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Bioinformatics exploration into potential mechanisms of Calamus draco for colorectal cancer treatment 9th

Bioinformatics exploration into potential mechanisms of Cala...

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9th International Conference on computer science and Computational Intelligence, ICCSCI 2024

作者： Kenneth, Christian Nirwantono, Rudi Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Biotechnology Department Faculty of Engineering Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Computer Science Program Bina Nusantara University Jakarta11480 Indonesia

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. © 2024 The Authors.

关键词： Lung cancer

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Design of Medical Privacy Protection Circuit Based on Cross Obfuscation 9

Design of Medical Privacy Protection Circuit Based on Cross ...

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9th International Conference on Integrated Circuits and Microsystems, ICICM 2024

作者： Chen, Zhikang Zhang, Yuejun Ren, Mingze Xia, Donghao Wang, Pengjun Zhang, Huihong Wen, Liang Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo China College of Electrical and Electronic Engineering Wenzhou University Wenzhou China China Coast Guard Academy Department of Electronic Technology Ningbo China

ISBN: (纸本)9798331509453

With the rise of the Internet of Things, smart healthcare with remote monitoring and diagnosis enables patients to reduce the number and duration of hospitalizations, thereby reducing the pressure on doctors. However, the transmission data of disease detection chips is susceptible to cache attacks, leading to user health data leakage. Hardware level encryption design has become a research hotspot. Firstly, by adding a Mask channel at the upper level of each convolution layer, the preprocessed data is transmitted simultaneously with the obfuscated data, and the channel exchange during transmission is utilized to protect user privacy;Secondly, there is a redundancy mechanism between the input matrix and weight matrix of the storage module, and invalid values are added to the redundant parts to prevent cache attacks from stealing user information;Finally, add multi temporal obfuscation to the first input data to achieve the different lengths between the first and subsequent data. The protection circuit designed in this article achieves user privacy protection on the basis of only increasing hardware overhead by 2.5%, power consumption overhead by 3.61%, and not affecting disease monitoring accuracy. © 2024 IEEE.

关键词： Information leakage

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An optimized Faster R-CNN model for Cassava Brown Streak Disease Classification 3

An optimized Faster R-CNN model for Cassava Brown Streak Dis...

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3rd International Conference on Advances in Computing, Communication, Embedded and Secure Systems, ACCESS 2023

作者： Rajasree, R. Latha, C. Beulah Christalin Paul, Sujni Appu, M. Aswathy, N. Karunya Institute of Technology and Sciences Department of Digital Sciences Coimbatore India Higher College of Technology Department of Computer Science Dubai United Arab Emirates Sree Narayana Guru College Department of Computer Science Coimbatore India Adi Sankara Institute of Engineering and Technology Department of Electronics and Communication Engineering Ernakulam India

ISBN: (纸本)9798350346169

The scientific community has shown considerable interest in plant disease detection and classification based on deep learning. In order to address these research gaps, this study proposes an optimized, fine-tuned model for the detection of Cassava Brown Streak Diseases. Casѕava is a vital Thai manufacturing harvest. Thailand is a pioneer in cassava production;therefore, a lot of cassava has been produced and exported. But, caѕsava infection could be the key to cut back caѕsava creation and immediately has an effect on growers' earnings. This research is to develop a model using an effective deep learning algorithm for cassava leaf disease detection. We split the classification into two phases, with Model1 and Model2. First model is used to do the cassava disease classification and second model for identifying the Cassava Brown Streak Virus Disease using VGGNet, AlexNet and Faster R-CNN algorithm. Furthermore, data augmentation techniques are employed during network training to improve the performance of the proposed network. The proposed model has been evaluated its performance using accuracy and confusion matrix. The experimental results demonstrates that our approach can accurately classify Cassava Brown Streak Diseases with an accuracy of 96% using Faster R-CNN. © 2023 IEEE.

关键词： computer vision

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Revealing Top-k Dominant Individuals in Incomplete Data Based on Spark Environment 14th

Revealing Top-k Dominant Individuals in Incomplete Data Bas...

