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2nd IEEE International Conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2023

作者： Begum, Almas Alex david, S. Hemalatha, d. Kollipara, Lavanya Sita Sai Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350315905

This article discusses about potential of deep learning in lung cancer diagnosis using medical imaging, specifically focusing on the performance of various deep learning models in accurately identifying lung nodules from CT scans. Based on the findings, it was revealed that various deep learning algorithms, mainly deals with convolutional neural networks, demonstrate a remarkable degree of accuracy and an appropriate trade-off between sensitivity and specificity in correctly identifying true positives and true negatives. However, there is a need for more detailed analysis and comparison of these methods to fully understand their performance characteristics. The article also identifies several potential future scopes of deep learning in medical imaging and lung cancer diagnosis, including integrating deep learning models into clinical practice, large-scale multi-center studies to validate the effectiveness of deep learning-based approaches, improving the interpretability of deep learning models, integrating information from multiple imaging modalities, and implementing deep learning-based approaches in screening programs to improve early detection and patient outcomes. Overall, the results suggest that deep learning has significant potential to improve lung cancer diagnosis and treatment, but further research is needed to fully realize this potential. © 2023 IEEE.

关键词： Medical imaging

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Investigation of performance and emissions of dI diesel engine with cottonseed biodiesel along with diethyl ether

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International Journal of Ambient Energy 2022年第1期43卷 28-31页

作者： Sugash, G. rubalingam, S. Praveen Kumar, U. Vinod Kumar, Gurram Venkata Akashragam, L.r. Department of Automobile Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India

In this paper, we found a way to solve the demand for compression ignition engine fuels and their good fuel efficiency but they produce severe exhaust emissions. This will result in the further examination of ways which the composition of the fuel used in a diesel engine could be changed to decrease emissions. To get oxidation in diesel fuel by adding oxygen enriched fuel is a method for complete combustion. In addition, many oxygenates have higher cetane values and their addition with diesel fuel results in the increase of overall cetane number which in turns leads to higher combustion rate and enhanced combustion with lower emissions. diesel oxygenates blends are the recent research areas. In this paper, biodiesel was blended at different proportions of cottonseed oil and diesel with diethyl ether as diesel oxygenates. investigation of performance and emission characteristics was done on varying loads. © 2022 Informa UK Limited, trading as Taylor & Francis Group.

关键词： diesel fuels

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Possession of Alliance with distinct Organization for Contingency Model

Possession of Alliance with Distinct Organization for Contin...

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2023 IEEE International Conference on research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering, rMKMATE 2023

作者： rajesh, r. Vishnupriyan, K. Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (纸本)9798350305708

Contingency theory is an organizational approach that holds there is not a perfect way to build a firm, lead an enterprise, or make policies. The model employed to ascertain and obtain an accurate result is compared to existing accuracy outcomes. Contingent leaders are dynamic in their choice and adaptation of concise tactics to suite environmental shifts at a certain moment in the company's activities. Plans for emergencies are a crucial component of your organization's overall business continuity strategy and business logic since they assist make sure your company is prepared for anything. The contingency model uses feature selection to create sustainable enterprises. The Naïve Bayes and feature selection algorithm uses a search method for finding new feature subsets with a ranking metric that rates different subsets of features. Feature selection helps every firm develop a backup strategy that reduces the risk of its primary strategy failing. Given the necessary input from the user, this results in more efficiency and accuracy in the results, producing the most accurate prediction value and result. The quality of an evaluation metric has a significant impact on the algorithm, and it is these evaluation metrics that separate between wrappers, filters, and embedding techniques, the three main categories of feature selection algorithms. They will be categorized in order to improve calculation accuracy and data handling efficiency. © 2023 IEEE.

关键词： Contingency Model Evaluation Metric Feature Selection Feature Subsets Naïve Bayes

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Crop disease Prediction Using Machine Learning

Crop Disease Prediction Using Machine Learning

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2023 International Conference on System, Computation, Automation and Networking, ICSCAN 2023

作者： Sivaprakash, P. Sankar, M. Chithambaramani, r. Marichamy, d. Yazhinian, S. Vimalaithayan, J. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350315127

Food is one of life's vital resources that everyone needs in order to survive. But not everyone is entitled to a nice meal every day. The World Health Organization estimates that two-thirds of the world's population experiences food insecurity. The technologically advanced answer to this age-old problem is smart agriculture, which helps to maximize the produce of farmland with minimum resources. Crop diseases are a serious danger to food security, but it is still challenging to promptly diagnose them since the required infrastructure is lacking in many parts of the world. Since the emergence of precise techniques, impressive advancements in the field of leaf-based image categorization have been made. Training data sets are the first step in the two stages of the proposed system's operation. This involves utilizing training sets that contain both healthy and unhealthy data. Utilizing the CNN technique, which has numerous implementation steps including data set building, feature extraction, classifier training, and classification, the second stage involves crop monitoring and disease detection. CNN has been jointly trained on data sets of diseased and healthy leaves to categorise the images of damaged and healthy leaves. Overall, by using machine learning to train the big publicly available data sets, we can clearly identify the sickness existing in plants on a large scale. © 2023 IEEE.

