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检索条件"机构=Computer Science And Engineering V.S.B Engineering College"

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strategic and sustainable ERP Decision Framework: Integrating Multi-Criteria Analysis with Deep-A-CNN-LsTM Models in Corporate strategy

Strategic and Sustainable ERP Decision Framework: Integratin...

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2024 International Conference on Integration of Emerging Technologies for the Digital World, ICIETDW 2024

作者： Dahiya, suman Akram, Nawab srinath Yadav, K. Manikumar, T. Thulasimani, T. barodia, Neha Universal AI University Maharashtra India Magadh Professional Institute BiharDanapur Patna India V.S.B Engineering College Tamilnadu Karur India Deemed to be University Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Krishnankoil India Bannari Amman Institute of Technology Department of Mathematics Tamilnadu Sathyamangalam India University Department of Humanities and Social Sciences Dehradun India

ISBN: (纸本)9798331541170

Customizing enterprise resource planning (ERP) software to meet a company's unique operational requirements is a crucial consideration throughout implementation. It offers a framework in this post to help management make decisions about customization choices and the capabilities needed to implement them. This structure allows us to identify several openings for tailoring the ERP system and company processes. A preprocessing step, feature extraction, and training the model make up the three phases of the said approach. The preprocessing steps include extracting summary metrics and performing temporal principal component analysis (PCA). One method for extracting features is Kernel Principal Component Analysis, or Kernel PCA. During the training process, a Deep-A-CNN-LsTM was utilized. With an average accuracy of 92.46%, the suggested method outperforms LsTM and CNN. © 2024 IEEE.

关键词： Convolutional neural networks

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A Comparative study about Content-based Image Retrieval using Features from Deep Convolutional Neural Networks 2

A Comparative Study about Content-Based Image Retrieval usin...

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2nd IEEE International Conference on Communication, security and Artificial Intelligence, ICCsAI 2023

作者： Pinaki, budi srikrushna Reddy, Meka Dheeraj Kapardi, srisatya M.V.S.R Engineering College Osmania University Department of Electronics and Communication Engineering Hyderabad India Vasavi College of Engineering Osmania University Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350369960

'Content-based Image Retrieval (CbIR)' is a popular approach that is frequently used to handle the image retrieval problem. Despite the fact that the CbIR approach has seen tremendous improvements, the fundamental challenge of finding and retrieving pertinent photos with high levels of accuracy remains. A lot of demand exists for the creation of efficient CbIR systems that utilise modern techniques. Convolutional neural networks (CNN) were used, which led to a substantial paradigm shift in machine learning and outstanding performance. This study's major objective is to ascertain whether deep CNN models can be employed to boost picture retrieval effectiveness. In a range of image-processing tasks, such as object identification, classification, and clustering, among others, CNNs excel very well. This work proposed the CbIR methodology, which implements the extraction of features by using a range of pre-trained CNN models. For this experimental research project, radiology and meteorological datasets were used. The findings demonstrated that pre-trained deep CNN models' learnt features may be quickly applied to image retrieval tasks and outperform hand-crafted features utilized in conventional CbIR techniques in terms of performance. A CbIR framework using features from a 'pre-trained deep CNN model (vGG16)' was provided in accordance with the study's findings. Put the recently created CbIR algorithm through its paces using a 'multi-class dataset' made up of images connected to the weather. The innovative CbIR technique resulted in an average retrieval precision of 86.73%. The standard CbIR design, which relies on manually created features (such color, texture, and form attribute), has produced an average retrieval precision of 73.25% in comparison to this. The unique methodology outperformed the traditional method, which showed a 13.48% larger gain in average precision. © 2023 IEEE.

关键词： Content-based Image Retrieval Convolutional Neural Networks Data Analysis Deep Learning Image Extraction Image Retrieval

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Design and Development of an IoT based Intelligent Energy Meter using Less Expensive Controller Unit

Design and Development of an IoT based Intelligent Energy Me...

