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2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Kumar, Bura Vijay Baby Rani, N. Krishna Prakash, R. Adusumilli, Manasa Sunil Kumar, V. Sr University School of Computer Science and Artificial Intelligence Warangal India Malla Reddy Engineering College for Women Dhulapally It Department Hyderabad India Sona College of Technology Department of Information Technology Salem India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaadeswaram India Nitte Meenakshi Institute of Technology Yelahanka Department of Ai and Ml Bengaluru India

ISBN: (纸本)9798350313413

Residential pollution is portrayed as a key contributor to health problems and environmental damage since it comes from indoor and outdoor sources. To discover a solution, a monitoring system should be built and capable of measuring the level of pollutants in typical residential environments. The suggested method makes use of cloud computing and the Internet of Things to measure pollution in residential areas. The goal is to monitor indoor air quality, gather data, and then communicate those findings with users in real time through cloud storage. An STM32 CPU, an ESP8266 wireless LAN module, a multisensory and hazardous substances are used in suggested system. According to the findings, the error rates for PM 2.5 and noise are 6-7% and 1%, respectively, while the error rate for readings of hazardous gases is 6%. The sensors are updated in 0.76 seconds, and an alarm results in a response of 2 seconds. © 2023 IEEE.

关键词： Cloud Computing Data analysis Internet of Things Monitoring system Visualization Wireless communication technologies

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Landslide Detection and Early Warning System using Extended Kalman Filter with Convolutional Neural Networks based Long Short-Term Memory 2

Landslide Detection and Early Warning System using Extended ...

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2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Monisha, P.J. Obaid, Saif Madhavan, Sowmya Sivagami, S. Saranya, N. Naga Department of Computer Science Saveetha College of Liberal Arts and Sciences SIMATS Chennai India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Chemistry New Prince Shri Bhavani College of Engineering and Technology Chennai India Department of Computer Applications Saveetha Liberal Arts and Sciences SIMATS Chennai India

ISBN: (纸本)9798331504960

Nowadays, landslides are a major risk to human life and causing devastating consequences. and economic stability by resulting substantial losses annually. The effective landslide risk management relies on timely recognition of spatial changes in patterns. patterns. Traditional approaches for developing the warning system for landslide had faced challenges which includes linearity assumption and difficulty in handling non-stationary data. Therefore, this paper proposes Extended Kalman Filter-Convolutional Neural Networks-Long Short-Term Memory (EKF-CNN-LSTM) for landslide detection and early warning system. Initially, the data is collected from Fast Deploying Monitoring System (FDMS) which consists of ad-hoc router, sensors related to landslides. Then, the data is preprocessed by using Fast Fourier Transform (FFT), which effectively filtered out noise and irrelevant frequency components by improving signal quality. After that, model is trained by proposed EKF-CNN-LSTM which generated real time prediction, early warnings of potential landslides. Finally, the early warnings are predicted and give output as "no warning" or "warning," by enabling the timely interventions. By integrating these technologies into landslide monitoring and EWS, enhanced the prediction accuracy and provided timely warnings. The proposed EKF-CNN-LSTM succeeded with high results including accuracy (0.943), precision (0.832), recall (0.786), F1-score (0.798) and MCC (0.743) when compared with existing ResNet101. © 2024 IEEE.

关键词： Extended Kalman filters

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Enhanced Framework for Authentic and Anonymous Data Sharing Over Cloud Storage Service

Enhanced Framework for Authentic and Anonymous Data Sharing ...

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Intelligent Algorithms for Computational Intelligence Systems (IACIS), International Conference on

作者： Archana Naik Anjali M Ushashree P Kavitha Sooda Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Bangalore India Department of Computer Science and Engineering Geethanjali College of Engineering and Technology Hydrabad India Department of Computer Science and Engineering B.M.S. College of Engineering Bangalore India

ISBN: (数字)9798350360660

ISBN: (纸本)9798350360677

This study introduces the concept of anonymous data exchange in banking applications using cloud computing. By intertwining innovative methodologies, the proposed framework bolsters security while introducing novel dimensions to the authentication landscape. Key to the system’s architecture is the integration of RSA and SHA algorithms within the framework. In the contemporary digital landscape, the banking sector confronts escalating challenges in establishing robust authentication and security measures to safeguard sensitive customer data and transactions. Color-coded passwords offer a pioneering approach, enhancing traditional authentication methods with added security. Furthermore, the system advocates for integrate multi-factor authentication techniques, thereby reinforcing the authentication process. This integration ensures the confidentiality, integrity, and authenticity of sensitive data transmitted between clients. The performance of this integrated algorithm is compared with other encryption algorithms, and it is observed that the proposed mechanism encryption time is 10% faster. The encrypted message was tested on an online simulator to attempt deciphering and could not be decrypted, confirming its security.

