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4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： James, T. Prabahar Godwin Bala, K. Bharath Institute of Higher Education and Research Department of Computer Science and Engineering Tamil Nadu Chennai600073 India Bharath Institute of Higher Education and Research School of Computing Department of Computer Science and Engineering Tamil Nadu Chennai600073 India

ISBN: (纸本)9798331508845

The goal of this project is to improve the resistance of crops to fungal root rot diseases, including Fusarium, Rhizoctonia, and Pythium within vertical soilless farming systems using genomic prediction approaches. Beside merging of the phenotypic and genetic data the study aims at discovering relevant gene markers that are responsible for disease resistance. Molecular biology and bioinformatics approaches shall be used to design decision support systems that will predict the vulnerability of different cultivars to root rot infection given environmental and genetic factors. Vertical soilless farming imposes specific risks and opportunities for disease control and the management of crop disease requires accurate data *** investigation will contribute to better understanding the genetic variability of resistance-related genes in the target pathogens, as well as help to identify proper, genetically adapted for the high-stress conditions. Lastly, the purpose of the findings is to enhance the stability and productivity of crop yield and food security under conditions of limited soil availability and other aspects of soilless systems. This work therefore provides a call for adopting the best breeding practices combined with modern genomic tools for developing stronger agricultural systems that will be able to withstand biotic stresses. © 2024 IEEE.

关键词： Plant diseases

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Enhanced Approach to Study the Neural Stem Cell Migration through Image Processing using Deep Learning 4

Enhanced Approach to Study the Neural Stem Cell Migration th...

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4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Vaithiyanathan, M. Bala, K. Bharath Institute of Higher Education and Research Department of Computer Science and Engineering Tamil Nadu Chennai600073 India School of Computing Bharath Institute of Higher Education and Research Department or Computer Science and Engineering Tamil Nadu Chennai600073 India

ISBN: (纸本)9798331508845

Neural stem cells are a specific type of stem cells that are unique in their capacity to self-renew and differentiate into neurons and glial cells, including neurons, oligodendrocytes and astrocytes the latter contributing to the developmental process of the nervous system. In a bid to implement this approach in identifying imaging features related to neural stem cells, this study aims at using deep convolutional neural networks (CNNs) at a middle level. In the study, the primary focus is laid on the consideration of the distribution of the number of CD133+ neural stem cells as well as their possible migration in rat brain areas. Absolute quantification studies delineated marked qualitative differences in the localization of these cells across distinct regions of the brain. Most importantly, we found out that CD133+ neural stem cells could move from subventricular zone (SVZ) to almost near different parts of the brain including ganglia, corpus callosum and cerebral cortex. Before undergoing surgery, the activity of the rats was trained through water maze learning, respectively recording their escape latency (EL) at the time point of one week, two weeks, and one month. The findings clearly showed that EL was longer in the cerebral ischemia-reperfusion as compared with the sham-operated control group. Nevertheless, a significant decrease in EL was seen in the group treated with electroacupuncture after ischemia-reperfusion. From this it could be extrapolated that electroacupuncture may improve the viability and differentiation of newly generated neural stem cells into both glial and neuronal lineages. In extension, electroacupuncture promoted new neuron formation and a subset was found to be functional when they secreted acetylcholine. Taken together, the present study showed that electroacupuncture facilitated neurological recovery in cerebral ischemia-reperfusion rats. © 2024 IEEE.

关键词： Stem cells

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Oil Spill Detection Using 2D Convolution Neural Network and Generative Adversarial Network 4

Oil Spill Detection Using 2D Convolution Neural Network and ...

