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3rd International Conference on Innovative Practices in Technology and Management, ICIPTM 2023

作者： Subha, S. Inbamalar, T.M. Komala, C.R. Suresh, Lakshmi R Boopathi, Sampath Alaskar, Kamal Sri Sairam Engineering College Department of Electronics and Instrumentation Engineering Chennai India R.M.K. College of Engineering and Technology Department of Electronics and Communication Engineering Thiruvallur India Hkbk College of Engineering Department of Information Science and Engineering Bangalore India Technocrats Institute of Technology Excellence Department of Computer Science and Engineering Bhopal India Muthayammal Engineering College Department of Mechanical Engineering Namakkal India Bharati Vidyapeeth Institute of Management Department of Computer Application Kolhapur India

ISBN: (纸本)9798350336238

The Internet of Medical Things (IoMT) is one of the most promising technology solutions that is currently being developed to monitor health status remotely. A risk-stratified data transmission protocol has been used to construct the IoMT architecture for remote patient monitoring. All the sub-systems have undergone performance tests as well as clinical validation. Clinical validation of IoMT software on 100 patients was successful. Digital representations' size and complexity are reduced by up to 80%, making them appropriate for use in developing narrow-band IoT networks. In particular for low-power devices, performance measurement revealed that bandwidth and energy were reduced to 97% and 95%, respectively. © 2023 IEEE.

关键词： Internet of things

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Optimized Transfer Learning Model for Lung Cancer Stage Classification Using Computed Tomography Images 5

Optimized Transfer Learning Model for Lung Cancer Stage Clas...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Deepika, R. Shanmugam, P. Moorthi, K. Kumar, P. K. Manoj Swarna, S.L. Ramnath, K. B v Raju Institute of Technology Department of Artificial Intelligence and Data Science Telangana Narsapur India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Sri Sairam Engineering College Department of Electronics and Communication Engineering Chennai India School of Computational Intelligence Joy University Tirunelveli India Sona College of Technology Department of Computer Science and Engineering Tamilnadu Salem India K.L.N College of Information Technology Department of Computer Science and Engineering Tamilnadu Madurai India

ISBN: (纸本)9798331518097

Lung cancer has become one of the primary causes of death worldwide due to cancer, which indicates the necessity of early diagnosis. In this regard, the present work proposes an optimized model of transfer learning based on characterizing different stages of lung cancer by using computed tomography images acquired from the IQ-OTH/NCCD lung cancer database downloadable from Kaggle. It makes use of a two-step process: first, a U-Net model is used on the patient's CT images to reconstruct anatomical features and reduce noise;second, a ShuffleNet architecture is used to predict the patient's CT stage using the reconstructed image. In addition, to enhance classification, PSO was incorporated into the model so that it can optimize the hyperparameters. The accuracy of the model was at 97.85 percent, and it suggests that the model can become a very useful tool for clinicians to bring about earliest identification and staging for lung cancer. © 2024 IEEE.

关键词： Lung cancer

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B Smart Technologies in engineering Applications of Cyber Physical System in Healthcare: Sensing, Imaging, Computing and Networking

Recent Advances in Computer Science and Communications

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Recent Advances in computer Science and communications 2021年第1期14卷 225-226页

作者： Kumar, Rajiv Singh, Pradeep Kumar Sharma, Ashutosh Department of Electronics and Communication Engineering Jaypee University of Information Technology India Department of Computer Science & Engineering ABES Engineering College Uttar Pradesh Ghaziabad India Institute of Computer Technology and Information Security Southern Federal University Russia

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Anaesthesia Monitoring: From Conventional to AI-Enabled Data Acquisition Methods 20

Anaesthesia Monitoring: From Conventional to AI-Enabled Data...

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20th International Conference on Smart Technologies, EUROCON 2023

作者： Liu, Xiaoxiao McGrath, Sean Flanagan, Colin Lei, Yiming Zeng, Liaoyuan University of Limerick Electronic and Computer Engineering Limerick Ireland School of Electronics Peking University Ministry of Education Engineering Research Center on Mobile Digital Hospital System Beijing China University of Electronic Science and Technology of China Department of Information and Communication Engineering Chengdu China

ISBN: (纸本)9781665463973

It is widely recognized that the timely acquisition and presentation of accurate physiological data is a vital aid to the anaesthesiologist in monitoring and maintaining the well-being of a patient undergoing surgery. Collecting, storing, synchronizing, integrating, evaluating, and retrieving pertinent information from patient data remains very difficult due to limited computer power, the need for specialized software, inconsistency between monitoring devices, and inadequate data storage. This paper introduces the general steps of data acquisition in anaesthesia and summarizes some data acquisition systems. It also introduces some applications of the burgeoning field of artificial intelligence (AI) to data acquisition, illustrating how AI techniques may be used to advantage to assist the anaesthesiologist in monitoring and interpreting such data. © 2023 IEEE.

