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Optimizing land mine detection across diverse mining environments: A hyperspectral data approach with regression models

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International Journal of Intelligent Networks 2024年第1期5卷 351-363页

作者： Anand, R. Andrew, J. Makki, Ihab Department of Electrical and Electronics Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Coimbatore India Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal Karnataka 576104 India Department of Nouvelle Aquitaine France

The detection of landmines, namely anti-tank mines, explosive devices, and unexploded ordnance, is a formidable obstacle for the global community. The visible consequences of unobserved explosives in communities affected by war are characterized by significant devastation and human suffering. In order to effectively tackle this matter, it is imperative to use proactive strategies that focus on the identification and mitigation of these perilous substances prior to their potential infliction of harm. Nevertheless, the majority of current solutions exhibit significant deficiencies, such as exorbitant expenses, inefficiencies, and apprehensions over accuracy. These drawbacks are further compounded by the inherent trade-offs that exist between these elements, where improvements in one area often come at the expense of another. Contrarily, recent breakthroughs in the areas of deep learning, unmanned aerial vehicles, and sensor technologies are being recognized as potentially transformative elements in the domain of landmine identification and removal. This paper presents a thorough examination of recent scholarly investigations that integrate computerized technology in the field of landmine detection. To the extent of our current understanding, there has been no prior investigation that has thoroughly examined this particular domain. The main aim of this study is to investigate the incorporation of machine learning based regression methods in the field of landmine detection. The study specifically emphasizes the identification and resolution of existing issues that hinder the development of efficient automated solutions, hence enhancing performance optimization. The Sum of Sine Curve Fit Regression Model is proposed and proved a powerful and adaptable tool for extracting relevant information from this hyperspectral images. © 2024 The Authors

关键词： Unmanned aerial vehicles (UAV)

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Classification of Rice Variant using Deep Learning

Classification of Rice Variant using Deep Learning

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2024 IEEE International Conference on Modeling, Simulation and Intelligent Computing, MoSICom 2024

作者： Kumar, K Susheel Verma, Prem Kumari Chaurasia, Subham Singh, Nagendra Pratap Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Department of Information Technology Karnataka Manipal India National Institute of Technology Department of Computer Science and Engineering Punjab Jalandhar India Axis Institute of Technology and Management Department of Computer Science and Engineering Kanpur India

ISBN: (纸本)9798331533311

Rice is a popular crop in the world. This paper has constructed a model using deep Learning for classification. Classification is a critical process in identifying the class of an image. Here, the Rice dataset has been taken to classify the class of rice Breed. Image Classification is a complex process that is affected by various factors. The emphasis is placed on summarizing essential superior class procedures and the strategies used for improving classification accuracy. This paper uses the deep learning approach for classification, such as CNN, and some activation functions, such as ReLu Softmax with Dense layers. The classification process used various domains, such as Machine Learning and deep learning. In this model, five classes of rice breed have been taken and classified with an accuracy of 9 6%. © 2024 IEEE.

关键词： Adversarial machine learning

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A Review on the Impacts of Social Media on the Mental Health 6th

A Review on the Impacts of Social Media on the Mental Health

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6th International Conference on Intelligent Computing and Optimization, ICO 2023

作者： Tapu, Md. Abu Bakar Siddiq Akash, Rashik Shahriar Al Fahim, Hafiz Jarin, Tanin Mohammad Bhuiyan, Touhid Reza, Ahmed Wasif Arefin, Mohammad Shamsul Department of Computer Science and Engineering Daffodil International University Birulia Bangladesh Department of Computer Science and Engineering East West University Dhaka Bangladesh Department of Computer Science and Engineering Chittagong University of Engineering and Technology Chattogram Bangladesh

ISBN: (纸本)9783031501579

There are numerous effects of social media use on people’s daily lives. Every day we are connected a lot of time with social media. As a result, our brains become unbalanced, and we feel a lot of illness in our bodies. As a result, the primary objective of this analysis is to provide information on how social media affects its users. Nineteen studies were included in this paper regarding the main purpose. We categorized the papers into four types, Status and Post based research, Research paper and Database analysis-based research, Survey based research, and disease-based research. After a comprehensive analysis of the available literature, we have concluded that utilizing the social side has an effect on our mental health and actions. The results highlighted a number of critical aspects, such as the varied approaches to determining the influence of social media, the limitations of the studies, and our thoughts on what should be enhanced. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： COVID-19

