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Optics-based metaheuristic approach to assess critical failure surfaces in both circular and non-circular failure modes for slope stability analysis

Rock Mechanics Bulletin

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Rock Mechanics Bulletin 2024年第1期3卷 37-54页

作者： Jayraj Singh Amit Kumar Verma Haider Banka Ravishankar kumar Amit Jaiswal Department of Computer Science&Engineering NIIT UniversityRajasthanIndia Department of Civil&Environment Engineering Indian Institute of TechnologyPatnaBihar801106India Department of Computer Science&Engineering Indian Institute of Technology(ISM)DhanbadJharkhand826004India Department of Mining Bharat Coking Coal LimitedDhanbadJharkhand826005India

In many construction projects,a proactive slope stability evaluation is a *** many deterministic or non-deterministic approaches have been commonly used,metaheuristic approaches have resulted in high precision and significant outcomes for slope stability analysis *** current work focuses on the reliable assessment of critical failure surfaces associated with the least factor of safety value in both homogeneous and non-homogeneous slopes using a new simplified meta-heuristic approach called optics-inspired optimization(OIO).The algorithm utilizes six different LEM methods as a fitness function for deriving the factor of *** analysis over three benchmark studies has been performed to demonstrate the algorithm's robustness and *** implementation found more robust results as compared to previous ***,the algorithm's statistical implication is conducted using the ANOVA test,which inferred better *** this interpretation,the approach claims to be suitable and efficient for slope stability analysis.

关键词： OIO-Algorithm Metaheuristic algorithms Slope stability analysis Critical failure surface LEM'S procedure

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Hybrid Marine Predators Optimization and Improved Particle Swarm Optimization-Based Optimal Cluster Routing in Wireless Sensor Networks(WSNs)

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China Communications 2022年第6期19卷 219-247页

作者： A.Balamurugan Sengathir Janakiraman M.Deva Priya A.Christy Jeba Malar Department of Computer Science&Engineering KPR Institute of Engineering and TechnologyCoimbatoreTamilnaduIndia Department of Information Technology CVR College of EngineeringHyderabadTelanganaIndia Department of Computer Science&Engineering Sri Eshwar College of EngineeringCoimbatoreTamilnaduIndia Department of Information Technology Sri Krishna College of TechnologyCoimbatoreTamilnaduIndia

Wireless Sensor Networks(WSNs)play an indispensable role in the lives of human beings in the fields of environment monitoring,manufacturing,education,agriculture etc.,However,the batteries in the sensor node under deployment in an unattended or remote area cannot be replaced because of their wireless *** this context,several researchers have contributed diversified number of cluster-based routing schemes that concentrate on the objective of extending node survival ***,there still exists a room for improvement in Cluster Head(CH)selection based on the integration of critical *** meta-heuristic methods that concentrate on guaranteeing both CH selection and data transmission for improving optimal network performance are *** this paper,a hybrid Marine Predators Optimization and Improved Particle Swarm Optimizationbased Optimal Cluster Routing(MPO-IPSO-OCR)is proposed for ensuring both efficient CH selection and data *** robust characteristic of MPOA is used in optimized CH selection,while improved PSO is used for determining the optimized route to ensure sink *** specific,a strategy of position update is included in the improved PSO for enhancing the global searching efficiency of *** high-speed ratio,unit speed rate and low speed rate strategy inherited by MPOA facilitate better exploitation by preventing solution from being struck into local optimality *** simulation investigation and statistical results confirm that the proposed MPOIPSO-OCR is capable of improving the energy stability by 21.28%,prolonging network lifetime by 18.62%and offering maximum throughput by 16.79%when compared to the benchmarked cluster-based routing schemes.

