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Svm-pca based handwritten devanagari digit character recognition

Recent Advances in computer science and Communications 2021年第1期14卷 48-53页

作者： Khamparia, Aditya Singh, Sanjay K. Luhach, Ashish K. Department of Computer Science and Engineering Faculty of Technology and Sciences Lovely Professional Universi-ty Phagwara Punjab India Department of Electrical and Communication Engineering Faculty of Technology and Sciences The PNG University of Technology MorobePapua New Guinea Australia

Objective: The blended fusion of Support Vector Machine (SVM) and Principal Component Analysis (PCA) have been widely used in recognizing handwritten digit characters of Devanagari script. The feature information from the character is extracted using its skeleton structure which optimally reduce data dimensionality using PCA. There is ample information available on handwritten charac-ter recognition on Indian and Non-Indian scripts but very few article emphasized on recognition of Devanagari scripts. Therefore, this paper presents an efficient handwritten Devanagari character recognition system based on block based feature extraction and PCA-SVM classifier. Methods: We have collected samples of handwritten Devanagari characters from different handwritten experts for classification. Results: For experimental work, total of 100 images having Devanagari digit characters been used for the purpose of training and testing. The proposed system achieves a maximum recognition accuracy of 96.6 % and 96.5% for 5 & 10 fold validations with 70% training and 30% testing data using block based feature and SVM classifier having different kernels. Conclusion: The obtained results achieve maximum accuracy using SVM classifier for digit character recognition. In future deep learning networks will be considered for accuracy enhancement and precision. © 2021 Bentham science Publishers.

关键词： Principal component analysis

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Towards responsible AI for education: Hybrid human-AI to confront the Elephant in the room

arXiv

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arXiv 2025年

作者： Hooshyar, Danial Šír, Gustav Yang, Yeongwook Kikas, Eve Hämäläinen, Raija Kärkkäinen, Tommi Gašević, Dragan Azevedo, Roger School of digital Technologies Tallinn University Tallinn Estonia Department of Education University of Jyväskylä Jyväskylä Finland Department of Computer Science Czech Technical University Prague Czech Republic Department of Computer Science and Engineering Gangneung-Wonju National University Wonju Korea Republic of School of Natural Sciences and Health Tallinn University Tallinn Estonia Faculty of Information Technology University of Jyväskylä Jyväskylä Finland Faculty of Information Technology Monash University ClaytonVIC Australia School of Modeling Simulation and Training University of Central Florida Orlando United States

Despite significant advancements in AI-driven educational systems and ongoing calls for responsible AI for education, multiple critical issues remain unresolved—acting as the elephant in the room within the AI in education, learning analytics, educational data mining, learning sciences, and educational psychology communities. This critical analysis paper identifies and examines nine persistent challenges that continue to undermine the fairness, transparency, and effectiveness of current AI methods and applications in education. These include: 1) the lack of clarity around what AI for education truly means—often ignoring the distinct purposes, strengths, and limitations of different AI families—and the growing trend of equating it with domain-agnostic, company-driven large language models;2) the widespread neglect of essential learning processes such as motivation, emotion, and (meta)cognition in AI-driven learner modelling and their contextual nature;3) the limited integration of domain knowledge and lack of stakeholder involvement in AI design and development;4) the continued use of non-sequential machine learning models on temporal educational data, which fails to capture the dynamics and dependencies critical to understanding real learning progressions;5) the misuse of non-sequential metrics to evaluate sequential or temporal models;6) using unreliable explainable AI methods to provide explanations for black-box models;7) ignoring ethical guidelines in addressing data inconsistencies during model training;8) use of mainstream AI methods for pattern discovery and learning analytics without systematic benchmarking, which risks leading to unreliable conclusions;and 9) focusing on global prescriptions while overlooking localized, student-specific recommendations. Supported by both theoretical and empirical research, we demonstrate how hybrid AI methods—specifically neural-symbolic AI—address the elephant in the room and serve as the foundation for building responsibl

关键词： Teaching

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Project management practices: Success versus failure

Project management practices: Success versus failure

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International Symposium on information technology 2008, ITSim

作者： Attarzadeh, Iman Siew, Hock Ow Department of Software Engineering Faculty of Computer Science and Information Technology University of Malaya 50603 Kuala Lumpur Malaysia

ISBN: (纸本)9781424423286

Often, software managers have to monitor and manage many projects concurrently. Unfortunately, some projects were completed successfully but some were not completed on time, over budget or being cancelled. Some of the reasons of this project failure are: lack of user involvement, lack of planning, incomplete requirements, lack of resources, incorrect cost estimation, etc. There are many project planning and scheduling techniques to manage and help to ensure project success. Some of these techniques, however, may not be suitable for specific types of projects and thus, cause projects to fail. This paper discusses the issues involved in project success and failure, and presents the results of seven projects undertaken by the undergraduate students taking the course project management. © 2008 IEEE.

