Search results for: neural nets

Authors: Hediyeh Talebi, Shokoofeh Ghiam, Changiz Eslahchi

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Traditionally, Alzheimer’s disease research has focused on genes with significant fold changes, potentially neglecting subtle but biologically important alterations. Our study introduces an integrative approach that highlights genes crucial to underlying biological processes, regardless of their fold change magnitude. Alzheimer's Single-cell RNA-seq data related to the peripheral blood mononuclear cells (PBMC) was extracted from the Gene Expression Omnibus (GEO). After quality control, normalization, scaling, batch effect correction, and clustering, differentially expressed genes (DEGs) were identified with adjusted p-values less than 0.05. These DEGs were categorized based on cell-type, resulting in four datasets, each corresponding to a distinct cell type. To distinguish between cells from healthy individuals and those with Alzheimer's, an adversarial autoencoder with a classifier was employed. This allowed for the separation of healthy and diseased samples. To identify the most influential genes in this classification, the weight matrices in the network, which includes the encoder and classifier components, were multiplied, and focused on the top 20 genes. The analysis revealed that while some of these genes exhibit a high fold change, others do not. These genes, which may be overlooked by previous methods due to their low fold change, were shown to be significant in our study. The findings highlight the critical role of genes with subtle alterations in diagnosing Alzheimer's disease, a facet frequently overlooked by conventional methods. These genes demonstrate remarkable discriminatory power, underscoring the need to integrate biological relevance with statistical measures in gene prioritization. This integrative approach enhances our understanding of the molecular mechanisms in Alzheimer’s disease and provides a promising direction for identifying potential therapeutic targets.

Keywords: alzheimer's disease, single-cell RNA-seq, neural networks, blood biomarkers

Procedia PDF Downloads 36

Authors: M. P. Dandekar, A. P. Bharne, P. T. Borkar, D. M. Kokare, N. K. Subhedar

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Ethanol ingestion by the mother ensue adverse consequences for her offspring. Herein, we examine the behavioral phenotype and neural substrate of the offspring of the mother on ethanol. Female rats were fed with ethanol-containing liquid diet from 8 days prior of conception and continued till 25 days post-parturition to coincide with weaning. Behavioral changes associated with anxiety, depression and learning and memory were assessed in the offspring, after they attained adulthood (day 85), using elevated plus maze (EPM), forced swim (FST) and novel object recognition tests (NORT), respectively. The offspring of the alcoholic mother, compared to those of the pair-fed mother, spent significantly more time in closed arms of EPM and showed more immobility time in FST. Offspring at the age of 25 and 85 days failed to discriminate between novel versus familiar object in NORT, thus reflecting anxiogenic, depressive and amnesic phenotypes. Neuropeptide cocaine- and amphetamine-regulated transcript peptide (CART) is known to be involved in central effects of ethanol and hence selected for the current study. Twenty-five days old pups of the alcoholic mother showed significant augmentation in CART-immunoreactivity in the cells of Edinger-Westphal (EW) nucleus and lateral hypothalamus. However, a significant decrease in CART-immunoreactivity was seen in nucleus accumbens shell (AcbSh), lateral part of bed nucleus of the stria terminalis (BNSTl), locus coeruleus (LC), hippocampus (CA1, CA2 and CA3), and arcuate nucleus (ARC) of the pups and/or adults offspring. While no change in the CART-immunoreactive fibers of AcbSh and BNSTl, CA2 and CA3 was noticed in the 25 days old pups, the CART-immunoreactive cells in EW and paraventricular nucleus (PVN), and fibers in the central nucleus of amygdala of 85 days old offspring remained unaffected. We suggest that the endogenous CART system in these discrete areas, among other factors, may be a causal to the abnormalities in the next generation of an alcoholic mother.

Keywords: anxiety, depression, CART, ethanol, immunocytochemistry

Procedia PDF Downloads 373

Authors: Altilio Rosa, Chirico Francesco, Foglia Goffredo

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In recent years, the number of Unmanned Aerial Vehicles (UAVs) has significantly increased. The rapid development of commercial and recreational drones makes them an important part of our society. Despite the growing list of their applications, these vehicles pose a huge threat to civil and military installations: detection, classification and neutralization of such flying objects become an urgent need. Radar is an effective remote sensing tool for detecting and tracking flying objects, but scenarios characterized by the presence of a high number of tracks related to flying birds make especially challenging the drone detection task: operator PPI is cluttered with a huge number of potential threats and his reaction time can be severely affected. Flying birds compared to UAVs show similar velocity, RADAR cross-section and, in general, similar characteristics. Building from the absence of a single feature that is able to distinguish UAVs and birds, this paper uses a multiple features approach where an original feature selection technique is developed to feed binary classifiers trained to distinguish birds and UAVs. RADAR tracks acquired on the field and related to different UAVs and birds performing various trajectories were used to extract specifically designed target movement-related features based on velocity, trajectory and signal strength. An optimization strategy based on a genetic algorithm is also introduced to select the optimal subset of features and to estimate the performance of several classification algorithms (Neural network, SVM, Logistic regression…) both in terms of the number of selected features and misclassification error. Results show that the proposed methods are able to reduce the dimension of the data space and to remove almost all non-drone false targets with a suitable classification accuracy (higher than 95%).

Keywords: birds, classification, machine learning, UAVs

Procedia PDF Downloads 188

Authors: Richard Ren

Abstract:

Air pollution is a serious danger to international well-being and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.Air pollution is a serious danger to international wellbeing and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.Air pollution is a serious danger to international wellbeing and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.

Keywords: air quality prediction, air pollution, artificial intelligence, machine learning algorithms

Procedia PDF Downloads 95

Authors: Gholba Niranjan Dilip, Anil Kumar

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Conduct of accurate reliable mapping of oil palm plantations and census of individual palm trees is a huge challenge. This study addresses this challenge and developed an optimized solution implemented deep learning techniques on remote sensing data. The oil palm is a very important tropical crop. To improve its productivity and land management, it is imperative to have accurate census over large areas. Since, manual census is costly and prone to approximations, a methodology for automated census using panchromatic images from Cartosat-2, SkySat and World View-3 satellites is demonstrated. It is selected two different study sites in Indonesia. The customized set of training data and ground-truth data are created for this study from Cartosat-2 images. The pre-trained model of Single Shot MultiBox Detector (SSD) Lite MobileNet V2 Convolutional Neural Network (CNN) from the TensorFlow Object Detection API is subjected to transfer learning on this customized dataset. The SSD model is able to generate the bounding boxes for each oil palm and also do the counting of palms with good accuracy on the panchromatic images. The detection yielded an F-Score of 83.16 % on seven different images. The detections are buffered and dissolved to generate polygons demarcating the boundaries of the oil palm plantations. This provided the area under the plantations and also gave maps of their location, thereby completing the automated census, with a fairly high accuracy (≈100%). The trained CNN was found competent enough to detect oil palm crowns from images obtained from multiple satellite sensors and of varying temporal vintage. It helped to estimate the increase in oil palm plantations from 2014 to 2021 in the study area. The study proved that high-resolution panchromatic satellite image can successfully be used to undertake census of oil palm plantations using CNNs.

