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2nd International Conference on Advancement in Computation and computer Technologies, InCACCT 2024

作者： Priyadharshini Jayadurga, N. Chandralekha, M. Saleem, Kashif Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India King Saud University College of Applied Studies and Community Service Department of Computer Sciences and Engineering Riyadh Saudi Arabia

ISBN: (纸本)9798350371314

Brain-computer Interaction (BCI) is an indispensable component in the constantly evolving field of human-machine interaction, offering direct interaction between the human brain and external devices. The acquisition of electroencephalogram, or EEG, signals by BCI devices is vital for this connection. The objective of this study is to differentiate eye states (open and closed) with the goal of shedding light on the consequences of artifacts when performing various BCI tasks. The background study explores the key concepts of BCI, emphasizing the critical role that EEG signals play in the intricate relationship between humans and computers. Within the BCI framework, the problem statement accentuates the importance of eye state classification across tasks. In an effort to improve classification accuracy and eliminate artifacts, this research strategically integrates techniques through the implementation of an extensive approach and assessments of multiple ensemble strategies. The findings and analysis reveal the efficiency of ensemble approaches in understanding eye states, hence improving BCI applications and expanding human-machine interaction. © 2024 IEEE.

关键词： Electroencephalography

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Healthcare Monitoring Revolution through Face Detection Using BP and SpO2 5

Healthcare Monitoring Revolution through Face Detection Usin...

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5th IEEE International Conference on computing, Power, and Communication Technologies, IC2PCT 2024

作者： Singh, Nongmeikapam Thoiba Kaur, Harkamal Dhiman, Richa Kumar, Manish Hans, Aarti Sharma, Preety Chandigarh University Department of Computer Science and Engineering Punjab India University Institute of Computing Chandigarh University Punjab India

ISBN: (纸本)9798350383522

The integration of facial recognition technologies with the monitoring of vital signs, especially blood pressure (BP) and oxygen saturation (SpO2) measurements, has emerged as a promising approach in healthcare quality management. Facial recognition and SpO2 pressure detection represent a major advance in health and wellness monitoring, providing a seamless and comprehensive approach to monitoring individual well-being. As technology continues to evolve, this system has the potential to improve how we manage and manage healthcare, delivering positive outcomes and improving the lives of individuals around the world. © 2024 IEEE.

关键词： computer vision face detection healthcare monitoring machine learning SpO2 detection vital sign monitoring

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Design and Development of Algorithmic Trading in Stock Market Using Artificial Intelligence 4

Design and Development of Algorithmic Trading in Stock Marke...

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4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Verma, Vaibhav Jangra, Kushagra Chawla, Jasneet Singh, Harmanjeet Chandigarh University Department of Computer Science & Engineering Punjab India University Institute of Computing Chandigarh University Punjab India

ISBN: (纸本)9798350387490

Algorithmic trading has become an increasingly popular method of investing in stock requests due to its potential to offer progressive returns and lower risk in comparison to traditional styles. The current status of algorithmic trading in stock requests is examined in this review paper, along with the creative tactics used by dealers. We analyze the advantages and disadvantages of algorithmic trading, along with the variables that may affect its performance. We also discuss the difficulties dealers have enforcing algorithmic trading and the possibility that algorithmic trading will increase request insecurity. Finally, we conclude by making suggestions for future research in the area of algorithmic trading in stock requests. Overall, the objective of this review is to give a comprehensive overview of algorithmic trading in the stock request and the implicit effects it has on financial assiduity. © 2024 IEEE.

关键词： Decentralized finance

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A Deep Learning Model to Detect Fake News about COVID-19

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Recent Advances in computer science and Communications 2023年第9期16卷 58-66页

作者： Shanmugavel, Selva Birunda Rangaswamy, Kanniga Devi Muthukannan, Muthiah Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Tamil Nadu India School of Computer Science and Engineering Vellore Institute of Technology Chennai Tamil Nadu Chennai India Department of Civil Engineering KCG College of Technology Chennai India

