Search results for: mineral potential classification

Authors: Dingyang Hu, Dan Liu

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DNN (Deep Neural Network) deep learning models are widely used in classification, prediction, and other task scenarios. To address the difficulties of generic adversarial perturbation generation for deep learning models under black-box conditions, a generic adversarial ingestion generation method based on a saliency map (CJsp) is proposed to obtain salient image regions by counting the factors that influence the input features of an image on the output results. This method can be understood as a saliency map attack algorithm to obtain false classification results by reducing the weights of salient feature points. Experiments also demonstrate that this method can obtain a high success rate of migration attacks and is a batch adversarial sample generation method.

Keywords: adversarial sample, gradient, probability, black box

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Authors: Arpan Paul, Joy Sen

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Livability is a holistic concept whose factors include many complex characteristics and levels of interrelationships among them. It has been considered as people’s need for public amenities and is recognized as a major element to create social welfare. The concept and principles of livability are essential for recognizing the significance of community well-being. The attributes and dimensions of livability are also important aspects to measure the overall quality of environment. Livability potential is mainly considered as the capacity to develop into the overall well-being of an urban area in future. The intent of the present study is to identify the prime factors to evaluate livability potential within a metropolis. For ground level case study, the paper has selected Kolkata Metropolitan Area (KMA) as it has wide physical, social, and economic variations within it. The initial part of the study deals with detailed literature review on livability and its significance of evaluating its potential within a metropolis. The next segment is dedicated for identifying the primary factors which would evaluate livability potential within a metropolis. In pursuit of identifying primary factors, which have a direct impact on urban livability, this study delineates the metropolitan area into various clusters, having their distinct livability potential. As a final outcome of the study, variations of livability potential of those selected clusters are highlighted to explain the complexity of the metropolitan development.

Keywords: Kolkata Metropolitan Area (KMA), livability potential, metropolis, wellbeing

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Authors: Shivkanya Fuloria, Neeraj Kumar Fuloria, Sokinder Kumar

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m-Cresol and oxadiazoles are the potent antimicrobial moieties. 2-(m-Tolyloxy)acetohydrazide (1) on cyclization with aromatic acids yielded 2-(aryl)-5-(m-tolyloxymethyl)-1,3,4-oxadiazole (1A-E). The structures of newer oxadiazoles were confirmed by elemental and spectral analysis. The newer compounds were evaluated for their antimicrobial potential. The compound 1E containing strong electron withdrawing group showed maximum antimicrobial potential. Other compounds also displayed antimicrobial potential to certain extent. The SAR of newer oxadiazoles indicated that substitution of strong electronegative group in the tolyloxy derived oxadiazoles enhanced their antimicrobial potential.

Keywords: antibacterial, cresol, hydrazide, oxadiazoles

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Authors: Fuad M. Alkoot

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DNA data have been used in forensics for decades. However, current research looks at using the DNA as a biometric identity verification modality. The goal is to improve the speed of identification. We aim at using gene data that was initially used for autism detection to find if and how accurate is this data for identification applications. Mainly our goal is to find if our data preprocessing technique yields data useful as a biometric identification tool. We experiment with using the nearest neighbor classifier to identify subjects. Results show that optimal classification rate is achieved when the test set is corrupted by normally distributed noise with zero mean and standard deviation of 1. The classification rate is close to optimal at higher noise standard deviation reaching 3. This shows that the data can be used for identity verification with high accuracy using a simple classifier such as the k-nearest neighbor (k-NN). 

Keywords: biometrics, genetic data, identity verification, k nearest neighbor

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Authors: Nusaiba Al-Nemrawi, Belal Al-Husein

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Methotrexate (MTX) is a stoichiometric inhibitor of dihydrofolate reductase, which is essential for DNA synthesis. MTX is a chemotherapeutic agent used for treating many types of cancer cells. However, cells’ resistant to MTX is very common and its pharmacokinetic behavior is highly problematic. of MTX within tumor cells, we propose encapsulation of antitumor drugs in nanoparticulated systems. Chitosan (CS) is a naturally occurring polymer that is biocompatibe, biodegradable, non-toxic, cationic and bioadhesive. CS nanoparticles (CS-NPs) have been used as drug carrier for targeted delivery. Titanium dioxide (TiO2), a natural mineral oxide, which is used in biomaterials due to its high stability and antimicrobial and anticorrosive properties. TiO2 showed a potential as a tumor suppressor. In this study a new formulation of MTX loaded in CS NPs (CS-MTX NPs) and coated with Titanium oxide (TiO2) was prepared. The mean particle size, zeta potential, polydispersity index were measured. The interaction between CS NPs and TiO2 NPs was confirmed using FTIR and XRD. CS-MTX NPs was studied in vitro using the tumor cell line MCF-7 (human breast cancer). The results showed that CS-MTX has a size around 169 nm and as they were coated with TiO2, the size ranged between and depending on the ratio of CS-MTX to TiO2 ratio used in the preparation. All NPs (uncoated and coated carried positive charges and were monodispersed. The entrapment efficacy was around 65%. Both FTIR and XRD proved that TiO2 interacted with CS-MTX NPs. The drug invitro release was controlled and sustained over days. Finally, the studied in vitro using the tumor cell line MCF-7 suggested that combining nanomaterials with anticancer drugs CS-MTX NPs may be more effective than free MTX for cancer treatment. In conclusion, the combination of CS-MTX NPs and TiO2 NPs showed excellent time-dependent in vitro antitumor behavior, therefore, can be employed as a promising anticancer agent to attain efficient results towards MCF-7 cells.

