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5th IEEE International Conference for Emerging technology, INCET 2024

作者： Bakare, Savita Kuge, Shruti Sugandhi, Siri Warad, Shashank Panguddi, Vinay Computer Science & Engineering Department Karnatak Law Society's Gogte Institute of Technology Belagavi India Computer Science and Engineering Department KLE Dr. M. S. Sheshgiri College of Engineering and Technology Belagavi India

ISBN: (纸本)9798350361155

Mental stress, or psychological stress, arises when individuals perceive emotional or psychological strain beyond their coping abilities. Various factors such as personal relationships, work pressure, financial problems, or major life changes, impact both emotional and physical well-being. Electroencephalography (EEG) signals serve as insightful indicators of brain activity, resembling tiny electrical whispers exchanged between brain cells. These brain cells are characterized by EEG patterns such as alpha, beta, theta, and gamma frequency band waves, and offer valuable insights such as frequency analysis, categorization of valence and arousal, and machine learning techniques, into distinct mental states and neural activity. The exploratory data analytics (EDA) techniques using ML methods (KNN, SVM, and RF) on EEG dataset is being performed to analyze mental stress detection. Furthermore, Mean Absolute Error (MAE) scores of the KNN, SVM, and RF models are compared based on the number of folds, which shows that KNN has the lowest MAE of 0.62, compared to SVM scores of 0.83 and RF score of 1.47 respectively. As a result, the KNN model is selected for stress detection due to its superior performance in reducing MAE in both iterations and training data sizes. Furthermore, the study concisely also reviews an existing literature on mental stress detection using EEG signals, highlighting prevalent challenges and research gaps. © 2024 IEEE.

关键词： Electroencephalography

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LoRaWAN: A Boon for the Remote Region IoT Applications

LoRaWAN: A Boon for the Remote Region IoT Applications

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2023 IEEE International Conference on Modelling, Simulation and Intelligent Computing, MoSICom 2023

作者： Sharma, Anamika Muhuri, Samya Thapar Institute of Engineering and Technology Computer Science and Engineering Department Patiala India

ISBN: (纸本)9798350393415

IoT has paved the way for enhancing and transforming the quality of life through remote automation and monitoring via various short-range, long-range, and cellular network connectivity technologies. These technologies are very efficient in urban regions but struggle in remote geographically challenging environments hence stopping the implementation of IoT devices in such regions. In such a scenario, LoRaWAN emerges as a revolutionary technology, providing efficient connectivity and reliable communication for remote region IoT application devices. This paper aims to provide valuable insights into the network architecture capabilities of LoRaWAN for remote region IoT applications. © 2023 IEEE.

关键词： Network architecture

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Predicting Brain Tumor Region from MRI FLAIR Images using Ensemble Method 26

Predicting Brain Tumor Region from MRI FLAIR Images using En...

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26th International Conference on computer and Information technology, ICCIT 2023

作者： Chakroborty, Protik Mishu, Sadia Zaman Al Mamun, Md. Hossain, Md. Ali Yakin Srizon, Azmain Rajshahi University of Engineering and Technology Department of Computer Science & Engineering Rajshahi Bangladesh

ISBN: (纸本)9798350359015

The conventional approach to tumor detection primarily hinges on the expert evaluation of physicians, a process susceptible to time constraints and inherent inaccuracies, thereby posing imminent threats to patients' well-being. However, the integration of the Fluid Attenuated Inversion Recovery (FLAIR) modality offers a transformative avenue. It empowers medical practitioners to gain profound insights into the extent of tumor infiltration, representing a significant advancement in diagnostic capabilities and potential treatment strategies. This study delves into the realm of brain tumor assessment and prediction using various deep learning CNN architectures like ResNet50 U-Net, EfficientNetB3 U-Net, and InceptionV3 U-Net. The U-Net architecture, in particular, stands out for its adaptability, allowing the incorporation of several neural network pre-trained models within a single framework. The investigation of ensemble modeling resulted from a focus on improving the effectiveness of the segmentation approach. This method makes use of the previously described models' combined advantages, enabled using an average of softmax. This cooperative endeavor resulted in the Ensemble model's development, which surpasses its individual analogs in terms of performance measures. Combining multiple models with their unique strengths and weaknesses enhances overall ensemble robustness. Diverse segmentation models employing different features benefit from ensembling, capturing a wider range of patterns for improved performance. In this context, the ensemble model emerges as the most effective in terms of segmentation performance, achieving a Dice Similarity Coefficient of 93.29% and an Intersection of Union score of 87.65% on the datasets. These metrics quantify the overlap between actual and predicted regions, showcasing the superiority of the ensemble model which outperforms the state-of-the-art methods. © 2023 IEEE.

