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Advanced Deep Learning Techniques for Multivariate Weather Prediction Using RNN and LSTM Models 5

Advanced Deep Learning Techniques for Multivariate Weather P...

5th IEEE International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2025

作者： Ramana, Chintha Venkata Varma, B.Venkata Ramesh, U.V. Kumar, P. Anand Lakshmanarao, A. Raja Rao, P.B.V. Shri Vishnu Engineering College for Women Department of Cse Bhimavaram India Sreyas institute of engineering and Technology Department of Cse Telangana Bandlaguda India Aditya University Department of Cse Surampalem India Jayamukhi Institute of Technological Sciences Department of Cse Narsampet Warangal India Aditya university Department of It Surampalem India Department of Cse Andhra Pradesh Bhimavaram India

ISBN: (纸本)9798350356236

This paper investigated advanced deep learning and traditional machine learning techniques for multivariate weather forecasting using time series data. A dataset with weather features like temperature, humidity, wind speed, and precipitation over extended periods of time was collected. Experiments were conducted with each feature of temperature, humidity, wind speed, and precipitation as the target variable to evaluate predictive accuracy in multiple scenarios. Initially, traditional machine learning regression models like Random Forest Regression (RFR), Decision Tree Regression (DTR), Gradient Boosted Regression (GBR), and Multiple Linear Regression (MLR) were applied as base models for predictive evaluation. Later, advanced deep learning models like RNN, LSTMs and GRU were applied and observed to enhance performance with respect to measures MSE, MAE, RMSE, and r-squared values. The results reveal the strengths of advanced methods over traditional ML models. This work contributes to valuable insights into weather forecast modeling. © 2025 IEEE.

关键词： Deep learning

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A green synthetic approach of silver nanoparticles using Premna tomentosa leaf extract and their anti-microbial study

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Materials Today: Proceedings 2022年第P12期62卷 6776-6779页

作者： Hymavathi, Atmakur Sreyas Institute of Engineering and Technology Hyderabad 500068 India

By using eco-friendly ways leaf extract from Premna tomentosa was used in the synthesis of Ag NPs (Silver nanoparticles). The extract acts as stabilizing and also as reducing agent. The AgNO3 solution was used as Silver precursor, formation of reddish-brown coloured solution from colourless solution was clearly visible, thus indicating the nanoparticles formation, even surface Plasmon resonance in UV visible spectroscopy was observed at 434 nm, confirming the Ag nanoparticles. Using microbes like Bacillus subtilis, Escherichia coli, Pseudomonas aeruginosa and Staphylococcus aureus antimicrobial activity studies were studied for the synthesised nanoparticles. Green synthesized Ag nanoparticles showed good antimicrobial activities against all the four *** MIC values ranged from 25 to 100 mg/ml for Pseudomonas aeruginosa indicating more sensitivity. The Bacillus subtilis and Staphylococcus aureus had least sensitivity towards the prepared Premna tomentosa Silver nanoparticles. © 2022

关键词： Silver nanoparticles

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Deep CNN Based Whale Optimization for Predicting the Rice Plant Disease in Real Time 1st

Deep CNN Based Whale Optimization for Predicting the Rice Pl...

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1st International Conference on Artificial Intelligence and Data Science, ICAIDS 2021

作者： Reddy, S. Sai Satyanarayana Sowmya, G. Reddy, Vudara Bhaskar Kumar, Boodidha Deepak Kumar, Ashwani Sreyas Institute of Engineering and Technology Hyderabad500068 India Department of Computer Science and Engineering Sreyas Institute of Engineering and Technology Hyderabad500068 India Sreyas Institute of Engineering and Technology Hyderabad500068 India

ISBN: (纸本)9783031213847

Plant diseases have a devastating impact on the security of food production, and they cause significant reductions in both the quality and quantity of agricultural products. To reduce this threat, various approaches such as machine learning and deep learning are presented. Therefore, automated recognition and detection of plant diseases is highly preferred in the field of agricultural industry. Most preferred systems utilize a greater number of training parameters but the classification accuracy is low. This paper proposes a deep CNN classifier for disease prediction in rice plant which is optimized by the whale optimization. The performance of the proposed whale based deep-CNN is analyzed using measures such as Accuracy, Sensitivity and Specificity. Based on training percentage Accuracy, Sensitivity and Specificity is 95.60%, 9557, 96.58 and 84%, 81.50, 88.73% for the K-Fold analysis. The accuracy is high when the training percentage is increased thus shows the improved performance outcome when compared with the existing conventional methods. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Forecasting

