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Using a hybrid neural network architecture for DNA sequence representation: A study on N4-methylcytosine sites

Computers in Biology and Medicine 2024年 178卷 108664-108664页

作者： Nguyen, Van-Nui Ho, Trang-Thi Doan, Thu-Dung Le, Nguyen Quoc Khanh University of Information and Communication Technology Thai Nguyen University Thai Nguyen Viet Nam Department of Computer Science and Information Engineering TamKang University New Taipei251301 Taiwan International Degree Program in Animal Vaccine Technology International College National Pingtung University of Science and Technology Pingtung Taiwan Professional Master Program in Artificial Intelligence in Medicine College of Medicine Taipei Medical University Taipei110 Taiwan Research Center for Artificial Intelligence in Medicine Taipei Medical University Taipei110 Taiwan AIBioMed Research Group Taipei Medical University Taipei110 Taiwan Translational Imaging Research Center Taipei Medical University Hospital Taipei110 Taiwan

N4-methylcytosine (4mC) is a modified form of cytosine found in DNA, contributing to epigenetic regulation. It exists in various genomes, including the Rosaceae family encompassing significant fruit crops like apples, cherries, and roses. Previous investigations have examined the distribution and functional implications of 4mC sites within the Rosaceae genome, focusing on their potential roles in gene expression regulation, environmental adaptation, and evolution. This research aims to improve the accuracy of predicting 4mC sites within the genome of Fragaria vesca, a Rosaceae plant species. Building upon the original 4mc-w2vec method, which combines word embedding processing and a convolutional neural network (CNN), we have incorporated additional feature encoding techniques and leveraged pre-trained natural language processing (NLP) models with different deep learning architectures including different forms of CNN, recurrent neural networks (RNN) and long short-term memory (LSTM). Our assessments have shown that the best model is derived from a CNN model using fastText encoding. This model demonstrates enhanced performance, achieving a sensitivity of 0.909, specificity of 0.77, and accuracy of 0.879 on an independent dataset. Furthermore, our model surpasses previously published works on the same dataset, thus showcasing its superior predictive capabilities. © 2024 Elsevier Ltd

关键词： Embeddings

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IoT for Water Quality Categorization

IoT for Water Quality Categorization

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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Hermantoro Sastrohartono Andreas Wahyu Krisdiarto Arief Ika Uktoro Reza Rahutomo Teddy Suparyanto Bens Pardamean Agricultural Engineering Department Faculty of Agricultural Technology Institute of Agriculture STIPER Yogyakarta Indonesia Information System Department School of Information Systems Bina Nusantara University Jakarta Indonesia Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta Indonesia

In agricultural water research, the adoption of Internet of Things (IoT) technology has emerged as a pivotal approach for large-scale data collection. Water availability in the context of water quality is very important, both for domestic and industrial purposes. For domestic purposes, drinking water and bathing water are separated. Meanwhile, for the palm oil industry, boiler filler is differentiated from additional process water (dilution water). Water quality parameters can be assessed from turbidity and Total Dissolve Solid (TDS). Measurements using measuring instruments separately and repeatedly require significant energy, time, and costs. This research was conducted with the primary objective of presenting a novel method for categorizing water quality with the approach of IoT sensor technology. The research methodology entailed the utilization of an integrated IoT water sensors system in conjunction with manual water categorization. The methods consist of (1) system design, (2) design and installation of sensor and IoT-based microcontrollers, and (3) accuracy and precision testing compared with laboratory measurements. The precision of the integrated IoT water sensors was assessed through a dedicated sensor precision test, resulting in an accuracy rate of 94.4% for the turbidity sensor and 97.5% for the TDS sensor. Notably, this approach successfully discriminated drinking water with valid categorization, while other water types, including groundwater, water with tea, and water with coffee, yielded null categorization results.