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14th International Conference on Genetic and Evolutionary Computing, ICGEC 2021

作者： Wang, Ke Wu, Jimmy Ming-Tai Cui, Binge Lin, Jerry Chun-Wei College of Computer Science and Engineering Shandong University of Science and Technology Shandong China Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen Norway

ISBN: (纸本)9789811684296

A large number of incomplete datasets exist in our life, which may be caused by missing data transmission, privacy information protection, and sudden device failure. It is more difficult to mine the key information in incomplete datasets because of missing dimension values in some uncertain dimensions. top-k dominated (TKD) query can be better applied to the information mining of this dataset. Compared with the traditional query algorithm, this algorithm does not need to set the threshold before the beginning, and the top-k key information can be obtained by simply setting in the algorithm. At present, there are some algorithms to perform TKD query on incomplete datasets, but most of them are stand-alone algorithms applied to small datasets and parallel algorithms based on MapReduce applied to large datasets. For large datasets, the algorithm based on MapReduce framework is defective due to the distributed computing framework of MapReduce, for example, when multiple MapReduce jobs are serially calculated, some framework start-up time and communication time between nodes are often wasted, leading to the performance of the algorithm to be improved. In this paper, a TKD query algorithm based on Spark computing framework is proposed. The data interaction between multiple jobs can be realized based on memory, which avoids a large amount of disk interaction when the algorithm runs. In addition, we also propose a new method to calculate the dominant number of objects based on bitmap index table, which simplifies the calculation process. Therefore, the performance of the algorithm has been further improved by using the new computing method and the new computing framework. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： MapReduce

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Application of Disruptive Technologies in Intelligent Transportation System 2nd

Application of Disruptive Technologies in Intelligent Transp...

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2nd International Conference on Data science, Machine Learning and Applications, ICDSMLA 2020

作者： Santhiya, P. Jebadurai, Immanuel John Raja Paulraj, Getzi Jeba Leelipushpam Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (纸本)9789811636899

Intelligent Transportation system (ITS) forms the backbone of smart cities in developed and developing countries. They improve the economy, saves time and provides a safer journey. ITS exploits various technologies viz., Internet of Things, Cloud computing, Fog computing, Artificial Intelligence, Machine learning, deep learning and mathematical models to offer traffic management, route planning, object and lane detection, accident prevention and many more. This paper review various literature that applies these technologies in ITS. İt also discuss various issues and challenges in ITS. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Internet of things

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Recurrent Neural Network-Based Dynamic Cooling Adjustment for Energy-Efficient Air Conditioning with IoT Sensors 1

Recurrent Neural Network-Based Dynamic Cooling Adjustment fo...

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1st International Conference on Innovations in Communications, Electrical and computer engineering, ICICEC 2024

作者： Sujatha, V. Shanthi, S. Juliet, P. Sudha Muthumarilakshmi, S. Latha, N. Mohankumar, N. Shree Sathyam College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Salem India Care College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Tiruchirappalli India Meenakshi College of Engineering Department of Electronics and Instrumentation Engineering Tamil Nadu Chennai India Chennai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India Symbiosis Institute of Technology Nagpur Campus Maharashtra Pune India

ISBN: (纸本)9798350376517

This research introduces a novel approach to the problem of air conditioning (AC) system energy efficiency. It presents a dynamic method for cooling control by combining recurrent neural networks (RNNs) with Internet of Things (IoT) sensors. The suggested approach uses real-time environmental data collected by IoT sensors, in contrast to traditional systems that stick to static cooling schedules. To predict the best cooling parameters, RNNs examine this data in conjunction with past trends. The AC system may adjust the amount of cooling it provides in response to the current weather. This adaptive technique reduces energy use while simultaneously increasing comfort. By comparing the suggested technique to more conventional ones, experimental validation shows that it is effective and results in significant energy savings. This technology has the potential to improve the efficiency and longevity of AC systems in a wide variety of settings, according to the results. This study makes a significant contribution to the continuous quest for energy-efficient Heating, Ventilation, and Air Conditioning (HVAC) system solutions by integrating advanced neural network modeling with novel IoT technologies. © 2024 IEEE.

关键词： Air conditioning

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Smart Distributed Parking System using Internet of Things 6

Smart Distributed Parking System using Internet of Things

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6th International Conference on Intelligent Computing and Control Systems, ICICCS 2022

作者： Leelipushpam Paulraj, Getzi Jeba Johnraja Jebadurai, Immanuel Khan, Pathan Firoz Sumanth, Neela Reddy, Kothapally Vishal Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9781665410359

With the growth of cities and population, urban corporation faces potential challenges. One such challenge is regulating parking spaces. This become a tedious job during weekends and festival season. Improper regulation in parking spaces lead to traffic congestion and affects the fuel economy. Various parking solutions using sensor networks and image processing techniques have been proposed in literatures. For developing smart city environment, single parking space management is not sufficient and image processing based parking system can be applied only to large spaces. This paper proposes distributed parking management system. Three parking slots have been created. The IR sensors capture the status of the space and communicate to the cloud. The user can also check the real time parking space availability in mobile application. Prototype has been developed and checked under various condition. The proposed solution provides parking space information on real time and improves fuel efficiency. © 2022 IEEE.

