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13th IEEE Symposium on computer Applications and Industrial Electronics, ISCAIE 2023

作者： Wandy, Wandy Adi, Kusworo Ayu, Media Anugerah Diponegoro University Doctoral Program of Information Systems School of Postgraduate Studies Semarang Indonesia Diponegoro University Faculty of Science and Mathematics Department of Physics Semarang Indonesia Sampoerna University Faculty of Engineering and Technology Department of Computer Science South Jakarta Indonesia

ISBN: (纸本)9798350347319

Sports science is an interdisciplinary and multidisciplinary science that strives to increase athletic performance and endurance. Sport science recognizes and prevents injuries. Sensors and statistics formalize Sports science. Runners need coaches and teams to support them before, during, and after the run race. Coaches generate running training plans to boost performance. Running race performances may be impacted by air pollution exposure while training, so coaches should consider limiting air pollution exposure when training. One of the external factors is Particulate Matter (PM2.5 and PM10). Sensors connecting to the Internet can record external factors and produce csv data. The foundation of supervised machine learning is the labeling process. Labeling a set of data is one of the laborious and time-consuming phases in every machine-learning application because it requires verifying the accuracy of the labels and making any necessary revisions. This research aimed to find a solution to automatically label numerous air particulate matter raw data using a rule based on parameters to reduce manual work, human errors and faster processes. This labeled data will later be used for supervised machine learning classification to support the coach in generating training programs for the runners in a Sports information System. Based on Indonesia Air Quality index rule-based approach, labeled text data in csv has been generated and tested with PM2.5 and PM10 parameters in three scenarios with a 100% success rate. It was possible to automate the labeling process, and it explained how automation results in fast and accurate results. © 2023 IEEE.

关键词： Classification (of information)

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Physical Layer Security in Untrusted Diamond Relay Networks with Imperfect Source-Relay Links

Physical Layer Security in Untrusted Diamond Relay Networks ...

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2022 International Symposium on Networks, computers and Communications, ISNCC 2022

作者： Qian, Shen He, Xin Zhou, Xiaobo Faculty of Engineering Kanagawa University Department of Information Systems Creation Japan Anhui Normal University School of Computer and Information China Tianjin University School of Computer Science and Technology China

ISBN: (数字)9781665485449

ISBN: (纸本)9781665485449

A two-untrusted-relay transmission scheme is proposed with lossy-forward (LF) relaying being utilized. Two untrusted relays are located between one source and one common destination and there is no direct link between the source and the destination, which referred to as diamond relaying network. Since LF relaying allows intra-link errors and always forwards the decoded information sequences from the relays to the destination, it realizes the reliable and secure transmission of decoder-and-forward based relay networks with untrusted relays. Reliable-and-secure probability is derived to evaluate the performance of the untrusted relay networks, which represents the probability that the destination can recover the original message sent from the source whereas the relays cannot. We find that as long as the contributions of the transmit power of source and relays are balanced, a certain degree of reliable-and-secure probability can be held, even without the support from a friendly jamming signal. © 2022 IEEE.

关键词： Probability

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Artificial Intelligence in Next-Generation Networking: Energy Efficiency Optimization in IoT Networks Using Hybrid LEACH Protocol

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SN computer science 2024年第5期5卷 546页

作者： Khan, Surbhi Bhatia Kumar, Ankit Mashat, Arwa Pruthviraja, Dayananda Imam Rahmani, Mohammad Khalid Mathew, Jimson School of Science Engineering and Environment University of Salford Salford United Kingdom Department of Information Technology Guru Ghasidas Vishwavidyalaya Chhattisgarh Bilaspur India Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Rabigh 21911 Saudi Arabia Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Karnataka Manipal 576104 India Department of Computer Science College of Computing and Informatics Saudi Electronic University Riyadh 11673 Saudi Arabia Department of Computer Science and Engineering Indian Institute of Technology Patna Bihar Patna India