Keywords: object detection, oil palm tree census, panchromatic images, single shot multibox detector

Procedia PDF Downloads 141

Authors: K. R. Roopesh Bharatwaj, Avinash Maharana, Favour Tobi Aborisade, Roger Young

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Presently, there are few cases of complete automation of drones and its allied intelligence capabilities. In essence, the potential of the drone has not yet been fully utilized. This paper presents feasible methods to build an intelligent drone with smart capabilities such as self-driving, and obstacle avoidance. It does this through advanced Reinforcement Learning Techniques and performs object detection using latest advanced algorithms, which are capable of processing light weight models with fast training in real time instances. For the scope of this paper, after researching on the various algorithms and comparing them, we finally implemented the Deep-Q-Networks (DQN) algorithm in the AirSim Simulator. In future works, we plan to implement further advanced self-driving and object detection algorithms, we also plan to implement voice-based speech recognition for the entire drone operation which would provide an option of speech communication between users (People) and the drone in the time of unavoidable circumstances. Thus, making drones an interactive intelligent Robotic Voice Enabled Service Assistant. This proposed drone has a wide scope of usability and is applicable in scenarios such as Disaster management, Air Transport of essentials, Agriculture, Manufacturing, Monitoring people movements in public area, and Defense. Also discussed, is the entire drone communication based on the satellite broadband Internet technology for faster computation and seamless communication service for uninterrupted network during disasters and remote location operations. This paper will explain the feasible algorithms required to go about achieving this goal and is more of a reference paper for future researchers going down this path.

Keywords: convolution neural network, natural language processing, obstacle avoidance, satellite broadband technology, self-driving

Procedia PDF Downloads 220

Authors: Reihane Mehrparvar

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Human age could exceed further by altering gene expression through food intaking, although as a consequence of recent century eating patterns, human life-span getting shorter by emerging irregulating in autophagy mechanism, insulin, leptin, gut microbiota which are important etiological factors of type-2 diabetes, obesity, infertility, cancer, metabolic and autoimmune diseases. However, restricted calorie intake and vigorous exercise might be beneficial for losing weight and metabolic regulation in a short period but could not be implementable in the long term as a way of life. Therefore, the lack of a dietary program that is compatible with the genes of the body is essential. Sweet and high-glycemic-index (HGI) foods were associated with type-2 diabetes and cancer morbidity. The neuropsychological perspective characterizes the inclination of sweet and HGI-food consumption as addictive behavior; hence this process engages preference of gut microbiota, neural node, and dopaminergic functions. Moreover, meal composition is not the only factor that affects body hemostasis. In this narrative review, it is believed to attempt to investigate how the body responded to different food intakes and represent an accurate model based on current evidence. Eating frequently and ingesting unassimilable protein and carbohydrates may not be compatible with human genes and could cause impairments in the self-renovation mechanism. This trajectory indicates our body is more adapted to starvation and eating animal meat and marrow. Here has been recommended a model that takes into account three important factors: frequent eating, meal composition, and circadian rhythm, which may offer a promising intervention for obesity, inflammation, cardiovascular, autoimmune disorder, type-2 diabetes, insulin resistance, infertility, and cancer through intensifying autophagy-mechanism and eliminate medical costs.

Keywords: metabolic disease, anti-aging, type-2 diabetes, autophagy

Procedia PDF Downloads 61

Authors: Velimir "monty" Vesselinov, Trais Kliplhuis, Hope Jasperson

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Artificial intelligence (AI) is a powerful tool in the geothermal energy sector, aiding in both exploration and utilization. Identifying promising geothermal sites can be challenging due to limited surface indicators and the need for expensive drilling to confirm subsurface resources. Geothermal reservoirs can be located deep underground and exhibit complex geological structures, making traditional exploration methods time-consuming and imprecise. AI algorithms can analyze vast datasets of geological, geophysical, and remote sensing data, including satellite imagery, seismic surveys, geochemistry, geology, etc. Machine learning algorithms can identify subtle patterns and relationships within this data, potentially revealing hidden geothermal potential in areas previously overlooked. To address these challenges, a SIML (Science-Informed Machine Learning) technology has been developed. SIML methods are different from traditional ML techniques. In both cases, the ML models are trained to predict the spatial distribution of an output (e.g., pressure, temperature, heat flux) based on a series of inputs (e.g., permeability, porosity, etc.). The traditional ML (a) relies on deep and wide neural networks (NNs) based on simple algebraic mappings to represent complex processes. In contrast, the SIML neurons incorporate complex mappings (including constitutive relationships and physics/chemistry models). This results in ML models that have a physical meaning and satisfy physics laws and constraints. The prototype of the developed software, called GeoTGO, is accessible through the cloud. Our software prototype demonstrates how different data sources can be made available for processing, executed demonstrative SIML analyses, and presents the results in a table and graphic form.

Keywords: science-informed machine learning, artificial inteligence, exploration, utilization, hidden geothermal

Procedia PDF Downloads 15

Authors: Jeffrey Chase

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Historically the profession of Occupational Therapy was closely tied to the treatment of those suffering from mental illness; more recently, and especially in the U.S., the percentage of OTs identifying as working in the mental health area has declined significantly despite the estimate that by 2020 behavioral health disorders will surpass physical illnesses as the major cause of disability worldwide. In the U.S. less than 10% of OTs identify themselves as working with the mentally ill and/or practicing in mental health settings. Such a decline has implications for both those suffering from mental illness and the profession of Occupational Therapy. One reason cited for the decline of OT in mental health has been the limited research in the discipline addressing mental health practice. Despite significant advances in technology and growth in the field of neuroscience, major institutions and funding sources such as the National Institute of Mental Health (NIMH) have noted that research into the etiology and treatment of mental illness have met with limited success over the past 25 years. One major reason posited by NIMH is that research has been limited by how we classify individuals, that being mostly on what is observable. A new classification system being developed by NIMH, the Research Domain Criteria (RDoc), has the goal to look beyond just descriptors of disorders for common neural, genetic, and physiological characteristics that cut across multiple supposedly separate disorders. The hope is that by classifying individuals along RDoC measures that both reliability and validity will improve resulting in greater advances in the field. As a result of this change NIH and NIMH will prioritize research funding to those projects using the RDoC model. Multiple disciplines across many different setting will be required for RDoC or similar classification systems to be developed. During this shift in research methodology OT has an opportunity to reassert itself into the research and treatment of mental illness, both in developing new ways to more validly classify individuals, and to document the legitimacy of previously ill-defined and validated disorders such as sensory integration.

