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4th International Conference on Advancements in Computing, ICAC 2022

作者： Kahawanugoda, Adithya Gnanarathna, Kulanika Meegoda, Nilan Monarawila, Randika Samarasinghe, Pradeepa Lindamulage, Asiri Gawesha Department of Computer Science and Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Information Technology Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350398090

Automated cognitive assessment tools are state-of-the-art in assessing cognition development. Due to the low availability of resources, building automated cognitive ability evaluation tools is challenging. This study focuses on developing machine learning models using a limited amount of data to assess Reasoning IQ, Knowledge IQ, Mental Chronometry and Attention-levels of Sinhala-speaking children between the age of 7 to 9 years. Our solution includes Sinhala speech recognition systems, image classification models, gaze estimation, blink count detection and facial expression recognition models to evaluate the above four cognitive ability measuring factors. Open domain speech recognition has been used to evaluate complex Sinhala child verbal responses and limited vocabulary responses were assessed using an end-to-end speech recognition system, respectively achieving 40.1% WER and 97.14% accuracy. Additionally, the image classification models for handwritten Sinhala letter recognition and two shape recognition models have gained 97%, 89% and 99% accuracy. The linear regression model for attention level evaluation that utilizes the inputs from a combination of eye-gaze estimation, facial expression recognition and blink rate detection models has gained 85% accuracy. © 2022 IEEE.

关键词： Image classification

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Deer Hunting Optimization with Deep Learning Model for Lung Cancer Classification

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computers, Materials & Continua 2022年第10期73卷 533-546页

作者： Mahmoud Ragab Hesham A.Abdushkour Alaa F.Nahhas Wajdi H.Aljedaibi Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah 21589Saudi Arabia Center of Artificial Intelligence for Precision Medicines King Abdulaziz UniversityJeddah 21589Saudi Arabia Mathematics Department Faculty of ScienceAl-Azhar UniversityNaser City 11884CairoEgypt Nautical Science Department Faculty of Maritime StudiesKing Abdulaziz UniversityJeddah21589Saudi Arabia Biochemistry Department Faculty of ScienceKing Abdulaziz UniversityJeddah21589Saudi Arabia Computer Science Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah 21589Saudi Arabia

Lung cancer is the main cause of cancer related death owing to its destructive nature and postponed detection at advanced *** recognition of lung cancer is essential to increase the survival rate of persons and it remains a crucial problem in the healthcare *** aided diagnosis(CAD)models can be designed to effectually identify and classify the existence of lung cancer using medical *** recently developed deep learning(DL)models find a way for accurate lung nodule classification ***,this article presents a deer hunting optimization with deep convolutional neural network for lung cancer detection and classification(DHODCNNLCC)*** proposed DHODCNN-LCC technique initially undergoes pre-processing in two stages namely contrast enhancement and noise ***,the features extraction process on the pre-processed images takes place using the Nadam optimizer with RefineDet *** addition,denoising stacked autoencoder(DSAE)model is employed for lung nodule ***,the deer hunting optimization algorithm(DHOA)is utilized for optimal hyper parameter tuning of the DSAE model and thereby results in improved classification *** experimental validation of the DHODCNN-LCC technique was implemented against benchmark dataset and the outcomes are assessed under various *** experimental outcomes reported the superior outcomes of the DHODCNN-LCC technique over the recent approaches with respect to distinct measures.

关键词： Lung cancer image classification computer aided diagnosis deep learning medical imaging parameter optimization

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Expert system Diagnosis Corn Pests and Diseases Using Certainty Factor Method 2

Expert System Diagnosis Corn Pests and Diseases Using Certai...

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2nd International Conference on Mechanical, Electronics, computer, and Industrial technology, MECnIT 2018

作者： Sulindawaty Zarlis, Muhammad Situmorang, Zakarias Sihotang, Hengki Tamando Doctoral Program Faculty of Computer Science and InformationTechnology Universitas Sumatera Utara Indonesia Faculty of Computer Science and Information Technology Universitas Sumatera Utara Indonesia

The development of technology has been very significant, not only in the fields of information, industry, education, but in agriculture. Therefore technological sophistication is also utilized by corn farmers to obtain information about corn crops. Corn is included in plantation commodities in Indonesia. However, in corn cultivation there are various problems associated with the disease that is likely to attack the plant. Therefore, the authors designed the expert system of corn disease diagnosis using certainty method. This system will analyze based on symptoms - symptoms that are in the input by the user so that in obtaining a result of disease identification on corn in the name of the disease, the cause of the disease, the percentage value and how to handle it. This expert system is developed by using the visual basic application and Microsoft Access database with Certainty Factor method to measure the value of certainty of a hypothesis against a fact. © 2019 Published under licence by IOP Publishing Ltd.

