Search results for: artificial oil bodies

Authors: K. S. Ghosh, Sagnik Bose, Kapil Tripati

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Potentiodynamic polarization studies carried out on AA2024 and AA2014 Al-Cu-Mg alloys of various tempers in 3.5 wt. % NaCl and in 3.5 wt. % NaCl + 1.0 % H2O2 solution characteristic E-i curves. Corrosion potential (Ecorr) value has shifted towards more negative potential with the increase of artificial aging time. The Ecorr value for the alloy tempers has also shifted anodically in presence of H2O2 in 3.5 % NaCl solution. Further, passivity phenomenon has been observed in all the alloy tempers when tested in 3.5 wt. % NaCl solution at pH 12. Stress corrosion cracking (SCC) behaviour of friction stir weld (FSW) joint of AA2014 alloy has been studied bu slow strain rate test (SSRT) in 3.5 wt. % NaCl solution. Optical micrographs of the corroded surfaces of polarised samples showed general corrosion, extensive pitting and intergranular corrosion as well. Further, potentiodynamic cyclic polarization curves displayed wide hysteresis loop indicating that the alloy tempers are susceptible to pit growth damage. Attempts have been made to explain the variation of observed electrochemical and SCC behaviour of the alloy tempers and the electrolyte conditions with the help of microstructural features.

Keywords: AA 2014 and AA 2024 Al-C-Mg alloy, artificial ageing, potentiodynamic polarization, TEM micrographs, stress corrosion cracking (SCC)

Procedia PDF Downloads 305

Authors: Suphat Rittirat, Sutha Klaocheed, Kanchit Thammasiri

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The effects of thidiazuron (TDZ) and benzyladenine (BA) on protocorm-like bodies (PLBs) induction from leaf explants was investigated. It was found that TDZ was superior to BA. The highest percentage and number of PLBs per leaf explant at 30 and 5.3 respectively were obtained on ½ MS medium supplemented with 9µM TDZ. The regenerated plantlets were potted and acclimatized in the greenhouse. These plants grew well and developed into normal plants after 3 month of transplantation. The 100% survival of plantlets was achieved when planted on pots containing sphagnum moss.

Keywords: orchid, PLBs, sphagnum moss, thidiazuron

Procedia PDF Downloads 301

Authors: Marjan Golmaryami, Marzieh Behzadi

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Recent developments in stock exchange highlight the need for an efficient and accurate method that helps stockholders make better decision. Since stock markets have lots of fluctuations during the time and different effective parameters, it is difficult to make good decisions. The purpose of this study is to employ artificial neural network (ANN) which can deal with time series data and nonlinear relation among variables to forecast next day stock price. Unlike other evolutionary algorithms which were utilized in stock exchange prediction, we trained our proposed neural network with metaheuristic bat algorithm, with fast and powerful convergence and applied it in stock price prediction for the first time. In order to prove the performance of the proposed method, this research selected a 7 year dataset from Parsian Bank stocks and after imposing data preprocessing, used 3 types of ANN (back propagation-ANN, particle swarm optimization-ANN and bat-ANN) to predict the closed price of stocks. Afterwards, this study engaged MATLAB to simulate 3 types of ANN, with the scoring target of mean absolute percentage error (MAPE). The results may be adapted to other companies stocks too.

Keywords: artificial neural network (ANN), bat algorithm, particle swarm optimization algorithm (PSO), stock exchange

Procedia PDF Downloads 526

Authors: Dequn Teng, Tianshuo Yang, Mingrui Wang, Qiuyu Chen, Xiao Wang, Katie Atkinson

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Currently, many students are kind of at a loss in the university due to the complex environment within the campus, where every information within the campus is isolated with fewer interactions with each other. However, if the on-campus resources are gathered and combined with the artificial intelligence modelling techniques, there will be a bridge for not only students in understanding themselves, and the teachers will understand students in providing a much efficient approach in education. The objective of this paper is to provide a competency level analysis method, apparatus, electronic equipment, and storage medium. It uses a user’s target competency level analysis model from a plurality of predefined candidate competency level analysis models by obtaining a user’s promotion target parameters, promotion target parameters including at least one of the following parameters: target profession, target industry, and the target company, according to the promotion target parameters. According to the parameters, the model analyzes the user’s ability level, determines the user’s ability level, realizes the quantitative and personalized analysis of the user’s ability level, and helps the user to objectively position his ability level.

