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9th International Conference on Information Technology Trends, ITT 2023

作者： Agbo, Benjamin Al-Aqrabi, Hussain Alsboui, Tariq Hussain, Muhammad Hill, Richard Keele University School of Computing and Mathematics Keele United Kingdom Higher Colleges of Technology Faculty of Computer Information Science Sharjah United Arab Emirates University of Huddersfield School of Computing and Engineering Huddersfield United Kingdom

ISBN: (纸本)9798350327502

The development of low-cost sensor networks (LCSNs) has facilitated the generation of vast amounts of environmental monitoring data in air quality monitoring stations. State-of-the-art research has focused on developing robust models to detect and mitigate events which may impact societies. Making adequate predictions is important for meeting the sustainability needs of future generations. Therefore, quality data is required to produce accurate results for downstream analytics and visualisations. When dealing with large amounts of data, it is however common to observe numerous amounts of missing data. This is a challenge for data miners because various methods for data analysis only work well on complete datasets. A traditional approach to handling missing data is to discard instances of missing values and only use complete cases for analysis. However, research has shown that this approach is not practical especially when large amounts of data are missing. This led to an increased need to develop strategies for replacing missing values with plausible values through imputation. This study presents an imputation strategy called k-BFMVI for recovering missing values before training downstream regression models for low cost sensor calibration. Experiments simulated missingness from 10% to 40% using MCAR and MAR mechanisms and the performance of the proposed technique was measured against state-of-the-art techniques. Overall, the proposed algorithm recorded the best imputation accuracy as opposed to existing techniques and showed significant improvements on downstream learning. © 2023 IEEE.

关键词： Sensor networks

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Hybrid Music Recommendation System Utilizing Item Popularity and Content-Based Filtering Innovations in music

Hybrid Music Recommendation System Utilizing Item Popularity...

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2022 International Conference on Data science, Agents and Artificial Intelligence, ICDSAAI 2022

作者： Singh, Aditya Porwal, Utsav Selvaraj, P. SRM Institute of Science and Technology College of Engineering and Technology Faculty of Engineering and Technology Department of Computing Technologies Kattankulathur603203 India

ISBN: (纸本)9798350333848

recommendation systems are increasing, making it viable to customize the listening trends and fine-tune the selections per the user's preferences. Over the years, every user's personalized music playlist can be primarily customized based totally on the statement of that user's behavior. In this work, we advocate a Hybrid Music advice machine using object recognition and content analysis primarily based on innovative filtering. We illustrated its implementation for virtual audio, a product class with swiftly increasing trends. The proposed machine routinely indicates customized playlists of MP3 songs for every user. The machine works in real-time and is scalable to the significant statistics generally encountered in personalization applications. We applied this personalization system on the 'Million-Song Dataset' and examined its overall performance with several users. Moreover, our proposed recommendation system performs well in recommending the music based on the item's popularity and user-centric content filtering. © 2022 IEEE.

关键词： Recommender systems

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Improved Frequency Estimation for Range-Finding Applications using Phase Unwrapping

Improved Frequency Estimation for Range-Finding Applications...

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IEEE Instrumentation and Measurement Technology Conference

作者： Sivagunalan Sivanathan Mohammed Ali Roula Kang Li Faculty of Computing Engineering and Science University of South Wales Pontypridd United Kingdom

ISBN: (数字)9798350380903

ISBN: (纸本)9798350380910

The Fast Fourier Transform (FFT) is a common signal processing operation used in frequency estimation, a technique that has many applications in a wide range of fields including radar, LiDAR, sonar, medical sciences, telecommunications, and metrology. However, in real-time applications, computational intensity often imposes restrictions on frequency resolution, and consequently introducing spectral errors. To address this limitation, the frequency resolution of FFT can be refined for enhanced accuracy by unwrapping the phase information of the frequency component, while maintaining the required FFT points and sampling rate. This paper presents an analysis of diverse windowing techniques, showcasing the potential enhancement of FFT spectral accuracy, particularly for precision-centric measurements. The proposed method is adaptable for deployment in both programmable and non-programmable real-time systems, including Embedded Systems and Field-Programmable Gate Arrays (FPGAs).