The convergence of the Internet of Things (IoT) and Artificial Intelligence (AI) is significantly transforming the landscape of future networking. The Internet of Things (IoT) is a technological paradigm that encompasses embedded systems, wireless sensors, and automation, facilitating the integration of various applications ranging from smart homes to wearable devices. In addition, the advent of artificial intelligence (AI) amplifies this influence by providing data-driven analytics, optimising processes, and presenting novel opportunities for growth. Nevertheless, the widespread adoption of devices within Internet of Things (IoT) networks gives rise to apprehensions regarding increased energy consumption. In order to ensure the longevity of network operations, it is imperative to employ energy-efficient protocols for sensor nodes that possess limited power resources. One example of a protocol that demonstrates this concept is the Low Energy Adaptive Clustering Hierarchy (LEACH) protocol. This protocol effectively divides networks into clusters and dynamically adjusts the cluster heads to optimise the transmission of data to the base stations. Our study enhances the LEACH protocol by incorporating digital twin simulation, thereby enhancing the efficiency of IoT systems. Virtual network models and AI analytics are employed to assess energy consumption and performance. Cache nodes play a crucial role within this framework as they collect data from cluster heads in order to transmit it to the base station. By leveraging artificial intelligence (AI) and simulation techniques, we are able to improve the energy efficiency and reliability of the Internet of Things (IoT) systems. The findings indicate a significant reduction of 83% in non-functioning nodes and a notable increase of 1.66 times in energy levels of nodes compared to conventional approaches. This study highlights a potential direction for energy-efficient, AI-enhanced Internet of Things (IoT) networking through

关键词： Cluster head Clustering Data aggregation Gateway Internet of things LEACH Lifetime

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A hybrid blockchain and AI-based approach for attack protection to secure internet of medical things networks

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Alexandria Engineering Journal 2025年 128卷 223-230页

作者： Alsolai, Hadeel Al-Wesabi, Fahd N. Al-Sharafi, Ali M. Alshuhail, Asma Darem, Abdulbasit A. Alghamdi, Bandar M. Alallah, Fouad Shoie Alzahrani, Abdulrahman Department of Information Systems College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Saudi Arabia Department of Computer Science Applied College at Mahayil King Khalid University Saudi Arabia Department of Computer Science and Artificial Intelligence College of Computing and Information Technology University of Bisha Bisha67714 Saudi Arabia Department of Information Systems College of Computer Sciences & Information Technology King Faisal University Saudi Arabia Center for Scientific Research and Entrepreneurship Northern Border University Arar73213 Saudi Arabia Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia Department of Computer Science and Engineering College of Computer Science and Engineering University of Hafr Al Batin Saudi Arabia

The Internet of Medical Things (IoMT) has revolutionized healthcare by enabling real-time monitoring, remote diagnosis, and seamless data exchange among medical devices and providers. However, the interconnected and distributed nature of IoMT systems exposes them to significant cybersecurity threats, including data breaches and unauthorized access. This study aims to develop a secure and scalable framework to safeguard IoMT environments using blockchain technology, federated learning, and dynamic consensus algorithms. The proposed approach integrates artificial intelligence to detect threats and employs federated learning to maintain patient privacy while training models collaboratively. Experimental evaluation of the framework shows improved data security, reduced response times, and enhanced system trustworthiness. The results suggest that the integration of blockchain and AI-driven mechanisms offers a robust solution to IoMT cybersecurity challenges, paving the way for more reliable and efficient healthcare systems. © 2025 The Authors

关键词： Federated learning

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Systematic Review: Predictive Models for the Winning Team of Super Leagues (SL) 2

Systematic Review: Predictive Models for the Winning Team of...

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2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023

作者： Muneer, Salman Alvi, Muhammad Bux Al Sakhnani, Mahmoud Raza, Hamamd Ghazal, Taher M. Ahmad, Munir NCBA&E School of Computer Science Lahore Pakistan Applied Science Private University Applied Science Research Center Amman11937 Jordan The Islamia University of Bahawalpur Department of Computer Systems Engineering Bahawalpur Pakistan University City Sharjah School of Business Skyline University College Sharjah1797 United Arab Emirates National College of Business Administration & Economics School of Computer Science Lahore Pakistan Center for Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia School of Information Technology Skyline University College University City Sharjah 1797 United Arab Emirates

ISBN: (纸本)9798350335644

The Pakistan Super League (PSL) is one of the most popular cricket leagues in the world, attracting millions of fans and spectators each year. With the 2023 edition of the league just around the corner, the use of predictive models to determine the winning team has gained much attention. This systematic review analyzed the existing literature on predictive models for determining the winning team of PSL 2023. The study examined multiple approaches, including machine learning, statistical modeling, and expert opinion. The results of this review suggest that machine learning models, particularly ensemble models, are the most accurate predictors of PSL match outcomes. These models typically use a combination of features, such as team rankings, player performance statistics, match-winning ratio, and previous match outcomes, to predict the result of a match. This paper discovered that expert opinion-based models could provide valuable insights into the likely winner of a match. These models typically use input from cricket experts and analysts to predict the outcome of a match. © 2023 IEEE.

