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A machine learning method for predicting disease-associated microRNA connections using network internal topology data

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Healthcare Analytics 2023年 4卷 100215-100215页

作者： Jindal, Latika Sharma, Aditi Prasad, K.D.V. Irshad, Azeem Rivera, Richard Karimovna, Abdurakhimova Dilora Department of Computer Science and Engineering Medi-Caps university Indore India Department of computer science institute of engineering & technology Lucknow India Symbiosis Institute of Business Management Hyderabad India Symbiosis International (Deemed University) Pune India Asghar Mall College Rawalpindi Higher Education Department (HED) Govt. of the Punjab Pakistan Department of Informatics and Computer Science Escuela Politécnica Nacional Quito Ecuador Department of Corporate Finance and Securities Tashkent Institute of Finance Tashkent Uzbekistan

Recent studies have shown that aberrations or mutations of microRNAs (miRNA) can cause various diseases. Therefore, identifying disease-associated microRNAs can aid in diagnosing and treating related disorders. However, getting precise correlations through biological research is expensive and time-consuming. This research suggests a machine learning method (HNDLM) based on network internal topology data to anticipate disease-miRNA connections. HNDLM applies the recently described network embedding technique to biological networks rather than creating similar ones. According to the experimental findings, HNDLM performs better in terms of accuracy and Area Under the ROC Curve (AUC) value when compared to the traditional algorithms MIDPE, MIDP, WBSMDA, RLSMDA, Collective Prediction based on Transductive Learning (CPTL), and Human Disease-related MiRNA Prediction (HDMP). The top 30 potential miRNAs suggested by HNDLM can also be verified by earlier research in the case study. HNDLM can identify probable disease-miRNA correlations, which will aid in the investigation of illness path physiology and advance the field of bioinformatics. © 2023 The Authors

关键词： Bioinformatics Correlations Link prediction Machine learning MicroRNAs

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Expeditious Diagnosis of Cerebral Aneurysm on MR Brain Angiogram Images Utilizing Soft Computing Techniques

Expeditious Diagnosis of Cerebral Aneurysm on MR Brain Angio...

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International Conference on Inventive Computation Technologies (ICICT)

作者： Anjaneya L. H K. Maheswari N. Revathi Siva. K. P Manzoore Elahi M. Soudagar Joshuva Arockia Dhanraj Department of Electrical and Electronics Engineering Bapuji Institute of Engineering and Technology Davangere Karnataka India Department of Computer Science and Engineering CMR Technical Campus Kandlakoya Hyderabad Telangana India Department of Electronics and Communication Engineering Panimalar Engineering College Chennai Tamil Nadu India Department of Computer Science and Business Systems Sri Eshwar College of Engineering Coimbatore Tamil Nadu India Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Techinical Sciences Chennai Tamil Nadu India Department of Mechatronics Engineering Centre for Automation and Robotics (ANRO) Hindustan Institute of Technology and Science Padur Chennai Tamil Nadu India

ISBN: (数字)9798350359299

ISBN: (纸本)9798350359305

It is always a challenging task for radiologists to detect Cerebral Aneurysms (CA). Unenhanced magnetic resonance angiography (MRA) has become a popular screening tool because of its non-invasiveness, lack of contrast, and ionizing radiation. Because of its screening role, MRA requires a high recognition rate. This paper presents Machine Learning (ML) methods for detecting CA in MRA data. MRA scans of normal and aneurysms are collected and evaluated. This research study is divided into two sections: segmentation and classification. First, K-Means Clustering (KMC) is used for aneurysm segmentation from MR images. Second, image classification is done. For that, the appropriate features are retrieved using feature extraction and selection. Then Gray Level Run Length Matrix (GLRLM) is used for feature extraction and the Genetic Algorithm (GA) for feature selection. The GLRLM and GA features are provided independently to the ML models for training and testing. The ML models used in the research work are Artificial Neural Networks (ANN) and Modified Fuzzy C-Means (MFCM). The metrics are employed to evaluate the GLRM and GA features with ANN and MFCM. The combination of GA features with ANN produces the best results when compared to other combinations. The GA with ANN obtains an outstanding 98.33% accuracy, 100% specificity and precision, 96.67% sensitivity, and 98.31% F1 score.

