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检索条件"机构=Dept. of Scientific Computing and Computer Application"

共 113 条记录，以下是21-30 订阅

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Classifying the DNA sequence using Optimized Naive Bayes Approach: A Comparative Analysis of Machine Learning Algorithms

Classifying the DNA sequence using Optimized Naive Bayes App...

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Computational Intelligence, Communication Technology and Networking (CICTN), International Conference on

作者： Aditya Pai H Chandrappa S Guru Prasad M S Ganesh Pathak Atul Thakare Pratik Kumar MIT School of Computing MIT Art Design and Technology University Pune India Dept. of Computer Science and Engineering JAIN (Deemed-to-be University) Bengaluru India Dept. of Computer Science and Engineering Graphic Era (Deemed to be University) Dehradun India Department of Computer Application Graphic Era (Deemed-to-be University) Dehradun India

ISBN: (数字)9798331530389

ISBN: (纸本)9798331530396

In bioinformatics, the exact classification of DNA sequences is essential to increasing comprehension of genetic structures and functionalities. This research introduces an enhanced Naive Bayes approach (ENB) specifically designed to handle more complexities related to DNA sequence data. This research integrates advanced feature weighting and optimized probability estimation techniques to enhance classification accuracy substantially. The proposed work demonstrated superior efficacy to traditional ML algorithms like Random Forest, SVM, Decision-Trees, K-Nearest Neighbors, Gradient Boosting etc. The proposed method attained a level of accuracy of 98%, and proposed approach performs better than the other models, with 94% accuracy in Logistic Regression coming in second.

关键词： Support vector machines Logistic regression Accuracy Machine learning algorithms Nearest neighbor methods Boosting Bayes methods Complexity theory Medical diagnosis Random forests

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VGG-16 Technique to Reduce the Global Food Crises and Enhance the Crop Yields: Deep Learning Approaches

VGG-16 Technique to Reduce the Global Food Crises and Enhanc...

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Applied Artificial Intelligence and computing (ICAAIC), International Conference on

作者： Sivakumar Depuru K Amala Pera Supriya A Basi Reddy Ravala.Sai Gireesh Dept of CSE School of Computing Mohan Babu University Tirupati India Dept.of. ECE Sri Venkateswara College of Engineering (Autonomous) Tirupati A.P India Department of Computer Application Mohan babu University Tirupati Andhra Pradesh India Dept. of. Computer Application Mohan babu University Tirupati A.P India

ISBN: (数字)9798350375190

ISBN: (纸本)9798350375206

In the digital era, enhancing agriculture plays a critical role in addressing food crises by improving crop yield and ensuring food security. Early detection and management of crop diseases are paramount for agricultural sustainability, significantly affecting food security and farmer livelihoods. Farmers often struggle to identify crop diseases at early stages, leading to reduced yields and potential food shortages. This research aims to assist farmers in early disease identification, thereby increasing crop yield and preventing food crises. This study introduces a machine learning approach using Convolutional Neural Networks (CNNs), specifically the VGG-16 model, to accurately identify crop diseases. VGG-16’s architecture is particularly effective in recognizing patterns in images, making it a superior choice for diagnosing plant diseases compared to existing models. By leveraging VGG-16, the research demonstrates improved accuracy in detecting crop diseases at early stages, providing a valuable tool for farmers to manage crop health proactively. The findings of this research highlight the potential of advanced machine learning techniques to enhance agricultural practices and contribute to global food security.

关键词： Support vector machines Technological innovation Accuracy Machine learning algorithms Food security Agriculture Convolutional neural networks

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A scalable generative model for dynamical system reconstruction from neuroimaging data 38

A scalable generative model for dynamical system reconstruct...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Volkmann, Eric Brändle, Alena Durstewitz, Daniel Koppe, Georgia Medical Faculty Mannheim Heidelberg University Mannheim Germany Institute for Machine Learning Johannes Kepler University Linz Austria Interdisciplinary Center for Scientific Computing Heidelberg University Heidelberg Germany Faculty of Physics and Astronomy Heidelberg University Heidelberg Germany Hector Institute for AI in Psychiatry Dept. for Psychiatry and Psychotherapy CIMH Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany

