Search results for: market trend detection

Authors: Ethan Petersen

Abstract:

In this paper, we analyze the volatility of Apple’s stock beginning January 3, 2005 up to October 9, 2014, then focus on a range from 30 days prior to each product announcement until 30 days after. Product announcements are filtered; announcements whose 60 day range is devoid of other events are separated. This filtration is chosen to isolate, and study, a potential cross-effect. Concerning Apple keynotes, there are two significant dates: the day the invitations to the event are received and the day of the event itself. As such, the statistical analysis is conducted for both invite-centered and event-centered time frames. A comparison to the VIX is made to determine if the trend is simply following the market or deviating. Regardless of the filtration, we find that there is a clear deviation from the market. Comparing these data sets, there are significantly different trends: isolated events have a constantly decreasing, erratic trend in volatility but an increasing, linear trend is observed for clustered events. According to the Efficient Market Hypothesis, we would expect a change when new information is publicly known and the results of this study support this claim.

Keywords: efficient market hypothesis, event study, volatility, VIX

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Authors: Shashank Bhatnagar, W. Wilfred Godfrey

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We develop in this paper, an approach to find the trendy topic, which not only considers the user-topic interaction but also considers the community, in which user belongs. This method modifies the previous approach of user-topic interaction to user-community-topic interaction with better speed-up in the range of [1.1-3]. We assume that trend detection in a social network is dependent on two things. The one is, broadcast of messages in social network governed by self-exciting point process, namely called Hawkes process and the second is, Community Rank. The influencer node links to others in the community and decides the community rank based on its PageRank and the number of users links to that community. The community rank decides the influence of one community over the other. Hence, the Hawkes process with the kernel of user-community-topic decides the trendy topic disseminated into the social network.

Keywords: community detection, community rank, Hawkes process, influencer node, pagerank, trend detection

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Authors: Shervin Alaei, Reza Moradi

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Trading in financial markets is subject to risks due to their high volatilities. Here, using an LSTM neural network, and by doing some risk-based feature engineering tasks, we developed a method that can accurately predict trends of the Tehran stock exchange market index from a few days ago. Our test results have shown that the proposed method with an average prediction accuracy of more than 94% is superior to the other common machine learning algorithms. To the best of our knowledge, this is the first work incorporating deep learning and risk factors to accurately predict market trends.

Keywords: deep learning, LSTM, trend prediction, risk management, artificial neural networks

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Authors: Howard Chitimira

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The European Union (EU) was probably the first body to establish multinational anti-market abuse laws aimed at enhancing the detection and curbing of cross-border market abuse activities in its member states. Put differently, the EU Insider Dealing Directive was adopted in 1989 and was the first law that harmonised the insider trading ban among the EU member states. Thereafter, the European Union Directive on Insider Dealing and Market Manipulation (EU Market Abuse Directive) was adopted in a bid to improve and effectively discourage all the forms of market abuse in the EU’s securities and financial markets. However, the EU Market Abuse Directive had its own gaps and flaws. In light of this, the Market Abuse Regulation and the Criminal Sanctions for Market Abuse Directive were enacted to repeal and replace the EU Market Abuse Directive in 2016. The article examines the adequacy of the EU Market Abuse Directive and its implementation in the United Kingdom (UK) prior to the British exit (Brexit). This is done to investigate the possible implications of the Brexit referendum outcome of 23 June 2016 on the future regulation of market abuse in the UK.

Keywords: market abuse, insider trading, market manipulation, European Union, United Kingdom

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Authors: Nesrine Bouzaher, Okba Necira

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The financial aspect of the Algerian economy is part of all sectors that have undergone great changes these two last decades; the goal is to enable economic mechanisms for real growth. Insurance is an indispensable tool for stabilizing these mechanisms. Therefore the national economy needs to develop the insurance market in order to support the investments, externally and internally; it turns out that reinsurance is one of the area which could prove their performance in several markets mainly emerging ones. The expansion of reinsurance in the domestic market is the preoccupation of this work, focusing on factors that could enhance the demand of reinsurance in the Algerian market. This work will be based on an analytical research of the economic contribution of the reinsurance and it’s collusion with insurance; market, then it will be necessary to provide an overview of the product in the national emerging market, finally we will try to investigate on the factors that could enhance the demand in the national reinsurance market so as to determine the potential of Algeria in this area.

