Search results for: quantification accuracy

Authors: Arshia Aflaki, Hadis Karimipour, Anik Islam

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Generative Adversarial Attacks (GAAs) threaten critical sectors, ranging from fingerprint recognition to industrial control systems. Existing Deep Learning (DL) algorithms are not robust enough against this kind of cyber-attack. As one of the most critical industries in the world, the power grid is not an exception. In this study, a Deep Reinforcement Learning-based (DRL) framework assisting the DL model to improve the robustness of the model against generative adversarial attacks is proposed. Real-world smart grid stability data, as an IIoT dataset, test our method and improves the classification accuracy of a deep learning model from around 57 percent to 96 percent.

Keywords: generative adversarial attack, deep reinforcement learning, deep learning, IIoT, generative adversarial networks, power system

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Authors: Waseem Shahzad, Ayesha Tahir Khan, Hamid Hussain Awan

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Ant-Miner algorithm that lies under ACO algorithms is used to extract knowledge from data in the form of rules. A variant of Ant-Miner algorithm named as cAnt-MinerPB is used to generate list of rules using pittsburgh approach in order to maintain the rule interaction among the rules that are generated. In this paper, we propose a parallel Ant MinerPB in which Ant colony optimization algorithm runs parallel. In this technique, a data set is divided vertically (i-e attributes) into different subsets. These subsets are created based on the correlation among attributes using Mutual Information (MI). It generates rules in a parallel manner and then merged to form a final list of rules. The results have shown that the proposed technique achieved higher accuracy when compared with original cAnt-MinerPB and also the execution time has also reduced.

Keywords: ant colony optimization, parallel Ant-MinerPB, vertical partitioning, classification rule discovery

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Authors: Prabu Duplex

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Due to stringent emission regulation targets, large-scale transition to renewable energy sources is a global challenge, and wind power plays a significant role in the solution vector. This scenario has led to the construction of offshore wind farms, and several wind farms are planned in the shallow waters where the marine habitat exists. It raises concerns over impacts of underwater noise on marine species, for example bridge constructions in the ocean straits. Dangerous to aquatic life, the environmental organisations say, the bridge would be devastating, since ocean straits are important place of transit for marine mammals. One of the highest concentrations of biodiversity in the world is concentrated these areas. The investigation of ship noise and piling noise that may happen during bridge construction and in operation is therefore vital. Once the source levels are known the receiver levels can be modelled. With this objective this work investigates the key requirement of the software that can model transmission loss in high frequencies that may occur during construction or operation phases. Most propagation models are 2D solutions, calculating the propagation loss along a transect, which does not include horizontal refraction, reflection or diffraction. In many cases, such models provide sufficient accuracy and can provide three-dimensional maps by combining, through interpolation, several two-dimensional (distance and depth) transects. However, in some instances the use of 2D models may not be sufficient to accurately model the sound propagation. A possible example includes a scenario where an island or land mass is situated between the source and receiver. The 2D model will result in a shadow behind the land mass where the modelled transects intersect the land mass. Diffraction will occur causing bending of the sound around the land mass. In such cases, it may be necessary to use a 3D model, which accounts for horizontal diffraction to accurately represent the sound field. Other scenarios where 2D models may not provide sufficient accuracy may be environments characterised by a strong up-sloping or down sloping seabed, such as propagation around continental shelves. In line with these objectives by means of a case study, this work addresses the importance of 3D interactions in underwater acoustics. The methodology used in this study can also be used for other 3D underwater sound propagation studies. This work assumes special significance given the increasing interest in using underwater acoustic modeling for environmental impacts assessments. Future work also includes inter-model comparison in shallow water environments considering more physical processes known to influence sound propagation, such as scattering from the sea surface. Passive acoustic monitoring of the underwater soundscape with distributed hydrophone arrays is also suggested to investigate the 3D propagation effects as discussed in this article.

Keywords: underwater acoustics, naval, maritime, cetaceans

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Authors: Muhammmad Riandhy Anindika Yudhy, Harry Patria, Ramadhani Santoso