Data-driven inference of the generative dynamics underlying a set of observed time series is of growing interest in machine learning and the natural sciences. In neuroscience, such methods promise to alleviate the need to handcraft models based on biophysical principles and allow to automatize the inference of inter-individual differences in brain dynamics. Recent breakthroughs in training techniques for state space models (SSMs) specifically geared toward dynamical systems (DS) reconstruction (DSR) enable to recover the underlying system including its geometrical (attractor) and long-term statistical invariants from even short time series. These techniques are based on control-theoretic ideas, like modern variants of teacher forcing (TF), to ensure stable loss gradient propagation while training. However, as it currently stands, these techniques are not directly applicable to data modalities where current observations depend on an entire history of previous states due to a signal's filtering properties, as common in neuroscience (and physiology more generally). Prominent examples are the blood oxygenation level dependent (BOLD) signal in functional magnetic resonance imaging (fMRI) or Ca2+ imaging data. Such types of signals render the SSM's decoder model non-invertible, a requirement for previous TF-based methods. Here, exploiting the recent success of control techniques for training SSMs, we propose a novel algorithm that solves this problem and scales exceptionally well with model dimensionality and filter length. We demonstrate its efficiency in reconstructing dynamical systems, including their state space geometry and long-term temporal properties, from just short BOLD time series. © 2024 Neural information processing systems foundation. All rights reserved.

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AI-Driven Software Reuse: Optimization and Comparative Performance Analysis

AI-Driven Software Reuse: Optimization and Comparative Perfo...

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Computational Intelligence, Communication Technology and Networking (CICTN), International Conference on

作者： Aditya Pai H Chandrappa S Guru Prasad M S Atul Thakare Sharon Christa Pratik Kumar MIT School of Computing MIT Art Design and Technology University Pune India School of Computer Science and Engineering JAIN Deemed to be University Bengaluru India Dept. of Computer Science and Engineering Graphic Era (Deemed to be University) Dehradun India Department of Computer Application Graphic Era (Deemed-to-be University) Dehradun India

ISBN: (数字)9798331530389

ISBN: (纸本)9798331530396

Software reuse improves productivity and ensures that there is less time delivered to the market by enabling the proper usage of reusable code from the software repositories. The study provides the collaboration between the AI techniques, which includes Convolution Neural Networks (CNN), Random Forest (RF), and Support Vector Machines (SVM), to predict software components' reusability. The comparative analysis shows the effectiveness of different AI- driven algorithms, where CNN has shown the highest accuracy of 92%, thus reducing the human effort in finding the reusable modules. The Case studies in e-commerce and health care show the benefit of using AI-driven reuse of software; this means that the development time is reduced by 25%, and the maintenance cost is decreased by 20%. The paper highlights how different AI algorithm integration with software engineering has some limitations. The findings show that AI has greatly revolutionized software reuse. Thus bringing innovation and efficiency in software engineering.

关键词： Support vector machines Computational modeling Medical services Software Data models Convolutional neural networks Artificial intelligence Software reusability Random forests Software engineering

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Evaluation of Machine-Learned Price Prediction Models For Block-Chain Technology

Evaluation of Machine-Learned Price Prediction Models For Bl...

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Innovations in High Speed Communication and Signal Processing (IHCSP), International Conference on

作者： Vikas Goel Ashutosh Sharma Raju Ranjan Amit Kumar Sharma Dept. of IT KIET Group of Institutions Ghaziabad India Dept. of Computer Application IMSUC-Ghaziabad Ghaziabad India School of Computing Science & Engineering Galgotia University Greater Noida India Dept. of CS&E Ch. Charan Singh University Campus Meerut India

As a new form of technology, blockchain has much to offer. The technology has a wide range of potential uses and advancements, depending on the field of application. However, further study is required to realise the potential of advanced technologies like Blockchain and smart contracts. There is a lot of potential for investigation because the technology is so new. This research looked at the origins of the Blockchain and analysed several different blockchain solutions. Ethereum was discovered to be the most often used platform for constructing distributed apps after being compared to other platforms. Therefore, a distributed smart energy microgrid application will be built on the Ethereum platform. There is a coin, or token, exclusive to each blockchain platform. Bitcoin's relationship to other cryptocurrencies was measured using the Karl Pearson correlation coefficient. The research concludes that ether's price is most sensitive to changes in the price of bitcoin. On a preferred Ethereum network, a distributed application for managing smart microgrids has been built. The smart contract constitutes the heart of this programme. As a result, we also take care of the smart contract's layout, coding, and optimization. In comparison to the original, less GAS is consumed by this smart contract. The entire distributed app has been built and thoroughly tested. Smart Energy Microgrid Distributed application framework results demonstrate that the application functioned as expected.

关键词： Surveys Analytical models Technological innovation Smart contracts Time series analysis Bitcoin Microgrids

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An Effective Prediction of Rainfall Using Machine Learning Technique

An Effective Prediction of Rainfall Using Machine Learning T...

