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Systematic Analysis of Effective Segmentation and Classification for Land Use Land Cover in Hyperspectral Image using Deep Learning Methods: A Review of the State of the Art : viewing Deep Learning Techniques for Land Use and Cover in Hyperspectral Images 20

Systematic Analysis of Effective Segmentation and Classifica...

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20th CSI International Symposium on Artificial Intelligence and Signal Processing, AISP 2024

作者： Yele, Vijaykumar P. Sedamkar, R.R. Alegavi, Sujata Thakur College of Engineering and Technology Department of Electronics & Telecommunication Engineering Maharashtra Mumbai400101 India Thakur College of Engineering and Technology Department of Computer Engineering Maharashtra Mumbai400101 India Thakur College of Engineering and Technology Department of Internet of Things Maharashtra Mumbai400101 India

ISBN: (纸本)9798350383942

This review paper delves into the intricate realm of segmenting and classifying Hyperspectral Images (HSI), complex visuals spanning numerous electromagnetic spectrum bands. HSI, garnering significant research attention, holds pivotal roles in geospatial applications, notably Land Use/Land Cover (LULC) mapping, demanding precise object identification. Addressing challenges posed by imbalanced data and limited labelled examples, the survey scrutinizes how researchers navigate HSI segmentation and classification. Briefly touching upon the foundational background of these techniques, the study navigates through diverse processing methods - thresholding, clustering, watershed, Deep Learning (DL) and others. Systematically exploring literature trends, DL advancements, attention mechanisms, data types, achieved accuracies and existing weaknesses, it strives to guide future research. Critically evaluating current knowledge, the paper illuminates' gaps in HSI segmentation and classification, culminating in discussions on pertinent issues and prospective projects in this domain. Ultimately, this work aims to propel advancements by addressing knowledge lacunae and charting potential pathways for upcoming research initiatives. © 2024 IEEE.

关键词： Image segmentation

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User Profile Based Movie Recommender System Using Machine Learning 2

User Profile Based Movie Recommender System Using Machine Le...

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2nd International Conference on Recent Trends in Microelectronics, Automation, Computing, and Communications Systems, ICMACC 2024

作者： Vemula, Punna Rao Jannu Soloman, Shyam Sunder Rao, Y. Chalapathi Baydeti, Nagaraju National Institute of Technology Nagaland Department of Computer Science and Engineering Nagaland Chumukedima797103 India VNRVJIET Department of Electronics and Communication Engineering Hyderabad500090 India

ISBN: (纸本)9798350366570

With the abundance of information online, users often struggle to make informed decisions. Recommendation systems (RMS) are crucial for helping users find relevant items among vast choices thereby using Content-Based Filtering and Collaborative Filtering algorithms. These systems are widely used across e-commerce, music apps, and streaming platforms like Netflix and YouTube to enhance user experience and drive business goals. Despite their benefits, RMS face some hazards such as cold start problems and scalability issues, which can impact the quality of recommendations. The proposed method uses the MovieLens-100k dataset, specifically the movies and ratings files, to recommend movies based on Content-Based Filtering. By preprocessing the data and calculating TF-IDF vectors and calculating the similarity for movie genres, the system builds user profiles and recommends movies with high similarity scores. Additionally, Single Value Decomposition (SVD) is used to address issues related to sparsity and scalability in collaborative filtering by reducing dimensionality. © 2024 IEEE.

关键词： Collaborative filtering

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Dual Ring Star 16-QAM Constellation Partitioning Based SCMA Codebook 5th

Dual Ring Star 16-QAM Constellation Partitioning Based SCMA ...

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5th International Conference on Computing, Communications, and Cyber Security, IC4S 2023

作者： Press, Randall A. Balyan, Vipin Babalola, Oluwaseyi P. Department of Electrical Electronics and Computer Engineering Cape Peninsula University of Technology Bellville Cape Town7535 South Africa

ISBN: (纸本)9789819725496

The following paper presents a dual ring star 16-QAM modulation scheme. SCMA codebook is designed using the Dual Ring Star 16-QAM (DRS16) constellation. Optimisation of the rr is executed by calculating the RR that ensures equidistance between neighbouring constellation points. When compared with the amplitude shift keying (ASK) modulation, 16-QAM can reduce the symbols rate by four times that required by ASK modulation. The importance of ring ratios as the transition of amplitude levels of Star QAM constellation patterns critically affects the error detection. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Stars

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Realtime Object Distance Measurement Using Stereo Vision Image Processing 2nd

Realtime Object Distance Measurement Using Stereo Vision Ima...