关键词： Expert systems

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Energy-efficient Virtual Machine Allocation Technique Using Flower Pollination Algorithm in Cloud Datacenter:A Panacea to Green Computing

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Journal of Bionic Engineering 2019年第2期16卷 354-366页

作者： Mohammed Joda Usman Abdul Samad Ismail Hassan Chizari Gaddafi Abdul-Salaam Ali Muhammad Usman Abdulsalam Yau Gital Omprakash Kaiwartya Ahmed Aliyu Department of Maths Bauchi State University GadauBauchi 81007Nigeria Department of Computer Science Universiti Teknology MalaysiaSkudai Johor 81310Malaysia School of Computing and Technology Park CampusUniversity of Gloucestershire Cheltenham GL50 4SHUK Department of Computer Science Kwame Nkrumah University of Science and TechnologyKumasi 00233Ghana Department of Maths and Computer Federal College of Education Technical GombeGombe 072158Nigeria Department of Maths Abubakar Tafawa Balewa University BauchiBauchi 81027Nigeria Department of Computer and Information Technology Northumbria UniversityNewcastle NE1SSTUK

Cloud computing has attracted significant interest due to the increasing service demands from organizations offloading computationally intensive tasks to ***,datacenter infrastructure comprises hardware resources that consume high amount of energy and give out carbon emissions at hazardous *** cloud datacenter,Virtual Machines(VMs)need to be allocated on various Physical Machines(PMs)in order to minimize resource wastage and increase energy *** allocation problem is *** finding an exact solution is complicated especially for large-scale *** this con text,this paper proposes an Energy-oriented Flower Pollination Algorithm(E-FPA)for VM allocation in cloud datacenter environments.A system framework for the scheme was developed to enable energy-oriented allocation of various VMs on a *** allocation uses a strategy called Dynamic Switching Probability(DSP).The framework finds a near optimal solution quickly and balances the exploration of the global search and exploitation of the local *** considers a processor,storage,and memory constraints of a PM while prioritizing energy-oriented allocation for a set of *** performed on MultiRecCloudSim utilizing planet workload show that the E-FPA outperforms the Genetic Algorithm for Power-Aware(GAPA)by 21.8%,Order of Exchange Migration(OEM)ant colony system by 21.5%,and First Fit Decreasing(FFD)by 24.9%.Therefore,E-FPA significantly improves datacenter performance and thus,enhances environmental sustainability.

关键词： virtualization green computing cloud datacenter energy optimization

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Anti-Forensic Technique Using Alternate Data Streams for Data Hiding in NTFS 1

Anti-Forensic Technique Using Alternate Data Streams for Dat...

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1st International Conference on Futuristic Technologies, INCOFT 2022

作者： Hermon, Rahul Singh, Upasna Singh, Bhupendra Defense Institute of Advanced Technology Computer Science and Engineering Department Pune India Indian Institute of Information Technology Computer Science and Engineering Department Pune India

ISBN: (纸本)9781665450461

Data hiding techniques are one of the most commonly used anti-forensic methods by attackers or perpetrators. There are many data hiding techniques in various kinds of file system in different OS. Alternate data streams (ADS) in NTFS are one such means by which data can be hidden in Windows OS. In this work, ADS will be used to demonstrate how data can be hidden in it, to include different kinds of data files. The same will be implemented using command prompt. Lastly, we compared the data hiding technique using ADS in Windows 10 and 11 Operating system. © 2022 IEEE.

关键词： Windows operating system

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Voice Enabled Intelligent Programming Assistant 4

Voice Enabled Intelligent Programming Assistant

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4th International Conference on Advancements in Computing, ICAC 2022

作者： Wataketiya, Ravindu Chandrasiri, Navinda Kithsiri, Ramesh Malwatta, Hirush Nadeeshani, Madhuka Siriwardana, Samanthi Department of Computer Science and Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Information Technology Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350398090

In modern era where software development is of vital importance, software developers are challenged with conditions like Repetitive Strain Injury (RSI) which hinders their ability to work effectively. Furthermore, people with difficulties with using their hands also find it challenging to program in the traditional manner. As a solution, coding with one's voice has been experimented with, but current solutions lack interactivity and are harder to use and setup leaving much room for improvement in this domain. In this research work, by using input classifier models with accuracies over 90%, intent classifiers with accuracies over 70%, code parsing and various human computer interaction techniques, we developed a conversationally interactive, programming language agnostic, easy to setup and easy to use Voice Coding Assistant. This will potentially help a global audience of programmers to achieve their goals and improve productivity and lead a healthier life. We have named the system thus developed, "Venic". © 2022 IEEE.