关键词： Classification (of information)

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Batch Normalization based Convolutional Block YOLOv3 real Time Object detection of Moving Images with Backdrop Adjustment 9

Batch Normalization based Convolutional Block YOLOv3 Real Ti...

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9th International Conference on Smart Computing and Communications, ICSCC 2023

作者： Umanandhini, d. devi, M. Shyamala Beulah Jabaseeli, N. Sridevi, S. Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Tamilnadu Chennai India

ISBN: (纸本)9798350314090

Moving object detection and counting the number of objects is a prominent challenge for researchers in various application fields. The most significant things that play a vital part in the creation and advancement of computer vision systems are anomaly detection and object recognition. The most important aspect of object recognition utilizing an image, video, or webcam is the prediction of objects with high efficiency and accuracy. This study seeks to solve the performance issue by combining a deep Learning-based neural network method with the model You Only Look Once (YOLO), and OpenCV. Unlike previous Algorithms, YOLOv3 simply takes a whole image once and forecasts the class probabilities for the bounding boxes. To detect the items in the frame, final weights acquired by training the dataset on the darknet are necessary. The performance of the YOLOv3 model mainly depends on the convolutional block layer creation. The main contribution of this research work is that the convolution block creation is iteratively examined with several layers to suggest the optimal model that portray higher accuracy. When using moving cameras, the visual background frequently seems to rotate, translate, and scale. As a result, modelling the backdrop or detecting moving objects based on optical flow is challenging. With this overview, Batch Normalization based Convolutional Block YOLOv3 (BNCBY) is proposed for detecting the objects and the number of objects from the real time image. We first estimate the background movements using a perspective transformation model, and then use a deep learning classifier built on hand labelled datasets to recognize moving product contenders there in foreground subtracked image. We discover spatial-temporal traits with each floating entity candidate through optical flow matching and then prune them based on their motion patterns relative to the backdrop. The proposed BNCBY model offer a techniquefor detecting moving objects that combines backdrop adjust

关键词： Convolution

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deep Optimized Smart Healthcare System for the detection of diabetes and Cardiovascular Ailments

Deep Optimized Smart Healthcare System for the Detection of ...

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2024 IEEE International Conference on Information technology, Electronics and Intelligent Communication Systems, ICITEICS 2024

作者： Naidu, U. Soma rahul, S.G. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai600062 India

ISBN: (纸本)9798350382693

The Internet of Things (loT) is a valuable tool in the field of smart healthcare for diagnosing a wide range of illnesses in Healthcare Systems. It is noticed that the traditional diagnostic techniques consume much more time and may result in inaccuracies making the ailment identification faulty. This necessitates a smart using the concept of deep Learning (dL). As a motivation, the concept of blending ailment prediction with dL for precise prediction is attempted in this study. In this work, an innovative structure for detecting the occurrence of diabetes and cardiac ailment from the medical database is developed and it is referred to as the deep Optimized Smart Healthcare system. In this study, an advanced optimization technique, the Boosted Binary Harris Hawks (BB-HH) is used to reduce the dimensionality of the dataset to improve the performance of the classifier implemented. Subsequently, the traits of the BB-HH optimization technique are used for the development of the Gated recurrent deep Convolutional Network (GdrCNet) technique for performing the classification of the ailment. Furthermore, the loss function of the Gated recurrent deep Convolutional Network is estimated with the help of the Krill Herd Optimization technique to enhance the accuracy of the developed classifier. The performance evaluation and validation of the proposed deep Optimized Smart Healthcare System is carried out using diabetes and cardiovascular ailment datasets from PIMA. © 2024 IEEE.

关键词： deep learning

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A review on Biopolymers as an Eco-Friendly and Sustainable Solution for Soil Stabilization

A Review on Biopolymers as an Eco-Friendly and Sustainable S...

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Indian Geotechnical Conference, IGC 2022

作者： raj, Neha Selvakumar, S. Department of Civil Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Tamil Nadu Chennai600062 India

ISBN: (纸本)9789819717521

The impact of traditional stabilization method on the soil is now a major concern for all the researchers. The amount of carbon dioxide emitted by cementitious materials in the soil has made global researchers to think about materials quite sustainable that are eco-friendly too. While considering sustainability, there has been ample number of choices for soil stabilization. However, all the sustainable materials are not considered as eco-friendly and delivers an adverse effect to the soil. To overcome these adverse effects, bio products are introduced into the soil which has a promising future in the field of sustainable geotechnics. In this review paper, the center of attraction is biopolymer, an eco-friendly material which is derived from sources like plants, animals and microorganisms. There are enormous materials which are extracted from nature, a very few are commonly used in the field of soil engineering. Though biopolymers, such as Xanthan and guar gum have shown immense increase in the strength of soil when added in different proportions. When compared to traditional material with biopolymer, a very less amount is require to attain the comparable strength. Biopolymer has proved itself to be versatile in the strength property but was found to be lacking behind when brought in contact of water. Biopolymers for stabilization are suitable for cohesive and cohesion- less soil with higher strength, which makes it the best option in place of traditional technique. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Biopolymers

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Comparative Analysis of Image Based Uterus Fibroid detection and Classification Using deep Learning 2

Comparative Analysis of Image Based Uterus Fibroid Detection...

