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Rice Leaves Disease Diagnose Empowered with Transfer Learning

computer systems science & Engineering 2022年第9期42卷 1001-1014页

作者： Nouh Sabri Elmitwally Maria Tariq Muhammad Adnan Khan Munir Ahmad Sagheer Abbas Fahad Mazaed Alotaibi College of Computer and Information Sciences Jouf UniversitySakaka72341Saudi Arabia Department of Computer Science Faculty of Computers and Artificial IntelligenceCairo University12613Egypt School of Computer Science National College of Business Administration and EconomicsLahore54000Pakistan Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan Pattern Recognition and Machine Learning Lab Department of SoftwareGachon UniversitySeongnam13557Korea Department of Information System Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia

In the agricultural industry,rice infections have resulted in significant productivity and economic *** infections must be recognized early on to regulate and mitigate the effects of the *** diagnosis of disease severity effects or incidence can preserve production from quantitative and qualitative losses,reduce pesticide use,and boost ta country’s *** the health of a rice plant through its leaves is usually done as a manual ocular *** this manuscript,three rice plant diseases:Bacterial leaf blight,Brown spot,and Leaf smut,were identified using the Alexnet *** research shows that any reduction in rice plants will have a significant beneficial impact on alleviating global food hunger by increasing supply,lowering prices,and reducing production's environmental impact that affects the economy of any *** would be able to get more exact and faster results with this technology,allowing them to administer the most acceptable treatment *** Using Alex Net,the proposed approach achieved a 99.0%accuracy rate for diagnosing rice leaves disease.

关键词： Rice bacterial leaf blight brown spot leaf smut machine learning alexnet

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A Comparative Analysis on Three Duplication Elements in Copy-Move Forgery using PatchMatch-Based Detection Method 8

A Comparative Analysis on Three Duplication Elements in Copy...

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8th IEEE International Conference on Software Engineering and computer systems, ICSECS 2023

作者： Nor Azaimi, Nur Izzati Abd Warif, Nor Bakiah Ismail, Nor-Syahidatul N Universiti Tun Hussein Onn Malaysia Faculty of Computer Science and Information Technology Johor Malaysia Centre for Information Security Research Universiti Tun Hussein Onn Malaysia Faculty of Computer Science and Information Technology Johor Malaysia Universiti Malaysia Pahang Al-Sultan Abdullah Faculty of Computing Department of Computer System and Networking Pekan Pahang Malaysia

ISBN: (纸本)9798350310931

Image forgery is the alteration of a digital image to hide some of the important and useful information. Copy-move forgery (CMF) is one of the most difficult to detect because the copied part of the image has the same characteristics as the original image. Most of the existing datasets only highlight additional attacks in the copied part. Since there are no categories of duplication elements in the datasets, this research analyzed three categories of duplication elements in CMF which are animals, food and non-living things using DEFACTO and CoMo3Dataset. The analysis is performed on PatchMatch-based detection method and the results show that the method able to maintain at least 83% for all duplication elements in both DEFACTO and CoMo3Dataset. Furthermore, the method is able to detect a minimum 92% score for the food category in both datasets. © 2023 IEEE.

关键词： Copy-Move forgery DEFACTO duplication regions PatchMatch

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Detection of Student Sleepiness Using U-Net and Conv-LSTM Methods to Improve Learning Quality 1

Detection of Student Sleepiness Using U-Net and Conv-LSTM Me...

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1st International Conference on Technology, Engineering, and Computing Applications, ICTECA 2023

作者： Rahman, Ben Andryana, Septi Gunaryati, Aris Mantoro, Teddy Faculty of Information and Communication Technology Universitas Nasional Department of Information System Jakarta Indonesia Faculty of Information and Communication Technology Universitas Nasional Department of Information Technology Jakarta Indonesia Sampoerna University Jakarta Selatan Faculty of Science and Technology Department of Computer Science Jakarta Indonesia

