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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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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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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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Diagnosis and Classification of Fetal Health Based on CTG Data Using Machine Learning Techniques 1st

Diagnosis and Classification of Fetal Health Based on CTG...

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1st International Conference on Machine Intelligence and Emerging Technologies, MIET 2022

作者： Islam, Md. Monirul Rokunojjaman, Md. Amin, Al Akhtar, Md. Nasim Sarker, Iqbal H. Dhaka1212 Bangladesh Department of Computer Science and Mathematics Bangladesh Agricultural University Mymensingh2202 Bangladesh Department of Computer Science and Technology Chongqing University of Technology Banan District Chongqing China Department of Computer Science and Engineering Dhaka University of Engineering and Technology Gazipur1707 Bangladesh Department of Computer Science and Engineering Chittagong University of Engineering and Technology Chittagong4349 Bangladesh

ISBN: (纸本)9783031346217

Cardiotocograms (CTGs) is a simple and inexpensive way for healthcare providers to monitor fetal health, allowing them to take step to lessen infant as well as mother died. The technology operates by emitting ultrasound pulses and monitoring the response, revealing information such as fetal heart rate (FHR), fetal movements, uterine contractions, and more. Knowing the state of fetal, doctors and patients can take necessary steps in a time. Machine learning can play a vital role in this field. In this paper, we classified the state of fetal including normal state, suspect state, pathological state on the fetal disease dataset using seven machine learning model named AdaBoost (AdB), Random Forest (RF), K- nearest Neighbors (K-NN), Support Vector Machine (SVM), Gradient Boosting Classifier (GBC), Decision Tree Classifier (DTC), and Logistic Regression (LR). To validate the experimental task, we used several performance metrics containing accuracy, precision, recall, and F1-score. We also used a scaling technique named standard scalar for doing an unbiased dataset. Among the classification models, GCB outperforms the best by achieving the accuracy 95 %, precision (for normal 96 %, suspect 85 %, pathological 97 % ), recall (for normal 98 %, suspect 78 %, pathological 94 % ), and F1-score (for normal 97 %, suspect 81 %, pathological 96 % ). Although, RF, SVM, and K-NN perform better precision (100 % ) in the class of pathological state only. © 2023, ICST Institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： Classification (of information)

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An Effective Machine Learning Approach for Music Genre Classification with Mel Spectrograms and KNN

An Effective Machine Learning Approach for Music Genre Class...

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2023 International Conference on Communication, Circuits, and Systems, IC3S 2023

作者： Alam Siddiquee, Md. Nura Hossain, Md. Ali Wahida, Farah Rajshahi University of Engineering and Technology Department of Computer Science and Engineering Rajshahi Bangladesh

ISBN: (纸本)9798350325904

The automatic classification of musical genre is extremely useful in the indexing of music. In the area of music information retrieval (MIR), categorizing music files based on their genre is commonly a subjective task performed by humans. Our study develops a classifier to predict the genre of audio files. GTZAN genre classification dataset was used to extract important features from audio files. We implemented a K-Nearest Neighbor (KNN) and have chosen a value of k as 2, 5, and 10. With KNN, the best performance was achieved with the value of k=10. The proposed feature extraction and classification models end up with better accuracy in musical genre classification. © 2023 IEEE.

关键词： Music

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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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Secure blockchain bidding auction protocol against malicious adversaries

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High-Confidence Computing 2024年第3期4卷 58-63页

作者： Xiaobing Dai Jiajing Liu Xin Liu Xiaofen Tu Ruexue Wang Department of Computer and Information Engineering Baotou Vocational&Technical CollegeBaotou 014010China Department of Computer Tianjin Ren’ai CollegeTianjin 301636China School of Information Engineering Inner Mongolia University of Science and TechnologyBaotou 014010China

In recent years,with the development of blockchain,electronic bidding auction has received more and more *** at the possible problems of privacy leakage in the current electronic bidding and auction,this paper proposes an electronic bidding auction system based on blockchain against malicious adversaries,which uses the secure multi-party computation to realize secure bidding auction protocol without any trusted third *** protocol proposed in this paper is an electronic bidding auction scheme based on the threshold elliptic curve *** can be implemented without any third party to complete the bidding auction for some malicious behaviors of the participants,which can solve the problem of resisting malicious adversary *** security of the protocol is proved by the real/ideal model paradigm,and the efficiency of the protocol is *** efficiency of the protocol is verified by simulating experiments,and the protocol has practical value.