Keywords: Algerian reinsurance data, demand trend of Algerian reinsurance, reinsurance, reinsurance market

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Authors: Necira Okba, Nesrine Bouzaher

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The financial aspect of the Algerian economy is part of all sectors that have undergone great changes these two last decades; the goal is to enable economic mechanisms for real growth. Insurance is an indispensable tool for stabilizing these mechanisms. Therefore, the national economy needs to develop the insurance market in order to support the investments, externally and intern ally; it turns out that reinsurance is one of the area which could prove their performance in several markets mainly emerging ones. The expansion of reinsurance in the domestic market is the preoccupation of this work, focusing on factors that could enhance the demand of reinsurance in the Algerian market. This work will be based on an analytical research of the economic contribution of the reinsurance and it’s collusion with insurance market, then it will be necessary to provide an overview of the product in the national emerging market, finally we will try to investigate on the factors that could enhance the demand in the national reinsurance market so as to determine the potential of Algeria in this area.

Keywords: Algerian reinsurance data, demand trend of Algerian reinsurance, reinsurance, reinsurance market

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Authors: Sadam Alwadi

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Outlier values become a problem that frequently occurs in the data observation or recording process. Thus, the need for data imputation has become an essential matter. In this work, it will make use of the methods described in the prior work to detect the outlier values based on a collection of stock market data. In order to implement the detection and find some solutions that maybe helpful for investors, real closed price data were obtained from the Amman Stock Exchange (ASE). Tukey and Maximum Overlapping Discrete Wavelet Transform (MODWT) methods will be used to impute the detect the outlier values.

Keywords: outlier values, imputation, stock market data, detecting, estimation

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Authors: Raja Kannusamy

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The research focuses on the applicable technologies in the automobile industry and their effects on the productivity and annual revenue of the industry. A study has been conducted on 6 major automobile manufacturing industries represented in this research as M1, M2, M3, M4, M5 and M6. The results indicate that M1, which is a pioneer in technological applications, remains the market leader, followed by M5 & M2 taking the second and third positions, respectively. M3, M6 and M4 are the followers and are placed next in positions. It has also been observed that M1 and M2 have entered into an agreement to share the basic structural technologies and they maintain long-term and trusted relationships with their suppliers through the Keiretsu system. With technological giants such as Apple, Microsoft, Uber and Google entering the automobile industry in recent years, an upward trend is expected in the futuristic market with self-driving cars to dominate the automobile sector. To keep up with the market trend, it is essential for automobile manufacturers to understand the importance of developing technological capabilities and skills to be competitive in the marketplace.

Keywords: automobile manufacturing industries, competitiveness, performance improvement, technological applications

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Authors: Naci Büyükkaracığan

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Hydroclimatic observation values ​​are used in the planning of the project of water resources. Climate variables are the first of the values ​​used in planning projects. At the same time, the climate system is a complex and interactive system involving the atmosphere, land surfaces, snow and bubbles, the oceans and other water structures. The amount and distribution of precipitation, which is an important climate parameter, is a limiting environmental factor for dispersed living things. Trend analysis is applied to the detection of the presence of a pattern or trend in the data set. Many trends work in different parts of the world are usually made for the determination of climate change. The detection and attribution of past trends and variability in climatic variables is essential for explaining potential future alteration resulting from anthropogenic activities. Parametric and non-parametric tests are used for determining the trends in climatic variables. In this study, trend tests were applied to annual total precipitation data obtained in period of 1972 and 2012, in the Konya Basin. Non-parametric trend tests, (Sen’s T, Spearman’s Rho, Mann-Kendal, Sen’s T trend, Wald-Wolfowitz) and parametric test (mean square) were applied to annual total precipitations of 15 stations for trend analysis. The linear slopes (change per unit time) of trends are calculated by using a non-parametric estimator developed by Sen. The beginning of trends is determined by using the Mann-Kendall rank correlation test. In addition, homogeneities in precipitation trends are tested by using a method developed by Van Belle and Hughes. As a result of tests, negative linear slopes were found in annual total precipitations in Konya.