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Hybrid cryptosystem enhanced RSA and RC4 chaotic map: A tutorial

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AIP Conference Proceedings 2024年第1期3222卷

作者： Arief Rahman Hakim Mohammad Andri Budiman Mahyuddin K. M. Nasution Master of Informatics Program Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia

Since the RSA algorithm is no longer secure and a lot of research has been done to break down RSA security, it is necessary to do research by making a combination of modifications of RSA algorithms, in this case using enhanced RSA using fake modules, with RCCM (RC4 Chaotic Map) algorithms to produce a Hybrid Cryptosystem that is expected to be able to improve the security of secret messages. The combination process is done by encrypting the messages using the RCCM algorithm first, then the messages that have been encrypted back in the encryption using the enhanced RSA algorithm with fake modules. So the security of messages has been improved. As for the results of this study, the key generation process is automatically generated which produces keystream for RCCM and fake public key and fake private key for enhanced RSA. In addition, what distinguishes enhanced RSA from ordinary RSA is the use of four primes p, q, r, and s. as well as the application of chaotic maps to RCCM gives the advantage of the unnecessary use of generating keys manually. With these keys, the process of encryption and decryption can be carried out to generate a secret message. In addition, the program resulting from this research applies the kraitchik factorization algorithm as a medium of attack against the key. The conclusion of this study is that by doing a hybrid cryptosystem enhanced RSA and RCCM can be safer due to the occurrence of double encryption processes. In addition, an enhanced RSA algorithm determines a fake module where the value of X is a prime number so that if attacked using a kraitchik factorization algorithm it is difficult to find a factorization of X values that corresponds to the number of p, q, r, and s.

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Reduction of the dataset of Baitul Mal aid recipients using the principal component analysis (PCA) method to improve C5.0 algorithm classification results

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AIP Conference Proceedings 2024年第1期3222卷

作者： Evi Susanti Sawaluddin Poltak Sihombing Master of Informatics Program Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia

This research raises problems among Baitul Mal aid recipients regarding aid to micro businesses. The process of assisting is an important activity to develop micro-businesses so that they can continue to operate and develop. Selection is also needed to prevent aid from being distributed incorrectly. Therefore, the proposed solution is to utilize data mining techniques, especially classification methods (clustering) to identify potential aid recipients who truly deserve to receive aid. This research also uses the Principal Component Analysis (PCA) method to reduce attribute dimensions in the dataset to increase the accuracy of the classification process. By optimizing the classification process through appropriate attribute reduction, this research is expected to make a positive contribution to increasing the efficiency and accuracy of credit risk assessment in the banking sector. The results of this research are: accuracy after the dataset has been reduced by 2 (two) attributes has a higher value when compared to the dataset that has not been reduced, this happens because the attribute reduction process is carried out to reduce attributes that are not or less relevant. So it can be said that the reduction process influences the accuracy of the C5.0 algorithm classification results.

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Comparing Naïve Bayes, Decision Tree and Logistic Regression Methods in Fraudulent Credit Card Transactions

Comparing Naïve Bayes, Decision Tree and Logistic Regressio...

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2020 International Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy, ICDABI 2020

作者： Alanezi, Maha A. Homeed, Mawra T. Mohamed, Zahra S. Zeki, Ahmed M. University of Bahrain Big Data Science Analytics Master Program Collage of Science Sakhir Bahrain University of Bahrain Collage of Information Technology Department of Information System Sakhir Bahrain

ISBN: (纸本)9781728196756

Data mining is utilized to explore banks' data to unravel any hidden scams and detect potential frauds. The aim of this paper is to compare between the Naïve Bayes, Decision Tree and Logistic Regression in fraudulent credit card transactions. Cross-Industry Standard Process for Data Mining (CRISP-DM) is followed to achieve the aim of this research. In terms of accuracy, the best classification model was Logistic Regression with 94.6% accuracy, compared with the Decision Tree and Naïve Bayes that showed accuracy of 89.1% and 90.9% respectively. Other measures were also calculated like time needed to build the model among others. © 2020 IEEE.

关键词： Decision trees

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Ergometer-Based Lower Limb Rehabilitation Robot for Post-Stroke Hemiplegic Patients with Voluntary Movement Induction using Muscle Synergy

Ergometer-Based Lower Limb Rehabilitation Robot for Post-Str...

