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2023 7th International Conference On Computing, Communication, Control And Automation, ICCUBEA 2023

作者： Tarle, Sahil Balasaheb Paulkar, Pratik Parmeshwar Phad, Karan Govind Jadhav, Shreyas Vishwanath Chaudhari, Archana Y. Patil, Pramod d. Ahire, Prashant Dr. D. Y. Patil Institute of Technology Pimpri Computer Engineering Department Pune18 India Pimpri Chinchwad College of Engineering Computer Engineering Department Pune India

ISBN: (纸本)9798350304268

It is challenging to make predictions about the stock market since it requires in-depth expertise to weed out news stories, look at past data, and determine how news stories affect stock price movements. There is a drastic increase in challenges because of the fluctuating nature of stock values. The stock market prediction helps investors, policymakers, and businesses make informed decisions based on the expected future performance of the stock market. This paper presents an application of the Prophet algorithm in the stock market, to forecast stock prices. Prophet is a time series forecasting algorithm that takes into account seasonality, trends, and holidays. The data source for this investigation is downloaded from Yahoo Finance includes daily High, low, Open and Close prices of a selected stock from January 2015 to April 2023. The model is trained on the 70 percent of historic data, and the performance is evaluated on the test data which is remaining 30 percent of data. The research results indicate that the Prophet An algorithm is likely to accurately foresee the stock prices with an average MAPE of 20.8 percent. Additionally, the model sheds light into the trends and seasonality of the stock market, which can be useful for investors and traders. Overall, the Findings indicate that the Prophet algorithm can be a valuable tool for stock market prediction. © 2023 IEEE.

关键词： Equities Prediction Facebook Prophet Machine learning Time Series Variance

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Integrating Imaging and Clinical data for Parkinson's disease diagnosis using deep learning 7

Integrating Imaging and Clinical Data for Parkinson's Diseas...

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7th IEEE International Conference on Emerging Smart Computing and Informatics, ESCI 2025

作者： divya, d. Kshirsagar, Pravin R. Tak, Tan Kuan Naveed, Quadri Noorulhasan Department of Computer Science and Engineering Jerusalem College of Engineering Chennai India Department of Electronics & Telecommunication Engineering J D College of Engineering & Management Nagpur India Electrical Power Engineering Programme Singapore Institute of Technology Singapore Department of Computer Science and Engineering King Khalid University Saudi Arabia

ISBN: (纸本)9798331515683

That disorder affecting the nervous system, Parkinson's disorder can worsen with time and cause a host of health problems. Parkinson's disease diminishes cerebral function and may induce physical complications in senior individuals. Parkinson's disease can induce a range of disorders by impairing neurological in nature, behavioral, and physiological systems. Physical structures exhibit minimal damage at early diagnosis, complicating the prediction of Parkinson's illness in its initial stages based solely on physical observation. Recent studies on electroencephalogram (EEG) signs in the identification of Parkinson's illness have revealed significant aspects pertinent to early detection. Electroencephalography (EEG) is a multifaceted signal comprising several non-linear and non-stationary elements. Subsequently, an auto-encoder-based deep extraction of features method is employed to derive the spatial and temporal attributes from the four frequency partitions. Additionally, statistical and spectral attributes are obtained. The gathered attributes are utilized to analyze notable patterns associated with Parkinson's disease. Optimal weighted feature fusion is achieved through the precise adjustment of weights. The proposed dlB-lSTM (densely located Bidirectional long Short Term Memory) employs weighted features so that we can identify Pd. To impede the progression of Parkinson's disease, the algorithm autonomously learns from the signals and delivers targeted outcomes. © 2025 IEEE.

关键词： Bidirecional Electroencephalogram Feature Extraction long Short Term Memory Neurological condition Parkinson's disease Physical Structures Spatial Temporal Features

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development and Evaluation of Sequential Batch Reactor with Natural Coagulants for dairy Waste Water Treatment 5

Development and Evaluation of Sequential Batch Reactor with ...