关键词： Human computer interaction

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Efficient Authentication system Using Wavelet Embeddings of Otoacoustic Emission Signals

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computer systems science & Engineering 2023年第5期45卷 1851-1867页

作者： V.Harshini T.Dhanwin A.Shahina N.Safiyyah A.Nayeemulla Khan Department of Information Technology Sri Sivasubramaniya Nadar College of EngineeringChennai603110India School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India

Biometrics,which has become integrated with our daily lives,could fall prey to falsification attacks,leading to security *** our paper,we use Transient Evoked Otoacoustic Emissions(TEOAE)that are generated by the human cochlea in response to an external sound stimulus,as a biometric *** are robust to falsification attacks,as the uniqueness of an individual’s inner ear cannot be *** this study,we use both the raw 1D TEOAE signals,as well as the 2D time-frequency representation of the signal using Continuous Wavelet Transform(CWT).We use 1D and 2D Convolutional Neural Networks(CNN)for the former and latter,respectively,to derive the feature *** corresponding lower-dimensional feature maps are obtained using principal component analysis,which is then used as features to build classifiers using machine learning techniques for the task of person identification.T-SNE plots of these feature maps show that they discriminate well among the *** the various architectures explored,we achieve a best-performing accuracy of 98.95%and 100%using the feature maps of the 1D-CNN and 2D-CNN,respectively,with the latter performance being an improvement over all the earlier *** performance makes the TEOAE based person identification systems deployable in real-world situations,along with the added advantage of robustness to falsification attacks.

关键词： Person identification system cochlea:transient evoked otoacoustic emission wavelet transform convolutional neural network

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Smart UML - Assignment Management Tool for UML Diagrams 4

Smart UML - Assignment Management Tool for UML Diagrams

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4th International Conference on Advancements in Computing, ICAC 2022

作者： Weerasinghe, D.N.H. Thiwanka, K.A.T. Jayasith, H.B.C. Onella Natalie, P.A.D. Samantha Rajapaksha, U.U. Karunasena, Anuradha Department of Computer Science and Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Information Technology Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350398090

The great majority of manual processes have been automated in the current day. Nonetheless, there is no reliable approach for evaluating UML diagrams for plagiarism and correctness. Unified Modeling Language (UML) provides the basis for UML diagrams. UML Diagrams are typically used to graphically express a system's stakeholders, activities, objects, and classes to get a deep knowledge of a particular system. UML diagrams are widely used by industry experts as well as beginners. Class diagrams and use case diagrams are the most commonly used diagrams in UML. Though most computer science-related students get assessed in UML modelling, diagram plagiarism isn't considered in most exams since it's a tedious task to perform. The proposed system automates capturing details in UML diagram components and generating a model answer from the given question scenario to compare the diagrams and calculate the plagiarism percentage with the correction percentage. © 2022 IEEE.

关键词： Unified Modeling Language

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Twitter sentiment analysis using natural language toolkit and Vader sentiment

Twitter sentiment analysis using natural language toolkit an...

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2019 International MultiConference of Engineers and computer Scientists, IMECS 2019

作者： Elbagir, Shihab Yang, Jing College of Computer Science and Technology Harbin Engineering University Heilongjiang150001 China Faculty of Computer Science and Information Technology Shendi University Shendi Sudan College of Computer Science and Technology Harbin Engineering University Heilongjiang150001 China

The sentiment analysis of Twitter data has gained much attention as a topic of research. The ability to obtain information about a public opinion by analyzing Twitter data and automatically classifying their sentiment polarity has attracted researchers because of the concise language used in tweets. In this study, we aimed to use the Valence Aware Dictionary for sEntiment Reasoner (VADER) to classify the sentiments expressed in Twitter data. However, because most previous studies were oriented to binary classification, in this study, we propose a multi-classification system for analyzing tweets. We used VADER to classify tweets related to the 2016 US election. The results showed good accuracy in detecting ternary and multiple classes. © 2019 Turkiye Klinikleri Journal of Medical sciences. All rights reserved.

关键词： Sentiment analysis

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Thailand Food Price Forecasting: Comparative Analysis of Machine Learning Models 6

Thailand Food Price Forecasting: Comparative Analysis of Mac...

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6th International Conference on Cybernetics and Intelligent system, ICORIS 2024

作者： Watthananon, Julaluk Thongrak, Prapas Thwe, Yamin Saekhow, Navarat Computer Science Department Faculty of Science and Technology RMUTT Pathum Thani Thailand

ISBN: (纸本)9798331539603

This study aims to address food price fraud and predict pricing trends for key commodities—Cassava flour, Maize, Meat (chicken), and Rice—in Thailand, using advanced machine learning techniques to enhance forecasting accuracy and detect anomalies. We utilized historical food price data from 2000 to 2020 and incorporated USD exchange rate fluctuations to build and evaluate several regression models, including Linear Regression, Decision Tree Regressor, Random Forest Regressor, Gradient Boosting Regressor, and Support Vector Regressor. The Decision Tree and Random Forest models demonstrated superior performance with the lowest mean squared error (MSE) and root mean squared error (RMSE), effectively capturing price trends and fluctuations. In contrast, Linear Regression and Support Vector Regressor showed higher errors and less effectiveness in handling nonlinear patterns. This study highlights the efficacy of machine learning in improving price prediction accuracy and detecting potential fraud, offering a robust framework for analyzing commodity pricing dynamics. The findings provide actionable insights for policymakers and stakeholders to implement proactive strategies, promoting greater market stability and fairness in Thailand's agricultural sector. © 2024 IEEE.

关键词： Support vector regression

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