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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Lenin, J. Rohit, V.Robin Nisha, F. Vasudevan, B. Sivakumar, S. Murugan, S. Alliance College of Engineering and Design Alliance University Department of Computer Science & Engineering Bangalore India Presidency University Computer Science & Engineering School of Computer Science & Engineering and Information Science Bengaluru India Cmr University School of Engineering and Technology Computer Science & Engineering Bangalore India Srm Institute of Science and Technology Department of Electronics and Communication Engineering Kattankulathur SRM Nagar Tamil Nadu Chennai India Department of Computer Science Tamil Nadu Perambalur India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350376425

This research introduces a new approach to radon detection in homes utilizing Decision Trees (DTs) enabled by the cloud in real-time. High radon levels, a natural radioactive gas, are dangerous to human health. Quick evaluation and mitigation efforts are hindered by the long processes required by traditional detection systems. Using DTs, a machine learning methodology, the proposed method sorts of radon levels according to variables, including building materials, ventilation systems, and geographic location. These trees may handle both numerical and categorical data and provide interpretability. Fast processing of massive data sets and real-time analysis across varied residential areas are made possible by integrating cloud computing, increasing efficiency and scalability. Stakeholders may remotely access crucial information due to the solution's accessibility and flexibility. Results from controlled experiments demonstrate that this method can accurately identify radon levels in the air in real time, allowing for preventative actions to lessen potential health hazards. People living in areas prone to radon may benefit from this practice in terms of their health. © 2024 IEEE.

关键词： Indoor air pollution

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Development of Framework to Find Lung Canker Using GAN Technique 5

Development of Framework to Find Lung Canker Using GAN Techn...

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5th IEEE International Conference on Computing, Power, and communication Technologies, IC2PCT 2024

作者： Ramakrishnan, Rakesh Muniandi, Balakumar Yennapusa, Haritha Jiwani, Nasmin Kiruthiga, T. University of the Cumberlands Department of Information Technology Union CityCA United States Lawrence Technological University Department of Electrical and Computer Engineering SouthfieldMI United States University of Central Missouri Department of Computer Science AustinTX United States University of the Cumberlands Dept. of Information Technology WilliamsburgKY United States Vetri Vinayaha College of Engineering and Technology Department of Electronics and Communication Engineering Trichy India

ISBN: (纸本)9798350383522

Lung cancer is one of the main reasons of lack of existence globally, accounting for approximately 1. Eight million deaths in past one year. Early prognosis of lung most cancers is crucial for a hit remedy, however it stays a task due to its subtle early signs and symptoms and the limitations of present-day diagnostic techniques. In recent years, the improvement of deep information of era has proven promising consequences in medical photograph analysis and ailment evaluation. On this context, the improved Generative adverse network (GAN) framework has emerged as a powerful device for improving the accuracy and overall performance of lung cancer evaluation. The stepped forward GAN framework is an extension of the conventional GAN, which incorporates a generator and a discriminator working collectively in a competitive gaining knowledge of the approach. The generator generates pics which might be then evaluated with the aid of the discriminator, which identifies if they may be actual or fake. This technique is repeated till the generator efficiently generates sensible photos, fooling the discriminator. Within the context of lung most cancers prognosis, the progressed GAN framework makes use of radiological snap shots, along with CT scans, to generate practical snap shots of lung nodules. Those generated photos are used to enhance the training dataset, developing the range and complexity of the facts, main to a miles higher deep getting-to-know version. This lets in higher differentiation between cancerous and non-cancerous nodules. © 2024 IEEE.

关键词： Better Differentiation Discriminator Extension Prognosis

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Graph Neural Networks for Resource Allocation Optimization in Healthcare Industry 2

Graph Neural Networks for Resource Allocation Optimization i...

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2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Manivannan, S.K. Kavididevi, Venkatesh Muthukumaran, D. Nandhini, R. Vakula, V. Srinivasan, C. Srm Institute of Science and Technology Faculty of Management Kattankulathur Tamil Nadu Chennai India Vardhaman College of Engineering Department of Information Technology Telangana Hyderabad India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Rajalakshmi Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Sri Sai Ranganathan Engineering College Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350368413

Optimizing the allocation of healthcare resources has never been easier than with the help of Graph Neural Networks (GNNs). In healthcare systems, where complications abound, GNNs may help with the allocation of resources in a way that improves both patient outcomes and operational efficiency. The goal is to use GNNs' graph-modelling capabilities to describe healthcare networks, with hospitals, doctors, and other medical resources acting as nodes and potential transfers of patients or shared resources as edges. Using both past and present data, the goal is to optimize resource allocation by forecasting the most effective distribution patterns. The decision-making process is improved by using important methods including attention mechanisms, message-passing algorithms, and node embeddings. The accuracy and scalability of healthcare resource management are both enhanced by GNNs, which capture spatial and temporal dynamics. The general efficacy of healthcare delivery systems is improved by this method, which permits more precise forecasts and efficient use of resources. The results highlight how GNNs can change healthcare resource allocation models. From GitHub repository data the short interfering RNAs (siRNAs) and microRNAs (miRNAs) data with efficacy is calculated. Out of the 8 data collected as samples for various siRNAs the efficacy minimum value is 0.07 and the maximum value is 0.946 and in other data samples collected it is 0.386 and 0.777. For miRNAs it is 0.934 and 0.394 and in other data sample collected it is 0.738 and 0.432. © 2024 IEEE.

