. The analysis reveals that the newly suggested model exceeds the contemporary state-of-the-art classifiers, achieving the highest accuracy across all seven datasets, ranging from 55% to 100%, while attaining the lowest loss, which varies between 0.02 and 0.11. This work offers a viable method for classifying cancer subtypes into many categories using gene expression data. © (2024), (Society of Sytematic Innovation). All rights reserved.

关键词： Cancer subtypes Deep neural networks Deep-learning Gene expression Machine-learning Reinforcement learning

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Machine Learning based Anomaly and Threat Detection System in Real Time Social Internet of Things using Dimensionality Reduction Techniques 2

Machine Learning based Anomaly and Threat Detection System i...

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

作者： Jenifer, Anciline Piramu, Preethika S.K. Vels Institute of Science Technology & Advanced Studies Department of Computer Science Tamil Nadu Chennai India

ISBN: (纸本)9798331505745

The Social Internet-of-Things (SIoT) is an innovative model developing in the struggle to safeguard privacy and increase trust inside the Internet-of-Things(IoT). The revolutionary notion of SIoT combines the IoT with social platforms, allowing inanimate items to develop relationships with one another. The fast growth of SIoT has transformed networked networks, enabling effortless interaction between devices. However, integrating these systems creates major security issues, including anomalies and attacks that may compromise system integrity. This study presents a machine learning(ML) methods like Isolation Forest (IF), One class support vector Machine(SVM) and Kernel density estimation(KDE) for in SIoT networks using the Principal component Analysis(PCA) and Correlation Analysis as dimensionality reduction techniques. The findings shows that the IF method provides an accuracy of 93% with the dimensionality reduction techniques applied on RT-IoT dataset. By utilizing powerful machine learning algorithms and contextual analytics, the framework improves detection accuracy and enables strong device collaboration in highly dynamic contexts.. © 2025 IEEE.

关键词： Support vector machines

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Advancements in Speech Recognition: Exploring Feature Extraction Techniques for Enhanced Accuracy and Human-Machine Communication 4

Advancements in Speech Recognition: Exploring Feature Extrac...

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4th International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Bhavani, R. Kathirvelu, Kalaivani Vels Institute of Science Technology and Advanced Studies Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350343670

Speech Recognition (SR), for all diverse languages, is still a big challenge. SR is predominantly used in telecommunication, medical documentation, car control, aviation, etc. SR involves breaking down an audio signal of spoken words into discrete units and then matching those units to words in a language model. The main target of SR is to attain healthier verbal communication amid humans and machines. Therefore, it is a good technology of the human-machine crossing point. The SR uses the Feature Extraction (FE) techniques for better accuracy. In this paper, various techniques for FE are discussed, and an overview is provided to assist in selecting the most suitable technique for achieving higher accuracy. The review highlights the importance of developing more accurate and robust SR systems to improve the user experience in various domains. Overall, it provides the manuscript deals with five different techniques, and among them, the Mel-Frequency Cepstral Coefficients (MFCC) technique was found to exhibit a high level of accuracy. © 2024 IEEE.

关键词： feature extraction Signal processing Signals Sound Speech recognition

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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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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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Enhanced Proof of Authentication(E-PoA) Consensus Blockchain-Based Secured Health Care Data Transmission in Internet of Things

Enhanced Proof of Authentication(E-PoA) Consensus Blockchain...

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2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Keerthana, T. Krithika, D.R. Vels Institute of Science Technology and Advanced Studies Department of Computer Application Chennai India

ISBN: (纸本)9798350375442

Blockchain and the Internet of Things (IoT) are revolutionizing healthcare, presenting the potential for transformation. The IoT is achieving more and more confidence as the infrastructure for future applications. Therefore, based on the comprehensive integration of Blockchain (BC) technology with IoT, security requirements are provided through security frameworks built into BC. Healthcare IoT applications in BC technology are revolutionizing industries. It also analyzes the potential benefits of BC implementation in IoT and healthcare for securely transferring medical data. Blockchain's decentralized and immutable nature of medical data sharing offers secure ways to store and transmit data. However, it is necessary to protect the data's privacy stored and shared securely. This review paper evaluates the algorithm based on Rivest, Shamir, and Adelman (RSA), advanced Encryption Standard (AES), Lionized Golden Eagle-based Homomorphic Elapid Security (LGE-HES), and BC-Assisted Secure Data Management Framework (BSDMF). This survey paper describes Blockchain technology-based medical data transfer based on IoT medical systems. The performance evaluation regarding Root Mean Square Error (RMAE), MSE, time complexity, and security performance compared to standard technologies can be analyzed. © 2024 IEEE.

关键词： Electronic health record

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Analysis of Lung Cells with a Novel Segmentation Methodology Using FCN and Deeplab V3 3

Analysis of Lung Cells with a Novel Segmentation Methodology...