关键词： Training Image segmentation Sensitivity Artificial neural networks Aneurysm Feature extraction Reliability

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A Hybrid Model for Botnet Detection using Machine Learning

A Hybrid Model for Botnet Detection using Machine Learning

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Business Analytics for Technology and Security (ICBATS), International Conference on

作者： Anam Zaheer Sidra Tahir Maram Fahaad Almufareh Bushra Hamid University Institute of Information Technology University of Arid Agriculture Rawalpindi Pakistan Applied Science Research Center Applied Science Private University Amman Jordan Department of Information Systems College of Computer and Information Sciences Jouf University Sakakah Saudi Arabia

Botnet attacks are becoming a growing threat to the security of computer networks, and there is a need for effective and efficient methods for detecting these attacks. In this study, we proposed a hybrid machine-learning model for detecting botnet attacks in network traffic. The approach combines three machine learning algorithms: k-means, rule- based system, and decision tree. The experiment was conducted using the CTU-13 dataset and features extracted from the Barnacles Mating Optimizer for network traffic flow. The experiment results showed that the proposed hybrid machine learning approach can achieve high accuracy levels in detecting botnet attacks, with a computed accuracy of 99%. The combination of different machine learning algorithms can improve the accuracy of the system, making it more robust to different types of botnet attacks. The use of real-world data, such as the CTU-13 dataset, and incorporating features from the Barnacles Mating Optimizer, make the results of this experiment more representative of real-world botnet attack detection computed accuracy 99.32 for kmean, 99.11% for decision tree and 97.14% for rule-based system. Precision for kmean 98.93%, decision tree 98.37% and rule-based 95.93% computed.

关键词： Machine learning algorithms Network topology Botnet Computational modeling Telecommunication traffic Machine learning Feature extraction

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Modular commutator in gapped quantum many-body systems

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Physical Review B 2022年第7期106卷 075147-075147页

作者： Isaac H. Kim Bowen Shi Kohtaro Kato Victor V. Albert Department of Computer Science University of California Davis Davis California USA Centre for Engineered Quantum Systems School of Physics University of Sydney Sydney NSW Australia Department of Physics University of California at San Diego La Jolla California USA Center for Quantum Information and Quantum Biology Osaka University Osaka Japan Graduate School of Informatics Nagoya University Nagoya Japan Joint Center for Quantum Information and Computer Science National Institute of Standards and Technology and University of Maryland College Park Maryland USA

In Phys. Rev. Lett. 128, 176402 (2022), we argued that the chiral central charge—a topologically protected quantity characterizing the edge theory of a gapped (2+1)-dimensional system—can be extracted from the bulk by using an order parameter called the modular commutator. In this paper, we reveal general properties of the modular commutator and strengthen its relationship with the chiral central charge. First, we identify connections between the modular commutator and conditional mutual information, time reversal, and modular flow. Second, we prove, within the framework of the entanglement bootstrap program, that two topologically ordered media connected by a gapped domain wall must have the same modular commutator in their respective bulk. Third, we numerically calculate the value of the modular commutator for a bosonic lattice Laughlin state for finite sizes and extrapolate to the infinite-volume limit. The result of this extrapolation is consistent with the proposed formula up to an error of about 0.7%.

关键词： Anyons Edge states Heat transfer Quantum correlations in quantum information Quantum entanglement Quasiparticles & collective excitations Topological phases of matter 2-dimensional systems

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EEG Signal Analysis for Early Detection of Critical Road Events and Emergency Response in Autonomous Driving

EEG Signal Analysis for Early Detection of Critical Road Eve...

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International Conference on Intelligent Transportation

作者： Xiaofei Zhang Xin Xiao Yingkai Yang Zhenmao Hao Jun Li Hong Wang School of Vehicle and Mobility Tsinghua University Beijing China School of Mathematics and Systems Science Xinjiang University Xinjiang China Department of Electrical and Electronic Engineering Imperial College London London Britain School of Computer Science Carleton University Ottawa Canada

Autonomous driving technology has the potential to revolutionize the transportation industry. However, ensuring the safety of passengers and other traffic participants is a crucial challenge that must be addressed. One major concern is how the vehicle should react in emergency situations, where split-second decisions must be made to avoid collisions and minimize damage. This study presents a novel approach for the early detection of critical road events and timely emergency response in autonomous driving through the analysis of electroencephalogram (EEG) signals. Specifically, the focus is on predicting cut-in maneuvers by preceding vehicles. EEG data from drivers were collected in driving scenarios, and offline analysis revealed distinctive EEG patterns preceding the cut-in event. By analyzing these signals with advanced machine learning algorithms, we may predict whether the car in front will cut in or not. Furthermore, a comparison of event-related potentials (ERPs) showed significant differences in waveform and amplitude between cut-in events and non-cut-in driving situations. EEG topographic map analysis revealed changes in brain activity in specific regions during cut-in events, shedding light on driver cognition and emotional responses. Additionally, time-frequency analysis revealed that cut-in events were characterized by increased low-frequency activity (below 10Hz) in the EEG signals. This research show cases the potential of EEG signal analysis to enable prompt response in dangerous and emergency situations, enhancing the safety of autonomous driving.