Aims/Background: Twitter has rapidly become a go-to source for current events coverage. The more people rely on it, the more important it is to provide accurate data. Twitter makes it easy to spread misinformation, which can have a significant impact on how people feel, especially if false information spreads around COVID-19. Methodology: Unfortunately, twitter was also used to spread myths and misinformation about the illness and its preventative immunization. So, it is crucial to identify false information before its spread gets out of hand. In this research, we look into the efficacy of several different types of deep neural networks in automatically classifying and identifying fake news content posted on social media platforms in relation to the COVID-19 pandemic. These networks include long short-term memory (LSTM), bi-directional LSTM, convolutional-neural-networks (CNN), and a hybrid of CNN-LSTM networks. Results: The "COVID-19 Fake News" dataset includes 42,280, actual and fake news cases for the COVID-19 pandemic and associated vaccines and has been used to train and test these deep neural networks. Conclusion: The proposed models are executed and compared to other deep neural networks, the CNN model was found to have the highest accuracy at 95.6%. © 2023 Bentham science Publishers.

关键词： COVID-19

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Influence of performance and emission of diesel engine with alumina nano material-based catalyst biodiesel using IoT

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International Journal of Power and Energy Conversion 2024年第4期15卷 338-351页

作者： Venkatesh, B. Khan, Mudassir Babu, J. Chinna Nagaraju, C.H. Mohan, R. Madhan Department of Mechanical Engineering Annamacharya Institute of Technology and Sciences Andhra Pradesh Rajampet India Department of Computer Science College of Computer Science Applied College Tanumah King Khalid University P.O. Box 960-61421 Abha Saudi Arabia Department of Electronics and Communication Engineering Annamacharya Institute of Technology and Sciences Andhra Pradesh Rajampet India Department of Electrical and Electronics Engineering Annamacharya Institute of Technology and Sciences India

In this study, the performance and emissions of Al2O3 alkaline cottonseed biodiesel-powered compression ignition engines were predicted. An experiment was conducted using different biodiesel blends made from cottonseed oil under various loading conditions. Biodiesel was produced using ethanol and Cao-mg/Al2O3 as catalysts through the transesterification process and blended with diesel to produce CBDN10, CBDN20, CBDN30, and CBDN40. The CBDN20 blends showed an evident reduction in fuel consumption by 8% and a 12% improvement in thermal efficiency at high static thrust. The use of these blends resulted in minimal CO, CO2, and NOx emissions from engines. These emissions were monitored by an IoT system and analysed with gas analysers, which revealed CBDN20’s ability to reduce nitrogen oxide and hydrocarbon emissions compared to diesel. Additionally, CBDN20 demonstrated improved thermal efficiency and reduced brake-specific fuel consumption compared to the other blends. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Nitrogen oxides

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Fuzzy clustering for electric field characterization and its application to thunderstorm interpretability

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Digital Communications and Networks 2025年第2期11卷 299-307页

作者： Xu Yang Hongyan Xing Xinyuan Ji Wei Xu Witold Pedrycz School of Electronics and Information Engineering Nanjing University of Information Science and TechnologyNanjing 210044China Department of Electrical and Computer Engineering University of AlbertaEdmonton AB T6G 2R3Canada Systems Research Institute Polish Academy of Sciences00-901 WarsawPoland Department of Computer Engineering Faculty of Engineering and Natural SciencesIstinye University34010 IstanbulTurkey

Changes in the Atmospheric Electric Field Signal(AEFS)are highly correlated with weather changes,especially with thunderstorm ***,little attention has been paid to the ambiguous weather information implicit in AEFS *** this paper,a Fuzzy C-Means(FCM)clustering method is used for the first time to develop an innovative approach to characterize the weather attributes carried by ***,a time series dataset is created in the time domain using AEFS *** AEFS-based weather is evaluated according to the time-series Membership Degree(MD)changes obtained by inputting this dataset into the ***,thunderstorm intensities are reflected by the change in distance from a thunderstorm cloud point charge to an AEF ***,a matching relationship is established between the normalized distance and the thunderstorm dominant MD in the space ***,the rationality and reliability of the proposed method are verified by combining radar charts and expert *** results confirm that this method accurately characterizes the weather attributes and changes in the AEFS,and a negative distance-MD correlation is obtained for the first *** detection of thunderstorm activity by AEF from the perspective of fuzzy set technology provides a meaningful guidance for interpretable thunderstorms.

