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An Integrated Approach to Potato Leaf Disease Detection Using Convolutional Neural Networks and Random Forest

An Integrated Approach to Potato Leaf Disease Detection Usin...

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2023 international conference on Innovative computing, Intelligent Communication and Smart electrical Systems, ICSES 2023

作者： Suryavanshi, Ankita Tanwar, Sarvesh Kukreja, Vinay Choudhary, Ankur Chamoli, Sushant Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Amity University Amity Institute of Information Technology Noida India Graphic Era Deemed to Be University Computer Science and Engineering Uttarakhand Dehradun248002 India Computer Science and Engineering Graphic Era Hill University Uttarakhand India

ISBN: (纸本)9798350319200

The worldwide agricultural sector is vital to feeding the growing population. Crop health and production must be protected for food security. Sustainable agriculture requires timely plant disease identification and management. This study examines potato disease of leaf detection, an important part of modern agriculture. They test machine learning and deep learning on a Kaggle dataset of plant leaves, including potato leaves. This study aims to determine the best disease detection tool. CNN and Random Forest are the main models. Pooling layers after many convolutional layers extract leaf image features. These models are precisely optimized for performance. In diagnosing potato leaf diseases, CNNs achieve 95-97% accuracy. Precision, recall, F1-score, and confusion matrix data reveal the models' strengths and limitations and their ability to distinguish illness classifications. This research benefits scientists and farmers. The findings aid early disease diagnosis and potato agriculture disease control. As data-driven agriculture advances, machine learning as well as deep learning can mitigate crop diseases and ensure global food security. This research advances the use of innovative technologies to solve agricultural problems. © 2023 IEEE.

关键词： accuracy CNN potato leaves precision random forest recall

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Suspect Identification Based on Facial Recognition from CCTV Using Hybrid Bat Algorithm 11

Suspect Identification Based on Facial Recognition from CCTV...

引用

11th international conference on electrical engineering, Computer science and Informatics, EECSI 2024

作者： Sari, Sabrina Adinda Sulistiyo, Mahmud Dwi School of Computing Telkom University Bandung Indonesia

ISBN: (纸本)9798350355314

The incidence of criminal activity is rising steadily, necessitating the use of surveillance cameras to monitor and control such activities effectively. However, traditional video evaluation by human guards and manual image processing often fails to accurately analyze footage, especially when individuals use disguises like glasses, masks, hats, or different hairstyles. To address this issue, this research proposes a surveillance system that leverages CCTV footage to detect and identify individuals attempting to conceal their identities. The system first detects facial areas using the Multi-task Cascaded Convolutional Networks (MTCNN) method. Once a facial area is detected, the system performs face identification by matching facial features of suspects with those captured on CCTV. If a match of 70% or higher is found, the individual is flagged as a suspect, with the output showing bounding boxes around the face labeled with their name. To enhance the system's efficiency, the FaceNet embedding model is integrated with the Hybrid Bat Algorithm (HBA) to reduce the dimensionality of face embeddings. HBA, which combines modifications from the Bat Algorithm (BA) with Differential Evolution (DE) techniques, effectively optimizes the process. Evaluation results showed the best performance with a maximum iteration of 50 and a population size of 30, achieving an accuracy of 0.877, recall of 0.830, precision of 0.907, and f1-score of 0.867. The face embedding dimension was reduced from 512 to 201 using HBA. © 2024 IEEE.

关键词： Guards (shields)

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Characterization Parameters and control Criteria for Conductor Tension Stringing Defects 8th

Characterization Parameters and Control Criteria for Conduct...

引用

8th international conference on computing, control and Industrial engineering, CCIE 2024

作者： Wan, Jiancheng Qiao, Liang He, Ronghao Qin, Jian Xue, Haijie China Electric Power Research Institute Co. Ltd. Beijing100055 China Fujian Provincial Power Transmission and Transformation Engineering Company Fujian Province Fuzhou350000 China

ISBN: (纸本)9789819769339

The accumulation of conductor torque during tension stringing construction is the main factor causing stringing defects (such as scattered strands, lantern, etc.), but conductor torque cannot be directly measured during tension stringing construction. This article converts torque detection into measuring the axial rotation of the conductor, proposes a detection scheme for the rotation count of the conductor during stringing, designs and develops wireless torque measurement sensors, conducts tension stringing torsion count measurement experiments. Characterization parameters and indicators of conductor stringing defects are extracted, and these indicators are compared and verified with simulation results. The results indicate that the characterization parameters for detecting conductor stringing defects are determined by the number of turns per hundred meters of conductor stringing between the tension machine output and the first base tower. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Torque meters

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Integrating photovoltaic systems into HVAC control using fuzzy algorithms for optimizing thermal comfort 30

Integrating photovoltaic systems into HVAC control using fuz...

