Search results for: flavor forecasting

Authors: Mehrnoosh Abouzari

Abstract:

Artificial intelligence has covered all areas of human life and has helped or replaced many jobs. One of the areas of application of artificial intelligence in the police is to detect crime, identify the accused or victim and prove the crime. It will play an effective role in implementing preventive justice and creating security in the community, and improving judicial decisions. This will help improve the performance of the police, increase the accuracy of criminal investigations, and play an effective role in preventing crime and high-risk behaviors in society. This article presents and analyzes the capabilities and capacities of artificial intelligence in police and similar examples used worldwide to prove the necessity of using artificial intelligence in the police. The main topics discussed include the performance of artificial intelligence in crime detection and prediction, the risk capacity of criminals and the ability to apply arbitray institutions, and the introduction of artificial intelligence programs implemented worldwide in the field of criminal investigation for police.

Keywords: police, artificial intelligence, forecasting, prevention, software

Procedia PDF Downloads 180

Authors: Abd Alati Ali Abushnaq, Malek Essnni, Abduraouf Eteer

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The present study proposes a comprehensive approach to effectively utilize associated gas from the KK remote station, eliminating the practice of flaring and mitigating greenhouse gas (GHG) emissions. The proposed integrated system involves diverting the associated gas via a newly designed pipeline, seamlessly connecting to the existing 12-inch pipeline at the tie-in point. The proposed destination is the low-pressure system at A-100 or 3rd stage, where the associated gas will be channeled towards the NGL (natural gas liquid) plant for processing. To ensure the system's efficacy under varying gas production scenarios, the study employs two industry-standard simulation software packages, Aspen HYSYS and PIPSIM. The simulated results demonstrate the system's ability to handle the projected increase in gas production, reaching up to 38 MMSCFD. This comprehensive analysis ensures the system's robustness and adaptability to future production demands.

Keywords: associated gas, flaring mitigation, GHG emissions, pipeline diversion, NGL plant, KK remote station, production forecasting, Aspen HYSYS, PIPSIM

Procedia PDF Downloads 41

Authors: Ahmed Kayad Abdourazak, Abderafi Souad, Zejli Driss, Idriss Abdoulkader Ibrahim

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In this paper Artificial Neural Network (ANN) is used to predict the solar irradiation in Djibouti for the first Time that is useful to the integration of Concentrating Solar Power (CSP) and sites selections for new or future solar plants as part of solar energy development. An ANN algorithm was developed to establish a forward/reverse correspondence between the latitude, longitude, altitude and monthly solar irradiation. For this purpose the German Aerospace Centre (DLR) data of eight Djibouti sites were used as training and testing in a standard three layers network with the back propagation algorithm of Lavenber-Marquardt. Results have shown a very good agreement for the solar irradiation prediction in Djibouti and proves that the proposed approach can be well used as an efficient tool for prediction of solar irradiation by providing so helpful information concerning sites selection, design and planning of solar plants.

Keywords: artificial neural network, solar irradiation, concentrated solar power, Lavenberg-Marquardt

Procedia PDF Downloads 335

Authors: Sadhana Sharma, P. K. Nema, Siddhartha Singha

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Paneer or Indian cottage cheese is an acid and heat coagulated milk product, highly perishable because of high moisture (58-60 %). Typically paneer is marble to light creamy white in appearance. A good paneer should have cohesive body with slight sponginess or springiness. The texture must be smooth and velvety with close-knit compactness. It should have pleasing mild acidic, slightly sweet and nutty flavour. Consumers today demand simple to prepare, convenient, healthy and natural foods. Dehydrated paneer finds numerous ways to be used. It can be used in curry preparation similar to paneer-in-curry, a delicacy in Indian cuisine. It may be added to granola/ trail mix yielding a high energy snack. If grounded to a powder, it may be used as a cheesy spice mix or used as popcorn seasoning. Dried paneer powder may be added to pizza dough or to a white sauce to turn it into a paneer sauce. Drying of such food hydrogels by conventional methods is associated with several undesirable characteristics including case hardening, longer drying time, poor rehydration ability and fat loss during drying. The present study focuses on developing cost-effective protocol for freeze-drying of paneer. The dehydrated product would be shelf-stable and can be rehydrated to its original state having flavor and texture comparable to the fresh form. Moreover, the final product after rehydration would be more fresh and softer than its frozen counterparts. The developed product would be shelf-stable at room temperature without any addition of preservatives.