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4th International Conference on Expert Clouds and Applications, ICOECA 2024

作者： Madhura, R. Nikkath Bushra, S. Syed Ismail, A. Haleema, P.K. SRM Institute of Science and Technology Department of Computing Technologies KattankulathurTamil Nadu Chennai India SRM Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur Tamil Nadu Chennai India School of Computing and Engineering University of West London RAK Branch Campus United Arab Emirates

ISBN: (纸本)9798350385793

Finding oil spills in the ocean is one of the most crucial tasks in preserving our ecosystem. Using satellite or aerial photographs as input to a deep learning model that makes use of 2D CNN (Convolution Neural Network) and GAN (Generative Adversarial Network). Deep learning assists in automatically detecting oil spills in the ocean. A strong model that may be applied to volumetric data is 2D CNN. To protect the environment and its biodiversity, it is critical to identify and minimize oil spills in delicate maritime environments. Conventional techniques for tracking oil spills in satellite imagery sometimes depend on labor-intensive, human error-prone manual inspection. When deep learning models are taught with a lot of data, they perform well. The Gathering of datasets, however the situation proved to be challenging. As a result, employing Generative Adversarial Networks (GAN) to produce new photos by utilizing preexisting ones. As a result, by giving the model a suitable number of photos to use for training and testing, it improves accuracy. © 2024 IEEE.

关键词： Satellite imagery

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Advancements and applications of position-sensitive detector(PSD): a review

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Optoelectronics Letters 2024年第6期20卷 330-338页

作者： Shaher Dwik G.Sasikala S.Natarajan Department of Electronics&Communication Engineering School of Electrical&CommunicationVel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai 600062India Department of Computer Science and Engineering Amrita School of ComputingAmrita Vishwa VidyapeethamChennai 601103India1

This paper presents a review of the position-sensitive detector(PSD) sensor, covering different types of PSD and recent works related to this field. Furthermore, it explains the theoretical concepts and provides information about its structure and principles of operation. Moreover, it includes the main information about the available commercial PSDs from different companies, along with a comparison between the common modules. The PSD features include high position resolution, fast response, and a wide dynamic range. These features make it suitable for various fields and applications, such as imaging, spectrometry, spectroscopy and others.

关键词： Advancements and applications of position-sensitive detector a review PSD

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An Efficient Classification of Cassava Leaf Disease using VGG 16

An Efficient Classification of Cassava Leaf Disease using VG...

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2023 International Conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Soujanya, A. Cherukuvada, Srikanth Murugan, V. Ramya, Singaraju Agalya, K. Gayathri, S. Ibrahimpatnam India School of Computing SRM Institute of Science and Technology Department of Networking and Communications Kattankulathur Chennai India CARE College of Engineering Department of Artificial Intelligence and Data Science Thayanur Trichy India SRM Institute of Science and Technology School of Computing Department of Computing Technologies Kattankulathur Chennai India Sri Eshwar College of Engineering Department of Computer Science and Engineering Coimbatore India K. Ramakrishnan College of Technology Department of Computer Science and Engineering Samayapuram Trichy India

ISBN: (纸本)9798350319200

Cassava is an essential food item in the tropics, it may origin enormous productivity decreases in cassava, leading to significant financial losses and a shortage of basic foods. Small-scale farmers grow the essential food and nutrition crop cassava because it can withstand harsh conditions. In many African nations, it provides a substantial source of carbohydrates. Leaf infections can occasionally harm cassava crops, limiting total productivity and farmers' income. In this study, Pre trained models are utilized for the determination of disease identification and classification on plant leaf issues. VGG 16 has been put into use, and its performance has been examined. An exploratory study is carried out using an online Cassava image collection, which contains pictures of Cassava leaves afflicted with five different disease categories. The vgg16 classifier that was suggested surpasses its competitors greatly and achieves an accuracy of 97.58%. The results of the test demonstrate that improving classification performance by using a database of images that is well balanced is possible. © 2023 IEEE.

关键词： Classification CNN Deep learning Transfer learning VGG16

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Diabetes Prediction using Enhanced and Optimized DRL-CNN Approaches 21

Diabetes Prediction using Enhanced and Optimized DRL-CNN App...

