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Smart Techniques for LULC Micro Class Classification Using Landsat8 Imagery

computers, Materials & Continua 2023年第3期74卷 5545-5557页

作者： Mutiullah Jamil Hafeez ul Rehman SaleemUllah Imran Ashraf Saqib Ubaid Department of Computer Science Khwaja Fareed University of Engineering and Information TechnologyRahim Yar Khan64200Pakistan Department of Information and Communication Engineering Yeungnam UniversityGyeongsan-si38541Korea

Wheat species play important role in the price of products and wheat production *** are several mathematical models used for the estimation of the wheat crop but these models are implemented without considering the wheat species which is an important independent *** task of wheat species identification is challenging both for human experts as well as for computer vision-based *** the use of satellite remote sensing,it is possible to identify and monitor wheat species on a large scale at any stage of the crop life *** this work,nine popular wheat species are identified by using Landsat8 operational land imager(OLI)and thermal infrared sensor(TIRS)*** thousand samples of eachwheat crop species are acquired every fifteen days with a temporal resolution of ten multispectral bands(band two to band eleven).This study employs random forest(RF),artificial neural network,support vector machine,Naive Bayes,and logistic regression for nine types of wheat *** addition,deep neural networks are also *** results indicate that RF shows the best performance of 91%accuracy while DNN obtains a 90.2%*** suggest that remotely sensed data can be used in wheat type estimation and to improve the performance of the mathematical models.

关键词： Mathematical model wheat crop estimation landsat8 remote sensing machine learning random forest deep neural network

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Human intelligence-based metaverse for co-learning of students and smart machines

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Journal of Ambient Intelligence and Humanized Computing 2023年第6期14卷 7695-7718页

作者： Lee, Chang-Shing Wang, Mei-Hui Reformat, Marek Huang, Sheng-Hui Department of Computer Science and Information Engineering National University of Tainan Tainan Taiwan Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Information Technology Institute University of Social Sciences Warsaw Poland

This paper proposes a Human Intelligence (HI)-based Computational Intelligence (CI) and Artificial Intelligence (AI) Fuzzy Markup Language (CI&AI-FML) Metaverse as an educational environment for co-learning of students and machines. The HI-based CI&AI-FML Metaverse is based on the spirit of the Heart Sutra that equips the environment with teaching principles and cognitive intelligence of ancient words of wisdom. There are four stages of the Metaverse: preparation and collection of learning data, data preprocessing, data analysis, and data evaluation. During the data preparation stage, the domain experts construct a learning dictionary with fuzzy concept sets describing different terms and concepts related to the course domains. Then, the students and teachers use the developed CI&AI-FML learning tools to interact with machines and learn together. Once the teachers prepare relevant material, students provide their inputs/texts representing their levels of understanding of the learned concepts. A Natural Language Processing (NLP) tool, Chinese Knowledge information Processing (CKIP), is used to process data/text generated by students. A focus is put on speech tagging, word sense disambiguation, and named entity recognition. Following that, the quantitative and qualitative data analysis is performed. Finally, the students’ learning progress, measured using progress metrics, is evaluated and analyzed. The experimental results reveal that the proposed HI-based CI&AI-FML Metaverse can foster students' motivation to learn and improve their performance. It has been shown in the case of young students studying Software engineering and learning English. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Students

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Statistical Detection of Malicious iOS Apps through Ensemble and Active Learning 2023

Statistical Detection of Malicious iOS Apps through Ensemble...