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Intelligent waste classification approach based on improved multi-layered convolutional neural network

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Multimedia Tools and Applications 2024年第36期83卷 84095-84120页

作者： Chhabra, Megha Sharan, Bhagwati Elbarachi, May Kumar, Manoj Department of Computer Science & amp Engineering School of Engineering & ampTechnology Sharda University UP Gr. Noida India School of Engineering and Sciences Department of Computer Science and Engineering SRM University-AP Andhra Pradesh Amaravati India Head of School of Computer Science University of Wollongong in Dubai Dubai Knowledge Park Dubai United Arab Emirates School of Computer Science University of Wollongong in Dubai Dubai Knowledge Park Dubai United Arab Emirates MEU Research Unit Middle East University Amman11831 Jordan

This study aims to improve the performance of organic to recyclable waste through deep learning techniques. Negative impacts on environmental and Social development have been observed relating to the poor waste segregation schemes. Separating organic waste from recyclable waste can lead to a faster and more effective recycling process. Manual waste classification is a time-consuming, costly, and less accurate recycling process. Automated segregation in the proposed work uses Improved Deep Convolutional Neural Network (DCNN). The dataset of 2 class category with 25077 images is divided into 70% training and 30% testing images. The performance metrics used are classification Accuracy, Missed Detection Rate (MDR), and False Detection Rate (FDR). The results of Improved DCNN are compared with VGG16, VGG19, MobileNetV2, DenseNet121, and EfficientNetB0 after transfer learning. Experimental results show that the image classification accuracy of the proposed model reaches 93.28%. © The Author(s) 2024.

关键词： Convolutional neural networks

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WBC Subtype Detection using Deep Learning with Optimizing Hyperparameters by Genetic Algorithm

WBC Subtype Detection using Deep Learning with Optimizing Hy...

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2023 International Conference on Next-Generation Computing, IoT and Machine Learning, NCIM 2023

作者： Shuvo, Mehedi Hasan Rahman, Md. Tahamidur Ahsan, Sk. Md. Masudul Khulna University of Engineering & Technology Department of Computer Science and Engineering Khulna Bangladesh

ISBN: (纸本)9798350316001

The human body's defense mechanism, the immune system, heavily relies on white blood cells (WBCs). These cells come in five subtypes: Eosinophil, Basophil, Neutrophils, Monocyte, and Lymphocyte. Each subtype plays a vital role in the diagnosis of hematological diseases, such as leukemia, as their presence or absence can indicate the presence of a disease. In the past, hematologists have manually examined blood smears under a microscope to detect the subtype of WBCs. However, this method is both time-consuming and prone to errors. This study proposes a new approach, which uses a deep learning-based convolutional neural network (CNN) architecture in combination with an evolution-based algorithm (genetic algorithm) to detect WBC subtypes with greater accuracy. A large dataset of 12,000 images was used for training and analysis. The proposed model, which utilizes the You Only Look Once version 5 (YOLOv5) for image localization and segmentation, was able to accurately localize WBCs with an IoU score of 94% for more than 80% of images and detect the subtypes with an accuracy of 98%. This study aims to overcome the limitations of previous research by providing a more efficient and accurate method for detecting WBC subtypes. © 2023 IEEE.

关键词： Genetic algorithms

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Submerged rigid vegetation effects on flow hydrodynamics within the pool morphology

River

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River 2024年第3期3卷 260-271页

作者： Kourosh Nosrati Hossein Afzalimehr Hamidreza Raeisifar Mohammad Nazari-Sharabian Moses Karakouzian Faculty of Civil Engineering Iran University of Science and TechnologyTehranIran Department of Mathematics Engineeringand Computer ScienceWest Virginia State UniversityInstituteWest VirginiaUSA Department of Civil and Environmental Engineering University of Nevada Las VegasLas VegasNevadaUSA

