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4th International Conference on Computing and Applied Informatics 2019, ICCAI 2019

作者： Siahaan, W.F.A. Sitompul, O.S. Situmorang, Z. Graduate Program of Computer Science Universitas Sumatera Utara Medan Indonesia Department of Information Technology Universitas Sumatera Utara Medan Indonesia Department of Computer Science Universitas Katolik Santo Thomas Medan Indonesia

Classification is an important technique in data mining to create patterns and data modeling. Hepatitis is a disease that is dangerous to humans as this disease affects the human liver which is a vital organ. Early detection of hepatitis by diagnosing its occurrence on patients is needed to give them help, care, and treatment. Therefore, a classification technique is required to create patterns and models of data. To achieve accuracy in classifying hepatitis there would be requirements to separate relevant from irrelevant features. By only considering relevance features in classifying hepatitis, it is expected that the classification process will be faster, easier, and more accurate. In this research, the ANFIS algorithm and genetic algorithm are used in classifying the diagnosis of hepatitis based on a dataset from the UCI data mining repository. Results obtained showed that the genetic algorithm gave a slightly higher accuracy of 98.73% compared to 86.67% results obtained using ANFIS. © Published under licence by IOP Publishing Ltd.

关键词： Genetic algorithms

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Front-end circuit design for electroencephalography (EEG) signal 2

Front-end circuit design for electroencephalography (EEG) si...

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Indo - Taiwan 2nd International Conference on Computing, Analytics and Networks, Indo-Taiwan ICAN 2020

作者： Salahuddin Morsalin, S.M. Lai, Shin-Chi Master's Program of Green Technology for Sustainability Nanhua University Chiayi County Taiwan Nanhua University Department of Computer Science and Information Engineering Chiayi County Taiwan

ISBN: (纸本)9781728149998

Electroencephalography (EEG) signals are recorded for knowing the electrical activity of the brain from the scalp, and the recorded waveform provides acquits into the dynamic aspects of brain activity. This study incorporates the circuit design and device development to achieve the Electroencephalography signal acquisition front-end circuit design for future Brain-computer Interface (BCI) applications. The amplitude of acquired signals should be strong enough and is usually expressed in the unit of millivolts. The data acquisition procedure consists three stages: 1) The acquisition of original EEG signal can be done by the active electrode and an instrumentation amplifier with a smaller gain;2) Improves the signal quality by using band-pass filter and band-stop filter;3) Those EEG signals convert into the digital code through the analog-to-digital converter (ADC) that will be integrated into a micro-controller. The digital code is stored into an embedded memory and is further transmitted via the Bluetooth module. The experimental results show that the system could implement the acquisition and storage of the EEG signals efficiently. The size of the printed circuit board (PCB) for the proposed design is smaller than 5.5 cm2. This system would be a benefit to all involved in the use of EEG for clinical diagnosis and monitoring, or even for the braincomputer interface. © 2020 IEEE.

关键词： Electrodes

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Improved Transform Algorithm of Direct Computation of Discrete Cosine for Mel-scale Frequency Cepstral Coefficient

Improved Transform Algorithm of Direct Computation of Discre...

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2021 IEEE International Conference on Electronic Communications, Internet of Things and Big Data, ICEIB 2021

作者： Lai, Shin-Chi Hung, Ying-Hsiu Zhu, Yi-Chang Wang, Szu-Ting Sheu, Ming-Hwa Juang, Wen-Ho National Formosa University Dept. of Automation Engineering Yunlin Taiwan National Yunlin University of Science and Technology Dept. of Electronic Engineering Yunlin Taiwan National Formosa University Doctor's Program of Smart Industry Technology Research and Design Yunlin Taiwan Nanhua University Dept. of Computer Science and Information Engineering Chiayi County Taiwan

ISBN: (纸本)9781665437554

In this work, we proposed a direct computation of the DCT algorithm that is improved by utilizing the coefficients of cosine and sine to calculate the DCT bins for Mel-scale frequency cepstral coefficients (MFCC). Instead of computing directly, the cosine coefficient is sharply declined more than 85% at the expense of the amount of additions and multiplications. According to the PSNR comparison results, and the proposed method is similar to Jo et al.'s algorithm. which uses approximate computation, so that the PSNR value is closed to 52 dB. Although the proposed algorithm must pay for some accuracy and extra complexity in computation, it still can greatly reduce more sizes of ROM. Based on the point of interest of simple control and straightforward structure, the algorithm is a selectable design for applications to the design of MFCC. © 2021 IEEE.

