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Rice Diseases Detection and Localization with Only Image-Level Disease Training Labels

Rice Diseases Detection and Localization with Only Image-Lev...

International Conference on Electrical engineering/electronics, computer, Telecommunications and Information technology, ECTI-CON

作者： J. Thanawiparat T. Kasetkasem T. Patrapornnant S. Patarapuwadol Department of Electrical Engineering Faculty of Engineering Kasetsart University Bangkok Thailand National Electronics and Computer-Technology Center Pathunthani Thailand Department of Plant Pathology Faculty of Agriculture at Kamphaeng Saen Kasetsart University Nakorn Pathom Thailand

In this paper, we propose a new approach for the detection and classification of rice diseases that combines the use of deep learning and level set methods. Our approach aims to improve the performance of rice disease classification and object detection by using an efficient image processing technique with deep learning. The proposed method will be evaluated based on Mean Average Precision (mAP), Accuracy, Precision, Recall, and F1-score. The results indicate that our proposed method improved the performance for most of the 16 classes of rice diseases and also it was able to improve performance even when using a small amount of labeled data. We also found that our proposed algorithm produced segmentation maps with significantly smaller computational time and the use of bias field estimation helped in the segmentation of complicated foreground objects in images by modifying unnecessary details. Overall, this research has the potential to be a valuable tool in the field of agriculture, where early and accurate detection of rice diseases is crucial for preventing the spread of the disease and maintaining crop yields.

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Image Cryptography Using Fibonacci Bit-plane Decomposition and Quantum Chaotic Permutation

Image Cryptography Using Fibonacci Bit-plane Decomposition a...

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International Conference on Computing, Intelligence and Data Analytics, ICCIDA 2022

作者： Ravi, Renjith V. Goyal, S.B. Djeddi, Chawki Kustov, Vladimir Department of Electronics and Communication Engineering M.E.A Engineering College Kerala Malappuram India Faculty of Information Technology City University Petaling Jaya46100 Malaysia Department of Mathematics and Computer Science Larbi Tebessi University Tebessa Algeria LITIS Lab Rouen University Rouen France Saint Petersburg Railway Transport University of Emperor Alexander I Saint Petersburg Russia

ISBN: (纸本)9783031270987

The use of image encryption to safeguard the privacy and security of images is a good idea. This study presents a unique image encryption technique that encrypts images using Fibonacci Bitplanes as security key images. The Fibonacci numbers are used in this research to present a new encryption technique. Furthermore, a unique bit-plane decomposition for Fibonacci weights is explored, which has cryptographic advantages. The novel lossless image encryption technique provided may encrypt an image for privacy protection utilizing this new decomposition approach. It also offers two layers of encryption, allowing it to handle both pay-preview apps and encrypted communications simultaneously. The algorithm’s performance in image encryption is confirmed by modeling and analysis. Finally, the suggested algorithm’s encryption performance is shown using results obtained and security assessments. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Number theory

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Identification of Different Medicinal Plants Through Image Processing

Identification of Different Medicinal Plants Through Image P...

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Smart Technologies for Sustainable Development Goals (ICSTSDG), International Conference on

作者： J. Anvar Shathik S. Hashini A. Pandiaraj N. Ramshankar Naveen G Amirthavarshini K B AIML and Data science Anjuman Institute of Technology and Management Uttarkannada Karnataka India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computing Technologies Faculty of Engineering and Technology SRM Institute of Science and Technology Chennai Department of Computer Science and Engineering RMD Engineering College Kavaraipettai Tamilnadu Department of Electronics and Communication Madha Institute of Engineering and Technology

ISBN: (数字)9798331516284

ISBN: (纸本)9798331516291

Identification of medicinal plants and raw materials is crucial for various applications including pharmaceuticals, herbal remedies, and conservation efforts. Conventional methods of recognition often rely on expert intelligence and instructions investigation which container be time-timewasting and lying to oversights. In this study, we propose a novel approach for automating the identification process using image processing and machine learning algorithms. Specifically, we leverage advanced techniques such as deep learning with ResNet-50, and Random Forest algorithms to accurately classify images of medicinal plants and raw materials. Additionally, we integrate a leaf disease detection system using Keras to further enhance the functionality of the platform. The developed system includes a user-friendly website with features such as a blog page for sharing information and seamless integration of the image processing algorithms using the Django framework. Investigational results display the efficacy of the anticipated method in precisely discovering medicinal plants and raw materials, paving the way for applications in various domains.

