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Screening of Citrus Diseases Using Deep learning Embedders and machine learning Techniques

Screening of Citrus Diseases Using Deep Learning Embedders a...

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international Symposium on Artificial Intelligence and signal processing (AISP)

作者： Yagnesh Challagundla Lohitha Rani Chintalapati Trilok Sai Charan Tunuguntla Amit Sur Bappadittya Roy Eugenia R. Zhuo School of Computer Science & Engineering VIT-AP University Amaravati Andhra Pradesh India Department of IT HIT West Bengal India VIT-AP University School of Electronics Amaravati Andhra Pradesh India College of Information and Computing Sciences University of Santo Tomas Manila Philippines

A tropical fruit with great economic significance, citrus is cultivated and farmed in almost 150 different nations. A total of about 157.98 million tons of citrus fruit are produced worldwide. Citrus plant diseases have a major negative impact on the citrus fruit market as well as production in terms of quantity as well as quality. Attention must be paid to citrus fruits and leaves in order to promptly diagnose and treat problems; failing to do so results in severe financial loss. Citrus plant diseases cause a yearly loss of about 50% of citrus fruits at citrus farming operations. In this study, to produce the vector representation of the images, we used three deep learning image embedders namely Inception V3, VGG16, and Squeeze net. We used five machine learning models, including Random Forest, KNN, Gradient Boosting, SGD, and Neural Network with 5-fold Cross Validation, to predict citrus diseases. Using the considered dataset, the classification performance of each independently tuned ML model across all of its ensembles has been obtained. The neural network with Inception V3 as image embedder has obtained maximum average accuracy of 96.6%, 96.6% recall, 96.5% precision, and 96.5% F1 Score. Finally, our prediction model offers a reliable technique for accurate detection of citrus diseases to assist farmers.

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Pre Cardiac Failure Detection using different supervised machine learning Methods with CNN

Pre Cardiac Failure Detection using different supervised Mac...

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international Symposium on Artificial Intelligence and signal processing (AISP)

作者： Amit Gupta Meeravali Shaikh G. Jithender Reddy Arun Singh Chouhan Department of Cyber Security School of Engineering Malla Reddy University Telangana Department of CSE School of Engineering Malla Reddy University Telangana GITAM University Visakhapatnam

For aspect, a convolution Neural Network (CNN) for cardiac arrest would help establish a better output. Whilst a cardiac arrest occurs, the limited interventions available to shop-affected person’s lives are related to badly affected outcomes. Therefore, the best way to enhance affected person results and lower related healthcare prices might be to prevent cardiac arrest. This remark highlights the significance of prediction models that continuously perceive excessive-risk individuals and assist healthcare providers in providing centered care to the appropriate patient at the appropriate time for the early identification of cardiac arrest patients who may need immediate medical attention. Moreover, the significant aspect lies in the proper symmetry formation or can, say, the better scenario to compute or make a clear recognition of the image having a clear resolution of the image. Starting with a small model to determine things as simple as CNN (Convolution Neural Network). In this proposed paper, we are used in seven different supervised machine learning method for simulation of ECG data set results in different time interval with different duration and amplitude. In the CNN with a partially connected convolution layer, the image has a single neuron connected to a small layer region. The CNN (convolution Neural Network) image classification takes the input, processes it and classification is done on the dimensions organized in three dimensions width, height, and depth.

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A Survey on Intelligent Reconfigurable/Reflecting Surface in 6G Communication

A Survey on Intelligent Reconfigurable/Reflecting Surface in...

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Device Intelligence, Computing and Communication Technologies, (DICCT), international conference on

作者： Diya Singh Ayush Kumar Rohan Patil Saurav Gupta School of Electronics Engineering Vellore Institute of Technology Chennai India

ISBN: (数字)9798331543358

ISBN: (纸本)9798331543365

Intelligent Reflecting/Re-configurable surfaces (IRSs) aligns and regulates the wireless network environment and efficiently controls the wavefront (Phase, Amplitude, Frequency, Polarization). Given the recent emphasis on establishing a secure, cost-efficient 6G network, IRS can potentially be a key factor in establishing 6G wireless network from urban to remote areas. It has emerged to be a highly promising technology. The survey paper classifies the recent research investigations on IRS into various different yet important categories. For IRS aided vehicular communications, the paper presents a detailed analysis on the development, impact and effects on V2X, V2V and UAV communications. The paper also includes a detailed analysis on machine learning models for IRS based wireless communication towards 6G, which highlights the complex problems in wireless communication such as beamforming. The paper covers major categories related to deployment, future works and challenges etc.

关键词： 6G mobile communication Surveys Array signal processing Surface waves Wireless networks Line-of-sight propagation machine learning Autonomous aerial vehicles Vehicle dynamics Vehicle-to-everything

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Natural Language processing to Detect Fake News in Arabic: A Survey Paper

Natural Language Processing to Detect Fake News in Arabic: A...