关键词： Brain MRI convolutional neural networks (CNN) Ensemble Segmentation Tumor U-Net

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A Trust-based Framework for the Detection and Avoidance of Blackhole Nodes in Opportunistic Networks 9

A Trust-based Framework for the Detection and Avoidance of B...

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9th International Conference on Signal Processing and Communication, ICSC 2023

作者： Kundu, Protyasha Saha, Swarnadeep Kundu, Anindita Barai, Smritikona Department of Computer Science and Engineering Heritage Institute of Technology Kolkata India Department of Software Systems School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350383201

An Opportunistic Network (OppNet), as opposed to a ubiquitous centralized network, relies on sporadic and opportunistic encounters between nodes to facilitate communication. The uncertainty about the node's nature presents challenges when nodes with malicious intents are present in the network. One category of malicious nodes includes blackhole nodes, which receive messages but drop them intentionally. In this paper, we introduce a novel trust function designed to detect blackhole nodes within the network. Our function has been widely tested and simulated to validate and increase the efficacy of transmission of messages in the network. We have performed a comparative analysis with various protocols (PROPHET, Epidemic and Spray And Wait) on several parameters such as Delivery Ratio, Overhead Ratio and Number of Messages dropped. The results demonstrate that our trust function is able to correctly detect and avoid blackhole nodes. We observed significant improvement in delivery ratio in Epidemic Routing (47%), Prophet (41%), and Spray And Wait Routing (17%) protocols when adjusting blackhole nodes while keeping the total network nodes constant. © 2023 IEEE.

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Machine Learning and Deep Learning Based Time Series Prediction and Forecasting of Ten Nations’ COVID-19 Pandemic

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SN computer science 2023年第1期4卷 91页

作者： Kumar, Yogesh Koul, Apeksha Kaur, Sukhpreet Hu, Yu-Chen Department of Computer Science and Engineering School of Technology Pandit Deendayal Energy University Gujarat Gandhinagar India Department of Computer Engineering Punjabi University Patiala India Department of Computer Science and Engineering Chandigarh Engineering College Mohali Landran India Department of Computer Science and Information Management Providence University Taichung Taiwan

In the paper, the authors investigated and predicted the future environmental circumstances of a COVID-19 to minimize its effects using artificial intelligence techniques. The experimental investigation of COVID-19 instances has been performed in ten countries, including India, the United States, Russia, Argentina, Brazil, Colombia, Italy, Turkey, Germany, and France using machine learning, deep learning, and time series models. The confirmed, deceased, and recovered datasets from January 22, 2020, to May 29, 2021, of Novel COVID-19 cases were considered from the Kaggle COVID dataset repository. The country-wise Exploratory Data Analysis visually represents the active, recovered, closed, and death cases from March 2020 to May 2021. The data are pre-processed and scaled using a MinMax scaler to extract and normalize the features to obtain an accurate prediction rate. The proposed methodology employs Random Forest Regressor, Decision Tree Regressor, K Nearest Regressor, Lasso Regression, Linear Regression, Bayesian Regression, Theilsen Regression, Kernel Ridge Regressor, RANSAC Regressor, XG Boost, Elastic Net Regressor, Facebook Prophet Model, Holt Model, Stacked Long Short-Term Memory, and Stacked Gated Recurrent Units to predict active COVID-19 confirmed, death, and recovered cases. Out of different machine learning, deep learning, and time series models, Random Forest Regressor, Facebook Prophet, and Stacked LSTM outperformed to predict the best results for COVID-19 instances with the lowest root-mean-square and highest R² score values. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： COVID-19 Facebook Prophet Holt model Prediction Random forest regressor RANSAC regressor Stacked gated recurrent units Stacked long short-term memory XG Boost

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Nature-inspired whale optimization technique for efficient information exchange in vehicular networks 12

Nature-inspired whale optimization technique for efficient i...