关键词： Medicinal plants Deep learning Machine learning algorithms Medical services Raw materials Classification algorithms Sustainable development Random forests Diseases Pharmaceuticals

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Detection of Human Stress Using Optimized Feature Selection and Classification in ECG Signals

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Mathematical Problems in engineering 2023年第1期2023卷

作者： Narmadha, G. Deivasigamani, S. Vellaisamy, Muthukumar Freitas, Lídio Inácio Ahmad, Badlishah R. Sakthivel, B. Department of Electrical and Electronics Engineering Sethu Institute of Technology Tamil Nadu Virudhunagar626115 India Faculty of Engineering Technology and Built Environment UCSI University Cheras Kuala Lumpur56000 Malaysia School of Engineering Faculty of Computing and Engineering Quest International University Ipoh Perak30250 Malaysia Department of Mechanical Engineering Dili Institute of Technology Dili Timor-leste Computer and Communication System University Malaysia Perlis Arau Malaysia Department of Electronics and Communication Engineering PSY Engineering College Tamil Nadu Sivagangai630561 India

An autonomic nervous system (ANS) of humans is majorly affected by psychological stress. The changes in ANS may cause several chronic diseases in humans. The electrocardiogram (ECG) signal is used to observe the variation in ANS. Numerous techniques are presented for an ECG stress signal handling feature extraction and classification. This work managed a heart rate variability feature acquired from smaller peak waveforms such as P, Q, S, and T waves. Also, the R peak is detected, which is a significant part of the ECG waveform. In this work, the proposed stress classification work has been categorized into two main processes: feature selection (FS) and classification. The main aim of the proposed work is to propose an optimized FS and classifier model for the detection of stress in ECG signals. The Metaheuristics model of the African vulture optimization (AVO) technique is presented to perform an FS. This selection is made to choose the required features and minimize the data for classification. The AVO-based modified Elman recurrent neural network (MERNN) technique is proposed to perform an efficient classification. The AVO is used for fine-tuning the weight of the MERNN technique. The experimental result of this technique is evaluated in terms of Recall (91.56%), Accuracy (92.43%), Precision (92.78%), and F1 score (95.86%). Thus, the proposed performance achieved a superior result than the conventional techniques. © 2023 Narmadha G et al.

关键词： Classification (of information)

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Nuclei Instance Segmentation in Colon Histology Images with YOLOv7 2nd

Nuclei Instance Segmentation in Colon Histology Images with...

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2nd International Conference on Advanced engineering, technology and Applications, ICAETA 2023

作者： Yıldız, Serdar Memiş, Abbas Varlı, Songül Department of Computer Engineering Faculty of Electrical and Electronics Engineering Yıldız Technical University İstanbul Turkey Department of Software Engineering Faculty of Engineering and Natural Sciences İstanbul Health and Technology University İstanbul Turkey Health Institutes of Türkiye İstanbul Turkey BİLGEM TÜBİTAK Kocaeli Turkey

