Search results for: Amar Deep
1641 Mutiple Medical Landmark Detection on X-Ray Scan Using Reinforcement Learning
Authors: Vijaya Yuvaram Singh V M, Kameshwar Rao J V
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The challenge with development of neural network based methods for medical is the availability of data. Anatomical landmark detection in the medical domain is a process to find points on the x-ray scan report of the patient. Most of the time this task is done manually by trained professionals as it requires precision and domain knowledge. Traditionally object detection based methods are used for landmark detection. Here, we utilize reinforcement learning and query based method to train a single agent capable of detecting multiple landmarks. A deep Q network agent is trained to detect single and multiple landmarks present on hip and shoulder from x-ray scan of a patient. Here a single agent is trained to find multiple landmark making it superior to having individual agents per landmark. For the initial study, five images of different patients are used as the environment and tested the agents performance on two unseen images.Keywords: reinforcement learning, medical landmark detection, multi target detection, deep neural network
Procedia PDF Downloads 1371640 NiAl-Layered Double Hydroxide: Preparation, Characterization and Applications in Photo-Catalysis and Hydrogen Storage
Authors: Ahmed Farghali, Heba Amar, Mohamed Khedr
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NiAl-Layered Double Hydroxide (NiAl-LDH), one of anionic functional layered materials, has been prepared by a simple co-precipitation process. X-ray diffraction patterns confirm the formation of the desired compounds of NiAl hydroxide single phase and the crystallite size was found to be about 4.6 nm. The morphology of the prepared samples was investigated using scanning electron microscopy and the layered structure was appeared under the transmission electron microscope. The thermal stability and the function groups of NiAl-LDH were investigated using thermal gravimetric analysis (TGA) and Fourier transform infrared (FTIR) respectively. NiAl-LDH was investigated as a photo-catalyst for the degradation of some toxic dyes such as toluidine blue and bromopyrogallol red. It shows good catalytic efficiency in visible light and even in dark. For the first time NiAl-LDH was used for hydrogen storage application. NiAl-LDH samples were exposed to 20 bar applied hydrogen pressure at room temperature, 100 and -193 oC. NiAl-LDH samples appear to have feasible hydrogen storage capacity. It was capable to adsorb 0.1wt% at room temperature, 0.15 wt% at 100oC and storage capacity reached 0.3 wt% at -193 oC.Keywords: NiAl-LDH, preparation, characterization, photo-catalysis, hydrogen storage
Procedia PDF Downloads 3071639 Developing a DNN Model for the Production of Biogas From a Hybrid BO-TPE System in an Anaerobic Wastewater Treatment Plant
Authors: Hadjer Sadoune, Liza Lamini, Scherazade Krim, Amel Djouadi, Rachida Rihani
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Deep neural networks are highly regarded for their accuracy in predicting intricate fermentation processes. Their ability to learn from a large amount of datasets through artificial intelligence makes them particularly effective models. The primary obstacle in improving the performance of these models is to carefully choose the suitable hyperparameters, including the neural network architecture (number of hidden layers and hidden units), activation function, optimizer, learning rate, and other relevant factors. This study predicts biogas production from real wastewater treatment plant data using a sophisticated approach: hybrid Bayesian optimization with a tree-structured Parzen estimator (BO-TPE) for an optimised deep neural network (DNN) model. The plant utilizes an Upflow Anaerobic Sludge Blanket (UASB) digester that treats industrial wastewater from soft drinks and breweries. The digester has a working volume of 1574 m3 and a total volume of 1914 m3. Its internal diameter and height were 19 and 7.14 m, respectively. The data preprocessing was conducted with meticulous attention to preserving data quality while avoiding data reduction. Three normalization techniques were applied to the pre-processed data (MinMaxScaler, RobustScaler and StandardScaler) and compared with the Non-Normalized data. The RobustScaler approach has strong predictive ability for estimating the volume of biogas produced. The highest predicted biogas volume was 2236.105 Nm³/d, with coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE) values of 0.712, 164.610, and 223.429, respectively.Keywords: anaerobic digestion, biogas production, deep neural network, hybrid bo-tpe, hyperparameters tuning
Procedia PDF Downloads 291638 Complicated Corneal Ulceration in Cats: Clinical Diagnosis and Surgical Management of 80 Cases
Authors: Khaled M. Ali, Ayman A. Mostafa, Soliman M. Soliman
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Objectives: To describe the most common clinical and endoscopic findings associated with complicated corneal ulcers in cats, and to determine the short-term outcomes after surgical treatment of these cats. Animals Eighteen client-owned cats of different breeds (52 females and 28 males), ranging in age from 3 months to 6 years, with corneal ulcers. Procedures: Cats were clinically evaluated to initially determine the concurrent corneal abnormalities. Endoscopic examination was performed to determine the anterior and posterior segments abnormalities. Superficial and deep stromal ulcers were treated using conjunctival flap. Corneal sequestrum was treated by partial keratectomy and conjunctival flap. Anterior synechia was treated via peripheral iridectomy and separation of the adhesion between the iris and the inner cornea. Symblepharon was treated by removal of the adhered conjunctival membrane from the cornea. Incurable endophthalmitis was treated surgically by extirpation. Short-term outcomes after surgical managements of selected corneal abnormalities were then assessed clinically and endoscopically. Results: Deep stromal ulcer with descemetocele, endophthalmitis, symblepharon, corneal sequestration and anterior synechia with secondary glaucoma and corneal scarring were the most common complications of corneal ulcer. FHV-1 was a common etiologic factor of corneal ulceration. Persistent corneal scars of varying shape and size developed in cats with deep stromal ulcer, anterior synechia, and corneal sequestration. Conclusions: Domestic shorthaired and Persian cats were the most predisposed breeds to FHV-1 infection and subsequent corneal ulceration. Immediate management of patients with corneal ulcer would prevent serious complications. No age or sex predisposition to complicated corneal ulceration in cats.Keywords: cats, complicated corneal ulceration, clinical, endoscopic diagnosis, FHV-1
