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Crop Classification using Convolutional Neural Network 15

Crop Classification using Convolutional Neural Network

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Kavitha, K. Gopalakrishnan, K. Balaji, S. Jeevanantham, J. Aakhila Hayathunisa, M. Department of Information Technology Velalar College of Engineering Erode India Department of Computer Science and Engineering Kongu Engineering College Erode India Department of Electronics and Communication Kongu Engineering College Erode India

ISBN: (纸本)9798331300579

Deep learning is a powerful machine learning technique that has the potential to revolutionize agriculture. By leveraging deep learning models, we can develop innovative solutions to critical agricultural challenges, such as crop classification, weed percentage estimation, and crop quality identification. Deep learning models can be trained on remote sensing data to accurately classify different crop types. This information can be used by farmers and agricultural stakeholders to monitor and manage their crops more effectively. For example, crop classification models can be used to identify areas of a field that are underperforming or at risk of disease. Weed infestations can significantly reduce crop yields and quality. Deep learning models can be trained on high-resolution imagery and sensor data to accurately estimate weed percentages within fields. This information can be used to develop precise and timely weed management strategies, reducing the need for excessive herbicide usage and minimizing the environmental impact. Assessing crop quality is crucial for optimizing harvest decisions and ensuring the delivery of high-quality produce to consumers. Deep learning techniques, such as image analysis and spectroscopy, can be employed to identify various quality parameters in crops, such as size, color, ripeness, and the presence of diseases or blemishes. This information can help farmers make informed decisions about harvesting, storage, and distribution. Assessing crop quality is crucial for optimizing harvest decisions and ensuring the delivery of high-quality produce to consumers. Deep learning techniques, such as image analysis and spectroscopy, can be employed to identify various quality parameters in crops, such as size, color, ripeness, and the presence of diseases or blemishes. The continued development and integration of deep learning technologies into agriculture hold promise for advancing global food production and agricultural sustainability. © Grenze Sc

关键词： Weed control

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Enhanced Mini-YOLOv7: A AI Approach for Safety Helmet Detection in the Construction Industry

Enhanced Mini-YOLOv7: A AI Approach for Safety Helmet Detect...

引用

2024 IEEE International Women in engineering (WIE) Conference on Electrical and computer engineering, WIECON-ECE 2024

作者： Blessie, A. Alice Sankarasubramanian, R.S. John, Joseph Krishnapriya, S. Bhuvanesh, A. Keerthanadevi, R. Government College of Engineering Department of Electronics and Communication Engineering Tirunelveli India Psg Institute of Technology and Applied Research Department of Mathematics Coimbatore India Gregorian Institute of Technology Department of Mechnanical Engineering Kottayam India Sri Eshwar College of Engineering Department of English Coimbatore India Psn College of Engineering and Technology Department of Electrical and Electronics Engineering Tirunelveli India Psn College of Engineering and Technology Department of Computer Science and Engineering Tirunelveli India

ISBN: (纸本)9798331535476

Wearing safety helmets is an effective way to keep construction workers safe on the job. Workers often opt to remove their helmets due to discomfort or a lack of security knowledge, leaving them vulnerable to unsaid threats. A worker's risk of injury from falls, both physical and otherwise, increases when they do not wear a safety helmet. Because detecting whether workers are wearing safety helmets is an important aspect of managing safety on construction sites, a fast and accurate detector is required. However, standard manual monitors aren't particularly popular, and attaching sensors to a safety helmet is a laborious operation. With that in mind, this study presents EM-YOLOv7, a DL method for autonomous safety helmet recognition in real-time that has been developed for use on construction sites. A multi-scale non-local attention module (MSNA) is incorporated into the system to improve detection accuracy in complicated situations. This module enhances feature recovery by aggregating semantic context across multiple scales. Further, to improve detection performance and handle mutual occlusion, a loss function named Wise-IoUv3 (WIoUv3) is used. This method adjusts the loss depending on the overlap between the predicted and ground truth bounding boxes. To train and test the model, we will utilize the safety helmet-wearing (SHWD) dataset, which includes a wide variety of construction workers and whether or not they wore helmets. The goal of this technique is to provide a 99% accurate solution for real-time helmet recognition, which would greatly improve safety management and decrease the likelihood of accidents on construction sites. © 2024 IEEE.