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12th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2023

作者： Tandon, Righa Verma, Ajay Gupta, P.K. Chitkara University Institute of Engineering and Technology Chitkara University Department of Computer Science and Engineering Punjab India Jaypee University of Information Technology Department of Computer Science and Engineering Solan India

ISBN: (纸本)9781665462617

Vehicle Ad-Hoc Networks (VANETs) offer roadside connectivity to facilitate user information exchange while driving. It connects other vehicles and hierarchical infrastructure components for applications including real-time traffic and vehicle control. Concord information sharing is needed for application answers because of the increase in vehicle and information density. In this paper, a secure information exchange model has been proposed using nature-inspired whale optimisation algorithm as a way to provide faster data transmission speeds and security. The proposed model makes use of a social behavior model of humpback whales to explore and exploit the search space for finding the optimal solution. Successful information selection and traffic information retrieval depend on the prior and present judgements. This keeps extra data from clogging up the traffic and vehicle management application while the driver is using it. © 2023 IEEE.

关键词： Vehicular ad hoc networks

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Exploring Deep Learning Architectures for Enhanced Cyber Threat Detection: A Survey

Exploring Deep Learning Architectures for Enhanced Cyber Thr...

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2024 International Conference on science, engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Oyinloye, Toluwase Sunday Arowolo, Micheal Olaolu Ogundoku, Roseline Oluwaseun Prasad, Rajesh African University of Science and Technology Department of Computer Science Abuja Nigeria University of Missouri Department of Electrical Engineering and Computer Science ColumbiaMO65201 United States Landmark University Department of Computer Science Omu-Aran Nigeria

ISBN: (纸本)9798350358155

As new organizational innovations and ideal model implementations take place;current methods of interruption detection and protection are becoming outdated. Due to the next flexible innovation, which will transmit incredibly rapid data volumes, new problems will arise for network safety frameworks. Apps on mobile devices are gradually displacing more conventional methods of managing data and information that depend on the web. As a result, there are several threats that could compromise or steal these data and information. A complete system shutdown is possible in the event of one of these attacks. This study introduces a deep learning-based cyber-attack detection system for wireless sensor networks (WSN). This method considers both the actions of the WSN nodes, and the data transmissions made possible by the MQTT protocol. By switching from a traditional machine learning model to a deep learning one, this strategy achieves much better detection accuracy. Using deep learning models that combine real information gathered from network streams, cyberthreats can be identified as digital risks in the network. © 2024 IEEE.

关键词： Cyber attacks

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Integrating AI, IoT, and Drones for Sustainable Apple Orchard Monitoring in Society 5.0 2nd

Integrating AI, IoT, and Drones for Sustainable Apple Orch...

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2nd International Conference on Applied Algorithms, ICAA 2025

作者： Datta, Ankana Paul, Sukalpa Pal, Anidipta Biswas, Sounav Bag, Anil Kumar Sengupta, Diganta Department of Computer Science and Engineering - AIML Heritage Institute of Technology Chowbaga Road Kolkata700107 India Department of Applied Electronics and Instrumentation Engineering Heritage Institute of Technology Chowbaga Road Kolkata700107 India Department of Computer Science and Engineering Heritage Institute of Technology Chowbaga Road Kolkata700107 India

ISBN: (纸本)9783031845420

Sustainable smart agriculture forms one of the focal points in Society 5.0. We propose an AI-IOT enabled framework capable of processing multi-modal data for apple orchard monitoring. Real-time data acquisition is done from ground sensors as well as drone fitted cameras. The ground sensors monitor soil moisture, pH levels, nutrient composition (nitrogen, phosphorous, and potassium), ambient temperature, and humidity, while ESP32-CAMs fitted on drones capture images of apples, leaves, and trees. IOT enabled Unmanned Aerial Vehicle (UAV) as well as the ground sensor framework feed the data to the cloud. YOLOv8 and ResNet152 have been used to process the images for classifying the health of the apple plants. Machine learning models predict the farm yield using the ground sensor data reflecting the soil conditions. Our framework fares better than prior art in terms of accuracy. Although existing literature exhibits processing of soil data for prediction of health and yield, our study takes into consideration three further nutrient components - nitrogen, phosphorous, and potassium. Our study shows accuracy of 98.19% (apple counting) 53.66% (apple classification) 96.00% (leaf classification). To the entirety of our understanding, this study is the first to use multi-modal data inclusive of extended soil nutrients. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Orchards