ISBN: (纸本)9798350360554

In the context of monitoring student engagement during learning sessions, the ability to accurately detect sleepiness can be a valuable asset. Early detection of sleepiness is critical to evaluate the need for intervention to ensure effective learning outcomes. Prolonged student sleepiness can negatively impact learning effectiveness and may result in academic challenges. This research used a semantic segmentation network to monitor and detect the level of sleepiness in students during learning sessions by modifying the U-Net architecture, which is enhanced with Convolutional Long Short-Term Memory (Conv-LSTM) blocks. Conv-LSTM blocks enable efficient feature sharing between network layers and improve accuracy in detecting sleepiness. In this study, experiments were carried out and the proposed method for monitoring students in the classroom was evaluated. The results of this research showed that the percentage of students who made movements detected in the video and extracted in image detection was 92% accurate and not sleepy. © 2023 IEEE.

关键词： Long short-term memory

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Expectation-Confirmation Model (ECM) to See Satisfaction and Continued Intention of e-Learning ('Cyber') 6

Expectation-Confirmation Model (ECM) to See Satisfaction and...

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6th International Conference on information Technology, InCIT 2022

作者： Prasetya, Fx Hendra Harnadi, Bernardinus Widiantoro, Albertus Dwiyoga Pamudji, Andre Kurniawan University Semarang Faculty Computer Science Soegijapranata Catholic Department of Information System Indonesia

ISBN: (纸本)9781665489126

The Expectation-Confirmation Model (ECM) has been widely used to test technology adoption by many researchers. This study aims to examine the ECM used for e-learning used at Soegijapranata Catholic University Semarang. The respondents used in this study were Soegijapranata Catholic University students from various study programs, both D3, Sl, S2, and S3. The methodology used in this study is to distribute questionnaires to cyber user respondents at Soegijapranata Catholic University, then test the data with statistical tests using Smar PLS, the expected results are models that are compiled based on hypotheses from literature studies as expected. Based on the hypothesis test, it turns out that all the hypotheses that have been prepared can be accepted because all P Value prices are below 5% or 0.05. The results of this study turned out to be a correlation between cyber user satisfaction on the sustainability of cyber use in the future. © 2022 IEEE.

关键词： E-learning

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Optimizing Multi-Media Database Performance: A Comprehensive Review of Fragmentation Methods and Dynamic Access Patterns 11

Optimizing Multi-Media Database Performance: A Comprehensive...

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11th IEEE International Conference on Intelligent Computing and information systems, ICICIS 2023

作者： Mahmoud, Shymaa H. Abdelraouf, Ahmed E. Badr, Nagwa L. Ali, Mohamed I. Ain Shams University Faculty of Archeology Museums and Archaeology Sites Management Department Cairo Egypt Ain Shams University Faculty of Computer and Information Science Information System Department Cairo Egypt

ISBN: (纸本)9798350322101

Large databases with a lot of data and many users can be difficult to manage and slow to query. Fragmentation is a technique that can be used to improve performance by dividing the database into smaller pieces that can be stored and accessed more efficiently. This research provides a systematic overview of the literature on fragmentation methods for databases. We performed a comprehensive literature review on fragmentation techniques proposed in articles published between 2012 and 2022, aiming to gather a systematic understanding of the subject. We examined the articles to determine if they took into account multimedia data or another data type, as well as other criteria such as the use of a cost model, ease of implementation, completeness, different algorithms applied to databases, and partitioning schemes. We also investigated whether the methods were dynamic, meaning that they could be adapted to changes in the database. Our investigation discovered a significant finding about the underutilization of dynamic fragmentation approaches and cost models in the context of multi-media databases. By discovering this gap, we have thrown light on an underutilized area of database management, providing opportunities for increased efficiency and cost-effectiveness. Furthermore, we stress the critical need of addressing the integration of a heritage database into existing infrastructure, which poses a substantial difficulty in the overall database management process. This discovery necessitates additional investigation and integration of dynamic fragmentation algorithms and cost models, which will result in improved performance, scalability, and resource allocation for multi-media databases. © 2023 IEEE.

关键词： Database systems

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The Impact of Social Media in Marketing on Brand Awareness and Customer Trust in Increasing Purchase Intention

The Impact of Social Media in Marketing on Brand Awareness a...

