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11th International Conference on ICT for Smart Society, ICISS 2024

作者： Hermanus, Davy Ronald Sasmoko Indrianti, Yasinta Research Interest Group in Educational Technology Bina Nusantara University Computer Science Department Indonesia School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia Research Interest Group in Educational Technology Bina Nusantara University Faculty of Humanities Primary Teacher Education Department Jakarta Indonesia Universitas Agung Podomoro Entrepreneurship Department Jakarta Indonesia

ISBN: (纸本)9798350389654

This paper examines the incorporation of DNA testing into intelligent education systems to develop customized learning plans that respond to the specific needs of each student. Through the examination of distinct genetic indicators associated with personality traits, talent, and inborn abilities, educators can formulate approaches that optimize academic achievement and boost the overall welfare of students. This approach not only enhances learning outcomes but also promotes a supportive and inclusive educational environment. The study critically examines the present state of literature regarding the correlation between genetics and learning. It digs into the ethical implications surrounding this topic and shows case studies that provide evidence of the effectiveness of teaching methods informed by DNA analysis. The results underscore the capacity of DNA testing to transform education by enhancing its adaptability and responsiveness to the distinct genetic composition of individual students. This underscores the need for additional research and ethical guidelines to guarantee responsible integration in educational environments. © 2024 IEEE.

关键词： Federated learning

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Multi-talker Verbal Interaction for Humanoid Robots

Multi-talker Verbal Interaction for Humanoid Robots

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International Conference on Methods and Models in Automation and Robotics (MMAR)

作者： Bartlomiej Klin Ryszard Beniak Michal Podpora Arkadiusz Gardecki Joanna Rut Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Department of Computer Science Opole University of Technology Faculty of Production Engineering and Logistics Opole University of Technology

ISBN: (数字)9798350362343

ISBN: (纸本)9798350362350

Working in multi-talker mode is viable under certain conditions, such as the fusion of audio and video stimuli along with smart adaptive beamforming of received audio signals. In this article, the authors verify part of the researched novel framework, which focuses on adapting to dynamic interlocutor’s location changes in the engagement zone of humanoid robots during the multi-talker conversation. After evaluating the framework, the authors confirm the necessity of a complementary and independent method of increasing the interlocutor’s signal isolation accuracy. It is necessary when video analysis performance plummets. The authors described the leading cause as insufficient performance during dynamic conversations. The video analysis cannot derive a new configuration when the interlocutor’s speech apparatus moves beyond the expected margin and the video frame rate drops.

关键词： Automation Accuracy Array signal processing Humanoid robots Oral communication Human factors

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A Novel Inherited Modeling Structure of Automatic Brain Tumor Segmentation from MRI

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Computers, Materials & Continua 2022年第11期73卷 3983-4002页

作者： Abdullah AAsiri Tariq Ali Ahmad Shaf Muhammad Aamir Muhammad Shoaib Muhammad Irfan Hassan A.Alshamrani Fawaz F.Alqahtani Osama M.Alshehri Radiological Sciences Department College of Applied Medical SciencesNajran UniversityNajran61441Saudi Arabia Department of Computer Science COMSATS University IslamabadSahiwal CampusSahiwal57000Pakistan Department of Informatics and Systems University of Management and TechnologyLahore54000Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Department of Clinical Laboratory Sciences Collage of Applied Medical ScienceNajran UniversityNajranSaudi Arabia

Brain tumor is one of the most dreadful worldwide types of cancer and affects people leading to *** resonance imaging methods capture skull images that contain healthy and affected *** checked the affected tissue in the slice-by-slice manner,which was timeconsuming and hectic ***,auto segmentation of the affected part is needed to facilitate ***,we have considered a hybrid model that inherits the convolutional neural network(CNN)properties to the support vector machine(SVM)for the auto-segmented brain tumor *** CNN model is initially used to detect brain tumors,while SVM is integrated to segment the tumor region *** proposed method was evaluated on a publicly available BraTS2020 *** statistical parameters used in this work for the mathematical measures are precision,accuracy,specificity,sensitivity,and dice ***,our method achieved an accuracy value of 0.98,which is most prominent than existing ***,the proposed approach is more suitable for medical experts to diagnose the early stages of the brain tumor.