Keywords: artificial intelligence, model, university, education, recommendation system, evaluation, job hunting

Procedia PDF Downloads 125

Authors: Wu You, Burra Venkata Durga Kumar

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This study examines the promising potential of integrating Blockchain and Artificial Intelligence (AI) technologies to scalability in Distributed Systems within the field of supply chain finance. The finance industry is continually confronted with scalability challenges in its Distributed Systems, particularly within the supply chain finance sector, impacting efficiency and security. Blockchain, with its inherent attributes of high scalability and secure distributed ledger system, coupled with AI's strengths in optimizing data processing and decision-making, holds the key to innovating the industry's approach to these issues. This study elucidates the synergistic interplay between Blockchain and AI, detailing how their fusion can drive a significant transformation in the supply chain finance sector's Distributed Systems. It offers specific use-cases within this field to illustrate the practical implications and potential benefits of this technological convergence. The study also discusses future possibilities and current challenges in implementing this groundbreaking approach within the context of supply chain finance. It concludes that the intersection of Blockchain and AI could ignite a new epoch of enhanced efficiency, security, and transparency in the Distributed Systems of supply chain finance within the financial industry.

Keywords: blockchain, artificial intelligence (AI), scaled distributed systems, supply chain finance, efficiency and security

Procedia PDF Downloads 60

Authors: Shahrzad Zolfagharnassab, Abdul Rashid Mohamed Shariff, Reza Ehsani, Hawa Ze Jaffar, Ishak Aris

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Ripeness estimation of oil palm fresh fruit is important processes that affect the profitableness and salability of oil palm fruits. The adulthood or ripeness of the oil palm fruits influences the quality of oil palm. Conventional procedure includes physical grading of Fresh Fruit Bunches (FFB) maturity by calculating the number of loose fruits per bunch. This physical classification of oil palm FFB is costly, time consuming and the results may have human error. Hence, many researchers try to develop the methods for ascertaining the maturity of oil palm fruits and thereby, deviously the oil content of distinct palm fruits without the need for exhausting oil extraction and analysis. This research investigates the potential of infrared images (Thermal Images) as a predictor to classify the oil palm FFB ripeness. A total of 270 oil palm fresh fruit bunches from most common cultivar of oil palm bunches Nigresens according to three maturity categories: under ripe, ripe and over ripe were collected. Each sample was scanned by the thermal imaging cameras FLIR E60 and FLIR T440. The average temperature of each bunches were calculated by using image processing in FLIR Tools and FLIR ThermaCAM researcher pro 2.10 environment software. The results show that temperature content decreased from immature to over mature oil palm FFBs. An overall analysis-of-variance (ANOVA) test was proved that this predictor gave significant difference between underripe, ripe and overripe maturity categories. This shows that the temperature as predictors can be good indicators to classify oil palm FFB. Classification analysis was performed by using the temperature of the FFB as predictors through Linear Discriminant Analysis (LDA), Mahalanobis Discriminant Analysis (MDA), Artificial Neural Network (ANN) and K- Nearest Neighbor (KNN) methods. The highest overall classification accuracy was 88.2% by using Artificial Neural Network. This research proves that thermal imaging and neural network method can be used as predictors of oil palm maturity classification.

Keywords: artificial neural network, maturity classification, oil palm FFB, thermal imaging

Procedia PDF Downloads 331

Authors: Uliana N. Tumanova, Sergey M. Voevodin, Veronica A. Sinitsyna, Alexandr I. Shchegolev

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An important part of forensic and autopsy research in perinatology is the answer to the question of life and stillbirth. Postmortem magnetic resonance imaging (MRI) is an objective non-invasive research method that allows to store data for a long time and not to exhume the body to clarify the diagnosis. The purpose of the research is to study the possibilities of a postmortem MRI to determine the stillbirth and death of a newborn who had spontaneous breathing and died on the first day after birth. MRI and morphological data of a study of 23 stillborn bodies, prenatally dead at a gestational age of 22-39 weeks (Group I) and the bodies of 16 newborns who died from 2 to 24 hours after birth (Group II) were compared. Before the autopsy, postmortem MRI was performed on the Siemens Magnetom Verio 3T device in the supine position of the body. The control group for MRI studies consisted of 7 live newborns without lung disease (Group III). On T2WI in the sagittal projection was measured MR-signal intensity (SI) in the lung tissue (L) and shoulder muscle (M). During the autopsy, a pulmonary swimming test was evaluated, and macro- and microscopic studies were performed. According to the postmortem MRI, the highest values of mean SI of the lung (430 ± 27.99) and of the muscle (405.5 ± 38.62) on T2WI were detected in group I and exceeded the corresponding value of group II by 2.7 times. The lowest values were found in the control group - 77.9 ± 12.34 and 119.7 ± 6.3, respectively. In the group II, the lung SI was 1.6 times higher than the muscle SI, whereas in the group I and in the control group, the muscle SI was 2.1 times and 1.8 times larger than the lung. On the basis of clinical and morphological data, we calculated the formula for determining the breathing index (BI) during postmortem MRI: BI = SIL x SIM / 100. The mean value of BI in the group I (1801.14 ± 241.6) (values ranged from 756 to 3744) significantly higher than the corresponding average value of BI in the group II (455.89 ± 137.32, p < 0.05) (305-638.4). In the control group, the mean BI value was 91.75 ± 13.3 (values ranged from 53 to 154). The BI with the results of pulmonary swimming tests and microscopic examination of the lungs were compared. The boundary value of BI for the differential diagnosis of stillborn and newborn death was 700. Using the postmortem MRI allows to differentiate the stillborn with the death of the breathing newborn.