关键词： Data acquisition

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Mobile Device Security in Wireless Networks: Challenges, issues and Solutions 15

Mobile Device Security in Wireless Networks: Challenges, iss...

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15th International Conference on Computing communication and Networking Technologies, ICCCNT 2024

作者： Gopalakrishna, K. Sahoo, Laxman Arunadevi, R. Wasatkar, Namrata N. Sahoo, Girija Shankar Singh, Dhiraj Faculty of Engineering and Technology Department of Electronics and Communication Engineering Karnataka Ramnagar District562112 India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai127 India Vishwakarma Institute of Technology Department of Computer Engineering Pune India Maharishi University of Information Technology Maharishi School of Engineering and Technology Uttar Pradesh India Chitkara University Institute of Engineering and technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India

ISBN: (纸本)9798350370249

Mobile device protection has emerged as an increasingly important difficulty as more and more customers depend upon wireless networks for everyday conversation and other duties. Wi-Fi networks offer convenience and versatility for users;however, they also come with safety worries. This paper presents an overview of the safety-demanding situations related to Wi -Fi networks, discusses safety issues related to cellular devices, and offers more than a few answers for creating cozy cell surroundings. It focuses on defensive person identification, privacy, statistics, and defending against possible malicious activities. Primarily, the paper explores security solutions, encryption, authentication, community access control, and tool control. Moreover, it appears on the role of service carriers in cell tool protection and shows an aggregate of quality practices and generation to sell secure usage. Finally, the paper recommends persevering with studies to identify new threats and answers to decorate mobile safety similarly. © 2024 IEEE.

关键词： Wireless local area networks (WLAN)

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Synergizing Collaborative Filtering and Popularity Scores in a Machine Learning Approach for Content Suggestions 7

Synergizing Collaborative Filtering and Popularity Scores in...

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7th International Conference on electronics, communication and Aerospace Technology, ICECA 2023

作者： Chaturvedi, Abhay Mathews, Boban Kumar Patra, Sasi Anil Kumar, N. Kavitha, R. Kumar Lakineni, Prasanna GLA University Department of Electronics and Communication Engineering Uttar Pradesh Mathura India Brindavan College of Engineering Department of ECE Karnataka Bangalore India GIET Engineering College Department of Computer Science & Engineering Andhra Pradesh India School of Engineering Department of Electronics and Communication Engineering Mohan Babu University Tirupati India Velammal College of Engineering and Technology Department of Information Technology Tamilnadu Madurai India GITAM School of Technology Department of CSE GITAM University Visakhapatnam India

ISBN: (纸本)9798350340600

Today, it is common to rely on reviews from other people when making decisions on a variety of life decisions, including which movies to watch, which items to buy, and which books to read. Recommender systems are crucial in meeting this need. By projecting a user's expected rating for an item, these systems function as filtering algorithms. Recommender systems provide advice to consumers via a prioritized list of projected item ratings after filtering through large data repositories. Online book recommender systems focus primarily on book aficionados. People frequently take into account and put their faith in the comments and reviews written by previous readers when choosing which book to read. The following study proposes a cleverly combined hybrid recommender system that combines content-based and collaborative filtering techniques. The author uses clustering techniques to divide data points into various clusters in the context of collaborative techniques. For this, methods like K-means clustering and Gaussian mixture models are employed, with the best algorithm being selected through the use of the silhouette score. The clustering procedure is then guided by the selected algorithm. Additionally, to efficiently reduce the dimensions of a sparse dataset, the research applies the Matrix Factorization method, specifically using Truncated Singular Value Decomposition (Truncated-SVD). The system uses the TF-IDF vectorizer for Content-Based Filtering, which takes textual assertions as input and converts them into a matrix of related vectors. The Root Mean Square Error (RMSE), a metric that measures the difference between absolute and obtained values, is used in the study to assess the system's correctness. This measure sheds light on the recommender system's underlying accuracy. © 2023 IEEE.

关键词： Recommender systems

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Microsoft Malware Detection using Machine Learning 6

Microsoft Malware Detection using Machine Learning

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6th International Conference on Intelligent Computing and Control Systems, ICICCS 2022

作者： Garg, Aayushi Tahilramani, Nikunj Chauhan, Parth Patel, Ritu Silver Oak University Electronics & Communication Dept Ahmedabad India Aiie Information Communication Technology Dept. Ahmedabad India Sage University Computer Engineering Dept Madhya Pradesh India

ISBN: (纸本)9781665410359

A malware is any software intentionally designed to cause damage to a computer, server, client or network. Malware is very challenging issue and major concern for privacy of data. This detection can give information about Malicious and Benign data. Machine learning contain Evaluation measures like multi-class log loss, Confusion matrix, precision and recall these four measures are used. Exploratory data analysis, ANOVA, Feature engineering and evaluation are methods for classification machine learning models. In this paper Decision tree model is used for the classification. Max depth for 16 value gives more accuracy score 0.63 as compare to Max depth for 32 value which gives accuracy score 0.5. © 2022 IEEE.