Procedia PDF Downloads 2771637 Design of an Automated Deep Learning Recurrent Neural Networks System Integrated with IoT for Anomaly Detection in Residential Electric Vehicle Charging in Smart Cities
Authors: Wanchalerm Patanacharoenwong, Panaya Sudta, Prachya Bumrungkun
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The paper focuses on the development of a system that combines Internet of Things (IoT) technologies and deep learning algorithms for anomaly detection in residential Electric Vehicle (EV) charging in smart cities. With the increasing number of EVs, ensuring efficient and reliable charging systems has become crucial. The aim of this research is to develop an integrated IoT and deep learning system for detecting anomalies in residential EV charging and enhancing EV load profiling and event detection in smart cities. This approach utilizes IoT devices equipped with infrared cameras to collect thermal images and household EV charging profiles from the database of Thailand utility, subsequently transmitting this data to a cloud database for comprehensive analysis. The methodology includes the use of advanced deep learning techniques such as Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) algorithms. IoT devices equipped with infrared cameras are used to collect thermal images and EV charging profiles. The data is transmitted to a cloud database for comprehensive analysis. The researchers also utilize feature-based Gaussian mixture models for EV load profiling and event detection. Moreover, the research findings demonstrate the effectiveness of the developed system in detecting anomalies and critical profiles in EV charging behavior. The system provides timely alarms to users regarding potential issues and categorizes the severity of detected problems based on a health index for each charging device. The system also outperforms existing models in event detection accuracy. This research contributes to the field by showcasing the potential of integrating IoT and deep learning techniques in managing residential EV charging in smart cities. The system ensures operational safety and efficiency while also promoting sustainable energy management. The data is collected using IoT devices equipped with infrared cameras and is stored in a cloud database for analysis. The collected data is then analyzed using RNN, LSTM, and feature-based Gaussian mixture models. The approach includes both EV load profiling and event detection, utilizing a feature-based Gaussian mixture model. This comprehensive method aids in identifying unique power consumption patterns among EV owners and outperforms existing models in event detection accuracy. In summary, the research concludes that integrating IoT and deep learning techniques can effectively detect anomalies in residential EV charging and enhance EV load profiling and event detection accuracy. The developed system ensures operational safety and efficiency, contributing to sustainable energy management in smart cities.Keywords: cloud computing framework, recurrent neural networks, long short-term memory, Iot, EV charging, smart grids
Procedia PDF Downloads 571636 A Recognition Method of Ancient Yi Script Based on Deep Learning
Authors: Shanxiong Chen, Xu Han, Xiaolong Wang, Hui Ma
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Yi is an ethnic group mainly living in mainland China, with its own spoken and written language systems, after development of thousands of years. Ancient Yi is one of the six ancient languages in the world, which keeps a record of the history of the Yi people and offers documents valuable for research into human civilization. Recognition of the characters in ancient Yi helps to transform the documents into an electronic form, making their storage and spreading convenient. Due to historical and regional limitations, research on recognition of ancient characters is still inadequate. Thus, deep learning technology was applied to the recognition of such characters. Five models were developed on the basis of the four-layer convolutional neural network (CNN). Alpha-Beta divergence was taken as a penalty term to re-encode output neurons of the five models. Two fully connected layers fulfilled the compression of the features. Finally, at the softmax layer, the orthographic features of ancient Yi characters were re-evaluated, their probability distributions were obtained, and characters with features of the highest probability were recognized. Tests conducted show that the method has achieved higher precision compared with the traditional CNN model for handwriting recognition of the ancient Yi.Keywords: recognition, CNN, Yi character, divergence
Procedia PDF Downloads 1581635 Geotechnical Characterization of Landslide in Dounia Park, Algiers, Algeria
Authors: Mira Filali, Amar Nechnech
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Most landslides in Algiers take place in Piacenzian marls of the Sahel (port in Arabic) and cause severe damage to properties and infrastructures. The aim of this paper is to describe the results of experimental as well as theoretical analysis of landslides. In order to understand the process which caused this slope instabilities, the results of geotechnical investigation carried out by the laboratory of construction (LNHC) laboratory in the area of Dounia park were analyzed, including particle size distribution, Atterberg limits, shear strength, odometer and pressuremeter tests. The study shows that the soils exhibited a high capacity to swelling according to index plasticity and clay content. Highs limit liquidity (LL) (53.45%) means that the soils are susceptible to landslides. The stability analysis carried out using finite element method, shows that the slope is stable (Fs > 1) in dry condition and in static state. Despite this results, the stable site could be described as only conditionally stable because slope failure can occur under combined effect of different factors. In fact the safety factor obtained by applying load when the phreatic surface is at ground, less than 1.5.Keywords: index properties, landslides, safety factor, slope stability
Procedia PDF Downloads 2281634 Neural Network Approaches for Sea Surface Height Predictability Using Sea Surface Temperature
Authors: Luther Ollier, Sylvie Thiria, Anastase Charantonis, Carlos E. Mejia, Michel Crépon