ISBN: (纸本)9783031509193

In histology image analysis, instance-based nuclei segmentation is one of the challenging tasks within the segmentation-guided studies since it is quite troublesome to detect each distinct nuclei instance of each nuclei type in images in contrast to the semantic segmentation in which all the image pixels of a nuclei type are labelled with the same mask ID although the segmented region may comprise of multiple instances. In this paper, an instance-based medical image segmentation task is addressed, and in this context, instances of multiple types of nuclei in colon histology images are aimed to be delineated distinctly. For the instance-based segmentation of the nuclei in colon histology images, the YOLOv7 algorithm and its built-in instance segmentation module are utilized. In the experimental studies performed on Colon Nuclei Identification and Counting (CoNIC) Challenge 2022 colon histology image dataset by using a 5-fold cross-validation performance evaluation strategy, nuclei instances belonging to 6 classes as the neutrophil, epithelial, lymphocyte, plasma, eosinophil and connective were segmented. To calculate the overall system accuracy, the quantification metrics of mean average precision (mAP) and mean panoptic quality (mPQ) were measured. In performance evaluations, quite promising accuracy values were obtained. The mAP values of 0.2885 and 0.2903, and mPQ values of 0.1659 and 0.1704 were observed by using the YOLOv7 algorithm. To the best of our knowledge, this is the first nuclei instance segmentation study with YOLOv7. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Colon histology images CoNIC 2022 dataset Nuclei instance segmentation Nuclei segmentation YOLOv7

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Investigation of Beam Steering Using a Rotatable All-Dielectric Hemispherical Lens Assembly with an Ultra-Wideband Antenna

Investigation of Beam Steering Using a Rotatable All-Dielect...

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Research, Invention, and Innovation Congress (RI2C)

作者： Patchadaporn Sangpet Virote Pirajnanchai Suramate Chalermwisutkul Sukanya Chudpooti Prayoot Akkaraekthalin Nonchanutt Chudpooti Deparment of Electronics and Telecommunication Engineering Faculty of Engineering Rajamangala University of Technology Thanyaburi Pathum Thani Thailand The Sirindhorn International Thai-German Graduate School of Engineering King Mongkut's University of Technology North Bangkok Bangkok Thailand Department of Industrial Physics and Medical Instrumentation King Mongkut's University of Technology North Bangkok Bangkok Thailand Deparment of Electrical and Computer Engineering Faculty of Engineering King Mongkut's University of Technology North Bangkok Bangkok Bangkok Thailand

ISBN: (数字)9798331506018

ISBN: (纸本)9798331506025

This study presents a 3D-printed all-dielectric lens assembly integrated with an ultra-wideband (UWB) antenna, designed specifically for beam-steering applications. The proposed system achieves beam steering by combining a rotatable 3D-printed all-dielectric lens assembly with a hemispherical lens and a UWB antenna. The UWB antenna is fabricated on an FR-4 substrate, while the hemispherical lens and rotatable stage assembly are made from Acrylonitrile Butadiene Styrene (ABS). The lens assembly includes a set of 3D-printed lenses capable of rotating to specific angles, with predefined positions at ±60° and a step size of 30°. Based on the simulated results, at the lower frequency of 3.1 GHz, the UWB antenna integrated with the 3D-printed all-dielectric hemispherical lens assembly does not achieve effective beam steering, as the main beam direction deviates from the rotatable lens angle. However, at intermediate frequencies, such as 7.0 GHz, the system demonstrates successful beam steering by adjusting the rotation angle of the hemispherical lens on the rotatable stage.

关键词： Technological innovation Beam steering Accuracy Directive antennas Ultra wideband antennas Frequency measurement Assembly Substrates Frequency control Lenses

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Label-free Identification of Nonelectrogenic Cancer Cells using Adhesion Noise

Label-free Identification of Nonelectrogenic Cancer Cells us...

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BioSensors Conference (BioSensors), IEEE

作者： Maximilian Ell Ralf Zeitler Roland Thewes Günther Zeck TU Wien Faculty of Electrical Engineering and Information Technology Institute of Biomedical Electronics Vienna Austria zerodev Stuttgart Germany TU Berlin Faculty of Electrical Engineering and Computer Science Chair of Sensor and Actuator Systems Berlin Germany

Time-continuous detection of cells on substrates without the need of optical microscopic imaging is of broad interest in biotechnological applications. We present a method how to detect cancer cells using voltage noise caused by cell adhesion which is recorded by high-density CMOS-based microelectrode arrays. We analyze our data in terms of spectral power density for two different types of arrays, each of them being optimized in a different working regime.

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Exploring the Influence of Digital Media on Secondary School Students' Daily Lives

Exploring the Influence of Digital Media on Secondary School...