Keywords: color, freeze-drying, paneer, texture

Procedia PDF Downloads 137

Authors: Obe Olumide Olayinka, Victor Balanica, Eugen Neagoe

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The effects of hypertension are often lethal thus its early detection and prevention is very important for everybody. In this paper, a neural network (NN) model was developed and trained based on a dataset of hypertension causative parameters in order to forecast the likelihood of occurrence of hypertension in patients. Our research goal was to analyze the potential of the presented NN to predict, for a period of time, the risk of hypertension or the risk of developing this disease for patients that are or not currently hypertensive. The results of the analysis for a given patient can support doctors in taking pro-active measures for averting the occurrence of hypertension such as recommendations regarding the patient behavior in order to lower his hypertension risk. Moreover, the paper envisages a set of three example scenarios in order to determine the age when the patient becomes hypertensive, i.e. determine the threshold for hypertensive age, to analyze what happens if the threshold hypertensive age is set to a certain age and the weight of the patient if being varied, and, to set the ideal weight for the patient and analyze what happens with the threshold of hypertensive age.

Keywords: neural network, hypertension, data set, training set, supervised learning

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Authors: Vinayaka Gude Divya Sampath

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The research introduces an integrated prediction model to assess the susceptibility of roads in a future flooding event. The model consists of deep learning algorithm for forecasting gauge height data and Flood Inundation Mapper (FIM) for spatial flooding. An optimal architecture for Long short-term memory network (LSTM) was identified for the gauge located on Tangipahoa River at Robert, LA. Dropout was applied to the model to evaluate the uncertainty associated with the predictions. The estimates are then used along with FIM to identify the spatial flooding. Further geoprocessing in ArcGIS provides the susceptibility values for different roads. The model was validated based on the devastating flood of August 2016. The paper discusses the challenges for generalization the methodology for other locations and also for various types of flooding. The developed model can be used by the transportation department and other emergency response organizations for effective disaster management.

Keywords: deep learning, disaster management, flood prediction, urban flooding

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Authors: Tianyu Wang, Nikita Karandikar

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The uncertainty around cost, safety, and feasibility of the decarbonized shipping fuels has made it increasingly complex for the shipping companies to set pricing strategies and forecast their freight revenues going forward. The increase in the green fuel surcharges will ultimately influence the automobile’s consumer prices. The auto shipping demand (ton-miles) has been gradually shifting from long-haul to short-sea trade over the past years following the relocation of the original equipment manufacturer (OEM) manufacturing to regions such as South America and Southeast Asia. The objective of this paper is twofold: 1) to investigate the car-carriers freight revenue development over the years when the trade pattern is gradually shifting towards short-sea exports 2) to empirically identify the quantitative impact of such trade pattern shifting to mainly freight rate, but also vessel size, fleet size as well as Green House Gas (GHG) emission in Roll on-Roll Off (Ro-Ro) shipping. In this paper, a model of analyzing and forecasting ton-miles and freight revenues for the trade routes of AS-NA (Asia to North America), EU-NA (Europe to North America), and SA-NA (South America to North America) is established by deploying Automatic Identification System (AIS) data and the financial results of a selected car carrier company. More specifically, Wallenius Wilhelmsen Logistics (WALWIL), the Norwegian Ro-Ro carrier listed on Oslo Stock Exchange, is selected as the case study company in this paper. AIS-based ton-mile datasets of WALWIL vessels that are sailing into North America region from three different origins (Asia, Europe, and South America), together with WALWIL’s quarterly freight revenues as reported in trade segments, will be investigated and compared for the past five years (2018-2022). Furthermore, ordinary‐least‐square (OLS) regression is utilized to construct the ton-mile demand and freight revenue forecasting. The determinants of trade pattern shifting, such as import tariffs following the China-US trade war and fuel prices following the 0.1% Emission Control Areas (ECA) zone requirement after IMO2020 will be set as key variable inputs to the machine learning model. The model will be tested on another newly listed Norwegian Car Carrier, Hoegh Autoliner, to forecast its 2022 financial results and to validate the accuracy based on its actual results. GHG emissions on the three routes will be compared and discussed based on a constant emission per mile assumption and voyage distances. Our findings will provide important insights about 1) the trade-off evaluation between revenue reduction and energy saving with the new ton-mile pattern and 2) how the trade flow shifting would influence the future need for the vessel and fleet size.