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Robust Indian Currency Recognition Using Deep Learning 4th

Robust Indian Currency Recognition Using Deep Learning

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4th International Conference on Advanced Informatics for Computing Research, ICAICR 2020

作者： Gowroju, Swathi Sravani, K. Ramchandar, N. Santhosh Kamesh, D. Sai Murthy, J. Nasrasimha Sreyas Institute of Engineering and Technology Telangana Hyderabad Telangana India Sreyas Institute of Engineering and Technology Hyderabad Telangana India

ISBN: (纸本)9789811636592

The applications of currency counting are vast in real-time. From the ATM to small entrepreneur business applications includes currency recognition. In contrary, currency recognition is one of the HCI applications that can be taken for any human-computer application. This technology limits the manpower and human supervision. The emerging technologies such as AI and Machine Learning were now part of every application. In this regard, the proposed methodology is a robust methodology built on deep learning technique using relatives less number of images in the database with high noise, including rotated notes, tilted notes, held by hand note images. The proposed methodology extracts denomination in 90 s. It uses a Convolution Neural Network (Inception using transfer learning) to extract currency denomination. We acquired the accuracy of 83% in predicting the denomination. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Deep learning

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Real-Image Transformation into a Caricature Image Using Neural Network 4th

Real-Image Transformation into a Caricature Image Using Neur...

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4th International Conference on Advanced Informatics for Computing Research, ICAICR 2020

作者： Swaroop, K. Sonali Kumar, Sandeep Sowjanya, A. Sreyas Institute of Engineering and Technology Hyderabad India

ISBN: (纸本)9789811636592

Multimedia is an area which is evolving at a greater extent day by day and it forms a great are of interest in machine learning and artificial intelligence. The caricature of a real time image is defined as a cartoon style of the corresponding object. It can be widely used process in the area of computer graphics as performing the same process manually for each picture. And hence this paper proposes a method of converting a real-time image into its caricature. The proposed methodology uses a concept of fully connected network to convert the real time image into its cartoon style. The experiments show improvised results up to 90% when compared to the existing methods. The experiments also prove that our model has strong applicability for translating real time images. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Machine learning

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NavRobotVac: A Navigational Robotic Vacuum Cleaner Using Raspberry Pi and Python 1

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4th International Conference on Smart Computing and Informatics, SCI 2020

作者： Nabi, Shaik Abdul Vardhan, Mettu Krishna Department of CSE Sreyas Institute of Engineering and Technology Hyderabad Telangana India Sreyas Institute of Engineering and Technology Hyderabad Telangana India

ISBN: (数字)9789811615023

ISBN: (纸本)9789811615016

Human life is becoming more advanced day by day and the use of technology is making our lives easier. By this consideration, one of our daily task is to keep our surroundings clean by natural way using a broomstick, as technology is increasing day by day we came with manual vacuum cleaners and then moved to robotic vacuum cleaners. In robotic vacuum cleaner there exist many different methods which are used to clean in home or hotels. In NavRobotVac (Navigational Robotic Vacuum cleaner) model, we consider the science of architecture (vastu) and implemented using python programming with Raspberry Pi. It automatically scans area around and starts cleaning. In this model, IoT sensors are used to read real-time data thus it gives technical efficiency and economic efficiency. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Python

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Document Summarization Model Using Modified Pagerank Algorithm 1st

Document Summarization Model Using Modified Pagerank Algorit...

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1st International Conference on Artificial Intelligence and Data Science, ICAIDS 2021

作者： Reddy, S. Sai Satyanarayana Kumar, Chithram Deeven Pisati, Sreekanth Reddy Kolli, Rama Devi Sreyas Institute of Engineering and Technology Hyderabad500068 India Department of Computer Science and Engineering Sreyas Institute of Engineering and Technology Hyderabad500068 India Sreyas Institute of Engineering and Technology Hyderabad500068 India

ISBN: (纸本)9783031213847

Document summarization refers to the writing of huge documents in a precise and short manner without losing the vital information. Automatic summarization of documents is important due to the fact that it consumes less time, less biased compared with human summarizations and the selection process can be made easier using these document summarization methods. Advanced natural language processing techniques are utilized to interpret the text and providing summarizations. In this research, a pagerank-based document summarization model is implemented to effectively summarize the document. Initially, the documents are collected and all the documents present in various formats are converted into text format and summarized using the modified pagerank algorithm. The experimentation results are analyzed using Rouge-N and Rouge-L with F1 measure, precision and recall and the results shows that the proposed model works more efficient than the existing methods with an f1 measure of 76.85%. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Natural language processing systems

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Design and Development of Novel Ensemble Classifier for Visual Sentiment Analysis 3

Design and Development of Novel Ensemble Classifier for Visu...

