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An Automatic Threshold Selection Using ALO for Healthcare Duplicate Record Detection with Reciprocal Neuro-Fuzzy Inference System

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computers, Materials & Continua 2023年第3期74卷 5821-5836页

作者： Ala Saleh Alluhaidan Pushparaj Anitha Subbappa Ved Prakash Mishra P.V.Chandrika Anurika Vaish Sarthak Sengupta Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadhKSA.P.O.Box:8442811671 Department of Electronics and Communication Engineering National Institute of Technical Teacher Training and ResearchChandigarh160019India Department of Periodontology JSS Dental College&HospitalJSS Academy of Higher Education and ResearchMysuru570015India Department of Computer Science and Engineering Amity UniversityDubai345019United Arab Emirates Department of Management/Economics Prin LN Welingkar Institute of Management Development and ResearchMumbai400019India Department of Management Studies Indian Institute of Information TechnologyAllahabad211012India

ESystems based on EHRs(Electronic health records)have been in use for many years and their amplified realizations have been felt *** still have been pioneering collections of massive volumes of health *** detections involve discovering records referring to the same practical components,indicating tasks,which are generally dependent on several input parameters that experts *** linkage specifies the issue of finding identical records across various data *** similarity existing between two records is characterized based on domain-based similarity functions over different ***-duplication of one dataset or the linkage of multiple data sets has become a highly significant operation in the data processing stages of different data mining *** objective is to match all the records associated with the same *** measures have been in use for representing the quality and complexity about data linkage algorithms,and many other novel metrics have been *** outline of the problem existing in themeasurement of data linkage and de-duplication quality and complexity is *** article focuses on the reprocessing of health data that is horizontally divided among data custodians,with the purpose of custodians giving similar features to sets of *** first step in this technique is about an automatic selection of training examples with superior quality from the compared record pairs and the second step involves training the reciprocal neuro-fuzzy inference system(RANFIS)*** the Optimal Threshold classifier,it is presumed that there is information about the original match status for all compared record pairs(i.e.,Ant Lion Optimization),and therefore an optimal threshold can be computed based on the respective ***,Clinical Decision(CD),and Cork Open research Archive(CORA)data repository help analyze the proposed method with evaluated benchmarks with current techniques.

关键词： Duplicate detection healthcare record linkage dataset pre-processing reciprocal neuro-fuzzy inference system and ant lion optimization fuzzy system

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Key performance indicators analysis for 4 G-LTE cellular networks based on real measurements

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International Journal of Information Technology (Singapore) 2023年第3期15卷 1347-1355页

作者： Shakir, Zaenab Mjhool, Ahmed Yaseen Al-Thaedan, Abbas Al-Sabbagh, Ali Alsabah, Ruaa Al-Muthanna University Samawah Iraq Information Technology Research and Development Center (ITRDC) University of Kufa Najaf Iraq Computer Technology Engineering Department Al-Taff University College Kerbala Iraq Ministry of Communication ITPC Kerbala Iraq Information Technology Department College of Information Technology and Computer Sciences University of Kerbala Kerbala Iraq

Key Performance Indicator (KPI) gives potential information that needs for successful network deploying, performance study, and enhanced networks. But, insufficient KPI data may prevent effective network design, which would increase operating costs and have a negative impact on network users. As known, Iraq launched the 4 G-LTE license very recently. Therefore, this work demonstrates Long Term-Evolution (LTE) data measurements and performance analysis of the KPI at the 2100MHz frequency band via a bandwidth of 20MHz for three mobile operators in Iraq, including Najaf city called (OP1, OP2, and OP3) for data confidentiality. Data collection is done by drive tests, from routes to characterize the cellular network. The data measurements have been focused on the parameters that affect the network directly, such as Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), Signal to Interference and noise Ratio (SINR), Received Signal Strength Indicator (RSSI), and Downlink Throughput (DL Throughput). Studying the analysis behavior of these parameters and the probability distribution function (PDF) for the KPIs demonstrates the relationship and dependency among these parameters for the three mentioned operators. Finally, these KPIs provide useful information for network management, assessment, and suitable requirements for Cellular network operators for voice and data services. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Cellular network Drive test KPI LTE

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Design and development of Embedded Controller with Wireless Sensor for Power Monitoring through Smart Interface Design Models 2

Design and Development of Embedded Controller with Wireless ...

