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EfficientNetB0-based Rice Disease Classification: A Strategic Fine-Tuning Approach

EfficientNetB0-based Rice Disease Classification: A Strategi...

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Smart Electronics and Communication (ICOSEC), International Conference on

作者： Shivani Mishra Chavali Sai Sreeram Yadav Akansha Raju A Balasundaram Ayesha Shaik Dept. of Computer Science and Engineering Vellore Institute of Technology Chennai Chennai India Center for Cyber Physical Systems Vellore Institute of Technology Chennai Chennai India

ISBN: (数字)9798331504403

ISBN: (纸本)9798331504410

Rice, a major stape food, is crucial for both sustenance and the living of millions of farmers. Detecting rice leaf diseases is vital to prevent widespread crop losses, safeguarding farmers’ incomes and global food supply. While current detection systems face accuracy and robustness challenges, deep learning models, particularly the EfficientNetB0, show promise. Despite their sensitivity to noise and variations in leaf appearance, ongoing research addresses these issues. This study introduces the use of EfficientNetB0 for accurate classification, incorporating a fine-tuning strategy in a brief five-epoch training period. This approach optimally balances model adaptation and prevents overfitting, leveraging the deep neural network’s ability to capture complex hierarchical features. The limited fine-tuning duration addresses potential risks while maximizing the model’s robust features. This unique amalgamation of advanced architecture and tailored training strategy aims to achieve efficient and accurate disease classification. The research signifies a step toward revolutionizing rice disease detection, emphasizing the importance of striking a balance between cutting-edge methodologies and practical considerations in the pursuit of agricultural sustainability. Before Fine Tuning the Training Accuracy was $\mathbf{9 8. 4 6 \%}$, Validation Accuracy was $\mathbf{9 9. 1 5 \%}$ and Test Accuracy was $\mathbf{9 8. 4 4 \%}$.After Fine Tuning there was a significant increase in the accuracy. The Training Accuracy came to be $\mathbf{9 9. 7 7 \%}$, Validation Accuracy is $\mathbf{9 9. 4 3 \%}$ and Test Accuracy: 99.12%

关键词： Training Adaptation models Accuracy Sensitivity Computational modeling Noise Robustness Sustainable development Tuning Diseases

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Non-Invasive Device for Hearing Rehabilitation

Non-Invasive Device for Hearing Rehabilitation

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E-Health and Bioengineering Conference (EHB)

作者： Daniela Goldan Mihai Aron Teofil Ilie Ursache Gladiola Petroiu Cristian Rotariu Dept. of Biomedical Sciences Faculty of Medical Bioengineering Grigore T. Popa University of Medicine and Pharmacy of Iasi Iași Romania Dept. of Automatic Control and Applied Informatics Faculty of Automatic Control and Computer Engineering Gheorghe Asachi Technical University Iași Romania

ISBN: (数字)9798331532147

ISBN: (纸本)9798331532154

Hearing loss impacts over 5% of the world's population, a percent rising due to aging populations, noise exposure, and chronic health conditions. To address this, we developed a non-invasive hearing device designed for individuals with hearing challenges that limit the use of hearing aids. The proposed device integrates a set of Bluetooth headphones with a smartphone, that process the ambient sound in real time through a software application and delivers to the hear impaired patient via bone conduction, bypassing traditional auditory pathways. Laboratory tests confirmed that the proposed device effectively replicates auditory perception. The developed device offers an accessible and user-friendly alternative to patients who cannot use traditional hearing aids.

关键词： Irrigation Noise Auditory system Ear Receivers Hearing aids Propagation losses Real-time systems Software Testing

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Identification and control of a Linear Time-Periodic Test Bench Using Harmonic Transfer Functions and LQR controllers

Identification and Control of a Linear Time-Periodic Test Be...