Keywords: lung, newborn, postmortem MRI, stillborn

Procedia PDF Downloads 106

Authors: Ş. Karabulut, A. Güllü, A. Güldaş, R. Gürbüz

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This study investigates the effects of the lead angle and chip thickness variation on surface roughness during the machining of compacted graphite iron using ceramic cutting tools under dry cutting conditions. Analytical models were developed for predicting the surface roughness values of the specimens after the face milling process. Experimental data was collected and imported to the artificial neural network model. A multilayer perceptron model was used with the back propagation algorithm employing the input parameters of lead angle, cutting speed and feed rate in connection with chip thickness. Furthermore, analysis of variance was employed to determine the effects of the cutting parameters on surface roughness. Artificial neural network and regression analysis were used to predict surface roughness. The values thus predicted were compared with the collected experimental data, and the corresponding percentage error was computed. Analysis results revealed that the lead angle is the dominant factor affecting surface roughness. Experimental results indicated an improvement in the surface roughness value with decreasing lead angle value from 88° to 45°.

Keywords: CGI, milling, surface roughness, ANN, regression, modeling, analysis

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Authors: Alireza Keshmiri, Shahriar Bagheri, Nan Wu

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This research presents an analytical model for the development of an energy harvester with piezoelectric rings stacked at the boundary of the structure based on the Adomian decomposition method. The model is applied to geometrically non-uniform beams to derive the steady-state dynamic response of the structure subjected to base motion excitation and efficiently harvest the subsequent vibrational energy. The in-plane polarization of the piezoelectric rings is employed to enhance the electrical power output. A parametric study for the proposed energy harvester with various design parameters is done to prepare the dataset required for optimization. Finally, simulation-based optimization technique helps to find the optimum structural design with maximum efficiency. To solve the optimization problem, an artificial neural network is first trained to replace the simulation model, and then, a genetic algorithm is employed to find the optimized design variables. Higher geometrical non-uniformity and length of the beam lowers the structure natural frequency and generates a larger power output.

Keywords: piezoelectricity, energy harvesting, simulation-based optimization, artificial neural network, genetic algorithm

Procedia PDF Downloads 104

Authors: Monique Du Plessis, Anina Coetzee, Colleen L. Seymour, Claire N. Spottiswoode

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Artificial nectar feeders are used to attract nectarivorous birds to gardens and are increasing in popularity. The costs and benefits of these feeders remain controversial, however. Nectar feeders may have positive effects by attracting nectarivorous birds towards suburbia, facilitating their urban adaptation, and supplementing bird diets when floral resources are scarce. However, this may come at the cost of luring them away from the plants they pollinate in neighboring indigenous vegetation. This study investigated the effect of nectar feeders on an African pollinator-plant mutualism. Given that birds are important pollinators to many fynbos plant species, this study was conducted in gardens and natural vegetation along the urban edge of the Cape Peninsula. Feeding experiments were carried out to compare relative bird abundance and local distribution patterns for nectarivorous birds (i.e., sunbirds and sugarbirds) between feeder and control treatments. Resultant changes in their visitation rates to Erica flowers in the natural vegetation were tested by inspection of their anther ring status. Nectar feeders attracted higher densities of nectarivores to gardens relative to natural vegetation and decreased their densities in the neighboring fynbos, even when floral abundance in the neighboring vegetation was high. The consequent changes to their distribution patterns and foraging behavior decreased their visitation to at least Erica plukenetii flowers (but not to Erica abietina). This study provides evidence that nectar feeders may have positive effects for birds themselves by reducing their urban sensitivity but also highlights the unintended negative effects feeders may have on the surrounding fynbos ecosystem. Given that nectar feeders appear to compete with the flowers of Erica plukenetii, and perhaps those of other Erica species, artificial feeding may inadvertently threaten bird-plant pollination networks.