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2nd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Ramesh, S.M. Rajeshkannan, S. Pundir, Sumit Dhaliwal, Navdeep Mishra, Sanjay Saravana, B Shanthi Kpr Institute of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India St.Joseph's College of Engineering Omr Department of Electronics and Communication Engineering Tamil Nadu Chennai India Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttarakhand Dehradun India Lovely Professional University Division of Research and Development Punjab Phagwara India Uttaranchal University Division of Research and Innovation Uttarakhand Dehradun India Adhiparasakthi College of Engineering Department of Electrical and Electronics Engineering Tamil Nadu Kalavai India

ISBN: (纸本)9798350325799

The advanced innovation in the power electronics system paved a numerous ways in the development and monitoring of power consumption. This helps to maintain the demand side management to adopt several measures in limiting the consumption of power. This is implemented through embedded system with artificial intelligence techniques. These systems are highly reliable and stable in performing operations. The important objective of the proposed system includes the power monitoring and controlling through embedded controllers. These systems are integrated with wireless sensor network for enabling data storage in the network with higher security concerns. The stored data are used for optimization process. This includes image processing techniques with feature extraction. The power monitoring system is implemented by automatically evaluating the environmental conditions through sensors and functions based upon the priority. They are implemented with remote monitoring and control systems with providing real time information to the user's mobile phone. The communication network are enhanced through the internet of things. This is enhanced through priority scheduling techniques through random forest algorithm. Thus, the overall system helps in enabling optimum power consumption through machine learning. © 2023 IEEE.

关键词： Machine learning

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Comparison of Image Quality Estimation Methods for JPEG Compression 15

Comparison of Image Quality Estimation Methods for JPEG Comp...

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

作者： Khurana, Jatin Chitra, S. Khurana, Shivam Akkur, Malatesh Kshirsagar, Pranali S. Ravivarman, G. Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India School of Sciences Physics & Electronics Karnataka Bangalore India Vishwakarma Institute of Technology Department of Computer Engineering Pune India Karpagam Academy of Higher Education Department of Electrical and Electronics Engineering Coimbatore641021 India

ISBN: (纸本)9798350370249

This paper gives a comparative check of modern-day photograph pleasant estimation techniques for JPEG Compression. The techniques in comparison consist of No-Reference (NR) and full-Reference (FR) best metrics. For the FR approach, the Structural Similarity (SSIM), peak sign to Noise Ratio (PSNR), and Mutual statistics (MI) are evaluated. For the NR method, the Blind/Reference less photo spatial first-class Evaluator (BRISQUE) and visible information constancy (VIF) are taken into consideration. The overall performance of the FR and NR methods are as compared on 3 synthetic photograph datasets and an unbiased database containing natural snap shots. The outcomes received from the evaluation of those databases display that the SSIM yields the most correct effects for each synthetic images and actual natural photos. In contrast, the NR strategies carry out better for artificial photos but yield poorer results for herbal pictures in comparison to FR strategies. The paper concludes with an advice that SSIM can be used for each FR and NR image estimation. © 2024 IEEE.

关键词： Consideration Databases Estimation Information Strategies Structural Techniques Unbiased

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The role of data governance in a high-level approach of data migration to open data

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Business Information Review 2024年第3期41卷 94-103页

作者： Yulfitri, Alivia Sensuse, Dana Indra Sumirat Hidayat, Deden Suryono, Ryan Randy Kautsarina Purwaningsih, Erisva Hakiki Prabuwono, Anton Satria Faculty of Computer Science Universitas Indonesia Depok Indonesia Faculty of Computer Science Universitas Esa Unggul West Jakarta Indonesia Research Center for Data and Information Sciences National Research and Innovation Agency (BRIN) Central Jakarta and Bandung Indonesia Faculty of Engineering and Computer Science Universitas Teknokrat Indonesia Lampung Indonesia Information and Communications Technology Human Resources Development Agency Ministry of Communication and Information Technology of the Republic of Indonesia Jakarta Central Jakarta Indonesia Faculty of Computing and Information Technology in Rabigh King Abdulaziz University Jeddah Saudi Arabia

Data quality issues are still a challenge in data migration. Existing methods mainly focus on technical issues. However, the problem of data quality is not only a technical problem, but the main one is the non-technical aspect. This paper aims to identify the role of data governance in the data migration process. The qualitative research method uses two primary references, namely the high-level data migration approach from Kazimir et al. and the DAMA-DMBOK framework. Both are highly compatible with data migration processes focusing on data quality and referencing governance roles. The case study was conducted in a central government working on data migration to the new One Data application. The results show that eight data governance roles are involved in 20 migration processes (65%). Non-technical activities related to data quality involve more than half of the data migration process. This paper proposes the role of data governance for non-technical aspects of the migration process. This paper presents a case study of the high-level data migration process and how data governance comes into play. This research can help the government and practitioners overcome problems that often occur in data migration, especially related to data quality. © The Author(s) 2024.

