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Bioinformatics exploration into potential mechanisms of Calamus draco for colorectal cancer treatment

Procedia computer Science 2024年 245卷 127-136页

作者： Christian Kenneth Rudi Nirwantono Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Biotechnology Department Faculty of Engineering Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program – Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

As one of the cancer types with the highest incidence rates, colorectal cancer (CRC) would benefit from treatments with fewer side effects and reduced treatment-resistant potential. One of the options is to harness the anti-CRC potential of natural products. Previous studies have shown that Calamus draco exudate, dragon's blood, has anticancer activity in liver cancer and acute myeloid leukemia, but its bioactivity has not been studied in CRC. Here we conduct a bioinformatics study based on network pharmacology to explore the anti-CRC potential and mechanism of C. draco -derived compounds. The bioinformatics pipeline is composed of compound and target collection, biological network evaluation, and enrichment analysis. We found that there are 43 bioactive compounds from C. draco targeting 91 CRC-related targets, of which most compounds target MEN1, PTGS2, and IDH1. Further analyses show that the targets of C. draco are involved in the cellular response to hypoxia. By inhibiting those targets, C. draco bioactive compounds can potentially hinder angiogenesis and increase treatment response efficacy.

关键词： bioinformatics network pharmacology Calamus draco colorectal cancer drug discovery

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Enhancing Plant Leaf Disease Identification with a CNN and DenseNet Hybrid Model 5

Enhancing Plant Leaf Disease Identification with a CNN and D...

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5th International Conference on Information Management and Machine Intelligence, ICIMMI 2023

作者： Dari, Sukhvinder Singh Tiwaskar, Shweta Raja Kumar, Jambi Ratna Langote, Vaishali Mohadikar, Gopal Madhukar Ashtagi, Rashmi Pune India VIIT Maharashtra Pune India Department of Computer Engineering Genba Sopanrao Moze College of Engineering Balewadi Maharashtra Pune India Program - CSE Department of Polytechnic Dr. Vishwanath Karad World Peace University Maharashtra Pune India Tolani Maritime Institute Induri Maharashtra Talegaon Dabhade India Department of Computer Engineering Vishwakarma Institute of Technology Bibwewadi Maharashtra Pune411037 India

ISBN: (纸本)9798400709418

The identification of plant leaf diseases is a essential section of agriculture, serving as a critical measure in mitigating the reduction in crop productivity. This study presents a novel methodology for improving the precision of plant leaf disease detection. This is achieved by employing a hybrid architecture that combines Convolutional Neural Networks (CNNs) and DenseNet models. The main aim of our study is to enhance the accuracy and effectiveness of disease detection by leveraging the combined capabilities of Convolutional Neural Networks (CNNs) and DenseNet. These two deep learning architectures are recognized for their proficiency in extracting and reusing features. In this study, we conduct a comparative analysis of our proposed hybrid model by evaluating its performance against traditional Convolutional Neural Networks (CNNs) as well as other advanced models such as Recurrent Neural Networks (RNNs) and Capsule Networks (CapsNets). The findings of our study demonstrate that the hybrid model, combining CNN and DenseNet architectures, showed an accuracy rate of 98.79%. This performance surpassed that of other models under comparison. The hybrid model demonstrated higher levels of precision, recall, F1 score, and AUC-ROC values, shows its effectiveness in the identification of plant leaf diseases. This study highlights the potential of hybrid deep learning models in the domain of precision agriculture and emphasizes the importance of integrating diverse architectural paradigms for accurate disease identification. The research conducted in this study makes a significant contribution to the progress of agricultural technology by providing a dependable mechanism for the early detection of diseases. As a result, this research contributes to the enhancement of crop health and the promotion of food security. © 2023 ACM.

关键词： Convolutional neural networks

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CAMRI Loss: Improving the Recall of a Specific Class without Sacrificing Accuracy

CAMRI Loss: Improving the Recall of a Specific Class without...