Keywords: Methotrexate, Titanium dioxide, Chitosan nanoparticles, cancer

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Authors: Moein Izadi, Ali Mohammadzadeh

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Detection of damaged parts of roads after earthquake is essential for coordinating rescuers. In this study, an approach is presented for the semi-automatic detection of damaged roads in a city using pre-event vector maps and both pre- and post-earthquake QuickBird satellite images. Damage is defined in this study as the debris of damaged buildings adjacent to the roads. Some spectral and texture features are considered for SVM classification step to detect damages. Finally, the proposed method is tested on QuickBird pan-sharpened images from the Bam City earthquake and the results show that an overall accuracy of 81% and a kappa coefficient of 0.71 are achieved for the damage detection. The obtained results indicate the efficiency and accuracy of the proposed approach.

Keywords: SVM classifier, disaster management, road damage detection, quickBird images

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Authors: Ella Sèdé Maforikan

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Accurate land cover mapping is essential for effective environmental monitoring and natural resources management. This study focuses on assessing the classification performance of two satellite datasets and evaluating the impact of different input feature combinations on classification accuracy in the Beterou catchment, situated in the northern part of Benin. Landsat-8 and Sentinel-2 images from June 1, 2020, to March 31, 2021, were utilized. Employing the Random Forest (RF) algorithm on Google Earth Engine (GEE), a supervised classification categorized the land into five classes: forest, savannas, cropland, settlement, and water bodies. GEE was chosen due to its high-performance computing capabilities, mitigating computational burdens associated with traditional land cover classification methods. By eliminating the need for individual satellite image downloads and providing access to an extensive archive of remote sensing data, GEE facilitated efficient model training on remote sensing data. The study achieved commendable overall accuracy (OA), ranging from 84% to 85%, even without incorporating spectral indices and terrain metrics into the model. Notably, the inclusion of additional input sources, specifically terrain features like slope and elevation, enhanced classification accuracy. The highest accuracy was achieved with Sentinel-2 (OA = 91%, Kappa = 0.88), slightly surpassing Landsat-8 (OA = 90%, Kappa = 0.87). This underscores the significance of combining diverse input sources for optimal accuracy in land cover mapping. The methodology presented herein not only enables the creation of precise, expeditious land cover maps but also demonstrates the prowess of cloud computing through GEE for large-scale land cover mapping with remarkable accuracy. The study emphasizes the synergy of different input sources to achieve superior accuracy. As a future recommendation, the application of Light Detection and Ranging (LiDAR) technology is proposed to enhance vegetation type differentiation in the Beterou catchment. Additionally, a cross-comparison between Sentinel-2 and Landsat-8 for assessing long-term land cover changes is suggested.

Keywords: land cover mapping, Google Earth Engine, random forest, Beterou catchment

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Authors: Yaojun Wang, Yaoqing Wang

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Stock selection is an important decision-making problem. Many machine learning and data mining technologies are employed to build automatic stock-selection system. A profitable stock-selection system should consider the stock’s investment value and the market timing. In this paper, we present a hybrid system including both engage for stock selection. This system uses a case-based reasoning (CBR) model to execute the stock classification, uses a decision-tree model to help with market timing and stock selection. The experiments show that the performance of this hybrid system is better than that of other techniques regarding to the classification accuracy, the average return and the Sharpe ratio.

Keywords: case-based reasoning, decision tree, stock selection, machine learning

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Authors: Tsega Asresa, Getahun Tigistu, Melaku Bayih

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Computer vision is a subfield of artificial intelligence that allows computers and systems to extract meaning from digital images and video. It is used in a wide range of fields of study, including self-driving cars, video surveillance, medical diagnosis, manufacturing, law, agriculture, quality control, health care, facial recognition, and military applications. Aromatic medicinal plants are botanical raw materials used in cosmetics, medicines, health foods, essential oils, decoration, cleaning, and other natural health products for therapeutic and Aromatic culinary purposes. These plants and their products not only serve as a valuable source of income for farmers and entrepreneurs but also going to export for valuable foreign currency exchange. In Ethiopia, there is a lack of technologies for the classification and identification of Aromatic medicinal plant parts and disease type cured by aromatic medicinal plants. Farmers, industry personnel, academicians, and pharmacists find it difficult to identify plant parts and disease types cured by plants before ingredient extraction in the laboratory. Manual plant identification is a time-consuming, labor-intensive, and lengthy process. To alleviate these challenges, few studies have been conducted in the area to address these issues. One way to overcome these problems is to develop a deep learning model for efficient identification of Aromatic medicinal plant parts with their corresponding disease type. The objective of the proposed study is to identify the aromatic medicinal plant parts and their disease type classification using computer vision technology. Therefore, this research initiated a model for the classification of aromatic medicinal plant parts and their disease type by exploring computer vision technology. Morphological characteristics are still the most important tools for the identification of plants. Leaves are the most widely used parts of plants besides roots, flowers, fruits, and latex. For this study, the researcher used RGB leaf images with a size of 128x128 x3. In this study, the researchers trained five cutting-edge models: convolutional neural network, Inception V3, Residual Neural Network, Mobile Network, and Visual Geometry Group. Those models were chosen after a comprehensive review of the best-performing models. The 80/20 percentage split is used to evaluate the model, and classification metrics are used to compare models. The pre-trained Inception V3 model outperforms well, with training and validation accuracy of 99.8% and 98.7%, respectively.