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5th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2024

作者： daniel, R. Sheela Sundararajan, S. Kumar, C. Antony Vasantha Jothi, J. Nirmal Krishnan, R. Santhana Ram, C. Ramasamy Sankar SCAD College of Engineering and Technology Department of Civil Engineering Cheranmahadevi India SCAD College of Engineering and Technology Department of Mechanical Engineering Cheranmahadevi India School of Electrical and Communication Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India SCAD College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India University College of Engineering Anna University Department of Computer Science and Engineering Tiruchirappalli India

ISBN: (纸本)9798350395235

India is often heralded as the "Cynosure"of the global dairy industry, boasting a staggering valuation of INR 14,899.8 billion in 2022. This industry has exhibited remarkable and sustained growth in milk product availability since the year 1998. However, it's imperative to concede that the dairy sector concurrently bears the dubious distinction of being one of the most ecologically burdensome industries, primarily due to its substantial effluent output. Approximately 50% of the total dairy wastewater emanates from the rinsing of processing units intricately involved in the production of dairy commodities. The sources of this wastewater are multifarious, encompassing spills and seepage from pipelines, suboptimal tanker drainage, foaming incidents, tank spillages, cleaning operations, and liquid losses. This wastewater comprises milk solids, detergents, sanitizers, milk remnants, and cleaning water. It is typified by elevated concentrations of nutrients, both organic and inorganic, and may also encompass acids, alkalis, active agents, disinfectants, in conjunction with a substantial microbiological load, inclusive of pathogenic viruses and bacteria. In recognition of the imperative need to reclaim valuable milk solids to reinvigorate agriculture, particularly in regions grappling with water scarcity, a comprehensive wastewater treatment strategy was proffered. This strategic framework encompassed the meticulous scrutiny of pivotal parameters such as pH, BOd (Biological Oxygen demand), and turbidity during the treatment regimen. Moreover, an ongoing discourse pertains to the utilization of natural coagulants for the efficacious treatment of dairy wastewater. diverse ratios of these coagulants were judiciously introduced to the wastewater under treatment, and the levels of pH, BOd, and turbidity were assiduously assessed upon the culmination of each processing stage. The determination of the treatment process's effectiveness hinged upon the discerning comparison of outc

关键词： Wastewater treatment

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Fine Tuning GANs for Image Colorization: Effects of Training data and Epochs 7

Fine Tuning GANs for Image Colorization: Effects of Training...

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2023 7th International Conference On Computing, Communication, Control And Automation, ICCUBEA 2023

作者： Patil, Rahul Patil, Pramod Joshi, Rohit Joshi, Atharv Hastak, Ninad Joshi, Akshay Pimpri Chinchwad College of Engineering Dept. of Computer Engineering Pune India Dr. D. Y. Patil Institute of Technology Dept. of Computer Engineering Pune India

ISBN: (纸本)9798350304268

Ever since the advent of deep learning, there has been a lot of experimentation and research being carried out in image processing. This gave rise to the conversion of black and white i.e. grayscale images into colorized images. Image colorization is a popular image-to-image translation problem. It finds its application in various domains like image restoration, movie colorization, colorization of old aerial surveillance shots, medical image colorization, etc. Earlier this was done manually by artists and photographers but recent advancements in deep learning have enabled automatic image colorization using techniques like Convolutional Neural Networks and Generative Adversarial Networks. CNN is a neural network that has layers that are essential for extracting features from images and can learn the complex mapping between grayscale images and their corresponding ground truth images. On the other hand, GANs is a relatively new technique that consists of two components: generator and discriminator. This research paper aims to present the results obtained by using both the above-mentioned methodologies for image colorization. Many examples of generated images and their respective accuracies and structure according to the technique used are discussed in the later part. © 2023 IEEE.

关键词： Convolutional Neural Network deep learning Generative Adversarial Network Grayscale Image Colorization

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Near-Field Communications: A Comprehensive Survey

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IEEE Communications Surveys and Tutorials 2025年第3期27卷 1687-1728页

作者： liu, Yuanwei Ouyang, Chongjun Wang, Zhaolin Xu, Jiaqi Mu, Xidong Swindlehurst, A. lee Queen Mary University of London School of Electronic Engineering and Computer Science LondonE1 4NS United Kingdom Kyung Hee University Department of Electronic Engineering Gyeonggi Yongin17104 Korea Republic of University College Dublin School of Electrical and Electronic Engineering College of Engineering and Architecture Dublin 4 D04 V1W8 Ireland University of California at Irvine Department of Electrical Engineering and Computer Science IrvineCA92697 United States Queen’s University Belfast Centre for Wireless Innovation BelfastBT3 9DT United Kingdom