关键词： Machine learning

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Air Quality Prediction Based on Air Pollution Emissions in the City Environment Using XGBoost with SMOTE 4

Air Quality Prediction Based on Air Pollution Emissions in t...

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4th IEEE International Conference of computer science and information Technology, ICOSNIKOM 2022

作者： Nababan, Adli A Sutarman Zarlis, Muhammad Nababan, Erna B Universitas Sumatera Utara Faculty of Computer Science and Information Technology Department of Computer Science Medan Indonesia Universitas Sumatera Utara Faculty of Mathematics and Natural Sciences Department of Mathematics Medan Indonesia Binus Graduate Program-Master of Information Systems Management Bina Nusantara University Department of Information Systems Management Jakarta Indonesia

ISBN: (纸本)9798350399073

Air pollution makes it worse in populated regions. Indonesia's major cities are also impacted by air pollution. Due to rising traffic, material consumption by vehicles, industrial growth, land burning, and garbage collection, air quality has altered significantly. Accurate measurement and classification of air quality are required. Results of accurate classification aid in forming governmental regulations. To reach the criteria for livable air quality, we intend to manage controls. Some of the characteristics of air pollution include particulate (PM10) or PM25, sulfides (in the form of SO2), carbon monoxide (CO), ozone (O3), and nitrites (NO2). Using the XGBoost algorithm and synthetic minority oversampling (SMOTE) methods based on the Air Pollution Standard Index (ISPU) category, this study seeks to uncover factors that affect air quality. XGBoost, an ensemble machine learning approach built on a decision tree and applying a gradient reinforcement framework, is the classification algorithm in use. Air quality categorization has been tested and shown to work with the XGBoost algorithm. The dataset used in this study is air quality in 2021 which was collected by the Jakarta Environmental Ministry The proposed classification model will be evaluated using the Repeated k-fold cross-validation method for objective results. The results of the study prove that SMOTE and XGBoost have better performance than using only the XGBoost method in predicting air quality, where the overall accuracy value of SMOTE with XGBoost is 99.60%, The overall precision value is 99.60%, the overall recall value is 99.60% and the overall f1-score is 99.60% and the overall ROC AUC value is 99.96%. The overall SMOTE method with the proposed XGBoost has given better performance in air pollution prediction. © 2022 IEEE.

关键词： Air quality

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Development of an Active Contour Method Based on Contrast Features to Detect Keratitis in the Cornea

Development of an Active Contour Method Based on Contrast Fe...

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International Conference on Informatics and Computing (ICIC)

作者： Wahyu Saptha Negoro Sumijan Saiful Bukhori Faculty of Engineering and Computer Science Universitas Potensi Utama Medan Indonesia Faculty of Engineering and Computer Science Universitas of UPI YPTK Padang Indonesia Faculty of Computer Science Information Systems Universitas of Jember Jember Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

Digital Image Processing (DIP) is a scientific discipline with techniques for processing images using computers. The digital image is expressed in a two-dimensional matrix. The research object is keratitis on the cornea. The keratitis image was obtained using a slit lamp photo at Rs. Padang Aye Center (PAC), based on the results ofthe diagnosis by looking at the development of the infiltrate or also called hypopyon, by measuring the ulcer borders horizontally and vertically with the aim of evaluating improvements that respond to treatment. The clinical results obtained cannot determine the extent and circumference of the keratitis layer area that responds to keratitis as a whole. The images used were 206 slit lamp images of keratitis. This research provides knowledge in the form of contrast values in the Active Contour method, resulting in an update called Contras Adjustment (CA)/CAimg, in correctly segmenting keratitis objects and providing measurements of the area and circumference of the keratitis area. The results of this study were from 206 slit lamp images, 195 slit lamp images of keratitis that detected keratitis correctly and 11 slit lamp images of keratitis that could not be detected, resulting in an accuracy of $\mathbf{9 5 \%}$.

关键词： Accuracy Cornea Digital images Picture archiving and communication systems Personnel MATLAB Informatics Standards Active contours Testing

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AI-driven Risk Assessment and Decision-making in Healthcare Applications Using Machine Learning: A Case Study on Customer Twitter Feeds 2

AI-driven Risk Assessment and Decision-making in Healthcare ...