Keywords: aromatic medicinal plant, computer vision, convolutional neural network, deep learning, plant classification, residual neural network

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Authors: Fathi Kallel, Abdulelah Alabd Uljabbar, Abdulrahman Aldukhail, Abdulaziz Alomran

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The brain is an important organ in our body since it is responsible about the majority actions such as vision, memory, etc. However, different diseases such as Alzheimer and tumors could affect the brain and conduct to a partial or full disorder. Regular diagnosis are necessary as a preventive measure and could help doctors to early detect a possible trouble and therefore taking the appropriate treatment, especially in the case of brain tumors. Different imaging modalities are proposed for diagnosis of brain tumor. The powerful and most used modality is the Magnetic Resonance Imaging (MRI). MRI images are analyzed by doctor in order to locate eventual tumor in the brain and describe the appropriate and needed treatment. Diverse image processing methods are also proposed for helping doctors in identifying and analyzing the tumor. In fact, a large Computer Aided Diagnostic (CAD) tools including developed image processing algorithms are proposed and exploited by doctors as a second opinion to analyze and identify the brain tumors. In this paper, we proposed a new advanced CAD for brain tumor identification, classification and feature extraction. Our proposed CAD includes three main parts. Firstly, we load the brain MRI. Secondly, a robust technique for brain tumor extraction is proposed. This technique is based on both Discrete Wavelet Transform (DWT) and Principal Component Analysis (PCA). DWT is characterized by its multiresolution analytic property, that’s why it was applied on MRI images with different decomposition levels for feature extraction. Nevertheless, this technique suffers from a main drawback since it necessitates a huge storage and is computationally expensive. To decrease the dimensions of the feature vector and the computing time, PCA technique is considered. In the last stage, according to different extracted features, the brain tumor is classified into either benign or malignant tumor using Support Vector Machine (SVM) algorithm. A CAD tool for brain tumor detection and classification, including all above-mentioned stages, is designed and developed using MATLAB guide user interface.

Keywords: MRI, brain tumor, CAD, feature extraction, DWT, PCA, classification, SVM

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Authors: Carlos Tapia Cortez, Serkan Saydam, Jeff Coulton, Claude Sammut

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Strategic mineral commodities (SMC) both energetic and metals have long been fundamental for human beings. There is a strong and long-run relation between the mineral resources industry and society's evolution, with the provision of primary raw materials, becoming one of the most significant drivers of economic growth. Due to mineral resources’ relevance for the entire economy and society, an understanding of the SMC market behaviour to simulate price fluctuations has become crucial for governments and firms. For any human activity, SMC price fluctuations are affected by economic, geopolitical, environmental, technological and psychological issues, where cognition has a major role. Cognition is defined as the capacity to store information in memory, processing and decision making for problem-solving or human adaptation. Thus, it has a significant role in those systems that exhibit dynamic equilibrium through time, such as economic growth. Cognition allows not only understanding past behaviours and trends in SCM markets but also supports future expectations of demand/supply levels and prices, although speculations are unavoidable. Technological developments may also be defined as a cognitive system. Since the Industrial Revolution, technological developments have had a significant influence on SMC production costs and prices, likewise allowing co-integration between commodities and market locations. It suggests a close relation between structural breaks, technology and prices evolution. SCM prices forecasting have been commonly addressed by econometrics and Gaussian-probabilistic models. Econometrics models may incorporate the relationship between variables; however, they are statics that leads to an incomplete approach of prices evolution through time. Gaussian-probabilistic models may evolve through time; however, price fluctuations are addressed by the assumption of random behaviour and normal distribution which seems to be far from the real behaviour of both market and prices. Random fluctuation ignores the evolution of market events and the technical and temporal relation between variables, giving the illusion of controlled future events. Normal distribution underestimates price fluctuations by using restricted ranges, curtailing decisions making into a pre-established space. A proper understanding of SMC's price dynamics taking into account the historical-cognitive relation between economic, technological and psychological factors over time is fundamental in attempting to simulate prices. The aim of this paper is to discuss the SMC market cognition hypothesis and empirically demonstrate its dynamic-cognitive capacity. Three of the largest and traded SMC's: oil, copper and gold, will be assessed to examine the economic, technological and psychological cognition respectively.

Keywords: commodity price simulation, commodity price uncertainties, dynamic-cognition, dynamic systems

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Authors: Andrea Bellucci, Martina Tofi

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The aim of paper is to analyze business models of bancassurance in Italy for life business. The life insurance business is very developed in the Italian market and banks branches have 80% of the market share. Given its maturity, the life insurance market needs to consolidate its organizational form to allow for the development of non-life business, which nowadays collects few premiums but represents a great opportunity to enlarge the market share of bancassurance using its strength in the distribution channel while the market share of independent agents is decreasing. Starting with the main business model of bancassurance for life business, this paper will analyze the performances of life companies in the Italian market by balance sheet indicators and by main discriminant variables of business models. The study will observe trends from 2013 to 2015 for the Italian market by exploiting a database managed by Associazione Nazionale delle Imprese di Assicurazione (ANIA). The applied approach is based on a bottom-up analysis starting with variables and indicators to define business models’ classification. The statistical classification algorithm proposed by Ward is employed to design business models’ profiles. Results from the analysis will be a representation of the main business models built by their profile related to indicators. In that way, an unsupervised analysis is developed that has the limit of its judgmental dimension based on research opinion, but it is possible to obtain a design of effective business models.