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15th International Conference on Contemporary Computing, IC3 2023

作者： Bhatt, Arpita Jadhav Agarwal, Parul Department of Computer Science & Engineering and Information Technology Jaypee Institute of Information Technology India

ISBN: (纸本)9798400700224

iOS is one of the most broadly used mobile operating systems after Android. In today's era, smartphones are widely used to perform several tasks such as net banking, GPS tracking, ordering products, etc. One of the main features of smartphones is that their capabilities can be expanded by installing third-party apps. However, these apps pose privacy threats to the user as they contain a lot of user's personal information, which may be vulnerable to attackers. Background studies have identified that a lot of user's personal and sensitive data is stored on smartphones and thus, they have become a perfect target for privacy threats in the whole mobile ecosystem. Machine learning techniques to detect malware apps have offered promising results for the Android platform whereas nominal work has been done for the iOS platform. In this paper, we are incorporating various machine learning techniques to detect malicious iOS apps from several categories. Some of the ensemble learning techniques with different classification algorithms are inculcated to enhance the precision of classifiers. A weighted average method on active and ensemble learning has been proposed which proved to be more efficient as compared to other machine learning techniques in most of the categories. Our empirical analysis has demonstrated that the proposed technique is statistically significant than active and ensemble learning. © 2023 ACM.

关键词： Smartphones

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Garbage collector-A Web Based System 2

Garbage collector-A Web Based System

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2nd International Conference on Informatics, ICI 2023

作者： Gupta, Piyush Khan, Fardeen Dixit, Varun Chaudhary, Laxmi Jaypee Institute of Information Technology Computer Science and Engineering and Information Technology Noida India

ISBN: (纸本)9798350343830

Smart cities are implementing web-based systems to improve the living conditions of their inhabitants. One such solution is the implementation of an efficient and eco-friendly garbage management system. Currently, garbage collection involves regular garbage trucks that pick-up garbage once a week in every zone of the city, which is a highly inefficient use of government resources. In this work, a web-based system is proposed to manage the large amount of garbage collected daily while also providing a better solution. The system involves users renting dumpsters to collect waste generated in their respective areas, which will then be decomposed at designated dumping yards. This approach aims to reduce emissions of toxic chemicals that pollute the environment. © 2023 IEEE.

关键词： Websites

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Facial Image-Based Autism Detection:A Comparative Study of Deep Neural Network Classifiers

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computers, Materials & Continua 2024年第1期78卷 105-126页

作者： Tayyaba Farhat Sheeraz Akram Hatoon SAlSagri Zulfiqar Ali Awais Ahmad Arfan Jaffar Faculty of Computer Science and Information Technology The Superior UniversityLahore54600Pakistan Intelligent Data Visual Computing Research(IDVCR) Faculty of Computer Science and Information TechnologyThe Superior UniversityLahore54600Pakistan Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh12571Saudi Arabia School of Computer Science and Electronic Engineering(CSEE) University of EssexWivenhoe ParkColchesterCO43SQUK

Autism Spectrum Disorder(ASD)is a neurodevelopmental condition characterized by significant challenges in social interaction,communication,and repetitive *** and precise ASD detection is crucial,particularly in regions with limited diagnostic resources like *** study aims to conduct an extensive comparative analysis of various machine learning classifiers for ASD detection using facial images to identify an accurate and cost-effective solution tailored to the local *** research involves experimentation with VGG16 and MobileNet models,exploring different batch sizes,optimizers,and learning rate *** addition,the“Orange”machine learning tool is employed to evaluate classifier performance and automated image processing capabilities are utilized within the *** findings unequivocally establish VGG16 as the most effective classifier with a 5-fold cross-validation ***,VGG16,with a batch size of 2 and the Adam optimizer,trained for 100 epochs,achieves a remarkable validation accuracy of 99% and a testing accuracy of 87%.Furthermore,the model achieves an F1 score of 88%,precision of 85%,and recall of 90% on test *** validate the practical applicability of the VGG16 model with 5-fold cross-validation,the study conducts further testing on a dataset sourced fromautism centers in Pakistan,resulting in an accuracy rate of 85%.This reaffirms the model’s suitability for real-world ASD *** research offers valuable insights into classifier performance,emphasizing the potential of machine learning to deliver precise and accessible ASD diagnoses via facial image analysis.