关键词： Construction industry

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MentalHealthAI: Utilizing Deep Learning for Accurate Stress Level Prediction 3

MentalHealthAI: Utilizing Deep Learning for Accurate Stress ...

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3rd International Conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Bharathi, S.T. Raj, J. Relin Francis Krishnan, R. Santhana Rajalakshmi, V. Kumar, V. Vinoth Anchitaalagammai, J.V. Department of Information Technology PSNA College of Engineering and Technology Dindigul India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Electronics and Communication Engineering SCAD College of Engineering and Technology Cheranmahadevi India Department of Management Studies SCAD College of Engineering and Technology Cheranmahadevi India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Avadi Chennai India Department of Computer Science and Engineering Velammal College of Engineering and Technology Madurai India

ISBN: (纸本)9798331532420

MentalHealthAI is an advanced deep learning system designed to accurately predict stress levels by integrating various data sources, including user surveys, heart rate metrics, sentiment scores from social media interactions, and demographic information. This innovative approach employs a Fully Connected Neural Network (FCNN) architecture, enabling the model to uncover complex relationships among multiple stress-related factors, such as PHQ-9 and GAD-7 questionnaire scores, heart rate variability, sleep patterns, and calculated sentiment scores. By utilizing Natural Language Processing (NLP) techniques, specifically the VADER model, the system analyzes social media text data to derive sentiment scores, providing insights into users' emotional states. This holistic assessment empowers individuals to understand the interplay between their mental health and daily activities, fostering proactive stress management. The advantages of MentalHealthAI include its capacity to deliver timely and personalized stress assessments, allowing users to make informed decisions about their mental well-being. By combining objective data from wearables with subjective sentiment analysis, the system offers a comprehensive perspective on stress dynamics. Ultimately, MentalHealthAI aims to enhance mental health support, providing valuable insights that encourage healthier lifestyles and improved emotional resilience. © 2024 IEEE.

关键词： Electronic health record

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Enhancing Data Security and Privacy in Cloud Computing: A Survey of Modern Techniques 3

Enhancing Data Security and Privacy in Cloud Computing: A Su...

引用

3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Suganya, N. Gouthami, P. Sathiya, R. Sivaranjani, S. Murugesan, M. Department of Computer Science and Business Systems Dr. N.G.P. Institute of Technology Coimbatore India Department of Information Technology Thangavelu Engineering College Chennai India Departement of Electrical and Electronics Engineering Sri Krishna College of Engineering and Technology Coimbatore India Departement of Electrical and Electronics Engineering Karpagam Institute of Technology Coimbatore India

ISBN: (纸本)9798331527549

A computing system that is based on the internet and offers users all resources as on-demand services. Servers, storage, databases, software, and networking are among these on-demand services. Usually, the user must pay for the use of the resources;they are not their property. The number of people using this technology is increasing dramatically as a result of these features. Although it has benefits, cloud computing poses certain security and privacy risks. The authorized user’s data can be stolen easily. This current research thoroughly investigates modern strategies to these problems, highlighting identity management protocols, effective encryption and blockchain-based solutions. The article identifies research gaps, focuses attention to the inadequacies of current approaches, and recommends the possible ways to improve the efficiency, scalability and trustworthiness. © 2025 IEEE.

关键词： Cloud security

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Managing Cloud Networks: A Classification of SDN-Enabled Clouds 5

Managing Cloud Networks: A Classification of SDN-Enabled Clo...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Singh, Vaishali Kehkeshan Jallal, S. Das, Sunanda Maharishi University of Information Technology Maharishi School of Engineering & Technology Uttar Pradesh India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India

ISBN: (纸本)9798350383522

The idea of separating the management plane from the data-forwarding gear was first introduced by Software-Defined Networking (SDN), which has brought about a revolutionary new age in networking. This innovation makes networks programmable, flexible, and dynamically reconfigurable. These characteristics have great potential for the field of computer science in the cloud, where dynamic adjustments and reconfiguration are essential owing to on-demand consumption patterns. The many simulation and empirical assessment techniques that have been created expressly for SDN-enabled cloud settings are also highlighted by us. Finally, study conducted perform a critical review of the state of the research, highlighting gaps and suggesting possibilities for further study. © 2024 IEEE.