Keywords: bancassurance, business model, non life bancassurance, insurance business value drivers

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Authors: Ho-Hsin Lee, Yue-Min Jiang, Shu-Hui Tsai, Jian-Ren Chen, Mei-Yu XU, Wen-Tien Wu

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This project presents an AI-based method and system for wound status analysis. The system uses a three-in-one sensor device to analyze wound status, including color, temperature, and a 3D sensor to provide wound information up to 2mm below the surface, such as redness, heat, and blood circulation information. The system has a 90% accuracy rate, requiring only one manual correction in 70% of cases, with a one-second delay. The system also provides an offline application that allows for manual correction of the wound bed range using color-based guidance to estimate wound bed size with 96% accuracy and a maximum of one manual correction in 96% of cases, with a one-second delay. Additionally, AI-assisted wound bed range selection achieves 100% of cases without manual intervention, with an accuracy rate of 76%, while AI-based wound tissue type classification achieves an 85.3% accuracy rate for five categories. The AI system also includes similar case search and expert recommendation capabilities. For AI-assisted wound range selection, the system uses WIFI6 technology, increasing data transmission speeds by 22 times. The project aims to save up to 64% of the time required for human wound record keeping and reduce the estimated time to assess wound status by 96%, with an 80% accuracy rate. Overall, the proposed AI method and system integrate multiple sensors to provide accurate wound information and offer offline and online AI-assisted wound bed size estimation and wound tissue type classification. The system decreases delay time to one second, reduces the number of manual corrections required, saves time on wound record keeping, and increases data transmission speed, all of which have the potential to significantly improve wound care and management efficiency and accuracy.

Keywords: wound status analysis, AI-based system, multi-sensor integration, color-based guidance

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Authors: Malik Muhammad Arslan, Muneeb Ullah, Dai Shihan, Daniyal Haider, Xiaodong Yang

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Chest X-rays are instrumental in the detection and monitoring of a wide array of diseases, including viral infections such as COVID-19, tuberculosis, pneumonia, lung cancer, and various cardiac and pulmonary conditions. To enhance the accuracy of diagnosis, artificial intelligence (AI) algorithms, particularly deep learning models like Convolutional Neural Networks (CNNs), are employed. However, these deep learning models demand a substantial and varied dataset to attain optimal precision. Generative Adversarial Networks (GANs) can be employed to create new data, thereby supplementing the existing dataset and enhancing the accuracy of deep learning models. Nevertheless, GANs have their limitations, such as issues related to stability, convergence, and the ability to distinguish between authentic and fabricated data. In order to overcome these challenges and advance the detection and classification of CXR normal and abnormal images, this study introduces a distinctive technique known as DCWGAN (Diverse Conditional Wasserstein GAN) for generating synthetic chest X-ray (CXR) images. The study evaluates the effectiveness of this Idiosyncratic DCWGAN technique using the ResNet50 model and compares its results with those obtained using the traditional GAN approach. The findings reveal that the ResNet50 model trained on the DCWGAN-generated dataset outperformed the model trained on the classic GAN-generated dataset. Specifically, the ResNet50 model utilizing DCWGAN synthetic images achieved impressive performance metrics with an accuracy of 0.961, precision of 0.955, recall of 0.970, and F1-Measure of 0.963. These results indicate the promising potential for the early detection of diseases in CXR images using this Inimitable approach.

Keywords: CNN, classification, deep learning, GAN, Resnet50

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Authors: Jui Afrin, Akhtarul Islam

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In this paper, the solution of glucose, yeast and glucose yeast mixture are being used as sample solution for determining the chemical oxygen demand (COD). In general COD determination method used to determine the different rang of oxidative potential. But in this work has shown to determine the definite oxidative potential for different concentration for known COD value and wanted to see the difference between experimental value and the theoretical value for evaluating the method drawbacks. In this study, made the values of oxidative potential like 400 mg/L, 500 mg/L, 600 mg/L, 700 mg/L and 800mg/L for various sample solutions and determined the oxidative potential according to our developed method. Plotting the experimental COD values vs. sample solutions of various concentrations in mg/L to draw the curve. From these curves see that the curves for glucose solution is not linear; its deviate from linearity for the lower concentration and the reason for this deviation is unknown. If these drawback can be removed this method can be effectively used to determine Oxidative Potential of Industrial wastewater (such as: Leather industry wastewater, Municipal wastewater, Food industry wastewater, Textile wastewater, Pharmaceuticals waste water) that’s why more experiment and study required.