关键词： Autism Autism Spectrum Disorder(ASD) disease segmentation features optimization deep learning models facial images classification

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Performance Evaluation of Smart Flower Optimization Algorithm Over Industrial Non-convex Constrained Optimization Problems 5th

Performance Evaluation of Smart Flower Optimization Algorith...

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5th International Conference on Applied Machine Learning and Data Analytics, AMLDA 2022

作者： Singh, Akhilesh Kumar Mehta, Shikha Computer Science Engineering and Information Technology Jaypee Institute of Information Technology Noida India

ISBN: (纸本)9783031342219

The large number of dimensions, complexity and constraints of real world problems is the deriving force to assess the algorithms in diverse applications scenarios. Over a past decade, nature inspired algorithm are seen as potential solutions to solve continuous optimization problems. These are non-deterministic algorithms with proven abilities to find near optimal solutions. However, according to not a single free lunch theorem, nor an any algorithm is suitable for solving all kinds of problems. This work present performance evaluation of recent meta-heuristic algorithm that is Sun Flower Optimization Algorithm (SFOA) over Non-Convex Constrained Optimization problems industrial problems from domains like mechanical engineering, chemical processes and Process synthesis and design problems. SFOA is compared with IUDE, MAgES, iLSHADE 424 for 15 problems with varied number of equality and inequality constraints. Results reveal that SFOA performs better for two out of four industrial chemical problems and five out of six process design and synthesis problems. Problems by mechanical engineering, IUDE, MAgES, iLSHADE 424 outperform SFOA for 4 out of five problems. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Heuristic algorithms

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An Enhanced Aspect-Based Sentiment Analysis Model Based on RoBERTa For Text Sentiment Analysis

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第14期49卷 193-202页

作者： Chauhan, Amit Sharma, Aman Mohana, Rajni Jaypee University of Information Technology HP Solan173234 India Department of Computer Science Amity School of Engineering and Technology Amity University Punjab Punjab Mohali140306 India

Using an aspect-based sentiment analysis task, sentiment polarity towards specific aspect phrases within the same sentence or document is to be identified. The process of mechanically determining the underlying attitude or opinion indicated in the text is known as sentiment analysis. One of the most important aspects of natural language processing is sentiment analysis. The RoBERTa transformer model was pretrained in a self-supervised manner using a substantial corpus of English data. This means it was pretrained solely with raw texts and an algorithmic process to generate inputs and labels from those texts. No human labelling was involved, allowing it to utilise a vast amount of publicly available data. The authors of this work provide a thorough investigation of aspect-based sentiment analysis with RoBERTa. The RoBERTa model and its salient characteristics are outlined in this work, followed by an analysis of the model's optimisation by the authors for aspect-based sentiment analysis. The authors compare the RoBERTa model with other state-of-the-art models and evaluate its performance on multiple benchmark datasets. Our experimental results show that the RoBERTa model is effective for this important natural language processing task, outperforming competing models on sentiment analysis tasks. Based on the SemEval-2014 variant benchmarking datasets, the restaurant and laptop domains have the highest accuracy, scoring 92.35 % and 82.33 %, respectively. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Natural language processing systems

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Energy-efficient virtual machine scheduling in IaaS cloud environment using energy-aware green-particle swarm optimization

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International Journal of information technology (Singapore) 2023年第4期15卷 1927-1935页

作者： Ajmera, Kashav Tewari, Tribhuwan Kumar Department of Computer Science and Engineering and Information Technology Jaypee Institute of Information Technology Noida-62 Noida India