关键词： Project management

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Handoff techniques for 4G wireless mobile Internet

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information technology Journal 2007年第5期6卷 745-750页

作者： Li, Xichun Salleh, Rosli Department of Computer System and Technology Faculty of Computer Science and Information Technology University of Malay 50603 Kuala Lumpur Malaysia

In this study, new handoff technique is developed to support wireless mobile bandwidth efficiency and higher data rates. This study will address handoff issues when a mobile node comes into WLAN overlapping region from a CDMA2000 coverage area and the mobile node will be supplied service by these two networks simultaneously through setting up a mix-bandwidth data path between the two networks. The performance of the proposed new data path will be evaluated and compared with current existing scheme through extensive simulations. The cost of the established new data path is also evaluated in terms of throughput and data transmission efficiency. © 2007 Asian Network for Scientific information.

关键词： Wireless networks

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ANN Based Skin Cancer Detection 1

ANN Based Skin Cancer Detection

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1st IEEE International Interdisciplinary Humanitarian Conference for Sustainability, IIHC 2022

作者： Sudhamsu, Gadug Mahesh, T.R. Faculty of Engineering and Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9781665456876

Melanoma is one of the most common types of skin cancer. A melanoma can be classified into benign and malignant. Fortunately, melanoma is completely curable if caught early. Melanoma, both benign and malignant, can present in a manner that is identical. As a result, distinguishing the two is quite challenging. The early identification of skin cancer presents a significant challenge. To determine which one is benign and which one is cancerous, you need the expertise of a board-certified dermatologist. Automated early detection systems for skin cancer are absolutely necessary for saving lives and reducing time and effort. This paper proposes an automatic skin lesion categorization system that uses a pretrained deep learning network and transfer learning. A detection system based on digital image processing was developed in order to make an early diagnosis of melanoma. In order to make the images of skin more appealing, dermoscopy images were first acquired, then preprocessed and postprocessed to remove noise. A modified k-means clustering algorithm is used to segment the processed image after k-means clustering. The attributes are then extracted using first-order statistics, gray-level co-occurrence matrices, and feature extraction techniques. Comparing the proposed method to the existing approaches, it was discovered that the classification rate of the suggested method exceeded the output of the existing approaches. © 2022 IEEE.

关键词： Image enhancement

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Just Use a Perceptron to Anticipate Dry 11

Just Use a Perceptron to Anticipate Dry

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11th International Conference on system Modeling and Advancement in Research Trends, SMART 2022

作者： Sinha, Deepak Kumar Reddy, Somashekhara Faculty of Engineering and Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9781665487320

Drought is considered one of the most terrifying disasters that humanity have ever experienced, and farmers all over the globe often deal with it. It may happen anywhere outside of the globe and is referred to as a 'slow catastrophe' since it lasts for a long time, and perhaps even further if it chooses to be more severe. Drought affects also human lives but also crops, global economy, and power that farmers have ingested. During a disaster, seems to be at risk. Basic necessities like food are difficult to get, and market forces imbalance causes irritation to reach its height. There are a variety of things that may be done to prevent the dry, such as desalinating water, crop planning, rainfall gathering, and sprinkler, which can all help preserve water during dry spells. The primary answer to this problem would have been to analyse the environment and the potential results of it, that could aid in planning for the worst-case scenario. Soil predictions may also be very helpful in forecasting this scenario. In order to forecast how floods might be averted, the article combines meteorological and soil data. Deep learning methods will make it possible to determine with remarkable accuracy if the droughts will occur or not. © 2022 IEEE.

关键词： Forecasting

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A Novel Approach for Detection of Agricultural Crops Using CNN 14

A Novel Approach for Detection of Agricultural Crops Using C...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Mahesh, T.R. Singh Chandel, Aman Gupta, Harshitha Jain, Rakshit Garg, Sadhvi Faculty of Engineering and Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350335095

Crop classification is an important aspect of farming because it improves crop management and increases crop yield. This study proposes a CNN-based crop classification technique using high-resolution images. To use this method, we created a database of 30 crops, including jute, maize, almonds, rice, sugarcane, banana, wheat, and others. The Convolutional Neural Network (CNN) is an Artificial Intelligence-based algorithm that learns similar features from images and is capable of performing a variety of tasks such as image classification, object detection, and segmentation. In this system, CNN or ConvNet is used to learn the features provided in the input images by sequentially entering several layers such as Convolution, Activation function (RELU), Pooling, and Flattening. Finally, with only a few explanations in the training set, this providing outstanding classifies new images. The proposed method has two major components: preprocessing and categorization. The experimental results show that the proposed tactic worked extremely well, with a ground truth of 0.9264. © 2023 IEEE.

关键词： Convolution

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Multi-Party Privacy Policy Management in the Context of Health Monitoring: An Exploratory Study 23

Multi-Party Privacy Policy Management in the Context of Heal...

