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The Role of Hyperparameter Tuning and Feature Engineering in Enhancing machine learning Models for Crop Disease Analysis

The Role of Hyperparameter Tuning and Feature Engineering in...

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Innovations in High Speed Communication and signal processing (IHCSP), international conference on

作者： M. Shyam P. Suresh Kumar Batani Raghavendra Rao Suruchi Gaurav Dedgaonkar A. Sivanantham Manvinder Brar ECE R.M.K. Engineering College Thiruvallur Tamilnadu Department of Electrical and Electronics Engineering Vignan's Institute of Information Technology Visakhapatnam Andhra Pradesh India School of Mangement - PG Management JAIN (Deemed to be University) Bangalore Karnataka India IT Vishwakarma Institute of Technology Pune India Department of Mechanical Engineering Karpagam Academy of Higher Education Coimbatore India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India

ISBN: (数字)9798350368949

ISBN: (纸本)9798350368956

In this study, we provide a novel approach to teaching machine learning models to analyze farm illnesses. This strategy uses feature engineering and hyperparameter optimization. They are recursive feature elimination, random forest, and genetic algorithm optimization. Random Forest (RF, GAO, RFE). The recommended technique uses three algorithms to create it. Random Forest, an ensemble learning approach, combines decision tree forecasts to improve accuracy and usefulness. Each decision tree makes its own forecasts. Genetic algorithm optimization uses flexible hyperparameter adjustments to optimize the model. Natural selection served as the inspiration for this strategy. Iterative feature elimination improves the model. This procedure selects key issue aspects one by one. This work aims to enhance machine learning models for faster and better crop disease detection. These approaches will enable this.

关键词： Accuracy Crops Feature extraction Ensemble learning Random forests Optimization Tuning Diseases Genetic algorithms Farming

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An Overview of the Role of IoT in Modern Agriculture Application

An Overview of the Role of IoT in Modern Agriculture Applica...

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international conference on signal processing, Communication, Power and Embedded System (SCOPES)

作者： Sitabhra Majumder Arunabha Pal Sumit Ray Sapparapu Sai Sivani Sanju Saha Ankita Priyadarshini Department of Agronomy Centurion University of Technology and Management Paralakhemundi Odisha India Department of Soil Science Centurion University of Technology and Management Paralakhemundi Odisha India Department of Agronomy SR University Warangal Telengana India Department of Agricultural Extension Education Centurion University of Technology and Management Paralakhemundi Odisha India Department of Agronomy Siksha ‘O’ Anusandhan Bhubaneshwar Odisha India

ISBN: (数字)9798331506452

ISBN: (纸本)9798331506469

The adoption of the Internet of Things (IoT) is revolutionizing conventional methods through precision farming, real-time data collection and automation in agriculture. This article highlights global agricultural challenges, such as resource constraints and growing food demand, positioning IoT as a solution. IoT enhances efficiency in areas like precision farming, irrigation, and livestock monitoring using sensors, cloud computing, and machine learning. Despite challenges such as high costs and rural connectivity, IoT offers opportunities to improve yield and sustainability, while reducing carbon footprints. Case studies from Europe, the USA, and India demonstrate successful adoption. Emerging technologies like AI, blockchain, and 5G promise further advancement, aligning IoT with Sustainable Development Goals 2, 6, 12, and 13.

关键词： Irrigation Automation Supply chains Food security machine learning Livestock Internet of Things Sustainable development Monitoring Farming

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A Robust Multi-Class Obesity Classification and Risk Analysis System by Leveraging the Potentiality of machine learning Techniques

A Robust Multi-Class Obesity Classification and Risk Analysi...

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2nd international conference on signal processing, Communication, Power and Embedded Systems, SCOPES 2024

作者： Maheswara Rao, V.V.R. Silpa, N. Kurada, Ramachandra Rao Devi, K. Priya, M Yagna Reddy, Shiva Shankar Shri Vishnu Engineering College for Women Department of C.S.E. Bhimavaram India S.R.K.R. Engineering College Department of C.S.E. Bhimavaram India