关键词： Brain tumor support vector machine convolutional neural network BraTS classification

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Comparison of Machine Learning Methods for Air Quality Prediction Based on Particulate Matter (PM2.5)

Comparison of Machine Learning Methods for Air Quality Predi...

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Advancement in Data science, E-learning and Information Systems (ICADEIS), International Conference on

作者： Farhanna Mar'i Susi Marianingsih Rifqi Abdillah Ersha Aisyah Elfaiz Riza Akhsani Setyo Prayoga Muhamad Fahmi Department of Informatics Engineering Faculty of Engineering Universitas Negeri Surabaya Surabaya Indonesia Department of Information System Jayapura University of Science and Technology Jayapura Indonesia Department of Information Technology Education Faculty of Engineering Universitas Negeri Surabaya Surabaya Indonesia Department of Biomedical Engineering Faculty of Science and Technology Universitas Airlangga Surabaya Indonesia

ISBN: (数字)9798331513320

ISBN: (纸本)9798331513337

Particulate PM 2.5 has a major effect on human health compared to other types of pollutants such as NO 2 , 03, CO, and SO 2 . PM 2.5 is the worst type of major pollutant for human health that can cause various types of serious diseases and even death. PM 2.5 is defined as an ambient air particle, which is one of the particle elements found in the free air of the earth's surface in the troposphere layer, which is very small in size, reaching <2.5 microns which contains causing substances include polycyclic aromatic hydrocarbons, lipopolysaccharides, and transition metals (copper, manganese, and zinc). Prediction of air quality index values in national cities can help the government make policies to carry out air pollution prevention and mitigation measures by looking at local air quality trends in advance, especially when there is a significant increase in the amount of PM 2.5 pollutants. The method of predicting air quality index values can be done using machine learning techniques with time series data analysis. This study will compare machine learning methods with two approaches, namely statistical regression with RF and ARIMA and deep learning method, namely LSTM. Based on the research results, the best machine learning method approach is statistical regression, namely the RF method with accuracy evaluation using RMSE, MAE, and MSE, which are respectively with a minimum value of 0.162, 0.108, and 0.026, followed by the LSTM and ARIMA methods.

关键词： Radio frequency Deep learning Prevention and mitigation Time series analysis Urban areas Air quality Indexes Long short term memory Zinc Testing

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Random Load Balancing in Mini-NDN Using Modified Inherent Topology 9

Random Load Balancing in Mini-NDN Using Modified Inherent To...

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9th International Conference on Wireless and Telematics, ICWT 2023

作者： Pratama, Cendika Sahid Arrachman, Muhammad Iqbal Nurrasyid, Muhammad Shafly Zaki Hamidi, Eki Ahmad Negara, Ridha Muldina Ahdan, Syaiful Mayasari, Ratna Syambas, Nana Rachmana School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia Uin Sunan Gunung Djati Bandung Electrical Engineering Department Bandung Indonesia School of Electrical Engineering Telkom University Bandung Indonesia Universitas Teknokrat Indonesia Faculty of Engineering & Computer Science Lampung35132 Indonesia

ISBN: (纸本)9798350305029

Named Data Networking (NDN) is a newly developing networking method that focuses on information, compared to TCP/IP, which focuses on hosts. This study proposed a solution to the growing complexity of computer networks. The fundamental problem will still be able to be found, one of them being difficulty in accessing a node as data traffic is piling up. One of the solutions is load balancing, which is implementing a system that will redirect packets to different service providers to ease up the load and reduce the number of packets that are dropped due to timeouts. This paper analyses the impact of load balancing in an extreme case emulated environment using Mini-NDN and random load balancing strategy. From the results gathered, implementing random load balancing to the consumer nodes decreases-and in some cases eliminates-dropped packets. In addition, implementing load balancing stabilizes packets' RTT (Round Trip Time) but increases the average RTT compared to using best route forwarding in an unloaded environment. © 2023 IEEE.

