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12th International Conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2024

作者： Ravi, C.N. Suhasini, T.S. Narasimharao, Jonnadula Mahendar, A. Amruthraj, B. Venkateswara Reddy, R. Department of Computer Science and Engineering CMR Engineering College Telangana Hyderabad India CMR Engineering College Telangana Hyderabad India Department of Computer Science and Engineering CMR Technical Campus Telangana Hyderabad India CMR Technical Campus Telangana Hyderabad India Department of Computer Science and Engineering CMR College of Engineering and Technology Telangana Hyderabad India CMR College of Engineering and Technology Telangana Hyderabad India

ISBN: (纸本)9789819601387

A significant factor in data transfer is computer science. Security considerations make it difficult to transmit sensitive data or have conversations via the internet. Typically, cryptography is used to transmit text-based underground messages and conceal in sequence. These days, there are numerous methods for concealing information on any media. A technique like this is steganography. The creation of a covert communication system that uses multi-image steganography will enable safe communication among the dispatcher and the recipient free from hacker intervention. Image steganography is the main technique for information hiding. It involves integrating the cipher text within a picture called the cover picture that is very difficult for strangers to view through an unassisted eye. Within a cover image, the concealed data may take the shape of any kind of text, picture, music, or even video. The secret code is split up into several pieces and etched into several cover images, according to the conceptual notion of multi-image steganography. Thus, in order to make it extremely difficult for the hackers to hide the data, we suggested two image steganography concepts. In order to create a safe and protected system, this learning suggest by means of the Advanced Encryption Standard (AES) approach of cryptography with the Least Significant Bit (LSB) method of steganography. Here, a symmetric key—which is used by both the sender and the recipient to encrypt and decode data—is used by both parties. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Stereo image processing

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A Delay-Tolerant low-duty cycle scheme in wireless sensor networks for IoT applications

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International Journal of Cognitive Computing in engineering 2023年第1期4卷 194-204页

作者： Singh, Shashank Anand, Veena Bera, Pallav Kumar Department of CSE National Institute of Technology Raipur India School of Engineering and Computer Science University of Evansville IN United States

Energy efficiency is an existing research challenge in wireless sensor networks (WSNs);to make WSNs energy efficient, many researchers moved to low-duty cycle WSNs with different data forwarding schemes. Low-duty cycle is considered to be a promising approach to design routing protocols for resource-constrained WSNs. Many applications have real-time constraints, which requires an event must be reported to the sink before deadline. Furthermore, wireless links between low-power radios are highly unreliable, and end-to-end latency requirements are challenging due to multiple transmissions for a single packet delivery. By considering probabilistic delay (i.e., delay bounded data delivery with reliability constraints) and energy efficiency issues, little research has been done using different data forwarding schemes, but still, the problem exists. In this work, two strategies are proposed for data forwarding: an energy-optimal path and a delay-optimal path. The early-arrived packet follows an optimal energy route and a diverse path to achieve a delay guarantee with minimum transmission cost. The proposed scheme improves the performance of low-duty cycle WSNs in terms of delay, reliability, stability, and transmission cost. It is observed that under different low-duty cycles, the proposed scheme achieves up to 25% energy conservation compared with both MinEEC and MinEED. At last, undirected spanning trees are considered to balance communication costs. The simulation results of the proposed scheme are compared with the existing methods. © 2023 The Authors

关键词： Wireless sensor networks

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Application and Effectiveness Evaluation of BP Neural Network Model Based on Rafflesia Optimization Algorithm Optimization in Daily Average Temperature Prediction 15th

Application and Effectiveness Evaluation of BP Neural Netw...