关键词： Laser radar Accuracy Fast Fourier transforms Sonar applications Size measurement Frequency estimation Real-time systems

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A Preference-Based Indicator Selection Hyper-Heuristic for Optimization Problems 9th

A Preference-Based Indicator Selection Hyper-Heuristic for ...

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19th International Conference on Advanced Data Mining and Applications, ADMA 2023

作者： Anwar, Adeem Ali Younas, Irfan Liu, Guanfeng Zhang, Xuyun School of Computing Faculty of Science and Engineering Macquarie University SydneyNSW Australia Odyssey Analytics 5757 Woodway Drive HoustonTX77057 United States

ISBN: (纸本)9783031466601

Heuristics have been effective in solving computationally difficult optimization issues, but because they are often created for certain problem domains, they perform poorly when the challenges are significantly altered. The currently available techniques are either designed to address single- or multi-objective optimization issues solely, or they perform poorly with the same parameters. The multi-domain approach known as hyper-heuristics (HHs) can be used to solve optimization issues with minor variations. Motivated by the notion of utilizing the benefits of low-level heuristics (LLHs) in order to obtain well-distributed and convergent optimum solutions along with taking into account the shortcomings of the work completed in many-objective HHs. For many-objective optimization problems, this paper develops a high-level selection approach that employs indicators by preference and offers a unique selection hyper-heuristic called Preference-based Indicator Selection Hyper-heuristic (PBI-HH). In order to establish fairness between exploration and exploitation, the method makes use of a randomization mechanism and a greedy strategy to address a significant problem faced by HHs. Three well-known many-objective evolutionary algorithms are combined in the unique technique that is being proposed. The efficacy of the proposed strategy is assessed by contrasting it with cutting-edge HHs. PBI-HH performs better or equal to the state-of-the-art HHs on 155 out of 160 cases employing the HV indicator and has the optimal μ norm mean values across all datasets. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Multiobjective optimization

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The Smart IoT based Automated Irrigation System using Arduino UNO and Soil Moisture Sensor 4

The Smart IoT based Automated Irrigation System using Arduin...

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4th International Conference on Smart Systems and Inventive Technology, ICSSIT 2022

作者： Gnanavel, S. Sreekrishna, M. Murugan, N. Durai Jaeyalakshmi, M. Loksharan, S. Dept of Computing Technologies Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur India Dept of Computer Science Engineering Rajalakshmi Engineering College Thandalam India

ISBN: (纸本)9781665401180

Agriculture is the important job in India. It needs water for irrigation. The main job of the farmer is to irrigate the field. In this research, the irrigation system is automated using the Internet of Things technology. Therefore, it reduces the needfor human intervention. This study will find the best crops suitable for the particular soil. When water is needed, the field is irrigated with sufficient water. When the water level in the field reaches a certain height or the water level in the well reaches the minimum level, the system sensor detects it and automatically shuts off the pump motor. From this, a physical exertion is reduced and the field is watered effectively. Moreover the working life of the pump motor is extended. © 2022 IEEE

关键词： Internet of things

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Machine Learning based Comparative Analysis of Cervical Cancer Risk Classifications Algorithms 2

Machine Learning based Comparative Analysis of Cervical Canc...