关键词： Graph neural networks

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Internet of Things Based Revolutionizing Healthcare Industry with Anesthesia Machine Control: The Future of Medical Technology

Internet of Things Based Revolutionizing Healthcare Industry...

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2024 International Conference on Cybernation and Computation, CYBERCOM 2024

作者： Kumuthapriya, K. Porselvi, R. Sasikala, S. Arulmozhiselvan, L. Anitha, C. Jayasree, S. Tagore Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Meenakshi Sundararajan Engineering College Department of Information Technology Chennai600 024 India Saveetha School of Engineering SIMATS Department of Computer Science and Engineering Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Chemistry Tamil Nadu Chennai India

ISBN: (纸本)9798331542344

Inducing a condition of regulated trance state, or anesthesia impact, while surgery helps alleviate discomfort and makes the procedure much easier for the surgeon. Some dosages of this medicine are administered by an anesthesiologist during surgery, although it has been discovered that human error is responsible for a considerable proportion of casualties. The consequences of anesthesia on individual real-Time patients can vary, but they can include diplopia, respiratory arrest, brain damage, or death if the dosage is wrong. In this study, we provide an Internet of Things (IoT) enabled Autonomous Anesthetic Management System that communicates with an application for mobile devices to facilitate the transfer of real-Time physiological data. Monitoring vital signs such as pulse rate, blood oxygen saturation, and core temperature allows for precise regulation of anesthetic dose. After then, a microcontroller-here an Arduino UNO-is tasked with analyzing the data. An individual's dose needs to be determined by their age, weight, and medical history. With the use of anesthetics, the patient will not experience any discomfort throughout the medical process, and the effects of the sedation should last throughout the surgery. Because of their critical nature, biological factors like heart rate and temperature must also be measured. The development of an automatic anesthetic control system is the goal of this project, as the failure to provide the correct amount of anesthesia at the prescribed intervals might lead to major health complications. By monitoring vital signs like temperature and respiration concurrently, this allows for the administration of the predetermined amount of doses at periodic times. © 2024 IEEE.

关键词： Anesthesiology

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An Efficient Direct Downlink Sensing Method Using 5G NR SSB Signals in Perceptive Mobile Networks

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IEEE Internet of Things Journal 2025年第11期12卷 15360-15369页

作者： Li, Hang Xiang, Yang Guo, Qinghua Liu, Lizhe Huang, Xiaojing Cheng, Zhiqun Pang, Yashan Hangzhou Dianzi University School of Electronics and Information Hangzhou310018 China Academy of Network and Communications of CETC National Key Laboratory of Advanced Communication Networks Hebei Shijiazhuang050081 China University of Wollongong School of Electrical Computer and Telecommunications Engineering WollongongNSW2522 Australia University of Technology Sydney Global Big Data Technologies Centre UltimoNSW2007 Australia

In perceptive mobile networks (PMNs), using 5G new radio (NR) signals for direct sensing poses a significant challenge to practical implementation due to the high computational complexity involved in estimating sensing parameters. In this article, an efficient sensing method is proposed to incorporate both downlink active sensing and passive sensing to estimate multiple sensing parameters, including delays, Angle of Arrival (AoA), Angle of Departure (AoD), and Doppler. In particular, it exploits the synchronization signal blocks (SSBs) to facilitate sensing with multiple remote radio units (RRUs). To reduce the computational complexity of direct sensing, a sparse model is developed to decouple multiple sensing parameter estimation, enabling efficient sensing method design. Then, leveraging unitary approximate message passing (UAMP) and sparse Bayesian learning (SBL), we propose an efficient method to achieve parameter estimation and association with corresponding RRUs. This method is further extended to general scenarios involving multiple path components with the same delay. Extensive simulations demonstrate the effectiveness of the proposed method, showing that it outperforms the existing ones in terms of sensing accuracy and complexity. © 2014 IEEE.