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MIMO-Aided Index Modulation-Based Orthogonal Frequency Division Multiple Access

MIMO-Aided Index Modulation-Based Orthogonal Frequency Divis...

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International Conference on Advanced Communication Technologies and Networking (CommNet)

作者： Widad Belaoura Saud Althunibat Mazen Hasna Khalid Qaraqe Electrical Systems Engineering Department University of M’Hamed Bougara Boumerdes Boumerdes Algeria Department of Electrical and Computer Engineering Texas A&M University at Qatar Doha Qatar Department of Electrical Engineering Qatar University Doha Qatar College of Science and Engineering Hamad Bin Khalifa University Doha Qatar

ISBN: (数字)9798350367027

ISBN: (纸本)9798350367034

Index modulation-aided orthogonal frequency division multiple access (IM-OFDMA) is envisioned as an advanced solution for the upcoming wireless communication systems. This paper proposes a highly efficient scheme by combining multiple-input multiple-output (MIMO) and IM-OFDMA to leverage both techniques collectively, offering a new possibility to boost spectrum efficiency compared with the IM-OFDMA and MIMO-OFDMA schemes. The basic principle of the resulting MIMO-IM-OFDMA scheme is to independently perform the IMOFDMA process on each transmit antenna, thereby achieving higher diversity. Considering different system configurations, Monte Carlo simulations demonstrate that the MIMO-IMOFDMA scheme not only enhances the communication error’s performance but also improves the spectral efficiency compared to the conventional IM-OFDMA and MIMO-OFDMA schemes.

关键词： Monte Carlo methods Spectral efficiency OFDM Spatial diversity Transmitting antennas Diversity reception Symbols Indexes MIMO communication Signal to noise ratio

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Exploring Fusion of the Face and Voice Modalities Using CNN Features for a Better Performance

Exploring Fusion of the Face and Voice Modalities Using CNN ...

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science, Engineering and Business for Sustainable Development Goals (SEB-SDG), International Conference on

作者： Adekunle A. Akinrinmade Emmanuel Adetiba Joke A. Badejo Comfort O. Lawal Department of Electrical and Information Engineering (Computer Engineering) College of Engineering Covenant University Covenant University Ota Ogun Nigeria Department of Electrical and Information Engineering College of Engineering Covenant University Ota Ogun Nigeria HRA Institute for Systems Science Durban University South Africa

Face recognition and speaker recognition have gained attention in recent times in the large-scale space. Large-scale face databases such as VGG-Face and VGGFace2 have been created using a semi-automated pipeline and used to develop methods for face recognition achieving state-of-the-art performance. This is also true for speaker recognition with large-scale databases such as VoxCeleb and VoxCeleb2. Howbeit, these two modalities have been treated individually. Although some works have explored the fusion of both modalities, they have played in small-scale space. This work aims at creating a large-scale face and corresponding voice database from YouTube under unconstrained conditions with a size comparable to the earlier mentioned and explores the fusion of both face and voice modalities for recognition in the large-scale space. To this end, a face and corresponding voice database of Nigerians available on YouTube was created for 2,656 Nigerians containing 2,055,169 face images and 195 hours of voice recording using a semi-automated curation pipeline. Convolutional Neural Networks (CNNs) were used to perform face recognition and speaker recognition individually. This was followed by the use of CNN for a combination of both modalities achieving an Equal Error Rate (EER) more than 5 times lower than the best result in the individual cases.

关键词： Video on demand Databases Face recognition Pipelines Speech recognition Feature extraction Speaker recognition

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Low-cost Modular Smart Home Controlling System for Minimising Energy Consumption: A Conceptual Study 1

Low-cost Modular Smart Home Controlling System for Minimisin...