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Sea Surface Height Anomaly (SLA) is a signature of the sub-mesoscale dynamics of the upper ocean. Sea Surface Temperature (SST) is driven by these dynamics and can be used to improve the spatial interpolation of SLA fields. In this study, we focused on the temporal evolution of SLA fields. We explored the capacity of deep learning (DL) methods to predict short-term SLA fields using SST fields. We used simulated daily SLA and SST data from the Mercator Global Analysis and Forecasting System, with a resolution of (1/12)◦ in the North Atlantic Ocean (26.5-44.42◦N, -64.25–41.83◦E), covering the period from 1993 to 2019. Using a slightly modified image-to-image convolutional DL architecture, we demonstrated that SST is a relevant variable for controlling the SLA prediction. With a learning process inspired by the teaching-forcing method, we managed to improve the SLA forecast at five days by using the SST fields as additional information. We obtained predictions of a 12 cm (20 cm) error of SLA evolution for scales smaller than mesoscales and at time scales of 5 days (20 days), respectively. Moreover, the information provided by the SST allows us to limit the SLA error to 16 cm at 20 days when learning the trajectory.Keywords: deep-learning, altimetry, sea surface temperature, forecast
Procedia PDF Downloads 811633 Sedimentary, Diagenesis and Evaluation of High Quality Reservoir of Coarse Clastic Rocks in Nearshore Deep Waters in the Dongying Sag; Bohai Bay Basin
Authors: Kouassi Louis Kra
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The nearshore deep-water gravity flow deposits in the Northern steep slope of Dongying depression, Bohai Bay basin, have been acknowledged as important reservoirs in the rift lacustrine basin. These deep strata term as coarse clastic sediment, deposit at the root of the slope have complex depositional processes and involve wide diagenetic events which made high-quality reservoir prediction to be complex. Based on the integrated study of seismic interpretation, sedimentary analysis, petrography, cores samples, wireline logging data, 3D seismic and lithological data, the reservoir formation mechanism deciphered. The Geoframe software was used to analyze 3-D seismic data to interpret the stratigraphy and build a sequence stratigraphic framework. Thin section identification, point counts were performed to assess the reservoir characteristics. The software PetroMod 1D of Schlumberger was utilized for the simulation of burial history. CL and SEM analysis were performed to reveal diagenesis sequences. Backscattered electron (BSE) images were recorded for definition of the textural relationships between diagenetic phases. The result showed that the nearshore steep slope deposits mainly consist of conglomerate, gravel sandstone, pebbly sandstone and fine sandstone interbedded with mudstone. The reservoir is characterized by low-porosity and ultra-low permeability. The diagenesis reactions include compaction, precipitation of calcite, dolomite, kaolinite, quartz cement and dissolution of feldspars and rock fragment. The main types of reservoir space are primary intergranular pores, residual intergranular pores, intergranular dissolved pores, intergranular dissolved pores, and fractures. There are three obvious anomalous high-porosity zones in the reservoir. Overpressure and early hydrocarbon filling are the main reason for abnormal secondary pores development. Sedimentary facies control the formation of high-quality reservoir, oil and gas filling preserves secondary pores from late carbonate cementation.Keywords: Bohai Bay, Dongying Sag, deep strata, formation mechanism, high-quality reservoir
Procedia PDF Downloads 1271632 Variation in Water Utilization of Typical Desert Shrubs in a Desert-Oasis Ecotone
Authors: Hai Zhou, Wenzhi Zhao
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Water is one of the most important factors limiting plant growth and development in desert ecosystems. In order to understand how desert shrubs cope with variation in water sources over time, it is important to understand plant–water relations in desert-oasis ecotone. We selected the typical desert shrubs: Nitraria sibirica, Calligonum mongolicum and Haloxylon ammodendron of 5-, 10-, 20- and 40-year old as the research species, to study the seasonal variation of plant water sources and response to precipitation in the desert-oasis ecotone of Linze, Northwestern China. We examined stable isotopic ratios of oxygen (δ18O) in stem water of desert shrubs as well as in precipitation, groundwater, and soil water in different soil layers and seasons to determine water sources for the shrubs. We found that the N. sibirica and H. ammodendron of 5-, 10-year old showed significant seasonal variation characteristics of δ18O value of stem water and water sources. However, the C. mongolicum and 20- and 40-year H. ammodendron main water sources were from deep soil water and groundwater, and less response to precipitation pulse. After 22.4 mm precipitation, the contribution of shallow soil water (0-50cm) to the use of N. sibirica increased from 6.7% to 36.5%; the C. mongolicum rarely use precipitation that were about 58.29% and 23.51%, absorbed from the deep soil water and groundwater; the contribution of precipitation to use of H. ammodendron had significantly differences among the four ages. The H. ammodendron of 5- and 10-year old about 86.3% and 42.5% water sources absorbed from the shallow soil water after precipitation. However, the contribution to 20- and 40-year old plant was less than 15%. So, the precipitation was one of the main water sources for desert shrubs, but the species showed different water utilization. We conclude that the main water source of the N. sibirica and H. ammodendron of 5-, 10-year was soil water recharged by precipitation, but the deeply rooted H. ammodendron of 20‐ and 40‐year‐old and the C. mongolicum have the ability to exploit a deep and reliable water source.Keywords: water use pattern, water resource, stable isotope, seasonal change, precipitation pulse
Procedia PDF Downloads 4251631 Image Classification with Localization Using Convolutional Neural Networks
Authors: Bhuyain Mobarok Hossain
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Image classification and localization research is currently an important strategy in the field of computer vision. The evolution and advancement of deep learning and convolutional neural networks (CNN) have greatly improved the capabilities of object detection and image-based classification. Target detection is important to research in the field of computer vision, especially in video surveillance systems. To solve this problem, we will be applying a convolutional neural network of multiple scales at multiple locations in the image in one sliding window. Most translation networks move away from the bounding box around the area of interest. In contrast to this architecture, we consider the problem to be a classification problem where each pixel of the image is a separate section. Image classification is the method of predicting an individual category or specifying by a shoal of data points. Image classification is a part of the classification problem, including any labels throughout the image. The image can be classified as a day or night shot. Or, likewise, images of cars and motorbikes will be automatically placed in their collection. The deep learning of image classification generally includes convolutional layers; the invention of it is referred to as a convolutional neural network (CNN).Keywords: image classification, object detection, localization, particle filter