关键词： DAMA-DMBOK framework data governance data migration Indonesia’s one data Kazimir et al.’s approach open data

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Smart Technique to Prevent Flood Disaster due to High Rainfall in Coastal Areas using IoT

Smart Technique to Prevent Flood Disaster due to High Rainfa...

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2023 IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2023

作者： Balaji, A. Srinivasan, V. Prasanna Rangarajan, J. Nagaraju, V. Bharathi, B. Murugan, Subbiah Rajalakshmi Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India R.M. D Engineering College Department of Information Technology Tamil Nadu Chennai India Muthayammal Engineering College Department of Electronics and Communication Engineering Tamil Nadu Rasipuram India Department of Computer Science Engineering Tamil Nadu Chennai India R.M.K Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Vee Eee Technologies Solution Pvt Ltd Research & Development Division Tamil Nadu Chennai India

ISBN: (纸本)9781665462631

Flooding has often been caused by an increase in the volume of water in a water system, such as a lake or river overflowing. On rare occasions, a dam will crack, releasing a tremendous amount of water. As a result, some water seeps into the soil, 'overflowing' the area. Rivers are intertwined with the banks of the river at a station. Aside from a lack of items and assets, municipal architecture rainwater contains germs and wastewater flows from dumping grounds and toxic leaks, resulting in various ailments. The purpose of this research is to identify the existence of water in river beds and assess whether it is in excellent condition. If the limit is surpassed, it alerts users via LED indicators, a buzzing sound, and internet apps. First, the physical unit is installed in places that are at risk of flooding. Next, the Internet of Things module serves as the transmitting unit. Finally, the sensors on the system measure the necessary data. The precise readings are shown on the LCD and sent to the IoT web application in real time. In this case, the Thing speaks web application stores data in a private channel, and the web application subsequently warns authorities and individuals when a flood arises. © 2023 IEEE.

关键词： Rivers

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Sensor Node communication based Selfish Node Detection in Mobile Wireless Sensor Networks

Sensor Node Communication based Selfish Node Detection in Mo...

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2023 IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2023

作者： Thangaraj, S.John Justin Ramshankar, N. Srividhya, E. Jayanthi, S. Kumudham, R. Srinivasan, C. Tamil Nadu Chennai India R.M.D. Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Department of Electronics and Communication Engineering Tamil Nadu Chennai India Vee Eee Technologies Solution Pvt Ltd Research & Development Division Tamil Nadu Chennai India

ISBN: (纸本)9781665462631

A mobile wireless sensor network (MWSN) is a self-configuring network that does not require a fixed structure, which minimizes its operation time. At the same time, every node in this network is free to move and make the network change its topology frequently. Several routing algorithms designed for MWSNs are established on every node that forwards data packets in the network. However, in real time, some of the nodes may act as selfish nodes. Those selfish nodes use the network and its services but do not cooperate with other nodes. Therefore, detecting these nodes is essential for the WSN. Thus, this approach, 'sensor node communication-based selfish node detection' (CSND) in MWSN, is proposed. In this approach, it can be verified that the node communicates based on the route request and reply messages sent within the communication network. The article's objective is to distinguish the selfish node in WSNs. This approach elects the forwarder node based on the highest energy level and node communication ratio (RNC). The BS is noticing the behaviour of every communication in the MWSN. The simulation illustrates that it enhances delivery and drop rates and improves energy efficiency during data transmission. © 2023 IEEE.

关键词： Energy efficiency

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Innovative System for Environmental Monitoring with Connected Sensors and Data Analysis for Chemical Pollutants

Innovative System for Environmental Monitoring with Connecte...

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2024 IEEE International Conference on Intelligent Systems and Advanced Applications, ICISAA 2024

作者： Suganya, A. Suryawanshi, Mahesh A. Bagal, Manisha V. Rawat, Ruchira Kaliappan, S. Manikandan, G. Karpagam College of Engineering Department of Information Technology Tamil Nadu Coimbatore India Bharati Vidyapeeth College of Engineering Department of Chemical Engineering CBD Navi Mumbai India Graphic Era Deemed to be University Department of Computer Science and Engineering Uttarakhand Dehradun India Lovely Professional University Division of Research and Development Punjab Phagwara India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798331539948

The Chemical Pollutants system, which combines sophisticated data processing capabilities with networked sensors to identify and control chemical pollutants, transforms environmental monitoring. It collects real-time data on pollutants using extremely accurate sensors, which are then evaluated using cloud infrastructure and a reliable wireless connection. Predictive insights using machine learning algorithms provide notable improvements: 97% accuracy in sensor data, 3 seconds of data transmission latency, and 99.5% system uptime. There are now 1200 daily active users, and the prediction model's accuracy is 92%. Environmentally, it resulted in a 30% decrease in PM2.5 levels, a halving of high pollution alerts to 10 per month, and an 85% increase in public awareness. The number of policy changes tripled to four each year, while community engagement quadrupled to twelve. The results highlight the system's contribution to improving the timeliness and quality of environmental data, influencing public health and policy, and highlighting its efficacy and potential for wider environmental management use. © 2024 IEEE.