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2024 International Conference on Intelligent systems for Cybersecurity, IsCs 2024

作者： balasubramani, M. Gohin, b. Jayashankari, J. samuthira Pandi, v. Ravi, Kiran Chand Arumugam, Deepak V.S.B Engineering College Department of Computer Science and Engineering Tamil Nadu Karur India Swarnandhra College of Engineering and Technolnogy Department of Computer Applications Andhra Pradesh Seetharamapuram India Prince Shri Venkateshwara Padmavathy Engineering College Department of Information Technology Tamil Nadu Chennai India Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Tamil Nadu Chennai India Mlr Institute of Technology Department of Ece Hyderabad India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Electrical and Electronics Tamil Nadu Chennai India

ISBN: (纸本)9798350375237

With an eye towards improving the efficacy of energy monitoring and management, this research offers a thorough approach for the creation of an Intelligent Energy Metre that is based on the Internet of Things. A key component of the system is the use of specialized sensors to continuously measure electrical characteristics like voltage and current. These sensors generate analog signals, which are conditioned, digitized, and processed by a microcontroller. Algorithms within the firmware/software calculate energy consumption metrics, which are transmitted wirelessly to a central server or cloud platform using secure communication protocols. The collected data is then stored and analyzed to provide insights into energy usage patterns, facilitating informed decision-making for optimizing energy efficiency and reducing costs. The methodology encompasses various stages, including market research, hardware and software development, integration, testing, deployment, and maintenance. Through the implementation of this methodology, a robust and scalable IoT-based energy monitoring system can be developed, empowering users to make informed decisions for sustainable energy management. © 2024 IEEE.

关键词： Energy management

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Deep Learning-based Diagnosis system for Liver Cancer Through Multimodal Determination for Early Treatment

Deep Learning-Based Diagnosis System for Liver Cancer Throug...

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2024 International Conference on Artificial Intelligence and Emerging Technology, Global AI summit 2024

作者： Fatima, N. sabiyath singh, Ramendra swathi, Akuthota Choudhary, Amar Ahmed, Azhar sharma, Chhaya B.S. Abdur Rahman Crescent Institute of Science & Technology Department of Computer Science and Engineering Chennai India Ghaziabad India Cvr College of Engineering Department of Computer Science and Engineering Mangalpalli Rangareddy India Alliance University Department Electronics and Communication Engineering Bengaluru India Kcc Institute of Technology and Management Department of Computer Science and Engineering Greater Noida India Raj Kumar Goel Institute of Technology Department of Computer Science and Engineering Ghaziabad India

ISBN: (纸本)9798350379716

The present research provides an analysis of the development and implementation of a deep learning-based diagnosis system for liver cancer. The provided system was developed using a dataset encompassing 2300 sensor readings, including imaging, clinical, and genomic data. A range of deep learning features was implemented to improve the early detection of liver cancer such as CNN, vGG 16, vGG 19, and Inception v3. The data were divided into two categories: one is for training, and another used for testing. The results of the data analysis have shown that vGG 19 provides the highest diagnostic accuracy of 98.76%, with vGG 16, Inception v3 and CNN displaying the levels of 94.5%, 93.4%, and 90.25%, respectively. The Model performance analysis was also augmented at the application of precision, recall, and F1 score indicators, as well as using AUC-ROC values which highlighted vGG 19 as the most effective algorithm. Another method of evaluation was the application of confusion matrices for plotting the results based on the number of true positive, true negative, false positive, and false negative detections, where all the samples from the dataset were added to each model calculation. The obtained results have shown the importance of multimodal data for the increase of diagnosis accuracy and revealed the promising potential of advanced computation techniques in medical analytics. In such a way, it can be stated that the developed deep learning models contribute to the ability of healthcare specialists in the definition of liver cancer at early stages, which allows timely interventions and facilitates the development of a range of personalized treatment protocols. The application of such algorithms has the potential to reshape liver cancer diagnostics, where major tasks are associated with further improvement and validation of the developed algorithms based on clinical case studies. © 2024 IEEE.

关键词： Contrastive Learning

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One-shot Approach for Multilingual Classification of Indic scripts

One-Shot Approach for Multilingual Classification of Indic S...