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15th International Conference on Genetic and Evolutionary Computing, ICGEC 2023

作者： Zhang, Zhen Chu, Shu-Chuan Pan, Jeng-Shyang Li, Wei Yang, Hongmei College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China College of Computer Science and Technology Harbin Engineering University Harbin China Department of Information Management Chaoyang University of Technology Taichung41349 Taiwan

ISBN: (纸本)9789819700677

With economic and social development, the impact of climate change on human social life is constantly deepening. The ability to accurately predict trends in meteorological data has become an important area of research at present. The main focus of this research is to explore the application and evaluation of the effectiveness of a BP neural network model based on the optimization of the Rafflesia Optimization Algorithm(ROA) for daily average temperature prediction. The data for this study uses a large amount of historical meteorological data as the training set, including date, time and the corresponding daily mean temperature. The prediction model was constructed using a BP neural network-based temperature prediction model, and the ROA was used to optimise the weights and biases of the BP neural network to improve the accuracy and reliability of the prediction model. The experimental results show that the optimized BP neural network model has higher accuracy and stability in predicting the daily average temperature compared with the traditional BP neural network model. The optimised model is also able to better capture the non-linear relationships and time-series features in the meteorological data, thus improving the accuracy and reliability of the prediction. This research can provide a reliable tool for meteorological prediction and climate research, which helps to accurately predict future climate change trends and provide scientific basis and decision support for future economic and social development and human life progress. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Climate change

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Enhancing Bitcoin Price Prediction Using LSTM and GRU Models 12th

Enhancing Bitcoin Price Prediction Using LSTM and GRU Models

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12th International Conference on Recent Trends in Computing, ICRTC 2024

作者： Anitha Rajakumari, P. Karthick, S. Gupta, Shruti Gupta, Shreya Mahapatra, R.P. Vinoth Kumar, S. Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai India Department of Computer Science and Engineering SRM Institute of Science and Technology Delhi NCR Campus Ghaziabad201204 India

ISBN: (纸本)9789819789450

Abstract Bitcoin, as the pioneering cryptocurrency, has witnessed significant fluctuations in its price since its inception. Predicting these fluctuations is of great interest to investors, traders, and researchers alike. This paper presents a comparative study of Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models for predicting Bitcoin prices. Cryptocurrency markets, particularly Bitcoin, exhibit high volatility and non-linearity, making accurate price prediction challenging. Recurrent Neural Networks (RNNs), including LSTM and GRU, have shown promise in capturing temporal dependencies and patterns in sequential data. In this study, we train and evaluate LSTM and GRU models separately on historical Bitcoin price data to forecast future price movements. Through comprehensive experimentation and analysis, we assess the performance of both models in terms of accuracy and effectiveness in Bitcoin price prediction. The performance of these models is evaluated based on various metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). The results demonstrate the effectiveness of LSTM and GRU in predicting Bitcoin prices and offer insights into their comparative performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Long short-term memory

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Radiative cooling for passive thermal management towards sustainable carbon neutrality

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National science Review 2023年第1期10卷 87-97页

作者： Jun Liang Jiawei Wu Jun Guo Huagen Li Xianjun Zhou Sheng Liang Cheng-Wei Qiu Guangming Tao Wuhan National Laboratory for Optoelectronics and Optics Valley Laboratory Huazhong University of Science and Technology Department of Electrical and Computer Engineering National University of Singapore MOE Key Laboratory of Thermo-Fluid Science and Engineering School of Energy and Power Engineering Xi'an Jiaotong University Key Laboratory of Education Ministry on Luminescence and Optical Information Technology National Physical Experiment Teaching Demonstration CenterDepartment of PhysicsSchool of Physical Science and Engineering Beijing Jiaotong University State Key Laboratory of Material Processing and Die &Mould Technology School of Materials Science and Engineering Huazhong University of Science and Technology

Photonic structures at the wavelength scale offer innovative energy solutions for a wide range of applications,from high-efficiency photovoltaics to passive cooling,thus reshaping the global energy *** cooling based on structural and material design presents new opportunities for sustainable carbon neutrality as a zero-energy,ecologically friendly cooling *** this review,in addition to introducing the fundamentals of the basic theory of radiative cooling technology,typical radiative cooling materials alongside their cooling effects over recent years are summarized and the current research status of radiative cooling materials is outlined and ***,technical challenges and potential advancements for radiative cooling are forecast with an outline of future application scenarios and development *** the future,radiative cooling is expected to make a significant contribution to global energy saving and emission reduction.