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Loss of containment is the primary hazard that process safety management is concerned within the oil and gas industry. Escalation to more serious consequences all begins with the loss of containment, starting with oil and gas release from leakage or spillage from primary containment resulting in pool fire, jet fire and even explosion when reacted with various ignition sources in the operations. Therefore, the heart of process safety management is avoiding loss of containment and mitigating its impact through the implementation of safeguards. The most effective safeguard for the case is an early detection system to alert Operations to take action prior to a potential case of loss of containment. The detection system value increases when applied to a long surface pipeline that is naturally difficult to monitor at all times and is exposed to multiple causes of loss of containment, from natural corrosion to illegal tapping. Based on prior researches and studies, detecting loss of containment accurately in the surface pipeline is difficult. The trade-off between cost-effectiveness and high accuracy has been the main issue when selecting the traditional detection method. The current best-performing method, Real-Time Transient Model (RTTM), requires analysis of closely positioned pressure, flow and temperature (PVT) points in the pipeline to be accurate. Having multiple adjacent PVT sensors along the pipeline is expensive, hence generally not a viable alternative from an economic standpoint.A conceptual approach to combine mathematical modeling using computational fluid dynamics and a supervised machine learning model has shown promising results to predict leakage in the pipeline. Mathematical modeling is used to generate simulation data where this data is used to train the leak detection and localization models. Mathematical models and simulation software have also been shown to provide comparable results with experimental data with very high levels of accuracy. While the supervised machine learning model requires a large training dataset for the development of accurate models, mathematical modeling has been shown to be able to generate the required datasets to justify the application of data analytics for the development of model-based leak detection systems for petroleum pipelines. This paper presents a review of key leak detection strategies for oil and gas pipelines, with a specific focus on crude oil applications, and presents the opportunities for the use of data analytics tools and mathematical modeling for the development of robust real-time leak detection and localization system for surface pipelines. A case study is also presented.

Keywords: pipeline, leakage, detection, AI

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Authors: I. Slim, H. Akkari, A. Ben Abdallah, I. Bhouri, M. Hedi Bedoui

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Osteoporosis is a common disease characterized by low bone mass and deterioration of micro-architectural bone tissue, which provokes an increased risk of fracture. This work treats the texture characterization of trabecular bone radiographs. The aim was to analyze according to clinical research a group of 174 subjects: 87 osteoporotic patients (OP) with various bone fracture types and 87 control cases (CC). To characterize osteoporosis, Fractal and MultiFractal (MF) methods were applied to images for features (attributes) extraction. In order to improve the results, a new method of MF spectrum based on the q-stucture function calculation was proposed and a combination of Fractal and MF attributes was used. The Support Vector Machines (SVM) was applied as a classifier to distinguish between OP patients and CC subjects. The features fusion (fractal and MF) allowed a good discrimination between the two groups with an accuracy rate of 96.22%.

Keywords: fractal, micro-architecture analysis, multifractal, osteoporosis, SVM

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Authors: Lily Farris, Chantel E. Canessa, Rena Heathcote, Susan Shumay, Suzanna V. McRae, Alissa Collingridge, Minna K. Miller

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Objective: To describe how the Lean approach can be applied to improve access, quality and safety of care in an ambulatory pediatric primary care setting. Background: Lean was originally developed by Toyota manufacturing in Japan, and subsequently adapted for use in the healthcare sector. Lean is a systematic approach, focused on identifying and reducing waste within organizational processes, improving patient-centered care and efficiency. Limited literature is available on the implementation of the Lean methodologies in a pediatric ambulatory care setting. Methods: A strategic continuous improvement event or Rapid Process Improvement Workshop (RPIW) was launched with the aim evaluating and structurally supporting clinic workflow, capacity building, sustainability, and ultimately improving access to care and enhancing the patient experience. The Lean process consists of five specific activities: Current state/process assessment (value stream map); development of a future state map (value stream map after waste reduction); identification, quantification and prioritization of the process improvement opportunities; implementation and evaluation of process changes; and audits to sustain the gains. Staff engagement is a critical component of the Lean process. Results: Through the implementation of the RPIW and shifting workload among the administrative team, four hours of wasted time moving between desks and doing work was eliminated from the Administrative Clerks role. To streamline clinic flow, the Nursing Assistants completed patient measurements and vitals for Nurse Practitioners, reducing patient wait times and adding value to the patients visit with the Nurse Practitioners. Additionally, through the Nurse Practitioners engagement in the Lean processes a need was recognized to articulate clinic vision, mission and the alignment of NP role and scope of practice with the agency and Ministry of Health strategic plan. Conclusions: Continuous improvement work in the Pediatric Primary Care NP Clinic has provided a unique opportunity to improve the quality of care delivered and has facilitated further alignment of the daily continuous improvement work with the strategic priorities of the Ministry of Health.

Keywords: ambulatory care, lean, pediatric primary care, system efficiency

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Authors: Debneil Saha Roy

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Weather forecasting is a complex topic and is well suited for analysis by deep learning approaches. With the wide availability of weather observation data nowadays, these approaches can be utilized to identify immediate comparisons between historical weather forecasts and current observations. This work explores the application of deep learning techniques to weather forecasting in order to accurately predict the weather over a given forecast hori­zon. Three deep neural networks are used in this study, namely, Multi-Layer Perceptron (MLP), Long Short Tunn Memory Network (LSTM) and a combination of Convolutional Neural Network (CNN) and LSTM. The predictive performance of these models is compared using two evaluation metrics. The results show that forecasting accuracy increases with an increase in the complexity of deep neural networks.