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2022 International Conference on Innovative Trends in Information Technology, ICITIIT 2022

作者： Mittur, Ajay shankar, Aravindh R Narasimhan, Adithya B.M.S. College of Engineering Computer Science and Engineering Bengaluru India

ISBN: (纸本)9781665406703

The use of multiple languages with different scripts is a common theme in India. There is an emerging need to digitise documents that may be handwritten or available solely as images. This necessitates a system for multilingual classification of different Indic scripts and the subsequent character recognition into digitised standards such as Unicode. However, a learning system for various languages with multiple character combinations can be computationally expensive and prove arduous with a dearth of available data. In this paper, the one-shot learning approach to the optical character recognition of different languages is explored, where there is a need to accurately classify the character given only one example of every additional class introduced. siamese neural networks are used for learning and to tune a network to work with entirely new, unseen data. Compelling results are attained in the classification of characters in nine different Indian languages using this approach with an accuracy ranging from 77.72 to 91.83 across the Indic languages in the best case. © 2022 IEEE.

关键词： Optical character recognition

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An extensive critique on advances in digital control techniques for smart grid and micro grid operation

An extensive critique on advances in digital control techniq...

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2025 IEEE International students' Conference on Electrical, Electronics and computer science, sCEECs 2025

作者： sarathkumar, D. bashkaran, K. Manivel, Murugesan Jayakumar, T. sivakumar, R. Anandhakumar, C. Kongu Engineering College Department of Electrical and Electronics Engineering Tamil Nadu 638 060 India Kongunadu College of Engineering and Technology Department of Biomedical Engineering Tamil Nadu Thottiam621215 India Karpagam Institute of Technology Department of Electrical and Electronics Engineering Tamilnadu Coimbatore641021 India Nandha Engineering College Department of Electrical and Electronics Engineering Tamil Nadu 638052 India V.S.B Engineering College Department of Electrical and Electronics Engineering Tamil Nadu 639111 India Sri Ramakrishna Institute of Technology Department of Electrical and Electronic Engineering Tamil Nadu Coimbatore641010 India

ISBN: (纸本)9798331529833

The integration of digital control techniques within smart grids and microgrids represents a pivotal advancement in the optimization, reliability, and efficiency of modern electrical networks. This paper explores the latest developments in digital control strategies, focusing on their application to the operation of smart grids and microgrids. Key areas of discussion include the implementation of advanced algorithms for real-time monitoring and control, the role of artificial intelligence and machine learning in predictive maintenance and decision-making, and the impact of distributed control systems on grid stability and resilience. Furthermore, the paper examines the challenges and solutions associated with integrating renewable energy sources and energy storage systems into the grid, emphasizing the importance of adaptive and scalable control frameworks. Through a comprehensive review of recent research and case studies, this paper provides insights into the future trends and potential breakthroughs in digital control technologies, highlighting their critical role in the evolution of smart grid and microgrid infrastructures. The findings suggest that continued innovation in this field will significantly enhance the operational capabilities of electrical grids, paving the way for more sustainable and resilient energy systems. © 2025 IEEE.

关键词： smart power grids

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Automating ML Models Using MLOPs 2

Automating ML Models Using MLOPS

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2nd International Conference on Advancements in smart, secure and Intelligent Computing, AssIC 2024

作者： sirisha, N. Kiran, Ajmeera Arshad, Mohammad Mounika, M. Mlr Institute of Technology Department of Computer Science and Engineering Hyderabad Dundigal India Deccan College of Engineering and Technology Department of It India B v Raju Institute of Technology Department of Ai & Ds India

ISBN: (纸本)9798350370188

Machine learning engineering relies on MLOps, a collection of best practises for commercializing models. As fresh data is added, machine learning models can become less effective. ML models can't use all data. To do this, we must reimagine the model by changing the front and back ends. Our project regularly tracks and trains models to overcome this constraint. MLOps (Machine Learning Operations) can help. ML pipeline automation integrates the trained model into the cloud, which can take a dataset as input and output. To quickly update the cloud ML algorithm in response to fresh data, we don't need to change the model. This guarantees the chosen model is valid and can be used with fewer flaws, improving performance. Our main objective is to build a reliable automated pipeline and install, maintain, and optimise machine learning models and systems securely and efficiently. © 2024 IEEE.

关键词： Machine learning

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Prediction of Heart Disease Using Hybrid Naïve bayes Technique 22

Prediction of Heart Disease Using Hybrid Naïve Bayes Techni...