Keywords: avian nectarivores, bird feeders, bird pollination, indirect effects in human-wildlife interactions, sugar water feeders, supplementary feeding

Procedia PDF Downloads 130

Authors: Dheyaa W. Abbood, Rafa H. AL-Suhaili, May S. Saleh

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A model using the artificial neural networks and genetic algorithm technique is developed for obtaining optimum dimensions of the foundation length and protections of small hydraulic structures. The procedure involves optimizing an objective function comprising a weighted summation of the state variables. The decision variables considered in the optimization are the upstream and downstream cutoffs length sand their angles of inclination, the foundation length, and the length of the downstream soil protection. These were obtained for a given maximum difference in head, depth of impervious layer and degree of anisotropy.The optimization carried out subjected to constraints that ensure a safe structure against the uplift pressure force and sufficient protection length at the downstream side of the structure to overcome an excessive exit gradient. The Geo-studios oft ware, was used to analyze 1200 different cases. For each case the length of protection and volume of structure required to satisfy the safety factors mentioned previously were estimated. An ANN model was developed and verified using these cases input-output sets as its data base. A MatLAB code was written to perform a genetic algorithm optimization modeling coupled with this ANN model using a formulated optimization model. A sensitivity analysis was done for selecting the cross-over probability, the mutation probability and level ,the number of population, the position of the crossover and the weights distribution for all the terms of the objective function. Results indicate that the most factor that affects the optimum solution is the number of population required. The minimum value that gives stable global optimum solution of this parameters is (30000) while other variables have little effect on the optimum solution.

Keywords: inclined cutoff, optimization, genetic algorithm, artificial neural networks, geo-studio, uplift pressure, exit gradient, factor of safety

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Authors: Aishi Kundu, Elhan Raza

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"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

Procedia PDF Downloads 82

Authors: Mariyam Arif, Ye Liu, Israr Ul Haq, Ahsan Ashfaq

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High cost of fossil fuels and intensifying installations of alternate energy generation sources are intimidating main challenges in power systems. Making accurate load forecasting an important and challenging task for optimal energy planning and management at both distribution and generation side. There are many techniques to forecast load but each technique comes with its own limitation and requires data to accurately predict the forecast load. Artificial Neural Network (ANN) is one such technique to efficiently forecast the load. Comparison between two different ranges of input datasets has been applied to dynamic ANN technique using MATLAB Neural Network Toolbox. It has been observed that selection of input data on training of a network has significant effects on forecasted results. Day-wise input data forecasted the load accurately as compared to year-wise input data. The forecasted load is then distributed among the six generators by using the linear programming to get the optimal point of generation. The algorithm is then verified by comparing the results of each generator with their respective generation limits.

Keywords: artificial neural networks, demand-side management, economic dispatch, linear programming, power generation dispatch

Procedia PDF Downloads 168

Authors: Shi-Yu Xu

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The top three risk factors that have contributed to building collapses during past earthquake events in Taiwan are: "irregular floor plans or elevations," "insufficient columns in single-bay buildings," and the "weak-story problem." Fortunately, these unsound structural characteristics can be directly identified from the floor plans. However, due to the vast number of old buildings, conducting manual inspections to identify these compromised structural features in all existing structures would be time-consuming and prone to human errors. This study aims to develop an algorithm that utilizes artificial intelligence techniques to automatically pinpoint the structural components within a building's floor plans. The obtained spatial information will be utilized to construct a digital structural model of the building. This information, particularly regarding the distribution of columns in the floor plan, can then be used to conduct preliminary seismic assessments of the building. The study employs various image processing and pattern recognition techniques to enhance detection efficiency and accuracy. The study enables a large-scale evaluation of structural vulnerability for numerous old buildings, providing ample time to arrange for structural retrofitting in those buildings that are at risk of significant damage or collapse during earthquakes.

Keywords: structural vulnerability detection, object recognition, seismic capacity assessment, old buildings, artificial intelligence

Procedia PDF Downloads 65

Authors: Shivendra Pratap Singh, Rishabh Sharma

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The widespread adoption of mobile technology in recent years has revolutionized the way we communicate and access information. The evolution of mobile systems has been rapid and impactful, shaping our lives and changing the way we live and work. However, despite its significant influence, the history and development of mobile technology are not well understood by the general public. This research paper aims to examine the history, technology and future of mobile systems, exploring their evolution from early mobile phones to the latest smartphones and beyond. The study will analyze the technological advancements and innovations that have shaped the mobile industry, from the introduction of mobile internet and multimedia capabilities to the integration of artificial intelligence and 5G networks. Additionally, the paper will also address the challenges and opportunities facing the future of mobile technology, such as privacy concerns, battery life, and the increasing demand for high-speed internet. Finally, the paper will also provide insights into potential future developments and innovations in the mobile sector, such as foldable phones, wearable technology, and the Internet of Things (IoT). The purpose of this research paper is to provide a comprehensive overview of the history, technology, and future of mobile systems, shedding light on their impact on society and the challenges and opportunities that lie ahead.