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21st International Learning and Technology Conference, L and T 2024

作者： Usha Nandhini, A. Dharmarajan, K. Sharma, Bhisham Chowdhury, Subrata Ben Dhaou, Imed Vistas School of Computing Chennai India Vistas Department of Information Technology Chennai India Chitkara University Institute of Engineering and Technology Chitkara University Department of Computer Science and Engineering Punjab India Sreenivasa Institute of Technology and Management Studies Department of Computer Science and Engineering Andra Pradesh Chittoor India Dar Al-Hekma University Hekma School of Engineering Computing and Design Department of Computer Science Jeddah Saudi Arabia

ISBN: (纸本)9798350393569

This study develops an advanced automated prediction system using Machine Learning (ML) techniques to identify diabetes early. The research employs the WBSMOTE method for data preprocessing, addresses class imbalances with the WB-SMOTE approach, and introduces two novel classification methods, DRL-CNN and DRL-OCNN. Reliable datasets from sources like The Data World repository, PIMA Indian Diabetes (PIDD) dataset, and Real Time (RT) Diabetes Dataset are used, with an emphasis on data consistency and feature relevance. If we examine these datasets, it contains general attributes and no internal specific attributes related to diabetes is used. So, prediction results will be what type of diabetes we won't get any specific characteristics and risk factors. Evaluation is based on Accuracy, Precision, and Recall criteria. The DRL-OCNN model, integrating SGD optimization and Delta Rule Learning, outperforms other ML models with a accuracy of 96%, precision of 0.93 and recall of 0.91, demonstrating its potential for accurate early diabetes identification and classification. © 2024 IEEE.

关键词： Machine learning

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Application of Bayes Rule for Precise Analysis of Class-Imbalanced Stroke Data

Application of Bayes Rule for Precise Analysis of Class-Imba...

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2023 International Conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Viswapriya, S.E. Rajeswari, D. SRM Institute of Science and Technology School of Computing College of Engineering and Technology Department of Data Science and Business Systems Kattankulathur Chennai India

ISBN: (纸本)9798350319200

The interpretability of stroke risk prediction is still lagging and it can be solved by using deep analysisofdata with the application of the Bayes rule. The prediction model has to be produced with accuracy, clarity, and comprehensibility without ambiguity, especially in machine learning and data analytics. The class imbalance problem is a common issue affecting the raw data, which means the imbalanced categorization of outcome feature value. In many fields, such as fraud detection, network intrusion detection, and medical diagnostics, this class imbalance issue is present where negative class samples outnumber positive class samples. If some datasets have class imbalance and imperfect issues, there will be no proper classification pattern. There are many resampling methods to address this class imbalance problem. However, these resampling methods can make an even distribution of classes by generating or omitting some samples, but theycannot handle imperfect data. The probabilistic analysis can lead to the identification of issues that exist in the dataset and provide direction to medical experts on dealing with the condition to avoid *** strength of applying the Bayes rule will provide a meaningful interpretation of stroke data. This paper investigates the Baye rule's significance in stroke data analysis. © 2023 IEEE.

关键词： Bayes rule Class imbalance probabilistic analysis Resampling Stroke data

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A Novel Method to Predict Chronic Kidney Disease using Optimized Deep Learning Algorithm 21

A Novel Method to Predict Chronic Kidney Disease using Optim...

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21st International Learning and Technology Conference, L and T 2024

作者： Preethi, I. Dharmarajan, K. Sharma, Bhisham Chowdhury, Subrata Dhaou, Imed Ben Vistas School of Computing Sciences Chennai India Vistas Department of Information Technology Chennai India Chitkara University Institute of Engineering and Technology Chitkara University Department of Computer Science and Engineering Punjab India Sreenivasa Institute of Technology and Management Studies Department of Computer Science and Engineering Andhra Pradesh Chittoor India Dar Al-Hekma University Hekma School of Engineering Computing Design Department of Computer Science Jeddah Saudi Arabia