Keywords: convolutional neural network, deep learning, long short term memory, multi-layer perceptron

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Authors: Kunal Shah, Swati Patel

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Many real world problems involve data which can be considered as multi-label data streams. Efficient methods exist for multi-label classification in non streaming scenarios. However, learning in evolving streaming scenarios is more challenging, as the learners must be able to adapt to change using limited time and memory. Classification is used to predict class of unseen instance as accurate as possible. Multi label classification is a variant of single label classification where set of labels associated with single instance. Multi label classification is used by modern applications, such as text classification, functional genomics, image classification, music categorization etc. This paper introduces the task of multi-label classification, methods for multi-label classification and evolution measure for multi-label classification. Also, comparative analysis of multi label classification methods on the basis of theoretical study, and then on the basis of simulation was done on various data sets.

Keywords: binary relevance, concept drift, data stream mining, MLSC, multiple window with buffer

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Authors: Nutcha Taneepanichskul, Helen C. Hailes, Mark Miodownik

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Plastic waste has emerged as a critical global environmental challenge, primarily driven by the prevalent use of conventional plastics derived from petrochemical refining and manufacturing processes in modern packaging. While these plastics serve vital functions, their persistence in the environment post-disposal poses significant threats to ecosystems. Addressing this issue necessitates approaches, one of which involves the development of biodegradable plastics designed to degrade under controlled conditions, such as industrial composting facilities. It is imperative to note that compostable plastics are engineered for degradation within specific environments and are not suited for uncontrolled settings, including natural landscapes and aquatic ecosystems. The full benefits of compostable packaging are realized when subjected to industrial composting, preventing environmental contamination and waste stream pollution. Therefore, effective sorting technologies are essential to enhance composting rates for these materials and diminish the risk of contaminating recycling streams. In this study, it leverage hyperspectral imaging technology (HSI) coupled with advanced machine learning algorithms to accurately identify various types of plastics, encompassing conventional variants like Polyethylene terephthalate (PET), Polypropylene (PP), Low density polyethylene (LDPE), High density polyethylene (HDPE) and biodegradable alternatives such as Polybutylene adipate terephthalate (PBAT), Polylactic acid (PLA), and Polyhydroxyalkanoates (PHA). The dataset is partitioned into three subsets: a training dataset comprising uncontaminated conventional and biodegradable plastics, a validation dataset encompassing contaminated plastics of both types, and a testing dataset featuring real-world packaging items in both pristine and contaminated states. Five distinct machine learning algorithms, namely Partial Least Squares Discriminant Analysis (PLS-DA), Support Vector Machine (SVM), Convolutional Neural Network (CNN), Logistic Regression, and Decision Tree Algorithm, were developed and evaluated for their classification performance. Remarkably, the Logistic Regression and CNN model exhibited the most promising outcomes, achieving a perfect accuracy rate of 100% for the training and validation datasets. Notably, the testing dataset yielded an accuracy exceeding 80%. The successful implementation of this sorting technology within recycling and composting facilities holds the potential to significantly elevate recycling and composting rates. As a result, the envisioned circular economy for plastics can be established, thereby offering a viable solution to mitigate plastic pollution.

Keywords: biodegradable plastics, sorting technology, hyperspectral imaging technology, machine learning algorithms

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Authors: Shahrzad Zolfagharnassab, Abdul Rashid Mohamed Shariff, Siti Khairunniza Bejo

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Conventional methods for nutrient soil mapping are based on laboratory tests of samples that are obtained from surveys. The time and cost involved in gathering and analyzing soil samples are the reasons that researchers use Predictive Soil Mapping (PSM). PSM can be defined as the development of a numerical or statistical model of the relationship among environmental variables and soil properties, which is then applied to a geographic database to create a predictive map. Kriging is a group of geostatistical techniques to spatially interpolate point values at an unobserved location from observations of values at nearby locations. The main problem with using kriging as an interpolator is that it is excessively data-dependent and requires a large number of closely spaced data points. Hence, there is a need to minimize the number of data points without sacrificing the accuracy of the results. In this paper, an Artificial Neural Networks (ANN) scheme was used to predict macronutrient values at un-sampled points. ANN has become a popular tool for prediction as it eliminates certain difficulties in soil property prediction, such as non-linear relationships and non-normality. Back-propagation multilayer feed-forward network structures were used to predict nitrogen, phosphorous and potassium values in the soil of the study area. A limited number of samples were used in the training, validation and testing phases of ANN (pattern reconstruction structures) to classify soil properties and the trained network was used for prediction. The soil analysis results of samples collected from the soil survey of block C of Sawah Sempadan, Tanjung Karang rice irrigation project at Selangor of Malaysia were used. Soil maps were produced by the Kriging method using 236 samples (or values) that were a combination of actual values (obtained from real samples) and virtual values (neural network predicted values). For each macronutrient element, three types of maps were generated with 118 actual and 118 virtual values, 59 actual and 177 virtual values, and 30 actual and 206 virtual values, respectively. To evaluate the performance of the proposed method, for each macronutrient element, a base map using 236 actual samples and test maps using 118, 59 and 30 actual samples respectively produced by the Kriging method. A set of parameters was defined to measure the similarity of the maps that were generated with the proposed method, termed the sample reduction method. The results show that the maps that were generated through the sample reduction method were more accurate than the corresponding base maps produced through a smaller number of real samples. For example, nitrogen maps that were produced from 118, 59 and 30 real samples have 78%, 62%, 41% similarity, respectively with the base map (236 samples) and the sample reduction method increased similarity to 87%, 77%, 71%, respectively. Hence, this method can reduce the number of real samples and substitute ANN predictive samples to achieve the specified level of accuracy.