This research is centered on a comprehensive investigation into the impact of turbulence on the movement and dispersion of materials within a three-dimensional(3D)bedform,speciﬁcally when there is a continuous presence of rigid vegetation submerged in theﬂ*** achieve our research objectives,we conducted extensive velocity measurements within a channel featuring this submerged *** measurements were carried out using an Acoustic Doppler Velocimeter(ADV).Additionally,our study delved into the intricate structures and turbulent characteristics of theﬂow,considering the coexistence of submerged vegetation and a 3D gravel *** pool featured entrance and exit slopes measuring 3 and 2.5°,*** experimental setup took place in a straightﬂume,measuring 14 m in length,0.9 m in width,and 0.6 m in ***ﬂume was equipped with transparent side walls to facilitate ***,our investigation extended to the spatial variations in velocity and turbulence *** analyzed various parameters including turbulence kinetic energy,integral turbulence lengths,dispersion coefﬁcients,and advective *** results revealed that integral length scales offer key insights into turbulent eddy *** the presence of vegetation and a 3D bedform,turbulent eddies undergo notable changes,ﬂattening in the longitudinal direction and expanding in the transverse and vertical ***,longitudinal advection is notably higher compared toﬂows without vegetation in a uniformﬂow or bare channel,especially for z/H>*** indicates that the presence of vegetation and a 3D bedform leads to an increase in turbulent kinetic energy(k values)that surpasses the reduction in the time-averaged velocity component(“U”)in the U×k term,thereby enhancing longitudinal advection.

关键词： 3D pool advection dispersion integral turbulent length submerged rigid vegetation

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Exploring Integration of Multimodal Deep Learning Approaches for Enhanced Alzheimer's Disease Diagnosis: A Review of Recent Literature

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SN computer science 2024年第7期5卷 852页

作者： Deshpande, Sonali Kulkarni, Nilima Computer Science & amp Engineering Department MIT SoC MIT Art Design and Technology University Pune India

Alzheimer's disease (AD), is the most common form of dementia that affects the nervous system. In the past few years, non-invasive early AD diagnosis has become more popular as a way to improve patient care and treatment results. Imaging methods, electroencephalogram (EEG) tests, and sound evaluations are some of the new ways that researchers have looked into. This review covers 60 papers published from 2020. They are compared in terms of how they use basic deep learning models such as CNN, LSTM, Alex Net, Inception Net, VGG19, and ResNet to identify AD. But not many studies use more than one method together, like image and EEG, EEG and sounds, or images and sounds. The information from the Scopus database makes it easy to look at the newest information and work. This means that using more than one method to find AD isn't getting as much attention. Our review says that combining the best parts of each method in a mixed way could make Alzheimer's research much more useful and lead to better ways to diagnose. The paper talks about problems and opportunities in the field right now as well as possible study topics and issues for the future. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Alzheimer's disease (AD) Deep learning Electroencephalography Machine learning Magnetic resonance imaging Neurological disorders Sound

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Enhanced Fake News Detection through the Fusion of Deep Learning and Repeat Vector Representations 2

Enhanced Fake News Detection through the Fusion of Deep Lear...

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Mahmud, Tanjim Hasan, Imran Aziz, Mohammad Tarek Rahman, Taohidur Hossain, Mohammad Shahadat Andersson, Karl Rangamati Science and Technology University Department of Computer Science and Engineering Rangamati4500 Bangladesh University of Chittagong Department of Computer Science and Engineering Chittagong4331 Bangladesh Pervasive and Mobile Computing Laboratory Luleå University of Technology Luleå97187 Sweden

ISBN: (纸本)9798350327533

The rapid dissemination of information across social networks, online news portals, and digital platforms has given rise to a pervasive issue - fake news. This misinformation, deliberately propagated by groups with ulterior motives, has the potential to divert societies in alarming directions. In a digital landscape inundated with millions of daily stories, distinguishing fact from fabrication has become a formidable challenge, casting a dark cloud of uncertainty over the information age. In response to this critical issue, this paper presents an innovative approach for enhanced fake news detection through the fusion of deep learning and Repeat Vector representations. In an era where individuals of all ages increasingly rely on online news sources, the dissemination of misinformation has become an alarming norm. It is a challenge to discern deceptive narratives that aim to sow discord and create undue *** confront this challenge head-on, we propose a deep neural network model that combines Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) architectures using the Repeat Vector method. This hybrid model excels in deciphering complex patterns within 1D data, effectively tackling the menace of fake news. Our rigorous evaluation demonstrates that our proposed model outperforms contemporary methods, achieving an impressive validation accuracy of up to 98.94% on our *** a world increasingly plagued by misinformation, our approach represents a significant stride toward safeguarding the integrity of information in the digital age. © 2024 IEEE.