Keywords: bod (biological oxygen demand), cod (chemical oxygen demand), oxidative potential, titration, waste water, development

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Authors: Endrick Barnacin, Jean-Luc Henry, Jimmy Nagau, Jack Molinie

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Palynology is a field of interest in many disciplines due to its multiple applications: chronological dating, climatology, allergy treatment, and honey characterization. Unfortunately, the analysis of a pollen slide is a complicated and time consuming task that requires the intervention of experts in the field, which are becoming increasingly rare due to economic and social conditions. That is why the need for automation of this task is urgent. A lot of studies have investigated the subject using different standard image processing descriptors and sometimes hand-crafted ones.In this work, we make a comparative study between classical feature extraction methods (Shape, GLCM, LBP, and others) and Deep Learning (CNN, Autoencoders, Transfer Learning) to perform a recognition task over 80 regional pollen species. It has been found that the use of Transfer Learning seems to be more precise than the other approaches

Keywords: pollens identification, features extraction, pollens classification, automated palynology

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Authors: Liping Wu, Durga Bhakta Pokharel, Junhua Dong, Changgang Wang, Lin Zhao, Wei Ke, Nan Chen

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Hazenite conversion coating was deposited on AZ31 Mg alloy in a deaerated phosphate solution containing 0.1 M K₂HPO₄ and 0.1 M Na₂HPO₄ (Na₀.₁K0₀.₁) with pH 9 at −0.8 V. The coating mechanism of hazenite was elucidated by in situ potentiostatic current decay, scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDS), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), Fourier transform infrared spectroscopy (FT-IR), electron probe micro-analyzer (EPMA) and differential scanning calorimetry (DSC). The volume of H₂ evolved during potentiostatic polarization was measured by a gas collection apparatus. The degradation resistance of the hazenite coating was evaluated in simulated body fluid (SBF) at 37℃ by using potentiodynamic polarization (PDP). The results showed that amorphous Mg(OH)₂ was deposited first, followed by the transformation of Mg(OH)₂ to amorphous MgHPO₄, subsequently the conversion of MgHPO₄ to crystallized K-struvite (KMgPO₄·6H₂O), finally the crystallization of crystallized hazenite (NaKMg₂(PO₄)₂·14H₂O). The deposited coating was composed of four layers where the inner layer is comprised of Mg(OH)₂, the middle layer of Mg(OH)₂ and MgHPO₄, the top layer of Mg(OH)₂, MgHPO₄ and K-struvite, the topmost layer of Mg(OH)₂, MgHPO₄, K-struvite and hazenite (NaKMg₂(PO₄)₂·14H₂O). The PD results showed that the hazenite coating decreased the corrosion rate by two orders of magnitude.

Keywords: magnesium alloy, potentiostatic technique, hazenite, mineral conversion coating

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Authors: Magdy B. Eteiba, Wael Ismael Wahba, Shimaa Barakat

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This paper presents a fault identification, classification and fault location estimation method based on Discrete Wavelet Transform and Adaptive Network Fuzzy Inference System (ANFIS) for medium voltage cable in the distribution system. Different faults and locations are simulated by ATP/EMTP, and then certain selected features of the wavelet transformed signals are used as an input for a training process on the ANFIS. Then an accurate fault classifier and locator algorithm was designed, trained and tested using current samples only. The results obtained from ANFIS output were compared with the real output. From the results, it was found that the percentage error between ANFIS output and real output is less than three percent. Hence, it can be concluded that the proposed technique is able to offer high accuracy in both of the fault classification and fault location.

Keywords: ANFIS, fault location, underground cable, wavelet transform

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Authors: Hung-Sheng Lin, Cheng-Hsuan Li

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Over the past few years, kernel-based algorithms have been widely used to extend some linear feature extraction methods such as principal component analysis (PCA), linear discriminate analysis (LDA), and nonparametric weighted feature extraction (NWFE) to their nonlinear versions, kernel principal component analysis (KPCA), generalized discriminate analysis (GDA), and kernel nonparametric weighted feature extraction (KNWFE), respectively. These nonlinear feature extraction methods can detect nonlinear directions with the largest nonlinear variance or the largest class separability based on the given kernel function. Moreover, they have been applied to improve the target detection or the image classification of hyperspectral images. The double nearest proportion feature extraction (DNP) can effectively reduce the overlap effect and have good performance in hyperspectral image classification. The DNP structure is an extension of the k-nearest neighbor technique. For each sample, there are two corresponding nearest proportions of samples, the self-class nearest proportion and the other-class nearest proportion. The term “nearest proportion” used here consider both the local information and other more global information. With these settings, the effect of the overlap between the sample distributions can be reduced. Usually, the maximum likelihood estimator and the related unbiased estimator are not ideal estimators in high dimensional inference problems, particularly in small data-size situation. Hence, an improved estimator by shrinkage estimation (regularization) is proposed. Based on the DNP structure, LDA is included as a special case. In this paper, the kernel method is applied to extend DNP to kernel-based DNP (KDNP). In addition to the advantages of DNP, KDNP surpasses DNP in the experimental results. According to the experiments on the real hyperspectral image data sets, the classification performance of KDNP is better than that of PCA, LDA, NWFE, and their kernel versions, KPCA, GDA, and KNWFE.