Keywords: global mental health and neuroscience, research opportunities for ot, greater integration of ot in mental health research, research and funding opportunities, research domain criteria (rdoc)

Procedia PDF Downloads 246

Authors: Talal Alsulaiman, Khaldoun Khashanah

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In this paper, we provided a literature survey on the artificial stock problem (ASM). The paper began by exploring the complexity of the stock market and the needs for ASM. ASM aims to investigate the link between individual behaviors (micro level) and financial market dynamics (macro level). The variety of patterns at the macro level is a function of the AFM complexity. The financial market system is a complex system where the relationship between the micro and macro level cannot be captured analytically. Computational approaches, such as simulation, are expected to comprehend this connection. Agent-based simulation is a simulation technique commonly used to build AFMs. The paper proceeds by discussing the components of the ASM. We consider the roles of behavioral finance (BF) alongside the traditionally risk-averse assumption in the construction of agent's attributes. Also, the influence of social networks in the developing of agents’ interactions is addressed. Network topologies such as a small world, distance-based, and scale-free networks may be utilized to outline economic collaborations. In addition, the primary methods for developing agents learning and adaptive abilities have been summarized. These incorporated approach such as Genetic Algorithm, Genetic Programming, Artificial neural network and Reinforcement Learning. In addition, the most common statistical properties (the stylized facts) of stock that are used for calibration and validation of ASM are discussed. Besides, we have reviewed the major related previous studies and categorize the utilized approaches as a part of these studies. Finally, research directions and potential research questions are argued. The research directions of ASM may focus on the macro level by analyzing the market dynamic or on the micro level by investigating the wealth distributions of the agents.

Keywords: artificial stock markets, market dynamics, bounded rationality, agent based simulation, learning, interaction, social networks

Procedia PDF Downloads 331

Authors: Sandra Baez, Mariana Pino, Mildred Berrio, Hernando Santamaria-Garcia, Lucas Sedeno, Adolfo Garcia, Sol Fittipaldi, Agustin Ibanez

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Schadenfreude refers to the perceiver’s experience of pleasure at another’s misfortune. This is a multidetermined emotion which can be evoked by hostile feelings and envy. The experience of Schadenfreude engages mechanisms implicated in diverse social cognitive processes. For instance, Schadenfreude involves heightened reward processing, accompanied by increased striatal engagement and it interacts with mentalizing and perspective-taking abilities. Patients with Huntington's disease (HD) exhibit reductions of Schadenfreude experience, suggesting a role of striatal degeneration in such an impairment. However, no study has directly assessed the relationship between regional brain atrophy in HD and reduced Schadenfreude. This study investigated whether gray matter (GM) atrophy in HD patients correlates with ratings of Schadenfreude. First, we compared the performance of 20 HD patients and 23 controls on an experimental task designed to trigger Schadenfreude and envy (another social emotion acting as a control condition). Second, we compared GM volume between groups. Third, we examined brain regions where atrophy might be associated with specific impairments in the patients. Results showed that while both groups showed similar ratings of envy, HD patients reported lower Schadenfreude. The latter pattern was related to atrophy in regions of the reward system (ventral striatum) and the mentalizing network (precuneus and superior parietal lobule). Our results shed light on the intertwining of reward and socioemotional processes in Schadenfreude, while offering novel evidence about their neural correlates. In addition, our results open the door to future studies investigating social emotion processing in other clinical populations characterized by striatal or mentalizing network impairments (e.g., Parkinson’s disease, schizophrenia, autism spectrum disorders).

Keywords: envy, Gray matter atrophy, Huntigton's disease, Schadenfreude, social emotions

Procedia PDF Downloads 311

Authors: Kevin Wang, Jian Ge, Yinan Zhao, Kevin Willis

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Kepler has discovered over 4000 exoplanets and candidates. However, current transit planet detection techniques based on the wavelet analysis and the Box Least Squares (BLS) algorithm have limited sensitivity in detecting minor planets with a low signal-to-noise ratio (SNR) and long periods with only 3-4 repeated signals over the mission lifetime of 4 years. This paper presents a novel precise-period transit signal detection methodology based on a new Graphics Processing Unit (GPU) Fast Folding algorithm in conjunction with a Convolutional Neural Network (CNN) to detect low SNR and/or long-period transit planet signals. A comparison with BLS is conducted on both simulated light curves and real data, demonstrating that the new method has higher speed, sensitivity, and reliability. For instance, the new system can detect transits with SNR as low as three while the performance of BLS drops off quickly around SNR of 7. Meanwhile, the GPU Fast Folding method folds light curves 25 times faster than BLS, a significant gain that allows exoplanet detection to occur at unprecedented period precision. This new method has been tested with all known transit signals with 100% confirmation. In addition, this new method has been successfully applied to the Kepler of Interest (KOI) data and identified a few new Earth-sized Ultra-short period (USP) exoplanet candidates and habitable planet candidates. The results highlight the promise for GPU Fast Folding as a replacement to the traditional BLS algorithm for finding small and/or long-period habitable and Earth-sized planet candidates in-transit data taken with Kepler and other space transit missions such as TESS(Transiting Exoplanet Survey Satellite) and PLATO(PLAnetary Transits and Oscillations of stars).

Keywords: algorithms, astronomy data analysis, deep learning, exoplanet detection methods, small planets, habitable planets, transit photometry

Procedia PDF Downloads 194

Authors: Bin Feng, Zhulin Zong

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Radar systems are critical in the modern military, aviation, and maritime operations, and their proper functioning is essential for the success of these operations. However, due to the complexity and sensitivity of radar systems, they are susceptible to various faults that can significantly affect their performance. Traditional radar fault diagnosis strategies rely on expert knowledge and rule-based approaches, which are often limited in effectiveness and require a lot of time and resources. Deep learning has recently emerged as a promising approach for fault diagnosis due to its ability to learn features and patterns from large amounts of data automatically. In this paper, we propose a radar fault diagnosis strategy based on deep learning that can accurately identify and classify faults in radar systems. Our approach uses convolutional neural networks (CNN) to extract features from radar signals and fault classify the features. The proposed strategy is trained and validated on a dataset of measured radar signals with various types of faults. The results show that it achieves high accuracy in fault diagnosis. To further evaluate the effectiveness of the proposed strategy, we compare it with traditional rule-based approaches and other machine learning-based methods, including decision trees, support vector machines (SVMs), and random forests. The results demonstrate that our deep learning-based approach outperforms the traditional approaches in terms of accuracy and efficiency. Finally, we discuss the potential applications and limitations of the proposed strategy, as well as future research directions. Our study highlights the importance and potential of deep learning for radar fault diagnosis. It suggests that it can be a valuable tool for improving the performance and reliability of radar systems. In summary, this paper presents a radar fault diagnosis strategy based on deep learning that achieves high accuracy and efficiency in identifying and classifying faults in radar systems. The proposed strategy has significant potential for practical applications and can pave the way for further research.