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30th IEEE international Symposium for Design and Technology in Electronic Packaging, SIITME 2024

作者： Boicu, Mihaela-Gabriela Dobrea, Marius-Alexandru Vasluianu, Mihaela Olteanu, Gabriela-Valentina Neculoiu, Giorgian University POLITEHNICA of Bucharest Faculty of Automatic Control and Computer Science Bucharest Romania Technical University of Civil Engineering of Bucharest Faculty of Hidrotechnics Bucharest Romania

ISBN: (纸本)9798331539511

This article presents a study on the use of photovoltaic panels for storing electrical energy intended to power an HVAC unit placed in a residential room. To highlight this aspect, two specific scenarios will be considered. Thus, we aim to analyze whether the generated and stored energy is sufficient to fulfil the thermal comfort requirements of the user, while also considering the primary objective of minimizing energy consumption. To achieve this goal, the HVAC unit will be controlled by implementing a fuzzy system that determines its optimal activation based on the comfort level obtained from the user. The fuzzy system presents as inputs both environmental parameters and specific personal perception parameters of the user. The research results provide an insight into the efficiency of implementing photovoltaic energy storage systems for residential applications and propose solutions for optimizing energy consumption in the context of ensuring adequate thermal comfort. Thus, the study contributes to the development of sustainable and personalized energy solutions for smart homes. © 2024 IEEE.

关键词： Thermal comfort

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Design of an Adaptive PID controller Based Linear Neural Network for Two-Area Interconnected Power System 11

Design of an Adaptive PID Controller Based Linear Neural Net...

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11th IEEE international conference on Systems and control, ICSC 2023

作者： Ali, Issa Askir, Alyaseh Nagi Nazlibilek, Sedat Higher Institute of Science and Technology Department of Electric and Electronics Jadui Libya College of Engineering Al Zawia University Zwara Libya Baskent University Electrical and Electronics Engineering Ankara Turkey

ISBN: (纸本)9798350304886

The automatic load frequency control for multi-area power systems has presented a significant challenge for power system engineers since It requires to design a robust controller for maintaining the balance between the generation and consumption of electricity within each area, as well as the entire power grid. This study focuses on the load-frequency control of an interconnected two-area power system, aiming to minimize the area control error through adaptive control method. Specifically, an adaptive linear neural network known as ADALINE is employed to dynamically adjust the parameters of a PID controller in real-time. This approach effectively governs both the frequency deviation and tie-line power flow deviation at the interconnection point. The outcomes of the study revealed that the employing of the adaptive LNN-PID controller results in reduced frequency deviations, small overshoots and minimal oscillations. Furthermore, the power system demonstrated more robustness against wide range of operational conditions, even when faced with significant load changes and system uncertainties. These findings underscore the effectiveness of intelligent control strategies in optimizing the performance of interconnected power systems. © 2023 IEEE.

关键词： Electric power system interconnection

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Wearable Airbag Technology: A Study 14

Wearable Airbag Technology: A Study

引用

14th international conference on Advances in computing, control, and Telecommunication Technologies, ACT 2023

作者： Girish, Abhinav Vishnuraj, N. Harigovind, C.B. Sreedath, M.K. Menon, Hema P. Devasia, Davidson Department of Mechanical Engineering Sreepathy Institute of Management And Technology Vavanoor India Department of Computer Science and Engineering Sreepathy Institute of Management and Technology Vavanoor India Department of Pediatric Neurology Aster Medicity Kochi India

The development of wearable airbag technology is a significant advance in the field of personal safety. This paper presents a study on various technologies available for the design of self-inflating devices which are presently available for the general public. These devices could be present on various entities such as cars, bikes, bicycles and people. This paper also focuses on the different design variations on deck which are altered according to the user and the extent of its service. As changes are made in terms of the inflating medium, electrical charge supply different models are fabricated accordingly. Consequently, a study on various techniques and available models would contribute to developing customized products. This technology has the potential to save countless lives and prevent serious injuries in the event of a fall or other accident. This technology has been used in the automotive industry for many years and is now being adapted for use in other industries, such as construction, where falls are a common hazard. Wearable airbag technology is a promising development and is used in various fields such as automation and medicine. Further research and development are needed to ensure that the technology is safe and effective. © Grenze Scientific Society, 2023.

关键词： Accidents

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Application of internet of things technology in automatic monitoring of geological disasters

Application of internet of things technology in automatic mo...

引用

2023 international conference on Remote Sensing, Mapping, and Geographic Systems, RSMG 2023

作者： Liu, Zhuoya Zeng, Guisen Mao, Cheng Lv, Qianyong Su, Li Electrical Science Institute of Guizhou Power Grid Co. Ltd. CSG Guiyang550000 China

ISBN: (纸本)9781510668898

In order to understand the Internet of Things technology, a research on the application of Internet of Things technology in automatic monitoring of geological disasters is put forward. In this paper, firstly, the Internet of Things technology is used to improve the accuracy and precision of geological disaster monitoring and make the prevention and control of geological disasters more timely. When using the Internet of Things technology, it is necessary to analyze the business requirements in advance, find the key Internet of Things technology, and effectively infiltrate it with the geological disaster prevention system, so that the advantages of the Internet of Things technology can be reflected. Secondly, in view of the shortcomings of geological disaster monitoring and early warning means and methods at the present stage and the future development needs, combined with Internet of Things, communication and other technologies, deepen the Understanding of geological disasters, analyzing and studying the application of Internet of Things technology in monitoring and early warning of geological disasters (debris flow, collapse, landslide, etc.). Finally, combined with an application example of slope monitoring in a project, the automatic monitoring and real-time early warning functions of geological disasters are realized, which has high application value in geological disaster monitoring. © 2023 SPIE.