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作者： Nishiyama, Daiki Fukuchi, Kazuto Akimoto, Youhei Sakuma, Jun Master's Program in Computer Science Degree Programs in Systems and Information Engineering Graduate School of Science and Technology University of Tsukuba Tsukuba-shi305-8577 Japan RIKEN Center for Advanced Intelligence Project Tokyo103-0027 Japan Department of Computer Science Faculty of Engineering Information and Systems University of Tsukuba Tsukuba-shi305-8577 Japan

In real world applications of multiclass classification models, misclassification in an important class (e.g., stop sign) can be significantly more harmful than in other classes (e.g., no parking). Thus, it is crucial to improve the recall of an important class while maintaining overall accuracy. For this problem, we found that improving the separation of important classes relative to other classes in the feature space is effective. Existing methods that give a class-sensitive penalty for cross-entropy loss do not improve the separation. Moreover, the methods designed to improve separations between all classes are unsuitable for our purpose because they do not consider the important classes. To achieve the separation, we propose a loss function that explicitly gives loss for the feature space, called class-sensitive additive angular margin (CAMRI) loss. CAMRI loss is expected to reduce the variance of an important class due to the addition of a penalty to the angle between the important class features and the corresponding weight vectors in the feature space. In addition, concentrating the penalty on only the important class hardly sacrifices separating the other classes. Experiments on CIFAR-10, GTSRB, and AwA2 showed that CAMRI loss could improve the recall of a specific class without sacrificing accuracy. In particular, compared with GTSRB's second-worst class recall when trained with cross-entropy loss, CAMRI loss improved recall by 9%. © 2023 The Institute of Electronics, Information and Communication Engineers.

关键词： Deep learning

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Robust Audio Watermarking Based on Empirical Mode Decomposition and Group Differential Relations

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AES: Journal of the Audio engineering Society 2023年第3期71卷 100-117页

作者： Lai, Wen-Hsing Chou, Tsung-Yuan Chou, Meng-Chen Schuller, Björn W. Department of Computer and Communication Engineering National Kaohsiung University of Science and Technology No. 1 University Rd. Yanchao Dist. Kaohsiung City82445 Taiwan ZD.B Chair of Embedded Intelligence for Health Care and Wellbeing University of Augsburg Germany Ph.D. Program in Engineering Science and Technology College of Engineering National Kaohsiung University of Science and Technology Taiwan GLAM – Group on Language Audio & Music Imperial College London United Kingdom

An audio watermarking technique using Complementary Ensemble Empirical Mode Decomposition and group differential relations of average absolute amplitudes of the last Intrinsic Mode Function (IMF) is proposed. By using group differential relations, the relationship with neighboring samples in the last IMF is well preserved, and near-imperceptibility can be achieved. Placing a watermark on low-frequency components, the last IMF, which is perceptually significant, therefore makes the watermark difficult to be removed. The embedding watermark, which is a logo image in our experiment, is processed by Arnold transformation, secret key encryption, and Bose–Chaudhuri–Hocquenghem coding to enhance robustness and security. Experimental results of the signal-to-noise ratio fit the recommendations of imperceptibility of the International Federation of the Phonographic Industry. The average Objective Difference Grade (an objective measure that correlates very well with subjective assessment) and subjective quality assessment were performed to evaluate the imperceptibility. Furthermore, our method accomplishes robustness under 13 different categories of attacks, including noise corruption, amplitude scaling, echo addition, resampling, re-quantization, low-pass filtering, MPEG-1 Audio Layer III compression, Digital-to-Analog/Analog-to-Digital conversion, cropping, time shift, zero thresholding, jittering, and combined attacks. © 2023 Authors. All rights reserved.

关键词： Intrinsic mode functions

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Enhancing the Performance of E-Mode AlGaN/GaN HEMTs With Recessed Gates Through Low-Damage Neutral Beam Etching and Post-Metallization Annealing

IEEE Open Journal of Nanotechnology

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IEEE Open Journal of Nanotechnology 2023年 4卷 150-155页

作者： Chen, Yi-Ho Ohori, Daisuke Aslam, Muhammad Lee, Yao-Jen Li, Yiming Samukawa, Seiji National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Graduate Degree Program of College of Electrical and Computer Engineering Hsinchu300093 Taiwan Tohoku University Institute of Fluid Science Sendai980-8577 Japan National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Institute of Pioneer Semiconductor Innovation Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Institute of Communications Engineering Institute of Biomedical Engineering Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Department of Electronics and Electrical Engineering Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Institute of Communications Engineering Hsinchu300093 Taiwan