Keywords: artificial neural network, kriging, macro nutrient, pattern recognition, precision farming, soil mapping

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Authors: F. J. Adeyeye, D. Eni, K. M. Okedoye

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In this paper we present a Hybrid of Adomian decomposition method (ADM). This is the substitution of a One-step method of Taylor’s series approximation of orders I and II, into the nonlinear part of Adomian decomposition method resulting in a convergent series scheme. This scheme is applied to solve some Logistic problems represented as Abelian differential equation and the results are compared with the actual solution and Runge-kutta of order IV in order to ascertain the accuracy and efficiency of the scheme. The findings shows that the scheme is efficient enough to solve logistic problems considered in this paper.

Keywords: Adomian decomposition method, nonlinear part, one-step method, Taylor series approximation, hybrid of Adomian polynomial, logistic problem, Malthusian parameter, Verhulst Model

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Authors: N. Ghatwary, A. Ahmed, H. Jalab

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In this paper, an approach for the liver tumor detection in computed tomography (CT) images is represented. The detection process is based on classifying the features of target liver cell to either tumor or non-tumor. Fractional differential (FD) is applied for enhancement of Liver CT images, with the aim of enhancing texture and edge features. Later on, a fusion method is applied to merge between the various enhanced images and produce a variety of feature improvement, which will increase the accuracy of classification. Each image is divided into NxN non-overlapping blocks, to extract the desired features. Support vector machines (SVM) classifier is trained later on a supplied dataset different from the tested one. Finally, the block cells are identified whether they are classified as tumor or not. Our approach is validated on a group of patients’ CT liver tumor datasets. The experiment results demonstrated the efficiency of detection in the proposed technique.

Keywords: fractional differential (FD), computed tomography (CT), fusion, aplha, texture features.

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Authors: H. Samadiyeh, R. Khajavi

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A new FD-based procedure, modal finite difference method (MFDM), is proposed for seismic wave propagation modeling, in which simulation is dealt with in the modal space. The method employs eigenvalues of a characteristic matrix formed by appropriate time-space FD stencils. Since MFD runs for different modes are totally independent of each other, MFDM can easily be parallelized while considerable simplicity in parallel-algorithm is also achieved. There is no requirement to any domain-decomposition procedure and inter-core data exchange. More important is the possibility to skip processing of less-significant modes, which enables one to adjust the procedure up to the level of accuracy needed. Thus, in addition to considerable ease of parallel programming, computation and storage costs are significantly reduced. The method is qualified for its efficiency by some numerical examples.

Keywords: Finite Difference Method, Graphics Processing Unit (GPU), Message Passing Interface (MPI), Modal, Wave propagation

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Authors: Jun Gil Ahn, Jong Kang Park, Jong Tae Kim

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In this paper, we analyze major components of activity recognition (AR) in wearable device with 9-axis sensors and sensor fusion filters. 9-axis sensors commonly include 3-axis accelerometer, 3-axis gyroscope and 3-axis magnetometer. We chose sensor fusion filters as Kalman filter and Direction Cosine Matrix (DCM) filter. We also construct sensor fusion data from each activity sensor data and perform classification by accuracy of AR using Naïve Bayes and SVM. According to the classification results, we observed that the DCM filter and the specific combination of the sensing axes are more effective for AR in wearable devices while classifying walking, running, ascending and descending.

