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4th International Conference on Circuits, Control, Communication and Computing, I4C 2022

作者： Muttaraju, Chakita Kabber, Anusha Parimala, S. Anagani, Prajwal Srinivasa, Gowri PES University Department of Computer Science and Engineering Bengaluru India PES University PES Center for Pattern Recognition Dept. of CSE Bengaluru India

ISBN: (纸本)9798350397475

The Covid-19 Pandemic that broke out in late December 2019 has had a widespread negative effect on the mental health of people around the world. This work aims to elicit features that had a major influence on mental health during the pandemic to better understand preventive measures and remedial actions that can be taken to help individuals in need. Along with factors such as demographic age, gender, marital status, and employment status, additional information such as the effect of media used as a source of information, coping methods, trust in the country's government, and healthcare organizations was analyzed to find their correlation (if any) to the perceived stress of the individual. Machine Learning techniques such as XGBoost, AdaBoost, Decision Trees, Ordinal regression, k-Nearest Neighbors, Lasso and Ridge regression were used to arrive at a relationship between the perceived stress scores and the features considered. On interpreting results from the different models, we conclude that the main factor influencing stress scores was loneliness followed by features indicating trust in government, compliance with Covid-19 preventive measures and concerns regarding the pandemic. © 2022 IEEE.

关键词： Decision trees

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A Comprehensive Study of Road Traffic Accidents: Hotspot Analysis and Severity Prediction Using Machine Learning 4

A Comprehensive Study of Road Traffic Accidents: Hotspot Ana...

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4th IEEE Bombay Section Signature Conference, IBSSC 2022

作者： Gupta, Utkarsh Varun, M.K. Srinivasa, Gowri Pes University Dept. of Computer Science and Engineering Bengaluru India Pes University Pes Center for Pattern Recognition Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9781665492911

This study analyses road traffic accident data recorded over a period of time to gain insights to the underlying pain points in the infrastructure and policies. Such insight allows us to focus our efforts in the right direction to make the lives of people safer. The data includes various geographical and meteorological factors affecting the severity of these accidents. We use Kernel density estimation (KDE) plots to analyse hotspots of accident-prone areas weighed against severity over years to understand the evolution of these dangerous zones. Furthermore, we use machine learning algorithms to predict the accident severity given certain parameters and to understand the factors that have a major influence on the severity of the accident. We have studied a publicly available dataset of road traffic accidents in the UK as a proof of concept of the pipeline to understand the underlying patterns of accidents occurring in a region of interest. © 2022 IEEE.

关键词： Machine learning

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PHUI-GA: GPU-based efficiency evolutionary algorithm for mining high utility itemsets

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Journal of Systems engineering and Electronics 2024年第4期35卷 965-975页

作者： JIANG Haipeng WU Guoqing SUN Mengdan LI Feng SUN Yunfei FANG Wei Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Department of Computer Science and TechnologyJiangnan UniversityWuxi 214122China China Ship Scientific Research Center Wuxi 214082China Taihu Laboratory of Deepsea Technological Science Wuxi 214082China Department of Mathematics Nanjing UniversityNanjing 210023China

Evolutionary algorithms(EAs)have been used in high utility itemset mining(HUIM)to address the problem of discover-ing high utility itemsets(HUIs)in the exponential search *** have good running and mining performance,but they still require huge computational resource and may miss many *** to the good combination of EA and graphics processing unit(GPU),we propose a parallel genetic algorithm(GA)based on the platform of GPU for mining HUIM(PHUI-GA).The evolution steps with improvements are performed in central processing unit(CPU)and the CPU intensive steps are sent to GPU to eva-luate with multi-threaded *** show that the mining performance of PHUI-GA outperforms the existing *** mining 90%HUIs,the PHUI-GA is up to 188 times better than the existing EAs and up to 36 times better than the CPU parallel approach.

关键词： high utility itemset mining(HUIM) graphics process-ing unit(GPU)parallel genetic algorithm(GA) mining perfor-mance

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Analysis of EEG Signals for Predicting Neurological Recovery in Post-Cardiac Arrest Comatose Patients

Analysis of EEG Signals for Predicting Neurological Recovery...

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Artificial Intelligence for Medicine, Health and Care (AIMHC), IEEE International Conference on

作者： Shria Guntunur Shreya Varma Shubangi Saxena Supreeta Sagere Gowri Srinivasa Dept. Computer Science Engineering PES Center for Pattern Recognition PES University Bengaluru India

Prognostic decisions made in the early days following cardiac arrest can significantly impact patient outcomes. This work leverages continuous electroencephalography (EEG) to predict neurological outcomes in patients who have become comatose after a cardiac arrest. We propose a two-stage stacked classifier (TSSC), combining an RNN model and an SVM model with a nonlinear kernel, with KNN serving as the meta-classifier. This approach involves an analysis of EEG features extracted using Welch's power spectral density, resulting in a reduced feature subset and a subsequent enhancement in accuracy. Experimental results demonstrate an impressive predictive accuracy of 95.1% with this approach. We also discuss the implications of our findings for clinical practice.

