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9th International Conference on informatics and Computing, ICIC 2024

作者： Ependi, Usman Putra, Ade Ahmad, Nahdatul Akma Widiantoro, Albertus Dwiyoga Arifin, Muhammad Zulkifli, Faiz Universitas Bina Darma Faculty Science Technology Informatics Department Palembang Indonesia Universitas Bina Darma Faculty of Vocational Computerized Accounting Department Palembang Indonesia Universiti Teknologi Mara College of Computing Informatics and Mathematics Perak Malaysia Soegijapranata Catholic University Faculty of Computer Science Information Systems Department Semarang Indonesia Muria Kudus University Faculty of Engineering Information Systems Department Kudus Indonesia

ISBN: (纸本)9798331517601

Imbalanced datasets pose critical challenges in text classification, often leading to biased predictions and reduced model performance. This study compares three undersampling techniques: Random UnderSampler, Near Miss, and Tomek Links, to evaluate their impact on five machine learning classifiers: Logistic Regression, Support Vector Machine, Random Forest, Naive Bayes, and Neural Network. The results demonstrate that the Random Forest classifier achieves the highest performance with precision of 97%, recall of 99%, and F1-score of 98% across all methods, showcasing its robustness against data imbalance. The Near Miss technique improves Neural Network performance to a recall of 83% and F1-score of 71%, while Tomek Links further boosts Neural Network precision to 68%, recall to 72%, and F1-score to 70%. In contrast, simpler models like Naive Bayes exhibit limited improvements, with precision and recall scores around 47-51%. These findings underscore the importance of selecting appropriate undersampling strategies to enhance text classification outcomes and address data imbalance effectively. © 2024 IEEE.

关键词： comparative study machine learning text classification undersampling

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Automated File Labeling for Heterogeneous Files Organization Using Machine Learning

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Computers, Materials & Continua 2023年第2期74卷 3263-3278页

作者： Sagheer Abbas Syed Ali Raza MAKhan Muhammad Adnan Khan Atta-ur-Rahman Kiran Sultan Amir Mosavi School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan Department of Computer Science GC University LahorePakistan Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Department of Software Pattern Recognition and Machine Learning LabGachon UniversitySeongnam13120Korea Department of Computer Science College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal University(IAU)P.O.Box 1982Dammam31441Saudi Arabia Department of CIT The Applied CollegeKing Abdulaziz UniversityJeddah31261Saudi Arabia John von Neumann Faculty of Informatics Obuda UniversityBudapest1034Hungary Institute of Information Engineering Automation and MathematicsSlovak University of Technology in BratislavaBratislava81107Slovakia Faculty of Civil Engineering TU-DresdenDresden01062Germany

File labeling techniques have a long history in analyzing the anthological trends in computational *** situation becomes worse in the case of files downloaded into systems from the ***,most users either have to change file names manually or leave a meaningless name of the files,which increases the time to search required files and results in redundancy and duplications of user ***,no significant work is done on automated file labeling during the organization of heterogeneous user files.A few attempts have been made in topic ***,one major drawback of current topic modeling approaches is better *** rely on specific language types and domain similarity of the *** this research,machine learning approaches have been employed to analyze and extract the information from heterogeneous corpus.A different file labeling technique has also been used to get the meaningful and`cohesive topic of the *** results show that the proposed methodology can generate relevant and context-sensitive names for heterogeneous data files and provide additional insight into automated file labeling in operating systems.

关键词： Automated file labeling file organization machine learning topic modeling

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Trans-Compiler-Based Database Code Conversion Model for Native Platforms and Languages 11th

Trans-Compiler-Based Database Code Conversion Model for Nati...

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11th International Conference on Model and Data engineering, MEDI 2022

作者： Barakat, Rameez Radwan, Moataz-Bellah A. Medhat, Walaa M. Yousef, Ahmed H. School of Information Technology and Computer Science Nile University Giza Egypt Center for Informatics Science Nile University Giza Egypt Department of Computer and Systems Faculty of Engineering Ain Shams University Cairo Egypt Faculty of computers and artificial intelligence Benha University Banha Egypt

ISBN: (纸本)9783031215940

Cross-platform mobile application development frameworks are now widely used among software companies and developers. Despite their time and cost-effectiveness, they still lack the performance and experience of natively developed applications. Many research tools have been proposed to solve this problem by converting a natively developed application from one platform to another. The Trans-Compiler Based Android to iOS Converter (TCAIOSC) was proposed to convert the front-end and back-end code of Android Java applications to iOS applications. Since databases are essential for mobile applications, this paper proposes a new database code conversion model based on trans-compilation and pattern matching. It proposes a model that can be used to support database code conversion between native languages and platforms and applies the proposed model to support the conversion of Firebase Firestore database code from Android (Java) to iOS (Swift) using TCAIOSC. The enhanced tool’s results show high accuracy for the converted database code and a noticeable improvement in the overall conversion results for TCAIOSC. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Pattern matching

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MobileDNN: Leveraging the Potential of Hybrid MobileNetV2-DNN for Advanced Image Analysis

MobileDNN: Leveraging the Potential of Hybrid MobileNetV2-DN...