ISBN: (纸本)9798350393569

Chronic Kidney Disease (CKD), a significant global health issue and has a major impact on a vast number of people. In the early stages, it is impossible to identify the disease's symptoms. Very few people are aware of this illness and are able to foresee the symptoms earlier. The disorder is considered as potential risk factor. As a result, a potent deep learning framework is used in this paper to reduce the risk of acquiring these diseases as a way to prevent them. The machine learning algorithms used in early disease prediction, has been found to be computationally expensive, frequently overfit, and underperform in terms of accuracy since they must examine the large amount of clinical data until the model converges. Therefore, in this paper three novel work has been proposed an efficient novel hybrid feature selection strategy RFITLO algorithm is used to find the optimal features that gives the major contribution is classifying the CKD disease. Then two proposed classification algorithms namely Enhanced Multi-Layer Perceptron (HW-MLP) and Optimized Multi-Layer Perceptron (PKD-OMLP) are used in prediction model to capture the complex patterns and optimize the learning algorithm to predict the CKD at prior stage from the data gathered in Kaggle, Real Time and UCI Machine Learning Repository Dataset. In order to measure the classifications of disease, performance measures including accuracy, precision and recall are analyzed. The experimental findings show that the PKD-OMLP strategy produces better results than the proposed HW-MLP and other conventional approaches like Support Vector Machine (SVM), Linear Regression (LR) and Multi-Layer Perceptron (MLP). Among the preceding four models PKD-OMLP renders the best outcome as per its performance level producing a high accuracy of 94.89% on testing Real Time (RT) CKD dataset comparatively with other datasets such as Kaggle and UCI repository. Therefore, these proposed algorithms can support clinicians to enable secured an

关键词： Classification (of information)

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Enhancing Healthcare Record Privacy Through the Integration of Visual Cryptography and Diverse Image Encryption Techniques 9

Enhancing Healthcare Record Privacy Through the Integration ...

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Chinnasamy, P. Deepalakshmi, P. Sandeep, Dayina Ganesh, Addala Siva Krishna, Angalakurthi Jaya Priya, D. Divya School of Computing Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Krishnankoil Virudhunagar India Mlr Institute of Technology Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350377972

The methodology of this research focuses on analyzing real-world breaches, such as the Star Health Insurance leak, and demonstrating how multi-layered encryption techniques could have mitigated the impact. By splitting sensitive healthcare information into multiple shares using visual cryptography, unauthorized access is prevented unless all shares are combined, adding an additional layer of security. Furthermore, image-based encryption adds another line of defense, ensuring that even if hackers gain access to data, it remains unusable without the correct decryption methods. Through the implementation of these techniques, we demonstrate a significant enhancement in the security of healthcare records. By combining diverse encryption methods, our approach not only strengthens the protection of personal health information but also prevents unauthorized data dissemination, as seen in the Star Health breach. The results indicate that our model is resilient to various cyber-attacks, reducing the likelihood of large-scale data leaks and improving the integrity of healthcare systems. © 2024 IEEE.

关键词： Electronic health record

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A survey for light field super-resolution

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High-Confidence computing 2024年第1期4卷 118-129页

作者： Mingyuan Zhao Hao Sheng Da Yang Sizhe Wang Ruixuan Cong Zhenglong Cui Rongshan Chen Tun Wang Shuai Wang Yang Huang Jiahao Shen State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Key Laboratory of Data Science and Intelligent Computing International Innovation InstituteBeihang UniversityHangzhou 311115China State Key Laboratory of Software Development Environment School of Computer Science and EngineeringBeihang UniversityBeijing 100191China

Compared to 2D imaging data,the 4D light field(LF)data retains richer scene’s structure information,which can significantly improve the computer’s perception capability,including depth estimation,semantic segmentation,and LF ***,there is a contradiction between spatial and angular resolution during the LF image acquisition *** overcome the above problem,researchers have gradually focused on the light field super-resolution(LFSR).In the traditional solutions,researchers achieved the LFSR based on various optimization frameworks,such as Bayesian and Gaussian *** learning-based methods are more popular than conventional methods because they have better performance and more robust generalization *** this paper,the present approach can mainly divided into conventional methods and deep learning-based *** discuss these two branches in light field spatial super-resolution(LFSSR),light field angular super-resolution(LFASR),and light field spatial and angular super-resolution(LFSASR),***,this paper also introduces the primary public datasets and analyzes the performance of the prevalent approaches on these ***,we discuss the potential innovations of the LFSR to propose the progress of our research field.

关键词： Light field super-resolution Convolutional neural network Transformer Sub-aperture image Epipolar-plane image

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