关键词： photonic structures radiative cooling energy saving and emission reduction carbon neutrality

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Machine learning based cardiovascular disease prediction

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Materials Today: Proceedings 2022年 64卷 459-463页

作者： Chinnasamy, P. Arun Kumar, S. Navya, V. Lakshmi Priya, K. Sruthi Boddu, Siva Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India Department of Computer Science and Engineering Bethesda Institute of Technology and Science Gwalior India Department of Electronics and Communication Engineering East Point College of Engineering & Technology Bangalore India Department of Electronics and Communication Engineering PERI Institute of Technology Chennai India UG Student Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

In past few years, one of the really tough situations in medicine is already making predictions cardiac dysfunction. Almost any moment, roughly one patient dies through myocardial infarction with in present era. Due to the difficulty of forecasting cardiac sickness, it is vital to systematize the procedure in place to minimize the risk associated with and to educate the patient well enough in preparation. The risk of cardiovascular disease in the World is tremendous. The accurate and thorough assessment of a physician's cardiovascular risk is critical for reducing the incidence and severity attack and stroke and improving cardiovascular protection. In order to solve this issue, we are proposing method to discover these heart abnormalities as soon as reasonably practicable to avert fatal consequences. The next step is to determine whether or not the user is at risk of developing cardiovascular disease. It also answers difficult problems concerning heart detection of diseases, allowing clinicians to make better clinical decisions. Medical specialists have accumulated a large number of medical data that can be analysed and important fact retrieved. Machine learning approaches can improve in the prognosis and early identification of heart illness after hypothesis testing. © 2022

关键词： Machine learning

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Perpetual Preservation of Pesticide Free Products Using IoT

Perpetual Preservation of Pesticide Free Products Using IoT

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2023 Intelligent Computing and Control for engineering and Business Systems, ICCEBS 2023

作者： Manimala, G. Kavitha, P. Selvakrishnan, S. Shivani, T.S. Sri Sairam Engineering College The Department of Computer Science and Engineering India R.M.K Engineering College The Department of Computer Science and Engineering India Hindustan Institute of Technology and Science The Department of Information Technology India

ISBN: (纸本)9798350394580

Grains are the primary source of daily sustenance for most people. Grain hoarding is among the most crucial elements of our economy and way of life. In India, a sizable group of workers oversee assuring the safety and quality of grain storage facilities. Traditional grain storage, for instance, had drawbacks such a wide connection scope, higher impedance and lighting strike, and a high maintenance cost. Waste reduction may be made possible via sensors and other gadgets. The storage of harvested grains is crucial to the economics of both rich and developing nations since it affects hundreds of millions of people. Therefore, it is crucial that food storage facilities, like FCI, monitor and control environmental factors. The main objective of a grain storage facility is to maintain the grain's quality and safety. Thanks to technological advancements, it is now possible to monitor distant sites in real-time from a variety of locations. Using NODEMCU, a real-time grain storage monitoring system has been developed with the goals of increasing grain storage levels, reducing grain losses, and lowering labour intensity generally. It is possible to create a granary monitoring system by fusing embedded and wireless technologies. © 2023 IEEE.

关键词： HumiditySensor NodeMCU Temperature Sensor Toggle Switch UltraSonic Sensor

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An Efficient way of Emotion and Gesture Recognition using Deep Learning Algorithms 5

An Efficient way of Emotion and Gesture Recognition using De...

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5th IEEE International Conference on Electrical, computer and Communication Technologies, ICECCT 2023

作者： Periyanayaki, M. Duraisamy, M. Duraisamy, Premkumar Natarajan, Yuvaraj KPR Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Government Arts and Science College Department of Computer Science Tirupattur India

ISBN: (纸本)9781665493604

Researchers in psychology, computer science, linguistics, neurology, and allied fields have become more interested in a human-computer interface system for autonomous face recognition or facial expression recognition. An Automatic Facial Expression Recognition System (AFERS) has been suggested in this study. Three steps comprise the proposed methodology: face detection, feature extraction, facial expression recognition, and gesture recognition. Face detection's initial steps include YCbCr-color skin colour detection, illumination correction for uniformity on the face, and morphological procedures for maintaining the necessary face region. Using the AAM (Active Appearance Model) approach, the first phase's output is utilized to extract facial features such as the mouth, nose, and eyes. Automatic facial expression recognition, the third stage, entails straightforward. Using the AAM (Active Appearance Model) approach, the first phase's output is utilized to extract facial features such as the mouth, nose, and eyes. © 2023 IEEE.