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Deep learning for diabetic retinopathy detection and classification Using Long Short-Term Memory (LSTM) 4

Deep learning for diabetic retinopathy detection and classif...

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4th IEEE International Conference on Data engineering and Communication Systems, ICDECS 2024

作者： Prema Kumar, M. Srinivas Goud, D. Sankar, S. Sharath Kumar, S. Department of Electronics and Communication Engineering Andhra Pradesh Bhimavaram India Vignana Bharathi Institute of Technology Department of Computer Science and Engineering Andhra Pradesh India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Rmd Engineering College Kavaraipettai Department of Computer Science and Engineering Tiruvallur India

ISBN: (纸本)9798350393354

Macular edema, diabetic retinopathy, and other apparent microvascular abnormalities in the human retina are common consequences of diabetes. The primary cause of blindness in the past ten years, diabetic retinopathy (DR), is the subject of this study. Preserving blood glucose control and preventing certain diabetic reactions require immediate or early therapy. Due of its intricacy and depth, DR is difficult to diagnose by laborious manual diagnostic procedures. This paper uses a novel methodology. Instead of manually creating features, we used the Long Short-Term Memory (LSTM) model to extract features for the Region-based Convolutional Neural Network (R-CNN) system. It is an R-CNN-LSTM model with two parts. The proposed R-CNN-LSTM system adds certain ground truth labels to training samples, which enables it to first identify the most informative patches and images before learning basic to complicated retinal properties. Using two independent public datasets, the suggested method investigates the retina vasculature and hard-exudate symptoms of diabetic retinopathy. The recommended illumination normalisation methodology improved the deep learning model and excelled other cutting-edge techniques in terms of accuracy, recall, and precision, according to extensive experimental results. © 2024 IEEE.

关键词： Long short-term memory

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Silver nano-particles' size prediction using deep learning model with green method

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Materials Today: Proceedings 2022年 69卷 1193-1199页

作者： Mathumathi, M. Vetriselvi, T. Matheswaran, P. Nandhini, R. Selvi, A. Department of Computer Science and Engineering K.Ramakrishnan College of Technology Trichy India Department of Computer Science and Engineering K.Ramakrishnan College of Engineering Trichy India Department of Computer Science and Engineering M.Kumarasamy College of Engineering Karur India

When it comes to casting technologies and other production processes such as welding, DL models minimise the need for costly experimental inquiry. One of the most important criteria in nanotechnology is the measurement of particle size. They've received a lot of interest for their chemical, physical, and clinical uses because of their outstanding qualities. By adopting a green chemical reduction process, the nano-silver crystals are generated in the biopolymer interface without any agglomeration. Nano-metal production relies on precise control of particle size, and this approach provides that. For the prediction size of Ag-NPs via the green approach, a hybrid ideal of Long-Short Term Memory (LSTM) and Convolutional Neural Network (CNN) and model is presented in this research. Temperature, starch stabiliser, and AgNO3 concentration are among the experimental variables fed into the CNN-LSTM model. The size of a nano particle is the output of the CNN-LSTM model. Making three subsets of the experimental data for training, validation, and testing was one of the initial steps in creating an adapting model. The silver nanoparticles were identified using X-ray diffraction and transmission electron microscopy. Ag-NPs' crystallographic airplanes were found to be face-centered cubic, according to XRD analysis (fcc).When comparing with other existing classifiers, DNN achieved high performance on precision, i.e. 89.87%, and this is due to the presence of simple structure. The CNN and auto encoder achieved nearly 82% and 80% of accuracy, where LSTM achieved 77.25% of accuracy and proposed CNN-LSTM achieved 83.50% of recall. © 2022

关键词： Long short-term memory

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