引用

International Conference on control, Decision and Information Technologies (CoDIT)

作者： Basak Sert Bahadir Catalbas Ismail Uyanik Dept. of Electrical and Electronics Engineering Hacettepe University Ankara Turkey Defence Systems Technologies Division Aselsan Inc. Ankara Turkey Dept. of Control and Automation Engineering Yildiz Technical University İstanbul Turkey

The increased need for accurately modeling the input-output characteristics of linear time-periodic (LTP) systems necessitates novel identification and control algorithms as well as new test benches for their experimental validation. This paper introduces a simple-to-build test bench for the identification and control of LTP systems. We mechanically coupled the shafts of two DC motors and fed back the angular velocity to the second motor with a time-periodic modulation. This allowed us to imitate a time-periodic load for the first DC motor, thereby yielding an experimental LTP plant. We used Matlab/Simulink target hardware support to implement the entire software in Simulink, which greatly simplifies input design, data collection, and analysis as compared to embedded programming. We used constant-frequency sinusoidal signals for the data collection on the experimental test bench. Subsequently, we estimated the harmonic transfer functions and identified the parameters of a state-space model of the proposed LTP system. We then designed a time-periodic controller in order to regulate the output of the LTP system. We designed a reference-tracking controller along with a Linear Quadratic Integrator (LQI) to improve the system performance. We validated our results through experiments on the physical LTP system plant.

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Architecture development of middleware and API for IOT-based Red Onion farming

Architecture development of middleware and API for IOT-based...

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International Seminar on Application for Technology of Information and Communication (ISemantic)

作者： Alvin Steven Suharnawi Farrikh Alzami Filmada Ocky Saputra Rama Aria Megantara Pulung Nurtantio Andono Dept. of Information Systems Universitas Dian Nuswantoro Indonesia Dept. of Information Engineering Universitas Dian Nuswantoro Indonesia Faculty of Computer Science Universitas Dian Nuswantoro Indonesia

IoT plays a crucial role in transforming the agricultural industry by offering diverse use cases starting from crop monitoring and precision farming to yield optimization. The fundamental reason why the agricultural community benefits immensely from IoT is due to its capacity to gather real-time data straight from their fields. Farming requires considerable attention, and wireless sensor networks enable farmers to deploy IoT devices throughout various parts of their farm so they can obtain information on soil moisture levels, temperature, humidity, among other essential parameters that are integral for effective agriculture management. This gathered data empowers farmers with accurate weather forecasts through machine learning techniques which assist them in making informed decisions related to planting or harvesting of crops - vital components critical for successful outcomes towards better yields. Moreover, middleware architectures/APIs developed specifically for IOT-based red onion farming utilize cloud computing services wherein these enlightening patterns generated get stored safely using analytics tools &automated systems. These intensify insights derived as it simplifies accessing important metrics aiding ease while analyzing historical trends advantageously helping make prudent future course corrective measures concerning planting/harvesting schedules based entirely on solid empirical evidence combined with artificial intelligence ensuring unprecedented efficiency gains and higher returns than previously achievable without technology guidance.

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Hash Tag-Based Sentiment Analysis Using A Deep Learning Approach

Hash Tag-Based Sentiment Analysis Using A Deep Learning Appr...

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International Conference on Advanced Computing and Communication systems (ICACCS)

作者： R. Venkatesan M. Sangeetha S. Nivedha M. Selvakumar K.S. Chandru P. Parthasarathi Department of Information Technology Bannari Amman Institute of Technology Erode India Department of Information Technology K S Rangasamy College of Technology Namakkal India Dept. of Computer Science & Engineering Hindusthan College of Engg and Technology Coimbatore India Dept. Computer Science & Business Systems Bannari Amman Institute of Technology Erode India Dept. Computer Science and Engineering Bannari Amman Institute of Technology Erode India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384376

Social commerce uses a variety of social networking sites like Facebook, Instagram, Twitter, Pinterest, and business Whatsapp as a platform to promote products and services. Since advertising, a product, or service in s-comm will reach as high as a possible number of users on networking sites. Ads will spread virally by sharing among friends and friends of friends. So that wider reachability is possible and shortly will get the customer for our business. The success of an s-comm campaign is measured by the degree to which consumers interact with the sellers through review comments, likes, and shares between friends. Customer behavior analysis is needed for s-comm marketing because social commerce can significantly boost economic growth. The sentiment analysis of customer recommendations was investigated using a dataset of women's s-comm apparel hashtags and reviews. So for such reviews are experimented with different machine learning algorithms (Decision tree, SVM, Random Forest, KNN, Naïve Bayes and LSTM). These models handled the common English reviews and compared the metrics (Accuracy, Sensitivity and Specificity) with one another. Instead of working with individual model, this paper introduces a novel hybrid approach that integrates CNN and Ada Boost LSTM algorithms to provide better results in sentiment analysis when compared to other machine learning algorithms mentioned above. While considering the hash tag with common review comments the hybrid system is out performed. The comparison analysis is done for performance matrices namely accuracy of 98%, specificity of 98.1%, and sensitivity of 98.1%. In the s-commerce transaction industry, this hybrid approach will help to create a deeper understanding of consumer sentiment analysis and psychologically attract clients.