Procedia PDF Downloads 2931630 Cultivating Concentration and Flow: Evaluation of a Strategy for Mitigating Digital Distractions in University Education
Authors: Vera G. Dianova, Lori P. Montross, Charles M. Burke
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In the digital age, the widespread and frequently excessive use of mobile phones amongst university students is recognized as a significant distractor which interferes with their ability to enter a deep state of concentration during studies and diminishes their prospects of experiencing the enjoyable and instrumental state of flow, as defined and described by psychologist M. Csikszentmihalyi. This study has targeted 50 university students with the aim of teaching them to cultivate their ability to engage in deep work and to attain the state of flow, fostering more effective and enjoyable learning experiences. Prior to the start of the intervention, all participating students completed a comprehensive survey based on a variety of validated scales assessing their inclination toward lifelong learning, frequency of flow experiences during study, frustration tolerance, sense of agency, as well as their love of learning and daily time devoted to non-academic mobile phone activities. Several days after this initial assessment, students received a 90-minute lecture on the principles of flow and deep work, accompanied by a critical discourse on the detrimental effects of excessive mobile phone usage. They were encouraged to practice deep work and strive for frequent flow states throughout the semester. Subsequently, students submitted weekly surveys, including the 10-item CORE Dispositional Flow Scale, a 3-item agency scale and furthermore disclosed their average daily hours spent on non-academic mobile phone usage. As a final step, at the end of the semester students engaged in reflective report writing, sharing their experiences and evaluating the intervention's effectiveness. They considered alterations in their love of learning, reflected on the implications of their mobile phone usage, contemplated improvements in their tolerance for boredom and perseverance in complex tasks, and pondered the concept of lifelong learning. Additionally, students assessed whether they actively took steps towards managing their recreational phone usage and towards improving their commitment to becoming lifelong learners. Employing a mixed-methods approach our study offers insights into the dynamics of concentration, flow, mobile phone usage and attitudes towards learning among undergraduate and graduate university students. The findings of this study aim to promote profound contemplation, on the part of both students and instructors, on the rapidly evolving digital-age higher education environment. In an era defined by digital and AI advancements, the ability to concentrate, to experience the state of flow, and to love learning has never been more crucial. This study underscores the significance of addressing mobile phone distractions and providing strategies for cultivating deep concentration. The insights gained can guide educators in shaping effective learning strategies for the digital age. By nurturing a love for learning and encouraging lifelong learning, educational institutions can better prepare students for a rapidly changing labor market, where adaptability and continuous learning are paramount for success in a dynamic career landscape.Keywords: deep work, flow, higher education, lifelong learning, love of learning
Procedia PDF Downloads 651629 Convergence Analysis of Training Two-Hidden-Layer Partially Over-Parameterized ReLU Networks via Gradient Descent
Authors: Zhifeng Kong
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Over-parameterized neural networks have attracted a great deal of attention in recent deep learning theory research, as they challenge the classic perspective of over-fitting when the model has excessive parameters and have gained empirical success in various settings. While a number of theoretical works have been presented to demystify properties of such models, the convergence properties of such models are still far from being thoroughly understood. In this work, we study the convergence properties of training two-hidden-layer partially over-parameterized fully connected networks with the Rectified Linear Unit activation via gradient descent. To our knowledge, this is the first theoretical work to understand convergence properties of deep over-parameterized networks without the equally-wide-hidden-layer assumption and other unrealistic assumptions. We provide a probabilistic lower bound of the widths of hidden layers and proved linear convergence rate of gradient descent. We also conducted experiments on synthetic and real-world datasets to validate our theory.Keywords: over-parameterization, rectified linear units ReLU, convergence, gradient descent, neural networks
Procedia PDF Downloads 1381628 Applying the Crystal Model to Different Nuclear Systems
Authors: A. Amar
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The angular distributions of the nuclear systems under consideration have been analyzed in the framework of the optical model (OM), where the real part was taken in the crystal model form. A crystal model (CM) has been applied to deuteron elastically scattered by ⁶,⁷Li and ⁹Be. A crystal model (CM) + distorted-wave Born approximation (DWBA) + dynamic polarization potential (DPP) potential has been applied to deuteron elastically scattered by ⁶,⁷Li and 9Be. Also, a crystal model has been applied to ⁶Li elastically scattered by ¹⁶O and ²⁸Sn in addition to the ⁷Li+⁷Li system and the ¹²C(alpha,⁸Be) ⁸Be reaction. The continuum-discretized coupled-channels (CDCC) method has been applied to the ⁷Li+⁷Li system and agreement between the crystal model and the continuum-discretized coupled-channels (CDCC) method has been observed. In general, the models succeeded in reproducing the differential cross sections at the full angular range and for all the energies under consideration.Keywords: optical model (OM), crystal model (CM), distorted-wave born approximation (DWBA), dynamic polarization potential (DPP), the continuum-discretized coupled-channels (CDCC) method, and deuteron elastically scattered by ⁶, ⁷Li and ⁹Be
Procedia PDF Downloads 701627 Parametric Study of the Structures: Influence of the Shells
Authors: Serikma Mourad, Mezidi Amar