关键词： 5G mobile communication systems

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Secure Key Management Based Mobile Authentication in Cloud

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computer Systems Science & engineering 2022年第12期43卷 887-896页

作者： S.Shanmuga Priya D.Yuvaraj T.Satyanarayana Murthy Varghese S.Chooralil S.Navaneetha Krishnan P.Banumathy P.SundaraVadivel Department of Computer Science and Engineering M.I.E.T Engineering CollegeTrichyTamilnaduIndia Department of Computer Science and Engineering Cihan UniversityDuhokKurdistan RegionIraq Department of Computer Science and Engineering Bapatla Engineering CollegeBapatlaAndhra PradeshIndia Department of Computer Science and Engineering Rajagiri School of Engineering and TechnologyKakkanadKeralaIndia Department Electronics and Communication Engineering SACS MAVMM Engineering CollegeMaduraiTamilnaduIndia Department of Information Technology Karaikal Polytechnic CollegeVarichikudy-KaraikalIndia Department of Computer Science and Engineering SACS MAVMM Engineering CollegeMaduraiIndia

Authentication is important to the security of user data in a mobilecloud environment. Because of the server’s user credentials, it is subject toattacks. To maintain data authentication, a novel authentication mechanism is proposed. It consists of three independent phases: Registration, login, and authentication and key agreement. The user registers with the Registration Center (RC)by producing a secret number that isn’t stored in the phone, which protects againstprivileged insider attacks. The user and server generate a nonce for dynamic useridentity and agree on a session secret key for safe communication. The passwordsare not stored on the computer or provided in plain text, they are resistant toreplay, guessing, and stolen verification attacks. The suggested protocol uses aone-way hash function and XOR operations, with the client having remote accessto a large number of servers over a secure communication channel. Concentrateson HMAC and SHA3 for Collision Free Hashing and to overcome length extension attacks. HMACs are substantially less affected by collisions than their underlying hashing algorithms alone. So adding an HMAC to an MD5 or SHA hashwould make it substantially more difficult to break via a rainbow table.

关键词： Registration center SIM IMEI

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Phishing Website Detection using Natural Language Processing 7

Phishing Website Detection using Natural Language Processing

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Mariappan, E. Jean Celia Grace, C. Joe Patrick Gnanaraj, S. Elizabeth Paulsyah, D. Muthukumaran, N. St. Mother Theresa Engineering College Department of Computer Science And Engineering Tamilnadu Tuticorin India Francis Xavier Engineering College Department of Information Technology Tamilnadu Tirunelveli India St. Mother Theresa Engineering College Department of Mechanical Engineering Tamilnadu Tuticorin India Francis Xavier Engineering College Department of Computer Science And Engineering Tamilnadu Tirunelveli India Sri Eswar Engineering College Department of Electronics And Communication Engineering Tamilnadu Coimbatore India

ISBN: (纸本)9798350359299

Given the rapid proliferation of online services, there has been a corresponding uptick in nefarious activities aimed at misleading users into unintended actions. The internet, as a platform, has become a fertile ground for innovative methods employed by attackers, particularly the widespread menace of phishing. By leveraging counterfeit websites, malicious entities seek to unlawfully gather sensitive data including user information, login credentials, social security numbers, and financial particulars. Accurately distinguishing between a legitimate website and a phishing attempt presents a significant challenge. This research work introduces a model for analyzing phishing websites employing machine learning techniques. The model strives to forecast the authenticity of a website by utilizing a variety of classification Algorithms and features rooted in natural language processing (NLP). © 2024 IEEE.

关键词： Websites

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Polarization-induced photocurrent switching effect in heterojunction photodiodes

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Nano Research 2023年第4期16卷 5503-5510页

作者： Dingbo Chen Yu-Chang Chen Guang Zeng Yu-Chun Li Xiao-Xi Li Dong Li Chao Shen Nan Chi Boon S.Ooi David Wei Zhang Hong-Liang Lu State Key Laboratory of ASIC and System Shanghai Institute of Intelligent Electronics&SystemsSchool of MicroelectronicsFudan UniversityShanghai 200433China Key Laboratory for Information Science of Electromagnetic Waves Department of Communication Science and EngineeringFudan UniversityShanghai 200433China Photonics Laboratory Division of ComputerElectricaland Mathematical Sciences and EngineeringKing Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Kingdom of Saudi Arabia

The unipolar photocurrent in conventional photodiodes(PDs)based on photovoltaic effect limits the output modes and potential versatility of these devices in *** photodiodes with photocurrent switching are emerging as a promising solution for obtaining photoelectric devices with unique and attractive functions,such as optical logic ***,we design an all-solid-state chip-scale ultraviolet(UV)PD based on a hybrid GaN heterojunction with engineered bipolar polarized electric *** introducing the polarization-induced photocurrent switching effect,the photocurrent direction can be switched in response to the wavelength of incident light at 0 V *** particular,the photocurrent direction exhibits negative when the irradiation wavelength is less than 315 nm,but positive when the wavelength is longer than 315 *** device shows a responsivity of up to−6.7 mA/W at 300 nm and 5.3 mA/W at 340 nm,*** particular,three special logic gates in response to different dual UV light inputs are demonstrated via a single bipolar PD,which may be beneficial for future multifunctional UV photonic integrated devices and systems.