Keywords: accelerometer, activity recognition, directiona cosine matrix filter, gyroscope, Kalman filter, magnetometer

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Authors: Wajahat Munir, Muhammad Haseeb, Adeel Anjum, Basit Raza, Ahmad Kamran Malik

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Databases are the key components of the software systems. Due to the exponential growth of data, it is the concern that the data should be accurate and available. The data in databases is vulnerable to internal and external threats, especially when it contains sensitive data like medical or military applications. Whenever the data is changed by malicious intent, data analysis result may lead to disastrous decisions. Autonomic self-healing is molded toward computer system after inspiring from the autonomic system of human body. In order to guarantee the accuracy and availability of data, we propose a technique which on a priority basis, tries to avoid any malicious transaction from execution and in case a malicious transaction affects the system, it heals the system in an isolated mode in such a way that the availability of system would not be compromised. Using this autonomic system, the management cost and time of DBAs can be minimized. In the end, we test our model and present the findings.

Keywords: autonomic computing, self-healing, threat avoidance, security

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Authors: Mohamed R. Al-Mulla, Bader Al-Bader, Firouz K. Ghaaedi, Francisco Sepulveda

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Surface electromyography (sEMG) is utilised in numerous studies on muscle activity. In the beginning, single electrodes were utilised; however, the newest approach is to use an array of electrodes or a grid of electrodes to improve the accuracy of the recorded reading. This research focuses on electrode placement on the biceps brachii, using an array of electrodes placed longitudinal and diagonally on the muscle belly. Trials were conducted on four healthy males, with sEMG signal acquisition from fatiguing isometric contractions. The signal was analysed using the power spectrum density. The separation between the two classes of fatigue (non-fatigue and fatigue) was calculated using the Davies-Bouldin Index (DBI). Results show that higher separability between the fatigue content of the sEMG signal when placed longitudinally, in the same direction as the muscle fibers.

Keywords: array electrodes, biceps brachii, electrode placement, EMG, isometric contractions, muscle fatigue

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Authors: I. K. Sharma

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Over the years world over system are being developed by research labs continuously monitor under water parameters in the coastal waters of sea such as conductivity, salinity, pressure, temperature, chlorophyll and biological blooms at different levels of water column. The research institutions have developed profilers which are launched by ship connected through cable, glider type profilers following underwater trajectory, buoy any driven profilers, wire guided profilers etc. In all these years, the effect was to design autonomous profilers with no cable quality connection, simple operation and on line date transfer in terms accuracy, repeatability, reliability and consistency. Hence for the Ministry of Communication and Information Technology, India sponsored research project to National Institute of Oceanography, GOA, India to design and develop autonomous vertical profilers, it has taken system and AVP has been successfully developed and tested.

Keywords: oceanography, water column, autonomous profiler, buoyancy

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Authors: Sergio J. Calleja, Adria Roca, José D. Santotoribio

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Pancreatic endocrine diseases include pathologies like insulin resistance (IR), prediabetes, and type 2 diabetes mellitus (DM2). Some of them are highly prevalent in the U.S.—40% of U.S. adults have IR, 38% of U.S. adults have prediabetes, and 12% of U.S. adults have DM2—, as reported by the National Center for Biotechnology Information (NCBI). Building upon this imperative, the objective of the present study was to develop a non-invasive test for the assessment of the patient’s pancreatic endocrine function and to evaluate its accuracy in detecting various pancreatic endocrine diseases, such as IR, prediabetes, and DM2. This approach to a routine blood and urine test is based around serum and urine biomarkers. It is made by the combination of several independent public algorithms, such as the Adult Treatment Panel III (ATP-III), triglycerides and glucose (TyG) index, homeostasis model assessment-insulin resistance (HOMA-IR), HOMA-2, and the quantitative insulin-sensitivity check index (QUICKI). Additionally, it incorporates essential measurements such as the creatinine clearance, estimated glomerular filtration rate (eGFR), urine albumin-to-creatinine ratio (ACR), and urinalysis, which are helpful to achieve a full image of the patient’s pancreatic endocrine disease. To evaluate the estimated accuracy of this test, an iterative process was performed by a machine learning (ML) algorithm, with a training set of 9,391 patients. The sensitivity achieved was 97.98% and the specificity was 99.13%. Consequently, the area under the receiver operating characteristic (AUROC) curve, the positive predictive value (PPV), and the negative predictive value (NPV) were 92.48%, 99.12%, and 98.00%, respectively. The algorithm was validated with a randomized controlled trial (RCT) with a target sample size (n) of 314 patients. However, 50 patients were initially excluded from the study, because they had ongoing clinically diagnosed pathologies, symptoms or signs, so the n dropped to 264 patients. Then, 110 patients were excluded because they didn’t show up at the clinical facility for any of the follow-up visits—this is a critical point to improve for the upcoming RCT, since the cost of each patient is very high and for this RCT almost a third of the patients already tested were lost—, so the new n consisted of 154 patients. After that, 2 patients were excluded, because some of their laboratory parameters and/or clinical information were wrong or incorrect. Thus, a final n of 152 patients was achieved. In this validation set, the results obtained were: 100.00% sensitivity, 100.00% specificity, 100.00% AUROC, 100.00% PPV, and 100.00% NPV. These results suggest that this approach to a routine blood and urine test holds promise in providing timely and accurate diagnoses of pancreatic endocrine diseases, particularly among individuals aged 40 and above. Given the current epidemiological state of these type of diseases, these findings underscore the significance of early detection. Furthermore, they advocate for further exploration, prompting the intention to conduct a clinical trial involving 26,000 participants (from March 2025 to December 2026).