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1st International Visualization, informatics and Technology Conference, IVIT 2022

作者： Putra Ahmad Baidowi, Zaid Mujaiyid Danial Hakim Nazrin Izuan Gerard, Muhammad Ramly, Ruwaida Mohd Isa, Mohd Rizal Yusof Darus, Mohamad Universiti Teknologi MARA Cawangan Selangor Kampus Dengkil Centre of Foundation Studies Selangor Dengkil43800 Malaysia Malaysian Institute of Information Technology Universiti Kuala Lumpur Malaysia National Defence University of Malaysia Computer Science Department Kuala Lumpur Malaysia College of Computing Informatics and Media Universiti Teknologi MARA School of Computing Sciences Malaysia

ISBN: (纸本)9781665486484

Internet of Things (IoT) has become one of the popular topics in Information and Communication Technology (ICT) recently. IoT has been widely used in many fields, and one of the most topics that had gained significant interest of many researchers is minimising energy consumption. IoT which is implemented in a wireless, connects multiple devices via the Internet. It allows data to be gathered and transferred without human intervention. In this paper, a new solution is to propose an automatic control of several electrical appliances such as air-conditioners, lights, and fans. These three electrical appliances contribute to the highest energy consumption. This paper aims to contribute to the solution of minimising energy consumption by deploying a low-cost modular system to be installed into readily houses. Thus, it can adapt any old infrastructure to an efficient home. Minimising power consumption has been studied in a smart home but less work deployed Arduino Nano 33 IoT as compared to the existing available Arduino system. Finally, this work is hoped lead to related studies in minimising energy consumption. © 2022 IEEE.

关键词： Energy utilization

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A Multi-Player Potential Game Approach for Sensor Network Localization with Noisy Measurements

A Multi-Player Potential Game Approach for Sensor Network Lo...

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IEEE Conference on Decision and Control

作者： Gehui Xu Guanpu Chen Baris Fidan Yiguang Hong Hongsheng Qi Thomas Parisini Karl H. Johansson Department of Electrical and Electronic Engineering Imperial College London London UK Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Department of Mechanical and Mechatronics Engineering University of Waterloo Waterloo ON Canada Department of Control Science and Engineering Tongji University shanghai China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai China Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Beijing China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing China Department of Electronic Systems Aalborg University Denmark Department of Engineering and Architecture University of Trieste Italy

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

Sensor network localization (SNL) is a challenging problem due to its inherent non-convexity and the effects of noise in inter-node ranging measurements and anchor node position. We formulate a non-convex SNL problem as a multiplayer non-convex potential game and investigate the existence and uniqueness of a Nash equilibrium (NE) in both the ideal setting without measurement noise and the practical setting with measurement noise. We first show that the NE exists and is unique in the noiseless case, and corresponds to the precise network localization. Then, we study the SNL for the case with errors affecting the anchor node position and the inter-node distance measurements. Specifically, we establish that in case these errors are sufficiently small, the NE exists and is unique. It is shown that the NE is an approximate solution to the SNL problem, and that the position errors can be quantified accordingly. Based on these findings, we apply the results to case studies involving only inter-node distance measurement errors and only anchor position information inaccuracies.

关键词： Location awareness Noise Games Position measurement Nash equilibrium Distance measurement Noise measurement

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Function for Tracing Diffusion of Classified Information to Support Multiple VMs with KVM 9

Function for Tracing Diffusion of Classified Information to ...

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9th International Symposium on Computing and Networking Workshops, CANDARW 2021

作者： Otani, Kohei Okazaki, Toshiki Yamauchi, Toshihiro Moriyama, Hideaki Sato, Masaya Taniguchi, Hideo Graduate School of Natural Science And Technology Okayama University Okayama Japan National Institute of Technology Ariake College Department of Creative Engineering Fukuoka Japan Faculty of Computer Science And Systems Engineering Okayama Prefectural University Okayama Japan

ISBN: (纸本)9781665428354

To handle information leaks caused by administrative errors or mishandling, a function for tracing the diffusion of classified information using a virtual machine monitor (VMM) was proposed. However, the proposed function has not been investigated in cases in which virtual machines (VMs) allocated by multiple virtual central processing units (vCPUs) are to be monitored. In addition, cases in which multiple VMs are monitored have not been examined. In this study, we describe the support of multiple VMs for the proposed VMM-based tracing function. We also show how to deal with VMs allocated by multiple vCPUs. Furthermore, we report the evaluation results from assessing the traceability of the improved proposed method and its overhead for classified information when a VM with multiple vCPUs is monitored. © 2021 IEEE.

关键词： Virtual machine

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