关键词： GaN heterostructure ultraviolet(UV)photodiodes bipolar photocurrent optical logic device

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A Machine Learning Approach for Wastewater Treatment using Feedforward Neural Network and Batch Normalization 1

A Machine Learning Approach for Wastewater Treatment using F...

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1st International Conference on Software, Systems and information Technology, SSITCON 2024

作者： Naveen, I.G. Peerbasha, S. Fallah, Mohammed Hussein Jebaseeli, S. Kanmani Das, Abhijeet Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India Department of Computer Science and Engineering Tiruchirapalli India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq New Prince Shri Bhavani College of Engineering and Technology Department of Information Technology Chennai India Department of Civil Engineering Bhubaneswar India

ISBN: (纸本)9798350352931

In present scenario, the freshwater is a contest as the world's population raises. Most of Earth's water comes from brackish water and seawater those are the largest sources of water. Water purification is crucial during this water crisis, and can be achieved through desalination and various water treatment techniques, making water safe for human consumption and other uses. Wastewater treatment downsides including with high energy consumption and chemical usage in mixture of water. Limited removal of certain pollutants sludge generation release are also concerns which are included with various pollutants with concern of overfitting leads to the overreliance on a specific treatment. To overcome these issues, the method of (Feedforward Neural Network (FFNN) and Batch Normalization (BN) (FFNN-BN)) is proposed by collecting water pollutants. This process involves with the collection of input from the dataset of Wastewater Treatment Plant (WWTP) and then features are extracted by proposed method FF-BN and classifying extracted features by the SVR handles high-dimensional data with a large number of features. The proposed FFNN-BN produce more results than traditional methods of Advanced Oxidation Processes (AOP) for modeling extreme values such as R2 Mean Absolute Error (MAE), Mean Squared Error and Root Mean Squared Error (RMSE) respectively. © 2024 IEEE.

关键词： Desalination

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Text Analysis on Early Reactions to ChatGPT as a Tool for Academic Progress or Exploitation

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SN computer Science 2024年第4期5卷 366页

作者： Bukar, Umar Ali Sayeed, Md Shohel Razak, Siti Fatimah Abdul Yogarayan, Sumendra Amodu, Oluwatosin Ahmed Raja Mahmood, Raja Azlina Centre for Intelligent Cloud Computing (CICC) Faculty of Information Science & amp Technology Multimedia University Jalan Ayer Keroh Lama Bukit Beruang Melaka 75450 Malaysia Department of Electrical Electronics & amp Systems Engineering Universiti Kebangsaan Malaysia (UKM) Selangor Bangi 43600 Malaysia Information and Communication Engineering Department Elizade University Ondo State Ilara-Mokin Nigeria Department of Communication Technology and Network Faculty of Computer Science and Information Technology Universiti Putra Malaysia Selangor Darul Ehsan Serdang 43400 Malaysia

Large language models (LLMs) have garnered significant attention lately, and one particular implementation that has captivated users is ChatGPT, a first-of-its kind innovation that sparks intense debates among professionals regarding its ethical boundaries, especially after it was released for public use. Some of the debates are ongoing, with several people getting more used to this tool and some losing touch with several ethical concerns. Thus, it is crucial to explore the early professional’s perspectives surrounding ChatGPT to comprehend its potential impact on the educational sector and, in particular, society in general. This research aims to address this knowledge gap by employing data mining techniques on LinkedIn to analyze ChatGPT-related discussions and provide a comprehensive textual examination. The dataset for this investigation consists of 166 records written in English by LinkedIn users discussing ChatGPT in its early stage. The study utilized VOSviewer software to clean, analyze, mine, and visualize the data. Among the sample, it was observed that 49% of the users are PhD holders, predominantly with experience in scientific research and data science fields. Hence, the findings reveal that issues such as plagiarism, references, citations, manuscripts, papers, and literature reviews stand out as the major concerns. These results suggest that while ChatGPT can contribute to academic progress by fostering new knowledge, thoughts, and ideas, it can also facilitate academic misconduct, including plagiarism and inaccurate information dissemination. Therefore, this paper also provides discussions on the implications and ethical issues of ChatGPT. In particular, it is imperative to establish clear ethical boundaries and guidelines for the appropriate use of LLMs in academic settings. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024.

关键词： Academic ChatGPT Ethics LinkedIn LLMs Text analysis Visualization VOSviewer

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