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5th International Conference on Advancements in computing, ICAC 2023

作者： Jayakody, E.R.T. Rabel, W.T.T. Estrop, R.D.V. Herath, K.G.L.P. Harshanath, S.M.B. Abeygunawardhana, Pradeep K. W. Sri Lanka Institute Information Technology Dept. of Computer Systems Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Dept. of Software Engineering Colombo Sri Lanka

ISBN: (纸本)9798350358131

The automation of processes in greenhouses provides a significant advantage to the agricultural sector. Due to the rapid growth of the population, increasing demand for food and labor shortage make the autonomous monitoring and management of agricultural work more important. Capsicum is a largely consumed vegetable in South Asia, which can be efficiently grown in greenhouses. Although there are many capsicum farms, they lack quality and efficient production to match the increasing demand. This paper proposes a robotic system for use in capsicum greenhouses that can navigate and monitor the plants to identify diseases, track the yield, and take remediation actions. The robot navigates to each plant in the greenhouse autonomously by using the SLAM approach with the use of laser range sensor data. The 4 DoF robotic arm of the robot has a camera and a sprayer attached to its end to investigate plants and spray remedies to diseased plants. The robotic arm moves around the plant to capture the images to detect deceases and count the pods to estimate the yield. The sprayer of the robot sprays remedies precisely to targeted plants to control pests and diseases. The system was successfully implemented and tested with the proposed functions. © 2023 IEEE.

关键词： Greenhouses

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An Empirical Comparison of Machine and Deep Learning Algorithms for Predicting Maternal Health Risk 5th

An Empirical Comparison of Machine and Deep Learning Algorit...

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5th EAI International Conference on Cognitive computing and Cyber Physical systems, IC4S 2024

作者： N, Srinivasa Gupta Valarmathi, B. Kavitha, A. Gopini, Sai Bhuvan Seela, Hari Kumar Department of Manufacturing Engineering School of Mechanical Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India Department of Mathematics School of Advanced of Sciences Vellore Institute of Technology Tamil Nadu Vellore India

ISBN: (纸本)9783031770777

Maternal health is among the greatest challenges in the world, especially in rural areas as there lack medical practitioners, they do not have easily accessible publics clinics and transport is difficult. Therefore, high rates of maternal as well as infant morbidity and mortalities are recorded. This research utilizes Artificial Intelligence (AI) with machine learning algorithms to forecast and address maternal health hazards right at their onset stage. The current research utilizes the concept of AI along with many Machine Learning (ML) methods like the Ensemble Learning Model (ELM), Random Forest (RF), K-Nearest Neighbour (KNN), Decision-Tree (DT), XG-Boost (XGB), Cat Boost (CB), and Gradient Boosting (GB), along with Synthetic Minority Over-sampling Technique (SMOTE) algorithm used for dealing with the problem class imbalance within the data set. SMOTE algorithm is utilized for the dataset balancing process. The handling system involves refining data preprocessing with the help of feature engineering and robust data cleaning which makes sure that anomalies do not erode the reliability of the predictive model. The existing methods [1] used RF (90%), DT (87%), XGB (85%), CB (86%), and GB (81%) algorithms and were compared with the accuracies of the proposed models like Logistic Regression (LR), Ensemble Learning Bagging (ELB), Ensemble Learning Stacking (ELS), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). The existing methods used only imbalance dataset. The accuracies of the proposed models with using SMOTE algorithm (balanced dataset) are LR (61.33%), KNN (81%), ELB (92.33%), ELS (90.66%) CNN (40.67%), RNN (59.67%), LSTM (54%), GRU (56%) respectively. Among these methods, ELB achieved 92.33% of accuracy with using SMOTE algorithm using imbalanced dataset. Whereas the accuracies of the proposed models without using SMOTE algorithm (imbalanced dataset) are LR (66.09%), KNN (68.47%)

关键词： Convolutional neural networks

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Feature Selection in Socio-Economic Analysis: A Multi-Method Approach for Accurate Predictive Outcomes