Keywords: AIS, automobile exports, maritime big data, trade flows

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Authors: Marlene Perez Villalpando, Kelly Joel Gurubel Tun

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In this paper, we propose an optimal control strategy for a hydroelectric power plant subject to input disturbances like meteorological phenomena. The engineering characteristics of the system are described by a nonlinear model. The random availability of renewable sources is predicted by a high-order neural network trained with an extended Kalman filter, whereas the power generation is regulated by the optimal control law. The main advantage of the system is the stabilization of the amount of power generated in the plant. A control supervisor maintains stability and availability in hydropower reservoirs water levels for power generation. The proposed approach demonstrated a good performance to stabilize the reservoir level and the power generation along their desired trajectories in the presence of disturbances.

Keywords: hydropower, high order neural network, Kalman filter, optimal control

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Authors: M. M. Wagh, V. V. Kulkarni

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The rising economic activities, growing population and improving living standards of world have led to a steady growth in its appetite for quality and quantity of energy services. As the economy expands the electricity demand is going to grow further, increasing the challenges of the more generation and stresses on the utility grids. Appropriate energy model will help in proper utilization of the locally available renewable energy sources such as solar, wind, biomass, small hydro etc. to integrate in the available grid, reducing the investments in energy infrastructure. Further to these new technologies like smart grids, decentralized energy planning, energy management practices, energy efficiency are emerging. In this paper, the attempt has been made to study and review the recent energy planning models, energy forecasting models, and renewable energy integration models. In addition, various modeling techniques and tools are reviewed and discussed.

Keywords: energy modeling, integration of renewable energy, energy modeling tools, energy modeling techniques

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Authors: Gleda Kutrolli, Maksi Kutrolli, Etjon Meco

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SARS-CoV-2 virus is currently one of the most infectious pathogens for humans. It started in China at the end of 2019 and now it is spread in all over the world. The origin and diffusion of the SARS-CoV-2 epidemic, is analysed based on the discussion of viral phylogeny theory. With the aim of understanding the spread of infection in the affected countries, it is crucial to modelize the spread of the virus and simulate its activity. In this paper, the prediction of coronavirus outbreak is done by using SIR model without vital dynamics, applying different numerical technique solving ordinary differential equations (ODEs). We find out that ABM and MRT methods perform better than other techniques and that the activity of the virus will decrease in April but it never cease (for some time the activity will remain low) and the next cycle will start in the middle July 2020 for Norway and Denmark, and October 2020 for Sweden, and September for Finland.

Keywords: forecasting, ordinary differential equations, SARS-COV-2 epidemic, SIR model

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Authors: Yuri Katz, Kun Liu, Arunram Atmacharan

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Due to illiquidity, mispricing on Credit Markets is inevitable. This creates huge challenges to banks and investors as they seek to find new ways of risk valuation and portfolio management in a post-credit crisis world. Here, we analyze the difference in behavior of the spread-to-maturity in investment and high-yield categories of US corporate bonds between 2014 and 2023. Deviation from the theoretical dependency of this measure in the universe under study allows to identify multiple cases of mispriced credit risk. Remarkably, we observe mispriced bonds in both categories of credit ratings. This identification is supported by the application of the state-of-the-art machine learning model in more than 90% of cases. Noticeably, the ML-driven model-based forecasting of a category of bond’s credit ratings demonstrate an excellent out-of-sample accuracy (AUC = 98%). We believe that these results can augment conventional valuations of credit portfolios.

Keywords: credit risk, credit ratings, bond pricing, spread-to-maturity, machine learning

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Authors: Ekapon Thienthaworn

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This research aims to study the reliability of the forecasting factor in social media by using survey research methods with questionnaires. The sampling is the group of undergraduate students in Bangkok. A multiple-step random number of 400 persons, data analysis are descriptive statistics with multivariate regression analysis. The research found the average of the overall trust at the intermediate level for reading the news in social media and the results of the multivariate regression analysis to find out the factors that forecast credibility of the media found the only content that has the power to forecast reliability of undergraduate students in Bangkok to reading the news on social media at the significance level.at 0.05.These can be factors with forecasts reliability of news in social media by a variable that has the highest influence factor of the media content and the speed is also important for reliability of the news.