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2024 International Conference on information Technology Research and Innovation, ICITRI 2024

作者： Fernando, Erick Prabowo, Yulius Denny Winanti Universitas Multimedia Nusantara Faculty of Engineering and Informatics Department of Information Systems Tangerang Indonesia Binus Online Learning Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Universitas Insan Pembangunan Department of Information Systems Indonesia

ISBN: (纸本)9798350376210

In the current digital era, marketing has developed using technology through media to reach consumers. One prominent feature is influencer marketing, where individuals with significant social media influence promote products or services to their audiences. This strategy has become dominant because of its potential to expand brand reach and boost sales. However, there are challenges in ensuring the effectiveness of influencer marketing, particularly in delivering content with sufficient information and entertainment value. Overcoming these challenges is critical to maximizing customer purchase intent. This research explores the influence of information value, entertainment value, brand awareness, and customer trust on customer purchase intentions in influencer marketing. This research used an online survey (google form) with 398 valid respondents used for analysis PLS-SEM with SMART PLS 3.0. The indicator test results are valid and reliable with loading value for each item with a value >0.5, AVE value ⩾ 0.50, Cronbach's alpha value ⩾ 0.70 and Composite Reliability (CR) value ⩾ 0.7. The findings show that these factors have a significant effect on purchase intention. In emphasizing the importance of influencers delivering educational and entertaining content to increase Brand Awareness in building customer trust, with the consistency of influencers. Overall, providing valuable insights for companies looking to optimize their influencer marketing strategy to increase brand awareness and customer trust, ultimately driving customer purchase intent. © 2024 IEEE.

关键词： Sales

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The Role of the Internet of Things in Monitoring Air Pollution-A Survey 11

The Role of the Internet of Things in Monitoring Air Polluti...

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11th International Conference on information Technology, ICIT 2023

作者： Abusukhon, Ahmad Alghannam, Bareeq Al-Zaytoonah University of Jordan Faculty of Science and Information Technology Computer Science Dep. Amman Jordan College of Business Studies the Public Authority for Applied Education and Training Department of Computer Science and Information System Kuwait City Kuwait

ISBN: (纸本)9798350320060

Air pollution is one of the most common problems that the world is facing today. In fact, there are numerous causes of air pollution, including the large number of industries and automobiles that emit carbon dioxide (CO2) and carbon monoxide. Pollution is increasing on a daily basis, making air quality monitoring and management a major challenge for governments in both developed and developing countries. Air pollution affects human health as well as the atmosphere, causing climate change. One of the techniques used for monitoring air pollution is the Internet of Things (IoT). In this paper, we demonstrate various techniques for monitoring air quality using IoT and make a comparison based on various factors elicited from the previous work. © 2023 IEEE.

关键词： Internet of things

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Enhancing Network Design through Statistical Evaluation of MANET Routing Protocols

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computers, Materials & Continua 2024年第7期80卷 319-339页

作者： Ibrahim Alameri Tawfik Al-Hadhrami Anjum Nazir Abdulsamad Ebrahim Yahya Atef Gharbi Faculty of Medical Sciences Jabir ibn Hayyan Medical UniversityAlkufa QuNajaf54001Iraq Computer Science Department School of Science and TechnologyNottingham Trent UniversityNottinghamNG118NSUK Department of Computing Faculty of Engineering Sciences&Technology(FEST)Hamdard UniversityKarachi74600Pakistan Department of Information Technology College of Computing and Information TechnologyNorthern Border UniversityArar91431Saudi Arabia Department of Information System College of Computing and Information TechnologyNorthern Border UniversityArar91431Saudi Arabia

This paper contributes a sophisticated statistical method for the assessment of performance in routing protocols salient Mobile Ad Hoc Network(MANET)routing protocols:Destination Sequenced Distance Vector(DSDV),Ad hoc On-Demand Distance Vector(AODV),Dynamic Source Routing(DSR),and Zone Routing Protocol(ZRP).In this paper,the evaluation will be carried out using complete sets of statistical tests such as Kruskal-Wallis,Mann-Whitney,and *** articulates a systematic evaluation of how the performance of the previous protocols varies with the number of nodes and the mobility *** study is premised upon the Quality of Service(QoS)metrics of throughput,packet delivery ratio,and end-to-end delay to gain an adequate understanding of the operational efficiency of each protocol under different network *** findings explained significant differences in the performance of different routing protocols;as a result,decisions for the selection and optimization of routing protocols can be taken effectively according to different network *** paper is a step forward in the general understanding of the routing dynamics of MANETs and contributes significantly to the strategic deployment of robust and efficient network infrastructures.

