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Classification of Millet Cultivars Using Image Processing and Machine Learning Techniques

Classification of Millet Cultivars Using Image Processing an...

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2025 IEEE international conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Bharambe, Dewendra Aher, Pushpalata Sandip University Department of Computer Science and Engineering Nashik India

ISBN: (纸本)9798331512965

India is one of the leading producers of various millet cultivars. Indian millets are a class of highly nutritious, drought-resistant crops that are mostly cultivated in the country's semi-arid and drought-prone regions. The most popularly cultivated millet varieties in India are Sorghum (Jowar), Pearl millet (Bajra), Barnyard Millet (Bhagar), Foxtail millet (Kangi), Amaranth (Rajgira), Red sorghum (Chikani), Finger millet (Nachani), and White sorghum (Dadar). In addition to being a wholesome dietary source, millets offer several health advantages. Since they don't contain gluten, they're an excellent alternative for anyone with gluten sensitivity or coeliac disease. Additionally low in glycemic index;millets are a great meal choice for diabetics. Each cultivar has a different nutritional composition and uses. Also, the majority of the time the farmers adapted the millet seeds obtained from their crop hence to preserve the purity of seeds and quality of produce it is very important to classify different millet cultivars. However, the classification of millet cultivars is a complex and time-consuming process. automation in millet cultivar classification becomes greatly beneficial with improved accuracy and efficiency. This study presents the effective classification of eight different millet cultivars using different machine-learning classifiers along with image processing techniques, based on the derived color features. © 2025 IEEE.

关键词： Seed

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Evaluating Kubernetes Distributions: Insights from Stress Testing Scenarios 17

Evaluating Kubernetes Distributions: Insights from Stress Te...

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17th international conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Ramadan, Ibrahim Mohamed Centofanti, Carlo Marotta, Andrea Graziosi, Fabio University of L'Aquila Department of Information Engineering Computer Science and Mathematics L'Aquila67100 Italy

ISBN: (纸本)9798331531195

In the modern era, Kubernetes has gained widespread adoption for its capability to manage containerized applications through automation. With a variety of Kubernetes distributions aimed at enhancing and extending core functionalities, there's a pressing need for a thorough evaluation to identify the most efficient solutions for orchestrating workloads. This study aims to assess a variety of Kubernetes distributions including k3s, k3d, Kind, MicroK8s, Minikube, and standard streamlined Kubernetes, focusing on pod orchestration efficiency under stress conditions. Utilizing the open-source tool kube-burner for stress testing, we propose a testing architecture and methodology to measure pod initiation, scheduling, and overall readiness times. The results show that streamlined Kubernetes and k3d outperform the others overall, with Minikube excelling in initiation time, while Kind's performance suggests it might not be suitable for production environments. This analysis highlights the trade-offs and performance impacts of different Kubernetes distributions, guiding users in selecting the most efficient option for their needs. © 2025 IEEE.

关键词： Kubernetes Microservices Orchestration Performance Evaluation Workload Optimization

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Design and Fabrication of Wearable Flexible 3D Tactile Sensors 11

Design and Fabrication of Wearable Flexible 3D Tactile Senso...

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11th international conference on Mechatronics and Robotics engineering, ICMRE 2025

作者： Xu, Naijia Zhang, Daohui Fu, Xin Han, BaiSong Hou, Xudong Zhao, Xingang Shenyang Institute of Automation Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang China University of Chinese Academy of Sciences Beijing China Shenyang University of Technology Shenyang China

