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Lung cancer prediction using multi-gene genetic programming by selecting automatic features from amino acid sequences

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Computational Biology and Chemistry 2022年第0期98卷 107638-107638页

作者： Sattar, Mohsin Majid, Abdul Kausar, Nabeela Bilal, Muhammad Kashif, Muhammad Biomedical Informatics Research Lab Department of Computer and Information Sciences Pakistan Institute of Engineering and Applied Sciences Islamabad Pakistan MIS Division Pakistan Institute of Nuclear Science and Technology Islamabad Pakistan Department of Computing and Technology Iqra University Islamabad Pakistan

Lung cancer is one of the leading causes of cancer related deaths. Early diagnosis of lung cancer using automatic feature selection from large number of features is a challenging task. Conventionally, cancer diagnosis approaches use physical features that appear in later stages, while harmful effects have already been occurred due to abnormal somatic mutations. In order to extract useful novel patterns to efficiently predict cancer at early stages, we analyzed lung cancer related mutated genes that reveal useful information in protein amino acid sequences. For this, we developed a new evolutionary learning technique with biologically inspired multi-gene genetic programming algorithm using discriminant information of protein amino acids. The proposed model efficiently selects 23 discriminant features out of 1500 features. Then it combines the selected features and related primitive functions optimally for prediction of lung cancer. Hence, an efficient predictive model is constructed that helps in understanding the complex heterogeneous nature of lung cancer. The proposed system achieved area under ROC curve and accuracy values of 98.79% and 95.67%, respectively outperforming related lung cancer prediction approaches. © 2022 Elsevier Ltd

关键词： Proteins

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Regression models for predicting the effect of trash rack on flow properties at power intakes

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engineering Applications of Computational Fluid Mechanics 2024年第1期18卷

作者： Li, Shuguang Qasem, Sultan Noman Karami, Hojat Salwana, Ely Rezaei, Alireza Shahmirzadi, Danyal Band, Shahab S. School of Computer Science and Technology Shandong Technology and Business University Yantai China Computer Science Department Imam Muhammad Ibn Saud Islamic University Riyadh Saudi Arabia Computer Science Department Faculty of Applied Science Taiz University Taiz Yemen Civil Engineering Department Semnan University Semnan Iran Institute of Visual Informatics Universiti Kebangsaan Malaysia Selangor Malaysia Graduate School of Engineering Science and Technology National Yunlin University of Science and Technology Douliou Taiwan International Graduate School of AI National Yunlin University of Science and Technology Yunlin Taiwan

Vortex flow characteristics in a reservoir and horizontal water intake have been predicted by using regression models in this numerical research. In this paper, three standalone machine learning models–random Forest (RF), K-nearest neighbours (KNN), Gradient Boosting (GB)–and a proposed hybrid model based on Lévy Jaya Algorithm (LJA) and GB (LJA-GB) are employed to estimate the effect of trash racks on flow properties at power intakes. The experimental data which are prepared for the proposed study in this paper were obtained through a rectangular laboratory tank 8.3 m³ with various submergence depths and Froude numbers on nine trash racks with 63.7%–84.1% opening, made out of 2, 2.5, 4, and 6 mm thick copper wire. The outcomes revealed that the proposed LJA-GB model shows the best overall performance among the four models used for estimation. Thus, the LJA-GB model has the lowest mean absolute error (MAE) (0.3344), mean squared error (MSE) (0.1784), and root mean squared error (RMSE) (0.4223) values and highest R-squared ((Formula presented.)) (0.9899) and Willmott’s index (WI) values (0.9508) in the testing stage metrics for (Formula presented.) estimation and MAE (0.0061), MSE (0.0001), RMSE (0.0073), (Formula presented.) (0.9971), WI (0.9727) for (Formula presented.) estimation. Whereas the RF and KNN models exhibited poor performance in both stages of estimation. © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： experimental modelling Hydro power plant regression models vortex strength

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Photonic Frequency Space Simulation of Tight-Binding Lattices Using Integrated LNOI Ring Resonators 16

Photonic Frequency Space Simulation of Tight-Binding Lattice...