Keywords: mobile technology, artificial intelligence, networking, iot, technological advancements, smartphones

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Authors: Anton V. Doroshin

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This article contains a description of main ideas for the attitude reorientation of spacecraft (small dual-spin spacecraft, nanosatellites) using properties of its chaotic attitude motion under the action of internal perturbations. The considering method based on intentional initiations of chaotic modes of attitude motion with big amplitudes of the nutation oscillations, and also on the redistributions of the angular momentum between coaxial bodies of the dual-spin spacecraft (DSSC), which perform in the purpose of system’s phase space changing.

Keywords: spacecraft, attitude dynamics, control, chaos

Procedia PDF Downloads 371

Authors: William Batson, Yunsik Song, Abel Simie

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Recent trends in suburban design and planning have centered on economic efficiency in construction and completion. In doing so, developers, builders, and architects have bypassed free and reliable sustainable solutions to minimize the carbon footprint and improve the environment. Often, suburban areas are designed without landscape features, sidewalks, parks, adequate lighting, or walking space. Much of the design concern involves minimizing construction costs and streamlining streets and utilities. A new development in creating retention ponds to mitigate flooding and slow runoff is one step in the positive direction. However, "if you build them (suburbs), they (fauna) will come." The inevitable flora and fauna that soon propagate and take refuge within these artificial retention ponds create an additional dilemma. Architects, planners, and developers know the requirements and current strategies to provide residents and wildlife with a viable and sustainable environment. This includes habitat for hibernating animals and facilitating opportunities, especially for cold-blooded mammals. Many species that migrate to these artificial ponds struggle to survive, especially during flooding and when the water table drains below the artificial rim, preventing aquatic mammals from climbing on land. This flooding often results from large areas of impervious asphalt and concrete. These impervious surfaces retain and dispense large amounts of rainwater and contaminants that carry industrial pollutants, oil, plastics, animal waste, and fertilizers into storm drains and then deposited in these retention ponds. This paper will identify and show how simple and logical solutions are used to create a sustainable suburb and reduce the carbon footprint using landscape architectural strategies and cost-free design solutions. We will also demonstrate simple changes in the present suburban design model to provide a viable and sustainable suburb for the 21st century.

Keywords: sustainavilty, suburban, flora, fauna, carbon footprint

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Authors: Harsh Sadawarti, Kamal Malik

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Humans are the unique and the most powerful creature on this planet just because of the high level of intelligence gifted by nature. Computational Intelligence is highly influenced by the term natural intelligence, neurosciences and mathematics. To deal with the in-depth study of computational intelligence and to utilize it in real-life applications, it is quite important to understand its simulation with the human brain. In this paper, the three important parts, Frontal Lobe, Occipital Lobe and Parietal Lobe of the human brain, are compared with the ANN(Artificial Neural Network), CNN(Convolutional Neural network), and RNN(Recurrent Neural Network), respectively. Intelligent computational systems are created by combining deductive reasoning, logical concepts and high-level algorithms with the simulation and study of the human brain. Human brain is a combination of Physiology, Psychology, emotions, calculations and many other parameters which are of utmost importance that determines the overall intelligence. To create intelligent algorithms, smart machines and to simulate the human brain in an effective manner, it is quite important to have an insight into the human brain and the basic concepts of biological neurosciences.

Keywords: computational intelligence, neurosciences, convolutional neural network, recurrent neural network, artificial neural network, frontal lobe, occipital lobe, parietal lobe

Procedia PDF Downloads 93

Authors: Ioan Corneliu Salisteanu, Valentin Dogaru Ulieru, Mihaita Nicolae Ardeleanu, Alin Pohoata, Bogdan Salisteanu, Stefan Broscareanu

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Creating a personalized statistic for an individual within the population using IT systems, based on the searches and intercepted spheres of interest they manifest, is just one 'atom' of the artificial intelligence analysis network. However, having the ability to generate statistics based on individual data intercepted from large demographic areas leads to reasoning like that issued by a human mind with global strategic ambitions. The DiaMOTO device is a technical sensory system that allows the interception of car events caused by a driver, positioning them in time and space. The device's connection to the vehicle allows the creation of a source of data whose analysis can create psychological, behavioural profiles of the drivers involved. The SiaMOTO system collects data from many vehicles equipped with DiaMOTO, driven by many different drivers with a unique fingerprint in their approach to driving. In this paper, we aimed to explain the software infrastructure of the SiaMOTO system, a system designed to monitor and improve driver driving behaviour, as well as the criteria and algorithms underlying the intelligent analysis process.