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The conception (design) of an earthquake-resistant structure is a complex problem seen the necessity of meeting the requirements of security been imperative by the regulations, and of economy been imperative by the increasing costs of the structures. The resistance of a building in the horizontal actions (shares) is mainly ensured by a mixed brace system; for a concrete building, this system is constituted by frame or shells; or both at the same time. After the earthquake of Boumerdes (May 23; 2003) in Algeria, the studies made by experts, ended in modifications of the Algerian Earthquake-resistant Regulation (AER 99). One of these modifications was to widen the use of shells for the brace system. This modification has create a conflict on the quantities, the positions and the type of the shells at adopt. In the present project, we suggest seeing the effect of the variation of the dimensions, the localization and the conditions of rigidity in extremities of shells. The study will be led on a building (F+5) implanted in zone of seismicity average. To do it, we shall proceed to a classic dynamic study of a structure by using 4 alternatives for shells by varying the lengths and number in order to compare the cost of the structure for 4 dispositions of the shells with a technical-economic study of the brace system by the use of different dispositions of shells and to estimate the quantities of necessary materials (concrete and steel).Keywords: reinforced concrete, mixed brace system, dynamic analysis, beams, shells
Procedia PDF Downloads 3221626 Sensing Characteristics of Gold Nanoparticles Decorated Sputtered Tin Oxide Thin Films as Nitrogen Oxide Sensor
Authors: Qasem Drmosh, Zain Yamai, Amar Mohamedkhair, Abdulmajid Hendi
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In recent years, there has been a growing interest in the reduction of the nitrogen oxides NOx (NO2, NO) gases resulting from automotive or combustion emissions. Recently, metal additives in nanometer dimension onto the surface of SnO2 nanorods, nanowires and nanotubes sensitizer to further increase the sensor response have been used. In contrast, there is a lack study focused on modifying the surface of SnO2 thin films by nanoparticles. The challenge in case of thin films is how to fabricate these nanoparticles on the surfaces in cost-effective method, high purity as well as without hampering electrical and topographical properties. Here in this report, a simple and facile strategy has been demonstrated to acquire high sensitive and fast response NO2 gas sensor. Structural, electrical, morphological, optical, and compositional properties of the fabricated sensors were investigated through different analytical technique including X-ray diffraction (XRD), Field emission scanning emission microscope (FESEM) and X-ray photoelectron spectroscopy (XPS). The sensing performance of the prepared sensors are studied at different temperatures for various concentrations of NO2 and compared with pristine SnO2 film.Keywords: NO2 sensor, SnO2, sputtering, thin films
Procedia PDF Downloads 2071625 Quality Assessment of New Zealand Mānuka Honeys Using Hyperspectral Imaging Combined with Deep 1D-Convolutional Neural Networks
Authors: Hien Thi Dieu Truong, Mahmoud Al-Sarayreh, Pullanagari Reddy, Marlon M. Reis, Richard Archer
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New Zealand mānuka honey is a honeybee product derived mainly from Leptospermum scoparium nectar. The potent antibacterial activity of mānuka honey derives principally from methylglyoxal (MGO), in addition to the hydrogen peroxide and other lesser activities present in all honey. MGO is formed from dihydroxyacetone (DHA) unique to L. scoparium nectar. Mānuka honey also has an idiosyncratic phenolic profile that is useful as a chemical maker. Authentic mānuka honey is highly valuable, but almost all honey is formed from natural mixtures of nectars harvested by a hive over a time period. Once diluted by other nectars, mānuka honey irrevocably loses value. We aimed to apply hyperspectral imaging to honey frames before bulk extraction to minimise the dilution of genuine mānuka by other honey and ensure authenticity at the source. This technology is non-destructive and suitable for an industrial setting. Chemometrics using linear Partial Least Squares (PLS) and Support Vector Machine (SVM) showed limited efficacy in interpreting chemical footprints due to large non-linear relationships between predictor and predictand in a large sample set, likely due to honey quality variability across geographic regions. Therefore, an advanced modelling approach, one-dimensional convolutional neural networks (1D-CNN), was investigated for analysing hyperspectral data for extraction of biochemical information from honey. The 1D-CNN model showed superior prediction of honey quality (R² = 0.73, RMSE = 2.346, RPD= 2.56) to PLS (R² = 0.66, RMSE = 2.607, RPD= 1.91) and SVM (R² = 0.67, RMSE = 2.559, RPD=1.98). Classification of mono-floral manuka honey from multi-floral and non-manuka honey exceeded 90% accuracy for all models tried. Overall, this study reveals the potential of HSI and deep learning modelling for automating the evaluation of honey quality in frames.Keywords: mānuka honey, quality, purity, potency, deep learning, 1D-CNN, chemometrics
Procedia PDF Downloads 1311624 CyberSteer: Cyber-Human Approach for Safely Shaping Autonomous Robotic Behavior to Comply with Human Intention
Authors: Vinicius G. Goecks, Gregory M. Gremillion, William D. Nothwang
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Modern approaches to train intelligent agents rely on prolonged training sessions, high amounts of input data, and multiple interactions with the environment. This restricts the application of these learning algorithms in robotics and real-world applications, in which there is low tolerance to inadequate actions, interactions are expensive, and real-time processing and action are required. This paper addresses this issue introducing CyberSteer, a novel approach to efficiently design intrinsic reward functions based on human intention to guide deep reinforcement learning agents with no environment-dependent rewards. CyberSteer uses non-expert human operators for initial demonstration of a given task or desired behavior. The trajectories collected are used to train a behavior cloning deep neural network that asynchronously runs in the background and suggests actions to the deep reinforcement learning module. An intrinsic reward is computed based on the similarity between actions suggested and taken by the deep reinforcement learning algorithm commanding the agent. This intrinsic reward can also be reshaped through additional human demonstration or critique. This approach removes the need for environment-dependent or hand-engineered rewards while still being able to safely shape the behavior of autonomous robotic agents, in this case, based on human intention. CyberSteer is tested in a high-fidelity unmanned aerial vehicle simulation environment, the Microsoft AirSim. The simulated aerial robot performs collision avoidance through a clustered forest environment using forward-looking depth sensing and roll, pitch, and yaw references angle commands to the flight controller. This approach shows that the behavior of robotic systems can be shaped in a reduced amount of time when guided by a non-expert human, who is only aware of the high-level goals of the task. Decreasing the amount of training time required and increasing safety during training maneuvers will allow for faster deployment of intelligent robotic agents in dynamic real-world applications.Keywords: human-robot interaction, intelligent robots, robot learning, semisupervised learning, unmanned aerial vehicles