关键词： Discrete cosine transforms

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A Genetic Algorithm to Optimize Cargo Stability in Container Transportation

A Genetic Algorithm to Optimize Cargo Stability in Container...

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IEEE Global Conference on Consumer Electronics (GCCE)

作者： Chi-Jung Hsieh Kuang-Sung Wu Tin-Yu Wu Fu Jie Tey Yi-Kai Chen Jye Yi Ho Department of Computer Science and Information Engineering National Ilan University Ilan County Taiwan Master Program of E-Learning for Multimedia and Network Communications National Ilan University Yilan Taiwan Department of Management Information Systems National Pingtung University of Science and Technology Pingtung County Taiwan Department of Electrical Engineering National Taiwan University of Science and Technology Taipei Taiwan

ISBN: (纸本)9781665492331

In the era of the global village, frequent cross-border trade in goods has made container transportation a significant part in delivery of cargo. However, rollover accidents of container trucks often occur because of tall truck height, improper cargo stowage or crooked roads. Therefore, how to find the optimal center of gravity (CG) of the packed cargo remains an issue that the transportation industry should not neglect. To optimize cargo stability and reduce the incidence of rollovers, this paper proposes a genetic algorithm that scores the cargo stacking process to find the best CG of the cargo based on the CG of the vehicle. According to the deviation angle and the point closest to the best CG, we can Figure out the optimal cargo stacking method.

关键词： Rollover Transportation industry Roads Stacking Transportation Containers Safety

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Transformative Sport Science Using Social Media as a Collaboration Tool 7

Transformative Sport Science Using Social Media as a Collabo...

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7th International Conference on ICT for Smart Society, ICISS 2020

作者： Oktavia, Tanty Gaol, Ford Lumban Hosoda, Takaaki Syahir, Arsyan Bina Nusantara University School of Information Systems Information Systems Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program - Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia Advanced Institute of Industrial Technology Decision Science Department Tokyo Japan Bina Nusantara University BINUS Graduate Program - Master of Information Systems Management Information Systems Management Department Jakarta11480 Indonesia

ISBN: (纸本)9781665404228

Sports science can have a significant and positive impact on coaches and athletes, as well as for the professional sports organization. According to this situation, more and more organizations are competing to implement sports science as the monitoring systems. Along with the development of technology, it becomes easier for humans to carry out their activities, including communication with each other. Advances in the field of communication technologies have led to the use of social networks to support all activity. With social media, all kinds of information that people want to search for can be found easily. Social networks have their uniqueness in their characteristics. Like LINE has free calling and video calling features, while Instagram has features like Instagram Story, where users can document their activities in the form of photos or recordings. By all the characteristics of social networks, it will support the activity of sports science. The methodology of this research focused on quantitative research based on the T-test approach. The result shows that social media involvement can significantly support collaboration in sports science, so it can increase people's motivation to interact with other participants in sport organizations. © 2020 IEEE.

关键词： Sports

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Deep Learning with Greedy Layer-Wise Compound Scaling for Temperature and Humidity Prediction in Solar Dryer Dome

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SSRN 2022年

作者： Cenggoro, Tjeng Wawan Elwirehardja, Gregorius Natanael Dominic, Nicholas Setiawan, Karli Eka Rahutomo, Reza Djuana, Endang Gunawan, Fergyanto E. Budiman, Arief S. Romeli, Sugiarto Pardamean, Bens Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta11480 Indonesia Electrical Engineering Department Faculty of Industrial Technology Universitas Trisakti Jakarta11440 Indonesia Industrial Engineering Department BINUS Graduate Program Master of Industrial Engineering Bina Nusantara University Jakarta11480 Indonesia Department of Manufacturing and Mechanical Engineering and Technology Oregon Institute of Technology Klamath FallsOR97601 United States PT. Impack Pratama Industri Jakarta Indonesia