关键词： Internet of things

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An Intelligent automatic Lung Images Classification with an ANFIS based Deep Q-Network Learning Model 14

An Intelligent Automatic Lung Images Classification with an ...

引用

14th international conference on Advances in computing, control, and Telecommunication Technologies, ACT 2023

作者： Jeyanthi, S. Maheswari, N. Uma Government Polytechnic College Karur India PSNA College of Engineering and Technology Department of Computer Science and Engineering Dindigul India

Lung images classifications have attracted extensive concentration from great many researchers in the field of medical image processing. Persistence with that Deep Q-Network learning system give an idea of successful methodology in the field of Machine intelligence investigation which has been applied in various medical applications. With medical image processing turning into a remarkable function in disease screening and finding, numerous machine learning methodologies have been raised as incredible strategies in diagnosing the diseases. An automated feature extraction and classification prompting wonderful achievements in the medical field are found out legitimately and consequently from the images. This research work proposed the significant deep learning methods of fast advancement, its key commitments and best accuracy in classification results. The proposed deep q-network processing system which includes number of phases like classification, segmentation and detection on medical images with respect to various lung images, datasets and benchmarks. In this proposed work, DQN learning model suggested for Multi Detector Computed Tomography lung images which is used to classify the lung images like interstitial pneumonia, pulmonary nodule and pulmonary embolism based on level of infection. In this research work, the utilization of deep learning procedures with machine intelligence to the medical image and an investigation of their future difficulties and potential challenges are examined. © Grenze Scientific Society, 2023.

关键词： Medical applications

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Prediction of Noise-Type and its Elimination from Photoplethysmograph Signal 3

Prediction of Noise-Type and its Elimination from Photopleth...

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3rd IEEE international conference on control, Instrumentation, Energy and Communication, CIEC 2024

作者： Chowdhury, Soujanya Bose, Aytrik Mukherjee, Shatanik Chandra, Jayanta K. Ramkrishna Mahato Govt. Engg. College Dept. of Electrical Engineering West Bengal Purulia India Cognizant West Bengal Kolkata India

ISBN: (纸本)9798350313703

Non-invasive measurement of photoplethysmograph (PPG) signal is an emerging research area. However the presence of noises in such signals may give erroneous interpretations. This paper thus proposes a technique to identify the noise model in the PPG signal, followed by the suitable de-noising techniques. Reservoir computing (RC) model has been used for the classification purpose which reduces numbers of learnable parameters and provided fast training. Cepstrum analysis has been applied for the elimination of convolutive noises. The method has been tested on 700 PPG windows and yielded good results from the point of view of PPG de-noising and estimation of respiration rate (RR) in terms of NRMSE and PCC with a standalone hardware implementation on a low-cost resource-constrained embedded device (Raspberry Pi Zero W) having an end-to-end latency and memory engagement of 165.34 s and 331.10 KB respectively. © 2024 IEEE.

关键词： Additive noise

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FoCLBP+2DGF: Fusion of Complete Local Binary Pattern with 2D Geometry Features for Facial Emotion Recognition in Videos 1

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19th international conference of Intelligent Unmanned Systems, ICIUS 2023

作者： Singh, Lovejit Singh, Sarbjeet Aggarwal, Naveen Singh, Ranjit Department of Computer Science and Engineering Chandigarh University Punjab Mohali140413 India Department of Computer Science and Engineering Panjab University Chandigarh160014 India

ISBN: (数字)9789819765911

ISBN: (纸本)9789819765904

People have different facial attributes in terms of their skin color, shape, and size. This diversity makes a challenge in the development of generalized automatic intelligent system for human’s emotions recognition through their facial expressions. Over the decades, researchers have significantly contributed to geometry and texture-based methods to act for facial contents in image/video in the form of feature vector. This paper presents a fusion of texture based complete local binary pattern (CLBP) method with two-dimensional (2D) geometry features (FoCLBP+2DGF) method to represent the facial expressions for emotion recognition in videos. The proposed method better describes the contrast and structural behavior of facial contents in spatial domain with fusion of CLBP and 2D geometry feature computing. It aggregates the facial expression’s behavior in temporal domain by sum normalization method to capture ratio among the features. The constructed final feature vector with proposed method is independent from the duration of video. Further, Bayesian optimization method is used to identify the best hyperparameters of multiclass support vector machines (SVMs) with proposed FoCLBP+2DGF method. Experimental results show that the proposed FoCLBP+2DGF method attained ≈ 88.64 ± 0.38% emotion recognition accuracy with SVMs using Gaussian kernel function in videos of crowd-sourced emotional multimodal actors’ dataset (CREMA-D). The proposed method significantly improved accuracy of intelligent system for human emotion recognition in videos over the human accuracy and existing work. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Intelligent systems

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