关键词： Malware

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SFT-based Channel Estimation for OFDM communication Systems 9

SFT-based Channel Estimation for OFDM Communication Systems

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9th International Conference on computer and communications, ICCC 2023

作者： Zhu, Ran Lv, Jiayi Li, Lintao Li, Yimin Han, Yue Wang, Pu Beijing Institute of Technology School of Information and Electronics Beijing China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing China Beijing Institute of Technology School of Cyberspace Science and Technology Beijing China

ISBN: (纸本)9798350317251

Channel estimation plays an important role in OFDM, which is favored for broadband systems. Conventional channel estimation methods transform domain by DFT to suppress the time domain noise of the LS estimates. Sparse Fourier Transform (SFT) has been proposed as a solution to shorten the computational complexity of the Fourier transform of sparse signal. In this paper, we propose an enhanced algorithm to make a better trade-off between performance and complexity. The dominant entries of channel impulse response are obtained directly by sparse inverse Fourier transform. Simulation results demonstrate that the proposed algorithm yields a satisfactory estimation performance at a lower computational complexity. © 2023 IEEE.

关键词： Fourier transforms

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Integrating Random Forest and Support Vector Regression Models for Optimized Energy Consumption Evaluation in Cloud Computing Data Centers 3

Integrating Random Forest and Support Vector Regression Mode...

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3rd International Conference on Technological Advancements in Computational Sciences, ICTACS 2023

作者： Shaik, Nazeer Kumar, B. Vinoth Bhati, Anita Kumar, Sandeep Singh, Jagendra Sahu, Dinesh Prasad SRIT-Autonomous Department of Computer Science Engineering Anantapur India Ksr Institute for Engineering and Technology Dept. of Electronics and Communication Engineering Tiruchengode India Jecrc University Department of Computer Applications Jaipur India Raj Kumar Goel Institute of Technology Department of Information Technology Ghaziabad India School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (纸本)9798350342338

This study proposes a novel approach for energy consumption evaluation in cloud computing data centers. The immense energy consumption of these data centers presents significant environmental and economic challenges. Traditional evaluation methods often fail to provide accurate estimations due to the complex dynamics of cloud computing environments. The research introduces an integrated model, combining Random Forest Regression and Support Vector Regression, along with a Gradient Boosting Regressor for iterative training of regression parameters. We measure the model's performance using Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE), replacing traditional relative deviation measures. The proposed approach aims to provide more accurate and efficient energy consumption evaluations, offering potential benefits in energy management, operational costs reduction, and minimizing environmental impact. © 2023 IEEE.

关键词： Cloud Computing Energy Consumption Evaluation Gradient Boosting Regressor Random Forest Regression Support Vector Regression

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Early Cervical Cancer Diagnostics Detection Based on RMSE and Extreme Learning Machine

Early Cervical Cancer Diagnostics Detection Based on RMSE an...

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2023 International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023

作者： Singh Pundir, Aditya Kumar Nayak, Ullal Akshatha Gayathri, D. Sucharitha, M. Jamalpur, Bhavana Rastogi, Ravi Arya College of Engineering and Information Technology Department of Electronics and Communication Engineering Rajasthan Jaipur India Nitte Meenakshi Institute of Technology Department of Information Science and Engineering Karnataka Bangalore India Psna College of Engineering and Technology Department of Ece Tamilnadu Dindigul India School of Electronics Engineering VIT-AP University Andhra Pradesh Amaravati India School of Computer Science and Artificial Intelligence Sr University Telangana Warangal India Nielit Gorakhpur Electronics Department Uttar Pradesh India

ISBN: (纸本)9798350300857

Among Indian women, cervical cancer continues to be the most prevalent form of the disease. The death and incidence rates from cervical cancer are dramatically higher in India than in any other country. Cervical cancer is unique among malignancies in that it can be prevented through screening by detecting and treating precancerous lesions at any point in the disease's lengthy natural history. Women can be screened for cervical precancers and malignancies using a number of different procedures, both older and more cutting-edge technologies. Given the limitations of any given screening tool, selecting the appropriate tool for a given situation is essential. Cervical precancers can be treated in a number of ways;the one chosen will rely on a number of factors, including the patient's preferences, the available budget, the quality of the biopsy specimens, etc. Cervical cancer screening guidelines for Indian women are examined. The proposed method consists of three main stages: preprocessing, feature extraction, and model training. In the preprocessing phase, image processing techniques like HE and sharpening and smoothing are applied. The Fourier Transform is used for feature extraction in GLCM. The RMSE-ELM technique is utilized for model evaluation. Better results are achieved with the proposed technique than with CNN or ELM. © 2023 IEEE.

关键词： Feature extraction

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