Keywords: algorithm, diabetes, laboratory medicine, non-invasive

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Authors: Leonardo Parra, Daniel Martínez, Jorge Pizarro, Fernando Ortega, Manuel Chacón-Fuentes, Andrés Quiroz

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Control of Dalaca pallens or blackworms, one of the most important hypogeous pest in grassland in southern Chile, is based on the use of broad-spectrum insecticides such as organophosphates and pyrethroids. However, the rapid development of insecticide resistance in field populations of this insect and public concern over the environmental impact of these insecticides has resulted in the search for other control methods. Specifically, the use of endophyte fungi for controlling pest has emerged as an interesting and promising strategy. Endophytes from ryegrass (Lolium perenne), establish a biotrophic relationship with the host, defined as mutualistic symbiosis. The plant-fungi association produces alkaloids where peramine is the main toxic substance against Listronotus bonariensis, the most important epigean pest of ryegrass. Nevertheless, the effect of peramina on others pest insects, such as D. pallens, to our knowledge has not been studied, and also its possible metabolization in the body of the larvae. Therefore, we addressed the following research question: Do larvae of D. pallens store peramine after consumption of ryegrass endophyte infected (E+)? For this, specimens of blackworms were fed with ryegrass plant of seven experimental lines and one commercial cultivar endophyte free (E-) sown at the Instituto de Investigaciones Agropecuarias Carillanca (Vilcún, Chile). Once the feeding period was over, ten larvae of each treatment were examined. Individuals were dissected, and their gut was removed to exclude any influence of remaining material. The rest of the larva's body was dried at 60°C by 24-48 h and ground into a fine powder using a mortar. 25 mg of dry powder was transferred to a microcentrifuge tube and extracted in 1 mL of a mixture of methanol:water:formic acid. Then, the samples were centrifuged at 16,000 rpm for 3 min, and the supernatant was colected and injected in the liquid chromatography of high resolution (HPLC). The results confirmed the presence of peramine in the larva's body of D. pallens. The insects that fed the experimental lines LQE-2 and LQE-6 were those where peramine was present in high proportion (0.205 and 0.199 ppm, respectively); while LQE-7 and LQE-3 obtained the lowest concentrations of the alkaloid (0.047 and 0.053 ppm, respectively). Peramine was not detected in the insects when the control cultivar Jumbo (E-) was tested. These results evidenced the storage and metabolism of peramine during consumption of the larvae. However, the effect of this alkaloid present in 'future ryegrass cultivars' (LQE-2 and LQE-6) on the performance and survival of blackworms must be studied and confirmed experimentally.

Keywords: blackworms, HPLC, alkaloid, pest

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Authors: Spoorthi Srupad

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Environmental Impact Assessment (EIA) stands as a critical engineering application facilitated by Earth Resources and Environmental Remote Sensing. Employing advanced technologies, this process enables a systematic evaluation of potential environmental impacts arising from engineering projects. Remote sensing techniques, including satellite imagery and geographic information systems (GIS), play a pivotal role in providing comprehensive data for assessing changes in land cover, vegetation, water bodies, and air quality. This abstract delves into the significance of EIA in engineering, emphasizing its role in ensuring sustainable and environmentally responsible practices. The integration of remote sensing technologies enhances the accuracy and efficiency of impact assessments, contributing to informed decision-making and the mitigation of adverse environmental consequences associated with engineering endeavors.

Keywords: environmental impact assessment, engineering applications, sustainability, environmental monitoring, remote sensing, geographic information systems, environmental management

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Authors: R. K. Chaurasiya, N. D. Londhe, S. Ghosh

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The study of the electrical signals produced by neural activities of human brain is called Electroencephalography. In this paper, we propose an automatic and efficient EEG signal classification approach. The proposed approach is used to classify the EEG signal into two classes: epileptic seizure or not. In the proposed approach, we start with extracting the features by applying Discrete Wavelet Transform (DWT) in order to decompose the EEG signals into sub-bands. These features, extracted from details and approximation coefficients of DWT sub-bands, are used as input to Principal Component Analysis (PCA). The classification is based on reducing the feature dimension using PCA and deriving the support-vectors using Support Vector Machine (SVM). The experimental are performed on real and standard dataset. A very high level of classification accuracy is obtained in the result of classification.