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2nd International Conference on Big Data, Machine Learning, and Applications, BigDML 2021

作者： Arunakumari, B.N. Shashidhar, R. Naziya Farheen, H.S. Roopa, M. Department of Computer Science and Engineering BMS Institute of Technology and Management Karnataka Bengaluru560064 India Department of Electronics and Communication Engineering JSS Science and Technology University Karnataka Mysuru570006 India Department of Electronics and Communication Engineering Navkis College of Engineering Hassan Karnataka Hassan573217 India Department of Electronics and Communication Engineering Dayananda Sagar College of Engineering Bengaluru560078 India

ISBN: (纸本)9789819934805

In recent years, great progress has been made on 2D and 3D image understanding tasks, such as object detection and instance segmentation. The recent trends in technology driverless cars are making a difference in daily life. The basic principle in these driverless cars is object detection and localization using multiple video cameras and LIDAR and it is one of the current trends in research and development, so attempts to achieve the same on small scale using the available resources. In the proposed method, firstly the stereo images are captured in a dual-lens camera, and secondly, converting the RGB image into a grayscale image. The third step is to apply a global threshold to separate the background, to get the same size of the image using morphological operation. Blob detection is used to detect the points and regions in the image. The fourth step is to detect the object distance and size measurement using the pinhole camera formula. Further, in the proposed work, an effort is made to determine the linear space between the camera and the object from the pictures taken from the camera. Typically, stereo images are used for computation. Binocular stereopsis, or stereo vision, is the capability to derive information about how far left the objects are, grounded uniquely on the comparative places of the object in the two eyes. It depends on both sensory and motor capabilities, using the similar principle the human brain employs, taking two images of the same object taken from two different linearly separated distances. The frame rate of the system can go a maximum of up to 15 frames per second. 15 frames per second can be considered as acceptable for most autonomous systems, and it will work in realtime. Effective convolutional matching technique between embeddings are used for localization that leads LIDAR to increase centimeter level accuracy by about 97%. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Pinhole cameras

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Boosting Reweighting Convolutional Neural Network for Microscopic Images to Identify and Classify Malaria Parasites 1

Boosting Reweighting Convolutional Neural Network for Micros...

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1st International Conference on Software, Systems and Information technology, SSITCON 2024

作者： Challaraj Emmanuel, E.S. Husain, Saif O. Raju, K. Butchi Ravi, G. Venkatasubramanian, R. Department of Life Sciences Bengaluru India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Gokaraju Rangaraju Institute of Engineering and Technology Department of Computer Science and Engineering Hyderabad India Sona College of Technology Department of Electronics and Communication Engineering Salem India New Prince Shri Bhavani College of Engineering and Technology Department of Electrical and Electronics Engineering Chennai India

ISBN: (纸本)9798350352931

Malaria is a critical health condition that affects both sultry and frigid region worldwide, its rise to millions of cases disease. The malaria is caused by parasites through to pass in the human blood cells, damage them over the time and occur the noise because it blends in the human blood cells. Therefore, it is identifying by using microscope Image to exanimated the blood cells. This paper, proposed Boosting Reweighting Convolutional Neural Network (BR-CNN) technique efficiently analysis the region to malaria parasites classify the disease image. Data obtained from the NIH Malaria dataset and pre-processing utilized remove unwanted noise in the image and normalization of the feature vector. The feature extraction involved the ResNet 50 utilized residual connections, which allows gradients to flow through the network, effectively extracted the malaria parasites. The Classification utilized BR-CNN technique is automatically extract features from the input blood cell image. The CNN processes the image through layer of the convolutional operations and non-linear activation to build a hierarchical representation of data. The performance of proposed model evaluated the various metrics such as accuracy, precision, recall and sensitivity metrics on NIH dataset. © 2024 IEEE.

关键词： Convolutional neural networks

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Design and Implementation of Advanced Driver Assistance Systems

Design and Implementation of Advanced Driver Assistance Syst...