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

作者： Ramya, J. Poongodi, A. Vels Institute Of Science Technology And Advanced Studies Department Of Computer Sciences Chennai India

ISBN: (数字)9798350318609

ISBN: (纸本)9798350318609

One of the primary causes of death globally, cancer is estimated to have affected more people in less developed nations than in more developed ones throughout time in terms of both cases and mortality. The unchecked growth of abnormal cells that begins in one or both of the lungs is known as lung cancer. The tumors impede the lungs' capacity to provide oxygen to the circulation as they enlarge. Medical images are analyzed using image processing, machine learning, and other technologies to help in early illness identification and treatment. When it comes to diseases like cancer, time is of the essence and should be investigated as soon as feasible. To diagnose lung cancer, we suggested a novel hybrid segmentation technique in this work. Using a fully convolutional network (FCN) and DeeplabV3,we were able to separate lung cancer cells hybridly. According to the findings, our approach performs well and is very accurate when compared to other currently used approaches. The proposed hybrid FCN with the Deeplabv3 model segments the input lung image with foreground and background with 93% and 97% of accuracy. And also shows 86% of IOU and 93% of the score showing the system performs well in the lung cancer segmentation process. © 2024 IEEE.

关键词： Median filters

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Cervical Disc Herniation Detection Using Morphological Image Processing and Neural Network Classification 3

Cervical Disc Herniation Detection Using Morphological Image...

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3rd International Conference on Computational Modelling, Simulation and Optimization, ICCMSO 2024

作者： Malarvizhi, C. Muruganantham, A. PSG Institute of Advanced Studies Department of Computer Science Coimbatore India Bengaluru India

ISBN: (纸本)9798350361391

Intervertebral discs, the rubbery cushions between spinal bones, can bulge or rupture (herniate) beyond their normal boundaries. This can irritate nearby nerves, causing pain, particularly in the upper back with cervical disc herniation. This work explores the use of morphological transformations of image and simulated neural network classification to analyze magnetic resonance imaging (MRI) data for automatic detection of disc herniation. Morphological operations manipulate individual pixels in an image based on the measurement of their surrounding pixels. In this paper, these techniques are employed for herniated regions identified in the magnetic resonance image scans. Specifically, the edge-detected canny image is suitable for identifying the edges of these regions, followed by morphological operations with structuring elements to refine and enhance the image quality. Using non-flat structuring elements such as arbitrary and ball applied to the images. Morphological operations named dilation and erosion are applied for four stages of herniated canny images. A neural network classification method is employed to analyze the processed images at different stages of disc herniation. This allows for the calculation of the effectiveness of each structuring element in accurately identifying herniated discs. The results of the morphological operations could be used to extract quantitative measurements of hernia size, shape, or other relevant features, aiding in treatment planning and monitoring. © 2024 IEEE.

关键词： Image enhancement

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Deep Learning-Based Plant Leaf Disease Detection Using Scaled Immutable Feature Selection Using Adaptive Deep Convolutional Recurrent Neural Network

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SN computer science 2023年第5期4卷 592页

作者： Jayashree, S. Sumalatha, V. Department of Computer Application Vels Institute of Science Technology and Advanced Studies Tamil Nadu Chennai India Department of Computer Science Vels Institute of Science Technology and Advanced Studies Tamil Nadu Chennai India

Agriculture provides food and raw materials that are the bedrock of all nations. It is very essential to humans as a source of food. Plant diseases are major factors that affect the yield and quality of the plant. Digital image processing can determine plant diseases. Prediction of plant diseases has been implicated in the early stages of the agricultural sector. Thus, existing image process techniques employed plant disease features manually which is inefficient and results in poor recognition accuracy. This is often time-consuming and can result in poor precision and reproducibility. To tackle this issue, we introduce a proposed Hyper-Spectral Immutable Scaled Feature Selection (HSISFS) using Adaptive Deep Convolutional Recurrent Neural Network (ADCRNN) for leaf disease identification. It starts by pre-processing and scaling the region of plant leaf features to normalize the image pixel by reducing the noisy image based on Sampling Subset Feature Filtering (S²F²). Then, Ada Boosting Region-Based Segmentation (ABRS) technique employs for segregating leaf region parts. Based on the segmentation, the image analyzes the nearest maximum feature weight using Spider Optimization-Based Maximum Features Weight (SO-MFW). After that, the HSISFS method selects the plant leaf disease’s finest features. Finally, the ADCRNN algorithm with softmax logical activate function categorizes the leaf disease based on the finest features. Thus, the proposed produced high classification and detection accuracy, precision, recall, and F-measure performance, with a low false rate. The classification accuracy performance results are superior than the existing methods. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Classification Feature selection Filtering Image processing Plant leaf disease RNN Segmentation

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