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soft computing based Implementation of Autonomous Drone Vehicle using Fuzzy Logic based Control Systems 2

Soft Computing based Implementation of Autonomous Drone Vehi...

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2nd international conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Mishra, Soni Punitha, S. Harikrishns, T. Swamy, K. Narayana Loganayagi, S. Graphic Era Hill University Department of Physics Dehradun India Department of Mathematics Tamil Nadu Salem India Annamacharya Institute of Technology & Sciences Department of Ai&ml Andhra Pradesh Rajampet India Kattigenahalli Yelahanka Bengaluru North Bengaluru560064 India Department of Computer Science and Engineering Tamil Nadu Thandalam Chennai602105 India

ISBN: (纸本)9798350325799

In recent years, there has been a lot of interest in the use of fuzzy logic-based control systems to construct autonomous drone vehicles. This method makes use of fuzzy logic's adaptability and flexibility to guide drones safely through challenging surroundings. This study gives a comprehensive framework for the design and implementation of fuzzy logic-based control systems for autonomous drone vehicles. The three primary parts of the proposed framework are perception, decision-making, and control. The perception module processes sensor input, including visual and depth data, to provide accurate representations of the world. The decision-making module interprets the perception data and uses fuzzy logic methods to arrive at sensible choices for navigation and obstacle avoidance. The fuzzy logic system's decisions are converted by the control module into the right control signals that the drone will follow. The fuzzy logic-based control system also shows resilience and adaptation to changing environmental circumstances and uncertainties, making it appropriate for real-time applications. New opportunities for autonomous drone applications, such as surveillance, delivery services, and search and rescue missions, are made possible by the adoption of this architecture. Using fuzzy logic-based control systems to create autonomous drone vehicles is a viable way to provide effective and precise drone navigation in challenging conditions. By offering a holistic framework that blends perception, decision-making, and control utilizing fuzzy logic approaches, this research advances the development of autonomous systems. © 2023 IEEE.

关键词： Decision making

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soft computing Application in Electrical, Electronics and Communication Field 2

Soft Computing Application in Electrical, Electronics and Co...

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2nd IEEE international conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Jindal, Ruby Yadav, Renu Singh, Dharmendra Kumar, R. Gowtham, M.S. Birare, Komal Madhukar School of Basic & Applied Sciences K. R. Mangalam University Department of Physics Haryana Gurugram122103 India Electronics and Communications Engineering Dev Bhoomi Uttarakhand University Uttarakhand 248007 India Invertis University Department of Applied Sciences Uttar Pradesh Bareilly243123 India Karpagam Academy of Higher Education Department of Chemistry Coimbatore641021 India Karpagam Institute of Technology Department of Electronics and Communication Engineering Coimbatore641105 India It Vishwakarma Institute of Information Technology Pune India

ISBN: (纸本)9798350369960

The term 'soft technologies' signifies a school of thought, rational thought, and explanation that acknowledges the practical benefits of combining, giving in, including categorising several variables during an investigation that takes shape in actuality. Its capacity to control intricate structures is despite the fact that it derives primarily feelings defined without exact verifiable designation set limitations for each component. It varies with difficult technologies because it is not as forgiving of errors, uncertainties, and unfinished data. In reality, IT constructs an environment using how people think. Using an allowance for mistake, unknowns, as well as limited truth to promote tractability, flexibility, and low reaction expense is the fundamental tenet of computers. Technologies primary methods includes Bayesian analysis of information, neural networks, inaccurate mathematics, and Darwinian computations. Although certain strategies may be employed alone, a few of the key advantages of computer science is that each approach complements the others. Mixing methodologies can offer answers to issues which conventional strategies have proved incapable to tackle due to the fact that become overly complicated or intrinsically unstable. Some key benefits of smart applications for robotics were evident. It seemed feasible to address challenging issues early on despite using computer-based techniques. It also contributed to the introduction of human wisdom in a variety of computing domains, including thinking, praise, comprehension, education, among a host of more. It was possible to create complicated equipment as an outcome, including electronics planning programmes and autonomously self-tuning equipment. The present piece emphasises a number of different themes. © 2023 IEEE.