Cloud data centers have a huge pool of heterogeneous servers to seamlessly meet the ever-increasing demand for cloud resources. Most of these servers belong to different server generations and have varying power ratings. Virtual machine (VM) consolidation schedules VMs over this pool of heterogeneous servers to improve resource utilization and reduce power consumption. However, it also increases the Service Level Agreement(SLA) violation since servers operate near full capacity. Therefore, to optimize this tradeoff between energy consumption and SLA violation, a Green-Particle Swarm Optimization (GPSO) algorithm is proposed that schedules VMs over energy-efficient green servers. These green servers (green particles) are energy-efficient, explore the global search space, and find a VM schedule with minimum active servers that reduce data center power consumption and ensure SLA. The proposed GPSO algorithm is implemented on the cloudsim simulator and compared with the state-of-the-art VM scheduling algorithms. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Bin packing Cloud computing Cloud data center Energy-efficient VM scheduling Green computing Particle swarm ptimization Power consumption Virtualization

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Fake News Encoder Classifier (FNEC) for Online Published News Related to COVID-19 Vaccines

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Intelligent Automation & Soft Computing 2023年第7期37卷 73-90页

作者： Asma Qaiser Saman Hina Abdul Karim Kazi Saad Ahmed Raheela Asif Department of Computer Science and Information Technology NED University of Engineering and TechnologyKarachi75270Pakistan Department of Computer Science Iqra UniversityKarachi76400Pakistan Department of Software Engineering NED University of Engineering and TechnologyKarachi75270Pakistan

In the past few years,social media and online news platforms have played an essential role in distributing news content *** of the authenticity of news has become a major *** the COVID-19 outbreak,misinformation and fake news were major sources of confusion and insecurity among the general *** the first quarter of the year 2020,around 800 people died due to fake news relevant to *** major goal of this research was to discover the best learning model for achieving high accuracy and performance.A novel case study of the Fake News Classification using ELECTRA model,which achieved 85.11%accuracy score,is thus reported in this *** addition to that,a new novel dataset called COVAX-Reality containing COVID-19 vaccine-related news has been *** the COVAX-Reality dataset,the performance of FNEC is compared to several traditional learning models i.e.,Support Vector Machine(SVM),Naive Bayes(NB),Passive Aggressive Classifier(PAC),Long Short-Term Memory(LSTM),Bi-directional LSTM(Bi-LSTM)and Bi-directional Encoder Representations from Transformers(BERT).For the evaluation of FNEC,standard metrics(Precision,Recall,Accuracy,and F1-Score)were utilized.

关键词： Deep learning fake news detection machine learning transformer model classification

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Transfer Learning Empowered Skin Diseases Detection in Children

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computer Modeling in engineering & Sciences 2024年第12期141卷 2609-2623页

作者： Meena N.Alnuaimi Nourah S.Alqahtani Mohammed Gollapalli Atta Rahman Alaa Alahmadi Aghiad Bakry Mustafa Youldash Dania Alkhulaifi Rashad Ahmed Hesham Al-Musallam Department of Computer Science College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of Computer Information Systems College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of Computer Engineering College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia ICS Department King Fahd University of Petroleum and MineralsDhahran31261Saudi Arabia

Human beings are often affected by a wide range of skin diseases,which can be attributed to genetic factors and environmental influences,such as exposure to sunshine with ultraviolet(UV)*** left untreated,these diseases can have severe consequences and spread,especially among *** detection is crucial to prevent their spread and improve a patient’s chances of ***,the branch of medicine dealing with skin diseases,faces challenges in accurately diagnosing these conditions due to the difficulty in identifying and distinguishing between different diseases based on their appearance,type of skin,and *** study presents a method for detecting skin diseases using Deep Learning(DL),focusing on the most common diseases affecting children in Saudi Arabia due to the high UV value in most of the year,especially in the *** method utilizes various Convolutional Neural Network(CNN)architectures to classify skin conditions such as eczema,psoriasis,and *** proposed method demonstrates high accuracy rates of 99.99%and 97%using famous and effective transfer learning models MobileNet and DenseNet121,*** illustrates the potential of DL in automating the detection of skin diseases and offers a promising approach for early diagnosis and treatment.

关键词： Deep learning MobileNet DenseNet121 skin diseases detection transfer learning

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