This study investigated the electrical properties of AlGaN/GaN high-electron-mobility transistors (HEMTs) with varied recess depths under the gate electrode. We demonstrated a recess depth of approximately 6 nm, which was achieved through neutral beam etching (NBE) technique with a low etch rate of 1.8 nm/min, resulting in device enhancement-mode (E-mode) behavior with threshold voltage (Vth) of 0.49 V. The effects of post-metallization annealing (PMA) on the device performance were also examined. The results revealed that PMA treatment improves the DC characteristics of the devices, including maximum drain current (IDMAX), transconductance (gm), subthreshold swing (SS), on-off ratio, and off-state leakage current, with maximum enhancement percentage of 18.3% for IDMAX, 3758% for on-off ratio, and 54.3% for SS. Moreover, this study compared the recess depths of metal-insulator-semiconductor high-electron-mobility transistors (MIS-HEMTs) with the SiN dielectric layer. The results showed that MIS-HEMTs exhibit more negative Vth values, which can be attributed to the controlled surface states achieved through passivation. © 2020 IEEE.

关键词： Etching

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Strategies for Enhancing Static Object Detection Range in Autonomous Vehicles Using Monocular Camera Vision

Strategies for Enhancing Static Object Detection Range in Au...

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International Conference on Control, Automation and Systems ( ICCAS)

作者： Xingyou Li Hyoungrae Kim Hakil Kim Department of Electrical and Computer Engineering Inha University Incheon South Korea Department of Program in Future Vehicle Engineering Inha University Incheon South Korea

ISBN: (数字)9788993215380

ISBN: (纸本)9798331517939

The performance of object detection models for autonomous driving is increasingly advancing. However, to recognize static traffic targets such as traffic light recognition and traffic sign recognition, the detectable distance in images plays a crucial role in the planning strategies and performance of recognition algorithms for autonomous driving systems. This study analyzes the detection performance and detection range of traffic lights by a monocular camera on an autonomous driving vehicle. The detection model used is the YOLOv7-x model, with the original images segmented to 1x, 1/2x, and 1/3x of the model input size. The distances are obtained through high-precision maps and vehicle-mounted GPS/RTK. The results of this study indicate the scalability of the detection distance of a monocular camera’s object detection in an autonomous driving environment using a single model. It further demonstrates a proportional relationship between the detection distance and the model input size. It confirms the scalability of detection distances with cameras of different fields of view using a single model.

关键词： Image segmentation Image recognition Target recognition Scalability Object detection Cameras Market research Image restoration Planning Autonomous vehicles

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Enhancing Healthcare Monitoring Systems: A Case Analysis in Optimization

Enhancing Healthcare Monitoring Systems: A Case Analysis in ...

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International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Mohit Lalit Gaurav Bathla Surender Singh Computer Science and Engineering Department Chandigarh University Mohali India Head Career Program Code Quotient Mohali India

In the past few years, fog computing (FC) has emerged as a promising complement to cloud computing. It offers reduced latency, minimal bandwidth consumption, and real-time data transfer. In healthcare, particularly in IoT-enabled systems, FC integration has become pivotal for real-time monitoring and immediate data transfer to health experts. The researchers all around the world proposed and developed various communication frameworks and come up with certain outputs in terms of quality-of-service parameters (QoS). However, these QoS parameters are not always appropriate for effective execution of the application. Following that, this study explores the integration of FC with multi-objective Optimization Algorithms (such as FFLY and GWO) to optimize crucial quality of service (QoS) metrics using a proposed linear function to optimize multi objectives, essential for sustained communication. The research focuses on optimizing multi objectives in healthcare monitoring systems. Experimental results reveal the superiority of the Grey Wolf Optimizer (GWO) over the Firefly Algorithm (FFLY). This study emphasizes the crucial significance of multi-objective optimisation algorithms in optimizing critical parameters for successful healthcare communication frameworks, which can lead to improvements in healthcare monitoring efficiency.