关键词： Cloud computing Multi-factor authentication Image color analysis Soft sensors Banking Passwords Encryption Performance analysis Servers Reliability

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SE-Resnet152 Model: Early Corn Leaf Disease Identification and Classification using Feature Based Transfer Learning Technique

SE-Resnet152 Model: Early Corn Leaf Disease Identification a...

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2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Kalpana, Ponugoti Chanti, Yerrolla Ravi, G. Regan, D. Pareek, Piyush Kumar Avn Institute of Engineering & Technology Department of Cse Hyderabad India Sr University School of Computer Science & Artificial Intelligence Warangal India Sona College of Technology Dept of Ece Salem India Siddartha Institute of Science and Technology Department of Ece Puttur India Nitte Meenakshi Institute of Technology Department of Ai and Ml Bengaluru India

ISBN: (纸本)9798350313413

Various corn leaf diseases reduce the quantity and quality of corn crop production, so early detection and classification are important for preventing crop yield. However, the detection and classification of corn leaf disease are more difficult due to the regions of the leaf blur and noise effect. To solve the above-mentioned problems, a feature-based transfer learning approach called the Convolutional Deep Learning (CDL) model is proposed. First, the images of corn leaves taken from Plant Village are preprocessed with Green Channel Conversion (GCC) and Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithms to enhance low-contrast images. Then the important features are extracted through ResNet152, which contains two fully connected layers with a sigmoid function. Next, squeeze and excitation are combined with ResNet152 to gain better performance in detection and classification. The proposed SE-ResNet152 model outperforms with a Precision 98.71%, Recall 98.14%, F1-score 96.87% and accuracy 99.02% respectively compared to existing model like U-Net and ResNet50 models. © 2023 IEEE.

关键词： Convolutional Neural Network Deep Learning Green Channel Extraction Low-Contrast Images ResNet152

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Hybrid Particle Swarm Optimization Algorithm with Deep Learning for Hyperspectral Images in Crops 2

Hybrid Particle Swarm Optimization Algorithm with Deep Learn...

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2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Natarajan, Vignesh Prasanna Adnan, Myasar Mundher Shalini, G. Chanti, Yerrolla Shanmugam, Sathiya Priya Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Ece Chennai India College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Civil Engineering Bengaluru India School of Computer Science & Artificial Intelligence Sr University Warangal India Panimalar Engineering College Department of Ece Chennai India

ISBN: (纸本)9798350373110

Mapping is a crucial part of using remote sensing to monitor agricultural resources. Crop maps provide essential baseline data in image categorization for a range of agricultural monitoring and decision support applications. For this reason, the research employs a Hybrid Particle Swarm Optimization-Deep Learning-Driven Crop Type Classification technique (HPSODL-CTC) in Hyper Spectral Image (HSI). The proposed HPSODL-CTC objective is to classify the different crop types on HSI. Initially, the suggested model preprocesses images to increase quality of images. Furthermore, the model under description builds a dilated Convolutional Neural Network (CNN) for extracting the features. The dilated CNN algorithm for hyperparameters are adjusted using the HPSO approach. Furthermore, the HPSODL-CTC model that is being described Extreme Learning Machine (ELM) method for crop categorization. The results showed that the proposed method performed better in terms of accuracy of (99.81%), precision of (99.63%), F1-measure of (99.71%), and recall of (99.86%) than the existing models, such as Multi-Objective Binary Chimp Optimization Algorithm (MOBChOA), Multi-Objective Binary Grey Wolf Optimizer (MOBGWO), and Adaptive Fick's Law technique - SCNN (AFLA-SCNN). © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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An Intelligent Context based Movie Recommendation System using Bi-Directional Gated Recurrent Unit 2

An Intelligent Context based Movie Recommendation System usi...