关键词： Topology

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Joint Radar-Communication Systems by Optimizing Radar Performance and Quality of Service for Communication Users

IEEE Transactions on Radar Systems

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IEEE Transactions on Radar Systems 2024年 2卷 778-790页

作者： Tsinos, Christos G. Arora, Aakash Tsiftsis, Theodoros A. National and Kapodistrian University of Athens Department of Digital Industry Technologies Evripus Campus Euboea 34400 Greece University of Twente Radio Systems Group Faculty of Electrical Engineering Mathematics and Computer Science AE Enschede7500 Netherlands Indian Institute of Technology Delhi Centre for Applied Research in Electronics New Delhi110016 India University of Thessaly Department of Informatics and Telecommunications Lamia35100 Greece Jinan University Zhuhai Campus School of Intelligent Systems Science and Engineering Zhuhai519070 China

In this article, the problem of linear precoding and radar receive beamforming design for joint radar-communication (JRC) systems is studied. A multiple antenna base station (BS) that serves multiple single-antenna user terminals on the downlink is assumed. Furthermore, the BS employs a simultaneous radar function in the form of point-like target detection from the reflected return signals in a signal-dependent interference environment. In this work, we jointly design the JRC linear precoder and the radar receive beamformer, thus aiming to optimize the performance of the radar part while maintaining a desired quality of service (QoS) for the communication one subject to a total transmit power constraint. To that end, we formulate a challenging fractional nonconvex optimization problem via which the optimal precoder and radar receive beamformer are derived. Then, we develop algorithmic solutions based on the majorization-minimization (MM) principle and the semidefinite relaxation (SDR) methodology for the formulated optimization problem. The performance of both the proposed solutions is examined and compared to the one of a system that supports only the radar functionality via numerical results. © 2023 IEEE.

关键词： Signal to noise ratio

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The 4D Nucleome Data Portal as a resource for searching and visualizing curated nucleomics data (vol 13, 2365, 2022)

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NATURE COMMUNICATIONS 2022年第1期13卷 1-11页

作者： Reiff, Sarah B. Schroeder, Andrew J. Kirli, Koray Cosolo, Andrea Bakker, Clara Mercado, Luisa Lee, Soohyun Veit, Alexander D. Balashov, Alexander K. Vitzthum, Carl Ronchetti, William Pitman, Kent M. Johnson, Jeremy Ehmsen, Shannon R. Kerpedjiev, Peter Abdennur, Nezar Imakaev, Maxim Ozturk, Serkan Utku Camoglu, Ugur Mirny, Leonid A. Gehlenborg, Nils Alver, Burak H. Park, Peter J. Department of Biomedical Informatics Harvard Medical School Boston MA 02115 USA Institute for Medical Engineering and Science Massachusetts Institute of Technology Cambridge MA 02139 USA Karya SMD Software Solutions İzmir 35040 Turkey Department of Physics Massachusetts Institute of Technology Cambridge MA 02139 USA

The 4D Nucleome (4DN) Network aims to elucidate the complex structure and organization of chromosomes in the nucleus and the impact of their disruption in disease biology. We present the 4DN Data Portal (https://***/), a repository for datasets generated in the 4DN network and relevant external datasets. Datasets were generated with a wide range of experiments, including chromosome conformation capture assays such as Hi-C and other innovative sequencing and microscopy-based assays probing chromosome architecture. All together, the 4DN data portal hosts more than 1800 experiment sets and 36000 files. Results of sequencing-based assays from different laboratories are uniformly processed and quality-controlled. The portal interface allows easy browsing, filtering, and bulk downloads, and the integrated HiGlass genome browser allows interactive visualization and comparison of multiple datasets. The 4DN data portal represents a primary resource for chromosome contact and other nuclear architecture data for the scientific community.

关键词： Computational platforms and environments Genetic databases Genomics

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Analytical Model for Underwater Wireless Sensor Network Energy Consumption Reduction

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Computers, Materials & Continua 2022年第7期72卷 1611-1626页

作者： Huma Hasan Rizvi Sadiq Ali Khan Rabia Noor Enam Kashif Nisar Muhammad Reazul Haque Computer Science Department UBIT Karachi UniversityKarachi75270Pakistan Sir Syed University of Engineering and Technology Karachi75300Pakistan Faculty of Computing and Informatics University Malaysia SabahJalan UMS88400Kota KinabaluSabahMalaysia Faculty of Computing&Informatics Multimedia UniversityPersiaran Multimedia63100CyberjayaSelangorMalaysia