Keywords: artificial intelligence, data processing, driver behaviour, driver monitoring, SiaMOTO

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Authors: Hsin Lee, Hsuan Lee

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Legal artificial intelligence (LegalAI) has been increasing applications within legal systems, propelled by advancements in natural language processing (NLP). Compared with general documents, legal case documents are typically long text sequences with intrinsic logical structures. Most existing language models have difficulty understanding the long-distance dependencies between different structures. Another unique challenge is that while the Judiciary of Taiwan has released legal judgments from various levels of courts over the years, there remains a significant obstacle in the lack of labeled datasets. This deficiency makes it difficult to train models with strong generalization capabilities, as well as accurately evaluate model performance. To date, models in Taiwan have yet to be specifically trained on judgment data. Given these challenges, this research proposes a Longformer-based pre-trained language model explicitly devised for retrieving similar judgments in Taiwanese legal documents. This model is trained on a self-constructed dataset, which this research has independently labeled to measure judgment similarities, thereby addressing a void left by the lack of an existing labeled dataset for Taiwanese judgments. This research adopts strategies such as early stopping and gradient clipping to prevent overfitting and manage gradient explosion, respectively, thereby enhancing the model's performance. The model in this research is evaluated using both the dataset and the Average Entropy of Offense-charged Clustering (AEOC) metric, which utilizes the notion of similar case scenarios within the same type of legal cases. Our experimental results illustrate our model's significant advancements in handling similarity comparisons within extensive legal judgments. By enabling more efficient retrieval and analysis of legal case documents, our model holds the potential to facilitate legal research, aid legal decision-making, and contribute to the further development of LegalAI in Taiwan.

Keywords: legal artificial intelligence, computation and language, language model, Taiwanese legal cases

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Authors: H. Bonakdari, I. Ebtehaj

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The prediction of scour depth around bridge piers is frequently considered in river engineering. One of the key aspects in efficient and optimum bridge structure design is considered to be scour depth estimation around bridge piers. In this study, scour depth around bridge piers is estimated using two methods, namely the Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN). Therefore, the effective parameters in scour depth prediction are determined using the ANN and ANFIS methods via dimensional analysis, and subsequently, the parameters are predicted. In the current study, the methods’ performances are compared with the nonlinear regression (NLR) method. The results show that both methods presented in this study outperform existing methods. Moreover, using the ratio of pier length to flow depth, ratio of median diameter of particles to flow depth, ratio of pier width to flow depth, the Froude number and standard deviation of bed grain size parameters leads to optimal performance in scour depth estimation.

Keywords: adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN), bridge pier, scour depth, nonlinear regression (NLR)

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Authors: Noor Azwa Azreen Binti Abd. Aziz, Muhamad Zaim Bin Mohd Rozi

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Moving towards the 4th Industrial Revolution, artificial intelligence (AI) is now more popular than ever. This subject is gaining significance every day and is continually expanding, often merging with other fields. Instead of merely being passive observers, there are benefits to understanding modern technology by delving into its inner workings. However, in a world teeming with digital information, the impact of AI on the spread of disinformation has garnered significant attention. The dissemination of inaccurate or misleading information is referred to as misinformation, posing a serious threat to democratic society, public debate, and individual decision-making. This article delves deep into the connection between AI and the dissemination of false information, exploring its potential, risks, and ethical issues as AI technology advances. The rise of AI has ushered in a new era in the dissemination of misinformation as AI-driven technologies are increasingly responsible for curating, recommending, and amplifying information on online platforms. While AI holds the potential to enhance the detection and mitigation of misinformation through natural language processing and machine learning, it also raises concerns about the amplification and propagation of false information. AI-powered deepfake technology, for instance, can generate hyper-realistic videos and audio recordings, making it increasingly challenging to discern fact from fiction.

Keywords: artificial intelligence, digital information, disinformation, ethical issues, misinformation

Procedia PDF Downloads 57

Authors: Tong He, Long Chen, Irag Mantegh, Wen-Fang Xie

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This paper proposes an image-based obstacle avoidance and tracking target identification strategy in GPS-degraded or GPS-denied environment for an Unmanned Aerial Vehicle (UAV). The traditional force algorithm for obstacle avoidance could produce local minima area, in which UAV cannot get away obstacle effectively. In order to eliminate it, an artificial potential approach based on harmonic potential is proposed to guide the UAV to avoid the obstacle by using the vision system. And image-based visual servoing scheme (IBVS) has been adopted to implement the proposed obstacle avoidance approach. In IBVS, the pixel accuracy is a key factor to realize the obstacle avoidance. In this paper, the deep reinforcement learning framework has been applied by reducing pixel errors through constant interaction between the environment and the agent. In addition, the combination of OpenTLD and Tensorflow based on neural network is used to identify the type of tracking target. Numerical simulation in Matlab and ROS GAZEBO show the satisfactory result in target identification and obstacle avoidance.