关键词： Marine Predators Optimization Algorithm(MPOA) Particle Swarm Optimization(PSO) Optimal Cluster-based Routing Cluster Head(CH)selection Wireless Sensor Networks(WSNs)

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AEECA for Reliable Communication to Enhance the Network Life Time for WSN

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Intelligent Automation & Soft Computing 2023年第2期35卷 1705-1719页

作者： Ganesh Jayaraman V R Sarma Dhulipala Department of Computer Science&Engineering Anjalai Ammal Mahalingam Engineering CollegeKovilvenniTamil NaduIndia Department of Physics Anna UniversityBIT CampusTiruchirappalliTamil NaduIndia

Nowadays,wireless sensor networks play a vital role in our day to day *** communication is preferred for many sensing applications due its convenience,ﬂexibility and *** sensors to sense the environmental factor are versatile and send sensed data to central station *** cluster based protocols are provided an optimal solution for enhancing the lifetime of the sensor *** this paper,modiﬁed K-means++algorithm is used to form the cluster and cluster head in an efﬁcient way and the Advanced Energy-Efﬁcient Cluster head selection Algorithm(AEECA)is used to calculate the weighted fac-tor of the transmission path and effective data collection using gateway *** experimental results show the proposed algorithm outperforms the existing routing algorithms.

关键词： Wireless sensor network reliable communication energy management energy clustering sensors base station gateway node

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Heart Diseases Prediction Using machine learning and Deep learning Models 6

Heart Diseases Prediction Using machine learning and Deep le...

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6th International Conference on Computational Intelligence and Communication Technologies, CCICT 2024

作者： Himanshi Pattanaik, Srinibas Nayak, Kanishk Department of CSE Computer Science&Engineering Chandigarh University Mohali India Department of CSE UIE University Institute of Engineering Chandigarh University Mohali India

ISBN: (纸本)9798350374629

Heart disorders cause a great deal of illness and mortality, making them a major worldwide health concern. In order to lessen the effects of cardiovascular illnesses, early detection and intervention are essential. In order to improve the precision and effectiveness of heart disease prediction, this study investigates the application of machine learning (ML) and deep learning (DL) approaches. Using a variety of datasets that include clinical measurements, medical histories, and patient demographics, predictive models are created to identify complex patterns that may indicate cardiovascular risk. In addition to sophisticated DL models like neural networks and convolutional neural networks (CNN), traditional machine learning algorithms like logistic regression, decision trees, and ensemble approaches are also used on the job. The study explores the technical nuances of model construction as well as ethical issues, privacy concerns with data, and the relationship between data scientists and medical experts. The aim is to construct precise and comprehensible models that enable doctors to provide proactive insights for individualized patient care, so contributing to the improvement of cardiovascular healthcare. It is expected that the study project's results will have an influence on cardiovascular health, promoting early identification, well-informed choices, and eventually bettering patient outcomes in the field of heart disease treatment. ©2024 IEEE.

关键词： Risk assessment

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Lung Cancer Segmentation with Three-Parameter Logistic Type Distribution Model

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computers, Materials & Continua 2023年第4期75卷 1447-1465页

作者： Debnath Bhattacharyya EaliStephen Neal Joshua N.Thirupathi Rao Yung-cheol Byun Department of Computer Science and Engineering Koneru Lakshmaiah Education FoundationVaddeswaramGuntur522302Andhra PradeshIndia Department of Computer Science&Engineering Vignan’s Institute of Information Technology(A)Visakhapatnam530049Andhra PradeshIndia Department of Computer Engineering Jeju National University102 Jejudaehak-roJeju-siJeju-do690-756Korea

Lung cancer is the leading cause of mortality in the world affectingboth men and women *** a radiologist just focuses on the patient’sbody, it increases the amount of strain on the radiologist and the likelihoodof missing pathological information such as abnormalities are *** of the primary objectives of this research work is to develop computerassisteddiagnosis and detection of lung cancer. It also intends to make iteasier for radiologists to identify and diagnose lung cancer accurately. Theproposed strategy which was based on a unique image feature, took intoconsideration the spatial interaction of voxels that were next to one *** the U-NET+Three parameter logistic distribution-based technique, wewere able to replicate the situation. The proposed technique had an averageDice co-efficient (DSC) of 97.3%, a sensitivity of 96.5% and a specificity of94.1% when tested on the Luna-16 dataset. This research investigates howdiverse lung segmentation, juxta pleural nodule inclusion, and pulmonarynodule segmentation approaches may be applied to create computer AidedDiagnosis (CAD) systems. When we compared our approach to four otherlung segmentation methods, we discovered that ours was the most *** employed 40 patients from Luna-16 datasets to evaluate this. In termsof DSC performance, the findings demonstrate that the suggested techniqueoutperforms the other strategies by a significant margin.