ISBN: (纸本)9798331506452

Obesity, a significant global health issue, is linked to numerous multifactorial inherited conditions, including cardiovascular disease, hypertension, diabetes, and various other serious medical disorders. The etiology of obesity is complex, involving genetic, physiological, and environmental contributors, in addition to inadequate dietary habits and insufficient physical activity. Consequently, there is an urgent need for precise diagnosis and assessment of obesity to develop and implement effective preventive strategies and remedial interventions. The diverse results produced by existing diagnostic techniques have incited significant debate over the optimal methodology for accurately identifying and evaluating obesity. This has complicated efforts to establish a standardized method for diagnosis and treatment. The primary goal of present research is to leverage ML techniques to develop a robust analytical model for classifying individuals who are overweight or obese. A significant benefit of this work is that it provides insights into the risk issues allied with obesity and the said underlying causes contributing to the condition. The developed model, which utilizes an individual's physical attributes and dietary patterns, was evaluated through various ML algorithms, Logistic Regression (LR), Boosted Trees (BT), Optimized Trees (OT), and Robust Support Vector machine (Robust-SVM). In this study, the authors have synthesized essential information about the employed learning models, including details on their development, key findings, and underlying methodologies. The model's hyperparameters were meticulously optimized to improve accuracy. The study's findings indicated that among the evaluated algorithms, the Robust Support Vector machine (SVM) demonstrated the highest accuracy. © 2024 IEEE.

关键词： Classification (of information)

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Research on SSVEP Recognition Method Based on Residual Network

Research on SSVEP Recognition Method Based on Residual Netwo...

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signal processing and Intelligent Computing (SPIC), international conference on

作者： Xue Bai Yueqiu Jiang Xiaolin Wu School of Information Science and Engineering Shenyang Ligong University Shenyang China School of Mathematics and Statistics Liaoning University Shenyang China

ISBN: (数字)9798350368888

ISBN: (纸本)9798350368895

Currently, Brain-Computer Interface (BCI) is a research hotspot, playing a crucial role in various fields. The difficulty of research is to identify the obtained Electroencephalography (EEG) signals. There are now a large number of recognition methods, but they all have certain shortcomings. Canonical Correlation Analysis (CCA), which is highly sensitive to noise and has poor performance for frequency or phase dense stimuli. Some researchers also use machine learning for signal recognition, but it requires relatively complex feature extraction. Research results indicate that its accuracy is relatively low. In order to tackle the aforementioned challenges such as low recognition accuracy and complex feature extraction, the paper proposes a deep learning-based method for recognizing steady-state visually evoked potentials (SSVEPs). The neural network employs a residual network (ResNet)for implementation. This method utilizes residual networks to extract features from signal data and complete model training through fully connected operations. Input samples into the model to achieve adaptive feature extraction and end-to-end classification of EEG signals. The recognition method proposed in this article can fully extract signal features, achieve effective recognition of SSVEP signals under short-term visual stimuli, and have high target recognition efficiency. The experimental results indicate that compared to the aforementioned recognition methods, the method proposed in this paper exhibits improvements in both recognition accuracy and efficiency.

关键词： Training Adaptation models Visualization Accuracy Target recognition machine learning Feature extraction Brain modeling Electroencephalography Residual neural networks

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Skin Disease Diagnosis System Using machine and Deep learning Algorithms

Skin Disease Diagnosis System Using Machine and Deep Learnin...

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international conference on signal processing, Communication, Power and Embedded System (SCOPES)

作者： Ashis Kumar Ashis Kumar Sahoo Nishant Kumar Bhaul Sidhant Nayak Adyasha Rath Ganapati Panda Computer Science and Engineering C. V. Raman Global University Bhubaneswar India

ISBN: (数字)9798331506452

ISBN: (纸本)9798331506469

Automating the diagnosing process has shown promising results in recent years due to the advancements in deep learning approaches. In this work, we provide a unique skin disease diagnosis system that uses Convolutional Neural Networks (CNN) and Generative Adversarial Networks (GAN) to provide effective diagnosis. In our work, the system is trained on the HAM10000 dataset, which contains a diverse collection of skin lesion images categorized into seven classes, including melanoma, basal cell carcinoma, and others. GANs are used to address the challenges of dataset imbalance and missing data by generating realistic synthetic images that augment the existing dataset and CNNs are used for image pre-processing, image classification, etc. Utilizing these techniques, our proposed system achieves an impressive classification accuracy of 90.7%. In conclusion, using deep learning methods helps our research improve the skin disease diagnosis system and also gives medical professionals a useful tool for the accurate identification of skin diseases.