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science, engineering and Business for Sustainable Development Goals (SEB-SDG), International Conference on

作者： Roseline Oluwaseun Ogundokun Rytis Maskeliūnas Robertas Damaševičius Marion Olubunmi Adebiyi Department of Computer Science Landmark University Omu Aran Nigeria Department of Multimedia Engineering Kaunas University of Technology Lithuania Faculty of Informatics Kaunas University of Technology Kaunas Lithuania

ISBN: (数字)9798350358155

ISBN: (纸本)9798350358162

Computer vision systems designed for posture recognition could offer valuable solutions in the healthcare sector, improving healthy ageing and supporting elderly individuals in their daily routines. One area of interest is the automatic detection of falls, which has garnered attention from computer vision and pattern recognition communities. While many existing methods rely on wearable sensors, which demonstrate high accuracy in fall detection, some individuals may be reluctant to wear such devices. Consequently, alternative approaches, such as vision-based techniques, have been developed. The proposed method utilizes a vision-based system that employs a convolutional neural network (CNN) to recognize and categorize various postures (e.g., sitting, lying, standing) in each frame, if available. This approach begins by detecting the person in the frame, applying background subtraction for human body shape extraction, and associating each daily activity with the extracted shape. The proposed model achieved an accuracy of 93.75%, which shows an outstanding performance over existing models. Subsequently, it estimates bounding boxes encompassing the predicted person's body shape. Overall, the proposed technique shows promising results for indoor monitoring systems.

关键词： Computer vision Accuracy Shape Computer architecture Feature extraction Convolutional neural networks Sustainable development

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Relip: Reliable in-Band Parallel Communication for Magnetic MIMO Wireless Power Transfer System

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IEEE Transactions on Mobile Computing 2025年

作者： Wang, Xinyu Zhou, Wangqiu Zhou, Hao Jiang, Shenyao Liu, Zhi Wang, Xiaoyan Ji, Yusheng Song, Qi City University of Hong Kong Department of Computer Science Hong Kong University of Science and Technology of China School of Computer Science and Technology Hefei230027 China The University of Electro-Communications Graduate School of Informatics and Engineering Tokyo182-8585 Japan Ibaraki University Graduate School of Science and Engineering Ibaraki310-8512 Japan National Institute of Informatics Information Systems Architecture Science Research Division Tokyo101-0003 Japan

In magnetic resonant coupling (MRC) based wireless power transfer (WPT) systems, receiver (RX) feedback communication is promising to enhance the capability and efficiency of the system. Although some studies have explored in-band implementations with low overhead costs, it has not been comprehensively investigated. In this paper, we propose Relip, a Reliable layer-level in-band parallel feedback communication mechanism for MIMO MRC-WPT systems, which addresses the impact of RX-RX couplings (i.e., non-negligible interference from strong couplings and positive effects of relay phenomenon), and provides a theoretical analysis of communication reliability. Technically, we first devise an On-Off based two-phase modulation mechanism to achieve RX identification and dependency detection under relay phenomenon. Then, we utilize observed channel decomposability to collect group-level power transfer channel conditions for eliminating the interference caused by strong RX-RX couplings. Furthermore, we perform RX selection to optimize the trade-off between communication reliability and time overhead. We design and implement the Relip prototype and conduct extensive experiments. The results validate the effectiveness of our mechanism, i.e., Relip can provide ≥99% average decoding accuracy for concurrent feedback communication of 14 devices, achieving an 18.31% improvement compared to the state-of-the-art solution. © 2002-2012 IEEE.

关键词： Inductive power transmission

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Generic Multi-agent Cooperative Control via Finite-time Distributed MPC 19

Generic Multi-agent Cooperative Control via Finite-time Dist...

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19th IEEE International Conference on Networking, Sensing and Control, ICNSC 2022

作者： Bao, HanQiu Shi, XuDong An, Jing Li, HaoJun Kang, Qi Tongji University Department of Control Science and Engineering China Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai China School of Electrical and Electronic Engineering Shanghai Institute of Technology Shanghai China College of Surveying and Geo-informatics Tongji University Shanghai China

ISBN: (纸本)9781665472432

This paper introduces a generic multi-agent cooperative control framework using finite-time distributed model predictive control, which applies to multiple types and heterogeneous multi-agent systems with directed topology. We design a detect and avoid collisions strategy using the prediction trajectory of agents, and the agents can safely move toward their goals. The formation time upper bound of multi-agents achieves consensus are derived with given connected communication topology. Numerical simulations validated the feasibility and effectiveness of the proposed approach. © 2022 IEEE.