关键词： application framework cloud computing hardware resources programmable controller resource provisioning simulation and energy modeling Software-Defined Networking (SDN)

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An Efficient Shortest Path Optimized Algorithm for Vehicle Adhoc Networks System 7

An Efficient Shortest Path Optimized Algorithm for Vehicle A...

引用

7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Shalini, K. Reddy, K. Harinath Punati, Kondalarao Kavin, K.S. Veena, P. Pushpalatha, K. Vardhaman College of Engineering Department of Artificial Intelligence and Data Science Shamshabad India Annamacharya Institute of Technology and Sciences Department of Electrical and Electronics Engineering Rajampet India Hyderabad Institute of Technology and Management Faculty of Electronics and Communication Engineering Hyderabad500090 India Government College of Engineering Department of Electrical and Electronics Engineering Tamilnadu Tirunelveli India N. S. S. Polytechnic College Department of Computer Engineering Pandalam Pathanamthitta India Coimbatore Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore641109 India

ISBN: (纸本)9798350372816

VANET (Vehicular ad-hoc network) is a network of advanced automobiles designed to solve the problem of mobility and, as a result, reduce the number of accidents. Because VANET is a type of MANET (mobile ad hoc network) surrounded by human-driven nodes, the social concept can be used to explain VANET routing decisions. This motivates academics to look into the possibilities of using Social Network Analysis (SNA) to create routing strategies. The most critical problem in any system is parcel drop. Social characteristics-based architectures encounter comparable challenges, resulting in inefficient message delivery. In this research, the dragonfly method is used to the shortest path methodology for fixed line VANET with the purpose of reducing package drop and increasing network reliability. This is made achievable by sculpted system of values centralization analysis, which improves the decision via dragonfly optimization methodology. The behavior of the using nodes capability then be determined by the importance computation. In this study, the sustainability of the optimized shortest path system is measured in terms of End to End delay, Energy Consumption, Packet Loss, and Throughput. The dragonfly algorithm's throughput evaluation is 650 kbps as compared to the traditional way. © 2024 IEEE.

关键词： Vehicular ad hoc networks

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An On Chip BIST Architecture for ADC Parameter Testing and Measurement 1

An On Chip BIST Architecture for ADC Parameter Testing and M...

引用

1st International Conference on Innovative Sustainable Technologies for Energy, Mechatronics and Smart Systems, ISTEMS 2024

作者： Jangir, Anuradha Dixit, Monika Kumar, Amit Greater Noida Institute of Technology Electronics and Communication Department UP Greater Noida India Computer Science and Engineering Department Geu India

ISBN: (纸本)9798350384246

This paper introduces a ADC BIST mechanism that uses 12-bit SAR ADC and 14-bit R2R DAC technology. This solution has been used for 40nm automotive microcontroller. On - chip method is used for validating SARADC and DAC static parameter. There are two methods to test BIST off-chip and on-chip. Evaluating the parameter i.e., INL, DNL offline using raw data names off-chip method which required some offline calculation to characterize the BIST circuit, whereas on-chip method does not require this kind of extra calculation. In this method parameter evaluate in background using USER_SMILE algorithm and display pass/fail status based on pre-loaded limits of parameter. Solution has been purposed for reducing cost of testing equipment and testing time. © 2024 IEEE.

关键词： Parameter estimation

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Achieving Beamfocusing via Two Separated Uniform Linear Arrays 58

Achieving Beamfocusing via Two Separated Uniform Linear Arra...

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58th Asilomar Conference on Signals, Systems and computers, ACSSC 2024

作者： Kosasih, Alva Demir, Özlem Tuǧfe Björnson, Emil Kth Royal Institute of Technology Department of Computer Science Stockholm Sweden Tobb Etu Department of Electrical-Electronics Engineering Ankara Turkey

ISBN: (纸本)9798350354058

This paper investigates coordinated beamforming using a modular linear array (MLA), composed of a pair of physically separated uniform linear arrays (ULAs), treated as sub-arrays. We focus on how such setups can give rise to near-field effects in 6G networks without requiring many antennas. Unlike conventional far-field beamforming, near-field beamforming enables simultaneous data service to multiple users at different distances in the same angular direction, offering significant multiplexing gains. We present a detailed analysis, including analytical expressions of the beamwidth and beamdepth for the MLA. Our findings reveal that using the MLA approach, we can remove approximately 36% of the antennas in the ULA while achieving the same level of beamfocusing. © 2024 IEEE.