Keywords: feature extraction, kernel method, double nearest proportion feature extraction, kernel double nearest feature extraction

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Authors: Esmael Makarian, Ayub Elyasi, Fatemeh Saberi, Olusegun Stanley Tomomewo

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Geothermal reservoirs (GTRs) have garnered global recognition as a sustainable energy source. The Thermo-Hydro-Mechanical-Chemical (THMC) integration coupling proves to be a practical and effective method for optimizing production in GTRs. The study outcomes demonstrate that THMC coupling serves as a versatile and valuable tool, offering in-depth insights into GTRs and enhancing their operational efficiency. This is achieved through temperature analysis and pressure changes and their impacts on mechanical properties, structural integrity, fracture aperture, permeability, and heat extraction efficiency. Moreover, THMC coupling facilitates potential benefits assessment and risks associated with different geothermal technologies, considering the complex thermal, hydraulic, mechanical, and chemical interactions within the reservoirs. However, THMC-coupling utilization in GTRs presents a multitude of challenges. These challenges include accurately modeling and predicting behavior due to the interconnected nature of processes, limited data availability leading to uncertainties, induced seismic events risks to nearby communities, scaling and mineral deposition reducing operational efficiency, and reservoirs' long-term sustainability. In addition, material degradation, environmental impacts, technical challenges in monitoring and control, accurate assessment of resource potential, and regulatory and social acceptance further complicate geothermal projects. Addressing these multifaceted challenges is crucial for successful geothermal energy resources sustainable utilization. This paper aims to illuminate the challenges and opportunities associated with THMC coupling in enhanced geothermal systems. Practical solutions and strategies for mitigating these challenges are discussed, emphasizing the need for interdisciplinary approaches, improved data collection and modeling techniques, and advanced monitoring and control systems. Overcoming these challenges is imperative for unlocking the full potential of geothermal energy making a substantial contribution to the global energy transition and sustainable development.

Keywords: geothermal reservoirs, THMC coupling, interdisciplinary approaches, challenges and opportunities, sustainable utilization

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Authors: Aly Elshafei, Daniela Romano

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With the development of autonomous vehicles, a human-machine interaction (HMI) system is needed for a safe transition of control when a takeover request (TOR) is required. An important part of the HMI system is the ability to monitor the level of situational awareness (SA) of any driver in real-time, in different scenarios, and without any pre-calibration. Presenting state-of-the-art machine learning models used to measure SA is the purpose of this systematic review. Investigating the limitations of each type of sensor, the gaps, and the most suited sensor and computational model that can be used in driving applications. To the author’s best knowledge this is the first literature review identifying online and offline classification methods used to measure SA, explaining which measurements are subject or session-specific, and how many classifications can be done with each classification model. This information can be very useful for researchers measuring SA to identify the most suited model to measure SA for different applications.

Keywords: situational awareness, autonomous driving, gaze metrics, EEG, ECG

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Authors: Seokbeen Lim, Gilwon Yoon

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The readiness potential in brain waves is a brain activity related with an intention whose potential arises even before its conscious intention. This study was carried out in order to understand the generation and mechanism of the readiness potential more. The experiment with two subjects was conducted in two ways following the Oddball task protocol. Firstly, auditory stimuli were randomly presented to the subjects. The subject was allowed to press the keyboard with the right index finger only when the subject heard the target stimulus but not the standard stimulus. Secondly, unlike the first one, the auditory stimuli were randomly presented, and the subjects pressed the keyboard in the same manner, but at the same time with grasping action of the left hand. The readiness potential showed up for both of these experiments. In the first Oddball experiment, the readiness potential was detected only when the target stimulus was presented. However, in the second Oddball experiment with the left hand action of grasping something, the readiness potential was detected at the presentation of for both standard and target stimuli. However, detected readiness potentials with the target stimuli were larger than those of the standard stimuli. We found an interesting phenomenon that the readiness potential was able to be detected even the standard stimulus. This indicates that motor-related readiness potentials can be generated only by the intention to move. These results present a new perspective in psychology and brain engineering since subconscious brain action may be prior to conscious recognition of the intention.

Keywords: readiness potential, auditory stimuli, event-related potential, electroencephalography, oddball task

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Authors: Ghada A. Alfattni

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Analysing unbalanced datasets is one of the challenges that practitioners in machine learning field face. However, many researches have been carried out to determine the effectiveness of the use of the synthetic minority over-sampling technique (SMOTE) to address this issue. The aim of this study was therefore to compare the effectiveness of the SMOTE over different models on unbalanced datasets. Three classification models (Logistic Regression, Support Vector Machine and Nearest Neighbour) were tested with multiple datasets, then the same datasets were oversampled by using SMOTE and applied again to the three models to compare the differences in the performances. Results of experiments show that the highest number of nearest neighbours gives lower values of error rates. 