关键词： Multi agent systems

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Early Detection and Classification of Melanoma Based on Android MobileNet V2 Convolutional Neural Network

Early Detection and Classification of Melanoma Based on Andr...

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2023 International Seminar on Application for technology of Information and Communication, iSemantic 2023

作者： Prabandari, Jasmine Iswarini Dyah Sari, Christy Atika Rachmawanto, Eko Hari Irawan, Candra Altamer, Bilal R. Alkhafaji, Mohamed Ayad University of Dian Nuswantoro Study Program in Informatics Engineering Semarang Indonesia University of Dian Nuswantoro Study Program in Information Systems Semarang Indonesia Mosul University Presidency University of Mosul Mosul Iraq National University of Science and Technology Department of Computer Science DhiQar Iraq

ISBN: (纸本)9798350339215

Melanoma is a malignant form of cancer that affects the skin and has a particularly high mortality rate, so it requires early detection to increase the level of safety for users. Diagnosis and detection of skin cancer are usually done through manual screening and visual inspection. This process requires a long time, has high complexity, is subjective, and is prone to errors. CNN is one of the algorithms with advantages in accurate classification. In this research, early detection and classification of melanoma cancer were carried out based on two classes, namely benign and malignant using the Convolutional Neural Network method. Our proposed method yields an accuracy of 81.11% for the validation data. The accuracy results obtained can be improved by using more datasets and increasing the number of layers used. This study uses the CNN method using MobileNet V2 architecture to detect melanoma skin cancer. The class used is benign and malignant. © 2023 IEEE.

关键词： Convolutional neural networks

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Deciphering the toxic effects of polystyrene nanoparticles on erythropoiesis at single-cell resolution

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Zoological Research 2025年第1期46卷 165-176页

作者： Eun Jung Kwon Hyeon Mi Sung Hansong Lee Soyul Ahn Yejin Kim Chae Rin Lee Kihun Kim Kyungjae Myung Won Kyu Kim Dokyoung Kim Sanghwa Jeong Chang-Kyu Oh Yun Hak Kim Medical Research Institute Pusan National UniversityYangsanGyeongsangnam-do 50612Republic of Korea Institute for Future Earth Pusan National UniversityBusan 46241Republic of Korea Department of Convergence Medical Sciences School of MedicinePusan National UniversityYangsanGyeongsangnam-do 50612Republic of Korea Department of Biomedical Informatics School of MedicinePusan National UniversityYangsanGyeongsangnam-do 50612Republic of Korea Department of Anatomy School of MedicinePusan National UniversityYangsanGyeongsangnam-do 50612Republic of Korea Center for Genomic Integrity Institute for Basic ScienceUlsan 689-798Republic of Korea Department of Biomedical Engineering College of Information-Bio Convergence EngineeringUlsan National Institute of Science and TechnologyUlsan 44919Republic of Korea Natural Product Research Center Korea Institute of Science and Technology(KIST)GangneungGangwon-do 25451Republic of Korea Department of Convergence Medicine Yonsei University Wonju College of MedicineWonjuGangwon-do 26426Republic of Korea Natural Product Applied Science KIST SchoolUniversity of Science and Technology(UST)GangneungGangwon-do 25451Republic of Korea Department of Biomedical Science Graduate SchoolKyung Hee UniversitySeoul 02447Republic of Korea Department of Anatomy and Neurobiology College of MedicineKyung Hee UniversitySeoul 02447Republic of Korea Department of Precision Medicine Graduate SchoolKyung Hee UniversitySeoul 02447Republic of Korea Center for Converging Humanities Kyung Hee UniversitySeoul 02447Republic of Korea KHU-KIST Department of Converging Science and Technology Kyung Hee UniversitySeoul 02447Republic of Korea UC San Diego Materials Research Science and Engineering Center 9500 Gilman DriveLa JollaCalifornia 92093USA Medical Research Center for Bioreaction to Reactive Oxygen Species and Biomedical Science Institute Core Research Institute(CRI)Kyung Hee UniversitySeoul 02447Republic of Korea Center for Brain Technology Brain Science Institute

Polystyrene nanoparticles pose significant toxicological risks to aquatic ecosystems,yet their impact on zebrafish(Danio rerio)embryonic development,particularly erythropoiesis,remains *** study used single-cell RNA sequencing to comprehensively evaluate the effects of polystyrene nanoparticle exposure on erythropoiesis in zebrafish *** vivo validation experiments corroborated the transcriptomic findings,revealing that polystyrene nanoparticle exposure disrupted erythrocyte differentiation,as evidenced by the decrease in mature erythrocytes and concomitant increase in immature ***,impaired heme synthesis further contributed to the diminished erythrocyte *** findings underscore the toxic effects of polystyrene nanoparticles on hematopoietic processes,highlighting their potential to compromise organismal health in aquatic environments.