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Computing and Information Technology (ICCIT-1441), 2020 international conference on

作者： Sharifah Alshehri Nourah Alessa Maryam Alhawiti Amal Majdoa Resan Aljohani Nojood Aljehane Mohammed Alotaibi Faculty of Computers and Information Technology University of Tabuk Tabuk Saudi Arabia

Due to recent progress in the World Wide Web and the expansion of social networking platforms, the dissemination of counterfeit information has gained momentum, leading to the proliferation of misinformation and misguided convictions among individuals and society in general. To address this escalating concern, artificial intelligence and machine learning techniques are being employed to construct automated detection models. Deep learning (DL) models exhibit substantial potential in managing intricate tasks related to natural language processing, thereby constituting a valuable tool in the context of detecting counterfeit news. Our objective is to formulate an Adam optimization-driven deep learning approach, termed the ADL-FNDC model, with the purpose of proficiently distinguishing fabricated news from genuine news sources. The ADL-FNDC model underwent preprocessing and word embedding using the GloVe algorithm. We then employed a deep belief network approach to detect and classify fake news.

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Advancing Brain-Computer Interfaces: A Deep learning Approach for Enhanced Neural signal processing 3

Advancing Brain-Computer Interfaces: A Deep Learning Approac...

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3rd IEEE international conference on Automation and Computation, AUTOCOM 2025

作者： Dharmendra, Kaku Riya Verma, Deepak Kumar Vats, Satvik Marwadi University Department of Computer Engineering Rajkot India Madan Mohan Malaviya University of Technology Department of Computer Science and Engineering U.P. Gorakhpur India

ISBN: (纸本)9798331542375

Brain-to-computer interfaces (BCIs) are a developing technology that has led to a straight link among the human brain and various systems. The complexity and lack of predictability of brain signals make the development of a trustworthy, accurate BCI system. In this study, we look into how advanced deep learning approaches like Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Graph Isomorphism Networks (GIN), and Convolutional Neural Network (CNN) can be used to improve neural signal processing performance in BCI. LSTM and GRU are good at discovering the temporally related EEG sequences, while CNNs extract the spatial feature of raw EEG data very well. We model and analyze graph-structured data from functional brain networks using GIN, revealing insights into spatial dynamics and inter-channel connections. Both graph-based and spatiotemporal features are integrated as proposed in the framework, and a deep learning architecture is constructed for signal decoding and classification that allows for a higher signal decoding and classification accuracy. Our experimental results on publicly available EEG datasets demonstrate that this approach outperforms standalone deep learning models and traditional machine learning approaches. It designs its solutions to attain higher accuracy, robustness, and the ability to generalize across various BCI tasks, including cognitive state recognition and motor imagery classification. The result indicates that our model LSTM is 96.92%, CNN is 95.95%, GRU is 95.40%, and GIN is 88.68%. Finally, this study demonstrates how advanced deep learning techniques can address the current limitations of BCI systems, opening the door for more reliable and successful real-world applications. © 2025 IEEE.

关键词： Convolutional neural networks

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machine learning based human recognition via robust Features from audio signals

Machine Learning based human recognition via robust Features...

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Artificial Intelligence (ICAI), international conference on

作者： Umar Sadique Muhammad Suleman Khan Shahzad Anwar Mehran Ahmad AI in Healthcare Lab National Centre of AI University of Engineering and Technology Peshawar Pakistan Department of Electrical Engineering University of Engineering and Technology Peshawar Pakistan Department of Mechatronics Engineering University of Engineering and Technology Peshawar Pakistan

Biometric verification techniques are commissioned throughout the world in different applications. Human voice recognition is one of the biometric techniques. This technique consists of identifying a human from their voice characteristic. This popular and beneficial biometric technique could be employed for identity human, security purposes, and many different applications. Human audio signal recognition consists of two phases i.e., Features Extraction and Classification. The proposed work consists of extracting features through the Mel Frequency Cepstral-Coefficient (MFCC) from the human audio signal, selecting robust features through Principal Component Analysis PCA, and classifying the selected features by comparing seven machine learning and proposed deep learning algorithms. Finally, compare the performance of different algorithms with different percentages of selected features to evaluate the acceptance rate of the correlated features. Support Vector machine SVM shows the best performance with an Accuracy of 99.27% with F1-score and ROC values of 1.00. In the comparison with other methods, the Random Forest and CNN-ANN are 2 nd top robust models with an accuracy of 98.7%. Some of the algorithm's accuracy decreased with fewer features, including Naïve Bayes accuracy suddenly decreases to 60% on 20% of total features. The experiment concludes the acceptance rate of correlated features in different ML and DL algorithms are different in speech processing data.