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International Journal of Crowd Science 2025年第1期9卷 64-78页

作者： Al-Qerem, Ahmad Ali, Ali Mohd Jebreen, Issam Nabot, Ahmad Rajab, Mohammed Alauthman, Mohammad Aldweesh, Amjad Aburub, Faisal Alangari, Someah Alzgol, Musab Computer Science Department Faculty of Information Technology Zarqa University Zarqa13110 Jordan Communications and Computer Engineering Department Faculty of Engineering Al-Ahliyya Amman University Amman19328 Jordan University Headquarter University of Anbar Ramadi31001 Iraq Department of Information Security University of Petra Amman11196 Jordan College of Computing and Information Technology Shaqra University Shaqra11911 Saudi Arabia Department of Business Intelligence and Data Analytics University of Petra Amman11196 Jordan Computer Information Systems Department Faculty of Information Technology Isra University Amman11622 Jordan

Feature selection is a cornerstone in advancing the accuracy and efficiency of predictive models, particularly in nuanced domains like socio-economic analysis. This study explores nine distinct feature selection methods, utilizing a heart disease dataset as a representative model for complex socio-economic systems. Our findings identified four universally recognized features as critical across all selection methods. However, the divergence in significance attributed to other features by different methods underscores the inherent variability in selection techniques. When the top four features were incorporated into twelve classification models, a noticeable surge in predictive accuracy was observed, emphasizing their foundational role in enhancing model outcomes. The variations among methods stress the need for a methodical and discerning approach to feature selection, especially in data-rich socio-economic landscapes. As we venture further into an era defined by data-driven decision-making, rigour and precision in feature selection become indispensable. Future research should extend this approach to broader datasets, ensuring the robustness and adaptability of our findings. © The author(s) 2025.

关键词： Prediction models

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Nostalgic Analysis of Location Based Tweets 13th

Nostalgic Analysis of Location Based Tweets

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13th International Conference on Social Informatics, SocInfo 2022

作者： Gao, Larissa Hong, Lingzi Mashhadi, Afra Computing and Software Systems University of Washington Bothell United States Information Science University of North Texas Denton United States

ISBN: (纸本)9783031190964

Social media posts have been increasingly used as mediums for gaining insight into a wide spectrum of people’s experiences including those in relation to physical spaces. We examine features of social media posts that characterized users’ memories of different visited Points-of-Interest (POI). Our results revealed that these recall of memories often mention other users, making them a collective experience. Furthermore, our analysis shows that nostalgic tweets are more than just tweets that mention the past but include an intertwine the past experiences with a positive outlook on the future and present. We also note that while these tweets do not mention specific locations in their content, their originating geo-location reflects places with a greater sense of belonging present. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Location

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Twitter Mining for Detecting Home Violence 3

Twitter Mining for Detecting Home Violence

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3rd International Conference on Advanced Research in computing, ICARC 2023

作者： Adeeba, Saleem Banujan, Kuhaneswaran Kumara, B.T.G.S. Li, Zhenni Sabaragamuwa University of Sri Lanka Department of Computing and Information Systems Belihuloya Sri Lanka Guangdong University of Technology School of Automation Guangzhou510006 China

ISBN: (纸本)9798350347371

Home Violence (HV) is any pattern of conduct intended to obtain or retain power and control over an intimate partner in any relationship. Identifying the HV occurrences will be critical in reducing them. Social Media (SM) is a vital and information-rich tool. HV-related information could well be detected using SM. The research proposed a method to detect HV-related Tweets. More than 20,000 Tweets were retrieved between May 2019 to May 2020 using Twitter API. Data preprocessing and word embedding were done, respectively. Then, to construct the model, the data set was split into training and testing datasets to detect HV-related Tweets;a deep learning model, LSTM, with different word embedding techniques, was used in this research (LSTM+TF-IDF, LSTM+BOW, LSTM+Word2Vec, LSTM+GloVe and LSTM+BERT). With an accuracy of 89.56%, the LSTM+BERT model surpassed the other implemented models. © 2023 IEEE.