关键词： Polystyrene nanoparticles Zebrafish embryos Single-cell RNA sequencing Erythropoiesis

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Metaheuristic Optimization Through Deep Learning Classification of COVID-19 in Chest X-Ray Images

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Computers, Materials & Continua 2022年第11期73卷 4193-4210页

作者： Nagwan Abdel Samee El-Sayed M.El-Kenawy Ghada Atteia Mona M.Jamjoom Abdelhameed Ibrahim Abdelaziz A.Abdelhamid Noha E.El-Attar Tarek Gaber Adam Slowik Mahmoud Y.Shams Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh 11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura 35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura 35712Egypt Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh 11671Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura University35516MansouraEgypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams University11566CairoEgypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Faculty of Computers and Artificial Intelligence Benha UniversityEgypt School of Science Engineeringand EnvironmentUniversity of SalfordUK Faculty of Computers and Informatics Suez Canal UniversityIsmailia41522Egypt Koszalin University of Technology Poland Faculty of Artificial Intelligence Kafrelsheikh UniversityKafrelsheikh33511Egypt

As corona virus disease(COVID-19)is still an ongoing global outbreak,countries around the world continue to take precautions and measures to control the spread of the *** of the excessive number of infected patients and the resulting deficiency of testing kits in hospitals,a rapid,reliable,and automatic detection of COVID-19 is in extreme need to curb the number of *** analyzing the COVID-19 chest X-ray images,a novel metaheuristic approach is proposed based on hybrid dipper throated and particle swarm *** lung region was segmented from the original chest X-ray images and augmented using various transformation ***,the augmented images were fed into the VGG19 deep network for feature *** the other hand,a feature selection method is proposed to select the most significant features that can boost the classification ***,the selected features were input into an optimized neural network for *** neural network is optimized using the proposed hybrid *** experimental results showed that the proposed method achieved 99.88%accuracy,outperforming the existing COVID-19 detection *** addition,a deep statistical analysis is performed to study the performance and stability of the proposed *** results confirm the effectiveness and superiority of the proposed approach.

关键词： Covid-19 feature selection dipper throated optimization particle swarm optimization deep learning

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Indoor Channel Characterization With Extremely Large Reconfigurable Intelligent Surfaces at 300 GHz 19

Indoor Channel Characterization With Extremely Large Reconfi...

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19th European Conference on Antennas and Propagation, EuCAP 2025

作者： Cardoso, Fabio Matos, Sérgio Pessoa, Luís M. Alexandropoulos, George C. University Institute of Lisbon Department of Information Science and Technology Portugal Instituto de Telecomunicações Portugal Inesc Tec Portugal University of Porto Faculty of Engineering Portugal National and Kapodistrian University of Athens Department of Informatics and Telecommunications Greece University of Illinois Chicago Department of Electrical and Computer Engineering IL United States

ISBN: (纸本)9788831299107

The technology of Reconfigurable Intelligent Surfaces (RISs) is lately being considered as a boosting component for various indoor wireless applications, enabling wave propagation control and coverage extension. However, the incorporation of extremely large RISs, as recently being considered for ultrahigh capacity industrial environments at subTHz frequencies, imposes certain challenges for indoor channel characterization. In particular, such RISs contribute additional multipath components and their large sizes with respect to the signal wavelength lead to near-field propagation. To this end, ray tracing approaches become quite cumbersome and need to be rerun for different RIS unit cell designs. In this paper, we present a novel approach for the incorporation of RISs in indoor multipath environments towards their efficient channel characterization. An 100 × 100 RIS design with 2-bit resolution unit cells realizing a fixed anomalous reflection at 300 GHz is presented, whose radar cross section patterns are obtained via full-wave simulations. It is showcased that the RIS behavior can be conveniently approximated by a three-ray model, which can be efficiently incorporated within available ray tracing tools, and that the far-field approximation is valid for even very small distances from the RIS. © 2025 European Association on Antennas and Propagation.

关键词： channel characterization indoor near field Reconfigurable intelligent surface reflectarray THz

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