Drying has been an eco-friendly and cost-efficient method to reduce post-harvest losses of agricultural crops. In various countries, the technique has been widely utilized in the form of Solar Dryer Dome (SDD) buildings. Accurate indoor microclimate forecasting is very important to implement smart control systems in SDDs that can maintain the ideal conditions, as the availability of solar energy is limited only at daytimes and the temperature may drop significantly during nighttimes in remote areas. The microclimate variables that play an important role in maintaining ideal environmental conditions include temperature and relative humidity. © 2022, The Authors. All rights reserved.

关键词： Forecasting

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First Demonstration of HZO-LNOI Integrated Ferroelectric Electro-Optic Modulator and Memory to Enable Reconfigurable Photonic Systems

First Demonstration of HZO-LNOI Integrated Ferroelectric Ele...

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International Electron Devices Meeting (IEDM)

作者： Zefeng Xu Chun-Kuei Chen Hong-Lin Lin Yuan Gao Wei Ke Baochang Xu Pavel Dmitriev Carlan Arbiz Evgeny Zamburg Steven Touzard Xinlun Cai James Lee Suresh Venkatesan Aaron Danner Aaron Voon-Yew Thean Department of Electrical and Computer Engineering National University of Singapore (NUS) Integrative Sciences and Engineering Program NUS Graduate School Singapore Next-Generation Hybrid μ-Electronics Center (SHINE) Singapore School of Electronics and Information Technology Sun Yat-Sen University China 510275 Singapore Centre for Quantum Technologies NUS POET Technologies

We have successfully demonstrated, for the first time, an innovative back-end-of-line (BEOL) compatible electro-optic modulator and memory (EOMM) based on Lithium Niobate on Insulator (LNOI) micro-ring resonator (MRR) integrated with Ferroelectric Hafnium Zirconate Hf 0.5 Zr 0.5 O (HZO) non-volatile analog memory. High non-volatile memory and modulation performances are both achieved in a single compact device, exhibiting high extinction ratio of 13.3 dB, excellent efficiency of 66pm/V, stable nine-state switching, record-high endurance exceeding 10 9 cycles. This is accomplished by utilizing Pockels effect in LNOI, induced by electric-field effect from remnant HZO ferroelectric polarization. We studied the system implementation of reconfigurable chiplet-interposer photonic interconnect, enabled by the EOMM and EOMM with hybrid thermal-optical modulation. Our model shows a potential 70% energy efficiency improvement over conventional electrical interposer interconnect. We have also tested the integration of the EOMM with POET technologies’ 400G Tx/Rx optical interposer chip and studied a limited scale demonstration of the EOMM device.

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Extraction of corn seed image texture using machine learning method

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AIP Conference Proceedings 2024年第1期2987卷

作者： Yampi R. Kaesmetan Fuzy Yustika Manik Wisard Kalengkongan Informatics Techniques Study Program STIKOM Uyelindo Kupang Kupang Indonesia Faculty of Computer Sciences and Information Technology Universitas Sumatera Utara Medan Indonesia Information System Study Program Universitas Sam Ratulangi Manado Manado Indonesia

Indonesia is the largest corn exporter in the world. Corn (Zea mays I.) Problems in determining the selection of corn seed to replant, especially corn in East Nusa Tenggara, are still a critical issue. The things that affect the quality of corn are found: the seeds are damaged, the seeds are dull, the seeds are dirty, the beans are broken due to the drying process, and the shell of the corn. The determination of the quality of corn grains usually is done manually with visual observation. The manual system requires a long time and produces good quality products that are not consistent due to the limitation of visual fatigue and differences in the perception of each observer. This research uses image texture extraction comparison with statistical methods I orde (color moment) and orde statistics II (GLCM) to select the corn seed. Orde statistics I (color moment) shows the emergence of the value of the degree of gray probability pixels in an image, while orde statistics II (GLCM) shows the relationship between two probability pixels forming a concurrence matrix from the image data. This research is expected to help the process of classification in determining the corn seed. The algorithm k of the nearest neighbor (K-NN) who used to research the classification of the object of the image that will be examined. The results of this study successfully performed using k-Nearest neighbor (k-NN) with a distance of euclidean distance and k=1 with the extraction of the color moment got the highest accuracy is 88%, and the extraction GLCM to characterize the homogeneity of 75.5%, correlation of 78.67%, a contrast of 65.75% and energy of 63.82% with an average accuracy of 70.93%.