关键词： Embeddings

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A Unified Decision-Making Technique for Analysing Treatments in Pandemic Context

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Computers, Materials & Continua 2022年第11期73卷 2591-2618页

作者： Fawaz Alsolami Abdullah Saad Al-Malaise Alghamdi Asif Irshad Khan Yoosef B.Abushark Abdulmohsen Almalawi Farrukh Saleem Alka Agrawal Rajeev Kumar Raees Ahmad Khan Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information Technology Babasaheb Bhimrao Ambedkar UniversityLucknow226025Uttar PradeshIndia Department of Computer Science and Engineering Babu Banarasi Das UniversityLucknow226028India

The COVID-19 pandemic has triggered a global humanitarian disaster that has never been seen *** experts,on the other hand,are undecided on the most valuable treatments of therapy because people ill with this infection exhibit a wide range of illness indications at different phases of ***,this project aims to undertake an experimental investigation to determine which treatments for COVID-19 disease is the most effective and *** research analysis is based on vast data gathered from professionals and research journals,making this study a comprehensive *** solve this challenging task,the researchers used the HF AHPTOPSIS Methodology,which is a well-known and highly effective MultiCriteria Decision Making(MCDM)*** technique assesses the many treatment options identified through various research papers and guidelines proposed by various countries,based on the recommendations of medical practitioners and *** review process begins with a ranking of different treatments based on their effectiveness using the HF-AHP approach and then evaluates the results in five different hospitals chosen by the authors as *** also perform robustness analysis to validate the conclusions of our *** a result,we obtained highly corroborative results that can be used as a *** results suggest that convalescent plasma has the greatest rank and priority in terms of effectiveness and demand,implying that convalescent plasma is the most effective treatment for SARS-CoV-2 in our *** also has the lowest priority in the estimation.

关键词： AHP-TOPSIS hesitant fuzzy sets SARS-CoV-2 healthcare sector preventive drugs

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Comparison of Visual Occupancy Detection Approaches for Parking Lots and Dedicated Containerized REST-API Server Application 9

Comparison of Visual Occupancy Detection Approaches for Park...

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9th Workshop on software Quality Analysis, Monitoring, Improvement, and Applications, SQAMIA 2022

作者： Petričko, Andrej Dakić, Pavle Vranić, Valentino Institute of Informatics Information Systems and Software Engineering Faculty of Informatics and Information Technologies Slovak University of Technology Bratislava Slovakia

With the development of new technology, the urban infrastructure, which is necessary to meet the social, economic, and physical needs of the population, is also gradually improving. Therefore, cities face significant urban planning challenges. With the increasing population and number of cars on the road parking space occupancy detection techniques are of great importance for efficient parking space management. Knowing the availability of parking spaces within a city in real-time and informing residents can be of great help in reducing the number of car park councils, reducing traffic congestion in cities, or improving the scalability of car parks and the time needed to find empty parking space. In many car parks, ground sensors are used to determine occupancy status. However, this requires the installation and maintenance of these sensors in every parking space. This can be expensive, especially in car parks with a large number of available spaces. With increasing computing power, new approaches to parking space occupancy detection using computer vision are emerging. However, these approaches are tailored to the site of application. In this work, we propose a comparison of two general approaches: classification and object detection. The reliability, precision, and scalability of these approaches are compared using a REST-API server application implemented in Python. In order to get as close as possible to general use, we use docker, which enables us easier develop and deploy this application independently of the hardware. © 2022 Copyright for this paper by its authors.