关键词： Magnetic resonance imaging(MRI) lung cancer Luna-16 logistic distribution segmentation deep learning juxta plural pulmonary nodules

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PSO-DBNet for Peak-to-Average Power Ratio Reduction Using Deep Belief Network

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computer Systems science & engineering 2023年第5期45卷 1483-1493页

作者： A.Jameer Basha M.Ramya Devi S.Lokesh P.Sivaranjani D.Mansoor Hussain Venkat Padhy Department of Computer Science and Engineering Hindusthan Institute of TechnologyCoimbatore641032India Department of Computer Science and Engineering Hindusthan College of Engineering and TechnologyCoimbatore641032India Department of Electronics and Communication Engineering Kongu Engineering CollegeErode638060India Department of Computer Science and Engineering Sri Krishna College of Engineering and TechnologyCoimbatore641008India School of Computing Science&Engineering VIT Bhopal UniversityBhopal466114India

Data transmission through a wireless network has faced various signal problems in the past *** orthogonal frequency division multiplexing(OFDM)technique is widely accepted in multiple data transfer patterns at various frequency bands.A recent wireless communication network uses OFDM in longterm evolution(LTE)and 5G,among *** main problem faced by 5G wireless OFDM is distortion of transmission signals in the *** transmission loss is called peak-to-average power ratio(PAPR).This wireless signal distortion can be reduced using various *** study uses machine learning-based algorithm to solve the problem of PAPR in 5G wireless *** transmit sequence(PTS)helps in the fast transfer of data in wireless *** is merged with deep belief neural network(DBNet)for the efficient processing of signals in wireless 5G *** indicates that the proposed system outperforms other existing ***,PAPR reduction in OFDM by DBNet is optimized with the help of an evolutionary algorithm called particle swarm ***,the specified design supports in improving the proposed PAPR reduction architecture.

关键词： 5G wireless network orthogonal frequency division multiplexing signal distortion peak to average power ratio partial transmit sequence deep belief network

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EEG Sequence Dependencies Learning With Fine-grained Sleep Transition Graph for Sleep Stage Classification

EEG Sequence Dependencies Learning With Fine-grained Sleep T...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Liang, Jingxin Philip, C.L. Xu, Yuebin Zhang, Tong South China University of Technology School of Computer Science&Engineering Guangzhou510006 China Pazhou Lab Guangzhou510335 China

ISBN: (纸本)9798350386226

Sleep stage classification assists in evaluating sleep quality and diagnosing sleep disorders. Existing methods based on spatial-temporal graph convolutional networks (STGCNs) have shown excellent performance in recognizing sleep stages. However, current works learn transition dependencies in sleep sequence in a coarse manner and restrict the modeling of brain activity to a significantly brief period. To address the above issues, we propose a fine-grained sleep transition graph (FSTG) method to learn EEG sequence dependencies for sleep stage classification in this paper. Specifically, the novel sleep transition graph (STG) constructor (STG-C) is proposed to model sleep sequence transition dependencies in a more fine-grained manner, extracting effective auxiliary information from sleep contextual sequences for sleep staging. In the STG-C, we initially explore brain connectivity over extended sleep temporal scales, utilizing the graph fusion mechanism (GFM) to learn the multiple temporal correlations of brain channels. Furthermore, to better capture dependencies of sleep sequence, a skip-gated STGCN (SG-STGCN) is designed as the strong STG sequence learner. The proposed FSTG model achieves state-of-the-art performance in public sleep staging datasets, ISRUC-S3 and ISRUC-S1. Additionally, visualization experiments demonstrate the effectiveness and interpretability of FSTG in sleep staging classification. © 2024 IEEE.