关键词： Atmospheric electric field(AEF) Thunderstorm Fuzzy C-means(FCM) Attribute

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Deep Learning-Based Mask Identification System Using ResNet Transfer Learning Architecture

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computer Systems science & engineering 2024年第2期48卷 341-362页

作者： Arpit Jain Nageswara Rao Moparthi A.Swathi Yogesh Kumar Sharma Nitin Mittal Ahmed Alhussen Zamil S.Alzamil MohdAnul Haq Department of Computer Science and Engineering KoneruLakshmaiah Education FoundationVaddeswaram522302India Department of Computer Science and Engineering Sreyas Institute of Engineering and TechnologyHyderabad500068India University Centre for Research and Development Chandigarh UniversityMohali140413India Department of Computer Engineering College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia

Recently,the coronavirus disease 2019 has shown excellent attention in the global community regarding health and the *** Health Organization(WHO)and many others advised controlling Corona Virus Disease in *** limited treatment resources,medical resources,and unawareness of immunity is an essential horizon to *** all resources,wearing a mask is the primary non-pharmaceutical intervention to stop the spreading of the virus caused by Severe Acute Respiratory Syndrome Coronavirus 2(SARS-CoV-2)*** countries made masks mandatory to prevent *** such enforcement,automatic and effective face detection systems are *** study presents a face mask identification approach for static photos and real-time movies that distinguishes between images with and without *** contribute to society,we worked on mask detection of an individual to adhere to the rule and provide awareness to the public or *** paper aims to get detection accuracy using transfer learning from Residual Neural Network 50(ResNet-50)architecture and works on detection *** experiment is tested with other popular pre-trained models such as Deep Convolutional Neural Networks(AlexNet),Residual Neural Networks(ResNet),and Visual Geometry Group Networks(VGG-Net)advanced *** proposed system generates an accuracy of 98.4%when modeled using Residual Neural Network 50(ResNet-50).Also,the precision and recall values are proved as better when compared to the existing *** outstanding work also can be used in video surveillance applications.

关键词： Transfer learning depth analysis convolutional neural networks(CNN) COVID-19

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Ultra-low power MoS2 optoelectronic synapse with wavelength sensitivity for color target recognition

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science China(Information sciences) 2025年第4期68卷 187-196页

作者： Bo WEI Yabo CHEN Xiaotong HAN Yan KANG Bujia LIANG Cheng LI Xiaokuo YANG Liang FANG Yuanxi PENG Institute for Quantum Information & State Key Laboratory of High Performance Computing College of ComputerNational University of Defense Technology Fundamentals Department Air Force Engineering University College of Advanced Interdisciplinary Studies National University of Defense Technology Institute of Quantum Information Science and Technology College of ScienceNational University of Defense Technology College of Computer National University of Defense Technology

Optoelectronic synapses that integrate visual perception and pre-processing hold significant potential for neuromorphic vision systems（NVSs）. However, due to a lack of wavelength sensitivity, existing NVS mainly focuses on gray-scale image processing, making it challenging to recognize color images. Additionally, the high power consumption of optoelectronic synapses, compared to the 10 fJ energy consumption of biological synapses, limits their broader application. To address these challenges, an energy-efficient NVS capable of color target recognition in a noisy environment was developed,utilizing a MoS2optoelectronic synapse with wavelength sensitivity. Benefiting from the distinct photon capture capabilities of 450, 535, and 650 nm light, the optoelectronic synapse exhibits wavelength-dependent synaptic plasticity, including excitatory postsynaptic current（EPSC）, paired-pulse facilitation（PPF）, and long-term plasticity（LTP）. These properties can effectively mimic the visual memory and color discrimination functions of the human vision system. Results demonstrate that the NVS, based on MoS2optoelectronic synapses, can eliminate the color noise at the sensor level, increasing color image recognition accuracy from 50% to 90%. Importantly, the optoelectronic synapse operates at a low voltage spike of0.0005 V, consuming only 0.075 fJ per spike, surpassing the energy efficiency of both existing optoelectronic and biological synapses. This ultra-low power, color-sensitive device eliminates the need for color filters and offers great promise for future deployment in filter-free NVS.