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International IEEE Conference and Workshop: Óbuda on Electrical and Power engineering (CANDO-EPE)

作者： Előd Gőgh Attila Kovari Institute of Digital Technology Faculty of Computer Science Eszterházy Károly Catholic University Eger Hungary Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Obuda University Budapest Hungary Institute of Computer Science University of Dunaujvaros Dunaujvaros Hungary

ISBN: (数字)9798331531119

ISBN: (纸本)9798331531126

The focus of this study is to investigate how digital media use affects the daily activities, emotional well-being, and general lifestyle of high school students in relation to education amidst concerns about a “new terrain or venue” for learning in an era moving full swing towards digitization. The study, conducted with students from grades 9–11, seeks to identify overall trends and associations involving digital media interactions: considering both byproducts of using new technology as an educational tool- in addition discursive aspects allowing for different forms of distraction. In summary, the results showed that digital media use is common and often regarded as educationally beneficial or a positive form of relaxation; however, daily activities are it affected by substantial adverse impacts on everyday life and time management. Suggestions focus on supporting healthy digital media habits, including content-wise engagement and time management tools.

关键词： Surveys Power engineering Mood Education Media Market research

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ChatGPT the Omniscient? A Guide to Effective Prompting

ChatGPT the Omniscient? A Guide to Effective Prompting

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International IEEE Conference and Workshop: Óbuda on Electrical and Power engineering (CANDO-EPE)

作者： Attila Kovari Faculty of Computer Science Institute of Digital Technology Eszterházy Károly Catholic University Eger Hungary Kandó Kálmán Faculty of Electrical Engineering Institute of Electronics and Communication Systems Obuda University Budapest Hungary Institute of Computer Science University of Dunaujvaros Dunaujvaros Hungary

ISBN: (数字)9798331531119

ISBN: (纸本)9798331531126

The invention of artificial intelligence and natural language processing has revolutionised human-machine interaction, and OpenAI's ChatGPT models are at the forefront of this. GPT-3 and GPT-4 models generate human-like text, answer questions and perform all kinds of language-related tasks. ChatGPT relies heavily on the quality of the requests it receives. This paper addresses the issue of efficient prompting in order to maximise its potential. Different prompting techniques are described in the paper: prompt template design, continuous prompts, few-shot learning, chain of thought prompting and metadata expansion. The conclusions show that learning how to write good prompts is crucial for realizing the full potential of ChatGPT in various natural language processing tasks.

关键词： Technological innovation Steganography Power engineering Plagiarism Metadata Chatbots Real-time systems Reliability Artificial intelligence Few shot learning

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A Novel Hybrid Evolutionary Mating Algorithm for Covid19 Confirmed Cases Prediction based on Vaccination

A Novel Hybrid Evolutionary Mating Algorithm for Covid19 Con...

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Next-Generation Computing, IoT and Machine Learning (NCIM), International Conference on

作者： Marzia Ahmed Ahmad Johari Mohamad Mostafijur Rahman Mohd Herwan Sulaiman Mohammod Abul Kashem Faculty of Electrical & Electronics Engineering Technology Universiti Malaysia Pahang (UMP) Pahang Malaysia Department of Software Engineering Daffodil International university Dhaka Bangladesh Department of Computer Science and Engineering Green University of Bangladesh Department of Computer Science and Engineering DUET Gazipur Bangladesh

Microorganisms may cause illness when they enter the body, multiply, and spread to other parts. The rapid spread of COVID-19 to neighboring countries is examined in this research. Anticipating a positive COVID-19 occurrence helps in determining risks and creating countermeasures. As a result, developing robust mathematical models with small error margins for predictions is crucial. Based on these findings, a combined method of evaluating confirmed cases of COVID-19 with universal immunization is recommended. First, the best hyperparameter values of the RBF kernel-based LSSVM (least square support vector machine) were determined using the most recent Evolutionary Mating Algorithm (EMA). After that, LSSVM will complete the task of prediction. This hybrid method has been utilized for time series forecasting in Malaysia since the country's immunization program against COVID-19 got underway. We evaluate our results next to those of well-known methodologies in nature-inspired metaheuristics.

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