Keywords: imbalanced datasets, SMOTE, machine learning, logistic regression, support vector machine, nearest neighbour

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Authors: Shiamaa Eltantawy, Gihan Sobhy, Alif Alaam

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Wilson disease, or hepatolenticular degeneration, is an autosomal recessive disease that results in excess copper buildup in the body. It primarily affects the liver and basal ganglia of the brain, but it can affect other organ systems. Musculoskeletal abnormalities, including premature osteoarthritis, skeletal deformity, and pathological bone fractures, can occasionally be found in WD patients with a hepatic or neurologic type. The aim was to assess the prevalence of osteoporosis and osteopenia in Wilson’s disease patients. This case-control study was conducted on ninety children recruited from the inpatient ward and outpatient clinic of the Paediatric Hepatology, Gastroenterology, and Nutrition department of the National Liver Institute at Menofia University, aged from 1 to 18 years. Males were 49, and females were 41. Children were divided into three groups: (Group I) consisted of thirty patients with WD; (Group II) consisted of thirty patients with chronic liver disease other than WD; (Group III) consisted of thirty age- and sex-matched healthy The exclusion criteria were patients with hyperparathyroidism, hyperthyroidism, renal failure, Cushing's syndrome, and patients on certain drugs such as chemotherapy, anticonvulsants, or steroids. All patients were subjected to the following: 1- Full history-taking and clinical examination. 2-Laboratory investigations: (FBC,ALT,AST,serum albumin, total protein, total serum bilirubin,direct bilirubin,alkaline phosphatase, prothrombin time, serum critine,parathyroid hormone, serum calcium, serum phosphrus). 3-Bone mineral density (BMD, gm/cm2) values were measured by dual-energy X-ray absorptiometry (DEXA). The results revealed that there was a highly statistically significant difference between the three groups regarding the DEXA scan, and there was no statistically significant difference between groups I and II, but the WD group had the lowest bone mineral density. The WD group had a large number of cases of osteopenia and osteoporosis, but there was no statistically significant difference with the group II mean, while a high statistically significant difference was found when compared to group III. In the WD group, there were 20 patients with osteopenia, 4 patients with osteoporosis, and 6 patients who were normal. The percentages were 66.7%, 13.3%, and 20%, respectively. Therefore, the largest number of cases in the WD group had osteopenia. There was no statistically significant difference found between WD patients on different treatment regimens regarding DEXA scan results (Z-Score). There was no statistically significant difference found between patients in the WD group (normal, osteopenic, or osteoporotic) regarding phosphorus (mg/dL), but there was a highly statistically significant difference found between them regarding ionised Ca (mmol/L). Therefore, there was a decrease in bone mineral density when the Ca level was decreased. In summary, Wilson disease is associated with bone demineralization. The largest number of cases in the WD group in our study had osteopenia (66.7%). Different treatment regimens (zinc monotherapy, Artamin, and zinc) as well as different laboratory parameters have no effect on bone mineralization in WD cases. Decreased ionised Ca is associated with low BMD in WD patients. Children with WD should be investigated for BMD.

Keywords: wilson disease, Bone mineral density, liver disease, osteoporosis

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Authors: Rifaldy, Misbahudin, Khalid Rizky, Ricky Aryanto, M. Alfiyan Bagus, Fahri Septianto, Firman Najib Wibisana, Excobar Arman

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Indonesia is a country that has a high level of disaster because it is on the ring of fire, and there are several regions with three major plates meeting in the world. So that disaster analysis must always be done to see the potential disasters that might always occur, especially in this research are landslides and liquefaction. This research was conducted to analyze areas that are vulnerable to landslides and liquefaction hazards and their relationship with the assessment of the issue of moving the new capital of the Republic of Indonesia to the island of Kalimantan with a total area of 612,267.22 km². The method in this analysis uses the Analytical Hierarchy Process and consistency ratio testing as a complex and unstructured problem-solving process into several parameters by providing values. The parameters used in this analysis are the slope, land cover, lithology distribution, wetness index, earthquake data, peak ground acceleration. Weighted overlay was carried out from all these parameters using the percentage value obtained from the Analytical Hierarchy Process and confirmed its accuracy with a consistency ratio so that a percentage of the area obtained with different vulnerability classification values was obtained. Based on the analysis results obtained vulnerability classification from very high to low vulnerability. There are (0.15%) 918.40083 km² of highly vulnerable, medium (20.75%) 127,045,44815 km², low (56.54%) 346,175.886188 km², very low (22.56%) 138,127.484832 km². This research is expected to be able to map landslides and liquefaction disasters on the island of Kalimantan and provide consideration of the suitability of regional development of the new capital of the Republic of Indonesia. Also, this research is expected to provide input or can be applied to all regions that are analyzing the vulnerability of landslides and liquefaction or the suitability of the development of certain regions.

Keywords: analytic hierarchy process, Borneo Island, landslide and liquefaction, vulnerability analysis

Procedia PDF Downloads 154

Authors: Chongya Song, Kang Yen, Alexander Pons, Jin Liu

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In the field of machine learning, the ensemble has been employed as a common methodology to improve the performance upon multiple base classifiers. However, the true predictions are often canceled out by the false ones during consensus due to a phenomenon called “curse of correlation” which is represented as the strong interferences among the predictions produced by the base classifiers. In addition, the existing practices are still not able to effectively mitigate the problem of imbalanced classification. Based on the analysis on our experiment results, we conclude that the two problems are caused by some inherent deficiencies in the approach of consensus. Therefore, we create an enhanced ensemble algorithm which adopts a designed rank-based chain-mode consensus to overcome the two problems. In order to evaluate the proposed ensemble algorithm, we employ a well-known benchmark data set NSL-KDD (the improved version of dataset KDDCup99 produced by University of New Brunswick) to make comparisons between the proposed and 8 common ensemble algorithms. Particularly, each compared ensemble classifier uses the same 22 base classifiers, so that the differences in terms of the improvements toward the accuracy and reliability upon the base classifiers can be truly revealed. As a result, the proposed rank-based chain-mode consensus is proved to be a more effective ensemble solution than the traditional consensus approach, which outperforms the 8 ensemble algorithms by 20% on almost all compared metrices which include accuracy, precision, recall, F1-score and area under receiver operating characteristic curve.