Keywords: credibility of news, behaviors and attitudes, social media, web board

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Authors: Karim Hamidi Machekposhti, Hossein Sedghi, Abdolrasoul Telvari, Hossein Babazadeh

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Rainfall and runoff phenomenon is a chaotic and complex outcome of nature which requires sophisticated modelling and simulation methods for explanation and use. Time Series modelling allows runoff data analysis and can be used as forecasting tool. In the paper attempt is made to model river runoff data and predict the future behavioural pattern of river based on annual past observations of annual river runoff. The river runoff analysis and predict are done using ARIMA model. For evaluating the efficiency of prediction to hydrological events such as rainfall, runoff and etc., we use the statistical formulae applicable. The good agreement between predicted and observation river runoff coefficient of determination (R²) display that the ARIMA (4,1,1) is the suitable model for predicting Karkheh River runoff at Iran.

Keywords: time series modelling, ARIMA model, river runoff, Karkheh River, CLS method

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Authors: Hidir S. Nogay

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In this study, two systems were created to predict interior temperature in induction motor. One of them consisted of a simple ANN model which has two layers, ten input parameters and one output parameter. The other one consisted of eight ANN models connected each other as cascaded. Cascaded ANN system has 17 inputs. Main reason of cascaded system being used in this study is to accomplish more accurate estimation by increasing inputs in the ANN system. Cascaded ANN system is compared with simple conventional ANN model to prove mentioned advantages. Dataset was obtained from experimental applications. Small part of the dataset was used to obtain more understandable graphs. Number of data is 329. 30% of the data was used for testing and validation. Test data and validation data were determined for each ANN model separately and reliability of each model was tested. As a result of this study, it has been understood that the cascaded ANN system produced more accurate estimates than conventional ANN model.

Keywords: cascaded neural network, internal temperature, inverter, three-phase induction motor

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Authors: A. Vinay, Mary Josephine, Shreesha. S. Rao, Dhande Kranthi Kumar, J. Nandini

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This paper looks at the development of Fisheries education in Karnataka and the supply of skilled human capital to the sector. The study tries to analyse their job occupancy patterns, Compound Growth Rate (CGR) and forecasts the fisheries graduates supply using the Holt method. In Karnataka, fisheries are one of the neglected allied sectors of agriculture in spite of having enormous scope and potential to contribute to the State's agriculture GDP. The State Government has been negligent in absorbing skilled human capital for the development of fisheries, as there are so many vacant positions in both education institutes, as well as the State fisheries department. CGR and forecasting of fisheries graduates shows a positive growth rate and increasing trend, from which we can understand that by proper utilization of skilled human capital can bring development in the fisheries sector of Karnataka.

Keywords: compound growth rate, fisheries education, holt method, skilled human capital

Procedia PDF Downloads 243

Authors: Sopio Gogilava, Goga Chakhaia, Levan Tsulukidze, Zurab Laoshvili, Irina Khubulava, Shalva Bosikashvili, Teimuraz Gugushvili

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Geoinformatics systems (GIS) integrated computer program RAMMS is widely used for forecasting debris flows and accordingly for the determination of anticipating risks with 85% accuracy. In view of the above, the work introduces new capabilities of the computer program RAMMS, which evaluates the effectiveness of anti-debris flow engineering construction, namely: the possibility of decreasing the expected velocity, kinetic energy, and output cone volume in the Mletiskhevi River. As a result of research has been determined that the anti-debris flow engineering construction designed to reduce the expected debris flow risk in the Mletiskhevi River is an effective environmental protection technology, that's why its introduction is promising.

