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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

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

ISBN: (纸本)9798331504403

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 98.46%, Validation Accuracy was 99.15% and Test Accuracy was 98.44%.After Fine Tuning there was a significant increase in the accuracy. The Training Accuracy came to be 99.77%, Validation Accuracy is 99.43% and Test Accuracy: 99.12% © 2024 IEEE.

关键词： Plant diseases

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Human Activity Recognition in Free Living Using a Wearable Sensor: A Two-Stage Approach 12

Human Activity Recognition in Free Living Using a Wearable S...

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12th International Conference on Awareness Science and Technology, iCAST 2023

作者： Li, Tianhui Zhou, Xue Kokubun, Kota Chen, Wenxi Division of Information System The University of Aizu Aizu-Wakamatsu Japan Dept. of Computer and Information Systems The University of Aizu Aizu-Wakamatsu Japan Dept. of Computer Science and Engineering The University of Aizu Aizu-Wakamatsu Japan

ISBN: (纸本)9798350324693

A novel two-stage approach for human activity recognition (HAR) using a wearable sensor is proposed in this study. The goal is to achieve both coarse-level and fine-level recognition of activities. The study focuses on five common activities: desk-working, driving, walking, downstairs, and upstairs. Three-axis acceleration signals were collected using a wrist-worn accelerometer sensor, and the activities were annotated during the participant’s daily routines. The acceleration data were preprocessed through segmentation, feature extraction, and normalization. In the first s tage, a t raditional m achine learning model (decision tree) was used for coarse-level classification, distinguishing between activities with exercise and without exercise. In the second stage, deep learning models were employed for fine-level classification to recognize specific activities within the coarse classes. The performance of the models was evaluated using accuracy, sensitivity, and specificity. T he p roposed approach showed promising results with accuracies of 93.53%, 93.52%, 85.70%, 98.11%, and 85.91% respectively for desk-working, driving, walking, downstairs, and upstairs, demonstrating the effectiveness of the two-stage strategy for HAR. The proposed two-stage approach outperforms the one-stage method (decision tree classifier a nd d eep l earning m odel), p articularly i n imbalanced data, achieving a graceful equilibrium between sensitivity and specificity. T he s tudy h ighlights t he i mportance o f considering the complexity and variability of daily activities for accurate classification a nd r esource optimization. © 2023 IEEE.

关键词： Wearable sensors

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Evaluation of Meteorological Factors’ Effect on Daily Heart Rate Variability Over Five Years 12

Evaluation of Meteorological Factors’ Effect on Daily Heart...

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12th International Conference on Awareness Science and Technology, iCAST 2023

作者： Kokubun, Kota Li, Tianhui Chen, Wenxi Dept. of Computer and Information Systems The University of Aizu Aizu-Wakamatsu Japan Division of Information System The University of Aizu Aizu-Wakamatsu Japan Dept. of Computer Science and Engineering The University of Aizu Aizu-Wakamatsu Japan

ISBN: (纸本)9798350324693

The aim of this study is to investigate the effect of meteorological factors on heart rate variability (HRV) derived from daily electrocardiogram (ECG) data. Daily meteorological data were obtained from the Japan Meteorological Agency’s database. Daily ECG data were measured while bathing for five y ears. T he s ubject w as a h ealthy m ale i n h is fi fties. After collecting ECG data, R-R intervals were calculated to perform HRV analysis in time domain and frequency domain. The evaluation of link between meteorological factors and HRV parameters was realized by utilizing meteorological factors’ importance calculated by Random Forest, and Steel-Dwass significant difference analysis of the values. Additionally, the evaluation was also performed by analyzing the obtained meteorological factors’ importance values using Granger Causality test. In Steel-Dwass test, several significant d ifferences (p © 2023 IEEE.

关键词： Electrocardiograms

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EHR based Prediction and Diet Recommender For Diabetes using Blockchain 3

EHR based Prediction and Diet Recommender For Diabetes using...

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3rd International Conference on Smart Technologies and systems for Next Generation Computing, ICSTSN 2024

作者： Poonguzhali, N. Amarabalan, N. Jawaharlal Nehru University School of Computer and Systems Sciences New Delhi India Manakula Vinayagar Institute of Technology Dept. of Electrical and Electronics Engineering Puducherry India