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16th Pacific Rim Conference on Lasers and Electro-Optics, CLEO-PR 2024

作者： Balčytis, Armandas Dinh, Hiep Ozawa, Tomoki Ota, Yasutomo Baba, Toshihiko Iwamoto, Satoshi Mitchell, Arnan Nguyen, Thach G. Integrated Photonics and Applications Centre Rmit University MelbourneVIC3000 Australia Advanced Institute for Materials Research Tohoku University Sendai980-8577 Japan Keio University Department of Applied Physics and Physico-Informatics Yokohama223-8522 Japan Yokohama National University Department of Electrical and Computer Engineering 79-5 Tokiwadai Hodogaya-ku Yokohama240-8501 Japan Research Center for Advanced Science and Technology The University of Tokyo 4-6-1 Komaba Meguro-ku Tokyo153-8904 Japan

We use electro-optically modulated ring cavities, integrated on thin-film lithium niobate, to model frequency dimension tight-binding lattices with versatile connectivity. Inter-mode coupling range, strength, and hopp... 详细信息

ISBN: (纸本)9798350372076

关键词： Photonics

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Multimodal E-Commerce Product Classification Using Hierarchical Fusion 2

Multimodal E-Commerce Product Classification Using Hierarchi...

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2nd IEEE Conference on Information technology and Data Science, CITDS 2022

作者： Tashu, Tsegaye Misikir Fattouh, Sara Kiss, Peter Horvath, Tomas Elte - Eötvös Loránd University Faculty of Informatics Department of Data Science and Engineering Pázmány Péter sétány 1/C Budapest1117 Hungary Faculty of Science Institute of Computer Science Pavol Jozef Šafárik University Jesenná5 Košice040 01 Slovakia College of Informatics Kombolcha Institute of Technology Wollo University Kombolcha208 Ethiopia

ISBN: (数字)9781665496537

ISBN: (纸本)9781665496537

In this work, we present a multi-modal model for commercial product classification, that combines features extracted by multiple neural network models from textual (Camem-BERT and FlauBERT) and visual data (SE-ResNeXt-50), using simple fusion techniques. The proposed method significantly outperformed the performance of the unimodal models, as well as the reported performance of similar models on our specific task. We made experiments with multiple fusing techniques, and found, that the best preforming technique to combine the individual embedding of the unimodal network is based on the combination of concatenation and averaging the feature vectors. Each modality complemented the shortcomings of the other modalities, demonstrating that increasing the number of modalities can be an effective method for improving the performance of multi-label and multimodal classification problems. © 2022 IEEE.

关键词： Neural network models

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Multimodal Feature Extraction for Memes Sentiment Classification 2

Multimodal Feature Extraction for Memes Sentiment Classifica...

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2nd IEEE Conference on Information technology and Data Science, CITDS 2022

作者： Ouaari, Sofiane Tashu, Tsegaye Misikir Horvath, Tomas Elte - Eötvös Loránd University Faculty of Informatics Department of Data Science and Engineering Pázmány Péter sétány 1/C Budapest1117 Hungary Institute of Computer Science Faculty of Science Pavol Jozef Šafárik University Jesenná 5 Košice040 01 Slovakia College of Informatics Kombolcha Institute of Technology Wollo University Kombolcha208 Ethiopia

ISBN: (数字)9781665496537

ISBN: (纸本)9781665496537

In this study, we propose feature extraction for multimodal meme classification using Deep Learning approaches. A meme is usually a photo or video with text shared by the young generation on social media platforms that expresses a culturally relevant idea. Since they are an efficient way to express emotions and feelings, a good classifier that can classify the sentiment behind the meme is important. To make the learning process more efficient, reduce the likelihood of overfitting, and improve the generalizability of the model, one needs a good approach for joint feature extraction from all modalities. In this work, we proposed to use different multimodal neural network approaches for multimodal feature extraction and use the extracted features to train a classifier to identify the sentiment in a meme. © 2022 IEEE.