Multiple-antenna technologies are evolving towards larger aperture sizes, extremely high frequencies, and innovative antenna types. This evolution is fostering the emergence of near-field communications (NFC) in future wireless systems. Considerable attention has been directed towards this cutting-edge technology due to its potential to enhance the capacity of wireless networks by introducing increased spatial degrees of freedom (doFs) in the range domain. Within this context, a comprehensive review of the state of the art on NFC is presented, with a specific focus on its i) fundamental operating principles, ii) channel modeling, iii) performance analysis, iv) signal processing techniques, and v) integration with other emerging applications. Specifically, i) the basic principles of NFC are characterized from both physics and communications perspectives, unveiling its unique properties in contrast to far-field communications. ii) Building on these principles, deterministic and stochastic near-field channel models are explored for spatially-discrete (SPd) and continuous-aperture (CAP) arrays. iii) Based on these models, existing contributions to near-field performance analysis are reviewed in terms of doFs/effective doFs (EdoFs), the power scaling law, and transmission rate. iv) Existing signal processing techniques for NFC are systematically surveyed, which include channel estimation, beamforming design, and low-complexity beam training. v) Major issues and research opportunities in incorporating near-field models into other promising technologies are identified to advance NFC’s deployment in next-generation networks. Throughout this paper, promising directions are highlighted to inspire future research endeavors in the realm of NFC, underscoring its significance in the advancement of wireless communication technologies. © 1998-2012 IEEE.

关键词： Near field communication

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Smart lawn Mower with Stability Control using AVR Controller 2

Smart Lawn Mower with Stability Control using AVR Controller

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2nd International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2024

作者： Selvalakshmi, S. Senthil Kumar, S. Rajeswari, N.P. Yamini, G. Vasudhevan, G. Kamalarajan, P. Department of Electronics and Instrumentation Engineering R.M.K Engineering College Kavaraipettai India Department of Mechanical Engineering R.M.K College of Engineering and Technology Puthuval India Department of Computer Science and Engineering Velammal College of Engineering and Technology Ponneri India Department of English Veltech Rangarajan Dr.Sagunthala Institute of Science and Technology Chennai India Department of Electronics and Communication Engineering Panimalar Engineering College Chennai India Department of Chemistry R.M.D. Engineering College Kavaraipettai India

ISBN: (纸本)9798331541217

This paper describes a robotic lawn mower that precisely and intelligently mow grass on playgrounds and lawns. To carry out the mowing operation, a collection of concurrently operating behaviors is defined. The dynamics of the terrain are obtained by utilizing a variety of accelerometers. AVR microcontroller utilizes the data from the sensors to adjust the mower's mechanisms in order to precisely remove grass. The controller will be relatively easy to use, making it accessible to all regular human users. design will place a high priority on energy efficiency. © 2024 IEEE.

关键词： lawn mowers

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Stress detection Using SVM and Virtual Assistant

Stress Detection Using SVM and Virtual Assistant

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2025 IEEE International Students' Conference on Electrical, Electronics and computer Science, SCEECS 2025

作者： Amale, Pranav Vibhute, Anup Mathpati, Mahesh S. Jatti, Ashwini V Thigale, Somnath B. Dr. D. Y. Patil Institute of Technology Electronics and Telecommunications Department Pune India Dr. D. Y. Patil Institute of Technology Instrumentation Engineering Department Pune India SVERIs College of Engineering Electronics and Telecommunications Department Pandharpur India Fabtech College of Engineering Computer Science and Engineering Department Sangola India

ISBN: (纸本)9798331529833

This paper presents a chatbot model designed to detect stress in users through simple yes-or-no questions. Utilizing a Support Vector Machine (SVM) algorithm, the model achieves high accuracy in identifying stress levels and provides users with actionable advice or medical recommendations. The chatbot's user-friendly interface is built using Anaconda Navigator and Python, ensuring ease of use for non-technical users. The model achieves a 100% accuracy rate on the given dataset, making it a promising tool for stress detection and early intervention. © 2025 IEEE.