Keywords: trend analysis, precipitation, hydroclimatology, Konya

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Authors: A. J. Regan, F. J. Lidgey, M. Betteridge, P. Georgiou, C. Toumazou, K. Hayatleh, J. R. Dibble

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Our goal is development of an algorithm capable of predicting the directional trend of the Standard and Poor’s 500 index (S&P 500). Extensive research has been published attempting to predict different financial markets using historical data testing on an in-sample and trend basis, with many authors employing excessively complex mathematical techniques. In reviewing and evaluating these in-sample methodologies, it became evident that this approach was unable to achieve sufficiently reliable prediction performance for commercial exploitation. For these reasons, we moved to an out-of-sample strategy based on linear regression analysis of an extensive set of financial data correlated with historical closing prices of the S&P 500. We are pleased to report a directional trend accuracy of greater than 55% for tomorrow (t+1) in predicting the S&P 500.

Keywords: financial algorithm, GPU, S&P 500, stock market prediction

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Authors: Zhen Peng, Baifeng Wu

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Modern computing technology enters the era of parallel computing with the trend of sustainable and scalable parallelism. Single Instruction Multiple Data (SIMD) is an important way to go along with the trend. It is able to gather more and more computing ability by increasing the number of processor cores without the need of modifying the program. Meanwhile, in the field of scientific computing and engineering design, many computation intensive applications are facing the challenge of increasingly large amount of data. Data parallel computing will be an important way to further improve the performance of these applications. In this paper, we take the accurate collision detection in building information modeling as an example. We demonstrate a model for constructing a data parallel algorithm. According to the model, a complex object is decomposed into the sets of simple objects; collision detection among complex objects is converted into those among simple objects. The resulting algorithm is a typical SIMD algorithm, and its advantages in parallelism and scalability is unparalleled in respect to the traditional algorithms.

Keywords: data parallelism, collision detection, single instruction multiple data, building information modeling, continuous scalability

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Authors: Galuh Pramesti

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In Indonesia, the rise of the middle class and consumer purchasing power has created a trend of shifting the traditional into a modern retail market. Development of the creative economy as an impact of the global economy has invaded the traditional market, due to low rents and minimum innovation, raising the issue of sustainability and urban resilience for survival of the traditional market. The study aims to understand the current market conditions by examining the challenges, resiliency, and identify the relationship between the traditional market and creative market. Using a single-case study approach as the research methodology, Santa Market has been chosen as the case study. It is a pilot project of collaboration between a traditional market and creative economy in Jakarta, Indonesia. The research was conducted as a qualitative study through in-depth interviews with the market vendors and the market management, besides a desk-based study of the leasing data and spatial analysis. The findings indicate traffic fluctuation as the main challenge. It is related to the tenant’s presence, rental fluctuation, gentrification, infrastructure, and market competition. Thus, the findings on resilience show a different response for creative and traditional markets. The traditional market’s response remained stable with minimum innovation, whereas the creative market relies on technological development. Regarding the relationship, supply and demand have become the main relationship occurring in Santa Market. It is then developed into the context of society and regulation. The conclusion provides recommendations for more solid regulation to protect the market tenants from stakeholder interests that can disrupt market viability, and a critical discussion on the concept of collaboration between traditional and creative markets. There is also a suggestion for further study on relation with the surroundings, to create a holistic study on how the collaboration can work well in the traditional market.

Keywords: creative economy, market sustainability, traditional market, urban resilience

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Authors: Samaneh Nadali, Masrah Azrifah Azmi Murad, Nurfadhlina Mohammad Sharef

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Sarcasm is a common phenomenon in social media which is a nuanced form of language for stating the opposite of what is implied. Due to the intentional ambiguity, analysis of sarcasm is a difficult task not only for a machine but even for a human. Although sarcasm detection has an important effect on sentiment, it is usually ignored in social media analysis because sarcasm analysis is too complicated. While there is a few systems exist which can detect sarcasm, almost no work has been carried out on a study and the review of the existing work in this area. This survey presents a nearly full image of sarcasm detection techniques and the related fields with brief details. The main contributions of this paper include the illustration of the recent trend of research in the sarcasm analysis and we highlight the gaps and propose a new framework that can be explored.

