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Automated Recognition of Wild Animal Species in Camera Trap Images Using Deep Learning Models 3

Automated Recognition of Wild Animal Species in Camera Trap ...

3rd International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2023

作者： Thangaraj, Rajasekaran Rajendar, Sivaramakrishnan Sanjith, M. Rithick Saran, K. Sasikumar, Sudev Chandhru, L. KPR Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9781665494007

Wild animal monitoring is one of the essential factors in balancing the ecosystem in protecting the animal habitat. Camera traps are used to capture the images, which play a vital role in the automatic monitoring of wildlife owing to their effectiveness in collecting wildlife data. The images captured from the camera traps are large volumes in nature, and they are inexpensive. However, the manual processing of such a large number of images is a time-consuming, expensive, and challenging task. Such a kind of process provides major hurdles to ecologists in monitoring wildlife. This paper evaluates deep learning models such as DeneseNet169, InceptionResNetV2, ResNetV2-50, and Xception for the automatic identification and recognition of individual animals in different patterns. The effectiveness of the models is measured using precision, recall, and F1 score indicators. Finally, the experimental findings show that the InceptionResNetV2 model outperforms the other models with an accuracy of 99%. © 2023 IEEE.

关键词： Cameras

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Smart Techniques for LULC Micro Class Classification Using Landsat8 Imagery

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computers, Materials & Continua 2023年第3期74卷 5545-5557页

作者： Mutiullah Jamil Hafeez ul Rehman SaleemUllah Imran Ashraf Saqib Ubaid Department of Computer Science Khwaja Fareed University of Engineering and Information TechnologyRahim Yar Khan64200Pakistan Department of Information and Communication Engineering Yeungnam UniversityGyeongsan-si38541Korea

Wheat species play important role in the price of products and wheat production *** are several mathematical models used for the estimation of the wheat crop but these models are implemented without considering the wheat species which is an important independent *** task of wheat species identification is challenging both for human experts as well as for computer vision-based *** the use of satellite remote sensing,it is possible to identify and monitor wheat species on a large scale at any stage of the crop life *** this work,nine popular wheat species are identified by using Landsat8 operational land imager(OLI)and thermal infrared sensor(TIRS)*** thousand samples of eachwheat crop species are acquired every fifteen days with a temporal resolution of ten multispectral bands(band two to band eleven).This study employs random forest(RF),artificial neural network,support vector machine,Naive Bayes,and logistic regression for nine types of wheat *** addition,deep neural networks are also *** results indicate that RF shows the best performance of 91%accuracy while DNN obtains a 90.2%*** suggest that remotely sensed data can be used in wheat type estimation and to improve the performance of the mathematical models.

关键词： Mathematical model wheat crop estimation landsat8 remote sensing machine learning random forest deep neural network

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Detection of Mental Stress using EEG signals - Alpha, Beta, Theta, and Gamma Bands 5

Detection of Mental Stress using EEG signals - Alpha, Beta, ...

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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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