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2024 International Telecommunications Conference, ITC-Egypt 2024

作者： Ali, Waleed Abd-Elshafi Saleh, Hatem Abdelhamid Eid, Haneen Salah Fathi, Ahmed Mohamed Mohamed, Abd-El Rahman Amr Higher Institute of Engineering EL- Shorouk Academy Department of Communications and Computer Engineering Cairo Egypt Misr University for Science and Technology Department of Electronics and Communications Engineering Giza Egypt

ISBN: (纸本)9798350351408

Advanced Driver Assistance Systems (ADAS) are a pivotal advancement in automotive technology, enhancing vehicle safety and driving experience. In our project, we have successfully designed and built a robotic prototype car. We have implemented a comprehensive control system for the car's movement, and the following features of ADAS systems: Bump Detection, Precise Lane Tracking, Adaptive Cruise Control, Traffic Sign Recognition, and Blind Spot Detection. Bump detection systems are equipped to detect and alert drivers of speed bumps and other road irregularities, in which we use NVIDIA Jetson Nano, Astra Pro Plus Depth camera as RGB and Depth sensor and use Custom AI Model based on YOLOv5 to detect different Bumps on the road and we use Python, TensorRT, and OpenCV. Lane Departure Warning (LDW) alerts drivers when their vehicle unintentionally drifts out of their lane, we use NVIDIA Jetson Nano as our main microcontroller unit (MCU) responsible for our Machine learning and Astra Pro Plus Depth camera as our RGB sensor, we use Python, Python's libraries such as OpenCV, and NumPy to implement LDW. Adaptive Cruise Control (ACC) adjusts the vehicle's speed automatically to maintain a safe distance from the vehicle ahead, we use NVIDIA Jetson Nano, Astra Pro Plus Depth camera as RGB and Depth sensor and use Custom AI Model based on YOLOv5 to detect different vehicles on the road, we use Python, TensorRT, and OpenCV. Traffic Sign Recognition can interpret and display traffic signs to keep drivers informed about speed limits, no-entry signs, and more, we use NVIDIA Jetson Nano, and Astra Pro Plus Depth camera as our RGB sensor and use Custom AI Model based on YOLOv5 to detect different Traffic Signs on the road, we use Python, TensorRT, and OpenCV. Blind Spot Detection (BSD) technology helps drivers identify vehicles in their blind spots and alerts them to avoid dangerous maneuvers, we use ESP32 as an MCU and Ultrasonic sensor, and use C++ to program the BSD feature. © 2024

关键词： Advanced driver assistance systems

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Novel Optimized Feature Selection Using Metaheuristics Applied to Physical Benchmark Datasets

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computers, Materials & Continua 2023年第2期74卷 4027-4041页

作者： Doaa Sami Khafaga El-Sayed M.El-kenawy Fadwa Alrowais Sunil Kumar Abdelhameed Ibrahim Abdelaziz A.Abdelhamid Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt School of Computer Science University of Petroleum and Energy StudiesDehradun248001India Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia

In data mining and machine learning,feature selection is a critical part of the process of selecting the optimal subset of features based on the target *** are 2n potential feature subsets for every n features in a dataset,making it difficult to pick the best set of features using standard ***,in this research,a new metaheuristics-based feature selection technique based on an adaptive squirrel search optimization algorithm(ASSOA)has been *** using metaheuristics to pick features,it is common for the selection of features to vary across runs,which can lead to *** of this,we used the adaptive squirrel search to balance exploration and exploitation duties more evenly in the optimization *** the selection of the best subset of features,we recommend using the binary ASSOA search strategy we developed *** to the suggested approach,the number of features picked is reduced while maximizing classification accuracy.A ten-feature dataset from the University of California,Irvine(UCI)repository was used to test the proposed method’s performance *** other state-of-the-art approaches,including binary grey wolf optimization(bGWO),binary hybrid grey wolf and particle swarm optimization(bGWO-PSO),bPSO,binary stochastic fractal search(bSFS),binary whale optimization algorithm(bWOA),binary modified grey wolf optimization(bMGWO),binary multiverse optimization(bMVO),binary bowerbird optimization(bSBO),binary hybrid GWO and genetic algorithm 4028 CMC,2023,vol.74,no.2(bGWO-GA),binary firefly algorithm(bFA),and *** results confirm the superiority and effectiveness of the proposed algorithm for solving the problem of feature selection.

关键词： Metaheuristics adaptive squirrel search algorithm optimization methods binary optimizer

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Deep Learning-Based Image Processing for Defect Detection in Electronic Components Manufacturing

Deep Learning-Based Image Processing for Defect Detection in...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Mathivanan, B. Maruthupandi, J. Lakshmi, Namala Vijaya Suvitha, S. Karthik, K. Swaroop, Anand Sri Ramakrishna Engineering College Department of Computer Science and Engineering Tamil Nadu Coimbatore641022 India New Horizon College of Engineering Department of Computer Science and Engineering Karnataka Bengaluru560103 India Kkr & Ksr Institute of Technology and Sciences Department of Computer Science and Engineering Andhra Pradesh 522017 India S.A. Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai600077 India Velammal College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Madurai625009 India Institute of Engineering and Technology Department of Computer Science and Engineering Uttar Pradesh Lucknow226021 India