关键词： Routing protocols statistical approach friedman MANET

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Implementing a Model-Based Approach for Digital Health Adoption Model Development

Implementing a Model-Based Approach for Digital Health Adopt...

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2023 International Conference on Informatics, multimedia, Cyber and information systems, ICIMCIS 2023

作者： Rahayu, Puji Dwiasnati, Saruni Rahim, Normala Saputra, Ipung Dwi Ari Ranadhan, Farhan Universitas Mercu Buana Faculty of Computer Science Information System Department Jakarta Indonesia Universitas Mercu Buana Faculty of Computer Science Informatic Department Jakarta Indonesia Universiti Sultan Zainal Abidin Fakulti Informatik Dan Komputeran Malaysia

ISBN: (纸本)9798350307306

The purpose of this study is to create a digital health adoption model based on parameters that have been found in previous studies, using the Model Development Engineering (MDE) method. This method is used to overcome the challenges of the Covid-19 pandemic in validating the model design to be implemented. One of the main advantages of the Model Development Engineering (MDE) approach is that all models produced are formal extensions of the Unified Modeling Language (UML). MDE is a system used to build a framework for organizing, planning, and controlling the information systems development process. In the verification stage of the Digital Health model, the presentation stage is adopted which involves two approaches, namely the design approach and the system approach. The design approach adopts Model Driven Engineering (MDE), which is used in the development of system architecture. The results of this study are the model designs used to adjust validation in developing digital health models using the Model Development Engineering technique. © 2023 IEEE.

关键词： COVID-19

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Catalyzing EEG signal analysis: unveiling the potential of machine learning-enabled smart K nearest neighbor outlier detection

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International Journal of information Technology (Singapore) 2024年 1-12页

作者： Aymen, Abid Khediri, Salim El Thaljaoui, Adel Miladi, Moahmed Kachouri, Abdennaceur Computer & amp Embedded Systems Lab National Engineering School of Sfax University of Sfax Sfax Tunisia Department of Information Technology College of Computer Qassim University Buraydah Saudi Arabia Department of Computer Science Faculty of Science of Gafsa University of Gafsa Gafsa Tunisia Preparatory Institute for Engineering Studies of Gafsa Gafsa Tunisia Department of Computer Science and Information College of Science at Zulfi Majmaah University Al-Majmaah 11952 Saudi Arabia Higher Institute of Computer Science and Multimedia of Sfax University of Sfax Sfax Tunisia Advanced Fluid Dynamics Energetics and Environment Lab National Engineering School of Sfax University of Sfax Sfax Tunisia

Electroencephalogram (EEG) data are susceptible to artifacts, such as lapses in concentration or poor imagination, which can significantly impact the accuracy of disease diagnosis in e-health applications. To mitigate this issue, the use of machine learning (ML) and potentially artificial intelligence (AI) solutions to accurately identify outliers becomes crucial. Unlike many AI methods that incorporate unnecessary or redundant input variables, our study focuses on detecting anomalous values in EEG data through the K nearest neighbor (KNN) process and Euclidean distance metric. Our proposed unsupervised non-parametric algorithm, known as the smart KNN outlier detector (SKOD), eliminates the need for initial parameter configurations such as the number of neighbors (K), while achieving high performance. Evaluation of SKOD using real EEG data from 140 trials demonstrated sensitivity and specificity exceeding 60%, with nearly perfect accuracy in detecting outliers reaching close to 100%. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Data analysis EEG signal Healthcare Knowledge-based systems Machine learning Outlier detection

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