Keywords: construction, debris flow, geoinformatics systems, program RAMMS

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Authors: Sol Girouard, Zona Kostic

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A home is often the largest and most expensive purchase a person makes. Whether the decision leads to a successful outcome will be determined by a combination of critical factors. In this paper, we propose a method that efficiently handles all the factors in residential real estate and performs predictions given a feature space with high dimensionality while controlling for overfitting. The proposed method was built on gradient descent and boosting algorithms and uses a mixed optimizing technique to improve the prediction power. Usually, a single model cannot handle all the cases thus our approach builds multiple models based on different subsets of the predictors. The algorithm was tested on 3 million homes across the U.S., and the experimental results demonstrate the efficiency of this approach by outperforming techniques currently used in forecasting prices. With everyday changes on the real estate market, our proposed algorithm capitalizes from new events allowing more efficient predictions.

Keywords: real estate prediction, gradient descent, boosting, ensemble methods, active learning, training

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Authors: Arslan Murtaza

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RFID (Radio Frequency Identification) and WSN (Wireless sensor network) are two significant wireless technologies that have extensive diversity of applications and provide limitless forthcoming potentials. RFID is used to identify existence and location of objects whereas WSN is used to intellect and monitor the environment. Incorporating RFID with WSN not only provides identity and location of an object but also provides information regarding the condition of the object carrying the sensors enabled RFID tag. It can be widely used in stock management, asset tracking, asset counting, security, military, environmental monitoring and forecasting, healthcare, intelligent home, intelligent transport vehicles, warehouse management, and precision agriculture. This assessment presents a brief introduction of RFID, WSN, and integration of WSN and RFID, and then applications related to both RFID and WSN. This assessment also deliberates status of the projects on RFID technology carried out in different computing group projects to be taken on WSN and RFID technology.

Keywords: wireless sensor network, RFID, embedded sensor, Wi-Fi, Bluetooth, integration, time saving, cost efficient

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Authors: Shahin Mirshekari, Mohammadreza Moradi, Hossein Jafari, Mehdi Jafari, Mohammad Ensaf

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This research employs Gaussian Process Regression (GPR) with an ensemble kernel, integrating Exponential Squared, Revised Matern, and Rational Quadratic kernels to analyze pharmaceutical sales data. Bayesian optimization was used to identify optimal kernel weights: 0.76 for Exponential Squared, 0.21 for Revised Matern, and 0.13 for Rational Quadratic. The ensemble kernel demonstrated superior performance in predictive accuracy, achieving an R² score near 1.0, and significantly lower values in MSE, MAE, and RMSE. These findings highlight the efficacy of ensemble kernels in GPR for predictive analytics in complex pharmaceutical sales datasets.

Keywords: Gaussian process regression, ensemble kernels, bayesian optimization, pharmaceutical sales analysis, time series forecasting, data analysis

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Authors: Arindam Chaudhuri

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The research presents epsilon- hierarchical fuzzy twin support vector regression (epsilon-HFTSVR) based on epsilon-fuzzy twin support vector regression (epsilon-FTSVR) and epsilon-twin support vector regression (epsilon-TSVR). Epsilon-FTSVR is achieved by incorporating trapezoidal fuzzy numbers to epsilon-TSVR which takes care of uncertainty existing in forecasting problems. Epsilon-FTSVR determines a pair of epsilon-insensitive proximal functions by solving two related quadratic programming problems. The structural risk minimization principle is implemented by introducing regularization term in primal problems of epsilon-FTSVR. This yields dual stable positive definite problems which improves regression performance. Epsilon-FTSVR is then reformulated as epsilon-HFTSVR consisting of a set of hierarchical layers each containing epsilon-FTSVR. Experimental results on both synthetic and real datasets reveal that epsilon-HFTSVR has remarkable generalization performance with minimum training time.

Keywords: regression, epsilon-TSVR, epsilon-FTSVR, epsilon-HFTSVR

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Authors: Salako Rotimi, Oshungade Stephen, Ojewoye Opeyemi

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The maintenance of price stability is one of the macroeconomic challenges facing Nigeria as a nation. This paper attempts to build a co-integration multivariate time series model for inflation movement in Nigeria using data extracted from the abstract of statistics of the Central Bank of Nigeria (CBN) from 2008 to 2017. The Johansen cointegration test suggests at least one co-integration vector describing the long run relationship between Consumer Price Index (CPI), Food Price Index (FPI) and Non-Food Price Index (NFPI). All three series show increasing pattern, which indicates a sign of non-stationary in each of the series. Furthermore, model predictability was established with root-mean-square-error, mean absolute error, mean average percentage error, and Theil’s unbiased statistics for n-step forecasting. The result depicts that the long run coefficient of a consumer price index (CPI) has a positive long-run relationship with the food price index (FPI) and non-food price index (NFPI).