ISBN: (纸本)9798331509293

In recent years, flexible haptic sensors have gained significant attention for their potential in robotics, inspired by the tactile sensing capabilities of human skin. These sensors enable robots to detect a wide range of haptic information, such as pressure and slip, thereby enhancing their flexibility and sensory capabilities. This paper introduces a flexible 3D haptic sensor with high sensitivity and low hysteresis, designed to detect both normal and tangential forces. The sensor features a three-layer structure: the top layer consists of a flexible square magnet that deforms under external forces, enabling force detection;the middle layer is made of Ecoflex 00-30 elastomer;and the bottom layer is a flexible circuit board (FPCB) embedded with Hall sensors that convert magnetic field changes into electrical signals. The flexible sensor is integrated into a smart glove by sewing it onto the thumb and index finger positions. By combining sensor data with machine learning algorithms, the glove achieves handwriting recognition with a classification accuracy of 96.67%. Additionally, the glove incorporates slip sensing capabilities to monitor grip states in real-time during object manipulation. These features make the sensor highly suitable for applications in wearable technology and human-computer interaction, with promising prospects for advancing human-robot interaction and robotics in general. © 2025 IEEE.

关键词： Human robot interaction

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A New Method for One-Pedal Driving With Fuzzy-Based Efficiency-Optimization Algorithm

A New Method for One-Pedal Driving With Fuzzy-Based Efficien...

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2025 IEEE international conference on Mechatronics, ICM 2025

作者： Heydrich, Marius Hammer, Tim Mitsching, Thomas Ivanov, Valentin Technische Universitä t Ilmenau Dept. of Computer Science and Automation Ilmenau Germany Technische Universitä t Ilmenau Dept. of Mechanical Engineering Ilmenau Germany

ISBN: (纸本)9798331533892

This paper presents a case study on adaptive one pedal driving for a battery electric sport utility vehicle with in wheel based rear-wheel drive that is able to adjust the drive pedal curve automatically. In addition, a fuzzy logic observes the state of the electric machines to adapt the motor torque to the most efficient region. The control method was tested in a co-simulation using a validated vehicle model and a driver-in-the-loop strategy. Within the experiments, an improvement in energy recovery of up to 4.8 % was achieved compared to the classic brake blending. In addition, the total number of brake pedal applications is ten times lower, which translates into less driving stress and greater driving comfort, especially on long distance trips, proving the effectiveness of the controller. © 2025 IEEE.

关键词： Vehicle wheels

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Integrating automation and Sustainability: Arduino-Based Solar Panel Cleaning System for Renewable Energy Optimization 3

Integrating Automation and Sustainability: Arduino-Based Sol...

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3rd IEEE international conference on automation and Computation, AUTOCOM 2025

作者： Pandey, Anju Dubey, Parul Rao, S.Rupendra Prasad, Sumit Dhawle, Krupali Bhagat, Dhananjay Anjaneya university Department of Cse Raipur India Nagpur Campus Pune India University Department of Psychology Bilaspur India School of Management Studies and Commerce Uttarakhand Open University Haldwani India G H Raisoni College of Engineering Department of Ai Nagpur India School of Computer Science and Engineering Dr. Vishwanath Karad Mit World Peace University Pune India

ISBN: (纸本)9798331542375

This paper realizes the design and implementation of an automatic cleaning system for solar cells through Arduinos, so they can work better and last longer with less maintenance. The system combines an Arduino microcontroller, servo motors, water spray devices, and sensors to automate the cleaning process. This not only eliminates dirt, dust, or debris;it ensures consistent efficacy. Testing showed that output from panels improved significantly after cleaning. Each time only 2.4 liters of water and 0.05 kWh of power were used, for a minimal consumption level. Its modular and inexpensive design makes it suitable for residential and small-scale use. From the optimum interval determined for monthly cleaning, a balance of efficiency gains and cost may be struck. This project forms a practical, not to mention scaleable, and sustainable solution for maintaining solar panels. Use of renewable energy technologies will further reduce the environmental impact. Next-generation developments might incorporate advanced dirt recognition or the convergence of renewable energy integration into their capabilities. © 2025 IEEE.

关键词： Sustainable development

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Automated Immune Cell Classification using Image Processing Techniques 6

Automated Immune Cell Classification using Image Processing ...