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An Intelligent System Based on Evolutionary Algorithm for Scheduling University Course Timetable

SSRN

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SSRN 2022年

作者： Bimantara, I. Made Satria Yuhana, Umi Laili Supriana, I. Wayan Pardede, Eric Department of Informatics Institut Teknologi Sepuluh Nopember 60117 Indonesia Informatics Study Program Faculty of Mathematics and Natural Sciences Udayana University Indonesia Department of Computer Science and Information Technology La Trobe University BundooraVIC3086 Australia

University Course Timetabling (UCT) is a common problem in educational institutions. The preparation of the class schedule must pay attention to available resources without violating set constraints. This research applies Evolutionary Algorithm such as Genetic Algorithm into timetabling system, to replace human expertise in generating optimal lecture schedules. Genetic Algorithm (GA) uses chromosomes to represent the solution of the UCT. One chromosome represents a set of schedule solutions that AG generates. Genes and alleles become components of chromosomes that represent the value of each entity to be optimized. We modeled chromosomes into four gene types, each of which has three allele types, namely day, hour, and room alleles which will be optimized with GA. The N-point and cycle crossovers were implemented into the new chromosome encoding scheme and do not take place at the gene level, but at the allele level by grouping similar allele types before the crossover process is carried out. The success rate is measured by the percentage of the optimal number of schedules generated from the trials. The proposed method provides a success rate of 80 - 85% and can produce an optimal schedule with an average computation time of 8 minutes. GA can reduce human resources and save time in the process of preparing lecture schedules. © 2022, The Authors. All rights reserved.

关键词： Genetic algorithms

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Deep Learning in Smart Healthcare: A GAN-based Approach for Imbalanced Alzheimer's Disease Classification

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Procedia computer Science 2024年 241卷 146-153页

作者： Hina Tufail Abdul Ahad Ira Puspitasari Ibraheem Shayea Paulo Jorge Coelho Ivan Miguel Pires Department of Computer Science University of Management and Technology Sialkot Pakistan Information System Study Program Faculty of Science and Technology Universitas Airlangga School of Software Northwestern Polytechnical University Xian Shaanxi P.R. China Department of Electronics and Communication Engineering Istanbul Technical University(ITU) Turkey Polytechnic University of Leiria Leiria Portugal Institute for Systems Engineering and Computers at Coimbra (INESC Coimbra) Coimbra Portugal Instituto de Telecomunicações Escola Superior de Tecnologia e Gestão de Águeda Universidade de Aveiro Águeda Portugal

Alzheimer's disease (AD) is a type of dementia that leads to memory loss and impairment, which afects patients’ lives badly. It is not curable yet, but its progression can be slowed down if detected at earlier stages. In this research study, we propose a transfer learning-based convolutional neural network (CNN) model to classify magnetic resonance imaging (MRI) into one of four stages of Alzheimer's disease. One of the major limitations of the deep learning-based classification model is the non-availability of healthcare datasets related to AD. The widely used Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset has a major class imbalance issue. We propose a generative adversarial network (GAN) based data augmentation technique to overcome the data imbalance. This promotes the investigation of applying GANs to generate synthetic samples for minority classes in Alzheimer's disease datasets to enhance classification performance. The results show the progression in the overall classification process of AD.

关键词： Alzheimer disease (AD) Deep learning Convolutional Neural Network (CNN) computer-aided diagnosis (CAD)

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