Keywords: radar system, fault diagnosis, deep learning, radar fault

Procedia PDF Downloads 59

Authors: Roongtiwa Chobchuen, Angkana Srikhan, Pattra Wattanapan

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Background: Neurogenic bowel is common condition after spinal cord injury. Most of spinal cord injured patients have motor weakness, mobility impairment which leads to constipation. Moreover, the neural pathway involving bowel function is interrupted. Therefore, the bowel management program should be implemented in nursing care in the earliest time after the onset of the disease to prevent the morbidity and mortality. Objective: To study the outcome of bowel management program of the patients with spinal cord injury who admitted for rehabilitation program. Study design: Descriptive study. Setting: Rehabilitation ward in Srinagarind Hospital. Populations: patients with subacute to chronic spinal cord injury who admitted at rehabilitation ward, Srinagarind hospital, aged over 18 years old. Instrument: The neurogenic bowel dysfunction score (NBDS) was used to determine the severity of neurogenic bowel. Procedure and statistical analysis: All participants were asked to complete the demographic data; age gender, duration of disease, diagnosis. The individual bowel function was assessed using NBDS at admission. The patients and caregivers were trained by nurses about the bowel management program which consisted of diet modification, abdominal massage, digital stimulation, stool evacuation including medication and physical activity. The outcome of the bowel management program was assessed by NBDS at discharge. The chi-square test was used to detect the difference in severity of neurogenic bowel at admission and discharge. Results: Sixteen spinal cord injured patients were enrolled in the study (age 45 ± 17 years old, 69% were male). Most of them (50%) were tetraplegia. On the admission, 12.5%, 12.5%, 43.75% and 31.25% were categorized as very minor (NBDS 0-6), minor (NBDS 7-9), moderate (NBDS 10-13) and severe (NBDS 14+) respectively. The severity of neurogenic bowel was decreased significantly at discharge (56.25%, 18.755%, 18.75% and 6.25% for very minor, minor, moderate and severe group respectively; p < 0.001) compared with NBDS at admission. Conclusions: Implementation of the effective bowel program decrease the severity of the neurogenic bowel in patient with spinal cord injury.

Keywords: neurogenic bowel, NBDS, spinal cord injury, bowel program

Procedia PDF Downloads 218

Authors: Ali Al Orf, Khawaja Bilal Waheed

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Background and objective: Joubert syndrome (JS) is a rare, autosomal-recessive condition. Early recognition is important for management and counseling. Magnetic resonance imaging (MRI) can help in diagnosis. Therefore, we sought to evaluate clinical presentation and MRI findings in Joubert syndrome and related disorders. Method: A retrospective review of genetically proven cases of Joubert syndromes and related disorders was reviewed for their clinical presentation, demographic information, and magnetic resonance imaging findings in a period of the last 10 years. Two radiologists documented magnetic resonance imaging (MRI) findings. The presence of hypoplasia of the cerebellar vermis with hypoplasia of the superior cerebellar peduncle resembling the “Molar Tooth Sign” in the mid-brain was documented. Genetic testing results were collected to label genes linked to the diagnoses. Results: Out of 12 genetically proven JS cases, most were females (9/12), and nearly all presented with hypotonia, ataxia, developmental delay, intellectual impairment, and speech disorders. 5/12 children presented at age of 1 or below. The molar tooth sign was seen in 10/12 cases. Two cases were associated with other brain findings. Most of the cases were found associated with consanguineous marriage Conclusion and discussion: The molar tooth sign is a frequent and reliable sign of JS and related disorders. Genes related to defective cilia result in malfunctioning in the retina, renal tubule, and neural cell migration, thus producing heterogeneous syndrome complexes known as “ciliopathies.” Other ciliopathies like Senior-Loken syndrome, Bardet Biedl syndrome, and isolated nephronophthisis must be considered as the differential diagnosis of JS. The main imaging findings are the partial or complete absence of the cerebellar vermis, hypoplastic cerebellar peduncles (giving MTS), and (bat-wing appearance) fourth ventricular deformity. LimitationsSingle-center, small sample size, and retrospective nature of the study were a few of the study limitations.

Keywords: Joubart syndrome, magnetic resonance imaging, molar tooth sign, hypotonia

Procedia PDF Downloads 70

Authors: Nathenal Thomas

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In the food chain and in many other agricultural products, corn, also known as maize, which goes by the scientific name Zea mays subsp, is a widely produced agricultural product. Corn has the highest adaptability. It comes in many different types, is employed in many different industrial processes, and is more adaptable to different agro-climatic situations. In Ethiopia, maize is among the most widely grown crop. Small-scale corn farming may be a household's only source of food in developing nations like Ethiopia. The aforementioned data demonstrates that the country's requirement for this crop is excessively high, and conversely, the crop's productivity is very low for a variety of reasons. The most damaging disease that greatly contributes to this imbalance between the crop's supply and demand is the corn disease. The failure to diagnose diseases in maize plant until they are too late is one of the most important factors influencing crop output in Ethiopia. This study will aid in the early detection of such diseases and support farmers during the cultivation process, directly affecting the amount of maize produced. The diseases in maize plants, such as northern leaf blight and cercospora leaf spot, have distinct symptoms that are visible. This study aims to detect the most frequent and degrading maize diseases using the most efficiently used subset of machine learning technology, deep learning so, called Image Processing. Deep learning uses networks that can be trained from unlabeled data without supervision (unsupervised). It is a feature that simulates the exercises the human brain goes through when digesting data. Its applications include speech recognition, language translation, object classification, and decision-making. Convolutional Neural Network (CNN) for Image Processing, also known as convent, is a deep learning class that is widely used for image classification, image detection, face recognition, and other problems. it will also use this algorithm as the state-of-the-art for my research to detect maize diseases by photographing maize leaves using a mobile phone.