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Deep Learning for Classifying Stages of Retinopathy of Prematurity

Deep Learning for Classifying Stages of Retinopathy of Prema...

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Advances in Modern Age Technologies for Health and engineering science (AMATHE), International Conference on

作者： Ayush Satish Govind Atharva Gadad Dhruv Jyoti Garodia Ayush Gupta Anand Vinekar Gowri Srinivasa Department of Computer Science and Engineering PES Center for Pattern Recognition PES University Bengaluru India Department of Pediatric Retina Narayana Nethralaya Bengaluru India

ISBN: (数字)9798350371567

ISBN: (纸本)9798350371574

This work presents a deep learning based approach to classify stages of retinopathy of prematurity (ROP). In particular, the approach focuses on delineating stages 1, 2 and 3 that are relatively crucial for treatment. Training and validation of the models is carried out on multiple subsets of data from 82,295 images collected by Narayana Nethralaya, Bengaluru, using 3nethra fundus cameras. A pre-trained EfficientNetV2S model is used for feature extraction and the datasets are used to train classification models to predict stage, plus disease status and decision for treatment. Experimental results demonstrate the efficacy of training models only on images capturing the temporal region of the retina and manually filtering out images that show no visible signs of ROP, with the best performing model distinguishing stage 2 and stage 3 with an accuracy of 93.44 % , AUC of 98.71% and sensitivity of 93.30%.

关键词： Training Deep learning Accuracy Sensitivity Filters Retinopathy Predictive models

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UPLIFT: Usage of Poverty Level Indicators to Facilitate Transformation 3

UPLIFT: Usage of Poverty Level Indicators to Facilitate Tran...

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3rd IEEE Bombay Section Signature Conference, IBSSC 2021

作者： Ramesh, Namrata Chandra, Diya Jawahar, Chiranth Srinivasa, Gowri Pes University Pes Center for Pattern Recognition Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9781665417587

UPLIFT: Usage of Poverty Level Indicators to Facilitate Transformation, as the name suggests, is a study that aims at analysing data consisting of various socio-economic factors that affect households in order to alleviate poverty by understanding and identifying various aspects that may have an impact on poverty levels. As a use case, we have studied data pertaining to the Latin American nation of Costa Rica. Initially, we perform exploratory data analysis to discover patterns in the data and gain an insight to the relationship between attributes. We then perform feature selection and normalization to render the data suitable for modeling with machine learning techniques. We use these models to predict the economic health of each household, mapping them into four categories corresponding to the level of poverty. Finally, the data is reinterpreted in the light of this classification to elicit the factors that strongly affect each category, towards recommending methods for improvement and an appropriate allocation of social welfare benefits. © 2021 IEEE.

关键词： Feature extraction

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A Survey of Artificial Hummingbird Algorithm and Its Variants: Statistical Analysis, Performance Evaluation, and Structural Reviewing

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Archives of Computational Methods in engineering 2025年第1期32卷 269-310页

作者： Hosseinzadeh, Mehdi Rahmani, Amir Masoud Husari, Fatimatelbatoul Mahmoud Alsalami, Omar Mutab Marzougui, Mehrez Nguyen, Gia Nhu Lee, Sang-Woong Institute of Research and Development Duy Tan University Da Nang Viet Nam School of Medicine and Pharmacy Duy Tan University Da Nang Viet Nam Future Technology Research Center National Yunlin University of Science and Technology Yunlin Taiwan Department of Communication and Computer Engineering Faculty of Engineering Cihan University-Erbil Kurdistan Region Iraq Department of Electrical Engineering College of Engineering Taif University Taif21944 Saudi Arabia Department of Computer Engineering College of Computer Science King Khalid University Abha61413 Saudi Arabia School of Computer Science Duy Tan University Da Nang Viet Nam Pattern Recognition and Machine Learning Lab Gachon University 1342 Seongnamdaero Sujeonggu Seongnam13120 Korea Republic of

In the last few decades, metaheuristic algorithms that use the laws of nature have been used dramatically in numerous and complex optimization problems. The artificial hummingbird algorithm (AHA) is one of the metaheuristic algorithms that was invented in 2022 based on the foraging and migration behavior of the hummingbird for modeling and solving optimization problems. The algorithm initially starts with an initial random population of solutions. It then uses iterative processes and hummingbird position updates to balance exploration and exploitation toward the most optimal solutions. This paper has a detailed and extensive review of the AHA algorithm considering the aspects of hybrid, improved, binary, multi-objective, and optimization problems. In addition, a wide range of applications of AHA in various fields such as feature selection, image processing, scheduling, Internet of Things, classification, clustering, financial and economic issues, forecasting, wireless sensor networks, and many engineering challenges are explored. The statistical and numerical results showed that the AHA algorithm with deep learning methods, Levy flight, and opposition-based learning had the best performance. Also, the AHA algorithm is most widely used in solving multimodal optimization problems and continuous functions. © The Author(s) under exclusive licence to International center for Numerical Methods in engineering (CIMNE) 2024.