关键词： computer vision

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Evolutionary Trends of Intelligent IoT Networking for Commercial and Industrial Use Cases

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中国通信（英文版） 2023年第8期20卷前插1-前插4页

作者： Shuai Han Rong Bo Nadra Guizani Sai Xu Ruoyu Su Liang Xiao Cheng Li Department of Electronics and Communication Engineering Harbin Institute of Technology Communications Research Centre Canada Engineering program and the computing Research Association School of Cybersecurity Northwestern Polytechnical UniversityXi'anChina Nanjing University of Posts and Telecommunications Department of Informatics and Communication Engineering Xiamen Univer- sityFujianChina Department of Electrical and Computer Engineering of Memorial University St.John'sCanada

The intelligent Internet of Things(IIoT)is one of the key enablers for the digital transformation of traditional industries towards Industry *** services have diversified requirements due to the nature of their commercial and industrial *** various devices in IIoT environments have their unique properties in terms of resource limita-tions,network lifetimes,and application Quali-ty-of-Service(QoS)requirements which affect the performance under such applications ***,IoT devices typically have constrained power,storage,computing,high latency,massive multiple access and communications resources which limit the performance of IIoT for commercial and industrial Use *** the other hand,devices used in IIoT were not initially designed with security in mind and they had been considered *** response to the above challenges,domestic and foreign scholars have gradually carried out research on network architecture,collaborative scheduling strategy,energy supply technology,resource man-agement,and ***,the research on IIoT is still in its infancy,and has not yet formed comprehen-sive and systematic theoretical methods and technical standards.

关键词： Special issues and sections Internet of Things Intelligent systems Commercialization Requirements engineering Industrial engineering

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Generalized planar Turán numbers related to short cycles

arXiv

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arXiv 2024年

作者： Gyo, Ervin Karim, Hilal Hama Alfréd Rényi Institute of Mathematics HUN-REN Budapest Hungary Department of Computer Science and Information Theory Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Műegyetem rkp. 3. BudapestH-1111 Hungary

Given two graphs H and F, the generalized planar Turán number exP(n, H, F) is the maximum number of copies of H that an n-vertex F-free planar graph can have. We investigate this function when H and F are short cycles. Namely, for large n, we find the exact value of exP(n, Cl, C3), where Cl is a cycle of length l, for 4 ≤ l ≤ 6, and determine the extremal graphs in each case. Also, considering the converse of these problems, we determine sharp upper bounds for exP(n, C3, Cl), for 4 ≤ l ≤ 6. © 2024, CC BY-NC-SA.

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Improving Smart Healthcare Safety and Security Using Kinect 1st

Improving Smart Healthcare Safety and Security Using Kinect

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1st International Conference on Cybersecurity in Emerging Digital Era, ICCEDE 2020

作者： Singh, Vijai Saxena, Neetesh Pathak, Drashti Saini, Garima Bhatnagar, Divya Department of CSE G.L. Bajaj Institute of Technology and Management Gr Noida India School of Computer Science and Informatics Cardiff University Wales United Kingdom Department of CSE IMS Engineering College Ghaziabad India

ISBN: (纸本)9783030848415

Modern healthcare operating rooms are cumbersome, especially in the current pandemic of Coronavirus (COVID-19) to manage the safety and security of the patients and the staff. Conditions in a modern operating room are like, a patient’s medical diagnosis or x-ray or the MRI images are the vital resources to be considered. In general, the doctors are impelled to scrub out their hands every time they scroll through or examine the images in mid-operation. To avoid departing the operating table, several doctors put their faith in assistants or nurses and ask to examine the system that can be clumsy and disappointing and can rise the gathering in the room itself. To overcome this problem and emphasizing the use of the touchless smart healthcare system in medical diagnosis we have developed an application for the same. We propose and demonstrate a gesture and speech-based system to help staff to deal with such critical conditions. The proposed system is composed of two main parts: the healthcare operating software and sensor Microsoft Kinect. Test results conclude the efficiency of the viewing system in terms of time complexity, and the value and the safety of the recording system have increased. © 2021, Springer Nature Switzerland AG.

关键词： Diagnosis

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Hybrid genetic-discretized algorithm to handle data uncertainty in diagnosing stenosis of coronary arteries

Hybrid genetic-discretized algorithm to handle data uncertai...