ISBN: (纸本)9798350391565

Diabetes is an illness which is triggered due to high blood sugar levels. Ignoring diabetes is undesirable since the condition can have adverse impacts on the heart, kidneys, cholesterol levels, eyes, and almost all other organs. Diabetes will be easier to manage the earlier it is detected. To do this, researchers use a variety of machine learning techniques to more accurately forecast the onset of early diabetes in human bodies or in its patients. By constructing models using datasets that are available, machine learning techniques produce more accurate predictions. In the proposed work, Random Forest Algorithm is used to predict diabetes, as this technique outperforms in terms of accuracy. Individuals with diabetes, maintaining an appropriate diet is crucial. The majority of them aim to assist users in managing their blood sugar levels. A person's weekly meal preparation habits can be attributed to a myriad of intersecting elements, such as taste, healthfulness, financial constraints, personal preferences, and lifestyle. An individual might experience it difficult to choose what to eat. To assist with the management of diabetes, the proposed system advises using a customized meal planning strategy. The diet plan suggested in the system is based on decision tree technique which is diabetic-friendly that is also socially, economically, and culturally acceptable and that can satisfy all nutritional needs while being mindful of calories. Following the prediction of diabetes and the suggestion of a diet plan, the patient's medical information is stored in the EHR for convenient access. © 2024 IEEE.

关键词： Electronic health record

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Xception CNN-Ensemble Learning Based Facial Emotion Recognition 6

Xception CNN-Ensemble Learning Based Facial Emotion Recognit...

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6th International Conference on Computing, Communication, control and Automation, ICCUBEA 2022

作者： Shirsath, Supriya Vikhe, Vaishali Vikhe, Pratap Pravara Rural Engineering College Dept. of Computer Engineering Ahmednagar India Pravara Rural Engineering College Dept. of Instrumentation & Control Engineering Ahmednagar India

ISBN: (纸本)9781665484510

Emotions in human is an effective medium to study the mindset of a person. Since, expression on the face of human is significant approach to understand the condition and communicate with him to release the pressure or stress of person. The emotions and behavior have a strong relationship and is noteworthy in non-verbal form of communication. The advancement in medical science and use of image processing tools like Artificial Intelligence (AI) and Machine Learning (ML), can be helpful to recognize emotion, detect stress and depression level of a person. Thus, in this research work emotion recognition system is developed using Convolution Neural Networks (CNNs) with increased dept. and width. CNNs have been shown to enhance prediction accuracy. In terms of proper emotion categorization and accurate prediction, the suggested ensemble technique is effective. In this paper Xception CNN architecture is proposed for accurate facial emotions prediction along with an ensemble model, using Max Voting ensemble technique, mainly contributes in accurate classification. In proposed technique trained CNN models were loaded and for each trained model prediction probabilities were generated. Furthermore, it is then used for Max Voting to generate final emotion prediction. The CNN models are trained and evaluated on FER-2013 dataset. © 2022 IEEE.

关键词： Emotion Recognition

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Benchmarking Convolutional Neural Network Inference on Low-Power Edge Devices 48

Benchmarking Convolutional Neural Network Inference on Low-P...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Ferraz, Oscar Araujo, Helder Silva, Vitor Falcao, Gabriel Instituto de Telecomunicações Portugal University of Coimbra Dept. of Electrical and Computer Engineering Portugal Institute of Systems and Robotics Portugal

ISBN: (纸本)9781728163277

The massive adoption of IoT devices, the recent developments in the efficiency of AI systems, and the increase of edge computational power, accelerated the deployment of edge AI systems. The implementation of these systems through the use of low-power embedded devices scattered across the edges of a network allows for reduced latency and cost, compared to traditional cloud-based AI computing systems. As a result of the low-complexity AI models and the available low-power embedded systems on the market, this paper provides a comparative study on the inference performance of convolutional neural networks for different edge devices, by exploiting low-power GPUs and dedicated AI hardware. The benchmark results were able to achieve 864 inferences/s for the Jetson AGX Xavier board on a pre-trained SqueezeNet, while reaching a high power efficiency of 52.6 inferences/s per Watt. For the dedicated Movidius neural stick, the system requires only 1.5 W for processing 24.2 inferences/s. © 2023 IEEE.

关键词： Artificial Intelligence Benchmarking Edge devices GPUs Inference Engine IoT Low-power

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Velocity-aided Channel Estimation for Spatially Selective mmWave Massive MIMO Communications 31

Velocity-aided Channel Estimation for Spatially Selective mm...