关键词： Deep learning Accuracy Generative adversarial networks Skin Data models Convolutional neural networks Medical diagnosis Lesions Medical diagnostic imaging Diseases

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Enhanced Seizure Prediction Using XGBoost and EEG Data: A machine learning Approach with increasing Model Metrics

Enhanced Seizure Prediction Using XGBoost and EEG Data: A Ma...

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2nd international conference on signal processing, Communication, Power and Embedded Systems, SCOPES 2024

作者： Panda, Sanghamitra Dash, Sushree Sasmita Pandey, Rakesh Sahoo, Swapnajit Sasmal, Annimesh Nayak, Debasish Swapnesh Kumar Centurion University of Technology and management Department of Computer Science and Engineering Odisha Bhubaneswar India Kiit University Deparment of Cse Bhubaneswar India Iimt College of Engineering Department of Ai & Ds Greater Noida201310 India School of Computer Science and Engineering Iilm University Uttar Pradesh Greater Noida India

ISBN: (纸本)9798331506452

Epilepsy is a neurological disorder marked by abnormal brain activity. Normally, brain cells (neurons) communicate with each other through electrical signals. In epilepsy, these signals become disrupted, leading to excessive and synchronized firing, resulting in seizures. But, if it can be predicted early, then it may be prevented or made to progress slowly. For prediction, machine learning has played a great role nowadays. In this study, we have used models like Random Forest, Decision Tree, Extra-trees classifier, Gradient Boost (XGBoost) has given better result for prediction. In this paper we have taken total 500 individuals EEG data with each has 4097 data points. The EEG scans provided detailed images of brain structures, allowing for precise analysis of epileptic activity using EEG data. The prediction of seizures has proven to be a formidable challenge, with existing studies failing to yield consistently accurate results. Our methodology involved a 60/40 train-test split and again 20/20 for test-validation split ensuring the model was trained on the majority of the data. By utilizing the XGBoost, we achieved an accuracy of 98%, demonstrating its effectiveness in handling complex spatial data inherent in EEG scans. Besides, we have presented roc-auc curve, f1-score, recall, support, precision and confusion matrix to show the execution of the model. This localization capability represents a significant advancement in epilepsy research, enabling more targeted treatment strategies. The researches already performed on prediction of Epilepsy disease has given poor performance of 95% classification accuracy in comparison to our model. © 2024 IEEE.

关键词： learning systems

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Decision Support System in Healthcare for Heart Disease Prediction

Decision Support System in Healthcare for Heart Disease Pred...

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international conference on signal processing, Communication, Power and Embedded System (SCOPES)

作者： Bipin Bihari Jayasingh K. Mary Sudha Rani Aruna Thotapalli Sumitra Mallick IT Department CVR College of Engineering Hyderabad India AIML Department Chaitanya Bharathi Institute of Technology Hyderabad India Department of computer science Guru Nanak Institute of Technical Campus Hyderabad India

ISBN: (数字)9798331506452

ISBN: (纸本)9798331506469

Heart conditions include heart failure, arrhythmias, and coronary artery disease are still among the leading causes of death in the world. Improving patient outcomes, lowering death rates, and efficiently allocating healthcare resources all depend on early identification and accurate prognosis of heart disorders. The goal of this research is to create a predictive model for cardiac problems using machine learning techniques to produce a Decision Support System (DSS). The system will be able to forecast an individual's chance of acquiring heart disease by examining a variety of risk factors and medical data. The research focuses on the use of a dataset that includes patient medical information, such as their demographics, medical history, and test results. In order to forecast accuracy, precision, and recall where the XGBoost algorithm performed well. In this work, we used many machine learning models to present a Heart Disease Prediction Support System because it will enable physicians to diagnose heart disease earlier and prevent its onset. Treatment of cardiac illnesses becomes easier with the system's outstanding performance and promising major breakthroughs in detection. The XGBoost algorithm outperforms all other models with outstanding accuracy of 93.24%, Precision of 94.2%, sensitivity of 93.4%, specificity of 92.9%, and F1 score of 93.8%.

关键词： Heart Decision support systems Accuracy machine learning algorithms signal processing algorithms Medical services Predictive models Prediction algorithms Medical diagnostic imaging Diseases

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Distributed Multi-kernel learning Based on Gaussian Mixture Model with Missing Data 23

Distributed Multi-kernel Learning Based on Gaussian Mixture ...