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作者： Alizadehsani, Roohallah Roshanzamir, Mohamad Abdar, Moloud Beykikhoshk, Adham Khosravi, Abbas Nahavandi, Saeid Plawiak, Pawel Tan, Ru San Acharya, U rajendra Deakin University GeelongVIC Australia Department of Engineering Fasa Branch Islamic Azad University Fars Fasa Iran Centre for Pattern Recognition and Data Analytics Deakin University GeelongVIC Australia Department of Information and Communications Technology Faculty of Computer Science and Telecommunications Cracow University of Technology Warszawska 24 st. F-3 31-155 Krakow Poland Institute of Theoretical and Applied Informatics Polish Academy of Sciences Baltycka 5 Gliwice44-100 Poland National Heart Centre Singapore Singapore Department of Electronics and Computer Engineering Ngee Ann Polytechnic Singapore Department of Biomedical Engineering School of Science and Technology Singapore University of Social Sciences Singapore Department of Bioinformatics and Medical Engineering Asia University Taiwan

Coronary artery disease (CAD) is the leading cause of morbidity and death worldwide. Invasive coronary angiography is the most accurate technique for diagnosing CAD, but is invasive and costly. Hence, analytical methods such as machine learning and data mining techniques are becoming increasingly more popular. Although physicians need to know which arteries are stenotic, most of the researchers focus only on CAD detection and few studies have investigated stenosis of the right coronary artery (RCA), left circumflex (LCX) artery and left anterior descending (LAD) artery separately. Meanwhile, most of the datasets in this field are noisy (data uncertainty). However, to the best of our knowledge, there is no study conducted to address this important problem. This study uses the extension of the Z-Alizadeh Sani dataset, containing 303 records with 54 features. A new feature selection algorithm is proposed in this work. Meanwhile, by discretization of data, we also handle the uncertainty in CAD prediction. To the best of our knowledge, this is the first study attempted to handle uncertainty in CAD prediction. Finally, the genetic algorithm (GA) is used to determine the hyper-parameters of the support vector machine (SVM) kernels. We have achieved high accuracy for the stenosis diagnosis of each main coronary artery. The results of this study can aid the clinicians to validate their manual stenosis diagnosis of RCA, LCX and LAD coronary arteries. © 2020 John Wiley & Sons, Ltd.

关键词： Feature Selection

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An Artificial Intelligence (AI) Based X-AODV Routing Protocol in Wireless Networks

An Artificial Intelligence (AI) Based X-AODV Routing Protoco...

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International Conference on Emerging Trends in Networks and computer Communications (ETNCC)

作者： Khalid Hussain Md Amin Ullah Sheikh Husin Bin Jazri Saira Muzzaffar Sohini Chowdhury Kavita School of Computing and Informatics Albukhary International University Alor Setar Kedah Malaysia Faculty of Innovation and Technology School of Computer Science Taylor's University Subang Jaya Malaysia Division of Research and Development Lovely Professional University Phagwara India KKR and KSR Institute of Technology and Sciences Guntur Andhra Pradesh Department of Computer Science and Engineering Chandigarh Group of Colleges Jhanjeri Mohali Punjab India

ISBN: (数字)9798350353266

ISBN: (纸本)9798350353273

The multi-hopping mechanism features in the wireless network environment, in which all nodes contain the information of link and connectivity in any infrastructure less manner. When mobile devices either jump from their original network or change their location without describing any fashion, it is named mobile ad hoc network MANET. To ensure the up to date information related to the location and to provide the routing traffic of the node, numerous protocols have been used to remain efficient in achieving this for instance, AODV, DSR, DSDV etc. While we, the peer-to-peer networking, have challenges in many places, security is something that warrants attention more than any other things. The signal corruption hampers data transmission in bedwire networks when the attacks take place. This kind of activity can touch anyone including the layers lying above the physical and below the network layer. In the current discussion we developed, proposition of diversified, efficient and secure AI-based X-AODV routing protocol. We have investigated our proposed protocol against the recent modified two protocols which are SAODV and AraN. The outcome of simulation reveals that our proposed routing protocol takes place in successfully performing of attack detection and is also the best when it comes to providing security and performance. The Simulation outcomes exhibit the Networks' performance improvement.

关键词： Technological innovation Throughput Routing Routing protocols Real-time systems Mobile handsets Peer-to-peer computing Artificial intelligence AODV Mobile ad hoc networks

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