Keywords: discrete wavelet transform, electroencephalogram, pattern recognition, principal component analysis, support vector machine

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Authors: Muhammad Saqlain, Nazar Abbas Saqib, Muazzam A. Khan

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Heart Failure (HF) has become the major health problem in our society. The prevalence of HF has increased as the patient’s ages and it is the major cause of the high mortality rate in adults. A successful identification and progression of HF can be helpful to reduce the individual and social burden from this syndrome. In this study, we use a real data set of cardiac patients to propose a classification model for the identification and progression of HF. The data set has divided into three age groups, namely young, adult, and old and then each age group have further classified into four classes according to patient’s current physical condition. Contemporary Data Mining classification algorithms have been applied to each individual class of every age group to identify the HF. Decision Tree (DT) gives the highest accuracy of 90% and outperform all other algorithms. Our model accurately diagnoses different stages of HF for each age group and it can be very useful for the early prediction of HF.

Keywords: decision tree, heart failure, data mining, classification model

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Authors: Naveed Tahir, Adnan Maqsood

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Cognitive workload is a significant factor affecting user performance, and it has been broadly investigated for its application in ergonomics as well as in designing and optimizing effective human-machine interactions. It is mentally challenging to maneuver an aircraft, and pilots must control the aircraft and adequately communicate to the verbal-auditory stimuli. Several physiological measures have long been researched and used to demonstrate the cognitive workload. In our current study, we have summarized recent findings of the effectiveness, accuracy, and applicability of commonly used physiological measures in evaluating cognitive workload. We have also highlighted on the advancements in physiological measures. The strength and limitations of physiological measures have also been discussed to assess the cognitive workload of people, especially the aircrews in laboratory settings and real-time situations. We have presented the research findings of the physiological measures to base suggestions on the proper applications of the measures and settings demanding the use of single measure or their combinations.

Keywords: aircrew, cognitive workload, subjective measure, physiological measure, performance measure

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Authors: Zhao Pengcheng, Yin Siyuan

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Fintech is the new hot topic of the 21st century, a discipline that combines financial theory with computer modelling. It can provide both digital analysis methods for investment banks and investment decisions for users. Given the variety of services available, it is necessary to provide a superior method of requirements elicitation to ensure that users' needs are addressed in the software development process. The accuracy of traditional software requirements elicitation methods is not sufficient, so this study attempts to use a multi-perspective based requirements heuristic framework. Methods such as interview and questionnaire combination, card sorting, and model driven are proposed. The collection results from PCA show that the new methods can better help with requirements elicitation. However, the method has some limitations and, there are some efficiency issues. However, the research in this paper provides a good theoretical extension that can provide researchers with some new research methods and perspectives viewpoints.

Keywords: requirement elicitation, FinTech, mobile application, survey, interview, model-driven

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Authors: Katarzyna Miśkiewicz, Magdalena Lasoń-Rydel, Małgorzata Krępska, Katarzyna Sieczyńska, Iwona Masłowska-Lipowicz, Katarzyna Ławińska

Abstract:

The aim of the study was to analyse a selection of 12 pasteurised products made from fruit and vegetables, such as fruit juices, fruit drinks, jams, marmalades and jam produced by small regional factories as well as large food corporations. The research was carried out as part of the project "Innovative system of healthy and regional food distribution", funded by the Ministry of Education and Science (Poland), which aims to create an economically and organisationally strong agri-food industry in Poland through effective cooperation between scientific and socio-economic actors. The main activities of the project include support for the creation of new distribution channels for regional food products and their easy access to a wide group of potential customers while maintaining the highest quality standards. One of the key areas of the project is food quality analyses conducted to indicate the competitive advantage of regional products. Presented here are studies on the content of sugars, vitamin C, preservatives, synthetic colours, sweeteners, sodium and potassium, as well as studies on the microbiological purity of selected products made from fruit and vegetables. The composition of products made from fruit and vegetables varies greatly and depends on both the type of raw material and the way it is processed. Of the samples tested, fruit drinks contained the least amount of sugars, and jam and marmalade made by large producers and bought in large chain stores contained the most. However, the low sugar content of some fruit drinks is due to the presence of the sweetener sucralose in their composition. The vitamin C content of the samples varied, being higher in products where it was added during production. All products made in small local factories were free of food additives such as preservatives, sweeteners and synthetic colours, indicating their superiority over products made by large producers. Products made in small local factories were characterised by a relatively high potassium content. The microbiological purity of commercial products was confirmed - no Salmonella spp. were detected, and the number of mesophilic bacteria, moulds, yeasts, and β-glucuronidase-positive E. coli was below the limit of quantification.