Keywords: sarcasm detection, sentiment analysis, social media, sarcasm analysis

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Authors: Victoria Siriaki Jorry, I. S. Mbalawata, Hayong Shin

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The main objective in a change detection problem is to develop algorithms for efficient detection of gradual and/or abrupt changes in the parameter distribution of a process or time series data. In this paper, we present a modified cumulative (MCUSUM) algorithm to detect the start and end of a time-varying linear drift in mean value of a time series data based on likelihood ratio test procedure. The design, implementation and performance of the proposed algorithm for a linear drift detection is evaluated and compared to the existing CUSUM algorithm using different performance measures. An approach to accurately approximate the threshold of the MCUSUM is also provided. Performance of the MCUSUM for gradual change-point detection is compared to that of standard cumulative sum (CUSUM) control chart designed for abrupt shift detection using Monte Carlo Simulations. In terms of the expected time for detection, the MCUSUM procedure is found to have a better performance than a standard CUSUM chart for detection of the gradual change in mean. The algorithm is then applied and tested to a randomly generated time series data with a gradual linear trend in mean to demonstrate its usefulness.

Keywords: average run length, CUSUM control chart, gradual change detection, likelihood ratio test

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Authors: Aswathy Vijayan, Sharath Sunder Rajeev

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Trade has been fundamental in the footprint of human civilization since ancient time. In most of the historic cities, city development was along trading routes, where marketplaces are the major entrance to a city and hence a major element of the urban fabric. Marketplaces are where the commercial activities flourish, people, having a sense of belonging to the place, where they easily fit in. Acknowledging the built environment in and around the market in a way, creating a sense of place is an important factor in the success of public spaces. Local markets are developed in an organic manner, which adds on to the people experience and perception of urban space. With the city development, the commercial needs within the city increase, hence marketplaces flourish, irrespective of the functional segregation within. The work-live culture in the marketplaces diminishes as the commercial expansion washes away the residential patches within it. Real estate flourishes as the newer infills are without considering the carrying capacity of the place. Chalai market is a prominent business center serving the regional level of Thiruvananthapuram city. The transformation trend of marketplaces in city cores are understood from case study on Fatimid Cairo Marketplace. The parameters that led to transformation of marketplaces in a global context is considered for the analysis of the Chalai market. The structure of the marketplace over the years is analyzed in terms of transformation in location, transformation in the land- use, change in commodity, and transformation in movement and activity. The aim of the research is to emphasize the need to understand the transformation trend, in creating a suitable development pattern for the city. The unregulated transformation within the city core has led to tremendous transformation in the user group and user pattern and eventually to the commercial trend. With the change in lifestyle and need for new amenities have led to addition of new infills leading to the degradation of the native commerce. Hence addressing the transformation of marketplaces are crucial to maintaining the locational significance and cultural importance and heritage of the place.

Keywords: bazaar, market centers, marketplaces, traditional city, traditional market, urban fabric

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Authors: A. A. Adebayo, O. Osibanjo

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Informal electronic waste (e-waste) processing is a crude method of recycling, which is on the increase in Nigeria. The release of hazardous substances such as heavy metals (HMs) into the environment during informal e-waste processing has been a major concern. However, there is insufficient information on environmental contamination from e-waste recycling, associated ecological risk in Alaba International Market, a major electronic market in Lagos, Nigeria. The aims of this study were to determine the levels of HMs in soil, resulting from the e-waste recycling; and also assess associated ecological risks in Alaba international market. Samples of soils (334) were randomly collected seasonally for three years from fourteen selected e-waste activity points and two control sites. The samples were digested using standard methods and HMs analysed by inductive coupled plasma optical emission. Ecological risk was estimated using Ecological Risk index (ER), Potential Ecological Risk index (RI), Index of geoaccumulation (Igeo), Contamination factor (Cf) and degree of contamination factor (Cdeg). The concentrations range of HMs (mg/kg) in soil were: 16.7-11200.0 (Pb); 14.3-22600.0 (Cu); 1.90-6280.0 (Ni), 39.5-4570.0 (Zn); 0.79-12300.0 (Sn); 0.02-138.0 (Cd); 12.7-1710.0 (Ba); 0.18-131.0 (Cr); 0.07-28.0 (V), while As was below detection limit. Concentrations range in control soils were 1.36-9.70 (Pb), 2.06-7.60 (Cu), 1.25-5.11 (Ni), 3.62-15.9 (Zn), BDL-0.56 (Sn), BDL-0.01 (Cd), 14.6-47.6 (Ba), 0.21–12.2 (Cr) and 0.22-22.2 (V). The trend in ecological risk index was in the order Cu > Pb > Ni > Zn > Cr > Cd > Ba > V. The potential ecological risk index with respect to informal e-waste activities were: burning > dismantling > disposal > stockpiling. The index of geo accumulation indices revealed that soils were extremely polluted with Cd, Cu, Pb, Zn and Ni. The contamination factor indicated that 93% of the studied areas have very high contamination status for Pb, Cu, Ba, Sn and Co while Cr and Cd were in the moderately contaminated status. The degree of contamination decreased in the order of Sn > Cu > Pb >> Zn > Ba > Co > Ni > V > Cr > Cd. Heavy metal contamination of Alaba international market environment resulting from informal e-waste processing was established. Proper management of e-waste and remediation of the market environment are recommended to minimize the ecological risks.