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2nd International Conference on Computational Intelligence and Network systems, CINS 2024

作者： Darwiesh, Abdelaziz Elhoseny, Mohamed El-Baz, A.H. Atassi, Reem Mathematics Dep. Faculty of Science Damietta University New Damietta Egypt Information Systems Dep. Faculty of Computing and Info University of Sharjah Sharjah United Arab Emirates Computer Science Dep. Faculty of Computers and Artificial Intelligence Damietta University New Damietta Egypt Higher Colleges of Technology Dubai United Arab Emirates

ISBN: (纸本)9798331504106

This study presents an AI-driven strategy for enhancing decision-making in healthcare applications by assessing and managing potential risks through the analysis of customer interactions on social media platforms, specifically Twitter. We propose the use of machine learning models to outperform traditional lexical approaches, addressing the complexities inherent in risk detection. Natural language processing methods are employed to efficiently process and analyze textual data, enabling healthcare institutions to make informed decisions about risk management. By leveraging customer feedback from the X platform, our approach aids in the development of effective healthcare programs by identifying and classifying risks. We create three specialized datasets for risk analysis, risk assessment, and hazard classification to optimize the training and deployment phases. In our case study, which focuses on tweets related to healthcare for US consumers, we find that over 50% of the sample identifies potential risks, with nearly 80% indicating medium levels of risk. Commonly identified risks include financial, operational, and strategic threats. The proposed methodology demonstrates an average accuracy of 82.66% in risk detection, identification, and assessment, confirming its effectiveness in supporting AI-driven decision-making in healthcare application development. ©2024 IEEE.

关键词： Tweets

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A Glimpse of chatGPT: An Introduction of Features, Challenges, and Threads in Higher Education

A Glimpse of chatGPT: An Introduction of Features, Challenge...

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International Conference on Informatics, Multimedia, Cyber and information System (ICIMCIS)

作者： Artika Arista Liyana Shuib Maizatul Akmar Ismail Information Systems Faculty of Computer Science Universitas Pembangunan Nasional Veteran Jakarta Jakarta Indonesia Department of Information Systems Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur Malaysia

When used in conjunction with education, ChatGPT is a helpful tool for teaching and understanding fundamental concepts. Students who struggle to find study partners or lack the time to attend tutoring sessions may discover that reviewing and studying with the chatGPT are helpful. Students' access to material and completion of assignments can be accelerated using this technology in research and education. More than one-third of university students who participated in a poll said that they used the ChatGPT for writing assessments. Professors have referred to these technologies as a "threat" and a "plague on education," and ChatGPT has been prohibited from several educational institutions. Thus, this research focuses on the introduction of Features, Challenges, and Threads chatGPT in Higher Education. This study is conducted using the literature review method. According to this research result, this tool can boost student engagement by providing immersive, dynamic, and customized learning environments. However, there are some related issues that need to be considered such as integrity, accuracy and reliability, information bias, and privacy issues. We can therefore infer that there may be benefits as well as drawbacks to employing AI in education that need to be taken into account.

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Backhoe Detection in Surveillance Area from Aerial Imagery 23

Backhoe Detection in Surveillance Area from Aerial Imagery

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Proceedings of the 2023 International Joint Conference on Robotics and Artificial Intelligence

作者： Khantharat Anekboon Nattapat Pratchayatiwat Department of Computer and Information Science Faculty of Applied Science King Mongkut's University of Technology North Bangkok Thailand

ISBN: (纸本)9798400707704

The rapid expansion of urban infrastructure and construction projects require efficient monitoring and security measures in surveillance areas. Traditional methods of ground-level surveillance have limitations such as time-consuming, small exploring areas, and requiring a lot of people or ground-level surveillance devices. In response to this challenge, aerial images are valuable and widely used for various reasons in different fields. They can be used to monitor various areas from the top view. This study provides a method for the detection of backhoes in the surveillance area using aerial images with modified Faster-RCNN. Initially, aerial images were sent to the Resnet-101 structured convolution network to automatically extract features from the input image. The outcome will be the input of two sub-networks, a region proposal network and a Fast-RCNN. This proposed method modified the anchor box sizes in the region proposal network to be suitable to detect very small backhoes from aerial images. Additionally, RoI pooling and RoI align were evaluated and compared a result of a backhoe detection. To evaluate the proposed approach, five iterations of hold-out method with RoI pooling and RoI align technique with different anchor box sizes were experimented. The experimental results demonstrate the efficacy of the proposed system, achieving high precision and recall rates in backhoe detection tasks. This study found that RoI align gives the best backhoe detection result, 86.8% of the mean average precision value while the RoI pooling, the original pooling technique from Faster R-RCNN give mean average precision only 76.7%.

关键词： Aerial Images Backhoe Detection Faster-RCNN RoI Align

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