关键词： Face recognition

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Face Recognition-Based Door Locking System with OTP Verification 6th

Face Recognition-Based Door Locking System with OTP Verifica...

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6th International Conference on Smart Computing and Informatics, SCI 2024

作者： Rajavel, M. Santhosh Kumaar, R. Suvedha, D. Dharani, S. Department of Computer Science and Engineering SRM Institute of Science and Technology Tamil Nadu Chennai India

ISBN: (纸本)9789819619801

In the world of the digital era, AI has had a major influence. With the advancement of technology, people can experience extraordinary innovations that not only make life more comfortable, but also make it simpler. As a part of AI, we have created a door locking mechanism that uses face recognition with OTP verification using OpenCV. At the stage of identification of faces, Histogram of oriented Gradients algorithm [HOG] is used for finding the face at the backend. This project is developed using both hardware and software. With the help of smart doorbells, homeowners can get notifications when a visitor is at the door and identify the visitor. They significantly raise people’s quality of life and advance the development of smart homes. The purpose of this project is to make the home or office area secure and can also be used to minimize criminality. Only one form of authentication is offered by the existing systems, which makes them less secure. Thus, for secure access, we have proposed a safe door locking system with two factor authentication that can authenticate and validate the user as well as open the door in real time. We have come up with an idea which should recognize the guest, relatives or a stranger and alert the user. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Authentication

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High Resolution Solar Image Generation Using Generative Adversarial Networks

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Annals of Data science 2024年第5期11卷 1545-1561页

作者： Dash, Ankan Ye, Junyi Wang, Guiling Jin, Huiran Department of Computer Science New Jersey Institute of Technology NewarkNJ07102 United States School of Applied Engineering and Technology New Jersey Institute of Technology NewarkNJ07102 United States

We applied Deep Learning algorithm known as Generative Adversarial Networks (GANs) to perform solar image-to-image translation. That is, from Solar Dynamics Observatory (SDO)/Helioseismic and Magnetic Imager (HMI) line of sight magnetogram images to SDO/Atmospheric Imaging Assembly (AIA) 0304-Å images. The Ultraviolet (UV)/Extreme Ultraviolet observations like the SDO/AIA 0304-Å images were only made available to scientists in the late 1990s even though the magnetic field observations like the SDO/HMI have been available since the 1970s. Therefore, by leveraging Deep Learning algorithms like GANs we can give scientists access to complete datasets for analysis. For generating high resolution solar images, we use the Pix2PixHD and Pix2Pix algorithms. The Pix2PixHD algorithm was specifically designed for high resolution image generation tasks, and the Pix2Pix algorithm is by far the most widely used image to image translation algorithm. For training and testing we used the data for the year 2012, 2013 and 2014. After model training, we evaluated the model on the test data. The results show that our deep learning models are capable of generating high resolution (1024 × 1024 pixels) SDO/AIA0304 images from SDO/HMI line of sight magnetograms. Specifically, the pixel-to-pixel Pearson Correlation Coefficient of the images generated by Pix2PixHD and original images is as high as 0.99. The number is 0.962 if Pix2Pix is used to generate images. The results we get for our Pix2PixHD model is better than the results obtained by previous works done by others to generate SDO/AIA 0304 images. Thus, we can use these models to generate AIA0304 images when the AIA0304 data is not available which can be used for understanding space weather and giving researchers the capability to predict solar events such as Solar Flares and Coronal Mass Ejections. As far as we know, our work is the first attempt to leverage Pix2PixHD algorithm for SDO/HMI to SDO/AIA0304 image-to-image translation. © Th

关键词： Generative adversarial networks

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