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2nd IEEE International Conference on Advances in computing, Communication and Applied Informatics, ACCAI 2023

作者： Ganguly, Tania Pati, Peeta Basa Deepa, K. Singh, Tripty Ozer, Tolga Amrita School of Computing Department of Computer Science and Engineering Bengaluru India Amrita School of Engineering Department of Electrical and Electronics Engineering Bengaluru India Afyon Kocatepe University Technology Faculty Electric Electronic Engineering Afyonkarahisar Turkey

ISBN: (纸本)9798350315905

Cervical cancer is a major health concern for women worldwide, and early detection is essential for successful treatment. Since symptoms often do not appear until later stages, early screening is necessary. Machine learning can help classify cervical cancer risk by analyzing patient datasets and identifying the important factors that predict the likelihood of emerging cervical cancer. This paper evaluates six different machine-learning approaches for analyzing risk factors associated with cervical cancer using a dataset of 838 instances with 36 features. Results show that the SVM classifier performs the best, with an accuracy of 99.60% which emphasize the possibility of utilizing machine learning to enhance the precision of cervical cancer risk assessment. This can result in the development of better screening and prevention techniques for cervical cancer, which can be more effective in identifying and managing this disease. © 2023 IEEE.

关键词： Logistic regression

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Sentiment Analysis of ChatGPT Healthcare Discourse: Insights from Twitter Data 15

Sentiment Analysis of ChatGPT Healthcare Discourse: Insights...

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15th International Conference on Software, Knowledge, Information Management and Applications, SKIMA 2023

作者： Kumar, Roshan Ayyasamy, Ramesh Kumar Sangodiah, Anbuselvan Krishnan, Kesavan Jebna, Abdulkarim Kanaan Theam, Lim Jit Universiti Tunku Abdul Rahman Faculty of Information and Comunication Technology Kampar Malaysia Quest International University Faculty of Computing and Engineering Ipoh Malaysia Universiti Malaya Faculty of Computer Science and Information Technology Kuala Lumpur Malaysia

ISBN: (纸本)9798350316551

This paper explores the application of Chat Generative Pretrained Transformer (ChatGPT) in the healthcare domain, introducing a sentiment analysis model to evaluate ChatGPT-related tweets in healthcare contexts. The study aims to uncover predominant sentiments, thematic content, and diverse perspectives concerning ChatGPT's integration into healthcare, utilizing an extensive dataset from Twitter comprising 10,330 healthcare-related tweets. Leveraging advanced Natural Language Processing (NLP) techniques, we systematically categorized topics and emotional content within these tweets. Additionally, we conducted a comprehensive analysis of frequently occurring words in tweets expressing positive and negative sentiments. The findings reveal that the majority of healthcare-related ChatGPT tweets express either positive or negative sentiments, with a minor proportion conveying neutral viewpoints. Furthermore, to enhance our comprehension of sentiment dynamics in healthcare discussions involving ChatGPT, we applied four machine learning classifiers Support Vector Machine, K-Nearest Neighbors, Naive Bayes and Random Forest. Remarkably, the SVM classifier demonstrated the highest accuracy at 85.6%, affirming its efficacy in healthcare sentiment analysis. In summary, this research sheds light on prevailing sentiments and perspectives regarding ChatGPT in the healthcare sector, highlighting its predominantly positive and neutral reception on platforms like Twitter. Additionally, the success of SVM as a sentiment analysis tool underscores its potential for discerning sentiments in healthcare-related ChatGPT discussions, contributing to ongoing debates on AI integration in healthcare and guiding future endeavors in this evolving field. © 2023 IEEE.

关键词： Sentiment analysis

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Data-Driven Approach for Condition Monitoring and Improving Power Output of Photovoltaic Systems

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Computers, Materials & Continua 2023年第3期74卷 5757-5776页

作者： Nebras M.Sobahi Ahteshamul Haque V S Bharath Kurukuru Md.Mottahir Alam Asif Irshad Khan Department of Electrical and Computer Engineering(ECE) King Abdulaziz UniversityJeddah 21589Saudi Arabia Advance Power Electronics Research Lab Department of Electrical EngineeringJamia Millia IslamiaNew DelhiIndia Computer Science Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah 21589Saudi Arabia