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Conference on Human System Interactions, HSI

作者： Daisuke Chugo Yuya Miyazaki Honoka Kubo Satoshi Muramatsu Sho Yokota Jin-Hua She Keio Ishiguro Hiroshi Hashimoto Graduate School of Science and Technology Kwansei Gakuin University Sanda Hyogo Japan School of Engineering Kwansei Gakuin University Sanda Hyogo Japan School of Information Science and Technology Tokai University Hiratsuka Kanagawa Japan Faculty of Science and Engineering Toyo University Kawagoe Saitama Japan School of Engineering Tokyo University of Technology Hachioji Tokyo Japan School of Health Sciences Tokyo University of Technology Kamata Tokyo Japan Master Program of Innovation for Design and Engineering Advanced Institute of Industrial Technology Shinagawa Tokyo Japan

ISBN: (数字)9798350362916

ISBN: (纸本)9798350362923

This paper proposes a lower limb rehabilitation robot for normal walking in post-stroke hemiplegic patients. Ergometers are commonly used for lower limb rehabilitation, but their use has depended on the experience of physiotherapists. Therefore, this paper proposes a new rehabilitation robot based on an ergometer that leads the patient to voluntary muscle activity and learns how to use the muscles required for normal walking. The paper first compares how pedestrians walk between a normal person and a hemiplegic patient using muscle synergy analysis to identify the lower limb usage required for normal gait. The paper then proposes a method to approach the use of the lower limb required for normal gait by changing the load depending on the position of the pedals when a hemiplegic patient uses the ergometer. This will enable stroke hemiplegic patients to train for normal gait by pedaling the ergometer. The above proposed method has been implemented in our prototype robot and the effectiveness of our proposed method has been confirmed in experiments with post-stroke hemiplegic patients as subjects.

关键词： Legged locomotion Training Pedestrians Prototypes Muscles Stroke (medical condition) Assistive robots

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Applying Parallel Processing to Improve the Computation Speed of K-Nearest Neighbor Algorithm

Applying Parallel Processing to Improve the Computation Spee...

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2020 International Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy, ICDABI 2020

作者： Alanezi, Maha A. Alqaddoumi, Abdulla University of Bahrain Collage of Science Big Data Science and Analytics Master Program Sakhir Bahrain University of Bahrain Collage of Information Technology Department of Information System Sakhir Bahrain

ISBN: (纸本)9781728196756

K-Nearest Neighbor (KNN) is a widely used algorithm to gain an accurate and efficient classification. One of the drawbacks of the algorithm is the time required to calculate the distance for each point. In this paper, the aim is to speed up the KNN algorithm with the implementation of multi threads and multiprocessors to reduce the time to execute the algorithm. There are two medical datasets used to apply the KNN algorithm for the comparison of the sequential and parallel performance. The datasets utilized are Heart Test and Breast Cancer Wisconsin (Diagnostic) Data Sets. The multiprocessing is used to parallelize the computation of the distance of KNN. The parallel KNN outperforms the sequential version with the speedup increase, leading to reduction in time for both the processes and threads. © 2020 IEEE.

关键词： Learning algorithms

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White blood cell subtype detection and classification

arXiv

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arXiv 2021年

作者： Praveen, Nalla Punn, Narinder Singh Sonbhadra, Sanjay Kumar Agarwal, Sonali Syafrullah, M. Adiyarta, Krisna Dept. of Information Technology Indian Institute of Information Technology Allahabad Prayagraj India Program of Master of Computer Science Universitas Budi Luhur Indonesia

—Machine learning has endless applications in the health care industry. White blood cell classification is one of the interesting and promising area of research. The classification of the white blood cells plays an important part in the medical diagnosis. In practise white blood cell classification is performed by the haematologist by taking a small smear of blood and careful examination under the microscope. The current procedures to identify the white blood cell subtype is more time taking and error-prone. The computer aided detection and diagnosis of the white blood cells tend to avoid the human error and reduce the time taken to classify the white blood cells. In the recent years several deep learning approaches have been developed in the context of classification of the white blood cells that are able to identify but are unable to localize the positions of white blood cells in the blood cell image. Following this, the present research proposes to utilize YOLOv3 object detection technique to localize and classify the white blood cells with bounding boxes. With exhaustive experimental analysis, the proposed work is found to detect the white blood cell with 99.2% accuracy and classify with 90% accuracy. © 2021, CC BY.