关键词： optoelectronic synapse neuromorphic vision system color recognition 2D materials image pre-processing

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An Innovative URL-Based System Approach with ML Based Prevention 5

An Innovative URL-Based System Approach with ML Based Preven...

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5th IEEE International Conference on computing, Power, and Communication Technologies, IC2PCT 2024

作者： Singh, Nongmeikapam Thoiba Rani, Jyoti Sharma, Prince Mishra, Akriti Yadav, Ankit Raj, Shivani Chandigarh University Department of Computer Science and Engineering Punjab India University Institute of Computing Chandigarh University Punjab India

ISBN: (纸本)9798350383522

This paper delves into the pervasive threat of phishing in the e-commerce sector, where adversaries impersonate financial institutions to perpetrate identity theft. The modus operandi involves sending deceptive emails urging users to click on fraudulent links, leading them to fake websites mimicking authentic ones. Focused on URL-based detection, the study employs sophisticated algorithms and machine learning techniques, utilizing a dataset for training and testing to identify phishing URLs. The paper outlines various types of phishing attacks, including deceptive, spell, clone, whale, link manipulation, and voice phishing. Prevention strategies, such as spam avoidance, secure communication, and user education, are discussed. The methodology involves frontend and backend development using Python, JavaScript, HTML, CSS, PHP, and MySQL, incorporating machine learning for analysis. The website provides a user-friendly interface with a reporting option, contributing to a robust defense against phishing threats. Results indicate the effectiveness of URL-based detection, emphasizing the importance of staying updated on evolving phishing trends and employing a multifaceted approach for enhanced organizational cybersecurity. © 2024 IEEE.

关键词： data security phishing phishing detection URL

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Dynamic Multi-Layer Perceptron for Fetal Health Classification Using Cardiotocography Data

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computers, Materials & Continua 2024年第8期80卷 2301-2330页

作者： Uddagiri Sirisha Parvathaneni Naga Srinivasu Panguluri Padmavathi Seongki Kim Aruna Pavate Jana Shafi Muhammad Fazal Ijaz Department of Computer Science and Engineering Prasad V Potluri Siddhartha Institute of TechnologyVijayawada520007India Amrita School of Computing Amrita Vishwa VidyapeethamAmaravatiAndhra Pradesh522503India Department of Tele informatics Engineering Federal University of CearáFortaleza60455-970Brazil Department of Computer Science and Engineering Koneru Lakshmaiah Education FoundationVaddeswaramGunturAndhra Pradesh522302India Department of Computer Engineering Chosun UniversityGwangju61452Republic of Korea School of CSIT Symbiosis Skills and Professional UniversityPune412101India Department of Computer Engineering and Information College of Engineering inWadi AlddawasirPrince Sattam bin Abdulaziz UniversityWadi Alddawasir11991Saudi Arabia School of IT and Engineering Melbourne Institute of TechnologyMelbourne3000Australia

Fetal health care is vital in ensuring the health of pregnant women and the *** check-ups need to be taken by the mother to determine the status of the fetus’growth and identify any potential *** know the status of the fetus,doctors monitor blood reports,Ultrasounds,cardiotocography(CTG)data,***,in this research,we have considered CTG data,which provides information on heart rate and uterine contractions during *** researchers have proposed various methods for classifying the status of fetus *** processing of CTG data is time-consuming and ***,automated tools should be used to classify fetal *** study proposes a novel neural network-based architecture,the Dynamic Multi-Layer Perceptron model,evaluated from a single layer to several layers to classify fetal *** strategies were applied,including pre-processing data using techniques like Balancing,Scaling,Normalization hyperparameter tuning,batch normalization,early stopping,etc.,to enhance the model’s performance.A comparative analysis of the proposed method is done against the traditional machine learning models to showcase its accuracy(97%).An ablation study without any pre-processing techniques is also *** study easily provides valuable interpretations for healthcare professionals in the decision-making process.

关键词： Fetal health cardiotocography data deep learning dynamic multi-layer perceptron feature engineering

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