关键词： Blockchain Secure multi-party computation Electronic bidding auction Malicious model Real/ideal model paradigm

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IPH2O: Island Parallel-Harris Hawks Optimizer-Based CLSTM for Stock Price Movement Prediction

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Annals of Data science 2024年第6期11卷 1959-1974页

作者： Joel, Linda Parthasarathy, S. Venkatesan, P. Nandhini, S. Department of Mathematics SRM Institute of Science and Technology Ramapuram Tamil Nadu Chennai89 India Research and Statistics Sri Ramachandra Medical College and RI Sri Ramachandra University Chennai India Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India

Stock price movement forecasting is the process of predicting the future price of a financial and company stock from chaotic data. In recent years, many financial institutions and academics have shown interest in stock market forecasting. The accurate and successful predictions of the future price of stock yield a substantial profit. However, the current approaches are a major challenge due to the dynamic, chaotic, high-noise, non-linear, highly complex, and nonparametric characteristics of stock data. Furthermore, it is not sufficient to consider only the target firms' information because the stock prices of the target firms may be influenced by their related firms. Significant profits can be made by correct forecasting of stock prices, while poor forecasts can cause huge problems. Thus, we propose a novel Island Parallel-Harris Hawks Optimizer (IP-HHO)-optimized Convolutional Long Short Term Memory (ConvLSTM) with an autocorrelation model to predict stock price movement. Then, using the IP-HHO algorithm, the hyperparameters of ConvLSTM are optimized to minimize the Mean Absolute Percentage Error (MAPE). Four different types of financial time series datasets are utilized to validate the performance of the evaluation measures such as root mean square error, MAPE, Index of Agreement, accuracy, and F1 score. The results show that the IP-HHO-optimized ConvLSTM model outperforms others by improving the prediction rate accuracy and effectively minimizing the MAPE rate by 19.62%. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023.

关键词： Profitability

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Evaluation of Logistic Regression and Decision Tree Models for Lung Cancer Prediction Using Survey Data

Evaluation of Logistic Regression and Decision Tree Models f...

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2024 IEEE International Conference on Electronic Systems and Intelligent Computing, ICESIC 2024

作者： Narayana, K.E. Jayashree, K. ThavaVinu, A. Sindhuja, M. Jaeyalakshmi, M. Rajalakshmi Engineering College Department of Information Technology Tamilnadu Chennai India Panimalar Engineering College Department of Artificial Intelligence and Data Science Tamilnadu Chennai India Prathyusha Engineering College Department of Information Technology Chennai India Acharya University Department of Computer Science and Engineering Karakul Uzbekistan Rajalakshmi Engineering College Department Of Computer Science And Engineering Chennai India

ISBN: (纸本)9798331522988

Lung cancer is one of the most deadly and ubiquitous forms of cancer globally. Early detection can make a significant difference in survival rates, prognosis, etc. Background The present study compares the performance of logistic regression and decision trees using a survey-based dataset for predicting lung cancer. This dataset has a total of 284 cases with 16 features;behavioral patterns, symptomatic data, and demographic details. Logistic regression is a binary classification algorithm that uses input features to model the probability of lung cancer presence given these input features using for example a sigmoid function. Using Decision Trees can identify significant fields in a dataset while reducing Gini Impurity for models that categorize patients based on feature values. The performance of both models is evaluated according to accuracy, F1-score, precision, and recall measures. From the preliminary results, it looks like the Decision Tree works a little better than the logistic regression model which gets an accuracy of 94%, but this one does with more accuracy (97%). This result emphasizes the inclusion of logistic regression within this context, as it may be able to predict lung cancer with greater accuracy. Additionally, to ensure the generalization and robustness of the model, further investigation and trials will focus on optimizing performance through ensemble approaches, hyperparameter tuning, and cross-validation. This comparative analysis demonstrates the effectiveness of machine learning techniques in developing prediction models for early lung cancer detection, an important step toward a quick and appropriate intervention. © 2024 IEEE.

关键词： Lung cancer

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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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