关键词： Image classification

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Analysis of the musculoskeletal effects of lower limb slippage when indoor dogs walk on the wooden floor

Analysis of the musculoskeletal effects of lower limb slippa...

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IEEE International Symposium on Industrial Electronics (ISIE)

作者： Daisuke Chugo Miyabi Igarashi Satoshi Muramatsu Sho Yokota Jin-Hua She Hiroshi Hashimoto Uemura Takashi Hiroaki Kamishina Yoshiharu Hata Takayuki Yamada Takahiro Uchida Graduate School of Science and Technology Kwansei Gakuin University Sanda Hyogo Japan School of Science and Technology Kwansei Gakuin University Sanda Hyogo Japan School of Information Science and Technology Tokai University Hiratsuka Kanagawa Japan Faculty of Science and Engineering Toyo University Kawagoe Saitama Japan School of Engineering Tokyo University of Technology Hachioji Tokyo Japan Master Program of Innovation for Design and Engineering Advanced Institute of Industrial Technology Shinagawa Tokyo Japan KyotoAR Animal Advanced Medical Center Kuse Kyoto Japan Rinrei Co. Ltd. Chuo Tokyo Japan

ISBN: (数字)9798350394085

ISBN: (纸本)9798350394092

In recent years, dogs are commonly kept indoors. Dogs, as animals that inherently walk on grippy outdoor ground, sometimes slip when walking on indoor wooden flooring, which can lead to injuries and arthritis. We have therefore developed a method to increase the coefficient of friction by applying a special coating to the wooden flooring to make it less slippery when the dog walks on it. The objective of this paper is to investigate whether the friction coefficient of the flooring can prevent dogs’ lower limbs from slipping when they walk, and to analyze the effects of lower limb slippage on the musculoskeletal system of dogs when they walk on the flooring. In the measurement experiment, walking movements were performed with and without paint coating on the wooden flooring, the test dogs were applied to a 3D musculoskeletal system simulation software model, and the amount of muscular force exerted was calculated through a simulation experiment with measured data. In addition, the floor reaction forces of the lower limbs during walking were measured and compared, and the effects of the friction coefficient of the floor environment on walking behavior were discussed.

关键词： Legged locomotion Solid modeling Force measurement Three-dimensional displays Friction Force Dogs

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Tweets Sentiment Analysis during COVID-19 Pandemic

Tweets Sentiment Analysis during COVID-19 Pandemic

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2020 International Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy, ICDABI 2020

作者： Alanezi, Maha A. Hewahi, Nabil M. University of Bahrain Big Data Science Analytics Master Program Collage of Science Sakhir Bahrain University of Bahrain Collage of Information Technology Department of Computer Science Sakhir Bahrain

ISBN: (纸本)9781728196756

The aim of this paper is to investigate the impact of social distance on people during COVID-19 pandemic using twitter sentiment analysis through a comparison between the k-means clustering and Mini-Batch k-means clustering approaches. To find the most common frequent words, two datasets have been investigated (WHO and Bahrain ministry of health datasets) to be as data preparation and exploration. Another two datasets (English and Arabic datasets) are used in the clustering of k-means. In this paper, a comparison between k-means and Mini-Batch k-means is performed to find a pattern. The word frequency shows that there are several words related to the pandemic. The sentiment analysis result show that in USA, Australia, Nigeria, Canada, and England, most tweets are neutral. However, the majority of tweets are positive tweets from both Italy and India. In addition, the k-means cluster in the English dataset reveals several cluster trends where COVID-19 pandemic procedures are addressed in cluster 1, and health workers are encouraged in cluster 3. © 2020 IEEE.

关键词： COVID-19

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