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International Conference on Electronics, Communication and computing Technologies (ICECCT)

作者： Nisha Rana Guru Prasad M S Ashutosh Sharma Pratap M S M Anand Kumar Aditya Pai H Dept. of Computer Science and Engineering Doon Institute of Engineering And Technology (DIET) Rishikesh India Dept. Computer Science and Engineering Graphic Era Deemed University Dehradun India Dept. of Applied Science (Chemistry) Doon Institute of Engineering And Technology (DIET) Rishikesh India School of Computing and Information Technology Reva University Bengaluru India Dept. of Computer Application Graphic Era (Deemed to be University) Dehradun India Dept. of Computer Science and Engg. Graphic Era (Deemed to be University) Dehradun India

Climatic information such as temperature, humidity, and precipitation are useful for agriculturists, businesses, researchers, and the government. Estimating precipitation is a major factor that affects the environment and is one of the most important aspects of meteorological science. In this study, factual approaches and machine learning methods are used to estimate and forecast meteorological parameters for the government. Estimating precipitation is a major factor that affects the environment and is one of the most important aspects of meteorological science. Approaches are used to forecast and estimate precipitation. Daily observations were used as part of the experiment. Validation of results using real-world data is used to check the accuracy of forecasting model experimentation. The experimental results show that distribution, correlation, line-plots, and neural networks perform well for forecasting meteorological characteristics and have the best classification accuracy when compared to other types of machine learning methods for precipitation forecasting.

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Industrial IoT Smart Manufacturing Systems based on Cloud computing Energy Efficiency Analysis using Machine Learning Algorithms

Industrial IoT Smart Manufacturing Systems based on Cloud Co...

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Electronics and Renewable Systems (ICEARS), International Conference on

作者： P. Chinnasamy Patan Feridoz Khan C Sasikala Mavalluru Swathi M Prema Kumar Ajay Kumar Yadav Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Tamilnadu India Department of Computer Science and Engineering School of Computing Mohan Babu University Tirupati Andhra Pradesh India Dept of CSE Srinivasa Ramanujan Institute of Technology (Autonomous) Ananthapuramu Andhra Pradesh India Audisankara College of Engineering and Technology Andhra Pradesh India Dept. of ECE Shri Vishnu Engineering College for Women (A) Bhimavaram Andhra Pradesh India Department of Computer Application United Institute of Management Prayagraj India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

With open connectivity and emergent computing, the Industrial Internet of Things (IIoT) combined with cloud computing resources provides significant breakthroughs in the field of industrial automation. These developments lead to the evolution of supplemental services, which improve automation in the production and industrial sectors. This research proposes novel technique in smart manufacturing system based on cloud computing with IIoT in energy efficiency using machine learning. Here the smart manufacturing process has been analysed based on IIoT with cloud computing network, where the energy efficiency is analysed using virtual machine-based reinforcement markov chain based linear regression. Experimental analysis has been carried out in terms of latency, energy efficiency, task response time, accuracy. proposed technique obtained 97% of Latency, 95% of Energy efficiency, 98% of Accuracy, 94% of Task response time.

关键词： Cloud computing Machine learning algorithms Automation Accuracy Linear regression Energy efficiency Virtual machines Time factors Industrial Internet of Things Smart manufacturing

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An Effective Classification of Ddos Cloud Based Attack Through Tree Founded Classifiers

An Effective Classification of Ddos Cloud Based Attack Throu...

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Innovations in High Speed Communication and Signal Processing (IHCSP), International Conference on

作者： Vikas Goel Pragati Goel Raju Ranjan Amit Kumar Sharma Dept. of IT KIET Group of Institutions Delhi-NCR Ghaziabad India Dept. of Computer Application NCRD'S Sterling Institute of Management Studies Nerul Navi Mumbai India School of Computing Science & Engineering Galgotia University Greater Noida India Dept. of CS&E Ch. Charan Singh University Campus Meerut India

The term “cloud computing” refers to a relatively new technology that provides users with access to various computing services and resources, including software, data storage, and communication infrastructure. There are currently certain security concerns with cloud computing. An Intrusion Detection System (IDS) is a form of network security that monitors for malicious activity. In the cloud, an IDS might operate on individual hosts or over an entire network. DoS attacks, which aim to disrupt, disable, or otherwise harm a service, have become increasingly common against cloud applications and internet services. Due to the cloud's complexity and decentralized nature, it is difficult to detect attacks on its infrastructures. Moreover, by allowing a wide variety of smart devices to access cloud-based infrastructures, it raises both the difficulty and the complexity of assaults. A decision tree is a method of categorization used in data mining. It is recommended to utilize this method when developing a classification scheme from a reclassified data set. Classifiers such as the Classification and Regression Tree (CART), C4.5, and the Random Tree (RT) are proposed in this work. The utilized CART method has a tendency to generate additional trees on nodes, until no tree can be formed judging whether or not a node is terminal. Because of its adaptability, the CART technique can handle gaps in the data. The tree is pruned to make it smaller and more manageable in C4.5. A classifier's complexity is decreased while its prediction accuracy is improved. Decision trees were used to generate the chain of basic decisions used in RT's label assignment process.