Procedia PDF Downloads 2551623 Graph Clustering Unveiled: ClusterSyn - A Machine Learning Framework for Predicting Anti-Cancer Drug Synergy Scores
Authors: Babak Bahri, Fatemeh Yassaee Meybodi, Changiz Eslahchi
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In the pursuit of effective cancer therapies, the exploration of combinatorial drug regimens is crucial to leverage synergistic interactions between drugs, thereby improving treatment efficacy and overcoming drug resistance. However, identifying synergistic drug pairs poses challenges due to the vast combinatorial space and limitations of experimental approaches. This study introduces ClusterSyn, a machine learning (ML)-powered framework for classifying anti-cancer drug synergy scores. ClusterSyn employs a two-step approach involving drug clustering and synergy score prediction using a fully connected deep neural network. For each cell line in the training dataset, a drug graph is constructed, with nodes representing drugs and edge weights denoting synergy scores between drug pairs. Drugs are clustered using the Markov clustering (MCL) algorithm, and vectors representing the similarity of drug pairs to each cluster are input into the deep neural network for synergy score prediction (synergy or antagonism). Clustering results demonstrate effective grouping of drugs based on synergy scores, aligning similar synergy profiles. Subsequently, neural network predictions and synergy scores of the two drugs on others within their clusters are used to predict the synergy score of the considered drug pair. This approach facilitates comparative analysis with clustering and regression-based methods, revealing the superior performance of ClusterSyn over state-of-the-art methods like DeepSynergy and DeepDDS on diverse datasets such as Oniel and Almanac. The results highlight the remarkable potential of ClusterSyn as a versatile tool for predicting anti-cancer drug synergy scores.Keywords: drug synergy, clustering, prediction, machine learning., deep learning
Procedia PDF Downloads 681622 Comparative Analysis of Predictive Models for Customer Churn Prediction in the Telecommunication Industry
Authors: Deepika Christopher, Garima Anand
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To determine the best model for churn prediction in the telecom industry, this paper compares 11 machine learning algorithms, namely Logistic Regression, Support Vector Machine, Random Forest, Decision Tree, XGBoost, LightGBM, Cat Boost, AdaBoost, Extra Trees, Deep Neural Network, and Hybrid Model (MLPClassifier). It also aims to pinpoint the top three factors that lead to customer churn and conducts customer segmentation to identify vulnerable groups. According to the data, the Logistic Regression model performs the best, with an F1 score of 0.6215, 81.76% accuracy, 68.95% precision, and 56.57% recall. The top three attributes that cause churn are found to be tenure, Internet Service Fiber optic, and Internet Service DSL; conversely, the top three models in this article that perform the best are Logistic Regression, Deep Neural Network, and AdaBoost. The K means algorithm is applied to establish and analyze four different customer clusters. This study has effectively identified customers that are at risk of churn and may be utilized to develop and execute strategies that lower customer attrition.Keywords: attrition, retention, predictive modeling, customer segmentation, telecommunications
Procedia PDF Downloads 511621 KCBA, A Method for Feature Extraction of Colonoscopy Images
Authors: Vahid Bayrami Rad
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In recent years, the use of artificial intelligence techniques, tools, and methods in processing medical images and health-related applications has been highlighted and a lot of research has been done in this regard. For example, colonoscopy and diagnosis of colon lesions are some cases in which the process of diagnosis of lesions can be improved by using image processing and artificial intelligence algorithms, which help doctors a lot. Due to the lack of accurate measurements and the variety of injuries in colonoscopy images, the process of diagnosing the type of lesions is a little difficult even for expert doctors. Therefore, by using different software and image processing, doctors can be helped to increase the accuracy of their observations and ultimately improve their diagnosis. Also, by using automatic methods, the process of diagnosing the type of disease can be improved. Therefore, in this paper, a deep learning framework called KCBA is proposed to classify colonoscopy lesions which are composed of several methods such as K-means clustering, a bag of features and deep auto-encoder. Finally, according to the experimental results, the proposed method's performance in classifying colonoscopy images is depicted considering the accuracy criterion.Keywords: colorectal cancer, colonoscopy, region of interest, narrow band imaging, texture analysis, bag of feature
Procedia PDF Downloads 451620 CO₂ Storage Capacity Assessment of Deep Saline Aquifers in Malaysia
Authors: Radzuan Junin, Dayang Zulaika A. Hasbollah
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The increasing amount of greenhouse gasses in the atmosphere recently has become one of the discussed topics in relation with world’s concern on climate change. Developing countries’ emissions (such as Malaysia) are now seen to surpass developed country’s emissions due to rapid economic development growth in recent decades. This paper presents the potential storage sites suitability and storage capacity assessment for CO2 sequestration in sedimentary basins of Malaysia. This study is the first of its kind that made an identification of potential storage sites and assessment of CO2 storage capacity within the deep saline aquifers in the country. The CO2 storage capacity in saline formation assessment was conducted based on the method for quick assessment of CO2 storage capacity in closed, and semi-closed saline formations modified to suit the geology setting of Malaysia. Then, an integrated approach that involved geographic information systems (GIS) analysis and field data assessment was adopted to provide the potential storage sites and its capacity for CO2 sequestration. This study concentrated on the assessment of major sedimentary basins in Malaysia both onshore and offshore where potential geological formations which CO2 could be stored exist below 800 meters and where suitable sealing formations are present. Based on regional study and amount of data available, there are 14 sedimentary basins all around Malaysia that has been identified as potential CO2 storage. Meanwhile, from the screening and ranking exercises, it is obvious that Malay Basin, Central Luconia Province, West Baram Delta and Balingian Province are respectively ranked as the top four in the ranking system for CO2 storage. 27% of sedimentary basins in Malaysia were evaluated as high potential area for CO2 storage. This study should provide a basis for further work to reduce the uncertainty in these estimates and also provide support to policy makers on future planning of carbon capture and sequestration (CCS) projects in Malaysia.Keywords: CO₂ storage, deep saline aquifer, GIS, sedimentary basin