关键词： Deep learning Sentiment analysis Analytical models Accuracy Sensitivity Machine learning algorithms Reviews Social networking (online) Biological system modeling Long short term memory

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Thermal Model Validation of a Power Electronics Module for Digital Twin Application

Thermal Model Validation of a Power Electronics Module for D...

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European Conference on Power Electronics and Applications

作者： Svenja Zöller Claudia Cabre-Piqueras Boris Schilder Alber Filba-Martinez Lukas Acker Faculty of Computer Science and Engineering Frankfurt University of Applied Science Frankfurt Germany Power Systems Dept. Catalonia Institute for Energy Research (IREC) Barcelona Spain

ISBN: (数字)9798350364446

ISBN: (纸本)9798350364453

Thermal stress is a critical failure factor for power electronics components. Hence, when developing a precise digital twin for a power system, it is imperative that thermal models provide a reliable portrayal of the component physics. In that regard, manufacturers offer simplified thermal models that enable designers and users to estimate its behaviour. Nevertheless, the accuracy of these models remains unknown, thus compromising the overall certainty of the digital twin. Therefore, it is of interest to discern whether these simplified models are sufficient for the precision required by the digital twin, and to assess the need of developing a more accurate model. Hence, this paper focuses on validating the theoretical thermal model of a power electronics switch module setup by using the deviation of the module mean time to failure (MTTF) as the acceptance parameter. To achieve this, a novel experimental validation methodology is proposed to retrieve an experimental thermal model and compare it to the theoretical one. This validation is performed considering the discrete thermal impedances and introducing the study of the coupling effects within the module.

关键词： Couplings Accuracy Europe Switches Power electronics Digital twins Power system reliability Reliability Thermal stresses Stress

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Reinforcement Learning-enabled Auctions for Self-Healing in Service Function Chaining

Reinforcement Learning-enabled Auctions for Self-Healing in ...

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IEEE International Conference on Communications Workshops, ICC

作者： Marios Avgeris Aris Leivadeas Ioannis Lambadaris Dept. of Systems and Computer Eng. Carleton University Ottawa Canada Dept. of Software Engineering and IT École de Technologie Supérieure (ÉTS) Montréal Canada

Service Function Chaining (SFC), defines the capability of interconnecting a number of ordered Service Functions (SFs) to create composite network services. A critical issue in SFC is the autonomic fault recovery, i.e., bringing the system back to its normal operation after a hardware or software failure. To address this challenge, in this paper, we propose a novel distributed methodology that treats the SFC Self-Healing problem in an Edge-Cloud infrastructure, while accounting for the various stakeholders. In particular, the individual SFC healing decisions are iteratively optimized and determined, while a Reinforcement Learning (RL)-based SFC-to-datacenter association procedure is realized. This process is complemented by a combinatorial auction-based resource allocation mechanism that resolves the potential SFC collocations at the end of each iteration. The proper operation, effectiveness and efficiency of our proposed healing mechanism is assessed under various evaluation scenarios.