关键词： Beamforming

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Efficient Optimization of Energy Consumption at Home through Machine Learning 5

Efficient Optimization of Energy Consumption at Home through...

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5th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2024

作者： Sharada Prasad, N. Lakshmi, Shree M.S. Manjunatha, S. Vekariya, Vipul Benal, Ganga D. Patil, Harshal RNS Institute of Technology Department of Electrical and Electronics Engineering Karnataka Bangalore560098 India Cambridge Institute of Technology Department of Computer Science and Engineering Karnataka Bangalore560036 India Parul University Faculty of Engineering and Technology Parul Institute of Engineering and Technology Department of Computer Science and Engineering Gujarat Waghodia391760 India Symbiosis Institute of Technology Maharashtra Pune412115 India

ISBN: (纸本)9798350395235

In recent years, the imperative need for efficient energy utilization in residential buildings has become increasingly evident due to the unwarranted wastage of electrical energy. This has spurred significant interest in optimizing energy consumption while maintaining user comfort. Accurate energy prediction is a crucial component of this optimization. This study focus on minimizing energy consumption by accurately predicting it through advanced Machine Learning (ML) models and optimization techniques. The study begins with the collection of energy data from a reliable source, Kaggle, comprising 29 features. To streamline the dataset, unnecessary features are discarded, and data normalization is performed to ensure consistency and reliability. Subsequently, ML models, specifically Long Short-Term Memory (LSTM), are designed and optimized through the Genetic Algorithm (GA) and Grey Wolf Optimization (GWO) to fine-tune hyperparameters. The prediction results are evaluated using error values to assess the accuracy and reliability of the models. Notably, our findings indicate that the GWO-LSTM model outperforms the others, exhibiting minimal errors and therefore showcasing its superior predictive capabilities. Accurate energy prediction not only serves as a valuable tool in its own right but also plays a pivotal role in enabling proactive energy management. By accurately forecasting future energy requirements, it becomes possible to optimize energy consumption further. Such precision paves the way for intelligent scheduling of home appliances, which, in turn, leads to significant reductions in energy consumption. © 2024 IEEE.

关键词： Energy utilization

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An Optimized Switching Scheme to Reduce the Leakage Current and Power Loss of Transformerless Inverter

An Optimized Switching Scheme to Reduce the Leakage Current ...

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2025 IEEE International Conference on Mechatronics, ICM 2025

作者： Mondal, Sudipto Rashid, Mamun Or Biswas, Shuvra Prokash Islam, Md. Rabiul Rajshahi University of Engineering & Technology Department of Electronics & Telecommunication Engineering Rajshahi6204 Bangladesh Rajshahi University of Engineering & Technology Department of Mechatronics Engineering Rajshahi6204 Bangladesh School of Electrical Computer & Telecommunications Engineering University of Wollongong NSW2522 Australia

ISBN: (纸本)9798331533892

Transformer-less grid-connected inverters gained its popularity in recent times due to their compact design, lower cost, lower weight and overall higher efficiency compared to transformer-based systems. However, among the other issues of the transformer-less solution, leakage current caused by common-mode (CM) voltage fluctuations remains a significant problem, impacting power output, system safety. This paper proposes an optimized switching scheme for a modified H7 transformerless photovoltaic (TLPV) inverter to address the issue by reducing the leakage current and increasing the power quality. Moreover, an optimized unipolar pulse width modulation (UPPWM) strategy is also presented to maintain fairly constant common mode voltage (CMV), effectively suppressing leakage current further. The modified H7 TLPV along with the optimized UPPWM strategy also ensures lower semiconductor power loss and overall better efficiency. The total harmonic distortion (THD) of the grid voltage and current with the optimized pulse width modulation (PWM) strategy found satisfactory which met the standards of IEEE-519. Simulation is conducted in the MATLAB/Simulink environment to validate the performance criteria over the other TLPV structures, feasibility of leakage current reduction and overall power loss. This novel strategy provides an efficient and minimal solution for the next generation TLPV. © 2025 IEEE.

关键词： Pulse width modulation

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