Keywords: Alaba international market, ecological risk, electronic waste, heavy metal contamination

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Authors: Rodrigo Arnaldo Scarpel

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Trend following is a widely accepted investment strategy that adopts a rule-based trading mechanism that rather than striving to predict market direction or on information gathering to decide when to buy and when to sell a stock. Thus, in trend following one must respond to market’s movements that has recently happen and what is currently happening, rather than on what will happen. Optimally, in trend following strategy, is to catch a bull market at its early stage, ride the trend, and liquidate the position at the first evidence of the subsequent bear market. For applying the trend following strategy one needs to find the trend and identify trade signals. In order to avoid false signals, i.e., identify fluctuations of short, mid and long terms and to separate noise from real changes in the trend, most academic works rely on moving averages and other technical analysis indicators, such as the moving average convergence divergence (MACD) and the relative strength index (RSI) to uncover intelligible stock trading rules following trend following strategy philosophy. Recently, some works has applied machine learning techniques for trade rules discovery. In those works, the process of rule construction is based on evolutionary learning which aims to adapt the rules to the current environment and searches for the global optimum rules in the search space. In this work, instead of focusing on the usage of machine learning techniques for creating trading rules, a time series trend classification employing a semi-supervised approach was used to early identify both the beginning and the end of upward and downward trends. Such classification model can be employed to identify trade signals and the decision-making procedure is that if an up-trend (down-trend) is identified, a buy (sell) signal is generated. Semi-supervised learning is used for model training when only part of the data is labeled and Semi-supervised classification aims to train a classifier from both the labeled and unlabeled data, such that it is better than the supervised classifier trained only on the labeled data. For illustrating the proposed approach, it was employed daily trade information, including the open, high, low and closing values and volume from January 1, 2000 to December 31, 2022, of the São Paulo Exchange Composite index (IBOVESPA). Through this time period it was visually identified consistent changes in price, upwards or downwards, for assigning labels and leaving the rest of the days (when there is not a consistent change in price) unlabeled. For training the classification model, a pseudo-label semi-supervised learning strategy was used employing different technical analysis indicators. In this learning strategy, the core is to use unlabeled data to generate a pseudo-label for supervised training. For evaluating the achieved results, it was considered the annualized return and excess return, the Sortino and the Sharpe indicators. Through the evaluated time period, the obtained results were very consistent and can be considered promising for generating the intended trading signals.

Keywords: evolutionary learning, semi-supervised classification, time series data, trading signals generation

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Authors: Muhammad Rosli Abdullah, Noor Hasmiza Harun

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A microorganism detection system has a potential to be used with the advancement in a biosensor development. The detection system requires an optical sensing system, microfluidic device and biological reagent. Although, the biosensors are available in the market, a label free and a lab-on-chip approach will promote a flexible solution. As a preliminary study of microorganism detection, three mechanisms such as Total Internal Reflection (TIR), Micro Fluidic Channel (MFC) and magnetic-electric field propagation were study and simulated. The objective are to identify the TIR angle, MFC parabolic flow and the wavelength for the microorganism detection. The simulation result indicates that evanescent wave is achieved when TIR angle > 42°, the corner and centre of a parabolic velocity are 0.02 m/s and 0.06 m/s respectively, and a higher energy distribution of a perfect electromagnetic scattering with dipole resonance radiation occurs at 500 nm. This simulation is beneficial to determine the components of the microorganism detection system that does not rely on classical microbiological, immunological and genetic methods which are laborious, time-consuming procedures and confined to specialized laboratories with expensive instrumentation equipment.