Keywords: economic, inflation, model, series

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Authors: Enlin Lo, Ioannis Dogaris, George Philippidis

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Succinic acid is a compound used for manufacturing lacquers, resins, and other coating chemicals. It is also used in the food and beverage industry as a flavor additive. It is predominantly manufactured from petrochemicals, but it can also be produced by fermentation of sugars from renewable feedstocks, such as plant biomass. Bio-based succinic acid has great potential in becoming a platform chemical (building block) for commodity and high-value chemicals. In this study, the production of bio-based succinic acid from sweet sorghum was investigated. Sweet sorghum has high fermentable sugar content and can be cultivated in a variety of climates. In order to avoid competition with food feedstocks, its non-edible ‘bagasse’ (the fiber part after extracting the juice) was targeted. Initially, various conditions of pretreating sweet sorghum bagasse (SSB) were studied in an effort to remove most of the non-fermentable components and expose the cellulosic fiber containing the fermentable sugars (glucose). Concentrated (83%) phosphoric acid was utilized at temperatures 50-80 oC for 30-60 min at various SSB loadings (10-15%), coupled with enzymatic hydrolysis using commercial cellulase (Ctec2, Novozymes) enzyme, to identify the conditions that lead to the highest glucose yields for subsequent fermentation to succinic acid. As the pretreatment temperature and duration increased, the bagasse color changed from light brown to dark brown-black, indicating decomposition, which ranged from 15% to 72%, while the theoretical glucose yield is 91%. With Minitab software statistical analysis, a model was built to identify the optimal pretreatment condition for maximum glucose released. The projected theoretical bio-based succinic acid production is 23g per 100g of SSB, which will be confirmed with fermentation experiments using the bacterium Actinobacillus succinogenes.

Keywords: biomass, cellulose, enzymatic hydrolysis, fermentation, pretreatment, succinic acid

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Authors: Saba Arif, Anam Nadeem, Roman Kalvin, Tanzeel Rashid, Burhan Ali, Juntakan Taweekun

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Currently, the energy crisis is taking serious attention. Globally, industries and building are major share takers of energy. 72% of total global energy is consumed by residential houses, markets, and commercial building. Additionally, in appliances air conditioners are major consumer of electricity; about 60% energy is used for cooling purpose in houses due to HVAC units. Energy demand will aid in determining what changes will be needed whether it is the estimation of the required energy for households or instituting conservation measures. Bottom-up model is one of the most famous methods for forecasting. In current research bottom-up model of air conditioners' energy consumption in all income classes in comparison with seasonal variation and hourly consumption is calculated. By comparison of energy consumption of all income classes by usage of air conditioners, total consumption of actual demand and current availability can be seen.

Keywords: air conditioning, bottom up model, income classes, energy demand

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Authors: Ljubica Dokić, Ivana Nikolić, Dragana Šoronja–Simović, Zita Šereš, Biljana Pajin, Nils Juul, Nikola Maravić

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The possibility of application the dietary fibers in production of crackers was observed in this work, as well as their influence on rheological and textural properties on the dough for crackers and influence on sensory properties of obtained crackers. Three different dietary fibers, oat, potato and pea fibers, replaced 10% of wheat flour. Long fermentation process and baking test method were used for crackers production. The changes of dough for crackers were observed by rheological methods of determination the viscoelastic dough properties and by textural measurements. Sensory quality of obtained crackers was described using quantity descriptive method (QDA) by trained members of descriptive panel. Additional analysis of crackers surface was performed by videometer. Based on rheological determination, viscoelastic properties of dough for crackers were reduced by application of dietary fibers. Manipulation of dough with 10% of potato fiber was disabled, thus the recipe modification included increase in water content at 35%. Dough compliance to constant stress for samples with dietary fibers decreased, due to more rigid and stiffer dough consistency compared to control sample. Also, hardness of dough for these samples increased and dough extensibility decreased. Sensory properties of final products, crackers, were reduced compared to control sample. Application of dietary fibers affected mostly hardness, structure and crispness of the crackers. Observed crackers were low marked for flavor and taste, due to influence of fibers specific aroma. The sample with 10% of potato fibers and increased water content was the most adaptable to applied stresses and to production process. Also this sample was close to control sample without dietary fibers by evaluation of sensory properties and by results of videometer method.