Keywords: image-based visual servoing, obstacle avoidance, tracking target identification, deep reinforcement learning, artificial potential approach, neural network

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Authors: Stuti Modi, Aditi Kambli

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This paper addresses the need for intelligent water management and distribution system in smart cities to ensure optimal consumption and distribution of water for drinking and sanitation purposes. Water being a limited resource in cities require an effective system for collection, storage and distribution. In this paper, applications of two mostly widely used particular types of data-driven models, namely artificial neural networks (ANN) and fuzzy logic-based models, to modelling in the water resources management field are considered. The objective of this paper is to review the principles of various types and architectures of neural network and fuzzy adaptive systems and their applications to integrated water resources management. Final goal of the review is to expose and formulate progressive direction of their applicability and further research of the AI-related and data-driven techniques application and to demonstrate applicability of the neural networks, fuzzy systems and other machine learning techniques in the practical issues of the regional water management. Apart from this the paper will deal with water storage, using ANN to find optimum reservoir level and predicting peak daily demands.

Keywords: artificial neural networks, fuzzy systems, peak daily demand prediction, water management and distribution

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Authors: Sevde Altuntas, Fatih Buyukserin

Abstract:

Alzheimer’s disease is responsible for irreversible neural damage of brain parts. One of the disease markers is Amyloid-β 1-42 protein that accumulates in the brain in the form plaques. The basic problem for detection of the protein is the low amount of protein that cannot be detected properly in body liquids such as blood, saliva or urine. To solve this problem, tests like ELISA or PCR are proposed which are expensive, require specialized personnel and can contain complex protocols. Therefore, Surface-enhanced Raman Spectroscopy (SERS) a good candidate for detection of Amyloid-β 1-42 protein. Because the spectroscopic technique can potentially allow even single molecule detection from liquid and solid surfaces. Besides SERS signal can be improved by using nanopattern surface and also is specific to molecules. In this context, our study proposes to fabricate diagnostic test models that utilize Au-coated nanopatterned polycarbonate (PC) surfaces modified with Thioflavin - T to detect low concentrations of Amyloid-β 1-42 protein in water and artificial saliva medium by the enhancement of protein SERS signal. The nanopatterned PC surface that was used to enhance SERS signal was fabricated by using Anodic Alumina Membranes (AAM) as a template. It is possible to produce AAMs with different column structures and varying thicknesses depending on voltage and anodization time. After fabrication process, the pore diameter of AAMs can be arranged with dilute acid solution treatment. In this study, two different columns structures were prepared. After a surface modification to decrease their surface energy, AAMs were treated with PC solution. Following the solvent evaporation, nanopatterned PC films with tunable pillared structures were peeled off from the membrane surface. The PC film was then modified with Au and Thioflavin-T for the detection of Amyloid-β 1-42 protein. The protein detection studies were conducted first in water via this biosensor platform. Same measurements were conducted in artificial saliva to detect the presence of Amyloid Amyloid-β 1-42 protein. SEM, SERS and contact angle measurements were carried out for the characterization of different surfaces and further demonstration of the protein attachment. SERS enhancement factor calculations were also completed via experimental results. As a result, our research group fabricated diagnostic test models that utilize Au-coated nanopatterned polycarbonate (PC) surfaces modified with Thioflavin-T to detect low concentrations of Alzheimer’s Amiloid – β protein in water and artificial saliva medium. This work was supported by The Scientific and Technological Research Council of Turkey (TUBITAK) Grant No: 214Z167.

Keywords: alzheimer, anodic aluminum oxide, nanotopography, surface enhanced Raman spectroscopy

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Authors: Cameron Hamilton, Walter Potter, Gerrit Hoogenboom, Ronald McClendon, Will Hobbs

Abstract:

A model was constructed to predict the amount of solar radiation that will make contact with the surface of the earth in a given location an hour into the future. This project was supported by the Southern Company to determine at what specific times during a given day of the year solar panels could be relied upon to produce energy in sufficient quantities. Due to their ability as universal function approximators, an artificial neural network was used to estimate the nonlinear pattern of solar radiation, which utilized measurements of weather conditions collected at the Griffin, Georgia weather station as inputs. A number of network configurations and training strategies were utilized, though a multilayer perceptron with a variety of hidden nodes trained with the resilient propagation algorithm consistently yielded the most accurate predictions. In addition, a modeled DNI field and adjacent weather station data were used to bolster prediction accuracy. In later trials, the solar radiation field was preprocessed with a discrete wavelet transform with the aim of removing noise from the measurements. The current model provides predictions of solar radiation with a mean square error of 0.0042, though ongoing efforts are being made to further improve the model’s accuracy.