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6th international conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Sathiyaprasad, B. Reddy, Arva Hari Vardhan Abdurrahman, Usama Micheal, A. Annie Department of Computer Science and Engineering Sathyabama institute of science and technology Chennai India

ISBN: (纸本)9798331522667

Advances in immunological research are essential for elucidating immune responses and developing targeted therapeutic approaches. This study proposes an automated method for immune cell classification leveraging machine learning and image processing techniques. A novel framework is introduced, combining high-resolution imaging with computational algorithms to accurately classify various immune cell types from biological samples. The methodology incorporated preprocessing steps, like normalization and noise reduction. Then, deep learning models, such as CNNs and SVMs, will be applied for feature extraction and classification. These models were trained on annotated datasets derived from flow cytometry and microscopy, demonstrating better accuracy and reliability. Results demonstrate an improvement in processing speed and accuracy of classification over traditional approaches, which underlines the importance of automation in immunological diagnostics. The data obtained can be used in high-throughput immunotherapy and disease diagnostic screening;it allows for the identification of immune cell populations within a short period and therefore improves research efficiency. The work provides a basis for real-time analysis and incorporation of multiscale datasets that will help to better understand immunity and its associated disorders. © 2025 IEEE.

关键词： Convolutional neural networks

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Platform-specific code generation method for the tyche embedded operating system based on AADL models 3

Platform-specific code generation method for the tyche embed...

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3rd international conference on Algorithms, Network, and Communication Technology, ICANCT 2024

作者： Chen, Li Jia, Zhangtao An, Heng Li, Haoyu Beijing Computer Technology and Application Research Institute Beijing China

ISBN: (数字)9781510688902

ISBN: (纸本)9781510688896

The Tyche Embedded Operating System is a highly customized real-time embedded operating system, widely applied in fields such as aerospace, medical devices, and industrial automation. Renowned for its robust support for real-time tasks and efficient resource management, this system has become a core technology in embedded system development. The Architecture Analysis and Design Language (AADL), as a stringent standardized modeling language, possesses powerful expressiveness and scalability. This paper proposes a method for generating platform-specific code for the Tyche Embedded Operating System based on AADL models, which involves describing complex systems through AADL modeling and generating safety-critical software suitable for the Tyche Embedded Operating System. Firstly, the paper elaborates on the key aspects of the AADL standard and adheres to these aspects for modeling safety-critical systems. Secondly, an intermediate layer is introduced, outlining the mapping rules from the AADL common subset to the intermediate code, generating intermediate code for each component within the system architecture, and ultimately compiling it into C code applicable to the Tyche platform. Finally, a prototype tool for code generation is provided, and the effectiveness of the proposed method is validated through a case study of a temperature control system. © 2025 SPIE.

关键词： Program compilers

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SafeHome: automation and Security System Using Internet of Things with Mobile Application 5

SafeHome: Automation and Security System Using Internet of T...

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5th international conference on Advanced Research in Computing, ICARC 2025

作者： Abas, Patricia A. Reyes, Bryll Christopher O. Serquina, Jazzy Niega, Juliet O. Medina, Oliver A. National University - Philippines Department of Computer Engineering Calamba City Philippines

ISBN: (纸本)9798331530983

The rapid advancements in Internet of Things (IoT) technology have transformed home automation and security, addressing the demand for convenience, comfort, and energy efficiency. Despite global market growth, barriers such as limited user understanding, power dependency, and lack of localized functionality hinder adoption in the Philippines. To address these challenges, this study developed SafeHome, an IoT-enabled smart home automation and security system. SafeHome integrates ESP32 and Arduino Nano microcontrollers with Firebase Realtime Database, enabling real-time device synchronization and monitoring through an Android mobile application. Key features include PIN-based security, push notifications, ultrasonic presence detection, and a safety breaker to prevent electrical hazards. The system also incorporates offline keypad access and backup power to ensure uninterrupted security operations. Agile Scrum methodology guided the system's iterative development, emphasizing user-centric design and continuous testing. A quantitative approach was applied to the descriptive research method using a five-point Likert scale in the evaluation. Data were collected from 12 evaluators, comprising six industry professionals and six homeowners, using a survey instrument based on the ISO/IEC 25010:2023 product quality model. The evaluation result from industry professionals is 4.76 out of 5, and 4.93 out of 5 from homeowners, corresponding to an "Excellent"descriptive interpretation. This result shows the developed system meets high security, usability, and user acceptance performance. Overall, the study demonstrates the potential of IoT-driven solutions to bridge gaps in home automation, offering an accessible and adaptable system tailored to local contexts. SafeHome advances smart home technology and addresses critical challenges, ensuring broader adoption and impact in modern residential settings. © 2025 IEEE.