Procedia PDF Downloads 3511619 SEM Image Classification Using CNN Architectures
Authors: Güzi̇n Ti̇rkeş, Özge Teki̇n, Kerem Kurtuluş, Y. Yekta Yurtseven, Murat Baran
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A scanning electron microscope (SEM) is a type of electron microscope mainly used in nanoscience and nanotechnology areas. Automatic image recognition and classification are among the general areas of application concerning SEM. In line with these usages, the present paper proposes a deep learning algorithm that classifies SEM images into nine categories by means of an online application to simplify the process. The NFFA-EUROPE - 100% SEM data set, containing approximately 21,000 images, was used to train and test the algorithm at 80% and 20%, respectively. Validation was carried out using a separate data set obtained from the Middle East Technical University (METU) in Turkey. To increase the accuracy in the results, the Inception ResNet-V2 model was used in view of the Fine-Tuning approach. By using a confusion matrix, it was observed that the coated-surface category has a negative effect on the accuracy of the results since it contains other categories in the data set, thereby confusing the model when detecting category-specific patterns. For this reason, the coated-surface category was removed from the train data set, hence increasing accuracy by up to 96.5%.Keywords: convolutional neural networks, deep learning, image classification, scanning electron microscope
Procedia PDF Downloads 1181618 Meta Mask Correction for Nuclei Segmentation in Histopathological Image
Authors: Jiangbo Shi, Zeyu Gao, Chen Li
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Nuclei segmentation is a fundamental task in digital pathology analysis and can be automated by deep learning-based methods. However, the development of such an automated method requires a large amount of data with precisely annotated masks which is hard to obtain. Training with weakly labeled data is a popular solution for reducing the workload of annotation. In this paper, we propose a novel meta-learning-based nuclei segmentation method which follows the label correction paradigm to leverage data with noisy masks. Specifically, we design a fully conventional meta-model that can correct noisy masks by using a small amount of clean meta-data. Then the corrected masks are used to supervise the training of the segmentation model. Meanwhile, a bi-level optimization method is adopted to alternately update the parameters of the main segmentation model and the meta-model. Extensive experimental results on two nuclear segmentation datasets show that our method achieves the state-of-the-art result. In particular, in some noise scenarios, it even exceeds the performance of training on supervised data.Keywords: deep learning, histopathological image, meta-learning, nuclei segmentation, weak annotations
Procedia PDF Downloads 1361617 Construction of Strain Distribution Profiles of EDD Steel at Elevated Temperatures
Authors: K. Eshwara Prasad, R. Raman Goud, Swadesh Kumar Singh, N. Sateesh
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In the present work forming limit diagrams and strain distribution profile diagrams for extra deep drawing steel at room and elevated temperatures have been determined experimentally by conducting stretch forming experiments by using designed and fabricated warm stretchforming tooling setup. With the help of forming Limit Diagrams (FLDs) and strain distribution profile diagrams the formability of Extra Deep Drawing steel has been analyzed and co-related with mechanical properties like strain hardening COEFFICIENT (n) and normal anisotropy (r−).Mechanical properties of EDD steel from room temperature to 4500C were determined and discussed the impact of temperature on the properties like work hardening exponent (n) anisotropy(r-) and strength coefficient of the material. Also the fractured surfaces after stretching have undergone the some metallurgical investigations and attempt has been made to co-relate with the formability of EDD steel sheets. They are co-related and good agreement with FLDs at various temperatures.Keywords: FLD, microhardness, strain distribution profile, stretch forming
Procedia PDF Downloads 3181616 The Genesis of the Anomalous Sernio Fan (Valtellina, Northern Italy)
Authors: Erika De Finis, Paola Gattinoni, Laura Scesi
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Massive rock avalanches formed some of the largest landslide deposits on Earth and they represent one of the major geohazards in high-relief mountains. This paper interprets a very large sedimentary fan (the Sernio fan, Valtellina, Northern Italy), located 20 Km SW from Val Pola Rock avalanche (1987), as the deposit of a partial collapse of a Deep Seated Gravitational Slope Deformation (DSGSD), afterwards eroded and buried by debris flows. The proposed emplacement sequence has been reconstructed based on geomorphological, structural and mechanical evidences. The Sernio fan is actually considered anomalous with reference to the very high ratio between the fan area (about 4.5km2) and the basin area (about 3km2). The morphology of the fan area is characterised by steep slopes (dip about 20%) and the fan apex is extended for 1.8 km inside the small catchment basin. This sedimentary fan was originated by a landslide that interested a part of a large deep-seated gravitational slope deformation, involving a wide area of about 55 km². The main controlling factor is tectonic and it is related to the proximity to regional fault systems and the consequent occurrence of fault weak rocks (GSI locally lower than 10 with compressive stress lower than 20MPa). Moreover, the fan deposit shows sedimentary evidences of recent debris flow events. The best current explanation of the Sernio fan involves an initial failure of some hundreds of Mm3. The run-out was quite limited because of the morphology of Valtellina’s valley floor, and the deposit filled the main valley forming a landslide dam, as confirmed by the lacustrine deposits detected upstream the fan. Nowadays the debris flow events represent the main hazard in the study area.Keywords: anomalous sedimentary fans, deep seated gravitational slope deformation, Italy, rock avalanche