Keywords: artificial neural networks, resilient propagation, solar radiation, time series forecasting

Procedia PDF Downloads 362

Authors: Kritiyaporn Kunsook

Abstract:

Machine learning algorithms (MLAs) such us artificial neural networks (ANNs), decision tree, support vector machines (SVMs), Naïve Bayes, and ensemble classifier by voting are powerful data driven methods that are relatively less widely used in the mapping of technique of system, and thus have not been comparatively evaluated together thoroughly in this field. The performances of a series of MLAs, ANNs, decision tree, SVMs, Naïve Bayes, and ensemble classifier by voting in technique of hydroponic systems prospectively modeling are compared based on the accuracy of each model. Classification of hydroponic systems only covers the test samples from vegetables grown with Nutrient film technique (NFT) and Deep flow technique (DFT). The feature, which are the characteristics of vegetables compose harvesting height width, temperature, require light and color. The results indicate that the classification performance of the ANNs is 98%, decision tree is 98%, SVMs is 97.33%, Naïve Bayes is 96.67%, and ensemble classifier by voting is 98.96% algorithm respectively.

Keywords: artificial neural networks, decision tree, support vector machines, naïve Bayes, ensemble classifier by voting

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Authors: L. K. Davis

Abstract:

The effects of the evolution force are observable in nature at all structural levels ranging from small molecular systems to conversely enormous biospheric systems. However, the evolution force and work associated with formation of biological structures has yet to be described mathematically or theoretically. In addressing the conundrum, we consider evolution from a unique perspective and in doing so we introduce the “Fundamental Theory of the Evolution Force: FTEF”. We utilized synthetic evolution artificial intelligence (SYN-AI) to identify genomic building blocks and to engineer 14-3-3 ζ docking proteins by transforming gene sequences into time-based DNA codes derived from protein hierarchical structural levels. The aforementioned served as templates for random DNA hybridizations and genetic assembly. The application of hierarchical DNA codes allowed us to fast forward evolution, while dampening the effect of point mutations. Natural selection was performed at each hierarchical structural level and mutations screened using Blosum 80 mutation frequency-based algorithms. Notably, SYN-AI engineered a set of three architecturally conserved docking proteins that retained motion and vibrational dynamics of native Bos taurus 14-3-3 ζ.

Keywords: 14-3-3 docking genes, synthetic protein design, time-based DNA codes, writing DNA code from scratch

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Authors: Yuan-Hong Ho, Chiung-Ju Huang

Abstract:

This article presents a monitoring indicators system that predicts whether a local government in Taiwan is heading for fiscal distress and identifies a suitable fiscal policy that would allow the local government to achieve fiscal balance in the long run. This system is relevant to stockholders’ interest, simple for national audit bodies to use, and provides an early warning of fiscal distress that allows preventative action to be taken.

Keywords: fiscal health, fiscal distress, monitoring signals, 10-point scale

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Authors: Gilad Padva, Sigal Barak Brandes

Abstract:

This research centers on an unexpected Israeli advertising campaign for Elemento, a local furniture company, which eroticizes male nudity. The discussed campaign includes a series of printed ads that depict naked male models in effeminate positions. This campaign included a series of ads published in Haaretz, a small-scaled yet highly prestigious daily newspaper which is typically read by urban middle-upper-class left-winged Israelis. Apparently, this campaign embodies an alternative masculinity that challenges the prevalent machismo in Israeli society and advertising. Although some of the ads focus on young men in effeminate positions, they never expose their genitals and anuses, and their bodies are never permeable. The 2010s Elemento male models are seemingly contrasted to conventional representation of manhood in contemporary mainstream advertising. They display a somewhat inactive, passive and self-indulgent masculinity which involves 'conspicuous leisure'. In the process of commodity fetishism, the advertised furniture are emptied of the original meaning of their production, and then filled with new meanings in ways that both mystify the product and turn it into a fetish object. Yet, our research critically reconsiders this sensational campaign as sophisticated patriarchal parody that does not subvert but rather reconfirms and even fetishizes patriarchal premises; it parodizes effeminacy rather than the prevalent (Israeli) machismo. Following Pierre Bourdieu's politics of cultural taste, our research reconsiders and criticizes the male models' domesticated masculinity in a fantasized and cosmopolitan hedonistic habitus. Notwithstanding, we suggest that the Elemento campaign, despite its conformity, does question some Israeli and global axioms about gender roles, corporeal ideologies, idealized bodies, and domesticated phalluses and anuses. Although the naked truth is uncovered by this campaign, it does erect a vibrant discussion of contemporary masculinities and their exploitation in current mass consumption.

Keywords: male body, campaign, advertising, gender studies, men's studies, Israeli culture, masculinity, parody, effeminacy

Procedia PDF Downloads 192