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3rd International Conference on Smart and Intelligent Learning for Information Optimization, CONSILIO 2021

作者： Sai Satyanarayana Reddy, S. Sreyas Institute of Engineering and Technology Hyderabad India

Visual media is now become one of the pre-eminent methods utilized for conveying feelings or suppositions on the web. A huge number of photographs are being transferred by individuals on popular social network platform for interpersonal communication. Visual sentiment analysis (VSA) has pulled in wide consideration since an ever increasing number of individuals will communicate their feeling and conclusions through visual substance via online media. The domain of visual emotion evaluation is impractical because-of the great degree of discrimination in the human realization measure. In this paper, a novel ensemble classifier for visual sentiment analysis, which will use few labelled training specimens to attain an effective SA model, is proposed. The classified output is provided by the Ensemble classifier. The steps adopted in the proposed Visual sentimental analysis for the effective classification are preprocessing and the classification. The pre-processing is accomplished by the Resnet-101, which will boost the potential of the classifier. The effectiveness of the proposed sentimental analysis method is revealed by the comparative analysis and it is expected that the proposed method outperforms all the comparative methods. Then, broad datasets enable the quick advancement of profound neural organizations for recognition process. However, the explanation of enormous scope datasets is over the top expensive and tedious. The efficacy of the proposed sentimental analysis method will be revealed by the comparative analysis and it is expected that the proposed method will exceed all the comparative methods. © Published under licence by IOP Publishing Ltd.

关键词： Deep neural networks

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Effect of dual and new generation wide-base tire assembly on inverted pavements

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Journal of Road engineering 2022年第2期2卷 124-136页

作者： Rajan Singh Baghel Sridhar Reddy Kasu Anush K.Chandrappa School of Infrastructure Indian Institute of Technology BhubaneswarOdisha 752050India Department of Civil Engineering Sreyas Institute of Engineering and TechnologyHyderabad 500068India

The conventional flexible pavements have been constructed such that the stiffness of the layer reduces with *** crust thickness becomes significantly high for heavy traffic corridors resulting in the consumption of large quantities of construction materials and also increasing environmental *** pavements with the aggregate interlayer(AIL)or stress absorbing membrane interlayer(SAMI)are considered to be one of the alternatives for thick conventional flexible pavements for heavy traffic *** AIL or SAMI is placed between a stiff cement-treated base and asphalt concrete layer to function as crack relief *** change in the composition alters the behaviour of inverted pavements compared to the conventional flexible *** the other hand,wide-base tires are being increasingly preferred by trucking industries due to increased fuel economy and cargo ***,the effect of wide-base tires on the performance of inverted pavements is yet to be *** this study,the 3D finite element(FE)models of inverted pavements considering different crack relief layers were developed,and load from dual-wheel and wide-base tires were *** stress-strain evolution in the various layers of inverted pavements was investigated and discussed in this *** results indicated the higher stress and strains due to wide base tires compared to the dual-wheel ***,pavement with SAMI was found to result in lower stress and strains in the asphalt concrete layer compared to AIL pavements.

关键词： Aggregate interlayer Inverted pavement Shear strain Stress absorbing membrane interlayer Wide-base tire

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Beyond Automation: AI-Driven Project Management with OpenAI and Prompt engineering 4

Beyond Automation: AI-Driven Project Management with OpenAI ...

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4th IEEE International Conference on Electrical, Computer, and Energy Technologies, ICECET 2024

作者： Jayaram, M. Bhutkar, Yogesh Kumar Bojjanapalli, Indhra Lochan Yeshwanth, Giri Reddy, Burri Yashwanth Sreyas Institute of Engineering and Technology Hyderabad India

ISBN: (纸本)9798350395914

Managing project management tools is manual and lacks specific integrations of the Large Language Model making the entire process of retrieval and feeding useful information time-consuming, prone to errors and inefficient. This paper is an innovative approach to enhance project management and collaboration by integrating OpenAI's AI with web-based tools, utilizing Prompt engineering. Team members interact through simple prompts, facilitating tasks such as assignment, tracking, and resource allocation to querying useful information from context in natural language using specific prompts. The web-based system ensures accessibility and real-time updates, incorporating Next JS for versatility and integration with different project management methodologies. © 2024 IEEE.

关键词： Project management

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