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Auto Digit Selection for Most Significant Digit First Multiplication

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IEEE Embedded Systems Letters 2025年

作者： Gorgin, Saeid Jaberipur, Ghassem Lee, Jeong-A Shin, Seokjoo Kim, Jungrae Computer Engineering Iranian Research Organization for Science and Technology Tehran Iran Semiconductor Systems Engineering Department College of Information and Communication Engineering SungKyunKwan University Korea Republic of Brain Pool Program for Chosun University Gwangju Korea Republic of Shahid Beheshti University Tehran Iran Chosun University Department of Computer Engineering Gwangju Korea Republic of

Performance in modern embedded systems, particularly those executing computation-intensive signal/image processing and machine learning algorithms, is critically dependent on the efficiency of multiplication operations. Serial multiplication offers key advantages in these resource-constrained environments, including low memory usage and minimal interconnect complexity. The most significant digit first (MSDF) serial multiplication scheme further enhances efficiency by enabling early termination and supporting variable precision. It is ideal for embedded applications where power and performance are critical. This paper proposes an auto-selection scheme for MSDF multiplication, where product digits are generated as a byproduct of residual maintenance, leading to speed, cost, and power efficiency improvements. We demonstrate the implementation of serial-serial and serial-parallel multipliers, where the multiplier operand in the latter is fully available throughout the computation. Simulation and synthesis results, compared to the most relevant previous works, show reductions in delay (16% and 20%), area consumption (11% and 23%), and power dissipation (7% and 20%), making our approach well-suited for embedded systems requiring efficient, low-power multiplication hardware. © 2009-2012 IEEE.

关键词： Left-to-Right Arithmetic Machine Learning Algorithms MSDF Multiplication Online Arithmetic

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Human Activity Classification: A LSTM and MediaPipe Approach for Intelligent Systems Application 6

Human Activity Classification: A LSTM and MediaPipe Approach...

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6th International Conference on Control and Robotics, ICCR 2024

作者： Chen, Yi-An Yang, Pei-Ching Chang Jung Christian University Master Degree Program in Information and Design Tainan Taiwan Chang Jung Christian University Department of Computer Science and Information Engineering Tainan Taiwan Chang Jung Christian University Artificial Intelligence Research Center Tainan Taiwan

ISBN: (纸本)9798331518158

With advancements in artificial intelligence, computer vision technologies have seen significant progress, enabling applications like facial recognition, autonomous driving, and medical imaging. This study leverages the Multi-Camera Action Dataset (MCAD) and MediaPipe Pose to create a skeletal dataset for training a Long Short-Term Memory (LSTM) model to recognize 18 types of daily actions. Evaluation using confusion matrix and F1 score reveals that Action 16 (Object Carry) is often misclassified as Action 7 (Walk) due to similar leg movements, and Action 9 (Cell to Ear) as Action 1 (Wave) due to similar hand movements. Future work aims to enhance model accuracy and explore broader applications in physical activity and exercise analysis. © 2024 IEEE.

关键词： Intelligent systems

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Extraction of c- F os Protein Regions from Mouse Brain Slice Images using Multi-channelization, 1 x 1 Convolution and U -Net 13

Extraction of c- F os Protein Regions from Mouse Brain Slice...

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Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2024

作者： Wang, Ziyue Okamura, Hibiki Luo, Shijie Hayashi, Yu Takizawa, Hotaka Master's Program in Computer Science University of Tsukuba Tsukuba Japan International Institute for Integrative Sleep Medicine University of Tsukuba Ph.D. Program in Humanics Tsukuba Japan International Institute for Integrative Sleep Medicine University of Tsukuba Tsukuba Japan Institute of Systems and Information Engineering Academic Computing and Communications Center University of Tsukuba Tsukuba Japan

ISBN: (纸本)9798350373332

This study proposed a novel extraction method of c-Fos protein regions in DAB(3,3'-diaminobenzidine)-stained mouse brain slice images using the U-Net model combined with the multi-channelization and 1×1 convolution techniques. Our U-Net m odel, called 1× 1 conv U-Net, was applied to three DAB- strainedmouse slice images whose resolutions were 1000x1000 pixels. The experimental results demonstrated that the 1×1 conv U-Net outperformed the conventional U-Net. © 2024 IEEE.

关键词： Mammals

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