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2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Soma Sundari, V. Al-Fatlawy, Ramy Riad Manohara, H.T. Annie Rathnakumari, E. Naga Saranya, N. Department of Computer Science Saveetha College of Liberal Arts and Sciences SIMATS Chennai India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Physics New Prince Shri Bhavani College of Engineering and Technology Chennai India Department of Computer Applications Saveetha Liberal Arts and Sciences SIMATS Chennai India

ISBN: (纸本)9798331504960

In recent years, there is the rapid expansion of digital content and streaming platforms. There are several existing recommendation systems developed based on collaborative filtering and content-based filtering but still they face challenges such as privacy concerns, overcoming the cold-start problem for new users, managing scalability for large datasets, and mitigating biases in recommendations. The motive of this study is to develop a robust movie recommendation system that enhances user satisfaction by offering content tailored to individual perceptions. This paper suggests a novel approach to develop an effective movie recommendation model using Bi-Directional Gated Recurrent Unit (Bi-GRU). The proposed Bi-GRU recommendation model is imposed on a Movie Lens dataset for movie preferences. Firstly, at the classification stage, Auto Regressive Integrated Moving Average and Convolutional Neural Networks (ARIMA-CNN) is employed to classify the movies with the assistance of temporal data. Furthermore, Bi-Directional Gated Recurrent Unit (Bi-GRU) is employed is incorporated for recommendation model as it can assist in summarization of the text and enhancement of the model this review from the audience signifies optimistic movie recommendation. The Bi-GRU model outperformed the existing method i.e. Unsupervised Boltzmann Machine-Based Time-Aware Recommendation System (UBMTR) by providing effective results in RSME (0.62), MAPE (0.38). © 2024 IEEE.

关键词： Convolutional neural networks

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DevOps Life Cycle Implementation on Real Life Scenarios

DevOps Life Cycle Implementation on Real Life Scenarios

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2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Naresh, E. Murthy, S.V.N. Pareek, Piyush Kumar Reddy, Kadiri Thirupal Shiva Darshan, S.L. Kumar, B. P. Pradeep Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Department of Information Technology Manipal India SJC Institute of Technology Department of Cse Chikkaballapur India Nitte Meenakshi Institute of Technology Bangalore Yelahanka India Bharat Institute of Engineering and Technology Hyderabad India National Institute of Technology Department of Computer Science and Engineering Warangal506004 India Hkbkce Dept of ECE Karnataka Bangalore560045 India

ISBN: (纸本)9798350359688

DevOps is a collaboration of development team and the operational team working together towards developing a software from scratch to until the software is fully accomplished. Lately software products were built using primitive models like waterfall model. DevOps was introduced for faster delivery of the products, as in comparison to the models developed lately as well as developing foolproof software applications. DevOps provides a sound working environment, as well as promoting innovation and changes in the software developed. DevOps aims at building steady and fast operating working software. Some of the scenarios where DevOps lifecycle is used in resolving problems. DevOps promotes transparency among different teams of an organization, simplification of business processes. DevOps makes the process simplified by reducing complex tasks to simpler tasks, faster release cycles. DevOps promotes faster release cycles along with continuous improvement. Motivation behind using DevOps is to reduce the cost of delivery, release quality, increase release velocity, reduce mean time. DevOps lifecycle increases agility, shortens releases, improves reliability and is highly scalable. DevOps plays a major role in building software from the starting phase of requirements gathering to the final stage as well as the maintenance and updating versions once the product is out in the market for use. © 2024 IEEE.

关键词： Application programs

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Unsupervised feature extraction based on uncorrelated approach

TechRxiv

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TechRxiv 2023年

作者： Jayashree Prakash, Shiva T. Venugopal, K.R. Nitte Meenakshi Intitute of Technology Yelahanka Bangalore560064 India Vijaya Vittala Institute of Technology Karnataka Bangalore560077 India University Visvesraraya College of Enginering Karnataka Bangalore560001 India The Department of Computer Science and Engineering Nitte Meenakshi Intitute of Technology Bangalore India R Vijaya Vittala Institute of Technology Bangalore India VTU Belgaum India The Department of Computer Science and Engineering Vijaya Vittala Institute of Technology Bangalore India VTU Belgaum India Department of Computer Science and Engineering University Visvesraraya College of Enginering Bangalore India

In high dimensional spaces, methods for data processing that are mathematically driven have recently attracted a lot of attention. We take into account the situation when information is obtained by sampling a probability distribution that has support on or close to a sub-manifold of Euclidean space. In this paper, we provide an innovative unsupervised learning method called Uncorrelated Neighborhood Preserving Embedding(UNPE), which identifies the underlying manifold structure of a data set and which favors keeping the neighbourhood structure of the data set. We provide a concrete formulations with UNPE, together with a successful solution algorithm, to demonstrate the usefulness of the framework, that has been confirmed by experimental findings on datasets Coil20, Pie, Tox, and Prostate-GE using three different parameters such as Fscore, NMI and accuracy. The UNPE is resolved using iterative techniques, and convergence has been demonstrated. It is observed that performance is better than by LPP algorithm by 1%, PCA by 2% and more than 2% of LLE and LE algorithms. © 2023, CC BY.