Increasing renewable energy targets globally has raised the requirement for the efficient and profitable operation of solar photovoltaic(PV)*** light of this requirement,this paper provides a path for evaluating the operating condition and improving the power output of the PV system in a grid integrated *** achieve this,different types of faults in grid-connected PV systems(GCPVs)and their impact on the energy loss associated with the electrical network are analyzed.A data-driven approach using neural networks(NNs)is proposed to achieve root cause analysis and localize the fault to the component level in the *** localized fault condition is combined with a parallel operation of adaptive neurofuzzy inference units(ANFIUs)to develop a power mismatch-based control unit(PMCU)for improving the power output of the *** develop the proposed framework,a 10-kW single-phase GCPV is simulated for training the NN-based anomaly detection approach with 14 deviation ***,the developed algorithm is combined with the PMCU implemented with the experimental setup of *** results identified 98.2%training accuracy and 43000 observations/sec prediction speed for the trained classifier,and improved power output with reduced voltage and current harmonics for the grid-connected PV operation.

关键词： Condition monitoring anomaly detection performance evaluation fault classification optimization

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Blockchain-Based Access Control with Decentralized Architecture for Data Storage and Transfer 1

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6th International Conference on Innovative computing and Communication, ICICC 2023

作者： Vanjipriya, V. Suresh, A. Department of Networking and Communications School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu SRM Nagar Kattankulathur Chennai603203 India

ISBN: (数字)9789819933150

ISBN: (纸本)9789819933143

When it comes to the security of data in the cloud storage model, access control is the most crucial factor. However, traditional methods of data sharing and access control present significant difficulties in the field of study due to issues like privacy data leakage and key abuse. The blockchain model also offers security because of the various encryption methods used for user authentication. Since this research focuses on the blockchain-based cloud, two major contributions are developed: one for effective access control and the other for privacy-based data sharing and retrieval. The first contribution implements a blockchain-based model for cloud-based access control and data sharing. This issue of a single point of failure in cloud architecture is effectively addressed by the developed blockchain-based access control and data sharing process. According to the credentials, the data user (DU) generates a registration request that is sent to the data owner (DO). In order to ensure safe transmission, an EHR application implements a data protection procedure. The cloud infrastructure incorporates components such as data consumers, data producers, blockchain transactions, smart contracts, and the Interplanetary File System (IPFS). To top it all off, DO includes a data protection algorithm for securing HER, wherein encrypted EHR is converted to IPFS before being shared with the data consumer. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Blockchain

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Non-GPS-based ETA Models Constructed from Historical GPS Data and Traffic Contexts 8

Non-GPS-based ETA Models Constructed from Historical GPS Dat...

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8th International Conference on Business and Industrial Research, ICBIR 2023

作者： Phon-Amnuaisuk, Somnuk Tan, Soon-Jiann Khor, Kok-Chin Tham, Mau-Luen Chong, Yung-Wey Omar, Saiful Ibrahim, Noor Farizah Binti Yusoff, Mohd Najwadi Mashud, Idham School of Computing and Informatics Universiti Teknologi Brunei Brunei Universiti Teknologi Brunei Faculty of Engineering Brunei Universiti Tunku Abdul Rahman Lkc Faculty of Engineering and Science Malaysia Universiti Sains Malaysia School of Computer Sciences Malaysia

ISBN: (纸本)9798350399646

Without GPS data, ETA can be estimated with good precision from historical GPS data and the context of the traffic environment. In this paper, the estimated travel duration between bus stops was computed based on historical GPS data with time stamps. Three approaches were employed to compute the duration between bus stops: (i) statistical central tendencies, (ii) nonlinear regression techniques i.e., decision tree regressor, random forest regressor and k-nearest neighbors regressor, and (iii) an ANN predictive model. These three approaches capture different characteristics of the traffic environment. Statistical central tendencies give the overall view of the ETA information while nonlinear regressors can capture context from the time stamps (e.g., weekdays vs holidays). Finally, predicting the duration required to reach the next bus stop based on previous timespans on the same route implicitly captures temporal dynamics of the traffic environment from previous time steps and the ANN predictive model exploits this temporal information. The feasibility of ETA estimation using these three approaches was explained, evaluated and discussed. © 2023 IEEE.

关键词： Decision trees

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