关键词： Fuzzy Logic Neuro computing soft computing

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Classifying breast invasive carcinoma subtypes: A feature selection-based machine learning approach 2

Classifying breast invasive carcinoma subtypes: A feature se...

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2nd Odisha international conference on Electrical Power Engineering, Communication and computing Technology, ODICON 2022

作者： Khatun, Lutfunnesa Hossain, Sk Md Mosaddek Ray, Sumanta Mukhopadhyay, Anirban University of Kalyani Computer Science & Engineering W.B. Kalyani India Aliah University Computer Science & Engineering W.B. Kolkata India

ISBN: (纸本)9781665478397

Breast invasive carcinoma (BRCA) is one of the most common breast cancer types with multiple distinct subtypes, each with a distinguished morphological structure. Identifying discriminating genes to classify the subtypes plays an essential role upon the pathogenesis and treatment of carcinomas. In the present work, we propose a machine learning framework to classify BRCA subtypes based on the gene expression profiles of differentially expressed (DE) genes identified from TCGA BRCA data. We have utilized several feature selection algorithms to discover numerous key discriminating DE genes to classify BRCA subtypes through different classification models. The efficacy of the classification framework is evaluated through several performance metrics like accuracy, Matthews correlation coefficient (MCC), logistic loss, precision, recall, and f1-score. Results show that the JMIM feature selection algorithm, in conjunction with the SVM-RBF and the soft voting classifiers, outperforms the other algorithms with high precision. Moreover, we have identified several hub genes most significantly associated with patients' survival in BRCA. © 2022 IEEE.

关键词： Feature Selection

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An Efficient Detection System For Cardio Vascular Disease Using Machine Learning Algorithms 2

An Efficient Detection System For Cardio Vascular Disease Us...

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2nd international conference on Advance computing and Innovative Technologies in Engineering, ICACITE 2022

作者： Sarmiladevi, N. Basker, K. Tamilarasu, R. Nivetha, L. Kongunadu College of Engineering and Technology Department of Artificial Intelligence and Data Science Trichy India Cihan University-Duhok Kurdistan Region Department of Artificial Intelligence and Data Science Iraq

ISBN: (纸本)9781665437899

Mining and acquiring knowledge technologies have played an increasingly important role in the healthcare system in latest generations, with the objective of transforming all data into an intelligible format. Medical professionals make 12 percent to 13 percent inaccuracies throughout the prognosis process, according to several design dictates. As a result, an automated ailment forecasting system is required for improved health diagnosis, and greater reliability is emphasised for assessment purposes. Furthermore, numerous researchers have emphasised the importance of prediction performance in order to improve the design of a computerized method. By minimising computational burden and factors that add to computational effort, the developed automation methodology is especially suited to predict cardiovascular diseases. The retrieved and chosen information are input into the classifier model in terms of enhancing forecast accuracy. For varying degree of success, the publicly available baseline heart disease database is being explored. Pattern learning techniques are used to examine the characteristics in both linear and non-linear ways. To improve the prediction rate, Linearly Iterated Support Vector Regression (LISVR) and stacked monkey optimization (SMO) are used for classification. This calculations were carried out in the MATLAB 2016b framework, and the analysis is carried out using a variety of available methods. The predicted model's exactness is 98.5 percent, specificity is 98.8 percent, recall is 99.5%, and F -measure is 99.28 percent. In comparison to current procedures, the obtained findings indicate that perhaps the expected LISVR- SMO operates more effectively than previous techniques and provides a good trading. © 2022 IEEE.

关键词： Metadata

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Prediction of Influenza A Cases in Tropical Climate Country using Deep Learning Model 2

Prediction of Influenza A Cases in Tropical Climate Country ...