Keywords: consensus, curse of correlation, imbalance classification, rank-based chain-mode ensemble

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Authors: Reza Ziaie Moayad, Azam Kouhpeyma

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In-situ testing is preferred to evaluate the liquefaction potential in cohesionless soils due to high disturbance during sampling. Although new in-situ methods with high accuracy have been developed, standard penetration test, the simplest and the oldest in-situ test, is still used due to the profusion of the recorded data. This paper reviews the Iranian standard of evaluating liquefaction potential in soils (codes 525) and compares the liquefaction assessment methods based on SPT results on cohesionless soil in this standard with the international standards. To this, methods for assessing liquefaction potential which are presented by Cetin et al. (2004), Boulanger and Idriss (2014) are compared with what is presented in standard 525. It is found that although the procedure used in Iranian standard of evaluating the potential of liquefaction has not been updated according to the new findings, it is a conservative procedure.

Keywords: cohesionless soil, liquefaction, SPT, standard 525

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Authors: Afaf Alharbi, Qianni Zhang

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The identification of malignant tissue in histopathological slides holds significant importance in both clinical settings and pathology research. This paper introduces a methodology aimed at automatically categorizing cancerous tissue through the utilization of a multiple-instance learning framework. This framework is specifically developed to acquire knowledge of the Bernoulli distribution of the bag label probability by employing neural networks. Furthermore, we put forward a neural network based permutation-invariant aggregation operator, equivalent to attention mechanisms, which is applied to the multi-instance learning network. Through empirical evaluation of an openly available colon cancer histopathology dataset, we provide evidence that our approach surpasses various conventional deep learning methods.

Keywords: attention multiple instance learning, MIL and transfer learning, histopathological slides, cancer tissue classification

Procedia PDF Downloads 93

Authors: Mohamed Aboelghar, Ayman Abou Hadid, Usama Albehairy, Asmaa Khater

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Sustainable agricultural development of the desert areas of Egypt under the pressure of irrigation water scarcity is a significant national challenge. Existing water harvesting techniques on the northwest coast of Egypt do not ensure the optimal use of rainfall for agricultural purposes. Basin-scale hydrology potentialities were studied to investigate how available annual rainfall could be used to increase agricultural production. All data related to agricultural production included in the form of geospatial layers. Thematic classification of Sentinal-2 imagery was carried out to produce the land cover and crop maps following the (FAO) system of land cover classification. Contour lines and spot height points were used to create a digital elevation model (DEM). Then, DEM was used to delineate basins, sub-basins, and water outlet points using the Soil and Water Assessment Tool (Arc SWAT). Main soil units of the study area identified from Land Master Plan maps. Climatic data collected from existing official sources. The amount of precipitation, surface water runoff, potential, and actual evapotranspiration for the years (2004 to 2017) shown as results of (Arc SWAT). The land cover map showed that the two tree crops (olive and fig) cover 195.8 km2 when herbaceous crops (barley and wheat) cover 154 km2. The maximum elevation was 250 meters above sea level when the lowest one was 3 meters below sea level. The study area receives a massive variable amount of precipitation; however, water harvesting methods are inappropriate to store water for purposes.

Keywords: water catchements, remote sensing, GIS, sustainable agricultural development

Procedia PDF Downloads 101

Authors: Danutė Jablonskytė-Raščė, Audronė MankevičIenė, Laura Masilionytė

Abstract:

During the period 2009–2015, in Joniškėlis Experimental Station of the Lithuanian Research Centre for Agriculture and Forestry, the effect of ecologic fertilizers Ekoplant, bio-activators Biokal 01 and Terra Sorb Foliar and their combinations on the formation of the productivity elements, grain yield and quality of winter wheat, spelt (Triticum spelta L.), and common wheat (Triticum aestivum L.) was analysed in ecological agro-system. The soil under FAO classification – Endocalcari-Endo-hypogleyic-Cambisol. In a clay loam soil, ecological fertilizer produced from sunflower hull ash and this fertilizer in combination with plant extracts and bio-humus exerted an influence on the grain yield of spelt and common wheat and their mixture (increased the grain yield by 10.0%, compared with the unfertilized crops). Spelt grain yield was by on average 16.9% lower than that of common wheat and by 11.7% lower than that of the mixture, but the role of spelt in organic production systems is important because with no mineral fertilization it produced grains with a higher (by 4%) gluten content and exhibited a greater ability to suppress weeds (by on average 61.9% lower weed weight) compared with the grain yield and weed suppressive ability of common wheat and mixture. Spelt cultivation in a mixture with common wheat significantly improved quality indicators of the mixture (its grain contained by 2.0% higher protein content and by 4.0% higher gluten content than common wheat grain), reduced disease incidence (by 2-8%), and weed infestation level (by 34-81%).

Keywords: common and spelt-wheat, ecological fertilizers, bio-activators, productivity elements, yield, quality

Procedia PDF Downloads 286