Keywords: crackers, dietary fibers, rheology, sensory properties

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Authors: Shih-Pin Chen, Shih-Syuan You

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This study proposes a regression model with flexible spreads for fuzzy input-output data to cope with the situation that the existing measures cannot reflect the actual estimation error. The main idea is that a dissemblance index (DI) is carefully identified and defined for precisely measuring the actual estimation error. Moreover, the graded mean integration (GMI) representation is adopted for determining more representative numeric regression coefficients. Notably, to comprehensively compare the performance of the proposed model with other ones, three different criteria are adopted. The results from commonly used test numerical examples and an application to Taiwan's business monitoring indicator illustrate that the proposed dissemblance index method not only produces valid fuzzy regression models for fuzzy input-output data, but also has satisfactory and stable performance in terms of the total estimation error based on these three criteria.

Keywords: dissemblance index, forecasting, fuzzy sets, linear regression

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Authors: Hosuk Shin, Young-Hyun Seo, Eunhak Lee, Seung-Young Kho

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Currently, in Seoul, users have the privilege to avoid riding crowded buses with the installation of Bus Information System (BIS). BIS has three levels of on-board bus ridership level information (spacious, normal, and crowded). However, there are flaws in the system due to it being real time which could provide incomplete information to the user. For example, a bus comes to the station, and on the BIS it shows that the bus is crowded, but on the stop that the user is waiting many people get off, which would mean that this station the information should show as normal or spacious. To fix this problem, this study predicts the bus ridership level using smart card data to provide more accurate information about the passenger ridership level on the bus. An Artificial Neural Network (ANN) is an interconnected group of nodes, that was created based on the human brain. Forecasting has been one of the major applications of ANN due to the data-driven self-adaptive methods of the algorithm itself. According to the results, the ANN algorithm was stable and robust with somewhat small error ratio, so the results were rational and reasonable.

Keywords: smartcard data, ANN, bus, ridership

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Authors: I. Szer, J. Szer, M. Pieńko, A. Robak, P. Jamińska-Gadomska

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Work at height, such as construction works on scaffoldings, is associated with a considerable risk. Scaffolding workers are usually exposed to changing weather conditions what can additionally increase the risk of dangerous situations. Therefore, it is very important to foresee the risk of adverse conditions to which the worker may be exposed. The data from meteorological stations may be used to asses this risk. However, the dependency between weather conditions on a scaffolding and in the vicinity of meteorological station, should be determined. The paper presents an analysis of two selected environmental parameters which have influence on the behavior of workers – air temperature and wind speed. Measurements of these parameters were made between April and November of 2016 on ten scaffoldings located in different parts of Poland. They were compared with the results taken from the meteorological stations located closest to the studied scaffolding. The results gathered from the construction sites and meteorological stations were not the same, but statistical analyses have shown that they were correlated.