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31st European Signal Processing Conference, EUSIPCO 2023

作者： Vlachos, Evangelos Mavrokefalidis, Christos Berberidis, Kostas Industrial Systems Institute ATHENA Research Center Patras Greece Dept. of Computer Engineering and Informatics University of Patras Patras Greece

ISBN: (纸本)9789464593600

This paper focuses on addressing the challenge of estimating multiple-input multiple-output (MIMO) channels for wireless communication between a ground base-station and a moving vehicle. One recently recognised model for time-varying channels incorporates spatial selectivity, which is referred to as beam squint, and is particularly relevant in the millimeter-wave (mmWave) range. In such scenarios, it is essential to account for the beam squint when attempting to recover channel parameters using a training sequence. However, the use of a training sequence alone may be insufficient for this purpose. To overcome this issue, in this work, we propose a channel estimation approach that exploits information provided by the control module of the vehicle, namely its velocity. The estimation problem that is designed, regards the channel both in a parametric and a non-parametric form and the alternating direction method of multipliers is utilised to efficiently solve it. It is demonstrated via simulations that considerable gains can be achieved if information from the control unit of the vehicle can be appropriately introduced and exploited. © 2023 European Signal Processing Conference, EUSIPCO. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Mathematical Model of Damping of UAV Oscillations in the Cargo Delivery Problem 4

Mathematical Model of Damping of UAV Oscillations in the Car...

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4th IEEE KhPI Week on Advanced Technology, KhPI Week 2023

作者： Kucherov, Dmytro Tkachenko, Valerii Shmelova, Tetiana Miroshnichenko, Ignat Poshyvailo, Olexii Ogirko, Ihor Dept. Computerized Control Systems Faculty of Computer Science and Technology National Aviation University Kyiv Ukraine Dept. Air Navigation Systems Faculty of Air Navigation Electronics and Telecommunications National Aviation University Kyiv Ukraine Dept. Mathematic Ukrainian Academy of Printing City Lviv Ukraine

ISBN: (纸本)9798350395532

Currently, the use of unmanned vehicles for the delivery of goods is becoming a trend in many areas of human activity. At the same time, it is necessary to solve the problem of the integrity of the cargo because of its possible fluctuations. The paper deals with the problem of delivering cargo attached to a 4-motor UAV on a rigid cable. A control system is needed to achieve the UAV equilibrium position that can compensate for significant mismatches in the XY coordinate plane. A combination of two control algorithms is proposed for solving this problem, one of which provides a given position in the horizontal plane at small mismatch angles and the second at large mismatch angles. The first controller uses the PID controller algorithm with the corresponding optimal setting of its parameters, and the second one uses the bang-bang control scheme. An additional controller is used to coordinate these controllers, which ensures their timely switching. Some simulation is also proposed. © 2023 IEEE.

关键词： Damping

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Fuzzy Logic controller for the Chemotherapy of Brain Tumor 9

Fuzzy Logic Controller for the Chemotherapy of Brain Tumor

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9th International Conference on control, Decision and Information Technologies, CoDIT 2023

作者： Zubair, Muhammad Iacoviello, Daniela Mughal, Iqra Shafeeq Sapienza University of Rome Dept. Computer Control and Management Engineering Rome Italy University of Catania Dept. di Ingegneria Electrica Electronica e Informatica Dieei Catania Italy

ISBN: (纸本)9798350311402

An advanced Fuzzy Logic controller (FLC) that considers all the states of the brain tumor system is designed for the chemotherapy treatment. A Mamdani-type FLC is proposed for dynamically controlling the chemotherapy drug for the tumor system;the chemotherapy treatment of brain tumors requires advanced strategies which mainly depend upon the severity of the tumor. In this work, the advanced FLC designed aims both at determining the amount of chemotherapy to eliminate tumor cells, and at preserving the minimum amount of healthy and immune cells. The controller's performance is verified using MATLAB software based on different control parameters, showing its effectiveness in reducing the tumor cells. It has shown favorable results in terms of steady-state error, rate of convergence, and amount of drug consumed. © 2023 IEEE.

关键词： Fuzzy control

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Multi-Point Positional Insertion Tuning for Small Object Detection

arXiv

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arXiv 2024年

作者： Goto, Kanoko Karasawa, Takumi Hirose, Takumi Kawakami, Rei Inoue, Nakamasa Dept. of Computer Science Institute of Science Tokyo Tokyo Japan Dept. of Systems and Control Engineering Institute of Science Tokyo Tokyo Japan

Small object detection aims to localize and classify small objects within images. With recent advances in large-scale vision-language pretraining, finetuning pretrained object detection models has emerged as a promising approach. However, finetuning large models is computationally and memory expensive. To address this issue, this paper introduces multi-point positional insertion (MPI) tuning, a parameter-efficient finetuning (PEFT) method for small object detection. Specifically, MPI incorporates multiple positional embeddings into a frozen pretrained model, enabling the efficient detection of small objects by providing precise positional information to latent features. Through experiments, we demonstrated the effectiveness of the proposed method on the SODA-D dataset. MPI performed comparably to conventional PEFT methods, including CoOp and VPT, while significantly reducing the number of parameters that need to be tuned. Copyright © 2024, The Authors. All rights reserved.

关键词： Visual languages

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