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8th International Conference on Sustainable information Engineering and technology, SIET 2023

作者： Sun, Luyi Yeng, Prosper Fauzi, Muhammad Ali Khatiwada, Pankaj Yang, Bian Department of Information Security and Communication Technology Norwegian University of Science and Technology Gjøvik Norway Faculty of Computer Science Brawijaya University Malang Indonesia

ISBN: (纸本)9798400708503

There is an increasing number of assisted living technologies and health monitoring systems to provide electronic health services and help patients and the elderly stay at home longer. It ensures users' safety and reduces caregivers' workload. Regardless of the benefits that health monitoring systems have brought to users, they are in the face of privacy challenges, which can influence users' adoption of these technologies. One of the challenges is multi-party privacy policy management. As different parties may have different considerations in the same scenario, they can make different privacy decisions. Therefore, a privacy policy management scheme is required to deal with conflicts. In this paper, we present the work of a user study with the scope of collecting several participants' opinions on multi-party privacy policy management in the context of health monitoring. One focus group and eight semi-structured interviews were conducted. Our questions for participants adopt strategies from online social networks. After that, we discuss participants' views on privacy decision conflict resolution in different health conditions and whether a recommendation system is needed to help with multi-party privacy policy management. We try to make prioritization based on participants' views and the emergency level of events in different scenarios, increase technology adoption, and ensure health services are better delivered. The results reflect the importance of adopting flexible strategies like majority weighted voting in different health conditions, which serves as a basis for multi-party privacy policy scheme development. © 2023 Owner/Author.

关键词： Social networking (online)

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Evaluation of properties in the transition of capability based agent organization

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Web Intelligence and Agent systems 2009年第1期7卷 1-21页

作者： Matson, Eric T. Deloach, Scott A. Bhatnagar, Raj Department of Computer and Information Technology Purdue University West Lafayette IN United States Department of Computer Science University of Cincinnati Cincinnati OH United States Department of Computing and Information Sciences Kansas State University Manhattan KS United States

It has been said that the only constant in life is change. This rule can also be directly applied to the lives of organizations. Any organization of non-trivial size, scope, life expectancy or function, is destined to change. An organization without the ability to transition is not robust, evolvable or adaptable within its environment. These basic preconditions to human organizations must also hold in viable agent organizations. To model an adaptable agent organization, the capability must be present to transition from one state to the next over the life of the organization. The organization model must include not only the structural objects, but also the ability to facilitate change. The ability to change empowers the organization to transition from one state to the next, over its useful life. To enable transition, we must formally capture and define what triggers an organization transition. In this paper, we will define the properties to formally model the ability of an adaptable organization to transition throughout its useful life. The properties will be instantiated, using an implemented system, allowing the evaluation of internal and external stimuli to cause transition to the organization. These transitions will be evaluated from several perspectives to determine their effectivity on basis of design and use. © 2009 - IOS Press and the authors. ALL rights reserved.

关键词： Multi agent systems

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Prediction of Geopolymer Concrete Compressive Strength Using Convolutional Neural Networks

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computer Modeling in Engineering & sciences 2024年第5期139卷 1455-1486页

作者： Kolli Ramujee Pooja Sadula Golla Madhu Sandeep Kautish Abdulaziz S.Almazyad Guojiang Xiong Ali Wagdy Mohamed Department of Civil Engineering VNR Vignana Jyothi Institute of Engineering and TechnologyHyderabadTelangana500090India Department of Information Technology VNR Vignana Jyothi Institute of Engineering and TechnologyHyderabadTelangana500090India Department of Computer Science LBEF Campus(Asia Pacific University of Technology&InnovationMalaysia)Kathmandu44600Nepal Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Guizhou Key Laboratory of Intelligent Technology in Power System College of Electrical EngineeringGuizhou UniversityGuiyang550025China Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmmanJordan

Geopolymer concrete emerges as a promising avenue for sustainable development and offers an effective solution to environmental *** attributes as a non-toxic,low-carbon,and economical substitute for conventional cement concrete,coupled with its elevated compressive strength and reduced shrinkage properties,position it as a pivotal material for diverse applications spanning from architectural structures to transportation *** this context,this study sets out the task of using machine learning(ML)algorithms to increase the accuracy and interpretability of predicting the compressive strength of geopolymer concrete in the civil engineering *** achieve this goal,a new approach using convolutional neural networks(CNNs)has been *** study focuses on creating a comprehensive dataset consisting of compositional and strength parameters of 162 geopolymer concrete mixes,all containing Class F fly *** selection of optimal input parameters is guided by two distinct *** first criterion leverages insights garnered from previous research on the influence of individual features on compressive *** second criterion scrutinizes the impact of these features within the model’s predictive *** to enhancing the CNN model’s performance is the meticulous determination of the optimal *** a systematic trial-and-error process,the study ascertains the ideal number of epochs for data division and the optimal value of k for k-fold cross-validation—a technique vital to the model’s *** model’s predictive prowess is rigorously assessed via a suite of performance metrics and comprehensive score ***,the model’s adaptability is gauged by integrating a secondary dataset into its predictive framework,facilitating a comparative evaluation against conventional prediction *** unravel the intricacies of the CNN model’s learning trajectory,a loss plot is deployed to elucidate its learning rat

关键词： Class F fly ash compressive strength geopolymer concrete prediction deep learning convolutional neural network

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