关键词： Optimization

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6G-Enabled Internet of Things:Vision,Techniques,and Open Issues

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computer Modeling in engineering & sciences 2022年第12期133卷 509-556页

作者： Mehdi Hosseinzadeh Atefeh Hemmati Amir Masoud Rahmani Pattern Recognition and Machine Learning Lab Gachon UniversitySeongnam13120Korea Department of Computer Engineering Science and Research BranchIslamic Azad UniversityTehran***Iran Future Technology Research Center National Yunlin University of Science and TechnologyYunlin64002Taiwan

There are changes in the development of wireless technology systems every decade.6G(sixth generation)wireless networks improve on previous generations by increasing dependability,accelerating networks,increasing available bandwidth,decreasing latency,and increasing data transmission speed to standardize communication *** purpose of this article is to comprehend the current directions in 6G studies and their relationship to the Internet of Things(IoT).Also,this paper discusses the impacts of 6G on IoT,critical requirements and trends for 6G-enabled IoT,new service classes of 6G and IoT technologies,and current 6G-enabled IoT studies selected by the systematic literature review(SLR)method published from 2018 to *** addition,we present a technical taxonomy for the classification of 6G-enabled IoT,which includes self-organization systems,energy efficiency,channel assessment,and ***,according to the articles reviewed,we consider the evaluation factors in this domain,including data transmission,delay,energy consumption,and ***,we focus on open issues and future research challenges in 6G-enabled *** mention important future challenges and directions,we can point to migration,data storage,data resource,data security,data sharing,data offloading,availability,scalability,portability,user experience,reliability,authentication,and authorization.

关键词： Internet of Things 6G wireless network machine learning artificial intelligence

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Resource Management approaches to Internet of Vehicles

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Multimedia Tools and Applications 2023年第30期82卷 46811-46844页

作者： Hosseinzadeh, Mehdi Abbasi, Shirin Rahmani, Amir Masoud Pattern Recognition and Machine Learning Lab Gachon University 1342 Seongnamdaero Sujeonggu Seongnam13120 Korea Republic of Computer Engineering Department Science and Research Branch Islamic Azad University Tehran Iran Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan

The Internet of Vehicles (IoV) has become a significant issue in designing smart cities. Many applications and services are provided in IoV for various purposes. They need the resources for the computation and data collection. One of the open issues in IoV is resource management because of resource limitation, resource heterogeneity, and dynamic networks. To our knowledge, no systematic and thorough research in resource management methodologies was conducted in IoV, despite the critical nature of resource management challenges. This paper analyzes the recently published studies on resource management in IoV. Our analysis shows that resource allocation has the most utilization rate of resource management approaches at 30%. The most critical parameter in resource allocation is cost, which includes energy, time, price, and processing capacity. The analytical reports show those cost parameters have the most evaluation in the approaches by 19%. Furthermore, we propose a taxonomy for resource management in IoV and extract challenges and open issues in this field. It can be a start point to suggest new methods for resource management in IoV. © 2023, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Resource allocation

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Solving the N-Queens and Golomb Ruler Problems Using DQN and an Approximation of the Convergence 28th

Solving the N-Queens and Golomb Ruler Problems Using DQN a...

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28th International Conference on Neural Information Processing, ICONIP 2021

作者： Prudhvi Raj, Patnala Saha, Snehanshu Srinivasa, Gowri PES Center for Pattern Recognition and Department of Computer Science and Engineering PES University Bengaluru India Department of Computer Science and Information Systems and APPCAIR BITS Pilani K K Birla Goa Campus Goa India

ISBN: (纸本)9783030923099

We build on the Deep Q-Learning Network (DQN) to solve the N-Queens problem to propose a solution to the Golomb Ruler problem, a popular example of a one dimensional constraint satisfaction problem. A comparison of the DQN approach with standard solution approaches to solve constraint satisfaction problems, such as backtracking and branch-and-bound, demonstrates the efficacy of the DQN approach, with significant computational savings as the order of the problem increases. The convergence behavior of the DQN model has been approximated using Locally Weighted Regression and Cybenko Approximation, demonstrating an improvement in the performance of the DQN with episodes, regardless of the order of the problem. © 2021, Springer Nature Switzerland AG.

关键词： Reinforcement learning

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