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An Effective Detection of Litchi Disease using Deep Learning 14

An Effective Detection of Litchi Disease using Deep Learning

引用

14th International Conference on Computing communication and Networking Technologies, ICCCNT 2023

作者： Dahiya, Mansi Guru Prasad, M.S. Anand, Tanmay Kumar, Khushi Bansal, Sparsh Naveen Kumar, H.N. Dept. of Computer Science and Engg. Dehradun India Vidyavardhaka College of Engineering Dept. of Electronics and Communication Engg. Karnataka Mysuru India

ISBN: (纸本)9798350335095

Litchi plant diseases are a major threat to global agricultural productivity, economies, and the environment as they cause significant losses. Therefore, it is necessary to have an early and accurate litchi plant monitoring system for farmers, managers, and decision-makers. To develop a constraint-free and reliable work plan for total litchi disease management, a comprehensive review of the literature and industry practices was conducted. A conceptual framework for litchi disease classification was outlined, which uses a structured approach combining professional visual interpretation, pathological analysis, and feature extraction using convolutional neural networks. The diseases in the litchi plant were identified, described, and divided into groups for broad classification. The next step is to recommend a course of treatment for the related litchi plant disease and provide contact information of a pathologist for further queries and suggestions. The aim is to create a user-friendly interface to offer farmers affordable, simple, and quick assistance. © 2023 IEEE.

关键词： Diseases

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A Hybrid Model for Accurate Prediction of Progression in Parkinson's Disease 5

A Hybrid Model for Accurate Prediction of Progression in Par...

引用

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

作者： Anirudh, S. Tummala, Manushri Sanisetty, Sri Sai Suhas Kumar, K. Dinesh Nasser, Nidal Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India Amrita Vishwa Vidyapeetham Amrita School of Engineering Dept. of Electronics and Communication Engineering Bengaluru India Al Faisal University College of Engineering Saudi Arabia

ISBN: (纸本)9798350383522

Evaluating the extent of motor impairment is an important aspect in Parkinson's disease, where the Unified Parkinson's Disease Rating Scale (UPDRS) plays a crucial role. The UPDRS assessment is traditional, time-consuming and labor intensive. This paper presents a stacked regression model using Light gradient-boosting machine (LightGBM) and CatBoost to automate and enhance UPDRS score predictions. It is an important advancement in managing Parkinson's disease that provides more efficient and individualized care for patients through ensemble learning. © 2024 IEEE.

关键词： CatBoost Ensemble Learning Light-GBM Parkinson's Disease Unified Parkinson's Disease Rating Scale

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Quantitative Assessment of Hayward Kiwi Soluble Solids Content Prediction Using Hyperspectral Imaging 13

Quantitative Assessment of Hayward Kiwi Soluble Solids Conte...

引用

13th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, WHISPERS 2023

作者： Francis, Jobin Devassy, Binu Melit George, Sudhish N George, Sony Norwegian University of Science and Technology Dept. of Computer Science Gjovik Norway National Institute of Technology Dept. of Electronics and Communication Engineering Calicut India

ISBN: (纸本)9798350395570

Soluble Solids Content (SSC) of kiwi fruit is an important internal quality attribute that influences its consumption quality and post-harvest shelf life. In this study, a non-invasive measurement and quantitative prediction of SSC indices in Hayward kiwi cultivars was conducted using hyperspectral imaging techniques. Hyperspectral images of 495 kiwi fruits were collected in reflectance mode between the wavelengths of 400 and 1000 nm. This study employed Partial Least Square Regression (PLSR) for calibration and prediction, combined with various preprocessing methods. Amongst, combination of SGS+MSC+PLS obtained better calibration and prediction outcomes, with RC2 = 0.42, RMSEP = 1.3023 and RP2 = 0.31, RMSEP = 1.3211 respectively. This study also looked at the feasibility of employing a Moving Window PLSR (MWPLSR) approach to find the best spectral interval and reduce the impact of multicollinearity in the spectral data. Then, MWPLSR approach delivered the best calibration and prediction values in a spectral region spanning 628-947 nm. © 2023 IEEE.

关键词： Calibration

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Speaker Identification Using MFCC Feature Extraction : A Comparative Study Using GMM, CNN, RNN, KNN and Random Forest Classifier 2

Speaker Identification Using MFCC Feature Extraction : A Com...