Keywords: CNN, zea mays subsp, leaf blight, cercospora leaf spot

Procedia PDF Downloads 52

Authors: Jiangang Shen

Abstract:

Olfactory dysfunction is a common symptom companied by anxiety- and depressive-like behaviors in depressive patients. Chronic stress triggers hormone responses and inhibits the proliferation and differentiation of neural stem cells (NSCs) in the hippocampus and subventricular zone (SVZ)-olfactory bulb (OB), contributing to depressive behaviors and olfactory dysfunction. However, the cellular signaling molecules to regulate chronic stress mediated olfactory dysfunction are largely unclear. Adaptor proteins containing the pleckstrin homology domain, phosphotyrosine binding domain, and leucine zipper motif (APPLs) are multifunctional adaptor proteins. Herein, we tested the hypothesis that APPL2 could inhibit hippocampal neurogenesis by affecting glucocorticoid receptor (GR) signaling, subsequently contributing to depressive and anxiety behaviors as well as olfactory dysfunctions. The major discoveries are included: (1) APPL2 Tg mice had enhanced GR phosphorylation under basic conditions but had no different plasma corticosterone (CORT) level and GR phosphorylation under stress stimulation. (2) APPL2 Tg mice had impaired hippocampal neurogenesis and revealed depressive and anxiety behaviors. (3) GR antagonist RU486 reversed the impaired hippocampal neurogenesis in the APPL2 Tg mice. (4) APPL2 Tg mice displayed higher GR activity and less capacity for neurogenesis at the olfactory system with lesser olfactory sensitivity than WT mice. (5) APPL2 negatively regulates olfactory functions by switching fate commitments of NSCs in adult olfactory bulbs via interaction with Notch1 signaling. Furthermore, baicalin, a natural medicinal compound, was found to be a promising agent targeting APPL2/GR signaling and promoting adult neurogenesis in APPL2 Tg mice and chronic corticosterone-induced depression mouse models. Behavioral tests revealed that baicalin had antidepressant and olfactory-improving effects. Taken together, APPL2 is a critical therapeutic target for antidepressant treatment.

Keywords: APPL2, hippocampal neurogenesis, depressive behaviors and olfactory dysfunction, stress

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Authors: Jason Clarke, Michaela Porubanova, Angela Mazzoli, Gulsah Kut

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What accounts for our perception that time inexorably passes in one direction, from the past to the future, the so-called arrow of time, given that the laws of physics permit motion in one temporal direction to also happen in the reverse temporal direction? Modern physics says that the reason for time’s unidirectional physical arrow is the relationship between time and entropy, the degree of disorder in the universe, which is evolving from low entropy (high order; thermal disequilibrium) toward high entropy (high disorder; thermal equilibrium), the second law of thermodynamics. Accordingly, our perception of the direction of time, from past to future, is believed to emanate as a result of the natural evolution of entropy from low to high, with low entropy defining our notion of ‘before’ and high entropy defining our notion of ‘after’. Here we explored this proposed relationship between entropy and the perception of time’s arrow. We predicted that if the brain has some mechanism for detecting entropy, whose output feeds into processes involved in constructing our perception of the direction of time, presentation of violations to the expectation that low entropy defines ‘before’ and high entropy defines ‘after’ would alert this mechanism, leading to measurable behavioral effects, namely a disruption in duration perception. To test this hypothesis, participants were shown briefly-presented (1000 ms or 500 ms) computer-generated visual dynamic events: novel 3D shapes that were seen either to evolve from whole figures into parts (low to high entropy condition) or were seen in the reverse direction: parts that coalesced into whole figures (high to low entropy condition). On each trial, participants were instructed to reproduce the duration of their visual experience of the stimulus by pressing and releasing the space bar. To ensure that attention was being deployed to the stimuli, a secondary task was to report the direction of the visual event (forward or reverse motion). Participants completed 60 trials. As predicted, we found that duration reproduction was significantly longer for the high to low entropy condition compared to the low to high entropy condition (p=.03). This preliminary data suggests the presence of a neural mechanism that detects entropy, which is used by other processes to construct our perception of the direction of time or time’s arrow.

Keywords: time perception, entropy, temporal illusions, duration perception

Procedia PDF Downloads 142

Authors: Aishi Kundu, Elhan Raza

Abstract:

"Necessity is the mother of invention" - Rapid increase in the global human population has directed the agricultural domain toward machine learning. The basic need of human beings is considered to be food which can be satisfied through farming. Farming is one of the major revenue generators for the Indian economy. Agriculture is not only considered a source of employment but also fulfils humans’ basic needs. So, agriculture is considered to be the source of employment and a pillar of the economy in developing countries like India. This paper provides a brief review of the progress made in implementing Machine Learning in the agricultural sector. Accurate predictions are necessary at the right time to boost production and to aid the timely and systematic distribution of agricultural commodities to make their availability in the market faster and more effective. This paper includes a thorough analysis of various machine learning algorithms applied in different aspects of agriculture (crop management, soil management, water management, yield tracking, livestock management, etc.).Due to climate changes, crop production is affected. Machine learning can analyse the changing patterns and come up with a suitable approach to minimize loss and maximize yield. Machine Learning algorithms/ models (regression, support vector machines, bayesian models, artificial neural networks, decision trees, etc.) are used in smart agriculture to analyze and predict specific outcomes which can be vital in increasing the productivity of the Agricultural Food Industry. It is to demonstrate vividly agricultural works under machine learning to sensor data. Machine Learning is the ongoing technology benefitting farmers to improve gains in agriculture and minimize losses. This paper discusses how the irrigation and farming management systems evolve in real-time efficiently. Artificial Intelligence (AI) enabled programs to emerge with rich apprehension for the support of farmers with an immense examination of data.

Keywords: machine Learning, artificial intelligence, crop management, precision farming, smart farming, pre-harvesting, harvesting, post-harvesting

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Authors: Kemal Efe Eseller, Göktuğ Yazici

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Laser-induced breakdown spectroscopy (LIBS) is a rapid optical atomic emission spectroscopy which is used for material identification and analysis with the advantages of in-situ analysis, elimination of intensive sample preparation, and micro-destructive properties for the material to be tested. LIBS delivers short pulses of laser beams onto the material in order to create plasma by excitation of the material to a certain threshold. The plasma characteristics, which consist of wavelength value and intensity amplitude, depends on the material and the experiment’s environment. In the present work, medicine samples’ spectrum profiles were obtained via LIBS. Medicine samples’ datasets include two different concentrations for both paracetamol based medicines, namely Aferin and Parafon. The spectrum data of the samples were preprocessed via filling outliers based on quartiles, smoothing spectra to eliminate noise and normalizing both wavelength and intensity axis. Statistical information was obtained and principal component analysis (PCA) was incorporated to both the preprocessed and raw datasets. The machine learning models were set based on two different train-test splits, which were 70% training – 30% test and 80% training – 20% test. Cross-validation was preferred to protect the models against overfitting; thus the sample amount is small. The machine learning results of preprocessed and raw datasets were subjected to comparison for both splits. This is the first time that all supervised machine learning classification algorithms; consisting of Decision Trees, Discriminant, naïve Bayes, Support Vector Machines (SVM), k-NN(k-Nearest Neighbor) Ensemble Learning and Neural Network algorithms; were incorporated to LIBS data of paracetamol based pharmaceutical samples, and their different concentrations on preprocessed and raw dataset in order to observe the effect of preprocessing.