Keywords: fruit and vegetable products, sugars, food additives, HPLC, ICP-OES

Procedia PDF Downloads 94

Authors: Ying Ying Liu, Austin Cooke, Parimala Thulasiraman

Abstract:

Internet of Things (IoT) has attracted a recent spark in research on Internet of Vehicles (IoV). In this paper, we focus on one research area in IoV: preserving location privacy of vehicle data. We discuss existing location privacy preserving techniques and provide a scheme for evaluating these techniques under IoV traffic condition. We propose a different strategy in applying Differential Privacy using k-d tree data structure to preserve location privacy and experiment on real world Gowalla data set. We show that our strategy produces differentially private data, good preservation of utility by achieving similar regression accuracy to the original dataset on an LSTM (Long Term Short Term Memory) neural network traffic predictor.

Keywords: differential privacy, internet of things, internet of vehicles, location privacy, privacy preservation scheme

Procedia PDF Downloads 179

Authors: A. Kavya Sri, K. Sai Vineela, R. Vanitha, S. Rohith

Abstract:

Large pieces of ice that break from the glaciers are known as icebergs. The threat that icebergs pose to navigation, production of offshore oil and gas services, and underwater pipelines makes their detection crucial. In this project, an automated iceberg tracking method using deep learning techniques and satellite images of icebergs is to be developed. With a temporal resolution of 12 days and a spatial resolution of 20 m, Sentinel-1 (SAR) images can be used to track iceberg drift over the Southern Ocean. In contrast to multispectral images, SAR images are used for analysis in meteorological conditions. This project develops a web-based graphical user interface to detect and track icebergs using sentinel-1 images. To track the movement of the icebergs by using temporal images based on their latitude and longitude values and by comparing the center and area of all detected icebergs. Testing the accuracy is done by precision and recall measures.

Keywords: synthetic aperture radar (SAR), icebergs, deep learning, spatial resolution, temporal resolution

Procedia PDF Downloads 91

Authors: Sheldon McCall, Miao Yu, Liyun Gong, Shigang Yue, Stefanos Kollias

Abstract:

Falls are a significant health concern for older adults globally, and prompt identification is critical to providing necessary healthcare support. Our study proposes a new fall detection method using computer vision based on modern deep learning techniques. Our approach involves training a trans- former model on a large 2D pose dataset for general action recognition, followed by transfer learning. Specifically, we freeze the first few layers of the trained transformer model and train only the last two layers for fall detection. Our experimental results demonstrate that our proposed method outperforms both classical machine learning and deep learning approaches in fall/non-fall classification. Overall, our study suggests that our proposed methodology could be a valuable tool for identifying falls.

Keywords: healthcare, fall detection, transformer, transfer learning

Procedia PDF Downloads 147

Authors: Asmaa M. Hussein, Amr Wassal, Ahmed Farouk Al-Sadek, A. F. Abd El-Rahman

Abstract:

In this study, a multivariate analysis of potato spectroscopic data was presented to detect the presence of brown rot disease or not. Near-Infrared (NIR) spectroscopy (1,350-2,500 nm) combined with multivariate analysis was used as a rapid, non-destructive technique for the detection of brown rot disease in potatoes. Spectral measurements were performed in 565 samples, which were chosen randomly at the infection place in the potato slice. In this study, 254 infected and 311 uninfected (brown rot-free) samples were analyzed using different advanced statistical analysis techniques. The discrimination performance of different multivariate analysis techniques, including classification, pre-processing, and dimension reduction, were compared. Applying a random forest algorithm classifier with different pre-processing techniques to raw spectra had the best performance as the total classification accuracy of 98.7% was achieved in discriminating infected potatoes from control.

Keywords: Brown rot disease, NIR spectroscopy, potato, random forest

Procedia PDF Downloads 190

Authors: Norshakirah Ab.Aziz, Nurul Atiqah Jamaluddin

Abstract:

Consequently, electronic word-of-mouth (e-WoM) becomes one of the resources in the decision making process and considered a valuable marketing channel for consumers and organizations. Admittedly, there is increasing concern on the accuracy and genuine of e-WoM content because consumers prefer to look out product or service information available online. Thus, the focus of this study is to propose a model and guidelines how to select trusted online review content according to domain chosen –undergrad students smartphone online review. Undeniable, mobile devices like smartphone has now become a necessity in today are daily life to complete our daily chores. The model and guideline focused on product competency review and the message integrity. In other words, this study aims to enable consumers to identify trusted online review content, which helps them in buying decisions.

Keywords: electronic word of mouth, e-WoM, WoM, online review

Procedia PDF Downloads 328