In an Underwater Wireless Sensor Network(UWSN),extreme energy loss is carried out by the early expiration of sensor nodes and causes a reduction in efficiency in the submerged acoustic sensor *** based on clustering strategies,instead of each node sending information by itself,utilize cluster heads to collect information inside the clusters for forwarding collective information to *** can effectively minimize the total energy loss during *** environment of UWSN is 3D architecture-based and follows a complex hierarchical clustering strategy involving its most effecting unique parameters such as propagation delay and limited transmission *** base clustering strategy works in rounds,where each round comprises three fundamental stages:cluster head selection,grouping or node association,and data aggregation followed by forwarding data to the *** UWSN,the energy consumed during the formation of clusters has been considered casually or completely evaded in the previous *** this paper,the cluster head setup period has been considered the main contributor to extra energy utilizer.A numerical channel model is proposed to compute extra *** is performed by using a UWSN broad *** results have shown that extra maximum energy consumption is approximately 12.9 percent of the system total energy consumed in information transmissions.

关键词： Analytical model submerged acoustic sensor systems energy loss round base clustering UWSN extra energy

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A Review on Leveraging Language Agnostic Automated Text Data Augmentation with Explainable Semantic Integrity

A Review on Leveraging Language Agnostic Automated Text Data...

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Advanced Research in Computing (ICARC), International Conference on

作者： Sarith Wijesundera Suresh Michael Peiris Department of Computer Science and Engineering University of Westminster London UK School of Computing Informatics Institute of Technology Colombo Sri Lanka

ISBN: (数字)9798331530983

ISBN: (纸本)9798331530990

Data Augmentation (DA) is an effective strategy to increase model generalisation. In Natural Language Processing (NLP), DA remains in its early stages, primarily due to the inherent sensitivity of textual data, which complicates the augmentation process. These complexities are further increased in multilingual contexts, as DA techniques effective in one language may not yield comparable results in others. As a result, manually identifying optimal DA techniques that preserve semantic integrity and are language agnostic remains a significant challenge. It emphasises the necessity for automating the text DA process with Explainable Artificial Intelligence (XAI), which offers a promising approach by calculating word importance scores to effectively guide the augmentation. In this paper, a comprehensive review of existing approaches in auto-text DA is presented, beginning with an exploration of the concept of auto-text DA, followed by a discussion on its cross-lingual applications, and concluding with the integration of XAI for semantic preservation. The paper highlights the critical need for further research to enhance the effectiveness and applicability of XAI in auto-text DA, enabling its use across diverse languages.

关键词： Sensitivity Explainable AI Reviews Semantics Transfer learning Data augmentation Search problems Natural language processing Multilingual Optimization

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An Early Warning System to Predict Earthquakes based on WT-ARIMA-ANN Model 3

An Early Warning System to Predict Earthquakes based on WT-A...

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3rd International Conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Babu, Uppara Raghu Raha, Shrinwantu Khan, Mujtaba Ali Yamini, C. Jayadurga, R. Kalra, Gourav Srinivasa Ramanujan Institute of Technology Department of Civil Engineering Andhra Pradesh India Bhairab Ganguly College Department of Geography Belgharia Kolkata India University of North Carolina Department of Computer Science College of Computing and Informatics Charlotte United States PSNA College of Engineering and Technology Department of Mathematics Tamil Nadu Dindigul India Prince Shri Venkateshwara Padmavathy Engineering College Department of MBA Tamil Nadu Chennai India Department of Mechanical Engineering Mullana India

ISBN: (纸本)9798350343632

A devastating earthquake is one of the Earth's most destructive natural disasters. Real-time seismic monitoring facilities can evolve into Earthquake Early Warning (EEW) systems with the help of modern technology, property damage, reducing the number of casualties, and economic losses. The proposed EEW system's goal is to supply a precise damage assessment just before an earthquake hits a certain area. The proposed method involves preprocessing, feature extraction, and model training. It is recommended to preprocess the data by first filtering it and then reformatting it. Filtering is essential for getting rid of irrelevant or noisy information. In feature extraction, the Principal Component Analysis (PCA) provides a useful perspective on the data by establishing a new coordinate system, and it also offers a method for reducing the data's dimensionality. Once the features have been obtained, the models are trained with WT-ARIMA-ANN. The proposed method outperforms two widely used alternatives, ARIMA and ANN. © 2023 IEEE.

关键词： Autoregressive Integrated Moving Average (ARIMA) Earthquake Early Warning (EEW) Principal Component Analysis (PCA)

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