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Arabic Sentiment Analysis of Mobile Banking Services Reviews

Arabic Sentiment Analysis of Mobile Banking Services Reviews

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International Conference on Social Networks Analysis, Management and Security (SNAMS)

作者： Safaa M. Khabour Dheya Mustafa Qutaibah AlThebyan Dept. of Information Systems Faculty of Information Technology and Computer Sciences Yarmouk University Irbid Jordan Dept. of Computer Engineering Faculty of Engineering The Hashemite University Zarqa Jordan Dept. of Cybersecurity Al Ain University Al Ain UAE Jordan University of Science and Technology Irbid Jordan

Online customer reviews have developed into a significant source of information about a business's performance. Due to shifting consumer expectations and growing internet penetration, the Middle East, especially Jordan, is seeing an increase in the usage of online banking. This propensity toward digital transition has been accelerated by the COVID-19 pandemic. Mobile banking service companies aim to effectively use customers' feedback to identify areas for improvement in customer satisfaction. Although Arabic is becoming one of the most widely used languages on the Internet, only a few studies have focused on Arabic sentiment analysis to date. The present study conducts an extensive emotion mining and sentiment analysis on mobile banking reviews in Arabic, exploiting machine learning, natural language processing, and resampling methods to obtain subjective feedback, determine polarity, and identify customers' feelings in the banking domain. This work walks the reader through detailed steps of cleaning and preprocessing a manually annotated dataset of online banking reviews in Arabic, followed by features extraction and modeling, dataset splitting, and balancing training data. Finally, we examined classification algorithms including Decision Tree (DT), Support Vector Machine (SVM), Logistic Regression (LR), Naïve Bayes (NB), K-Nearest Neighbors (KNN), and Random Forest (RF). Ensemble hard and soft voting ML methods were also evaluated. We achieved a maximum accuracy of about 94% using the LR classifier and a maximum precision of 94% using the LR and hard voting methods. Our methodology will enable banks to acquire insights on how to develop their online presence and meet the demands of customers and stakeholders.

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Utilization of Online Collaborative Tools in Software engineering: An Empirical Study on Review Meetings 6

Utilization of Online Collaborative Tools in Software Engine...

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6th International Conference on computer Science and engineering, UBMK 2021

作者： Akman, Ibrahim Turhan, Cigdem Hacaloglu, Tuna Dept. of Computer Engineering Atilim University Ankara Turkey Dept of Software Engineering Atilim University Ankara Turkey Dept of Information Systems Engineering Atilim University Ankara Turkey

ISBN: (纸本)9781665429085

Software development involves a significant amount of team effort where collaboration and communication of the team members are crucial. The team meetings are core activities in all stages of the software development process. Even though these meetings often are conducted face-to-face (F2F) with a lackof technology utilization, changing global conditions such as the COVID-19 pandemic require other solutions urgently without interrupting the software development schedule. For this purpose, online collaborative tools provide new opportunities for software teams to work together avoiding waste in time and resources and the relevant literature is immature. This study aims to assess the factors affecting the integration of online collaborative tools to SE practices with a special reference to review meetings. For this purpose, a sample of 73 SE sophomore and junior students who are future software professionals participated in experimental review meetings based on predefined scenarios. The findings indicate that the utilization of OCT's has positive effects on the participants' actual performance and improves the interaction between team members compared to F2F meetings. © 2021 IEEE

关键词： Software design

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Sampled-Data Output Feedback Stabilization of a Family of High-Order systems with Delays

Sampled-Data Output Feedback Stabilization of a Family of Hi...

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American control Conference (ACC)

作者： Congran Zhao Wei Lin School of Mathematics and Statistics Jiangsu Normal University Xuzhou China Dept. of Electrical Systems and Computer Engineering Case Western Reserve University Cleveland Ohio USA

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

Sampled-data control via output feedback is studied for a family of strongly nonlinear systems with state and input delays. Under sample and hold, a delay-free output feedback control scheme is developed based on the emulation method, adding a power integrator (AAPI) technique, and recursive design of nonlinear observers. With the aid of Lyapunov-Krasovskii functional theorem, together with the idea of homogeneous domination, we prove that the proposed sampled-data output feedback controller makes the hybrid closed-loop systems with delays and uncertainty globally asymptotically stable, if the input delay and sampling period are limited. The family of time-delay uncertain systems under consideration goes beyond the Lipschitz or linear growth condition and is genuinely nonlinear as it contains uncontrollable/unobservable linearization and is not stabilizable, even locally, by any linear or smooth feedback. Application of the sampled-data control scheme presented in this paper is illustrated by an example with simulation.

关键词： Uncertain systems Uncertainty Emulation Observers Delays Closed loop systems Nonlinear systems

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