Keywords: scaffolding, health and safety at work, temperature, wind velocity

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Authors: Dila Ram Bhandari

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Throughout the 20th century, new governments were created where identities such as ethnic, religious, linguistic, caste, communal, tribal, and others played a part in the development of constitutions and the legal system of victim and criminal justice. Acute issues with extremism, poverty, environmental degradation, cybercrimes, human rights violations, crime against, and victimization of both individuals and groups have recently plagued South Asian nations. Everyday massive number of crimes are steadfast, these frequent crimes have made the lives of common citizens restless. Crimes are one of the major threats to society and also for civilization. Crime is a bone of contention that can create a societal disturbance. The old-style crime solving practices are unable to live up to the requirement of existing crime situations. Crime analysis is one of the most important activities of the majority of intelligent and law enforcement organizations all over the world. The South Asia region lacks such a regional coordination mechanism, unlike central Asia of Asia Pacific regions, to facilitate criminal intelligence sharing and operational coordination related to organized crime, including illicit drug trafficking and money laundering. There have been numerous conversations in recent years about using data mining technology to combat crime and terrorism. The Data Detective program from Sentient as a software company, uses data mining techniques to support the police (Sentient, 2017). The goals of this internship are to test out several predictive model solutions and choose the most effective and promising one. First, extensive literature reviews on data mining, crime analysis, and crime data mining were conducted. Sentient offered a 7-year archive of crime statistics that were daily aggregated to produce a univariate dataset. Moreover, a daily incidence type aggregation was performed to produce a multivariate dataset. Each solution's forecast period lasted seven days. Statistical models and neural network models were the two main groups into which the experiments were split. For the crime data, neural networks fared better than statistical models. This study gives a general review of the applied statistics and neural network models. A detailed image of each model's performance on the available data and generalizability is provided by a comparative analysis of all the models on a comparable dataset. Obviously, the studies demonstrated that, in comparison to other models, Gated Recurrent Units (GRU) produced greater prediction. The crime records of 2005-2019 which was collected from Nepal Police headquarter and analysed by R programming. In conclusion, gated recurrent unit implementation could give benefit to police in predicting crime. Hence, time series analysis using GRU could be a prospective additional feature in Data Detective.

Keywords: time series analysis, forecasting, ARIMA, machine learning

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Authors: Danladi Ali

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This work proposed to adopt and optimize the conventional method of estimating power generated from campus solid waste (CSW) using an intelligent technique. The chemical content of the CSW was analyzed, the population responsible for the generation of the CSW, the amount of CSW generated, power to grid predicted and forecasted were obtained, and sources of supply of electricity for Adamawa State University (ADSU) were compared with the PGPs estimated from the CSW. The percentage content of the chemical elements was obtained as 56.90% carbon, 8.40% hydrogen, 27.70% oxygen, 6.00% nitrogen and 1.00% sulfur. The amount of the CSW generated and power to grid predicted and forecasted for 10 years was determined as 287.74 tons/day, 13.12MW and 12.90 MW, respectively. A model for estimating power potential from CSW for ADSU was developed, and also the work revealed that the PGPs estimated from the CSW are adequate to power the University for 24 hours on a daily basis.

Keywords: prediction, intelligent, forecasting, environment, power to grid, campus solid waste

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Authors: Doostishoar Farzad, Forootanfar Hamid, Hasan-Bikdashti Morvarid, Faramarzi Mohammad Ali, Ameri Atefe

Abstract:

Introduction: Chili peppers and related plants in the family of capsaicum produce a mixture of capsaicins represent anticarcinogenic, antimutagenic, and chemopreventive properties. Vanillylamine, the main product of capsaicin hydrolysis is applied as a precursor for manufacturing of natural vanillin (a famous flavor). It is also used in the production of synthetic capsaicins harboring a wide variety of physiological and biological activities such as antibacterial and anti-inflammatory effects as well as enhancing of adrenal catecholamine secretion, analgesic, and antioxidative activities. The ability of some lipases, such as Novozym 677 BG and Novozym 435 and also some proteases e.g. trypsine and penicillin acylase, in capsaicin hydrolysis and green synthesis of vanillylamine has been investigated. In the present study the optimized culture broth of a newly isolated lipase-producing bacterial strain (Stenotrophomonas maltophilia) applied for the hydrolysis of capsaicin. Materials and methods: In order to compare hydrolytic activity of optimized and basal culture broth through capsaicin 2 mL of each culture broth (as sources of lipase) was introduced to capsaicin solution (500 mg/L) and then the reaction mixture (total volume of 3 mL) was incubated at 40 °C and 120 rpm. Samples were taken every 2 h and analyzed for vanillylamine formation using HPLC. Same reaction mixture containing boiled supernatant (to inactivate lipase) designed as blank and each experiment was done in triplicate. Results: 215 mg/L of vanillylamine was produced after the treatment of capsaicin using the optimized medium for 18 h, while only 61 mg/L of vanillylamine was detected in presence of the basal medium under the same conditions. No capsaicin conversion was observed in the blank sample, in which lipase activity was suppressed by boiling of the sample for 10 min. Conclusion: The application of optimized broth culture for the hydrolysis of capsaicin led to a 43% conversion of that pungent compound to vanillylamine.

Keywords: Capsaicin, green synthesis, lipase, stenotrophomonas maltophilia

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