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2nd IEEE National Biomedical Engineering conference, NBEC 2023

作者： Abd Rahim, Muhamad Sharifuddin Yakub, Fitri Omar, Mas Abd Ghani, Rasli Dhamanti, Inge Sivakumar, Soubraylu Universiti Teknologi Malaysia Wee Laboratory Malaysia-Japan International Institute of Technology Kuala Lumpur Malaysia Universiti Teknologi Malaysia Malaysia-Japan International Institute of Technology Department of Electronic System Engineering Kuala Lumpur Malaysia Faculty of Public Health Department of Health Policy and Administration Surabaya Indonesia Srm Institute of Science and Technology Computing Technologies Chennai India

ISBN: (纸本)9798350338546

Influenza remains a significant public health concern particularly in tropical climate countries. Accurate prediction of influenza cases is crucial for effective resource allocation and public health planning. This study aimed to design and evaluate deep learning models for the monthly prediction of influenza A cases in a tropical climate country specifically Malaysia. The models considered both univariate and multivariate input configurations incorporating temperature and humidity variables. The study utilized a dataset spanning from January 2006 to December 2019 with training data from January 2006 to December 2016 and test data until December 2019. Various deep learning models such as RNN, LSTM, complex LSTM, GRU, Transformer and Informer were implemented and evaluated. The results demonstrated that the RNN variants specifically LSTM and GRU consistently outperformed the transformer and informer models for both univariate and multivariate prediction. The shorter input sequences (2 months) yielded better performance compared to longer sequences (12 months) with mean square error (MSE) of 0.0069, capturing more relevant temporal patterns and dependencies. The deep learning models produce a better ability to capture complex relationships and temporal patterns in the data. The findings highlight the effectiveness of the RNN variants and the impact of input configurations and sequence lengths on predictive performance. These findings provide valuable insights that can support public health planning and decision-making processes. dataset can be obtained from the https://***/joqxA. © 2023 IEEE.

关键词： Forecasting

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A Bibliometric and Social Media Analysis of the Nexus Between Blockchain Technology and Finance 2nd

A Bibliometric and Social Media Analysis of the Nexus Betwee...

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2nd international conference on Intelligent Manufacturing and Robotics, ICIMR 2024

作者： Ayodele, Olamide Emmanuel Ajibade, Temiloluwa Iyanuoluwa Oyebode, Oluwadare Joshua Akintoye, Kayode Akinlekan Ajayi, Babatunde Adedotun Adediran, Anthonia Oluwatosin Ajibade, Samuel-Soma M. Majeed, Anwar P. P. Abdul Luo, Yang Department of Banking and Finance Ekiti State University Ekiti State Ado Ekiti Nigeria Department of Business Management Scholar School System Birmingham United Kingdom Civil and Environmental Engineering Afe Babalola University Ekiti State Ado Ekiti Nigeria Department of Computer Science The Federal Polytechnic Ekiti State Ado Ekiti Nigeria Department of Cyber Security Faculty of Computing and Informatics Ladoke Akintola University of Technology Ogbomoso Nigeria Department of Real Estate Faculty of Built Environment Universiti Malaya KL Kuala Lumpur Malaysia Department of Data Science and Artificial Intelligence Faculty of Engineering and Technology Sunway University Selangor Malaysia Department of Computer Engineering Istanbul Ticaret University Istanbul Turkey Suzhou Taicang215400 China