关键词： Sensor networks

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Hand Gesture Recognition using Shape-based Image Features for Music Controller 3

Hand Gesture Recognition using Shape-based Image Features fo...

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3rd International Conference on Smart Electronics and Communication, ICOSEC 2022

作者： Chitra, R. Anusha Bamini, A.M. Brindha, D. Reddy, Allam Prathyusha Kezia, Naru Beulah, Gyara Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India

ISBN: (数字)9781665497640

ISBN: (纸本)9781665497640

The main objective of this article is to develop a system for recognizing the user's hand gesture by detecting some significant shape-based features. The features are orientation, finger status, thumb in either in folded or raised form as well posture of hand fingers of hand, and their relevant position in the given input image. There is a similarity between the shape of the hands and the fingers of the human. The goal of work is to make a real-time system for passing the information to other devices or controlling the device by recognizing the gesture motion. The suggested method is particularly intended for use in a multitasking environment. While working on multiple programs on desktop at the same time like hearing music and playing in different windows. To skip from one song to another song or stop a song some appropriate action must be taken. This is a long and time-consuming procedure. Users will save time and remain connected to the job that can be executed this activity without using the music system and with only one hand movement. The Music Controller switches or pause the current music track by making a simple hand movement in front of the webcam. © 2022 IEEE.

关键词： Real time systems

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Savitzky-Golay Pre-processed Kulczynski Similarity Feature Extractive Convolutional Deep Belief Network for Brain Tumor Detection 6

Savitzky-Golay Pre-processed Kulczynski Similarity Feature E...

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6th International Conference on Devices, Circuits and Systems, ICDCS 2022

作者： Kurian, Simy Mary Juliet, Sujitha Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9781665480949

Brain cancer recognition at earlier stage is essential for treatment and patient survival rate. Many researchers carried out their research in the field of brain tumor discovery. But, the accuracy of brain cancer detection using traditional works was not higher. For resolving the above issues, SavitzkyGolay Pre-processed Kulczynski Similarity Feature Extractive Convolutional Deep Belief Network (SGPKSFE-CDBN) Model is introduced for detecting the brain tumor with minimal time consumption. The input MRI image is pre-processed using Savitzky-Golay Filter and applied to Deformable Surface based Skull Stripping Process. The skull stripped image is then sent to Kulczynski Similarity based Image Segmentation Process, which partitions the preprocessed image into number of segments. The multiple features are extracted using Gabor wavelet transformation from the input image. The image attributes are forwarded to the output layer where it employs the softmax activation function to match the extracted features with disease features for brain tumor detection. By this way, efficient tumor detection is carried out with higher accuracy and lesser time consumption. © 2022 IEEE.

关键词： Wavelet transforms Image segmentation Surface waves Magnetic resonance imaging Feature extraction Brain modeling Gabor filters

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Stock Market Prediction Using Multi-Objective Optimization 12

Stock Market Prediction Using Multi-Objective Optimization

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12th International Conference on computer and Knowledge engineering, ICCKE 2022

作者： Zolfaghari, Mahshid Fadishei, Hamid Tajgardan, Mohsen Khoshkangini, Reza University of Bojnord Computer Engineering Department Bojnord Iran Qom University of Technology Faculty of Electrical and Computer Engineering Qom Iran Malmö University Faculty of Technology and Society Department of Computer Science and Media Technology Sweden

ISBN: (数字)9781665476133

ISBN: (纸本)9781665476133

Forecasting in financial markets is challenging due to the inherent randomness of financial data sources and the vast number of factors that affect the market trends. Thus, it is essential to find informative elements within the vast number of available factors to enhance the performance of the predictive models in such a vital context. This makes the feature selection process an integral part of the financial prediction. In this paper, we propose a multi-objective evolutionary algorithm to reduce the number of features employed to predict the yearly performance of the US stock market. The primary idea is to select a smaller set of features with the slightest similarity and the best prediction accuracy. In this practice, we have utilized genetic algorithm, XGBoost and correlation in order to obtain a more diverse set of features which increases the performance. Experiential results show that our proposed approach is able to reduce the number of features significantly while maintaining comparable prediction accuracy. © 2022 IEEE.

关键词： Financial markets

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