关键词： ISO Standards

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Facial Expression Recognition in Real-Time Surveillance Using CNN and Transfer Learning with ResNet-50 3

Facial Expression Recognition in Real-Time Surveillance Usin...

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3rd IEEE international conference on automation and Computation, AUTOCOM 2025

作者： Mohamed, Aezeden Nii, Rainier Binga, Kipas Modi, Sanjay Nagaveni, P. Nnadhini, T.J. School of Mechanical Engineering Png University of Technology Lae Papua New Guinea School of Mathematics and Computer Science Ng University of Technology Lae Papua New Guinea Angau Memorial General Hospital Lae Papua New Guinea Lovely Professional University Punjab Phagwara India Karpagam Academy of Higher Education Department of Electrical and Electronics Engineering Coimbatore India Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331542375

This paper describes the implementation of real-time FER system implemented using CNN with transfer learning from ResNet-50 for surveillance video analysis. The system is designed to efficiently identify seven basic facial expressions: segment by real-time video frames in the form of Happy, Sad, Angry, Surprised, Disgusted, Fearful and Neutral. Since there are lighting changes, occlusion and diverse orientations of the face in surveillance environment, the utilization of pretrained ResNet-50 allows the system to perform well and properly distinguish facial expressions. The proposed method uses the transfer learning to fine tune the ResNet-50 on a large FER dataset and obtain good classification accuracy, precision, recall, F1-Score as well. Evaluation data prove that the suggested model yields a 96.2% recognition rate as well as high precision (97.9%) and recall (96.9) ratios. In addition, inference time works as low as at 35 ms per frame to achieve 28.5 FPS for real-time application. Further, the proposed model provides better accuracy as compared to the regular models like the VGGNet and InceptionNet, along with less computation time. This work gives a strong real-time technique to recognize the facial expression which is prepared for use in security and surveillance systems. © 2025 IEEE.

关键词： Convolutional neural networks

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Applying CNNs for the Early Detection of Bone Malignant Tumors in X-Ray Images 3

Applying CNNs for the Early Detection of Bone Malignant Tumo...

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3rd IEEE international conference on automation and Computation, AUTOCOM 2025

作者： Punia, Ankit Saluja, Ashish Singh Garg, Mohan Rajendran, Naveen Kumar Das, Neeraj Narayana, Lakshmi Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Department of Computer Science and Engineering Odisha Bhubaneswar India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh Baddi174103 India Department of Aerospace Engineering Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Bengaluru India

ISBN: (纸本)9798331542375

Bone malignant tumors are cancerous growths that develop in the bones and can be fatal if they remain undiagnosed or untreated for a long time. Since these tumors often grow slowly, early detection is important for successful treatment and high survival rates. Existing medical imaging tools to detect bone malignant tumors, such as X-rays, utilize human expertise and are time-consuming. Thus, recently, computer-aided diagnosis systems have been developed primarily with Convolutional Neural Networks (CNNs). Convolutional neural networks are deep learning models proven to perform highly on pattern recognition tasks, especially in image and video analysis. In this paper, we suggest that CNNs be used in the early finding of bone malignant tumors using X-ray images. It starts by preprocessing the X-ray images before entering them into CNN to perform feature extraction. The resampling data was then used to teach the CNN where one class is 'normal,' and another class has an abnormal, namely a bone malignant tumor. The system's performance was tested on a dataset consisting of X-ray images obtained from patients with bone malignant tumors. Results showed high detection accuracy for both these tumors with sensitivity and specificity of © 2025 IEEE.

关键词： Feature Selection

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