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proceedings of the 2nd international conference on signal processing, Computer Networks and Communications

作者： Sicong Chen Ying Liu College of Information Science and Electronic Engineering Zhejiang University China

ISBN: (纸本)9798400716430

Distributed classification, as an important distributed learning task, has received much attention for more than two decades. However, it is still a challenge to achieve acceptable classification performance on some real datasets that follow complex distributions. Moreover, in various practical scenarios, some data features are unavoidably missing due to environmental interference or human factors, which affect the performance of classification. Considering this, in this paper, we propose a distributed Gaussian Mixture Model-based Multi-Kernel learning (dGMM-MKL) algorithm for classification of missing data distributed over a network. In the proposed algorithm, a distributed imputation method based on Gaussian Mixture Model (GMM) is used to make imputation for the missing components. Then, a distributed multi-kernel support vector machine (SVM) is developed to achieve classification based on the imputed data. Finally, we present experiment results on some datasets. Compared with state-of-the-art algorithms, the proposed dGMM-MKL algorithm shows better classification performance and higher robustness under different missing probabilities.

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Devising a Textual Analogous Computation-Centric Approach for Profiling Exercise Levels

Devising a Textual Analogous Computation-Centric Approach fo...

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Innovations in High Speed Communication and signal processing (IHCSP), international conference on

作者： G. Indira Sudhanshu Dev Uma C Swadimath Monali Ravindra Borade G. R. Vijayshankar Pooja Sharma Department of EEE Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India School of Mangement PG Management JAIN (Deemed to be University) Bangalore Karnataka India AI & Ds Vishwakarma Institute of Technology Pune India Department of Civil Engineering Karpagam Academy of Higher Education Coimbatore India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India

ISBN: (数字)9798350368949

ISBN: (纸本)9798350368956

This paper introduces the Textual Analogous Computation-Centric Approach (TACCA) for textual data-based exercise level monitoring. TACCA uses natural language processing and machine learning to classify written exercise reports by difficulty to address concerns with smart devices and self-reported surveys. Semantic analysis and word embeddings extract textual characteristics. machine learning algorithms use these properties to categorize exercise levels in a labelled dataset. Memory, accuracy, and Area Under the Curve (AUC) compare TACCA to standard research methods. The results show that TACCA accurately measures exercise intensity better than other methods. TACCA distinguishes exercise types and reduces classification mistakes with 0.85 accuracy, 0.87 precision, 0.83 recall, and 0.92 AUC. This research's ablation study separates TACCA's key components. It shows how powerful machine learning and semantic analysis raise performance standards. These results show that TACCA's general design approach works when components interact to improve prediction accuracy and flexibility. Healthcare systems may employ TACCA due to its fast and large processor. TACCA can handle large data sets and function in various healthcare situations, making it ideal for health and fitness monitoring.

关键词： Surveys Accuracy machine learning algorithms Semantics Medical services machine learning Natural language processing Wearable devices Monitoring Standards

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Research on Dispersion Equalization Technology in Coherent Optical Communication 2

Research on Dispersion Equalization Technology in Coherent O...

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2nd international conference on Electronic Information Technology, EIT 2023

作者： Yang, Junhong Liu, Hao Wang, Jiazan Key&Core Technology Innovation Institute of the Greater Bay Area Guangzhou510535 China

ISBN: (数字)9781510666627

ISBN: (纸本)9781510666610

High-speed coherent optical communication systems can effectively solve the current problems of transmission capacity and transmission distance in the field of communication, and have become an important research direction in optical communication. The optical signals generated by lasers are composed of different frequency components, which have different transmission speeds in optical fibers, causing dispersion effects. Dispersion causes inter-code crosstalk, which has an impact on coherent demodulation and threshold judgments at the receiving end. Dispersion has a constraining effect on the performance of high-speed coherent optical communication systems, and the development of optical communication requires the study of equalization methods to solve the dispersion problem. In this paper, we examine the dispersion problems and the corresponding solutions in coherent optical communication by reviewing various domestic and foreign literature, focusing on the classical optical communication dispersion equalization technique and analyzing the phenomenon of limited dispersion equalization effect due to the constant modal value of the classical technique tapping coefficient. We study and design the dispersion equalizer CMGD-FIR based on the momentum gradient descent optimization algorithm, which exponentially weights the average optimized gradient and corrects the deviation at each update iteration. Simulation results show that this technique can quickly update the tap weights close to the minimum value and improve the convergence speed while ensuring the dispersion equalization effect. © 2023 SPIE.

关键词： Digital signal processing

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