Keywords: microorganism, microfluidic, total internal reflection, lab on chip

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Authors: Tamar Lazariashvili

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Introduction: The agro-insurance has great importance for agricultural development in the country. In the article, the insurance market of the Georgian agricultural sector has been studied, the level of interest of farmers with insurance products and the trend of demand for those products are revealed; also, the importance of insurance is substantiated. Methodology: The following research methods are applied in the presented paper: statistical (selection, grouping, observation, trend) and qualitative research (in-depth interview with farmers). They claim that the main reason for aggravation is the low level of trust, less awareness about the conditions of the insurance contract. In order to eradicate distrust towards agro-insurance, it is recommended to increase awareness of insured farmers in terms of an insurance agreement. In the case of disputable issues between insurance companies and the customers (farmers), it is advisable to enact the Mediation Service, which will be able to protect the rights of insured farmers. Main Findings: Insurance companies prefer to deal with large farmers, the number of them is very small in Georgia as the credit market. The government interference in this sector is also a very cautious topic. However, the government can strengthen the awareness of farmers about the characteristics and advantages of the insurance system in order to increase the number of insured and reduce insurance premiums for farmers. Conclusion: Enactment of agro-insurance will increase the interest and confidence of financial institutions in the farming sector, financial resources will be accessible to the farmers that will facilitate the stable development of the sector in the country. The size of the agro-insurance market in the country should be increased, and the new territories should be covered. The State must have an obligation to ensure the risk of farmers and subsidize insurance companies. Based on the analysis of the insurance market, the conclusions on agro-insurance issues and the relevant recommendations are proposed.

Keywords: Agro-insurance, agricultural product, Agro-market, farming

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Authors: Mo-Se Lee, Cheol-Hwi Ahn, Kee-Young Kwahk, Hyunchul Ahn

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Over the past decade, deep learning has been in spotlight among various machine learning algorithms. In particular, CNN (Convolutional Neural Network), which is known as effective solution for recognizing and classifying images, has been popularly applied to classification and prediction problems in various fields. In this study, we try to apply CNN to stock market prediction, one of the most challenging tasks in the machine learning research. In specific, we propose to apply CNN as the binary classifier that predicts stock market direction (up or down) by using a graph as its input. That is, our proposal is to build a machine learning algorithm that mimics a person who looks at the graph and predicts whether the trend will go up or down. Our proposed model consists of four steps. In the first step, it divides the dataset into 5 days, 10 days, 15 days, and 20 days. And then, it creates graphs for each interval in step 2. In the next step, CNN classifiers are trained using the graphs generated in the previous step. In step 4, it optimizes the hyper parameters of the trained model by using the validation dataset. To validate our model, we will apply it to the prediction of KOSPI200 for 1,986 days in eight years (from 2009 to 2016). The experimental dataset will include 14 technical indicators such as CCI, Momentum, ROC and daily closing price of KOSPI200 of Korean stock market.

Keywords: convolutional neural network, deep learning, Korean stock market, stock market prediction

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Authors: Rebeca Ramirez Acosta, Sebastian Lenhoff

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Decision makers worldwide need to determine if the implementation of a new market mechanism will contribute to the sustainability and resilience of the power system. Due to smart grid technologies, new products in the distribution and transmission system can be traded; however, the impact of changing a market rule will differ between several regions. To test systematically those impacts, a market categorization has been compiled and organized in a smart grid market testing toolbox. This toolbox maps all actual energy products and sets the basis for running a co-simulation test with the new rule to be implemented. It will help to measure the impact of the new rule, based on the sustainable and resilience indicators.

Keywords: co-simulation, electricity market, smart grid market, market testing

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Authors: K. Sruthi, Sai Snehitha Yadavalli, Swathi Gosh Acharyya