Procedia PDF Downloads 4731615 Peg@GDF3:TB3+ – Rb Nanocomposites for Deep-Seated X-Ray Induced Photodynamic Therapy in Oncology
Authors: E.A. Kuchma
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Photodynamic therapy (PDT) is considered an alternative and minimally invasive cancer treatment modality compared to chemotherapy and radiation therapy. PDT includes three main components: a photosensitizer (PS), oxygen, and a light source. PS is injected into the patient's body and then selectively accumulates in the tumor. However, the light used in PDT (spectral range 400–700 nm) is limited to superficial lesions, and the light penetration depth does not exceed a few cm. The problem of PDT (poor visible light transmission) can be solved by using X-rays. The penetration depth of X-rays is ten times greater than that of visible light. Therefore, X-ray radiation easily penetrates through the tissues of the body. The aim of this work is to develop universal nanocomposites for X-ray photodynamic therapy of deep and superficial tumors using scintillation nanoparticles of gadolinium fluoride (GdF3), doped with Tb3+, coated with a biocompatible coating (PEG) and photosensitizer RB (Rose Bengal). PEG@GdF3:Tb3+(15%) – RB could be used as an effective X-ray, UV, and photoluminescent mediator to excite a photosensitizer for generating reactive oxygen species (ROS) to kill tumor cells via photodynamic therapy. GdF3 nanoparticles can also be used as contrast agents for computed tomography (CT) and magnetic resonance imaging (MRI).Keywords: X-ray induced photodynamic therapy, scintillating nanoparticle, radiosensitizer, photosensitizer
Procedia PDF Downloads 731614 A Comparison of Convolutional Neural Network Architectures for the Classification of Alzheimer’s Disease Patients Using MRI Scans
Authors: Tomas Premoli, Sareh Rowlands
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In this study, we investigate the impact of various convolutional neural network (CNN) architectures on the accuracy of diagnosing Alzheimer’s disease (AD) using patient MRI scans. Alzheimer’s disease is a debilitating neurodegenerative disorder that affects millions worldwide. Early, accurate, and non-invasive diagnostic methods are required for providing optimal care and symptom management. Deep learning techniques, particularly CNNs, have shown great promise in enhancing this diagnostic process. We aim to contribute to the ongoing research in this field by comparing the effectiveness of different CNN architectures and providing insights for future studies. Our methodology involved preprocessing MRI data, implementing multiple CNN architectures, and evaluating the performance of each model. We employed intensity normalization, linear registration, and skull stripping for our preprocessing. The selected architectures included VGG, ResNet, and DenseNet models, all implemented using the Keras library. We employed transfer learning and trained models from scratch to compare their effectiveness. Our findings demonstrated significant differences in performance among the tested architectures, with DenseNet201 achieving the highest accuracy of 86.4%. Transfer learning proved to be helpful in improving model performance. We also identified potential areas for future research, such as experimenting with other architectures, optimizing hyperparameters, and employing fine-tuning strategies. By providing a comprehensive analysis of the selected CNN architectures, we offer a solid foundation for future research in Alzheimer’s disease diagnosis using deep learning techniques. Our study highlights the potential of CNNs as a valuable diagnostic tool and emphasizes the importance of ongoing research to develop more accurate and effective models.Keywords: Alzheimer’s disease, convolutional neural networks, deep learning, medical imaging, MRI
Procedia PDF Downloads 651613 Strain DistributionProfiles of EDD Steel at Elevated Temperatures
Authors: Eshwara Prasad Koorapati, R. Raman Goud, Swadesh Kumar Singh
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In the present work forming limit diagrams and strain distribution profile diagrams for extra deep drawing steel at room and elevated temperatures have been determined experimentally by conducting stretch forming experiments by using designed and fabricated warm stretch forming tooling setup. With the help of forming Limit Diagrams (FLDs) and strain distribution profile diagrams the formability of Extra Deep Drawing steel has been analyzed and co-related with mechanical properties like strain hardening coefficient (n) and normal anisotropy (r−).Mechanical properties of EDD steel from room temperature to 4500 C were determined and discussed the impact of temperature on the properties like work hardening exponent (n) anisotropy (r-) and strength coefficient of the material. Also, the fractured surfaces after stretching have undergone the some metallurgical investigations and attempt has been made to co-relate with the formability of EDD steel sheets. They are co-related and good agreement with FLDs at various temperatures.Keywords: FLD, micro hardness, strain distribution profile, stretch forming
Procedia PDF Downloads 4151612 The Use of Waste Fibers as Reinforcement in Biopolymer Green Composites
Authors: Dalila Hammiche, Lisa Klaai, Amar Boukerrou
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Following this trend, natural fiber reinforcements have been gaining importance in the composites sector. The effectiveness of natural fiber–reinforced PLA composite as an alternative material to substitute the non-renewable petroleum-based materials has been examined by researchers. In this study, we investigated the physicochemical, particle size and distribution, and thermal behavior of prickly pear seed flour (PPSF). Then, composites were manufactured with 20% in PPSF. Thermal, morphological, and mechanical properties have been studied, and water absorption tests as well. The characterization of this fiber has shown that cellulose is the majority constituent (30%), followed by hemicellulose (27%). To improve the fiber-matrix adhesion, the PPS was chemically treated with alkali treatment. The addition of PPSF decreases the thermal properties, and the study of the mechanical properties showed that the increase in the fiber content from 0 to 20% increased Young’s modulus. According to the results, the mechanical and thermal behaviors of composites are improved after fiber treatment. However, there is an increase in water absorption of composites compared to the PLA matrix. The moisture sensitivity of natural fiber composites limits their use in structural applications. Degradation of the fiber-matrix interface is likely to occur when the material is subjected to variable moisture conditions.Keywords: biopolymer, composites, alcali treatment, mechanical properties
Procedia PDF Downloads 124