引用

2nd International Conference on Trends in Electrical, Electronics and computer engineering, TEECCON 2023

作者： Yerramreddy, Dhanvanth Reddy Marasani, Jayasurya Gowtham, Ponnuru Sathwik Venkata Yashwanth, Samudrala Poorna, S.S. Anuraj, K. Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science and Engineering Amritapuri India Amrita Vishwa Vidyapeetham Dept. of Electronics and Communication Engineering Amritapuri India

ISBN: (纸本)9798350339949

Speech recognition plays a major role in technology that enables computers and devices to understand and interpret human speech, while speaker identification is a specialized application within speech recognition that focuses on recognizing and distinguishing individual speakers based on their unique vocal characteristics. By utilizing techniques such as feature extraction, pattern recognition and machine learning algorithms speaker identification systems can match an input speech sample to a known set of speaker profiles and determine the most likely speaker's identity. This process involves comparing the extracted features of the input speech with the features stored in a database or model. In this project we are performing a comparative study which mainly focuses on extracting features through MFCC feature extraction for all models in common and then the implementation of identifying speakers using Gaussian Mixture Model, CNN, LSTM in case of RNN, k-Nearest Neighbour and a Random Forest Classifier.A comparative study on various types of models helps us to pave way on analyzing these to implement one of the best among these in day to day life. Among all the models, GMM performed very well with an accuracy of 98.68% followed by LSTM with an accuracy of 95.77%. © 2023 IEEE.

关键词： Speech recognition

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Secrecy Capacity Optimization in RF/FSO systems: Impact of Mixed Rayleigh and Log-Normal Fading with Atmospheric Turbulence

Secrecy Capacity Optimization in RF/FSO Systems: Impact of M...

引用

2024 IEEE International Conference on Intelligent Signal Processing and Effective communication Technologies, INSPECT 2024

作者： Upadhyay, Deepak Sharma, Kunj Bihari Gupta, Mridul Upadhyay, Abhay Venu, Nookala Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Electronics and Communication Engineering Dehradun India ABV Indian Institute of Information Technology and Management Data Science and Quantum Computing Gwalior India

ISBN: (纸本)9798350379525

This paper investigates the secrecy capacity optimization in RF/FSO systems with mixed Rayleigh and log-normal fading, while masking eavesdroppers channel state information () as well as suffering from atmospheric turbulence. We explore the impact of several parameters including beamforming angles, modulation schemes and hybrid RF/FSO link configurations through a combination of simulations as well as analytical models. Secrecy capacity is enhanced for an improved SNR and vice versa under higher turbulence conditions and cooler influents eavesdropped scenarios. Performance of the hybrid RF/FSO systems dominates considerably over single-link solutions, especially at longer distances. All these mechanisms appreciably increase the secrecy capacity, while beamforming and advanced modulation techniques offer most of the improvement. The study also demonstrates the interdependencies of system parameters, providing guidelines for designing secure communication systems under different environmental and operational conditions. © 2024 IEEE.

关键词： Beamforming

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Implementation of Modulo Multiplication with CAM-Based Massive-Parallel SIMD Matrix Core

Implementation of Modulo Multiplication with CAM-Based Massi...

引用

2023 International Technical Conference on Circuits/systems, computers, and communications, ITC-CSCC 2023

作者： Kageyama, Kyosuke Hamano, Hajime Kayama, Ryogo Koide, Tetsushi Kumaki, Takeshi Kindai University Dept. of Electrical Electronic and Communication Engineering Osaka Japan Ritsumeikan University Dept. of Electronic and Computer Engineering Shiga Japan Hiroshima University Hiroshima Japan

ISBN: (纸本)9798350326413

Recently, semiconductor technology has been growing rapidly, and mobile devices are required to have digital convergence. In this paper, a CAM-based massive-parallel SIMD matrix core (CAMX) is proposed as a mobile device accelerator for high-performance, programmability, and versatility. The CAMX can process repeated arithmetic and table-lookup coding operations in parallel. Mobile devices need to keep secure communication on the Internet, and the cipher techniques are wide spread for secure communication like the Advanced Encryption Standard (AES), Rivest-Shamir-Adleman (RSA), and so on. In particular, RSA encryption needs to calculate modulo multiplication. In this paper, the CAMX executes modulo multiplication for secure communication. This processing involves multiplication and division processing. This processing is implemented in the CAMX, and the CAMX can execute modulo multiplication at 1,104 clock cycles in parallel. In addition, the clock cycles of the CAMX and an ARM core are compared. From the result, the CAMX can reduce clock cycles by approximately 86 % for 1,024 parallel processing data relative to the ARM core with NEON. © 2023 IEEE.

关键词： Semiconductor device manufacture

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Optimal Resource Allocation in Dynamic Fading Channels via Meta-Learning Techniques 2

Optimal Resource Allocation in Dynamic Fading Channels via M...

引用

2nd IEEE International Conference on Advancements and Key Challenges in Green Energy and Computing, AKGEC 2024

作者： Upadhyay, Deepak Sharma, Kunj Bihari Gupta, Mridul Upadhyay, Abhay Venu, Nookala Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Electronics and Communication Engineering Dehradun India Madhav Institute of Technology & Science Deemed University Centre for Internet of Things Gwalior India

ISBN: (纸本)9798350390179

In this paper, we address the optimally dynamical resource allocation problem for fading channels utilizing meta-learning methods with an application to improve efficiency and performance in wireless communication systems. Extensive experiments are conducted and our meta-learning model has demonstrated the ability to adapt itself dynamically in various channel conditions, leading to improvement of Signal-to-Noise Ratio (SNR), reduction of Bit Error Rate (BER) and enhancement on throughput when compared with traditional methods. Visualizations like performance analysis comparisons, heatmaps or 3D plots are used to show how the model can be leverage as effectively manager of resources where they are and when they suit best. The performance improvements demonstrated here are statistically significant testifying for the scalability of our work. The findings of this study demonstrate the major benefits that can be achieved by meta-learning to overcome immanent issues in fast-changing wireless conditions and its potential to propel future communication technologies forward. © 2024 IEEE.