Keywords: machine learning, laser-induced breakdown spectroscopy, medicines, principal component analysis, preprocessing

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Authors: Maftunakhon Latipova, Feruza Khaydarova

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Obesity is considered a chronic metabolic disease that occurs at any age. Regulation of body weight in the body is carried out through complex interaction of a complex of interrelated systems that control the body's energy system. Energy imbalance is the cause of obesity and overweight, in which the supply of energy from food exceeds the energy needs of the body. Obesity is closely related to impaired appetite regulation, and a hypothalamus is a key place for neural regulation of food consumption. The nucleus of the hypothalamus is connected and interdependent on receiving, integrating and sending hunger signals to regulate appetite. Purpose of the study: to identify markers of food behavior. Materials and methods: The screening was carried out to identify eating disorders in 200 men and women aged 18 to 35 years with overweight and obesity and to check the effects of Orexin A and Neuropeptide Y markers. A questionnaire and questionnaires were conducted with over 200 people aged 18 to 35 years. Questionnaires were for eating disorders and hidden depression (on the Zang scale). Anthropometry is measured by OT, OB, BMI, Weight, and Height. Based on the results of the collected data, 3 groups were divided: People with obesity, People with overweight, Control Group of Healthy People. Results: Of the 200 analysed persons, 86% had eating disorders. Of these, 60% of eating disorders were associated with childhood. According to the Zang test result: Normal condition was about 37%, mild depressive disorder 20%, moderate depressive disorder 25% and 18% of people suffered from severe depressive disorder without knowing it. One group of people with obesity had eating disorders and moderate and severe depressive disorder, and group 2 was overweight with mild depressive disorder. According to laboratory data, the first group had the lowest concentration of Orexin A and Neuropeptide U in blood serum. Conclusions: Being overweight and obese are the first signal of many diseases, and prevention and detection of these disorders will prevent various diseases, including type 2 diabetes. Obesity etiology is associated with eating disorders and signal transmission of the orexinorghetic system of the hypothalamus.

Keywords: obesity, endocrinology, hypothalamus, overweight

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Authors: Noreen Noreen, Aamir Ijaz, Hamza Akhtar

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Background: Screening plays a major role to detect chromosomal abnormalities, Down syndrome, neural tube defects and other inborn diseases of the newborn. Serum biomarkers in the second trimester are useful in determining risk of most common chromosomal anomalies; these test include Alpha-fetoprotein (AFP), Human chorionic gonadotropin (hCG), Unconjugated Oestriol (UEȝ)and inhibin-A. Quadruple biomarkers are worth test in diagnosing the congenital pathology during pregnancy, these procedures does not form a part of routine health care of pregnant women in Pakistan, so the median value is lacking for population in Pakistan. Objective: To determine median values of biochemical maternal serum markers in local population during second trimester maternal screening. Study settings: Department of Chemical Pathology and Endocrinology, Armed Forces Institute of Pathology (AFIP) Rawalpindi. Methods: Cross-Sectional study for estimation of reference values. Non-probability consecutive sampling, 155 healthy pregnant women, of 30-40 years of age, will be included. As non-parametric statistics will be used, the minimum sample size is 120. Result: Total 155 women were enrolled into this study. The age of all women enrolled ranged from 30 to39 yrs. Among them, 39 per cent of women were less than 34 years. Mean maternal age 33.46±2.35 SD and maternal body weight were 54.98±2.88. Median value of quadruple markers calculated from 15-18th week of gestation that will be used for calculation of MOM for screening of trisomy21 in this gestational age. Median value at 15 week of gestation were observed hCG 36650 mIU/ml, AFP 23.3 IU/ml, UEȝ 3.5 nmol/L, InhibinA 198 ng/L, at 16 week of gestation hCG 29050 mIU/ml, AFP 35.4 IU/ml, UEȝ 4.1 nmol/L, InhibinA 179 ng/L, at 17 week of gestation hCG 28450 mIU/ml, AFP 36.0 IU/ml, UEȝ 6.7 nmol/L, InhibinA 176 ng/L and at 18 week of gestation hCG 25200 mIU/ml, AFP 38.2 IU/ml, UEȝ 8.2 nmol/L, InhibinA 190 ng/L respectively.All the comparisons were significant (p-Value <0.005) with 95% confidence Interval (CI) and level of significance of study set by going through literature and set at 5%. Conclusion: The median values for these four biomarkers in Pakistani pregnant women can be used to calculate MoM.

Keywords: screening, down syndrome, quadruple test, second trimester, serum biomarkers

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Authors: Zhenzhen Xu, Chunfu Shao, Shengyou Wang, Chunjiao Dong

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To improve the satisfaction of residents' travel, this paper analyzes the characteristics and influencing factors of urban residents' travel behavior. First, a Multinominal Logit Model (MNL) model is built to analyze the characteristics of residents' travel behavior, reveal the influence of individual attributes, family attributes and travel characteristics on the choice of travel mode, and identify the significant factors. Then put forward suggestions for policy improvement. Finally, Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP) models are introduced to evaluate the policy effect. This paper selects Futian Street in Futian District, Shenzhen City for investigation and research. The results show that gender, age, education, income, number of cars owned, travel purpose, departure time, journey time, travel distance and times all have a significant influence on residents' choice of travel mode. Based on the above results, two policy improvement suggestions are put forward from reducing public transportation and non-motor vehicle travel time, and the policy effect is evaluated. Before the evaluation, the prediction effect of MNL, SVM and MLP models was evaluated. After parameter optimization, it was found that the prediction accuracy of the three models was 72.80%, 71.42%, and 76.42%, respectively. The MLP model with the highest prediction accuracy was selected to evaluate the effect of policy improvement. The results showed that after the implementation of the policy, the proportion of public transportation in plan 1 and plan 2 increased by 14.04% and 9.86%, respectively, while the proportion of private cars decreased by 3.47% and 2.54%, respectively. The proportion of car trips decreased obviously, while the proportion of public transport trips increased. It can be considered that the measures have a positive effect on promoting green trips and improving the satisfaction of urban residents, and can provide a reference for relevant departments to formulate transportation policies.