ISBN: (纸本)9789819639489

This study explores Blockchain technology in the global financial industry using trends analysis, bibliometric analysis, and a literature appraisal using Elsevier Scopus data from 2016 to 2023. The Blockchain in Finance (BCF) study produced 185 publications: 53.5% papers, 45.4% conference proceedings, and 1.1% reviews. Analysis of the subject area revealed three important Computer science, Engineering, and Decision sciences articles. However, source title analysis showed that ACM international conference Proceeding Series, Sustainability Switzerland, and E3S Web of conferences publish BCF research most often. According to the stakeholders study, Jon M. Truby and Qatar University (Qatar) are the top BCFin researchers and affiliations with 4% and % publications, respectively. China produces the most research on the topic. Due to active support from funding sources like the Chinese National Natural science Foundation (NNSF), 18 publications have been funded. Co-occurrence of keywords The investigation indicated that BCFin’s top keywords include Blockchain, Finance, and Supply Chain Finance. Three clusters with 4–8 keywords, 132 linkages, and 866 TLS were found via cluster analysis. Keywords classify the clusters as Integrated Business Solutions (Cluster 1), Digital Commerce Infrastructure (Cluster 2), and Decentralized Financial Network (Cluster 3). The findings indicate that BCFin is a dynamic and influential multidisciplinary study field. BCFin has transformed the global financial sector by improving trust, transaction security, and communication. It still struggles with interoperability, system integration, and sustainability. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Decentralized finance

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PriorHealth: A Priority-Aware Task Scheduling Framework for Managing Healthcare data in Fog computing Applications

PriorHealth: A Priority-Aware Task Scheduling Framework for ...

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Intelligent data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： Sujit Bebortta Subhranshu Sekhar Tripathy Niva Tripathy Department of Computer Science Ravenshaw University Cuttack India School of Computer Engineering KIIT Deemed to be University Bhubaneswar India Department of Computer Science and Engineering DRIEMS Autonomous College Cuttack India

The advent of wearable sensor technology, like oximeters, accelerometers, and gyroscope-based monitoring devices, are being widely used to address the important problem of health monitoring for mobility patients. Unusual patient data prompts healthcare facilities to take immediate action by sending out alerts. The major goal of this paper is to investigate the field of fog computing-assisted healthcare service with regard to priority-aware healthcare task offloading. When it comes to providing end users with extra processing power for time-sensitive tasks, the fog server is essential. But this ever-changing environment is really hard when some jobs demand a far slower response time than others. This problem is solved by introducing a priority-aware scheduling and task offloading technique that prioritizes high-priority jobs, especially those with strict deadlines, when allocating CPU resources. Any CPU resources that are left over are then carefully allocated to provide for a longer average response time for the low priority tasks, which are lower on the task hierarchy. Furthermore, a basic lower-bound is obtained for the mean response time of assignments with hard and soft deadlines. This study demonstrates the effectiveness of the proposed task scheduling strategy with a wide range of simulations, skillfully allocating resources for end users and edge servers by giving hard-deadline tasks top priority and carefully scheduling lower priority soft-deadline tasks.

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Early-Stage Diabetes Prediction using data Mining Algorithms 22

Early-Stage Diabetes Prediction using Data Mining Algorithms

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2nd international conference on computing Advancements: Age of computing and Augmented Life, ICCA 2022

作者： Moniruzzaman, Md Zaman, A.G.M. Tasnia, Rifah Biswas, Sutopa Khanam, Mehnur Department of Computer Science American International University-Bangladesh Dhaka Bangladesh

ISBN: (纸本)9781450397346

Diabetes is a very common disease nowadays. If not treated early diabetes can pose a profoundly serious health threat. Much research has been conducted to find out the optimal solution for diabetes detection by applying different data mining algorithms, where the dataset consists of different medicinal attributes. In this study, our aim is to examine whether diabetes can be detected at early-stage by applying different data mining algorithms to the non-medicinal dataset;as well as to investigate whether data normalization techniques can improve the classifiers accuracy. Naive Bayes, K-Nearest Neighbor (KNN), Support Vector Machines (SVM), Decision Tree, Random Forest, and Gradient Boosting Classifier (GBC) algorithms are applied to the Early Stage Diabetes Risk Prediction dataset in conjunction with Decimal Point Scaling, Z-Score Normalization, Pareto Scaling, Variable Stability Scaling, Min-Max normalization, Max normalization, Maximum Absolute Scaling, Mean Centered Scaling, soft-max normalization, Power Transformer, Median and Median Absolute Deviation Normalization, Robust Scaling and Log Scaling normalization methods. In this experiment, we discovered that early-stage diabetes detection is possible without any medical diagnosis data. The result shows that GBC performs better compared to other classification algorithms in combination with data normalization and achieved an impressive 99.038% prediction accuracy. © 2022 ACM.