关键词： Object detection

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UNet Based onMulti-Object Segmentation and Convolution Neural Network for Object Recognition

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Computers, Materials & Continua 2024年第7期80卷 1563-1580页

作者： Nouf Abdullah Almujally Bisma Riaz Chughtai Naif Al Mudawi Abdulwahab Alazeb Asaad Algarni Hamdan A.Alzahrani Jeongmin Park Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Information Technology Department College of Computing and InformaticsSaudi Electronic UniversityRiyadh13316Saudi Arabia Department of Computer Engineering Korea Polytechnic UniversitySiheung-siGyeonggi-do237Republic of Korea

The recent advancements in vision technology have had a significant impact on our ability to identify multiple objects and understand complex *** technologies,such as augmented reality-driven scene integration,robotic navigation,autonomous driving,and guided tour systems,heavily rely on this type of scene *** paper presents a novel segmentation approach based on the UNet network model,aimed at recognizing multiple objects within an *** methodology begins with the acquisition and preprocessing of the image,followed by segmentation using the fine-tuned UNet ***,we use an annotation tool to accurately label the segmented *** labeling,significant features are extracted from these segmented objects,encompassing KAZE(Accelerated Segmentation and Extraction)features,energy-based edge detection,frequency-based,and blob *** the classification stage,a convolution neural network(CNN)is *** comprehensive methodology demonstrates a robust framework for achieving accurate and efficient recognition of multiple objects in *** experimental results,which include complex object datasets like MSRC-v2 and PASCAL-VOC12,have been *** analyzing the experimental results,it was found that the PASCAL-VOC12 dataset achieved an accuracy rate of 95%,while the MSRC-v2 dataset achieved an accuracy of 89%.The evaluation performed on these diverse datasets highlights a notably impressive level of performance.

关键词： UNet segmentation blob fourier transform convolution neural network

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Competitive strategies for ensuring Fintech platform performance: Evidence from multiple case studies

Competitive strategies for ensuring Fintech platform perform...

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作者： Ng, Evelyn Pan, Shan L. Discipline of Business Information Systems The University of Sydney SydneyNSW Australia School of Computing Macquarie University SydneyNSW Australia School of Information System and Technology Management University of New South Wales SydneyNSW Australia

The emergence of Fintech platforms has revolutionized the way financial services are provided. And yet, in spite of their growing prominence in the global financial sector, there remains a lack of understanding of the competitive strategies that are appropriate for these platform-based businesses, and the implications of those strategies for their performance. Examining four case studies of some of the most successful Fintech platforms in China from the theoretical perspective of the core logics of strategy, we develop a theoretical framework that suggests that the nature of the competitive strategies deployed by a Fintech platform should be contingent on (1) the extent to which their services can be differentiated, as well as (2) the tangibility and physical presence of the platform’s service offerings. More specifically, our framework presents four different combinations of competitive strategies that Fintech platforms can adopt contingent on the nature of their services along these two dimensions. In addition, beyond the two dimensions, our framework suggests that all Fintech platforms should be underpinned by a common strategic core consisting of strategies that align the platform with social pressures. These pressures include those exerted by the government, the market, and society in general. With its findings, it is hoped that our study will provide specific guidance for Fintech practitioners on the appropriate competitive strategies to adopt in order to set their platforms on the path of commercial success. © 2022 The Authors. information systems Journal published by John Wiley & Sons Ltd.

关键词： Finance

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Toward Organizational Pattern Ontology 27

Toward Organizational Pattern Ontology

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2022 27th European Conference on Pattern Languages of Programs, EuroPLoP 2022

作者： Waseeb, Shakirullah Vranic, Valentino Institute of Informatics Information Systems and Software Engineering Faculty of Informatics and Information Technologies Slovak University of Technology in Bratislava Slovakia

ISBN: (纸本)9781450395946

Organizational patterns of agile software development are proven practices for dealing organizational principles. Finding and selecting the right pattern is difficult. One way to select a pattern is to follow the sequence and compositions given in a pattern language. However, in general, pattern languages do not always reveal every reliable connection. Patterns are described in informal and unstructured text, making it difficult to understand their connections. This work attempts to provide a conceptual ontology model for organizational patterns-describing the collection of concepts (terms) and their relations. This contribution can be an attempt to express, expose, and share semantic knowledge between patterns using ontology. The resulting ontology can be used on top of the organizational patterns repositories to help retrieve patterns based on their logical connections. © 2022 ACM.

关键词： Semantics

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