关键词： Contrastive Learning

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Deep Learning for Channel Prediction in Non-Stationary Wireless Fading Environments

Deep Learning for Channel Prediction in Non-Stationary Wirel...

引用

2024 IEEE International Conference on Intelligent Signal Processing and Effective communication Technologies, INSPECT 2024

作者： Upadhyay, Deepak Sharma, Kunj Bihari Gupta, Mridul Upadhyay, Abhay Venu, Nookala Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Electronics and Communication Engineering Dehradun India Data Science and Quantum Computing ABV Indian Institute of Information Technology and Management Gwalior India

ISBN: (纸本)9798350379525

In non-stationary wireless fading channels deep learning models are effective in predicting channel states as indicated by this study. This work compared the LSTM and GRU models with autoregressive methods or Kalman filter, implying that deep learning is indeed able to capture temporal dependence between variables and was likely more adapted than linear method when dealing with many changes in dynamics. From the perspective of pre-processing data, keys like attention mechanism and transfer learning were decisive elements in helping them to improve prediction scores and make models more robust. These results showed that these enhanced procedures allow the models to generalise over a wide range of non-stationary situations, generating consistent performance in various diverse environments. This study indicates the promise of deep learning for enhancing wireless network operation, which could lead to greater self-awareness and utilization features in emerging future-generation wireless systems. © 2024 IEEE.

关键词： Prediction models

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Delta-γ Modeling of Wireless system Resilience in Multipath Fading and Interference Environments 7

Delta-γ Modeling of Wireless System Resilience in Multipath...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Upadhyay, Deepak Upadhyay, Abhay Sharma, Kunj Bihari Gupta, Mridul Venu, Nookala Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Electronics and Communication Engineering Dehradun India Madhav Institute of Technology & Science Deemed University Centre for Internet of Things Gwalior India

ISBN: (纸本)9798350350067

In this paper, we pursue the resilience of wireless communication systems in multipath fading and interference environments utilizing Delta- γ modelling. Furthermore, fading phenomena such as Rayleigh/Rician/Nakagami channels can be simulated to examine the impact on important performance metrics like SNR, BER and throughput. Different types of interference scenarios, covering co-channel and adjacent channel interferences are also studied to understand impact on system capacity as well as reliability. This article investigates the use of adaptive modulation techniques, multiple-input multiple-output (MIMO) strategies and advanced interference management schemes by extensive *** results illustrate the importance of adaptive strategies in overcoming fading and interference which leads to an increase in system resilience as well as a robust communication link. Metrics for the outage probability and mean time to failure (MTTF) can inform how reliable systems are across operational scenarios. This work reconciles theory with practice to provide a systematic way of controlling the operational parameters in wireless networks under varying conditions. These lessons will be crucial in steering the design of forthcoming wireless systems that must more reliably satisfy a broader set of application requirements. © 2024 IEEE.

关键词： Multipath fading

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Voice or Content? - Exploring Impact of Speech Content on Age Estimation from Voice 31

Voice or Content? - Exploring Impact of Speech Content on Ag...

引用

31st European Signal Processing Conference, EUSIPCO 2023

作者： Ide, Yuta Tawara, Naohiro Saito, Susumu Nakano, Teppei Ogawa, Tetsuji Dept. of Communications and Computer Engineering Waseda University Tokyo Japan NTT Communication Science Laboratories Kyoto Japan

ISBN: (纸本)9789464593600

To investigate the impact of speech content on age estimation accuracy from voice data, we created a corpus of speech utterances featuring identical content spoken by individuals of varying ages. Subsequently, we analyzed the age estimation outcomes derived from this dataset. Previous studies have identified biases in age-labeled speech corpora regarding speaker age and vocabulary usage. Given that speech content typically varies with the speaker's age during conversations, it's plausible that age estimation results could be influenced by speech content. To address this concern, we developed a dataset in which speakers of different ages delivered speech content that was consistent across all speakers and tailored to the characteristics of each age group. We estimated the speakers' ages both manually, through crowdsourcing, and automatically and then conducted a significance test to assess whether speech content affected the age estimation results. Our findings indicated that neither the automatic age estimation system employed nor the manual age estimation outcomes were significantly impacted by speech content. © 2023 European Signal Processing Conference, EUSIPCO. All rights reserved.

关键词： Crowdsourcing

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