关键词： Autoencoder Technique Convolutional Neural Network Deep Learning Long Short-Term Memory

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GLCM Based Feature Extraction and Medical X-ray Image Classification Using Machine Learning Techniques 1st

GLCM Based Feature Extraction and Medical X-ray Image Classi...

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1st EAI International Conference on Intelligent Systems and Machine Learning, ICISML 2022

作者： Rout, Jyotiranjan Das, Swagat Kumar Mohalik, Priyabrata Mohanty, Subhashree Mohanty, Chandan Kumar Behera, Susil Kumar Department of Computer Science and Engineering Balasore College of Engineering and Technology Balasore India

ISBN: (纸本)9783031350771

The automated system is now created with excellent accuracy to detect abnormalities in X-ray images. To enhance the appearance of medical photographs, image pre-processing methods are applied, so that high accuracy can be achieved with constrained means. Images are often classified based on their textural properties, which are measured using the Gray Level Co-occurrence Matrix (GLCM). The grey level correlation matrix provides statistical information of the second order on the grey levels of neighboring pixels in a picture (GLCM). In this proposed paper, medical X-ray images are classified and their features are extracted using an ensemble learning model. By extracting image features using the GLCM feature extraction method, this proposed model is able to distinguish between healthy and sick images (Gray level co-occurrence matrix).to improve the efficiency of the Ensemble learning classification method, it is compared against various algorithms using performance indicators, including Logistic regression, Gaussian Naive Bayes, as well as Random Forest. When this approach is compared to existing methods, the proposed ensemble model has an accuracy rate of 97% in classifying normal and diseased images. © 2023, ICST Institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： Image enhancement

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Utilizing Transfer Learning and Class-Selective Image Processing for COVID-19 Detection 26

Utilizing Transfer Learning and Class-Selective Image Proces...

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

作者： Tabassum, Noshin Parvin, Farjana Rajshahi University of Engineering & Technology Department of Computer Science and Engineering Rajshahi Bangladesh

ISBN: (纸本)9798350359015

The year 2019 marked the emergence of a novel contagious illness named COVID-19 which was brought about by the SARS-CoV-2 virus. This newly identified virus swiftly spread across the globe, ultimately resulting in the unprecedented COVID-19 pandemic, that impacted populations, healthcare systems, and economies worldwide. Globally, there is a growing need for COVID-19 non-invasive detection by medical image analysis. In the realm of medical diagnostics, deep learning stands out as a supremely efficient approach for analyzing extensive collections of chest X-ray (CXR) images, particularly influencing the realm of COVID-19 screening. A specific technique within deep learning, known as Convolutional Neural Network (CNN), holds a preeminent position due to its effectiveness. In the scope of this research, Convolutional Neural Networks (CNN) have been utilized to differentiate COVID-19 cases from both normal cases and instances of Pneumonia, leveraging information extracted from chest X-ray images. This utilization underscores the potency of advanced machine learning in confronting pivotal healthcare hurdles. The approach involved fine-tuning a pre-trained CNN model, InceptionV3, via transfer learning, wherein certain layers were adjusted before training on the dataset. The initial classification accuracy exhibited strong performance across all image classes, except for one category that displayed a marginally lower accuracy rate. A straightforward yet efficient approach to enhance image contrast has been utilized exclusively in the lower-performance class. This was done to enhance the image quality within that particular class, leading to improved accuracy. Since the entire dataset did not have to go through image processing, the model was computationally efficient and yielded an overall accuracy of 99.92% outperforming existing works. © 2023 IEEE.

关键词： chest X-ray Convolutional Neural Network COVID-19 image processing Pneumonia transfer learning

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