关键词： Python

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Gaussian PI Controller Network Classifier for Grid-Connected Renewable Energy System

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Intelligent Automation & Soft Computing 2023年第1期35卷 983-995页

作者： Ravi Samikannu K.Vinoth Narasimha Rao dasari Senthil Kumar Subburaj Department of Electrical Computer and Telecommunications Engineering Botswana International University of Science and TechnologyBotswana Department of Electrical and Electronics Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennaiTamil NaduIndia Department of Electrical Power Engineering College of EngineeringDefence UniversityEthiopia Department Electrical and Electronics Engineering New Prince Shri Bhavani College of Engineering and TechnologySembakkamTamil NaduIndia

Multi-port converters are considered as exceeding earlier period decade owing to function in a combination of different energy sources in a single processing *** energy sources are playing a significant role in the modern energy system with rapid *** renewable sources like fuel combustion and solar energy,the generated voltages change due to their environmental *** develop energy resources,electric power generation involved huge *** power and output voltages are plays important role in our work but it not considered in the existing *** considering the power and voltage,Gaussian PI Controller-Maxpooling deep Convolutional Neural Network Classifier(GPIC-MdCNNC)Model is introduced for the grid-connected renewable energy *** input information is collected from two input *** that,input layer transfer information to hidden layer 1 fuzzy PI is employed for controlling voltage in GPIC-MdCNNC *** layer 1 is transferred to hidden layer *** activation is employed for determining the output voltage with help of the *** last,the output layer offers the last value in GPIC-MdCNNC *** designed method was confirmed using one and multiple sources by stable and unpredictable input ***-MdCNNC Model increases the performance of grid-connected renewable energy systems by enhanced voltage value compared with state-of-the-art *** control technique using GPIC-MdCNNC Model increases the dynamics of hybrid energy systems connected to the grid.

关键词： Multi-port converters renewable sources fuzzy PI controller gaussian activation function fuel cell

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detection of dysrhythmias from Electrocardiogram Signals Using Noise Removal and deep learning Techniques

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SN computer Science 2024年第7期5卷 882页

作者： Rajesh, P. Balasubramaniyan, M. Thiyagu, T.M. Azhagiri, M. Shanmuganathan, C. Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R & amp D Institute of Science and Technology Tamil Nadu Chennai India Department of Computer Science and Engineering Sri Venkateswara College of Engineering and Technology (Autonomous) Andhra Pradesh Chittoor 517127 India Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Tamil Nadu Chennai 600089 India

Heart related diseases have created a major loss in human lives. Healthy heart ensures good standard of living from infant to aged people for a long period. Predicting heart related diseases prior to serious cause would mostly reduce death and save human life. One such heart related issue is dysrhythmias which is an unstable rhythm occurs in the normal heart beat of the humans due to improper electrical dispositions that synchronizes the heartbeat. This endangered disease would definitely lead to the end of life irrespective of the age group. This unstable rhythm would be diagnosed mostly using Electrocardiogram (ECG) which is an electrical waveform that depicts the heartbeat. This research aimed to discover a novel method that makes use of the ECG waveforms as a training and testing dataset with the most incipient deep learning (dl) algorithms’ to predict dysrhythmia earlier. The dl models like Convolutional Neural Network (CNN) and Bi directional long Short Term Memory (BlSTM) are combined to experiment the input data by segmenting the signals. A noise removal technique was used to remove the noise in the waveforms of the ECG data and built a two dimensional image which is the input to the proposed method. The proposed method produced remarkable results as 99.36 percent in accuracy, 0.99 as F1 score. The objective of the investigation is to save human lives from serious and severe dysrhythmias by predicting it as early as possible by using the proposed methodology. © The Author(s), under exclusive licence to Springer Nature Singapore Pte ltd. 2024.

关键词： BlSTM CNN dysrhythmias ECG Noise removal

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Your Interest, Your Summaries: Query-Focused long Video Summarization

arXiv

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arXiv 2024年

作者： Patel, Nirav Prajapati, Payal Shah, Maitrik The Department of Computer Engineering L. D. College of Engineering Gujarat Ahmedabad380015 India

Generating a concise and informative video summary from a long video is important, yet subjective due to varying scene importance. Users’ ability to specify scene importance through text queries enhances the relevance of such summaries. This paper introduces an approach for query-focused video summarization, aiming to align video summaries closely with user queries. To this end, we propose the Fully Convolutional Sequence Network with Attention (FCSNA-QFVS), a novel approach designed for this task. leveraging temporal convolutional and attention mechanisms, our model effectively extracts and highlights relevant content based on user-specified queries. Experimental validation on a benchmark dataset for query-focused video summarization demonstrates the effectiveness of our approach. Copyright © 2024, The Authors. All rights reserved.

关键词： Structured Query language

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