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Water is the most crucial element for sustaining life on earth. Increasing water pollution directly or indirectly leads to harmful effects on human life. Most of the heavy metal ions are harmful in their cationic form. These heavy metal ions are released by various activities like disposing of batteries, industrial wastes, automobile emissions, and soil contamination. Ions like (Pb, Co, Cd) are carcinogenic and show many harmful effects when consumed more than certain limits proposed by WHO. The simultaneous detection of the heavy metal ions (Pb, Co, Cd), which are highly toxic, is reported in this study. There are many analytical methods for quantifying, but electrochemical techniques are given high priority because of their sensitivity and ability to detect and recognize lower concentrations. Square wave voltammetry was preferred in electrochemical methods due to the absence of background currents which is interference. Square wave voltammetry was performed on GCE for the quantitative detection of ions. Three electrode system consisting of a glassy carbon electrode as the working electrode (3 mm diameter), Ag/Agcl electrode as the reference electrode, and a platinum wire as the counter electrode was chosen for experimentation. The mechanism of detection was done by optimizing the experimental parameters, namely pH, scan rate, and temperature. Under the optimized conditions, square wave voltammetry was performed for simultaneous detection. Scan rates were varied from 5 mV/s to 100 mV/s and found that at 25 mV/s all the three ions were detected simultaneously with proper peaks at particular stripping potential. The variation of pH from 3 to 8 was done where the optimized pH was taken as pH 5 which holds good for three ions. There was a decreasing trend at starting because of hydrogen gas evolution, and after pH 5 again there was a decreasing trend that is because of hydroxide formation on the surface of the working electrode (GCE). The temperature variation from 25˚C to 45˚C was done where the optimum temperature concerning three ions was taken as 35˚C. Deposition and stripping potentials were given as +1.5 V and -1.5 V, and the resting time of 150 seconds was given. Three ions were detected at stripping potentials of Cd²⁺ at -0.84 V, Pb²⁺ at -0.54 V, and Co²⁺ at -0.44 V. The parameters of detection were optimized on a glassy carbon electrode for simultaneous detection of the ions at lower concentrations by square wave voltammetry.

Keywords: cadmium, cobalt, lead, glassy carbon electrode, square wave anodic stripping voltammetry

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Authors: Jae-Jeong Kim, Ki-Ro Kim, Hyoung-Kyu Song

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In this paper, we propose an efficient signal detector that switches M parameter of QRD-M detection scheme is proposed for MIMO-OFDM system. The proposed detection scheme calculates the threshold by 1-norm condition number and then switches M parameter of QRD-M detection scheme according to channel information. If channel condition is bad, the parameter M is set to high value to increase the accuracy of detection. If channel condition is good, the parameter M is set to low value to reduce complexity of detection. Therefore, the proposed detection scheme has better trade off between BER performance and complexity than the conventional detection scheme. The simulation result shows that the complexity of proposed detection scheme is lower than QRD-M detection scheme with similar BER performance.

Keywords: MIMO-OFDM, QRD-M, channel condition, BER

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Authors: Jae-Hyun Ro, Yong-Jun Kim, Chang-Bin Ha, Hyoung-Kyu Song

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In multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) systems, many detection schemes have been developed to improve the error performance and to reduce the complexity. Maximum likelihood (ML) detection has optimal error performance but it has very high complexity. Thus, this paper proposes reduced complexity of ML detection combined with decision feedback equalizer (DFE). The error performance of the proposed detection scheme is higher than the conventional DFE. But the complexity of the proposed scheme is lower than the conventional ML detection.

Keywords: detection, DFE, MIMO-OFDM, ML

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Authors: Maryam Heidari, James H. Jones Jr.

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Millions of online posts about different topics and products are shared on popular social media platforms. One use of this content is to provide crowd-sourced information about a specific topic, event or product. However, this use raises an important question: what percentage of information available through these services is trustworthy? In particular, might some of this information be generated by a machine, i.e., a bot, instead of a human? Bots can be, and often are, purposely designed to generate enough volume to skew an apparent trend or position on a topic, yet the consumer of such content cannot easily distinguish a bot post from a human post. In this paper, we introduce a model for social media bot detection which uses Bidirectional Encoder Representations from Transformers (Google Bert) for sentiment classification of tweets to identify topic-independent features. Our use of a Natural Language Processing approach to derive topic-independent features for our new bot detection model distinguishes this work from previous bot detection models. We achieve 94\% accuracy classifying the contents of data as generated by a bot or a human, where the most accurate prior work achieved accuracy of 92\%.

Keywords: bot detection, natural language processing, neural network, social media

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Authors: Aristeidis Samitas, Evangelos Vasileiou

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The aim of this study is to examine the month and the trading month effect under changing financial trends. We choose the Greek stock market to implement our assumption because there are clear and long term periods of financial growth and recession. Daily financial data from Athens Exchange General Index for the period 2002-2012 are considered. The paper employs several linear and non-linear models, although the TGARCH asymmetry model best fits in this sample and for this reason we mainly present the TGARCH results. Empirical results show that changing economic and financial conditions influences the calendar effects. Especially, the trading month effect totally changes in each fortnight according to the financial trend. On the other hand, in Greece the January effect exists during the growth periods, although it does not exist when the financial trend changes. The findings are helpful to anybody who invest and deals with the Greek stock market. Moreover, they may pave the way for an alternative calendar anomalies research approach, so it may be useful to investors who take into account these anomalies when they draw their investment strategy.