ISBN: (纸本)9798331543617

In electronic factors manufacturing, icing product quality through disfigurement discovery is pivotal for maintaining trustability and performance norms. This exploration investigates the operation of deep literacy ways in image processing for disfigurement discovery. Deep literacy algorithms, particularly convolutional neural networks(CNNs), have shown remarkable success in automated image analysis tasks. This study explores their effectiveness in relating blights similar to face abnormalities, cracks, or indecorous assembly in electronic factors. By using annotated datasets and advanced neural network infrastructures, the proposed approach aims to enhance delicacy and effectiveness in disfigurement discovery compared to traditional styles. The methodology involves preprocessing raw images, training CNN models on labeled data, and validating their performance through rigorous testing and evaluation. Results demonstrate the eventuality of deep literacy to revise disfigurement discovery processes in manufacturing, offering more dependable examinations that contribute to bettered product quality and reduced product costs. This exploration contributes precious perceptivity to the integration of AI technologies for enhancing quality control in artificial settings. © 2024 IEEE.

关键词： Smart manufacturing

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Integrating Decision Trees with Cloud Computing for Predicting Cognitive Decline in Alzheimer's Patients 2

Integrating Decision Trees with Cloud Computing for Predicti...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Meenakshi, K. Ravinder, Bathini Pasha, Mohammed Rehaman Thinakaran, K. Jayanayudu, Dabbara Maniraj, S.P. SRM Institute of Science and Technology Faculty of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Vadapalani India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Guntur India Malla Reddy Institute of Engineering and Technology Department of Computer Science and Engineering Telangana Hyderabad India Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Saveetha School of Engineering Tamil Nadu Chennai India Siddartha Institute of Science and Technology Department of Electronics and Communication Engineering Andhra Pradesh Puttur India SRM Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350317558

The proposed research work suggests a combined technique based on decision trees and cloud computing for predicting cognitive decline in Alzheimer's patients. Medical records and data from wearable devices are among the many sources that perform preprocessing and feature engineering. Decision tree models can make the most efficient use of computing resources. Decision tree topologies are tested for performance criteria, including accuracy and F1 score. The system's predictive powers are rather encouraging since it frequently achieves accuracy rates over 90%. However, limitations in data quality and the intricacy of decision tree interpretation provide challenges. The prediction of cognitive deterioration in Alzheimer's patients may be made more scalable and interpretable by integrating decision trees with cloud computing. This method has promise for early identification and intervention despite its limitations. To further improve prediction accuracy and clinical value, future research should increase data quality and make it easier to understand. © 2024 IEEE.

关键词： Alzheimer's disease Cloud computing Decision trees Early detection Healthcare

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U-Net-Attention-TBNet: A Cutting-Edge Solution for Accurate TB Lesion Segmentation and Classification 8

U-Net-Attention-TBNet: A Cutting-Edge Solution for Accurate ...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Karthikeyan, M. Saravana Raj, J. Relin Francis Parvathi, R. Anushya, S. Thanga Krishnan, R. Santhana Joshua, K. Paul Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai Avadi India SaveethaInstitute of Medical and Technical Sciences Saveetha School of Engineering Department of Electronics and Communication Engineering Chennai India Department of Smart Computing Vellore India Scad College of Engineering and Technology Department of Computer Science and Engineering Cheranmahadevi India Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India

ISBN: (纸本)9798350376425

U-Net-Attention-TBNet is an innovative deep-learning model developed for precisely segmenting and classifying tuberculosis (TB) lesions in chest X-ray images. This Model combines the U-Net architecture with an advanced attention mechanism, enhancing feature extraction and boosting detection accuracy. Trained on the CheXpert dataset, U-Net-Attention-TBNet demonstrates superior performance compared to existing models, including DenseNet with U-Net, ResNet with U-Net, and VGGNet with U-Net. It achieves significantly higher accuracy, precision, recall, and F1 score, showcasing its effectiveness in distinguishing between primary TB lesions, secondary TB lesions, miliary TB lesions, and calcified granulomas. The attention mechanism refines the Model's ability to focus on pertinent features, leading to improved segmentation and reduced false positives. This progress represents a significant advancement in TB diagnosis, offering enhanced reliability and efficiency in medical imaging and contributing to better management of tuberculosis. © 2024 IEEE.

关键词： Medical imaging

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