关键词： Sleep research

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A survey of machine learning techniques for detecting and diagnosing COVID-19 from imaging

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Quantitative Biology 2022年第2期10卷 188-207页

作者： Aishwarza Panday Muhammad Ashad Kabir Nihad Karim Chowdhury Department of Computer Science&Engineering Stamford UniversityDhaka 1217Bangladesh School of Computing and Mathematics Charles Sturt UniversityNSW 2795Australia Department of Computer Science&Engineering University of ChittagongChittagong 4349Bangladesh

Background: Due to the limited availability and high cost of the reverse transcription-polymerase chain reaction (RT- PCR) test, many studies have proposed machine learning techniques for detecting COVID-19 from medical imaging. The purpose of this study is to systematically review, assess and synthesize research articles that have used different machine learning techniques to detect and diagnose COVID-19 from chest X-ray and CT scan ***: A structured literature search was conducted in the relevant bibliographic databases to ensure that the survey solely centered on reproducible and high-quality research. We selected papers based on our inclusion ***: In this survey, we reviewed 98 articles that fulfilled our inclusion criteria. We have surveyed a complete pipeline of chest imaging analysis techniques related to COVID-19, including data collection, pre-processing, feature extraction, classification, and visualization. We have considered CT scans and X-rays as both are widely used to describe the latest developments in medical imaging to detect ***: This survey provides researchers with valuable insights into different machine learning techniques and their performance in the detection and diagnosis of COVID-19 from chest imaging. At the end, the challenges and limitations in detecting COVID-19 using machine learning techniques and the future direction of research are discussed.

关键词： COVID-19 machine learning deep learning detection classification diagnosing X-ray CT scan

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Novel method for ranking batsmen in Indian Premier League

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Data science and Management 2023年第3期6卷 158-173页

作者： M.K.Manju Abin Oommen Philip Department of Computer Science&Engineering Toch Institute of Science&TechnologyErnakulamKerala682313India Department of CSE-Cyber Security Indian Institute of Information TechnologyKottayam686635India

Sports analytics have benefited immensely from the growth and popularity of artificial intelligence and machine *** techniques enable sports analysts to evaluate player performance more effectively.A literature review of player performance evaluation methods shows the need to develop a new performance evaluation index for Twenty20(T20)cricket.A novel framework was proposed to evaluate batsman strength based on individual performance,role in the team,and team ***,proposed ranking systems are derived from static networks,that is,the aggregation of game results over ***,the scores of the players(or teams)fluctuate over ***,defeating a renowned player during peak performance is more rewarding than defeating the same player during other *** account for this,we propose a new method and apply it to the T20 format Indian Premier *** method serves three main purposes:First,it creates a new performance index for players to rank them more accurately and ***,the players are clustered based on their *** the third phase,a social network analysis approach is applied to visualize and analyze crickets as a network to gain better insights into players’team *** novel approach is a helpful index for sports coaches,analysts,cricket fans,and managers to evaluate player performance and rank for future aspects.

关键词： Indian Premier League Social network analysis Artificial intelligence Machine learning

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Empirical Assessment of Bacillus Calmette-Guérin Vaccine to Combat COVID-19

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computers, Materials & Continua 2022年第1期70卷 213-231页

作者： Nikita Jain Vedika Gupta Chinmay Chakraborty Agam Madan Deepali Virmani Lorenzo Salas-Morera Laura Garcia-Hernandez Department of Computer Science&Engineering Bharati Vidyapeeth’s College of EngineeringNew Delhi110063India Department of Electronics&Communication Engineering Birla Institute of TechnologyMesra814142India Department of Computer Science&Engineering Bhagwan Parshuram Institute of TechnologyNew Delhi110063India Area of Project Engineering University of CórdobaCórdoba14071Spain

COVID-19 has become one of the critical health issues globally,which surfaced first in latter part of the year *** is the topmost concern for many nations’governments as the contagious virus started mushrooming over adjacent regions of infected *** 1980,a vaccine called Bacillus Calmette-Guérin(BCG)was introduced for preventing tuberculosis and lung *** that have made the BCG vaccine mandatory have witnessed a lesser COVID-19 fatality rate than the countries that have not made it *** paper’s initial research shows that the countries with a longtermcompulsory BCGvaccination system are less affected by COVID-19 than those without a BCG vaccination *** paper discusses analytical data patterns for medical applications regarding COVID-19 impact on countries with mandatory BCG status on fatality *** paper has tackled numerous analytical challenges to realize the full potential of heterogeneous *** analogy is drawn to demonstrate how other factors can affect fatality and infection rates other than BCG vaccination only,such as age groups affected,other diseases,and stringency *** data of Spain,Portugal,and Germany have been taken for a case study of BCG impact analysis.

关键词： Bacillus Calmette-Guérin COVID-19 fatality rate lockdown gross domestic product vaccine

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