关键词： Feature extraction

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Real-Time Audio Noise Reduction and Speech Enhancement Using LadderNet With Hybrid Spectrogram Time-Domain Audio Separation Network 4

Real-Time Audio Noise Reduction and Speech Enhancement Using...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Siddiqua, Ayesha Basha, C.H. Hussaian Abbas, Haider Mohammed Sheela, P. Merlin Indhumathi, V. Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India Sr University Department of Electrical & Electronics Engineering School of Engineering Warangal India The Islamic University Department of Computers Techniques Engineering College of Technical Engineering Najaf Iraq Department of English New Prince Shri Bhavani College of Engineering and Technology Chennai India College of Arts and Science Department of Computer Applications Sona Salem India

ISBN: (纸本)9798350352931

In recent years, the rapid growth of modern technology has improved people lives, but it also comes with a downside: increased exposure to noise from complex industrial infrastructure and machinery. To overcome this issue, the existing method namely a single-channel Deep Neural Network (DNN) model have developed for speech signal processing, but it led to limitations in online processing. Therefore, this research proposes a LadderNet with Hybrid Spectrogram Time-domain Audio Separation Network (HSTasNet) model for reducing the noise and enhancing speech enhancement in real-time audio processing. This procedure begins with the collection of data from real-time audio signal and then preprocessed with spectral gating filter to reduce noise from signals. After that, t-distributed Stochastic Neighbor Embedding (t-SNE) technique to extract the optimal features from preprocessed signals. Finally, LadderNet and HS-TasNet techniques are employed for noise reduction and speech enhancement. As per the results, the proposed LadderNet-HSTasNet model offered outstanding results in terms of Perceptual Evaluation of Speech Quality (PESQ) of 3.847, Short-Time Objective Intelligibility (STOI) of 1.874, and Scale-Invariant Signal-to-Distortion Ratio (SI-SDR) of 16.792 when compared with existing Dual-Path Recurrent Neural Network (DPRNN) model. © 2024 IEEE.

关键词： Noise abatement

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Detection of Electrical Transmission Line Overload with Blockchain using Isolation Forest with Recurrent Neural Network 4

Detection of Electrical Transmission Line Overload with Bloc...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Veeramachaneni, Vinod Fallah, Mohammed H. Reddy, Gurram Vijendar Pareek, Piyush Kumar Monisha Jothi, R. Colorado Technical University Department of Information Technology Colorado Springs United States College of Technical Engineering The Islamic University of Al Diwaniyah Department of Computers Techniques Engineering Al Diwaniyah Iraq Gokaraju Rangaraju Institute of Engineering and Technology Department of Information Technology Hyderabad India Nitte Meenakshi Institute of Technology Department of Aiml & Ipr Cell Bengaluru India New Prince Shri Bhavani College of Engineering and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350352931

Nowadays, energy has emerged as a dynamic support of modern society, with electricity playing a crucial role in powering our daily lives. As the world's dependence on electricity continues to grow, the significance of a consistent and reliable supply cannot be exaggerated. Power companies face the daunting task of meeting this escalating demand, while also ensuring the delivery of high-quality energy services to consumers, where even a momentary disruption can have severe consequences for businesses, communities, and individuals alike. To overcome these limitations, an Isolation Forest with Recurrent Neural Network (IF-RNN) for detecting overload in electrical transmission lines using Power Demand Benchmarking Electric Power Load (PDB-EPL) dataset. For preprocessing, a Singular Value Decomposition (SVD) is employed for reducing noise and irrelevant features, for improving generalization, and dimensionality. Then, Blockchain is considered for feature extraction that performs decentralization, fault tolerance, immutability, auditability, and transparency. Eventually, IF-RNN is utilized as classifier for anomaly detection, high-dimensional data handling, and scalability. The results conclusively demonstrate the efficacy of the proposed IF-RNN interms of balanced accuracy, and accuracy when compared to the existing method like Feed-forward Neural Network (FNN). © 2024 IEEE.

关键词： Power transmission lines

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