Keywords: neural network, travel characteristics analysis, transportation choice, travel sharing rate, traffic resource allocation

Procedia PDF Downloads 112

Authors: Jorge Rodriguez-Guerra, Carlos Calleja, Aron Pujana, Iker Elorza, Ana Maria Macarulla

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Society demands more reliable manufacturing processes capable of producing high quality products in shorter production cycles. New control algorithms have been studied to satisfy this paradigm, in which Fault-Tolerant Control (FTC) plays a significant role. It is suitable to detect, isolate and adapt a system when a harmful or faulty situation appears. In this paper, a general overview about FTC characteristics are exposed; highlighting the properties a system must ensure to be considered faultless. In addition, a research to identify which are the main FTC techniques and a classification based on their characteristics is presented in two main groups: Active Fault-Tolerant Controllers (AFTCs) and Passive Fault-Tolerant Controllers (PFTCs). AFTC encompasses the techniques capable of re-configuring the process control algorithm after the fault has been detected, while PFTC comprehends the algorithms robust enough to bypass the fault without further modifications. The mentioned re-configuration requires two stages, one focused on detection, isolation and identification of the fault source and the other one in charge of re-designing the control algorithm by two approaches: fault accommodation and control re-design. From the algorithms studied, one has been selected and applied to a case study based on an industrial hydraulic-press. The developed model has been embedded under a real-time validation platform, which allows testing the FTC algorithms and analyse how the system will respond when a fault arises in similar conditions as a machine will have on factory. One AFTC approach has been picked up as the methodology the system will follow in the fault recovery process. In a first instance, the fault will be detected, isolated and identified by means of a neural network. In a second instance, the control algorithm will be re-configured to overcome the fault and continue working without human interaction.

Keywords: fault-tolerant control, electro-hydraulic actuator, fault detection and isolation, control re-design, real-time

Procedia PDF Downloads 152

Authors: Yasamin Rahimi, Ali Kamandi, Abbas Hoseini, Hesam Haddad

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Online/offline chat is a convenient approach in the electronic markets of second-hand products in which potential customers would like to have more information about the products to fill the information gap between buyers and sellers. Online peer in peer market is trying to create artificial intelligence-based systems that help customers ask more informative questions in an easier way. In this article, we introduce a method for the question/answer system that we have developed for the top-ranked electronic market in Iran called Divar. When it comes to secondhand products, incomplete product information in a purchase will result in loss to the buyer. One way to balance buyer and seller information of a product is to help the buyer ask more informative questions when purchasing. Also, the short time to start and achieve the desired result of the conversation was one of our main goals, which was achieved according to A/B tests results. In this paper, we propose and evaluate a method for suggesting questions and answers in the messaging platform of the e-commerce website Divar. Creating such systems is to help users gather knowledge about the product easier and faster, All from the Divar database. We collected a dataset of around 2 million messages in Persian colloquial language, and for each category of product, we gathered 500K messages, of which only 2K were Tagged, and semi-supervised methods were used. In order to publish the proposed model to production, it is required to be fast enough to process 10 million messages daily on CPU processors. In order to reach that speed, in many subtasks, faster and simplistic models are preferred over deep neural models. The proposed method, which requires only a small amount of labeled data, is currently used in Divar production on CPU processors, and 15% of buyers and seller’s messages in conversations is directly chosen from our model output, and more than 27% of buyers have used this model suggestions in at least one daily conversation.

Keywords: smart reply, spell checker, information retrieval, intent detection, question answering

Procedia PDF Downloads 160

Authors: Mane Darbinyan, Lusine Hayrapetyan, Elen Matevosyan

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Artificial Intelligence in Education (AIED) has been created to help students get ready for the workforce, and over the past 25 years, it has grown significantly, offering a variety of technologies to support academic, institutional, and administrative services. However, this is still challenging, especially considering the labor market's rapid change. While choosing a career, people face various obstacles because they do not take into consideration their own preferences, which might lead to many other problems like shifting jobs, work stress, occupational infirmity, reduced productivity, and manual error. Besides preferences, people should properly evaluate their technical and non-technical skills, as well as their personalities. Professional counseling has become a difficult undertaking for counselors due to the wide range of career choices brought on by changing technological trends. It is necessary to close this gap by utilizing technology that makes sophisticated predictions about a person's career goals based on their personality. Hence, there is a need to create an automated model that would help in decision-making based on user inputs. Improving career guidance can be achieved by embedding machine learning into the career consulting ecosystem. There are various systems of career guidance that work based on the same logic, such as the classification of applicants, matching applications with appropriate departments or jobs, making predictions, and providing suitable recommendations. Methodologies like KNN, Neural Networks, K-means clustering, D-Tree, and many other advanced algorithms are applied in the fields of data and compute some data, which is helpful to predict the right careers. Besides helping users with their career choice, these systems provide numerous opportunities which are very useful while making this hard decision. They help the candidate to recognize where he/she specifically lacks sufficient skills so that the candidate can improve those skills. They are also capable to offer an e-learning platform, taking into account the user's lack of knowledge. Furthermore, users can be provided with details on a particular job, such as the abilities required to excel in that industry.

Keywords: career guidance system, machine learning, career prediction, predictive decision, data mining, technical and non-technical skills

Procedia PDF Downloads 59

Authors: Mane Darbinyan, Lusine Hayrapetyan, Elen Matevosyan

Abstract:

Artificial Intelligence in Education (AIED) has been created to help students get ready for the workforce, and over the past 25 years, it has grown significantly, offering a variety of technologies to support academic, institutional, and administrative services. However, this is still challenging, especially considering the labor market's rapid change. While choosing a career, people face various obstacles because they do not take into consideration their own preferences, which might lead to many other problems like shifting jobs, work stress, occupational infirmity, reduced productivity, and manual error. Besides preferences, people should evaluate properly their technical and non-technical skills, as well as their personalities. Professional counseling has become a difficult undertaking for counselors due to the wide range of career choices brought on by changing technological trends. It is necessary to close this gap by utilizing technology that makes sophisticated predictions about a person's career goals based on their personality. Hence, there is a need to create an automated model that would help in decision-making based on user inputs. Improving career guidance can be achieved by embedding machine learning into the career consulting ecosystem. There are various systems of career guidance that work based on the same logic, such as the classification of applicants, matching applications with appropriate departments or jobs, making predictions, and providing suitable recommendations. Methodologies like KNN, neural networks, K-means clustering, D-Tree, and many other advanced algorithms are applied in the fields of data and compute some data, which is helpful to predict the right careers. Besides helping users with their career choice, these systems provide numerous opportunities which are very useful while making this hard decision. They help the candidate to recognize where he/she specifically lacks sufficient skills so that the candidate can improve those skills. They are also capable of offering an e-learning platform, taking into account the user's lack of knowledge. Furthermore, users can be provided with details on a particular job, such as the abilities required to excel in that industry.