关键词： Volatile organic compounds

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Deep Learning Approaches for Solving Challenges in computer Vision Applications and Exploring Evolving Patterns 7

Deep Learning Approaches for Solving Challenges in Computer ...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Gejji, Sujay Supreeth, B.R. Khan, Pathan Firoze Venkat, S. Pradeepa, K. Kaliappan, S. S. G. Balekundri Institute of Technology Department of Electronics & Communication Engineering Karnataka Belagavi India Oxford Degree and Pg College Department of Computer Science Sir. M. Visveshwaraiah Layout Bangalore India Madanapalle institute of Technology & Science Department of Computer Science & Technology Andhra Pradesh Madanapalle India S. A. Engineering College Department of Electronics and communication Engineering Tamil Nadu Chennai India Kpr College of Arts Science and Research Department of Artificial Intelligence and Data Science Tamil Nadu Coimbatore India Lovely Professional University Division of Research and Development Punjab Phagwara India

ISBN: (纸本)9798350350067

Deep learning has brought substantial advancements to computer vision in recent years by providing answers to challenging visual interpretation problems. It investigates several DL methodologies, such as self-supervised learning strategies, hybrid CNN-RNN models, enhanced CNNs, generative adversarial networks (GANs), and vision transformers (ViTs). Novel CNN architectures are designed, GANs are used for image synthesis, hybrid models are used to integrate spatial and temporal information, self-attention is used in ViTs, and self-supervised learning is used for feature extraction. The results of the study show significant improvements: the accuracy of Enhanced CNNs was 81.6%, the accuracy of GANs was 14.8%, the accuracy of Hybrid CNN-RNN models was 86.2%, the accuracy of ViTs was 79.2%, and the accuracy of transfer for self-supervised learning techniques was 76.8%. These findings demonstrate the improved effectiveness and performance of the suggested techniques, highlighting their potential to resolve challenging problems in feature extraction, video analysis, object recognition, picture enhancement, and image classification. It concludes that advances in computer vision will be facilitated by the ongoing development of DL methods. These advancements will lead to more precise and adaptable applications in a range of fields, including autonomous driving, medical imaging, surveillance, and augmented reality. © 2024 IEEE.

关键词： Generative adversarial networks

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Deep learning and encryption algorithms based model for enhancing biometric security for artificial intelligence era

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Journal of Ambient Intelligence and Humanized Computing 2024年 1-14页

作者： Byeon, Haewon Shabaz, Mohammad Surbakti, Herison Keshta, Ismail Soni, Mukesh Bhatnagar, Vaibhav Department of Digital Anti-Aging Healthcare Inje University Gimhae50834 Korea Republic of Model Institute of Engineering and Technology J&K Jammu India Information and Communication Technology Rangsit University International College Pathumthani Lak Hok Thailand Computer Science and Information Systems Department College of Applied Sciences AlMaarefa University Riyadh Saudi Arabia Department of CSE University Centre for Research & ampDevelopment Chandigarh University Punjab Mohali140413 India Department of Computer Applications Manipal University Jaipur Jaipur India

The significance of facial recognition in the era of artificial intelligence lies in its utilization of facial features as a type of biometric characteristic possessing uniqueness and irreversibility. However, exposing these features to attacks, tampering, or unauthorized disclosure poses considerable threats to user privacy and security. A privacy and security solution based on deep learning and encryption algorithms is proposed to tackle this issue. This solution employs the FaceNet deep learning algorithm to extract facial features efficiently. The combination of biometric feature blurriness and cryptographic system precision is achieved, utilizing the CKKS fully homomorphic encryption algorithm for operations in the ciphertext domain of facial recognition. The SM4 algorithm is used to enhance the resilience of facial feature ciphertext against malicious attacks. By leveraging the properties of symmetric ciphers, a balance is achieved between security and computational efficiency. The management of the symmetric key used in the SM4 algorithm is conducted through the employment of the SM9 asymmetric encryption algorithm. Experimental results and analysis demonstrate that the proposed solution enhances the security of data transmission, storage, and comparison without compromising the accuracy and efficiency of facial recognition. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Differential privacy

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