关键词： computers Cloud computing Technological innovation Web and internet services Intrusion detection Signal processing Denial-of-service attack

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Analysis of Bones Abnormality Detection and Segmentation

Analysis of Bones Abnormality Detection and Segmentation

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IEEE International Conference on Intelligent computing and Information Systems (ICICIS)

作者： Mennat-Allah Ismail Yasmine Mohammed Nadine Gamal Maram Hatem Sara Ibrahim Esraa Hamdi Abbas Mayar Mohsen Maram Nasser Hala M. Ebied Dept. of Bioinformatics Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt Dept. Bioinformatics Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt Information System Department Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt Scientific Computing Department Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt

Numerous musculoskeletal disorders can be caused by bone diseases, which are widespread. Musculoskeletal conditions are estimated to affect 1.71 billion people worldwide. In addition to musculoskeletal fractures, knee osteoarthritis, fractures, and femoral neck injuries are all relatively prevalent bone illnesses. Missed fractures, on the other hand, are a frequent prognostic failure in accidents and emergencies. As a result, patients’ treatment and care are complicated and delayed. Deep Learning (DL) and Artificial Intelligence (AI) are currently garnering a lot of interest for their potential to help physicians identify bone fractures. Studies in traumatology and orthopedics have demonstrated the utility and potential of DL in the radiograph-based diagnosis of fractures and illnesses. In this paper, different classification techniques have been compared to classify the bone. The methods used for detection and segmentation of wrist bones are Inception-v3, VGG16, Densenet-121, and Resnet-50while for elbow bones, Resnet-50, Inception-v3, Densenet-121, and Densenet-169 are used. Two datasets are used in the experiments.: Kaggle dataset and MURA dataset. The experimental results show that the Densenet-169 and Inception-v3 performed better than the other models using MURA dataset and Kaggle dataset respectively.

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The Symphony of Style: An Extensive Examination of Determination Mechanisms in Deep Learning-Inspired Outfit Suggestions

The Symphony of Style: An Extensive Examination of Determina...

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Computational Intelligence, Communication Technology and Networking (CICTN), International Conference on

作者： Guru Prasad M S Ravatappa A Bhosagi Madhusudhana K Prithviraj Chandrappa S Vikash Kumar Department of Computer Science and Engineering Graphic Era (Deemed to be University) Dehradun India School of Computing and Information Technology Reva University Bengaluru India Dept. of Electronics & Communication Engineering SDM Institute of Technology Ujire India Department of Computer Science and Engineering Nitte (Deemed to be University) NMAMIT Mangalore India Department of Computer Science and Engineering Jain (Deemed-to-be University) Bengaluru India Department of Computer Application Graphic Era Deemed University Dehradun India

ISBN: (数字)9798331530389

ISBN: (纸本)9798331530396

In the field of the fashion industry, the introduction of deep learning technology has taken an innovative revolution by providing assistance in the construction of intellectual systems that are intelligent to deliver personalized ensemble recommendations. In this research work, an innovative ensemble recommendation structure called Deep Learning VGG16 (Visual Geometry Group 16) recommender is presented. The proposed method allows a deep learning model VGG16 to understand complex patterns and developments resulting from a vast group of fashion images. In order to increase the user experience and greater participation in the well-established fashion industry, the system offers personalized ensemble ideas. The important, core and fundamental component of the system is VGG16, consisting of 13 convolutional layers and 3 fully connected layers, which is used to support the analysis of fashion images. A cutting-edge convolutional neural network (CNN) model is improved over a large and diverse collection of fashion images. Model will extract advanced high-level information and statistics with the capability to distinguish particulars in colour patterns, clothing combinations, and stylistic elements. Using this process of development, the system becomes better at identifying user preferences and fashion trends.

关键词： Deep learning Adaptation models Visualization Virtual assistants Clothing Market research Clothing industry User experience Convolutional neural networks Recommender systems

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