关键词： data mining

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A Novel Multivariate Recurrent Neural Network based Analysis Model for Predicting Stock Prices 2

A Novel Multivariate Recurrent Neural Network based Analysis...

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2nd international conference on Innovative Sustainable Computational Technologies, CISCT 2022

作者： Singh, Harsimran Jit Ghosh, Kaushik Sharma, Sugandha University of Petroleum and Energy Studies School of Computer Science Uttarakhand India

ISBN: (纸本)9781665474160

Stock prices are a challenging issue in the real world because of their complicated dynamics and dependence on several factors. Complex time series issues have been successfully handled by recurrent neural networks (RNN). We examined several multivariate models built using various RNN architectures, such as GRU and LSTM, and compared them to their corresponding univariate models as well as to one another. This paper's goal is to examine how well a neural network can deal with this situation. We have suggested a multivariate RNN-based analytical model in this work to forecast stock price movement. To determine the temporal relationships and anticipate stock prices of a certain firm from its prior historical data of stocks, we employed a soft computing technique based on RNN. In order to forecast stock price, we present a multivariate neural network structure and contrast and compare the estimation error of mentioned networks with a univariate LSTM model. The results highlighted that, when trained on the same data, the proposed multivariate prediction model readily beat the univariate prediction models. We have also demonstrated that multivariate prediction models may be trained and used in corporate environments more quickly and efficiently. The results suggest that the proposed RNN model gives the most accurate predicted result for the prediction with accuracy of 88.91% taking the time of 1.76 seconds. The accuracy for the LSTM and GRU is 85.43% and 81.24%. Time taken by GRU and LSTM ais 1.78 and 1.80 seconds respectively. © 2022 IEEE.

关键词： Neural network models

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Comparative Hybrid Deep Convolutional Learning Framework with Transfer Learning for Diagnosis of Lung Cancer 1

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14th international conference on soft computing and Pattern Recognition, SoCPaR 2022, and the 14th World Congress on Nature and Biologically Inspired computing, NaBIC 2022

作者： Dadgar, Sajad Neshat, Mehdi Department of Mathematics and Computer Science Amirkabir University of Technology Tehran15875-4413 Iran Australian Centre for Precision Health University of South Australia Cancer Research Institute University of South Australia AdelaideSA5000 Australia

ISBN: (数字)9783031275241

ISBN: (纸本)9783031275234

Lung cancer stands as the most common cancer in men and has 2nd rank in women worldwide. Detecting the type of cancer and running the appropriate treatments in the early stages play a pivotal role in the survival of patients. As obtaining a considerable number of lung cancer sample data is challenging and expensive due to privacy constraints, the majority of classifiers have encountered overfitting issues due to the difficulty of the small sample size. In this paper, we proposed a comparative hybrid convolutional deep transfer learning model, including VGG16, ResNet152V2, MobileNetV3 (small and large), InceptionResNetV2, and EfficientNetV2 to detect three widespread lung cancer types: Squamous Cell Carcinoma (SCC), Large Cell Carcinoma (LCC), and Adenocarcinoma. Furthermore, we developed and compared five architectures of the pre-trained convolutional deep learning model combined with Fully connected, Dropout, and Batch-Normalisation layers, and the hyper-parameters were adjusted. We used 1000 CT scans from the public dataset after proper pre-processing. The best-performed model was InceptionResNetV2 with transfer learning, and it achieved 91.1 % accuracy, 84.9 % precision, 95.8 % AUC, and 81.5 % F1-score to classify three different lung cancers from normal samples. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Computerized tomography

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