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22nd International symposium on Communications and Information Technologies, IsCIT 2023

作者： Patil, shantakumar Lutimath, Nagaraj M. Jogish, D. Premjyoti Patil, bhargav s. Sai Vidya Institute of Technology Computer Science and Engineering Bengaluru India Dayananda Sagar Academy of Technology and Management Computer Science and Engineering Bengaluru India Bengaluru India B.M.S. College of Engineering Computer Science and Engineering Karnataka Bengaluru560019 India

ISBN: (纸本)9781665457316

The domain of medical diagnosis has attracted many researchers. several cases of human early mortality have been predicted by investigating the diseases. Different reasons are the cause of several diseases and one such cause is heart disease. set of disorders in the heart lead to heart disease. It consists of blood vessel issues such as irregular heart beat problems, weak heart muscle problems, congenital heart difficulties, cardio vascular defects and coronary artery disorder. Many researchers have summarized idiosyncratic procedures to preserve human life and help health care experts to recognize, prevent and manage heart disorder. Machine Learning is utilized for decision making in many domains. Naive bayes technique is one approach which uses conditional probability. In this paper we have taken UCI machine learning data repository including of patients affected from heart disease is analyzed using Complement Naive bayes probability methodology along with correlated features of the heart disease data set. The prediction is done using this concept as hybrid Naive bayes probability technique. The advantage of Complement Naive bayes probability prediction is that, it can be used for imbalanced data set. Accuracy of classification of patients suffering from heart disease is predicted. Implementation is done in Python environment. © 2023 IEEE.

关键词： Python

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Classifying Grape Leaf Disease using an Optimized YOLOv7 Deep Learning Model 4

Classifying Grape Leaf Disease using an Optimized YOLOv7 Dee...

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4th International Conference on soft Computing for security Applications, ICsCsA 2024

作者： Abraham, John T Hussain, Mohammad Manzoor srividhya, R. Linda, P Edith Karthik, s. Nareshkumar, R. Bharata Mata College Department of Computer Science Kochi India B v Raju Institute of Technology Department of Computer Science and Engineering Telangana Medak India Dr G R Damodaran College of Science Department of Computer Science Coimbatore India Kristu Jayanti College Department of Computer Science Bengaluru India SRM Institute of Science and Technology School of Computing College of Engineering and Technology Department of Networking and Communications Tamilnadu Kattankulathur India

ISBN: (纸本)9798331515720

The prevalence of grape leaf diseases has had a profound influence on the worldwide grape industry, resulting in considerable financial setbacks. For effective disease management, it is essential to classify the disease quickly and accurately. This research presents an enhanced YOLOv7 deep-learning model for accurately classifying grape leaf diseases. The dataset is obtained from Kaggle and consists of four categories: black rot, leaf blight, black measles, and healthy. The Adam optimizer is utilized to optimize hyperparameters in order to enhance the performance of the model. The refined YOLOv7 model achieved a remarkable accuracy of 99.24% on the test set, showcasing its potential for detecting grape leaf diseases at an early stage. The model in precision agriculture can aid in the management of grape production, categorization of leaf diseases, and monitoring of operations. © 2024 IEEE.

关键词： Contrastive Learning

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Translation of Architectural sketch Images to building Images Using CycleGAN

Translation of Architectural Sketch Images to Building Image...

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2022 IEEE North Karnataka subsection Flagship International Conference, NKCon 2022

作者： Divya, I.b. sooda, Kavitha B.M.S. College of Engineering Computer Science Engineering Department Bengaluru India B.M.S. College of Engineering Computer Sciences Engineering Department Bengaluru India

ISBN: (纸本)9781665453424

sketching and drawing is the hallmark of an architect's toolkit. Architect needs to somehow show his idea from his sketch and also designing a sketch image from a building image will help the architects to record the different designs. Hence, using image-to-image translation strategies, it is possible to convert architectural vector designs into actual building images and vice versa. Image-to-image translation is becoming more popular as a result of advancements in information technology and computer vision. In a class of vision and graphic problems known as 'image-to-image translation', the objective is to discover the relationship between an output image and an input image. The main objective of the model in this study is to convert architectural sketches into building images and vice versa using Image to image translation on unpaired image datasets to help reduce the communication gap between architects and their clients. A suitable GAN model called Cycle GAN has been used to train on collected unpaired image dataset. Utilizing the trained model, a web application is created that is open for everyone. A Python library called flask is used to create the web application's front end in order to provide a straight-forward and user-friendly interface. © 2022 IEEE.

关键词： Deep learning

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