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2nd IEEE International Conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2023

作者： Shanthini Watson Benjamin, Minu Inba Visumathi, J. Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science & Engineering Chennai India

ISBN: (纸本)9798350315905

Fibroids are abnormal muscular tissue growing in and on the uterine wall. The fibroids are almost benign, but they can cause severe problems like abruption of the placenta, restriction of fetal growth, and repeated miscarriages. Fibroids ultimately grow in size and number if they are not detected at an early stage. Finding fibroid at the beginning is crucial so that appropriate treatment can be provided. In spite of a numerous big attempts and encouraging results in this area, the difficulty of precise segmentation and classification still exists. Fibroid detection is challenging due to differences in fibroid location, number, shape and dimension. This survey analyzed the enhancement methods, segmentation, feature extraction, classification, and deep learning approaches used for uterus fibroid analysis. Additionally, this review provides an overview of all relevant literature regarding fibroid detection, its advantages, constraints, advancements, and subsequent prospects. © 2023 IEEE.

关键词： Image classification

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Systematic Analysis and Classification of Enhanced diabetes mellitus Prediction Through Machine Learning 1

Systematic Analysis and Classification of Enhanced Diabetes ...

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1st International Conference on Advanced Computing and Emerging technologies, ACET 2024

作者： Manivannan, d. Manikandan, N.K. Sathiya Priya, S. Kujani, T. Bharathi, S. Gokila, E. Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Tamilnadu Chennai India

ISBN: (纸本)9798350367720

The healthcare field is rapidly incorporating machine learning, a powerful technology that can analyze massive datasets of medical information. This facilitates the making of more informed decisions, which improves patient care. To ensure the safe and efficient application of machine learning in healthcare, research is essential. By studying and analyzing medical data, researchers can identify potential risks and develop algorithms that not only improve decision-making but also provide valuable predictions to aid in preventive measures. This study expand on how machine learning is revolutionizing healthcare, particularly in the fight against diabetes. This rapidly growing disease demands constant monitoring. Here, machine learning steps in as a powerful tool. researchers are exploring various machine learning techniques to aid in the early detection of diabetes, allowing for timely intervention and improved health outcomes. We are now working on more than three different types of machine learning methods for this model. By analyzing vast sets of medical data, researchers can provide insights that help people make informed choices about their health. Machine learning's potential in this area is particularly exciting when it comes to diabetes, a rapidly growing global concern that requires close monitoring. The ultimate goal of research is to create a user-friendly website that is accessible to everyone, regardless of their present health state. To that end, researchers are investigating several machine learning algorithms to forecast the start of diabetes. This tool has the potential to enable people to adopt proactive behaviours and enhance their general well-being. © 2024 IEEE.

关键词： Adversarial machine learning

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ThresholdedreLU Orthogonal Layer Weight regularized densely Connected Convolutional Networks CNN for Strawberry disease Prediction 2

ThresholdedReLU Orthogonal Layer Weight Regularized Densely ...

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2nd International Conference for Innovation in technology, INOCON 2023

作者： devi, M. Shyamala Kumar, Ankit ravikumar, S. Yadav, rohit Singh, deepanshu Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamilnadu Chennai India

ISBN: (纸本)9798350320923

Strawberry fruits will be affected by metabolic or climatic problems, causing a significant threat to the crop's production and quality as well as raising the risk of strawberry illnesses. Traditional identification methods, however, also have high rate of assessment inaccuracies and operate inadequately in real-time. This is completely obvious that in today's environment of increasing strawberry output and reliability requirements, the traditional methods for strawberry disease diagnosis, which primarily rely on individual perspective and untrained eye monitoring, cannot meet the expectations of people for strawberry disease identification and control. A more comprehensive system must be created in order to accurately identify strawberry fruit diseases and provide suitable treatment alternatives. With this intent, this paper recommends ThresholdedreLU Orthogonal Layer Weight regularized denseNet (TOdN) CNN model to identify the damaged strawberry using the Fruit360 disease dataset from KAGGLE. The proposed TOdN model uses Fruit360 disease dataset from KAGGLE with 2000 Strawberry fruit leaflets having 1000 damaged strawberry fruit and 1000 healthy strawberry fruit. The 3 X 3 convolutional layers, 3 X 3 softmax pooling layer, 3 X 3 Transition and dense Layer and 1 X 1 dropout and Classification Layer were used to create the TOdN model. It was then trained using the learning rate of orthogonal regularised Gradient descent and ThresholdedreLU activation layer. The TOdN was designed using a denseNet network and ThresholdedreLU activation with 0.5 dropout and weight decay of orthogonal regularization. The dataset for Strawberry disease was partitioned into training and testing data, and the training data were fitted to both the IAGLr model and other CNN models in order to compare the effectiveness of the proposed IAGLr model. Python was coded on a Gtx Geforce Tesla V100 Nvidia graphics card server with a batch size of 64 and 40 training epochs. results of execution indic

关键词： Convolution

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