Keywords: career guidance system, machine learning, career prediction, predictive decision, data mining, technical and non-technical skills

Procedia PDF Downloads 47

Authors: Godwin Dennison, C. E. Boulind, O. Gould, B. de Lacy Costello, J. Allison, P. White, P. Ewings, A. Wicaksono, N. J. Curtis, A. Pullyblank, D. Jayne, J. A. Covington, N. Ratcliffe, N. K. Francis

Abstract:

Background: Colorectal symptoms are common but only infrequently represent serious pathology, including colorectal cancer (CRC). A large number of invasive tests are presently performed for reassurance. We investigated the feasibility of urinary volatile organic compound (VOC) testing as a potential triage tool in patients fast-tracked for assessment for possible CRC. Methods: A prospective, multi-centre, observational feasibility study was performed across three sites. Patients referred on NHS fast-track pathways for potential CRC provided a urine sample which underwent Gas Chromatography Mass Spectrometry (GC-MS), Field Asymmetric Ion Mobility Spectrometry (FAIMS) and Selected Ion Flow Tube Mass Spectrometry (SIFT-MS) analysis. Patients underwent colonoscopy and/or CT colonography and were grouped as either CRC, adenomatous polyp(s), or controls to explore the diagnostic accuracy of VOC output data supported by an artificial neural network (ANN) model. Results: 558 patients participated with 23 (4.1%) CRC diagnosed. 59% of colonoscopies and 86% of CT colonographies showed no abnormalities. Urinary VOC testing was feasible, acceptable to patients, and applicable within the clinical fast track pathway. GC-MS showed the highest clinical utility for CRC and polyp detection vs. controls (sensitivity=0.878, specificity=0.882, AUROC=0.884). Conclusion: Urinary VOC testing and analysis are feasible within NHS fast-track CRC pathways. Clinically meaningful differences between patients with cancer, polyps, or no pathology were identified therefore suggesting VOC analysis may have future utility as a triage tool. Acknowledgment: Funding: NIHR Research for Patient Benefit grant (ref: PB-PG-0416-20022).

Keywords: colorectal cancer, volatile organic compound, gas chromatography mass spectrometry, field asymmetric ion mobility spectrometry, selected ion flow tube mass spectrometry

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Authors: Javier Muro, Anja Linstadter, Florian Manner, Lisa Schwarz, Stephan Wollauer, Paul Magdon, Gohar Ghazaryan, Olena Dubovyk

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Monitoring the trade-off between biomass production and biodiversity in grasslands is critical to evaluate the effects of management practices across environmental gradients. New generations of remote sensing sensors and machine learning approaches can model grasslands’ characteristics with varying accuracies. However, studies often fail to cover a sufficiently broad range of environmental conditions, and evidence suggests that prediction models might be case specific. In this study, biomass production and biodiversity indices (species richness and Fishers’ α) are modeled in 150 grassland plots for three sites across Germany. These sites represent a North-South gradient and are characterized by distinct soil types, topographic properties, climatic conditions, and management intensities. Predictors used are derived from Sentinel-1 & 2 and a set of topoedaphic variables. The transferability of the models is tested by training and validating at different sites. The performance of feed-forward deep neural networks (DNN) is compared to a random forest algorithm. While biomass predictions across gradients and sites were acceptable (r2 0.5), predictions of biodiversity indices were poor (r2 0.14). DNN showed higher generalization capacity than random forest when predicting biomass across gradients and sites (relative root mean squared error of 0.5 for DNN vs. 0.85 for random forest). DNN also achieved high performance when using the Sentinel-2 surface reflectance data rather than different combinations of spectral indices, Sentinel-1 data, or topoedaphic variables, simplifying dimensionality. This study demonstrates the necessity of training biomass and biodiversity models using a broad range of environmental conditions and ensuring spatial independence to have realistic and transferable models where plot level information can be upscaled to landscape scale.

Keywords: ecosystem services, grassland management, machine learning, remote sensing

Procedia PDF Downloads 190

Authors: Samaneh Nazeri, Bagher Mojazi Amiri, Hamid Farahmand

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Production of high quality fish eggs with reasonable hatching rate makes a success in aquaculture industries. It is influenced by the environmental stimulators and inhibitors. Diazinon is a widely-used pesticide in Golestan province (Southern Caspian Sea, North of Iran) which is washed to the aquatic environment (3 mg/L in the river). It is little known about the effect of this pesticide on the embryogenesis of sturgeon fish, the valuable species of the Caspian Sea. Hormonal content of the egg is an important factor to guaranty the successful passes of embryonic stages. In this study, the fate of Persian sturgeon embryo to 24, 48, 72, and 96-hours exposure of diazinon (LC₅₀ dose) was tested. Also, the effect of thyroid hormones (T3 and T4) on these embryos was tested concurrently or separately with diazinon LC ₅₀ dose. Fertilized eggs are exposed to T3 (low dose: 1 ng/ml, high dose: 10 ng/ml), T4 (low dose: 1 ng/ml, high dose: 10 ng/ml). Six eggs were randomly selected from each treatment (with three replicates) in five developmental stages (two cell- division, neural, heart present, heart beaten, and hatched larvae). The possibility of changing T3, T4, and cortisol contents of the embryos were determined in all treated groups and in every mentioned embryonic stage. The hatching rate in treated groups was assayed at the end of the embryogenesis to clarify the effect of thyroid hormones and diazinon. The results indicated significant differences in thyroid hormone contents, but no significant differences were recognized in cortisol levels at various early life stages of embryos. There was also significant difference in thyroid hormones in (T3, T4) + diazinon treated embryos (P˂0.05), while no significant difference between control and treatments in cortisol levels was observed. The highest hatching rate was recorded in HT3 treatment, while the lowest hatching rate was recorded for diazinon LC₅₀ treatment. The result confirmed that Persian sturgeon embryo is less sensitive to diazinon compared to teleost embryos, and thyroid hormones may increase hatching rate even in the presence of diazinon.

Keywords: Persian sturgeon, diazinon, thyroid hormones, cortisol, embryo

Procedia PDF Downloads 282