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Federated learning Based User Scheduling for Real-Time Multimedia Tasks in Edge Devices 1

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3rd EAI international conference on Edge Computing and IoT, EAI ICECI 2022

作者： Wen, Wenkan Liu, Yiwen Gao, Yanxia Zhu, Zhirong Shi, Yuanquan Peng, Xiaoning School of Computer and Artificial Intelligence Huaihua University Huaihua418000 China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Hunan Huaihua418000 China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Hunan Huaihua418000 China

ISBN: (数字)9783031289903

ISBN: (纸本)9783031289897

Edge networks are highly volatile and the quality of device communication and computational resources change not only over time but also according to the movement of users. Current federation learning suffers from poor device network state and failure of devices to upload models in a timely manner. To address these problems, an intelligent scheduling mechanism that uses the predicted device state based on device information to select the appropriate device for federated learning is proposed in this paper. By focusing on information such as communication quality, computational resources, and location information, the information of edge devices is collected to analyze and predict the device network and computing resources to further analyze the state of devices in depth. Experiments are conducted on real datasets, and the experimental results show that the proposed scheduling method can make the global model fit faster than without the algorithm, which significantly improves the training efficiency of federated learning. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： machine learning

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Tibetan medical named entity recognition study for Tibetan clinical electronic medical records 3

Tibetan medical named entity recognition study for Tibetan c...

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3rd international conference on Computer Science and Communication Technology, ICCSCT 2022

作者： Cuo, Zhuoma Jia, Cairang Duanzhu, Sangjie Zhuoma, Yangmao Zhaxi, Zhuoma The State Key Laboratory of Tibetan Intelligent Information Processing and Application Qinghai Xining810008 China

ISBN: (纸本)9781510661240

In current years, there are many open corpus and research results for Chinese clinical natural language processing (for short NLP) of Biomedical and Traditional Chinese Medicine. However, the research in this area for Traditional Tibetan Medicine lags behind. We know that Traditional Tibetan Medicine has its own unique set of theory system in treating and saving people. Therefore, it is imperative to speed up the research on the Tibetan Clinical Natural Language processing. Medical named entity recognition is an important subtask of Clinical Natural Language processing. So Tibetan medical named entity recognition is an urgent and basic research work for Traditional Tibetan Medicine. Due to the scarcity of labeled datasets, the Medical Named Entity Recognition task of Traditional Tibetan Medicine clinical text is still an unvisited researching area. In this work, we firstly manually construct a labeled dataset for this task and then explore this area with deep learning approaches by designing a Tibetan Lattice-LSTM-CRF neural network architecture. To further improve the model performance, we also incorporate both syllable and word level pre-trained representation. The final empirical results show that the proposed models could produce accuracy rate, recall rate and F1 values of 91.89%, 93.15% and 92.52%, respectively on our test set, which shows the validity of the model in the paper. © 2022 SPIE.

关键词： Natural language processing systems

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Diabetes Prediction Using Nature-Inspired Optimization Algorithms

Diabetes Prediction Using Nature-Inspired Optimization Algor...

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international Symposium on Artificial Intelligence and signal processing (AISP)

作者： J. Vijaya Divyanshu Tirkey Priyank Singh Aditya Goel Department of DSAI International Institute of Information Technology Naya Raipur chhattisgarh

Diabetes is a common genetic disease across the world. It is a chronic disease spurred by either production of insufficient production of insulin by the pancreas or less insulin use by the body. Insulin maintains the blood sugar level of the body. Uncontrolled diabetes can cause hypertension, also known as high blood glucose. Hypertension in the long run can be a cause of heart disease. Therefore, the prediction of diabetes is a major problem in the health sector. Various machine learning methods are already in use for the prediction of diabetes. Traditional methods were first used to address diabetes forecasting, but they take a while to deploy. Therefore, future research has adopted the use of various optimization strategies to solve the issue of diabetes forecasting as well as to boost system selectivity and operating speed. These methods have all proven to be successful in solving different optimization issues and also improve the model significantly. This work proposes a Logistic Regression model by the use of nature-inspired algorithms. We proposed a methodology which uses seven different optimization algorithms instead of gradient descent for linear regression. We implemented a linear regression algorithm from scratch and then used it with different optimization algorithms which improve the overall performance.

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Internet of Things: A Survey on Fused machine learning-Based Intrusion Detection Approaches 1

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3rd international conference on machine Intelligence and signal processing, MISP 2021

作者： Gupta, Priyanka Yadav, Lokesh Tomar, Deepak Singh Department of Computer Science & Engineering Maulana Azad National Institute of Technology Bhopal India

ISBN: (数字)9789811908408

ISBN: (纸本)9789811908392

The Internet of Things (IoT) connects billions of interconnected devices that can exchange information with each other with minimal user intervention. The goal of IoT is to become accessible to anyone, anytime, and anywhere. IoT has engaged in multiple fields, including education, health care, businesses, and smart home. Security and privacy issues have been significant obstacles to the widespread adoption of IoT. IoT devices cannot be entirely secure from threats;detecting attacks in real-time is essential for securing devices. In the real-time communication domain and especially in IoT, security and protection are the major issues. The resource-constrained nature of IoT devices makes traditional security techniques difficult. In this paper, the research work carried out in IoT intrusion detection system is presented. The deep learning methods are explored to provide an effective security solution for IoT intrusion detection systems. Then, the advantages and disadvantages of the methodology are discussed. Further, the open issues for future trends are provided. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Intrusion detection

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