Keywords: month effect, trading month effect, economic cycles, crisis

Procedia PDF Downloads 393

Authors: Sitaram Dahal

Abstract:

Sustainable Development, a universal need for the present generation and the future generation, is an accepted way to assure intra and inter-generational equity. International movements like Rio Earth Summit 1992, Stockholm Conference 1972, Kyoto Protocol, Sustainable Development Goals (SDGs) proclaim the need of sustainable globe. The socio- economic disparity prevailing in the society shows that the indigenous peoples are living life far below poverty line. These indigenous people, aboriginal social groups sharing common cultural values and with a unique identity, are away from development being merely focused on the growth. Though studies suggest that most of the indigenous practices are often environment-friendly, alert about the plunging trend of the practices. This study explores the trend of intergenerational transmission of indigenous profession of pottery making of Kumal community (Meghauli Village Development Committee of Chitwan district) and factors affecting the trend. The SD indicators - contribution of IP to well-being of pottery makers had been query in the study. The study reveals that the pottery making profession can stand sustainable in terms of environment and socio-economic capital compared to modern technologies. However, the number of practitioners has been decreasing and youths hardly show interest to continue their indigenous profession. The new generations are not in a stage of accepting pottery in complete profession, that challenges the social and cultural sustainability of the profession. Indigenous profession demand people investments over modern technology and innovations. The relative investment of human labour is dramatically high with the indigenous profession. In addition, the fashion and innovations of market rule challenge the sustainability of the pottery making profession. The practice is limited to small cluster as a show piece at present. The study illustrates the market driven unsustainability of indigenous profession of Kumal community.

Keywords: professional unsustainability, pottery making, Kumal Community, Indigenous Professoin

Procedia PDF Downloads 230

Authors: S. Avdasheva, D. Tsytsulina

Abstract:

In many regulated industries over the world price cap as a method of price regulation replaces cost-plus pricing. It is a kind of incentive regulation introduced in order to enhance productive efficiency by strengthening sellers’ incentives for cost reduction as well as incentives for more efficient pricing. However pricing under cap is not neutral for competition in the market. We consider influence on competition on the markets where benchmark for cap is chosen from when sellers are multi-market. We argue that the impact of price cap regulation on market competition depends on the design of cap. More specifically if cap for one (regulated) market depends on the price of the supplier in other (non-regulated) market, there is sub-type of price cap regulation (known in Russian tariff regulation as ‘netback minus’) that enhance incentives to collude in non-regulated market.

Keywords: price regulation, competition, collusion

Procedia PDF Downloads 480

Authors: Wei Li, Tuo Yang

Abstract:

In order to satisfy the real-time and accurate requirements of cigarette smoke detection in complex scenes, a cigarette smoke detection technology based on the combination of deep learning and color features was proposed. Firstly, based on the color features of cigarette smoke, the suspicious cigarette smoke area in the image is extracted. Secondly, combined with the efficiency of cigarette smoke detection and the problem of network overfitting, a network model for cigarette smoke detection was designed according to YOLOV3 algorithm to reduce the false detection rate. The experimental results show that the method is feasible and effective, and the accuracy of cigarette smoke detection is up to 99.13%, which satisfies the requirements of real-time cigarette smoke detection in complex scenes.

Keywords: deep learning, computer vision, cigarette smoke detection, YOLOV3, color feature extraction

Procedia PDF Downloads 53

Authors: Bartholomew Badar

Abstract:

An NFT is a digital certificate with rights to own an asset, including various valuable digital goods such as art pieces, music items, collectibles, etc. The market for NFTs started developing in 2017 and has lately seen increased growth as crypto-currencies and the blockchain market continue to gain popularity. This study aims to understand potential uses for NFTs concerning the music industry and record labels. Independent artists struggle to distribute and sell their music without the help of a record label. The NFT marketplace could be a great tool to eliminate this problem. The research objective is to identify possibilities for independent artists to own their music rights and share value with an audience. We see a trend of new-school music artists trying to enter the music NFT market by creating visualizers, beats, cover art, etc. To analyze various existing music NFT assets and determine whether or not independent artists could monetize their music without a record label is the main focus of this scholarly paper.

Keywords: blockchain, crypto-currency, music, artist, NFT

Procedia PDF Downloads 149